Author: "Wray, Naomi R." / Publication Type: Magazines - Searchworks@Jio Institute Digital Library Search Results

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1. Patterns of stressful life events and polygenic scores for five mental disorders and neuroticism among adults with depression

Author: Crouse, Jacob J., Park, Shin Ho, Byrne, Enda M., Mitchell, Brittany L., Scott, Jan, Medland, Sarah E., Lin, Tian, Wray, Naomi R., Martin, Nicholas G., and Hickie, Ian B.
Abstract: The dominant (‘general’) version of the diathesis-stress theory of depression views stressors and genetic vulnerability as independent risks. In the Australian Genetics of Depression Study (N= 14,146; 75% female), we tested whether polygenic scores (PGS) for major depression, bipolar disorder, schizophrenia, anxiety, ADHD, and neuroticism were associated with reported exposure to 32 childhood, past-year, lifetime, and accumulated stressful life events (SLEs). In false discovery rate-corrected models, the clearest PGS-SLE relationships were for the ADHD- and depression-PGSs, and to a lesser extent, the anxiety- and schizophrenia-PGSs. We describe the associations for childhood and accumulated SLEs, and the 2–3 strongest past-year/lifetime SLE associations. Higher ADHD-PGS was associated with all childhood SLEs (emotional abuse, emotional neglect, physical neglect; ORs = 1.09–1.14; p’s < 1.3 × 10−5), more accumulated SLEs, and reported exposure to sudden violent death (OR = 1.23; p= 3.6 × 10−5), legal troubles (OR = 1.15; p= 0.003), and sudden accidental death (OR = 1.14; p= 0.006). Higher depression-PGS was associated with all childhood SLEs (ORs = 1.07–1.12; p’s < 0.013), more accumulated SLEs, and severe human suffering (OR = 1.17; p= 0.003), assault with a weapon (OR = 1.12; p= 0.003), and living in unpleasant surroundings (OR = 1.11; p= 0.001). Higher anxiety-PGS was associated with childhood emotional abuse (OR = 1.08; p= 1.6 × 10−4), more accumulated SLEs, and serious accident (OR = 1.23; p= 0.004), physical assault (OR = 1.08; p= 2.2 × 10−4), and transportation accident (OR = 1.07; p= 0.001). Higher schizophrenia-PGS was associated with all childhood SLEs (ORs = 1.12–1.19; p’s < 9.3−8), more accumulated SLEs, and severe human suffering (OR = 1.16; p= 0.003). Higher neuroticism-PGS was associated with living in unpleasant surroundings (OR = 1.09; p= 0.007) and major financial troubles (OR = 1.06; p= 0.014). A reversed pattern was seen for the bipolar-PGS, with lower odds of reported physical assault (OR = 0.95; p= 0.014), major financial troubles (OR = 0.93; p= 0.004), and living in unpleasant surroundings (OR = 0.92; p= 0.007). Genetic risk for several mental disorders influences reported exposure to SLEs among adults with moderately severe, recurrent depression. Our findings emphasise that stressors and diatheses are inter-dependent and challenge diagnosis and subtyping (e.g., reactive/endogenous) based on life events.
Published: 2024
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2. Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries

Author: Zheng, Zhili, Liu, Shouye, Sidorenko, Julia, Wang, Ying, Lin, Tian, Yengo, Loic, Turley, Patrick, Ani, Alireza, Wang, Rujia, Nolte, Ilja M., Snieder, Harold, Yang, Jian, Wray, Naomi R., Goddard, Michael E., Visscher, Peter M., and Zeng, Jian
Abstract: We develop a method, SBayesRC, that integrates genome-wide association study (GWAS) summary statistics with functional genomic annotations to improve polygenic prediction of complex traits. Our method is scalable to whole-genome variant analysis and refines signals from functional annotations by allowing them to affect both causal variant probability and causal effect distribution. We analyze 50 complex traits and diseases using ∼7 million common single-nucleotide polymorphisms (SNPs) and 96 annotations. SBayesRC improves prediction accuracy by 14% in European ancestry and up to 34% in cross-ancestry prediction compared to the baseline method SBayesR, which does not use annotations, and outperforms other methods, including LDpred2, LDpred-funct, MegaPRS, PolyPred-S and PRS-CSx. Investigation of factors affecting prediction accuracy identifies a significant interaction between SNP density and annotation information, suggesting whole-genome sequence variants with annotations may further improve prediction. Functional partitioning analysis highlights a major contribution of evolutionary constrained regions to prediction accuracy and the largest per-SNP contribution from nonsynonymous SNPs.
Published: 2024
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3. Genome-wide copy number variation association study in anorexia nervosa

Author: Walker, Alicia, Karlsson, Robert, Szatkiewicz, Jin P., Thornton, Laura M., Yilmaz, Zeynep, Leppä, Virpi M., Savva, Androula, Lin, Tian, Sidorenko, Julia, McRae, Allan, Kirov, George, Davies, Helena L., Fundín, Bengt T., Chawner, Samuel J. R. A., Song, Jie, Borg, Stina, Wen, Jia, Watson, Hunna J., Munn-Chernoff, Melissa A., Baker, Jessica H., Gordon, Scott, Berrettini, Wade H., Brandt, Harry, Crawford, Steven, Halmi, Katherine A., Kaplan, Allan S., Kaye, Walter H., Mitchell, James, Strober, Michael, Woodside, D. Blake, Pedersen, Nancy L., Parker, Richard, Jordan, Jennifer, Kennedy, Martin A., Birgegård, Andreas, Landén, Mikael, Martin, Nicholas G., Sullivan, Patrick F., Bulik, Cynthia M., and Wray, Naomi R.
Abstract: This study represents the first large-scale investigation of rare (<1% population frequency) copy number variants (CNVs) in anorexia nervosa (AN). Large, rare CNVs are reported to be causally associated with anthropometric traits, neurodevelopmental disorders, and schizophrenia, yet their role in the genetic basis of AN is unclear. Using genome-wide association study (GWAS) array data from the Anorexia Nervosa Genetics Initiative (ANGI), which included 7414 AN case and 5044 controls, we investigated the association of 67 well-established syndromic CNVs and 178 pleiotropic disease-risk dosage-sensitive CNVs with AN. To identify novel CNV regions (CNVRs) that increase the risk of AN, we conducted genome-wide association studies with a focus on rare CNV-breakpoints (CNV-GWAS). We found no net enrichment of rare CNVs, either deletions or duplications, in AN, and none of the well-established syndromic or pleiotropic CNVs had a significant association with AN status. However, the CNV-GWAS found 21 nominally associated CNVRs that contribute to AN risk, covering protein-coding genes implicated in synaptic function, metabolic/mitochondrial factors, and lipid characteristics, like the CD36(7q21.11) gene, which transports long-chain fatty acids into cells. CNVRs intersecting genes previously related to neurodevelopmental traits include deletions of NRXN1intron 5 (2p16.3), IMMP2L(7q31.1), and PTPRD(9p23). Overall, given that our study is well powered to detect the CNV burden level reported for schizophrenia, we can conclude that rare CNVs have a limited role in the etiology of AN, as reported for bipolar disorder. Our nominal associations for the 21 discovered CNVRs are consistent with AN being a metabo-psychiatric trait, as demonstrated by the common genetic architecture of AN, and we provide association results to allow for replication in future research.
Published: 2024
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4. 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, Jacqueline, Meltzer-Brody, Samantha, Milgrom, Jeannette, Guintivano, Jerry, Hickie, Ian B., Whiteman, David C., Olsen, Catherine M., Medland, Sarah E., Martin, Nicholas G., Wray, Naomi R., and Byrne, Enda M.
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= 1.4 × 10−3). The male depression sample did not report any significant results, and the point estimate of total protein (OR = 0.98, CI = [0.92–1.04], p= 4.7 × 10−1) did not indicate any association. Local genetic correlation analysis highlighted significant genetic correlation between PND and total protein, located in 5q13.3 (rG= 0.68, CI = [0.33, 1.0], p= 3.6 × 10−4). Discussion and Conclusion:Multiple lines of evidence from genetic analysis highlight an association between total serum protein levels and depression in females. Further research involving prospective measurement of total protein and depressive symptoms is warranted.
Published: 2023
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5. Interactions between the lipidome and genetic and environmental factors in autism

Author: Yap, Chloe X., Henders, Anjali K., Alvares, Gail A., Giles, Corey, Huynh, Kevin, Nguyen, Anh, Wallace, Leanne, McLaren, Tiana, Yang, Yuanhao, Hernandez, Leanna M., Gandal, Michael J., Hansell, Narelle K., Cleary, Dominique, Grove, Rachel, Hafekost, Claire, Harun, Alexis, Holdsworth, Helen, Jellett, Rachel, Khan, Feroza, Lawson, Lauren P., Leslie, Jodie, Levis Frenk, Mira, Masi, Anne, Mathew, Nisha E., Muniandy, Melanie, Nothard, Michaela, Miller, Jessica L., Nunn, Lorelle, Strike, Lachlan T., Cadby, Gemma, Moses, Eric K., de Zubicaray, Greig I., Thompson, Paul M., McMahon, Katie L., Wright, Margaret J., Visscher, Peter M., Dawson, Paul A., Dissanayake, Cheryl, Eapen, Valsamma, Heussler, Helen S., Whitehouse, Andrew J. O., Meikle, Peter J., Wray, Naomi R., and Gratten, Jacob
Abstract: Autism omics research has historically been reductionist and diagnosis centric, with little attention paid to common co-occurring conditions (for example, sleep and feeding disorders) and the complex interplay between molecular profiles and neurodevelopment, genetics, environmental factors and health. Here we explored the plasma lipidome (783 lipid species) in 765 children (485 diagnosed with autism spectrum disorder (ASD)) within the Australian Autism Biobank. We identified lipids associated with ASD diagnosis (n= 8), sleep disturbances (n= 20) and cognitive function (n= 8) and found that long-chain polyunsaturated fatty acids may causally contribute to sleep disturbances mediated by the FADSgene cluster. We explored the interplay of environmental factors with neurodevelopment and the lipidome, finding that sleep disturbances and unhealthy diet have a convergent lipidome profile (with potential mediation by the microbiome) that is also independently associated with poorer adaptive function. In contrast, ASD lipidome differences were accounted for by dietary differences and sleep disturbances. We identified a large chr19p13.2 copy number variant genetic deletion spanning the LDLRgene and two high-confidence ASD genes (ELAVL3and SMARCA4) in one child with an ASD diagnosis and widespread low-density lipoprotein-related lipidome derangements. Lipidomics captures the complexity of neurodevelopment, as well as the biological effects of conditions that commonly affect quality of life among autistic people.
Published: 2023
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6. Clinical, demographic, and genetic risk factors of treatment‐attributed suicidality in >10,000 Australian adults taking antidepressants

Author: Campos, Adrian I., Byrne, Enda M., Iorfino, Frank, Fabbri, Chiara, Hickie, Ian B., Lewis, Cathryn M., Wray, Naomi R., Medland, Sarah E., Rentería, Miguel E., and Martin, Nicholas G.
Abstract: Emergence of suicidal symptoms has been reported as a potential antidepressant adverse drug reaction. Identifying risk factors associated could increase our understanding of this phenomenon and stratify individuals at higher risk. Logistic regressions were used to identify risk factors of self‐reported treatment‐attributed suicidal ideation (TASI). We then employed classifiers to test the predictive ability of the variables identified. A TASI GWAS, as well as SNP‐based heritability estimation, were performed. GWAS replication was sought from an independent study. Significant associations were found for age and comorbid conditions, including bipolar and personality disorders. Participants reporting TASI from one antidepressant were more likely to report TASI from other antidepressants. No genetic loci associated with TAS I (p< 5e‐8) were identified. Of 32 independent variants with suggestive association (p< 1e‐5), 27 lead SNPs were available in a replication dataset from the GENDEP study. Only one variant showed a consistent effect and nominal association in the independent replication sample. Classifiers were able to stratify non‐TASI from TASI participants (AUC = 0.77) and those reporting treatment‐attributed suicide attempts (AUC = 0.85). The pattern of TASI co‐occurrence across participants suggest nonspecific factors underlying its etiology. These findings provide insights into the underpinnings of TASI and serve as a proof‐of‐concept of the use of classifiers for risk stratification.
Published: 2022
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7. Polygenic risk score analysis for amyotrophic lateral sclerosis leveraging cognitive performance, educational attainment and schizophrenia

Author: Restuadi, Restuadi, Garton, Fleur C., Benyamin, Beben, Lin, Tian, Williams, Kelly L., Vinkhuyzen, Anna, van Rheenen, Wouter, Zhu, Zhihong, Laing, Nigel G., Mather, Karen A., Sachdev, Perminder S., Ngo, Shyuan T., Steyn, Frederik J., Wallace, Leanne, Henders, Anjali K., Visscher, Peter M., Needham, Merrilee, Mathers, Susan, Nicholson, Garth, Rowe, Dominic B., Henderson, Robert D., McCombe, Pamela A., Pamphlett, Roger, Blair, Ian P., Wray, Naomi R., and McRae, Allan F.
Abstract: Amyotrophic Lateral Sclerosis (ALS) is recognised to be a complex neurodegenerative disease involving both genetic and non-genetic risk factors. The underlying causes and risk factors for the majority of cases remain unknown; however, ever-larger genetic data studies and methodologies promise an enhanced understanding. Recent analyses using published summary statistics from the largest ALS genome-wide association study (GWAS) (20,806 ALS cases and 59,804 healthy controls) identified that schizophrenia (SCZ), cognitive performance (CP) and educational attainment (EA) related traits were genetically correlated with ALS. To provide additional evidence for these correlations, we built single and multi-trait genetic predictors using GWAS summary statistics for ALS and these traits, (SCZ, CP, EA) in an independent Australian cohort (846 ALS cases and 665 healthy controls). We compared methods for generating the risk predictors and found that the combination of traits improved the prediction (Nagelkerke-R2) of the case–control logistic regression. The combination of ALS, SCZ, CP, and EA, using the SBayesR predictor method gave the highest prediction (Nagelkerke-R2) of 0.027 (Pvalue = 4.6 × 10−8), with the odds-ratio for estimated disease risk between the highest and lowest deciles of individuals being 3.15 (95% CI 1.96–5.05). These results support the genetic correlation between ALS, SCZ, CP and EA providing a better understanding of the complexity of ALS.
Published: 2022
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8. Mapping genomic loci implicates genes and synaptic biology in schizophrenia

