Practical considerations for imputation of untyped markers in admixed populations.

Genetic Epidemiology
Daniel ShrinerCharles N Rotimi

Abstract

Imputation of genotypes for markers untyped in a study sample has become a standard approach to increase genome coverage in genome-wide association studies at practically zero cost. Most methods for imputing missing genotypes extend previously described algorithms for inferring haplotype phase. These algorithms generally fall into three classes based on the underlying model for estimating the conditional distribution of haplotype frequencies: a cluster-based model, a multinomial model, or a population genetics-based model. We compared BEAGLE, PLINK, and MACH, representing the three classes of models, respectively, with specific attention to measures of imputation success and selection of the reference panel for an admixed study sample of African Americans. Based on analysis of chromosome 22 and after calibration to a fixed level of 90% concordance between experimentally determined and imputed genotypes, MACH yielded the largest absolute number of successfully imputed markers and the largest gain in coverage of the variation captured by HapMap reference panels. Following the common practice of performing imputation once, the Yoruba in Ibadan, Nigeria (YRI) reference panel outperformed other HapMap reference panels, including (1)...Continue Reading

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Citations

Oct 20, 2012·Theoretical Population Biology·Lucy HuangNoah A Rosenberg
Dec 12, 2012·Animal : an International Journal of Animal Bioscience·Z WengX Ding
Jun 1, 2012·Journal of Human Genetics·Pritam ChandaDan E Arking
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Feb 8, 2011·BMC Medical Genomics·Bashira A CharlesCharles N Rotimi
Oct 13, 2015·PloS One·Shelina RamnarineNancy L Saccone

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