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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: Sep 2, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

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Datasets for a simulated family-based exome-sequencing study.

Nirodha Epasinghege Dona1, Jinko Graham1

  • 1Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby V5A 1S6, Canada.

Data in Brief
|August 5, 2022
PubMed
Summary
This summary is machine-generated.

We generated simulated exome-sequencing data for 150 families with lymphoid cancer. This dataset aids in identifying genomic patterns and disease inheritance in families with multiple affected members.

Keywords:
Ascertained pedigreesExome sequencingFamily studiesLymphoid cancer

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Area of Science:

  • Genomics
  • Cancer Research
  • Population Genetics

Background:

  • Lymphoid cancers represent a significant health burden.
  • Understanding familial aggregation of cancer is crucial for genetic research.
  • Admixed populations present unique challenges and opportunities for genetic studies.

Purpose of the Study:

  • To create a simulated dataset of exome-sequencing data for 150 North American admixed families.
  • To facilitate research into genomic patterns and inheritance of lymphoid cancer in multi-affected families.
  • To provide accessible resources for the scientific community.

Main Methods:

  • Simulation of exome-sequencing data for 150 families.
  • Inclusion of single-nucleotide variants for affected family members.
  • Development and provision of associated software scripts for data generation.

Main Results:

  • Generated a comprehensive dataset of simulated exome-sequencing data.
  • Data includes family ascertainment and variant information for affected individuals.
  • The simulation process and scripts are documented and shared.

Conclusions:

  • The simulated data are valuable for studying genomic patterns in families with multiple lymphoid cancer cases.
  • This resource can advance the understanding of complex disease inheritance.
  • Facilitates future research in cancer genetics and population studies.