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1. Genome-wide association study of borderline personality disorder reveals genetic overlap with bipolar disorder, major depression and schizophrenia.

Author: Witt, SH, Streit, F, Jungkunz, M, Frank, J, Awasthi, S, Reinbold, CS, Treutlein, J, Degenhardt, F, Forstner, AJ, Heilmann-Heimbach, S, Dietl, L, Schwarze, CE, Schendel, D, Strohmaier, J, Abdellaoui, A, Adolfsson, R, Air, TM, Akil, H, Alda, M, Alliey-Rodriguez, N, Andreassen, OA, Babadjanova, G, Bass, NJ, Bauer, M, Baune, BT, Bellivier, F, Bergen, S, Bethell, A, Biernacka, JM, Blackwood, DHR, Boks, MP, Boomsma, DI, Børglum, AD, Borrmann-Hassenbach, M, Brennan, P, Budde, M, Buttenschøn, HN, Byrne, EM, Cervantes, P, Clarke, T-K, Craddock, N, Cruceanu, C, Curtis, D, Czerski, PM, Dannlowski, U, Davis, T, de Geus, EJC, Di Florio, A, Djurovic, S, Domenici, E, Edenberg, HJ, Etain, B, Fischer, SB, Forty, L, Fraser, C, Frye, MA, Fullerton, JM, Gade, K, Gershon, ES, Giegling, I, Gordon, SD, Gordon-Smith, K, Grabe, HJ, Green, EK, Greenwood, TA, Grigoroiu-Serbanescu, M, Guzman-Parra, J, Hall, LS, Hamshere, M, Hauser, J, Hautzinger, M, Heilbronner, U, Herms, S, Hitturlingappa, S, Hoffmann, P, Holmans, P, Hottenga, J-J, Jamain, S, Jones, I, Jones, LA, Juréus, A, Kahn, RS, Kammerer-Ciernioch, J, Kirov, G, Kittel-Schneider, S, Kloiber, S, Knott, SV, Kogevinas, M, Landén, M, Leber, M, Leboyer, M, Li, QS, Lissowska, J, Lucae, S, Martin, NG, Mayoral-Cleries, F, McElroy, SL, McIntosh, AM, McKay, JD, and McQuillin, A
Subjects: Bipolar Disorders Working Group of the Psychiatric Genomics Consortium, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Humans, Genetic Predisposition to Disease, Case-Control Studies, Bipolar Disorder, Depressive Disorder, Major, Borderline Personality Disorder, Schizophrenia, Genotype, Multifactorial Inheritance, Adolescent, Adult, Aged, Middle Aged, Female, Male, Genome-Wide Association Study, Young Adult, Depressive Disorder, Major, Clinical Sciences, Public Health and Health Services, Psychology
Abstract: Borderline personality disorder (BOR) is determined by environmental and genetic factors, and characterized by affective instability and impulsivity, diagnostic symptoms also observed in manic phases of bipolar disorder (BIP). Up to 20% of BIP patients show comorbidity with BOR. This report describes the first case-control genome-wide association study (GWAS) of BOR, performed in one of the largest BOR patient samples worldwide. The focus of our analysis was (i) to detect genes and gene sets involved in BOR and (ii) to investigate the genetic overlap with BIP. As there is considerable genetic overlap between BIP, major depression (MDD) and schizophrenia (SCZ) and a high comorbidity of BOR and MDD, we also analyzed the genetic overlap of BOR with SCZ and MDD. GWAS, gene-based tests and gene-set analyses were performed in 998 BOR patients and 1545 controls. Linkage disequilibrium score regression was used to detect the genetic overlap between BOR and these disorders. Single marker analysis revealed no significant association after correction for multiple testing. Gene-based analysis yielded two significant genes: DPYD (P=4.42 × 10-7) and PKP4 (P=8.67 × 10-7); and gene-set analysis yielded a significant finding for exocytosis (GO:0006887, PFDR=0.019; FDR, false discovery rate). Prior studies have implicated DPYD, PKP4 and exocytosis in BIP and SCZ. The most notable finding of the present study was the genetic overlap of BOR with BIP (rg=0.28 [P=2.99 × 10-3]), SCZ (rg=0.34 [P=4.37 × 10-5]) and MDD (rg=0.57 [P=1.04 × 10-3]). We believe our study is the first to demonstrate that BOR overlaps with BIP, MDD and SCZ on the genetic level. Whether this is confined to transdiagnostic clinical symptoms should be examined in future studies.
Published: 2017

2. Metabolic activity of CYP2C19 and CYP2D6 on antidepressant response from 13 clinical studies using genotype imputation: a meta-analysis.

Author: Li, D, Pain, O, Chiara, F, Wong, WLE, Lo, CWH, Ripke, S, Cattaneo, A, Souery, D, Dernovsek, MZ, Henigsberg, N, Hauser, J, Lewis, G, Mors, O, Perroud, N, Rietschel, M, Uher, R, Maier, W, Baune, BT, Biernacka, JM, Bondolfi, G, Domschke, K, Kato, M, Liu, Y-L, Serretti, A, Tsai, S-J, Weinshilboum, R, GSRD Consortium, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, McIntosh, AM, Lewis, CM, Li, D, Pain, O, Chiara, F, Wong, WLE, Lo, CWH, Ripke, S, Cattaneo, A, Souery, D, Dernovsek, MZ, Henigsberg, N, Hauser, J, Lewis, G, Mors, O, Perroud, N, Rietschel, M, Uher, R, Maier, W, Baune, BT, Biernacka, JM, Bondolfi, G, Domschke, K, Kato, M, Liu, Y-L, Serretti, A, Tsai, S-J, Weinshilboum, R, GSRD Consortium, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, McIntosh, AM, and Lewis, CM
Abstract: Cytochrome P450 enzymes including CYP2C19 and CYP2D6 are important for antidepressant metabolism and polymorphisms of these genes have been determined to predict metabolite levels. Nonetheless, more evidence is needed to understand the impact of genetic variations on antidepressant response. In this study, individual clinical and genetic data from 13 studies of European and East Asian ancestry populations were collected. The antidepressant response was clinically assessed as remission and percentage improvement. Imputed genotype was used to translate genetic polymorphisms to metabolic phenotypes (poor, intermediate, normal, and rapid+ultrarapid) of CYP2C19 and CYP2D6. CYP2D6 structural variants cannot be imputed from genotype data, limiting the determination of metabolic phenotypes, and precluding testing for association with response. The association of CYP2C19 metabolic phenotypes with treatment response was examined using normal metabolizers as the reference. Among 5843 depression patients, a higher remission rate was found in CYP2C19 poor metabolizers compared to normal metabolizers at nominal significance but did not survive after multiple testing correction (OR = 1.46, 95% CI [1.03, 2.06], p = 0.033, heterogeneity I2 = 0%, subgroup difference p = 0.72). No metabolic phenotype was associated with percentage improvement from baseline. After stratifying by antidepressants primarily metabolized by CYP2C19, no association was found between metabolic phenotypes and antidepressant response. Metabolic phenotypes showed differences in frequency, but not effect, between European- and East Asian-ancestry studies. In conclusion, metabolic phenotypes imputed from genetic variants using genotype were not associated with antidepressant response. CYP2C19 poor metabolizers could potentially contribute to antidepressant efficacy with more evidence needed. Sequencing and targeted pharmacogenetic testing, alongside information on side effects, antidepressant dosage, depression me
Published: 2024

3. Predictors of Functional Impairment in Bipolar Disorder: Results from 13 Cohorts from Seven Countries by The Global Bipolar Cohort Collaborative

Author: Burdick, KE, Millett, CE, Yocum, AK, Altimus, CM, Andreassen, OA, Aubin, V, Belzeaux, R, Berk, Michael, Biernacka, JM, Blumberg, HP, Cleare, AJ, Diaz‐Byrd, C, Dubertret, C, Etain, B, Eyler, L, Forester, BP, Fullerton, JM, Frye, MA, Gard, S, Godin, O, Haffen, E, Klaus, F, Lagerberg, TV, Leboyer, M, Martinez‐Aran, A, McElroy, S, Mitchell, PB, Olie, E, Olorunfemi, P, Passerieux, C, Peters, A, Pham, DL, Polosan, M, Potter, JR, Sajatovic, M, Samalin, L, Schwan, R, Shanahan, M, Solé, B, Strawbridge, R, Stuart, Amanda, Torres, I, Ueland, T, Vieta, E, Williams, Lana, Wrobel, Anna, Yatham, LN, Young, AH, Nierenberg, A, McInnis, MG, Burdick, KE, Millett, CE, Yocum, AK, Altimus, CM, Andreassen, OA, Aubin, V, Belzeaux, R, Berk, Michael, Biernacka, JM, Blumberg, HP, Cleare, AJ, Diaz‐Byrd, C, Dubertret, C, Etain, B, Eyler, L, Forester, BP, Fullerton, JM, Frye, MA, Gard, S, Godin, O, Haffen, E, Klaus, F, Lagerberg, TV, Leboyer, M, Martinez‐Aran, A, McElroy, S, Mitchell, PB, Olie, E, Olorunfemi, P, Passerieux, C, Peters, A, Pham, DL, Polosan, M, Potter, JR, Sajatovic, M, Samalin, L, Schwan, R, Shanahan, M, Solé, B, Strawbridge, R, Stuart, Amanda, Torres, I, Ueland, T, Vieta, E, Williams, Lana, Wrobel, Anna, Yatham, LN, Young, AH, Nierenberg, A, and McInnis, MG
Published: 2022

4. Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Author: Mullins, N, Kang, J, Campos, A, Coleman, JR, Edwards, AC, Galfalvy, H, Levey, DF, Lori, A, Shabalin, A, Starnawska, A, Su, M-H, Watson, HJ, Adams, M, Awasthi, S, Ganda, M, Hafferty, JD, Hishimoto, A, Kim, M, Okazaki, S, Otsuka, I, Ripke, S, Ware, EB, Bergen, AW, Berrettini, WH, Bohus, M, Brandt, H, Chang, X, Chen, WJ, Chen, H-C, Crawford, S, Crow, S, DiBlasi, E, Duriez, P, Fernandez-Aranda, F, Fichter, MM, Gallinger, S, Glatt, SJ, Gorwood, P, Guo, Y, Hakonarson, H, Halmi, KA, Hwu, H-G, Jain, S, Jamain, S, Jimenez-Murcia, S, Johnson, C, Kaplan, AS, Kaye, WH, Keel, PK, Kennedy, JL, Klump, KL, Li, D, Liao, S-C, Lieb, K, Lilenfeld, L, Liu, C-M, Magistretti, PJ, Marshall, CR, Mitchell, JE, Monson, ET, Myers, RM, Pinto, D, Powers, A, Ramoz, N, Roepke, S, Rozanov, V, Scherer, SW, Schmahl, C, Sokolowski, M, Strober, M, Thornton, LM, Treasure, J, Tsuang, MT, Witt, SH, Woodside, DB, Yilmaz, Z, Zillich, L, Adolfsson, R, Agartz, I, Air, TM, Alda, M, Alfredsson, L, Andreassen, OA, Anjorin, A, Appadurai, V, Artigas, MS, Van der Auwera, S, Azevedo, MH, Bass, N, Bau, CHD, Baune, BT, Bellivier, F, Berger, K, Biernacka, JM, Bigdeli, TB, Binder, EB, Boehnke, M, Boks, MP, Bosch, R, Braff, DL, Bryant, R, Budde, M, Byrne, EM, Cahn, W, Casas, M, Castelao, E, Cervilla, JA, Chaumette, B, Cichon, S, Corvin, A, Craddock, N, Craig, D, Degenhardt, F, Djurovic, S, Edenberg, HJ, Fanous, AH, Foo, JC, Forstner, AJ, Frye, M, Fullerton, JM, Gatt, JM, Gejman, P, Giegling, I, Grabe, HJ, Green, MJ, Grevet, EH, Grigoroiu-Serbanescu, M, Gutierrez, B, Guzman-Parra, J, Hamilton, SP, Hamshere, ML, Hartmann, A, Hauser, J, Heilmann-Heimbach, S, Hoffmann, P, Ising, M, Jones, I, Jones, LA, Jonsson, L, Kahn, RS, Kelsoe, JR, Kendler, KS, Kloiber, S, Koenen, KC, Kogevinas, M, Konte, B, Krebs, M-O, Lander, M, Lawrence, J, Leboyer, M, Lee, PH, Levinson, DF, Liao, C, Lissowska, J, Lucae, S, Mayoral, F, McElroy, SL, McGrath, P, McGuffin, P, McQuillin, A, Medland, SE, Mehta, D, Melle, I, Milaneschi, Y, Mitchell, PB, Molina, E, Morken, G, Mortensen, PB, Mueller-Myhsok, B, Nievergelt, C, Nimgaonkar, V, Noethen, MM, O'Donovan, MC, Ophoff, RA, Owen, MJ, Pato, C, Pato, MT, Penninx, BWJH, Pimm, J, Pistis, G, Potash, JB, Power, RA, Preisig, M, Quested, D, Ramos-Quiroga, JA, Reif, A, Ribases, M, Richarte, V, Rietschel, M, Rivera, M, Roberts, A, Roberts, G, Rouleau, GA, Rovaris, DL, Rujescu, D, Sanchez-Mora, C, Sanders, AR, Schofield, PR, Schulze, TG, Scott, LJ, Serretti, A, Shi, J, Shyn, S, Sirignano, L, Sklar, P, Smeland, OB, Smoller, JW, Sonuga-Barke, EJS, Spalletta, G, Strauss, JS, Swiatkowska, B, Trzaskowski, M, Turecki, G, Vilar-Ribo, L, Vincent, JB, Voelzke, H, Walters, JTR, Weickert, CS, Weickert, TW, Weissman, MM, Williams, LM, Wray, NR, Zai, CC, Ashley-Koch, AE, Beckham, JC, Hauser, ER, Hauser, MA, Kimbrel, NA, Lindquist, JH, McMahon, B, Oslin, DW, Qin, X, Agerbo, E, Borglum, AD, Breen, G, Erlangsen, A, Esko, T, Gelernter, J, Hougaard, DM, Kessler, RC, Kranzler, HR, Li, QS, Martin, NG, McIntosh, AM, Mors, O, Nordentoft, M, Olsen, CM, Porteous, D, Ursano, RJ, Wasserman, D, Werge, T, Whiteman, DC, Bulik, CM, Coon, H, Demontis, D, Docherty, AR, Kuo, P-H, Lewis, CM, Mann, JJ, Renteria, ME, Smith, DJ, Stahl, EA, Stein, MB, Streit, F, Willour, V, Ruderfer, DM, Mullins, N, Kang, J, Campos, A, Coleman, JR, Edwards, AC, Galfalvy, H, Levey, DF, Lori, A, Shabalin, A, Starnawska, A, Su, M-H, Watson, HJ, Adams, M, Awasthi, S, Ganda, M, Hafferty, JD, Hishimoto, A, Kim, M, Okazaki, S, Otsuka, I, Ripke, S, Ware, EB, Bergen, AW, Berrettini, WH, Bohus, M, Brandt, H, Chang, X, Chen, WJ, Chen, H-C, Crawford, S, Crow, S, DiBlasi, E, Duriez, P, Fernandez-Aranda, F, Fichter, MM, Gallinger, S, Glatt, SJ, Gorwood, P, Guo, Y, Hakonarson, H, Halmi, KA, Hwu, H-G, Jain, S, Jamain, S, Jimenez-Murcia, S, Johnson, C, Kaplan, AS, Kaye, WH, Keel, PK, Kennedy, JL, Klump, KL, Li, D, Liao, S-C, Lieb, K, Lilenfeld, L, Liu, C-M, Magistretti, PJ, Marshall, CR, Mitchell, JE, Monson, ET, Myers, RM, Pinto, D, Powers, A, Ramoz, N, Roepke, S, Rozanov, V, Scherer, SW, Schmahl, C, Sokolowski, M, Strober, M, Thornton, LM, Treasure, J, Tsuang, MT, Witt, SH, Woodside, DB, Yilmaz, Z, Zillich, L, Adolfsson, R, Agartz, I, Air, TM, Alda, M, Alfredsson, L, Andreassen, OA, Anjorin, A, Appadurai, V, Artigas, MS, Van der Auwera, S, Azevedo, MH, Bass, N, Bau, CHD, Baune, BT, Bellivier, F, Berger, K, Biernacka, JM, Bigdeli, TB, Binder, EB, Boehnke, M, Boks, MP, Bosch, R, Braff, DL, Bryant, R, Budde, M, Byrne, EM, Cahn, W, Casas, M, Castelao, E, Cervilla, JA, Chaumette, B, Cichon, S, Corvin, A, Craddock, N, Craig, D, Degenhardt, F, Djurovic, S, Edenberg, HJ, Fanous, AH, Foo, JC, Forstner, AJ, Frye, M, Fullerton, JM, Gatt, JM, Gejman, P, Giegling, I, Grabe, HJ, Green, MJ, Grevet, EH, Grigoroiu-Serbanescu, M, Gutierrez, B, Guzman-Parra, J, Hamilton, SP, Hamshere, ML, Hartmann, A, Hauser, J, Heilmann-Heimbach, S, Hoffmann, P, Ising, M, Jones, I, Jones, LA, Jonsson, L, Kahn, RS, Kelsoe, JR, Kendler, KS, Kloiber, S, Koenen, KC, Kogevinas, M, Konte, B, Krebs, M-O, Lander, M, Lawrence, J, Leboyer, M, Lee, PH, Levinson, DF, Liao, C, Lissowska, J, Lucae, S, Mayoral, F, McElroy, SL, McGrath, P, McGuffin, P, McQuillin, A, Medland, SE, Mehta, D, Melle, I, Milaneschi, Y, Mitchell, PB, Molina, E, Morken, G, Mortensen, PB, Mueller-Myhsok, B, Nievergelt, C, Nimgaonkar, V, Noethen, MM, O'Donovan, MC, Ophoff, RA, Owen, MJ, Pato, C, Pato, MT, Penninx, BWJH, Pimm, J, Pistis, G, Potash, JB, Power, RA, Preisig, M, Quested, D, Ramos-Quiroga, JA, Reif, A, Ribases, M, Richarte, V, Rietschel, M, Rivera, M, Roberts, A, Roberts, G, Rouleau, GA, Rovaris, DL, Rujescu, D, Sanchez-Mora, C, Sanders, AR, Schofield, PR, Schulze, TG, Scott, LJ, Serretti, A, Shi, J, Shyn, S, Sirignano, L, Sklar, P, Smeland, OB, Smoller, JW, Sonuga-Barke, EJS, Spalletta, G, Strauss, JS, Swiatkowska, B, Trzaskowski, M, Turecki, G, Vilar-Ribo, L, Vincent, JB, Voelzke, H, Walters, JTR, Weickert, CS, Weickert, TW, Weissman, MM, Williams, LM, Wray, NR, Zai, CC, Ashley-Koch, AE, Beckham, JC, Hauser, ER, Hauser, MA, Kimbrel, NA, Lindquist, JH, McMahon, B, Oslin, DW, Qin, X, Agerbo, E, Borglum, AD, Breen, G, Erlangsen, A, Esko, T, Gelernter, J, Hougaard, DM, Kessler, RC, Kranzler, HR, Li, QS, Martin, NG, McIntosh, AM, Mors, O, Nordentoft, M, Olsen, CM, Porteous, D, Ursano, RJ, Wasserman, D, Werge, T, Whiteman, DC, Bulik, CM, Coon, H, Demontis, D, Docherty, AR, Kuo, P-H, Lewis, CM, Mann, JJ, Renteria, ME, Smith, DJ, Stahl, EA, Stein, MB, Streit, F, Willour, V, and Ruderfer, DM
Abstract: BACKGROUND: Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. METHODS: We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. RESULTS: Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. CONCLUSIONS: Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.
Published: 2022

5. Identifying the Common Genetic Basis of Antidepressant Response.

Author: Pain, O, Hodgson, K, Trubetskoy, V, Ripke, S, Marshe, VS, Adams, MJ, Byrne, EM, Campos, AI, Carrillo-Roa, T, Cattaneo, A, Als, TD, Souery, D, Dernovsek, MZ, Fabbri, C, Hayward, C, Henigsberg, N, Hauser, J, Kennedy, JL, Lenze, EJ, Lewis, G, Müller, DJ, Martin, NG, Mulsant, BH, Mors, O, Perroud, N, Porteous, DJ, Rentería, ME, Reynolds, CF, Rietschel, M, Uher, R, Wigmore, EM, Maier, W, Wray, NR, Aitchison, KJ, Arolt, V, Baune, BT, Biernacka, JM, Bondolfi, G, Domschke, K, Kato, M, Li, QS, Liu, Y-L, Serretti, A, Tsai, S-J, Turecki, G, Weinshilboum, R, GSRD Consortium, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, McIntosh, AM, Lewis, CM, Pain, O, Hodgson, K, Trubetskoy, V, Ripke, S, Marshe, VS, Adams, MJ, Byrne, EM, Campos, AI, Carrillo-Roa, T, Cattaneo, A, Als, TD, Souery, D, Dernovsek, MZ, Fabbri, C, Hayward, C, Henigsberg, N, Hauser, J, Kennedy, JL, Lenze, EJ, Lewis, G, Müller, DJ, Martin, NG, Mulsant, BH, Mors, O, Perroud, N, Porteous, DJ, Rentería, ME, Reynolds, CF, Rietschel, M, Uher, R, Wigmore, EM, Maier, W, Wray, NR, Aitchison, KJ, Arolt, V, Baune, BT, Biernacka, JM, Bondolfi, G, Domschke, K, Kato, M, Li, QS, Liu, Y-L, Serretti, A, Tsai, S-J, Turecki, G, Weinshilboum, R, GSRD Consortium, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, McIntosh, AM, and Lewis, CM
Abstract: BACKGROUND: Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction. METHODS: Genome-wide analysis of remission (n remit = 1852, n nonremit = 3299) and percentage improvement (n = 5218) was performed. Single nucleotide polymorphism-based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA. RESULTS: Neither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism-based heritability was significantly different from zero for remission (h 2 = 0.132, SE = 0.056) but not for percentage improvement (h 2 = -0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response. CONCLUSIONS: This study demonstrates that antidepressant response is influenced by common genetic variation, has a genetic overlap sc
Published: 2022

6. Combining schizophrenia and depression polygenic risk scores improves the genetic prediction of lithium response in bipolar disorder patients (vol 12, 278, 2022)

Author: Schubert, KO, Thalamuthu, A, Amare, AT, Frank, J, Streit, F, Adl, M, Akula, N, Akiyama, K, Ardau, R, Arias, B, Aubry, J-M, Backlund, L, Bhattacharjee, AK, Bellivier, F, Benabarre, A, Bengesser, S, Biernacka, JM, Birner, A, Marie-Claire, C, Cearns, M, Cervantes, P, Chen, H-C, Chillotti, C, Cichon, S, Clark, SR, Cruceanu, C, Czerski, PM, Dalkner, N, Dayer, A, Degenhardt, F, Del Zompo, M, DePaulo, JR, Etain, B, Falkai, P, Forstner, AJ, Frisen, L, Frye, MA, Fullerton, JM, Gard, S, Garnham, JS, Goes, FS, Grigoroiu-Serbanescu, M, Grof, P, Hashimoto, R, Hauser, J, Heilbronner, U, Herms, S, Hoffmann, P, Hou, L, Hsu, Y-H, Jamain, S, Jimenez, E, Kahn, J-P, Kassem, L, Kuo, P-H, Kato, T, Kelsoe, J, Kittel-Schneider, S, Ferensztajn-Rochowiak, E, Konig, B, Kusumi, I, Laje, G, Landen, M, Lavebratt, C, Leboyer, M, Leckband, SG, Maj, M, Manchia, M, Martinsson, L, McCarthy, MJ, McElroy, S, Colom, F, Mitjans, M, Mondimore, FM, Monteleone, P, Nievergelt, CM, Nothen, MM, Novak, T, O'Donovan, C, Ozaki, N, Osby, U, Papiol, S, Pfennig, A, Pisanu, C, Potash, JB, Reif, A, Reininghaus, E, Rouleau, GA, Rybakowski, JK, Schalling, M, Schofield, PR, Schweizer, BW, Severino, G, Shekhtman, T, Shilling, PD, Shimoda, K, Simhandl, C, Slaney, CM, Squassina, A, Stamm, T, Stopkova, P, Tekola-Ayele, F, Tortorella, A, Turecki, G, Veeh, J, Vieta, E, Witt, SH, Roberts, G, Zandi, PP, Alda, M, Bauer, M, McMahon, FJ, Mitchell, PB, Schulze, TG, Rietschel, M, Baune, BT, Schubert, KO, Thalamuthu, A, Amare, AT, Frank, J, Streit, F, Adl, M, Akula, N, Akiyama, K, Ardau, R, Arias, B, Aubry, J-M, Backlund, L, Bhattacharjee, AK, Bellivier, F, Benabarre, A, Bengesser, S, Biernacka, JM, Birner, A, Marie-Claire, C, Cearns, M, Cervantes, P, Chen, H-C, Chillotti, C, Cichon, S, Clark, SR, Cruceanu, C, Czerski, PM, Dalkner, N, Dayer, A, Degenhardt, F, Del Zompo, M, DePaulo, JR, Etain, B, Falkai, P, Forstner, AJ, Frisen, L, Frye, MA, Fullerton, JM, Gard, S, Garnham, JS, Goes, FS, Grigoroiu-Serbanescu, M, Grof, P, Hashimoto, R, Hauser, J, Heilbronner, U, Herms, S, Hoffmann, P, Hou, L, Hsu, Y-H, Jamain, S, Jimenez, E, Kahn, J-P, Kassem, L, Kuo, P-H, Kato, T, Kelsoe, J, Kittel-Schneider, S, Ferensztajn-Rochowiak, E, Konig, B, Kusumi, I, Laje, G, Landen, M, Lavebratt, C, Leboyer, M, Leckband, SG, Maj, M, Manchia, M, Martinsson, L, McCarthy, MJ, McElroy, S, Colom, F, Mitjans, M, Mondimore, FM, Monteleone, P, Nievergelt, CM, Nothen, MM, Novak, T, O'Donovan, C, Ozaki, N, Osby, U, Papiol, S, Pfennig, A, Pisanu, C, Potash, JB, Reif, A, Reininghaus, E, Rouleau, GA, Rybakowski, JK, Schalling, M, Schofield, PR, Schweizer, BW, Severino, G, Shekhtman, T, Shilling, PD, Shimoda, K, Simhandl, C, Slaney, CM, Squassina, A, Stamm, T, Stopkova, P, Tekola-Ayele, F, Tortorella, A, Turecki, G, Veeh, J, Vieta, E, Witt, SH, Roberts, G, Zandi, PP, Alda, M, Bauer, M, McMahon, FJ, Mitchell, PB, Schulze, TG, Rietschel, M, and Baune, BT
Published: 2022

