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

There is great interest in understanding the impact of rare variants in human diseases using large sequence datasets. In deep sequence 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 d...

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Veröffentlicht in:Genes 2020-05, Vol.11 (5), p.586
Hauptverfasser: Jiang, Yu, Chen, Sai, Wang, Xingyan, Liu, Mengzhen, Iacono, William G, Hewitt, John K, Hokanson, John E, Krauter, Kenneth, Laakso, Markku, Li, Kevin W, Lutz, Sharon M, McGue, Matthew, Pandit, Anita, Zajac, Gregory J M, Boehnke, Michael, Abecasis, Goncalo R, Vrieze, Scott I, Jiang, Bibo, Zhan, Xiaowei, Liu, Dajiang J
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Sprache:eng
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Zusammenfassung:There is great interest in understanding the impact of rare variants in human diseases using large sequence datasets. In deep sequence 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. We discuss practical issues and 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 among methods that can properly estimate allele effects, and enhanced gene-level tests over existing approaches. Software packages implementing these methods are available online.
ISSN:2073-4425
2073-4425
DOI:10.3390/genes11050586