Jiang Yingda, Chiu Chi-Yang, Yan Qi, Chen Wei, Gorin Michael B, Conley Yvette P, Lakhal-Chaieb M'Hamed Lajmi, Cook Richard J, Amos Christopher I, Wilson Alexander F, Bailey-Wilson Joan E, McMahon Francis J, Vazquez Ana I, Yuan Ao, Zhong Xiaogang, Xiong Momiao, Weeks Daniel E, Fan Ruzong
Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA.
Division of Biostatistics, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN.
J Am Stat Assoc. 2021;116(534):531-545. doi: 10.1080/01621459.2020.1799809. Epub 2020 Jul 28.
Genetics plays a role in age-related macular degeneration (AMD), a common cause of blindness in the elderly. There is a need for powerful methods for carrying out region-based association tests between a dichotomous trait like AMD and genetic variants on family data. Here, we apply our new generalized functional linear mixed models (GFLMM) developed to test for gene-based association in a set of AMD families. Using common and rare variants, we observe significant association with two known AMD genes: and . Using rare variants, we find suggestive signals in four genes: , , , and . Intriguingly, is down-regulated in AMD aqueous humor, and deficiency leads to retinal inflammation and increased vulnerability to oxidative stress. These findings were made possible by our GFLMM which model the effect of a major gene as a fixed mean, the polygenic contributions as a random variation, and the correlation of pedigree members by kinship coefficients. Simulations indicate that the GFLMM likelihood ratio tests (LRTs) accurately control the Type I error rates. The LRTs have similar or higher power than existing retrospective kernel and burden statistics. Our GFLMM-based statistics provide a new tool for conducting family-based genetic studies of complex diseases. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
遗传学在年龄相关性黄斑变性(AMD)中发挥作用,AMD是老年人失明的常见原因。需要有强大的方法来对AMD等二分性状与家族数据中的基因变异进行基于区域的关联测试。在此,我们应用我们新开发的广义功能线性混合模型(GFLMM)来测试一组AMD家族中基于基因的关联。使用常见变异和罕见变异,我们观察到与两个已知的AMD基因存在显著关联: 和 。使用罕见变异,我们在四个基因中发现了提示性信号: 、 、 和 。有趣的是, 在AMD房水中表达下调, 缺乏会导致视网膜炎症并增加对氧化应激的易感性。我们的GFLMM将主要基因的效应建模为固定均值,将多基因贡献建模为随机变异,并通过亲属系数对系谱成员的相关性进行建模,从而使这些发现成为可能。模拟表明,GFLMM似然比检验(LRT)准确地控制了I型错误率。LRT比现有的回顾性核统计和负担统计具有相似或更高的功效。我们基于GFLMM的统计为开展复杂疾病的基于家族的遗传研究提供了一种新工具。本文的补充材料,包括可用于重现该工作的材料的标准化描述,可作为在线补充材料获取