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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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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.
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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Updated: Dec 12, 2025

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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A framework for pathway knowledge driven prioritization in genome-wide association studies.

Shrayashi Biswas1, Soumen Pal1, Partha P Majumder1

  • 1National Institute of Biomedical Genomics, Kalyani, India.

Genetic Epidemiology
|August 12, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces GKnowMTest, a novel method for genome-wide association studies (GWAS) that prioritizes variants using gene pathway knowledge. This approach enhances the detection of low-frequency variants and improves overall study power.

Keywords:
GWASLASSO penaltylogistic regressionpathwaysprioritizationweighted p values

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) often miss variants with low frequencies or modest effects due to stringent detection thresholds.
  • Prioritizing variants using functional annotations and epigenetic data has improved GWAS power.
  • Gene-level knowledge, such as pathway and ontology annotations, offers a promising avenue for further enhancing variant detection.

Purpose of the Study:

  • To develop and validate a novel method for prioritizing variants in GWAS by leveraging gene-level pathway knowledge.
  • To improve the power of GWAS in detecting low-frequency or low-effect variants.
  • To introduce a computationally scalable framework for pathway-guided GWAS scans.

Main Methods:

  • Developed GKnowMTest (Genomic Knowledge-guided Multiple Testing), a framework using penalized logistic regression for prioritized pathway-guided GWAS.
  • The method incorporates gene-level pathway annotations and adaptively reweights p-values using cross-validation to avoid overfitting.
  • Applied the framework to genome-wide summary statistics and user-specified pathway lists, working with large-scale gene annotations.

Main Results:

  • Demonstrated that the GKnowMTest strategy significantly improves the overall power of GWAS.
  • Showed that the method effectively maintains the global Type 1 error rate.
  • Validated the approach using whole-genome simulations and publicly available GWAS datasets.

Conclusions:

  • GKnowMTest offers a powerful and statistically sound approach to enhance variant discovery in GWAS by integrating gene pathway information.
  • The method is computationally scalable and applicable to large datasets, providing an open-source R package for broader use.
  • Leveraging gene-level knowledge represents a significant advancement in maximizing the utility of GWAS data for identifying disease-associated variants.