Gosik Kirk, Sun Lidan, Chinchilli Vernon M, Wu Rongling
Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA17033, USA.
Curr Genomics. 2018 Aug;19(5):384-394. doi: 10.2174/1389202919666171218162210.
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.
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.
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.
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.
涉及两个以上基因座的遗传相互作用被认为比之前所认识到的更广泛地影响数量遗传性状和疾病。然而，对于绘制完整遗传结构图谱的此类高阶相互作用的检测尚未得到充分探索。
我们提出了一个超高维模型，用于在遗传定位或关联研究中系统地表征所有可能标记之间不同阶次的遗传主效应和相互作用效应。该模型基于一种称为iFORM的变量选择程序的扩展构建，iFORM源自向前选择。该模型除了能够估计主效应和成对上位效应的大小和符号外，还显示出其独特的能力来估计高阶上位效应的大小和符号。
通过模拟研究对该模型的统计特性进行了测试和验证。通过分析木本植物梅花（Prunus mume）作图群体中枝条生长的真实数据，我们证明了该模型在实际遗传研究中的有用性和实用性。该模型识别出了对梅花枝条生长有贡献的重要高阶相互作用。
该模型提供了一种工具，通过识别当前遗传文献中经常被忽视的任何可能的高阶上位性，来精确构建数量性状的基因型 - 表型图谱。
