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

Nat Genet
Authors
Keywords
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.

Year of Publication
2015
Journal
Nat Genet
Volume
47
Issue
12
Pages
1385-92
Date Published
2015 Dec
ISSN
1546-1718
URL
DOI
10.1038/ng.3431
PubMed ID
26523775
PubMed Central ID
PMC4666835
Links
Grant list
S10 RR028832 / RR / NCRR NIH HHS / United States
R01 HG006399 / HG / NHGRI NIH HHS / United States
1S10RR028832-01 / RR / NCRR NIH HHS / United States
F32 HG007805 / HG / NHGRI NIH HHS / United States
R01 MH101244 / MH / NIMH NIH HHS / United States
MR/L010305/1 / Medical Research Council / United Kingdom
G0800509 / Medical Research Council / United Kingdom