PT - JOURNAL ARTICLE AU - Jo Nishino AU - Kochi Yuta AU - Daichi Shigemizu AU - Mamoru Kato AU - Katsunori Ikari AU - Hidenori Ochi AU - Hisashi Noma AU - Kota Matsui AU - Takashi Morizono AU - Keith A Boroevich AU - Tatsuhiko Tsunoda AU - Shigeyuki Matsui TI - Empirical Bayes estimation of semi-parametric hierarchical mixture models for unbiased characterization of polygenic disease architectures AID - 10.1101/080945 DP - 2016 Jan 01 TA - bioRxiv PG - 080945 4099 - http://biorxiv.org/content/early/2016/10/21/080945.short 4100 - http://biorxiv.org/content/early/2016/10/21/080945.full AB - Genome-wide association studies (GWAS) suggest that the genetic architecture of complex diseases consists of unexpectedly numerous variants with small effect sizes. However, the polygenic architecture of many diseases has not been accurately assessed due to lack of simple and fast methods for unbiased estimation of the underlying proportion of disease-associated variants and their effect-size distribution. Applying empirical Bayes estimation of semi-parametric hierarchical mixture models to GWAS summary statistics, we confirmed that schizophrenia was extremely polygenic (~30% risk variants of independent genome-wide SNPs, most within odds ratio (OR)=1.03), whereas rheumatoid arthritis was less polygenic (2.5~5.0% risk variants, significant portion reaching OR=1.05~1.1). For rheumatoid arthritis, stratified estimations revealed that expression quantitative loci in blood explained large genetic variance, and low- and high-frequency derived alleles were prone to be risk and protective, respectively, suggesting a predominance of deleterious-risk and advantageous-protective mutation. Despite genetic correlation, effect-size distributions for schizophrenia and bipolar disorder differed across allele frequency. These analyses distinguished disease polygenic architectures and provided clues for etiological differences in complex diseases.