7. HLA-DRB1 and HLA-DQB1 genetic diversity modulates response to lithium in bipolar affective disorders

Author: Le Clerc, S, Lombardi, L, Baune, BT, Amare, AT, Schubert, KO, Hou, L, Clark, SR, Papiol, S, Cearns, M, Heilbronner, U, Degenhardt, F, Tekola-Ayele, F, Hsu, YH, Shekhtman, T, Adli, M, Akula, N, Akiyama, K, Ardau, R, Arias, B, Aubry, JM, Backlund, L, Bhattacharjee, AK, Bellivier, F, Benabarre, A, Bengesser, S, Biernacka, JM, Birner, A, Brichant-Petitjean, C, Cervantes, P, Chen, HC, Chillotti, C, Cichon, S, Cruceanu, C, Czerski, PM, Dalkner, N, Dayer, A, Del Zompo, M, DePaulo, JR, Étain, B, Jamain, S, Falkai, P, Forstner, AJ, Frisen, L, Frye, MA, Fullerton, JM, Gard, S, Garnham, JS, Goes, FS, Grigoroiu-Serbanescu, M, Grof, P, Hashimoto, R, Hauser, J, Herms, S, Hoffmann, P, Jiménez, E, Kahn, JP, Kassem, L, Kuo, PH, Kato, T, Kelsoe, JR, Kittel-Schneider, S, Ferensztajn-Rochowiak, E, König, B, Kusumi, I, Laje, G, Landén, M, Lavebratt, C, Leckband, SG, Tortorella, A, Manchia, M, Martinsson, L, McCarthy, MJ, McElroy, SL, Colom, F, Millischer, V, Mitjans, M, Mondimore, FM, Monteleone, P, Nievergelt, CM, Nöthen, MM, Novák, T, O’Donovan, C, Ozaki, N, Ösby, U, Pfennig, A, Potash, JB, Reif, A, Reininghaus, E, Rouleau, GA, Rybakowski, JK, Schalling, M, Schofield, PR, Schweizer, BW, Severino, G, Shilling, PD, Shimoda, K, Simhandl, C, Slaney, CM, Pisanu, C, Squassina, A, Le Clerc, S, Lombardi, L, Baune, BT, Amare, AT, Schubert, KO, Hou, L, Clark, SR, Papiol, S, Cearns, M, Heilbronner, U, Degenhardt, F, Tekola-Ayele, F, Hsu, YH, Shekhtman, T, Adli, M, Akula, N, Akiyama, K, Ardau, R, Arias, B, Aubry, JM, Backlund, L, Bhattacharjee, AK, Bellivier, F, Benabarre, A, Bengesser, S, Biernacka, JM, Birner, A, Brichant-Petitjean, C, Cervantes, P, Chen, HC, Chillotti, C, Cichon, S, Cruceanu, C, Czerski, PM, Dalkner, N, Dayer, A, Del Zompo, M, DePaulo, JR, Étain, B, Jamain, S, Falkai, P, Forstner, AJ, Frisen, L, Frye, MA, Fullerton, JM, Gard, S, Garnham, JS, Goes, FS, Grigoroiu-Serbanescu, M, Grof, P, Hashimoto, R, Hauser, J, Herms, S, Hoffmann, P, Jiménez, E, Kahn, JP, Kassem, L, Kuo, PH, Kato, T, Kelsoe, JR, Kittel-Schneider, S, Ferensztajn-Rochowiak, E, König, B, Kusumi, I, Laje, G, Landén, M, Lavebratt, C, Leckband, SG, Tortorella, A, Manchia, M, Martinsson, L, McCarthy, MJ, McElroy, SL, Colom, F, Millischer, V, Mitjans, M, Mondimore, FM, Monteleone, P, Nievergelt, CM, Nöthen, MM, Novák, T, O’Donovan, C, Ozaki, N, Ösby, U, Pfennig, A, Potash, JB, Reif, A, Reininghaus, E, Rouleau, GA, Rybakowski, JK, Schalling, M, Schofield, PR, Schweizer, BW, Severino, G, Shilling, PD, Shimoda, K, Simhandl, C, Slaney, CM, Pisanu, C, and Squassina, A
Abstract: Bipolar affective disorder (BD) is a severe psychiatric illness, for which lithium (Li) is the gold standard for acute and maintenance therapies. The therapeutic response to Li in BD is heterogeneous and reliable biomarkers allowing patients stratification are still needed. A GWAS performed by the International Consortium on Lithium Genetics (ConLiGen) has recently identified genetic markers associated with treatment responses to Li in the human leukocyte antigens (HLA) region. To better understand the molecular mechanisms underlying this association, we have genetically imputed the classical alleles of the HLA region in the European patients of the ConLiGen cohort. We found our best signal for amino-acid variants belonging to the HLA-DRB1*11:01 classical allele, associated with a better response to Li (p < 1 × 10−3; FDR < 0.09 in the recessive model). Alanine or Leucine at position 74 of the HLA-DRB1 heavy chain was associated with a good response while Arginine or Glutamic acid with a poor response. As these variants have been implicated in common inflammatory/autoimmune processes, our findings strongly suggest that HLA-mediated low inflammatory background may contribute to the efficient response to Li in BD patients, while an inflammatory status overriding Li anti-inflammatory properties would favor a weak response.
Published: 2021

8. Gearing up for the future: Exploring facilitators and barriers to inform clinical trial design in frontotemporal lobar degeneration

Author: Banga, YB, Banga, YB, Lai, Y, Kim, P, Boeve, BF, Boxer, AL, Rosen, HJ, Forsberg, LK, Heuer, HW, Brushaber, D, Appleby, B, Biernacka, JM, Bordelon, YM, Botha, H, Bozoki, AC, Brannelly, P, Dickerson, BC, Dickinson, S, Dickson, DW, Domoto-Reilly, K, Faber, K, Fagan, AM, Fields, JA, Fishman, A, Foroud, TM, Galasko, DR, Gavrilova, RH, Gendron, TF, Geschwind, DH, Ghoshal, N, Goldman, J, Graff-Radford, J, Graff-Radford, NR, Grant, I, Grossman, M, Hsiung, GYR, Huang, EJ, Huey, ED, Irwin, DJ, Jones, DT, Kantarci, K, Karydas, AM, Kaufer, D, Knopman, DS, Kramer, JH, Kremers, WK, Kornak, J, Kukull, WA, Lagone, E, Leger, GC, Litvan, I, Ljubenkov, PA, Lucente, DE, Mackenzie, IR, Manoochehri, M, Masdeu, JC, McGinnis, S, Mendez, MF, Miller, BL, Miyagawa, T, Nelson, KM, Onyike, CU, Pantelyat, A, Pascual, B, Pearlman, R, Petrucelli, L, Pottier, CP, Rademakers, R, Ramos, EM, Rankin, KP, Rascovsky, K, Rexach, JE, Ritter, A, Roberson, ED, Rojas, JC, Sabbagh, MN, Salmon, DP, Savica, R, Seeley, WW, Staffaroni, AM, Syrjanen, JA, Tartaglia, MC, Tatton, N, Taylor, JC, Toga, AW, Weintraub, S, Wheaton, D, Wong, B, Wszolek, Z, Banga, YB, Banga, YB, Lai, Y, Kim, P, Boeve, BF, Boxer, AL, Rosen, HJ, Forsberg, LK, Heuer, HW, Brushaber, D, Appleby, B, Biernacka, JM, Bordelon, YM, Botha, H, Bozoki, AC, Brannelly, P, Dickerson, BC, Dickinson, S, Dickson, DW, Domoto-Reilly, K, Faber, K, Fagan, AM, Fields, JA, Fishman, A, Foroud, TM, Galasko, DR, Gavrilova, RH, Gendron, TF, Geschwind, DH, Ghoshal, N, Goldman, J, Graff-Radford, J, Graff-Radford, NR, Grant, I, Grossman, M, Hsiung, GYR, Huang, EJ, Huey, ED, Irwin, DJ, Jones, DT, Kantarci, K, Karydas, AM, Kaufer, D, Knopman, DS, Kramer, JH, Kremers, WK, Kornak, J, Kukull, WA, Lagone, E, Leger, GC, Litvan, I, Ljubenkov, PA, Lucente, DE, Mackenzie, IR, Manoochehri, M, Masdeu, JC, McGinnis, S, Mendez, MF, Miller, BL, Miyagawa, T, Nelson, KM, Onyike, CU, Pantelyat, A, Pascual, B, Pearlman, R, Petrucelli, L, Pottier, CP, Rademakers, R, Ramos, EM, Rankin, KP, Rascovsky, K, Rexach, JE, Ritter, A, Roberson, ED, Rojas, JC, Sabbagh, MN, Salmon, DP, Savica, R, Seeley, WW, Staffaroni, AM, Syrjanen, JA, Tartaglia, MC, Tatton, N, Taylor, JC, Toga, AW, Weintraub, S, Wheaton, D, Wong, B, and Wszolek, Z
Abstract: BACKGROUND: Frontotemporal lobar degeneration (FTLD) refers to a group of neurodegenerative conditions, affecting the frontal and/or temporal lobes. Ongoing research has provided insight into developing clinical trials for FTLD and key clinical measures such as structural MRI. To inform clinical trial design and optimize participation, it is imperative to explore facilitators and barriers for potential candidates. OBJECTIVE: The objective of this study is to explore facilitators and barriers to participating in future clinical trials for FTLD. METHODS: Advancing Research and Treatment for Frontotemporal Lobar Degeneration (ARTFL) and Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects (LEFFTDS) are observational studies focused on characterizing FTLD syndromes in preparation for clinical trials. The 584 participants enrolled across 18 research sites in the United States and Canada completed a survey assessing interest in clinical trial participation. RESULTS: 29% of respondents self-reported as patients (63±10 years), 26% self-reported as caregivers answering on behalf of patients (65±10 years), and 45% self-reported as healthy but at risk for FTLD (48±14 years). Travel reimbursement was the most common factor reported to positively influence participation (≧66%), with the healthy but at risk group showing the strongest endorsement (83%). Cost and time involved in travel were possible barriers for about half of the patients (48%) and healthy but at risk respondents (53%). The respondents value receiving feedback on the study findings (≧80%) and being informed of their individual disease progression (≧75%). Particularly, keeping participation confidential was very important for the healthy but at risk group (62%). In regard to research assessments, most participants demonstrated a high interest in physical and neurological exams at a research center (≧87%) whereas only half were interested in doing more invasive procedures such as the lumbar puncture
Published: 2021

9. Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology

Author: Mullins, N, Forstner, AJ, O'Connell, KS, Coombes, B, Coleman, JR, Qiao, Z, Als, TD, Bigdeli, TB, Borte, S, Bryois, J, Charney, AW, Drange, OK, Gandal, MJ, Hagenaars, SP, Ikeda, M, Kamitaki, N, Kim, M, Krebs, K, Panagiotaropoulou, G, Schilder, BM, Sloofman, LG, Steinberg, S, Trubetskoy, V, Winsvold, BS, Won, H-H, Abramova, L, Adorjan, K, Agerbo, E, Al Eissa, M, Albani, D, Alliey-Rodriguez, N, Anjorin, A, Antilla, V, Antoniou, A, Awasthi, S, Baek, JH, Baekvad-Hansen, M, Bass, N, Bauer, M, Beins, EC, Bergen, SE, Birner, A, Pedersen, CB, Boen, E, Boks, MP, Bosch, R, Brum, M, Brumpton, BM, Brunkhorst-Kanaan, N, Budde, M, Bybjerg-Grauholm, J, Byerley, W, Cairns, M, Casas, M, Cervantes, P, Clarke, T-K, Cruceanu, C, Cuellar-Barboza, A, Cunningham, J, Curtis, D, Czerski, PM, Dale, AM, Dalkner, N, David, FS, Degenhardt, F, Djurovic, S, Dobbyn, AL, Douzenis, A, Elvsashagen, T, Escott-Price, V, Ferrier, IN, Fiorentino, A, Foroud, TM, Forty, L, Frank, J, Frei, O, Freimer, NB, Frisen, L, Gade, K, Garnham, J, Gelernter, J, Pedersen, MG, Gizer, IR, Gordon, SD, Gordon-Smith, K, Greenwood, TA, Grove, J, Guzman-Parra, J, Ha, K, Haraldsson, M, Hautzinger, M, Heilbronner, U, Hellgren, D, Herms, S, Hoffmann, P, Holmans, PA, Huckins, L, Jamain, S, Johnson, JS, Kalman, JL, Kamatani, Y, Kennedy, JL, Kittel-Schneider, S, Knowles, JA, Kogevinas, M, Koromina, M, Kranz, TM, Kranzler, HR, Kubo, M, Kupka, R, Kushner, SA, Lavebratt, C, Lawrence, J, Leber, M, Lee, H-J, Lee, PH, Levy, SE, Lewis, C, Liao, C, Lucae, S, Lundberg, M, MacIntyre, DJ, Maier, W, Maihofer, A, Malaspina, D, Maratou, E, Martinsson, L, Mattheisen, M, McCarroll, SA, McGregor, NW, McGuffin, P, McKay, JD, Medeiros, H, Medland, SE, Millischer, V, Montgomery, GW, Moran, JL, Morris, DW, Muhleisen, TW, O'Brien, N, O'Donovan, C, Loohuis, LMO, Oruc, L, Papiol, S, Pardinas, AF, Perry, A, Pfennig, A, Porichi, E, Potash, JB, Quested, D, Raj, T, Rapaport, MH, DePaulo, JR, Regeer, EJ, Rice, JP, Rivas, F, Rivera, M, Roth, J, Roussos, P, Ruderfer, DM, Sanchez-Mora, C, Schulte, EC, Senner, F, Sharp, S, Shilling, PD, Sigurdsson, E, Sirignano, L, Slaney, C, Smeland, OB, Sobell, JL, Hansen, CS, Artigas, MS, Spijker, AT, Stein, DJ, Strauss, JS, Swiatkowska, B, Terao, C, Thorgeirsson, TE, Toma, C, Tooney, P, Tsermpini, E-E, Vawter, MP, Vedder, H, Walters, JTR, Witt, SH, Xi, S, Xu, W, Yang, JMK, Young, AH, Young, H, Zandi, PP, Zhou, H, Zillich, L, Adolfsson, R, Agartz, I, Alda, M, Alfredsson, L, Babadjanova, G, Backlund, L, Baune, BT, Bellivier, F, Bengesser, S, Berrettini, WH, Blackwood, DHR, Boehnke, M, Borglum, AD, Breen, G, Carr, VJ, Catts, S, Corvin, A, Craddock, N, Dannlowski, U, Dikeos, D, Esko, T, Etain, B, Ferentinos, P, Frye, M, Fullerton, JM, Gawlik, M, Gershon, ES, Goes, F, Green, MJ, Grigoroiu-Serbanescu, M, Hauser, J, Henskens, F, Hillert, J, Hong, KS, Hougaard, DM, Hultman, CM, Hveem, K, Iwata, N, Jablensky, A, Jones, I, Jones, LA, Kahn, RS, Kelsoe, JR, Kirov, G, Landen, M, Leboyer, M, Lewis, CM, Li, QS, Lissowska, J, Lochner, C, Loughland, C, Martin, NG, Mathews, CA, Mayoral, F, McElroy, SL, McIntosh, AM, McMahon, FJ, Melle, I, Michie, P, Milani, L, Mitchell, PB, Morken, G, Mors, O, Mortensen, PB, Mowry, B, Muller-Myhsok, B, Myers, RM, Neale, BM, Nievergelt, CM, Nordentoft, M, Nothen, MM, ODonovan, MC, Oedegaard, KJ, Olsson, T, Owen, MJ, Paciga, SA, Pantelis, C, Pato, C, Pato, MT, Patrinos, GP, Perlis, RH, Posthuma, D, Ramos-Quiroga, JA, Reif, A, Reininghaus, EZ, Ribases, M, Rietschel, M, Ripke, S, Rouleau, GA, Saito, T, Schall, U, Schalling, M, Schofield, PR, Schulze, TG, Scott, LJ, Scott, RJ, Serretti, A, Weickert, CS, Smoller, JW, Stefansson, H, Stefansson, K, Stordal, E, Streit, F, Sullivan, PF, Turecki, G, Vaaler, AE, Vieta, E, Vincent, JB, Waldman, ID, Weickert, TW, Werge, T, Wray, NR, Zwart, J, Biernacka, JM, Nurnberger, J, Cichon, S, Edenberg, HJ, Stahl, EA, McQuillin, A, Di Florio, A, Ophoff, RA, Andreassen, OA, Mullins, N, Forstner, AJ, O'Connell, KS, Coombes, B, Coleman, JR, Qiao, Z, Als, TD, Bigdeli, TB, Borte, S, Bryois, J, Charney, AW, Drange, OK, Gandal, MJ, Hagenaars, SP, Ikeda, M, Kamitaki, N, Kim, M, Krebs, K, Panagiotaropoulou, G, Schilder, BM, Sloofman, LG, Steinberg, S, Trubetskoy, V, Winsvold, BS, Won, H-H, Abramova, L, Adorjan, K, Agerbo, E, Al Eissa, M, Albani, D, Alliey-Rodriguez, N, Anjorin, A, Antilla, V, Antoniou, A, Awasthi, S, Baek, JH, Baekvad-Hansen, M, Bass, N, Bauer, M, Beins, EC, Bergen, SE, Birner, A, Pedersen, CB, Boen, E, Boks, MP, Bosch, R, Brum, M, Brumpton, BM, Brunkhorst-Kanaan, N, Budde, M, Bybjerg-Grauholm, J, Byerley, W, Cairns, M, Casas, M, Cervantes, P, Clarke, T-K, Cruceanu, C, Cuellar-Barboza, A, Cunningham, J, Curtis, D, Czerski, PM, Dale, AM, Dalkner, N, David, FS, Degenhardt, F, Djurovic, S, Dobbyn, AL, Douzenis, A, Elvsashagen, T, Escott-Price, V, Ferrier, IN, Fiorentino, A, Foroud, TM, Forty, L, Frank, J, Frei, O, Freimer, NB, Frisen, L, Gade, K, Garnham, J, Gelernter, J, Pedersen, MG, Gizer, IR, Gordon, SD, Gordon-Smith, K, Greenwood, TA, Grove, J, Guzman-Parra, J, Ha, K, Haraldsson, M, Hautzinger, M, Heilbronner, U, Hellgren, D, Herms, S, Hoffmann, P, Holmans, PA, Huckins, L, Jamain, S, Johnson, JS, Kalman, JL, Kamatani, Y, Kennedy, JL, Kittel-Schneider, S, Knowles, JA, Kogevinas, M, Koromina, M, Kranz, TM, Kranzler, HR, Kubo, M, Kupka, R, Kushner, SA, Lavebratt, C, Lawrence, J, Leber, M, Lee, H-J, Lee, PH, Levy, SE, Lewis, C, Liao, C, Lucae, S, Lundberg, M, MacIntyre, DJ, Maier, W, Maihofer, A, Malaspina, D, Maratou, E, Martinsson, L, Mattheisen, M, McCarroll, SA, McGregor, NW, McGuffin, P, McKay, JD, Medeiros, H, Medland, SE, Millischer, V, Montgomery, GW, Moran, JL, Morris, DW, Muhleisen, TW, O'Brien, N, O'Donovan, C, Loohuis, LMO, Oruc, L, Papiol, S, Pardinas, AF, Perry, A, Pfennig, A, Porichi, E, Potash, JB, Quested, D, Raj, T, Rapaport, MH, DePaulo, JR, Regeer, EJ, Rice, JP, Rivas, F, Rivera, M, Roth, J, Roussos, P, Ruderfer, DM, Sanchez-Mora, C, Schulte, EC, Senner, F, Sharp, S, Shilling, PD, Sigurdsson, E, Sirignano, L, Slaney, C, Smeland, OB, Sobell, JL, Hansen, CS, Artigas, MS, Spijker, AT, Stein, DJ, Strauss, JS, Swiatkowska, B, Terao, C, Thorgeirsson, TE, Toma, C, Tooney, P, Tsermpini, E-E, Vawter, MP, Vedder, H, Walters, JTR, Witt, SH, Xi, S, Xu, W, Yang, JMK, Young, AH, Young, H, Zandi, PP, Zhou, H, Zillich, L, Adolfsson, R, Agartz, I, Alda, M, Alfredsson, L, Babadjanova, G, Backlund, L, Baune, BT, Bellivier, F, Bengesser, S, Berrettini, WH, Blackwood, DHR, Boehnke, M, Borglum, AD, Breen, G, Carr, VJ, Catts, S, Corvin, A, Craddock, N, Dannlowski, U, Dikeos, D, Esko, T, Etain, B, Ferentinos, P, Frye, M, Fullerton, JM, Gawlik, M, Gershon, ES, Goes, F, Green, MJ, Grigoroiu-Serbanescu, M, Hauser, J, Henskens, F, Hillert, J, Hong, KS, Hougaard, DM, Hultman, CM, Hveem, K, Iwata, N, Jablensky, A, Jones, I, Jones, LA, Kahn, RS, Kelsoe, JR, Kirov, G, Landen, M, Leboyer, M, Lewis, CM, Li, QS, Lissowska, J, Lochner, C, Loughland, C, Martin, NG, Mathews, CA, Mayoral, F, McElroy, SL, McIntosh, AM, McMahon, FJ, Melle, I, Michie, P, Milani, L, Mitchell, PB, Morken, G, Mors, O, Mortensen, PB, Mowry, B, Muller-Myhsok, B, Myers, RM, Neale, BM, Nievergelt, CM, Nordentoft, M, Nothen, MM, ODonovan, MC, Oedegaard, KJ, Olsson, T, Owen, MJ, Paciga, SA, Pantelis, C, Pato, C, Pato, MT, Patrinos, GP, Perlis, RH, Posthuma, D, Ramos-Quiroga, JA, Reif, A, Reininghaus, EZ, Ribases, M, Rietschel, M, Ripke, S, Rouleau, GA, Saito, T, Schall, U, Schalling, M, Schofield, PR, Schulze, TG, Scott, LJ, Scott, RJ, Serretti, A, Weickert, CS, Smoller, JW, Stefansson, H, Stefansson, K, Stordal, E, Streit, F, Sullivan, PF, Turecki, G, Vaaler, AE, Vieta, E, Vincent, JB, Waldman, ID, Weickert, TW, Werge, T, Wray, NR, Zwart, J, Biernacka, JM, Nurnberger, J, Cichon, S, Edenberg, HJ, Stahl, EA, McQuillin, A, Di Florio, A, Ophoff, RA, and Andreassen, OA
Abstract: Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.
Published: 2021

10. Association of polygenic score for major depression with response to lithium in patients with bipolar disorder

Author: Amare, AT, Schubert, KO, Hou, L, Clark, SR, Papiol, S, Cearns, M, Heilbronner, U, Degenhardt, F, Tekola-Ayele, F, Hsu, Y-H, Shekhtman, T, AdIi, M, Akula, N, Akiyama, K, Ardau, R, Arias, B, Aubry, J-M, Backlund, L, Bhattacharjee, K, Bellivier, F, Benabarre, A, Bengesser, S, Biernacka, JM, Birner, A, Brichant-Petitjean, C, Cervantes, P, Chen, H-C, Chillotti, C, Cichon, S, Cruceanu, C, Czerski, PM, Dalkner, N, Dayer, A, Del Zompo, M, DePaulo, JR, Etain, B, Jamain, S, Falkai, P, Forstner, AJ, Frisen, L, Frye, MA, Fullerton, JM, Gard, S, Garnham, JS, Goes, FS, Grigoroiu-Serbanescu, M, Grof, P, Hashimoto, R, Hauser, J, Herms, S, Hoffmann, P, Hofmann, A, Jimenez, E, Kahn, J-P, Kassem, L, Kuo, P-H, Kato, T, Kelsoe, JR, Kittel-Schneider, S, Kliwicki, S, Koenig, B, Kusumi, I, Laje, G, Landen, M, Lavebratt, C, Leboyer, M, Leckband, SG, Tortorella, A, Manchia, M, Martinsson, L, McCarthy, MJ, McElroy, SL, Colom, F, Mitjans, M, Mondimore, FM, Monteleone, P, Nievergelt, CM, Noethen, MM, Novak, T, O'Donovan, C, Ozaki, N, Osby, U, Pfennig, A, Potash, JB, Reif, A, Reininghaus, E, Rouleau, GA, Rybakowski, JK, Schalling, M, Schofield, PR, Schweizer, BW, Severino, G, Shilling, PD, Shimoda, K, Simhandl, C, Slaney, CM, Squassina, A, Stamm, T, Stopkova, P, Maj, M, Turecki, G, Vieta, E, Veeh, J, Witt, SH, Wright, A, Zandi, PP, Mitchell, PB, Bauer, M, Alda, M, Rietschel, M, McMahon, FJ, Schulze, TG, Baune, BT, Amare, AT, Schubert, KO, Hou, L, Clark, SR, Papiol, S, Cearns, M, Heilbronner, U, Degenhardt, F, Tekola-Ayele, F, Hsu, Y-H, Shekhtman, T, AdIi, M, Akula, N, Akiyama, K, Ardau, R, Arias, B, Aubry, J-M, Backlund, L, Bhattacharjee, K, Bellivier, F, Benabarre, A, Bengesser, S, Biernacka, JM, Birner, A, Brichant-Petitjean, C, Cervantes, P, Chen, H-C, Chillotti, C, Cichon, S, Cruceanu, C, Czerski, PM, Dalkner, N, Dayer, A, Del Zompo, M, DePaulo, JR, Etain, B, Jamain, S, Falkai, P, Forstner, AJ, Frisen, L, Frye, MA, Fullerton, JM, Gard, S, Garnham, JS, Goes, FS, Grigoroiu-Serbanescu, M, Grof, P, Hashimoto, R, Hauser, J, Herms, S, Hoffmann, P, Hofmann, A, Jimenez, E, Kahn, J-P, Kassem, L, Kuo, P-H, Kato, T, Kelsoe, JR, Kittel-Schneider, S, Kliwicki, S, Koenig, B, Kusumi, I, Laje, G, Landen, M, Lavebratt, C, Leboyer, M, Leckband, SG, Tortorella, A, Manchia, M, Martinsson, L, McCarthy, MJ, McElroy, SL, Colom, F, Mitjans, M, Mondimore, FM, Monteleone, P, Nievergelt, CM, Noethen, MM, Novak, T, O'Donovan, C, Ozaki, N, Osby, U, Pfennig, A, Potash, JB, Reif, A, Reininghaus, E, Rouleau, GA, Rybakowski, JK, Schalling, M, Schofield, PR, Schweizer, BW, Severino, G, Shilling, PD, Shimoda, K, Simhandl, C, Slaney, CM, Squassina, A, Stamm, T, Stopkova, P, Maj, M, Turecki, G, Vieta, E, Veeh, J, Witt, SH, Wright, A, Zandi, PP, Mitchell, PB, Bauer, M, Alda, M, Rietschel, M, McMahon, FJ, Schulze, TG, and Baune, BT
Abstract: Lithium is a first-line medication for bipolar disorder (BD), but only one in three patients respond optimally to the drug. Since evidence shows a strong clinical and genetic overlap between depression and bipolar disorder, we investigated whether a polygenic susceptibility to major depression is associated with response to lithium treatment in patients with BD. Weighted polygenic scores (PGSs) were computed for major depression (MD) at different GWAS p value thresholds using genetic data obtained from 2586 bipolar patients who received lithium treatment and took part in the Consortium on Lithium Genetics (ConLi+Gen) study. Summary statistics from genome-wide association studies in MD (135,458 cases and 344,901 controls) from the Psychiatric Genomics Consortium (PGC) were used for PGS weighting. Response to lithium treatment was defined by continuous scores and categorical outcome (responders versus non-responders) using measurements on the Alda scale. Associations between PGSs of MD and lithium treatment response were assessed using a linear and binary logistic regression modeling for the continuous and categorical outcomes, respectively. The analysis was performed for the entire cohort, and for European and Asian sub-samples. The PGSs for MD were significantly associated with lithium treatment response in multi-ethnic, European or Asian populations, at various p value thresholds. Bipolar patients with a low polygenic load for MD were more likely to respond well to lithium, compared to those patients with high polygenic load [lowest vs highest PGS quartiles, multi-ethnic sample: OR = 1.54 (95% CI: 1.18-2.01) and European sample: OR = 1.75 (95% CI: 1.30-2.36)]. While our analysis in the Asian sample found equivalent effect size in the same direction: OR = 1.71 (95% CI: 0.61-4.90), this was not statistically significant. Using PGS decile comparison, we found a similar trend of association between a high genetic loading for MD and lower response to lithium. Our findin
Published: 2021

