Search

Your search keyword '"Cox, Nancy J"' showing total 2,309 results
Start Over Author "Cox, Nancy J"
2,309 results on '"Cox, Nancy J"'

2. Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes

Author

Toikumo, Sylvanus, Jennings, Mariela V, Pham, Benjamin K, Lee, Hyunjoon, Mallard, Travis T, Bianchi, Sevim B, Meredith, John J, Vilar-Ribo, Laura, Xu, Heng, Hatoum, Alexander S, Johnson, Emma C, Pazdernik, Vanessa K, Jinwala, Zeal, Pakala, Shreya R, Leger, Brittany S, Niarchou, Maria, Ehinmowo, Michael, Jenkins, Greg D, Batzler, Anthony, Pendegraft, Richard, Palmer, Abraham A, Zhou, Hang, Biernacka, Joanna M, Coombes, Brandon J, Gelernter, Joel, Xu, Ke, Hancock, Dana B, Cox, Nancy J, Smoller, Jordan W, Davis, Lea K, Justice, Amy C, Kranzler, Henry R, Kember, Rachel L, and Sanchez-Roige, Sandra

Subjects
Biomedical and Clinical Sciences, Health Sciences, Substance Misuse, Drug Abuse (NIDA only), Prevention, Tobacco, Brain Disorders, Genetics, Human Genome, Tobacco Smoke and Health, 2.1 Biological and endogenous factors, Good Health and Well Being, Humans, Tobacco Use Disorder, Genetic Predisposition to Disease, Genome-Wide Association Study, United States, Male, Female, Electronic Health Records, Penn Medicine BioBank, Biomedical and clinical sciences, Health sciences, Psychology
Abstract

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviours and although strides have been made using genome-wide association studies to identify risk variants, most variants identified have been for nicotine consumption, rather than TUD. Here we leveraged four US biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records) in 653,790 individuals (495,005 European, 114,420 African American and 44,365 Latin American) and data from UK Biobank (ncombined = 898,680). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviours in children and hundreds of medical outcomes, including HIV infection, heart disease and pain. This work furthers our biological understanding of TUD and establishes electronic health records as a source of phenotypic information for studying the genetics of TUD.

Published
2024

3. Determinants of mosaic chromosomal alteration fitness

Author

Pershad, Yash, Mack, Taralynn, Poisner, Hannah, Jakubek, Yasminka A., Stilp, Adrienne M., Mitchell, Braxton D., Lewis, Joshua P., Boerwinkle, Eric, Loos, Ruth J. F., Chami, Nathalie, Wang, Zhe, Barnes, Kathleen, Pankratz, Nathan, Fornage, Myriam, Redline, Susan, Psaty, Bruce M., Bis, Joshua C., Shojaie, Ali, Silverman, Edwin K., Cho, Michael H., Yun, Jeong H., DeMeo, Dawn, Levy, Daniel, Johnson, Andrew D., Mathias, Rasika A., Taub, Margaret A., Arnett, Donna, North, Kari E., Raffield, Laura M., Carson, April P., Doyle, Margaret F., Rich, Stephen S., Rotter, Jerome I., Guo, Xiuqing, Cox, Nancy J., Roden, Dan M., Franceschini, Nora, Desai, Pinkal, Reiner, Alex P., Auer, Paul L., Scheet, Paul A., Jaiswal, Siddhartha, Weinstock, Joshua S., and Bick, Alexander G.

Published
2024
Full Text
View/download PDF

4. Clinical associations with a polygenic predisposition to benign lower white blood cell counts

Author

Mosley, Jonathan D., Shelley, John P., Dickson, Alyson L., Zanussi, Jacy, Daniel, Laura L., Zheng, Neil S., Bastarache, Lisa, Wei, Wei-Qi, Shi, Mingjian, Jarvik, Gail P., Rosenthal, Elisabeth A., Khan, Atlas, Sherafati, Alborz, Kullo, Iftikhar J., Walunas, Theresa L., Glessner, Joseph, Hakonarson, Hakon, Cox, Nancy J., Roden, Dan M., Frangakis, Stephan G., Vanderwerff, Brett, Stein, C. Michael, Van Driest, Sara L., Borinstein, Scott C., Shu, Xiao-Ou, Zawistowski, Matthew, Chung, Cecilia P., and Kawai, Vivian K.

Published
2024
Full Text
View/download PDF

6. Advancing genomics to improve health equity

Author

Madden, Ebony B., Hindorff, Lucia A., Bonham, Vence L., Akintobi, Tabia Henry, Burchard, Esteban G., Baker, Kellan E., Begay, Rene L., Carpten, John D., Cox, Nancy J., Di Francesco, Valentina, Dillard, Denise A., Fletcher, Faith E., Fullerton, Stephanie M., Garrison, Nanibaa’ A., Hammack-Aviran, Catherine M., Hiratsuka, Vanessa Y., Hildreth, James E. K., Horowitz, Carol R., Hughes Halbert, Chanita A., Inouye, Michael, Jackson, Amber, Landry, Latrice G., Kittles, Rick A., Leek, Jeff T., Limdi, Nita A., Lockhart, Nicole C., Ofili, Elizabeth O., Pérez-Stable, Eliseo J., Sabatello, Maya, Saulsberry, Loren, Schools, Lorjetta E., Troyer, Jennifer L., Wilfond, Benjamin S., Wojcik, Genevieve L., Cho, Judy H., Lee, Sandra S.-J., and Green, Eric D.

Published
2024
Full Text
View/download PDF

8. The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits

Author

Costanzo, Maria C, von Grotthuss, Marcin, Massung, Jeffrey, Jang, Dongkeun, Caulkins, Lizz, Koesterer, Ryan, Gilbert, Clint, Welch, Ryan P, Kudtarkar, Parul, Hoang, Quy, Boughton, Andrew P, Singh, Preeti, Sun, Ying, Duby, Marc, Moriondo, Annie, Nguyen, Trang, Smadbeck, Patrick, Alexander, Benjamin R, Brandes, MacKenzie, Carmichael, Mary, Dornbos, Peter, Green, Todd, Huellas-Bruskiewicz, Kenneth C, Ji, Yue, Kluge, Alexandria, McMahon, Aoife C, Mercader, Josep M, Ruebenacker, Oliver, Sengupta, Sebanti, Spalding, Dylan, Taliun, Daniel, Consortium, AMP-T2D, Abecasis, Gonçalo, Akolkar, Beena, Allred, Nicholette D, Altshuler, David, Below, Jennifer E, Bergman, Richard, Beulens, Joline WJ, Blangero, John, Boehnke, Michael, Bokvist, Krister, Bottinger, Erwin, Bowden, Donald, Brosnan, M Julia, Brown, Christopher, Bruskiewicz, Kenneth, Burtt, Noël P, Cebola, Inês, Chambers, John, Chen, Yii-Der Ida, Cherkas, Andriy, Chu, Audrey Y, Clark, Christopher, Claussnitzer, Melina, Cox, Nancy J, Hoed, Marcel den, Dong, Duc, Duggirala, Ravindranath, Dupuis, Josée, Elders, Petra JM, Engreitz, Jesse M, Fauman, Eric, Ferrer, Jorge, Flannick, Jason, Flicek, Paul, Flickinger, Matthew, Florez, Jose C, Fox, Caroline S, Frayling, Timothy M, Frazer, Kelly A, Gaulton, Kyle J, Gloyn, Anna L, Hanis, Craig L, Hanson, Robert, Hattersley, Andrew T, Im, Hae Kyung, Iqbal, Sidra, Jacobs, Suzanne BR, Jang, Dong-Keun, Jordan, Tad, Kamphaus, Tania, Karpe, Fredrik, Keane, Thomas M, Kim, Seung K, Lage, Kasper, Lange, Leslie A, and Lazar, Mitchell

Subjects
Genetics, Diabetes, Human Genome, Metabolic and endocrine, Good Health and Well Being, Humans, Diabetes Mellitus, Type 2, Access to Information, Prospective Studies, Genomics, Phenotype, AMP-T2D Consortium, CMDKP, GWAS, T2DKP, data sharing, diabetes, effector genes, genetic associations, genetic support, genomics, portal, Biochemistry and Cell Biology, Medical Biochemistry and Metabolomics, Endocrinology & Metabolism
Abstract

Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP's comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results.

Published
2023

9. Ancestral diversity improves discovery and fine-mapping of genetic loci for anthropometric traits—The Hispanic/Latino Anthropometry Consortium

Author

Fernández-Rhodes, Lindsay, Graff, Mariaelisa, Buchanan, Victoria L, Justice, Anne E, Highland, Heather M, Guo, Xiuqing, Zhu, Wanying, Chen, Hung-Hsin, Young, Kristin L, Adhikari, Kaustubh, Palmer, Nicholette D, Below, Jennifer E, Bradfield, Jonathan, Pereira, Alexandre C, Glover, LáShauntá, Kim, Daeeun, Lilly, Adam G, Shrestha, Poojan, Thomas, Alvin G, Zhang, Xinruo, Chen, Minhui, Chiang, Charleston WK, Pulit, Sara, Horimoto, Andrea, Krieger, Jose E, Guindo-Martínez, Marta, Preuss, Michael, Schumann, Claudia, Smit, Roelof AJ, Torres-Mejía, Gabriela, Acuña-Alonzo, Victor, Bedoya, Gabriel, Bortolini, Maria-Cátira, Canizales-Quinteros, Samuel, Gallo, Carla, González-José, Rolando, Poletti, Giovanni, Rothhammer, Francisco, Hakonarson, Hakon, Igo, Robert, Adler, Sharon G, Iyengar, Sudha K, Nicholas, Susanne B, Gogarten, Stephanie M, Isasi, Carmen R, Papnicolaou, George, Stilp, Adrienne M, Qi, Qibin, Kho, Minjung, Smith, Jennifer A, Langefeld, Carl D, Wagenknecht, Lynne, Mckean-Cowdin, Roberta, Gao, Xiaoyi Raymond, Nousome, Darryl, Conti, David V, Feng, Ye, Allison, Matthew A, Arzumanyan, Zorayr, Buchanan, Thomas A, Chen, Yii-Der Ida, Genter, Pauline M, Goodarzi, Mark O, Hai, Yang, Hsueh, Willa, Ipp, Eli, Kandeel, Fouad R, Lam, Kelvin, Li, Xiaohui, Nadler, Jerry L, Raffel, Leslie J, Roll, Kathryn, Sandow, Kevin, Tan, Jingyi, Taylor, Kent D, Xiang, Anny H, Yao, Jie, Audirac-Chalifour, Astride, Peralta Romero, Jose de Jesus, Hartwig, Fernando, Horta, Bernando, Blangero, John, Curran, Joanne E, Duggirala, Ravindranath, Lehman, Donna E, Puppala, Sobha, Fejerman, Laura, John, Esther M, Aguilar-Salinas, Carlos, Burtt, Noël P, Florez, Jose C, García-Ortíz, Humberto, González-Villalpando, Clicerio, Mercader, Josep, Orozco, Lorena, Tusié-Luna, Teresa, Blanco, Estela, Gahagan, Sheila, Cox, Nancy J, and Hanis, Craig

Abstract

[This corrects the article DOI: 10.1016/j.xhgg.2022.100099.].

Published
2023

10. SOX6 expression and aneurysms of the thoracic and abdominal aorta

Author

Carmona-Berrio, David, Adarve-Rengifo, Isabel, Marshall, Andrea G., Vue, Zer, Hall, Duane D., Miller-Fleming, Tyne W., Actkins, Ky’Era V., Beasley, Heather K., Almonacid, Paula M., Barturen-Larrea, Pierina, Wells, Quinn S., Lopez, Marcos G., Garza-Lopez, Edgar, Dai, Dao-Fu, Shao, Jianqiang, Neikirk, Kit, Billings, Frederic T., IV, Curci, John A., Cox, Nancy J., Gama, Vivian, Hinton, Antentor, Jr., and Gomez, Jose A.

Published
2024
Full Text
View/download PDF

11. Genome-Wide Association Study Points to Novel Locus for Gilles de la Tourette Syndrome

Author

Barr, Cathy L., Batterson, James R., Berlin, Cheston, Budman, Cathy L., Coppola, Giovanni, Cox, Nancy J., Darrow, Sabrina, Dion, Yves, Freimer, Nelson B., Grados, Marco A., Greenberg, Erica, Hirschtritt, Matthew E., Huang, Alden Y., Illmann, Cornelia, King, Robert A., Kurlan, Roger, Leckman, James F., Lyon, Gholson J., Malaty, Irene A., McMahon, William M., Neale, Benjamin M., Okun, Michael S., Osiecki, Lisa, Robertson, Mary M., Rouleau, Guy A., Sandor, Paul, Singer, Harvey S., Smit, Jan H., Sul, Jae Hoon, Androutsos, Christos, Basha, Entela, Farkas, Luca, Fichna, Jakub, Janik, Piotr, Kapisyzi, Mira, Karagiannidis, Iordanis, Koumoula, Anastasia, Nagy, Peter, Puchala, Joanna, Szejko, Natalia, Szymanska, Urszula, Tsironi, Vaia, Apter, Alan, Ball, Juliane, Bodmer, Benjamin, Bognar, Emese, Buse, Judith, Vela, Marta Correa, Fremer, Carolin, Garcia-Delgar, Blanca, Gulisano, Mariangela, Hagen, Annelieke, Hagstrøm, Julie, Madruga-Garrido, Marcos, Pellico, Alessandra, Ruhrman, Daphna, Schnell, Jaana, Silvestri, Paola Rosaria, Skov, Liselotte, Steinberg, Tamar, Gloor, Friederike Tagwerker, Turner, Victoria L., Weidinger, Elif, Alexander, John, Aranyi, Tamas, Buisman, Wim R., Buitelaar, Jan K., Driessen, Nicole, Drineas, Petros, Fan, Siyan, Forde, Natalie J., Gerasch, Sarah, van den Heuvel, Odile A., Jespersgaard, Cathrine, Kanaan, Ahmad S., Möller, Harald E., Nawaz, Muhammad S., Nespoli, Ester, Pagliaroli, Luca, Poelmans, Geert, Pouwels, Petra J.W., Rizzo, Francesca, Veltman, Dick J., van der Werf, Ysbrand D., Widomska, Joanna, Zilhäo, Nuno R., Brown, Lawrence W., Cheon, Keun-Ah, Coffey, Barbara J., Fernandez, Thomas V., Gilbert, Donald L., Hong, Hyun Ju, Ibanez-Gomez, Laura, Kim, Eun-Joo, Kim, Young Key, Kim, Young-Shin, Koh, Yun-Joo, Kook, Sodahm, Kuperman, Samuel, Leventhal, Bennett L., Maras, Athanasios, Murphy, Tara L., Shin, Eun-Young, Song, Dong-Ho, Song, Jungeun, State, Matthew W., Visscher, Frank, Wang, Sheng, Zinner, Samuel H., Tsetsos, Fotis, Topaloudi, Apostolia, Jain, Pritesh, Yang, Zhiyu, Yu, Dongmei, Kolovos, Petros, Tumer, Zeynep, Rizzo, Renata, Hartmann, Andreas, Depienne, Christel, Worbe, Yulia, Müller-Vahl, Kirsten R., Cath, Danielle C., Boomsma, Dorret I., Wolanczyk, Tomasz, Zekanowski, Cezary, Barta, Csaba, Nemoda, Zsofia, Tarnok, Zsanett, Padmanabhuni, Shanmukha S., Buxbaum, Joseph D., Grice, Dorothy, Glennon, Jeffrey, Stefansson, Hreinn, Hengerer, Bastian, Yannaki, Evangelia, Stamatoyannopoulos, John A., Benaroya-Milshtein, Noa, Cardona, Francesco, Hedderly, Tammy, Heyman, Isobel, Huyser, Chaim, Mir, Pablo, Morer, Astrid, Mueller, Norbert, Munchau, Alexander, Plessen, Kerstin J., Porcelli, Cesare, Roessner, Veit, Walitza, Susanne, Schrag, Anette, Martino, Davide, Tischfield, Jay A., Heiman, Gary A., Willsey, A. Jeremy, Dietrich, Andrea, Davis, Lea K., Crowley, James J., Mathews, Carol A., Scharf, Jeremiah M., Georgitsi, Marianthi, Hoekstra, Pieter J., and Paschou, Peristera

Published
2024
Full Text
View/download PDF

12. Ancestral diversity improves discovery and fine-mapping of genetic loci for anthropometric traits—The Hispanic/Latino Anthropometry Consortium

Author

Fernández-Rhodes, Lindsay, Graff, Mariaelisa, Buchanan, Victoria L, Justice, Anne E, Highland, Heather M, Guo, Xiuqing, Zhu, Wanying, Chen, Hung-Hsin, Young, Kristin L, Adhikari, Kaustubh, Palmer, Nicholette D, Below, Jennifer E, Bradfield, Jonathan, Pereira, Alexandre C, Glover, LáShauntá, Kim, Daeeun, Lilly, Adam G, Shrestha, Poojan, Thomas, Alvin G, Zhang, Xinruo, Chen, Minhui, Chiang, Charleston WK, Pulit, Sara, Horimoto, Andrea, Krieger, Jose E, Guindo-Martínez, Marta, Preuss, Michael, Schumann, Claudia, Smit, Roelof AJ, Torres-Mejía, Gabriela, Acuña-Alonzo, Victor, Bedoya, Gabriel, Bortolini, Maria-Cátira, Canizales-Quinteros, Samuel, Gallo, Carla, González-José, Rolando, Poletti, Giovanni, Rothhammer, Francisco, Hakonarson, Hakon, Igo, Robert, Adler, Sharon G, Iyengar, Sudha K, Nicholas, Susanne B, Gogarten, Stephanie M, Isasi, Carmen R, Papnicolaou, George, Stilp, Adrienne M, Qi, Qibin, Kho, Minjung, Smith, Jennifer A, Langefeld, Carl D, Wagenknecht, Lynne, Mckean-Cowdin, Roberta, Gao, Xiaoyi Raymond, Nousome, Darryl, Conti, David V, Feng, Ye, Allison, Matthew A, Arzumanyan, Zorayr, Buchanan, Thomas A, Chen, Yii-Der Ida, Genter, Pauline M, Goodarzi, Mark O, Hai, Yang, Hsueh, Willa, Ipp, Eli, Kandeel, Fouad R, Lam, Kelvin, Li, Xiaohui, Nadler, Jerry L, Raffel, Leslie J, Roll, Kathryn, Sandow, Kevin, Tan, Jingyi, Taylor, Kent D, Xiang, Anny H, Yao, Jie, Audirac-Chalifour, Astride, de Jesus Peralta Romero, Jose, Hartwig, Fernando, Horta, Bernando, Blangero, John, Curran, Joanne E, Duggirala, Ravindranath, Lehman, Donna E, Puppala, Sobha, Fejerman, Laura, John, Esther M, Aguilar-Salinas, Carlos, Burtt, Noël P, Florez, Jose C, García-Ortíz, Humberto, González-Villalpando, Clicerio, Mercader, Josep, Orozco, Lorena, Tusié-Luna, Teresa, Blanco, Estela, Gahagan, Sheila, Cox, Nancy J, and Hanis, Craig

Subjects
Biological Sciences, Genetics, Obesity, Human Genome, Hispanic/Latino, anthropometrics, diversity, fine-mapping, obesity, population stratification, trans-ancestral or trans-ethnic
Abstract

Hispanic/Latinos have been underrepresented in genome-wide association studies (GWAS) for anthropometric traits despite their notable anthropometric variability, ancestry proportions, and high burden of growth stunting and overweight/obesity. To address this knowledge gap, we analyzed densely imputed genetic data in a sample of Hispanic/Latino adults to identify and fine-map genetic variants associated with body mass index (BMI), height, and BMI-adjusted waist-to-hip ratio (WHRadjBMI). We conducted a GWAS of 18 studies/consortia as part of the Hispanic/Latino Anthropometry (HISLA) Consortium (stage 1, n = 59,771) and generalized our findings in 9 additional studies (stage 2, n = 10,538). We conducted a trans-ancestral GWAS with summary statistics from HISLA stage 1 and existing consortia of European and African ancestries. In our HISLA stage 1 + 2 analyses, we discovered one BMI locus, as well as two BMI signals and another height signal each within established anthropometric loci. In our trans-ancestral meta-analysis, we discovered three BMI loci, one height locus, and one WHRadjBMI locus. We also identified 3 secondary signals for BMI, 28 for height, and 2 for WHRadjBMI in established loci. We show that 336 known BMI, 1,177 known height, and 143 known WHRadjBMI (combined) SNPs demonstrated suggestive transferability (nominal significance and effect estimate directional consistency) in Hispanic/Latino adults. Of these, 36 BMI, 124 height, and 11 WHRadjBMI SNPs were significant after trait-specific Bonferroni correction. Trans-ancestral meta-analysis of the three ancestries showed a small-to-moderate impact of uncorrected population stratification on the resulting effect size estimates. Our findings demonstrate that future studies may also benefit from leveraging diverse ancestries and differences in linkage disequilibrium patterns to discover novel loci and additional signals with less residual population stratification.

Published
2022

14. Integration of genetic, transcriptomic, and clinical data provides insight into 16p11.2 and 22q11.2 CNV genes

Author

Vysotskiy, Mikhail, Zhong, Xue, Miller-Fleming, Tyne W, Zhou, Dan, Cox, Nancy J, and Weiss, Lauren A

Subjects
Biological Sciences, Genetics, Human Genome, Brain Disorders, Intellectual and Developmental Disabilities (IDD), Mental Health, Genetic Testing, Schizophrenia, Autism, Biotechnology, Clinical Research, 2.1 Biological and endogenous factors, Aetiology, Mental health, Good Health and Well Being, Autism Spectrum Disorder, Autistic Disorder, Chromosome Deletion, Chromosome Disorders, Chromosomes, Human, Pair 16, DNA Copy Number Variations, DiGeorge Syndrome, Genotype, Humans, Intellectual Disability, Phenotype, Psychotic Disorders, Scavenger Receptors, Class F, Transcriptome, Tumor Suppressor Proteins, Copy number variants, Transcriptome imputation, Electronic health records, Psychiatric traits, Phenome-wide association studies, Autism Working Group of the Psychiatric Genomics Consortium^, Bipolar Disorder Working Group of the Psychiatric Genomics Consortium^, Schizophrenia Working Group of the Psychiatric Genomics Consortium^, Clinical Sciences
Abstract

BackgroundDeletions and duplications of the multigenic 16p11.2 and 22q11.2 copy number variant (CNV) regions are associated with brain-related disorders including schizophrenia, intellectual disability, obesity, bipolar disorder, and autism spectrum disorder (ASD). The contribution of individual CNV genes to each of these identified phenotypes is unknown, as well as the contribution of these CNV genes to other potentially subtler health implications for carriers. Hypothesizing that DNA copy number exerts most effects via impacts on RNA expression, we attempted a novel in silico fine-mapping approach in non-CNV carriers using both GWAS and biobank data.MethodsWe first asked whether gene expression level in any individual gene in the CNV region alters risk for a known CNV-associated behavioral phenotype(s). Using transcriptomic imputation, we performed association testing for CNV genes within large genotyped cohorts for schizophrenia, IQ, BMI, bipolar disorder, and ASD. Second, we used a biobank containing electronic health data to compare the medical phenome of CNV carriers to controls within 700,000 individuals in order to investigate the full spectrum of health effects of the CNVs. Third, we used genotypes for over 48,000 individuals within the biobank to perform phenome-wide association studies between imputed expressions of individual 16p11.2 and 22q11.2 genes and over 1500 health traits.ResultsUsing large genotyped cohorts, we found individual genes within 16p11.2 associated with schizophrenia (TMEM219, INO80E, YPEL3), BMI (TMEM219, SPN, TAOK2, INO80E), and IQ (SPN), using conditional analysis to identify upregulation of INO80E as the driver of schizophrenia, and downregulation of SPN and INO80E as increasing BMI. We identified both novel and previously observed over-represented traits within the electronic health records of 16p11.2 and 22q11.2 CNV carriers. In the phenome-wide association study, we found seventeen significant gene-trait pairs, including psychosis (NPIPB11, SLX1B) and mood disorders (SCARF2), and overall enrichment of mental traits.ConclusionsOur results demonstrate how integration of genetic and clinical data aids in understanding CNV gene function and implicates pleiotropy and multigenicity in CNV biology.

Published
2021

15. Novel ancestry-specific primary open-angle glaucoma loci and shared biology with vascular mechanisms and cell proliferation

Author

Lo Faro, Valeria, Bhattacharya, Arjun, Zhou, Wei, Zhou, Dan, Wang, Ying, Läll, Kristi, Kanai, Masahiro, Lopera-Maya, Esteban, Straub, Peter, Pawar, Priyanka, Tao, Ran, Zhong, Xue, Namba, Shinichi, Sanna, Serena, Nolte, Ilja M., Okada, Yukinori, Ingold, Nathan, MacGregor, Stuart, Snieder, Harold, Surakka, Ida, Shortt, Jonathan, Gignoux, Chris, Rafaels, Nicholas, Crooks, Kristy, Verma, Anurag, Verma, Shefali S., Guare, Lindsay, Rader, Daniel J., Willer, Cristen, Martin, Alicia R., Brantley, Milam A., Jr., Gamazon, Eric R., Jansonius, Nomdo M., Joos, Karen, Cox, Nancy J., and Hirbo, Jibril

Published
2024
Full Text
View/download PDF

16. Genome-Wide Association Study Meta-Analysis of 9619 Cases With Tic Disorders

Author

Yu, Dongmei, Sul, Jae Hoon, Tsetsos, Fotis, Nawaz, Muhammad S., Huang, Alden Y., Zelaya, Ivette, Illmann, Cornelia, Osiecki, Lisa, Darrow, Sabrina M., Hirschtritt, Matthew E., Greenberg, Erica, Muller-Vahl, Kirsten R., Stuhrmann, Manfred, Dion, Yves, Rouleau, Guy, Aschauer, Harald, Stamenkovic, Mara, Schlögelhofer, Monika, Sandor, Paul, Barr, Cathy L., Grados, Marco, Singer, Harvey S., Nöthen, Markus M., Hebebrand, Johannes, Hinney, Anke, King, Robert A., Fernandez, Thomas V., Barta, Csaba, Tarnok, Zsanett, Nagy, Peter, Depienne, Christel, Worbe, Yulia, Hartmann, Andreas, Budman, Cathy L., Rizzo, Renata, Lyon, Gholson J., McMahon, William M., Batterson, James R., Cath, Danielle C., Malaty, Irene A., Okun, Michael S., Berlin, Cheston, Woods, Douglas W., Lee, Paul C., Jankovic, Joseph, Robertson, Mary M., Gilbert, Donald L., Brown, Lawrence W., Coffey, Barbara J., Dietrich, Andrea, Hoekstra, Pieter J., Kuperman, Samuel, Zinner, Samuel H., Luðvigsson, Pétur, Sæmundsen, Evald, Thorarensen, Ólafur, Atzmon, Gil, Barzilai, Nir, Wagner, Michael, Moessner, Rainald, Ophoff, Roel, Pato, Carlos N., Pato, Michele T., Knowles, James A., Roffman, Joshua L., Smoller, Jordan W., Buckner, Randy L., Willsey, Jeremy A., Tischfield, Jay A., Heiman, Gary A., Stefansson, Hreinn, Stefansson, Kári, Posthuma, Danielle, Cox, Nancy J., Pauls, David L., Freimer, Nelson B., Neale, Benjamin M., Davis, Lea K., Paschou, Peristera, Coppola, Giovanni, Mathews, Carol A., Scharf, Jeremiah M., Agee, Michelle, Auton, Adam, Bell, Robert K., Bryc, Katarzyna, Elson, Sarah L., Fontanillas, Pierre, Furlotte, Nicholas A., Hicks, Barry, Huber, Karen E., Jewett, Ethan M., Jiang, Yunxuan, Kleinman, Aaron, Lin, Keng-Han, Litterman, Nadia K., McCreight, Jey C., McIntyre, Matthew H., McManus, Kimberly F., Mountain, Joanna L., Noblin, Elizabeth S., Northover, Carrie A.M., Pitts, Steven J., Poznik, G. David, Sathirapongsasuti, J. Fah, Shelton, Janie F., Shringarpure, Suyash, Tung, Joyce Y., Vacic, Vladimir, Wang, Xin, Strom, Nora I., Halvorsen, Matthew W., Grove, Jakob, Ásbjörnsdóttir, Bergrún, Luðvígsson, Pétur, de Schipper, Elles, Bäckmann, Julia, Andrén, Per, Tian, Chao, Als, Thomas Damm, Nissen, Judith Becker, Meier, Sandra M., Bybjerg-Grauholm, Jonas, Hougaard, David M., Werge, Thomas, Børglum, Anders D., Hinds, David A., Rück, Christian, Mataix-Cols, David, Stefánsson, Hreinn, Stefansson, Kari, Crowley, James J., and Mattheisen, Manuel

Published
2024
Full Text
View/download PDF

21. Genetic risk for major depressive disorder and loneliness in sex-specific associations with coronary artery disease

Author

Dennis, Jessica, Sealock, Julia, Levinson, Rebecca T, Farber-Eger, Eric, Franco, Jacob, Fong, Sarah, Straub, Peter, Hucks, Donald, Song, Wen-Liang, Linton, MacRae F, Fontanillas, Pierre, Elson, Sarah L, Ruderfer, Douglas, Abdellaoui, Abdel, Sanchez-Roige, Sandra, Palmer, Abraham A, Boomsma, Dorret I, Cox, Nancy J, Chen, Guanhua, Mosley, Jonathan D, Wells, Quinn S, and Davis, Lea K

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Brain Disorders, Genetics, Heart Disease - Coronary Heart Disease, Depression, Cardiovascular, Atherosclerosis, Major Depressive Disorder, Prevention, Heart Disease, Mental Health, Human Genome, Mental Illness, Serious Mental Illness, 2.1 Biological and endogenous factors, Mental health, Good Health and Well Being, Coronary Artery Disease, Depressive Disorder, Major, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Loneliness, Male, Multifactorial Inheritance, Risk Factors, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

Major depressive disorder (MDD) and loneliness are phenotypically and genetically correlated with coronary artery disease (CAD), but whether these associations are explained by pleiotropic genetic variants or shared comorbidities is unclear. To tease apart these scenarios, we first assessed the medical morbidity pattern associated with genetic risk factors for MDD and loneliness by conducting a phenome-wide association study in 18,385 European-ancestry individuals in the Vanderbilt University Medical Center biobank, BioVU. Polygenic scores for MDD and loneliness were developed for each person using previously published meta-GWAS summary statistics, and were tested for association with 882 clinical diagnoses ascertained via billing codes in electronic health records. We discovered strong associations with heart disease diagnoses, and next embarked on targeted analyses of CAD in 3893 cases and 4197 controls. We found odds ratios of 1.11 (95% CI, 1.04-1.18; P 8.43 × 10-4) and 1.13 (95% CI, 1.07-1.20; P 4.51 × 10-6) per 1-SD increase in the polygenic scores for MDD and loneliness, respectively. Results were similar in patients without psychiatric symptoms, and the increased risk persisted in females even after adjusting for multiple conventional risk factors and a polygenic score for CAD. In a final sensitivity analysis, we statistically adjusted for the genetic correlation between MDD and loneliness and re-computed polygenic scores. The polygenic score unique to loneliness remained associated with CAD (OR 1.09, 95% CI 1.03-1.15; P 0.002), while the polygenic score unique to MDD did not (OR 1.00, 95% CI 0.95-1.06; P 0.97). Our replication sample was the Atherosclerosis Risk in Communities (ARIC) cohort of 7197 European-ancestry participants (1598 incident CAD cases). In ARIC, polygenic scores for MDD and loneliness were associated with hazard ratios of 1.07 (95% CI, 0.99-1.14; P = 0.07) and 1.07 (1.01-1.15; P = 0.03), respectively, and we replicated findings from the BioVU sensitivity analyses. We conclude that genetic risk factors for MDD and loneliness act pleiotropically to increase CAD risk in females.

Published
2021

22. Alcohol and cigarette smoking consumption as genetic proxies for alcohol misuse and nicotine dependence

Author

Sanchez-Roige, Sandra, Cox, Nancy J, Johnson, Eric O, Hancock, Dana B, and Davis, Lea K

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Health Sciences, Pharmacology and Pharmaceutical Sciences, Tobacco, Substance Misuse, Tobacco Smoke and Health, Drug Abuse (NIDA only), Brain Disorders, Clinical Research, Prevention, Alcoholism, Alcohol Use and Health, Stroke, Cardiovascular, Cancer, Oral and gastrointestinal, Mental health, Good Health and Well Being, Adult, Alcohol Drinking, Alcoholism, Cigarette Smoking, Cohort Studies, Databases, Genetic, Female, Genome-Wide Association Study, Humans, Male, Phenotype, Tobacco Products, Tobacco Use Disorder, United Kingdom, White People, Alcohol, Nicotine, Consumption, Dependence, Polygenic analysis, PheWAS, Medical and Health Sciences, Psychology and Cognitive Sciences, Substance Abuse, Biochemistry and cell biology, Pharmacology and pharmaceutical sciences, Epidemiology
Abstract

PurposeTo investigate the role of consumption phenotypes as genetic proxies for alcohol misuse and nicotine dependence.MethodsWe leveraged GWAS data from well-powered studies of consumption, alcohol misuse, and nicotine dependence phenotypes measured in individuals of European ancestry from the UK Biobank (UKB) and other population-based cohorts (largest total N = 263,954), and performed genetic correlations within a medical-center cohort, BioVU (N = 66,915). For alcohol, we used quantitative measures of consumption and misuse via AUDIT from UKB. For smoking, we used cigarettes per day from UKB and non-UKB cohorts comprising the GSCAN consortium, and nicotine dependence via ICD codes from UKB and Fagerström Test for Nicotine Dependence from non-UKB cohorts.ResultsIn a large phenome-wide association study, we show that smoking consumption and dependence phenotypes show similar strongly negatively associations with a plethora of diseases, whereas alcohol consumption shows patterns of genetic association that diverge from those of alcohol misuse.ConclusionsOur study suggests that cigarette smoking consumption, which can be easily measured in the general population, may be good a genetic proxy for nicotine dependence, whereas alcohol consumption is not a direct genetic proxy of alcohol misuse.

