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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Detalhes bibliográficos

Principais autores:	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
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Frontiers Media S.A. 2018-04-01
coleção:	Frontiers in Genetics
Assuntos:	genome-wide association study (GWAS) polygenic disease architecture polygenicity effect-size distribution semi-parametric hierarchical mixture model
Acesso em linha:	http://journal.frontiersin.org/article/10.3389/fgene.2018.00115/full
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