ArticleGenome biology and evolution2016
Population Stratification and Underrepresentation of Indian Subcontinent Genetic Diversity in the 1000 Genomes Project Dataset.
Article in Genome biology and evolution, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- NAT2 global landscape: Genetic diversity and acetylation statuses from a systematic review.PloS one · 2023Pooled it
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- High-throughput sequencing: a breakthrough in molecular diagnosis for precision medicine.Functional & integrative genomics · 2025Review
- Precision nutrition across climates: decoding diet, tradition, and genomic adaptations from South Asia to the Arctic.Frontiers in nutrition · 2025Review
- Genetic and linguistic comparisons reveal complex sex-biased transmission of language features.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Pharmacogenetic analysis of structural variation in the 1000 genomes project using whole genome sequences.Scientific reports · 2024Article
- Quantifying variations associated with dental caries reveals disparity in effect allele frequencies across diverse populations.BMC genomic data · 2024Article
- The Inclusion of Underrepresented Populations in Cardiovascular Genetics and Epidemiology.Journal of cardiovascular development and disease · 2024Review
- South Asia: The Missing Diverse in Diversity.Behavior genetics · 2024Review
- Bridging the diversity gap: Analytical and study design considerations for improving the accuracy of trans-ancestry genetic prediction.HGG advances · 2023Article
- Polygenic Risk Scores for Alzheimer's Disease and General Cognitive Function Are Associated With Measures of Cognition in Older South Asians.The journals of gerontology. Series A, Biological sciences and medical sciences · 2023Article
- A roadmap to increase diversity in genomic studies.Nature medicine · 2022Review
- Comprehensive Assessment of Indian Variations in the Druggable Kinome Landscape Highlights Distinct Insights at the Sequence, Structure and Pharmacogenomic Stratum.Frontiers in pharmacology · 2022Article
- Ethnic Diversity and Warfarin Pharmacogenomics.Frontiers in pharmacology · 2022Review
- Article
- Genotypes of informative loci from 1000 Genomes data allude evolution and mixing of human populations.Scientific reports · 2021Article
- Genetic ancestry plays a central role in population pharmacogenomics.Communications biology · 2021Article
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4 authors.
Funding
Abstract
Genomic variation in Indian populations is of great interest due to the diversity of ancestral components, social stratification, endogamy and complex admixture patterns. With an expanding population of 1.2 billion, India is also a treasure trove to catalogue innocuous as well as clinically relevant rare mutations. Recent studies have revealed four dominant ancestries in populations from mainland India: Ancestral North-Indian (ANI), Ancestral South-Indian (ASI), Ancestral Tibeto-Burman (ATB) and Ancestral Austro-Asiatic (AAA). The 1000 Genomes Project (KGP) Phase-3 data include about 500 genomes from five linguistically defined Indian-Subcontinent (IS) populations (Punjabi, Gujrati, Bengali, Telugu and Tamil) some of whom are recent migrants to USA or UK. Comparative analyses show that despite the distinct geographic origins of the KGP-IS populations, the ANI component is predominantly represented in this dataset. Previous studies demonstrated population substructure in the HapMap Gujrati population, and we found evidence for additional substructure in the Punjabi and Telugu populations. These substructured populations have characteristic/significant differences in heterozygosity and inbreeding coefficients. Moreover, we demonstrate that the substructure is better explained by factors like differences in proportion of ancestral components, and endogamy driven social structure rather than invoking a novel ancestral component to explain it. Therefore, using language and/or geography as a proxy for an ethnic unit is inadequate for many of the IS populations. This highlights the necessity for more nuanced sampling strategies or corrective statistical approaches, particularly for biomedical and population genetics research in India.
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