Author: Trubetskoy, Vassily, Pardiñas, Antonio F., Qi, Ting, Panagiotaropoulou, Georgia, Awasthi, Swapnil, Bigdeli, Tim B., Bryois, Julien, Chen, Chia-Yen, Dennison, Charlotte A., Hall, Lynsey S., Lam, Max, Watanabe, Kyoko, Frei, Oleksandr, Ge, Tian, Harwood, Janet C., Koopmans, Frank, Magnusson, Sigurdur, Richards, Alexander L., Sidorenko, Julia, Wu, Yang, Zeng, Jian, Grove, Jakob, Kim, Minsoo, Li, Zhiqiang, Voloudakis, Georgios, Zhang, Wen, Adams, Mark, Agartz, Ingrid, Atkinson, Elizabeth G., Agerbo, Esben, Al Eissa, Mariam, Albus, Margot, Alexander, Madeline, Alizadeh, Behrooz Z., Alptekin, Köksal, Als, Thomas D., Amin, Farooq, Arolt, Volker, Arrojo, Manuel, Athanasiu, Lavinia, Azevedo, Maria Helena, Bacanu, Silviu A., Bass, Nicholas J., Begemann, Martin, Belliveau, Richard A., Bene, Judit, Benyamin, Beben, Bergen, Sarah E., Blasi, Giuseppe, Bobes, Julio, Bonassi, Stefano, Braun, Alice, Bressan, Rodrigo Affonseca, Bromet, Evelyn J., Bruggeman, Richard, Buckley, Peter F., Buckner, Randy L., Bybjerg-Grauholm, Jonas, Cahn, Wiepke, Cairns, Murray J., Calkins, Monica E., Carr, Vaughan J., Castle, David, Catts, Stanley V., Chambert, Kimberley D., Chan, Raymond C. K., Chaumette, Boris, Cheng, Wei, Cheung, Eric F. C., Chong, Siow Ann, Cohen, David, Consoli, Angèle, Cordeiro, Quirino, Costas, Javier, Curtis, Charles, Davidson, Michael, Davis, Kenneth L., de Haan, Lieuwe, Degenhardt, Franziska, DeLisi, Lynn E., Demontis, Ditte, Dickerson, Faith, Dikeos, Dimitris, Dinan, Timothy, Djurovic, Srdjan, Duan, Jubao, Ducci, Giuseppe, Dudbridge, Frank, Eriksson, Johan G., Fañanás, Lourdes, Faraone, Stephen V., Fiorentino, Alessia, Forstner, Andreas, Frank, Josef, Freimer, Nelson B., Fromer, Menachem, Frustaci, Alessandra, Gadelha, Ary, Genovese, Giulio, Gershon, Elliot S., Giannitelli, Marianna, Giegling, Ina, Giusti-Rodríguez, Paola, Godard, Stephanie, Goldstein, Jacqueline I., González Peñas, Javier, González-Pinto, Ana, Gopal, Srihari, Gratten, Jacob, Green, Michael F., Greenwood, Tiffany A., Guillin, Olivier, Gülöksüz, Sinan, Gur, Raquel E., Gur, Ruben C., Gutiérrez, Blanca, Hahn, Eric, Hakonarson, Hakon, Haroutunian, Vahram, Hartmann, Annette M., Harvey, Carol, Hayward, Caroline, Henskens, Frans A., Herms, Stefan, Hoffmann, Per, Howrigan, Daniel P., Ikeda, Masashi, Iyegbe, Conrad, Joa, Inge, Julià, Antonio, Kähler, Anna K., Kam-Thong, Tony, Kamatani, Yoichiro, Karachanak-Yankova, Sena, Kebir, Oussama, Keller, Matthew C., Kelly, Brian J., Khrunin, Andrey, Kim, Sung-Wan, Klovins, Janis, Kondratiev, Nikolay, Konte, Bettina, Kraft, Julia, Kubo, Michiaki, Kučinskas, Vaidutis, Kučinskiene, Zita Ausrele, Kusumawardhani, Agung, Kuzelova-Ptackova, Hana, Landi, Stefano, Lazzeroni, Laura C., Lee, Phil H., Legge, Sophie E., Lehrer, Douglas S., Lencer, Rebecca, Lerer, Bernard, Li, Miaoxin, Lieberman, Jeffrey, Light, Gregory A., Limborska, Svetlana, Liu, Chih-Min, Lönnqvist, Jouko, Loughland, Carmel M., Lubinski, Jan, Luykx, Jurjen J., Lynham, Amy, Macek, Milan, Mackinnon, Andrew, Magnusson, Patrik K. E., Maher, Brion S., Maier, Wolfgang, Malaspina, Dolores, Mallet, Jacques, Marder, Stephen R., Marsal, Sara, Martin, Alicia R., Martorell, Lourdes, Mattheisen, Manuel, McCarley, Robert W., McDonald, Colm, McGrath, John J., Medeiros, Helena, Meier, Sandra, Melegh, Bela, Melle, Ingrid, Mesholam-Gately, Raquelle I., Metspalu, Andres, Michie, Patricia T., Milani, Lili, Milanova, Vihra, Mitjans, Marina, Molden, Espen, Molina, Esther, Molto, María Dolores, Mondelli, Valeria, Moreno, Carmen, Morley, Christopher P., Muntané, Gerard, Murphy, Kieran C., Myin-Germeys, Inez, Nenadić, Igor, Nestadt, Gerald, Nikitina-Zake, Liene, Noto, Cristiano, Nuechterlein, Keith H., O’Brien, Niamh Louise, O’Neill, F. Anthony, Oh, Sang-Yun, Olincy, Ann, Ota, Vanessa Kiyomi, Pantelis, Christos, Papadimitriou, George N., Parellada, Mara, Paunio, Tiina, Pellegrino, Renata, Periyasamy, Sathish, Perkins, Diana O., Pfuhlmann, Bruno, Pietiläinen, Olli, Pimm, Jonathan, Porteous, David, Powell, John, Quattrone, Diego, Quested, Digby, Radant, Allen D., Rampino, Antonio, Rapaport, Mark H., Rautanen, Anna, Reichenberg, Abraham, Roe, Cheryl, Roffman, Joshua L., Roth, Julian, Rothermundt, Matthias, Rutten, Bart P. F., Saker-Delye, Safaa, Salomaa, Veikko, Sanjuan, Julio, Santoro, Marcos Leite, Savitz, Adam, Schall, Ulrich, Scott, Rodney J., Seidman, Larry J., Sharp, Sally Isabel, Shi, Jianxin, Siever, Larry J., Sigurdsson, Engilbert, Sim, Kang, Skarabis, Nora, Slominsky, Petr, So, Hon-Cheong, Sobell, Janet L., Söderman, Erik, Stain, Helen J., Steen, Nils Eiel, Steixner-Kumar, Agnes A., Stögmann, Elisabeth, Stone, William S., Straub, Richard E., Streit, Fabian, Strengman, Eric, Stroup, T. Scott, Subramaniam, Mythily, Sugar, Catherine A., Suvisaari, Jaana, Svrakic, Dragan M., Swerdlow, Neal R., Szatkiewicz, Jin P., Ta, Thi Minh Tam, Takahashi, Atsushi, Terao, Chikashi, Thibaut, Florence, Toncheva, Draga, Tooney, Paul A., Torretta, Silvia, Tosato, Sarah, Tura, Gian Battista, Turetsky, Bruce I., Üçok, Alp, Vaaler, Arne, van Amelsvoort, Therese, van Winkel, Ruud, Veijola, Juha, Waddington, John, Walter, Henrik, Waterreus, Anna, Webb, Bradley T., Weiser, Mark, Williams, Nigel M., Witt, Stephanie H., Wormley, Brandon K., Wu, Jing Qin, Xu, Zhida, Yolken, Robert, Zai, Clement C., Zhou, Wei, Zhu, Feng, Zimprich, Fritz, Atbaşoğlu, Eşref Cem, Ayub, Muhammad, Benner, Christian, Bertolino, Alessandro, Black, Donald W., Bray, Nicholas J., Breen, Gerome, Buccola, Nancy G., Byerley, William F., Chen, Wei J., Cloninger, C. Robert, Crespo-Facorro, Benedicto, Donohoe, Gary, Freedman, Robert, Galletly, Cherrie, Gandal, Michael J., Gennarelli, Massimo, Hougaard, David M., Hwu, Hai-Gwo, Jablensky, Assen V., McCarroll, Steven A., Moran, Jennifer L., Mors, Ole, Mortensen, Preben B., Müller-Myhsok, Bertram, Neil, Amanda L., Nordentoft, Merete, Pato, Michele T., Petryshen, Tracey L., Pirinen, Matti, Pulver, Ann E., Schulze, Thomas G., Silverman, Jeremy M., Smoller, Jordan W., Stahl, Eli A., Tsuang, Debby W., Vilella, Elisabet, Wang, Shi-Heng, Xu, Shuhua, Adolfsson, Rolf, Arango, Celso, Baune, Bernhard T., Belangero, Sintia Iole, Børglum, Anders D., Braff, David, Bramon, Elvira, Buxbaum, Joseph D., Campion, Dominique, Cervilla, Jorge A., Cichon, Sven, Collier, David A., Corvin, Aiden, Curtis, David, Forti, Marta Di, Domenici, Enrico, Ehrenreich, Hannelore, Escott-Price, Valentina, Esko, Tõnu, Fanous, Ayman H., Gareeva, Anna, Gawlik, Micha, Gejman, Pablo V., Gill, Michael, Glatt, Stephen J., Golimbet, Vera, Hong, Kyung Sue, Hultman, Christina M., Hyman, Steven E., Iwata, Nakao, Jönsson, Erik G., Kahn, René S., Kennedy, James L., Khusnutdinova, Elza, Kirov, George, Knowles, James A., Krebs, Marie-Odile, Laurent-Levinson, Claudine, Lee, Jimmy, Lencz, Todd, Levinson, Douglas F., Li, Qingqin S., Liu, Jianjun, Malhotra, Anil K., Malhotra, Dheeraj, McIntosh, Andrew, McQuillin, Andrew, Menezes, Paulo R., Morgan, Vera A., Morris, Derek W., Mowry, Bryan J., Murray, Robin M., Nimgaonkar, Vishwajit, Nöthen, Markus M., Ophoff, Roel A., Paciga, Sara A., Palotie, Aarno, Pato, Carlos N., Qin, Shengying, Rietschel, Marcella, Riley, Brien P., Rivera, Margarita, Rujescu, Dan, Saka, Meram C., Sanders, Alan R., Schwab, Sibylle G., Serretti, Alessandro, Sham, Pak C., Shi, Yongyong, St Clair, David, Stefánsson, Hreinn, Stefansson, Kari, Tsuang, Ming T., van Os, Jim, Vawter, Marquis P., Weinberger, Daniel R., Werge, Thomas, Wildenauer, Dieter B., Yu, Xin, Yue, Weihua, Holmans, Peter A., Pocklington, Andrew J., Roussos, Panos, Vassos, Evangelos, Verhage, Matthijs, Visscher, Peter M., Yang, Jian, Posthuma, Danielle, Andreassen, Ole A., Kendler, Kenneth S., Owen, Michael J., Wray, Naomi R., Daly, Mark J., Huang, Hailiang, Neale, Benjamin M., Sullivan, Patrick F., Ripke, Stephan, Walters, James T. R., and O’Donovan, Michael C.
Abstract: Schizophrenia has a heritability of 60–80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2Aand transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies.
Published: 2022
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9. Impact of CYP2C19 metaboliser status on SSRI response: a retrospective study of 9500 participants of the Australian Genetics of Depression Study

Author: Campos, Adrian I., Byrne, Enda M., Mitchell, Brittany L., Wray, Naomi R., Lind, Penelope A., Licinio, Julio, Medland, Sarah E., Martin, Nicholas G., Hickie, Ian B., and Rentería, Miguel E.
Abstract: Background: Variation within the CYP2C19gene has been linked to differential metabolism of selective serotonin reuptake inhibitors (SSRIs). Pharmacogenetic recommendations based on the effect of CYP2C19variants have been made available and are used increasingly by clinical practitioners. Nonetheless, the underlying assumption linking differential metabolism to efficacy or adverse side effects remains understudied. Here, we aim to fill this gap by studying CYP2C19polymorphisms and inferred metabolism and patient-reported antidepressant response in a sample of 9531 Australian adults who have taken SSRIs. Methods: Metaboliser status was inferred for participants based on CYP2C19alleles. Primary analysis consisted of assessing differences in treatment efficacy and tolerability between normal(reference) and: ultrarapid, rapid, intermediateand poormetabolisers. Results: Across medications, poor metabolisers reported a higher efficacy, whereas rapid metabolisers reported higher tolerability. When stratified by drug, associations between metaboliser status and efficacy did not survive multiple testing correction. Intermediate metabolisers were at greater odds of reporting any side effect for sertraline and higher number of side effects across medications and for sertraline. Conclusions: The effects between metaboliser status and treatment efficacy, tolerability and side effects were in the expected direction. Our power analysis suggests we would detect moderate to large effects, at least nominally. Reduced power may also be explained by heterogeneity in antidepressant dosages or concomitant medications, which we did not measure. The fact that we identify slower metabolisers to be at higher risk of side effects even without adjusting for clinical titration, and the nominally significant associations consistent with the expected metabolic effects provide new evidence for the link between CYP2C19 metabolism and SSRI response. Nonetheless, longitudinal and interventional designs such as randomized clinical trials that stratify by metaboliser status are necessary to establish the effects of CYP2C19metabolism on SSRI treatment efficacy or adverse effects.
Published: 2022
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10. From Mendel to quantitative genetics in the genome era: the scientific legacy of W. G. Hill

Author: Charlesworth, Brian, Goddard, Michael E., Meyer, Karin, Visscher, Peter M., Weir, Bruce S., and Wray, Naomi R.
Abstract: The quantitative geneticist W. G. (‘Bill’) Hill, awardee of the 2018 Darwin Medal of the Royal Society and the 2019 Mendel Medal of the Genetics Society (United Kingdom), died on 17 December 2021 at the age of 81 years. Here, we pay tribute to his multiple key scientific contributions, which span population and evolutionary genetics, animal and plant breeding and human genetics. We discuss his theoretical research on the role of linkage disequilibrium (LD) and mutational variance in the response to selection, the origin of the widely used LD metric r2in genomic association studies, the genetic architecture of complex traits, the quantification of the variation in realized relationships given a pedigree relationship and much more. We demonstrate that basic theoretical research in quantitative and statistical genetics has led to profound insights into the genetics and evolution of complex traits and made predictions that were subsequently empirically validated, often decades later.
Published: 2022
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11. Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology

Author: van Rheenen, Wouter, van der Spek, Rick A. A., Bakker, Mark K., van Vugt, Joke J. F. A., Hop, Paul J., Zwamborn, Ramona A. J., de Klein, Niek, Westra, Harm-Jan, Bakker, Olivier B., Deelen, Patrick, Shireby, Gemma, Hannon, Eilis, Moisse, Matthieu, Baird, Denis, Restuadi, Restuadi, Dolzhenko, Egor, Dekker, Annelot M., Gawor, Klara, Westeneng, Henk-Jan, Tazelaar, Gijs H. P., van Eijk, Kristel R., Kooyman, Maarten, Byrne, Ross P., Doherty, Mark, Heverin, Mark, Al Khleifat, Ahmad, Iacoangeli, Alfredo, Shatunov, Aleksey, Ticozzi, Nicola, Cooper-Knock, Johnathan, Smith, Bradley N., Gromicho, Marta, Chandran, Siddharthan, Pal, Suvankar, Morrison, Karen E., Shaw, Pamela J., Hardy, John, Orrell, Richard W., Sendtner, Michael, Meyer, Thomas, Başak, Nazli, van der Kooi, Anneke J., Ratti, Antonia, Fogh, Isabella, Gellera, Cinzia, Lauria, Giuseppe, Corti, Stefania, Cereda, Cristina, Sproviero, Daisy, D’Alfonso, Sandra, Sorarù, Gianni, Siciliano, Gabriele, Filosto, Massimiliano, Padovani, Alessandro, Chiò, Adriano, Calvo, Andrea, Moglia, Cristina, Brunetti, Maura, Canosa, Antonio, Grassano, Maurizio, Beghi, Ettore, Pupillo, Elisabetta, Logroscino, Giancarlo, Nefussy, Beatrice, Osmanovic, Alma, Nordin, Angelica, Lerner, Yossef, Zabari, Michal, Gotkine, Marc, Baloh, Robert H., Bell, Shaughn, Vourc’h, Patrick, Corcia, Philippe, Couratier, Philippe, Millecamps, Stéphanie, Meininger, Vincent, Salachas, François, Mora Pardina, Jesus S., Assialioui, Abdelilah, Rojas-García, Ricardo, Dion, Patrick A., Ross, Jay P., Ludolph, Albert C., Weishaupt, Jochen H., Brenner, David, Freischmidt, Axel, Bensimon, Gilbert, Brice, Alexis, Durr, Alexandra, Payan, Christine A. M., Saker-Delye, Safa, Wood, Nicholas W., Topp, Simon, Rademakers, Rosa, Tittmann, Lukas, Lieb, Wolfgang, Franke, Andre, Ripke, Stephan, Braun, Alice, Kraft, Julia, Whiteman, David C., Olsen, Catherine M., Uitterlinden, Andre G., Hofman, Albert, Rietschel, Marcella, Cichon, Sven, Nöthen, Markus M., Amouyel, Philippe, Traynor, Bryan J., Singleton, Andrew B., Mitne Neto, Miguel, Cauchi, Ruben J., Ophoff, Roel A., Wiedau-Pazos, Martina, Lomen-Hoerth, Catherine, van Deerlin, Vivianna M., Grosskreutz, Julian, Roediger, Annekathrin, Gaur, Nayana, Jörk, Alexander, Barthel, Tabea, Theele, Erik, Ilse, Benjamin, Stubendorff, Beatrice, Witte, Otto W., Steinbach, Robert, Hübner, Christian A., Graff, Caroline, Brylev, Lev, Fominykh, Vera, Demeshonok, Vera, Ataulina, Anastasia, Rogelj, Boris, Koritnik, Blaž, Zidar, Janez, Ravnik-Glavač, Metka, Glavač, Damjan, Stević, Zorica, Drory, Vivian, Povedano, Monica, Blair, Ian P., Kiernan, Matthew C., Benyamin, Beben, Henderson, Robert D., Furlong, Sarah, Mathers, Susan, McCombe, Pamela A., Needham, Merrilee, Ngo, Shyuan T., Nicholson, Garth A., Pamphlett, Roger, Rowe, Dominic B., Steyn, Frederik J., Williams, Kelly L., Mather, Karen A., Sachdev, Perminder S., Henders, Anjali K., Wallace, Leanne, de Carvalho, Mamede, Pinto, Susana, Petri, Susanne, Weber, Markus, Rouleau, Guy A., Silani, Vincenzo, Curtis, Charles J., Breen, Gerome, Glass, Jonathan D., Brown, Robert H., Landers, John E., Shaw, Christopher E., Andersen, Peter M., Groen, Ewout J. N., van Es, Michael A., Pasterkamp, R. Jeroen, Fan, Dongsheng, Garton, Fleur C., McRae, Allan F., Davey Smith, George, Gaunt, Tom R., Eberle, Michael A., Mill, Jonathan, McLaughlin, Russell L., Hardiman, Orla, Kenna, Kevin P., Wray, Naomi R., Tsai, Ellen, Runz, Heiko, Franke, Lude, Al-Chalabi, Ammar, Van Damme, Philip, van den Berg, Leonard H., and Veldink, Jan H.
Abstract: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects. ALS-associated risk loci were shared with multiple traits within the neurodegenerative spectrum but with distinct enrichment patterns across brain regions and cell types. Of the environmental and lifestyle risk factors obtained from the literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. The combination of all ALS-associated signals reveals a role for perturbations in vesicle-mediated transport and autophagy and provides evidence for cell-autonomous disease initiation in glutamatergic neurons.
Published: 2021
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12. Polygenic burden could explain high rates of affective disorders in a community with restricted founder population

