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

Shefali S. Verma, Mariza de Andrade, Gerard Tromp, Helena Kuivaniemi, Elizabeth Pugh, Bahram Namjou-Khales, Shubhabrata Mukherjee, Gail P. Jarvik, Leah C. Kottyan, Amber Burt, Yuki Bradford, Gretta D. Armstrong, Kimberly Derr, Dana C. Crawford, Jonathan L. Haines, Rongling Li, David Crosslin, Marylyn D. Ritchie

Research output: Contribution to journalArticle

59 Scopus citations

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.

Original languageEnglish (US)
Article number370
JournalFrontiers in Genetics
Volume5
Issue numberDEC
DOIs
StatePublished - 2014

Keywords

  • Electronic health records
  • Genome-wide association
  • Imputation
  • eMERGE

ASJC Scopus subject areas

  • Molecular Medicine
  • Genetics
  • Genetics(clinical)

Fingerprint Dive into the research topics of 'Imputation and quality control steps for combining multiple genome-wide datasets'. Together they form a unique fingerprint.

  • Cite this

    Verma, S. S., de Andrade, M., Tromp, G., Kuivaniemi, H., Pugh, E., Namjou-Khales, B., Mukherjee, S., Jarvik, G. P., Kottyan, L. C., Burt, A., Bradford, Y., Armstrong, G. D., Derr, K., Crawford, D. C., Haines, J. L., Li, R., Crosslin, D., & Ritchie, M. D. (2014). Imputation and quality control steps for combining multiple genome-wide datasets. Frontiers in Genetics, 5(DEC), [370]. https://doi.org/10.3389/fgene.2014.00370