11. Combining schizophrenia and depression polygenic risk scores improves the genetic prediction of lithium response in bipolar disorder patients

Author: Schubert, KO, Thalamuthu, A, Amare, AT, Frank, J, Streit, F, Adl, M, Akula, N, Akiyama, K, Ardau, R, Arias, B, Aubry, J-M, Backlund, L, Bhattacharjee, AK, Bellivier, F, Benabarre, A, Bengesser, S, Biernacka, JM, Birner, A, Marie-Claire, C, Cearns, M, Cervantes, P, Chen, H-C, Chillotti, C, Cichon, S, Clark, SR, Cruceanu, C, Czerski, PM, Dalkner, N, Dayer, A, Degenhardt, F, Del Zompo, M, DePaulo, JR, Etain, B, Falkai, P, Forstner, AJ, Frisen, L, Frye, MA, Fullerton, JM, Gard, S, Garnham, JS, Goes, FS, Grigoroiu-Serbanescu, M, Grof, P, Hashimoto, R, Hauser, J, Heilbronner, U, Herms, S, Hoffmann, P, Hou, L, Hsu, Y-H, Jamain, S, Jimenez, E, Kahn, J-P, Kassem, L, Kuo, P-H, Kato, T, Kelsoe, J, Kittel-Schneider, S, Ferensztajn-Rochowiak, E, Koenig, B, Kusumi, I, Laje, G, Landen, M, Lavebratt, C, Leboyer, M, Leckband, SG, Maj, M, Manchia, M, Martinsson, L, McCarthy, MJ, McElroy, S, Colom, F, Mitjans, M, Mondimore, FM, Monteleone, P, Nievergelt, CM, Noethen, MM, Novak, T, O'Donovan, C, Ozaki, N, Oesby, U, Papiol, S, Pfennig, A, Pisanu, C, Potash, JB, Reif, A, Reininghaus, E, Rouleau, GA, Rybakowski, JK, Schalling, M, Schofield, PR, Schweizer, BW, Severino, G, Shekhtman, T, Shilling, PD, Shimoda, K, Simhandl, C, Slaney, CM, Squassina, A, Stamm, T, Stopkova, P, Tekola-Ayele, F, Tortorella, A, Turecki, G, Veeh, J, Vieta, E, Witt, SH, Roberts, G, Zandi, PP, Alda, M, Bauer, M, McMahon, FJ, Mitchell, PB, Schulze, TG, Rietschel, M, Baune, BT, Schubert, KO, Thalamuthu, A, Amare, AT, Frank, J, Streit, F, Adl, M, Akula, N, Akiyama, K, Ardau, R, Arias, B, Aubry, J-M, Backlund, L, Bhattacharjee, AK, Bellivier, F, Benabarre, A, Bengesser, S, Biernacka, JM, Birner, A, Marie-Claire, C, Cearns, M, Cervantes, P, Chen, H-C, Chillotti, C, Cichon, S, Clark, SR, Cruceanu, C, Czerski, PM, Dalkner, N, Dayer, A, Degenhardt, F, Del Zompo, M, DePaulo, JR, Etain, B, Falkai, P, Forstner, AJ, Frisen, L, Frye, MA, Fullerton, JM, Gard, S, Garnham, JS, Goes, FS, Grigoroiu-Serbanescu, M, Grof, P, Hashimoto, R, Hauser, J, Heilbronner, U, Herms, S, Hoffmann, P, Hou, L, Hsu, Y-H, Jamain, S, Jimenez, E, Kahn, J-P, Kassem, L, Kuo, P-H, Kato, T, Kelsoe, J, Kittel-Schneider, S, Ferensztajn-Rochowiak, E, Koenig, B, Kusumi, I, Laje, G, Landen, M, Lavebratt, C, Leboyer, M, Leckband, SG, Maj, M, Manchia, M, Martinsson, L, McCarthy, MJ, McElroy, S, Colom, F, Mitjans, M, Mondimore, FM, Monteleone, P, Nievergelt, CM, Noethen, MM, Novak, T, O'Donovan, C, Ozaki, N, Oesby, U, Papiol, S, Pfennig, A, Pisanu, C, Potash, JB, Reif, A, Reininghaus, E, Rouleau, GA, Rybakowski, JK, Schalling, M, Schofield, PR, Schweizer, BW, Severino, G, Shekhtman, T, Shilling, PD, Shimoda, K, Simhandl, C, Slaney, CM, Squassina, A, Stamm, T, Stopkova, P, Tekola-Ayele, F, Tortorella, A, Turecki, G, Veeh, J, Vieta, E, Witt, SH, Roberts, G, Zandi, PP, Alda, M, Bauer, M, McMahon, FJ, Mitchell, PB, Schulze, TG, Rietschel, M, and Baune, BT
Abstract: Lithium is the gold standard therapy for Bipolar Disorder (BD) but its effectiveness differs widely between individuals. The molecular mechanisms underlying treatment response heterogeneity are not well understood, and personalized treatment in BD remains elusive. Genetic analyses of the lithium treatment response phenotype may generate novel molecular insights into lithium's therapeutic mechanisms and lead to testable hypotheses to improve BD management and outcomes. We used fixed effect meta-analysis techniques to develop meta-analytic polygenic risk scores (MET-PRS) from combinations of highly correlated psychiatric traits, namely schizophrenia (SCZ), major depression (MD) and bipolar disorder (BD). We compared the effects of cross-disorder MET-PRS and single genetic trait PRS on lithium response. For the PRS analyses, we included clinical data on lithium treatment response and genetic information for n = 2283 BD cases from the International Consortium on Lithium Genetics (ConLi+Gen; www.ConLiGen.org ). Higher SCZ and MD PRSs were associated with poorer lithium treatment response whereas BD-PRS had no association with treatment outcome. The combined MET2-PRS comprising of SCZ and MD variants (MET2-PRS) and a model using SCZ and MD-PRS sequentially improved response prediction, compared to single-disorder PRS or to a combined score using all three traits (MET3-PRS). Patients in the highest decile for MET2-PRS loading had 2.5 times higher odds of being classified as poor responders than patients with the lowest decile MET2-PRS scores. An exploratory functional pathway analysis of top MET2-PRS variants was conducted. Findings may inform the development of future testing strategies for personalized lithium prescribing in BD.
Published: 2021

12. Shared genetic risk between eating disorder- and substance-use-related phenotypes: Evidence from genome-wide association studies

Author: Munn-Chernoff, MA, Johnson, EC, Chou, YL, Coleman, JRI, Thornton, LM, Walters, RK, Yilmaz, Z, Baker, JH, Hübel, C, Gordon, S, Medland, SE, Watson, HJ, Gaspar, HA, Bryois, J, Hinney, A, Leppä, VM, Mattheisen, M, Ripke, S, Yao, S, Giusti-Rodríguez, P, Hanscombe, KB, Adan, RAH, Alfredsson, L, Ando, T, Andreassen, OA, Berrettini, WH, Boehm, I, Boni, C, Boraska Perica, V, Buehren, K, Burghardt, R, Cassina, M, Cichon, S, Clementi, M, Cone, RD, Courtet, P, Crow, S, Crowley, JJ, Danner, UN, Davis, OS, Zwaan, M, Dedoussis, G, Degortes, D, DeSocio, JE, Dick, DM, Dikeos, D, Dina, C, Dmitrzak-Weglarz, M, Docampo, E, Duncan, LE, Egberts, K, Ehrlich, S, Escaramís, G, Esko, T, Estivill, X, Farmer, A, Favaro, A, Fernández-Aranda, F, Fichter, MM, Fischer, K, Föcker, M, Foretova, L, Forstner, AJ, Forzan, M, Franklin, CS, Gallinger, S, Giegling, I, Giuranna, J, Gonidakis, F, Gorwood, P, Gratacos Mayora, M, Guillaume, S, Guo, Y, Hakonarson, H, Hatzikotoulas, K, Hauser, J, Hebebrand, J, Helder, SG, Herms, S, Herpertz-Dahlmann, B, Herzog, W, Huckins, LM, Hudson, JI, Imgart, H, Inoko, H, Janout, V, Jiménez-Murcia, S, Julià, A, Kalsi, G, Kaminská, D, Karhunen, L, Karwautz, A, Kas, MJH, Kennedy, JL, Keski-Rahkonen, A, Kiezebrink, K, Kim, YR, Klump, KL, Knudsen, GP, La Via, MC, Le Hellard, S, Levitan, RD, Li, D, Lilenfeld, L, Lin, BD, Lissowska, J, Luykx, J, Magistretti, PJ, Maj, M, Mannik, K, Marsal, S, Marshall, CR, Mattingsdal, M, McDevitt, S, McGuffin, P, Metspalu, A, Meulenbelt, I, Micali, N, Mitchell, K, Monteleone, A M, Monteleone, P, Nacmias, B, Navratilova, M, Ntalla, I, O'Toole, JK, Ophoff, Roel, Padyukov, L, Palotie, A, Pantel, J, Papezova, H, Pinto, D, Rabionet, R, Raevuori, A, Ramoz, N, Reichborn-Kjennerud, T, Ricca, V, Ripatti, S, Ritschel, F, Roberts, M, Rotondo, A, Rujescu, D, Rybakowski, F, Santonastaso, P, Scherag, A, Scherer, SW, Schmidt, U, Schork, NJ, Schosser, A, Seitz, J, Slachtova, L, Slagboom, PE, Slof-Op't Landt, MCT, Slopien, A, Sorbi, S, ?wi?tkowska, B, Szatkiewicz, JP, Tachmazidou, I, Tenconi, E, Tortorella, A, Tozzi, F, Treasure, J, Tsitsika, A, Tyszkiewicz-Nwafor, M, Tziouvas, K, van Elburg, AA, van Furth, EF, Wagner, G, Walton, E, Widen, E, Zeggini, E, Zerwas, S, Zipfel, S, Bergen, AW, Boden, JM, Brandt, H, Crawford, S, Halmi, KA, Horwood, LJ, Johnson, C, Kaplan, AS, Kaye, WH, Mitchell, J E, Olsen, CM, Pearson, JF, Pedersen, NL, Strober, M, Werge, T, Whiteman, DC, Woodside, DB, Grove, J, Henders, AK, Larsen, J T, Parker, R, Petersen, LV, Jordan, J, Kennedy, MA, Birgegård, A, Lichtenstein, P, Norring, C, Landén, M, Mortensen, PB, Polimanti, R, McClintick, JN, Adkins, AE, Aliev, F, Bacanu, SA, Batzler, A, Bertelsen, S, Biernacka, JM, Bigdeli, TB, Chen, L S, Clarke, TK, Degenhardt, F, Docherty, AR, Edwards, AC, Foo, JC, Fox, L, Frank, J, Hack, LM, Hartmann, AM, Hartz, SM, Heilmann-Heimbach, S, Hodgkinson, C, Hoffmann, P, Hottenga, JJ, Konte, B, Lahti, J, Lahti-Pulkkinen, M, Lai, D, Ligthart, L, Loukola, A, Maher, BS, Mbarek, H, McIntosh, AM, McQueen, MB, Meyers, JL, Milaneschi, Y, Palviainen, T, Peterson, RE, Ryu, E, Saccone, N L, Salvatore, JE, Sanchez-Roige, S, Schwandt, M, Sherva, R, Streit, F, Strohmaier, J, Thomas, N, Wang, JCY, Webb, BT, Wedow, R, Wetherill, L, Wills, AG, Zhou, H, Boardman, JD, Chen, D, Choi, D S, Copeland, WE, Culverhouse, RC, Dahmen, N, Degenhardt, L, Domingue, BW, Frye, MA, Gäebel, W, Hayward, C, Ising, M, Keyes, M, Kiefer, F, Koller, G, Kramer, J (John), Kuperman, S, Lucae, S, Lynskey, MT, Maier, W, Mann, K, Männistö, S, Müller-Myhsok, B, Murray, AD, Nurnberger, JI, Preuss, U, Räikkönen, K, Reynolds, MD, Ridinger, M, Scherbaum, N, Schuckit, MA, Soyka, M, Treutlein, J, Witt, SH, Wodarz, N, Zill, P, Adkins, DE, Boomsma, DI, Bierut, LJ, Brown, S, Bucholz, KK, Costello, EJ, Wit, HJ, Diazgranados, N, Eriksson, JG, Farrer, LA, Foroud, TM, Gillespie, NA, Goate, AM, Goldman, D, Grucza, RA, Hancock, DB, Harris, KM, Hesselbrock, V, Hewitt, JK, Hopfer, CJ, Iacono, WG, Johnson, E O, Karpyak, VM, Kendler, KS, Kranzler, HR, Krauter, K, Lind, PA, McGue, M, MacKillop, J, Madden, PA, Maes, HH, Magnusson, PKE, Nelson, EC, Nöthen, MM, Palmer, AA, Penninx, BWJH, Porjesz, B, Rice, JP, Rietschel, M, Riley, BP, Rose, RJ, Shen, PH, Silberg, J, Stallings, MC, Tarter, RE, Vanyukov, MM, Vrieze, S, Wall, TL, Whitfield, JB, Zhao, H, Neale, BM, Wade, TD, Heath, AC, Montgomery, GW, Martin, NG, Sullivan, PF, Kaprio, J, Breen, G, Gelernter, J, Edenberg, HJ, Bulik, CM, Agrawal, A, Munn-Chernoff, MA, Johnson, EC, Chou, YL, Coleman, JRI, Thornton, LM, Walters, RK, Yilmaz, Z, Baker, JH, Hübel, C, Gordon, S, Medland, SE, Watson, HJ, Gaspar, HA, Bryois, J, Hinney, A, Leppä, VM, Mattheisen, M, Ripke, S, Yao, S, Giusti-Rodríguez, P, Hanscombe, KB, Adan, RAH, Alfredsson, L, Ando, T, Andreassen, OA, Berrettini, WH, Boehm, I, Boni, C, Boraska Perica, V, Buehren, K, Burghardt, R, Cassina, M, Cichon, S, Clementi, M, Cone, RD, Courtet, P, Crow, S, Crowley, JJ, Danner, UN, Davis, OS, Zwaan, M, Dedoussis, G, Degortes, D, DeSocio, JE, Dick, DM, Dikeos, D, Dina, C, Dmitrzak-Weglarz, M, Docampo, E, Duncan, LE, Egberts, K, Ehrlich, S, Escaramís, G, Esko, T, Estivill, X, Farmer, A, Favaro, A, Fernández-Aranda, F, Fichter, MM, Fischer, K, Föcker, M, Foretova, L, Forstner, AJ, Forzan, M, Franklin, CS, Gallinger, S, Giegling, I, Giuranna, J, Gonidakis, F, Gorwood, P, Gratacos Mayora, M, Guillaume, S, Guo, Y, Hakonarson, H, Hatzikotoulas, K, Hauser, J, Hebebrand, J, Helder, SG, Herms, S, Herpertz-Dahlmann, B, Herzog, W, Huckins, LM, Hudson, JI, Imgart, H, Inoko, H, Janout, V, Jiménez-Murcia, S, Julià, A, Kalsi, G, Kaminská, D, Karhunen, L, Karwautz, A, Kas, MJH, Kennedy, JL, Keski-Rahkonen, A, Kiezebrink, K, Kim, YR, Klump, KL, Knudsen, GP, La Via, MC, Le Hellard, S, Levitan, RD, Li, D, Lilenfeld, L, Lin, BD, Lissowska, J, Luykx, J, Magistretti, PJ, Maj, M, Mannik, K, Marsal, S, Marshall, CR, Mattingsdal, M, McDevitt, S, McGuffin, P, Metspalu, A, Meulenbelt, I, Micali, N, Mitchell, K, Monteleone, A M, Monteleone, P, Nacmias, B, Navratilova, M, Ntalla, I, O'Toole, JK, Ophoff, Roel, Padyukov, L, Palotie, A, Pantel, J, Papezova, H, Pinto, D, Rabionet, R, Raevuori, A, Ramoz, N, Reichborn-Kjennerud, T, Ricca, V, Ripatti, S, Ritschel, F, Roberts, M, Rotondo, A, Rujescu, D, Rybakowski, F, Santonastaso, P, Scherag, A, Scherer, SW, Schmidt, U, Schork, NJ, Schosser, A, Seitz, J, Slachtova, L, Slagboom, PE, Slof-Op't Landt, MCT, Slopien, A, Sorbi, S, ?wi?tkowska, B, Szatkiewicz, JP, Tachmazidou, I, Tenconi, E, Tortorella, A, Tozzi, F, Treasure, J, Tsitsika, A, Tyszkiewicz-Nwafor, M, Tziouvas, K, van Elburg, AA, van Furth, EF, Wagner, G, Walton, E, Widen, E, Zeggini, E, Zerwas, S, Zipfel, S, Bergen, AW, Boden, JM, Brandt, H, Crawford, S, Halmi, KA, Horwood, LJ, Johnson, C, Kaplan, AS, Kaye, WH, Mitchell, J E, Olsen, CM, Pearson, JF, Pedersen, NL, Strober, M, Werge, T, Whiteman, DC, Woodside, DB, Grove, J, Henders, AK, Larsen, J T, Parker, R, Petersen, LV, Jordan, J, Kennedy, MA, Birgegård, A, Lichtenstein, P, Norring, C, Landén, M, Mortensen, PB, Polimanti, R, McClintick, JN, Adkins, AE, Aliev, F, Bacanu, SA, Batzler, A, Bertelsen, S, Biernacka, JM, Bigdeli, TB, Chen, L S, Clarke, TK, Degenhardt, F, Docherty, AR, Edwards, AC, Foo, JC, Fox, L, Frank, J, Hack, LM, Hartmann, AM, Hartz, SM, Heilmann-Heimbach, S, Hodgkinson, C, Hoffmann, P, Hottenga, JJ, Konte, B, Lahti, J, Lahti-Pulkkinen, M, Lai, D, Ligthart, L, Loukola, A, Maher, BS, Mbarek, H, McIntosh, AM, McQueen, MB, Meyers, JL, Milaneschi, Y, Palviainen, T, Peterson, RE, Ryu, E, Saccone, N L, Salvatore, JE, Sanchez-Roige, S, Schwandt, M, Sherva, R, Streit, F, Strohmaier, J, Thomas, N, Wang, JCY, Webb, BT, Wedow, R, Wetherill, L, Wills, AG, Zhou, H, Boardman, JD, Chen, D, Choi, D S, Copeland, WE, Culverhouse, RC, Dahmen, N, Degenhardt, L, Domingue, BW, Frye, MA, Gäebel, W, Hayward, C, Ising, M, Keyes, M, Kiefer, F, Koller, G, Kramer, J (John), Kuperman, S, Lucae, S, Lynskey, MT, Maier, W, Mann, K, Männistö, S, Müller-Myhsok, B, Murray, AD, Nurnberger, JI, Preuss, U, Räikkönen, K, Reynolds, MD, Ridinger, M, Scherbaum, N, Schuckit, MA, Soyka, M, Treutlein, J, Witt, SH, Wodarz, N, Zill, P, Adkins, DE, Boomsma, DI, Bierut, LJ, Brown, S, Bucholz, KK, Costello, EJ, Wit, HJ, Diazgranados, N, Eriksson, JG, Farrer, LA, Foroud, TM, Gillespie, NA, Goate, AM, Goldman, D, Grucza, RA, Hancock, DB, Harris, KM, Hesselbrock, V, Hewitt, JK, Hopfer, CJ, Iacono, WG, Johnson, E O, Karpyak, VM, Kendler, KS, Kranzler, HR, Krauter, K, Lind, PA, McGue, M, MacKillop, J, Madden, PA, Maes, HH, Magnusson, PKE, Nelson, EC, Nöthen, MM, Palmer, AA, Penninx, BWJH, Porjesz, B, Rice, JP, Rietschel, M, Riley, BP, Rose, RJ, Shen, PH, Silberg, J, Stallings, MC, Tarter, RE, Vanyukov, MM, Vrieze, S, Wall, TL, Whitfield, JB, Zhao, H, Neale, BM, Wade, TD, Heath, AC, Montgomery, GW, Martin, NG, Sullivan, PF, Kaprio, J, Breen, G, Gelernter, J, Edenberg, HJ, Bulik, CM, and Agrawal, A
Abstract: Eating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa and problem alcohol use (genetic correlation [rg], twin-based = 0.23-0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome-wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge eating, AN without binge eating, and a bulimia nervosa factor score), and eight substance-use-related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder and schizophrenia. Total study sample sizes per phenotype ranged from ~2400 to ~537 000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism-based genetic correlations between eating disorder- and substance-use-related phenotypes. Significant positive genetic associations emerged between AUD and AN (rg = 0.18; false discovery rate q = 0.0006), cannabis initiation and AN (rg = 0.23; q < 0.0001), and cannabis initiation and AN with binge eating (rg = 0.27; q = 0.0016). Conversely, significant negative genetic correlations were observed between three nondiagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge eating (rgs = −0.19 to −0.23; qs < 0.04). The genetic correlation between AUD and AN was no longer significant after co-varying for major depressive disorder loci. The patterns of association between eating disorder- and substance-use-related phenotyp
Published: 2021

13. Investigating polygenic burden in age at disease onset in bipolar disorder: Findings from an international multicentric study

