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Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Updated: Jun 14, 2025

Evaluation of Hepatic Glucose Production in a Polycystic Ovary Syndrome Mouse Model

Evaluation of Hepatic Glucose Production in a Polycystic Ovary Syndrome Mouse Model

Published on: March 5, 2022

Integrated data driven analysis identifies potential candidate genes associated with PCOS.

Shaini Joseph¹, Krutika Patil², Niharika Rahate¹

¹Genetic Research Center, ICMR-National Institute for Research in Reproductive and Child Health, J.M. Street, Parel, Mumbai 400012, India.

Computational Biology and Chemistry

|September 7, 2024

Summary

This summary is machine-generated.

This study integrated PCOS data to identify novel candidate genes, finding PROS1, C1QA, and KNG1 downregulated in affected women, offering insights into polycystic ovary syndrome pathophysiology.

Keywords:

Complement and coagulation cascade Gene prioritization Microarray analysis Network analysis Ovary PCOS

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Last Updated: Jun 14, 2025

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Published on: March 5, 2022

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Genomics
Molecular Biology
Reproductive Endocrinology

Background:

Polycystic ovary syndrome (PCOS) is a leading cause of anovulatory infertility.
Existing PCOS research has generated extensive data, yet etiological understanding remains incomplete.
Integrated analysis of PCOS data can identify novel candidate genes for its pathophysiology.

Purpose of the Study:

To integrate literature and gene expression data for PCOS.
To apply gene prioritization and network analysis for novel candidate gene discovery.
To assess evidence-based scoring for ranking PCOS-associated genes.

Main Methods:

Integrated evidence-based approach combining literature and gene expression data.
Gene prioritization and network analysis for candidate gene identification.
In silico and experimental validation of candidate genes, including protein level analysis in follicular fluid.

Main Results:

Identified approximately 2000 plausible candidate genes associated with PCOS.
In silico validation confirmed 938 genes involved in PCOS.
Experimental validation showed PROS1, C1QA, and KNG1 downregulation in PCOS patients, while VTN expression was unchanged.

Conclusions:

In silico approaches are effective for identifying and prioritizing candidate genes in complex diseases like PCOS.
The study provides clearer insights into PCOS molecular mechanisms by adding to the repertoire of PCOS-associated genes.
Findings highlight the role of complement and coagulation pathways in PCOS pathogenesis.