Page 110 - Read Online

Ratnapriya. J Transl Genet Genom 2022;6:240-256 https://dx.doi.org/10.20517/jtgg.2021.54 Page 244

Figure 1. Applications of AMD-GWAS findings in understanding the disease biology and clinical practices and interventions. AMD:
Age-related macular degeneration; GWAS: genome-wide association studies.

large resource for studying the relationship between genetic variations and gene expression levels across
multiple tissues and cell types . Unfortunately, the exclusion or limited representation of AMD-relevant
[51]
tissues (retina, macula and RPE) has delayed the functional understanding of most ocular phenotypes.
However, researchers in vision field were quick in recognizing this unmet need and have created several
excellent resources in the last few years [52-59] .

While tissue-shared regulation appears to underlie an appreciable proportion of the genetic component of
complex traits [51,60] , a series of studies have identified enrichment of GWAS signal in tissue-specific [60,61] or
cell-type specific [62,63] expression quantitative trait loci (eQTLs). A recent single-cell sequencing study shows
cell-type specificity for multiple AMD risk genes across diverse cell types, including bipolar cells (CFH,
CFHR1), horizontal cells (HTRA1 and ARMS2), astrocytes (APOE) and microglia (CXC3R1) . Thus,
[57]
aligning cell-type-specific expression profiles with GWAS results can lead us one step closer to functional
follow-up experiments [62,64] . However, human primary tissue samples are often a mixture of multiple cell-
types, and tissue-specific expression is a function of the distribution of cell-types present in that tissue. A
growing number of in silico deconvolution methods and associated reference panels with cell-type-specific
marker genes enable the robust estimation of the enrichment of specific cell-types from bulk tissue gene
expression data [65-67] . In recent years, single-cell sequencing technology has also emerged as key tool to gain
insights into complex cell-types directly [68-71] . A comparative analysis of normal and disease conditions can
help in characterizing complex cellular changes in response to pathology [72,73] .