Statistical driver genes as a means to uncover missing heritability for age-related macular degeneration

BMC Med Genomics. 2020 Jul 6;13(1):95. doi: 10.1186/s12920-020-00747-4.

Abstract

Background: Age-related macular degeneration (AMD) is a progressive retinal disease contributing to blindness worldwide. Multiple estimates for AMD heritability (h2) exist; however, a substantial proportion of h2 is not attributable to known genomic loci. The International AMD Genomics Consortium (IAMDGC) gathered the largest dataset of advanced AMD (ADV) cases and controls available and identified 34 loci containing 52 independent risk variants defining known AMD h2. To better define AMD heterogeneity, we used Pathway Analysis by Randomization Incorporating Structure (PARIS) on the IAMDGC data and identified 8 statistical driver genes (SDGs), including 2 novel SDGs not discovered by the IAMDGC. We chose to further investigate these pathway-based risk genes and determine their contribution to ADV h2, as well as the differential ADV subtype h2.

Methods: We performed genomic-relatedness-based restricted maximum-likelihood (GREML) analyses on ADV, geographic atrophy (GA), and choroidal neovascularization (CNV) subtypes to investigate the h2 of genotyped variants on the full DNA array chip, 34 risk loci (n = 2758 common variants), 52 variants from the IAMDGC 2016 GWAS, and the 8 SDGs, specifically the novel 2 SDGs, PPARA and PLCG2.

Results: Via GREML, full chip h2 was 44.05% for ADV, 46.37% for GA, and 62.03% for CNV. The lead 52 variants' h2 (ADV: 14.52%, GA: 8.02%, CNV: 13.62%) and 34 loci h2 (ADV: 13.73%, GA: 8.81%, CNV: 12.89%) indicate that known variants contribute ~ 14% to ADV h2. SDG variants account for a small percentage of ADV, GA, and CNV heritability, but estimates based on the combination of SDGs and the 34 known loci are similar to those calculated for known loci alone. We identified modest epistatic interactions among variants in the 2 SDGs and the 52 IAMDGC variants, including modest interactions between variants in PPARA and PLCG2.

Conclusions: Pathway analyses, which leverage biological relationships among genes in a pathway, may be useful in identifying additional loci that contribute to the heritability of complex disorders in a non-additive manner. Heritability analyses of these loci, especially amongst disease subtypes, may provide clues to the importance of specific genes to the genetic architecture of AMD.

Keywords: GREML; Genome-wide association study; Heritability; Pathway analysis; Statistical driver gene.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Female
  • Genetic Markers*
  • Genetic Predisposition to Disease*
  • Genome-Wide Association Study
  • Humans
  • Macular Degeneration / genetics*
  • Macular Degeneration / pathology
  • Male
  • Middle Aged
  • Polymorphism, Single Nucleotide*
  • Risk Factors
  • Software*

Substances

  • Genetic Markers