June 2020
Volume 61, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2020
Statistical driver genes and the missing heritability of age-related macular degeneration
Author Affiliations & Notes
  • Andrea R Waksmunski
    Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, United States
    Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, United States
  • Michelle Grunin
    Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, United States
    Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States
  • Tyler G Kinzy
    Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States
  • Robert Igo
    Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States
  • Jonathan L Haines
    Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, United States
    Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States
  • Jessica Cooke Bailey
    Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States
    Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, United States
  • Footnotes
    Commercial Relationships   Andrea Waksmunski, None; Michelle Grunin, None; Tyler Kinzy, None; Robert Igo, None; Jonathan Haines, None; Jessica Cooke Bailey, None
  • Footnotes
    Support  1X01HG006934-01 and R01 EY022310
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 1895. doi:
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      Andrea R Waksmunski, Michelle Grunin, Tyler G Kinzy, Robert Igo, Jonathan L Haines, Jessica Cooke Bailey; Statistical driver genes and the missing heritability of age-related macular degeneration. Invest. Ophthalmol. Vis. Sci. 2020;61(7):1895.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Age-related macular degeneration (AMD) is a progressive disease of the retina that is shaped by environmental and genetic risk factors. Previous studies have estimated the heritability (h2) of AMD based on variants identified in genome-wide association studies (GWAS); however, a substantial proportion of AMD h2 cannot be explained by known loci. The International AMD Genomics Consortium (IAMDGC) performed one of the largest GWAS on advanced AMD (ADV) cases and controls and identified 34 loci anchored by 52 independent genomic variants contributing to the majority of AMD h2. Using pathway analysis of the IAMDGC GWAS data, we identified 8 statistical driver genes (SDGs) including 2 novel SDGs not discovered by the IAMDGC GWAS: PPARA and PLCG2. We chose to further investigate these risk genes and estimate their contribution to the h2 of ADV and its subtypes.

Methods : We performed genomic-relatedness-based restricted maximum-likelihood (GREML) analyses on ADV (n = 16,144 cases), geographic atrophy (GA) (n = 3,235 cases) and choroidal neovascularization (CNV) (n = 10,749 cases) subtypes to investigate the h2 of the common genotyped variants in the 8 SDGs and separately in the 2 novel SDGs. These estimates were calculated in isolate and in combination with variants from the 34 AMD loci identified in the IAMDGC GWAS using various prevalence estimates. Covariates were included for downstream analyses. This research was conducted in compliance with institutional review boards as described in the IAMDGC GWAS.

Results : Variants from the 8 SDGs account for a small percentage of ADV, GA, and CNV h2 (3.76±0.39%, 2.53±0.36%, and 3.71±0.41%, respectively). Heritability estimates for ADV, GA, and CNV based on the combination of SDGs and the 34 known loci are not significantly different from those calculated for the known loci alone, indicating that the 2 novel loci we identified via pathway analysis may contribute to AMD in a non-additive manner. Covariates did not influence the h2 in any subtype tested.

Conclusions : Pathway analyses of GWAS data, which interrogate networks of genes in biological contexts, may be useful in identifying novel loci that contribute to the h2 of complex disorders, especially those with potentially non-additive effects. Heritability analyses of these loci, especially for disease subtypes, may provide insights into the importance of specific genes in the genetic architecture of AMD.

This is a 2020 ARVO Annual Meeting abstract.

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