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1. Polarized macrophages regulate fibro/adipogenic progenitor (FAP) adipogenesis through exosomes

Author

Mengyao Liu, Martin Ng, Tuan Phu, Laura Bouchareychas, Brian T. Feeley, Hubert T. Kim, Robert L. Raffai, and Xuhui Liu

Subjects
Macrophage polarization, Exosomes, Fibro/adipogenic progenitors, Beige fat differentiation, Muscle fatty infiltration, Rotator cuff injury, Medicine (General), R5-920, Biochemistry, QD415-436
Abstract

Abstract Background Macrophage polarization has been observed in the process of muscle injuries including rotator cuff (RC) muscle atrophy and fatty infiltration after large tendon tears. In our previous study, we showed that fibrogenesis and white adipogenesis of muscle residential fibro/adipogenic progenitors (FAPs) cause fibrosis and fatty infiltration and that brown/beige adipogenesis of FAPs promotes rotator cuff muscle regeneration. However, how polarized macrophages and their exosomes regulate FAP differentiation remains unknown. Methods We cultured FAPs with M0, M1, and M2 macrophages or 2 × 109 exosomes derived from M0, M1 and M2 with and without GW4869, an exosome inhibitor. In vivo, M0, M1, and M2 macrophages were transplanted or purified macrophage exosomes (M0, M1, M2) were injected into supraspinatus muscle (SS) after massive tendon tears in mice (n = 6). SS were harvested at six weeks after surgery to evaluate the level of muscle atrophy and fatty infiltration. Results Our results showed that M2 rather than M0 or M1 macrophages stimulates brown/beige fat differentiation of FAPs. However, the effect of GW4869, the exosome inhibitor, diminished this effect. M2 exosomes also promoted FAP Beige differentiation in vitro. The transplantation of M2 macrophages reduced supraspinatus muscle atrophy and fatty infiltration. In vivo injections of M2 exosomes significantly reduced muscle atrophy and fatty infiltration in supraspinatus muscle. Conclusion Results from our study demonstrated that polarized macrophages directly regulated FAP differentiation through their exosomes and M2 macrophage-derived exosomes may serve as a novel treatment option for RC muscle atrophy and fatty infiltration.

Published
2023
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3. Label-free single-vesicle based surface enhanced Raman spectroscopy: A robust approach for investigating the biomolecular composition of small extracellular vesicles.

Author

Zirui Liu, Martin Ng, Siddharth Srivastava, Tieyi Li, Jun Liu, Tuan Anh Phu, Bogdan Mateescu, Yi-Ting Wang, Chia-Feng Tsai, Tao Liu, Robert L Raffai, and Ya-Hong Xie

Subjects
Medicine, Science
Abstract

Small extracellular vesicles (sEVs) are cell-released vesicles ranging from 30-150nm in size. They have garnered increasing attention because of their potential for both the diagnosis and treatment of disease. The diversity of sEVs derives from their biological composition and cargo content. Currently, the isolation of sEV subpopulations is primarily based on bio-physical and affinity-based approaches. Since a standardized definition for sEV subpopulations is yet to be fully established, it is important to further investigate the correlation between the biomolecular composition of sEVs and their physical properties. In this study, we employed a platform combining single-vesicle surface-enhanced Raman spectroscopy (SERS) and machine learning to examine individual sEVs isolated by size-exclusion chromatography (SEC). The biomolecular composition of each vesicle examined was reflected by its corresponding SERS spectral features (biomolecular "fingerprints"), with their roots in the composition of their collective Raman-active bonds. Origins of the SERS spectral features were validated through a comparative analysis between SERS and mass spectrometry (MS). SERS fingerprinting of individual vesicles was effective in overcoming the challenges posed by EV population averaging, allowing for the possibility of analyzing the variations in biomolecular composition between the vesicles of similar and/or different sizes. Using this approach, we uncovered that each of the size-based fractions of sEVs contained particles with predominantly similar SERS spectral features. Indeed, more than 84% of the vesicles residing within a particular group were clearly distinguishable from that of the other EV sub-populations, despite some spectral variations within each sub-population. Our results suggest the possibility that size-based EV fractionation methods produce samples where similarly eluted sEVs are correlated with their respective biochemical contents, as reflected by their SERS spectra. Our findings therefore highlight the possibility that the biogenesis and respective biological functionalities of the various sEV fractions may be inherently different.

Published
2024
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4. ApoE enhances mitochondrial metabolism via microRNA-142a/146a-regulated circuits that suppress hematopoiesis and inflammation in hyperlipidemia

Author

Tuan Anh Phu, Ngan K. Vu, Martin Ng, Alex S. Gao, Joshua S. Stoolman, Navdeep S. Chandel, and Robert L. Raffai

Subjects
CP: Metabolism, Biology (General), QH301-705.5
Abstract

Summary: Apolipoprotein E (ApoE) is recognized for its pleiotropic properties that suppress inflammation. We report that ApoE serves as a metabolic rheostat that regulates microRNA control of glycolytic and mitochondrial activity in myeloid cells and hematopoietic stem and progenitor cells (HSPCs). ApoE expression in myeloid cells increases microRNA-146a, which reduces nuclear factor κB (NF-κB)-driven GLUT1 expression and glycolytic activity. In contrast, ApoE expression reduces microRNA-142a, which increases carnitine palmitoyltransferase 1a (CPT1A) expression, fatty acid oxidation, and oxidative phosphorylation. Improved mitochondrial metabolism by ApoE expression causes an enrichment of tricarboxylic acid (TCA) cycle metabolites and nicotinamide adenine dinucleotide (NAD+) in macrophages. The study of mice with conditional ApoE expression supports the capacity of ApoE to foster microRNA-controlled immunometabolism. Modulation of microRNA-146a and -142a in the hematopoietic system of hyperlipidemic mice using RNA mimics and antagonists, respectively, improves mitochondrial metabolism, which suppresses inflammation and hematopoiesis. Our findings unveil microRNA regulatory circuits, controlled by ApoE, that exert metabolic control over hematopoiesis and inflammation in hyperlipidemia.

Published
2023
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5. ApoE expression in macrophages communicates immunometabolic signaling that controls hyperlipidemia‐driven hematopoiesis & inflammation via extracellular vesicles

Author

Tuan Anh Phu, Martin Ng, Ngan K. Vu, Alex S. Gao, and Robert L. Raffai

Subjects
ApoE, extracellular vesicles, immunometabolism, inflammation, macrophage, microRNA, Cytology, QH573-671
Abstract

Abstract While apolipoprotein E (apoE) expression by myeloid cells is recognized to control inflammation, whether such benefits can be communicated via extracellular vesicles is not known. Through the study of extracellular vesicles produced by macrophages derived from the bone marrow of Wildtype (WT‐BMDM‐EV) and ApoE deficient (EKO‐BMDM‐EV) mice, we uncovered a critical role for apoE expression in regulating their cell signaling properties. WT‐BMDM‐EV communicated anti‐inflammatory properties to recipient myeloid cells by increasing cellular levels of apoE and miR‐146a‐5p, that reduced NF‐κB signalling. They also downregulated cellular levels of miR‐142a‐3p, resulting in increased levels of its target carnitine palmitoyl transferase 1A (CPT1A) which improved fatty acid oxidation (FAO) and oxidative phosphorylation (OxPHOS) in recipient cells. Such favorable metabolic polarization enhanced cell‐surface MerTK levels and the phagocytic uptake of apoptotic cells. In contrast, EKO‐BMDM‐EV exerted opposite effects by reducing cellular levels of apoE and miR‐146a‐5p, which increased NF‐κB−driven GLUT1‐mediated glucose uptake, aerobic glycolysis, and oxidative stress. Furthermore, EKO‐BMDM‐EV increased cellular miR‐142a‐3p levels, which reduced CPT1A levels and impaired FAO and OxPHOS in recipient myeloid cells. When cultured with naïve CD4+ T lymphocytes, EKO‐BMDM‐EV drove their activation and proliferation, and fostered their transition to a Th1 phenotype. While infusions of WT‐BMDM‐EV into hyperlipidemic mice resolved inflammation, infusions of EKO‐BMDM‐EV increased hematopoiesis and drove inflammatory responses in myeloid cells and T lymphocytes. ApoE‐dependent immunometabolic signaling by macrophage extracellular vesicles was dependent on transcriptional axes controlled by miR‐146a‐5p and miR‐142a‐3p that could be reproduced by infusing miR‐146a mimics & miR‐142a antagonists into hyperlipidemic apoE‐deficient mice. Together, our findings unveil a novel property for apoE expression in macrophages that modulates the immunometabolic regulatory properties of their secreted extracellular vesicles.

Published
2023
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6. Coronary microvascular dysfunction: A review of recent progress and clinical implications

Author

Rajan Rehan, Andy Yong, Martin Ng, James Weaver, and Rajesh Puranik

Subjects
coronary microcirculation, microvascular angina, endothelial dysfunction, inflammation, INOCA, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

The coronary microcirculation plays a cardinal role in regulating coronary blood flow to meet the changing metabolic demands of the myocardium. Coronary microvascular dysfunction (CMD) refers to structural and functional remodeling of the coronary microcirculation. CMD plays a role in the pathogenesis of obstructive and non-obstructive coronary syndromes as well as myocardial diseases, including heart failure with preserved ejection fraction (HFpEF). Despite recent diagnostic advancements, CMD is often under-appreciated in clinical practice, and may allow for the development of novel therapeutic targets. This review explores the diagnosis and pathogenic role of CMD across a range of cardiovascular diseases, its prognostic significance, and the current therapeutic landscape.

Published
2023
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7. A polymorphism in the OPRM1 3′-untranslated region is associated with methadone efficacy in treating opioid dependence

Author

Crist, RC, Doyle, GA, Nelson, EC, Degenhardt, L, Martin, NG, Montgomery, GW, Saxon, AJ, Ling, W, and Berrettini, WH

Subjects
Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Brain Disorders, Drug Abuse (NIDA only), Clinical Research, Prescription Drug Abuse, Genetics, Clinical Trials and Supportive Activities, Substance Misuse, Neurosciences, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Good Health and Well Being, 3' Untranslated Regions, Adult, Alleles, Analgesics, Opioid, Australia, Buprenorphine, Buprenorphine, Naloxone Drug Combination, Female, Genotype, Humans, Male, Methadone, Narcotic Antagonists, Opiate Substitution Treatment, Opioid-Related Disorders, Polymorphism, Genetic, Receptors, Opioid, mu, White People, Pharmacology & Pharmacy, Pharmacology and pharmaceutical sciences
Abstract

The μ-opioid receptor (MOR) is the primary target of methadone and buprenorphine. The primary neuronal transcript of the OPRM1 gene, MOR-1, contains a ~13 kb 3' untranslated region with five common haplotypes in European-Americans. We analyzed the effects of these haplotypes on the percentage of opioid positive urine tests in European-Americans (n=582) during a 24-week, randomized, open-label trial of methadone or buprenorphine/naloxone (Suboxone) for the treatment of opioid dependence. A single haplotype, tagged by rs10485058, was significantly associated with patient urinalysis data in the methadone treatment group. Methadone patients with the A/A genotype at rs10485058 were less likely to have opioid-positive urine drug screens than those in the combined A/G and G/G genotypes group (relative risk=0.76, 95% confidence intervals=0.73-0.80, P=0.0064). Genotype at rs10485058 also predicted self-reported relapse rates in an independent population of Australian patients of European descent (n=1215) who were receiving opioid substitution therapy (P=0.003). In silico analysis predicted that miR-95-3p would interact with the G, but not the A allele of rs10485058. Luciferase assays indicated miR-95-3p decreased reporter activity of constructs containing the G, but not the A allele of rs10485058, suggesting a potential mechanism for the observed pharmacogenetic effect. These findings suggest that selection of a medication for opioid dependence based on rs10485058 genotype might improve outcomes in this ethnic group.

Published
2018

8. 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

9. Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group

Author

Schmaal, L, Hibar, DP, Sämann, PG, Hall, GB, Baune, BT, Jahanshad, N, Cheung, JW, van Erp, TGM, Bos, D, Ikram, MA, Vernooij, MW, Niessen, WJ, Tiemeier, H, Hofman, A, Wittfeld, K, Grabe, HJ, Janowitz, D, Bülow, R, Selonke, M, Völzke, H, Grotegerd, D, Dannlowski, U, Arolt, V, Opel, N, Heindel, W, Kugel, H, Hoehn, D, Czisch, M, Couvy-Duchesne, B, Rentería, ME, Strike, LT, Wright, MJ, Mills, NT, de Zubicaray, GI, McMahon, KL, Medland, SE, Martin, NG, Gillespie, NA, Goya-Maldonado, R, Gruber, O, Krämer, B, Hatton, SN, Lagopoulos, J, Hickie, IB, Frodl, T, Carballedo, A, Frey, EM, van Velzen, LS, Penninx, BWJH, van Tol, M-J, van der Wee, NJ, Davey, CG, Harrison, BJ, Mwangi, B, Cao, B, Soares, JC, Veer, IM, Walter, H, Schoepf, D, Zurowski, B, Konrad, C, Schramm, E, Normann, C, Schnell, K, Sacchet, MD, Gotlib, IH, MacQueen, GM, Godlewska, BR, Nickson, T, McIntosh, AM, Papmeyer, M, Whalley, HC, Hall, J, Sussmann, JE, Li, M, Walter, M, Aftanas, L, Brack, I, Bokhan, NA, Thompson, PM, and Veltman, DJ

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Clinical Sciences, Psychology, Major Depressive Disorder, Serious Mental Illness, Neurosciences, Biomedical Imaging, Depression, Basic Behavioral and Social Science, Mental Health, Brain Disorders, Clinical Research, Pediatric, Behavioral and Social Science, 2.3 Psychological, social and economic factors, Aetiology, 2.1 Biological and endogenous factors, Neurological, Mental health, Adolescent, Adult, Brain, Cerebral Cortex, Depressive Disorder, Major, Female, Frontal Lobe, Gray Matter, Gyrus Cinguli, Humans, Magnetic Resonance Imaging, Male, Neuroimaging, Prefrontal Cortex, Temporal Lobe, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

The neuro-anatomical substrates of major depressive disorder (MDD) are still not well understood, despite many neuroimaging studies over the past few decades. Here we present the largest ever worldwide study by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Major Depressive Disorder Working Group on cortical structural alterations in MDD. Structural T1-weighted brain magnetic resonance imaging (MRI) scans from 2148 MDD patients and 7957 healthy controls were analysed with harmonized protocols at 20 sites around the world. To detect consistent effects of MDD and its modulators on cortical thickness and surface area estimates derived from MRI, statistical effects from sites were meta-analysed separately for adults and adolescents. Adults with MDD had thinner cortical gray matter than controls in the orbitofrontal cortex (OFC), anterior and posterior cingulate, insula and temporal lobes (Cohen's d effect sizes: -0.10 to -0.14). These effects were most pronounced in first episode and adult-onset patients (>21 years). Compared to matched controls, adolescents with MDD had lower total surface area (but no differences in cortical thickness) and regional reductions in frontal regions (medial OFC and superior frontal gyrus) and primary and higher-order visual, somatosensory and motor areas (d: -0.26 to -0.57). The strongest effects were found in recurrent adolescent patients. This highly powered global effort to identify consistent brain abnormalities showed widespread cortical alterations in MDD patients as compared to controls and suggests that MDD may impact brain structure in a highly dynamic way, with different patterns of alterations at different stages of life.

Published
2017

10. Subcortical brain structure and suicidal behaviour in major depressive disorder: a meta-analysis from the ENIGMA-MDD working group.

Author

Rentería, ME, Schmaal, L, Hibar, DP, Couvy-Duchesne, B, Strike, LT, Mills, NT, de Zubicaray, GI, McMahon, KL, Medland, SE, Gillespie, NA, Hatton, SN, Lagopoulos, J, Veltman, DJ, van der Wee, N, van Erp, TGM, Wittfeld, K, Grabe, HJ, Block, A, Hegenscheid, K, Völzke, H, Veer, IM, Walter, H, Schnell, K, Schramm, E, Normann, C, Schoepf, D, Konrad, C, Zurowski, B, Godlewska, BR, Cowen, PJ, Penninx, BWJH, Jahanshad, N, Thompson, PM, Wright, MJ, Martin, NG, Christensen, H, and Hickie, IB

Subjects
Brain, Humans, Magnetic Resonance Imaging, Prevalence, Risk Factors, Suicide, Depressive Disorder, Major, Adult, Aged, Middle Aged, Female, Male, Young Adult, Suicidal Ideation, Neuroimaging, Depressive Disorder, Major, Psychology, Clinical Sciences, Public Health and Health Services
Abstract

The aetiology of suicidal behaviour is complex, and knowledge about its neurobiological mechanisms is limited. Neuroimaging methods provide a noninvasive approach to explore the neural correlates of suicide vulnerability in vivo. The ENIGMA-MDD Working Group is an international collaboration evaluating neuroimaging and clinical data from thousands of individuals collected by research groups from around the world. Here we present analyses in a subset sample (n=3097) for whom suicidality data were available. Prevalence of suicidal symptoms among major depressive disorder (MDD) cases ranged between 29 and 69% across cohorts. We compared mean subcortical grey matter volumes, lateral ventricle volumes and total intracranial volume (ICV) in MDD patients with suicidal symptoms (N=451) vs healthy controls (N=1996) or MDD patients with no suicidal symptoms (N=650). MDD patients reporting suicidal plans or attempts showed a smaller ICV (P=4.12 × 10-3) or a 2.87% smaller volume compared with controls (Cohen's d=-0.284). In addition, we observed a nonsignificant trend in which MDD cases with suicidal symptoms had smaller subcortical volumes and larger ventricular volumes compared with controls. Finally, no significant differences (P=0.28-0.97) were found between MDD patients with and those without suicidal symptoms for any of the brain volume measures. This is by far the largest neuroimaging meta-analysis of suicidal behaviour in MDD to date. Our results did not replicate previous reports of association between subcortical brain structure and suicidality and highlight the need for collecting better-powered imaging samples and using improved suicidality assessment instruments.

