Gene-Based Association Testing of Dichotomous Traits With Generalized Functional Linear Mixed Models Using Extended Pedigrees: Applications to Age-Related Macular Degeneration

Yingda Jiang, Chi-Yang Chiu, Qi Yan, Wei Chen, Michael B. Gorin, Yvette P. Conley, M’Hamed Lajmi Lakhal-Chaieb, Richard J. Cook, Christopher I. Amos, Alexander F. Wilson, Joan E. Bailey-Wilson, Francis J. McMahon, Ana I. Vazquez, Ao Yuan, Xiaogang Zhong, Momiao Xiong, Daniel E. Weeks and Ruzong Fan

Journal of the American Statistical Association, 2021, vol. 116, issue 534, 531-545

Abstract: 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 (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.

Date: 2021
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DOI: 10.1080/01621459.2020.1799809

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