Today, genomic data holds great potential to improve healthcare strategies across various dimensions-be it disease prevention, enhanced diagnosis, or optimized treatment. The biggest hurdle faced by the medical and research community in India is the lack of genotype-phenotype correlations for Indians at a population-wide and an individual level. This leads to inefficient translation of genomic information during clinical decision making. Population-wide sequencing projects for Indian genomes help overcome hurdles and enable us to unearth and validate the genetic markers for different health conditions. Machine learning algorithms are essential to analyze huge amounts of genotype data in synergy with gene expression, demographic, clinical, and pathological data. Predictive models developed through these algorithms help in classifying the individuals into different risk groups, so that preventive measures and personalized therapies can be designed. They also help in identifying the impact of each genetic marker with the associated condition, from a clinical perspective. In India, genome sequencing technologies have now become more accessible to the general population. However, information on variants associated with several major diseases is not available in publicly-accessible databases. Creating a centralized database of variants facilitates early detection and mitigation of health risks in individuals. In this article, we discuss the challenges faced by genetic researchers and genomic testing facilities in India, in terms of dearth of public databases, people with knowledge on machine learning algorithms, computational resources and awareness in the medical community in interpreting genetic variants. Potential solutions to enhance genomic research in India, are also discussed.

Introduction:The adiponectin gene, ADIPOQ, encodes an adipocytokine, known as adiponectin hormone. This hormone is known to be associated with insulin sensitization, fat metabolism, immunity, and inflammatory response. Polymorphisms in ADIPOQ gene lower the adiponectin levels, increasing the risk for diabetes and cardiovascular diseases.Aims:The study aimed to calculate the prevalence rates of ADIPOQ polymorphisms in Indian population and to compare those prevalence rates with that of other populations.Subjects and Methods:Microarray-based genotypic data of 14 ADIPOQ polymorphisms from 703 individuals of Indian origin were used.Statistical Analysis Used:Frequency estimation, identity-by-descent, Hardy–Weinberg equilibrium, Chi-square test of significance were used for statistical analysis.Results:Allelic and genotypic frequencies of ADIPOQ polymorphisms, Chi-square tests of significance for allelic and genotypic frequencies across various populations.Conclusions:East Asians are very different from Indians in terms of allelic and genotypic frequencies of ADIPOQ polymorphisms. Europeans have similar genotypic and allelic patterns with Indians. Admixture Americans and Africans also showed significant differences with polymorphisms of the Indian population.