Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.
Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.
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DOI：
10.1371/journal.pgen.1005965
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发表时间：
2016-04
期刊：
影响因子：
4.5
通讯作者：
Xiong M
中科院分区：
文献类型：
--
作者：
Zhang F;Xie D;Liang M;Xiong M
到目前为止，大多数表型的遗传分析都集中在分析单个性状或独立分析每一种表型。然而，多个互补性状的联合上位性分析将增加统计能力，提高我们对复杂疾病复杂遗传结构的理解。尽管它们在揭示复杂性状的遗传结构方面很重要，但在多个表型中识别上位性的统计方法仍然基本上没有被探索。为了填补这一空白，我们将多个数量性状分析中的两个基因之间的交互作用作为多元函数回归(MFRG)进行了检验，在MFRG中，基因功能(遗传变量图谱)被定义为遗传变量的基因组位置的函数。我们使用大规模模拟来计算测试具有多表型的两个基因之间的互作的I型错误率，并用单变量函数回归模型来比较多变量成对互作分析和单性状互作分析的能力。为了进一步评估性能，用于上位性分析的MFRG被应用于来自NHLBI的外显子组测序项目(ESP)的五种外显子组序列数据，以检测多效性上位性。共有267对形成遗传互作网络的基因显示出上位性影响五个性状的显着证据。结果表明，多表型的联合互作分析比单一性状的互作分析具有更高的检测互作能力，为全面揭示多表型的遗传结构开辟了新的方向。广泛使用的统计方法检验单一表型的交互作用。然而，我们经常观察到多效性遗传互作效应。多个互补性状的同时基因-基因(GXG)互作分析将增加检测GXG互作的统计能力。尽管GXG互作在揭示复杂性状的遗传结构方面发挥了重要作用，但由于其潜在的复杂性，检测多表型GXG互作的统计方法仍然不太发达。因此，我们将函数回归模型从单变量扩展到多变量，用于多个相关表型的同时GXG交互作用分析。大规模的模拟被用来评估两个具有多表型的基因之间的互作测试的I类错误率，并与传统的多变量成对互作分析和单变量函数回归模型的单性状互作分析进行比较。为了进一步评估性能，用于相互作用分析的MFRG被应用于来自NHLBI的外显子组测序项目(ESP)的五种外显子序列数据，以检测多效性GXG相互作用。形成遗传互作网络的267对基因显示出交互作用影响五个性状的显著证据。
To date, most genetic analyses of phenotypes have focused on analyzing single traits or analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power and improve our understanding of the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two genes in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large-scale simulations to calculate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare the power with multivariate pairwise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for epistasis analysis is applied to five phenotypes of exome sequence data from the NHLBI’s Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 267 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has a much higher power to detect interaction than the interaction analysis of a single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes. The widely used statistical methods test interaction for single phenotype. However, we often observe pleotropic genetic interaction effects. The simultaneous gene-gene (GxG) interaction analysis of multiple complementary traits will increase statistical power to detect GxG interactions. Although GxG interactions play an important role in uncovering the genetic structure of complex traits, the statistical methods for detecting GxG interactions in multiple phenotypes remains less developed owing to its potential complexity. Therefore, we extend functional regression model from single variate to multivariate for simultaneous GxG interaction analysis of multiple correlated phenotypes. Large-scale simulations are conducted to evaluate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare power with traditional multivariate pair-wise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for interaction analysis is applied to five phenotypes of exome sequence data from the NHLBI’s Exome Sequencing Project (ESP) to detect pleiotropic GxG interactions. 267 pairs of genes that formed a genetic interaction network showed significant evidence of interactions influencing five traits.