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Open AccessArticle

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

1
Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA
2
Illumina Inc., 5200 Illuminay Way, San Diego, CA 92122, USA
3
Department of Psychology, University of Minnesota, Minneapolis, MN 55454, USA
4
Department of Psychiatry, University of Minnesota, Minneapolis, MN 55454, USA
5
Institute for Behavioral Genetics, University of Colorado Boulder, Aurora, CO 80045, USA
6
Department of Epidemiology, School of Public Health, University of Colorado Denver, Aurora, CO 80045, USA
7
Department of Medicine, University of Eastern Finland and Kuopio University Hospital, 70211 Kuopio, Finland
8
Center of Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
9
Department of Biostatistics and Informatics, University of Colorado, Anschutz Medical Campus, Aurora, CO 80045, USA
10
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; https://doi.org/10.3390/genes11050586
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. https://doi.org/10.3390/genes11050586

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. https://doi.org/10.3390/genes11050586

Chicago/Turabian Style

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. 2020. "Association Analysis and Meta-Analysis of Multi-Allelic Variants for Large-Scale Sequence Data" Genes 11, no. 5: 586. https://doi.org/10.3390/genes11050586

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