Detecting Genetic Interactions for Quantitative Traits Using m-Spacing Entropy Measure.
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...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 11 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/3/2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109030994&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109030994 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/3/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109030994 109030994 109030994 10.1155/2015/523641 109030994 ppf: 1 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Detecting Genetic Interactions for Quantitative Traits Using m-Spacing Entropy Measure. aug: au: Yee, Jaeyong Kwon, Min-Seok Jin, Seohoon Park, Taesung Park, Mira affil: Department of Physiology and Biophysics, Eulji University, Daejeon, Republic of Korea sug: subj: Instrument Validation Genotype Models, Statistical Mutation Genomics Mathematics Human Descriptive Statistics Data Analysis Software Simulations ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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