An OMICs based meta-analysis to support infection state stratification

Screening of Inherited Retinal Disease Patients in a Low-Resource Setting Using an Augmented Next-Generation Sequencing Panel

BACKGROUND

Inherited retinal diseases (IRDs) are a clinically and genetically heterogeneous group of disorders affecting millions worldwide. Despite the widespread adoption of next-generation sequencing (NGS) panels, there remains a critical gap in the genetically diverse and understudied African populations.

METHODS

One hundred and thirty-five South African patients affected by various IRDs underwent NGS using a custom-targeted panel sequencing over 100 known genes. The panel was supplemented by in silico screening for a MAK-Alu insertion and screening of seven functionally established deep intronic variants.

RESULTS

Through our combined screening strategy, we obtained a probable genetic diagnosis for 56% of the cohort. We identified 83 unique variants in 29 IRD genes underlying the disease, including 16 putative novel variants. Molecular findings prompted recommendations for clinical re-examination in ten patients. Resolution rates varied across clinical classifications and population groups.

CONCLUSIONS

This study reports the first use of a targeted NGS panel for IRDs in southern Africa, demonstrating a cost-effective, customisable approach that optimises both diagnostic yield and resource efficiency, making it a valuable tool for IRD molecular characterisation in resource-limited settings. Augmenting the panel by screening for variants relevant to South African patients allowed us to achieve a resolution rate in line with international studies. Our study underscores the importance of investigating diverse populations to bridge disparities in genomic research and improve diagnostic outcomes for underrepresented population groups.

Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach

The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology’s potential, a set of transcriptomic datasets are meta-analyzed as an example.