Author: Kalman, JL, Papiol, S, Forstner, AJ, Heilbronner, U, Degenhardt, F, Strohmaier, J, Adli, M, Adorjan, K, Akula, N, Alda, M, Anderson-Schmidt, H, Andlauer, TFM, Anghelescu, IG, Ardau, R, Arias, B, Arolt, V, Aubry, JM, Backlund, L, Bartholdi, K, Bauer, M, Baune, BT, Becker, T, Bellivier, F, Benabarre, A, Bengesser, S, Bhattacharjee, AK, Biernacka, JM, Birner, A, Brichant-Petitjean, C, Budde, M, Cervantes, P, Chillotti, C, Cichon, S, Clark, SR, Colom, F, Comes, AL, Cruceanu, C, Czerski, PM, Dannlowski, U, Dayer, A, Del Zompo, M, DePaulo, JR, Dietrich, DE, Étain, B, Ethofer, T, Falkai, P, Fallgatter, A, Figge, C, Flatau, L, Folkerts, H, Frisen, L, Frye, MA, Fullerton, JM, Gade, K, Gard, S, Garnham, JS, Goes, FS, Grigoroiu-Serbanescu, M, Gryaznova, A, Hake, M, Hauser, J, Herms, S, Hoffmann, P, Hou, L, Jäger, M, Jamain, S, Jiménez, E, Juckel, G, Kahn, JP, Kassem, L, Kelsoe, J, Kittel-Schneider, S, Kliwicki, S, Klohn-Sagatholislam, F, Koller, M, König, B, Konrad, C, Lackner, N, Laje, G, Landén, M, Lang, FU, Lavebratt, C, Leboyer, M, Leckband, SG, Maj, M, Manchia, M, Martinsson, L, McCarthy, MJ, McElroy, SL, McMahon, FJ, Mitchell, PB, Mitjans, M, Mondimore, FM, Monteleone, P, Nieratschker, V, Nievergelt, CM, Novák, T, Ösby, U, Pfennig, A, Potash, JB, Kalman, JL, Papiol, S, Forstner, AJ, Heilbronner, U, Degenhardt, F, Strohmaier, J, Adli, M, Adorjan, K, Akula, N, Alda, M, Anderson-Schmidt, H, Andlauer, TFM, Anghelescu, IG, Ardau, R, Arias, B, Arolt, V, Aubry, JM, Backlund, L, Bartholdi, K, Bauer, M, Baune, BT, Becker, T, Bellivier, F, Benabarre, A, Bengesser, S, Bhattacharjee, AK, Biernacka, JM, Birner, A, Brichant-Petitjean, C, Budde, M, Cervantes, P, Chillotti, C, Cichon, S, Clark, SR, Colom, F, Comes, AL, Cruceanu, C, Czerski, PM, Dannlowski, U, Dayer, A, Del Zompo, M, DePaulo, JR, Dietrich, DE, Étain, B, Ethofer, T, Falkai, P, Fallgatter, A, Figge, C, Flatau, L, Folkerts, H, Frisen, L, Frye, MA, Fullerton, JM, Gade, K, Gard, S, Garnham, JS, Goes, FS, Grigoroiu-Serbanescu, M, Gryaznova, A, Hake, M, Hauser, J, Herms, S, Hoffmann, P, Hou, L, Jäger, M, Jamain, S, Jiménez, E, Juckel, G, Kahn, JP, Kassem, L, Kelsoe, J, Kittel-Schneider, S, Kliwicki, S, Klohn-Sagatholislam, F, Koller, M, König, B, Konrad, C, Lackner, N, Laje, G, Landén, M, Lang, FU, Lavebratt, C, Leboyer, M, Leckband, SG, Maj, M, Manchia, M, Martinsson, L, McCarthy, MJ, McElroy, SL, McMahon, FJ, Mitchell, PB, Mitjans, M, Mondimore, FM, Monteleone, P, Nieratschker, V, Nievergelt, CM, Novák, T, Ösby, U, Pfennig, A, and Potash, JB
Abstract: Objectives: Bipolar disorder (BD) with early disease onset is associated with an unfavorable clinical outcome and constitutes a clinically and biologically homogenous subgroup within the heterogeneous BD spectrum. Previous studies have found an accumulation of early age at onset (AAO) in BD families and have therefore hypothesized that there is a larger genetic contribution to the early-onset cases than to late onset BD. To investigate the genetic background of this subphenotype, we evaluated whether an increased polygenic burden of BD- and schizophrenia (SCZ)-associated risk variants is associated with an earlier AAO in BD patients. Methods: A total of 1995 BD type 1 patients from the Consortium of Lithium Genetics (ConLiGen), PsyCourse and Bonn-Mannheim samples were genotyped and their BD and SCZ polygenic risk scores (PRSs) were calculated using the summary statistics of the Psychiatric Genomics Consortium as a training data set. AAO was either separated into onset groups of clinical interest (childhood and adolescence [≤18 years] vs adulthood [>18 years]) or considered as a continuous measure. The associations between BD- and SCZ-PRSs and AAO were evaluated with regression models. Results: BD- and SCZ-PRSs were not significantly associated with age at disease onset. Results remained the same when analyses were stratified by site of recruitment. Conclusions: The current study is the largest conducted so far to investigate the association between the cumulative BD and SCZ polygenic risk and AAO in BD patients. The reported negative results suggest that such a polygenic influence, if there is any, is not large, and highlight the importance of conducting further, larger scale studies to obtain more information on the genetic architecture of this clinically relevant phenotype.
Published: 2019

14. Analysis of the influence of microRNAs in lithium response in bipolar disorder

Author: Reinbold, CS, Forstner, AJ, Hecker, J, Fullerton, JM, Hoffmann, P, Hou, L, Heilbronner, U, Degenhardt, F, Adli, M, Akiyama, K, Akula, N, Ardau, R, Arias, B, Backlund, L, Benabarre, A, Bengesser, S, Bhattacharjee, AK, Biernacka, JM, Birner, A, Marie-Claire, C, Cervantes, P, Chen, GB, Chen, HC, Chillotti, C, Clark, SR, Colom, F, Cousins, DA, Cruceanu, C, Czerski, PM, Dayer, A, Étain, B, Falkai, P, Frisén, L, Gard, S, Garnham, JS, Goes, FS, Grof, P, Gruber, O, Hashimoto, R, Hauser, J, Herms, S, Jamain, S, Jiménez, E, Kahn, JP, Kassem, L, Kittel-Schneider, S, Kliwicki, S, König, B, Kusumi, I, Lackner, N, Laje, G, Landén, M, Lavebratt, C, Leboyer, M, Leckband, SG, Jaramillo, CAL, MacQueen, G, Manchia, M, Martinsson, L, Mattheisen, M, McCarthy, MJ, McElroy, SL, Mitjans, M, Mondimore, FM, Monteleone, P, Nievergelt, CM, Ösby, U, Ozaki, N, Perlis, RH, Pfennig, A, Reich-Erkelenz, D, Rouleau, GA, Schofield, PR, Schubert, KO, Schweizer, BW, Seemüller, F, Severino, G, Shekhtman, T, Shilling, PD, Shimoda, K, Simhandl, C, Slaney, CM, Smoller, JW, Squassina, A, Stamm, TJ, Stopkova, P, Tighe, SK, Tortorella, A, Turecki, G, Volkert, J, Witt, SH, Wright, AJ, Trevor Young, L, Zandi, PP, Potash, JB, DePaulo, JR, Bauer, M, Reininghaus, E, Novák, T, Aubry, JM, Reinbold, CS, Forstner, AJ, Hecker, J, Fullerton, JM, Hoffmann, P, Hou, L, Heilbronner, U, Degenhardt, F, Adli, M, Akiyama, K, Akula, N, Ardau, R, Arias, B, Backlund, L, Benabarre, A, Bengesser, S, Bhattacharjee, AK, Biernacka, JM, Birner, A, Marie-Claire, C, Cervantes, P, Chen, GB, Chen, HC, Chillotti, C, Clark, SR, Colom, F, Cousins, DA, Cruceanu, C, Czerski, PM, Dayer, A, Étain, B, Falkai, P, Frisén, L, Gard, S, Garnham, JS, Goes, FS, Grof, P, Gruber, O, Hashimoto, R, Hauser, J, Herms, S, Jamain, S, Jiménez, E, Kahn, JP, Kassem, L, Kittel-Schneider, S, Kliwicki, S, König, B, Kusumi, I, Lackner, N, Laje, G, Landén, M, Lavebratt, C, Leboyer, M, Leckband, SG, Jaramillo, CAL, MacQueen, G, Manchia, M, Martinsson, L, Mattheisen, M, McCarthy, MJ, McElroy, SL, Mitjans, M, Mondimore, FM, Monteleone, P, Nievergelt, CM, Ösby, U, Ozaki, N, Perlis, RH, Pfennig, A, Reich-Erkelenz, D, Rouleau, GA, Schofield, PR, Schubert, KO, Schweizer, BW, Seemüller, F, Severino, G, Shekhtman, T, Shilling, PD, Shimoda, K, Simhandl, C, Slaney, CM, Smoller, JW, Squassina, A, Stamm, TJ, Stopkova, P, Tighe, SK, Tortorella, A, Turecki, G, Volkert, J, Witt, SH, Wright, AJ, Trevor Young, L, Zandi, PP, Potash, JB, DePaulo, JR, Bauer, M, Reininghaus, E, Novák, T, and Aubry, JM
Abstract: Bipolar disorder (BD) is a common, highly heritable neuropsychiatric disease characterized by recurrent episodes of mania and depression. Lithium is the best-established long-term treatment for BD, even though individual response is highly variable. Evidence suggests that some of this variability has a genetic basis. This is supported by the largest genome-wide association study (GWAS) of lithium response to date conducted by the International Consortium on Lithium Genetics (ConLiGen). Recently, we performed the first genome-wide analysis of the involvement of miRNAs in BD and identified nine BD-associated miRNAs. However, it is unknown whether these miRNAs are also associated with lithium response in BD. In the present study, we therefore tested whether common variants at these nine candidate miRNAs contribute to the variance in lithium response in BD. Furthermore, we systematically analyzed whether any other miRNA in the genome is implicated in the response to lithium. For this purpose, we performed gene-based tests for all known miRNA coding genes in the ConLiGen GWAS dataset (n = 2,563 patients) using a set-based testing approach adapted from the versatile gene-based test for GWAS (VEGAS2). In the candidate approach, miR-499a showed a nominally significant association with lithium response, providing some evidence for involvement in both development and treatment of BD. In the genome-wide miRNA analysis, 71 miRNAs showed nominally significant associations with the dichotomous phenotype and 106 with the continuous trait for treatment response. A total of 15 miRNAs revealed nominal significance in both phenotypes with miR-633 showing the strongest association with the continuous trait (p = 9.80E-04) and miR-607 with the dichotomous phenotype (p = 5.79E-04). No association between miRNAs and treatment response to lithium in BD in either of the tested conditions withstood multiple testing correction. Given the limited power of our study, the investigation of miRNAs
Published: 2018

15. Association of polygenic score for schizophrenia and HLA antigen and inflammation genes with response to lithium in bipolar affective disorder: A genome-wide association study

Author: Amare, AT, Schubert, KO, Hou, L, Clark, SR, Papiol, S, Heilbronner, U, Degenhardt, F, Tekola-Ayele, F, Hsu, YH, Shekhtman, T, Adli, M, Akula, N, Akiyama, K, Ardau, R, Arias, B, Aubry, JM, Backlund, L, Bhattacharjee, AK, Bellivier, F, Benabarre, A, Bengesser, S, Biernacka, JM, Birner, A, Brichant-Petitjean, C, Cervantes, P, Chen, HC, Chillotti, C, Cichon, S, Cruceanu, C, Czerski, PM, Dalkner, N, Dayer, A, Del Zompo, M, DePaulo, JR, Étain, B, Falkai, P, Forstner, AJ, Frisen, L, Frye, MA, Fullerton, JM, Gard, S, Garnham, JS, Goes, FS, Grigoroiu-Serbanescu, M, Grof, P, Hashimoto, R, Hauser, J, Herms, S, Hoffmann, P, Hofmann, A, Jamain, S, Jiménez, E, Kahn, JP, Kassem, L, Kuo, PH, Kato, T, Kelsoe, J, Kittel-Schneider, S, Kliwicki, S, König, B, Kusumi, I, Laje, G, Landén, M, Lavebratt, C, Leboyer, M, Leckband, SG, Tortorella, A, Manchia, M, Martinsson, L, McCarthy, MJ, McElroy, S, Colom, F, Mitjans, M, Mondimore, FM, Monteleone, P, Nievergelt, CM, Nöthen, MM, Novák, T, O'Donovan, C, Ozaki, N, Ösby, U, Pfennig, A, Potash, JB, Reif, A, Reininghaus, E, Rouleau, GA, Rybakowski, JK, Schalling, M, Schofield, PR, Schweizer, BW, Severino, G, Shilling, PD, Shimoda, K, Simhandl, C, Slaney, CM, Squassina, A, Stamm, T, Stopkova, P, Maj, M, Turecki, G, Amare, AT, Schubert, KO, Hou, L, Clark, SR, Papiol, S, Heilbronner, U, Degenhardt, F, Tekola-Ayele, F, Hsu, YH, Shekhtman, T, Adli, M, Akula, N, Akiyama, K, Ardau, R, Arias, B, Aubry, JM, Backlund, L, Bhattacharjee, AK, Bellivier, F, Benabarre, A, Bengesser, S, Biernacka, JM, Birner, A, Brichant-Petitjean, C, Cervantes, P, Chen, HC, Chillotti, C, Cichon, S, Cruceanu, C, Czerski, PM, Dalkner, N, Dayer, A, Del Zompo, M, DePaulo, JR, Étain, B, Falkai, P, Forstner, AJ, Frisen, L, Frye, MA, Fullerton, JM, Gard, S, Garnham, JS, Goes, FS, Grigoroiu-Serbanescu, M, Grof, P, Hashimoto, R, Hauser, J, Herms, S, Hoffmann, P, Hofmann, A, Jamain, S, Jiménez, E, Kahn, JP, Kassem, L, Kuo, PH, Kato, T, Kelsoe, J, Kittel-Schneider, S, Kliwicki, S, König, B, Kusumi, I, Laje, G, Landén, M, Lavebratt, C, Leboyer, M, Leckband, SG, Tortorella, A, Manchia, M, Martinsson, L, McCarthy, MJ, McElroy, S, Colom, F, Mitjans, M, Mondimore, FM, Monteleone, P, Nievergelt, CM, Nöthen, MM, Novák, T, O'Donovan, C, Ozaki, N, Ösby, U, Pfennig, A, Potash, JB, Reif, A, Reininghaus, E, Rouleau, GA, Rybakowski, JK, Schalling, M, Schofield, PR, Schweizer, BW, Severino, G, Shilling, PD, Shimoda, K, Simhandl, C, Slaney, CM, Squassina, A, Stamm, T, Stopkova, P, Maj, M, and Turecki, G
Abstract: IMPORTANCE Lithium is a first-line mood stabilizer for the treatment of bipolar affective disorder (BPAD). However, the efficacy of lithium varies widely, with a nonresponse rate of up to 30%. Biological response markers are lacking. Genetic factors are thought to mediate treatment response to lithium, and there is a previously reported genetic overlap between BPAD and schizophrenia (SCZ). OBJECTIVES To test whether a polygenic score for SCZ is associated with treatment response to lithium in BPAD and to explore the potential molecular underpinnings of this association. DESIGN, SETTING, AND PARTICIPANTS A total of 2586 patients with BPAD who had undergone lithium treatment were genotyped and assessed for long-term response to treatment between 2008 and 2013.Weighted SCZ polygenic scores were computed at different P value thresholds using summary statistics from an international multicenter genome-wide association study (GWAS) of 36 989 individuals with SCZ and genotype data from patients with BPAD from the Consortium on Lithium Genetics. For functional exploration, a cross-trait meta-GWAS and pathway analysis was performed, combining GWAS summary statistics on SCZ and response to treatment with lithium. Data analysis was performed from September 2016 to February 2017. MAIN OUTCOMES AND MEASURES Treatment response to lithiumwas defined on both the categorical and continuous scales using the Retrospective Criteria of Long-Term Treatment Response in Research Subjects with Bipolar Disorder score. The effect measures include odds ratios and the proportion of variance explained. RESULTS Of the 2586 patients in the study (mean [SD] age, 47.2 [13.9] years), 1478 were women and 1108 were men. The polygenic score for SCZ was inversely associated with lithium treatment response in the categorical outcome, at a threshold P < 5 ? 10-2. Patients with BPAD who had a low polygenic load for SCZ responded better to lithium, with odds ratios for lithium response ranging from 3.46 (95%
Published: 2018

16. Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes

Author: Ruderfer, DM, Ripke, S, McQuillin, A, Boocock, J, Stahl, EA, Pavlides, JMW, Mullins, N, Charney, AW, Ori, APS, Loohuis, LMO, Domenici, E, Di Florio, A, Papiol, S, Kalman, JL, Trubetskoy, V, Adolfsson, R, Agartz, I, Agerbo, E, Akil, H, Albani, D, Albus, M, Alda, M, Alexander, M, Alliey-Rodriguez, N, Als, TD, Amin, F, Anjorin, A, Arranz, MJ, Awasthi, S, Bacanu, SA, Badner, JA, Baekvad-Hansen, M, Bakker, S, Band, G, Barchas, JD, Barroso, I, Bass, N, Bauer, M, Baune, BT, Begemann, M, Bellenguez, C, Belliveau, RA, Bellivier, F, Bender, S, Bene, J, Bergen, SE, Berrettini, WH, Bevilacqua, E, Biernacka, JM, Bigdeli, TB, Black, DW, Blackburn, H, Blackwell, JM, Blackwood, DHR, Pedersen, CB, Boehnke, M, Boks, M, Borglum, AD, Bramon, E, Breen, G, Brown, MA, Bruggeman, R, Buccola, NG, Buckner, RL, Budde, M, Bulik-Sullivan, B, Bumpstead, SJ, Bunney, W, Burmeister, M, Buxbaum, JD, Bybjerg-Grauholm, J, Byerley, W, Cahn, W, Cai, G, Cairns, MJ, Campion, D, Cantor, RM, Carr, VJ, Carrera, N, Casas, JP, Casas, M, Catts, SV, Cervantes, P, Chambert, KD, Chan, RCK, Chen, EYH, Chen, RYL, Cheng, W, Cheung, EFC, Chong, SA, Clarke, TK, Cloninger, CR, Cohen, D, Cohen, N, Coleman, JRI, Collier, DA, Cormican, P, Coryell, W, Craddock, N, Craig, DW, Ruderfer, DM, Ripke, S, McQuillin, A, Boocock, J, Stahl, EA, Pavlides, JMW, Mullins, N, Charney, AW, Ori, APS, Loohuis, LMO, Domenici, E, Di Florio, A, Papiol, S, Kalman, JL, Trubetskoy, V, Adolfsson, R, Agartz, I, Agerbo, E, Akil, H, Albani, D, Albus, M, Alda, M, Alexander, M, Alliey-Rodriguez, N, Als, TD, Amin, F, Anjorin, A, Arranz, MJ, Awasthi, S, Bacanu, SA, Badner, JA, Baekvad-Hansen, M, Bakker, S, Band, G, Barchas, JD, Barroso, I, Bass, N, Bauer, M, Baune, BT, Begemann, M, Bellenguez, C, Belliveau, RA, Bellivier, F, Bender, S, Bene, J, Bergen, SE, Berrettini, WH, Bevilacqua, E, Biernacka, JM, Bigdeli, TB, Black, DW, Blackburn, H, Blackwell, JM, Blackwood, DHR, Pedersen, CB, Boehnke, M, Boks, M, Borglum, AD, Bramon, E, Breen, G, Brown, MA, Bruggeman, R, Buccola, NG, Buckner, RL, Budde, M, Bulik-Sullivan, B, Bumpstead, SJ, Bunney, W, Burmeister, M, Buxbaum, JD, Bybjerg-Grauholm, J, Byerley, W, Cahn, W, Cai, G, Cairns, MJ, Campion, D, Cantor, RM, Carr, VJ, Carrera, N, Casas, JP, Casas, M, Catts, SV, Cervantes, P, Chambert, KD, Chan, RCK, Chen, EYH, Chen, RYL, Cheng, W, Cheung, EFC, Chong, SA, Clarke, TK, Cloninger, CR, Cohen, D, Cohen, N, Coleman, JRI, Collier, DA, Cormican, P, Coryell, W, Craddock, N, and Craig, DW
Abstract: Schizophrenia and bipolar disorder are two distinct diagnoses that share symptomology. Understanding the genetic factors contributing to the shared and disorder-specific symptoms will be crucial for improving diagnosis and treatment. In genetic data consisting of 53,555 cases (20,129 bipolar disorder [BD], 33,426 schizophrenia [SCZ]) and 54,065 controls, we identified 114 genome-wide significant loci implicating synaptic and neuronal pathways shared between disorders. Comparing SCZ to BD (23,585 SCZ, 15,270 BD) identified four genomic regions including one with disorder-independent causal variants and potassium ion response genes as contributing to differences in biology between the disorders. Polygenic risk score (PRS) analyses identified several significant correlations within case-only phenotypes including SCZ PRS with psychotic features and age of onset in BD. For the first time, we discover specific loci that distinguish between BD and SCZ and identify polygenic components underlying multiple symptom dimensions. These results point to the utility of genetics to inform symptomology and potential treatment. Genetic analysis of multiple bipolar disorder and schizophrenia cohorts reveals loci and polygenic risk scores that differentiate the clinical symptoms of these two highly correlated disorders.
Published: 2018

17. Association of the Polygenic Scores for Personality Traits and Response to Selective Serotonin Reuptake Inhibitors in Patients with Major Depressive Disorder

Author: Amare, AT, Schubert, KO, Tekola-Ayele, F, Hsu, Y-H, Sangkuhl, K, Jenkins, G, Whaley, RM, Barman, P, Batzler, A, Altman, RB, Arolt, V, Brockmoeller, J, Chen, C-H, Domschke, K, Hall-Flavin, DK, Hong, C-J, Illi, A, Ji, Y, Kampman, O, Kinoshita, T, Leinonen, E, Liou, Y-J, Mushiroda, T, Nonen, S, Skime, MK, Wang, L, Kato, M, Liu, Y-L, Praphanphoj, V, Stingl, JC, Bobo, WV, Tsai, S-J, Kubo, M, Klein, TE, Weinshilboum, RM, Biernacka, JM, Baune, BT, Amare, AT, Schubert, KO, Tekola-Ayele, F, Hsu, Y-H, Sangkuhl, K, Jenkins, G, Whaley, RM, Barman, P, Batzler, A, Altman, RB, Arolt, V, Brockmoeller, J, Chen, C-H, Domschke, K, Hall-Flavin, DK, Hong, C-J, Illi, A, Ji, Y, Kampman, O, Kinoshita, T, Leinonen, E, Liou, Y-J, Mushiroda, T, Nonen, S, Skime, MK, Wang, L, Kato, M, Liu, Y-L, Praphanphoj, V, Stingl, JC, Bobo, WV, Tsai, S-J, Kubo, M, Klein, TE, Weinshilboum, RM, Biernacka, JM, and Baune, BT
Abstract: Studies reported a strong genetic correlation between the Big Five personality traits and major depressive disorder (MDD). Moreover, personality traits are thought to be associated with response to antidepressants treatment that might partly be mediated by genetic factors. In this study, we examined whether polygenic scores (PGSs) derived from the Big Five personality traits predict treatment response and remission in patients with MDD who were prescribed selective serotonin reuptake inhibitors (SSRIs). In addition, we performed meta-analyses of genome-wide association studies (GWASs) on these traits to identify genetic variants underpinning the cross-trait polygenic association. The PGS analysis was performed using data from two cohorts: the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS, n = 529) and the International SSRI Pharmacogenomics Consortium (ISPC, n = 865). The cross-trait GWAS meta-analyses were conducted by combining GWAS summary statistics on SSRIs treatment outcome and on the personality traits. The results showed that the PGS for openness and neuroticism were associated with SSRIs treatment outcomes at p < 0.05 across PT thresholds in both cohorts. A significant association was also found between the PGS for conscientiousness and SSRIs treatment response in the PGRN-AMPS sample. In the cross-trait GWAS meta-analyses, we identified eight loci associated with (a) SSRIs response and conscientiousness near YEATS4 gene and (b) SSRI remission and neuroticism eight loci near PRAG1, MSRA, XKR6, ELAVL2, PLXNC1, PLEKHM1, and BRUNOL4 genes. An assessment of a polygenic load for personality traits may assist in conjunction with clinical data to predict whether MDD patients might respond favorably to SSRIs.
Published: 2018

18. Applying polygenic risk scoring for psychiatric disorders to a large family with bipolar disorder and major depressive disorder

