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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.
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Related Experiment Video

Updated: May 27, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Published on: June 23, 2012

Detecting rare variant associations: methods for testing haplotypes and multiallelic genotypes.

Rita M Cantor1, Marsha Wilcox

  • 1Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095-7088, USA. rcantor@mednet.ucla.edu

Genetic Epidemiology
|December 1, 2011
PubMed
Summary

Researchers analyzed genetic variants and single-nucleotide polymorphisms (SNPs) using diverse methods for simulated traits. Multiallelic approaches effectively identified associated genes, highlighting the impact of inheritance models and genetic entity selection.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genetic Analysis Workshop 17 (GAW17) provided simulated data for analyzing genetic associations.
  • Group 13 contributors explored various analytical strategies for complex genetic traits.

Purpose of the Study:

  • To synthesize and compare diverse analytical approaches used by Group 13 for GAW17 data.
  • To evaluate methods for testing associations of rare variants and common single-nucleotide polymorphisms (SNPs) with simulated traits.

Main Methods:

  • Contributors employed a range of statistical methods, incorporating multiallelic genotypes and haplotypes.
  • Genetic entities were selected and coded variably, including sliding windows, haplotypes, and gene-based approaches.
  • Analyses focused on identifying associations between genetic variants and simulated disease traits.

Main Results:

  • Most methods successfully identified two associated genes for each simulated trait.
  • The top gene was often associated with the most causal alleles, while the second was linked to a high-frequency causal SNP.
  • Multiallelic analytic approaches were consistently favored for potentially increasing statistical power.

Conclusions:

  • The choice of genetic entities and inheritance models significantly influences the outcomes of genetic association analyses.
  • Diverse analytical strategies, particularly those leveraging multiallelic information, show promise in identifying trait-associated genes.
  • Flexibility and creativity in coding and evaluating genetic variants are crucial for robust analysis.