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Efficient testing and effect size estimation for set-based genetic association inference via semiparametric multilevel mixture modeling: Application to a genome-wide association study of coronary artery disease

By Shonosuke Sugasawa and Hisashi Noma
In genetic association studies, rare variants with extremely small allele frequency play a crucial role in complex traits, and the set-based testing methods that jointly assess the effects of groups of single nucleotide polymorphisms (SNPs) were developed to improve powers for the association tests. However, the powers of these tests... Show more
March 12, 2020
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Efficient testing and effect size estimation for set-based genetic association inference via semiparametric multilevel mixture modeling: Application to a genome-wide association study of coronary artery disease
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