Published
2021

23. Synaptic processes and immune-related pathways implicated in Tourette syndrome.

Author

Tsetsos, Fotis, Yu, Dongmei, Sul, Jae Hoon, Huang, Alden Y, Illmann, Cornelia, Osiecki, Lisa, Darrow, Sabrina M, Hirschtritt, Matthew E, Greenberg, Erica, Muller-Vahl, Kirsten R, Stuhrmann, Manfred, Dion, Yves, Rouleau, Guy A, Aschauer, Harald, Stamenkovic, Mara, Schlögelhofer, Monika, Sandor, Paul, Barr, Cathy L, Grados, Marco A, Singer, Harvey S, Nöthen, Markus M, Hebebrand, Johannes, Hinney, Anke, King, Robert A, Fernandez, Thomas V, Barta, Csaba, Tarnok, Zsanett, Nagy, Peter, Depienne, Christel, Worbe, Yulia, Hartmann, Andreas, Budman, Cathy L, Rizzo, Renata, Lyon, Gholson J, McMahon, William M, Batterson, James R, Cath, Danielle C, Malaty, Irene A, Okun, Michael S, Berlin, Cheston, Woods, Douglas W, Lee, Paul C, Jankovic, Joseph, Robertson, Mary M, Gilbert, Donald L, Brown, Lawrence W, Coffey, Barbara J, Dietrich, Andrea, Hoekstra, Pieter J, Kuperman, Samuel, Zinner, Samuel H, Wagner, Michael, Knowles, James A, Jeremy Willsey, A, Tischfield, Jay A, Heiman, Gary A, Cox, Nancy J, Freimer, Nelson B, Neale, Benjamin M, Davis, Lea K, Coppola, Giovanni, Mathews, Carol A, Scharf, Jeremiah M, Paschou, Peristera, Tourette Association of America International Consortium for Genetics, Darrow, Sabrina, Kurlan, Roger, Leckman, James F, Smit, Jan H, and Gilles de la Tourette GWAS Replication Initiative

Subjects
Tourette Association of America International Consortium for Genetics, Gilles de la Tourette GWAS Replication Initiative, Tourette International Collaborative Genetics Study, Psychiatric Genomics Consortium Tourette Syndrome Working Group, Neurons, Humans, Tourette Syndrome, Genotype, Genome-Wide Association Study, Mental Health, Genetics, Neurosciences, Human Genome, Brain Disorders, Biotechnology, Neurodegenerative, 2.1 Biological and endogenous factors, Clinical Sciences, Public Health and Health Services, Psychology
Abstract

Tourette syndrome (TS) is a neuropsychiatric disorder of complex genetic architecture involving multiple interacting genes. Here, we sought to elucidate the pathways that underlie the neurobiology of the disorder through genome-wide analysis. We analyzed genome-wide genotypic data of 3581 individuals with TS and 7682 ancestry-matched controls and investigated associations of TS with sets of genes that are expressed in particular cell types and operate in specific neuronal and glial functions. We employed a self-contained, set-based association method (SBA) as well as a competitive gene set method (MAGMA) using individual-level genotype data to perform a comprehensive investigation of the biological background of TS. Our SBA analysis identified three significant gene sets after Bonferroni correction, implicating ligand-gated ion channel signaling, lymphocytic, and cell adhesion and transsynaptic signaling processes. MAGMA analysis further supported the involvement of the cell adhesion and trans-synaptic signaling gene set. The lymphocytic gene set was driven by variants in FLT3, raising an intriguing hypothesis for the involvement of a neuroinflammatory element in TS pathogenesis. The indications of involvement of ligand-gated ion channel signaling reinforce the role of GABA in TS, while the association of cell adhesion and trans-synaptic signaling gene set provides additional support for the role of adhesion molecules in neuropsychiatric disorders. This study reinforces previous findings but also provides new insights into the neurobiology of TS.

Published
2021

25. Genetic variants and functional pathways associated with resilience to Alzheimer’s disease

Author

Dumitrescu, Logan, Mahoney, Emily R, Mukherjee, Shubhabrata, Lee, Michael L, Bush, William S, Engelman, Corinne D, Lu, Qiongshi, Fardo, David W, Trittschuh, Emily H, Mez, Jesse, Kaczorowski, Catherine, Saucedo, Hector Hernandez, Widaman, Keith F, Buckley, Rachel, Properzi, Michael, Mormino, Elizabeth, Yang, Hyun-Sik, Harrison, Tessa, Hedden, Trey, Nho, Kwangsik, Andrews, Shea J, Tommet, Doug, Hadad, Niran, Sanders, R Elizabeth, Ruderfer, Douglas M, Gifford, Katherine A, Moore, Annah M, Cambronero, Francis, Zhong, Xiaoyuan, Raghavan, Neha S, Vardarajan, Badri, Initiative, The Alzheimer’s Disease Neuroimaging, Consortium, A4 Study Team Alzheimer’s Disease Genetics, Pericak-Vance, Margaret A, Farrer, Lindsay A, Wang, Li-San, Cruchaga, Carlos, Schellenberg, Gerard, Cox, Nancy J, Haines, Jonathan L, Keene, C Dirk, Saykin, Andrew J, Larson, Eric B, Sperling, Reisa A, Mayeux, Richard, Bennett, David A, Schneider, Julie A, Crane, Paul K, Jefferson, Angela L, and Hohman, Timothy J

Subjects
Biological Psychology, Health Sciences, Psychology, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurosciences, Clinical Trials and Supportive Activities, Genetics, Dementia, Aging, Acquired Cognitive Impairment, Behavioral and Social Science, Brain Disorders, Clinical Research, Neurodegenerative, Human Genome, Alzheimer's Disease, Prevention, 2.1 Biological and endogenous factors, Aetiology, Neurological, Good Health and Well Being, Aged, 80 and over, Alzheimer Disease, Brain, Chromosomes, Human, Pair 18, Cognitive Dysfunction, Cognitive Reserve, Female, Genome-Wide Association Study, Genotype, Humans, Male, Polymorphism, Single Nucleotide, Alzheimer's disease, amyloid, resilience, GWAS, reserve, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s Disease Genetics Consortium (ADGC), A4 Study Team, Alzheimer’s disease, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Abstract

Approximately 30% of older adults exhibit the neuropathological features of Alzheimer's disease without signs of cognitive impairment. Yet, little is known about the genetic factors that allow these potentially resilient individuals to remain cognitively unimpaired in the face of substantial neuropathology. We performed a large, genome-wide association study (GWAS) of two previously validated metrics of cognitive resilience quantified using a latent variable modelling approach and representing better-than-predicted cognitive performance for a given level of neuropathology. Data were harmonized across 5108 participants from a clinical trial of Alzheimer's disease and three longitudinal cohort studies of cognitive ageing. All analyses were run across all participants and repeated restricting the sample to individuals with unimpaired cognition to identify variants at the earliest stages of disease. As expected, all resilience metrics were genetically correlated with cognitive performance and education attainment traits (P-values < 2.5 × 10-20), and we observed novel correlations with neuropsychiatric conditions (P-values < 7.9 × 10-4). Notably, neither resilience metric was genetically correlated with clinical Alzheimer's disease (P-values > 0.42) nor associated with APOE (P-values > 0.13). In single variant analyses, we observed a genome-wide significant locus among participants with unimpaired cognition on chromosome 18 upstream of ATP8B1 (index single nucleotide polymorphism rs2571244, minor allele frequency = 0.08, P = 2.3 × 10-8). The top variant at this locus (rs2571244) was significantly associated with methylation in prefrontal cortex tissue at multiple CpG sites, including one just upstream of ATPB81 (cg19596477; P = 2 × 10-13). Overall, this comprehensive genetic analysis of resilience implicates a putative role of vascular risk, metabolism, and mental health in protection from the cognitive consequences of neuropathology, while also providing evidence for a novel resilience gene along the bile acid metabolism pathway. Furthermore, the genetic architecture of resilience appears to be distinct from that of clinical Alzheimer's disease, suggesting that a shift in focus to molecular contributors to resilience may identify novel pathways for therapeutic targets.

Published
2020

27. The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits

Author

Abecasis, Gonçalo, Akolkar, Beena, Alexander, Benjamin R., Allred, Nicholette D., Altshuler, David, Below, Jennifer E., Bergman, Richard, Beulens, Joline W.J., Blangero, John, Boehnke, Michael, Bokvist, Krister, Bottinger, Erwin, Boughton, Andrew P., Bowden, Donald, Brosnan, M. Julia, Brown, Christopher, Bruskiewicz, Kenneth, Burtt, Noël P., Carmichael, Mary, Caulkins, Lizz, Cebola, Inês, Chambers, John, Ida Chen, Yii-Der, Cherkas, Andriy, Chu, Audrey Y., Clark, Christopher, Claussnitzer, Melina, Costanzo, Maria C., Cox, Nancy J., Hoed, Marcel den, Dong, Duc, Duby, Marc, Duggirala, Ravindranath, Dupuis, Josée, Elders, Petra J.M., Engreitz, Jesse M., Fauman, Eric, Ferrer, Jorge, Flannick, Jason, Flicek, Paul, Flickinger, Matthew, Florez, Jose C., Fox, Caroline S., Frayling, Timothy M., Frazer, Kelly A., Gaulton, Kyle J., Gilbert, Clint, Gloyn, Anna L., Green, Todd, Hanis, Craig L., Hanson, Robert, Hattersley, Andrew T., Hoang, Quy, Im, Hae Kyung, Iqbal, Sidra, Jacobs, Suzanne B.R., Jang, Dong-Keun, Jordan, Tad, Kamphaus, Tania, Karpe, Fredrik, Keane, Thomas M., Kim, Seung K., Kluge, Alexandria, Koesterer, Ryan, Kudtarkar, Parul, Lage, Kasper, Lange, Leslie A., Lazar, Mitchell, Lehman, Donna, Liu, Ching-Ti, Loos, Ruth J.F., Ma, Ronald Ching-wan, MacDonald, Patrick, Massung, Jeffrey, Maurano, Matthew T., McCarthy, Mark I., McVean, Gil, Meigs, James B., Mercader, Josep M., Miller, Melissa R., Mitchell, Braxton, Mohlke, Karen L., Morabito, Samuel, Morgan, Claire, Mullican, Shannon, Narendra, Sharvari, Ng, Maggie C.Y., Nguyen, Lynette, Palmer, Colin N.A., Parker, Stephen C.J., Parrado, Antonio, Parsa, Afshin, Pawlyk, Aaron C., Pearson, Ewan R., Plump, Andrew, Province, Michael, Quertermous, Thomas, Redline, Susan, Reilly, Dermot F., Ren, Bing, Rich, Stephen S., Richards, J. Brent, Rotter, Jerome I., Ruebenacker, Oliver, Ruetten, Hartmut, Salem, Rany M., Sander, Maike, Sanders, Michael, Sanghera, Dharambir, Scott, Laura J., Sengupta, Sebanti, Siedzik, David, Sim, Xueling, Singh, Preeti, Sladek, Robert, Small, Kerrin, Smith, Philip, Stein, Peter, Spalding, Dylan, Stringham, Heather M., Sun, Ying, Susztak, Katalin, ’t Hart, Leen M., Taliun, Daniel, Taylor, Kent, Thomas, Melissa K., Todd, Jennifer A., Udler, Miriam S., Voight, Benjamin, von Grotthuss, Marcin, Wan, Andre, Welch, Ryan P., Wholley, David, Yuksel, Kaan, Zaghloul, Norann A., Jang, Dongkeun, Moriondo, Annie, Nguyen, Trang, Smadbeck, Patrick, Brandes, MacKenzie, Dornbos, Peter, Huellas-Bruskiewicz, Kenneth C., Ji, Yue, McMahon, Aoife C., Fauman, Eric B., Kamphaus, Tania Nayak, and Abecasis, Gonçalo R.

