Bilow Michael, Crespo Fernando, Pan Zhicheng, Eskin Eleazar, Eyheramendy Susana
Department of Computer Science, University of California, Los Angeles, California.
Department of Statistics, Pontificia Universidad Católica de Chile, Santiago, Chile.
Genetics. 2017 Mar;205(3):1041-1047. doi: 10.1534/genetics.116.198473. Epub 2017 Jan 27.
Genome-wide association studies have identified thousands of variants implicated in dozens of complex diseases. Most studies collect individuals with and without disease and search for variants with different frequencies between the groups. For many of these studies, additional disease traits are also collected. Jointly modeling clinical phenotype and disease status is a promising way to increase power to detect true associations between genetics and disease. In particular, this approach increases the potential for discovering genetic variants that are associated with both a clinical phenotype and a disease. Standard multivariate techniques fail to effectively solve this problem, because their case-control status is discrete and not continuous. Standard approaches to estimate model parameters are biased due to the ascertainment in case-control studies. We present a novel method that resolves both of these issues for simultaneous association testing of genetic variants that have both case status and a clinical covariate. We demonstrate the utility of our method using both simulated data and the Northern Finland Birth Cohort data.
全基因组关联研究已经识别出数千个与数十种复杂疾病相关的变异。大多数研究收集了患病人群和未患病人群，并在两组之间寻找频率不同的变异。对于许多此类研究，还收集了其他疾病特征。联合建模临床表型和疾病状态是提高检测基因与疾病之间真实关联能力的一种有前景的方法。特别是，这种方法增加了发现与临床表型和疾病都相关的基因变异的可能性。标准的多变量技术无法有效解决这个问题，因为它们的病例对照状态是离散的而非连续的。由于病例对照研究中的确定方式，估计模型参数的标准方法存在偏差。我们提出了一种新方法，该方法解决了这两个问题，用于对具有病例状态和临床协变量的基因变异进行同时关联测试。我们使用模拟数据和芬兰北部出生队列数据证明了我们方法的实用性。
