Abstract
The analysis of less common variants in genome-wide association studies promises to elucidate complex trait genetics but is hampered by low power to reliably detect association. We show that addition of population-specific exome sequence data to global reference data allows more accurate imputation, particularly of less common SNPs (minor allele frequency 1–10%) in two very different European populations. The imputation improvement corresponds to an increase in effective sample size of 28–38%, for SNPs with a minor allele frequency in the range 1–3%.
Highlights
Genome-wide association study (GWAS) meta-analyses routinely use genotype imputation [1]
We found a significant increase in accuracy (r2 of imputed against known allele dosages across samples for a given SNP) from use of a local reference panel, which was often substantial for less common variants (Table 1)
Variants with a minor allele frequency in the range 0.01–0.032 showed an increase in imputation accuracy of 0.193/0.167 (38%/ 28% improvement) for CROATIA-Korcula/Orkney Complex Disease studies (ORCADES) and 0.112/0.089 (15%/11% improvement) for variants with MAF between 0.032 and 0.100
Summary
Genome-wide association study (GWAS) meta-analyses routinely use genotype imputation [1]. Theoretical studies and empirical studies using other primary reference panels, have shown that imputation accuracy in a study population can be increased by use of an additional reference panel such as whole genome or exome sequence data drawn from a subset of the population under study [2] [4] [5] [6] [7] [8] [9]. We used data from the CROATIA-Korcula and Orkney Complex Disease studies (ORCADES) [10] [11]. Both studies are family-based, cross-sectional community studies of the genetics of complex traits.
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