Published
2017

11. Comprehensive Sex-Stratified Genetic Analysis of 28 Blood Biomarkers and Depression Reveals a Significant Association between Depression and Low Levels of Total Protein in Females.

Author

Kiewa, J, Meltzer-Brody, S, Milgrom, J, Guintivano, J, Hickie, IB, Whiteman, DC, Olsen, CM, Medland, SE, Martin, NG, Wray, NR, Byrne, EM, Kiewa, J, Meltzer-Brody, S, Milgrom, J, Guintivano, J, Hickie, IB, Whiteman, DC, Olsen, CM, Medland, SE, Martin, NG, Wray, NR, and Byrne, EM

Abstract

INTRODUCTION: Major depression (MD) is more common amongst women than men, and MD episodes have been associated with fluctuations in reproductive hormones amongst women. To investigate biological underpinnings of heterogeneity in MD, the associations between depression, stratified by sex and including perinatal depression (PND), and blood biomarkers, using UK Biobank (UKB) data, were evaluated, and extended to include the association of depression with biomarker polygenic scores (PGS), generated as proxy for each biomarker. METHOD: Using female (N = 39,761) and male (N = 38,821) UKB participants, lifetime MD and PND were tested for association with 28 blood biomarkers. A GWAS was conducted for each biomarker and genetic correlations with depression subgroups were estimated. Using independent data from the Australian Genetics of Depression Study, PGS were constructed for each biomarker, and tested for association with depression status (n [female cases/controls] = 9,006/6,442; n [male cases/controls] = 3,106/6,222). Regions of significant local genetic correlation between depression subgroups and biomarkers highlighted by the PGS analysis were identified. RESULTS: Depression in females was significantly associated with levels of twelve biomarkers, including total protein (OR = 0.90, CI = [0.86, 0.94], p = 3.9 × 10-6) and vitamin D (OR = 0.94, CI = [0.90, 0.97], p = 2.6 × 10-4), and PND with five biomarker levels, also including total protein (OR = 0.88, CI = [0.81, 0.96], p = 4.7 × 10-3). Depression in males was significantly associated with levels of eleven biomarkers. In the independent Australian Genetics of Depression Study, PGS analysis found significant associations for female depression and PND with total protein (female depression: OR = 0.93, CI = [0.88, 0.98], p = 3.6 × 10-3; PND: OR = 0.91, CI = [0.86, 0.96], p = 1.1 × 10-3), as well as with vitamin D (female depression: OR = 0.93, CI = [0.89, 0.97], p = 2.0 × 10-3; PND: OR = 0.92, CI = [0.87, 0.97], p = 1.

Published
2024

12. Understanding the genetic complexity of puberty timing across the allele frequency spectrum

Author

Kentistou, KA, Kaisinger, LR, Stankovic, S, Vaudel, M, Mendes de Oliveira, E, Messina, A, Walters, RG, Liu, X, Busch, AS, Helgason, H, Thompson, DJ, Santoni, F, Petricek, KM, Zouaghi, Y, Huang-Doran, I, Gudbjartsson, DF, Bratland, E, Lin, K, Gardner, EJ, Zhao, Y, Jia, RY, Terao, C, Riggan, MJ, Bolla, MK, Yazdanpanah, M, Yazdanpanah, N, Bradfield, JP, Broer, L, Campbell, A, Chasman, DI, Cousminer, DL, Franceschini, N, Franke, LH, Girotto, G, He, C, Järvelin, M-R, Joshi, PK, Kamatani, Y, Karlsson, R, Luan, J, Lunetta, KL, Mägi, R, Mangino, M, Medland, SE, Meisinger, C, Noordam, R, Nutile, T, Concas, MP, Polašek, O, Porcu, E, Ring, SM, Sala, C, Smith, AV, Tanaka, T, van der Most, PJ, Vitart, V, Wang, CA, Willemsen, G, Zygmunt, M, Ahearn, TU, Andrulis, IL, Anton-Culver, H, Antoniou, AC, Auer, PL, Barnes, CLK, Beckmann, MW, Berrington de Gonzalez, A, Bogdanova, NV, Bojesen, SE, Brenner, H, Buring, JE, Canzian, F, Chang-Claude, J, Couch, FJ, Cox, A, Crisponi, L, Czene, K, Daly, MB, Demerath, EW, Dennis, J, Devilee, P, De Vivo, I, Dörk, T, Dunning, AM, Dwek, M, Eriksson, JG, Fasching, PA, Fernandez-Rhodes, L, Ferreli, L, Fletcher, O, Gago-Dominguez, M, García-Closas, M, García-Sáenz, JA, González-Neira, A, Grallert, H, Guénel, P, Haiman, CA, Hall, P, Hamann, U, Hakonarson, H, Hart, RJ, Hickey, M, Hooning, MJ, Hoppe, R, Hopper, JL, Hottenga, J-J, Hu, FB, Huebner, H, Hunter, DJ, ABCTB Investigators, Jernström, H, John, EM, Karasik, D, Khusnutdinova, EK, Kristensen, VN, Lacey, JV, Lambrechts, D, Launer, LJ, Lind, PA, Lindblom, A, Magnusson, PKE, Mannermaa, A, McCarthy, MI, Meitinger, T, Menni, C, Michailidou, K, Millwood, IY, Milne, RL, Montgomery, GW, Nevanlinna, H, Nolte, IM, Nyholt, DR, Obi, N, O'Brien, KM, Offit, K, Oldehinkel, AJ, Ostrowski, SR, Palotie, A, Pedersen, OB, Peters, A, Pianigiani, G, Plaseska-Karanfilska, D, Pouta, A, Pozarickij, A, Radice, P, Rennert, G, Rosendaal, FR, Ruggiero, D, Saloustros, E, Sandler, DP, Schipf, S, Schmidt, CO, Schmidt, MK, Small, K, Spedicati, B, Stampfer, M, Stone, J, Tamimi, RM, Teras, LR, Tikkanen, E, Turman, C, Vachon, CM, Wang, Q, Winqvist, R, Wolk, A, Zemel, BS, Zheng, W, van Dijk, KW, Alizadeh, BZ, Bandinelli, S, Boerwinkle, E, Boomsma, DI, Ciullo, M, Chenevix-Trench, G, Cucca, F, Esko, T, Gieger, C, Grant, SFA, Gudnason, V, Hayward, C, Kolčić, I, Kraft, P, Lawlor, DA, Martin, NG, Nøhr, EA, Pedersen, NL, Pennell, CE, Ridker, PM, Robino, A, Snieder, H, Sovio, U, Spector, TD, Stöckl, D, Sudlow, C, Timpson, NJ, Toniolo, D, Uitterlinden, A, Ulivi, S, Völzke, H, Wareham, NJ, Widen, E, Wilson, JF, Lifelines Cohort Study, Danish Blood Donor Study, Ovarian Cancer Association Consortium, Breast Cancer Association Consortium, Biobank Japan Project, China Kadoorie Biobank Collaborative Group, Pharoah, PDP, Li, L, Easton, DF, Njølstad, PR, Sulem, P, Murabito, JM, Murray, A, Manousaki, D, Juul, A, Erikstrup, C, Stefansson, K, Horikoshi, M, Chen, Z, Farooqi, IS, Pitteloud, N, Johansson, S, Day, FR, Perry, JRB, Ong, KK, Kentistou, KA, Kaisinger, LR, Stankovic, S, Vaudel, M, Mendes de Oliveira, E, Messina, A, Walters, RG, Liu, X, Busch, AS, Helgason, H, Thompson, DJ, Santoni, F, Petricek, KM, Zouaghi, Y, Huang-Doran, I, Gudbjartsson, DF, Bratland, E, Lin, K, Gardner, EJ, Zhao, Y, Jia, RY, Terao, C, Riggan, MJ, Bolla, MK, Yazdanpanah, M, Yazdanpanah, N, Bradfield, JP, Broer, L, Campbell, A, Chasman, DI, Cousminer, DL, Franceschini, N, Franke, LH, Girotto, G, He, C, Järvelin, M-R, Joshi, PK, Kamatani, Y, Karlsson, R, Luan, J, Lunetta, KL, Mägi, R, Mangino, M, Medland, SE, Meisinger, C, Noordam, R, Nutile, T, Concas, MP, Polašek, O, Porcu, E, Ring, SM, Sala, C, Smith, AV, Tanaka, T, van der Most, PJ, Vitart, V, Wang, CA, Willemsen, G, Zygmunt, M, Ahearn, TU, Andrulis, IL, Anton-Culver, H, Antoniou, AC, Auer, PL, Barnes, CLK, Beckmann, MW, Berrington de Gonzalez, A, Bogdanova, NV, Bojesen, SE, Brenner, H, Buring, JE, Canzian, F, Chang-Claude, J, Couch, FJ, Cox, A, Crisponi, L, Czene, K, Daly, MB, Demerath, EW, Dennis, J, Devilee, P, De Vivo, I, Dörk, T, Dunning, AM, Dwek, M, Eriksson, JG, Fasching, PA, Fernandez-Rhodes, L, Ferreli, L, Fletcher, O, Gago-Dominguez, M, García-Closas, M, García-Sáenz, JA, González-Neira, A, Grallert, H, Guénel, P, Haiman, CA, Hall, P, Hamann, U, Hakonarson, H, Hart, RJ, Hickey, M, Hooning, MJ, Hoppe, R, Hopper, JL, Hottenga, J-J, Hu, FB, Huebner, H, Hunter, DJ, ABCTB Investigators, Jernström, H, John, EM, Karasik, D, Khusnutdinova, EK, Kristensen, VN, Lacey, JV, Lambrechts, D, Launer, LJ, Lind, PA, Lindblom, A, Magnusson, PKE, Mannermaa, A, McCarthy, MI, Meitinger, T, Menni, C, Michailidou, K, Millwood, IY, Milne, RL, Montgomery, GW, Nevanlinna, H, Nolte, IM, Nyholt, DR, Obi, N, O'Brien, KM, Offit, K, Oldehinkel, AJ, Ostrowski, SR, Palotie, A, Pedersen, OB, Peters, A, Pianigiani, G, Plaseska-Karanfilska, D, Pouta, A, Pozarickij, A, Radice, P, Rennert, G, Rosendaal, FR, Ruggiero, D, Saloustros, E, Sandler, DP, Schipf, S, Schmidt, CO, Schmidt, MK, Small, K, Spedicati, B, Stampfer, M, Stone, J, Tamimi, RM, Teras, LR, Tikkanen, E, Turman, C, Vachon, CM, Wang, Q, Winqvist, R, Wolk, A, Zemel, BS, Zheng, W, van Dijk, KW, Alizadeh, BZ, Bandinelli, S, Boerwinkle, E, Boomsma, DI, Ciullo, M, Chenevix-Trench, G, Cucca, F, Esko, T, Gieger, C, Grant, SFA, Gudnason, V, Hayward, C, Kolčić, I, Kraft, P, Lawlor, DA, Martin, NG, Nøhr, EA, Pedersen, NL, Pennell, CE, Ridker, PM, Robino, A, Snieder, H, Sovio, U, Spector, TD, Stöckl, D, Sudlow, C, Timpson, NJ, Toniolo, D, Uitterlinden, A, Ulivi, S, Völzke, H, Wareham, NJ, Widen, E, Wilson, JF, Lifelines Cohort Study, Danish Blood Donor Study, Ovarian Cancer Association Consortium, Breast Cancer Association Consortium, Biobank Japan Project, China Kadoorie Biobank Collaborative Group, Pharoah, PDP, Li, L, Easton, DF, Njølstad, PR, Sulem, P, Murabito, JM, Murray, A, Manousaki, D, Juul, A, Erikstrup, C, Stefansson, K, Horikoshi, M, Chen, Z, Farooqi, IS, Pitteloud, N, Johansson, S, Day, FR, Perry, JRB, and Ong, KK

Abstract

Pubertal timing varies considerably and is associated with later health outcomes. We performed multi-ancestry genetic analyses on ~800,000 women, identifying 1,080 signals for age at menarche. Collectively, these explained 11% of trait variance in an independent sample. Women at the top and bottom 1% of polygenic risk exhibited ~11 and ~14-fold higher risks of delayed and precocious puberty, respectively. We identified several genes harboring rare loss-of-function variants in ~200,000 women, including variants in ZNF483, which abolished the impact of polygenic risk. Variant-to-gene mapping approaches and mouse gonadotropin-releasing hormone neuron RNA sequencing implicated 665 genes, including an uncharacterized G-protein-coupled receptor, GPR83, which amplified the signaling of MC3R, a key nutritional sensor. Shared signals with menopause timing at genes involved in DNA damage response suggest that the ovarian reserve might signal centrally to trigger puberty. We also highlight body size-dependent and independent mechanisms that potentially link reproductive timing to later life disease.

Published
2024

13. Phase 2 of extracellular RNA communication consortium charts next-generation approaches for extracellular RNA research

Author

Bogdan Mateescu, Jennifer C. Jones, Roger P. Alexander, Eric Alsop, Ji Yeong An, Mohammad Asghari, Alex Boomgarden, Laura Bouchareychas, Alfonso Cayota, Hsueh-Chia Chang, Al Charest, Daniel T. Chiu, Robert J. Coffey, Saumya Das, Peter De Hoff, Andrew deMello, Crislyn D’Souza-Schorey, David Elashoff, Kiarash R. Eliato, Jeffrey L. Franklin, David J. Galas, Mark B. Gerstein, Ionita H. Ghiran, David B. Go, Stephen Gould, Tristan R. Grogan, James N. Higginbotham, Florian Hladik, Tony Jun Huang, Xiaoye Huo, Elizabeth Hutchins, Dennis K. Jeppesen, Tijana Jovanovic-Talisman, Betty Y.S. Kim, Sung Kim, Kyoung-Mee Kim, Yong Kim, Robert R. Kitchen, Vaughan Knouse, Emily L. LaPlante, Carlito B. Lebrilla, L. James Lee, Kathleen M. Lennon, Guoping Li, Feng Li, Tieyi Li, Tao Liu, Zirui Liu, Adam L. Maddox, Kyle McCarthy, Bessie Meechoovet, Nalin Maniya, Yingchao Meng, Aleksandar Milosavljevic, Byoung-Hoon Min, Amber Morey, Martin Ng, John Nolan, Getulio P. De Oliveira Junior, Michael E. Paulaitis, Tuan Anh Phu, Robert L. Raffai, Eduardo Reátegui, Matthew E. Roth, David A. Routenberg, Joel Rozowsky, Joseph Rufo, Satyajyoti Senapati, Sigal Shachar, Himani Sharma, Anil K. Sood, Stavros Stavrakis, Alessandra Stürchler, Muneesh Tewari, Juan P. Tosar, Alexander K. Tucker-Schwartz, Andrey Turchinovich, Nedyalka Valkov, Kendall Van Keuren-Jensen, Kasey C. Vickers, Lucia Vojtech, Wyatt N. Vreeland, Ceming Wang, Kai Wang, ZeYu Wang, Joshua A. Welsh, Kenneth W. Witwer, David T.W. Wong, Jianping Xia, Ya-Hong Xie, Kaichun Yang, Mikołaj P. Zaborowski, Chenguang Zhang, Qin Zhang, Angela M. Zivkovic, and Louise C. Laurent

Subjects
Biological sciences, Biochemistry, Molecular biology, Cell biology, Science
Abstract

Summary: The extracellular RNA communication consortium (ERCC) is an NIH-funded program aiming to promote the development of new technologies, resources, and knowledge about exRNAs and their carriers. After Phase 1 (2013–2018), Phase 2 of the program (ERCC2, 2019–2023) aims to fill critical gaps in knowledge and technology to enable rigorous and reproducible methods for separation and characterization of both bulk populations of exRNA carriers and single EVs. ERCC2 investigators are also developing new bioinformatic pipelines to promote data integration through the exRNA atlas database. ERCC2 has established several Working Groups (Resource Sharing, Reagent Development, Data Analysis and Coordination, Technology Development, nomenclature, and Scientific Outreach) to promote collaboration between ERCC2 members and the broader scientific community. We expect that ERCC2’s current and future achievements will significantly improve our understanding of exRNA biology and the development of accurate and efficient exRNA-based diagnostic, prognostic, and theranostic biomarker assays.

Published
2022
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14. Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group.

