Gorski, Mathias and Winkler, Thomas W. and Stark, Klaus and Mueller-Nurasyid, Martina and Ried, Janina S. and Grallert, Harald and Weber, Bernhard H. F. and Heid, Iris M. (2014) Harmonization of Study and Reference Data by PhaseLift: Saving Time When Imputing Study Data. GENETIC EPIDEMIOLOGY, 38 (5). pp. 381-388. ISSN 0741-0395, 1098-2272
Full text not available from this repository. (Request a copy)Abstract
Genome-wide association studies are usually accompanied by imputation techniques to complement genome-wide SNP chip genotypes. Current imputation approaches separate the phasing of study data from imputing, which makes the phasing independent from the reference data. The two-step approach allows for updating the imputation for a new reference panel without repeating the tedious phasing step. This advantage, however, does no longer hold, when the build of the study data differs from the build of the reference data. In this case, the current approach is to harmonize the study data annotation with the reference data (prephasing lift-over), requiring rephasing and re-imputing. As a novel approach, we propose to harmonize study haplotypes with reference haplotypes (postphasing lift-over). This allows for updating imputed study data for new reference panels without requiring rephasing. With continuously updated reference panels, our approach can save considerable computing time of up to 1 month per re-imputation. We evaluated the rephasing and postphasing lift-over approaches by using data from 1,644 unrelated individuals imputed by both approaches and comparing it with directly typed genotypes. On average, both approaches perform equally well with mean concordances of 93% between imputed and typed genotypes for both approaches. Also, imputation qualities are similar (mean difference in RSQ < 0.1%). We demonstrate that our novel postphasing lift-over approach is a practical and time-saving alternative to the prephasing lift-over. This might encourage study partners to accommodate updated reference builds and ultimately improve the information content of study data. Our novel approach is implemented in the software PhaseLift. Genet Epidemiol 38: 381-388, 2014. (C) 2014 Wiley Periodicals, Inc.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | GENOME-WIDE ASSOCIATION; GENOTYPE IMPUTATION; MACULAR DEGENERATION; GENETIC-VARIATION; DISEASE; genome-wide association studies; meta-analysis; HapMap; 1000 Genomes; genotype imputation; imputation quality |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Humangenetik Medicine > Institut für Epidemiologie und Präventivmedizin |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 08 Oct 2019 13:23 |
| Last Modified: | 08 Oct 2019 13:23 |
| URI: | https://pred.uni-regensburg.de/id/eprint/9919 |
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