Semiparametric Allelic Tests for Mapping Multiple Phenotypes: Binomial Regression and Mahalanobis Distance.
Semiparametric Allelic Tests for Mapping Multiple Phenotypes: Binomial Regression and Mahalanobis Distance.
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DOI：
10.1002/gepi.21930
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发表时间：
2015-12
影响因子：
2.1
通讯作者：
Ghosh S
中科院分区：
文献类型：
--
作者：
Majumdar A;Witte JS;Ghosh S
二元表型通常是由于多个潜在的定量前体而出现的。遗传变异可能以多效性方式影响多个性状。因此，同时分析这些相关性状可能比分析单个性状更强大。已经开发了各种基因型水平的方法，例如MultiPhen，以鉴定多变量表型背后的遗传因素。对于单变量表型，等位基因水平测试的有用性和适用性进行了研究。病例与对照之间等位基因频率差异的检验通常用于绘制病例-对照关联。然而，多变量关联作图的等位基因方法还没有得到很多研究。我们探讨了两个等位基因测试的多变量关联：一个使用二项回归模型的基础上反向回归的基因型表型（BAMP），和其他采用马氏距离的两个样本之间的平均值的多变量表型向量的两个等位基因在一个SNP（DAMP）。这些方法可以合并离散和连续的表型。研究了BAMP的一些理论性质。使用模拟，检测多变量关联的方法的功率与基因型水平测试MultiPhen进行比较。对于多变量表型，等位基因检测的功效略高于MultiPhen。对于隐性遗传模式下的一/二个二元性状，等位基因测试被发现实质上更强大。这三种检验均应用于两个真实的数据，其结果为模拟研究提供了一定的支持。由于等位基因的方法假设哈迪-温伯格平衡（HWE），我们提出了一种混合的方法来测试多变量关联，实现MultiPhen时，违反HWE和BAMP否则。
Binary phenotypes commonly arise due to multiple underlying quantitative precursors. Genetic variants may impact multiple traits in a pleiotropic manner. Hence, simultaneously analyzing such correlated traits may be more powerful than analyzing individual traits. Various genotype-level methods, e.g. MultiPhen, have been developed to identify genetic factors underlying a multivariate phenotype. For univariate phenotypes, the usefulness and applicability of allele-level tests have been investigated. The test of allele frequency difference among cases and controls is commonly used for mapping case-control association. However, allelic methods for multivariate association mapping have not been studied much. We explore two allelic tests of multivariate association: one using a Binomial regression model based on inverted regression of genotype on phenotype (BAMP), and the other employing the Mahalanobis distance between two sample means of the multivariate phenotype vector for two alleles at a SNP (DAMP). These methods can incorporate both discrete and continuous phenotypes. Some theoretical properties for BAMP are studied. Using simulations, the power of the methods for detecting multivariate association are compared with the genotype-level test MultiPhen. The allelic tests yield marginally higher power than MultiPhen for multivariate phenotypes. For one/two binary traits under recessive mode of inheritance, allelic tests are found substantially more powerful. All three tests are applied to two real data and the results offer some support for the simulation study. Since the allelic approaches assume Hardy-Weinberg Equilibrium (HWE), we propose a hybrid approach for testing multivariate association that implements MultiPhen when HWE is violated and BAMP otherwise.