Examining the Impact of Imputation Errors on Fine-Mapping Using DNA Methylation QTL as a Model Trait. Issue 3 (30th April 2019)

• Record Type: Journal Article
• Authors: Chundru, V Kartik; Marioni, Riccardo E; Prendergast, James G D; Vallerga, Costanza L; Lin, Tian; Beveridge, Allan J; Gratten, Jacob; Hume, David A; Deary, Ian J; Wray, Naomi R; Visscher, Peter M; McRae, Allan F
• Is Part Of: Genetics. Volume 212:Issue 3(2019)
• Page Start: 577
• Page End: 586
• Publication Date: 2019-04-30
• Subjects: fine-mapping -- DNA-methylation -- imputation -- CpG-SNPs; Genetics -- Periodicals; 576.5
• DOI: 10.1534/genetics.118.301861
• Languages: English
• ISSNs: 0016-6731

Abstract

Abstract: This study highlights dangers in over-interpreting fine-mapping results. Chundru et al. show that genotype imputation accuracy has a large impact on fine-mapping accuracy. They used DNA methylation at CpG-sites with a variant... Genetic variants disrupting DNA methylation at CpG dinucleotides (CpG-SNP) provide a set of known causal variants to serve as models to test fine-mapping methodology. We use 1716 CpG-SNPs to test three fine-mapping approaches (Bayesian imputation-based association mapping, Bayesian sparse linear mixed model, and the J-test), assessing the impact of imputation errors and the choice of reference panel by using both whole-genome sequence (WGS), and genotype array data on the same individuals ( n = 1166). The choice of imputation reference panel had a strong effect on imputation accuracy, with the 1000 Genomes Project Phase 3 (1000G) reference panel ( n = 2504 from 26 populations) giving a mean nonreference discordance rate between imputed and sequenced genotypes of 3.2% compared to 1.6% when using the Haplotype Reference Consortium (HRC) reference panel ( n = 32, 470 Europeans). These imputation errors had an impact on whether the CpG-SNP was included in the 95% credible set, with a difference of ∼23% and ∼7% between the WGS and the 1000G and HRC imputed datasets, respectively. All of the fine-mapping methods failed to reach the expected 95% coverage of the CpG-SNP. This is attributed to secondary cis genetic effects that are unable to be statistically separated from the CpG-SNP, and through a masking mechanism where the effect of the methylation disrupting allele at the CpG-SNP is hidden by the effect of a nearby SNP that has strong linkage disequilibrium with the CpG-SNP. The reduced accuracy in fine-mapping a known causal variant in a low-level biological trait with imputed genetic data has implications for the study of higher-order complex traits and disease.