Author

Schmaal, L, Veltman, DJ, van Erp, TGM, Sämann, PG, Frodl, T, Jahanshad, N, Loehrer, E, Tiemeier, H, Hofman, A, Niessen, WJ, Vernooij, MW, Ikram, MA, Wittfeld, K, Grabe, HJ, Block, A, Hegenscheid, K, Völzke, H, Hoehn, D, Czisch, M, Lagopoulos, J, Hatton, SN, Hickie, IB, Goya-Maldonado, R, Krämer, B, Gruber, O, Couvy-Duchesne, B, Rentería, ME, Strike, LT, Mills, NT, de Zubicaray, GI, McMahon, KL, Medland, SE, Martin, NG, Gillespie, NA, Wright, MJ, Hall, GB, MacQueen, GM, Frey, EM, Carballedo, A, van Velzen, LS, van Tol, MJ, van der Wee, NJ, Veer, IM, Walter, H, Schnell, K, Schramm, E, Normann, C, Schoepf, D, Konrad, C, Zurowski, B, Nickson, T, McIntosh, AM, Papmeyer, M, Whalley, HC, Sussmann, JE, Godlewska, BR, Cowen, PJ, Fischer, FH, Rose, M, Penninx, BWJH, Thompson, PM, and Hibar, DP

Subjects
Brain, Hippocampus, Humans, Magnetic Resonance Imaging, Case-Control Studies, Depressive Disorder, Major, Adult, Middle Aged, Female, Male, Neuroimaging, major depressive disorder, structural volumes, ENIGMA consortium, meta-analysis, hippocampus, Depressive Disorder, Major, Psychiatry, Medical and Health Sciences, Biological Sciences, Psychology and Cognitive Sciences
Abstract

The pattern of structural brain alterations associated with major depressive disorder (MDD) remains unresolved. This is in part due to small sample sizes of neuroimaging studies resulting in limited statistical power, disease heterogeneity and the complex interactions between clinical characteristics and brain morphology. To address this, we meta-analyzed three-dimensional brain magnetic resonance imaging data from 1728 MDD patients and 7199 controls from 15 research samples worldwide, to identify subcortical brain volumes that robustly discriminate MDD patients from healthy controls. Relative to controls, patients had significantly lower hippocampal volumes (Cohen's d=-0.14, % difference=-1.24). This effect was driven by patients with recurrent MDD (Cohen's d=-0.17, % difference=-1.44), and we detected no differences between first episode patients and controls. Age of onset ⩽21 was associated with a smaller hippocampus (Cohen's d=-0.20, % difference=-1.85) and a trend toward smaller amygdala (Cohen's d=-0.11, % difference=-1.23) and larger lateral ventricles (Cohen's d=0.12, % difference=5.11). Symptom severity at study inclusion was not associated with any regional brain volumes. Sample characteristics such as mean age, proportion of antidepressant users and proportion of remitted patients, and methodological characteristics did not significantly moderate alterations in brain volumes in MDD. Samples with a higher proportion of antipsychotic medication users showed larger caudate volumes in MDD patients compared with controls. This currently largest worldwide effort to identify subcortical brain alterations showed robust smaller hippocampal volumes in MDD patients, moderated by age of onset and first episode versus recurrent episode status.

Published
2016

15. Genome-wide association study of lifetime cannabis use based on a large meta-analytic sample of 32 330 subjects from the International Cannabis Consortium.

Author

Stringer, S, Minică, CC, Verweij, KJH, Mbarek, H, Bernard, M, Derringer, J, van Eijk, KR, Isen, JD, Loukola, A, Maciejewski, DF, Mihailov, E, van der Most, PJ, Sánchez-Mora, C, Roos, L, Sherva, R, Walters, R, Ware, JJ, Abdellaoui, A, Bigdeli, TB, Branje, SJT, Brown, SA, Bruinenberg, M, Casas, M, Esko, T, Garcia-Martinez, I, Gordon, SD, Harris, JM, Hartman, CA, Henders, AK, Heath, AC, Hickie, IB, Hickman, M, Hopfer, CJ, Hottenga, JJ, Huizink, AC, Irons, DE, Kahn, RS, Korhonen, T, Kranzler, HR, Krauter, K, van Lier, PAC, Lubke, GH, Madden, PAF, Mägi, R, McGue, MK, Medland, SE, Meeus, WHJ, Miller, MB, Montgomery, GW, Nivard, MG, Nolte, IM, Oldehinkel, AJ, Pausova, Z, Qaiser, B, Quaye, L, Ramos-Quiroga, JA, Richarte, V, Rose, RJ, Shin, J, Stallings, MC, Stiby, AI, Wall, TL, Wright, MJ, Koot, HM, Paus, T, Hewitt, JK, Ribasés, M, Kaprio, J, Boks, MP, Snieder, H, Spector, T, Munafò, MR, Metspalu, A, Gelernter, J, Boomsma, DI, Iacono, WG, Martin, NG, Gillespie, NA, Derks, EM, and Vink, JM

Subjects
Humans, Marijuana Abuse, Carrier Proteins, Potassium Channels, Cell Adhesion Molecules, Membrane Proteins, Marijuana Smoking, Adolescent, Adult, Aged, Aged, 80 and over, Middle Aged, Female, Male, Genome-Wide Association Study, Young Adult, CD56 Antigen, Potassium Channels, Sodium-Activated, and over, Sodium-Activated, Antigens, CD56, Genetics, Tobacco, Substance Abuse, Human Genome, Drug Abuse, Brain Disorders, Pediatric Research Initiative, Tobacco Smoke and Health, Prevention, Biotechnology, Clinical Research, 2.1 Biological and endogenous factors, Mental Health, Psychology, Clinical Sciences, Public Health and Health Services
Abstract

Cannabis is the most widely produced and consumed illicit psychoactive substance worldwide. Occasional cannabis use can progress to frequent use, abuse and dependence with all known adverse physical, psychological and social consequences. Individual differences in cannabis initiation are heritable (40-48%). The International Cannabis Consortium was established with the aim to identify genetic risk variants of cannabis use. We conducted a meta-analysis of genome-wide association data of 13 cohorts (N=32 330) and four replication samples (N=5627). In addition, we performed a gene-based test of association, estimated single-nucleotide polymorphism (SNP)-based heritability and explored the genetic correlation between lifetime cannabis use and cigarette use using LD score regression. No individual SNPs reached genome-wide significance. Nonetheless, gene-based tests identified four genes significantly associated with lifetime cannabis use: NCAM1, CADM2, SCOC and KCNT2. Previous studies reported associations of NCAM1 with cigarette smoking and other substance use, and those of CADM2 with body mass index, processing speed and autism disorders, which are phenotypes previously reported to be associated with cannabis use. Furthermore, we showed that, combined across the genome, all common SNPs explained 13-20% (P

Published
2016

16. Novel loci associated with usual sleep duration: the CHARGE Consortium Genome-Wide Association Study

Author

Gottlieb, DJ, Hek, K, Chen, T-H, Watson, NF, Eiriksdottir, G, Byrne, EM, Cornelis, M, Warby, SC, Bandinelli, S, Cherkas, L, Evans, DS, Grabe, HJ, Lahti, J, Li, M, Lehtimäki, T, Lumley, T, Marciante, KD, Pérusse, L, Psaty, BM, Robbins, J, Tranah, GJ, Vink, JM, Wilk, JB, Stafford, JM, Bellis, C, Biffar, R, Bouchard, C, Cade, B, Curhan, GC, Eriksson, JG, Ewert, R, Ferrucci, L, Fülöp, T, Gehrman, PR, Goodloe, R, Harris, TB, Heath, AC, Hernandez, D, Hofman, A, Hottenga, J-J, Hunter, DJ, Jensen, MK, Johnson, AD, Kähönen, M, Kao, L, Kraft, P, Larkin, EK, Lauderdale, DS, Luik, AI, Medici, M, Montgomery, GW, Palotie, A, Patel, SR, Pistis, G, Porcu, E, Quaye, L, Raitakari, O, Redline, S, Rimm, EB, Rotter, JI, Smith, AV, Spector, TD, Teumer, A, Uitterlinden, AG, Vohl, M-C, Widen, E, Willemsen, G, Young, T, Zhang, X, Liu, Y, Blangero, J, Boomsma, DI, Gudnason, V, Hu, F, Mangino, M, Martin, NG, O'Connor, GT, Stone, KL, Tanaka, T, Viikari, J, Gharib, SA, Punjabi, NM, Räikkönen, K, Völzke, H, Mignot, E, and Tiemeier, H

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Mental Health, Sleep Research, Clinical Research, Brain Disorders, Genetics, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Adult, Black or African American, Aged, Dyssomnias, Female, Genetic Association Studies, Genome-Wide Association Study, Humans, Male, Middle Aged, Polymorphism, Single Nucleotide, Self Report, Sleep, White People, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

Usual sleep duration is a heritable trait correlated with psychiatric morbidity, cardiometabolic disease and mortality, although little is known about the genetic variants influencing this trait. A genome-wide association study (GWAS) of usual sleep duration was conducted using 18 population-based cohorts totaling 47 180 individuals of European ancestry. Genome-wide significant association was identified at two loci. The strongest is located on chromosome 2, in an intergenic region 35- to 80-kb upstream from the thyroid-specific transcription factor PAX8 (lowest P=1.1 × 10(-9)). This finding was replicated in an African-American sample of 4771 individuals (lowest P=9.3 × 10(-4)). The strongest combined association was at rs1823125 (P=1.5 × 10(-10), minor allele frequency 0.26 in the discovery sample, 0.12 in the replication sample), with each copy of the minor allele associated with a sleep duration 3.1 min longer per night. The alleles associated with longer sleep duration were associated in previous GWAS with a more favorable metabolic profile and a lower risk of attention deficit hyperactivity disorder. Understanding the mechanisms underlying these associations may help elucidate biological mechanisms influencing sleep duration and its association with psychiatric, metabolic and cardiovascular disease.

Published
2015

17. Visualizing transfer of microbial biomolecules by outer membrane vesicles in microbe‐host‐communication in vivo

Author

Miriam Bittel, Patrick Reichert, Ilann Sarfati, Anja Dressel, Stefanie Leikam, Stefan Uderhardt, Iris Stolzer, Tuan Anh Phu, Martin Ng, Ngan K. Vu, Stefan Tenzer, Ute Distler, Stefan Wirtz, Veit Rothhammer, Markus F. Neurath, Robert L. Raffai, Claudia Günther, and Stefan Momma

Subjects
Cre‐Loxp, extracellular vesicles, inflammation, intestinal stem cells, microbiome, outer membrane vesicles, Cytology, QH573-671
Abstract

Abstract The intestinal microbiota influences mammalian host physiology in health and disease locally in the gut but also in organs devoid of direct contact with bacteria such as the liver and brain. Extracellular vesicles (EVs) or outer membrane vesicles (OMVs) released by microbes are increasingly recognized for their potential role as biological shuttle systems for inter‐kingdom communication. However, physiologically relevant evidence for the transfer of functional biomolecules from the intestinal microbiota to individual host cells by OMVs in vivo is scarce. By introducing Escherichia coli engineered to express Cre‐recombinase (E. coliCre) into mice with a Rosa26.tdTomato‐reporter background, we leveraged the Cre‐LoxP system to report the transfer of bacterial OMVs to recipient cells in vivo. Colonizing the intestine of these mice with E. coliCre, resulted in Cre‐recombinase induced fluorescent reporter gene‐expression in cells along the intestinal epithelium, including intestinal stem cells as well as mucosal immune cells such as macrophages. Furthermore, even far beyond the gut, bacterial‐derived Cre induced extended marker gene expression in a wide range of host tissues, including the heart, liver, kidney, spleen, and brain. Together, our findings provide a method and proof of principle that OMVs can serve as a biological shuttle system for the horizontal transfer of functional biomolecules between bacteria and mammalian host cells.

Published
2021
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18. High glucose macrophage exosomes enhance atherosclerosis by driving cellular proliferation & hematopoiesis

Author

Laura Bouchareychas, Phat Duong, Tuan Anh Phu, Eric Alsop, Bessie Meechoovet, Rebecca Reiman, Martin Ng, Ryo Yamamoto, Hiromitsu Nakauchi, Warren J. Gasper, Kendall Van Keuren-Jensen, and Robert L. Raffai

Subjects
Endocrine system physiology, Cell biology, Science
Abstract

Summary: We investigated whether extracellular vesicles (EVs) produced under hyperglycemic conditions could communicate signaling to drive atherosclerosis. We did so by treating Apoe−/− mice with exosomes produced by bone marrow-derived macrophages (BMDM) exposed to high glucose (BMDM–HG-exo) or control. Infusions of BMDM–HG-exo increased hematopoiesis, circulating myeloid cell numbers, and atherosclerotic lesions with an accumulation of macrophage foam and apoptotic cells. Transcriptome-wide analysis of cultured macrophages treated with BMDM–HG-exo or plasma EVs isolated from subjects with type II diabetes revealed a reduced inflammatory state and increased metabolic activity. Furthermore, BMDM–HG-exo induced cell proliferation and reprogrammed energy metabolism by increasing glycolytic activity. Lastly, profiling microRNA in BMDM–HG-exo and plasma EVs from diabetic subjects with advanced atherosclerosis converged on miR-486-5p as commonly enriched and recognized in dysregulated hematopoiesis and Abca1 control. Together, our findings show that EVs serve to communicate detrimental properties of hyperglycemia to accelerate atherosclerosis in diabetes.

Published
2021
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20. A genome-wide meta-analysis of association studies of Cloninger's Temperament Scales.

Author

Service, SK, Verweij, KJH, Lahti, J, Congdon, E, Ekelund, J, Hintsanen, M, Räikkönen, K, Lehtimäki, T, Kähönen, M, Widen, E, Taanila, A, Veijola, J, Heath, AC, Madden, PAF, Montgomery, GW, Sabatti, C, Järvelin, M-R, Palotie, A, Raitakari, O, Viikari, J, Martin, NG, Eriksson, JG, Keltikangas-Järvinen, L, Wray, NR, and Freimer, NB

Subjects
Humans, Cohort Studies, Longitudinal Studies, Reproducibility of Results, Personality, Temperament, Personality Inventory, Psychometrics, Twins, Genotype, Linkage Disequilibrium, Phenotype, Genetic Heterogeneity, Polymorphism, Single Nucleotide, Adult, Middle Aged, Australia, Finland, Female, Male, Genome-Wide Association Study, association, genetics, genome-wide, meta-analysis, personality, temperament, Polymorphism, Single Nucleotide, Clinical Sciences, Public Health and Health Services, Psychology
Abstract

Temperament has a strongly heritable component, yet multiple independent genome-wide studies have failed to identify significant genetic associations. We have assembled the largest sample to date of persons with genome-wide genotype data, who have been assessed with Cloninger's Temperament and Character Inventory. Sum scores for novelty seeking, harm avoidance, reward dependence and persistence have been measured in over 11,000 persons collected in four different cohorts. Our study had >80% power to identify genome-wide significant loci (P

Published
2012

21. IL-4 polarized human macrophage exosomes control cardiometabolic inflammation and diabetes in obesity

Author

Tuan Anh Phu, Martin Ng, Ngan K. Vu, Laura Bouchareychas, and Robert L. Raffai

Subjects
obesity, Technology, PPARγ, exosomes, macrophage, Inbred C57BL, adipocyte, Cardiovascular, Diet, High-Fat, Exosomes, Medical and Health Sciences, Oral and gastrointestinal, Mice, mitochondrial respiration, Drug Discovery, Diabetes Mellitus, Genetics, 2.1 Biological and endogenous factors, Animals, Humans, Insulin, Obesity, Aetiology, Molecular Biology, microRNA-33, Metabolic and endocrine, Nutrition, Inflammation, Pharmacology, cardiometabolic inflammation, type II diabetes, Macrophages, Diabetes, Biological Sciences, Diet, Mice, Inbred C57BL, High-Fat, MicroRNAs, Adipose Tissue, Cardiovascular Diseases, Molecular Medicine, Original Article, Interleukin-4, Insulin Resistance, beiging, Biotechnology
Abstract

Cardiometabolic disease is an increasing cause of morbidity and death in society. While M1-like macrophages contribute to metabolic inflammation and insulin resistance, those polarized to an M2-like phenotype exert protective properties. Building on our observations reporting M2-like macrophage exosomes in atherosclerosis control, we tested whether they could serve to control inflammation in the liver and adipose tissue of obese mice. In thinking of clinical translation, we studied human THP-1 macrophages exposed to interleukin (IL)-4 as a source of exosomes (THP1-IL4-exo). Our findings show that THP1-IL4-exo polarized primary macrophages to an anti-inflammatory phenotype and reprogramed their energy metabolism by increasing levels of microRNA-21/99a/146b/378a (miR-21/99a/146b/378a) while reducing miR-33. This increased lipophagy, mitochondrial activity, and oxidative phosphorylation (OXPHOS). THP1-IL4-exo exerted a similar regulation of these miRs in cultured 3T3-L1 adipocytes. This enhanced insulin-dependent glucose uptake through increased peroxisome proliferator activated receptor gamma (PPARγ)-driven expression of GLUT4. It also increased levels of UCP1 and OXPHOS activity, which promoted lipophagy, mitochondrial activity, and beiging of 3T3-L1 adipocytes. Intraperitoneal infusions of THP1-IL4-exo into obese wild-type and Ldlr(−/−) mice fed a Western high-fat diet reduced hematopoiesis and myelopoiesis, and favorably reprogramed inflammatory signaling and metabolism in circulating Ly6C(hi) monocytes. This also reduced leukocyte numbers and inflammatory activity in the circulation, aorta, adipose tissue, and the liver. Such treatments reduced hepatic steatosis and increased the beiging of white adipose tissue as revealed by increased UCP1 expression and OXPHOS activity that normalized blood insulin levels and improved glucose tolerance. Our findings support THP1-IL4-exo as a therapeutic approach to control cardiometabolic disease and diabetes in obesity.