Author: Wang, Xiaotong, Lin, Tian, Yengo, Loic, Sidorenko, Julia, Wray, Naomi R., and Levinson, Douglas F.
Abstract: This study investigates if genetic factors could contribute to the high rate of mood disorders reported in a U.S. community known to have a restricted early founder population (confirmed here through runs of homozygosity analysis). Polygenic scores (PGSs) for eight common diseases, disorders, or traits, including psychiatric disorders, were calculated in 274 participants (125 mood disorder cases) who each reported three or four grandparents born in the community. Ancestry‐matched controls were selected from the UK Biobank (UKB; three sets of N= 1,822 each). The mean PGSs were significantly higher in the community for major depression PRS (p= 2.1 × 10−19, 0.56 SDunits), bipolar disorder (p= 2.5 × 10−15, 0.56 SDunits), and schizophrenia (p= 3.8 × 10−21, 0.64 SDunits). The PGSs were not significantly different between the community participants and UKB controls for the traits of body mass index, Type 2 diabetes, coronary artery disease, and chronotype. The mean PGSs for height were significantly lower in the community sample compared to controls (−0.21 SDunits, p= 1.2 × 10−5). The results are consistent with enrichment of polygenic risk factors for psychiatric disorders in this community.
Published: 2021
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13. Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation

Author: Min, Josine L., Hemani, Gibran, Hannon, Eilis, Dekkers, Koen F., Castillo-Fernandez, Juan, Luijk, René, Carnero-Montoro, Elena, Lawson, Daniel J., Burrows, Kimberley, Suderman, Matthew, Bretherick, Andrew D., Richardson, Tom G., Klughammer, Johanna, Iotchkova, Valentina, Sharp, Gemma, Al Khleifat, Ahmad, Shatunov, Aleksey, Iacoangeli, Alfredo, McArdle, Wendy L., Ho, Karen M., Kumar, Ashish, Söderhäll, Cilla, Soriano-Tárraga, Carolina, Giralt-Steinhauer, Eva, Kazmi, Nabila, Mason, Dan, McRae, Allan F., Corcoran, David L., Sugden, Karen, Kasela, Silva, Cardona, Alexia, Day, Felix R., Cugliari, Giovanni, Viberti, Clara, Guarrera, Simonetta, Lerro, Michael, Gupta, Richa, Bollepalli, Sailalitha, Mandaviya, Pooja, Zeng, Yanni, Clarke, Toni-Kim, Walker, Rosie M., Schmoll, Vanessa, Czamara, Darina, Ruiz-Arenas, Carlos, Rezwan, Faisal I., Marioni, Riccardo E., Lin, Tian, Awaloff, Yvonne, Germain, Marine, Aïssi, Dylan, Zwamborn, Ramona, van Eijk, Kristel, Dekker, Annelot, van Dongen, Jenny, Hottenga, Jouke-Jan, Willemsen, Gonneke, Xu, Cheng-Jian, Barturen, Guillermo, Català-Moll, Francesc, Kerick, Martin, Wang, Carol, Melton, Phillip, Elliott, Hannah R., Shin, Jean, Bernard, Manon, Yet, Idil, Smart, Melissa, Gorrie-Stone, Tyler, Shaw, Chris, Al Chalabi, Ammar, Ring, Susan M., Pershagen, Göran, Melén, Erik, Jiménez-Conde, Jordi, Roquer, Jaume, Lawlor, Deborah A., Wright, John, Martin, Nicholas G., Montgomery, Grant W., Moffitt, Terrie E., Poulton, Richie, Esko, Tõnu, Milani, Lili, Metspalu, Andres, Perry, John R. B., Ong, Ken K., Wareham, Nicholas J., Matullo, Giuseppe, Sacerdote, Carlotta, Panico, Salvatore, Caspi, Avshalom, Arseneault, Louise, Gagnon, France, Ollikainen, Miina, Kaprio, Jaakko, Felix, Janine F., Rivadeneira, Fernando, Tiemeier, Henning, van IJzendoorn, Marinus H., Uitterlinden, André G., Jaddoe, Vincent W. V., Haley, Chris, McIntosh, Andrew M., Evans, Kathryn L., Murray, Alison, Räikkönen, Katri, Lahti, Jari, Nohr, Ellen A., Sørensen, Thorkild I. A., Hansen, Torben, Morgen, Camilla S., Binder, Elisabeth B., Lucae, Susanne, Gonzalez, Juan Ramon, Bustamante, Mariona, Sunyer, Jordi, Holloway, John W., Karmaus, Wilfried, Zhang, Hongmei, Deary, Ian J., Wray, Naomi R., Starr, John M., Beekman, Marian, van Heemst, Diana, Slagboom, P. Eline, Morange, Pierre-Emmanuel, Trégouët, David-Alexandre, Veldink, Jan H., Davies, Gareth E., de Geus, Eco J. C., Boomsma, Dorret I., Vonk, Judith M., Brunekreef, Bert, Koppelman, Gerard H., Alarcón-Riquelme, Marta E., Huang, Rae-Chi, Pennell, Craig E., van Meurs, Joyce, Ikram, M. Arfan, Hughes, Alun D., Tillin, Therese, Chaturvedi, Nish, Pausova, Zdenka, Paus, Tomas, Spector, Timothy D., Kumari, Meena, Schalkwyk, Leonard C., Visscher, Peter M., Davey Smith, George, Bock, Christoph, Gaunt, Tom R., Bell, Jordana T., Heijmans, Bastiaan T., Mill, Jonathan, and Relton, Caroline L.
Abstract: Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15–17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype–phenotype map than previously anticipated.
Published: 2021
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14. Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology

Author: Mullins, Niamh, Forstner, Andreas J., O’Connell, Kevin S., Coombes, Brandon, Coleman, Jonathan R. I., Qiao, Zhen, Als, Thomas D., Bigdeli, Tim B., Børte, Sigrid, Bryois, Julien, Charney, Alexander W., Drange, Ole Kristian, Gandal, Michael J., Hagenaars, Saskia P., Ikeda, Masashi, Kamitaki, Nolan, Kim, Minsoo, Krebs, Kristi, Panagiotaropoulou, Georgia, Schilder, Brian M., Sloofman, Laura G., Steinberg, Stacy, Trubetskoy, Vassily, Winsvold, Bendik S., Won, Hong-Hee, Abramova, Liliya, Adorjan, Kristina, Agerbo, Esben, Al Eissa, Mariam, Albani, Diego, Alliey-Rodriguez, Ney, Anjorin, Adebayo, Antilla, Verneri, Antoniou, Anastasia, Awasthi, Swapnil, Baek, Ji Hyun, Bækvad-Hansen, Marie, Bass, Nicholas, Bauer, Michael, Beins, Eva C., Bergen, Sarah E., Birner, Armin, Bøcker Pedersen, Carsten, Bøen, Erlend, Boks, Marco P., Bosch, Rosa, Brum, Murielle, Brumpton, Ben M., Brunkhorst-Kanaan, Nathalie, Budde, Monika, Bybjerg-Grauholm, Jonas, Byerley, William, Cairns, Murray, Casas, Miquel, Cervantes, Pablo, Clarke, Toni-Kim, Cruceanu, Cristiana, Cuellar-Barboza, Alfredo, Cunningham, Julie, Curtis, David, Czerski, Piotr M., Dale, Anders M., Dalkner, Nina, David, Friederike S., Degenhardt, Franziska, Djurovic, Srdjan, Dobbyn, Amanda L., Douzenis, Athanassios, Elvsåshagen, Torbjørn, Escott-Price, Valentina, Ferrier, I. Nicol, Fiorentino, Alessia, Foroud, Tatiana M., Forty, Liz, Frank, Josef, Frei, Oleksandr, Freimer, Nelson B., Frisén, Louise, Gade, Katrin, Garnham, Julie, Gelernter, Joel, Giørtz Pedersen, Marianne, Gizer, Ian R., Gordon, Scott D., Gordon-Smith, Katherine, Greenwood, Tiffany A., Grove, Jakob, Guzman-Parra, José, Ha, Kyooseob, Haraldsson, Magnus, Hautzinger, Martin, Heilbronner, Urs, Hellgren, Dennis, Herms, Stefan, Hoffmann, Per, Holmans, Peter A., Huckins, Laura, Jamain, Stéphane, Johnson, Jessica S., Kalman, Janos L., Kamatani, Yoichiro, Kennedy, James L., Kittel-Schneider, Sarah, Knowles, James A., Kogevinas, Manolis, Koromina, Maria, Kranz, Thorsten M., Kranzler, Henry R., Kubo, Michiaki, Kupka, Ralph, Kushner, Steven A., Lavebratt, Catharina, Lawrence, Jacob, Leber, Markus, Lee, Heon-Jeong, Lee, Phil H., Levy, Shawn E., Lewis, Catrin, Liao, Calwing, Lucae, Susanne, Lundberg, Martin, MacIntyre, Donald J., Magnusson, Sigurdur H., Maier, Wolfgang, Maihofer, Adam, Malaspina, Dolores, Maratou, Eirini, Martinsson, Lina, Mattheisen, Manuel, McCarroll, Steven A., McGregor, Nathaniel W., McGuffin, Peter, McKay, James D., Medeiros, Helena, Medland, Sarah E., Millischer, Vincent, Montgomery, Grant W., Moran, Jennifer L., Morris, Derek W., Mühleisen, Thomas W., O’Brien, Niamh, O’Donovan, Claire, Olde Loohuis, Loes M., Oruc, Lilijana, Papiol, Sergi, Pardiñas, Antonio F., Perry, Amy, Pfennig, Andrea, Porichi, Evgenia, Potash, James B., Quested, Digby, Raj, Towfique, Rapaport, Mark H., DePaulo, J. Raymond, Regeer, Eline J., Rice, John P., Rivas, Fabio, Rivera, Margarita, Roth, Julian, Roussos, Panos, Ruderfer, Douglas M., Sánchez-Mora, Cristina, Schulte, Eva C., Senner, Fanny, Sharp, Sally, Shilling, Paul D., Sigurdsson, Engilbert, Sirignano, Lea, Slaney, Claire, Smeland, Olav B., Smith, Daniel J., Sobell, Janet L., Søholm Hansen, Christine, Soler Artigas, Maria, Spijker, Anne T., Stein, Dan J., Strauss, John S., Świątkowska, Beata, Terao, Chikashi, Thorgeirsson, Thorgeir E., Toma, Claudio, Tooney, Paul, Tsermpini, Evangelia-Eirini, Vawter, Marquis P., Vedder, Helmut, Walters, James T. R., Witt, Stephanie H., Xi, Simon, Xu, Wei, Yang, Jessica Mei Kay, Young, Allan H., Young, Hannah, Zandi, Peter P., Zhou, Hang, Zillich, Lea, Adolfsson, Rolf, Agartz, Ingrid, Alda, Martin, Alfredsson, Lars, Babadjanova, Gulja, Backlund, Lena, Baune, Bernhard T., Bellivier, Frank, Bengesser, Susanne, Berrettini, Wade H., Blackwood, Douglas H. R., Boehnke, Michael, Børglum, Anders D., Breen, Gerome, Carr, Vaughan J., Catts, Stanley, Corvin, Aiden, Craddock, Nicholas, Dannlowski, Udo, Dikeos, Dimitris, Esko, Tõnu, Etain, Bruno, Ferentinos, Panagiotis, Frye, Mark, Fullerton, Janice M., Gawlik, Micha, Gershon, Elliot S., Goes, Fernando S., Green, Melissa J., Grigoroiu-Serbanescu, Maria, Hauser, Joanna, Henskens, Frans, Hillert, Jan, Hong, Kyung Sue, Hougaard, David M., Hultman, Christina M., Hveem, Kristian, Iwata, Nakao, Jablensky, Assen V., Jones, Ian, Jones, Lisa A., Kahn, René S., Kelsoe, John R., Kirov, George, Landén, Mikael, Leboyer, Marion, Lewis, Cathryn M., Li, Qingqin S., Lissowska, Jolanta, Lochner, Christine, Loughland, Carmel, Martin, Nicholas G., Mathews, Carol A., Mayoral, Fermin, McElroy, Susan L., McIntosh, Andrew M., McMahon, Francis J., Melle, Ingrid, Michie, Patricia, Milani, Lili, Mitchell, Philip B., Morken, Gunnar, Mors, Ole, Mortensen, Preben Bo, Mowry, Bryan, Müller-Myhsok, Bertram, Myers, Richard M., Neale, Benjamin M., Nievergelt, Caroline M., Nordentoft, Merete, Nöthen, Markus M., O’Donovan, Michael C., Oedegaard, Ketil J., Olsson, Tomas, Owen, Michael J., Paciga, Sara A., Pantelis, Chris, Pato, Carlos, Pato, Michele T., Patrinos, George P., Perlis, Roy H., Posthuma, Danielle, Ramos-Quiroga, Josep Antoni, Reif, Andreas, Reininghaus, Eva Z., Ribasés, Marta, Rietschel, Marcella, Ripke, Stephan, Rouleau, Guy A., Saito, Takeo, Schall, Ulrich, Schalling, Martin, Schofield, Peter R., Schulze, Thomas G., Scott, Laura J., Scott, Rodney J., Serretti, Alessandro, Shannon Weickert, Cynthia, Smoller, Jordan W., Stefansson, Hreinn, Stefansson, Kari, Stordal, Eystein, Streit, Fabian, Sullivan, Patrick F., Turecki, Gustavo, Vaaler, Arne E., Vieta, Eduard, Vincent, John B., Waldman, Irwin D., Weickert, Thomas W., Werge, Thomas, Wray, Naomi R., Zwart, John-Anker, Biernacka, Joanna M., Nurnberger, John I., Cichon, Sven, Edenberg, Howard J., Stahl, Eli A., McQuillin, Andrew, Di Florio, Arianna, Ophoff, Roel A., and Andreassen, Ole A.
Abstract: Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.
Published: 2021
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15. Conditional GWAS analysis to identify disorder-specific SNPs for psychiatric disorders

