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Genome-wide association analysis of GAW17 data using an empirical Bayes variable selection

By Vitara Pungpapong, Libo Wang, Yanzhu Lin, Dabao Zhang and Min Zhang

Abstract

Next-generation sequencing technologies enable us to explore rare functional variants. However, most current statistical techniques are too underpowered to capture signals of rare variants in genome-wide association studies. We propose a supervised coalescing of single-nucleotide polymorphisms to obtain gene-based markers that can stably reveal possible genetic effects related to rare alleles. We use a newly developed empirical Bayes variable selection algorithm to identify associations between studied traits and genetic markers. Using our novel method, we analyzed the three continuous phenotypes in the GAW17 data set across 200 replicates, with intriguing results

Topics: Proceedings
Publisher: BioMed Central
OAI identifier: oai:pubmedcentral.nih.gov:3287887
Provided by: PubMed Central

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Citations

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