Comparison of Strategies to Detect Epistasis from eQTL Data

Autor: Thierry Schüpbach, Zoltán Kutalik, Ioannis Xenarios, Karen Kapur, Sven Bergmann
Jazyk: angličtina
Rok vydání: 2011
Předmět:
False discovery rate
Heredity
Genetic Linkage
Quantitative Trait Loci
Gene Expression
lcsh:Medicine
Single-nucleotide polymorphism
Computational biology
Saccharomyces cerevisiae
Quantitative trait locus
Biology
03 medical and health sciences
0302 clinical medicine
Genetic linkage
Genome Analysis Tools
Genome-Wide Association Studies
Genetics
Humans
False Positive Reactions
Trait Locus Analysis
lcsh:Science
Genetic Association Studies
030304 developmental biology
Genetic association
0303 health sciences
Evolutionary Biology
Multidisciplinary
Population Biology
lcsh:R
Computational Biology
Human Genetics
Epistasis
Genetic
Molecular Sequence Annotation
Genomics
Human genetics
Expression quantitative trait loci
Genetic Polymorphism
Epistasis
lcsh:Q
030217 neurology & neurosurgery
Population Genetics
Research Article
Zdroj: PLoS One, vol. 6, no. 12, pp. e28415
PLoS ONE, Vol 6, Iss 12, p e28415 (2011)
PLoS ONE
PLOS ONE
Popis: 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.
Databáze: OpenAIRE
Externí odkaz: