Genotype imputation of Metabochip SNPs using a study-specific reference panel of ~4,000 haplotypes in African Americans from the Women's Health Initiative

Abstract

Genetic imputation has become standard practice in modern genetic studies. However, several important issues have not been adequately addressed including the utility of study-specific reference, performance in admixed populations, and quality for less common (minor allele frequency [MAF] 0.005-0.05) and rare (MAF < 0.005) variants. These issues only recently became addressable with genome-wide association studies (GWAS) follow-up studies using dense genotyping or sequencing in large samples of non-European individuals. In this work, we constructed a study-specific reference panel of 3,924 haplotypes using African Americans in the Women's Health Initiative (WHI) genotyped on both the Metabochip and the Affymetrix 6.0 GWAS platform. We used this reference panel to impute into 6,459 WHI SNP Health Association Resource (SHARe) study subjects with only GWAS genotypes. Our analysis confirmed the imputation quality metric Rsq (estimated r(2) , specific to each SNP) as an effective post-imputation filter. We recommend different Rsq thresholds for different MAF categories such that the average (across SNPs) Rsq is above the desired dosage r(2) (squared Pearson correlation between imputed and experimental genotypes). With a desired dosage r(2) of 80%, 99.9% (97.5%, 83.6%, 52.0%, 20.5%) of SNPs with MAF > 0.05 (0.03-0.05, 0.01-0.03, 0.005-0.01, and 0.001-0.005) passed the post-imputation filter. The average dosage r(2) for these SNPs is 94.7%, 92.1%, 89.0%, 83.1%, and 79.7%, respectively. These results suggest that for African Americans imputation of Metabochip SNPs from GWAS data, including low frequency SNPs with MAF 0.005-0.05, is feasible and worthwhile for power increase in downstream association analysis provided a sizable reference panel is available.

Fig. 1

Reference construction and imputation pipeline using a study-specific reference panel. This schematic cartoon shows how we constructed our study-specific reference panel using five individuals genotyped on both the Affymetrix 6.0 and the Metabochip platform and how we performed imputation into the remaining five individuals with Affymetrix 6.0 data only.

Fig. 2

Imputation accuracy by chromosome for 2% randomly masked GWAS SNPs. Imputation accuracy (as measured by average dosage r²) for 2% GWAS SNPs masked at random is plotted by chromosome.

Fig. 3

Rsq by dosage r² for 2% randomly masked GWAS SNPs. Estimated imputation accuracy (minimac output Rsq) is plotted against the true dosage r², for the 2% GWAS SNPs masked at random.

Fig. 4

Accuracy and calibration of imputation. Percentages of SNPs passing post-imputation QC (left y axis) and average dosage r² (right y axis) are plotted against Rsq threshold used for post-imputation QC for SNPs in different MAF categories.

Fig. 5

Rsq by dosage r² Heatmap for Metabochip SNPs (estimated by masking 100 reference individuals). Estimated imputation accuracy (minimac output Rsq) is plotted against the true dosage r², for Metabochip SNPs by masking 100 reference individuals. Color scheme is defined by the number of underlying SNPs, specifically, log10 (Frequency).