RT Journal Article
SR Electronic
T1 Association Analysis and Meta-Analysis of Multi-allelic Variants for Large Scale Sequence Data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 197913
DO 10.1101/197913
A1 Xiaowei Zhan
A1 Sai Chen
A1 Yu Jiang
A1 Mengzhen Liu
A1 William G. Iacono
A1 John K. Hewitt
A1 John E Hokanson
A1 Kenneth Krauter
A1 Markku Laakso
A1 Kevin W. Li
A1 Sharon M Lutz
A1 Matthew McGue
A1 Anita Pandit
A1 Gregory JM Zajac
A1 Michael Boehnke
A1 Goncalo R. Abecasis
A1 Bibo Jiang
A1 Scott I. Vrieze
A1 Dajiang J. Liu
YR 2017
UL http://biorxiv.org/content/early/2017/10/03/197913.abstract
AB 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).Contact: xiaowei.zhan@utsouthwestem.edu; dajiang.liu@psu.edu