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: CFH and ARMS2. Using rare variants, we find suggestive signals in four genes: ASAH1, CLEC6A, TMEM63C, and SGSM1. Intriguingly, ASAH1 is down-regulated in AMD aqueous humor, and ASAH1 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 (LRT) accurately control the Type I error rates. The LRT 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.

遗传因素在与年龄有关的黄斑变性（AMD）中起作用，AMD是老年人失明的常​​见原因。需要有力的方法来进行基于二分性的特征（如AMD）和家庭数据的遗传变异之间的基于区域的关联测试。在这里，我们应用我们开发的新的广义功能线性混合模型（GFLMM），以测试一组AMD系列中基于基因的关联。使用常见和罕见变体，我们观察到与两个已知的AMD基因显着相关：CFH和ARMS2。使用罕见的变体，我们在四个基因中发现提示信号：ASAH1，CLEC6A，TMEM63C和SGSM1。有趣的是，ASAH1在AMD房水中被下调，并且ASAH1缺乏导致视网膜发炎并增加了对氧化应激的脆弱性。这些发现是通过我们的GFLMM得以实现的，该模型将主要基因的作用作为固定均值，将多基因贡献作为随机变量进行建模，并通过亲属系数对谱系成员的相关性进行建模。仿真表明，GFLMM似然比测试（LRT）可以精确控制I型错误率。与现有的回顾性内核和负担统计数据相比，LRT具有相似或更高的功能。我们基于GFLMM的统计数据为进行基于家庭的复杂疾病基因研究提供了新工具。