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Imputation of low-frequency variants using the HapMap3 benefits from large, diverse reference sets.

ABSTRACT: Imputation allows the inference of unobserved genotypes in low-density data sets, and is often used to test for disease association at variants that are poorly captured by standard genotyping chips (such as low-frequency variants). Although much effort has gone into developing the best imputation algorithms, less is known about the effects of reference set choice on imputation accuracy. We assess the improvements afforded by increases in reference size and diversity, specifically comparing the HapMap2 data set, which has been used to date for imputation, and the new HapMap3 data set, which contains more samples from a more diverse range of populations. We find that, for imputation into Western European samples, the HapMap3 reference provides more accurate imputation with better-calibrated quality scores than HapMap2, and that increasing the number of HapMap3 populations included in the reference set grant further improvements. Improvements are most pronounced for low-frequency variants (frequency <5%), with the largest and most diverse reference sets bringing the accuracy of imputation of low-frequency variants close to that of common ones. For low-frequency variants, reference set diversity can improve the accuracy of imputation, independent of reference sample size. HapMap3 reference sets provide significant increases in imputation accuracy relative to HapMap2, and are of particular use if highly accurate imputation of low-frequency variants is required. Our results suggest that, although the sample sizes from the 1000 Genomes Pilot Project will not allow reliable imputation of low-frequency variants, the larger sample sizes of the main project will allow.

SUBMITTER: Jostins L

PROVIDER: S-EPMC3110048 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature

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Imputation of low-frequency variants using the HapMap3 benefits from large, diverse reference sets.

Jostins Luke L Morley Katherine I KI Barrett Jeffrey C JC

European journal of human genetics : EJHG 20110302 6

Imputation allows the inference of unobserved genotypes in low-density data sets, and is often used to test for disease association at variants that are poorly captured by standard genotyping chips (such as low-frequency variants). Although much effort has gone into developing the best imputation algorithms, less is known about the effects of reference set choice on imputation accuracy. We assess the improvements afforded by increases in reference size and diversity, specifically comparing the H ...[more]

PMID: 21364697

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Project description:Genome-wide association (GWA) studies have been limited by the reliance on common variants present on microarrays or imputable from the HapMap Project data. More recently, the completion of the 1000 Genomes Project has provided variant and haplotype information for several million variants derived from sequencing over 1,000 individuals. To help understand the extent to which more variants (including low frequency (1% ? MAF <5%) and rare variants (<1%)) can enhance previously identified associations and identify novel loci, we selected 93 quantitative circulating factors where data was available from the InCHIANTI population study. These phenotypes included cytokines, binding proteins, hormones, vitamins and ions. We selected these phenotypes because many have known strong genetic associations and are potentially important to help understand disease processes. We performed a genome-wide scan for these 93 phenotypes in InCHIANTI. We identified 21 signals and 33 signals that reached P<5×10(-8) based on HapMap and 1000 Genomes imputation, respectively, and 9 and 11 that reached a stricter, likely conservative, threshold of P<5×10(-11) respectively. Imputation of 1000 Genomes genotype data modestly improved the strength of known associations. Of 20 associations detected at P<5×10(-8) in both analyses (17 of which represent well replicated signals in the NHGRI catalogue), six were captured by the same index SNP, five were nominally more strongly associated in 1000 Genomes imputed data and one was nominally more strongly associated in HapMap imputed data. We also detected an association between a low frequency variant and phenotype that was previously missed by HapMap based imputation approaches. An association between rs112635299 and alpha-1 globulin near the SERPINA gene represented the known association between rs28929474 (MAF?=?0.007) and alpha1-antitrypsin that predisposes to emphysema (P?=?2.5×10(-12)). Our data provide important proof of principle that 1000 Genomes imputation will detect novel, low frequency-large effect associations.

Dataset Information

Imputation of low-frequency variants using the HapMap3 benefits from large, diverse reference sets.

Publications

Imputation of low-frequency variants using the HapMap3 benefits from large, diverse reference sets.

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