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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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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.
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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Updated: Nov 3, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Multitrait transcriptome-wide association study (TWAS) tests.

Helian Feng1, Nicholas Mancuso2,3, Bogdan Pasaniuc4,5

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

Genetic Epidemiology
|June 3, 2021
PubMed
Summary
This summary is machine-generated.

Multitrait transcriptome-wide association (TWAS) tests enhance the power to detect genetic associations with multiple related traits. This new framework significantly increases the number of identified genes compared to single-trait methods.

Keywords:
GWASTWASmultitrait genetic association testsmultivariate testing

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Multitrait tests increase the power to detect associations between single-nucleotide polymorphisms (SNPs) and multiple related traits.
  • Transcriptome-wide association studies (TWAS) test associations between predicted gene expression and phenotypes.
  • Extending multitrait methods to the multi-SNP TWAS setting is a logical advancement.

Purpose of the Study:

  • To develop and evaluate multi-SNP TWAS methods for testing associations with multiple phenotypes simultaneously.
  • To assess the statistical power and Type I error rates of various multitrait TWAS approaches.
  • To demonstrate the utility of multitrait TWAS in real-world genetic studies.

Main Methods:

  • Developed methods for multi-SNP TWAS by extending established multitrait statistical frameworks.
  • Evaluated eight multitrait TWAS methods using simulations with varying gene-phenotype effects and phenotype covariance structures.
  • Applied the multitrait Wald TWAS approach to analyze four circulating lipid traits from the Global Lipids Genetics Consortium.

Main Results:

  • Multitrait TWAS tests demonstrated well-calibrated Type I error rates across most methods.
  • Multitrait TWAS significantly improved statistical power compared to single-trait TWAS followed by Bonferroni correction.
  • The multitrait Wald TWAS identified 506 genes associated with lipid levels, versus 87 genes using single-trait methods.

Conclusions:

  • Multitrait TWAS provides a powerful framework for identifying genetic associations with multiple phenotypes.
  • This approach outperforms single-trait methods, particularly for functionally correlated traits and studies with overlapping samples.
  • Multitrait TWAS offers valuable insights into the genetic architecture underlying complex, multiple traits.