Author: de Jong, S, Abdalla Diniz, MJ, Saloma, A, Gadelha, A, Santoro, ML, Ota, VK, Noto, C, Curtis, C, Newhouse, SJ, Patel, H, Hall, LS, O'Reilly, PF, Belangero, S, Bressan, RA, Breen, G, Wray, NR, Ripke, S, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Adams, MJ, Agerbo, E, Air, TM, Andlauer, TFM, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Binder, EB, Blackwood, DHR, Bryois, J, Buttenschon, HN, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Clarke, T-K, Coleman, JR, Colodro-Conde, L, Couvy-Duchesne, B, Craddock, N, Crawford, GE, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Direk, N, Dolan, C, Dunn, EC, Eley, TC, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Forstner, AJ, Frank, J, Gaspar, HA, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Ising, M, Jansen, R, Jones, I, Jones, LA, Jorgenson, E, Knowles, JA, Kohane, IS, Kraft, J, Kretzschmar, WW, Krogh, J, Kutalik, Z, Li, Y, Lind, PA, MacIntyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, Mcgrath, P, Mcguffin, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milaneschi, Y, Milani, L, Mondimore, FM, Montgomery, GW, Mostafavi, S, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Peyrot, WJ, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Shyn, S, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Smith, DJ, Stefansson, H, Steinberg, S, Streit, F, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thomson, PA, Thorgeirsson, TE, Traylor, M, Treutlein, J, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, Van der Auwera, S, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Witt, SH, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Baune, BT, Berger, K, Boomsma, D, Cichon, S, Dannlowski, U, de Geus, EJC, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Grabe, HJ, Hamilton, SP, Hayward, C, Heath, AC, Kendler, KS, Kloiber, S, Lewis, G, Li, QS, Lucae, S, Madden, PAF, Magnusson, PK, Martin, NG, McIntosh, AM, Metspalu, A, Mors, O, Mortensen, PB, Mueller-Myhsok, B, Nordentoft, M, Noethen, MM, O'Donovan, MC, Paciga, SA, Pedersen, NL, Penninx, BWJH, Perlis, RH, Porteous, DJ, Potash, JB, Preisig, M, Rietschel, M, Schaefer, C, Schulze, TG, Smoller, JW, Stefansson, K, Tiemeier, H, Uher, R, Voelzke, H, Weissman, MM, Werge, T, Lewis, CM, Levinson, DF, Borglum, AD, Sullivan, PF, Meier, S, Strauss, J, Xu, W, Vincent, JB, Matthews, K, Ferreira, M, O'Dushlaine, C, Purcell, S, Raychaudhuri, S, Ruderfer, DM, Sklar, P, Scott, LJ, Flickinger, M, Burmeister, M, Li, J, Guan, W, Absher, D, Thompson, RC, Meng, FG, Schatzberg, AF, Bunney, WE, Barchas, JD, Watson, SJ, Myers, RM, Akil, H, Boehnke, M, Chambert, K, Moran, J, Scolnick, E, Djurovic, S, Melle, I, Morken, G, Corvin, A, Anjorin, A, Kandaswamy, R, Lawrence, J, McLean, AW, Pickard, BS, Bergen, SE, Nimgaonkar, V, Landen, M, Schalling, M, Osby, U, Backlund, L, Frisen, L, Langstrom, N, Stahl, E, Dobbyn, A, Jamain, S, Etain, B, Bellivier, F, Leber, M, Maaser, A, Fischer, SB, Reinbold, CS, Kittel-Schneider, S, Fullerton, JM, Oruc, L, Para, JG, Mayoral, F, Rivas, F, Czerski, PM, Kammerer-Ciernioch, J, Vedder, H, Borrmann-Hassenbach, M, Pfennig, A, Brennan, P, McKay, JD, Kogevinas, M, Schwarz, M, Schofield, PR, Muehleisen, TW, Schumacher, J, Bauer, M, Wright, A, Mitchell, PB, Hautzinger, M, Kelsoe, JR, Greenwood, TA, Nievergelt, CM, Shilling, PD, Smith, EN, Bloss, CS, Edenberg, HJ, Koller, DL, Gershon, ES, Liu, C, Badner, JA, Scheftner, WA, Lawson, WB, Nwulia, EA, Hipolito, M, Coryell, W, Rice, J, Byerley, W, McMahon, FJ, Lohoff, FW, Zandi, PP, Mahon, PB, McInnis, MG, Zollner, S, Zhang, P, Szelinger, S, St Clair, D, Caesar, S, Gordon-Smith, K, Fraser, C, Green, EK, Grozeva, D, Hamshere, ML, Kirov, G, Nikolov, I, Collier, DA, Elkin, A, Williamson, R, Young, AH, Ferrier, IN, Milanova, V, Alda, M, Cervantes, P, Cruceanu, C, Rouleau, GA, Turecki, G, Paciga, S, Winslow, AR, Grigoroiu-Serbanescu, M, Ophoff, R, Adolfsson, R, Adolfsson, AN, Del-Favero, J, Pato, C, Biernacka, JM, Frye, MA, Morris, D, Schork, NJ, Reif, A, Lissowska, J, Hauser, J, Szeszenia-Dabrowska, N, McGhee, K, Quinn, E, Moskvina, V, Holmans, PA, Farmer, A, Kennedy, JL, Andreassen, OA, Mattingsdal, M, Bass, NJ, Gurling, H, McQuillin, A, Breuer, R, Hultman, C, Lichtenstein, P, Huckins, LM, Leboyer, M, Lathrop, M, Nurnberger, J, Steffens, M, Foroud, TM, Berrettini, WH, Craig, DW, Shi, J, de Jong, S, Abdalla Diniz, MJ, Saloma, A, Gadelha, A, Santoro, ML, Ota, VK, Noto, C, Curtis, C, Newhouse, SJ, Patel, H, Hall, LS, O'Reilly, PF, Belangero, S, Bressan, RA, Breen, G, Wray, NR, Ripke, S, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Adams, MJ, Agerbo, E, Air, TM, Andlauer, TFM, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Binder, EB, Blackwood, DHR, Bryois, J, Buttenschon, HN, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Clarke, T-K, Coleman, JR, Colodro-Conde, L, Couvy-Duchesne, B, Craddock, N, Crawford, GE, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Direk, N, Dolan, C, Dunn, EC, Eley, TC, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Forstner, AJ, Frank, J, Gaspar, HA, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Ising, M, Jansen, R, Jones, I, Jones, LA, Jorgenson, E, Knowles, JA, Kohane, IS, Kraft, J, Kretzschmar, WW, Krogh, J, Kutalik, Z, Li, Y, Lind, PA, MacIntyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, Mcgrath, P, Mcguffin, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milaneschi, Y, Milani, L, Mondimore, FM, Montgomery, GW, Mostafavi, S, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Peyrot, WJ, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Shyn, S, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Smith, DJ, Stefansson, H, Steinberg, S, Streit, F, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thomson, PA, Thorgeirsson, TE, Traylor, M, Treutlein, J, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, Van der Auwera, S, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Witt, SH, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Baune, BT, Berger, K, Boomsma, D, Cichon, S, Dannlowski, U, de Geus, EJC, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Grabe, HJ, Hamilton, SP, Hayward, C, Heath, AC, Kendler, KS, Kloiber, S, Lewis, G, Li, QS, Lucae, S, Madden, PAF, Magnusson, PK, Martin, NG, McIntosh, AM, Metspalu, A, Mors, O, Mortensen, PB, Mueller-Myhsok, B, Nordentoft, M, Noethen, MM, O'Donovan, MC, Paciga, SA, Pedersen, NL, Penninx, BWJH, Perlis, RH, Porteous, DJ, Potash, JB, Preisig, M, Rietschel, M, Schaefer, C, Schulze, TG, Smoller, JW, Stefansson, K, Tiemeier, H, Uher, R, Voelzke, H, Weissman, MM, Werge, T, Lewis, CM, Levinson, DF, Borglum, AD, Sullivan, PF, Meier, S, Strauss, J, Xu, W, Vincent, JB, Matthews, K, Ferreira, M, O'Dushlaine, C, Purcell, S, Raychaudhuri, S, Ruderfer, DM, Sklar, P, Scott, LJ, Flickinger, M, Burmeister, M, Li, J, Guan, W, Absher, D, Thompson, RC, Meng, FG, Schatzberg, AF, Bunney, WE, Barchas, JD, Watson, SJ, Myers, RM, Akil, H, Boehnke, M, Chambert, K, Moran, J, Scolnick, E, Djurovic, S, Melle, I, Morken, G, Corvin, A, Anjorin, A, Kandaswamy, R, Lawrence, J, McLean, AW, Pickard, BS, Bergen, SE, Nimgaonkar, V, Landen, M, Schalling, M, Osby, U, Backlund, L, Frisen, L, Langstrom, N, Stahl, E, Dobbyn, A, Jamain, S, Etain, B, Bellivier, F, Leber, M, Maaser, A, Fischer, SB, Reinbold, CS, Kittel-Schneider, S, Fullerton, JM, Oruc, L, Para, JG, Mayoral, F, Rivas, F, Czerski, PM, Kammerer-Ciernioch, J, Vedder, H, Borrmann-Hassenbach, M, Pfennig, A, Brennan, P, McKay, JD, Kogevinas, M, Schwarz, M, Schofield, PR, Muehleisen, TW, Schumacher, J, Bauer, M, Wright, A, Mitchell, PB, Hautzinger, M, Kelsoe, JR, Greenwood, TA, Nievergelt, CM, Shilling, PD, Smith, EN, Bloss, CS, Edenberg, HJ, Koller, DL, Gershon, ES, Liu, C, Badner, JA, Scheftner, WA, Lawson, WB, Nwulia, EA, Hipolito, M, Coryell, W, Rice, J, Byerley, W, McMahon, FJ, Lohoff, FW, Zandi, PP, Mahon, PB, McInnis, MG, Zollner, S, Zhang, P, Szelinger, S, St Clair, D, Caesar, S, Gordon-Smith, K, Fraser, C, Green, EK, Grozeva, D, Hamshere, ML, Kirov, G, Nikolov, I, Collier, DA, Elkin, A, Williamson, R, Young, AH, Ferrier, IN, Milanova, V, Alda, M, Cervantes, P, Cruceanu, C, Rouleau, GA, Turecki, G, Paciga, S, Winslow, AR, Grigoroiu-Serbanescu, M, Ophoff, R, Adolfsson, R, Adolfsson, AN, Del-Favero, J, Pato, C, Biernacka, JM, Frye, MA, Morris, D, Schork, NJ, Reif, A, Lissowska, J, Hauser, J, Szeszenia-Dabrowska, N, McGhee, K, Quinn, E, Moskvina, V, Holmans, PA, Farmer, A, Kennedy, JL, Andreassen, OA, Mattingsdal, M, Bass, NJ, Gurling, H, McQuillin, A, Breuer, R, Hultman, C, Lichtenstein, P, Huckins, LM, Leboyer, M, Lathrop, M, Nurnberger, J, Steffens, M, Foroud, TM, Berrettini, WH, Craig, DW, and Shi, J
Abstract: Psychiatric disorders are thought to have a complex genetic pathology consisting of interplay of common and rare variation. Traditionally, pedigrees are used to shed light on the latter only, while here we discuss the application of polygenic risk scores to also highlight patterns of common genetic risk. We analyze polygenic risk scores for psychiatric disorders in a large pedigree (n ~ 260) in which 30% of family members suffer from major depressive disorder or bipolar disorder. Studying patterns of assortative mating and anticipation, it appears increased polygenic risk is contributed by affected individuals who married into the family, resulting in an increasing genetic risk over generations. This may explain the observation of anticipation in mood disorders, whereby onset is earlier and the severity increases over the generations of a family. Joint analyses of rare and common variation may be a powerful way to understand the familial genetics of psychiatric disorders.
Published: 2018

19. Genetic variants associated with response to lithium treatment in bipolar disorder: A genome-wide association study

Author: Hou, L, Heilbronner, U, Degenhardt, F, Adli, M, Akiyama, K, Akula, N, Ardau, R, Arias, B, Backlund, L, Banzato, CEM, Benabarre, A, Bengesser, S, Bhattacharjee, AK, Biernacka, JM, Birner, A, Brichant-Petitjean, C, Bui, ET, Cervantes, P, Chen, GB, Chen, HC, Chillotti, C, Cichon, S, Clark, SR, Colom, F, Cousins, DA, Cruceanu, C, Czerski, PM, Dantas, CR, Dayer, A, Étain, B, Falkai, P, Forstner, AJ, Frisén, L, Fullerton, JM, Gard, S, Garnham, JS, Goes, FS, Grof, P, Gruber, O, Hashimoto, R, Hauser, J, Herms, S, Hoffmann, P, Hofmann, A, Jamain, S, Jiménez, E, Kahn, JP, Kassem, L, Kittel-Schneider, S, Kliwicki, S, König, B, Kusumi, I, Lackner, N, Laje, G, Landén, M, Lavebratt, C, Leboyer, M, Leckband, SG, Jaramillo, CAL, Macqueen, G, Manchia, M, Martinsson, L, Mattheisen, M, McCarthy, MJ, McElroy, SL, Mitjans, M, Mondimore, FM, Monteleone, P, Nievergelt, CM, Nöthen, MM, Ösby, U, Ozaki, N, Perlis, RH, Pfennig, A, Reich-Erkelenz, D, Rouleau, GA, Schofield, PR, Schubert, KO, Schweizer, BW, Seemüller, F, Severino, G, Shekhtman, T, Shilling, PD, Shimoda, K, Simhandl, C, Slaney, CM, Smoller, JW, Squassina, A, Stamm, T, Stopkova, P, Tighe, SK, Tortorella, A, Turecki, G, Volkert, J, Witt, S, Wright, A, Young, LT, Zandi, PP, Potash, JB, Depaulo, JR, Hou, L, Heilbronner, U, Degenhardt, F, Adli, M, Akiyama, K, Akula, N, Ardau, R, Arias, B, Backlund, L, Banzato, CEM, Benabarre, A, Bengesser, S, Bhattacharjee, AK, Biernacka, JM, Birner, A, Brichant-Petitjean, C, Bui, ET, Cervantes, P, Chen, GB, Chen, HC, Chillotti, C, Cichon, S, Clark, SR, Colom, F, Cousins, DA, Cruceanu, C, Czerski, PM, Dantas, CR, Dayer, A, Étain, B, Falkai, P, Forstner, AJ, Frisén, L, Fullerton, JM, Gard, S, Garnham, JS, Goes, FS, Grof, P, Gruber, O, Hashimoto, R, Hauser, J, Herms, S, Hoffmann, P, Hofmann, A, Jamain, S, Jiménez, E, Kahn, JP, Kassem, L, Kittel-Schneider, S, Kliwicki, S, König, B, Kusumi, I, Lackner, N, Laje, G, Landén, M, Lavebratt, C, Leboyer, M, Leckband, SG, Jaramillo, CAL, Macqueen, G, Manchia, M, Martinsson, L, Mattheisen, M, McCarthy, MJ, McElroy, SL, Mitjans, M, Mondimore, FM, Monteleone, P, Nievergelt, CM, Nöthen, MM, Ösby, U, Ozaki, N, Perlis, RH, Pfennig, A, Reich-Erkelenz, D, Rouleau, GA, Schofield, PR, Schubert, KO, Schweizer, BW, Seemüller, F, Severino, G, Shekhtman, T, Shilling, PD, Shimoda, K, Simhandl, C, Slaney, CM, Smoller, JW, Squassina, A, Stamm, T, Stopkova, P, Tighe, SK, Tortorella, A, Turecki, G, Volkert, J, Witt, S, Wright, A, Young, LT, Zandi, PP, Potash, JB, and Depaulo, JR
Abstract: Background Lithium is a first-line treatment in bipolar disorder, but individual response is variable. Previous studies have suggested that lithium response is a heritable trait. However, no genetic markers of treatment response have been reproducibly identified. Methods Here, we report the results of a genome-wide association study of lithium response in 2563 patients collected by 22 participating sites from the International Consortium on Lithium Genetics (ConLiGen). Data from common single nucleotide polymorphisms (SNPs) were tested for association with categorical and continuous ratings of lithium response. Lithium response was measured using a well established scale (Alda scale). Genotyped SNPs were used to generate data at more than 6 million sites, using standard genomic imputation methods. Traits were regressed against genotype dosage. Results were combined across two batches by meta-analysis. Findings A single locus of four linked SNPs on chromosome 21 met genome-wide significance criteria for association with lithium response (rs79663003, p=1·37×10-8; rs78015114, p=1·31×10-8; rs74795342, p=3·31×10-9; and rs75222709, p=3·50×10-9). In an independent, prospective study of 73 patients treated with lithium monotherapy for a period of up to 2 years, carriers of the response-associated alleles had a significantly lower rate of relapse than carriers of the alternate alleles (p=0·03268, hazard ratio 3·8, 95% CI 1·1-13·0). Interpretation The response-associated region contains two genes for long, non-coding RNAs (lncRNAs), AL157359.3 and AL157359.4. LncRNAs are increasingly appreciated as important regulators of gene expression, particularly in the CNS. Confirmed biomarkers of lithium response would constitute an important step forward in the clinical management of bipolar disorder. Further studies are needed to establish the biological context and potential clinical utility of these findings. Funding Deutsche Forschungsgemeinschaft, National Institute of Mental Hea
Published: 2016

20. Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder

Author: Hou, L, Bergen, SE, Akula, N, Song, J, Hultman, CM, Landén, M, Adli, M, Alda, M, Ardau, R, Arias, B, Aubry, JM, Backlund, L, Badner, JA, Barrett, TB, Bauer, M, Baune, BT, Bellivier, F, Benabarre, A, Bengesser, S, Berrettini, WH, Bhattacharjee, AK, Biernacka, JM, Birner, A, Bloss, CS, Brichant-Petitjean, C, Bui, ET, Byerley, W, Cervantes, P, Chillotti, C, Cichon, S, Colom, F, Coryell, W, Craig, DW, Cruceanu, C, Czerski, PM, Davis, T, Dayer, A, Degenhardt, F, Del Zompo, M, DePaulo, JR, Edenberg, HJ, Étain, B, Falkai, P, Foroud, T, Forstner, AJ, Frisén, L, Frye, MA, Fullerton, JM, Gard, S, Garnham, JS, Gershon, ES, Goes, FS, Greenwood, TA, Grigoroiu-Serbanescu, M, Hauser, J, Heilbronner, U, Heilmann-Heimbach, S, Herms, S, Hipolito, M, Hitturlingappa, S, Hoffmann, P, Hofmann, A, Jamain, S, Jiménez, E, Kahn, JP, Kassem, L, Kelsoe, JR, Kittel-Schneider, S, Kliwicki, S, Koller, DL, König, B, Lackner, N, Laje, G, Lang, M, Lavebratt, C, Lawson, WB, Leboyer, M, Leckband, SG, Liu, C, Maaser, A, Mahon, PB, Maier, W, Maj, M, Manchia, M, Martinsson, L, McCarthy, MJ, McElroy, SL, McInnis, MG, McKinney, R, Mitchell, PB, Mitjans, M, Mondimore, FM, Monteleone, P, Mühleisen, TW, Nievergelt, CM, Nöthen, MM, Novák, T, Nurnberger, JI, Nwulia, EA, Ösby, U, Hou, L, Bergen, SE, Akula, N, Song, J, Hultman, CM, Landén, M, Adli, M, Alda, M, Ardau, R, Arias, B, Aubry, JM, Backlund, L, Badner, JA, Barrett, TB, Bauer, M, Baune, BT, Bellivier, F, Benabarre, A, Bengesser, S, Berrettini, WH, Bhattacharjee, AK, Biernacka, JM, Birner, A, Bloss, CS, Brichant-Petitjean, C, Bui, ET, Byerley, W, Cervantes, P, Chillotti, C, Cichon, S, Colom, F, Coryell, W, Craig, DW, Cruceanu, C, Czerski, PM, Davis, T, Dayer, A, Degenhardt, F, Del Zompo, M, DePaulo, JR, Edenberg, HJ, Étain, B, Falkai, P, Foroud, T, Forstner, AJ, Frisén, L, Frye, MA, Fullerton, JM, Gard, S, Garnham, JS, Gershon, ES, Goes, FS, Greenwood, TA, Grigoroiu-Serbanescu, M, Hauser, J, Heilbronner, U, Heilmann-Heimbach, S, Herms, S, Hipolito, M, Hitturlingappa, S, Hoffmann, P, Hofmann, A, Jamain, S, Jiménez, E, Kahn, JP, Kassem, L, Kelsoe, JR, Kittel-Schneider, S, Kliwicki, S, Koller, DL, König, B, Lackner, N, Laje, G, Lang, M, Lavebratt, C, Lawson, WB, Leboyer, M, Leckband, SG, Liu, C, Maaser, A, Mahon, PB, Maier, W, Maj, M, Manchia, M, Martinsson, L, McCarthy, MJ, McElroy, SL, McInnis, MG, McKinney, R, Mitchell, PB, Mitjans, M, Mondimore, FM, Monteleone, P, Mühleisen, TW, Nievergelt, CM, Nöthen, MM, Novák, T, Nurnberger, JI, Nwulia, EA, and Ösby, U
Abstract: Bipolar disorder (BD) is a genetically complex mental illness characterized by severe oscillations of mood and behaviour. Genome-wide association studies (GWAS) have identified several risk loci that together account for a small portion of the heritability. To identify additional risk loci, we performed a two-stage meta-analysis of > 9 million genetic variants in 9,784 bipolar disorder patients and 30,471 controls, the largest GWAS of BD to date. In this study, to increase power we used ~2,000 lithium-treated cases with a long-term diagnosis of BD from the Consortium on Lithium Genetics, excess controls, and analytic methods optimized for markers on the X-chromosome. In addition to four known loci, results revealed genome-wide significant associations at two novel loci: an intergenic region on 9p21.3 (rs12553324, P= 5.87×10-9; odds ratio (OR)=1.12) and markers within ERBB2 (rs2517959, P= 4.53×10-9; OR=1.13). No significant X-chromosome associations were detected and Xlinked markers explained very little BD heritability. The results add to a growing list of common autosomal variants involved in BD and illustrate the power of comparing well-characterized cases to an excess of controls in GWAS.
Published: 2016

21. The International SSRI Pharmacogenomics Consortium (ISPC): a genome-wide association study of antidepressant treatment response

Author: Biernacka, JM, Sangkuhl, K, Jenkins, G, Whaley, RM, Barman, P, Batzler, A, Altman, RB, Arolt, V, Brockmoeller, J, Chen, CH, Domschke, K, Hall-Flavin, DK, Hong, CJ, Illi, A, Ji, Y, Kampman, O, Kinoshita, T, Leinonen, E, Liou, YJ, Mushiroda, T, Nonen, S, Skime, MK, Wang, L, Baune, BT, Kato, M, Liu, YL, Praphanphoj, V, Stingl, JC, Tsai, SJ, Kubo, M, Klein, TE, Weinshilboum, R, Biernacka, JM, Sangkuhl, K, Jenkins, G, Whaley, RM, Barman, P, Batzler, A, Altman, RB, Arolt, V, Brockmoeller, J, Chen, CH, Domschke, K, Hall-Flavin, DK, Hong, CJ, Illi, A, Ji, Y, Kampman, O, Kinoshita, T, Leinonen, E, Liou, YJ, Mushiroda, T, Nonen, S, Skime, MK, Wang, L, Baune, BT, Kato, M, Liu, YL, Praphanphoj, V, Stingl, JC, Tsai, SJ, Kubo, M, Klein, TE, and Weinshilboum, R
Abstract: Response to treatment with selective serotonin reuptake inhibitors (SSRIs) varies considerably between patients. The International SSRI Pharmacogenomics Consortium (ISPC) was formed with the primary goal of identifying genetic variation that may contribute to response to SSRI treatment of major depressive disorder. A genome-wide association study of 4-week treatment outcomes, measured using the 17-item Hamilton Rating Scale for Depression (HRSD-17), was performed using data from 865 subjects from seven sites. The primary outcomes were percent change in HRSD-17 score and response, defined as at least 50% reduction in HRSD-17. Data from two prior studies, the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomics Study (PGRN-AMPS) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, were used for replication, and a meta-analysis of the three studies was performed (N=2394). Although many top association signals in the ISPC analysis map to interesting candidate genes, none were significant at the genome-wide level and the associations were not replicated using PGRN-AMPS and STAR*D data. The top association result in the meta-analysis of response represents SNPs 5′ upstream of the neuregulin-1 gene, NRG1 (P = 1.20E - 06). NRG1 is involved in many aspects of brain development, including neuronal maturation and variations in this gene have been shown to be associated with increased risk for mental disorders, particularly schizophrenia. Replication and functional studies of these findings are warranted.
Published: 2015

22. Assessment of Response to Lithium Maintenance Treatment in Bipolar Disorder: A Consortium on Lithium Genetics (ConLiGen) Report

Author: Manchia, M, Adli, M, Akula, N, Ardau, R, Aubry, JM, Backlund, L, Banzato, CEM, Baune, BT, Bellivier, F, Bengesser, S, Biernacka, JM, Brichant-Petitjean, C, Bui, E, Calkin, CV, Cheng, ATA, Chillotti, C, Cichon, S, Clark, S, Czerski, PM, Dantas, C, Del Zompo, M, DePaulo, JR, Detera-Wadleigh, SD, Etain, B, Falkai, P, Frisén, L, Frye, MA, Fullerton, J, Gard, S, Garnham, J, Goes, FS, Grof, P, Gruber, O, Hashimoto, R, Hauser, J, Heilbronner, U, Hoban, R, Hou, L, Jamain, S, Kahn, JP, Kassem, L, Kato, T, Kelsoe, JR, Kittel-Schneider, S, Kliwicki, S, Kuo, PH, Kusumi, I, Laje, G, Lavebratt, C, Leboyer, M, Leckband, SG, López Jaramillo, CA, Maj, M, Malafosse, A, Martinsson, L, Masui, T, Mitchell, PB, Mondimore, F, Monteleone, P, Nallet, A, Neuner, M, Novák, T, O'Donovan, C, Ösby, U, Ozaki, N, Perlis, RH, Pfennig, A, Potash, JB, Reich-Erkelenz, D, Reif, A, Reininghaus, E, Richardson, S, Rouleau, GA, Rybakowski, JK, Schalling, M, Schofield, PR, Schubert, OK, Schweizer, B, Seemüller, F, Grigoroiu-Serbanescu, M, Severino, G, Seymour, LR, Slaney, C, Smoller, JW, Squassina, A, Stamm, T, Steele, J, Stopkova, P, Tighe, SK, Tortorella, A, Turecki, G, Wray, NR, Wright, A, Zandi, PP, Zilles, D, Bauer, M, Rietschel, M, McMahon, FJ, Schulze, TG, Alda, M, Manchia, M, Adli, M, Akula, N, Ardau, R, Aubry, JM, Backlund, L, Banzato, CEM, Baune, BT, Bellivier, F, Bengesser, S, Biernacka, JM, Brichant-Petitjean, C, Bui, E, Calkin, CV, Cheng, ATA, Chillotti, C, Cichon, S, Clark, S, Czerski, PM, Dantas, C, Del Zompo, M, DePaulo, JR, Detera-Wadleigh, SD, Etain, B, Falkai, P, Frisén, L, Frye, MA, Fullerton, J, Gard, S, Garnham, J, Goes, FS, Grof, P, Gruber, O, Hashimoto, R, Hauser, J, Heilbronner, U, Hoban, R, Hou, L, Jamain, S, Kahn, JP, Kassem, L, Kato, T, Kelsoe, JR, Kittel-Schneider, S, Kliwicki, S, Kuo, PH, Kusumi, I, Laje, G, Lavebratt, C, Leboyer, M, Leckband, SG, López Jaramillo, CA, Maj, M, Malafosse, A, Martinsson, L, Masui, T, Mitchell, PB, Mondimore, F, Monteleone, P, Nallet, A, Neuner, M, Novák, T, O'Donovan, C, Ösby, U, Ozaki, N, Perlis, RH, Pfennig, A, Potash, JB, Reich-Erkelenz, D, Reif, A, Reininghaus, E, Richardson, S, Rouleau, GA, Rybakowski, JK, Schalling, M, Schofield, PR, Schubert, OK, Schweizer, B, Seemüller, F, Grigoroiu-Serbanescu, M, Severino, G, Seymour, LR, Slaney, C, Smoller, JW, Squassina, A, Stamm, T, Steele, J, Stopkova, P, Tighe, SK, Tortorella, A, Turecki, G, Wray, NR, Wright, A, Zandi, PP, Zilles, D, Bauer, M, Rietschel, M, McMahon, FJ, Schulze, TG, and Alda, M
Abstract: Objective:The assessment of response to lithium maintenance treatment in bipolar disorder (BD) is complicated by variable length of treatment, unpredictable clinical course, and often inconsistent compliance. Prospective and retrospective methods of assessment of lithium response have been proposed in the literature. In this study we report the key phenotypic measures of the "Retrospective Criteria of Long-Term Treatment Response in Research Subjects with Bipolar Disorder" scale currently used in the Consortium on Lithium Genetics (ConLiGen) study.Materials and Methods:Twenty-nine ConLiGen sites took part in a two-stage case-vignette rating procedure to examine inter-rater agreement [Kappa (κ)] and reliability [intra-class correlation coefficient (ICC)] of lithium response. Annotated first-round vignettes and rating guidelines were circulated to expert research clinicians for training purposes between the two stages. Further, we analyzed the distributional properties of the treatment response scores available for 1,308 patients using mixture modeling.Results:Substantial and moderate agreement was shown across sites in the first and second sets of vignettes (κ = 0.66 and κ = 0.54, respectively), without significant improvement from training. However, definition of response using the A score as a quantitative trait and selecting cases with B criteria of 4 or less showed an improvement between the two stages (ICC1 = 0.71 and ICC2 = 0.75, respectively). Mixture modeling of score distribution indicated three subpopulations (full responders, partial responders, non responders).Conclusions:We identified two definitions of lithium response, one dichotomous and the other continuous, with moderate to substantial inter-rater agreement and reliability. Accurate phenotypic measurement of lithium response is crucial for the ongoing ConLiGen pharmacogenomic study.
Published: 2013