Published
2022

22. The genetic basis of endometriosis and comorbidity with other pain and inflammatory conditions

Author

Rahmioglu, N, Mortlock, S, Ghiasi, M, Moller, PL, Stefansdottir, L, Galarneau, G, Turman, C, Danning, R, Law, MH, Sapkota, Y, Christofidou, P, Skarp, S, Giri, A, Banasik, K, Krassowski, M, Lepamets, M, Marciniak, B, Noukas, M, Perro, D, Sliz, E, Sobalska-Kwapis, M, Thorleifsson, G, Topbas-Selcuki, NF, Vitonis, A, Westergaard, D, Arnadottir, R, Burgdorf, KS, Campbell, A, Cheuk, CSK, Clementi, C, Cook, J, De Vivo, I, DiVasta, A, Dorien, O, Donoghue, JF, Edwards, T, Fontanillas, P, Fung, JN, Geirsson, RT, Girling, JE, Harkki, P, Harris, HR, Healey, M, Heikinheimo, O, Holdsworth-Carson, S, Hostettler, IC, Houlden, H, Houshdaran, S, Irwin, JC, Jarvelin, M-R, Kamatani, Y, Kennedy, SH, Kepka, E, Kettunen, J, Kubo, M, Kulig, B, Kurra, V, Laivuori, H, Laufer, MR, Lindgren, CM, MacGregor, S, Mangino, M, Martin, NG, Matalliotaki, C, Matalliotakis, M, Murray, AD, Ndungu, A, Nezhat, C, Olsen, CM, Opoku-Anane, J, Padmanabhan, S, Paranjpe, M, Peters, M, Polak, G, Porteous, DJ, Rabban, J, Rexrode, KM, Romanowicz, H, Saare, M, Saavalainen, L, Schork, AJ, Sen, S, Shafrir, AL, Siewierska-Gorska, A, Slomka, M, Smith, BH, Smolarz, B, Szaflik, T, Szyllo, K, Takahashi, A, Terry, KL, Tomassetti, C, Treloar, SA, Vanhie, A, Vincent, K, Vo, KC, Werring, DJ, Zeggini, E, Zervou, M, Stefansson, K, Nyegaard, M, Uimari, O, Yurttas-Beim, P, Tung, JY, Adachi, S, Buring, JE, Ridker, PM, D'Hooghe, T, Goulielmos, GN, Hapangama, DK, Hayward, C, Horne, AW, Low, S-K, Martikainen, H, Chasman, D, Rogers, PAW, Saunders, PT, Sirota, M, Spector, T, Strapagiel, D, Whiteman, DC, Giudice, LC, Velez-Edwards, DR, Kraft, P, Salumets, A, Nyholt, DR, Magi, R, Becker, CM, Steinthorsdottir, V, Missmer, SA, Montgomery, GW, Morris, AP, Zondervan, KT, Rahmioglu, N, Mortlock, S, Ghiasi, M, Moller, PL, Stefansdottir, L, Galarneau, G, Turman, C, Danning, R, Law, MH, Sapkota, Y, Christofidou, P, Skarp, S, Giri, A, Banasik, K, Krassowski, M, Lepamets, M, Marciniak, B, Noukas, M, Perro, D, Sliz, E, Sobalska-Kwapis, M, Thorleifsson, G, Topbas-Selcuki, NF, Vitonis, A, Westergaard, D, Arnadottir, R, Burgdorf, KS, Campbell, A, Cheuk, CSK, Clementi, C, Cook, J, De Vivo, I, DiVasta, A, Dorien, O, Donoghue, JF, Edwards, T, Fontanillas, P, Fung, JN, Geirsson, RT, Girling, JE, Harkki, P, Harris, HR, Healey, M, Heikinheimo, O, Holdsworth-Carson, S, Hostettler, IC, Houlden, H, Houshdaran, S, Irwin, JC, Jarvelin, M-R, Kamatani, Y, Kennedy, SH, Kepka, E, Kettunen, J, Kubo, M, Kulig, B, Kurra, V, Laivuori, H, Laufer, MR, Lindgren, CM, MacGregor, S, Mangino, M, Martin, NG, Matalliotaki, C, Matalliotakis, M, Murray, AD, Ndungu, A, Nezhat, C, Olsen, CM, Opoku-Anane, J, Padmanabhan, S, Paranjpe, M, Peters, M, Polak, G, Porteous, DJ, Rabban, J, Rexrode, KM, Romanowicz, H, Saare, M, Saavalainen, L, Schork, AJ, Sen, S, Shafrir, AL, Siewierska-Gorska, A, Slomka, M, Smith, BH, Smolarz, B, Szaflik, T, Szyllo, K, Takahashi, A, Terry, KL, Tomassetti, C, Treloar, SA, Vanhie, A, Vincent, K, Vo, KC, Werring, DJ, Zeggini, E, Zervou, M, Stefansson, K, Nyegaard, M, Uimari, O, Yurttas-Beim, P, Tung, JY, Adachi, S, Buring, JE, Ridker, PM, D'Hooghe, T, Goulielmos, GN, Hapangama, DK, Hayward, C, Horne, AW, Low, S-K, Martikainen, H, Chasman, D, Rogers, PAW, Saunders, PT, Sirota, M, Spector, T, Strapagiel, D, Whiteman, DC, Giudice, LC, Velez-Edwards, DR, Kraft, P, Salumets, A, Nyholt, DR, Magi, R, Becker, CM, Steinthorsdottir, V, Missmer, SA, Montgomery, GW, Morris, AP, and Zondervan, KT

Abstract

Endometriosis is a common condition associated with debilitating pelvic pain and infertility. A genome-wide association study meta-analysis, including 60,674 cases and 701,926 controls of European and East Asian descent, identified 42 genome-wide significant loci comprising 49 distinct association signals. Effect sizes were largest for stage 3/4 disease, driven by ovarian endometriosis. Identified signals explained up to 5.01% of disease variance and regulated expression or methylation of genes in endometrium and blood, many of which were associated with pain perception/maintenance (SRP14/BMF, GDAP1, MLLT10, BSN and NGF). We observed significant genetic correlations between endometriosis and 11 pain conditions, including migraine, back and multisite chronic pain (MCP), as well as inflammatory conditions, including asthma and osteoarthritis. Multitrait genetic analyses identified substantial sharing of variants associated with endometriosis and MCP/migraine. Targeted investigations of genetically regulated mechanisms shared between endometriosis and other pain conditions are needed to aid the development of new treatments and facilitate early symptomatic intervention.

Published
2023

23. P2/N95 filtering facepiece respirators: Results of a large-scale quantitative mask fit testing program in Australian health care workers

Author

Maxim Milosevic, Raaj Kishore Biswas, Lesley Innes, Martin Ng, Ali Mehmet Darendeliler, Alice Wong, and Elizabeth Denney-Wilson

Subjects
Male, SARS-CoV-2, N95 Respirators, Epidemiology, Health Personnel, Health Policy, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Australia, Public Health, Environmental and Occupational Health, COVID-19, airborne, Equipment Design, infection control, respiratory protection, Infectious Diseases, Occupational Exposure, Major Article, Humans, Female, Respiratory Protective Devices, Pandemics, ComputingMethodologies_COMPUTERGRAPHICS
Abstract

Graphical abstract Image, graphical abstract, Background In response to the COVID-19 pandemic, 6,287 Australian health care workers (HCWs) were fit tested to N95 filtering facepiece respirators (FFRs). This study determined how readily HCWs were fitted to 8 FFRs and how age and sex influenced testing. Methods HCWs were fit tested following the quantitative OSHA protocol. After bivariate analysis, a logistic regression model assessed the effect of FFR model, HCW age and sex on fit test results. Results Of 4,198 female and 2,089 male HCWs tested, 93.3% were successfully fitted. Fifty-five percent passed the first FFR, 21% required 2 and 23% required testing on 3 or more models. Males were 15% less likely to pass compared to females (P < .001). Individuals aged 18-29 were significantly more likely to pass compared to colleagues aged 30-59. Cup-style 3M 1860S was the most suitable model (95% CI: 1.94, 2.54) while the duckbill BSN TN01-11 was most likely to fail (95% CI: 0.11, 0.15). Conclusions Current N95 FFRs exhibit suboptimal fit such that a large proportion (45%) of HCWs require testing on multiple models. Older age and male sex were associated with significantly higher fit failure rates. QNFT programs should consider HCW characteristics like sex, age, racial and facial anthropometric measurements to improve the protection of the health workforce.

Published
2022

25. Remote Ischemic Preconditioning Acutely Improves Coronary Microcirculatory Function

Author

Jerrett K. Lau, Probal Roy, Ashkan Javadzadegan, Abouzar Moshfegh, William F. Fearon, Martin Ng, Harry Lowe, David Brieger, Leonard Kritharides, and Andy S. Yong

Subjects
coronary flow reserve, coronary physiology, microcirculation, microcirculatory resistance, remote ischemic preconditioning, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Background Remote ischemic preconditioning (RIPC) attenuates myocardial damage during elective and primary percutaneous coronary intervention. Recent studies suggest that coronary microcirculatory function is an important determinant of clinical outcome. The aim of this study was to assess the effect of RIPC on markers of microcirculatory function. Methods and Results Patients referred for cardiac catheterization and fractional flow reserve measurement were randomized to RIPC or sham. Operators and patients were blinded to treatment allocation. Comprehensive physiological assessments were performed before and after RIPC/sham including the index of microcirculatory resistance and coronary flow reserve after intracoronary glyceryl trinitrate and during the infusion of intravenous adenosine. Thirty patients were included (87% male; mean age: 63.1±10.0 years). RIPC and sham groups were similar with respect to baseline characteristics. RIPC decreased the calculated index of microcirculatory resistance (median, before RIPC: 22.6 [interquartile range [IQR]: 17.9–25.6]; after RIPC: 17.5 [IQR: 14.5–21.3]; P=0.007) and increased coronary flow reserve (2.6±0.9 versus 3.8±1.7, P=0.001). These RIPC‐mediated changes were associated with a reduction in hyperemic transit time (median: 0.33 [IQR: 0.26–0.40] versus 0.25 [IQR: 0.20–0.30]; P=0.010). RIPC resulted in a significant decrease in the calculated index of microcirculatory resistance compared with sham (relative change with treatment [mean±SD] was −18.1±24.8% versus +6.1±37.5; P=0.047) and a significant increase in coronary flow reserve (+41.2% [IQR: 20.0–61.7] versus −7.8% [IQR: −19.1 to 10.3]; P

Published
2018
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26. Multi-trait genetic analysis identifies auto-immune loci associated with cutaneous melanoma

Author

Liyanage, U, MacGregor, S, Bishop, DT, Shi, J, An, J, Ong, JS, Han, X, Scolyer, RA, Martin, NG, Medland, SE, Byrne, EM, Green, AC, Saw, RPM, Thompson, JF, Stretch, J, Spillane, A, Jiang, Y, Tian, C, 23andMe Research Team, Gordon, SG, Duffy, DL, Olsen, CM, Whiteman, DC, Long, GV, Iles, MM, Landi, MT, and Law, MH

Abstract

Genome-wide association studies (GWAS) have identified a number of risk loci for cutaneous melanoma. Cutaneous melanoma shares overlapping genetic risk (genetic correlation) with a number of other traits, including with its risk factors such as sunburn propensity. This genetic correlation can be exploited to identify additional cutaneous melanoma risk loci by multi-trait analysis of GWAS (MTAG). We used bivariate LD-score regression to identify traits that are genetically correlated with clinically-confirmed cutaneous melanoma, and then used publicly available GWAS for these traits in a MTAG. MTAG allows GWAS to be combined while accounting for sample overlap and incomplete genetic correlation. We identified a total of 74 genome-wide independent loci; 19 of them were not previously reported in the input cutaneous melanoma GWAS-meta-analysis. 55 of these loci were replicated (P < 0.05/74), Bonferroni corrected P -value in two independent cutaneous melanoma replication cohorts from Melanoma Institute Australia and 23andMe, Inc. Among the new cutaneous melanoma loci are ones that have also been associated with autoimmune traits including rs715199 near LPP, and rs10858023 near AP4B1. Our analysis indicates genetic correlation between traits can be leveraged to identify new risk genes for cutaneous melanoma.

Published
2022

27. Haemodynamic and metabolic adaptations in coronary microvascular disease

Author

Samer Noaman, David M Kaye, Shane Nanayakkara, Anthony M Dart, Andy S C Yong, Martin Ng, Donna Vizi, Stephen J Duffy, Nicholas Cox, and William Chan

Subjects
Cardiology and Cardiovascular Medicine
Abstract

ObjectiveWe aimed to evaluate the microcirculatory resistance (MR) and myocardial metabolic adaptations at rest and in response to increased cardiac workload in patients with suspected coronary microvascular dysfunction (CMD).MethodsPatients with objective ischaemia and/or myocardial injury and non-obstructive coronary artery disease underwent thermodilution-derived microcirculatory assessment and transcardiac blood sampling during graded exercise with adenosine-mediated hyperaemia. We measured MR at rest and following supine cycle ergometry. Patients (n=24) were stratified by the resting index of MR (IMR) into normal-IMR (IMRResultsThe mean age was 57 years; 67% were males and 38% had hypertension. The normal-IMR group had increased IMR response to exercise (16±5 vs 23±12U, p=0.03) compared with the high-IMR group, who had persistently elevated IMR at rest and following exercise (38±19 vs 33±15U, p=0.39) despite similar exercise duration and rate-pressure product between the groups, both p>0.05. The normal-IMR group had augmented oxygen extraction ratio following exercise (53±18 vs 64±11%, p=0.03) compared with the high-IMR group (65±14 vs 59±11%, p=0.26). The postexercise lactate uptake was greater in the high-IMR (0.04±0.05 vs 0.11±0.07 mmol/L, p=0.004) compared with normal-IMR group (0.08±0.06 vs 0.09±0.09 mmol/L, p=0.67). The high-IMR group demonstrated greater troponin release following exercise compared with the normal-IMR group (0.13±0.12 vs 0.001±0.05 ng/L, p=0.03).ConclusionsPatients with suspected CMD appear to have distinctive microcirculatory resistive and myocardial metabolic profiles at rest and in response to exercise. These differences in phenotypes may permit individualised therapies targeting microvascular responsiveness (normal-IMR group) and/or myocardial metabolic adaptations (normal-IMR and high-IMR groups).

Published
2023

28. 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

29. Enhancing Discovery of Genetic Variants for Posttraumatic Stress Disorder Through Integration of Quantitative Phenotypes and Trauma Exposure Information

Author

Maihofer, AX, Choi, KW, Coleman, JR, Daskalakis, NP, Denckla, CA, Ketema, E, Morey, RA, Polimanti, R, Ratanatharathorn, A, Torres, K, Wingo, AP, Zai, CC, Aiello, AE, Almli, LM, Amstadter, AB, Andersen, SB, Andreassen, OA, Arbisi, PA, Ashley-Koch, AE, Austin, SB, Avdibegovic, E, Borglum, AD, Babic, D, Baekvad-Hansen, M, Baker, DG, Beckham, JC, Bierut, LJ, Bisson, J, Boks, MP, Bolger, EA, Bradley, B, Brashear, M, Breen, G, Bryant, RA, Bustamante, AC, Bybjerg-Grauholm, J, Calabrese, JR, Caldas-de-Almeida, JM, Chen, C-Y, Dale, AM, Dalvie, S, Deckert, J, Delahanty, DL, Dennis, MF, Disner, SG, Domschke, K, Duncan, LE, Kulenovic, AD, Erbes, CR, Evans, A, Farrer, LA, Feeny, NC, Flory, JD, Forbes, D, Franz, CE, Galea, S, Garrett, ME, Gautam, A, Gelaye, B, Gelernter, J, Geuze, E, Gillespie, CF, Goci, A, Gordon, SD, Guffanti, G, Hammamieh, R, Hauser, MA, Heath, AC, Hemmings, SMJ, Hougaard, DM, Jakovljevic, M, Jett, M, Johnson, EO, Jones, I, Jovanovic, T, Qin, X-J, Karstoft, K-I, Kaufman, ML, Kessler, RC, Khan, A, Kimbrel, NA, King, AP, Koen, N, Kranzler, HR, Kremen, WS, Lawford, BR, Lebois, LAM, Lewis, C, Liberzon, I, Linnstaedt, SD, Logue, MW, Lori, A, Lugonja, B, Luykx, JJ, Lyons, MJ, Maples-Keller, JL, Marmar, C, Martin, NG, Maurer, D, Mavissakalian, MR, McFarlane, A, McGlinchey, RE, McLaughlin, KA, McLean, SA, Mehta, D, Mellor, R, Michopoulos, V, Milberg, W, Miller, MW, Morris, CP, Mors, O, Mortensen, PB, Nelson, EC, Nordentoft, M, Norman, SB, O'Donnell, M, Orcutt, HK, Panizzon, MS, Peters, ES, Peterson, AL, Peverill, M, Pietrzak, RH, Polusny, MA, Rice, JP, Risbrough, VB, Roberts, AL, Rothbaum, AO, Rothbaum, BO, Roy-Byrne, P, Ruggiero, KJ, Rung, A, Rutten, BPF, Saccone, NL, Sanchez, SE, Schijven, D, Seedat, S, Seligowski, A, Seng, JS, Sheerin, CM, Silove, D, Smith, AK, Smoller, JW, Sponheim, SR, Stein, DJ, Stevens, JS, Teicher, MH, Thompson, WK, Trapido, E, Uddin, M, Ursano, RJ, van den Heuvel, LL, Van Hooff, M, Vermetten, E, Vinkers, CH, Voisey, J, Wang, Y, Wang, Z, Werge, T, Williams, MA, Williamson, DE, Winternitz, S, Wolf, C, Wolf, EJ, Yehuda, R, Young, KA, Young, RM, Zhao, H, Zoellner, LA, Haas, M, Lasseter, H, Provost, AC, Salem, RM, Sebat, J, Shaffer, RA, Wu, T, Ripke, S, Daly, MJ, Ressler, KJ, Koenen, KC, Stein, MB, Nievergelt, CM, Maihofer, AX, Choi, KW, Coleman, JR, Daskalakis, NP, Denckla, CA, Ketema, E, Morey, RA, Polimanti, R, Ratanatharathorn, A, Torres, K, Wingo, AP, Zai, CC, Aiello, AE, Almli, LM, Amstadter, AB, Andersen, SB, Andreassen, OA, Arbisi, PA, Ashley-Koch, AE, Austin, SB, Avdibegovic, E, Borglum, AD, Babic, D, Baekvad-Hansen, M, Baker, DG, Beckham, JC, Bierut, LJ, Bisson, J, Boks, MP, Bolger, EA, Bradley, B, Brashear, M, Breen, G, Bryant, RA, Bustamante, AC, Bybjerg-Grauholm, J, Calabrese, JR, Caldas-de-Almeida, JM, Chen, C-Y, Dale, AM, Dalvie, S, Deckert, J, Delahanty, DL, Dennis, MF, Disner, SG, Domschke, K, Duncan, LE, Kulenovic, AD, Erbes, CR, Evans, A, Farrer, LA, Feeny, NC, Flory, JD, Forbes, D, Franz, CE, Galea, S, Garrett, ME, Gautam, A, Gelaye, B, Gelernter, J, Geuze, E, Gillespie, CF, Goci, A, Gordon, SD, Guffanti, G, Hammamieh, R, Hauser, MA, Heath, AC, Hemmings, SMJ, Hougaard, DM, Jakovljevic, M, Jett, M, Johnson, EO, Jones, I, Jovanovic, T, Qin, X-J, Karstoft, K-I, Kaufman, ML, Kessler, RC, Khan, A, Kimbrel, NA, King, AP, Koen, N, Kranzler, HR, Kremen, WS, Lawford, BR, Lebois, LAM, Lewis, C, Liberzon, I, Linnstaedt, SD, Logue, MW, Lori, A, Lugonja, B, Luykx, JJ, Lyons, MJ, Maples-Keller, JL, Marmar, C, Martin, NG, Maurer, D, Mavissakalian, MR, McFarlane, A, McGlinchey, RE, McLaughlin, KA, McLean, SA, Mehta, D, Mellor, R, Michopoulos, V, Milberg, W, Miller, MW, Morris, CP, Mors, O, Mortensen, PB, Nelson, EC, Nordentoft, M, Norman, SB, O'Donnell, M, Orcutt, HK, Panizzon, MS, Peters, ES, Peterson, AL, Peverill, M, Pietrzak, RH, Polusny, MA, Rice, JP, Risbrough, VB, Roberts, AL, Rothbaum, AO, Rothbaum, BO, Roy-Byrne, P, Ruggiero, KJ, Rung, A, Rutten, BPF, Saccone, NL, Sanchez, SE, Schijven, D, Seedat, S, Seligowski, A, Seng, JS, Sheerin, CM, Silove, D, Smith, AK, Smoller, JW, Sponheim, SR, Stein, DJ, Stevens, JS, Teicher, MH, Thompson, WK, Trapido, E, Uddin, M, Ursano, RJ, van den Heuvel, LL, Van Hooff, M, Vermetten, E, Vinkers, CH, Voisey, J, Wang, Y, Wang, Z, Werge, T, Williams, MA, Williamson, DE, Winternitz, S, Wolf, C, Wolf, EJ, Yehuda, R, Young, KA, Young, RM, Zhao, H, Zoellner, LA, Haas, M, Lasseter, H, Provost, AC, Salem, RM, Sebat, J, Shaffer, RA, Wu, T, Ripke, S, Daly, MJ, Ressler, KJ, Koenen, KC, Stein, MB, and Nievergelt, CM

