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Page 251 Ratnapriya. J Transl Genet Genom 2022;6:240-256 https://dx.doi.org/10.20517/jtgg.2021.54

Figure 4. Applications of transcriptome data generated in control and AMD donors. Transcriptome data alone can help in identifying
genes and pathways that are dysregulated in the disease process. They can provide insights into the disease biology as well as possibly
identify disease biomarkers and therapeutic targets. Integrative analysis of transcriptome with the genetic data can be applied for eQTL
and transcriptome-wide association analysis. These approaches can help in identifying causal variants and target genes at known AMD
loci as well as could potentially reveal new candidates. SNP: Single nucleotide polymorphism; AMD: age-related macular degeneration;
eQTL: expression quantitative trait loci.

level in AMD patients when compared to controls . Similarly, the identification of complement proteins
[110]
in histopathological and genetic studies prompted several comparative studies of complement regulators,
complements components, and activation products in serums and plasma of AMD cases and controls
[110]
(reviewed in Ref. ). Despite considerable inter-individual variability in complement level, few
complement genes have shown a reproducible change in AMD cases, especially when integrated with
AMD-associated common and rare variants in these genes . This further enforces the value of genetic-
[117]
data-driven biomarker discovery in AMD.

Whilst the potential of gene expression signatures remains largely unfulfilled, transcriptome studies in
AMD hold great promises for interrogation of the biomarkers that could lead to novel insights into the
underlying molecular processes. The increased technical advances in gene expression profiling and analysis
including deep learning methods provide hope that signatures can begin to progress more frequently
beyond the development phase and translate to patient benefit by identification of AMD biomarkers with
predictive accuracy compared to more established prognostic factors.

CONCLUDING REMARKS AND FUTURE PERSPECTIVE
Elucidating the disease mechanisms underlying AMD-GWAS loci holds great potential [Figure 4]. In this
review, we have focused mainly on the application of transcriptome data. However, with the increased
availability of different data types such as histone modification, methylome, and proteome data, it is likely