Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance components analysis

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Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance components analysis

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Title: 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; Price, Alkes L

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Citation: Loh, P., G. Bhatia, A. Gusev, H. K. Finucane, B. K. Bulik-Sullivan, S. J. Pollack, T. R. de Candia, et al. 2015. “Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance components analysis.” Nature genetics 47 (12): 1385-1392. doi:10.1038/ng.3431. http://dx.doi.org/10.1038/ng.3431.
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Abstract: Heritability analyses of 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 architecture 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 1Mb 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) among several pairs of GERA diseases; genetic correlations were on average 1.3x stronger than correlations of overall disease liabilities. To accomplish these analyses, we developed a fast algorithm for multi-component, multi-trait variance components analysis that overcomes prior computational barriers that made such analyses intractable at this scale.
Published Version: doi:10.1038/ng.3431
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4666835/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:27320464
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