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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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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Related Experiment Video

Updated: May 10, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Efficient network-guided multi-locus association mapping with graph cuts.

Chloé-Agathe Azencott1, Dominik Grimm, Mahito Sugiyama

  • 1Machine Learning and Computational Biology Research Group, Max Planck Institute for Developmental Biology & Max Planck Institute for Intelligent Systems Spemannstr 38, 72076 Tübingen, Germany. chloe-agathe.azencott@tuebingen.mpg.de

Bioinformatics (Oxford, England)
|July 2, 2013
PubMed
Summary
This summary is machine-generated.

We developed SConES, an efficient method to identify connected sets of genetic loci associated with complex traits. This approach improves upon existing multi-locus mapping methods by integrating network information for better phenotype prediction.

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Last Updated: May 10, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

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Published on: July 27, 2021

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) increasingly highlight the limitations of single-locus analyses for complex traits.
  • Existing multi-locus mapping methods struggle to integrate biological pathway and network information effectively.
  • Current network-aware methods are often limited in scope or do not scale to genome-wide datasets.

Purpose of the Study:

  • To develop an efficient computational method for identifying sets of genetically linked loci associated with complex phenotypes.
  • To overcome the scalability and integration limitations of existing multi-locus mapping approaches.
  • To leverage biological network structures for enhanced genetic association analysis.

Main Methods:

  • SConES (Sparse and Connected Environmental Selection) utilizes a minimum cut reformulation for feature selection under sparsity and connectivity constraints.
  • The method is designed for exact and rapid computation, enabling scalability to large genetic datasets.
  • It integrates genetic loci with underlying biological network information.

Main Results:

  • SConES demonstrates superior performance compared to state-of-the-art methods in terms of computational runtime.
  • The method scales efficiently to hundreds of thousands of genetic loci.
  • In simulation studies, SConES showed higher power in detecting causal single nucleotide polymorphisms (SNPs).
  • Application to Arabidopsis thaliana flowering time data identified relevant loci supporting literature findings and enabling accurate phenotype prediction.

Conclusions:

  • SConES provides an efficient and scalable solution for identifying network-connected genetic loci associated with complex traits.
  • The method effectively integrates biological network information into multi-locus association analysis.
  • SConES enhances the discovery of biologically relevant genetic factors for complex phenotypes.