Imputation and quality control steps for combining multiple genome-wide datasets

Abstract

The electronic MEdical Records and GEnomics (eMERGE) network brings together DNA biobanks linked to electronic health records (EHRs) from multiple institutions. Approximately 51,000 DNA samples from distinct individuals have been genotyped using genome-wide SNP arrays across the nine sites of the network. The eMERGE Coordinating Center and the Genomics Workgroup developed a pipeline to impute and merge genomic data across the different SNP arrays to maximize sample size and power to detect associations with a variety of clinical endpoints. The 1000 Genomes cosmopolitan reference panel was used for imputation. Imputation results were evaluated using the following metrics: accuracy of imputation, allelic R2 (estimated correlation between the imputed and true genotypes), and the relationship between allelic R2 and minor allele frequency. Computation time and memory resources required by two different software packages (BEAGLE and IMPUTE2) were also evaluated. A number of challenges were encountered due to the complexity of using two different imputation software packages, multiple ancestral populations, and many different genotyping platforms. We present lessons learned and describe the pipeline implemented here to impute and merge genomic data sets. The eMERGE imputed dataset will serve as a valuable resource for discovery, leveraging the clinical data that can be mined from the EHR.

Population-specific genotype imputations using minimac or IMPUTE2

“…9) and Beagle 8 . Multiple aspects of the three tools, e.g., their imputation accuracy, error rates and computational performance, have been compared previously 6,10,16,17 . The choice for a given tool depends on the target set that is to be imputed and on the type of computational resources available, as discussed in this paper.…”

“…The purpose of these filters is to exclude both markers and samples with low-quality data. Anderson et al 7 and Verma et al 10 provide a detailed protocol that deals with both per-maker and per-individual filtering.…”

“…The quality of an imputation experiment can be assessed by various metrics 10 . These metrics can be divided into two categories on the basis of whether true genotypes are available or not.…”

“…A guideline for imputations with the Beagle 8 and IMPUTE2 (ref. 9) tools, as well as postimputation quality control, has also been published by Verma et al 10 , and a protocol for doing meta-analysis of GWAS results for large numbers of cohorts is described in Winkler et al 11 .…”

“…This protocol differs from the previous guidelines in the study by Verma et al 10 , providing instructions for imputations with IMPUTE2 (ref. 9) and minimac 12 .…”