Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas.
Genet Epidemiol. 2019 Dec;43(8):996-1017. doi: 10.1002/gepi.22258. Epub 2019 Sep 23.
In genetic association studies, joint modeling of related traits/phenotypes can utilize the correlation between them and thereby provide more power and uncover additional information about genetic etiology. Moreover, detecting rare genetic variants are of current scientific interest as a key to missing heritability. Logistic Bayesian LASSO (LBL) has been proposed recently to detect rare haplotype variants using case-control data, that is, a single binary phenotype. As there is currently no haplotype association method that can handle multiple binary phenotypes, we extend LBL to fill this gap. We develop a bivariate model by using a latent variable to induce correlation between the two outcomes. We carry out extensive simulations to investigate the bivariate LBL and compare with the univariate LBL. The bivariate LBL performs better or similar to the univariate LBL in most settings. It has the highest gain in power when a haplotype is associated with both traits and it affects at least one trait in a direction opposite to the direction of the correlation between the traits. We analyze two data sets-Genetic Analysis Workshop 19 sequence data on systolic and diastolic blood pressures and a genome-wide association data set on lung cancer and smoking and detect several associated rare haplotypes.
在遗传关联研究中，对相关性状/表型进行联合建模可以利用它们之间的相关性，从而提供更多的能力并揭示有关遗传病因的更多信息。此外，检测罕见的遗传变异是当前科学研究的热点，因为这是解决遗传率缺失的关键。最近已经提出了逻辑贝叶斯 LASSO（LBL）来使用病例对照数据（即单个二元表型）检测罕见的单倍型变异。由于目前没有可以处理多个二元表型的单倍型关联方法，我们将 LBL 扩展以填补这一空白。我们通过使用潜在变量来诱导两个结果之间的相关性，开发了一个双变量模型。我们进行了广泛的模拟来研究双变量 LBL 并与单变量 LBL 进行比较。在大多数情况下，双变量 LBL 的性能优于或与单变量 LBL 相似。当单倍型与两个性状都相关且它至少影响一个性状的方向与性状之间的相关性方向相反时，双变量 LBL 的功效增益最大。我们分析了两个数据集——收缩压和舒张压的遗传分析研讨会 19 序列数据和肺癌与吸烟的全基因组关联数据集，并检测到几个相关的罕见单倍型。
