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Mutational Landscape Screening Through Comprehensive In Silico Analysis for Polycystic Ovarian Syndrome–Related Genes

  • Genetics: Original Article
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Abstract

Polycystic ovary syndrome (PCOS) is a multifactorial endocrinopathy of indistinguishable etiopathogenesis that is liable to entail genetic and environmental machinery synergistically interacting with its phenotypic expression. It has been hypothesized that the environment secondarily interacts with genes to define the quantifiable phenotype in a primary, genetically determined, hyper-androgenic ovarian defect. The severity and prevalence of the disease are escalating due to uncontrolled diet and lifestyle, the influence of multiple environmental factors as well as genetic disorders. Many candidate genes have been identified to be one of the causes of PCOS. Different studies have been carried out to find the genetic correlation of PCOS. The mutational landscape analysis scans the entire genes for SNPs which usually occurs more frequently in patients and not in healthy individuals. In this study, an extensive computational analysis of all reported nsSNPs of the 27 selected PCOS-related genes was performed to infer the most pathogenic forms associated with PCOS. As a result, 28 genetic variants from 11 genes were predicted to be most harmful. Results of the present study can be useful for building an integrative genotype–phenotype database for further studies.

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Authors and Affiliations

  1. Department of Environmental Science, University of Calcutta, 35, Ballygunge Circular Road, Kolkata, 700019, India

    Shrinjana Dhar, Saptarshi Mridha & Pritha Bhattacharjee

  2. Department of Biotechnology, St. Xavier’s College (Autonomous), 30, Mother Teresa Sarani, Kolkata, 700016, India

    Saptarshi Mridha

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SD: Study design, Data analysis, Manuscript writing; SM: Data mining, Data analysis, Manuscript writing; PB: Study design, Manuscript editing & Supervision of the experiment.

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Dhar, S., Mridha, S. & Bhattacharjee, P. Mutational Landscape Screening Through Comprehensive In Silico Analysis for Polycystic Ovarian Syndrome–Related Genes. Reprod. Sci. 29, 480–496 (2022). https://doi.org/10.1007/s43032-021-00752-7

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