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Whole-genome ordinary ridge regression including gene-gene interaction effects
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The methodology of applying markers across the entire genome for the purpose of predicting genomic values, can improve prediction of complex traits. In this study, we considered all main effects (1981 markers) and epistatic effects (1962180 markers) in a wheat data set with 280 accessions to investigate  if the inclusion of epistatic effects can improve genomic prediction. The results of our simulations using the real data showed that the contribution of epistasis to phenotypic prediction is very small. However, including epistatic effects in the model allows us to separate the epistatic effects from the main effects (estimated by a model without epistasis).

Place, publisher, year, edition, pages
IT, 13 017
National Category
Engineering and Technology
URN: urn:nbn:se:uu:diva-196300OAI: oai:DiVA.org:uu-196300DiVA: diva2:609722
Educational program
Master Programme in Computational Science
Available from: 2013-03-07 Created: 2013-03-07 Last updated: 2013-03-07Bibliographically approved

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