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 language | English (US) |
|---|---|
| Pages (from-to) | 384-394 |
| Number of pages | 11 |
| Journal | Current Genomics |
| Volume | 19 |
| Issue number | 5 |
| DOIs | |
| State | Published - Jan 1 2018 |
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All Science Journal Classification (ASJC) codes
- Genetics
- Genetics(clinical)
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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 journal › Article
TY - JOUR
T1 - An ultrahigh-dimensional mapping model of high-order epistatic networks for complex traits
AU - Gosik, Kirk
AU - Sun, Lidan
AU - Chinchilli, Vernon M.
AU - Wu, Rongling
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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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UR - http://www.scopus.com/inward/citedby.url?scp=85048966543&partnerID=8YFLogxK
U2 - 10.2174/1389202919666171218162210
DO - 10.2174/1389202919666171218162210
M3 - Article
C2 - 30065614
AN - SCOPUS:85048966543
VL - 19
SP - 384
EP - 394
JO - Current Genomics
JF - Current Genomics
SN - 1389-2029
IS - 5
ER -