Modeling and Testing for Joint Association Using a Genetic Random Field Model
Modeling and Testing for Joint Association Using a Genetic Random Field Model
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DOI：
10.1111/biom.12160
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发表时间：
2014-09-01
期刊：
影响因子：
1.9
通讯作者：
Lu, Qing
中科院分区：
文献类型：
--
作者：
He, Zihuai;Zhang, Min;Lu, Qing
在确定易患常见复杂疾病的单一遗传变异方面取得了实质性进展。尽管如此，人类疾病的遗传病因仍然在很大程度上未知。人类复杂疾病可能是由大量遗传变异而不是单一变异的联合作用所影响。考虑连锁不平衡（LD）和潜在相互作用的多个遗传变异的联合分析可以进一步加强发现过程，从而识别新的疾病易感性遗传变异。出于空间统计学的发展，我们提出了一个新的统计模型的基础上的随机场理论，称为遗传随机场模型（GenRF），考虑可能的基因-基因相互作用和LD的联合关联分析。使用伪似然方法，开发了用于多个遗传变体的联合关联的GenRF测试，其具有以下优点：（1）适应复杂的相互作用以提高性能;（2）自然降维;（3）在LD存在的情况下提高功率;以及（4）计算效率。模拟研究是在各种情景下进行的。发展一直集中在数量性状和稳健性的GenRF测试的其他性状，例如，二元性状，也进行了讨论。与通常采用的内核机器方法SKAT以及其他更标准的方法相比，GenRF在存在复杂相互作用的情况下显示出整体相当的性能和更好的性能。该方法进一步说明了应用程序的达拉斯心脏研究。
Substantial progress has been made in identifying single genetic variants predisposing to common complex diseases. Nonetheless, the genetic etiology of human diseases remains largely unknown. Human complex diseases are likely influenced by the joint effect of a large number of genetic variants instead of a single variant. The joint analysis of multiple genetic variants considering linkage disequilibrium (LD) and potential interactions can further enhance the discovery process, leading to the identification of new disease-susceptibility genetic variants. Motivated by development in spatial statistics, we propose a new statistical model based on the random field theory, referred to as a genetic random field model (GenRF), for joint association analysis with the consideration of possible gene-gene interactions and LD. Using a pseudo-likelihood approach, a GenRF test for the joint association of multiple genetic variants is developed, which has the following advantages: (1) accommodating complex interactions for improved performance; (2) natural dimension reduction; (3) boosting power in the presence of LD; and (4) computationally efficient. Simulation studies are conducted under various scenarios. The development has been focused on quantitative traits and robustness of the GenRF test to other traits, for example, binary traits, is also discussed. Compared with a commonly adopted kernel machine approach, SKAT, as well as other more standard methods, GenRF shows overall comparable performance and better performance in the presence of complex interactions. The method is further illustrated by an application to the Dallas Heart Study.