Empirical Bayes Estimation of Semi-parametric Hierarchical Mixture Models for Unbiased Characterization of Polygenic Disease Architectures

Empirical Bayes Estimation of Semi-parametric Hierarchical Mixture Models for Unbiased Characterization of Polygenic Disease Architectures

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 architectures of many diseases have not been well characterized due to lack of simple and fast methods for unbia...

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Bibliographic Details
Main Authors: Jo Nishino, Yuta Kochi, Daichi Shigemizu, Mamoru Kato, Katsunori Ikari, Hidenori Ochi, Hisashi Noma, Kota Matsui, Takashi Morizono, Keith A. Boroevich, Tatsuhiko Tsunoda, Shigeyuki Matsui
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-04-01
Series:Frontiers in Genetics
Subjects:
genome-wide association study (GWAS)
polygenic disease architecture
polygenicity
effect-size distribution
semi-parametric hierarchical mixture model
Online Access:http://journal.frontiersin.org/article/10.3389/fgene.2018.00115/full

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http://journal.frontiersin.org/article/10.3389/fgene.2018.00115/full

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