Published
2023
Full Text
View/download PDF

28. Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts

Author

Zhou, Wei, Kanai, Masahiro, Wu, Kuan-Han H., Rasheed, Humaira, Tsuo, Kristin, Hirbo, Jibril B., Wang, Ying, Bhattacharya, Arjun, Zhao, Huiling, Namba, Shinichi, Surakka, Ida, Wolford, Brooke N., Lo Faro, Valeria, Lopera-Maya, Esteban A., Läll, Kristi, Favé, Marie-Julie, Chapman, Sinéad B., Karjalainen, Juha, Kurki, Mitja, Mutaamba, Maasha, Partanen, Juulia J., Brumpton, Ben M., Chavan, Sameer, Chen, Tzu-Ting, Daya, Michelle, Ding, Yi, Feng, Yen-Chen A., Gignoux, Christopher R., Graham, Sarah E., Hornsby, Whitney E., Ingold, Nathan, Johnson, Ruth, Laisk, Triin, Lin, Kuang, Lv, Jun, Millwood, Iona Y., Palta, Priit, Pandit, Anita, Preuss, Michael H., Thorsteinsdottir, Unnur, Uzunovic, Jasmina, Zawistowski, Matthew, Zhong, Xue, Campbell, Archie, Crooks, Kristy, de Bock, Geertruida H., Douville, Nicholas J., Finer, Sarah, Fritsche, Lars G., Griffiths, Christopher J., Guo, Yu, Hunt, Karen A., Konuma, Takahiro, Marioni, Riccardo E., Nomdo, Jansonius, Patil, Snehal, Rafaels, Nicholas, Richmond, Anne, Shortt, Jonathan A., Straub, Peter, Tao, Ran, Vanderwerff, Brett, Barnes, Kathleen C., Boezen, Marike, Chen, Zhengming, Chen, Chia-Yen, Cho, Judy, Smith, George Davey, Finucane, Hilary K., Franke, Lude, Gamazon, Eric R., Ganna, Andrea, Gaunt, Tom R., Ge, Tian, Huang, Hailiang, Huffman, Jennifer, Koskela, Jukka T., Lajonchere, Clara, Law, Matthew H., Li, Liming, Lindgren, Cecilia M., Loos, Ruth J.F., MacGregor, Stuart, Matsuda, Koichi, Olsen, Catherine M., Porteous, David J., Shavit, Jordan A., Snieder, Harold, Trembath, Richard C., Vonk, Judith M., Whiteman, David, Wicks, Stephen J., Wijmenga, Cisca, Wright, John, Zheng, Jie, Zhou, Xiang, Awadalla, Philip, Boehnke, Michael, Cox, Nancy J., Geschwind, Daniel H., Hayward, Caroline, Hveem, Kristian, Kenny, Eimear E., Lin, Yen-Feng, Mägi, Reedik, Martin, Hilary C., Medland, Sarah E., Okada, Yukinori, Palotie, Aarno V., Pasaniuc, Bogdan, Sanna, Serena, Smoller, Jordan W., Stefansson, Kari, van Heel, David A., Walters, Robin G., Zöllner, Sebastian, Martin, Alicia R., Willer, Cristen J., Daly, Mark J., Neale, Benjamin M., Lopera, Esteban, Kerminen, Sini, Wu, Kuan-Han, Bhatta, Laxmi, Brumpton, Ben, Deelen, Patrick, Murakami, Yoshinori, Willer, Cristen, and Hirbo, Jibril

Published
2023
Full Text
View/download PDF

30. Publisher Correction: Gene expression imputation across multiple brain regions provides insights into schizophrenia risk

Author

Huckins, Laura M, Dobbyn, Amanda, Ruderfer, Douglas M, Hoffman, Gabriel, Wang, Weiqing, Pardiñas, Antonio F, Rajagopal, Veera M, Als, Thomas D, T. Nguyen, Hoang, Girdhar, Kiran, Boocock, James, Roussos, Panos, Fromer, Menachem, Kramer, Robin, Domenici, Enrico, Gamazon, Eric R, Purcell, Shaun, Demontis, Ditte, Børglum, Anders D, Walters, James TR, O’Donovan, Michael C, Sullivan, Patrick, Owen, Michael J, Devlin, Bernie, Sieberts, Solveig K, Cox, Nancy J, Im, Hae Kyung, Sklar, Pamela, and Stahl, Eli A

Subjects
Genetics, Neurosciences, Human Genome, Serious Mental Illness, Schizophrenia, Brain Disorders, Mental Health, Biotechnology, Mental health, CommonMind Consortium, Schizophrenia Working Group of the Psychiatric Genomics Consortium, iPSYCH-GEMS Schizophrenia Working Group, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

In the HTML version of the article originally published, the author group 'The Schizophrenia Working Group of the Psychiatric Genomics Consortium' was displayed incorrectly. The error has been corrected in the HTML version of the article.

Published
2019

31. Gene expression imputation across multiple brain regions provides insights into schizophrenia risk

Author

Huckins, Laura M, Dobbyn, Amanda, Ruderfer, Douglas M, Hoffman, Gabriel, Wang, Weiqing, Pardiñas, Antonio F, Rajagopal, Veera M, Als, Thomas D, T. Nguyen, Hoang, Girdhar, Kiran, Boocock, James, Roussos, Panos, Fromer, Menachem, Kramer, Robin, Domenici, Enrico, Gamazon, Eric R, Purcell, Shaun, Demontis, Ditte, Børglum, Anders D, Walters, James TR, O’Donovan, Michael C, Sullivan, Patrick, Owen, Michael J, Devlin, Bernie, Sieberts, Solveig K, Cox, Nancy J, Im, Hae Kyung, Sklar, Pamela, and Stahl, Eli A

Subjects
Biological Sciences, Genetics, Mental Health, Brain Disorders, Human Genome, Schizophrenia, 2.1 Biological and endogenous factors, Aetiology, Mental health, Brain, Case-Control Studies, Gene Expression, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Risk, Transcriptome, CommonMind Consortium, Schizophrenia Working Group of the Psychiatric Genomics Consortium, iPSYCH-GEMS Schizophrenia Working Group, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

Published
2019

32. Interrogating the Genetic Determinants of Tourette’s Syndrome and Other Tic Disorders Through Genome-Wide Association Studies

Author

Yu, Dongmei, Sul, Jae Hoon, Tsetsos, Fotis, Nawaz, Muhammad S, Huang, Alden Y, Zelaya, Ivette, Illmann, Cornelia, Osiecki, Lisa, Darrow, Sabrina M, Hirschtritt, Matthew E, Greenberg, Erica, Muller-Vahl, Kirsten R, Stuhrmann, Manfred, Dion, Yves, Rouleau, Guy, Aschauer, Harald, Stamenkovic, Mara, Schlögelhofer, Monika, Sandor, Paul, Barr, Cathy L, Grados, Marco, Singer, Harvey S, Nöthen, Markus M, Hebebrand, Johannes, Hinney, Anke, King, Robert A, Fernandez, Thomas V, Barta, Csaba, Tarnok, Zsanett, Nagy, Peter, Depienne, Christel, Worbe, Yulia, Hartmann, Andreas, Budman, Cathy L, Rizzo, Renata, Lyon, Gholson J, McMahon, William M, Batterson, James R, Cath, Danielle C, Malaty, Irene A, Okun, Michael S, Berlin, Cheston, Woods, Douglas W, Lee, Paul C, Jankovic, Joseph, Robertson, Mary M, Gilbert, Donald L, Brown, Lawrence W, Coffey, Barbara J, Dietrich, Andrea, Hoekstra, Pieter J, Kuperman, Samuel, Zinner, Samuel H, Luðvigsson, Pétur, Sæmundsen, Evald, Thorarensen, Ólafur, Atzmon, Gil, Barzilai, Nir, Wagner, Michael, Moessner, Rainald, Ophoff, Roel, Pato, Carlos N, Pato, Michele T, Knowles, James A, Roffman, Joshua L, Smoller, Jordan W, Buckner, Randy L, Willsey, A Jeremy, Tischfield, Jay A, Heiman, Gary A, Stefansson, Hreinn, Stefansson, Kári, Posthuma, Danielle, Cox, Nancy J, Pauls, David L, Freimer, Nelson B, Neale, Benjamin M, Davis, Lea K, Paschou, Peristera, Coppola, Giovanni, Mathews, Carol A, and Scharf, Jeremiah M

Subjects
Mental Health, Prevention, Brain Disorders, Human Genome, Neurosciences, Neurodegenerative, Serious Mental Illness, Tourette Syndrome, Genetics, Aetiology, 2.1 Biological and endogenous factors, Case-Control Studies, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Risk Factors, Severity of Illness Index, Tic Disorders, fms-Like Tyrosine Kinase 3, Tourette Association of America International Consortium for Genetics, the Gilles de la Tourette GWAS Replication Initiative, the Tourette International Collaborative Genetics Study, and the Psychiatric Genomics Consortium Tourette Syndrome Working Group, Child Psychiatry, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

ObjectiveTourette's syndrome is polygenic and highly heritable. Genome-wide association study (GWAS) approaches are useful for interrogating the genetic architecture and determinants of Tourette's syndrome and other tic disorders. The authors conducted a GWAS meta-analysis and probed aggregated Tourette's syndrome polygenic risk to test whether Tourette's and related tic disorders have an underlying shared genetic etiology and whether Tourette's polygenic risk scores correlate with worst-ever tic severity and may represent a potential predictor of disease severity.MethodsGWAS meta-analysis, gene-based association, and genetic enrichment analyses were conducted in 4,819 Tourette's syndrome case subjects and 9,488 control subjects. Replication of top loci was conducted in an independent population-based sample (706 case subjects, 6,068 control subjects). Relationships between Tourette's polygenic risk scores (PRSs), other tic disorders, ascertainment, and tic severity were examined.ResultsGWAS and gene-based analyses identified one genome-wide significant locus within FLT3 on chromosome 13, rs2504235, although this association was not replicated in the population-based sample. Genetic variants spanning evolutionarily conserved regions significantly explained 92.4% of Tourette's syndrome heritability. Tourette's-associated genes were significantly preferentially expressed in dorsolateral prefrontal cortex. Tourette's PRS significantly predicted both Tourette's syndrome and tic spectrum disorders status in the population-based sample. Tourette's PRS also significantly correlated with worst-ever tic severity and was higher in case subjects with a family history of tics than in simplex case subjects.ConclusionsModulation of gene expression through noncoding variants, particularly within cortico-striatal circuits, is implicated as a fundamental mechanism in Tourette's syndrome pathogenesis. At a genetic level, tic disorders represent a continuous spectrum of disease, supporting the unification of Tourette's syndrome and other tic disorders in future diagnostic schemata. Tourette's PRSs derived from sufficiently large samples may be useful in the future for predicting conversion of transient tics to chronic tic disorders, as well as tic persistence and lifetime tic severity.

