MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes

Abstract

Genome-wide association studies (GWAS) can identify common alleles that contribute to complex disease susceptibility. Despite the large number of SNPs assessed in each study, the effects of most common SNPs must be evaluated indirectly using either genotyped markers or haplotypes thereof as proxies. We have previously implemented a computationally efficient Markov Chain framework for genotype imputation and haplotyping in the freely available MaCH software package. The approach describes sampled chromosomes as mosaics of each other and uses available genotype and shotgun sequence data to estimate unobserved genotypes and haplotypes, together with useful measures of the quality of these estimates. Our approach is already widely used to facilitate comparison of results across studies as well as meta-analyses of GWAS. Here, we use simulations and experimental genotypes to evaluate its accuracy and utility, considering choices of genotyping panels, reference panel configurations, and designs where genotyping is replaced with shotgun sequencing. Importantly, we show that genotype imputation not only facilitates cross study analyses but also increases power of genetic association studies. We show that genotype imputation of common variants using HapMap haplotypes as a reference is very accurate using either genome-wide SNP data or smaller amounts of data typical in fine-mapping studies. Furthermore, we show the approach is applicable in a variety of populations. Finally, we illustrate how association analyses of unobserved variants will benefit from ongoing advances such as larger HapMap reference panels and whole genome shotgun sequencing technologies.

© 2010 Wiley-Liss, Inc.

Figures

Fig. 1

ROC curve comparing two measures of data quality. For imputed SNPs on chromosome 14, where both imputed and actual genotypes were available, we evaluated the ability of two different measures of data quality (the estimated concordance between imputed and true genotypes and the estimated r2 between imputed and true genotypes) to discriminate between poor and well imputed SNPs. Both estimates of imputation quality are calculated without using the actual observed genotypes.

Fig. 2

Imputation improves quality of LD estimates. For imputed SNPs on chromosome 14, the figure compares estimates of LD obtained by genotyping both SNPs (“Results from Actual Genotyping,” X axis) with estimates of LD obtained by imputing genotypes for both SNPs using markers on the 317K marker chip (“Results from Imputed Data,” Y axis, Top left), obtained by imputing genotypes for one of the SNPs (“Results from Imputed Data,” Y axis, Bottom Left) or obtained from the HapMap CEU panel (“Results from HapMap CEU,” Y axis, Top and Bottom Right).

Fig. 3

Evaluation of imputation accuracy across HGDP panels. For each of 52 populations in the Human Genome Diversity Panel (HGDP) a set of 872 SNPs distributed evenly across 32 regions, each ~330 kb in length, was used to impute 992 other SNPs. The 992 imputed SNPs were located near the middle of each imputed region. Imputation was done using either the HapMap YRI, CEU, CHB+JPT, or a combination of three HapMap panels (first four panels, best panel is shaded in gray) or using the remaining HGDP samples as a reference. In each case, the proportion of correctly imputed alleles is tabulated. The figure is based on a re-analysis of data of Conrad et al. [2006].

Fig. 4

Evaluation of imputation accuracy across HGDP panels. Genotypes for a set of 992 SNPs were imputed in the HGDP and then compared with actual genotypes. For each pair of true and imputed genotypes an r2 coefficient was calculated and averaged for each population. The best set of HapMap reference individuals for each population is shaded. The coverage obtained by using the best available tag SNP (rather than imputed genotypes) is overlaid in pink. See Figure 3 legend for further details.