Abstract

BACKGROUND: Posttraumatic stress disorder (PTSD) is heritable and a potential consequence of exposure to traumatic stress. Evidence suggests that a quantitative approach to PTSD phenotype measurement and incorporation of lifetime trauma exposure (LTE) information could enhance the discovery power of PTSD genome-wide association studies (GWASs). METHODS: A GWAS on PTSD symptoms was performed in 51 cohorts followed by a fixed-effects meta-analysis (N = 182,199 European ancestry participants). A GWAS of LTE burden was performed in the UK Biobank cohort (N = 132,988). Genetic correlations were evaluated with linkage disequilibrium score regression. Multivariate analysis was performed using Multi-Trait Analysis of GWAS. Functional mapping and annotation of leading loci was performed with FUMA. Replication was evaluated using the Million Veteran Program GWAS of PTSD total symptoms. RESULTS: GWASs of PTSD symptoms and LTE burden identified 5 and 6 independent genome-wide significant loci, respectively. There was a 72% genetic correlation between PTSD and LTE. PTSD and LTE showed largely similar patterns of genetic correlation with other traits, albeit with some distinctions. Adjusting PTSD for LTE reduced PTSD heritability by 31%. Multivariate analysis of PTSD and LTE increased the effective sample size of the PTSD GWAS by 20% and identified 4 additional loci. Four of these 9 PTSD loci were independently replicated in the Million Veteran Program. CONCLUSIONS: Through using a quantitative trait measure of PTSD, we identified novel risk loci not previously identified using prior case-control analyses. PTSD and LTE have a high genetic overlap that can be leveraged to increase discovery power through multivariate methods.

Published
2022

30. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects

Author

Howe, LJ, Nivard, MG, Morris, TT, Hansen, AF, Rasheed, H, Cho, Y, Chittoor, G, Ahlskog, R, Lind, PA, Palviainen, T, van der Zee, MD, Cheesman, R, Mangino, M, Wang, Y, Li, S, Klaric, L, Ratliff, SM, Bielak, LF, Nygaard, M, Giannelis, A, Willoughby, EA, Reynolds, CA, Balbona, JV, Andreassen, OA, Ask, H, Baras, A, Bauer, CR, Boomsma, DI, Campbell, A, Campbell, H, Chen, Z, Christofidou, P, Corfield, E, Dahm, CC, Dokuru, DR, Evans, LM, de Geus, EJC, Giddaluru, S, Gordon, SD, Harden, KP, Hill, WD, Hughes, A, Kerr, SM, Kim, Y, Kweon, H, Latvala, A, Lawlor, DA, Li, L, Lin, K, Magnus, P, Magnusson, PKE, Mallard, TT, Martikainen, P, Mills, MC, Njolstad, PR, Overton, JD, Pedersen, NL, Porteous, DJ, Reid, J, Silventoinen, K, Southey, MC, Stoltenberg, C, Tucker-Drob, EM, Wright, MJ, Hewitt, JK, Keller, MC, Stallings, MC, Lee, JJ, Christensen, K, Kardia, SLR, Peyser, PA, Smith, JA, Wilson, JF, Hopper, JL, Hagg, S, Spector, TD, Pingault, J-B, Plomin, R, Havdahl, A, Bartels, M, Martin, NG, Oskarsson, S, Justice, AE, Millwood, IY, Hveem, K, Naess, O, Willer, CJ, Asvold, BO, Koellinger, PD, Kaprio, J, Medland, SE, Walters, RG, Benjamin, DJ, Turley, P, Evans, DM, Smith, GD, Hayward, C, Brumpton, B, Hemani, G, Davies, NM, Howe, LJ, Nivard, MG, Morris, TT, Hansen, AF, Rasheed, H, Cho, Y, Chittoor, G, Ahlskog, R, Lind, PA, Palviainen, T, van der Zee, MD, Cheesman, R, Mangino, M, Wang, Y, Li, S, Klaric, L, Ratliff, SM, Bielak, LF, Nygaard, M, Giannelis, A, Willoughby, EA, Reynolds, CA, Balbona, JV, Andreassen, OA, Ask, H, Baras, A, Bauer, CR, Boomsma, DI, Campbell, A, Campbell, H, Chen, Z, Christofidou, P, Corfield, E, Dahm, CC, Dokuru, DR, Evans, LM, de Geus, EJC, Giddaluru, S, Gordon, SD, Harden, KP, Hill, WD, Hughes, A, Kerr, SM, Kim, Y, Kweon, H, Latvala, A, Lawlor, DA, Li, L, Lin, K, Magnus, P, Magnusson, PKE, Mallard, TT, Martikainen, P, Mills, MC, Njolstad, PR, Overton, JD, Pedersen, NL, Porteous, DJ, Reid, J, Silventoinen, K, Southey, MC, Stoltenberg, C, Tucker-Drob, EM, Wright, MJ, Hewitt, JK, Keller, MC, Stallings, MC, Lee, JJ, Christensen, K, Kardia, SLR, Peyser, PA, Smith, JA, Wilson, JF, Hopper, JL, Hagg, S, Spector, TD, Pingault, J-B, Plomin, R, Havdahl, A, Bartels, M, Martin, NG, Oskarsson, S, Justice, AE, Millwood, IY, Hveem, K, Naess, O, Willer, CJ, Asvold, BO, Koellinger, PD, Kaprio, J, Medland, SE, Walters, RG, Benjamin, DJ, Turley, P, Evans, DM, Smith, GD, Hayward, C, Brumpton, B, Hemani, G, and Davies, NM

Abstract

Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.

Published
2022

31. 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

32. Greater male than female variability in regional brain structure across the lifespan

Author

Wierenga, LM, Doucet, GE, Dima, D, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andreassen, OA, Anticevic, A, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, den Braber, A, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Calhoun, VD, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Chaim-Avancini, TM, Ching, CRK, Clark, VP, Conrod, PJ, Conzelmann, A, Crivello, F, Davey, CG, Dickie, EW, Ehrlich, S, Van't Ent, D, Fisher, SE, Fouche, J-P, Franke, B, Fuentes-Claramonte, P, de Geus, EJC, Di Giorgio, A, Glahn, DC, Gotlib, IH, Grabe, HJ, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Gurholt, TP, de Haan, L, Haatveit, B, Harrison, BJ, Hartman, CA, Hatton, SN, Heslenfeld, DJ, van den Heuvel, OA, Hickie, IB, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, AC, Jiang, J, Jonsson, EG, Joska, JA, Kalnin, AJ, Klein, M, Koenders, L, Kolskar, KK, Kramer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, IS, Lee, PH, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, BC, McDonald, C, McIntosh, AM, McMahon, KL, McPhilemy, G, van der Meer, D, Menchon, JM, Naaijen, J, Nyberg, L, Oosterlaan, J, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Radua, J, Reif, A, Richard, G, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Sim, K, Simmons, A, Smoller, JW, Sommer, IE, Soriano-Mas, C, Stein, DJ, Strike, LT, Szeszko, PR, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Trollor, JN, Uhlmann, A, Veer, IM, Veltman, DJ, Voineskos, A, Volzke, H, Walter, H, Wang, L, Wang, Y, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, HC, Williams, SCR, Wittfeld, K, Wolf, DH, Wright, MJ, Yoncheva, YN, Zanetti, M, Ziegler, GC, de Zubicaray, G, Thompson, PM, Crone, EA, Frangou, S, Tamnes, CK, Wierenga, LM, Doucet, GE, Dima, D, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andreassen, OA, Anticevic, A, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, den Braber, A, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Calhoun, VD, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Chaim-Avancini, TM, Ching, CRK, Clark, VP, Conrod, PJ, Conzelmann, A, Crivello, F, Davey, CG, Dickie, EW, Ehrlich, S, Van't Ent, D, Fisher, SE, Fouche, J-P, Franke, B, Fuentes-Claramonte, P, de Geus, EJC, Di Giorgio, A, Glahn, DC, Gotlib, IH, Grabe, HJ, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Gurholt, TP, de Haan, L, Haatveit, B, Harrison, BJ, Hartman, CA, Hatton, SN, Heslenfeld, DJ, van den Heuvel, OA, Hickie, IB, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, AC, Jiang, J, Jonsson, EG, Joska, JA, Kalnin, AJ, Klein, M, Koenders, L, Kolskar, KK, Kramer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, IS, Lee, PH, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, BC, McDonald, C, McIntosh, AM, McMahon, KL, McPhilemy, G, van der Meer, D, Menchon, JM, Naaijen, J, Nyberg, L, Oosterlaan, J, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Radua, J, Reif, A, Richard, G, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Sim, K, Simmons, A, Smoller, JW, Sommer, IE, Soriano-Mas, C, Stein, DJ, Strike, LT, Szeszko, PR, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Trollor, JN, Uhlmann, A, Veer, IM, Veltman, DJ, Voineskos, A, Volzke, H, Walter, H, Wang, L, Wang, Y, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, HC, Williams, SCR, Wittfeld, K, Wolf, DH, Wright, MJ, Yoncheva, YN, Zanetti, M, Ziegler, GC, de Zubicaray, G, Thompson, PM, Crone, EA, Frangou, S, and Tamnes, CK

Abstract

For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.

Published
2022

33. Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years

Author

Dima, D, Modabbernia, A, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, Ortiz-Garcia De la Foz, V, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, Van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Williams, SCR, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, Frangou, S, Dima, D, Modabbernia, A, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, Ortiz-Garcia De la Foz, V, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, Van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Williams, SCR, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, and Frangou, S

Abstract

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.

Published
2022

34. Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years

Author

Frangou, S, Modabbernia, A, Williams, SCR, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, de la Foz, VO-G, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, Dima, D, Frangou, S, Modabbernia, A, Williams, SCR, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, de la Foz, VO-G, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, and Dima, D

Abstract

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.

Published
2022

35. Self-reported impact of developmental stuttering across the lifespan

Author

Boyce, JO, Jackson, VE, van Reyk, O, Parker, R, Vogel, AP, Eising, E, Horton, SE, Gillespie, NA, Scheffer, IE, Amor, DJ, Hildebrand, MS, Fisher, SE, Martin, NG, Reilly, S, Bahlo, M, Morgan, AT, Boyce, JO, Jackson, VE, van Reyk, O, Parker, R, Vogel, AP, Eising, E, Horton, SE, Gillespie, NA, Scheffer, IE, Amor, DJ, Hildebrand, MS, Fisher, SE, Martin, NG, Reilly, S, Bahlo, M, and Morgan, AT

Abstract

AIM: To examine the phenomenology of stuttering across the lifespan in the largest prospective cohort to date. METHOD: Participants aged 7 years and older with a history of developmental stuttering were recruited. Self-reported phenotypic data were collected online including stuttering symptomatology, co-occurring phenotypes, genetic predisposition, factors associated with stuttering severity, and impact on anxiety, education, and employment. RESULTS: A total of 987 participants (852 adults: 590 males, 262 females, mean age 49 years [SD = 17 years 10 months; range = 18-93 years] and 135 children: 97 males, 38 females, mean age 11 years 4 months [SD = 3 years; range = 7-17 years]) were recruited. Stuttering onset occurred at age 3 to 6 years in 64.0%. Blocking (73.2%) was the most frequent phenotype; 75.9% had sought stuttering therapy and 15.5% identified as having recovered. Half (49.9%) reported a family history. There was a significant negative correlation with age for both stuttering frequency and severity in adults. Most were anxious due to stuttering (90.4%) and perceived stuttering as a barrier to education and employment outcomes (80.7%). INTERPRETATION: The frequent persistence of stuttering and the high proportion with a family history suggest that stuttering is a complex trait that does not often resolve, even with therapy. These data provide new insights into the phenotype and prognosis of stuttering, information that is critically needed to encourage the development of more effective speech therapies. WHAT THIS PAPER ADDS: Half of the study cohort had a family history of stuttering. While 75.9% of participants had sought stuttering therapy, only 15.5% identified as having recovered. There was a significant negative correlation between age and stuttering frequency and severity in adults.

Published
2022

36. Changing genetic architecture of body mass index from infancy to early adulthood: an individual based pooled analysis of 25 twin cohorts

Author

Silventoinen, K, Li, W, Jelenkovic, A, Sund, R, Yokoyama, Y, Aaltonen, S, Piirtola, M, Sugawara, M, Tanaka, M, Matsumoto, S, Baker, LA, Tuvblad, C, Tynelius, P, Rasmussen, F, Craig, JM, Saffery, R, Willemsen, G, Bartels, M, van Beijsterveldt, CEM, Martin, NG, Medland, SE, Montgomery, GW, Lichtenstein, P, Krueger, RF, McGue, M, Pahlen, S, Christensen, K, Skytthe, A, Kyvik, KO, Saudino, KJ, Dubois, L, Boivin, M, Brendgen, M, Dionne, G, Vitaro, F, Ullemar, V, Almqvist, C, Magnusson, PKE, Corley, RP, Huibregtse, BM, Knafo-Noam, A, Mankuta, D, Abramson, L, Haworth, CMA, Plomin, R, Bjerregaard-Andersen, M, Beck-Nielsen, H, Sodemann, M, Duncan, GE, Buchwald, D, Burt, SA, Klump, KL, Llewellyn, CH, Fisher, A, Boomsma, D, Sorensen, TIA, Kaprio, J, Silventoinen, K, Li, W, Jelenkovic, A, Sund, R, Yokoyama, Y, Aaltonen, S, Piirtola, M, Sugawara, M, Tanaka, M, Matsumoto, S, Baker, LA, Tuvblad, C, Tynelius, P, Rasmussen, F, Craig, JM, Saffery, R, Willemsen, G, Bartels, M, van Beijsterveldt, CEM, Martin, NG, Medland, SE, Montgomery, GW, Lichtenstein, P, Krueger, RF, McGue, M, Pahlen, S, Christensen, K, Skytthe, A, Kyvik, KO, Saudino, KJ, Dubois, L, Boivin, M, Brendgen, M, Dionne, G, Vitaro, F, Ullemar, V, Almqvist, C, Magnusson, PKE, Corley, RP, Huibregtse, BM, Knafo-Noam, A, Mankuta, D, Abramson, L, Haworth, CMA, Plomin, R, Bjerregaard-Andersen, M, Beck-Nielsen, H, Sodemann, M, Duncan, GE, Buchwald, D, Burt, SA, Klump, KL, Llewellyn, CH, Fisher, A, Boomsma, D, Sorensen, TIA, and Kaprio, J

Abstract

BACKGROUND: Body mass index (BMI) shows strong continuity over childhood and adolescence and high childhood BMI is the strongest predictor of adult obesity. Genetic factors strongly contribute to this continuity, but it is still poorly known how their contribution changes over childhood and adolescence. Thus, we used the genetic twin design to estimate the genetic correlations of BMI from infancy to adulthood and compared them to the genetic correlations of height. METHODS: We pooled individual level data from 25 longitudinal twin cohorts including 38,530 complete twin pairs and having 283,766 longitudinal height and weight measures. The data were analyzed using Cholesky decomposition offering genetic and environmental correlations of BMI and height between all age combinations from 1 to 19 years of age. RESULTS: The genetic correlations of BMI and height were stronger than the trait correlations. For BMI, we found that genetic correlations decreased as the age between the assessments increased, a trend that was especially visible from early to middle childhood. In contrast, for height, the genetic correlations were strong between all ages. Age-to-age correlations between environmental factors shared by co-twins were found for BMI in early childhood but disappeared altogether by middle childhood. For height, shared environmental correlations persisted from infancy to adulthood. CONCLUSIONS: Our results suggest that the genes affecting BMI change over childhood and adolescence leading to decreasing age-to-age genetic correlations. This change is especially visible from early to middle childhood indicating that new genetic factors start to affect BMI in middle childhood. Identifying mediating pathways of these genetic factors can open possibilities for interventions, especially for those children with high genetic predisposition to adult obesity.

