Chapter title |
Higher order interactions: detection of epistasis using machine learning and evolutionary computation.
|
---|---|
Chapter number | 24 |
Book title |
Genome-Wide Association Studies and Genomic Prediction
|
Published in |
Methods in molecular biology, May 2013
|
DOI | 10.1007/978-1-62703-447-0_24 |
Pubmed ID | |
Book ISBNs |
978-1-62703-446-3, 978-1-62703-447-0
|
Authors |
Nelson RM, Kierczak M, Carlborg O, Ronald M. Nelson, Marcin Kierczak, Örjan Carlborg |
Editors |
Cedric Gondro, Julius van der Werf, Ben Hayes |
Abstract |
Higher order interactions are known to affect many different phenotypic traits. The advent of large-scale genotyping has, however, shown that finding interactions is not a trivial task. Classical genome-wide association studies (GWAS) are a useful starting point for unraveling the genetic architecture of a phenotypic trait. However, to move beyond the additive model we need new analysis tools specifically developed to deal with high-dimensional genotypic data. Here we show that evolutionary algorithms are a useful tool in high-dimensional analyses designed to identify gene-gene interactions in current large-scale genotypic data. |
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