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Perception and preferences for food and beverages determine dietary behaviour and health outcomes. Inherent differences in chemosensory genes, ethnicity, geo-climatic conditions, and sociocultural practices are other determinants. We aimed to study the variation landscape of chemosensory genes involved in perception of taste, texture, odour, temperature and burning sensations through analysis of 1,029 genomes of the IndiGen project and diverse continental populations. SNPs from 80 chemosensory genes were studied in whole genomes of 1,029 IndiGen samples and 2054 from the 1000 Genomes project. Population genetics approaches were used to infer ancestry of IndiGen individuals, gene divergence and extent of differentiation among studied populations. 137,760 SNPs including common and rare variants were identified in IndiGenomes with 62,950 novel (46%) and 48% shared with the 1,000 Genomes. Genes associated with olfaction harbored most SNPs followed by those associated with differences in perception of salt and pungent tastes. Across species, receptors for bitter taste were the most diverse compared to others. Three predominant ancestry groups within IndiGen were identified based on population structure analysis. We also identified 1,184 variants that exhibit differences in frequency of derived alleles and high population differentiation (FST ≥0.3) in Indian populations compared to European, East Asian and African populations. Examples include ADCY10, TRPV1, RGS6, OR7D4, ITPR3, OPRM1, TCF7L2, and RUNX1. This is a first of its kind of study on baseline variations in genes that could govern cuisine designs, dietary preferences and health outcomes. This would be of enormous utility in dietary recommendations for precision nutrition both at population and individual level.

Host genetic variants can determine their susceptibility to COVID-19 infection and severity as noted in a recent Genome-wide Association Study (GWAS). Given the prominent genetic differences in Indian sub-populations as well as differential prevalence of COVID-19, here, we compute genetic risk scores in diverse Indian sub-populations that may predict differences in the severity of COVID-19 outcomes. We utilized the top 100 most significantly associated single-nucleotide polymorphisms (SNPs) from a GWAS by Pairo-Castineira et al. determining the genetic susceptibility to severe COVID-19 infection, to compute population-wise polygenic risk scores (PRS) for populations represented in the Indian Genome Variation Consortium (IGVC) database. Using a generalized linear model accounting for confounding variables, we found that median PRS was significantly associated (p < 2 x 10−16) with COVID-19 mortality in each district corresponding to the population studied and had the largest effect on mortality (regression coefficient = 10.25). As a control we repeated our analysis on randomly selected 100 non-associated SNPs several times and did not find significant association. Therefore, we conclude that genetic susceptibility may play a major role in determining the differences in COVID-19 outcomes and mortality across the Indian sub-continent. We suggest that combining PRS with other observed risk-factors in a Bayesian framework may provide a better prediction model for ascertaining high COVID-19 risk groups and to design more effective public health resource allocation and vaccine distribution schemes.