Fostering diversity in global genomics: a South Asian perspective

Area of Science:

• Genomics
• Population Genetics
• Bioinformatics

Background:

• The Global South, representing over two-thirds of the world's population, is significantly underrepresented in genetic research.
• This underrepresentation hinders the application of genetic discoveries for disease understanding, patient stratification, and precision medicine in these populations.
• Lack of diverse genetic data limits the translation of genomic knowledge into actionable health strategies for a large portion of the global population.

Purpose of the Study:

• To highlight the critical need for increased genetic representation from the Global South in research.
• To discuss the potential of initiatives like the GenomeIndia Project in enhancing disease genomics.
• To explore how diverse genomic data can improve understanding of evolutionary history and disease landscapes.

Main Methods:

• The GenomeIndia Project is a pan-India initiative focused on studying India's extensive genetic diversity.
• It emphasizes the inclusion of genetic data from small and isolated populations across India.
• The project integrates genomic data with baseline blood biochemistry, anthropological, and socio-economic information for genotype-phenotype correlation.

Main Results:

• The GenomeIndia Project is generating a comprehensive dataset of India's genetic landscape.
• This initiative ensures representation from diverse ethnic and geographic groups within India.
• The integrated data aims to provide unprecedented detail in connecting genetic variations to observable traits and health conditions.

Conclusions:

• Genomic data from the Global South, exemplified by the GenomeIndia Project, is crucial for advancing global health equity.
• This project will significantly enhance the understanding of disease genomics and human evolutionary history.
• The comprehensive data generated holds the potential for developing more effective precision medicine strategies tailored to diverse populations.