Author: Byrne, Enda M., Zhu, Zhihong, Qi, Ting, Skene, Nathan G., Bryois, Julien, Pardinas, Antonio F., Stahl, Eli, Smoller, Jordan W., Rietschel, Marcella, Owen, Michael J., Walters, James T. R., O’Donovan, Michael C., McGrath, John G., Hjerling-Leffler, Jens, Sullivan, Patrick F., Goddard, Michael E., Visscher, Peter M., Yang, Jian, and Wray, Naomi R.
Abstract: Substantial genetic liability is shared across psychiatric disorders but less is known about risk variants that are specific to a given disorder. We used multi-trait conditional and joint analysis (mtCOJO) to adjust GWAS summary statistics of one disorder for the effects of genetically correlated traits to identify putative disorder-specific SNP associations. We applied mtCOJO to summary statistics for five psychiatric disorders from the Psychiatric Genomics Consortium—schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), attention-deficit hyperactivity disorder (ADHD) and autism (AUT). Most genome-wide significant variants for these disorders had evidence of pleiotropy (i.e., impact on multiple psychiatric disorders) and hence have reduced mtCOJO conditional effect sizes. However, subsets of genome-wide significant variants had larger conditional effect sizes consistent with disorder-specific effects: 15 of 130 genome-wide significant variants for schizophrenia, 5 of 40 for major depression, 3 of 11 for ADHD and 1 of 2 for autism. We show that decreased expression of VPS29in the brain may increase risk to SCZ only and increased expression of CSE1Lis associated with SCZ and MD, but not with BIP. Likewise, decreased expression of PCDHA7in the brain is linked to increased risk of MD but decreased risk of SCZ and BIP.
Published: 2021
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16. Association and prediction of phenotypic traits from neuroimaging data using a multi-component mixed model excluding the target vertex

Author: Išgum, Ivana, Landman, Bennett A., Couvy-Duchesne, Baptiste, Zhang, Futao, Kemper, Kathryn E., Sidorenko, Julia, Wray, Naomi R., Visscher, Peter M., Yang, Jian, and Colliot, Olivier
Published: 2021
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17. Genome-wide gene-environment analyses of major depressive disorder and reported lifetime traumatic experiences in UK Biobank

Author: Coleman, Jonathan R. I., Peyrot, Wouter J., Purves, Kirstin L., Davis, Katrina A. S., Rayner, Christopher, Choi, Shing Wan, Hübel, Christopher, Gaspar, Héléna A., Kan, Carol, Van der Auwera, Sandra, Adams, Mark James, Lyall, Donald M., Choi, Karmel W., Dunn, Erin C., Vassos, Evangelos, Danese, Andrea, Maughan, Barbara, Grabe, Hans J., Lewis, Cathryn M., O’Reilly, Paul F., McIntosh, Andrew M., Smith, Daniel J., Wray, Naomi R., Hotopf, Matthew, Eley, Thalia C., and Breen, Gerome
Abstract: Depression is more frequent among individuals exposed to traumatic events. Both trauma exposure and depression are heritable. However, the relationship between these traits, including the role of genetic risk factors, is complex and poorly understood. When modelling trauma exposure as an environmental influence on depression, both gene-environment correlations and gene-environment interactions have been observed. The UK Biobank concurrently assessed Major Depressive Disorder (MDD) and self-reported lifetime exposure to traumatic events in 126,522 genotyped individuals of European ancestry. We contrasted genetic influences on MDD stratified by reported trauma exposure (final sample size range: 24,094–92,957). The SNP-based heritability of MDD with reported trauma exposure (24%) was greater than MDD without reported trauma exposure (12%). Simulations showed that this is not confounded by the strong, positive genetic correlation observed between MDD and reported trauma exposure. We also observed that the genetic correlation between MDD and waist circumference was only significant in individuals reporting trauma exposure (rg= 0.24, p= 1.8 × 10−7versus rg= −0.05, p= 0.39 in individuals not reporting trauma exposure, difference p= 2.3 × 10−4). Our results suggest that the genetic contribution to MDD is greater when reported trauma is present, and that a complex relationship exists between reported trauma exposure, body composition, and MDD.
Published: 2020
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18. A resource-efficient tool for mixed model association analysis of large-scale data

Author: Jiang, Longda, Zheng, Zhili, Qi, Ting, Kemper, Kathryn E., Wray, Naomi R., Visscher, Peter M., and Yang, Jian
Abstract: The genome-wide association study (GWAS) has been widely used as an experimental design to detect associations between genetic variants and a phenotype. Two major confounding factors, population stratification and relatedness, could potentially lead to inflated GWAS test statistics and hence to spurious associations. Mixed linear model (MLM)-based approaches can be used to account for sample structure. However, genome-wide association (GWA) analyses in biobank samples such as the UK Biobank (UKB) often exceed the capability of most existing MLM-based tools especially if the number of traits is large. Here, we develop an MLM-based tool (fastGWA) that controls for population stratification by principal components and for relatedness by a sparse genetic relationship matrix for GWA analyses of biobank-scale data. We demonstrate by extensive simulations that fastGWA is reliable, robust and highly resource-efficient. We then apply fastGWA to 2,173 traits on array-genotyped and imputed samples from 456,422 individuals and to 2,048 traits on whole-exome-sequenced samples from 46,191 individuals in the UKB.
Published: 2019
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19. An overview of DNA methylation-derived trait score methods and applications

Author: Nabais, Marta F., Gadd, Danni A., Hannon, Eilis, Mill, Jonathan, McRae, Allan F., and Wray, Naomi R.
Abstract: Microarray technology has been used to measure genome-wide DNA methylation in thousands of individuals. These studies typically test the associations between individual DNA methylation sites (“probes”) and complex traits or diseases. The results can be used to generate methylation profile scores (MPS) to predict outcomes in independent data sets. Although there are many parallels between MPS and polygenic (risk) scores (PGS), there are key differences. Here, we review motivations, methods, and applications of DNA methylation-based trait prediction, with a focus on common diseases. We contrast MPS with PGS, highlighting where assumptions made in genetic modeling may not hold in epigenetic data.
Published: 2023
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20. Prediction of individual genetic risk to disease from genome-wide association studies

Author: Wray, Naomi R., Goddard, Michael E., and Visscher, Peter M.
Subjects: Genetic disorders -- Risk factors, Genetic disorders -- Research, Genomes -- Research, Single nucleotide polymorphisms -- Research, Health
Abstract: The number and effect size of risk loci that underlie complex genetic diseases are examined to quantify the accuracy of risk prediction in individuals. Results from these genome-wide association studies (GWAS) can be used to identify healthy individuals with high risk of developing disease.
Published: 2007

21. Genetic correlations of polygenic disease traits: from theory to practice

Author: van Rheenen, Wouter, Peyrot, Wouter J., Schork, Andrew J., Lee, S. Hong, and Wray, Naomi R.
Abstract: The genetic correlation describes the genetic relationship between two traits and can contribute to a better understanding of the shared biological pathways and/or the causality relationships between them. The rarity of large family cohorts with recorded instances of two traits, particularly disease traits, has made it difficult to estimate genetic correlations using traditional epidemiological approaches. However, advances in genomic methodologies, such as genome-wide association studies, and widespread sharing of data now allow genetic correlations to be estimated for virtually any trait pair. Here, we review the definition, estimation, interpretation and uses of genetic correlations, with a focus on applications to human disease.
Published: 2019
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22. Quantifying between‐cohort and between‐sex genetic heterogeneity in major depressive disorder

Author: Trzaskowski, Maciej, Mehta, Divya, Peyrot, Wouter J., Hawkes, David, Davies, Daniel, Howard, David M., Kemper, Kathryn E., Sidorenko, Julia, Maier, Robert, Ripke, Stephan, Mattheisen, Manuel, Baune, Bernhard T., Grabe, Hans J., Heath, Andrew C., Jones, Lisa, Jones, Ian, Madden, Pamela A.F., McIntosh, Andrew M., Breen, Gerome, Lewis, Cathryn M., Børglum, Anders D., Sullivan, Patrick F., Martin, Nicholas G., Kendler, Kenneth S., Levinson, Douglas F., and Wray, Naomi R.
Abstract: Major depressive disorder (MDD) is clinically heterogeneous with prevalence rates twice as high in women as in men. There are many possible sources of heterogeneity in MDD most of which are not measured in a sufficiently comparable way across study samples. Here, we assess genetic heterogeneity based on two fundamental measures, between‐cohort and between‐sex heterogeneity. First, we used genome‐wide association study (GWAS) summary statistics to investigate between‐cohort genetic heterogeneity using the 29 research cohorts of the Psychiatric Genomics Consortium (PGC; Ncases = 16,823, Ncontrols = 25,632) and found that some of the cohort heterogeneity can be attributed to ascertainment differences (such as recruitment of cases from hospital vs. community sources). Second, we evaluated between‐sex genetic heterogeneity using GWAS summary statistics from the PGC, Kaiser Permanente GERA, UK Biobank, and the Danish iPSYCH studies but did not find convincing evidence for genetic differences between the sexes. We conclude that there is no evidence that the heterogeneity between MDD data sets and between sexes reflects genetic heterogeneity. Larger sample sizes with detailed phenotypic records and genomic data remain the key to overcome heterogeneity inherent in assessment of MDD.
Published: 2019
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23. Genome-wide association study identifies 30 loci associated with bipolar disorder

Author: Stahl, Eli A., Breen, Gerome, Forstner, Andreas J., McQuillin, Andrew, Ripke, Stephan, Trubetskoy, Vassily, Mattheisen, Manuel, Wang, Yunpeng, Coleman, Jonathan R. I., Gaspar, Héléna A., de Leeuw, Christiaan A., Steinberg, Stacy, Pavlides, Jennifer M. Whitehead, Trzaskowski, Maciej, Byrne, Enda M., Pers, Tune H., Holmans, Peter A., Richards, Alexander L., Abbott, Liam, Agerbo, Esben, Akil, Huda, Albani, Diego, Alliey-Rodriguez, Ney, Als, Thomas D., Anjorin, Adebayo, Antilla, Verneri, Awasthi, Swapnil, Badner, Judith A., Bækvad-Hansen, Marie, Barchas, Jack D., Bass, Nicholas, Bauer, Michael, Belliveau, Richard, Bergen, Sarah E., Pedersen, Carsten Bøcker, Bøen, Erlend, Boks, Marco P., Boocock, James, Budde, Monika, Bunney, William, Burmeister, Margit, Bybjerg-Grauholm, Jonas, Byerley, William, Casas, Miquel, Cerrato, Felecia, Cervantes, Pablo, Chambert, Kimberly, Charney, Alexander W., Chen, Danfeng, Churchhouse, Claire, Clarke, Toni-Kim, Coryell, William, Craig, David W., Cruceanu, Cristiana, Curtis, David, Czerski, Piotr M., Dale, Anders M., de Jong, Simone, Degenhardt, Franziska, Del-Favero, Jurgen, DePaulo, J. Raymond, Djurovic, Srdjan, Dobbyn, Amanda L., Dumont, Ashley, Elvsåshagen, Torbjørn, Escott-Price, Valentina, Fan, Chun Chieh, Fischer, Sascha B., Flickinger, Matthew, Foroud, Tatiana M., Forty, Liz, Frank, Josef, Fraser, Christine, Freimer, Nelson B., Frisén, Louise, Gade, Katrin, Gage, Diane, Garnham, Julie, Giambartolomei, Claudia, Pedersen, Marianne Giørtz, Goldstein, Jaqueline, Gordon, Scott D., Gordon-Smith, Katherine, Green, Elaine K., Green, Melissa J., Greenwood, Tiffany A., Grove, Jakob, Guan, Weihua, Guzman-Parra, José, Hamshere, Marian L., Hautzinger, Martin, Heilbronner, Urs, Herms, Stefan, Hipolito, Maria, Hoffmann, Per, Holland, Dominic, Huckins, Laura, Jamain, Stéphane, Johnson, Jessica S., Juréus, Anders, Kandaswamy, Radhika, Karlsson, Robert, Kennedy, James L., Kittel-Schneider, Sarah, Knowles, James A., Kogevinas, Manolis, Koller, Anna C., Kupka, Ralph, Lavebratt, Catharina, Lawrence, Jacob, Lawson, William B., Leber, Markus, Lee, Phil H., Levy, Shawn E., Li, Jun Z., Liu, Chunyu, Lucae, Susanne, Maaser, Anna, MacIntyre, Donald J., Mahon, Pamela B., Maier, Wolfgang, Martinsson, Lina, McCarroll, Steve, McGuffin, Peter, McInnis, Melvin G., McKay, James D., Medeiros, Helena, Medland, Sarah E., Meng, Fan, Milani, Lili, Montgomery, Grant W., Morris, Derek W., Mühleisen, Thomas W., Mullins, Niamh, Nguyen, Hoang, Nievergelt, Caroline M., Adolfsson, Annelie Nordin, Nwulia, Evaristus A., O’Donovan, Claire, Loohuis, Loes M. Olde, Ori, Anil P. S., Oruc, Lilijana, Ösby, Urban, Perlis, Roy H., Perry, Amy, Pfennig, Andrea, Potash, James B., Purcell, Shaun M., Regeer, Eline J., Reif, Andreas, Reinbold, Céline S., Rice, John P., Rivas, Fabio, Rivera, Margarita, Roussos, Panos, Ruderfer, Douglas M., Ryu, Euijung, Sánchez-Mora, Cristina, Schatzberg, Alan F., Scheftner, William A., Schork, Nicholas J., Shannon Weickert, Cynthia, Shehktman, Tatyana, Shilling, Paul D., Sigurdsson, Engilbert, Slaney, Claire, Smeland, Olav B., Sobell, Janet L., Søholm Hansen, Christine, Spijker, Anne T., St Clair, David, Steffens, Michael, Strauss, John S., Streit, Fabian, Strohmaier, Jana, Szelinger, Szabolcs, Thompson, Robert C., Thorgeirsson, Thorgeir E., Treutlein, Jens, Vedder, Helmut, Wang, Weiqing, Watson, Stanley J., Weickert, Thomas W., Witt, Stephanie H., Xi, Simon, Xu, Wei, Young, Allan H., Zandi, Peter, Zhang, Peng, Zöllner, Sebastian, Adolfsson, Rolf, Agartz, Ingrid, Alda, Martin, Backlund, Lena, Baune, Bernhard T., Bellivier, Frank, Berrettini, Wade H., Biernacka, Joanna M., Blackwood, Douglas H. R., Boehnke, Michael, Børglum, Anders D., Corvin, Aiden, Craddock, Nicholas, Daly, Mark J., Dannlowski, Udo, Esko, Tõnu, Etain, Bruno, Frye, Mark, Fullerton, Janice M., Gershon, Elliot S., Gill, Michael, Goes, Fernando, Grigoroiu-Serbanescu, Maria, Hauser, Joanna, Hougaard, David M., Hultman, Christina M., Jones, Ian, Jones, Lisa A., Kahn, René S., Kirov, George, Landén, Mikael, Leboyer, Marion, Lewis, Cathryn M., Li, Qingqin S., Lissowska, Jolanta, Martin, Nicholas G., Mayoral, Fermin, McElroy, Susan L., McIntosh, Andrew M., McMahon, Francis J., Melle, Ingrid, Metspalu, Andres, Mitchell, Philip B., Morken, Gunnar, Mors, Ole, Mortensen, Preben Bo, Müller-Myhsok, Bertram, Myers, Richard M., Neale, Benjamin M., Nimgaonkar, Vishwajit, Nordentoft, Merete, Nöthen, Markus M., O’Donovan, Michael C., Oedegaard, Ketil J., Owen, Michael J., Paciga, Sara A., Pato, Carlos, Pato, Michele T., Posthuma, Danielle, Ramos-Quiroga, Josep Antoni, Ribasés, Marta, Rietschel, Marcella, Rouleau, Guy A., Schalling, Martin, Schofield, Peter R., Schulze, Thomas G., Serretti, Alessandro, Smoller, Jordan W., Stefansson, Hreinn, Stefansson, Kari, Stordal, Eystein, Sullivan, Patrick F., Turecki, Gustavo, Vaaler, Arne E., Vieta, Eduard, Vincent, John B., Werge, Thomas, Nurnberger, John I., Wray, Naomi R., Di Florio, Arianna, Edenberg, Howard J., Cichon, Sven, Ophoff, Roel A., Scott, Laura J., Andreassen, Ole A., Kelsoe, John, and Sklar, Pamela
Abstract: Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P< 1 × 10−4in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P< 5 × 10−8) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder.
Published: 2019
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24. Population‐based identity‐by‐descent mapping combined with exome sequencing to detect rare risk variants for schizophrenia

