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.
journal_name
Biomed Res Intjournal_title
BioMed research internationalauthors
Yee J,Kwon MS,Jin S,Park T,Park Mdoi
10.1155/2015/523641subject
Has Abstractpub_date
2015-01-01 00:00:00pages
523641eissn
2314-6133issn
2314-6141journal_volume
2015pub_type
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journal_title:BioMed research international
pub_type: 杂志文章
doi:10.1155/2015/317403
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journal_title:BioMed research international
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pub_type: 杂志文章,meta分析
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journal_title:BioMed research international
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journal_title:BioMed research international
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pub_type: 杂志文章
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pub_type: 杂志文章
doi:10.1155/2015/823829
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pub_type: 杂志文章
doi:10.1155/2013/875048
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pub_type: 杂志文章,评审
doi:10.1155/2019/2025636
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pub_type: 临床试验,杂志文章
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journal_title:BioMed research international
pub_type: 临床试验,杂志文章,多中心研究
doi:10.1155/2019/9721781
更新日期:2019-03-13 00:00:00
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pub_type: 杂志文章
doi:10.1155/2014/473419
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pub_type: 杂志文章
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doi:10.1155/2014/238532
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pub_type: 杂志文章
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