Comparison of Strategies to Detect Epistasis from eQTL Data
Genome-wide association studies have been instrumental in identifying genetic variants associated with complex traits such as human disease or gene expression phenotypes. It has been proposed that extending existing analysis methods by considering interactions between pairs of loci may uncover addit...
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Public Library of Science
2011
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3242756/ |
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pubmed-32427562011-12-28 Comparison of Strategies to Detect Epistasis from eQTL Data Kapur, Karen Schüpbach, Thierry Xenarios, Ioannis Kutalik, Zoltán Bergmann, Sven Research Article Genome-wide association studies have been instrumental in identifying genetic variants associated with complex traits such as human disease or gene expression phenotypes. It has been proposed that extending existing analysis methods by considering interactions between pairs of loci may uncover additional genetic effects. However, the large number of possible two-marker tests presents significant computational and statistical challenges. Although several strategies to detect epistasis effects have been proposed and tested for specific phenotypes, so far there has been no systematic attempt to compare their performance using real data. We made use of thousands of gene expression traits from linkage and eQTL studies, to compare the performance of different strategies. We found that using information from marginal associations between markers and phenotypes to detect epistatic effects yielded a lower false discovery rate (FDR) than a strategy solely using biological annotation in yeast, whereas results from human data were inconclusive. For future studies whose aim is to discover epistatic effects, we recommend incorporating information about marginal associations between SNPs and phenotypes instead of relying solely on biological annotation. Improved methods to discover epistatic effects will result in a more complete understanding of complex genetic effects. Public Library of Science 2011-12-19 /pmc/articles/PMC3242756/ /pubmed/22205949 http://dx.doi.org/10.1371/journal.pone.0028415 Text en Kapur et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Kapur, Karen Schüpbach, Thierry Xenarios, Ioannis Kutalik, Zoltán Bergmann, Sven |
| spellingShingle |
Kapur, Karen Schüpbach, Thierry Xenarios, Ioannis Kutalik, Zoltán Bergmann, Sven Comparison of Strategies to Detect Epistasis from eQTL Data |
| author_facet |
Kapur, Karen Schüpbach, Thierry Xenarios, Ioannis Kutalik, Zoltán Bergmann, Sven |
| author_sort |
Kapur, Karen |
| title |
Comparison of Strategies to Detect Epistasis from eQTL Data |
| title_short |
Comparison of Strategies to Detect Epistasis from eQTL Data |
| title_full |
Comparison of Strategies to Detect Epistasis from eQTL Data |
| title_fullStr |
Comparison of Strategies to Detect Epistasis from eQTL Data |
| title_full_unstemmed |
Comparison of Strategies to Detect Epistasis from eQTL Data |
| title_sort |
comparison of strategies to detect epistasis from eqtl data |
| description |
Genome-wide association studies have been instrumental in identifying genetic variants associated with complex traits such as human disease or gene expression phenotypes. It has been proposed that extending existing analysis methods by considering interactions between pairs of loci may uncover additional genetic effects. However, the large number of possible two-marker tests presents significant computational and statistical challenges. Although several strategies to detect epistasis effects have been proposed and tested for specific phenotypes, so far there has been no systematic attempt to compare their performance using real data. We made use of thousands of gene expression traits from linkage and eQTL studies, to compare the performance of different strategies. We found that using information from marginal associations between markers and phenotypes to detect epistatic effects yielded a lower false discovery rate (FDR) than a strategy solely using biological annotation in yeast, whereas results from human data were inconclusive. For future studies whose aim is to discover epistatic effects, we recommend incorporating information about marginal associations between SNPs and phenotypes instead of relying solely on biological annotation. Improved methods to discover epistatic effects will result in a more complete understanding of complex genetic effects. |
| publisher |
Public Library of Science |
| publishDate |
2011 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3242756/ |
| _version_ |
1611495848844197888 |