Published
2019

33. Meta-analysis fine-mapping is often miscalibrated at single-variant resolution

Author

Zhou, Wei, Kanai, Masahiro, Wu, Kuan-Han H., Rasheed, Humaira, Tsuo, Kristin, Hirbo, Jibril B., Wang, Ying, Bhattacharya, Arjun, Zhao, Huiling, Namba, Shinichi, Surakka, Ida, Wolford, Brooke N., Lo Faro, Valeria, Lopera-Maya, Esteban A., Läll, Kristi, Favé, Marie-Julie, Partanen, Juulia J., Chapman, Sinéad B., Karjalainen, Juha, Kurki, Mitja, Maasha, Mutaamba, Brumpton, Ben M., Chavan, Sameer, Chen, Tzu-Ting, Daya, Michelle, Ding, Yi, Feng, Yen-Chen A., Guare, Lindsay A., Gignoux, Christopher R., Graham, Sarah E., Hornsby, Whitney E., Ingold, Nathan, Ismail, Said I., Johnson, Ruth, Laisk, Triin, Lin, Kuang, Lv, Jun, Millwood, Iona Y., Moreno-Grau, Sonia, Nam, Kisung, Palta, Priit, Pandit, Anita, Preuss, Michael H., Saad, Chadi, Setia-Verma, Shefali, Thorsteinsdottir, Unnur, Uzunovic, Jasmina, Verma, Anurag, Zawistowski, Matthew, Zhong, Xue, Afifi, Nahla, Al-Dabhani, Kawthar M., Al Thani, Asma, Bradford, Yuki, Campbell, Archie, Crooks, Kristy, de Bock, Geertruida H., Damrauer, Scott M., Douville, Nicholas J., Finer, Sarah, Fritsche, Lars G., Fthenou, Eleni, Gonzalez-Arroyo, Gilberto, Griffiths, Christopher J., Guo, Yu, Hunt, Karen A., Ioannidis, Alexander, Jansonius, Nomdo M., Konuma, Takahiro, Michael Lee, Ming Ta, Lopez-Pineda, Arturo, Matsuda, Yuta, Marioni, Riccardo E., Moatamed, Babak, Nava-Aguilar, Marco A., Numakura, Kensuke, Patil, Snehal, Rafaels, Nicholas, Richmond, Anne, Rojas-Muñoz, Agustin, Shortt, Jonathan A., Straub, Peter, Tao, Ran, Vanderwerff, Brett, Vernekar, Manvi, Veturi, Yogasudha, Barnes, Kathleen C., Boezen, Marike, Chen, Zhengming, Chen, Chia-Yen, Cho, Judy, Smith, George Davey, Finucane, Hilary K., Franke, Lude, Gamazon, Eric R., Ganna, Andrea, Gaunt, Tom R., Ge, Tian, Huang, Hailiang, Huffman, Jennifer, Katsanis, Nicholas, Koskela, Jukka T., Lajonchere, Clara, Law, Matthew H., Li, Liming, Lindgren, Cecilia M., Loos, Ruth J.F., MacGregor, Stuart, Matsuda, Koichi, Olsen, Catherine M., Porteous, David J., Shavit, Jordan A., Snieder, Harold, Takano, Tomohiro, Trembath, Richard C., Vonk, Judith M., Whiteman, David C., Wicks, Stephen J., Wijmenga, Cisca, Wright, John, Zheng, Jie, Zhou, Xiang, Awadalla, Philip, Boehnke, Michael, Bustamante, Carlos D., Cox, Nancy J., Fatumo, Segun, Geschwind, Daniel H., Hayward, Caroline, Hveem, Kristian, Kenny, Eimear E., Lee, Seunggeun, Lin, Yen-Feng, Mbarek, Hamdi, Mägi, Reedik, Martin, Hilary C., Medland, Sarah E., Okada, Yukinori, Palotie, Aarno V., Pasaniuc, Bogdan, Rader, Daniel J., Ritchie, Marylyn D., Sanna, Serena, Smoller, Jordan W., Stefansson, Kari, van Heel, David A., Walters, Robin G., Zöllner, Sebastian, Biobank of the Americas, Biobank Japan Project, BioMe, BioVU, CanPath - Ontario Health Study, China Kadoorie Biobank Collaborative Group, Colorado Center for Personalized Medicine, deCODE Genetics, Estonian Biobank, FinnGen, Generation Scotland, Genes & Health Research Team, LifeLines, Mass General Brigham Biobank, Michigan Genomics Initiative, National Biobank of Korea, Penn Medicine BioBank, Qatar Biobank, The Qskin Sun and Health Study, Taiwan Biobank, The Hunt Study, Ucla Atlas Community Health Initiative, Uganda Genome Resource, Uk Biobank, Martin, Alicia R., Willer, Cristen J., Daly, Mark J., Neale, Benjamin M., and Elzur, Roy

Published
2022
Full Text
View/download PDF

34. Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease

Author

Zhou, Wei, Kanai, Masahiro, Wu, Kuan-Han H., Rasheed, Humaira, Tsuo, Kristin, Hirbo, Jibril B., Wang, Ying, Bhattacharya, Arjun, Zhao, Huiling, Namba, Shinichi, Surakka, Ida, Wolford, Brooke N., Lo Faro, Valeria, Lopera-Maya, Esteban A., Läll, Kristi, Favé, Marie-Julie, Partanen, Juulia J., Chapman, Sinéad B., Karjalainen, Juha, Kurki, Mitja, Maasha, Mutaamba, Brumpton, Ben M., Chavan, Sameer, Chen, Tzu-Ting, Daya, Michelle, Ding, Yi, Feng, Yen-Chen A., Guare, Lindsay A., Gignoux, Christopher R., Graham, Sarah E., Hornsby, Whitney E., Ingold, Nathan, Ismail, Said I., Johnson, Ruth, Laisk, Triin, Lin, Kuang, Lv, Jun, Millwood, Iona Y., Moreno-Grau, Sonia, Nam, Kisung, Palta, Priit, Pandit, Anita, Preuss, Michael H., Saad, Chadi, Setia-Verma, Shefali, Thorsteinsdottir, Unnur, Uzunovic, Jasmina, Verma, Anurag, Zawistowski, Matthew, Zhong, Xue, Afifi, Nahla, Al-Dabhani, Kawthar M., Al Thani, Asma, Bradford, Yuki, Campbell, Archie, Crooks, Kristy, de Bock, Geertruida H., Damrauer, Scott M., Douville, Nicholas J., Finer, Sarah, Fritsche, Lars G., Fthenou, Eleni, Gonzalez-Arroyo, Gilberto, Griffiths, Christopher J., Guo, Yu, Hunt, Karen A., Ioannidis, Alexander, Jansonius, Nomdo M., Konuma, Takahiro, Lee, Ming Ta Michael, Lopez-Pineda, Arturo, Matsuda, Yuta, Marioni, Riccardo E., Moatamed, Babak, Nava-Aguilar, Marco A., Numakura, Kensuke, Patil, Snehal, Rafaels, Nicholas, Richmond, Anne, Rojas-Muñoz, Agustin, Shortt, Jonathan A., Straub, Peter, Tao, Ran, Vanderwerff, Brett, Vernekar, Manvi, Veturi, Yogasudha, Barnes, Kathleen C., Boezen, Marike, Chen, Zhengming, Chen, Chia-Yen, Cho, Judy, Smith, George Davey, Finucane, Hilary K., Franke, Lude, Gamazon, Eric R., Ganna, Andrea, Gaunt, Tom R., Ge, Tian, Huang, Hailiang, Huffman, Jennifer, Katsanis, Nicholas, Koskela, Jukka T., Lajonchere, Clara, Law, Matthew H., Li, Liming, Lindgren, Cecilia M., Loos, Ruth J.F., MacGregor, Stuart, Matsuda, Koichi, Olsen, Catherine M., Porteous, David J., Shavit, Jordan A., Snieder, Harold, Takano, Tomohiro, Trembath, Richard C., Vonk, Judith M., Whiteman, David C., Wicks, Stephen J., Wijmenga, Cisca, Wright, John, Zheng, Jie, Zhou, Xiang, Awadalla, Philip, Boehnke, Michael, Bustamante, Carlos D., Cox, Nancy J., Fatumo, Segun, Geschwind, Daniel H., Hayward, Caroline, Hveem, Kristian, Kenny, Eimear E., Lee, Seunggeun, Lin, Yen-Feng, Mbarek, Hamdi, Mägi, Reedik, Martin, Hilary C., Medland, Sarah E., Okada, Yukinori, Palotie, Aarno V., Pasaniuc, Bogdan, Rader, Daniel J., Ritchie, Marylyn D., Sanna, Serena, Smoller, Jordan W., Stefansson, Kari, van Heel, David A., Walters, Robin G., Zöllner, Sebastian, Martin, Alicia R., Willer, Cristen J., Daly, Mark J., and Neale, Benjamin M.

Published
2022
Full Text
View/download PDF

35. Leveraging global multi-ancestry meta-analysis in the study of idiopathic pulmonary fibrosis genetics

Author

Partanen, Juulia J., Häppölä, Paavo, Zhou, Wei, Lehisto, Arto A., Ainola, Mari, Sutinen, Eva, Allen, Richard J., Stockwell, Amy D., Leavy, Olivia C., Oldham, Justin M., Guillen-Guio, Beatriz, Cox, Nancy J., Hirbo, Jibril B., Schwartz, David A., Fingerlin, Tasha E., Flores, Carlos, Noth, Imre, Yaspan, Brian L., Jenkins, R. Gisli, Wain, Louise V., Ripatti, Samuli, Pirinen, Matti, Laitinen, Tarja, Kaarteenaho, Riitta, Myllärniemi, Marjukka, Daly, Mark J., and Koskela, Jukka T.

Published
2022
Full Text
View/download PDF

37. Automated Phenotyping Tool for Identifying Developmental Language Disorder Cases in Health Systems Data (APT-DLD): A New Research Algorithm for Deployment in Large-Scale Electronic Health Record Systems

Author

Walters, Courtney E., Nitin, Rachana, Margulis, Katherine, Boorom, Olivia, Gustavson, Daniel E., Bush, Catherine T., Davis, Lea K., Below, Jennifer E., Cox, Nancy J., Camarata, Stephen M., and Gordon, Reyna L.

