MaCH‐Admix: Genotype Imputation for Admixed Populations

Abstract

Imputation in admixed populations is an important problem but challenging due to the complex linkage disequilibrium (LD) pattern. The emergence of large reference panels such as that from the 1,000 Genomes Project enables more accurate imputation in general, and in particular for admixed populations and for uncommon variants. To efficiently benefit from these large reference panels, one key issue to consider in modern genotype imputation framework is the selection of effective reference panels. In this work, we consider a number of methods for effective reference panel construction inside a hidden Markov model and specific to each target individual. These methods fall into two categories: identity-by-state (IBS) based and ancestry-weighted approach. We evaluated the performance on individuals from recently admixed populations. Our target samples include 8,421 African Americans and 3,587 Hispanic Americans from the Women’s Health Initiative, which allow assessment of imputation quality for uncommon variants. Our experiments include both large and small reference panels; large, medium, and small target samples; and in genome regions of varying levels of LD. We also include BEAGLE and IMPUTE2 for comparison. Experiment results with large reference panel suggest that our novel piecewise IBS method yields consistently higher imputation quality than other methods/software. The advantage is particularly noteworthy among uncommon variants where we observe up to 5.1% information gain with the difference being highly significant (Wilcoxon signed rank test P-value < 0.0001). Our work is the first that considers various sensible approaches for imputation in admixed populations and presents a comprehensive comparison.

Imputing Genotypes in Biallelic Populations from Low-Coverage Sequence Data

“…Genome-wide association studies could be enhanced by the inference of additional markers using large multipopulation data sets such as the International HapMap Project (International HapMap Consortium et al 2010). The emergence of the meta-analysis led to a need for algorithms that could merge disparate data sets Howie et al 2009;Li et al 2010;Liu et al 2013;Fuchsberger et al 2015). These algorithms often employed large haplotype reference panels to improve imputation (Marchini et al 2007;Browning and Browning 2009;Howie et al 2009).…”

Genetic risk factors for the development of osteonecrosis in children under age 10 treated for acute lymphoblastic leukemia

“…For SNPs not interrogated by direct genotyping, genotypes were imputed using MaCH-Admix software (University of North Carolina), using phase 1 release v3 of the 1000 Genomes Project (http://www.1000genomes.org) as the reference genomes. 16 For a subset of 15 cases of osteonecrosis, whole exome sequencing was performed. Library generation was performed using NimbleGen SeqCap EZ Exome Enrichment Kit v2.0, and sequencing was performed using Illumina HiSequation 2000.…”

Pharmacogenomics: accessing important alleles by imputation from commercial genome-wide SNP arrays

“…This dispensed with the need of dealing with the complex choice of achieving the right combination of reference panels to mimic admixed populations, increasing imputation accuracy and even making it faster (Howie et al, 2011). Improved imputation algorithms can handle these large panels and account for admixture (Howie et al, 2009;Marchini and Howie, 2010;Li et al, 2010;Liu et al, 2013).…”

“…Imputation works by making use of linkage disequilibrium, finding haplotype segments that are shared by both the panel and study individuals and using them to fill in missing data (Kong et al, 2008;Marchini and Howie, 2010). Imputation of biallelic pharmacogenomic variants has been reported as a valid method (Mijatovic et al, 2012), but little methodological research exists for imputation in admixed populations (Liu et al, 2013).…”