Author: Harold, Denise, Connolly, Siobhan, Riley, Brien P., Kendler, Kenneth S., McCarthy, Shane E., McCombie, William R., Richards, Alex, Owen, Michael J., O'Donovan, Michael C., Walters, James, Donnelly, Peter, Bates, Lesley, Barroso, Ines, Blackwell, Jenefer M., Bramon, Elvira, Brown, Matthew A., Casas, Juan P., Corvin, Aiden, Deloukas, Panos, Duncanson, Audrey, Jankowski, Janusz, Markus, Hugh S., Mathew, Christopher G., Palmer, Colin N. A., Plomin, Robert, Rautanen, Anna, Sawcer, Stephen J., Trembath, Richard C., Viswanathan, Ananth C., Wood, Nicholas W., Spencer, Chris C. A., Band, Gavin, Bellenguez, Céline, Freeman, Colin, Hellenthal, Garrett, Giannoulatou, Eleni, Hopkins, Lucinda, Pirinen, Matti, Pearson, Richard, Strange, Amy, Su, Zhan, Vukcevic, Damjan, Langford, Cordelia, Hunt, Sarah E., Edkins, Sarah, Gwilliam, Rhian, Blackburn, Hannah, Bumpstead, Suzannah J., Dronov, Serge, Gillman, Matthew, Gray, Emma, Hammond, Naomi, Jayakumar, Alagurevathi, McCann, Owen T., Liddle, Jennifer, Potter, Simon C., Ravindrarajah, Radhi, Ricketts, Michelle, Waller, Matthew, Weston, Paul, Widaa, Sara, Whittaker, Pamela, Ripke, Stephan, Neale, Benjamin M., Corvin, Aiden, Walters, James T. R., Farh, Kai‐How, Holmans, Peter A., Lee, Phil, Bulik‐Sullivan, Brendan, Collier, David A., Huang, Hailiang, Pers, Tune H., Agartz, Ingrid, Agerbo, Esben, Albus, Margot, Alexander, Madeline, Amin, Farooq, Bacanu, Silviu A., Begemann, Martin, Belliveau, Richard A., Bene, Judit, Bergen, Sarah E., Bevilacqua, Elizabeth, Bigdeli, Tim B., Black, Donald W., Bruggeman, Richard, Buccola, Nancy G., Buckner, Randy L., Byerley, William, Cahn, Wiepke, Cai, Guiqing, Campion, Dominique, Cantor, Rita M., Carr, Vaughan J., Carrera, Noa, Catts, Stanley V., Chambert, Kimberley D., Chan, Raymond C. K., Chan, Ronald Y. L., Chen, Eric Y. H., Cheng, Wei, Cheung, Eric F. C., Chong, Siow Ann, Cloninger, C. Robert, Cohen, David, Cohen, Nadine, Cormican, Paul, Craddock, Nick, Crowley, James J., Curtis, David, Davidson, Michael, Davis, Kenneth L., Degenhardt, Franziska, Del Favero, Jurgen, Demontis, Ditte, Dikeos, Dimitris, Dinan, Timothy, Djurovic, Srdjan, Donohoe, Gary, Drapeau, Elodie, Duan, Jubao, Dudbridge, Frank, Durmishi, Naser, Eichhammer, Peter, Eriksson, Johan, Escott‐Price, Valentina, Essioux, Laurent, Fanous, Ayman H., Farrell, Martilias S., Frank, Josef, Franke, Lude, Freedman, Robert, Freimer, Nelson B., Friedl, Marion, Friedman, Joseph I., Fromer, Menachem, Genovese, Giulio, Georgieva, Lyudmila, Giegling, Ina, Giusti‐Rodríguez, Paola, Godard, Stephanie, Goldstein, Jacqueline I., Golimbet, Vera, Gopal, Srihari, Gratten, Jacob, Haan, Lieuwe, Hammer, Christian, Hamshere, Marian L., Hansen, Mark, Hansen, Thomas, Haroutunian, Vahram, Hartmann, Annette M., Henskens, Frans A., Herms, Stefan, Hirschhorn, Joel N., Hoffmann, Per, Hofman, Andrea, Hollegaard, Mads V., Hougaard, David M., Ikeda, Masashi, Joa, Inge, Julià, Antonio, Kalaydjieva, Luba, Karachanak‐Yankova, Sena, Karjalainen, Juha, Kavanagh, David, Keller, Matthew C., Kennedy, James L., Khrunin, Andrey, Kim, Yunjung, Klovins, Janis, Knowles, James A., Konte, Bettina, Kucinskas, Vaidutis, Kucinskiene, Zita Ausrele, Kuzelova‐Ptackova, Hana, Kähler, Anna K., Laurent, Claudine, Lee, Jimmy, Lee, S. Hong, Legge, Sophie E., Lerer, Bernard, Li, Miaoxin, Li, Tao, Liang, Kung‐Yee, Lieberman, Jeffrey, Limborska, Svetlana, Loughland, Carmel M., Lubinski, Jan, Lönnqvist, Jouko, Macek, Milan, Magnusson, Patrik K. E., Maher, Brion S., Maier, Wolfgang, Mallet, Jacques, Marsal, Sara, Mattheisen, Manuel, Mattingsdal, Morten, McCarley, Robert W., McDonald, Colm, McIntosh, Andrew M., Meier, Sandra, Meijer, Carin J., Melegh, Bela, Melle, Ingrid, Mesholam‐Gately, Raquelle I., Metspalu, Andres, Michie, Patricia T., Milani, Lili, Milanova, Vihra, Mokrab, Younes, Morris, Derek W., Mors, Ole, Murphy, Kieran C., Murray, Robin M., Myin‐Germeys, Inez, Müller‐Myhsok, Bertram, Nelis, Mari, Nenadic, Igor, Nertney, Deborah A., Nestadt, Gerald, Nicodemus, Kristin K., Nikitina‐Zake, Liene, Nisenbaum, Laura, Nordin, Annelie, O'Callaghan, Eadbhard, O'Dushlaine, Colm, O'Neill, F. Anthony, Oh, Sang‐Yun, Olincy, Ann, Olsen, Line, Van Os, Jim, Pantelis, Christos, Papadimitriou, George N., Papiol, Sergi, Parkhomenko, Elena, Pato, Michele T., Paunio, Tiina, Pejovic‐Milovancevic, Milica, Perkins, Diana O., Pietiläinen, Olli, Pimm, Jonathan, Pocklington, Andrew J., Price, Alkes, Pulver, Ann E., Purcell, Shaun M., Quested, Digby, Rasmussen, Henrik B., Reichenberg, Abraham, Reimers, Mark A., Richards, Alexander L., Roffman, Joshua L., Roussos, Panos, Ruderfer, Douglas M., Salomaa, Veikko, Sanders, Alan R., Schall, Ulrich, Schubert, Christian R., Schulze, Thomas G., Schwab, Sibylle G., Scolnick, Edward M., Scott, Rodney J., Seidman, Larry J., Shi, Jianxin, Sigurdsson, Engilbert, Silagadze, Teimuraz, Silverman, Jeremy M., Sim, Kang, Slominsky, Petr, Smoller, Jordan W., So, Hon‐Cheong, Spencer, Chris C. A., Stahl, Eli A., Stefansson, Hreinn, Steinberg, Stacy, Stogmann, Elisabeth, Straub, Richard E., Strengman, Eric, Strohmaier, Jana, Stroup, T. Scott, Subramaniam, Mythily, Suvisaari, Jaana, Svrakic, Dragan M., Szatkiewicz, Jin P., Söderman, Erik, Thirumalai, Srinivas, Toncheva, Draga, Tosato, Sarah, Veijola, Juha, Waddington, John, Walsh, Dermot, Wang, Dai, Wang, Qiang, Webb, Bradley T., Weiser, Mark, Wildenauer, Dieter B., Williams, Nigel M., Williams, Stephanie, Witt, Stephanie H., Wolen, Aaron R., Wong, Emily H. M., Wormley, Brandon K., Xi, Hualin Simon, Zai, Clement C., Zheng, Xuebin, Zimprich, Fritz, Wray, Naomi R., Stefansson, Kari, Visscher, Peter M., Adolfsson, Rolf, Andreassen, Ole A., Blackwood, Douglas H. R., Bramon, Elvira, Buxbaum, Joseph D., Børglum, Anders D., Darvasi, Ariel, Domenici, Enrico, Ehrenreich, Hannelore, Esko, Tõnu, Gejman, Pablo V., Gill, Michael, Gurling, Hugh, Hultman, Christina M., Iwata, Nakao, Jablensky, Assen V., Jönsson, Erik G., Kendler, Kenneth S., Kirov, George, Knight, Jo, Lencz, Todd, Levinson, Douglas F., Li, Qingqin S., Liu, Jianjun, Malhotra, Anil K., McCarroll, Steven A., McQuillin, Andrew, Moran, Jennifer L., Mortensen, Preben B., Mowry, Bryan J., Owen, Michael J., Palotie, Aarno, Pato, Carlos N., Petryshen, Tracey L., Posthuma, Danielle, Riley, Brien P., Rujescu, Dan, Sham, Pak C., Sklar, Pamela, Clair, David St, Weinberger, Daniel R., Wendland, Jens R., Werge, Thomas, Daly, Mark J., Sullivan, Patrick F., O'Donovan, Michael C., Donohoe, Gary, Gill, Michael, Corvin, Aiden, and Morris, Derek W.
Abstract: Genome‐wide association studies (GWASs) are highly effective at identifying common risk variants for schizophrenia. Rare risk variants are also important contributors to schizophrenia etiology but, with the exception of large copy number variants, are difficult to detect with GWAS. Exome and genome sequencing, which have accelerated the study of rare variants, are expensive so alternative methods are needed to aid detection of rare variants. Here we re‐analyze an Irish schizophrenia GWAS dataset (n= 3,473) by performing identity‐by‐descent (IBD) mapping followed by exome sequencing of individuals identified as sharing risk haplotypes to search for rare risk variants in coding regions. We identified 45 rare haplotypes (>1 cM) that were significantly more common in cases than controls. By exome sequencing 105 haplotype carriers, we investigated these haplotypes for functional coding variants that could be tested for association in independent GWAS samples. We identified one rare missense variant in PCNTbut did not find statistical support for an association with schizophrenia in a replication analysis. However, IBD mapping can prioritize both individual samples and genomic regions for follow‐up analysis but genome rather than exome sequencing may be more effective at detecting risk variants on rare haplotypes.
Published: 2019
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25. A genome-wide association study of shared risk across psychiatric disorders implicates gene regulation during fetal neurodevelopment

Author: Schork, Andrew J., Won, Hyejung, Appadurai, Vivek, Nudel, Ron, Gandal, Mike, Delaneau, Olivier, Revsbech Christiansen, Malene, Hougaard, David M., Bækved-Hansen, Marie, Bybjerg-Grauholm, Jonas, Giørtz Pedersen, Marianne, Agerbo, Esben, Bøcker Pedersen, Carsten, Neale, Benjamin M., Daly, Mark J., Wray, Naomi R., Nordentoft, Merete, Mors, Ole, Børglum, Anders D., Bo Mortensen, Preben, Buil, Alfonso, Thompson, Wesley K., Geschwind, Daniel H., and Werge, Thomas
Abstract: There is mounting evidence that seemingly diverse psychiatric disorders share genetic etiology, but the biological substrates mediating this overlap are not well characterized. Here we leverage the unique Integrative Psychiatric Research Consortium (iPSYCH) study, a nationally representative cohort ascertained through clinical psychiatric diagnoses indicated in Danish national health registers. We confirm previous reports of individual and cross-disorder single-nucleotide polymorphism heritability for major psychiatric disorders and perform a cross-disorder genome-wide association study. We identify four novel genome-wide significant loci encompassing variants predicted to regulate genes expressed in radial glia and interneurons in the developing neocortex during mid-gestation. This epoch is supported by partitioning cross-disorder single-nucleotide polymorphism heritability, which is enriched at regulatory chromatin active during fetal neurodevelopment. These findings suggest that dysregulation of genes that direct neurodevelopment by common genetic variants may result in general liability for many later psychiatric outcomes.
Published: 2019
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26. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions

Author: Howard, David M., Adams, Mark J., Clarke, Toni-Kim, Hafferty, Jonathan D., Gibson, Jude, Shirali, Masoud, Coleman, Jonathan R. I., Hagenaars, Saskia P., Ward, Joey, Wigmore, Eleanor M., Alloza, Clara, Shen, Xueyi, Barbu, Miruna C., Xu, Eileen Y., Whalley, Heather C., Marioni, Riccardo E., Porteous, David J., Davies, Gail, Deary, Ian J., Hemani, Gibran, Berger, Klaus, Teismann, Henning, Rawal, Rajesh, Arolt, Volker, Baune, Bernhard T., Dannlowski, Udo, Domschke, Katharina, Tian, Chao, Hinds, David A., Trzaskowski, Maciej, Byrne, Enda M., Ripke, Stephan, Smith, Daniel J., Sullivan, Patrick F., Wray, Naomi R., Breen, Gerome, Lewis, Cathryn M., and McIntosh, Andrew M.
Abstract: Major depression is a debilitating psychiatric illness that is typically associated with low mood and anhedonia. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximize sample size, we meta-analyzed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 genesets associated with depression, including both genes and gene pathways associated with synaptic structure and neurotransmission. An enrichment analysis provided further evidence of the importance of prefrontal brain regions. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant after multiple testing correction. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding etiology and developing new treatment approaches.
Published: 2019
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27. Association of Whole-Genome and NETRIN1 Signaling Pathway–Derived Polygenic Risk Scores for Major Depressive Disorder and White Matter Microstructure in the UK Biobank

