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

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.

Keywords:

• Age-related macular degeneration
• Association study
• Complex diseases
• Extended pedigree
• Generalized functional linear mixed models
• Rare variants

Supplementary Materials

The supplementary materials include additional results from the AMD analyses, as well as from our simulation studies. For links to the GitHub sites containing the R package implementing our statistics, as well as our simulation code, please see the Computer Program section below.

Acknowledgments

Three anonymous reviewers, an associate editor, and the editors, Dr. Fuentes and Dr. Zhang, provided very good and insightful comments for us to improve the article.

Computer Program

The software is released as an R package PedGFLMM available at https://github.com/DanielEWeeks/PedGFLMM. For reproducibility purposes, we have also released the code used to generate the results in this article via GitHub at https://github.com/DanielEWeeks/PedGFLMM-simulation-code.

Disclosure Statement

Drs. Gorin, Conley, and Weeks are listed as co-inventors on U.S. patents 7,695,909 and 8,053,190 held by University of Pittsburgh for 10q26 ARMS2/HTRA1 locus of AMD.

Additional information

Funding

This study was supported by U.S. National Science Foundation grant DMS-1915904 (Ruzong Fan), by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development at NIH (Chi-Yang Chiu), by the Intramural Research Program of the National Human Genome Research Institute at NIH (Ruzong Fan, Chi-Yang Chiu, Alexander F. Wilson, E. Bailey-Wilson), by the Intramural Research Program of the National Institute of Mental Health at NIH (Francis J. McMahon), National Institutes of Health, Bethesda, Maryland, by Wei Chen’s NIH grants R01EY024226 and R01HG007358 and the University of Pittsburgh (Ruzong Fan is an unpaid collaborator on the grant R01EY024226), by Christopher I. Amos’ Texas Cancer Prevention Research Institute grants RR170048 & RP190641, NIH grants U01CA196386, U19CA203654, R01CA242218 and U01CA243483, and by Ana I. Vazquez’s NIH grants R01GM101219 and R01GM099992. Michael B. Gorin received funding support from the NEI R01 EY09859 and the ARRA supplement, Harold and Pauline Price Foundation, Arnold and Mabel Beckman Foundation and unrestricted funds to the Department of Ophthalmology from Research to Prevent Blindness, N.Y. The genotyping of the age-related macular degeneration dataset was carried out by the Johns Hopkins University Genetic Resources Core Facility SNP Center. This work utilized the computational resources of the NIH HPC Biowulf cluster at the National Institutes of Health, Bethesda, MD (https://hpc.nih.gov).