Rare Variants Imputation in Admixed Populations: Comparison Across Reference Panels and Bioinformatics Tools.

Authors:
Sanjeev Sariya
Sanjeev Sariya
Center for Microbiomics and Human Health
Joseph H Lee
Joseph H Lee
Columbia University
United States
Richard Mayeux
Richard Mayeux
Taub Institute for Research on Alzheimer's Disease and the Aging Brain
United States
Badri N Vardarajan
Badri N Vardarajan
Boston University School of Medicine
United States
Jennifer J Manly
Jennifer J Manly
Taub Institute for Research on Alzheimer's Disease and the Aging Brain
New York | United States
Adam M Brickman
Adam M Brickman
Columbia University
United States
Rafael Lantigua
Rafael Lantigua
Columbia University
United States

Front Genet 2019 3;10:239. Epub 2019 Apr 3.

Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.

Background: Imputation has become a standard approach in genome-wide association studies (GWAS) to infer untyped markers. Although feasibility for common variants imputation is well established, we aimed to assess rare and ultra-rare variants' imputation in an admixed Caribbean Hispanic population (CH).

Methods: We evaluated imputation accuracy in CH ( = 1,000), focusing on rare (0.1% ≤ minor allele frequency (MAF) ≤ 1%) and ultra-rare (MAF < 0.1%) variants. We used two reference panels, the Haplotype Reference Consortium (HRC; = 27,165) and 1000 Genome Project (1000G phase 3; = 2,504) and multiple phasing (SHAPEIT, Eagle2) and imputation algorithms (IMPUTE2, MACH-Admix). To assess imputation quality, we reported: (a) high-quality variant counts according to imputation tools' internal indexes (e.g., IMPUTE2 "Info" ≥ 80%). (b) Wilcoxon Signed-Rank Test comparing imputation quality for genotyped variants that were masked and imputed; (c) Cohen's kappa coefficient to test agreement between imputed and whole-exome sequencing (WES) variants; (d) imputation of G206A mutation in the (ultra-rare in the general population an more frequent in CH) followed by confirmation genotyping. We also tested ancestry proportion (European, African and Native American) against WES-imputation mismatches in a Poisson regression fashion.

Results: SHAPEIT2 retrieved higher percentage of imputed high-quality variants than Eagle2 (rare: 51.02% vs. 48.60%; ultra-rare 0.66% vs. 0.65%, Wilcoxon -value < 0.001). SHAPEIT-IMPUTE2 employing HRC outperformed 1000G (64.50% vs. 59.17%; 1.69% vs. 0.75% for high-quality rare and ultra-rare variants, respectively, Wilcoxon -value < 0.001). SHAPEIT-IMPUTE2 outperformed MaCH-Admix. Compared to 1000G, HRC-imputation retrieved a higher number of high-quality rare and ultra-rare variants, despite showing lower agreement between imputed and WES variants (e.g., rare: 98.86% for HRC vs. 99.02% for 1000G). High Kappa ( = 0.99) was observed for both reference panels. Twelve G206A mutation carriers were imputed and all validated by confirmation genotyping. African ancestry was associated with higher imputation errors for uncommon and rare variants (-value < 1e-05).

Conclusion: Reference panels with larger numbers of haplotypes can improve imputation quality for rare and ultra-rare variants in admixed populations such as CH. Ethnic composition is an important predictor of imputation accuracy, with higher African ancestry associated with poorer imputation accuracy.

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Source
https://www.frontiersin.org/article/10.3389/fgene.2019.00239
Publisher Site
http://dx.doi.org/10.3389/fgene.2019.00239DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6456789PMC
April 2019
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