Evidence map›Paper›PMID 30065614›Full record

ArticleCurrent genomics2018

An Ultrahigh-Dimensional Mapping Model of High-order Epistatic Networks for Complex Traits.

Kirk Gosik, Lidan Sun, Vernon M Chinchilli, Rongling Wu

Abstract read
In one paragraph

Article in Current genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Kirk GosikDepartment of Public Health Sciences, Penn State College of Medicine, Hershey, PA17033, USA.
Lidan SunDepartment of Public Health Sciences, Penn State College of Medicine, Hershey, PA17033, USA.
Vernon M ChinchilliDepartment of Public Health Sciences, Penn State College of Medicine, Hershey, PA17033, USA.
Rongling WuDepartment of Public Health Sciences, Penn State College of Medicine, Hershey, PA17033, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenetic 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.

methodsWe 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.

resultsThe 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.

conclusionThe 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.

Indexed as

EpistasisHigh-order interactionsiFORMPrunus mumeQuantitative traitVariable selectionWoody plant

Identifiers

PMID30065614
PMCPMC6030858

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