Detecting Genetic Interactions for Quantitative Traits Using m-Spacing Entropy Measure

BioMed Research International
Jaeyong YeeMira Park

Abstract

A number of statistical methods for detecting gene-gene interactions have been developed in genetic association studies with binary traits. However, many phenotype measures are intrinsically quantitative and categorizing continuous traits may not always be straightforward and meaningful. Association of gene-gene interactions with an observed distribution of such phenotypes needs to be investigated directly without categorization. Information gain based on entropy measure has previously been successful in identifying genetic associations with binary traits. We extend the usefulness of this information gain by proposing a nonparametric evaluation method of conditional entropy of a quantitative phenotype associated with a given genotype. Hence, the information gain can be obtained for any phenotype distribution. Because any functional form, such as Gaussian, is not assumed for the entire distribution of a trait or a given genotype, this method is expected to be robust enough to be applied to any phenotypic association data. Here, we show its use to successfully identify the main effect, as well as the genetic interactions, associated with a quantitative trait.

References

Nov 1, 2007·European Journal of Human Genetics : EJHG·Changzheng DongYixue Li
Nov 13, 2007·Journal of Theoretical Biology·Guolian KangDai Zhang
Apr 9, 2008·Nature Genetics·Michael N WeedonTimothy M Frayling
May 28, 2011·Genetic Epidemiology·Ming LiQing Lu
Jan 10, 2012·Proceedings of the National Academy of Sciences of the United States of America·Or ZukEric S Lander
May 2, 2013·Current Genomics·Cen WuYuehua Cui

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Citations

Oct 23, 2016·Briefings in Bioinformatics·Paola G Ferrario, Inke R König
Dec 9, 2020·Entropy·Pritam ChandaRasna Walia

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Methods Mentioned

BETA
genotyping

Software Mentioned

QMDR

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