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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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.
GWAS does not require the identification of the target gene involved in...
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Related Experiment Video

Updated: Oct 10, 2025

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

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A Random Walk-Based Method to Identify Candidate Genes Associated With Lymphoma.

Minjie Sheng1, Haiying Cai1, Qin Yang1

  • 1Department of Ophthalmology, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, China.

Frontiers in Genetics
|December 13, 2021
PubMed
Summary

Researchers identified novel genes linked to lymphoma using a random walk algorithm. This method helps understand lymphoma

Keywords:
enrichment theorylymphomapermutation testprotein-protein interaction networkrandom walk with restart algorithm

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Area of Science:

  • Oncology
  • Bioinformatics
  • Genetics

Background:

  • Lymphoma is a rare but serious cancer, particularly affecting adolescents and elder adults.
  • The exact causes of lymphoma remain unclear, but genetic factors are strongly implicated in its development.
  • Identifying genes associated with lymphoma is crucial for understanding its pathogenesis.

Purpose of the Study:

  • To develop and apply a novel computational method for inferring new genes associated with lymphoma.
  • To identify candidate genes involved in the initiation and progression of lymphoma.

Main Methods:

  • A random walk-based algorithm with restart was employed on a protein-protein interaction network.
  • Initial candidate genes were identified from a dataset of 1,458 known lymphoma-associated genes.
  • Permutation, linkage, and enrichment tests were used to filter and validate candidate genes, controlling for false positives.

Main Results:

  • A total of 108 novel genes inferred to be associated with lymphoma were identified.
  • Key genes such as RAC3, TEC, IRAK2/3/4, PRKCE, SMAD3, BLK, TXK, and PRKCQ were highlighted.
  • These genes showed strong linkages and potential roles in lymphoma initiation and progression.

Conclusions:

  • The random walk-based method effectively identifies novel lymphoma-associated genes.
  • The study provides a validated list of 108 candidate genes for further research into lymphoma etiology.
  • Inferred genes like RAC3 and TEC may play significant roles in lymphoma development.