| Title: | Meta-imputation: An efficient method to combine genotype data after imputation with multiple reference panels |
| Journal: | American Journal of Human Genetics |
| Published: | 3 May 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35508176/ |
| DOI: | https://doi.org/10.1016/j.ajhg.2022.04.002 |
| URL: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9247833 |
Publication 10231
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
Genotype imputation is an integral tool in genome-wide association studies, in which it facilitates meta-analysis, increases power, and enables fine-mapping. With the increasing availability of whole-genome-sequence datasets, investigators have access to a multitude of reference-panel choices for genotype imputation. In principle, combining all sequenced whole genomes into a single large panel would provide the best imputation performance, but this is often cumbersome or impossible due to privacy restrictions. Here, we describe meta-imputation, a method that allows imputation results generated using different reference panels to be combined into a consensus imputed dataset. Our meta-imputation method requires small changes to the output of existing imputation tools to produce necessary inputs, which are then combined using dynamically estimated weights that are tailored to each individual and genome segment. In the scenarios we examined, the method consistently outperforms imputation using a single reference panel and achieves accuracy comparable to imputation using a combined reference panel.</p>
10 Authors
- Ketian Yu
- Sayantan Das
- Jonathon LeFaive
- Alan Kwong
- Jacob Pleiness
- Lukas Forer
- Sebastian Schönherr
- Christian Fuchsberger
- Albert Vernon Smith
- Gonçalo Rocha Abecasis
1 Application
| Application ID | Title |
|---|---|
| 24460 | Genetic causes of complex human traits: emphasis on psychiatric, ocular, dermatological, and cardiovascular diseases. |
Enabling scientific discoveries that improve human health