What's Happening?
A significant disparity exists in global health databases, with South Asians being largely absent from genomic and health datasets. According to Dr. Bhramar Mukherjee, senior associate dean of public health data science and data equity at Yale School
of Public Health, over 20% of the world's population is neglected in multi-modal data integration. The NHGRI-EBI GWAS Catalogue, an online database of human genome-wide association studies, reveals that between 2005 and 2025, more than 86% of participants were of European ancestry, while South Asians accounted for less than 1%. This data gap extends to newer tools like single-cell atlases, which show a pervasive European overrepresentation and underrepresentation of Asian and Latino individuals, as highlighted by a study published in Cell Genomics. This imbalance means that AI models and clinical tools built on these European-heavy datasets are less accurate for South Asian populations, who face higher rates of type 2 diabetes, cardiovascular disease, and asthma.
Why It's Important?
The underrepresentation of South Asians in global health databases has critical implications for medical research and healthcare. Polygenic risk scores, which estimate genetic disease risk, are less accurate when applied to South Asian populations, as demonstrated by a 2023 study on multiple sclerosis. This limits the ability to understand disease mechanisms and identify drug targets relevant to these communities. Diagnostic thresholds, risk scores, and prediction models developed primarily from European populations may not be effective for South Asians, necessitating recalibration with local data. The lack of diverse data also means that AI models, which are increasingly used to shape future research and care, will perpetuate existing biases, potentially leading to less effective or even harmful health interventions for a significant portion of the global population. This issue underscores a broader problem of inequity in global health research funding, where only about 10% addresses the needs of low- and middle-income countries, despite accounting for over 90% of the world's potential years of life lost.
What's Next?
To address this critical data gap, experts are advocating for increased regional collaboration and infrastructure development within South Asia. A recent perspective in the Lancet Regional Health – Southeast Asia suggests building greater collaboration between existing biobanks and cohorts, while ensuring South Asian researchers and institutions maintain a meaningful role in data usage. This includes safeguards such as co-authorship, joint intellectual property, technology transfer, training, and infrastructure support, along with priority access to their own data for South Asian researchers. The goal is to create a harmonized system where existing datasets can communicate, historically overlooked populations are included, and data generators share in scientific benefits. Without these changes, the biases embedded in current datasets will continue to be amplified by AI models and clinical tools, making it imperative to act now to ensure equitable and accurate healthcare for all.
Beyond the Headlines
The issue of underrepresentation in health databases extends beyond mere statistics, touching upon ethical and human rights dimensions. As Bhramar Mukherjee of Yale School of Public Health states, neglecting over 20% of the world in multi-modal data integration denies them the 'human right and opportunity to attain the maximal possible health.' This highlights a systemic problem where historical funding patterns and research priorities have created a global health landscape that is deeply unequal. The reliance on European-centric data not only creates less accurate medical tools for diverse populations but also perpetuates a cycle of exclusion in scientific discovery. Addressing this requires a fundamental shift in how global health research is funded, designed, and implemented, moving towards more inclusive and equitable practices that recognize the vast genetic and environmental diversity of human populations. The long-term implication is a more just and effective global healthcare system that serves all individuals, regardless of their ancestry.
