Imputation and quality control steps for combining multiple genome-wide datasets
| Autor: | Gail P. Jarvik, Kimberly Derr, David R. Crosslin, Amber A. Burt, Dana C. Crawford, Bahram Namjou-Khales, Shubhabrata Mukherjee, Shefali S. Verma, Jonathan L. Haines, Marylyn D. Ritchie, Yuki Bradford, Helena Kuivaniemi, Mariza de Andrade, Leah C. Kottyan, Elizabeth W. Pugh, Gretta D. Armstrong, Rongling Li, Gerard Tromp |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
IMPUTE2
lcsh:QH426-470 Computer science BEAGLE imputation Genomics Genome-wide association study computer.software_genre 3. Good health Minor allele frequency lcsh:Genetics eMERGE electronic health records Sample size determination genome-wide association Genetics Molecular Medicine Original Research Article Data mining 1000 Genomes Project Workgroup Genotyping computer Genetics (clinical) Imputation (genetics) |
| Zdroj: | Frontiers in Genetics Frontiers in Genetics, Vol 5 (2014) |
| ISSN: | 1664-8021 |
| Popis: | 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 R (2) (estimated correlation between the imputed and true genotypes), and the relationship between allelic R (2) 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|