Understanding retinal diseases with genotypic and transcriptomic data analysis

© 2021 SATYASAI ARAVIND PRASAD MANDAThe retina is light-sensitive eye tissue responsible for vision, but little is known about the genetic regulation of retinal gene expression. Investigating key drivers of gene regulation in the retina in healthy and diseased individuals remains a fundamental challenge in macular degeneration research, especially given the difficulty of accessing human retinal tissue. Deciphering the effects of genetic variation on retinal gene expression will underpin the development of novel treatment avenues for otherwise untreatable diseases causing blindness. A method to investigate these further focuses on the effects of genetic variants on gene expression levels derived from transcriptomic data. This type of ‘omics analysis, known as expression quantitative trait (eQTL) analysis integrates genotype and gene-expression data. The genotyping data for this thesis was generated in collaboration with scientists from the TIGEM, Italy, who first assembled the retinal transcriptome. We aimed to identify the genetic variants that modulate gene expression using a cohort of 41 individual donors of healthy retinal tissue. We performed retinal eQTL analysis using this independent cohort and compared our results with recently published retinal eQTL studies. After observing a weak eQTL signal potentially due to the small sample size, we explored potential strategies to mitigate the multiple testing burden so as to improve statistical power. To this end, we performed eQTL power analyses and limited both the set of variants and genes under consideration by thresholding on allele frequency and gene transcriptional abundance as well as disease relevance. Further, eQTL analysis was used to interpret the genetics of Macular Telangiectasia II, a blinding retinal degenerative disease. This included genome-wide and targeted interrogation of the signals from the largest genome-wide association study to date for this disease