Abstract

Purpose: Data mining algorithms using electronic health records (EHRs) are useful in large-scale population-wide studies to classify etiology and comorbidities (Casey et al., 2016). Here, we apply this approach to developmental language disorder (DLD), a prevalent communication disorder whose risk factors and epidemiology remain largely undiscovered. Method: We first created a reliable system for manually identifying DLD in EHRs based on speech-language pathologist (SLP) diagnostic expertise. We then developed and validated an automated algorithmic procedure, called, Automated Phenotyping Tool for identifying DLD cases in health systems data (APT-DLD), that classifies a DLD status for patients within EHRs on the basis of ICD (International Statistical Classification of Diseases and Related Health Problems) codes. APT-DLD was validated in a discovery sample (N = 973) using expert SLP manual phenotype coding as a gold-standard comparison and then applied and further validated in a replication sample of N = 13,652 EHRs. Results: In the discovery sample, the APT-DLD algorithm correctly classified 98% (concordance) of DLD cases in concordance with manually coded records in the training set, indicating that APT-DLD successfully mimics a comprehensive chart review. The output of APT-DLD was also validated in relation to independently conducted SLP clinician coding in a subset of records, with a positive predictive value of 95% of cases correctly classified as DLD. We also applied APT-DLD to the replication sample, where it achieved a positive predictive value of 90% in relation to SLP clinician classification of DLD. Conclusions: APT-DLD is a reliable, valid, and scalable tool for identifying DLD cohorts in EHRs. This new method has promising public health implications for future large-scale epidemiological investigations of DLD and may inform EHR data mining algorithms for other communication disorders.

Published
2020
Full Text
View/download PDF

40. Sex-specific genetic predictors of Alzheimer’s disease biomarkers

Author

Deming, Yuetiva, Dumitrescu, Logan, Barnes, Lisa L, Thambisetty, Madhav, Kunkle, Brian, Gifford, Katherine A, Bush, William S, Chibnik, Lori B, Mukherjee, Shubhabrata, De Jager, Philip L, Kukull, Walter, Huentelman, Matt, Crane, Paul K, Resnick, Susan M, Keene, C Dirk, Montine, Thomas J, Schellenberg, Gerard D, Haines, Jonathan L, Zetterberg, Henrik, Blennow, Kaj, Larson, Eric B, Johnson, Sterling C, Albert, Marilyn, Moghekar, Abhay, del Aguila, Jorge L, Fernandez, Maria Victoria, Budde, John, Hassenstab, Jason, Fagan, Anne M, Riemenschneider, Matthias, Petersen, Ronald C, Minthon, Lennart, Chao, Michael J, Van Deerlin, Vivianna M, Lee, Virginia M-Y, Shaw, Leslie M, Trojanowski, John Q, Peskind, Elaine R, Li, Gail, Davis, Lea K, Sealock, Julia M, Cox, Nancy J, Alzheimer’s Disease Neuroimaging Initiative (ADNI), The Alzheimer Disease Genetics Consortium (ADGC), Goate, Alison M, Bennett, David A, Schneider, Julie A, Jefferson, Angela L, Cruchaga, Carlos, and Hohman, Timothy J

Subjects
Biomedical and Clinical Sciences, Neurosciences, Brain Disorders, Acquired Cognitive Impairment, Human Genome, Neurodegenerative, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Aging, Dementia, Genetics, Alzheimer's Disease, Women's Health, 2.1 Biological and endogenous factors, Neurological, Aged, 80 and over, Alzheimer Disease, Amyloid beta-Peptides, Amyloidosis, Apolipoproteins E, Biomarkers, Brain, Claudins, Female, Genome-Wide Association Study, Genotype, Humans, Male, Muscle Proteins, Mutation, Peptide Fragments, Serpins, Sex Factors, Transcription Factors, tau Proteins, Alzheimer disease, Cerebrospinal fluid biomarkers, Neuropathology, Sex difference, APOE, Amyloid, Tau, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer Disease Genetics Consortium, Clinical Sciences, Neurology & Neurosurgery
Abstract

Cerebrospinal fluid (CSF) levels of amyloid-β 42 (Aβ42) and tau have been evaluated as endophenotypes in Alzheimer's disease (AD) genetic studies. Although there are sex differences in AD risk, sex differences have not been evaluated in genetic studies of AD endophenotypes. We performed sex-stratified and sex interaction genetic analyses of CSF biomarkers to identify sex-specific associations. Data came from a previous genome-wide association study (GWAS) of CSF Aβ42 and tau (1527 males, 1509 females). We evaluated sex interactions at previous loci, performed sex-stratified GWAS to identify sex-specific associations, and evaluated sex interactions at sex-specific GWAS loci. We then evaluated sex-specific associations between prefrontal cortex (PFC) gene expression at relevant loci and autopsy measures of plaques and tangles using data from the Religious Orders Study and Rush Memory and Aging Project. In Aβ42, we observed sex interactions at one previous and one novel locus: rs316341 within SERPINB1 (p = 0.04) and rs13115400 near LINC00290 (p = 0.002). These loci showed stronger associations among females (β = - 0.03, p = 4.25 × 10-8; β = 0.03, p = 3.97 × 10-8) than males (β = - 0.02, p = 0.009; β = 0.01, p = 0.20). Higher levels of expression of SERPINB1, SERPINB6, and SERPINB9 in PFC was associated with higher levels of amyloidosis among females (corrected p values  0.38). In total tau, we observed a sex interaction at a previous locus, rs1393060 proximal to GMNC (p = 0.004), driven by a stronger association among females (β = 0.05, p = 4.57 × 10-10) compared to males (β = 0.02, p = 0.03). There was also a sex-specific association between rs1393060 and tangle density at autopsy (pfemale = 0.047; pmale = 0.96), and higher levels of expression of two genes within this locus were associated with lower tangle density among females (OSTN p = 0.006; CLDN16 p = 0.002) but not males (p ≥ 0.32). Results suggest a female-specific role for SERPINB1 in amyloidosis and for OSTN and CLDN16 in tau pathology. Sex-specific genetic analyses may improve understanding of AD's genetic architecture.

Published
2018

41. LPA Variants are Associated with Residual Cardiovascular Risk in Patients Receiving Statins

Author

Wei, Wei-Qi, Li, Xiaohui, Feng, Qiping, Kubo, Michiaki, Kullo, Iftikhar J, Peissig, Peggy L, Karlson, Elizabeth W, Jarvik, Gail P, Lee, Ming Ta Michael, Shang, Ning, Larson, Eric A, Edwards, Todd, Shaffer, Christian M, Mosley, Jonathan D, Maeda, Shiro, Horikoshi, Momoko, Ritchie, Marylyn, Williams, Marc S, Larson, Eric B, Crosslin, David R, Bland, Harris T, Pacheco, Jennifer A, Rasmussen-Torvik, Laura J, Cronkite, David, Hripcsak, George, Cox, Nancy J, Wilke, Russell A, Stein, C Michael, Rotter, Jerome I, Momozawa, Yukihide, Roden, Dan M, Krauss, Ronald M, and Denny, Joshua C

Subjects
Human Genome, Genetics, Clinical Research, Heart Disease, Atherosclerosis, Cardiovascular, Heart Disease - Coronary Heart Disease, Aetiology, 2.1 Biological and endogenous factors, Case-Control Studies, Coronary Disease, Databases, Genetic, Dyslipidemias, Electronic Health Records, Gene Frequency, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Lipoprotein(a), Phenotype, Polymorphism, Single Nucleotide, Risk Assessment, Risk Factors, Time Factors, Treatment Outcome, cholesterol, coronary disease, electronic health records, hydroxymethylglutaryl-CoA, LDL reductase inhibitors, lysophosphatidic acid, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Public Health and Health Services, Cardiovascular System & Hematology
Abstract

BackgroundCoronary heart disease (CHD) is a leading cause of death globally. Although therapy with statins decreases circulating levels of low-density lipoprotein cholesterol and the incidence of CHD, additional events occur despite statin therapy in some individuals. The genetic determinants of this residual cardiovascular risk remain unknown.MethodsWe performed a 2-stage genome-wide association study of CHD events during statin therapy. We first identified 3099 cases who experienced CHD events (defined as acute myocardial infarction or the need for coronary revascularization) during statin therapy and 7681 controls without CHD events during comparable intensity and duration of statin therapy from 4 sites in the Electronic Medical Records and Genomics Network. We then sought replication of candidate variants in another 160 cases and 1112 controls from a fifth Electronic Medical Records and Genomics site, which joined the network after the initial genome-wide association study. Finally, we performed a phenome-wide association study for other traits linked to the most significant locus.ResultsThe meta-analysis identified 7 single nucleotide polymorphisms at a genome-wide level of significance within the LPA/PLG locus associated with CHD events on statin treatment. The most significant association was for an intronic single nucleotide polymorphism within LPA/PLG (rs10455872; minor allele frequency, 0.069; odds ratio, 1.58; 95% confidence interval, 1.35-1.86; P=2.6×10-10). In the replication cohort, rs10455872 was also associated with CHD events (odds ratio, 1.71; 95% confidence interval, 1.14-2.57; P=0.009). The association of this single nucleotide polymorphism with CHD events was independent of statin-induced change in low-density lipoprotein cholesterol (odds ratio, 1.62; 95% confidence interval, 1.17-2.24; P=0.004) and persisted in individuals with low-density lipoprotein cholesterol ≤70 mg/dL (odds ratio, 2.43; 95% confidence interval, 1.18-4.75; P=0.015). A phenome-wide association study supported the effect of this region on coronary heart disease and did not identify noncardiovascular phenotypes.ConclusionsGenetic variations at the LPA locus are associated with CHD events during statin therapy independently of the extent of low-density lipoprotein cholesterol lowering. This finding provides support for exploring strategies targeting circulating concentrations of lipoprotein(a) to reduce CHD events in patients receiving statins.

Published
2018

42. Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees

Author

Jun, Goo, Manning, Alisa, Almeida, Marcio, Zawistowski, Matthew, Wood, Andrew R, Teslovich, Tanya M, Fuchsberger, Christian, Feng, Shuang, Cingolani, Pablo, Gaulton, Kyle J, Dyer, Thomas, Blackwell, Thomas W, Chen, Han, Chines, Peter S, Choi, Sungkyoung, Churchhouse, Claire, Fontanillas, Pierre, King, Ryan, Lee, SungYoung, Lincoln, Stephen E, Trubetskoy, Vasily, DePristo, Mark, Fingerlin, Tasha, Grossman, Robert, Grundstad, Jason, Heath, Alison, Kim, Jayoun, Kim, Young Jin, Laramie, Jason, Lee, Jaehoon, Li, Heng, Liu, Xuanyao, Livne, Oren, Locke, Adam E, Maller, Julian, Mazur, Alexander, Morris, Andrew P, Pollin, Toni I, Ragona, Derek, Reich, David, Rivas, Manuel A, Scott, Laura J, Sim, Xueling, Tearle, Rick G, Teo, Yik Ying, Williams, Amy L, Zöllner, Sebastian, Curran, Joanne E, Peralta, Juan, Akolkar, Beena, Bell, Graeme I, Burtt, Noël P, Cox, Nancy J, Florez, Jose C, Hanis, Craig L, McKeon, Catherine, Mohlke, Karen L, Seielstad, Mark, Wilson, James G, Atzmon, Gil, Below, Jennifer E, Dupuis, Josée, Nicolae, Dan L, Lehman, Donna, Park, Taesung, Won, Sungho, Sladek, Robert, Altshuler, David, McCarthy, Mark I, Duggirala, Ravindranath, Boehnke, Michael, Frayling, Timothy M, Abecasis, Gonçalo R, and Blangero, John

Subjects
Diabetes, Genetics, Human Genome, Clinical Research, Obesity, Genetic Testing, 2.1 Biological and endogenous factors, Aetiology, Metabolic and endocrine, Diabetes Mellitus, Type 2, Family Health, Female, Gene Frequency, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Genotype, Humans, Male, Mexican Americans, Pedigree, Phenotype, Quantitative Trait Loci, Whole Genome Sequencing, genetics, sequencing, type 2 diabetes, eQTL, rare variants
Abstract

A major challenge in evaluating the contribution of rare variants to complex disease is identifying enough copies of the rare alleles to permit informative statistical analysis. To investigate the contribution of rare variants to the risk of type 2 diabetes (T2D) and related traits, we performed deep whole-genome analysis of 1,034 members of 20 large Mexican-American families with high prevalence of T2D. If rare variants of large effect accounted for much of the diabetes risk in these families, our experiment was powered to detect association. Using gene expression data on 21,677 transcripts for 643 pedigree members, we identified evidence for large-effect rare-variant cis-expression quantitative trait loci that could not be detected in population studies, validating our approach. However, we did not identify any rare variants of large effect associated with T2D, or the related traits of fasting glucose and insulin, suggesting that large-effect rare variants account for only a modest fraction of the genetic risk of these traits in this sample of families. Reliable identification of large-effect rare variants will require larger samples of extended pedigrees or different study designs that further enrich for such variants.

