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

Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA
Illumina Inc., 5200 Illuminay Way, San Diego, CA 92122, USA
Department of Psychology, University of Minnesota, Minneapolis, MN 55454, USA
Department of Psychiatry, University of Minnesota, Minneapolis, MN 55454, USA
Institute for Behavioral Genetics, University of Colorado Boulder, Aurora, CO 80045, USA
Department of Epidemiology, School of Public Health, University of Colorado Denver, Aurora, CO 80045, USA
Department of Medicine, University of Eastern Finland and Kuopio University Hospital, 70211 Kuopio, Finland
Center of Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
Department of Biostatistics and Informatics, University of Colorado, Anschutz Medical Campus, Aurora, CO 80045, USA
Department of Clinical Science, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Genes 2020, 11(5), 586;
Received: 30 April 2020 / Revised: 19 May 2020 / Accepted: 21 May 2020 / Published: 25 May 2020
(This article belongs to the Special Issue Statistical Genetics)
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. View Full-Text
Keywords: multi-allelic variants; GWAS; meta-analysis; smoking multi-allelic variants; GWAS; meta-analysis; smoking
MDPI and ACS Style

Jiang, Y.; Chen, S.; Wang, X.; Liu, M.; Iacono, W.G.; Hewitt, J.K.; Hokanson, J.E.; Krauter, K.; Laakso, M.; Li, K.W.; Lutz, S.M.; McGue, M.; Pandit, A.; Zajac, G.J.M.; Boehnke, M.; Abecasis, G.R.; Vrieze, S.I.; Jiang, B.; Zhan, X.; Liu, D.J. Association Analysis and Meta-Analysis of Multi-Allelic Variants for Large-Scale Sequence Data. Genes 2020, 11, 586.

AMA Style

Jiang Y, Chen S, Wang X, Liu M, Iacono WG, Hewitt JK, Hokanson JE, Krauter K, Laakso M, Li KW, Lutz SM, McGue M, Pandit A, Zajac GJM, Boehnke M, Abecasis GR, Vrieze SI, Jiang B, Zhan X, Liu DJ. Association Analysis and Meta-Analysis of Multi-Allelic Variants for Large-Scale Sequence Data. Genes. 2020; 11(5):586.

Chicago/Turabian Style

Jiang, Yu, Sai Chen, Xingyan Wang, 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 J.M. Zajac, Michael Boehnke, Goncalo R. Abecasis, Scott I. Vrieze, Bibo Jiang, Xiaowei Zhan, and Dajiang J. Liu. 2020. "Association Analysis and Meta-Analysis of Multi-Allelic Variants for Large-Scale Sequence Data" Genes 11, no. 5: 586.

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