Author: Barbu, Miruna C., Zeng, Yanni, Shen, Xueyi, Cox, Simon R., Clarke, Toni-Kim, Gibson, Jude, Adams, Mark J., Johnstone, Mandy, Haley, Chris S., Lawrie, Stephen M., Deary, Ian J., Wray, Naomi R., Ripke, Stephan, Mattheisen, Manuel, Trzaskowski, Maciej, Byrne, Enda M., Abdellaoui, Abdel, Adams, Mark J., Agerbo, Esben, Air, Tracy M., Andlauer, Till F.M., Bacanu, Silviu-Alin, Bækvad-Hansen, Marie, Beekman, Aartjan T.F., Bigdeli, Tim B., Binder, Elisabeth B., Blackwood, Douglas H.R., Bryois, Julien, Buttenschøn, Henriette N., Bybjerg-Grauholm, Jonas, Cai, Na, Castelao, Enrique, Christensen, Jane Hvarregaard, Clarke, Toni-Kim, Coleman, Jonathan R.I., Colodro-Conde, Lucía, Couvy-Duchesne, Baptiste, Craddock, Nick, Crawford, Gregory E., Davies, Gail, Deary, Ian J., Degenhardt, Franziska, Derks, Eske M., Direk, Nese, Dolan, Conor V., Dunn, Erin C., Eley, Thalia C., Escott-Price, Valentina, Hassan Kiadeh, Farnush Farhadi, Finucane, Hilary K., Forstner, Andreas J., Frank, Josef, Gaspar, Héléna A., Gill, Michael, Goes, Fernando S., Gordon, Scott D., Grove, Jakob, Hall, Lynsey S., Hansen, Christine Søholm, Hansen, Thomas F., Herms, Stefan, Hickie, Ian B., Hoffmann, Per, Homuth, Georg, Horn, Carsten, Hottenga, Jouke-Jan, Hougaard, David M., Ising, Marcus, Jansen, Rick, Jorgenson, Eric, Knowles, James A., Kohane, Isaac S., Kraft, Julia, Kretzschmar, Warren W., Krogh, Jesper, Kutalik, Zoltán, Li, Yihan, Lind, Penelope A., MacIntyre, Donald J., MacKinnon, Dean F., Maier, Robert M., Maier, Wolfgang, Marchini, Jonathan, Mbarek, Hamdi, McGrath, Patrick, McGuffin, Peter, Medland, Sarah E., Mehta, Divya, Middeldorp, Christel M., Mihailov, Evelin, Milaneschi, Yuri, Milani, Lili, Mondimore, Francis M., Montgomery, Grant W., Mostafavi, Sara, Mullins, Niamh, Nauck, Matthias, Ng, Bernard, Nivard, Michel G., Nyholt, Dale R., O'Reilly, Paul F., Oskarsson, Hogni, Owen, Michael J., Painter, Jodie N., Pedersen, Carsten Bøcker, Pedersen, Marianne Giørtz, Peterson, Roseann E., Pettersson, Erik, Peyrot, Wouter J., Pistis, Giorgio, Posthuma, Danielle, Quiroz, Jorge A., Qvist, Per, Rice, John P., Riley, Brien P., Rivera, Margarita, Mirza, Saira Saeed, Schoevers, Robert, Schulte, Eva C., Shen, Ling, Shi, Jianxin, Shyn, Stanley I., Sigurdsson, Engilbert, Sinnamon, Grant C.B., Smit, Johannes H., Smith, Daniel J., Stefansson, Hreinn, Steinberg, Stacy, Streit, Fabian, Strohmaier, Jana, Tansey, Katherine E., Teismann, Henning, Teumer, Alexander, Thompson, Wesley, Thomson, Pippa A., Thorgeirsson, Thorgeir E., Traylor, Matthew, Treutlein, Jens, Trubetskoy, Vassily, Uitterlinden, André G., Umbricht, Daniel, Van der Auwera, Sandra, van Hemert, Albert M., Viktorin, Alexander, Visscher, Peter M., Wang, Yunpeng, Webb, Bradley T., Weinsheimer, Shantel Marie, Wellmann, Jürgen, Willemsen, Gonneke, Witt, Stephanie H., Wu, Yang, Xi, Hualin S., Yang, Jian, Zhang, Futao, Arolt, Volker, Baune, Bernhard T., Berger, Klaus, Boomsma, Dorret I., Cichon, Sven, Dannlowski, Udo, de Geus, E.J.C., DePaulo, J. Raymond, Domenici, Enrico, Domschke, Katharina, Esko, Tõnu, Grabe, Hans J., Hamilton, Steven P., Hayward, Caroline, Heath, Andrew C., Kendler, Kenneth S., Kloiber, Stefan, Lewis, Glyn, Li, Qingqin S., Lucae, Susanne, Madden, Pamela A.F., Magnusson, Patrik K., Martin, Nicholas G., McIntosh, Andrew M., Metspalu, Andres, Mors, Ole, Mortensen, Preben Bo, Müller-Myhsok, Bertram, Nordentoft, Merete, Nöthen, Markus M., O'Donovan, Michael C., Paciga, Sara A., Pedersen, Nancy L., Penninx, Brenda W.J.H., Perlis, Roy H., Porteous, David J., Potash, James B., Preisig, Martin, Rietschel, Marcella, Schaefer, Catherine, Schulze, Thomas G., Smoller, Jordan W., Stefansson, Kari, Tiemeier, Henning, Uher, Rudolf, Völzke, Henry, Weissman, Myrna M., Werge, Thomas, Lewis, Cathryn M., Levinson, Douglas F., Breen, Gerome, Børglum, Anders D., Sullivan, Patrick F., Agee, Michelle, Alipanahi, Babak, Auton, Adam, Bell, Robert K., Bryc, Katarzyna, Elson, Sarah L., Fontanillas, Pierre, Furlotte, Nicholas A., Hinds, David A., Huber, Karen E., Kleinman, Aaron, Litterman, Nadia K., McCreight, Jennifer C., McIntyre, Matthew H., Mountain, Joanna L., Noblin, Elizabeth S., Northover, Carrie A.M., Pitts, Steven J., Sathirapongsasuti, J. Fah, Sazonova, Olga V., Shelton, Janie F., Shringarpure, Suyash, Tian, Chao, Tung, Joyce Y., Vacic, Vladimir, Wilson, Catherine H., McIntosh, Andrew M., and Whalley, Heather C.
Abstract: Major depressive disorder is a clinically heterogeneous psychiatric disorder with a polygenic architecture. Genome-wide association studies have identified a number of risk-associated variants across the genome and have reported growing evidence of NETRIN1 pathway involvement. Stratifying disease risk by genetic variation within the NETRIN1 pathway may provide important routes for identification of disease mechanisms by focusing on a specific process, excluding heterogeneous risk-associated variation in other pathways. Here, we sought to investigate whether major depressive disorder polygenic risk scores derived from the NETRIN1 signaling pathway (NETRIN1-PRSs) and the whole genome, excluding NETRIN1 pathway genes (genomic-PRSs), were associated with white matter microstructure.
Published: 2019
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28. Hypermetabolism in ALS is associated with greater functional decline and shorter survival

Author: Steyn, Frederik J, Ioannides, Zara A, van Eijk, Ruben P A, Heggie, Susan, Thorpe, Kathryn A, Ceslis, Amelia, Heshmat, Saman, Henders, Anjali K, Wray, Naomi R, van den Berg, Leonard H, Henderson, Robert D, McCombe, Pamela A, and Ngo, Shyuan T
Abstract: ObjectiveTo determine the prevalence of hypermetabolism, relative to body composition, in amyotrophic lateral sclerosis (ALS) and its relationship with clinical features of disease and survival.MethodsFifty-eight patients with clinically definite or probable ALS as defined by El Escorial criteria, and 58 age and sex-matched control participants underwent assessment of energy expenditure. Our primary outcome was the prevalence of hypermetabolism in cases and controls. Longitudinal changes in clinical parameters between hypermetabolic and normometabolic patients with ALS were determined for up to 12 months following metabolic assessment. Survival was monitored over a 30-month period following metabolic assessment.ResultsHypermetabolism was more prevalent in patients with ALS than controls (41% vs 12%, adjusted OR=5.4; p<0.01). Change in body weight, body mass index and fat mass (%) was similar between normometabolic and hypermetabolic patients with ALS. Mean lower motor neuron score (SD) was greater in hypermetabolic patients when compared with normometabolic patients (4 (0.3) vs 3 (0.7); p=0.04). In the 12 months following metabolic assessment, there was a greater change in Revised ALS Functional Rating Scale score in hypermetabolic patients when compared with normometabolic patients (−0.68 points/month vs −0.39 points/month; p=0.01). Hypermetabolism was inversely associated with survival. Overall, hypermetabolism increased the risk of death during follow-up to 220% (HR 3.2, 95% CI 1.1 to 9.4, p=0.03).Conclusions and relevanceHypermetabolic patients with ALS have a greater level of lower motor neuron involvement, faster rate of functional decline and shorter survival. The metabolic index could be important for informing prognosis in ALS.
Published: 2018
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29. Genome‐wide gene‐environment interaction in depression: A systematic evaluation of candidate genes

Author: Van der Auwera, Sandra, Peyrot, Wouter J., Milaneschi, Yuri, Hertel, Johannes, Baune, Bernhard, Breen, Gerome, Byrne, Enda, Dunn, Erin C., Fisher, Helen, Homuth, Georg, Levinson, Douglas, Lewis, Cathryn, Mills, Natalie, Mullins, Niamh, Nauck, Matthias, Pistis, Giorgio, Preisig, Martin, Rietschel, Marcella, Ripke, Stephan, Sullivan, Patrick, Teumer, Alexander, Völzke, Henry, Boomsma, Dorret I., Wray, Naomi R., Penninx, Brenda, and Grabe, Hans
Abstract: Gene by environment (GxE) interaction studies have investigated the influence of a number of candidate genes and variants for major depressive disorder (MDD) on the association between childhood trauma and MDD. Most of these studies are hypothesis driven and investigate only a limited number of SNPs in relevant pathways using differing methodological approaches. Here (1) we identified 27 genes and 268 SNPs previously associated with MDD or with GxE interaction in MDD and (2) analyzed their impact on GxE in MDD using a common approach in 3944 subjects of European ancestry from the Psychiatric Genomics Consortium who had completed the Childhood Trauma Questionnaire. (3) We subsequently used the genome‐wide SNP data for a genome‐wide case‐control GxE model and GxE case‐only analyses testing for an enrichment of associated SNPs. No genome‐wide significant hits and no consistency among the signals of the different analytic approaches could be observed. This is the largest study for systematic GxE interaction analysis in MDD in subjects of European ancestry to date. Most of the known candidate genes/variants could not be supported. Thus, their impact on GxE interaction in MDD may be questionable. Our results underscore the need for larger samples, more extensive assessment of environmental exposures, and greater efforts to investigate new methodological approaches in GxE models for MDD.
Published: 2018
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30. Concepts, estimation and interpretation of SNP-based heritability

Author: Yang, Jian, Zeng, Jian, Goddard, Michael E, Wray, Naomi R, and Visscher, Peter M
Abstract: Narrow-sense heritability (h2) is an important genetic parameter that quantifies the proportion of phenotypic variance in a trait attributable to the additive genetic variation generated by all causal variants. Estimation of h2previously relied on closely related individuals, but recent developments allow estimation of the variance explained by all SNPs used in a genome-wide association study (GWAS) in conventionally unrelated individuals, that is, the SNP-based heritability (). In this Perspective, we discuss recently developed methods to estimate for a complex trait (and genetic correlation between traits) using individual-level or summary GWAS data. We discuss issues that could influence the accuracy of , definitions, assumptions and interpretations of the models, and pitfalls of misusing the methods and misinterpreting the models and results.
Published: 2017
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31. Genome-wide study of DNA methylation shows alterations in metabolic, inflammatory, and cholesterol pathways in ALS

Author: Hop, Paul J., Zwamborn, Ramona A.J., Hannon, Eilis, Shireby, Gemma L., Nabais, Marta F., Walker, Emma M., van Rheenen, Wouter, van Vugt, Joke J.F.A., Dekker, Annelot M., Westeneng, Henk-Jan, Tazelaar, Gijs H.P., van Eijk, Kristel R., Moisse, Matthieu, Baird, Denis, Al Khleifat, Ahmad, Iacoangeli, Alfredo, Ticozzi, Nicola, Ratti, Antonia, Cooper-Knock, Jonathan, Morrison, Karen E., Shaw, Pamela J., Basak, A. Nazli, Chiò, Adriano, Calvo, Andrea, Moglia, Cristina, Canosa, Antonio, Brunetti, Maura, Grassano, Maurizio, Gotkine, Marc, Lerner, Yossef, Zabari, Michal, Vourc’h, Patrick, Corcia, Philippe, Couratier, Philippe, Mora Pardina, Jesus S., Salas, Teresa, Dion, Patrick, Ross, Jay P., Henderson, Robert D., Mathers, Susan, McCombe, Pamela A., Needham, Merrilee, Nicholson, Garth, Rowe, Dominic B., Pamphlett, Roger, Mather, Karen A., Sachdev, Perminder S., Furlong, Sarah, Garton, Fleur C., Henders, Anjali K., Lin, Tian, Ngo, Shyuan T., Steyn, Frederik J., Wallace, Leanne, Williams, Kelly L., Neto, Miguel Mitne, Cauchi, Ruben J., Blair, Ian P., Kiernan, Matthew C., Drory, Vivian, Povedano, Monica, de Carvalho, Mamede, Pinto, Susana, Weber, Markus, Rouleau, Guy A., Silani, Vincenzo, Landers, John E., Shaw, Christopher E., Andersen, Peter M., McRae, Allan F., van Es, Michael A., Pasterkamp, R. Jeroen, Wray, Naomi R., McLaughlin, Russell L., Hardiman, Orla, Kenna, Kevin P., Tsai, Ellen, Runz, Heiko, Al-Chalabi, Ammar, van den Berg, Leonard H., Van Damme, Philip, Mill, Jonathan, Veldink, Jan H., Veldink, Jan H., van den Berg, Leonard H., Moed, Matthijs, Al-Chalabi, Ammar, Wray, Naomi R., Hardiman, Orla, Chio, Adriano, and Mill, Jonathan
Abstract: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with an estimated heritability between 40 and 50%. DNA methylation patterns can serve as proxies of (past) exposures and disease progression, as well as providing a potential mechanism that mediates genetic or environmental risk. Here, we present a blood-based epigenome-wide association study meta-analysis in 9706 samples passing stringent quality control (6763 patients, 2943 controls). We identified a total of 45 differentially methylated positions (DMPs) annotated to 42 genes, which are enriched for pathways and traits related to metabolism, cholesterol biosynthesis, and immunity. We then tested 39 DNA methylation–based proxies of putative ALS risk factors and found that high-density lipoprotein cholesterol, body mass index, white blood cell proportions, and alcohol intake were independently associated with ALS. Integration of these results with our latest genome-wide association study showed that cholesterol biosynthesis was potentially causally related to ALS. Last, DNA methylation at several DMPs and blood cell proportion estimates derived from DNA methylation data were associated with survival rate in patients, suggesting that they might represent indicators of underlying disease processes potentially amenable to therapeutic interventions.
Published: 2022
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32. Epigenetic Signatures of Cigarette Smoking

Author: Joehanes, Roby, Just, Allan C., Marioni, Riccardo E., Pilling, Luke C., Reynolds, Lindsay M., Mandaviya, Pooja R., Guan, Weihua, Xu, Tao, Elks, Cathy E., Aslibekyan, Stella, Moreno-Macias, Hortensia, Smith, Jennifer A., Brody, Jennifer A., Dhingra, Radhika, Yousefi, Paul, Pankow, James S., Kunze, Sonja, Shah, Sonia H., McRae, Allan F., Lohman, Kurt, Sha, Jin, Absher, Devin M., Ferrucci, Luigi, Zhao, Wei, Demerath, Ellen W., Bressler, Jan, Grove, Megan L., Huan, Tianxiao, Liu, Chunyu, Mendelson, Michael M., Yao, Chen, Kiel, Douglas P., Peters, Annette, Wang-Sattler, Rui, Visscher, Peter M., Wray, Naomi R., Starr, John M., Ding, Jingzhong, Rodriguez, Carlos J., Wareham, Nicholas J., Irvin, Marguerite R., Zhi, Degui, Barrdahl, Myrto, Vineis, Paolo, Ambatipudi, Srikant, Uitterlinden, André G., Hofman, Albert, Schwartz, Joel, Colicino, Elena, Hou, Lifang, Vokonas, Pantel S., Hernandez, Dena G., Singleton, Andrew B., Bandinelli, Stefania, Turner, Stephen T., Ware, Erin B., Smith, Alicia K., Klengel, Torsten, Binder, Elisabeth B., Psaty, Bruce M., Taylor, Kent D., Gharib, Sina A., Swenson, Brenton R., Liang, Liming, DeMeo, Dawn L., O’Connor, George T., Herceg, Zdenko, Ressler, Kerry J., Conneely, Karen N., Sotoodehnia, Nona, Kardia, Sharon L. R., Melzer, David, Baccarelli, Andrea A., van Meurs, Joyce B. J., Romieu, Isabelle, Arnett, Donna K., Ong, Ken K., Liu, Yongmei, Waldenberger, Melanie, Deary, Ian J., Fornage, Myriam, Levy, Daniel, and London, Stephanie J.
Abstract: Supplemental Digital Content is available in the text.
Published: 2016
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33. Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis

Author: van Rheenen, Wouter, Shatunov, Aleksey, Dekker, Annelot M, McLaughlin, Russell L, Diekstra, Frank P, Pulit, Sara L, van der Spek, Rick A A, Võsa, Urmo, de Jong, Simone, Robinson, Matthew R, Yang, Jian, Fogh, Isabella, van Doormaal, Perry TC, Tazelaar, Gijs H P, Koppers, Max, Blokhuis, Anna M, Sproviero, William, Jones, Ashley R, Kenna, Kevin P, van Eijk, Kristel R, Harschnitz, Oliver, Schellevis, Raymond D, Brands, William J, Medic, Jelena, Menelaou, Androniki, Vajda, Alice, Ticozzi, Nicola, Lin, Kuang, Rogelj, Boris, Vrabec, Katarina, Ravnik-Glavač, Metka, Koritnik, Blaž, Zidar, Janez, Leonardis, Lea, Grošelj, Leja Dolenc, Millecamps, Stéphanie, Salachas, François, Meininger, Vincent, de Carvalho, Mamede, Pinto, Susana, Mora, Jesus S, Rojas-García, Ricardo, Polak, Meraida, Chandran, Siddharthan, Colville, Shuna, Swingler, Robert, Morrison, Karen E, Shaw, Pamela J, Hardy, John, Orrell, Richard W, Pittman, Alan, Sidle, Katie, Fratta, Pietro, Malaspina, Andrea, Topp, Simon, Petri, Susanne, Abdulla, Susanne, Drepper, Carsten, Sendtner, Michael, Meyer, Thomas, Ophoff, Roel A, Staats, Kim A, Wiedau-Pazos, Martina, Lomen-Hoerth, Catherine, Van Deerlin, Vivianna M, Trojanowski, John Q, Elman, Lauren, McCluskey, Leo, Basak, A Nazli, Tunca, Ceren, Hamzeiy, Hamid, Parman, Yesim, Meitinger, Thomas, Lichtner, Peter, Radivojkov-Blagojevic, Milena, Andres, Christian R, Maurel, Cindy, Bensimon, Gilbert, Landwehrmeyer, Bernhard, Brice, Alexis, Payan, Christine A M, Saker-Delye, Safaa, Dürr, Alexandra, Wood, Nicholas W, Tittmann, Lukas, Lieb, Wolfgang, Franke, Andre, Rietschel, Marcella, Cichon, Sven, Nöthen, Markus M, Amouyel, Philippe, Tzourio, Christophe, Dartigues, Jean-François, Uitterlinden, Andre G, Rivadeneira, Fernando, Estrada, Karol, Hofman, Albert, Curtis, Charles, Blauw, Hylke M, van der Kooi, Anneke J, de Visser, Marianne, Goris, An, Weber, Markus, Shaw, Christopher E, Smith, Bradley N, Pansarasa, Orietta, Cereda, Cristina, Del Bo, Roberto, Comi, Giacomo P, D'Alfonso, Sandra, Bertolin, Cinzia, Sorarù, Gianni, Mazzini, Letizia, Pensato, Viviana, Gellera, Cinzia, Tiloca, Cinzia, Ratti, Antonia, Calvo, Andrea, Moglia, Cristina, Brunetti, Maura, Arcuti, Simona, Capozzo, Rosa, Zecca, Chiara, Lunetta, Christian, Penco, Silvana, Riva, Nilo, Padovani, Alessandro, Filosto, Massimiliano, Muller, Bernard, Stuit, Robbert Jan, Blair, Ian, Zhang, Katharine, McCann, Emily P, Fifita, Jennifer A, Nicholson, Garth A, Rowe, Dominic B, Pamphlett, Roger, Kiernan, Matthew C, Grosskreutz, Julian, Witte, Otto W, Ringer, Thomas, Prell, Tino, Stubendorff, Beatrice, Kurth, Ingo, Hübner, Christian A, Leigh, P Nigel, Casale, Federico, Chio, Adriano, Beghi, Ettore, Pupillo, Elisabetta, Tortelli, Rosanna, Logroscino, Giancarlo, Powell, John, Ludolph, Albert C, Weishaupt, Jochen H, Robberecht, Wim, Van Damme, Philip, Franke, Lude, Pers, Tune H, Brown, Robert H, Glass, Jonathan D, Landers, John E, Hardiman, Orla, Andersen, Peter M, Corcia, Philippe, Vourc'h, Patrick, Silani, Vincenzo, Wray, Naomi R, Visscher, Peter M, de Bakker, Paul I W, van Es, Michael A, Pasterkamp, R Jeroen, Lewis, Cathryn M, Breen, Gerome, Al-Chalabi, Ammar, van den Berg, Leonard H, and Veldink, Jan H
Abstract: To elucidate the genetic architecture of amyotrophic lateral sclerosis (ALS) and find associated loci, we assembled a custom imputation reference panel from whole-genome-sequenced patients with ALS and matched controls (n = 1,861). Through imputation and mixed-model association analysis in 12,577 cases and 23,475 controls, combined with 2,579 cases and 2,767 controls in an independent replication cohort, we fine-mapped a new risk locus on chromosome 21 and identified C21orf2 as a gene associated with ALS risk. In addition, we identified MOBP and SCFD1 as new associated risk loci. We established evidence of ALS being a complex genetic trait with a polygenic architecture. Furthermore, we estimated the SNP-based heritability at 8.5%, with a distinct and important role for low-frequency variants (frequency 1–10%). This study motivates the interrogation of larger samples with full genome coverage to identify rare causal variants that underpin ALS risk.
Published: 2016
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34. A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases

Author: Han, Buhm, Pouget, Jennie G, Slowikowski, Kamil, Stahl, Eli, Lee, Cue Hyunkyu, Diogo, Dorothee, Hu, Xinli, Park, Yu Rang, Kim, Eunji, Gregersen, Peter K, Dahlqvist, Solbritt Rantapää, Worthington, Jane, Martin, Javier, Eyre, Steve, Klareskog, Lars, Huizinga, Tom, Chen, Wei-Min, Onengut-Gumuscu, Suna, Rich, Stephen S, Wray, Naomi R, and Raychaudhuri, Soumya
Abstract: There is growing evidence of shared risk alleles for complex traits (pleiotropy), including autoimmune and neuropsychiatric diseases. This might be due to sharing among all individuals (whole-group pleiotropy) or a subset of individuals in a genetically heterogeneous cohort (subgroup heterogeneity). Here we describe the use of a well-powered statistic, BUHMBOX, to distinguish between those two situations using genotype data. We observed a shared genetic basis for 11 autoimmune diseases and type 1 diabetes (T1D; P < 1 × 10−4) and for 11 autoimmune diseases and rheumatoid arthritis (RA; P < 1 × 10−3). This sharing was not explained by subgroup heterogeneity (corrected PBUHMBOX> 0.2; 6,670 T1D cases and 7,279 RA cases). Genetic sharing between seronegative and seropostive RA (P < 1 × 10−9) had significant evidence of subgroup heterogeneity, suggesting a subgroup of seropositive-like cases within seronegative cases (PBUHMBOX= 0.008; 2,406 seronegative RA cases). We also observed a shared genetic basis for major depressive disorder (MDD) and schizophrenia (P < 1 × 10−4) that was not explained by subgroup heterogeneity (PBUHMBOX= 0.28; 9,238 MDD cases).
Published: 2016
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35. Risk of psychiatric illness from advanced paternal age is not predominantly from de novo mutations

Author: Gratten, Jacob, Wray, Naomi R, Peyrot, Wouter J, McGrath, John J, Visscher, Peter M, and Goddard, Michael E
Abstract: The offspring of older fathers have higher risk of psychiatric disorders such as schizophrenia and autism. Paternal-age-related de novo mutations are widely assumed to be the underlying causal mechanism, and, although such mutations must logically make some contribution, there are alternative explanations (for example, elevated liability to psychiatric illness may delay fatherhood). We used population genetic models based on empirical observations of key parameters (for example, mutation rate, prevalence, and heritability) to assess the genetic relationship between paternal age and risk of psychiatric illness. These models suggest that age-related mutations are unlikely to explain much of the increased risk of psychiatric disorders in children of older fathers. Conversely, a model incorporating a weak correlation between age at first child and liability to psychiatric illness matched epidemiological observations. Our results suggest that genetic risk factors shared by older fathers and their offspring are a credible alternative explanation to de novo mutations for risk to children of older fathers.
Published: 2016
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36. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets

Author: Zhu, Zhihong, Zhang, Futao, Hu, Han, Bakshi, Andrew, Robinson, Matthew R, Powell, Joseph E, Montgomery, Grant W, Goddard, Michael E, Wray, Naomi R, Visscher, Peter M, and Yang, Jian
Abstract: Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with human complex traits. However, the genes or functional DNA elements through which these variants exert their effects on the traits are often unknown. We propose a method (called SMR) that integrates summary-level data from GWAS with data from expression quantitative trait locus (eQTL) studies to identify genes whose expression levels are associated with a complex trait because of pleiotropy. We apply the method to five human complex traits using GWAS data on up to 339,224 individuals and eQTL data on 5,311 individuals, and we prioritize 126 genes (for example, TRAF1 and ANKRD55 for rheumatoid arthritis and SNX19 and NMRAL1 for schizophrenia), of which 25 genes are new candidates; 77 genes are not the nearest annotated gene to the top associated GWAS SNP. These genes provide important leads to design future functional studies to understand the mechanism whereby DNA variation leads to complex trait variation.
Published: 2016
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37. Genetic variants associated with response to lithium treatment in bipolar disorder: a genome-wide association study

Author: Hou, Liping, Heilbronner, Urs, Degenhardt, Franziska, Adli, Mazda, Akiyama, Kazufumi, Akula, Nirmala, Ardau, Raffaella, Arias, Bárbara, Backlund, Lena, Banzato, Claudio E M, Benabarre, Antoni, Bengesser, Susanne, Bhattacharjee, Abesh Kumar, Biernacka, Joanna M, Birner, Armin, Brichant-Petitjean, Clara, Bui, Elise T, Cervantes, Pablo, Chen, Guo-Bo, Chen, Hsi-Chung, Chillotti, Caterina, Cichon, Sven, Clark, Scott R, Colom, Francesc, Cousins, David A, Cruceanu, Cristiana, Czerski, Piotr M, Dantas, Clarissa R, Dayer, Alexandre, Étain, Bruno, Falkai, Peter, Forstner, Andreas J, Frisén, Louise, Fullerton, Janice M, Gard, Sébastien, Garnham, Julie S, Goes, Fernando S, Grof, Paul, Gruber, Oliver, Hashimoto, Ryota, Hauser, Joanna, Herms, Stefan, Hoffmann, Per, Hofmann, Andrea, Jamain, Stephane, Jiménez, Esther, Kahn, Jean-Pierre, Kassem, Layla, Kittel-Schneider, Sarah, Kliwicki, Sebastian, König, Barbara, Kusumi, Ichiro, Lackner, Nina, Laje, Gonzalo, Landén, Mikael, Lavebratt, Catharina, Leboyer, Marion, Leckband, Susan G, Jaramillo, Carlos A López, MacQueen, Glenda, Manchia, Mirko, Martinsson, Lina, Mattheisen, Manuel, McCarthy, Michael J, McElroy, Susan L, Mitjans, Marina, Mondimore, Francis M, Monteleone, Palmiero, Nievergelt, Caroline M, Nöthen, Markus M, Ösby, Urban, Ozaki, Norio, Perlis, Roy H, Pfennig, Andrea, Reich-Erkelenz, Daniela, Rouleau, Guy A, Schofield, Peter R, Schubert, K Oliver, Schweizer, Barbara W, Seemüller, Florian, Severino, Giovanni, Shekhtman, Tatyana, Shilling, Paul D, Shimoda, Kazutaka, Simhandl, Christian, Slaney, Claire M, Smoller, Jordan W, Squassina, Alessio, Stamm, Thomas, Stopkova, Pavla, Tighe, Sarah K, Tortorella, Alfonso, Turecki, Gustavo, Volkert, Julia, Witt, Stephanie, Wright, Adam, Young, L Trevor, Zandi, Peter P, Potash, James B, DePaulo, J Raymond, Bauer, Michael, Reininghaus, Eva Z, Novák, Tomas, Aubry, Jean-Michel, Maj, Mario, Baune, Bernhard T, Mitchell, Philip B, Vieta, Eduard, Frye, Mark A, Rybakowski, Janusz K, Kuo, Po-Hsiu, Kato, Tadafumi, Grigoroiu-Serbanescu, Maria, Reif, Andreas, Del Zompo, Maria, Bellivier, Frank, Schalling, Martin, Wray, Naomi R, Kelsoe, John R, Alda, Martin, Rietschel, Marcella, McMahon, Francis J, and Schulze, Thomas G
Abstract: Lithium is a first-line treatment in bipolar disorder, but individual response is variable. Previous studies have suggested that lithium response is a heritable trait. However, no genetic markers of treatment response have been reproducibly identified.
Published: 2016
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38. Genome‐wide association study reveals greater polygenic loading for schizophrenia in cases with a family history of illness

Author: Bigdeli, Tim B., Ripke, Stephan, Bacanu, Silviu‐Alin, Lee, Sang Hong, Wray, Naomi R., Gejman, Pablo V., Rietschel, Marcella, Cichon, Sven, St Clair, David, Corvin, Aiden, Kirov, George, McQuillin, Andrew, Gurling, Hugh, Rujescu, Dan, Andreassen, Ole A., Werge, Thomas, Blackwood, Douglas H. R., Pato, Carlos N., Pato, Michele T., Malhotra, Anil K., O'Donovan, Michael C., Kendler, Kenneth S., and Fanous, Ayman H.
Abstract: Genome‐wide association studies (GWAS) of schizophrenia have yielded more than 100 common susceptibility variants, and strongly support a substantial polygenic contribution of a large number of small allelic effects. It has been hypothesized that familial schizophrenia is largely a consequence of inherited rather than environmental factors. We investigated the extent to which familiality of schizophrenia is associated with enrichment for common risk variants detectable in a large GWAS. We analyzed single nucleotide polymorphism (SNP) data for cases reporting a family history of psychotic illness (N = 978), cases reporting no such family history (N = 4,503), and unscreened controls (N = 8,285) from the Psychiatric Genomics Consortium (PGC1) study of schizophrenia. We used a multinomial logistic regression approach with model‐fitting to detect allelic effects specific to either family history subgroup. We also considered a polygenic model, in which we tested whether family history positive subjects carried more schizophrenia risk alleles than family history negative subjects, on average. Several individual SNPs attained suggestive but not genome‐wide significant association with either family history subgroup. Comparison of genome‐wide polygenic risk scores based on GWAS summary statistics indicated a significant enrichment for SNP effects among family history positive compared to family history negative cases (Nagelkerke's R2 = 0.0021; P = 0.00331; P‐value threshold <0.4). Estimates of variability in disease liability attributable to the aggregate effect of genome‐wide SNPs were significantly greater for family history positive compared to family history negative cases (0.32 and 0.22, respectively; P = 0.031). We found suggestive evidence of allelic effects detectable in large GWAS of schizophrenia that might be specific to particular family history subgroups. However, consideration of a polygenic risk score indicated a significant enrichment among family history positive cases for common allelic effects. Familial illness might, therefore, represent a more heritable form of schizophrenia, as suggested by previous epidemiological studies. © 2015 Wiley Periodicals, Inc.
Published: 2016
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39. Across-cohort QC analyses of GWAS summary statistics from complex traits

Author: Chen, Guo-Bo, Lee, Sang Hong, Robinson, Matthew R, Trzaskowski, Maciej, Zhu, Zhi-Xiang, Winkler, Thomas W, Day, Felix R, Croteau-Chonka, Damien C, Wood, Andrew R, Locke, Adam E, Kutalik, Zoltán, Loos, Ruth J F, Frayling, Timothy M, Hirschhorn, Joel N, Yang, Jian, Wray, Naomi R, and Visscher, Peter M
Abstract: Genome-wide association studies (GWASs) have been successful in discovering SNP trait associations for many quantitative traits and common diseases. Typically, the effect sizes of SNP alleles are very small and this requires large genome-wide association meta-analyses (GWAMAs) to maximize statistical power. A trend towards ever-larger GWAMA is likely to continue, yet dealing with summary statistics from hundreds of cohorts increases logistical and quality control problems, including unknown sample overlap, and these can lead to both false positive and false negative findings. In this study, we propose four metrics and visualization tools for GWAMA, using summary statistics from cohort-level GWASs. We propose methods to examine the concordance between demographic information, and summary statistics and methods to investigate sample overlap. (I) We use the population genetics Fststatistic to verify the genetic origin of each cohort and their geographic location, and demonstrate using GWAMA data from the GIANT Consortium that geographic locations of cohorts can be recovered and outlier cohorts can be detected. (II) We conduct principal component analysis based on reported allele frequencies, and are able to recover the ancestral information for each cohort. (III) We propose a new statistic that uses the reported allelic effect sizes and their standard errors to identify significant sample overlap or heterogeneity between pairs of cohorts. (IV) To quantify unknown sample overlap across all pairs of cohorts, we propose a method that uses randomly generated genetic predictors that does not require the sharing of individual-level genotype data and does not breach individual privacy.
Published: 2016
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40. Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis

Author: Loh, Po-Ru, Bhatia, Gaurav, Gusev, Alexander, Finucane, Hilary K, Bulik-Sullivan, Brendan K, Pollack, Samuela J, de Candia, Teresa R, Lee, Sang Hong, Wray, Naomi R, Kendler, Kenneth S, O'Donovan, Michael C, Neale, Benjamin M, Patterson, Nick, and Price, Alkes L
Abstract: Heritability analyses of genome-wide association study (GWAS) cohorts have yielded important insights into complex disease architecture, and increasing sample sizes hold the promise of further discoveries. Here we analyze the genetic architectures of schizophrenia in 49,806 samples from the PGC and nine complex diseases in 54,734 samples from the GERA cohort. For schizophrenia, we infer an overwhelmingly polygenic disease architecture in which ≥71% of 1-Mb genomic regions harbor ≥1 variant influencing schizophrenia risk. We also observe significant enrichment of heritability in GC-rich regions and in higher-frequency SNPs for both schizophrenia and GERA diseases. In bivariate analyses, we observe significant genetic correlations (ranging from 0.18 to 0.85) for several pairs of GERA diseases; genetic correlations were on average 1.3 tunes stronger than the correlations of overall disease liabilities. To accomplish these analyses, we developed a fast algorithm for multicomponent, multi-trait variance-components analysis that overcomes prior computational barriers that made such analyses intractable at this scale.
Published: 2015
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41. The value of polygenic analyses in psychiatry