Published
2018

43. Discerning asthma endotypes through comorbidity mapping

Author

Jia, Gengjie, Zhong, Xue, Im, Hae Kyung, Schoettler, Nathan, Pividori, Milton, Hogarth, D. Kyle, Sperling, Anne I., White, Steven R., Naureckas, Edward T., Lyttle, Christopher S., Terao, Chikashi, Kamatani, Yoichiro, Akiyama, Masato, Matsuda, Koichi, Kubo, Michiaki, Cox, Nancy J., Ober, Carole, Rzhetsky, Andrey, and Solway, Julian

Published
2022
Full Text
View/download PDF

47. EndoPRS: Incorporating Endophenotype Information to Improve Polygenic Risk Scores for Clinical Endpoints

Author

Kharitonova, Elena V., primary, Sun, Quan, additional, Ockerman, Frank, additional, Chen, Brian, additional, Zhou, Laura Y., additional, Cao, Hongyuan, additional, Mathias, Rasika A., additional, Auer, Paul L., additional, Ober, Carole, additional, Raffield, Laura M., additional, Reiner, Alexander P., additional, Cox, Nancy J., additional, Kelada, Samir, additional, Tao, Ran, additional, and Li, Yun, additional

Published
2024
Full Text
View/download PDF

48. Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis

Author

Yang, Fan, Wang, Jiebiao, Consortium, The GTEx, Pierce, Brandon L, Chen, Lin S, Aguet, François, Ardlie, Kristin G, Cummings, Beryl B, Gelfand, Ellen T, Getz, Gad, Hadley, Kane, Handsaker, Robert E, Huang, Katherine H, Kashin, Seva, Karczewski, Konrad J, Lek, Monkol, Li, Xiao, MacArthur, Daniel G, Nedzel, Jared L, Nguyen, Duyen T, Noble, Michael S, Segrè, Ayellet V, Trowbridge, Casandra A, Tukiainen, Taru, Abell, Nathan S, Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Battle, Alexis, Bogu, Gireesh K, Brown, Andrew, Brown, Christopher D, Castel, Stephane E, Chiang, Colby, Conrad, Donald F, Cox, Nancy J, Damani, Farhan N, Davis, Joe R, Delaneau, Olivier, Dermitzakis, Emmanouil T, Engelhardt, Barbara E, Eskin, Eleazar, Ferreira, Pedro G, Frésard, Laure, Gamazon, Eric R, Garrido-Martín, Diego, Gewirtz, Ariel DH, Gliner, Genna, Gloudemans, Michael J, Guigo, Roderic, Hall, Ira M, Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Im, Hae Kyung, Jo, Brian, Kang, Eun Yong, Kim, Yungil, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I, McDowell, Ian C, Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B, Muñoz-Aguirre, Manuel, Ndungu, Anne W, Nicolae, Dan L, Nobel, Andrew B, Oliva, Meritxell, Ongen, Halit, Palowitch, John J, Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J, Peterson, Christine B, Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Saha, Ashis, Sammeth, Michael, Scott, Alexandra J, Shabalin, Andrey A, Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E, Strober, Benjamin J, Sul, Jae Hoon, Tsang, Emily K, Urbut, Sarah, van de Bunt, Martijn, and Wang, Gao

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Biotechnology, Human Genome, 2.1 Biological and endogenous factors, Underpinning research, Aetiology, 1.1 Normal biological development and functioning, Generic health relevance, Good Health and Well Being, Databases, Genetic, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, Genetic Predisposition to Disease, Genome-Wide Association Study, Genomics, Humans, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Selection, Genetic, Tissue Distribution, GTEx Consortium, Medical and Health Sciences, Bioinformatics
Abstract

The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (cis-eQTLs). More research is needed to identify effects of genetic variation on distant genes (trans-eQTLs) and understand their biological mechanisms. One common trans-eQTLs mechanism is "mediation" by a local (cis) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are "cis-mediators" of trans-eQTLs, including those "cis-hubs" involved in regulation of many trans-genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans-eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis-mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis-hubs and trans-eQTL regulation across tissue types.

Published
2017

49. Co-expression networks reveal the tissue-specific regulation of transcription and splicing

Author

Saha, Ashis, Kim, Yungil, Gewirtz, Ariel DH, Jo, Brian, Gao, Chuan, McDowell, Ian C, Consortium, The GTEx, Engelhardt, Barbara E, Battle, Alexis, Aguet, François, Ardlie, Kristin G, Cummings, Beryl B, Gelfand, Ellen T, Getz, Gad, Hadley, Kane, Handsaker, Robert E, Huang, Katherine H, Kashin, Seva, Karczewski, Konrad J, Lek, Monkol, Li, Xiao, MacArthur, Daniel G, Nedzel, Jared L, Nguyen, Duyen T, Noble, Michael S, Segrè, Ayellet V, Trowbridge, Casandra A, Tukiainen, Taru, Abell, Nathan S, Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Bogu, Gireesh K, Brown, Andrew, Brown, Christopher D, Castel, Stephane E, Chen, Lin S, Chiang, Colby, Conrad, Donald F, Cox, Nancy J, Damani, Farhan N, Davis, Joe R, Delaneau, Olivier, Dermitzakis, Emmanouil T, Eskin, Eleazar, Ferreira, Pedro G, Frésard, Laure, Gamazon, Eric R, Garrido-Martín, Diego, Gliner, Genna, Gloudemans, Michael J, Guigo, Roderic, Hall, Ira M, Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Im, Hae Kyung, Kang, Eun Yong, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I, Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B, Muñoz-Aguirre, Manuel, Ndungu, Anne W, Nicolae, Dan L, Nobel, Andrew B, Oliva, Meritxell, Ongen, Halit, Palowitch, John J, Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J, Peterson, Christine B, Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Sammeth, Michael, Scott, Alexandra J, Shabalin, Andrey A, Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E, Strober, Benjamin J, and Sul, Jae Hoon

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Biotechnology, Human Genome, 1.1 Normal biological development and functioning, 2.1 Biological and endogenous factors, Generic health relevance, Bayes Theorem, Databases, Genetic, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, Genotyping Techniques, Humans, Organ Specificity, Polymorphism, Single Nucleotide, RNA Splicing, Sequence Analysis, RNA, GTEx Consortium, Medical and Health Sciences, Bioinformatics
Abstract

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.

Published
2017

50. Dynamic landscape and regulation of RNA editing in mammals

Author

Aguet, François, Ardlie, Kristin G, Cummings, Beryl B, Gelfand, Ellen T, Getz, Gad, Hadley, Kane, Handsaker, Robert E, Huang, Katherine H, Kashin, Seva, Karczewski, Konrad J, Lek, Monkol, Li, Xiao, MacArthur, Daniel G, Nedzel, Jared L, Nguyen, Duyen T, Noble, Michael S, Segrè, Ayellet V, Trowbridge, Casandra A, Tukiainen, Taru, Abell, Nathan S, Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Battle, Alexis, Bogu, Gireesh K, Brown, Andrew, Brown, Christopher D, Castel, Stephane E, Chen, Lin S, Chiang, Colby, Conrad, Donald F, Cox, Nancy J, Damani, Farhan N, Davis, Joe R, Delaneau, Olivier, Dermitzakis, Emmanouil T, Engelhardt, Barbara E, Eskin, Eleazar, Ferreira, Pedro G, Frésard, Laure, Gamazon, Eric R, Garrido-Martín, Diego, Gewirtz, Ariel DH, Gliner, Genna, Gloudemans, Michael J, Guigo, Roderic, Hall, Ira M, Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Kyung Im, Hae, Jo, Brian, Yong Kang, Eun, Kim, Yungil, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Gen, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I, McDowell, Ian C, Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B, Muñoz-Aguirre, Manuel, Ndungu, Anne W, Nicolae, Dan L, Nobel, Andrew B, Oliva, Meritxell, Ongen, Halit, Palowitch, John J, Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J, Peterson, Christine B, Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Saha, Ashis, Sammeth, Michael, Scott, Alexandra J, Shabalin, Andrey A, Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E, Strober, Benjamin J, Sul, Jae Hoon, Tsang, Emily K, Urbut, Sarah, van de Bunt, Martijn, Wang, Gao, Wen, Xiaoquan, Wright, Fred A, Xi, Hualin S, Yeger-Lotem, Esti, and Zappala, Zachary

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Adenosine Deaminase, Animals, Female, Genotype, HEK293 Cells, Humans, Male, Mice, Muscles, Nuclear Proteins, Organ Specificity, Primates, Proteolysis, RNA Editing, RNA-Binding Proteins, Spatio-Temporal Analysis, Species Specificity, Transcriptome, GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, Biospecimen Collection Source Site—RPCI, Biospecimen Core Resource—VARI, Brain Bank Repository—University of Miami Brain Endowment Bank, Leidos Biomedical—Project Management, ELSI Study, Genome Browser Data Integration &Visualization—EBI, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, General Science & Technology
Abstract

Adenosine-to-inosine (A-to-I) RNA editing is a conserved post-transcriptional mechanism mediated by ADAR enzymes that diversifies the transcriptome by altering selected nucleotides in RNA molecules. Although many editing sites have recently been discovered, the extent to which most sites are edited and how the editing is regulated in different biological contexts are not fully understood. Here we report dynamic spatiotemporal patterns and new regulators of RNA editing, discovered through an extensive profiling of A-to-I RNA editing in 8,551 human samples (representing 53 body sites from 552 individuals) from the Genotype-Tissue Expression (GTEx) project and in hundreds of other primate and mouse samples. We show that editing levels in non-repetitive coding regions vary more between tissues than editing levels in repetitive regions. Globally, ADAR1 is the primary editor of repetitive sites and ADAR2 is the primary editor of non-repetitive coding sites, whereas the catalytically inactive ADAR3 predominantly acts as an inhibitor of editing. Cross-species analysis of RNA editing in several tissues revealed that species, rather than tissue type, is the primary determinant of editing levels, suggesting stronger cis-directed regulation of RNA editing for most sites, although the small set of conserved coding sites is under stronger trans-regulation. In addition, we curated an extensive set of ADAR1 and ADAR2 targets and showed that many editing sites display distinct tissue-specific regulation by the ADAR enzymes in vivo. Further analysis of the GTEx data revealed several potential regulators of editing, such as AIMP2, which reduces editing in muscles by enhancing the degradation of the ADAR proteins. Collectively, our work provides insights into the complex cis- and trans-regulation of A-to-I editing.

Published
2017

Catalog

Books, media, physical & digital resources
See catalog results