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Genome-wide polygenic risk scores (PRS) for lifestyle disorders, like Type 2 Diabetes (T2D), are useful in identifying at-risk individuals early on in life, and to guide them towards healthier lifestyles. The current study was aimed at developing PRS for the Indian population using imputed genotype data from UK Biobank and testing the developed PRS on data from GenomegaDB of Indians living in India. 959 T2D cases and 2,818 controls were selected from Indian participants of UK Biobank to develop the PRS. Summary statistics available for South Asians, from the DIAMANTE consortium, were used to weigh genetic variants. LDpred2 algorithm was used to adjust the effect of linkage disequilibrium among the variants. The association of PRS with T2D, after adjusting for age, sex and top ten genetic principal components, was found to be very significant (AUC = 0.7953, OR = 2.9856 [95% CI: 2.7044-3.2961]). When participants were divided into four PRS quartile groups, the odds of developing T2D increased sequentially with the higher PRS groups. The highest PRS group (top 25%) showed 5.79 fold increased risk compared to the rest of the participants (75%). The PRS derived using the same set of variants was found to be significantly associated with T2D in the test dataset of 445 Indians (AUC = 0.7781, OR = 1.6656 [95%CI = 0.6127-4.5278]). Our study demonstrates a framework to derive Indian-specific PRS for T2D. The accuracy of the derived PRS shows it's potential to be used as a prognostic metric to stratify individuals, and to recommend personalized preventive strategies.
Asunto(s)
Diabetes Mellitus Tipo 2 , Humanos , Diabetes Mellitus Tipo 2/epidemiología , Diabetes Mellitus Tipo 2/genética , Predisposición Genética a la Enfermedad , Estudio de Asociación del Genoma Completo , Factores de Riesgo , Genotipo , Herencia Multifactorial/genéticaRESUMEN
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.
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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.
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The wild-type p53-induced phosphatase 1 (WIP1) is a serine/threonine phosphatase that negatively regulates multiple proteins involved in DNA damage response including p53, CHK2, Histone H2AX, and ATM, and it has been shown to be overexpressed or amplified in human cancers including breast and ovarian cancers. We examined WIP1 mRNA levels across multiple tumor types and found the highest levels in breast cancer, leukemia, medulloblastoma and neuroblastoma. Neuroblastoma is an exclusively TP53 wild type tumor at diagnosis and inhibition of p53 is required for tumorigenesis. Neuroblastomas in particular have previously been shown to have 17q amplification, harboring the WIP1 (PPM1D) gene and associated with poor clinical outcome. We therefore sought to determine whether inhibiting WIP1 with a selective antagonist, GSK2830371, can attenuate neuroblastoma cell growth through reactivation of p53 mediated tumor suppression. Neuroblastoma cell lines with wild-type TP53 alleles were highly sensitive to GSK2830371 treatment, while cell lines with mutant TP53 were resistant to GSK2830371. The majority of tested neuroblastoma cell lines with copy number gains of the PPM1D locus were also TP53 wild-type and sensitive to GSK2830371A; in contrast cell lines with no copy gain of PPM1D were mixed in their sensitivity to WIP1 inhibition, with the primary determinant being TP53 mutational status. Since WIP1 is involved in the cellular response to DNA damage and drugs used in neuroblastoma treatment induce apoptosis through DNA damage, we sought to determine whether GSK2830371 could act synergistically with standard of care chemotherapeutics. Treatment of wild-type TP53 neuroblastoma cell lines with both GSK2830371 and either doxorubicin or carboplatin resulted in enhanced cell death, mediated through caspase 3/7 induction, as compared to either agent alone. Our data suggests that WIP1 inhibition represents a novel therapeutic approach to neuroblastoma that could be integrated with current chemotherapeutic approaches.