Contrasting regional architectures of schizophrenia and other complex diseases using fast variance components analysis
Po-Ru Loh , Gaurav Bhatia , Alexander Gusev , Hilary K Finucane , Brendan K Bulik-Sullivan , Samuela J Pollack , Schizophrenia Working Group Psychiatric Genomics Consortium , Teresa R de Candia , Sang Hong Lee , Naomi R Wray , Kenneth S Kendler , Michael C O’Donovan , Benjamin M Neale , Nick Patterson , Alkes L Price
doi: http://dx.doi.org/10.1101/016527

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 ≥76% of 1Mb genomic regions harbor at least one 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 13 of 36 pairs of GERA diseases; genetic correlations were consistently stronger (1.3x on average) than correlations of overall disease liabilities. To accomplish these analyses, we developed a novel, fast algorithm for multi-component, multi-trait variance components analysis that overcomes prior computational barriers that made such analyses intractable at this scale.