23. Assessment of Response to Lithium Maintenance Treatment in Bipolar Disorder: A Consortium on Lithium Genetics (ConLiGen) Report

Author: J. Raymond DePaulo, Martin Alda, Alfonso Tortorella, Susan G. Leckband, Jean-Michel Aubry, James B. Potash, Clara Brichant-Petitjean, Layla Kassem, Joanna Hauser, Tomas Novak, David Zilles, Claire O'Donovan, Rebecca Hoban, Sarah Kittel-Schneider, Claire Slaney, Lena Backlund, Peter Falkai, Andrew T. A. Cheng, John R. Kelsoe, Andrea Pfennig, Janice M. Fullerton, Marcella Rietschel, Caterina Chillotti, Stéphane Jamain, Elise Bui, Po-Hsiu Kuo, Alessio Squassina, Claudio E. M. Banzato, Joanna M. Biernacka, Lisa R. Seymour, Naomi R. Wray, Ichiro Kusumi, Thomas Stamm, Fernando S. Goes, Audrey Nallet, Sarah K. Tighe, Giovanni Severino, Jean-Pierre Kahn, Maria Neuner, Nirmala Akula, Norio Ozaki, Francis J. McMahon, Frank Bellivier, Andreas Reif, Ryota Hashimoto, Barbara W. Schweizer, Palmiero Monteleone, Florian Seemüller, Urs Heilbronner, Sevilla D. Detera-Wadleigh, Pavla Stopkova, Martin Schalling, Julie Garnham, Sébastien Gard, Mark A. Frye, Eva Z. Reininghaus, Lina Martinsson, Urban Ösby, Maria Grigoroiu-Serbanescu, Sara Richardson, Tadafumi Kato, Marion Leboyer, Catharina Lavebratt, Piotr M. Czerski, Scott R. Clark, Daniela Reich-Erkelenz, Sebastian Kliwicki, Guy A. Rouleau, Oliver Gruber, Gonzalo Laje, Adam Wright, Gustavo Turecki, Roy H. Perlis, Takuya Masui, Mirko Manchia, Bernhard T. Baune, Mazda Adli, Michael Bauer, Sven Cichon, Frank Mondimore, Bruno Etain, Philip B. Mitchell, Thomas G. Schulze, Peter P. Zandi, Raffaella Ardau, Peter R. Schofield, Susanne Bengesser, Liping Hou, Janusz K. Rybakowski, Mario Maj, Clarissa de Rosalmeida Dantas, Louise Frisén, O. Schubert, Carlos Jaramillo, Maria Del Zompo, Paul Grof, Cynthia V. Calkin, Jo Steele, Jordan W. Smoller, Alain Malafosse, Manchia, M, Adli, M, Akula, N, Ardau, R, Aubry, Jm, Backlund, L, Banzato, Ce, Baune, Bt, Bellivier, F, Bengesser, S, Biernacka, Jm, Brichant Petitjean, C, Bui, E, Calkin, Cv, Cheng, At, Chillotti, C, Cichon, S, Clark, S, Czerski, Pm, Dantas, C, Zompo, Md, Depaulo, Jr, Detera Wadleigh, Sd, Etain, B, Falkai, P, Frisén, L, Frye, Ma, Fullerton, J, Gard, S, Garnham, J, Goes, F, Grof, P, Gruber, O, Hashimoto, R, Hauser, J, Heilbronner, U, Hoban, R, Hou, L, Jamain, S, Kahn, Jp, Kassem, L, Kato, T, Kelsoe, Jr, Kittel Schneider, S, Kliwicki, S, Kuo, Ph, Kusumi, I, Laje, G, Lavebratt, C, Leboyer, M, Leckband, Sg, López Jaramillo, Ca, Maj, Mario, Malafosse, A, Martinsson, L, Masui, T, Mitchell, Pb, Mondimore, F, Monteleone, P, Nallet, A, Neuner, M, Novák, T, O'Donovan, C, Osby, U, Ozaki, N, Perlis, Rh, Pfennig, A, Potash, Jb, Reich Erkelenz, D, Reif, A, Reininghaus, E, Richardson, S, Rouleau, Ga, Rybakowski, Jk, Schalling, M, Schofield, Pr, Schubert, Ok, Schweizer, B, Seemüller, F, Grigoroiu Serbanescu, M, Severino, G, Seymour, Lr, Slaney, C, Smoller, Jw, Squassina, A, Stamm, T, Steele, J, Stopkova, P, Tighe, Sk, Tortorella, Alfonso Antonio Vincenzo, Turecki, G, Wray, Nr, Wright, A, Zandi, Pp, Zilles, D, Bauer, M, Rietschel, M, Mcmahon, Fj, Schulze, Tg, Alda, M., Department of Psychiatry, Dalhousie University [Halifax], Department of Psychiatry and Psychotherapy, Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Human Genetics Branch, National Institutes of Health [Bethesda] (NIH)-National Institute of Mental Health (NIMH), Unit of Clinical Pharmacology, University-Hospital of Cagliari, Department of Mental Health and Psychiatry, University Hospital of Geneva, Trinity College Dublin-St. James's Hospital, University of Campinas [Campinas] (UNICAMP), University of Adelaide, Pôle de Psychiatrie, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Groupe Hospitalier Saint Louis - Lariboisière - Fernand Widal [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Medical University Graz, Mayo Clinic, Division of Epidemiology and Genetics, Academia Sinica-Institute of Biomedical Sciences (ICB/USP), Universidade de São Paulo (USP)-Universidade de São Paulo (USP), Department of Genomics, Life and Brain Center, University of Bonn, Psychiatric Genetic Unit, Poznan University of Medical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Institut Mondor de Recherche Biomédicale (IMRB), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-IFR10, University of Homburg, Genetics of Mental Illness and Brain Function, Neuroscience Research Australia, Service de psychiatrie adulte, Université Bordeaux Segalen - Bordeaux 2-Hôpital Charles Perrens-Centre Expert Trouble Bipolaire, Mood Disorders Center of Ottawa (MDCO), University of Ottawa [Ottawa], University of Toronto, Georg-August-University [Göttingen], Osaka University Graduate School of Medicine, University of California [San Diego] (UC San Diego), University of California-University of California, Service de Psychiatrie et Psychologie Clinique, Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Hôpital Jeanne-d'Arc, Laboratory for Molecular Dynamics of Mental Disorders, RIKEN Center for Brain Science [Wako] (RIKEN CBS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN)-RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), Mental Health Sciences Unit, University College of London [London] (UCL), Department of Adult Psychiatry, Institute of Epidemiology and Preventive Medicine, National Taiwan University [Taiwan] (NTU), Hokkaido University Graduate School of Medicine, Molecular Medicine and Surgery department, Karolinska Institutet [Stockholm], Center for Molecular Medicine, Karolinska University Hospital [Stockholm], Department of Pharmacy, Veterans Affairs San Diego Healthcare System, Skaggs School of Pharmacy and Pharmaceutical Sciences [San Diego], University of Antioquia, University of Napoli, School of Psychiatry, University of New South Wales [Sydney] (UNSW)-Black Dog Institute, Prague Psychiatric Center, University of Prague, Fujita Health University School of Medicine, Nagoya University Graduate School of Medicine, Harvard Medical School [Boston] (HMS)-Massachusetts General Hospital [Boston], Technische Universität Dresden = Dresden University of Technology (TU Dresden), University of Iowa [Iowa City], Center of Excellence in Neuroscience, CHU de Montréal, Department of Medicine, Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CR CHUM), Centre Hospitalier de l'Université de Montréal (CHUM), Université de Montréal (UdeM)-Université de Montréal (UdeM)-Centre Hospitalier de l'Université de Montréal (CHUM), Université de Montréal (UdeM)-Université de Montréal (UdeM), Biometric Psychiatric Genetics Research Unit, Alexandru Obregia Psychiatric Hospital, McGill Group for Suicide Studies, Douglas Mental Health University Institute, Queensland Brain Institute, University of Queensland [Brisbane], Department of Mental Health, Johns Hopkins Bloomberg School of Public Health [Baltimore], Johns Hopkins University (JHU)-Johns Hopkins University (JHU), Department of Genetic Epidemiology in Psychiatry [Mannhein], Universität Heidelberg [Heidelberg]-Central Institute of Mental Health Mannheim, The work on assessment of lithium response has been supported by a grant from Canadian Institutes of Health Research #64410 to MA. MG-S was supported by Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii (UEFISCDI), Bucharest, Romania (grant no. 89/2012). JMA and AN were supported by a grant from the Swiss National Foundation (#32003B_125469/1 to JM Aubry). ConLiGen is in part supported by funds from the Intramural Research Program of the National Institute of Mental Health (NIMH) at the National Institutes of Health (NIH), Department of Health and Human Services, United States Government, Aubry, Jean-Michel, Universidade Estadual de Campinas = University of Campinas (UNICAMP), Universidade de São Paulo = University of São Paulo (USP)-Universidade de São Paulo = University of São Paulo (USP), Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Georg-August-University = Georg-August-Universität Göttingen, University of California (UC)-University of California (UC), Guellaen, Georges, Université Bordeaux Segalen - Bordeaux 2-Centre hospitalier Charles Perrens [Bordeaux]-Centre Expert Trouble Bipolaire, and Ikeda, Kazutaka
Subjects: Male, Bipolar Disorder, Lithium (medication), Psychometrics, Psychopharmacology, Epidemiology, International Cooperation, lcsh:Medicine, Lithium, Maintenance Treatment, ConLiGen, ddc:616.89, 0302 clinical medicine, Antimanic Agents, Psychology, lcsh:Science, Reliability (statistics), Psychiatry, Genetics, 0303 health sciences, Multidisciplinary, ASSOCIATION, Genomics, Pharmacoepidemiology, 3. Good health, Mental Health, Phenotype, Treatment Outcome, MAPPING SUSCEPTIBILITY GENES, Behavioral Pharmacology, RELIABILITY, Lithium Compounds, Medicine, Female, Research Article, medicine.drug, Drugs and Devices, MAPPING SUSCEPTIBILITY GENES, PROPHYLACTIC LITHIUM, OBSERVER AGREEMENT, MOOD DISORDERS, ONSET, ASSOCIATION, RELIABILITY, MORTALITY, ILLNESS, AGE, ILLNESS, PROPHYLACTIC LITHIUM, 03 medical and health sciences, AGE, Neuropharmacology, Genomic Medicine, [SDV.BBM] Life Sciences [q-bio]/Biochemistry, Molecular Biology, Genome-Wide Association Studies, medicine, Humans, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Bipolar disorder, ddc:610, Biology, 030304 developmental biology, Mood Disorders, business.industry, MORTALITY, lcsh:R, Computational Biology, Reproducibility of Results, Models, Theoretical, medicine.disease, OBSERVER AGREEMENT, Mood disorders, Pharmacogenetics, Therapies, ONSET, lcsh:Q, Pharmacogenomics, business, 030217 neurology & neurosurgery, Kappa
Abstract: Objective: The assessment of response to lithium maintenance treatment in bipolar disorder (BD) is complicated by variable length of treatment, unpredictable clinical course, and often inconsistent compliance. Prospective and retrospective methods of assessment of lithium response have been proposed in the literature. In this study we report the key phenotypic measures of the "Retrospective Criteria of Long-Term Treatment Response in Research Subjects with Bipolar Disorder" scale currently used in the Consortium on Lithium Genetics (ConLiGen) study. Materials and Methods: Twenty-nine ConLiGen sites took part in a two-stage case-vignette rating procedure to examine inter-rater agreement [Kappa (\(\kappa\))] and reliability [intra-class correlation coefficient (ICC)] of lithium response. Annotated first-round vignettes and rating guidelines were circulated to expert research clinicians for training purposes between the two stages. Further, we analyzed the distributional properties of the treatment response scores available for 1,308 patients using mixture modeling. Results: Substantial and moderate agreement was shown across sites in the first and second sets of vignettes (\(\kappa\) = 0.66 and \(\kappa\) = 0.54, respectively), without significant improvement from training. However, definition of response using the A score as a quantitative trait and selecting cases with B criteria of 4 or less showed an improvement between the two stages (\(ICC_1 = 0.71\) and \(ICC_2 = 0.75\), respectively). Mixture modeling of score distribution indicated three subpopulations (full responders, partial responders, non responders). Conclusions: We identified two definitions of lithium response, one dichotomous and the other continuous, with moderate to substantial inter-rater agreement and reliability. Accurate phenotypic measurement of lithium response is crucial for the ongoing ConLiGen pharmacogenomic study.
Published: 2013

24. Correction: Social connectedness as a determinant of mental health: A scoping review.

Author: Wickramaratne PJ, Yangchen T, Lepow L, Patra BG, Glicksberg BS, Talati A, Adekkanattu P, Ryu E, Biernacka JM, Charney A, Mann JJ, Pathak J, Olfson M, and Weissman MM
Abstract: [This corrects the article DOI: 10.1371/journal.pone.0275004.]., (Copyright: © 2024 Wickramaratne et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Published: 2024
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25. Extracting social support and social isolation information from clinical psychiatry notes: comparing a rule-based natural language processing system and a large language model.

Author: Patra BG, Lepow LA, Kasi Reddy Jagadeesh Kumar P, Vekaria V, Sharma MM, Adekkanattu P, Fennessy B, Hynes G, Landi I, Sanchez-Ruiz JA, Ryu E, Biernacka JM, Nadkarni GN, Talati A, Weissman M, Olfson M, Mann JJ, Zhang Y, Charney AW, and Pathak J
Abstract: Objectives: Social support (SS) and social isolation (SI) are social determinants of health (SDOH) associated with psychiatric outcomes. In electronic health records (EHRs), individual-level SS/SI is typically documented in narrative clinical notes rather than as structured coded data. Natural language processing (NLP) algorithms can automate the otherwise labor-intensive process of extraction of such information., Materials and Methods: Psychiatric encounter notes from Mount Sinai Health System (MSHS, n = 300) and Weill Cornell Medicine (WCM, n = 225) were annotated to create a gold-standard corpus. A rule-based system (RBS) involving lexicons and a large language model (LLM) using FLAN-T5-XL were developed to identify mentions of SS and SI and their subcategories (eg, social network, instrumental support, and loneliness)., Results: For extracting SS/SI, the RBS obtained higher macroaveraged F1-scores than the LLM at both MSHS (0.89 versus 0.65) and WCM (0.85 versus 0.82). For extracting the subcategories, the RBS also outperformed the LLM at both MSHS (0.90 versus 0.62) and WCM (0.82 versus 0.81)., Discussion and Conclusion: Unexpectedly, the RBS outperformed the LLMs across all metrics. An intensive review demonstrates that this finding is due to the divergent approach taken by the RBS and LLM. The RBS was designed and refined to follow the same specific rules as the gold-standard annotations. Conversely, the LLM was more inclusive with categorization and conformed to common English-language understanding. Both approaches offer advantages, although additional replication studies are warranted., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)
Published: 2024
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26. Correction: Metabolic activity of CYP2C19 and CYP2D6 on antidepressant response from 13 clinical studies using genotype imputation: a meta-analysis.

Author: Li D, Pain O, Fabbri C, Wong WLE, Lo CWH, Ripke S, Cattaneo A, Souery D, Dernovsek MZ, Henigsberg N, Hauser J, Lewis G, Mors O, Perroud N, Rietschel M, Uher R, Maier W, Baune BT, Biernacka JM, Bondolfi G, Domschke K, Kato M, Liu YL, Serretti A, Tsai SJ, Weinshilboum R, McIntosh AM, and Lewis CM
Published: 2024
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27. Metabolic activity of CYP2C19 and CYP2D6 on antidepressant response from 13 clinical studies using genotype imputation: a meta-analysis.

Author: Li D, Pain O, Fabbri C, Wong WLE, Lo CWH, Ripke S, Cattaneo A, Souery D, Dernovsek MZ, Henigsberg N, Hauser J, Lewis G, Mors O, Perroud N, Rietschel M, Uher R, Maier W, Baune BT, Biernacka JM, Bondolfi G, Domschke K, Kato M, Liu YL, Serretti A, Tsai SJ, Weinshilboum R, McIntosh AM, and Lewis CM
Subjects: Female, Humans, Male, Asian People genetics, Depressive Disorder, Major drug therapy, Depressive Disorder, Major genetics, Depressive Disorder, Major metabolism, Genotype, Phenotype, Treatment Outcome, White People genetics, Antidepressive Agents therapeutic use, Cytochrome P-450 CYP2C19 genetics, Cytochrome P-450 CYP2D6 genetics, Cytochrome P-450 CYP2D6 metabolism
Abstract: Cytochrome P450 enzymes including CYP2C19 and CYP2D6 are important for antidepressant metabolism and polymorphisms of these genes have been determined to predict metabolite levels. Nonetheless, more evidence is needed to understand the impact of genetic variations on antidepressant response. In this study, individual clinical and genetic data from 13 studies of European and East Asian ancestry populations were collected. The antidepressant response was clinically assessed as remission and percentage improvement. Imputed genotype was used to translate genetic polymorphisms to metabolic phenotypes (poor, intermediate, normal, and rapid+ultrarapid) of CYP2C19 and CYP2D6. CYP2D6 structural variants cannot be imputed from genotype data, limiting the determination of metabolic phenotypes, and precluding testing for association with response. The association of CYP2C19 metabolic phenotypes with treatment response was examined using normal metabolizers as the reference. Among 5843 depression patients, a higher remission rate was found in CYP2C19 poor metabolizers compared to normal metabolizers at nominal significance but did not survive after multiple testing correction (OR = 1.46, 95% CI [1.03, 2.06], p = 0.033, heterogeneity I 2 = 0%, subgroup difference p = 0.72). No metabolic phenotype was associated with percentage improvement from baseline. After stratifying by antidepressants primarily metabolized by CYP2C19, no association was found between metabolic phenotypes and antidepressant response. Metabolic phenotypes showed differences in frequency, but not effect, between European- and East Asian-ancestry studies. In conclusion, metabolic phenotypes imputed from genetic variants using genotype were not associated with antidepressant response. CYP2C19 poor metabolizers could potentially contribute to antidepressant efficacy with more evidence needed. Sequencing and targeted pharmacogenetic testing, alongside information on side effects, antidepressant dosage, depression measures, and diverse ancestry studies, would more fully capture the influence of metabolic phenotypes., (© 2024. The Author(s).)
Published: 2024
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28. Exploring the genetics of lithium response in bipolar disorders.

Author: Herrera-Rivero M, Adli M, Akiyama K, Akula N, Amare AT, Ardau R, Arias B, Aubry JM, Backlund L, Bellivier F, Benabarre A, Bengesser S, Bhattacharjee AK, Biernacka JM, Birner A, Cearns M, Cervantes P, Chen HC, Chillotti C, Cichon S, Clark SR, Colom F, Cruceanu C, Czerski PM, Dalkner N, Degenhardt F, Del Zompo M, DePaulo JR, Etain B, Falkai P, Ferensztajn-Rochowiak E, Forstner AJ, Frank J, Frisén L, Frye MA, Fullerton JM, Gallo C, Gard S, Garnham JS, Goes FS, Grigoroiu-Serbanescu M, Grof P, Hashimoto R, Hasler R, Hauser J, Heilbronner U, Herms S, Hoffmann P, Hou L, Hsu YH, Jamain S, Jiménez E, Kahn JP, Kassem L, Kato T, Kelsoe J, Kittel-Schneider S, Kuo PH, Kusumi I, König B, Laje G, Landén M, Lavebratt C, Leboyer M, Leckband SG, Maj M, Manchia M, Marie-Claire C, Martinsson L, McCarthy MJ, McElroy SL, Millischer V, Mitjans M, Mondimore FM, Monteleone P, Nievergelt CM, Novák T, Nöthen MM, O'Donovan C, Ozaki N, Papiol S, Pfennig A, Pisanu C, Potash JB, Reif A, Reininghaus E, Richard-Lepouriel H, Roberts G, Rouleau GA, Rybakowski JK, Schalling M, Schofield PR, Schubert KO, Schulte EC, Schweizer BW, Severino G, Shekhtman T, Shilling PD, Shimoda K, Simhandl C, Slaney CM, Squassina A, Stamm T, Stopkova P, Streit F, Tekola-Ayele F, Thalamuthu A, Tortorella A, Turecki G, Veeh J, Vieta E, Viswanath B, Witt SH, Zandi PP, Alda M, Bauer M, McMahon FJ, Mitchell PB, Rietschel M, Schulze TG, and Baune BT
Abstract: Background: Lithium (Li) remains the treatment of choice for bipolar disorders (BP). Its mood-stabilizing effects help reduce the long-term burden of mania, depression and suicide risk in patients with BP. It also has been shown to have beneficial effects on disease-associated conditions, including sleep and cardiovascular disorders. However, the individual responses to Li treatment vary within and between diagnostic subtypes of BP (e.g. BP-I and BP-II) according to the clinical presentation. Moreover, long-term Li treatment has been linked to adverse side-effects that are a cause of concern and non-adherence, including the risk of developing chronic medical conditions such as thyroid and renal disease. In recent years, studies by the Consortium on Lithium Genetics (ConLiGen) have uncovered a number of genetic factors that contribute to the variability in Li treatment response in patients with BP. Here, we leveraged the ConLiGen cohort (N = 2064) to investigate the genetic basis of Li effects in BP. For this, we studied how Li response and linked genes associate with the psychiatric symptoms and polygenic load for medical comorbidities, placing particular emphasis on identifying differences between BP-I and BP-II., Results: We found that clinical response to Li treatment, measured with the Alda scale, was associated with a diminished burden of mania, depression, substance and alcohol abuse, psychosis and suicidal ideation in patients with BP-I and, in patients with BP-II, of depression only. Our genetic analyses showed that a stronger clinical response to Li was modestly related to lower polygenic load for diabetes and hypertension in BP-I but not BP-II. Moreover, our results suggested that a number of genes that have been previously linked to Li response variability in BP differentially relate to the psychiatric symptomatology, particularly to the numbers of manic and depressive episodes, and to the polygenic load for comorbid conditions, including diabetes, hypertension and hypothyroidism., Conclusions: Taken together, our findings suggest that the effects of Li on symptomatology and comorbidity in BP are partially modulated by common genetic factors, with differential effects between BP-I and BP-II., (© 2024. The Author(s).)
Published: 2024
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29. Lithium response in bipolar disorder is associated with focal adhesion and PI3K-Akt networks: a multi-omics replication study.

Author: Ou AH, Rosenthal SB, Adli M, Akiyama K, Akula N, Alda M, Amare AT, Ardau R, Arias B, Aubry JM, Backlund L, Bauer M, Baune BT, Bellivier F, Benabarre A, Bengesser S, Bhattacharjee AK, Biernacka JM, Cervantes P, Chen GB, Chen HC, Chillotti C, Cichon S, Clark SR, Colom F, Cousins DA, Cruceanu C, Czerski PM, Dantas CR, Dayer A, Del Zompo M, Degenhardt F, DePaulo JR, Étain B, Falkai P, Fellendorf FT, Ferensztajn-Rochowiak E, Forstner AJ, Frisén L, Frye MA, Fullerton JM, Gard S, Garnham JS, Goes FS, Grigoroiu-Serbanescu M, Grof P, Gruber O, Hashimoto R, Hauser J, Heilbronner U, Herms S, Hoffmann P, Hofmann A, Hou L, Jamain S, Jiménez E, Kahn JP, Kassem L, Kato T, Kittel-Schneider S, König B, Kuo PH, Kusumi I, Lackner N, Laje G, Landén M, Lavebratt C, Leboyer M, Leckband SG, Jaramillo CAL, MacQueen G, Maj M, Manchia M, Marie-Claire C, Martinsson L, Mattheisen M, McCarthy MJ, McElroy SL, McMahon FJ, Mitchell PB, Mitjans M, Mondimore FM, Monteleone P, Nievergelt CM, Nöthen MM, Novák T, Ösby U, Ozaki N, Papiol S, Perlis RH, Pisanu C, Potash JB, Pfennig A, Reich-Erkelenz D, Reif A, Reininghaus EZ, Rietschel M, Rouleau GA, Rybakowski JK, Schalling M, Schofield PR, Schubert KO, Schulze TG, Schweizer BW, Seemüller F, Severino G, Shekhtman T, Shilling PD, Shimoda K, Simhandl C, Slaney CM, Squassina A, Stamm T, Stopkova P, Tighe SK, Tortorella A, Turecki G, Vieta E, Volkert J, Witt S, Wray NR, Wright A, Young LT, Zandi PP, and Kelsoe JR
Subjects: Humans, Proto-Oncogene Proteins c-akt genetics, Phosphatidylinositol 3-Kinases genetics, Genome-Wide Association Study, Multiomics, Focal Adhesions, Lithium pharmacology, Lithium therapeutic use, Bipolar Disorder drug therapy, Bipolar Disorder genetics
Abstract: Lithium is the gold standard treatment for bipolar disorder (BD). However, its mechanism of action is incompletely understood, and prediction of treatment outcomes is limited. In our previous multi-omics study of the Pharmacogenomics of Bipolar Disorder (PGBD) sample combining transcriptomic and genomic data, we found that focal adhesion, the extracellular matrix (ECM), and PI3K-Akt signaling networks were associated with response to lithium. In this study, we replicated the results of our previous study using network propagation methods in a genome-wide association study of an independent sample of 2039 patients from the International Consortium on Lithium Genetics (ConLiGen) study. We identified functional enrichment in focal adhesion and PI3K-Akt pathways, but we did not find an association with the ECM pathway. Our results suggest that deficits in the neuronal growth cone and PI3K-Akt signaling, but not in ECM proteins, may influence response to lithium in BD., (© 2024. The Author(s).)
Published: 2024
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30. Pharmacogenomic overlap between antidepressant treatment response in major depression & antidepressant associated treatment emergent mania in bipolar disorder.

