A simple and fast two‐locus quality control test to detect false positives due to batch effects in genome‐wide association studies

Abstract

The impact of erroneous genotypes having passed standard quality control (QC) can be severe in genome-wide association studies, genotype imputation, and estimation of heritability and prediction of genetic risk based on single nucleotide polymorphisms (SNP). To detect such genotyping errors, a simple two-locus QC method, based on the difference in test statistic of association between single SNPs and pairs of SNPs, was developed and applied. The proposed approach could detect many problematic SNPs with statistical significance even when standard single SNP QC analyses fail to detect them in real data. Depending on the data set used, the number of erroneous SNPs that were not filtered out by standard single SNP QC but detected by the proposed approach varied from a few hundred to thousands. Using simulated data, it was shown that the proposed method was powerful and performed better than other tested existing methods. The power of the proposed approach to detect erroneous genotypes was ∼80% for a 3% error rate per SNP. This novel QC approach is easy to implement and computationally efficient, and can lead to a better quality of genotypes for subsequent genotype-phenotype investigations. Genet. Epidemiol. 34:854–862, 2010. © 2010 Wiley-Liss, Inc.

Local Joint Testing Improves Power and Identifies Hidden Heritability in Association Studies

“…Filtering false positives Lee et al (2010) describe an issue where genotyping errors that would go unnoticed in standard quality control (QC) procedures can cause inflation in joint and conditional tests of association. When SNPs are highly correlated, miscalled bases in only the cases or controls induce rare haplotypes.…”

“…While these approaches reduce the multiple-hypothesis correction penalty, we show that they do not capture much of the available power gain. Furthermore, prior approaches have not accounted for a known issue with genotyping error and joint tests (Lee et al 2010), which we show affects these methods.By testing pairs of SNPs for additive effects, rather than individual SNPs, we improve power to detect loci containing multiple independent causal variants, including those containing linkage-masked SNPs. Through local testing, we substantially reduce the multiple-hypothesis correction penalty, while simultaneously enriching for situations in which joint tests are more powerful, i.e., when there are independent additive genetic signals contained in each of the SNPs in the test.…”

“…While these approaches reduce the multiple-hypothesis correction penalty, we show that they do not capture much of the available power gain. Furthermore, prior approaches have not accounted for a known issue with genotyping error and joint tests (Lee et al 2010), which we show affects these methods.…”

“…When SNPs are highly correlated, miscalled bases in only the cases or controls induce rare haplotypes. As these haplotypes are present only in the cases or controls, this increases the association signal in the joint test (Lee et al 2010). …”

“…However, when the association appeared to be driven by genotyping error, the joint test statistic became insignificant after pgs imputation (Table S7). In this way, we overcome the false positive error identified by Lee et al (2010). …”