Published
2022

37. Smoking remains associated with education after controlling for social background and genetic factors in a study of 18 twin cohorts

Author

Silventoinen, K, Piirtola, M, Jelenkovic, A, Sund, R, Tarnoki, AD, Tarnoki, DL, Medda, E, Nistico, L, Toccaceli, V, Honda, C, Inui, F, Tomizawa, R, Watanabe, M, Sakai, N, Gatz, M, Butler, DA, Lee, J, Lee, SJ, Sung, J, Franz, CE, Kremen, WS, Lyons, MJ, Derom, CA, Vlietinck, RF, Loos, RJF, Tynelius, P, Rasmussen, F, Martin, NG, Medland, SE, Montgomery, GW, Brandt, I, Nilsen, TS, Harris, JR, Tyler, J, Hopper, JL, Magnusson, PKE, Pedersen, NL, Aslan, AKD, Ordonana, JR, Sanchez-Romera, JF, Colodro-Conde, L, Rebato, E, Zhang, D, Pang, Z, Tan, Q, Silberg, JL, Maes, HH, Boomsma, D, Sorensen, TIA, Korhonen, T, Kaprio, J, Silventoinen, K, Piirtola, M, Jelenkovic, A, Sund, R, Tarnoki, AD, Tarnoki, DL, Medda, E, Nistico, L, Toccaceli, V, Honda, C, Inui, F, Tomizawa, R, Watanabe, M, Sakai, N, Gatz, M, Butler, DA, Lee, J, Lee, SJ, Sung, J, Franz, CE, Kremen, WS, Lyons, MJ, Derom, CA, Vlietinck, RF, Loos, RJF, Tynelius, P, Rasmussen, F, Martin, NG, Medland, SE, Montgomery, GW, Brandt, I, Nilsen, TS, Harris, JR, Tyler, J, Hopper, JL, Magnusson, PKE, Pedersen, NL, Aslan, AKD, Ordonana, JR, Sanchez-Romera, JF, Colodro-Conde, L, Rebato, E, Zhang, D, Pang, Z, Tan, Q, Silberg, JL, Maes, HH, Boomsma, D, Sorensen, TIA, Korhonen, T, and Kaprio, J

Abstract

We tested the causality between education and smoking using the natural experiment of discordant twin pairs allowing to optimally control for background genetic and childhood social factors. Data from 18 cohorts including 10,527 monozygotic (MZ) and same-sex dizygotic (DZ) twin pairs discordant for education and smoking were analyzed by linear fixed effects regression models. Within twin pairs, education levels were lower among the currently smoking than among the never smoking co-twins and this education difference was larger within DZ than MZ pairs. Similarly, education levels were higher among former smoking than among currently smoking co-twins, and this difference was larger within DZ pairs. Our results support the hypothesis of a causal effect of education on both current smoking status and smoking cessation. However, the even greater intra-pair differences within DZ pairs, who share only 50% of their segregating genes, provide evidence that shared genetic factors also contribute to these associations.

Published
2022

38. Corrigendum: Potential genetic overlap between insomnia and sleep symptoms in major depressive disorder: A polygenic risk score analysis.

Author

Melhuish Beaupre, LM, Tiwari, AK, Gonçalves, VF, Zai, CC, Marshe, VS, Lewis, CM, Martin, NG, McIntosh, AM, Adams, MJ, Baune, BT, Levinson, DF, Boomsma, DI, Penninx, BWJH, Breen, G, Hamilton, S, Awasthi, S, Ripke, S, Jones, L, Jones, I, Byrne, EM, Hickie, IB, Potash, JP, Shi, J, Weissman, MM, Milaneschi, Y, Shyn, SI, de Geus, EJC, Willemsen, G, Brown, GM, Kennedy, JL, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Melhuish Beaupre, LM, Tiwari, AK, Gonçalves, VF, Zai, CC, Marshe, VS, Lewis, CM, Martin, NG, McIntosh, AM, Adams, MJ, Baune, BT, Levinson, DF, Boomsma, DI, Penninx, BWJH, Breen, G, Hamilton, S, Awasthi, S, Ripke, S, Jones, L, Jones, I, Byrne, EM, Hickie, IB, Potash, JP, Shi, J, Weissman, MM, Milaneschi, Y, Shyn, SI, de Geus, EJC, Willemsen, G, Brown, GM, Kennedy, JL, and Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium

Abstract

[This corrects the article DOI: 10.3389/fpsyt.2021.734077.].

Published
2022

39. The Australian Genetics of Depression Study: New Risk Loci and Dissecting Heterogeneity Between Subtypes

Author

Mitchell, BL, Campos, A, Whiteman, DC, Olsen, CM, Gordon, SD, Walker, AJ, Dean, OM, Berk, M, Hickie, IB, Medland, SE, Wray, NR, Martin, NG, Byrne, EM, Mitchell, BL, Campos, A, Whiteman, DC, Olsen, CM, Gordon, SD, Walker, AJ, Dean, OM, Berk, M, Hickie, IB, Medland, SE, Wray, NR, Martin, NG, and Byrne, EM

Abstract

BACKGROUND: Major depressive disorder (MDD) is a common and highly heterogeneous psychiatric disorder, but little is known about the genetic characterization of this heterogeneity. Understanding the genetic etiology of MDD can be challenging because large sample sizes are needed for gene discovery-often achieved with a trade-off in the depth of phenotyping. METHODS: The Australian Genetics of Depression Study is the largest stand-alone depression cohort with both genetic data and in-depth phenotyping and comprises a total of 15,792 participants of European ancestry, 92% of whom met diagnostic criteria for MDD. We leveraged the unique nature of this cohort to conduct a meta-analysis with the largest publicly available depression genome-wide association study to date and subsequently used polygenic scores to investigate genetic heterogeneity across various clinical subtypes of MDD. RESULTS: We increased the number of known genome-wide significant variants associated with depression from 103 to 126 and found evidence of association of novel genes implicated in neuronal development. We found that a polygenic score for depression explained 5.7% of variance in MDD liability in our sample. Finally, we found strong support for genetic heterogeneity in depression with differential associations of multiple psychiatric and comorbid traits with age of onset, longitudinal course, and various subtypes of MDD. CONCLUSIONS: Until now, this degree of detailed phenotyping in such a large sample of MDD cases has not been possible. Along with the discovery of novel loci, we provide support for differential pathways to illness models that recognize the overlap with other common psychiatric disorders as well as pathophysiological differences.

