Rönnegård, Lars
- Department of Animal Biosciences, Swedish University of Agricultural Sciences
- Dalarna University
Abstract
Genome-wide association studies (GWAS) are a powerful tool for identifying genes. They exploit the standing genetic variation and correlate phenotypic diversity to genetic markers close to or with genes of interest. However, their power is limited when it comes to complex phenotypes caused by highly epistatically interacting genes. To improve GWAS and to develop new methods, a computational model system could prove invaluable. In the computational model system presented here, the functionality of all genes in question can be identified using knockouts. This allows the comparison between the quantitative genetics results and the functional analysis. Here the goal is to perform a pilot study to investigate to which degree such a computational model can serve as a positive control for a GWAS. Surprisingly, even though the model used here is relatively simple and uses only a few genes, the GWAS struggles to identify all relevant genes. The advantages and limitations of this approach will be discussed to improve the model for future comparisons.
Published in
ALIFE
2022, volume: 34, pages: 80-88
Title: ALIFE 2022: The 2022 Conference on Artificial Life
Publisher: Massachusetts Institute of Technology
Conference
2022 Conference on Artificial Life, ALIFE 2022, July 18–22, 2022, Online
SLU Authors
UKÄ Subject classification
Bioinformatics (Computational Biology)
Publication identifier
- DOI: https://doi.org/10.1162/isal_a_00491
Permanent link to this page (URI)
https://res.slu.se/id/publ/142856