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Higher order interactions: detection of epistasis using machine learning and evolutionary computation.
2013 (English)In: Methods in Molecular Biology, ISSN 1064-3745, E-ISSN 1940-6029, Vol. 1019Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2013. Vol. 1019
National Category
Natural Sciences
Research subject
Bioinformatics
Identifiers
URN: urn:nbn:se:uu:diva-294214DOI: 10.1007/978-1-62703-447-0_24PubMedID: 23756908OAI: oai:DiVA.org:uu-294214DiVA: diva2:929268
Available from: 2016-05-18 Created: 2016-05-18 Last updated: 2016-05-18

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Carlborg, Örjan
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