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Association Analysis and Meta-Analysis of Multi-allelic Variants for Large Scale Sequence Data

By Xiaowei Zhan, Sai Chen, Yu Jiang, Mengzhen Liu, William G Iacono, John K. Hewitt, John E Hokanson, Kenneth Krauter, Markku Laakso, Kevin W Li, Sharon M Lutz, Matthew McGue, Anita Pandit, Gregory JM Zajac, Michael Boehnke, Goncalo R Abecasis, Bibo Jiang, Scott I Vrieze, Dajiang J Liu

Posted 03 Oct 2017
bioRxiv DOI: 10.1101/197913

Motivation: There is great interest to understand the impact of rare variants in human diseases using large sequence datasets. In deep sequences datasets of >10,000 samples, ~10% of the variant sites are observed to be multi-allelic. Many of the multi-allelic variants have been shown to be functional and disease relevant. Proper analysis of multi-allelic variants is critical to the success of a sequencing study, but existing methods do not properly handle multi-allelic variants and can produce highly misleading association results. Results: We propose novel methods to encode multi-allelic sites, conduct single variant and gene-level association analyses, and perform meta-analysis for multi-allelic variants. We evaluated these methods through extensive simulations and the study of a large meta-analysis of ~18,000 samples on the cigarettes-per-day phenotype. We showed that our joint modeling approach provided an unbiased estimate of genetic effects, greatly improved the power of single variant association tests, and enhanced gene-level tests over existing approaches. Availability: Software packages implementing these methods are available at (https://github.com/zhanxw/rvtests http://genome.sph.umich.edu/wiki/RareMETAL).

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