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Related Concept Videos

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...
16.0K

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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Fine mapping by composite genome-wide association analysis.

Joaquim Casellas1, Jhon Jacobo Cañas-Álvarez2, Marta Fina2

  • 1Grup de Recerca en Millora Genètica Molecular Veterinària,Departament de Ciència Animal i dels Aliments,Universitat Autònoma de Barcelona,08193 Bellaterra,Spain.

Genetics Research
|June 7, 2017
PubMed
Summary
This summary is machine-generated.

Genome-wide association (GWA) studies can be improved using a composite GWA approach. This method enhances precision in locating causal mutations by accounting for linked genetic markers, though it may slightly reduce statistical power.

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Area of Science:

  • Genetics and genomics
  • Statistical genetics
  • Bioinformatics

Background:

  • Genome-wide association (GWA) studies identify genomic regions linked to phenotypic traits.
  • Linkage disequilibrium (LD) can confound GWA results, especially with significant markers spanning large genomic regions.
  • Accurate identification of causal mutations is crucial for understanding trait heritability.

Purpose of the Study:

  • To adapt the composite interval mapping approach for GWA studies (composite GWA).
  • To evaluate the impact of including linked genetic markers on GWA analysis.
  • To assess the precision and power of the composite GWA model in identifying causal mutations.

Main Methods:

  • Adaptation of composite interval mapping to the GWA framework.
  • Testing model performance using simulated data under various scenarios.
  • Evaluation of factors including quantitative trait loci (QTL) effects, LD, and genomic region width.

Main Results:

  • The genomic region width had minimal impact on competing single nucleotide polymorphism (SNP) numbers and composite GWA precision.
  • Moderate-to-high LD between SNPs attenuated the loss of statistical power.
  • Composite GWA improved specificity and reduced the number of significant genetic markers identified.

Conclusions:

  • Composite GWA offers a novel approach for GWA analyses.
  • Conditioning on linked markers and focusing on delimited genomic regions increases precision in locating causal mutations.
  • The composite GWA model may increase precision at the potential expense of statistical power.