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Assessment of Genotype Imputation Performance Using 1000 Genomes in African American Studies

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Title
Assessment of Genotype Imputation Performance Using 1000 Genomes in African American Studies
Published in
PLOS ONE, November 2012
DOI 10.1371/journal.pone.0050610
Pubmed ID
Authors

Dana B. Hancock, Joshua L. Levy, Nathan C. Gaddis, Laura J. Bierut, Nancy L. Saccone, Grier P. Page, Eric O. Johnson

Abstract

Genotype imputation, used in genome-wide association studies to expand coverage of single nucleotide polymorphisms (SNPs), has performed poorly in African Americans compared to less admixed populations. Overall, imputation has typically relied on HapMap reference haplotype panels from Africans (YRI), European Americans (CEU), and Asians (CHB/JPT). The 1000 Genomes project offers a wider range of reference populations, such as African Americans (ASW), but their imputation performance has had limited evaluation. Using 595 African Americans genotyped on Illumina's HumanHap550v3 BeadChip, we compared imputation results from four software programs (IMPUTE2, BEAGLE, MaCH, and MaCH-Admix) and three reference panels consisting of different combinations of 1000 Genomes populations (February 2012 release): (1) 3 specifically selected populations (YRI, CEU, and ASW); (2) 8 populations of diverse African (AFR) or European (AFR) descent; and (3) all 14 available populations (ALL). Based on chromosome 22, we calculated three performance metrics: (1) concordance (percentage of masked genotyped SNPs with imputed and true genotype agreement); (2) imputation quality score (IQS; concordance adjusted for chance agreement, which is particularly informative for low minor allele frequency [MAF] SNPs); and (3) average r2hat (estimated correlation between the imputed and true genotypes, for all imputed SNPs). Across the reference panels, IMPUTE2 and MaCH had the highest concordance (91%-93%), but IMPUTE2 had the highest IQS (81%-83%) and average r2hat (0.68 using YRI+ASW+CEU, 0.62 using AFR+EUR, and 0.55 using ALL). Imputation quality for most programs was reduced by the addition of more distantly related reference populations, due entirely to the introduction of low frequency SNPs (MAF≤2%) that are monomorphic in the more closely related panels. While imputation was optimized by using IMPUTE2 with reference to the ALL panel (average r2hat = 0.86 for SNPs with MAF>2%), use of the ALL panel for African American studies requires careful interpretation of the population specificity and imputation quality of low frequency SNPs.

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Geographical breakdown

Country Count As %
United States 5 5%
France 2 2%
Denmark 1 <1%
Finland 1 <1%
Unknown 97 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 22%
Student > Ph. D. Student 17 16%
Student > Master 15 14%
Student > Bachelor 10 9%
Other 8 8%
Other 18 17%
Unknown 15 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 42 40%
Biochemistry, Genetics and Molecular Biology 18 17%
Medicine and Dentistry 12 11%
Computer Science 4 4%
Mathematics 3 3%
Other 10 9%
Unknown 17 16%