PSIKO2: a fast and versatile tool to infer population stratification on various levels in GWAS

Tools

Popescu, Andrei-Alin and Huber, Katharina (2015) PSIKO2: a fast and versatile tool to infer population stratification on various levels in GWAS. Bioinformatics, 31 (21). pp. 3552-3554. ISSN 1367-4803

Preview

PDF (final) - Accepted Version
Download (122kB) | Preview

Abstract

Genome-Wide Association Studies are an invaluable tool for identifying genotypic loci linked with agriculturally important traits or certain diseases. The signal on which such studies rely upon can however be obscured by population stratification making it necessary to account for it in some way. Population stratification is dependent on when admixture happend and thus can occur at various levels. To aid in its inference at the genome-level, we recently introduced PSIKO and comparison with leading methods indicate that it has attractive properties. However uptil now it could not be used for local ancestry inference (LAI) which is preferable in cases of recent admixture as the genome level tends to be too coarse to properly account for processes acting on small segments of a genome.To also bring the powerful ideas underpinning PSIKO to bear in such studies, we extended it to PSIKO2 which we introduce here. Availability: Source code, binaries, and user manual are freely available at \url{https://www.uea.ac.uk/computing/psiko}.

Item Type:	Article
Additional Information:	This is a pre-copyedited, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The version of record, Popescu and Huber, Bioinformatics (2015) doi: 10.1093/bioinformatics/btv396, is available online at: http://bioinformatics.oxfordjournals.org/content/early/2015/07/24/bioinformatics.btv396.
Uncontrolled Keywords:	q-matrix,population stratification ,local ancestry inference,gwas,genome-wide association studies
Faculty \ School:	Faculty of Science > School of Computing Sciences Faculty of Science
UEA Research Groups:	Faculty of Science > Research Groups > Computational Biology > Phylogenetics (former - to 2018) Faculty of Science > Research Groups > Computational Biology
Related URLs:	https://www.uea.ac.uk/computing/psiko
Depositing User:	Pure Connector
Date Deposited:	19 Oct 2015 10:00
Last Modified:	14 Jun 2023 12:12
URI:	https://ueaeprints.uea.ac.uk/id/eprint/54719
DOI:	10.1093/bioinformatics/btv396

Downloads

Downloads per month over past year

Actions (login required)

View Item