Journal article

Conditional GWAS analysis to identify disorder-specific SNPs for psychiatric disorders

Enda M Byrne, Zhihong Zhu, Ting Qi, Nathan G Skene, Julien Bryois, Antonio F Pardinas, Eli Stahl, Jordan W Smoller, Marcella Rietschel, Michael J Owen, James TR Walters, Michael C O'Donovan, John G McGrath, Jens Hjerling-Leffler, Patrick F Sullivan, Michael E Goddard, Peter M Visscher, Jian Yang, Naomi R Wray

MOLECULAR PSYCHIATRY | NATURE PUBLISHING GROUP | Published : 2020

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., ..

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University of Melbourne Researchers

Grants

Awarded by National Health and Medical Research Council of Australia


Awarded by US National Institute of Drug Abuse


Funding Acknowledgements

This work is supported by grants from the National Health and Medical Research Council of Australia (1087889, 1145645, 1113400, 1078901 and 1078037), and the Sylvia & Charles Viertel Charitable Foundation. The PGC has received major funding from the US National Institute of Mental Health and the US National Institute of Drug Abuse (U01 MH109528 and U01 MH1095320). We thank the research participants and employees of 23andMe, Inc. for contributing to this study. This paper would not have been possible without the generosity of participants in the many studies that comprise the final meta-analyses and the dedication of many clinicians and research staff who have collected the data and made them publically available. Acknowledgments for specific data sets are provided in the .