RT Journal Article
SR Electronic
T1 Empirical Bayes estimation of semi-parametric hierarchical mixture models for unbiased characterization of polygenic disease architectures
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 080945
DO 10.1101/080945
A1 Jo Nishino
A1 Kochi Yuta
A1 Daichi Shigemizu
A1 Mamoru Kato
A1 Katsunori Ikari
A1 Hidenori Ochi
A1 Hisashi Noma
A1 Kota Matsui
A1 Takashi Morizono
A1 Keith A Boroevich
A1 Tatsuhiko Tsunoda
A1 Shigeyuki Matsui
YR 2016
UL http://biorxiv.org/content/early/2016/10/21/080945.abstract
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.