| Title: | HaploBlocks: Efficient Detection of Positive Selection in Large Population Genomic Datasets |
| Journal: | Molecular Biology and Evolution |
| Published: | 15 Feb 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36790822/ |
| DOI: | https://doi.org/10.1093/molbev/msad027 |
Publication 11092
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
Genomic regions under positive selection harbor variation linked for example to adaptation. Most tools for detecting positively selected variants have computational resource requirements rendering them impractical on population genomic datasets with hundreds of thousands of individuals or more. We have developed and implemented an efficient haplotype-based approach able to scan large datasets and accurately detect positive selection. We achieve this by combining a pattern matching approach based on the positional Burrows-Wheeler transform with model-based inference which only requires the evaluation of closed-form expressions. We evaluate our approach with simulations, and find it to be both sensitive and specific. The computational resource requirements quantified using UK Biobank data indicate that our implementation is scalable to population genomic datasets with millions of individuals. Our approach may serve as an algorithmic blueprint for the era of "big data" genomics: a combinatorial core coupled with statistical inference in closed form.</p>
10 Authors
- Benedikt Kirsch-Gerweck
- Leonard Bohnenkämper
- Michel T Henrichs
- Jarno N Alanko
- Hideo Bannai
- Bastien Cazaux
- Pierre Peterlongo
- Joachim Burger
- Jens Stoye
- Yoan Diekmann
1 Application
| Application ID | Title |
|---|---|
| 63023 | Efficient whole-genome scans for positive selection in large population genomic datasets |
Enabling scientific discoveries that improve human health