Improving power of association tests using multiple sets of imputed genotypes from distributed reference panels

Abstract

The accuracy of genotype imputation depends upon two factors: the sample size of the reference panel and the genetic similarity between the reference panel and the target samples. When multiple reference panels are not consented to combine together, it is unclear how to combine the imputation results to optimize the power of genetic association studies. We compared the accuracy of 9,265 Norwegian genomes imputed from three reference panels-1000 Genomes phase 3 (1000G), Haplotype Reference Consortium (HRC), and a reference panel containing 2,201 Norwegian participants from the population-based Nord Trøndelag Health Study (HUNT) from low-pass genome sequencing. We observed that the population-matched reference panel allowed for imputation of more population-specific variants with lower frequency (minor allele frequency (MAF) between 0.05% and 0.5%). The overall imputation accuracy from the population-specific panel was substantially higher than 1000G and was comparable with HRC, despite HRC being 15-fold larger. These results recapitulate the value of population-specific reference panels for genotype imputation. We also evaluated different strategies to utilize multiple sets of imputed genotypes to increase the power of association studies. We observed that testing association for all variants imputed from any panel results in higher power to detect association than the alternative strategy of including only one version of each genetic variant, selected for having the highest imputation quality metric. This was particularly true for lower frequency variants (MAF < 1%), even after adjusting for the additional multiple testing burden.

Keywords: GWAS; genotype imputation; multiple reference panels; population-specific; study power.

Figure 1. Number of variants that are imputed by different reference panels

The corresponding percentage is the variants number out of all 23.8 million variants that are successfully imputed by any of the three reference panels.

Figure 2. Distribution of numbers of variants that are imputed from only one reference panel or from multiple reference panels in different MAF categories

Variants that are imputed by 1000G only are categorized as SNPs and non-SNP variants, including indels, deletions, complex short substitutions and other structural variant classes. 1000G, 1000 Genomes Phase 3; WGS, whole-genome sequencing; HRC, Haplotype Reference Consortium; MAF, minor allele frequency

Figure 3. HRC and HUNT WGS panels show comparable imputation quality

a. comparing the mean empirical R² (y axis) reported by different reference panels for variants that are directly genotyped categorized by the MAF (x axis) without any ImpRsq threshold applied. b. comparing the mean Imputation R² (y axis) reported by different reference panels for variants that are directly genotyped categorized by the MAF (x axis) without any ImpRsq threshold applied. c. comparing the mean Imputation R² (y axis) reported by different reference panels for all imputed variants (ImpRsq > 0.3) by the MAF (x axis). d. comparing the mean Imputation R² (y axis) reported by different reference panels for all imputed variants by the MAF (x axis) without any ImpRsq threshold applied. 1000G, 1000 Genomes Phase 3; WGS, whole-genome sequencing; HRC, Haplotype Reference Consortium; MAF, minor allele frequency; ImpRsq, imputation quality metric R²

Figure 4. Comparison of power to detect true associations between best p-value and best Rsq approaches via simulation studies

For each MAF category, 3,000 directly genotyped variants were randomly selected based on their MAF estimated with genotypes obtained from the chip array to estimate the power. The power is calculated as the proportion of significantly associated variants across three imputed panels based on each strategy given the corresponding significance threshold. ImpRsq > 0.3 was applied to remove poorly imputed genotypes. The numbers of variants that were successfully imputed from at least two reference panels and used in the simulation studies are: 2,513 with MAF > 0 and ≤ 0.001; 2,989 with MAF > 0.001 and ≤ 0.01; 3,000 with MAF > 0.01 and ≤ 0.05; and 3,000 with MAF > 0.05. MAF, minor allele frequency; ImpRsq, imputation quality metric R²