Ocular disease mechanisms elucidated by genetics of human fetal retinal pigment epithelium gene expression

Abstract

The eye is an intricate organ with limited representation in large-scale functional genomics datasets. The retinal pigment epithelium (RPE) serves vital roles in ocular development and retinal homeostasis. We interrogated the genetics of gene expression of cultured human fetal RPE (fRPE) cells under two metabolic conditions. Genes with disproportionately high fRPE expression are enriched for genes related to inherited ocular diseases. Variants near these fRPE-selective genes explain a larger fraction of risk for both age-related macular degeneration (AMD) and myopia than variants near genes enriched in 53 other human tissues.Increased mitochondrial oxidation of glutamine by fRPE promoted expression of lipid synthesis genes implicated in AMD. Expression and splice quantitative trait loci (e/sQTL) analysis revealed shared and metabolic condition-specific loci of each type and several eQTL not previously described in any tissue. Fine mapping of fRPE e/sQTL across AMD and myopia genome-wide association data suggests new candidate genes, and mechanisms by which the same common variant of RDH5 contributes to both increased AMD risk and decreased myopia risk. Our study highlights the unique transcriptomic characteristics of fRPE and provides a resource to connect e/sQTL in a critical ocular cell type to monogenic and complex eye disorders.3The importance of vision to humans and the accessibility of the eye to examination have motivated the characterization of more than one thousand genetic conditions involving ocular phenotypes 1 . Among these, numerous monogenic diseases exhibit significant inter-and intra-familial phenotypic variability 2-7 . Imbalance in allelic expression of a handful of causative genes has been documented 8 , but common genetic variants responsible for such effects remain to be discovered.Complementing our knowledge of numerous monogenic ocular disorders, recent genome-wide association studies (GWAS) 9 have identified hundreds of independent loci associated with polygenic ocular phenotypes such as age-related macular degeneration (AMD), the leading cause of blindness in elderly individuals in developed countries 10,11 , and myopia, the most common type of refractive error worldwide and an increasingly common cause of blindness 12-14 . Despite the rapid success of GWAS in mapping novel ocular disease susceptibility loci, the functional mechanisms underlying these associations are often obscure.Connecting changes in molecular functions such as gene expression and splicing with specific GWAS genomic variants has aided the elucidation of functional mechanisms. Non-coding variants account for a preponderance of the most significant GWAS loci 15,16 , and most expression quantitative trait loci (eQTL) map to non-coding variants 17 . Thousands of eQTL have been found in a variety of human tissues 18 , but ocular celltypes are underrepresented among eQTL maps across diverse tissues, and no systematic search for eQTL has so far been described for any cell type in the human eye.The retinal pigment epithelium ...

Abundant associations with gene expression complicate GWAS follow-up

“…We integrated into the web server more than 200 peer-reviewed GWAS studies across more than 800 unique traits and 642 disease-associated phenotypes from the UK Biobank rapid GWAS (downloaded from http://www.nealelab.is/uk-biobank/). In addition, LocusCompare integrates eQTLs from 48 tissues in the GTEx study (version 7) 3 ; eQTLs and splicing QTLs from coronary artery smooth muscle cells 9 and retinal pigment epithelial cells 10 ; and methylation QTLs from brain tissues 11,12 and whole blood 13 . Using preloaded association datasets, LocusCompare enables easy comparison between pairs of association signals.…”

Shared Genetics of Multiple System Atrophy and Inflammatory Bowel Disease

“…27 In addition, we scanned blood 28 (N > 30,000) and brain 29 (N > 520) eQTL summary statistics with substantially larger sample sizes than corresponding tissues in GTEx (N blood < 400, N brain < 200). LocusCompare 30 was then applied to check whether loci identified in conjFDR analysis colocalizes with eQTL signal. Although LocusCompare can be useful for visual assessment of the colocalization between conjFDR and eQTL signals, it cannot quantify contributions of specific SNPs to colocalization.…”