| Title: | Inferring population structure in biobank-scale genomic data |
| Journal: | American Journal of Human Genetics |
| Published: | 16 Mar 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35298920/ |
| DOI: | https://doi.org/10.1016/j.ajhg.2022.02.015 |
| URL: | http://www.cell.com/article/S0002929722000660/pdf |
| Citations: | 20 (16 in last 2 years) as of 8 Aug 2024 |
Publication 7175
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Abstract
Inferring the structure of human populations from genetic variation data is a key task in population and medical genomic studies. Although a number of methods for population structure inference have been proposed, current methods are impractical to run on biobank-scale genomic datasets containing millions of individuals and genetic variants. We introduce SCOPE, a method for population structure inference that is orders of magnitude faster than existing methods while achieving comparable accuracy. SCOPE infers population structure in about a day on a dataset containing one million individuals and variants as well as on the UK Biobank dataset containing 488,363 individuals and 569,346 variants. Furthermore, SCOPE can leverage allele frequencies from previous studies to improve the interpretability of population structure estimates.5 Authors
- Alec M. Chiu
- Erin K. Molloy
- Zilong Tan
- Ameet Talwalkar
- Sriram Sankararaman
1 Application
| Application ID | Title |
|---|---|
| 33127 | Methods for large-scale medical and population genetic data |
Enabling scientific discoveries that improve human health