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

Title
Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis
Publication Date
2015
Author(s)
Loh, Po-Ru
Bhatia, Gaurav
O'Donovan, Michael C
Neale, Benjamin M
Patterson, Nick
Price, Alkes L
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
Type of document
Journal Article
Language
en
Entity Type
Publication
Publisher
Nature Publishing Group
Place of publication
United States of America
DOI
10.1038/ng.3431
UNE publication id
une:18956
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.
Link
Citation
Nature Genetics, 47(12), p. 1385-1392
ISSN
1546-1718
1061-4036
Start page
1385
End page
1392

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