Abstract

Background: Genetic interactions involving more than two loci have been thought to affect quantitatively inherited traits and diseases more pervasively than previously appreciated. However, the detection of such high-order interactions to chart a complete portrait of genetic architecture has not been well explored. Methods: We present an ultrahigh-dimensional model to systematically characterize genetic main effects and interaction effects of various orders among all possible markers in a genetic mapping or association study. The model was built on the extension of a variable selection procedure, called iFORM, derived from forward selection. The model shows its unique power to estimate the magnitudes and signs of high-order epistatic effects, in addition to those of main effects and pairwise epistatic effects. Results: The statistical properties of the model were tested and validated through simulation studies. By analyzing a real data for shoot growth in a mapping population of woody plant, mei (Prunus mume), we demonstrated the usefulness and utility of the model in practical genetic studies. The model has identified important high-order interactions that contribute to shoot growth for mei. Conclusion: The model provides a tool to precisely construct genotype-phenotype maps for quantitative traits by identifying any possible high-order epistasis which is often ignored in the current genetic literature.

Original languageEnglish (US)
Pages (from-to)384-394
Number of pages11
JournalCurrent Genomics
Volume19
Issue number5
DOIs
StatePublished - Jan 1 2018

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Prunus
Statistical Models
Growth
Genotype
Phenotype
Population
Genetic Background

All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)

Cite this

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title = "An ultrahigh-dimensional mapping model of high-order epistatic networks for complex traits",
abstract = "Background: Genetic interactions involving more than two loci have been thought to affect quantitatively inherited traits and diseases more pervasively than previously appreciated. However, the detection of such high-order interactions to chart a complete portrait of genetic architecture has not been well explored. Methods: We present an ultrahigh-dimensional model to systematically characterize genetic main effects and interaction effects of various orders among all possible markers in a genetic mapping or association study. The model was built on the extension of a variable selection procedure, called iFORM, derived from forward selection. The model shows its unique power to estimate the magnitudes and signs of high-order epistatic effects, in addition to those of main effects and pairwise epistatic effects. Results: The statistical properties of the model were tested and validated through simulation studies. By analyzing a real data for shoot growth in a mapping population of woody plant, mei (Prunus mume), we demonstrated the usefulness and utility of the model in practical genetic studies. The model has identified important high-order interactions that contribute to shoot growth for mei. Conclusion: The model provides a tool to precisely construct genotype-phenotype maps for quantitative traits by identifying any possible high-order epistasis which is often ignored in the current genetic literature.",
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An ultrahigh-dimensional mapping model of high-order epistatic networks for complex traits. / Gosik, Kirk; Sun, Lidan; Chinchilli, Vernon M.; Wu, Rongling.

In: Current Genomics, Vol. 19, No. 5, 01.01.2018, p. 384-394.

Research output: Contribution to journalArticle

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