Published
2022

40. A saturated map of common genetic variants associated with human height

Author

Yengo, L, Vedantam, S, Marouli, E, Sidorenko, J, Bartell, E, Sakaue, S, Graff, M, Eliasen, AU, Jiang, Y, Raghavan, S, Miao, J, Arias, JD, Graham, SE, Mukamel, RE, Spracklen, CN, Yin, X, Chen, S-H, Ferreira, T, Highland, HH, Ji, Y, Karaderi, T, Lin, K, Lull, K, Malden, DE, Medina-Gomez, C, Machado, M, Moore, A, Rueger, S, Sim, X, Vrieze, S, Ahluwalia, TS, Akiyama, M, Allison, MA, Alvarez, M, Andersen, MK, Ani, A, Appadurai, V, Arbeeva, L, Bhaskar, S, Bielak, LF, Bollepalli, S, Bonnycastle, LL, Bork-Jensen, J, Bradfield, JP, Bradford, Y, Braund, PS, Brody, JA, Burgdorf, KS, Cade, BE, Cai, H, Cai, Q, Campbell, A, Canadas-Garre, M, Catamo, E, Chai, J-F, Chai, X, Chang, L-C, Chang, Y-C, Chen, C-H, Chesi, A, Choi, SH, Chung, R-H, Cocca, M, Concas, MP, Couture, C, Cuellar-Partida, G, Danning, R, Daw, EW, Degenhard, F, Delgado, GE, Delitala, A, Demirkan, A, Deng, X, Devineni, P, Dietl, A, Dimitriou, M, Dimitrov, L, Dorajoo, R, Ekici, AB, Engmann, JE, Fairhurst-Hunter, Z, Farmaki, A-E, Faul, JD, Fernandez-Lopez, J-C, Forer, L, Francescatto, M, Freitag-Wolf, S, Fuchsberger, C, Galesloot, TE, Gao, Y, Gao, Z, Geller, F, Giannakopoulou, O, Giulianini, F, Gjesing, AP, Goel, A, Gordon, SD, Gorski, M, Grove, J, Guo, X, Gustafsson, S, Haessler, J, Hansen, TF, Havulinna, AS, Haworth, SJ, He, J, Heard-Costa, N, Hebbar, P, Hindy, G, Ho, Y-LA, Hofer, E, Holliday, E, Horn, K, Hornsby, WE, Hottenga, J-J, Huang, H, Huang, J, Huerta-Chagoya, A, Huffman, JE, Hung, Y-J, Huo, S, Hwang, MY, Iha, H, Ikeda, DD, Isono, M, Jackson, AU, Jager, S, Jansen, IE, Johansson, I, Jonas, JB, Jonsson, A, Jorgensen, T, Kalafati, I-P, Kanai, M, Kanoni, S, Karhus, LL, Kasturiratne, A, Katsuya, T, Kawaguchi, T, Kember, RL, Kentistou, KA, Kim, H-N, Kim, YJ, Kleber, ME, Knol, MJ, Kurbasic, A, Lauzon, M, Le, P, Lea, R, Lee, J-Y, Leonard, HL, Li, SA, Li, X, Liang, J, Lin, H, Lin, S-Y, Liu, J, Liu, X, Lo, KS, Long, J, Lores-Motta, L, Luan, J, Lyssenko, V, Lyytikainen, L-P, Mahajan, A, Mamakou, V, Mangino, M, Manichaikul, A, Marten, J, Mattheisen, M, Mavarani, L, McDaid, AF, Meidtner, K, Melendez, TL, Mercader, JM, Milaneschi, Y, Miller, JE, Millwood, IY, Mishra, PP, Mitchell, RE, Mollehave, LT, Morgan, A, Mucha, S, Munz, M, Nakatochi, M, Nelson, CP, Nethander, M, Nho, CW, Nielsen, AA, Nolte, IM, Nongmaithem, SS, Noordam, R, Ntalla, I, Nutile, T, Pandit, A, Christofidou, P, Parna, K, Pauper, M, Petersen, ERB, Petersen, L, Pitkanen, N, Polasek, O, Poveda, A, Preuss, MH, Pyarajan, S, Raffield, LM, Rakugi, H, Ramirez, J, Rasheed, A, Raven, D, Rayner, NW, Riveros, C, Rohde, R, Ruggiero, D, Ruotsalainen, SE, Ryan, KA, Sabater-Lleal, M, Saxena, R, Scholz, M, Sendamarai, A, Shen, B, Shi, J, Shin, JH, Sidore, C, Sitlani, CM, Slieker, RKC, Smit, RAJ, Smith, A, Smith, JA, Smyth, LJ, Southam, LE, Steinthorsdottir, V, Sun, L, Takeuchi, F, Tallapragada, D, Taylor, KD, Tayo, BO, Tcheandjieu, C, Terzikhan, N, Tesolin, P, Teumer, A, Theusch, E, Thompson, DJ, Thorleifsson, G, Timmers, PRHJ, Trompet, S, Turman, C, Vaccargiu, S, van der Laan, SW, van der Most, PJ, van Klinken, JB, van Setten, J, Verma, SS, Verweij, N, Veturi, Y, Wang, CA, Wang, C, Wang, L, Wang, Z, Warren, HR, Wei, WB, Wickremasinghe, AR, Wielscher, M, Wiggins, KL, Winsvold, BS, Wong, A, Wu, Y, Wuttke, M, Xia, R, Xie, T, Yamamoto, K, Yang, J, Yao, J, Young, H, Yousri, NA, Yu, L, Zeng, L, Zhang, W, Zhang, X, Zhao, J-H, Zhao, W, Zhou, W, Zimmermann, ME, Zoledziewska, M, Adair, LS, Adams, HHH, Aguilar-Salinas, CA, Al-Mulla, F, Arnett, DK, Asselbergs, FW, Asvold, BO, Attia, J, Banas, B, Bandinelli, S, Bennett, DA, Bergler, T, Bharadwaj, D, Biino, G, Bisgaard, H, Boerwinkle, E, Boger, CA, Bonnelykke, K, Boomsma, D, Borglum, AD, Borja, JB, Bouchard, C, Bowden, DW, Brandslund, I, Brumpton, B, Buring, JE, Caulfield, MJ, Chambers, JC, Chandak, GR, Chanock, SJ, Chaturvedi, N, Chen, Y-DI, Chen, Z, Cheng, C-Y, Christophersen, IE, Ciullo, M, Cole, JW, Collins, FS, Cooper, RS, Cruz, M, Cucca, F, Cupples, LA, Cutler, MJ, Damrauer, SM, Dantoft, TM, de Borst, GJ, de Groot, LCPGM, De Jager, PL, de Kleijn, DP, de Silva, HJ, Dedoussis, G, den Hollander, A, Du, S, Easton, DF, Elders, PJM, Eliassen, AH, Ellinor, PT, Elmstahl, S, Erdmann, J, Evans, MK, Fatkin, D, Feenstra, B, Feitosa, MF, Ferrucci, L, Ford, I, Fornage, M, Franke, A, Franks, PW, Freedman, B, Gasparini, P, Gieger, C, Girotto, G, Goddard, ME, Golightly, YM, Gonzalez-Villalpando, C, Gordon-Larsen, P, Grallert, H, Grant, SFA, Grarup, N, Griffiths, L, Gudnason, V, Haiman, C, Hakonarson, H, Hansen, T, Hartman, CA, Hattersley, AT, Hayward, C, Heckbert, SR, Heng, C-K, Hengstenberg, C, Hewitt, AW, Hishigaki, H, Hoyng, CB, Huang, PL, Huang, W, Hunt, SC, Hveem, K, Hypponen, E, Iacono, WG, Ichihara, S, Ikram, MA, Isasi, CR, Jackson, RD, Jarvelin, M-R, Jin, Z-B, Jockel, K-H, Joshi, PK, Jousilahti, P, Jukema, JW, Kahonen, M, Kamatani, Y, Kang, KD, Kaprio, J, Kardia, SLR, Karpe, F, Kato, N, Kee, F, Kessler, T, Khera, A, Khor, CC, Kiemeney, LALM, Kim, B-J, Kim, EK, Kim, H-L, Kirchhof, P, Kivimaki, M, Koh, W-P, Koistinen, HA, Kolovou, GD, Kooner, JS, Kooperberg, C, Kottgen, A, Kovacs, P, Kraaijeveld, A, Kraft, P, Krauss, RM, Kumari, M, Kutalik, Z, Laakso, M, Lange, LA, Langenberg, C, Launer, LJ, Le Marchand, L, Lee, H, Lee, NR, Lehtimaki, T, Li, H, Li, L, Lieb, W, Lin, X, Lind, L, Linneberg, A, Liu, C-T, Loeffler, M, London, B, Lubitz, SA, Lye, SJ, Mackey, DA, Magi, R, Magnusson, PKE, Marcus, GM, Vidal, PM, Martin, NG, Marz, W, Matsuda, F, McGarrah, RW, McGue, M, McKnight, AJ, Medland, SE, Mellstrom, D, Metspalu, A, Mitchell, BD, Mitchell, P, Mook-Kanamori, DO, Morris, AD, Mucci, LA, Munroe, PB, Nalls, MA, Nazarian, S, Nelson, AE, Neville, MJ, Newton-Cheh, C, Nielsen, CS, Nothen, MM, Ohlsson, C, Oldehinkel, AJ, Orozco, L, Pahkala, K, Pajukanta, P, Palmer, CNA, Parra, EJ, Pattaro, C, Pedersen, O, Pennell, CE, Penninx, BWJH, Perusse, L, Peters, A, Peyser, PA, Porteous, DJ, Posthuma, D, Power, C, Pramstaller, PP, Province, MA, Qi, Q, Qu, J, Rader, DJ, Raitakari, OT, Ralhan, S, Rallidis, LS, Rao, DC, Redline, S, Reilly, DF, Reiner, AP, Rhee, SY, Ridker, PM, Rienstra, M, Ripatti, S, Ritchie, MD, Roden, DM, Rosendaal, FR, Rotter, J, Rudan, I, Rutters, F, Sabanayagam, C, Saleheen, D, Salomaa, V, Samani, NJ, Sanghera, DK, Sattar, N, Schmidt, B, Schmidt, H, Schmidt, R, Schulze, MB, Schunkert, H, Scott, LJ, Scott, RJ, Sever, P, Shiroma, EJ, Shoemaker, MB, Shu, X-O, Simonsick, EM, Sims, M, Singh, JR, Singleton, AB, Sinner, MF, Smith, JG, Snieder, H, Spector, TD, Stampfer, MJ, Stark, KJ, Strachan, DP, t' Hart, LM, Tabara, Y, Tang, H, Tardif, J-C, Thanaraj, TA, Timpson, NJ, Tonjes, A, Tremblay, A, Tuomi, T, Tuomilehto, J, Tusie-Luna, M-T, Uitterlinden, AG, van Dam, RM, van der Harst, P, Van der Velde, N, van Duijn, CM, van Schoor, NM, Vitart, V, Volker, U, Vollenweider, P, Volzke, H, Wacher-Rodarte, NH, Walker, M, Wang, YX, Wareham, NJ, Watanabe, RM, Watkins, H, Weir, DR, Werge, TM, Widen, E, Wilkens, LR, Willemsen, G, Willett, WC, Wilson, JF, Wong, T-Y, Woo, J-T, Wright, AF, Wu, J-Y, Xu, H, Yajnik, CS, Yokota, M, Yuan, J-M, Zeggini, E, Zemel, BS, Zheng, W, Zhu, X, Zmuda, JM, Zonderman, AB, Zwart, J-A, Chasman, D, Cho, YS, Heid, IM, McCarthy, M, Ng, MCY, O'Donnell, CJ, Rivadeneira, F, Thorsteinsdottir, U, Sun, Y, Tai, ES, Boehnke, M, Deloukas, P, Justice, AE, Lindgren, CM, Loos, RJF, Mohlke, KL, North, KE, Stefansson, K, Walters, RG, Winkler, TW, Young, KL, Loh, P-R, Esko, T, Assimes, TL, Auton, A, Abecasis, GR, Willer, CJ, Locke, AE, Berndt, S, Lettre, G, Frayling, TM, Okada, Y, Wood, AR, Visscher, PM, Hirschhorn, JN, Yengo, L, Vedantam, S, Marouli, E, Sidorenko, J, Bartell, E, Sakaue, S, Graff, M, Eliasen, AU, Jiang, Y, Raghavan, S, Miao, J, Arias, JD, Graham, SE, Mukamel, RE, Spracklen, CN, Yin, X, Chen, S-H, Ferreira, T, Highland, HH, Ji, Y, Karaderi, T, Lin, K, Lull, K, Malden, DE, Medina-Gomez, C, Machado, M, Moore, A, Rueger, S, Sim, X, Vrieze, S, Ahluwalia, TS, Akiyama, M, Allison, MA, Alvarez, M, Andersen, MK, Ani, A, Appadurai, V, Arbeeva, L, Bhaskar, S, Bielak, LF, Bollepalli, S, Bonnycastle, LL, Bork-Jensen, J, Bradfield, JP, Bradford, Y, Braund, PS, Brody, JA, Burgdorf, KS, Cade, BE, Cai, H, Cai, Q, Campbell, A, Canadas-Garre, M, Catamo, E, Chai, J-F, Chai, X, Chang, L-C, Chang, Y-C, Chen, C-H, Chesi, A, Choi, SH, Chung, R-H, Cocca, M, Concas, MP, Couture, C, Cuellar-Partida, G, Danning, R, Daw, EW, Degenhard, F, Delgado, GE, Delitala, A, Demirkan, A, Deng, X, Devineni, P, Dietl, A, Dimitriou, M, Dimitrov, L, Dorajoo, R, Ekici, AB, Engmann, JE, Fairhurst-Hunter, Z, Farmaki, A-E, Faul, JD, Fernandez-Lopez, J-C, Forer, L, Francescatto, M, Freitag-Wolf, S, Fuchsberger, C, Galesloot, TE, Gao, Y, Gao, Z, Geller, F, Giannakopoulou, O, Giulianini, F, Gjesing, AP, Goel, A, Gordon, SD, Gorski, M, Grove, J, Guo, X, Gustafsson, S, Haessler, J, Hansen, TF, Havulinna, AS, Haworth, SJ, He, J, Heard-Costa, N, Hebbar, P, Hindy, G, Ho, Y-LA, Hofer, E, Holliday, E, Horn, K, Hornsby, WE, Hottenga, J-J, Huang, H, Huang, J, Huerta-Chagoya, A, Huffman, JE, Hung, Y-J, Huo, S, Hwang, MY, Iha, H, Ikeda, DD, Isono, M, Jackson, AU, Jager, S, Jansen, IE, Johansson, I, Jonas, JB, Jonsson, A, Jorgensen, T, Kalafati, I-P, Kanai, M, Kanoni, S, Karhus, LL, Kasturiratne, A, Katsuya, T, Kawaguchi, T, Kember, RL, Kentistou, KA, Kim, H-N, Kim, YJ, Kleber, ME, Knol, MJ, Kurbasic, A, Lauzon, M, Le, P, Lea, R, Lee, J-Y, Leonard, HL, Li, SA, Li, X, Liang, J, Lin, H, Lin, S-Y, Liu, J, Liu, X, Lo, KS, Long, J, Lores-Motta, L, Luan, J, Lyssenko, V, Lyytikainen, L-P, Mahajan, A, Mamakou, V, Mangino, M, Manichaikul, A, Marten, J, Mattheisen, M, Mavarani, L, McDaid, AF, Meidtner, K, Melendez, TL, Mercader, JM, Milaneschi, Y, Miller, JE, Millwood, IY, Mishra, PP, Mitchell, RE, Mollehave, LT, Morgan, A, Mucha, S, Munz, M, Nakatochi, M, Nelson, CP, Nethander, M, Nho, CW, Nielsen, AA, Nolte, IM, Nongmaithem, SS, Noordam, R, Ntalla, I, Nutile, T, Pandit, A, Christofidou, P, Parna, K, Pauper, M, Petersen, ERB, Petersen, L, Pitkanen, N, Polasek, O, Poveda, A, Preuss, MH, Pyarajan, S, Raffield, LM, Rakugi, H, Ramirez, J, Rasheed, A, Raven, D, Rayner, NW, Riveros, C, Rohde, R, Ruggiero, D, Ruotsalainen, SE, Ryan, KA, Sabater-Lleal, M, Saxena, R, Scholz, M, Sendamarai, A, Shen, B, Shi, J, Shin, JH, Sidore, C, Sitlani, CM, Slieker, RKC, Smit, RAJ, Smith, A, Smith, JA, Smyth, LJ, Southam, LE, Steinthorsdottir, V, Sun, L, Takeuchi, F, Tallapragada, D, Taylor, KD, Tayo, BO, Tcheandjieu, C, Terzikhan, N, Tesolin, P, Teumer, A, Theusch, E, Thompson, DJ, Thorleifsson, G, Timmers, PRHJ, Trompet, S, Turman, C, Vaccargiu, S, van der Laan, SW, van der Most, PJ, van Klinken, JB, van Setten, J, Verma, SS, Verweij, N, Veturi, Y, Wang, CA, Wang, C, Wang, L, Wang, Z, Warren, HR, Wei, WB, Wickremasinghe, AR, Wielscher, M, Wiggins, KL, Winsvold, BS, Wong, A, Wu, Y, Wuttke, M, Xia, R, Xie, T, Yamamoto, K, Yang, J, Yao, J, Young, H, Yousri, NA, Yu, L, Zeng, L, Zhang, W, Zhang, X, Zhao, J-H, Zhao, W, Zhou, W, Zimmermann, ME, Zoledziewska, M, Adair, LS, Adams, HHH, Aguilar-Salinas, CA, Al-Mulla, F, Arnett, DK, Asselbergs, FW, Asvold, BO, Attia, J, Banas, B, Bandinelli, S, Bennett, DA, Bergler, T, Bharadwaj, D, Biino, G, Bisgaard, H, Boerwinkle, E, Boger, CA, Bonnelykke, K, Boomsma, D, Borglum, AD, Borja, JB, Bouchard, C, Bowden, DW, Brandslund, I, Brumpton, B, Buring, JE, Caulfield, MJ, Chambers, JC, Chandak, GR, Chanock, SJ, Chaturvedi, N, Chen, Y-DI, Chen, Z, Cheng, C-Y, Christophersen, IE, Ciullo, M, Cole, JW, Collins, FS, Cooper, RS, Cruz, M, Cucca, F, Cupples, LA, Cutler, MJ, Damrauer, SM, Dantoft, TM, de Borst, GJ, de Groot, LCPGM, De Jager, PL, de Kleijn, DP, de Silva, HJ, Dedoussis, G, den Hollander, A, Du, S, Easton, DF, Elders, PJM, Eliassen, AH, Ellinor, PT, Elmstahl, S, Erdmann, J, Evans, MK, Fatkin, D, Feenstra, B, Feitosa, MF, Ferrucci, L, Ford, I, Fornage, M, Franke, A, Franks, PW, Freedman, B, Gasparini, P, Gieger, C, Girotto, G, Goddard, ME, Golightly, YM, Gonzalez-Villalpando, C, Gordon-Larsen, P, Grallert, H, Grant, SFA, Grarup, N, Griffiths, L, Gudnason, V, Haiman, C, Hakonarson, H, Hansen, T, Hartman, CA, Hattersley, AT, Hayward, C, Heckbert, SR, Heng, C-K, Hengstenberg, C, Hewitt, AW, Hishigaki, H, Hoyng, CB, Huang, PL, Huang, W, Hunt, SC, Hveem, K, Hypponen, E, Iacono, WG, Ichihara, S, Ikram, MA, Isasi, CR, Jackson, RD, Jarvelin, M-R, Jin, Z-B, Jockel, K-H, Joshi, PK, Jousilahti, P, Jukema, JW, Kahonen, M, Kamatani, Y, Kang, KD, Kaprio, J, Kardia, SLR, Karpe, F, Kato, N, Kee, F, Kessler, T, Khera, A, Khor, CC, Kiemeney, LALM, Kim, B-J, Kim, EK, Kim, H-L, Kirchhof, P, Kivimaki, M, Koh, W-P, Koistinen, HA, Kolovou, GD, Kooner, JS, Kooperberg, C, Kottgen, A, Kovacs, P, Kraaijeveld, A, Kraft, P, Krauss, RM, Kumari, M, Kutalik, Z, Laakso, M, Lange, LA, Langenberg, C, Launer, LJ, Le Marchand, L, Lee, H, Lee, NR, Lehtimaki, T, Li, H, Li, L, Lieb, W, Lin, X, Lind, L, Linneberg, A, Liu, C-T, Loeffler, M, London, B, Lubitz, SA, Lye, SJ, Mackey, DA, Magi, R, Magnusson, PKE, Marcus, GM, Vidal, PM, Martin, NG, Marz, W, Matsuda, F, McGarrah, RW, McGue, M, McKnight, AJ, Medland, SE, Mellstrom, D, Metspalu, A, Mitchell, BD, Mitchell, P, Mook-Kanamori, DO, Morris, AD, Mucci, LA, Munroe, PB, Nalls, MA, Nazarian, S, Nelson, AE, Neville, MJ, Newton-Cheh, C, Nielsen, CS, Nothen, MM, Ohlsson, C, Oldehinkel, AJ, Orozco, L, Pahkala, K, Pajukanta, P, Palmer, CNA, Parra, EJ, Pattaro, C, Pedersen, O, Pennell, CE, Penninx, BWJH, Perusse, L, Peters, A, Peyser, PA, Porteous, DJ, Posthuma, D, Power, C, Pramstaller, PP, Province, MA, Qi, Q, Qu, J, Rader, DJ, Raitakari, OT, Ralhan, S, Rallidis, LS, Rao, DC, Redline, S, Reilly, DF, Reiner, AP, Rhee, SY, Ridker, PM, Rienstra, M, Ripatti, S, Ritchie, MD, Roden, DM, Rosendaal, FR, Rotter, J, Rudan, I, Rutters, F, Sabanayagam, C, Saleheen, D, Salomaa, V, Samani, NJ, Sanghera, DK, Sattar, N, Schmidt, B, Schmidt, H, Schmidt, R, Schulze, MB, Schunkert, H, Scott, LJ, Scott, RJ, Sever, P, Shiroma, EJ, Shoemaker, MB, Shu, X-O, Simonsick, EM, Sims, M, Singh, JR, Singleton, AB, Sinner, MF, Smith, JG, Snieder, H, Spector, TD, Stampfer, MJ, Stark, KJ, Strachan, DP, t' Hart, LM, Tabara, Y, Tang, H, Tardif, J-C, Thanaraj, TA, Timpson, NJ, Tonjes, A, Tremblay, A, Tuomi, T, Tuomilehto, J, Tusie-Luna, M-T, Uitterlinden, AG, van Dam, RM, van der Harst, P, Van der Velde, N, van Duijn, CM, van Schoor, NM, Vitart, V, Volker, U, Vollenweider, P, Volzke, H, Wacher-Rodarte, NH, Walker, M, Wang, YX, Wareham, NJ, Watanabe, RM, Watkins, H, Weir, DR, Werge, TM, Widen, E, Wilkens, LR, Willemsen, G, Willett, WC, Wilson, JF, Wong, T-Y, Woo, J-T, Wright, AF, Wu, J-Y, Xu, H, Yajnik, CS, Yokota, M, Yuan, J-M, Zeggini, E, Zemel, BS, Zheng, W, Zhu, X, Zmuda, JM, Zonderman, AB, Zwart, J-A, Chasman, D, Cho, YS, Heid, IM, McCarthy, M, Ng, MCY, O'Donnell, CJ, Rivadeneira, F, Thorsteinsdottir, U, Sun, Y, Tai, ES, Boehnke, M, Deloukas, P, Justice, AE, Lindgren, CM, Loos, RJF, Mohlke, KL, North, KE, Stefansson, K, Walters, RG, Winkler, TW, Young, KL, Loh, P-R, Esko, T, Assimes, TL, Auton, A, Abecasis, GR, Willer, CJ, Locke, AE, Berndt, S, Lettre, G, Frayling, TM, Okada, Y, Wood, AR, Visscher, PM, and Hirschhorn, JN

Abstract

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

Published
2022

41. Financial strain moderates genetic influences on self-rated health: support for diathesis-stress model of gene-environment interplay

Author

Finkel, D, Zavala, C, Franz, CE, Pahlen, S, Gatz, M, Pedersen, NL, Finch, BK, Aslan, AD, Catts, VS, Ericsson, M, Krueger, RF, Martin, NG, Mohan, A, Mosing, MA, Prescott, CA, Whitfield, KE, Finkel, D, Zavala, C, Franz, CE, Pahlen, S, Gatz, M, Pedersen, NL, Finch, BK, Aslan, AD, Catts, VS, Ericsson, M, Krueger, RF, Martin, NG, Mohan, A, Mosing, MA, Prescott, CA, and Whitfield, KE

Abstract

Data from the Interplay of Genes and Environment across Multiple Studies (IGEMS) consortium were used to examine predictions of different models of gene-by-environment interaction to understand how genetic variance in self-rated health (SRH) varies at different levels of financial strain. A total of 11,359 individuals from 10 twin studies in Australia, Sweden, and the United States contributed relevant data, including 2,074 monozygotic and 2,623 dizygotic twin pairs. Age ranged from 22 to 98 years, with a mean age of 61.05 (SD = 13.24). A factor model was used to create a harmonized measure of financial strain across studies and items. Twin analyses of genetic and environmental variance for SRH incorporating age, age2, sex, and financial strain moderators indicated significant financial strain moderation of genetic influences on self-rated health. Moderation results did not differ across sex or country. Genetic variance for SRH increased as financial strain increased, matching the predictions of the diathesis-stress and social comparison models for components of variance. Under these models, environmental improvements would be expected to reduce genetically based health disparities.

