Fast variance-components analysis to compare/contrast genetic architectures of several complex diseases

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. The phenotype of the complex disease schizophrenia is most likely not that different from that of any other mujltifactorial trait––including environment-induced disorders, dose-independent adverse drug reactions, and drug efficacy.

 In the attached study, authors analyze the genetic architectures of schizophrenia in 49,806 samples from the Psychiatric Genomics Consortium (PGC) and nine other complex diseases in 54,734 samples from the Genetic Epidemiology Research on Aging (GERA) cohort. For schizophrenia, authors infer an overwhelmingly polygenic disease architecture in which ≥71% of 1-Mb genomic regions harbor one or more variants influencing schizophrenia risk.

 Authors also observe significant enrichment of heritability in GC-rich regions and in higher-frequency SNPs for both schizophrenia and GERA diseases. In bi-variate analyses, they observed significant genetic correlations (ranging from 0.18 to 0.85) for several pairs of GERA diseases; genetic correlations were, on average, 1.3 times stronger than the correlations of overall disease liabilities. To accomplish these analyses, authors developed a fast algorithm for multicomponent, multi-trait variance-components analysis that overcomes prior computational barriers that made such analyses intractable at such a large scale as this.

 Nat Genet  Dec 2o15; 47: 1385–1392

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