Author: Nuñez NA, Coombes BJ, Beaupre LM, Ozerdem A, Resendez MG, Romo-Nava F, Bond DJ, Veldic M, Singh B, Moore KM, Betcher HK, Kung S, Prieto ML, Fuentes M, Ercis M, Miola A, Sanchez Ruiz JA, Jenkins G, Batzler A, Leung JG, Cuellar-Barboza A, Tye SJ, McElroy SL, Biernacka JM, and Frye MA
Subjects: Humans, Mania chemically induced, Mania drug therapy, Depression, Pharmacogenetics, Genome-Wide Association Study, Antidepressive Agents therapeutic use, Bipolar Disorder drug therapy, Bipolar Disorder genetics, Bipolar Disorder chemically induced, Depressive Disorder, Major drug therapy, Depressive Disorder, Major genetics
Abstract: There is increasing interest in individualizing treatment selection for more than 25 regulatory approved treatments for major depressive disorder (MDD). Despite an inconclusive efficacy evidence base, antidepressants (ADs) are prescribed for the depressive phase of bipolar disorder (BD) with oftentimes, an inadequate treatment response and or clinical concern for mood destabilization. This study explored the relationship between antidepressant response in MDD and antidepressant-associated treatment emergent mania (TEM) in BD. We conducted a genome-wide association study (GWAS) and polygenic score analysis of TEM and tested its association in a subset of BD-type I patients treated with SSRIs or SNRIs. Our results did not identify any genome-wide significant variants although, we found that a higher polygenic score (PGS) for antidepressant response in MDD was associated with higher odds of TEM in BD. Future studies with larger transdiagnostic depressed cohorts treated with antidepressants are encouraged to identify a neurobiological mechanism associated with a spectrum of depression improvement from response to emergent mania., (© 2024. The Author(s).)
Published: 2024
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31. Predictors of Functional Impairment in Bipolar Disorder: Results From 13 Cohorts From Seven Countries by the Global Bipolar Cohort Collaborative.

Author: Burdick KE, Millett CE, Yocum AK, Altimus CM, Andreassen OA, Aubin V, Belzeaux R, Berk M, Biernacka JM, Blumberg HP, Cleare AJ, Diaz-Byrd C, Dubertret C, Etain B, Eyler LT, Forester BP, Fullerton JM, Frye MA, Gard S, Godin O, Haffen E, Klaus F, Lagerberg TV, Leboyer M, Martinez-Aran A, McElroy S, Mitchell PB, Olie E, Olorunfemi P, Passerieux C, Peters AT, Pham DL, Polosan M, Potter JR, Sajatovic M, Samalin L, Schwan R, Shanahan M, Solé B, Strawbridge R, Stuart AL, Torres I, Ueland T, Vieta E, Williams LJ, Wrobel AL, Yatham LN, Young AH, Nierenberg AA, and McInnis MG
Abstract: Objectives: Persistent functional impairment is common in bipolar disorder (BD) and is influenced by a number of demographic, clinical, and cognitive features. The goal of this project was to estimate and compare the influence of key factors on community function in multiple cohorts of well-characterized samples of individuals with BD., Methods: Thirteen cohorts from 7 countries included n = 5882 individuals with BD across multiple sites. The statistical approach consisted of a systematic uniform application of analyses across sites. Each site performed a logistic regression analysis with empirically derived "higher versus lower function" as the dependent variable and selected clinical and demographic variables as predictors., Results: We found high rates of functional impairment, ranging from 41 to 75%. Lower community functioning was associated with depressive symptoms in 10 of 12 of the cohorts that included this variable in the analysis. Lower levels of education, a greater number of prior mood episodes, the presence of a comorbid substance use disorder, and a greater total number of psychotropic medications were also associated with low functioning., Conclusions: The bipolar clinical research community is poised to work together to characterize the multi-dimensional contributors to impairment and address the barriers that impede patients' complete recovery. We must also identify the core features which enable many to thrive and live successfully with BD. A large-scale, worldwide, prospective longitudinal study focused squarely on BD and its heterogeneous presentations will serve as a platform for discovery and promote major advances toward optimizing outcomes for every individual with this illness.Reprinted from Bipolar Disord 2022; 24:709-719 , with permission from John Wiley and Sons. Copyright © 2022., (Copyright © 2023 by the American Psychiatric Association.)
Published: 2023
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32. Long-Term Lithium Therapy and Thyroid Disorders in Bipolar Disorder: A Historical Cohort Study.

Author: Joseph B, Nunez NA, Pazdernik V, Kumar R, Pahwa M, Ercis M, Ozerdem A, Cuellar-Barboza AB, Romo-Nava F, McElroy SL, Coombes BJ, Biernacka JM, Stan MN, Frye MA, and Singh B
Abstract: Lithium has been a cornerstone treatment for bipolar disorder (BD). Despite descriptions in the literature regarding associations between long-term lithium therapy (LTLT) and development of a thyroid disorder (overt/subclinical hypo/hyperthyroidism, thyroid nodule, and goiter) in BD, factors such as time to onset of thyroid abnormalities and impact on clinical outcomes in the course of illness have not been fully characterized. In this study we aimed to compare clinical characteristics of adult BD patients with and without thyroid disorders who were on LTLT. We aimed to identify the incidence of thyroid disorders in patients with BD on LTLT and response to lithium between patients with and without thyroid disorders in BD. The Cox proportional model was used to find the median time to the development of a thyroid disorder. Our results showed that up to 32% of patients with BD on LTLT developed a thyroid disorder, of which 79% developed hypothyroidism, which was corrected with thyroid hormone replacement. We did not find significant differences in lithium response between patients with or without thyroid disorders in BD. Findings from this study suggest that patients with BD and comorbid thyroid disorders when adequately treated have a response to lithium similar to patients with BD and no thyroid disorders.
Published: 2023
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33. Social connectedness as a determinant of mental health: A scoping review.

Author: Wickramaratne PJ, Yangchen T, Lepow L, Patra BG, Glicksburg B, Talati A, Adekkanattu P, Ryu E, Biernacka JM, Charney A, Mann JJ, Pathak J, Olfson M, and Weissman MM
Subjects: Adult, Anxiety Disorders, Depression, Female, Humans, Loneliness psychology, Mental Health, Pregnancy, Social Support, Depressive Disorder, Major psychology
Abstract: Public health and epidemiologic research have established that social connectedness promotes overall health. Yet there have been no recent reviews of findings from research examining social connectedness as a determinant of mental health. The goal of this review was to evaluate recent longitudinal research probing the effects of social connectedness on depression and anxiety symptoms and diagnoses in the general population. A scoping review was performed of PubMed and PsychInfo databases from January 2015 to December 2021 following PRISMA-ScR guidelines using a defined search strategy. The search yielded 66 unique studies. In research with other than pregnant women, 83% (19 of 23) studies reported that social support benefited symptoms of depression with the remaining 17% (5 of 23) reporting minimal or no evidence that lower levels of social support predict depression at follow-up. In research with pregnant women, 83% (24 of 29 studies) found that low social support increased postpartum depressive symptoms. Among 8 of 9 studies that focused on loneliness, feeling lonely at baseline was related to adverse outcomes at follow-up including higher risks of major depressive disorder, depressive symptom severity, generalized anxiety disorder, and lower levels of physical activity. In 5 of 8 reports, smaller social network size predicted depressive symptoms or disorder at follow-up. In summary, most recent relevant longitudinal studies have demonstrated that social connectedness protects adults in the general population from depressive symptoms and disorders. The results, which were largely consistent across settings, exposure measures, and populations, support efforts to improve clinical detection of high-risk patients, including adults with low social support and elevated loneliness., Competing Interests: The authors have no conflicts of interest with regard to this study. Dr. John Mann receives royalties from the Research Foundation for Mental Hygiene for commercial use of the C-SSRS from the Research Foundation for Mental Hygiene. Dr. Benjamin Glicksberg has received consulting fees from Anthem AI and Prometheus Biosciences. Dr. Myrna Weissman has received research funding from NIMH, Brain and Behavior Foundation, Templeton Foundation and has received book royalties from the Perseus Press, Oxford Press, and American Association of Psychiatry press, and has received royalties on the social adjustment scale from Multihealth Systems. None of these represent a conflict of interest.
Published: 2022
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34. Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach - CORRIGENDUM.

Author: Cearns M, Amare AT, Schubert KO, Thalamuthu A, Frank J, Streit F, Adli M, Akula N, Akiyama K, Ardau R, Arias B, Aubry J, Backlund L, Bhattacharjee AK, Bellivier F, Benabarre A, Bengesser S, Biernacka JM, Birner A, Brichant-Petitjean C, Cervantes P, Chen H, Chillotti C, Cichon S, Cruceanu C, Czerski PM, Dalkner N, Dayer A, Degenhardt F, Zompo MD, DePaulo JR, Étain B, Falkai P, Forstner AJ, Frisen L, Frye MA, Fullerton JM, Gard S, Garnham JS, Goes FS, Grigoroiu-Serbanescu M, Grof P, Hashimoto R, Hauser J, Heilbronner U, Herms S, Hoffmann P, Hofmann A, Hou L, Hsu YH, Jamain S, Jiménez E, Kahn JP, Kassem L, Kuo PH, Kato T, Kelsoe J, Kittel-Schneider S, Kliwicki S, König B, Kusumi I, Laje G, Landén M, Lavebratt C, Leboyer M, Leckband SG, Maj M, Manchia M, Martinsson L, McCarthy MJ, McElroy S, Colom F, Mitjans M, Mondimore FM, Monteleone P, Nievergelt CM, Nöthen MM, Novák T, O'Donovan C, Ozaki N, Millischer V, Papiol S, Pfennig A, Pisanu C, Potash JB, Reif A, Reininghaus E, Rouleau GA, Rybakowski JK, Schalling M, Schofield PR, Schweizer BW, Severino G, Shekhtman T, Shilling PD, Shimoda K, Simhandl C, Slaney CM, Squassina A, Stamm T, Stopkova P, TekolaAyele F, Tortorella A, Turecki G, Veeh J, Vieta E, Witt SH, Roberts G, Zandi PP, Alda M, Bauer M, McMahon FJ, Mitchell PB, Schulze TG, Rietschel M, Clark SR, and Baune BT
Published: 2022
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35. Shared brain transcriptomic signature in TDP-43 type A FTLD patients with or without GRN mutations.

Author: Pottier C, Mateiu L, Baker MC, DeJesus-Hernandez M, Teixeira Vicente C, Finch NA, Tian S, van Blitterswijk M, Murray ME, Ren Y, Petrucelli L, Oskarsson B, Biernacka JM, Graff-Radford NR, Boeve BF, Petersen RC, Josephs KA, Asmann YW, Dickson DW, and Rademakers R
Subjects: Brain metabolism, DNA-Binding Proteins genetics, DNA-Binding Proteins metabolism, Humans, Intercellular Signaling Peptides and Proteins genetics, Intercellular Signaling Peptides and Proteins metabolism, Mutation, Transcriptome, Frontotemporal Dementia genetics, Frontotemporal Dementia metabolism, Frontotemporal Lobar Degeneration genetics, Frontotemporal Lobar Degeneration metabolism, Progranulins genetics, Progranulins metabolism
Abstract: Frontotemporal lobar degeneration with TDP-43 inclusions (FTLD-TDP) is a complex heterogeneous neurodegenerative disorder for which mechanisms are poorly understood. To explore transcriptional changes underlying FTLD-TDP, we performed RNA-sequencing on 66 genetically unexplained FTLD-TDP patients, 24 FTLD-TDP patients with GRN mutations and 24 control participants. Using principal component analysis, hierarchical clustering, differential expression and coexpression network analyses, we showed that GRN mutation carriers and FTLD-TDP-A patients without a known mutation shared a common transcriptional signature that is independent of GRN loss-of-function. After combining both groups, differential expression as compared to the control group and coexpression analyses revealed alteration of processes related to immune response, synaptic transmission, RNA metabolism, angiogenesis and vesicle-mediated transport. Deconvolution of the data highlighted strong cellular alterations that were similar in FTLD-TDP-A and GRN mutation carriers with NSF as a potentially important player in both groups. We propose several potentially druggable pathways such as the GABAergic, GDNF and sphingolipid pathways. Our findings underline new disease mechanisms and strongly suggest that affected pathways in GRN mutation carriers extend beyond GRN and contribute to genetically unexplained forms of FTLD-TDP-A., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Guarantors of Brain.)
Published: 2022
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36. Correction: Combining schizophrenia and depression polygenic risk scores improves the genetic prediction of lithium response in bipolar disorder patients.

Author: Schubert KO, Thalamuthu A, Amare AT, Frank J, Streit F, Adl M, Akula N, Akiyama K, Ardau R, Arias B, Aubry JM, Backlund L, Bhattacharjee AK, Bellivier F, Benabarre A, Bengesser S, Biernacka JM, Birner A, Marie-Claire C, Cearns M, Cervantes P, Chen HC, Chillotti C, Cichon S, Clark SR, Cruceanu C, Czerski PM, Dalkner N, Dayer A, Degenhardt F, Del Zompo M, DePaulo JR, Étain B, Falkai P, Forstner AJ, Frisen L, Frye MA, Fullerton JM, Gard S, Garnham JS, Goes FS, Grigoroiu-Serbanescu M, Grof P, Hashimoto R, Hauser J, Heilbronner U, Herms S, Hoffmann P, Hou L, Hsu YH, Jamain S, Jiménez E, Kahn JP, Kassem L, Kuo PH, Kato T, Kelsoe J, Kittel-Schneider S, Ferensztajn-Rochowiak E, König B, Kusumi I, Laje G, Landén M, Lavebratt C, Leboyer M, Leckband SG, Maj M, Manchia M, Martinsson L, McCarthy MJ, McElroy S, Colom F, Mitjans M, Mondimore FM, Monteleone P, Nievergelt CM, Nöthen MM, Novák T, O'Donovan C, Ozaki N, Ösby U, Papiol S, Pfennig A, Pisanu C, Potash JB, Reif A, Reininghaus E, Rouleau GA, Rybakowski JK, Schalling M, Schofield PR, Schweizer BW, Severino G, Shekhtman T, Shilling PD, Shimoda K, Simhandl C, Slaney CM, Squassina A, Stamm T, Stopkova P, Tekola-Ayele F, Tortorella A, Turecki G, Veeh J, Vieta E, Witt SH, Roberts G, Zandi PP, Alda M, Bauer M, McMahon FJ, Mitchell PB, Schulze TG, Rietschel M, and Baune BT
Published: 2022
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37. Genetic variants associated with acamprosate treatment response in alcohol use disorder patients: A multiple omics study.

Author: Ho MF, Zhang C, Wei L, Zhang L, Moon I, Geske JR, Skime MK, Choi DS, Biernacka JM, Oesterle TS, Frye MA, Seppala MD, Karpyak VM, Li H, and Weinshilboum RM
Subjects: Acamprosate therapeutic use, Alcohol Drinking, Ethanol, Genome-Wide Association Study, Humans, Taurine therapeutic use, Alcohol Deterrents therapeutic use, Alcoholism drug therapy, Alcoholism genetics
Abstract: Background and Purpose: Acamprosate is an anti-craving drug used for the pharmacotherapy of alcohol use disorder (AUD). However, only some patients achieve optimal therapeutic outcomes. This study was designed to explore differences in metabolomic profiles between patients who maintained sobriety and those who relapsed, to determine whether those differences provide insight into variation in acamprosate treatment response phenotypes., Experimental Approach: We previously conducted an acamprosate trial involving 442 AUD patients, and 267 of these subjects presented themselves for a 3-month follow-up. The primary outcome was abstinence. Clinical information, genomic data and metabolomics data were collected. Baseline plasma samples were assayed using targeted metabolomics., Key Results: Baseline plasma arginine, threonine, α-aminoadipic acid and ethanolamine concentrations were associated with acamprosate treatment outcomes and baseline craving intensity, a measure that has been associated with acamprosate treatment response. We next applied a pharmacometabolomics-informed genome-wide association study (GWAS) strategy to identify genetic variants that might contribute to variations in plasma metabolomic profiles that were associated with craving and/or acamprosate treatment outcome. Gene expression data for induced pluripotent stem cell-derived forebrain astrocytes showed that a series of genes identified during the metabolomics-informed GWAS were ethanol responsive. Furthermore, a large number of those genes could be regulated by acamprosate. Finally, we identified a series of single nucleotide polymorphisms that were associated with acamprosate treatment outcomes., Conclusion and Implications: These results serve as an important step towards advancing our understanding of disease pathophysiology and drug action responsible for variation in acamprosate response and alcohol craving in AUD patients., (© 2022 The Authors. British Journal of Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)
Published: 2022
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38. Clinical and Genetic Correlates of Bipolar Disorder With Childhood-Onset Attention Deficit Disorder.

Author: Nunez NA, Coombes BJ, Romo-Nava F, Bond DJ, Vande Voort J, Croarkin PE, Leibman N, Gardea Resendez M, Veldic M, Betcher H, Singh B, Colby C, Cuellar-Barboza A, Prieto M, Moore KM, Ozerdem A, McElroy SL, Frye MA, and Biernacka JM
Abstract: Background: Bipolar disorder (BD) with co-occurring attention deficit-hyperactivity disorder (ADHD) is associated with an unfavorable course of illness. We aimed to identify potential clinical and genetic correlates of BD with and without ADHD., Methods: Among patients with BD ( N = 2,198) enrolled in the Mayo Clinic Bipolar Biobank we identified those with ADHD diagnosed in childhood (BD+cADHD; N = 350), those with adult-onset attention deficit symptoms (BD+aAD; N = 254), and those without ADHD ( N = 1,594). We compared the groups using linear or logistic regression adjusting for age, sex, and recruitment site. For genotyped patients ( N = 1,443), logistic regression was used to compare ADHD and BD polygenic risk scores (PRSs) between the BD groups, as well as to non-BD controls ( N = 777)., Results: Compared to the non-ADHD BD group, BD+cADHD patients were younger, more often men and had a greater number of co-occurring anxiety and substance use disorders (all p < 0.001). Additionally, BD+cADHD patients had poorer responses to lithium and lamotrigine ( p = 0.005 and p = 0.007, respectively). In PRS analyses, all BD patient subsets had greater genetic risk for BD and ADHD when compared to non-BD controls ( p < 0.001 in all comparisons). BD+cADHD patients had a higher ADHD-PRS than non-ADHD BD patients ( p = 0.012). However, BD+aAD patients showed no evidence of higher ADHD-PRS than non-ADHD BD patients ( p = 0.38)., Conclusions: BD+cADHD was associated with a greater number of comorbidities and reduced response to mood stabilizing treatments. The higher ADHD PRS for the BD+cADHD group may reflect a greater influence of genetic factors on early presentation of ADHD symptoms., Competing Interests: JV reports a grant-in-kind from Assurex, unrelated to the current study. PC has received research support from Neuronetics, Inc., NeoSync, Inc., and Pfizer. He has received grant-in-kind equipment and laboratory support for research studies from Assurex Health, Neuronetics, Inc. and MagVenture, Inc. He has served as a consultant for Myriad Neuroscience and Procter & Gamble. BS received grant support from Clinical and Translational Science CCaTS Small Grants Award, and Mayo Clinic. SM is or has been a consultant to or member of the scientific advisory boards of Avanir, Allergan now AbbVie, Bracket now Signant Health, Naurex, Idorsia, Intra-Cellular Therapies, Inc., Shire now Takeda, Sunovion, and Takeda. She is or has been a principal or co-investigator on studies sponsored by the Agency for Healthcare Research & Quality AHRQ, Avenir, AstraZeneca, Cephalon, Forest, Marriott Foundation, Medibio, National Institute of Mental Health, Orexigen Therapeutics, Inc., Jazz, Shire now Takeda, Sunovian, and Takeda Pharmaceutical Company Ltd. She is also an inventor on United States Patent No. 6,323,236 B2, Use of Sulfamate Derivatives for Treating Impulse Control Disorders, and along with the patent's assignee, University of Cincinnati, Cincinnati, Ohio, has received payments from Johnson & Johnson, which has exclusive rights under the patent. MF has received grant support from Assurex Health, Myriad, Pfizer, National Institute of Mental Health RO1 MH079261, National Institute of Alcohol Abuse and Alcoholism P20AA017830, Mayo Foundation; has been a consultant to Janssen Global Services, LLC, Mitsubishi Tanabe Pharma Corporation, Myriad, Sunovion, and Teva Pharmaceuticals; has received CME/Travel Support/presentation from CME Outfitters Inc. and Sunovian; Mayo Clinic has a financial interest in AssureRx and OneOme. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Nunez, Coombes, Romo-Nava, Bond, Vande Voort, Croarkin, Leibman, Gardea Resendez, Veldic, Betcher, Singh, Colby, Cuellar-Barboza, Prieto, Moore, Ozerdem, McElroy, Frye and Biernacka.)
Published: 2022
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39. Identifying the Common Genetic Basis of Antidepressant Response.

Author: Pain O, Hodgson K, Trubetskoy V, Ripke S, Marshe VS, Adams MJ, Byrne EM, Campos AI, Carrillo-Roa T, Cattaneo A, Als TD, Souery D, Dernovsek MZ, Fabbri C, Hayward C, Henigsberg N, Hauser J, Kennedy JL, Lenze EJ, Lewis G, Müller DJ, Martin NG, Mulsant BH, Mors O, Perroud N, Porteous DJ, Rentería ME, Reynolds CF 3rd, Rietschel M, Uher R, Wigmore EM, Maier W, Wray NR, Aitchison KJ, Arolt V, Baune BT, Biernacka JM, Bondolfi G, Domschke K, Kato M, Li QS, Liu YL, Serretti A, Tsai SJ, Turecki G, Weinshilboum R, McIntosh AM, and Lewis CM
Abstract: Background: Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction., Methods: Genome-wide analysis of remission ( n remit = 1852, n nonremit = 3299) and percentage improvement ( n = 5218) was performed. Single nucleotide polymorphism-based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA., Results: Neither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism-based heritability was significantly different from zero for remission ( h 2 = 0.132, SE = 0.056) but not for percentage improvement ( h 2 = -0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response., Conclusions: This study demonstrates that antidepressant response is influenced by common genetic variation, has a genetic overlap schizophrenia and educational attainment, and provides a useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response., (© 2021 The Authors.)
Published: 2022
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40. Association of Attention-Deficit/Hyperactivity Disorder and Depression Polygenic Scores with Lithium Response: A Consortium for Lithium Genetics Study.

Author: Coombes BJ, Millischer V, Batzler A, Larrabee B, Hou L, Papiol S, Heilbronner U, Adli M, Akiyama K, Akula N, Amare AT, Ardau R, Arias B, Aubry JM, Backlund L, Bauer M, Baune BT, Bellivier F, Benabarre A, Bengesser S, Bhattacharjee AK, Cervantes P, Chen HC, Chillotti C, Cichon S, Clark SR, Colom F, Cruceanu C, Czerski PM, Dalkner N, Degenhardt F, Del Zompo M, DePaulo JR, Étain B, Falkai P, Ferensztajn-Rochowiak E, Forstner AJ, Frisen L, Gard S, Garnham JS, Goes FS, Grigoroiu-Serbanescu M, Grof P, Hashimoto R, Hauser J, Herms S, Hoffmann P, Jamain S, Jiménez E, Kahn JP, Kassem L, Kato T, Kelsoe JR, Kittel-Schneider S, König B, Kuo PH, Kusumi I, Laje G, Landén M, Lavebratt C, Leboyer M, Leckband SG, Maj M, Manchia M, Martinsson L, McCarthy MJ, McElroy SL, Mitchell PB, Mitjans M, Mondimore FM, Monteleone P, Nievergelt CM, Nöthen MM, Novák T, O'Donovan C, Osby U, Ozaki N, Pfennig A, Pisanu C, Potash JB, Reif A, Reininghaus E, Rietschel M, Rouleau GA, Rybakowski JK, Schalling M, Schofield PR, Schubert KO, Schweizer BW, Severino G, Shekhtman T, Shilling PD, Shimoda K, Simhandl C, Slaney CM, Squassina A, Stamm T, Stopkova P, Tortorella A, Turecki G, Vieta E, Witt SH, Zandi PP, Fullerton JM, Alda M, Frye MA, Schulze TG, McMahon FJ, and Biernacka JM
Abstract: Response to lithium varies widely between individuals with bipolar disorder (BD). Polygenic risk scores (PRSs) can uncover pharmacogenomics effects and may help predict drug response. Patients ( N = 2,510) with BD were assessed for long-term lithium response in the Consortium on Lithium Genetics using the Retrospective Criteria of Long-Term Treatment Response in Research Subjects with Bipolar Disorder score. PRSs for attention-deficit/hyperactivity disorder (ADHD), major depressive disorder (MDD), and schizophrenia (SCZ) were computed using lassosum and in a model including all three PRSs and other covariates, and the PRS of ADHD (β = -0.14; 95% confidence interval [CI]: -0.24 to -0.03; p value = 0.010) and MDD (β = -0.16; 95% CI: -0.27 to -0.04; p value = 0.005) predicted worse quantitative lithium response. A higher SCZ PRS was associated with higher rates of medication nonadherence (OR = 1.61; 95% CI: 1.34-1.93; p value = 2e-7). This study indicates that genetic risk for ADHD and depression may influence lithium treatment response. Interestingly, a higher SCZ PRS was associated with poor adherence, which can negatively impact treatment response. Incorporating genetic risk of ADHD, depression, and SCZ in combination with clinical risk may lead to better clinical care for patients with BD., Competing Interests: Eduard Vieta has received grants and served as consultant, advisor, or CME speaker for the following entities: AB-Biotics, Allergan, Angelini, AstraZeneca, Bristol-Myers Squibb, Dainippon Sumitomo Pharma, Farmindustria, Ferrer, Forest Research Institute, Gedeon Richter, GlaxoSmithKline, Janssen, Lundbeck, Otsuka, Pfizer, Roche, Sanofi-Aventis, Servier, Shire, Sunovion, Takeda, the Brain and Behaviour Foundation, the Spanish Ministry of Science and Innovation (CIBERSAM), and the Stanley Medical Research Institute. Michael Bauer has received grants from the Deutsche Forschungsgemeinschaft and Bundesministeriums für Bildung und Forschung and served as consultant, advisor, or CME speaker for the following entities: Allergan, Aristo, Janssen, Lilly, Lundbeck, neuraxpharm, Otsuka, Sandoz, Servier, and Sunovion outside the submitted work. Sarah Kittel-Schneider has received grants and served as consultant, advisor, or speaker for the following entities: Medice Arzneimittel Pütter GmbH and Shire. Bernhard Baune has received grants and served as consultant, advisor, or CME speaker for the following entities: AstraZeneca, Bristol-Myers Squibb, Janssen, Lundbeck, Otsuka, Servier, the National Health and Medical Research Council, the Fay Fuller Foundation, and the James and Diana Ramsay Foundation. Tadafumi Kato received honoraria for lectures, manuscripts, and/or consultancy, from Kyowa Hakko Kirin Co., Ltd., Eli Lilly Japan K.K., Otsuka Pharmaceutical Co., Ltd., GlaxoSmithKline K.K., Taisho Toyama Pharmaceutical Co., Ltd., Dainippon Sumitomo Pharma Co., Ltd., Meiji Seika Pharma Co., Ltd., Pfizer Japan Inc., Mochida Pharmaceutical Co., Ltd., Shionogi & Co., Ltd., Janssen Pharmaceutical K.K., Janssen Asia Pacific, Yoshitomiyakuhin, Astellas Pharma Inc., Wako Pure Chemical Industries, Ltd., Wiley Publishing Japan, Nippon Boehringer Ingelheim Co. Ltd., Kanae Foundation for the Promotion of Medical Science, MSD K.K., Kyowa Pharmaceutical Industry Co., Ltd., and Takeda Pharmaceutical Co., Ltd. T.K. also received a research grant from Takeda Pharmaceutical Co., Ltd. Peter Falkai has received grants and served as consultant, advisor, or CME speaker for the following entities Abbott, GlaxoSmithKline, Janssen, Essex, Lundbeck, Otsuka, Gedeon Richter, Servier, and Takeda as well as the German Ministry of Science and the German Ministry of Health. Eva Reininghaus has received grants and served as consultant, advisor, or CME speaker for the following entities: Janssen and Institut Allergosan. Mikael Landén declares that, over the past 36 months, he has received lecture honoraria from Lundbeck and served as scientific consultant for EPID Research Oy; no other equity ownership, profit-sharing agreements, royalties, or patent. Kazufumi Akiyama has received consulting honoraria from Taisho Toyama Pharmaceutical Co., Ltd. The other authors have no other conflict of interest to disclose., (Copyright © 2021 by S. Karger AG, Basel.)
Published: 2021
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41. Characterisation of age and polarity at onset in bipolar disorder.