Published
2022

42. Genome-wide analyses of individual differences in quantitatively assessed reading- and language-related skills in up to 34,000 people

Author

Eising, E, Mirza-Schreiber, N, de Zeeuw, EL, Wang, CA, Truong, DT, Allegrini, AG, Shapland, CY, Zhu, G, Wigg, KG, Gerritse, ML, Molz, B, Alagoz, G, Gialluisi, A, Abbondanza, F, Rimfeld, K, van Donkelaar, M, Liao, Z, Jansen, PR, Andlauer, TFM, Bates, TC, Bernard, M, Blokland, K, Bonte, M, Borglum, AD, Bourgeron, T, Brandeis, D, Ceronihh, F, Csepe, V, Dale, PS, de Jong, PF, DeFries, JC, Demonet, J-F, Demontis, D, Feng, Y, Gordon, SD, Guger, SL, Hayiou-Thomas, ME, Hernandez-Cabrera, JA, Hottenga, J-J, Hulme, C, Kere, J, Kerr, EN, Koomar, T, Landerl, K, Leonard, GT, Lovett, MW, Lyytinen, H, Martin, NG, Martinelli, A, Maurer, U, Michaelson, JJ, Moll, K, Monaco, AP, Morgan, AT, Nothen, MM, Pausova, Z, Pennell, CE, Pennington, BF, Price, KM, Rajagopal, VM, Ramus, F, Richer, L, Simpson, NH, Smith, SD, Snowling, MJ, Stein, J, Struguuu, LJ, Talcott, JB, Tiemeier, H, van der Schroeff, MP, Verhoef, E, Watkins, KE, Wilkinson, M, Wright, MJ, Barr, CL, Boomsma, D, Carreiras, M, Franken, M-CJ, Gruen, JR, Luciano, M, Muller-Myhsok, B, Newbury, DF, Olson, RK, Paracchini, S, Paus, T, Plomin, R, Reilly, S, Schulte-Korn, G, Tomblin, JB, Bergen, E, Whitehouse, AJO, Willcutt, EG, St Pourcain, B, Francks, C, Fisher, SE, Eising, E, Mirza-Schreiber, N, de Zeeuw, EL, Wang, CA, Truong, DT, Allegrini, AG, Shapland, CY, Zhu, G, Wigg, KG, Gerritse, ML, Molz, B, Alagoz, G, Gialluisi, A, Abbondanza, F, Rimfeld, K, van Donkelaar, M, Liao, Z, Jansen, PR, Andlauer, TFM, Bates, TC, Bernard, M, Blokland, K, Bonte, M, Borglum, AD, Bourgeron, T, Brandeis, D, Ceronihh, F, Csepe, V, Dale, PS, de Jong, PF, DeFries, JC, Demonet, J-F, Demontis, D, Feng, Y, Gordon, SD, Guger, SL, Hayiou-Thomas, ME, Hernandez-Cabrera, JA, Hottenga, J-J, Hulme, C, Kere, J, Kerr, EN, Koomar, T, Landerl, K, Leonard, GT, Lovett, MW, Lyytinen, H, Martin, NG, Martinelli, A, Maurer, U, Michaelson, JJ, Moll, K, Monaco, AP, Morgan, AT, Nothen, MM, Pausova, Z, Pennell, CE, Pennington, BF, Price, KM, Rajagopal, VM, Ramus, F, Richer, L, Simpson, NH, Smith, SD, Snowling, MJ, Stein, J, Struguuu, LJ, Talcott, JB, Tiemeier, H, van der Schroeff, MP, Verhoef, E, Watkins, KE, Wilkinson, M, Wright, MJ, Barr, CL, Boomsma, D, Carreiras, M, Franken, M-CJ, Gruen, JR, Luciano, M, Muller-Myhsok, B, Newbury, DF, Olson, RK, Paracchini, S, Paus, T, Plomin, R, Reilly, S, Schulte-Korn, G, Tomblin, JB, Bergen, E, Whitehouse, AJO, Willcutt, EG, St Pourcain, B, Francks, C, and Fisher, SE

Abstract

The use of spoken and written language is a fundamental human capacity. Individual differences in reading- and language-related skills are influenced by genetic variation, with twin-based heritability estimates of 30 to 80% depending on the trait. The genetic architecture is complex, heterogeneous, and multifactorial, but investigations of contributions of single-nucleotide polymorphisms (SNPs) were thus far underpowered. We present a multicohort genome-wide association study (GWAS) of five traits assessed individually using psychometric measures (word reading, nonword reading, spelling, phoneme awareness, and nonword repetition) in samples of 13,633 to 33,959 participants aged 5 to 26 y. We identified genome-wide significant association with word reading (rs11208009, P = 1.098 × 10-8) at a locus that has not been associated with intelligence or educational attainment. All five reading-/language-related traits showed robust SNP heritability, accounting for 13 to 26% of trait variability. Genomic structural equation modeling revealed a shared genetic factor explaining most of the variation in word/nonword reading, spelling, and phoneme awareness, which only partially overlapped with genetic variation contributing to nonword repetition, intelligence, and educational attainment. A multivariate GWAS of word/nonword reading, spelling, and phoneme awareness maximized power for follow-up investigation. Genetic correlation analysis with neuroimaging traits identified an association with the surface area of the banks of the left superior temporal sulcus, a brain region linked to the processing of spoken and written language. Heritability was enriched for genomic elements regulating gene expression in the fetal brain and in chromosomal regions that are depleted of Neanderthal variants. Together, these results provide avenues for deciphering the biological underpinnings of uniquely human traits.

Published
2022

43. Perinatal depression is associated with a higher polygenic risk for major depressive disorder than non-perinatal depression

Author

Kiewa, J, Meltzer-Brody, S, Milgrom, J, Guintivano, J, Hickie, IB, Whiteman, DC, Olsen, CM, Colodro-Conde, L, Medland, SE, Martin, NG, Wray, NR, Byrne, EM, Kiewa, J, Meltzer-Brody, S, Milgrom, J, Guintivano, J, Hickie, IB, Whiteman, DC, Olsen, CM, Colodro-Conde, L, Medland, SE, Martin, NG, Wray, NR, and Byrne, EM

Abstract

BACKGROUND: Distinctions between major depressive disorder (MDD) and perinatal depression (PND) reflect varying views of PND, from a unique etiological subtype of MDD to an MDD episode that happens to coincide with childbirth. This case-control study investigated genetic differences between PND and MDD outside the perinatal period (non-perinatal depression or NPD). METHODS: We conducted a genome-wide association study using PND cases (Edinburgh Postnatal Depression Scale score ≥ 13) from the Australian Genetics of Depression Study 2018 data (n = 3804) and screened controls (n = 6134). Results of gene-set enrichment analysis were compared with those of women with non-PND. For six psychiatric disorders/traits, genetic correlations with PND were evaluated, and logistic regression analysis reported polygenic score (PGS) association with both PND and NPD. RESULTS: Genes differentially expressed in ovarian tissue were significantly enriched (stdBeta = 0.07, p = 3.3e-04), but were not found to be associated with NPD. The genetic correlation between PND and MDD was 0.93 (SE = 0.07; p = 3.5e-38). Compared with controls, PGS for MDD are higher for PND cases (odds ratio [OR] = 1.8, confidence interval [CI] = [1.7-1.8], p = 9.5e-140) than for NPD cases (OR = 1.6, CI = [1.5-1.7], p = 1.2e-49). Highest risk is for those reporting both antenatal and postnatal depression, irrespective of prior MDD history. CONCLUSIONS: PND has a high genetic overlap with MDD, but points of distinction focus on differential expression in ovarian tissue and higher MDD PGS, particularly for women experiencing both antenatal and postpartum PND.

Published
2022

44. Hypothesis-driven genome-wide association studies provide novel insights into genetics of reading disabilities

Author

Price, KM, Wigg, KG, Eising, E, Feng, Y, Blokland, K, Wilkinson, M, Kerr, EN, Guger, SL, Abbondanza, F, Allegrini, AG, Andlauer, TFM, Bates, TC, Bernard, M, Bonte, M, Boomsma, DI, Bourgeron, T, Brandeis, D, Carreiras, M, Ceroni, F, Csepe, V, Dale, PS, DeFries, JC, de Jong, PF, Demonet, JF, de Zeeuw, EL, Franken, M-CJ, Francks, C, Gerritse, M, Gialluisi, A, Gordon, SD, Gruen, JR, Hayiou-Thomas, ME, Hernandez-Cabrera, J, Hottenga, J-J, Hulme, C, Jansen, PR, Kere, J, Koomar, T, Landerl, K, Leonard, GT, Liao, Z, Luciano, M, Lyytinen, H, Martin, NG, Martinelli, A, Maurer, U, Michaelson, JJ, Mirza-Schreiber, N, Moll, K, Monaco, AP, Morgan, AT, Mueller-Myhsok, B, Newbury, DF, Noethen, MM, Olson, RK, Paracchini, S, Paus, T, Pausova, Z, Pennell, CE, Pennington, BF, Plomin, RJ, Ramus, F, Reilly, S, Richer, L, Rimfeld, K, Schulte-Korne, G, Shapland, CY, Simpson, NH, Smith, SD, Snowling, MJ, St Pourcain, B, Stein, JF, Talcott, JB, Tiemeier, H, Tomblin, JB, Truong, DT, van Bergen, E, van der Schroeff, MP, Van Donkelaar, M, Verhoef, E, Wang, CA, Watkins, KE, Whitehouse, AJO, Willcutt, EG, Wright, MJ, Zhu, G, Fisher, SE, Lovett, MW, Strug, LJ, Barr, CL, Price, KM, Wigg, KG, Eising, E, Feng, Y, Blokland, K, Wilkinson, M, Kerr, EN, Guger, SL, Abbondanza, F, Allegrini, AG, Andlauer, TFM, Bates, TC, Bernard, M, Bonte, M, Boomsma, DI, Bourgeron, T, Brandeis, D, Carreiras, M, Ceroni, F, Csepe, V, Dale, PS, DeFries, JC, de Jong, PF, Demonet, JF, de Zeeuw, EL, Franken, M-CJ, Francks, C, Gerritse, M, Gialluisi, A, Gordon, SD, Gruen, JR, Hayiou-Thomas, ME, Hernandez-Cabrera, J, Hottenga, J-J, Hulme, C, Jansen, PR, Kere, J, Koomar, T, Landerl, K, Leonard, GT, Liao, Z, Luciano, M, Lyytinen, H, Martin, NG, Martinelli, A, Maurer, U, Michaelson, JJ, Mirza-Schreiber, N, Moll, K, Monaco, AP, Morgan, AT, Mueller-Myhsok, B, Newbury, DF, Noethen, MM, Olson, RK, Paracchini, S, Paus, T, Pausova, Z, Pennell, CE, Pennington, BF, Plomin, RJ, Ramus, F, Reilly, S, Richer, L, Rimfeld, K, Schulte-Korne, G, Shapland, CY, Simpson, NH, Smith, SD, Snowling, MJ, St Pourcain, B, Stein, JF, Talcott, JB, Tiemeier, H, Tomblin, JB, Truong, DT, van Bergen, E, van der Schroeff, MP, Van Donkelaar, M, Verhoef, E, Wang, CA, Watkins, KE, Whitehouse, AJO, Willcutt, EG, Wright, MJ, Zhu, G, Fisher, SE, Lovett, MW, Strug, LJ, and Barr, CL

Abstract

Reading Disability (RD) is often characterized by difficulties in the phonology of the language. While the molecular mechanisms underlying it are largely undetermined, loci are being revealed by genome-wide association studies (GWAS). In a previous GWAS for word reading (Price, 2020), we observed that top single-nucleotide polymorphisms (SNPs) were located near to or in genes involved in neuronal migration/axon guidance (NM/AG) or loci implicated in autism spectrum disorder (ASD). A prominent theory of RD etiology posits that it involves disturbed neuronal migration, while potential links between RD-ASD have not been extensively investigated. To improve power to identify associated loci, we up-weighted variants involved in NM/AG or ASD, separately, and performed a new Hypothesis-Driven (HD)-GWAS. The approach was applied to a Toronto RD sample and a meta-analysis of the GenLang Consortium. For the Toronto sample (n = 624), no SNPs reached significance; however, by gene-set analysis, the joint contribution of ASD-related genes passed the threshold (p~1.45 × 10-2, threshold = 2.5 × 10-2). For the GenLang Cohort (n = 26,558), SNPs in DOCK7 and CDH4 showed significant association for the NM/AG hypothesis (sFDR q = 1.02 × 10-2). To make the GenLang dataset more similar to Toronto, we repeated the analysis restricting to samples selected for reading/language deficits (n = 4152). In this GenLang selected subset, we found significant association for a locus intergenic between BTG3-C21orf91 for both hypotheses (sFDR q < 9.00 × 10-4). This study contributes candidate loci to the genetics of word reading. Data also suggest that, although different variants may be involved, alleles implicated in ASD risk may be found in the same genes as those implicated in word reading. This finding is limited to the Toronto sample suggesting that ascertainment influences genetic associations.

Published
2022

45. Australian Parkinson's Genetics Study (APGS): Pilot (n=1532)

Author

Bivol, S, Mellick, GD, Gratten, J, Parker, R, Mulcahy, A, Mosley, PE, Poortvliet, PC, Campos, AI, Mitchell, BL, Garcia-Marin, LM, Cross, S, Ferguson, M, Lind, PA, Loesch-Mdzewska, Danuta, Visscher, PM, Medland, SE, Scherzer, CR, Martin, NG, and Rentería, ME

Subjects
110999 Neurosciences not elsewhere classified, FOS: Clinical medicine, 111799 Public Health and Health Services not elsewhere classified, 110399 Clinical Sciences not elsewhere classified, FOS: Health sciences, Uncategorized
Abstract

Purpose Parkinson's disease (PD) is a neurodegenerative disorder associated with progressive disability. While the precise aetiology is unknown, there is evidence of significant genetic and environmental influences on individual risk. The Australian Parkinson's Genetics Study seeks to study genetic and patient-reported data from a large cohort of individuals with PD in Australia to understand the sociodemographic, genetic and environmental basis of PD susceptibility, symptoms and progression. Participants In the pilot phase reported here, 1819 participants were recruited through assisted mailouts facilitated by Services Australia based on having three or more prescriptions for anti-PD medications in their Pharmaceutical Benefits Scheme records. The average age at the time of the questionnaire was 64±6 years. We collected patient-reported information and sociodemographic variables via an online (93% of the cohort) or paper-based (7%) questionnaire. One thousand five hundred and thirty-two participants (84.2%) met all inclusion criteria, and 1499 provided a DNA sample via traditional post. Findings to date 65% of participants were men, and 92% identified as being of European descent. A previous traumatic brain injury was reported by 16% of participants and was correlated with a younger age of symptom onset. At the time of the questionnaire, constipation (36% of participants), depression (34%), anxiety (17%), melanoma (16%) and diabetes (10%) were the most reported comorbid conditions. Future plans We plan to recruit sex-matched and age-matched unaffected controls, genotype all participants and collect non-motor symptoms and cognitive function data. Future work will explore the role of genetic and environmental factors in the aetiology of PD susceptibility, onset, symptoms, and progression, including as part of international PD research consortia.

Published
2022
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46. Abstract C051: Ceramide signaling regulates PDA aggression through exosome reprogramming of the stroma

Author

Audrey M. Hendley, Atsushi Urano, Xianlu L. Peng, Sudipta Ashe, Natanya R. Kerper, Tuan A. Phu, Martin Ng, Simone Giacometti, David I. Berrios, Gun H. Jang, Jen J. Yeh, Steven Gallinger, David K. Chang, Andrew V. Biankin, Valerie M. Weaver, Grace E. Kim, David W. Dawson, Robert L. Raffai, and Matthias Hebrok

Subjects
Cancer Research, Oncology
Abstract

Ceramide is a bioactive lipid signaling molecule that regulates multiple cellular processes influencing pancreatic tumor progression and drug response. The pleiotropic role of ceramide signaling in cancer includes modulation of exosome biogenesis and secretion. Smpd3 encodes an enzyme that generates ceramide through hydrolysis of sphingomyelin. Employing the KPC mouse model of pancreatic cancer, we demonstrated that Smpd3 regulates exosome biogenesis in pancreatic ductal adenocarcinoma (PDA) cells and is pro-tumorigenic during PDA progression. Ablation of Smdp3 in KPC mice significantly extends survival by 19% when compared to KPC; Smpd3wt/wt controls. KPC; Smpd3f/f mice display significantly less PanIN and tumor burden compared to KPC; Smpd3wt/wt controls. Lipidomics analysis of epithelial cell lines generated from end-stage pancreatic tumors of KPC; Smpd3f/f and KPC; Smpd3wt/wt mice demonstrated an alteration in hundreds of lipid species including ceramides, triacylglycerides, sphingomyelins, and phosphatidylcholines. Analysis of RNA-seq data of these epithelial cell lines showed a switching of primary tumors from the predominant more aggressive basal-like subtype seen in KPC; Smpd3wt/wt mice to classical in KPC; Smpd3f/f mice. Pathways analysis of our RNA-seq dataset showed an enrichment for genes involved in cellular mechanics and regulation of the tumor microenvironment. To query if Smpd3-generated exosomes have a direct effect on pancreatic tumor progression, we injected KPC; Smpd3wt/wt and KPC; Smpd3f/f mice with exosomes isolated from KPC; Smpd3f/f and KPC; Smpd3wt/wt PDA cell lines. Injection of exosomes derived from KPC; Smpd3f/f mice significantly extended survival of both Smpd3wt/wt and KPC; Smpd3f/f mice when compared to injection of exosomes isolated from KPC; Smpd3wt/wt mice, suggesting an anti-tumorigenic effect of exosomes isolated from Smpd3-deficient PDA cell lines. We observed a decrease in extracellular matrix collagen abundance and fewer activated stellate cells and fibroblasts in KPC; Smpd3f/f compared to control KPC; Smpd3wt/wt pancreata. Abrogation of Smpd3 expression also affected immune cell infiltration, as demonstrated by a significant increase in iNOS+ F4/80+ double positive macrophages in KPC; Smpd3f/f pancreata when compared to KPC; Smpd3wt/wt pancreata. Loss of Smpd3 resulted in a significant reduction in CD31+ endothelial cells in pancreatic tumors of KPC; Smpd3f/f mice when compared to KPC; Smpd3wt/wt mice, which may influence the ability of chemotherapeutics to enter pancreatic tumors. Our patient data demonstrate that high SMPD3 expression in surgically resected, treatment naive PDA significantly correlated with longer patient survival when patients received adjuvant chemotherapy, more than 95% of which was gemcitabine. Collectively, our data show that ceramide-dependent exosomes promote tumorigenesis, specifically activation of stellate cells and fibroblasts – which may in turn induce a stiff, fibrotic, proinflammatory tumor microenvironment that also impedes vasculature formation. Citation Format: Audrey M. Hendley, Atsushi Urano, Xianlu L. Peng, Sudipta Ashe, Natanya R. Kerper, Tuan A. Phu, Martin Ng, Simone Giacometti, David I. Berrios, Gun H. Jang, Jen J. Yeh, Steven Gallinger, David K. Chang, Andrew V. Biankin, Valerie M. Weaver, Grace E. Kim, David W. Dawson, Robert L. Raffai, Matthias Hebrok. Ceramide signaling regulates PDA aggression through exosome reprogramming of the stroma [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr C051.

Published
2022

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