期刊
NATURE HUMAN BEHAVIOUR
卷 3, 期 5, 页码 513-525出版社
NATURE PORTFOLIO
DOI: 10.1038/s41562-019-0566-x
关键词
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资金
- NIH [R01HD083613, R01AG054628, P2CHD042849]
- Jacobs Foundation
- Royal Netherlands Academy of Science Professor Award [PAH/6635]
- ZonMw [531003014, 849200011]
- ` Aggression in children: unraveling gene-environment interplay to inform treatment and intervention strategies' (ACTION) project
- European Union [602768]
- ERC Consolidator Grant [647648 EdGe]
- University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross-council Lifelong Health and Wellbeing Initiative [MR/K026992/1]
- Age UK (Disconnected Mind Project)
- Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6]
- Scottish Funding Council [HR03006]
- Medical Research Council UK
- Wellcome Trust (Wellcome Trust Strategic Award 'STratifying Resilience and Depression Longitudinally' (STRADL) [104036/Z/14/Z]
- BBSRC [BB/F019394/1] Funding Source: UKRI
- MRC [G0700704] Funding Source: UKRI
Genetic correlations estimated from genome-wide association studies (GWASs) reveal pervasive pleiotropy across a wide variety of phenotypes. We introduce genomic structural equation modelling (genomic SEM): a multivariate method for analysing the joint genetic architecture of complex traits. Genomic SEM synthesizes genetic correlations and single-nucleotide polymorphism heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to model multivariate genetic associations among phenotypes, identify variants with effects on general dimensions of cross-trait liability, calculate more predictive polygenic scores and identify loci that cause divergence between traits. We demonstrate several applications of genomic SEM, including a joint analysis of summary statistics from five psychiatric traits. We identify 27 independent single-nucleotide polymorphisms not previously identified in the contributing univariate GWASs. Polygenic scores from genomic SEM consistently outperform those from univariate GWASs. Genomic SEM is flexible and open ended, and allows for continuous innovation in multivariate genetic analysis.
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