Author: Kalman JL, Olde Loohuis LM, Vreeker A, McQuillin A, Stahl EA, Ruderfer D, Grigoroiu-Serbanescu M, Panagiotaropoulou G, Ripke S, Bigdeli TB, Stein F, Meller T, Meinert S, Pelin H, Streit F, Papiol S, Adams MJ, Adolfsson R, Adorjan K, Agartz I, Aminoff SR, Anderson-Schmidt H, Andreassen OA, Ardau R, Aubry JM, Balaban C, Bass N, Baune BT, Bellivier F, Benabarre A, Bengesser S, Berrettini WH, Boks MP, Bromet EJ, Brosch K, Budde M, Byerley W, Cervantes P, Chillotti C, Cichon S, Clark SR, Comes AL, Corvin A, Coryell W, Craddock N, Craig DW, Croarkin PE, Cruceanu C, Czerski PM, Dalkner N, Dannlowski U, Degenhardt F, Del Zompo M, DePaulo JR, Djurovic S, Edenberg HJ, Eissa MA, Elvsåshagen T, Etain B, Fanous AH, Fellendorf F, Fiorentino A, Forstner AJ, Frye MA, Fullerton JM, Gade K, Garnham J, Gershon E, Gill M, Goes FS, Gordon-Smith K, Grof P, Guzman-Parra J, Hahn T, Hasler R, Heilbronner M, Heilbronner U, Jamain S, Jimenez E, Jones I, Jones L, Jonsson L, Kahn RS, Kelsoe JR, Kennedy JL, Kircher T, Kirov G, Kittel-Schneider S, Klöhn-Saghatolislam F, Knowles JA, Kranz TM, Lagerberg TV, Landen M, Lawson WB, Leboyer M, Li QS, Maj M, Malaspina D, Manchia M, Mayoral F, McElroy SL, McInnis MG, McIntosh AM, Medeiros H, Melle I, Milanova V, Mitchell PB, Monteleone P, Monteleone AM, Nöthen MM, Novak T, Nurnberger JI, O'Brien N, O'Connell KS, O'Donovan C, O'Donovan MC, Opel N, Ortiz A, Owen MJ, Pålsson E, Pato C, Pato MT, Pawlak J, Pfarr JK, Pisanu C, Potash JB, Rapaport MH, Reich-Erkelenz D, Reif A, Reininghaus E, Repple J, Richard-Lepouriel H, Rietschel M, Ringwald K, Roberts G, Rouleau G, Schaupp S, Scheftner WA, Schmitt S, Schofield PR, Schubert KO, Schulte EC, Schweizer B, Senner F, Severino G, Sharp S, Slaney C, Smeland OB, Sobell JL, Squassina A, Stopkova P, Strauss J, Tortorella A, Turecki G, Twarowska-Hauser J, Veldic M, Vieta E, Vincent JB, Xu W, Zai CC, Zandi PP, Di Florio A, Smoller JW, Biernacka JM, McMahon FJ, Alda M, Müller-Myhsok B, Koutsouleris N, Falkai P, Freimer NB, Andlauer TFM, Schulze TG, and Ophoff RA
Subjects: Age of Onset, Genome-Wide Association Study, Humans, Multifactorial Inheritance, Autism Spectrum Disorder, Bipolar Disorder diagnosis, Bipolar Disorder epidemiology, Bipolar Disorder genetics, Depressive Disorder, Major genetics
Abstract: Background: Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools., Aims: To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics., Method: Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts., Results: Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = -0.34 years, s.e. = 0.08), major depression (β = -0.34 years, s.e. = 0.08), schizophrenia (β = -0.39 years, s.e. = 0.08), and educational attainment (β = -0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO., Conclusions: AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.
Published: 2021
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42. Combining schizophrenia and depression polygenic risk scores improves the genetic prediction of lithium response in bipolar disorder patients.

Author: Schubert KO, Thalamuthu A, Amare AT, Frank J, Streit F, Adl M, Akula N, Akiyama K, Ardau R, Arias B, Aubry JM, Backlund L, Bhattacharjee AK, Bellivier F, Benabarre A, Bengesser S, Biernacka JM, Birner A, Marie-Claire C, Cearns M, Cervantes P, Chen HC, Chillotti C, Cichon S, Clark SR, Cruceanu C, Czerski PM, Dalkner N, Dayer A, Degenhardt F, Del Zompo M, DePaulo JR, Étain B, Falkai P, Forstner AJ, Frisen L, Frye MA, Fullerton JM, Gard S, Garnham JS, Goes FS, Grigoroiu-Serbanescu M, Grof P, Hashimoto R, Hauser J, Heilbronner U, Herms S, Hoffmann P, Hou L, Hsu YH, Jamain S, Jiménez E, Kahn JP, Kassem L, Kuo PH, Kato T, Kelsoe J, Kittel-Schneider S, Ferensztajn-Rochowiak E, König B, Kusumi I, Laje G, Landén M, Lavebratt C, Leboyer M, Leckband SG, Maj M, Manchia M, Martinsson L, McCarthy MJ, McElroy S, Colom F, Mitjans M, Mondimore FM, Monteleone P, Nievergelt CM, Nöthen MM, Novák T, O'Donovan C, Ozaki N, Ösby U, Papiol S, Pfennig A, Pisanu C, Potash JB, Reif A, Reininghaus E, Rouleau GA, Rybakowski JK, Schalling M, Schofield PR, Schweizer BW, Severino G, Shekhtman T, Shilling PD, Shimoda K, Simhandl C, Slaney CM, Squassina A, Stamm T, Stopkova P, Tekola-Ayele F, Tortorella A, Turecki G, Veeh J, Vieta E, Witt SH, Roberts G, Zandi PP, Alda M, Bauer M, McMahon FJ, Mitchell PB, Schulze TG, Rietschel M, and Baune BT
Subjects: Depression, Genetic Predisposition to Disease, Humans, Lithium therapeutic use, Multifactorial Inheritance, Risk Factors, Bipolar Disorder drug therapy, Bipolar Disorder genetics, Depressive Disorder, Major drug therapy, Depressive Disorder, Major genetics, Schizophrenia drug therapy, Schizophrenia genetics
Abstract: Lithium is the gold standard therapy for Bipolar Disorder (BD) but its effectiveness differs widely between individuals. The molecular mechanisms underlying treatment response heterogeneity are not well understood, and personalized treatment in BD remains elusive. Genetic analyses of the lithium treatment response phenotype may generate novel molecular insights into lithium's therapeutic mechanisms and lead to testable hypotheses to improve BD management and outcomes. We used fixed effect meta-analysis techniques to develop meta-analytic polygenic risk scores (MET-PRS) from combinations of highly correlated psychiatric traits, namely schizophrenia (SCZ), major depression (MD) and bipolar disorder (BD). We compared the effects of cross-disorder MET-PRS and single genetic trait PRS on lithium response. For the PRS analyses, we included clinical data on lithium treatment response and genetic information for n = 2283 BD cases from the International Consortium on Lithium Genetics (ConLi + Gen; www.ConLiGen.org ). Higher SCZ and MD PRSs were associated with poorer lithium treatment response whereas BD-PRS had no association with treatment outcome. The combined MET2-PRS comprising of SCZ and MD variants (MET2-PRS) and a model using SCZ and MD-PRS sequentially improved response prediction, compared to single-disorder PRS or to a combined score using all three traits (MET3-PRS). Patients in the highest decile for MET2-PRS loading had 2.5 times higher odds of being classified as poor responders than patients with the lowest decile MET2-PRS scores. An exploratory functional pathway analysis of top MET2-PRS variants was conducted. Findings may inform the development of future testing strategies for personalized lithium prescribing in BD., (© 2021. The Author(s).)
Published: 2021
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43. Extracting social determinants of health from electronic health records using natural language processing: a systematic review.

Author: Patra BG, Sharma MM, Vekaria V, Adekkanattu P, Patterson OV, Glicksberg B, Lepow LA, Ryu E, Biernacka JM, Furmanchuk A, George TJ, Hogan W, Wu Y, Yang X, Bian J, Weissman M, Wickramaratne P, Mann JJ, Olfson M, Campion TR, Weiner M, and Pathak J
Subjects: Data Management, Humans, Machine Learning, Social Determinants of Health, Electronic Health Records, Natural Language Processing
Abstract: Objective: Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs., Materials and Methods: A broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review., Results: Smoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9)., Conclusion: NLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)
Published: 2021
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44. Genetic contributions to alcohol use disorder treatment outcomes: a genome-wide pharmacogenomics study.

Author: Biernacka JM, Coombes BJ, Batzler A, Ho AM, Geske JR, Frank J, Hodgkinson C, Skime M, Colby C, Zillich L, Pozsonyiova S, Ho MF, Kiefer F, Rietschel M, Weinshilboum R, O'Malley SS, Mann K, Anton R, Goldman D, and Karpyak VM
Subjects: Female, Genome-Wide Association Study, Humans, Male, Naltrexone therapeutic use, Narcotic Antagonists therapeutic use, Pharmacogenetics, Taurine therapeutic use, Treatment Outcome, Alcohol Deterrents therapeutic use, Alcoholism drug therapy, Alcoholism genetics
Abstract: Naltrexone can aid in reducing alcohol consumption, while acamprosate supports abstinence; however, not all patients with alcohol use disorder (AUD) benefit from these treatments. Here we present the first genome-wide association study of AUD treatment outcomes based on data from the COMBINE and PREDICT studies of acamprosate and naltrexone, and the Mayo Clinic CITA study of acamprosate. Primary analyses focused on treatment outcomes regardless of pharmacological intervention and were followed by drug-stratified analyses to identify treatment-specific pharmacogenomic predictors of acamprosate and naltrexone response. Treatment outcomes were defined as: (1) time until relapse to any drinking (TR) and (2) time until relapse to heavy drinking (THR; ≥ 5 drinks for men, ≥4 drinks for women in a day), during the first 3 months of treatment. Analyses were performed within each dataset, followed by meta-analysis across the studies (N = 1083 European ancestry participants). Single nucleotide polymorphisms (SNPs) in the BRE gene were associated with THR (min p = 1.6E-8) in the entire sample, while two intergenic SNPs were associated with medication-specific outcomes (naltrexone THR: rs12749274, p = 3.9E-8; acamprosate TR: rs77583603, p = 3.1E-9). The top association signal for TR (p = 7.7E-8) and second strongest signal in the THR (p = 6.1E-8) analysis of naltrexone-treated patients maps to PTPRD, a gene previously implicated in addiction phenotypes in human and animal studies. Leave-one-out polygenic risk score analyses showed significant associations with TR (p = 3.7E-4) and THR (p = 2.6E-4). This study provides the first evidence of a polygenic effect on AUD treatment response, and identifies genetic variants associated with potentially medication-specific effects on AUD treatment response., (© 2021. The Author(s).)
Published: 2021
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45. HLA-DRB1 and HLA-DQB1 genetic diversity modulates response to lithium in bipolar affective disorders.

Author: Le Clerc S, Lombardi L, Baune BT, Amare AT, Schubert KO, Hou L, Clark SR, Papiol S, Cearns M, Heilbronner U, Degenhardt F, Tekola-Ayele F, Hsu YH, Shekhtman T, Adli M, Akula N, Akiyama K, Ardau R, Arias B, Aubry JM, Backlund L, Bhattacharjee AK, Bellivier F, Benabarre A, Bengesser S, Biernacka JM, Birner A, Brichant-Petitjean C, Cervantes P, Chen HC, Chillotti C, Cichon S, Cruceanu C, Czerski PM, Dalkner N, Dayer A, Del Zompo M, DePaulo JR, Étain B, Jamain S, Falkai P, Forstner AJ, Frisen L, Frye MA, Fullerton JM, Gard S, Garnham JS, Goes FS, Grigoroiu-Serbanescu M, Grof P, Hashimoto R, Hauser J, Herms S, Hoffmann P, Jiménez E, Kahn JP, Kassem L, Kuo PH, Kato T, Kelsoe JR, Kittel-Schneider S, Ferensztajn-Rochowiak E, König B, Kusumi I, Laje G, Landén M, Lavebratt C, Leckband SG, Tortorella A, Manchia M, Martinsson L, McCarthy MJ, McElroy SL, Colom F, Millischer V, Mitjans M, Mondimore FM, Monteleone P, Nievergelt CM, Nöthen MM, Novák T, O'Donovan C, Ozaki N, Ösby U, Pfennig A, Potash JB, Reif A, Reininghaus E, Rouleau GA, Rybakowski JK, Schalling M, Schofield PR, Schweizer BW, Severino G, Shilling PD, Shimoda K, Simhandl C, Slaney CM, Pisanu C, Squassina A, Stamm T, Stopkova P, Maj M, Turecki G, Vieta E, Veeh J, Witt SH, Wright A, Zandi PP, Mitchell PB, Bauer M, Alda M, Rietschel M, McMahon FJ, Schulze TG, Spadoni JL, Boukouaci W, Richard JR, Le Corvoisier P, Barrau C, Zagury JF, Leboyer M, and Tamouza R
Subjects: Adult, Alleles, Bipolar Disorder drug therapy, Female, Gene Frequency, Genetic Variation, Genotype, Haplotypes, Humans, Male, Middle Aged, Pharmacogenetics, Treatment Outcome, Bipolar Disorder genetics, Genetic Predisposition to Disease, HLA-DQ beta-Chains genetics, HLA-DRB1 Chains genetics, Lithium therapeutic use
Abstract: Bipolar affective disorder (BD) is a severe psychiatric illness, for which lithium (Li) is the gold standard for acute and maintenance therapies. The therapeutic response to Li in BD is heterogeneous and reliable biomarkers allowing patients stratification are still needed. A GWAS performed by the International Consortium on Lithium Genetics (ConLiGen) has recently identified genetic markers associated with treatment responses to Li in the human leukocyte antigens (HLA) region. To better understand the molecular mechanisms underlying this association, we have genetically imputed the classical alleles of the HLA region in the European patients of the ConLiGen cohort. We found our best signal for amino-acid variants belonging to the HLA-DRB1*11:01 classical allele, associated with a better response to Li (p < 1 × 10 -3 ; FDR < 0.09 in the recessive model). Alanine or Leucine at position 74 of the HLA-DRB1 heavy chain was associated with a good response while Arginine or Glutamic acid with a poor response. As these variants have been implicated in common inflammatory/autoimmune processes, our findings strongly suggest that HLA-mediated low inflammatory background may contribute to the efficient response to Li in BD patients, while an inflammatory status overriding Li anti-inflammatory properties would favor a weak response., (© 2021. The Author(s).)
Published: 2021
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46. Genetics and antiepileptic mood stabilizer treatment response in bipolar disorder: what do we know?

Author: Ho AM, Weinshilboum RM, Frye MA, and Biernacka JM
Subjects: Antimanic Agents therapeutic use, Antipsychotic Agents therapeutic use, Humans, Mood Disorders drug therapy, Mood Disorders genetics, Treatment Outcome, Anticonvulsants therapeutic use, Bipolar Disorder drug therapy, Bipolar Disorder genetics, Carbamazepine therapeutic use, Lamotrigine therapeutic use, Valproic Acid therapeutic use
Abstract: Antiepileptic mood stabilizers (AED-MS) are often used to treat bipolar disorder (BD). Similar to other mood disorder medications, AED-MS treatment response varies between patients. Identification of biomarkers associated with treatment response may ultimately help with the delivery of individualized treatment and lead to improved treatment efficacy. Here, we conducted a narrative review of the current knowledge of the pharmacogenomics of AED-MS (valproic acid, lamotrigine and carbamazepine) treatment response in BD, including genetic contributions to AED-MS pharmacokinetics. Genes involved in neurotransmitter systems and drug transport have been shown to be associated with AED-MS treatment response. As more studies are conducted, and experimental and analytical methods advance, knowledge of AED-MS pharmacogenomics is expected to grow and contribute to precision medicine in BD.
Published: 2021
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47. Impact of variant-level batch effects on identification of genetic risk factors in large sequencing studies.

Author: Wickland DP, Ren Y, Sinnwell JP, Reddy JS, Pottier C, Sarangi V, Carrasquillo MM, Ross OA, Younkin SG, Ertekin-Taner N, Rademakers R, Hudson ME, Mainzer LS, Biernacka JM, and Asmann YW
Subjects: Alleles, Alzheimer Disease genetics, Alzheimer Disease pathology, Apolipoproteins E genetics, Databases, Genetic, Exome, Female, Gene Frequency, Genetic Predisposition to Disease, Genotype, Humans, Male, Membrane Transport Proteins genetics, Mitochondrial Precursor Protein Import Complex Proteins, Polymorphism, Single Nucleotide, Principal Component Analysis, Sequence Analysis, DNA, Genome-Wide Association Study, High-Throughput Nucleotide Sequencing methods
Abstract: Genetic studies have shifted to sequencing-based rare variants discovery after decades of success in identifying common disease variants by Genome-Wide Association Studies using Single Nucleotide Polymorphism chips. Sequencing-based studies require large sample sizes for statistical power and therefore often inadvertently introduce batch effects because samples are typically collected, processed, and sequenced at multiple centers. Conventionally, batch effects are first detected and visualized using Principal Components Analysis and then controlled by including batch covariates in the disease association models. For sequencing-based genetic studies, because all variants included in the association analyses have passed sequencing-related quality control measures, this conventional approach treats every variant as equal and ignores the substantial differences still remaining in variant qualities and characteristics such as genotype quality scores, alternative allele fractions (fraction of reads supporting alternative allele at a variant position) and sequencing depths. In the Alzheimer's Disease Sequencing Project (ADSP) exome dataset of 9,904 cases and controls, we discovered hidden variant-level differences between sample batches of three sequencing centers and two exome capture kits. Although sequencing centers were included as a covariate in our association models, we observed differences at the variant level in genotype quality and alternative allele fraction between samples processed by different exome capture kits that significantly impacted both the confidence of variant detection and the identification of disease-associated variants. Furthermore, we found that a subset of top disease-risk variants came exclusively from samples processed by one exome capture kit that was more effective at capturing the alternative alleles compared to the other kit. Our findings highlight the importance of additional variant-level quality control for large sequencing-based genetic studies. More importantly, we demonstrate that automatically filtering out variants with batch differences may lead to false negatives if the batch discordances come largely from quality differences and if the batch-specific variants have better quality., Competing Interests: The authors have declared that no competing interests exist.
Published: 2021
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48. Dissecting clinical heterogeneity of bipolar disorder using multiple polygenic risk scores.

Author: Coombes BJ, Markota M, Mann JJ, Colby C, Stahl E, Talati A, Pathak J, Weissman MM, McElroy SL, Frye MA, and Biernacka JM
Subjects: Female, Humans, Male, Multifactorial Inheritance, Risk Factors, Suicide, Attempted, Bipolar Disorder epidemiology, Bipolar Disorder genetics, Psychotic Disorders
Abstract: Bipolar disorder (BD) has high clinical heterogeneity, frequent psychiatric comorbidities, and elevated suicide risk. To determine genetic differences between common clinical sub-phenotypes of BD, we performed a systematic polygenic risk score (PRS) analysis using multiple PRSs from a range of psychiatric, personality, and lifestyle traits to dissect differences in BD sub-phenotypes in two BD cohorts: the Mayo Clinic BD Biobank (N = 968) and Genetic Association Information Network (N = 1001). Participants were assessed for history of psychosis, early-onset BD, rapid cycling (defined as four or more episodes in a year), and suicide attempts using questionnaires and the Structured Clinical Interview for DSM-IV. In a combined sample of 1969 bipolar cases (45.5% male), those with psychosis had higher PRS for SCZ (OR = 1.3 per S.D.; p = 3e-5) but lower PRSs for anhedonia (OR = 0.87; p = 0.003) and BMI (OR = 0.87; p = 0.003). Rapid cycling cases had higher PRS for ADHD (OR = 1.23; p = 7e-5) and MDD (OR = 1.23; p = 4e-5) and lower BD PRS (OR = 0.8; p = 0.004). Cases with a suicide attempt had higher PRS for MDD (OR = 1.26; p = 1e-6) and anhedonia (OR = 1.22; p = 2e-5) as well as lower PRS for educational attainment (OR = 0.87; p = 0.003). The observed novel PRS associations with sub-phenotypes align with clinical observations such as rapid cycling BD patients having a greater lifetime prevalence of ADHD. Our findings confirm that genetic heterogeneity contributes to clinical heterogeneity of BD and consideration of genetic contribution to psychopathologic components of psychiatric disorders may improve genetic prediction of complex psychiatric disorders.
Published: 2020
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49. Potential pharmacogenomic targets in bipolar disorder: considerations for current testing and the development of decision support tools to individualize treatment selection.

Author: Cuéllar-Barboza AB, McElroy SL, Veldic M, Singh B, Kung S, Romo-Nava F, Nunez NA, Cabello-Arreola A, Coombes BJ, Prieto M, Betcher HK, Moore KM, Winham SJ, Biernacka JM, and Frye MA
Abstract: Background: Treatment in bipolar disorder (BD) is commonly applied as a multimodal therapy based on decision algorithms that lack an integrative understanding of molecular mechanisms or a biomarker associated clinical outcome measure. Pharmacogenetics/genomics study the individual genetic variation associated with drug response. This selective review of pharmacogenomics and pharmacogenomic testing (PGT) in BD will focus on candidate genes and genome wide association studies of pharmacokinetic drug metabolism and pharmacodynamic drug response/adverse event, and the potential role of decision support tools that incorporate multiple genotype/phenotype drug recommendations., Main Body: We searched PubMed from January 2013 to May 2019, to identify studies reporting on BD and pharmacogenetics, pharmacogenomics and PGT. Studies were selected considering their contribution to the field. We summarize our findings in: targeted candidate genes of pharmacokinetic and pharmacodynamic pathways, genome-wide association studies and, PGT platforms, related to BD treatment. This field has grown from studies of metabolizing enzymes (i.e., pharmacokinetics) and drug transporters (i.e., pharmacodynamics), to untargeted investigations across the entire genome with the potential to merge genomic data with additional biological information., Conclusions: The complexity of BD genetics and, the heterogeneity in BD drug-related phenotypes, are important considerations for the design and interpretation of BD PGT. The clinical applicability of PGT in psychiatry is in its infancy and is far from reaching the robust impact it has in other medical disciplines. Nonetheless, promising findings are discovered with increasing frequency with remarkable relevance in neuroscience, pharmacology and biology.
Published: 2020
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50. Inferring multimodal latent topics from electronic health records.

Author: Li Y, Nair P, Lu XH, Wen Z, Wang Y, Dehaghi AAK, Miao Y, Liu W, Ordog T, Biernacka JM, Ryu E, Olson JE, Frye MA, Liu A, Guo L, Marelli A, Ahuja Y, Davila-Velderrain J, and Kellis M
Subjects: Bayes Theorem, Databases, Factual, Electronic Health Records statistics & numerical data, Humans, Machine Learning, Models, Statistical, Phenotype, Electronic Health Records classification, Medical Informatics methods
Abstract: Electronic health records (EHR) are rich heterogeneous collections of patient health information, whose broad adoption provides clinicians and researchers unprecedented opportunities for health informatics, disease-risk prediction, actionable clinical recommendations, and precision medicine. However, EHRs present several modeling challenges, including highly sparse data matrices, noisy irregular clinical notes, arbitrary biases in billing code assignment, diagnosis-driven lab tests, and heterogeneous data types. To address these challenges, we present MixEHR, a multi-view Bayesian topic model. We demonstrate MixEHR on MIMIC-III, Mayo Clinic Bipolar Disorder, and Quebec Congenital Heart Disease EHR datasets. Qualitatively, MixEHR disease topics reveal meaningful combinations of clinical features across heterogeneous data types. Quantitatively, we observe superior prediction accuracy of diagnostic codes and lab test imputations compared to the state-of-art methods. We leverage the inferred patient topic mixtures to classify target diseases and predict mortality of patients in critical conditions. In all comparison, MixEHR confers competitive performance and reveals meaningful disease-related topics.
Published: 2020
Full Text: View/download PDF

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