Author: Middeldorp, Christel M. and Wray, Naomi R.
Published: 2018
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42. Parsimonious model for mass-univariate vertexwise analysis

Author: Couvy-Duchesne, Baptiste, Zhang, Futao, Kemper, Kathryn E., Sidorenko, Julia, Wray, Naomi R., Visscher, Peter M., Colliot, Olivier, and Yang, Jian
Published: 2022
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43. Author Correction: From Mendel to quantitative genetics in the genome era: the scientific legacy of W. G. Hill

Author: Charlesworth, Brian, Goddard, Michael E., Meyer, Karin, Visscher, Peter M., Weir, Bruce S., and Wray, Naomi R.
Published: 2022
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44. Genetic Overlap Between Diagnostic Subtypes of Ischemic Stroke

Author: Holliday, Elizabeth G., Traylor, Matthew, Malik, Rainer, Bevan, Steve, Falcone, Guido, Hopewell, Jemma C., Cheng, Yu-Ching, Cotlarciuc, Ioana, Bis, Joshua C., Boerwinkle, Eric, Boncoraglio, Giorgio B., Clarke, Robert, Cole, John W., Fornage, Myriam, Furie, Karen L., Ikram, M. Arfan, Jannes, Jim, Kittner, Steven J., Lincz, Lisa F., Maguire, Jane M., Meschia, James F., Mosley, Thomas H., Nalls, Mike A., Oldmeadow, Christopher, Parati, Eugenio A., Psaty, Bruce M., Rothwell, Peter M., Seshadri, Sudha, Scott, Rodney J., Sharma, Pankaj, Sudlow, Cathie, Wiggins, Kerri L., Worrall, Bradford B., Rosand, Jonathan, Mitchell, Braxton D., Dichgans, Martin, Markus, Hugh S., Levi, Christopher, Attia, John, and Wray, Naomi R.
Abstract: Despite moderate heritability, the phenotypic heterogeneity of ischemic stroke has hampered gene discovery, motivating analyses of diagnostic subtypes with reduced sample sizes. We assessed evidence for a shared genetic basis among the 3 major subtypes: large artery atherosclerosis (LAA), cardioembolism, and small vessel disease (SVD), to inform potential cross-subtype analyses.
Published: 2015
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45. Testing the role of circadian genes in conferring risk for psychiatric disorders

Author: Byrne, Enda M., Heath, Andrew C., Madden, Pamela A.F., Pergadia, Michele L., Hickie, Ian B., Montgomery, Grant W., Martin, Nicholas G., and Wray, Naomi R.
Published: 2014
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46. A recessive genetic model and runs of homozygosity in major depressive disorder

Author: Power, Robert A., Keller, Matthew C., Ripke, Stephan, Abdellaoui, Abdel, Wray, Naomi R., Sullivan, Patrick F., and Breen, Gerome
Published: 2014
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47. A genome wide survey supports the involvement of large copy number variants in schizophrenia with and without intellectual disability

Author: Derks, Eske M., Ayub, Muhammad, Chambert, Kimberly, Del Favero, Jurgen, Johnstone, Mandy, MacGregor, Stuart, Maclean, Alan, McKechanie, Andrew G., McRae, Allan F., Moran, Jennifer L., Pickard, Benjamin S., Purcell, Shaun, Sklar, Pamela, StCLair, David M., Wray, Naomi R., Visscher, Peter M., and Blackwood, Douglas H. R.
Published: 2013
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48. Identifying the Common Genetic Basis of Antidepressant Response

Author: Pain, Oliver, Hodgson, Karen, Trubetskoy, Vassily, Ripke, Stephan, Marshe, Victoria S., Adams, Mark J., Byrne, Enda M., Campos, Adrian I., Carrillo-Roa, Tania, Cattaneo, Annamaria, Als, Thomas Damm, Souery, Daniel, Dernovsek, Mojca Z., Fabbri, Chiara, Hayward, Caroline, Henigsberg, Neven, Hauser, Joanna, Kennedy, James L., Lenze, Eric J., Lewis, Glyn, Müller, Daniel J., Martin, Nicholas G., Mulsant, Benoit H., Mors, Ole, Perroud, Nader, Porteous, David J., Rentería, Miguel E., Reynolds, Charles F., Rietschel, Marcella, Uher, Rudolf, Wigmore, Eleanor M., Maier, Wolfgang, Wray, Naomi R., Aitchison, Katherine J., Arolt, Volker, Baune, Bernhard T., Biernacka, Joanna M., Bondolfi, Guido, Domschke, Katharina, Kato, Masaki, Li, Qingqin S., Liu, Yu-Li, Serretti, Alessandro, Tsai, Shih-Jen, Turecki, Gustavo, Weinshilboum, Richard, Kasper, Siegfried, Zohar, Joseph, Souery, Daniel, Montgomery, Stuart, Albani, Diego, Forloni, Gianluigi, Ferentinos, Panagiotis, Rujescu, Dan, Mendlewicz, Julien, Wray, Naomi R., Ripke, Stephan, Mattheisen, Manuel, Trzaskowski, Maciej, Byrne, Enda M., Abdellaoui, Abdel, Adams, Mark J., Agerbo, Esben, Air, Tracy M., Andlauer, Till F.M., Bacanu, Silviu-Alin, Bækvad-Hansen, Marie, Beekman, Aartjan T.F., Bigdeli, Tim B., Binder, Elisabeth B., Bryois, Julien, Buttenschøn, Henriette N., Bybjerg-Grauholm, Jonas, Cai, Na, Castelao, Enrique, Christensen, Jane Hvarregaard, Clarke, Toni-Kim, Coleman, Jonathan R.I., Colodro-Conde, Lucía, Couvy-Duchesne, Baptiste, Craddock, Nick, Crawford, Gregory E., Davies, Gail, Deary, Ian J., Degenhardt, Franziska, Derks, Eske M., Direk, Nese, Dolan, Conor V., Dunn, Erin C., Eley, Thalia C., Escott-Price, Valentina, Hassan Kiadeh, Farnush Farhadi, Finucane, Hilary K., Foo, Jerome C., Forstner, Andreas J., Frank, Josef, Gaspar, Héléna A., Gill, Michael, Goes, Fernando S., Gordon, Scott D., Grove, Jakob, Hall, Lynsey S., Hansen, Christine Søholm, Hansen, Thomas F., Herms, Stefan, Hickie, Ian B., Hoffmann, Per, Homuth, Georg, Horn, Carsten, Hottenga, Jouke-Jan, Hougaard, David M., Howard, David M., Ising, Marcus, Jansen, Rick, Jones, Ian, Jones, Lisa A., Jorgenson, Eric, Knowles, James A., Kohane, Isaac S., Kraft, Julia, Kretzschmar, Warren W., Kutalik, Zoltán, Li, Yihan, Lind, Penelope A., MacIntyre, Donald J., MacKinnon, Dean F., Maier, Robert M., Maier, Wolfgang, Marchini, Jonathan, Mbarek, Hamdi, McGrath, Patrick, McGuffin, Peter, Medland, Sarah E., Mehta, Divya, Middeldorp, Christel M., Mihailov, Evelin, Milaneschi, Yuri, Milani, Lili, Mondimore, Francis M., Montgomery, Grant W., Mostafavi, Sara, Mullins, Niamh, Nauck, Matthias, Ng, Bernard, Nivard, Michel G., Nyholt, Dale R., O’Reilly, Paul F., Oskarsson, Hogni, Owen, Michael J., Painter, Jodie N., Pedersen, Carsten Bøcker, Pedersen, Marianne Giørtz, Peterson, Roseann E., Peyrot, Wouter J., Pistis, Giorgio, Posthuma, Danielle, Quiroz, Jorge A., Qvist, Per, Rice, John P., Riley, Brien P., Rivera, Margarita, Mirza, Saira Saeed, Schoevers, Robert, Schulte, Eva C., Shen, Ling, Shi, Jianxin, Shyn, Stanley I., Sigurdsson, Engilbert, Sinnamon, Grant C.B., Smit, Johannes H., Smith, Daniel J., Stefansson, Hreinn, Steinberg, Stacy, Streit, Fabian, Strohmaier, Jana, Tansey, Katherine E., Teismann, Henning, Teumer, Alexander, Thompson, Wesley, Thomson, Pippa A., Thorgeirsson, Thorgeir E., Traylor, Matthew, Treutlein, Jens, Trubetskoy, Vassily, Uitterlinden, André G., Umbricht, Daniel, Van der Auwera, Sandra, van Hemert, Albert M., Viktorin, Alexander, Visscher, Peter M., Wang, Yunpeng, Webb, Bradley T., Weinsheimer, Shantel Marie, Wellmann, Jürgen, Willemsen, Gonneke, Witt, Stephanie H., Wu, Yang, Xi, Hualin S., Yang, Jian, Zhang, Futao, Arolt, Volker, Baune, Bernhard T., Berger, Klaus, Boomsma, Dorret I., Cichon, Sven, Dannlowski, Udo, de Geus, E.J.C., DePaulo, J. Raymond, Domenici, Enrico, Domschke, Katharina, Esko, Tõnu, Grabe, Hans J., Hamilton, Steven P., Hayward, Caroline, Heath, Andrew C., Kendler, Kenneth S., Kloiber, Stefan, Lewis, Glyn, Li, Qingqin S., Lucae, Susanne, Madden, Pamela A.F., Magnusson, Patrik K., Martin, Nicholas G., McIntosh, Andrew M., Metspalu, Andres, Mors, Ole, Mortensen, Preben Bo, Müller-Myhsok, Bertram, Nordentoft, Merete, Nöthen, Markus M., O’Donovan, Michael C., Paciga, Sara A., Pedersen, Nancy L., Penninx, Brenda W.J.H., Perlis, Roy H., Porteous, David J., Potash, James B., Preisig, Martin, Rietschel, Marcella, Schaefer, Catherine, Schulze, Thomas G., Smoller, Jordan W., Stefansson, Kari, Tiemeier, Henning, Uher, Rudolf, Völzke, Henry, Weissman, Myrna M., Werge, Thomas, Lewis, Cathryn M., Levinson, Douglas F., Breen, Gerome, Børglum, Anders D., Sullivan, Patrick F., McIntosh, Andrew M., and Lewis, Cathryn M.
Abstract: 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.
Published: 2021
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49. Author Correction: Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology

Author: van Rheenen, Wouter, van der Spek, Rick A. A., Bakker, Mark K., van Vugt, Joke J. F. A., Hop, Paul J., Zwamborn, Ramona A. J., de Klein, Niek, Westra, Harm-Jan, Bakker, Olivier B., Deelen, Patrick, Shireby, Gemma, Hannon, Eilis, Moisse, Matthieu, Baird, Denis, Restuadi, Restuadi, Dolzhenko, Egor, Dekker, Annelot M., Gawor, Klara, Westeneng, Henk-Jan, Tazelaar, Gijs H. P., van Eijk, Kristel R., Kooyman, Maarten, Byrne, Ross P., Doherty, Mark, Heverin, Mark, Al Khleifat, Ahmad, Iacoangeli, Alfredo, Shatunov, Aleksey, Ticozzi, Nicola, Cooper-Knock, Johnathan, Smith, Bradley N., Gromicho, Marta, Chandran, Siddharthan, Pal, Suvankar, Morrison, Karen E., Shaw, Pamela J., Hardy, John, Orrell, Richard W., Sendtner, Michael, Meyer, Thomas, Başak, Nazli, van der Kooi, Anneke J., Ratti, Antonia, Fogh, Isabella, Gellera, Cinzia, Lauria, Giuseppe, Corti, Stefania, Cereda, Cristina, Sproviero, Daisy, D’Alfonso, Sandra, Sorarù, Gianni, Siciliano, Gabriele, Filosto, Massimiliano, Padovani, Alessandro, Chiò, Adriano, Calvo, Andrea, Moglia, Cristina, Brunetti, Maura, Canosa, Antonio, Grassano, Maurizio, Beghi, Ettore, Pupillo, Elisabetta, Logroscino, Giancarlo, Nefussy, Beatrice, Osmanovic, Alma, Nordin, Angelica, Lerner, Yossef, Zabari, Michal, Gotkine, Marc, Baloh, Robert H., Bell, Shaughn, Vourc’h, Patrick, Corcia, Philippe, Couratier, Philippe, Millecamps, Stéphanie, Meininger, Vincent, Salachas, François, Mora Pardina, Jesus S., Assialioui, Abdelilah, Rojas-García, Ricardo, Dion, Patrick A., Ross, Jay P., Ludolph, Albert C., Weishaupt, Jochen H., Brenner, David, Freischmidt, Axel, Bensimon, Gilbert, Brice, Alexis, Durr, Alexandra, Payan, Christine A. M., Saker-Delye, Safa, Wood, Nicholas W., Topp, Simon, Rademakers, Rosa, Tittmann, Lukas, Lieb, Wolfgang, Franke, Andre, Ripke, Stephan, Braun, Alice, Kraft, Julia, Whiteman, David C., Olsen, Catherine M., Uitterlinden, Andre G., Hofman, Albert, Rietschel, Marcella, Cichon, Sven, Nöthen, Markus M., Amouyel, Philippe, Traynor, Bryan J., Singleton, Andrew B., Mitne Neto, Miguel, Cauchi, Ruben J., Ophoff, Roel A., Wiedau-Pazos, Martina, Lomen-Hoerth, Catherine, van Deerlin, Vivianna M., Grosskreutz, Julian, Roediger, Annekathrin, Gaur, Nayana, Jörk, Alexander, Barthel, Tabea, Theele, Erik, Ilse, Benjamin, Stubendorff, Beatrice, Witte, Otto W., Steinbach, Robert, Hübner, Christian A., Graff, Caroline, Brylev, Lev, Fominykh, Vera, Demeshonok, Vera, Ataulina, Anastasia, Rogelj, Boris, Koritnik, Blaž, Zidar, Janez, Ravnik-Glavač, Metka, Glavač, Damjan, Stević, Zorica, Drory, Vivian, Povedano, Monica, Blair, Ian P., Kiernan, Matthew C., Benyamin, Beben, Henderson, Robert D., Furlong, Sarah, Mathers, Susan, McCombe, Pamela A., Needham, Merrilee, Ngo, Shyuan T., Nicholson, Garth A., Pamphlett, Roger, Rowe, Dominic B., Steyn, Frederik J., Williams, Kelly L., Mather, Karen A., Sachdev, Perminder S., Henders, Anjali K., Wallace, Leanne, de Carvalho, Mamede, Pinto, Susana, Petri, Susanne, Weber, Markus, Rouleau, Guy A., Silani, Vincenzo, Curtis, Charles J., Breen, Gerome, Glass, Jonathan D., Brown, Robert H., Landers, John E., Shaw, Christopher E., Andersen, Peter M., Groen, Ewout J. N., van Es, Michael A., Pasterkamp, R. Jeroen, Fan, Dongsheng, Garton, Fleur C., McRae, Allan F., Davey Smith, George, Gaunt, Tom R., Eberle, Michael A., Mill, Jonathan, McLaughlin, Russell L., Hardiman, Orla, Kenna, Kevin P., Wray, Naomi R., Tsai, Ellen, Runz, Heiko, Franke, Lude, Al-Chalabi, Ammar, Van Damme, Philip, van den Berg, Leonard H., and Veldink, Jan H.
Published: 2022
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50. A genome‐wide association study of sleep habits and insomnia

Author: Byrne, Enda M., Gehrman, Philip R., Medland, Sarah E., Nyholt, Dale R., Heath, Andrew C., Madden, Pamela A. F., Hickie, Ian B., Van Duijn, Cornelia M., Henders, Anjali K., Montgomery, Grant W., Martin, Nicholas G., and Wray, Naomi R.
Published: 2013
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