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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...
16.6K

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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Integrative approaches for large-scale transcriptome-wide association studies.

Alexander Gusev1,2,3, Arthur Ko4,5, Huwenbo Shi6

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

Nature Genetics
|February 9, 2016
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Summary

This study introduces a new method combining gene expression and genome-wide association studies (GWAS) to find genes linked to complex traits. The approach identified 69 novel genes associated with obesity and related traits.

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Area of Science:

  • Genetics
  • Genomics
  • Molecular Biology

Background:

  • Complex traits are influenced by numerous genetic variants affecting gene expression and protein levels.
  • Identifying genes associated with complex traits is crucial for understanding disease mechanisms.

Purpose of the Study:

  • To develop and apply a powerful strategy integrating gene expression data with genome-wide association studies (GWAS) summary statistics.
  • To identify genes with cis-regulated expression linked to complex traits, specifically obesity-related phenotypes.

Main Methods:

  • Leveraged expression imputation from genetic data to perform transcriptome-wide association studies (TWAS).
  • Integrated gene expression measurements from blood and adipose tissue (∼3,000 individuals) with large-scale GWAS data (>900,000 phenotype measurements).

Main Results:

  • Identified 69 novel genes significantly associated with obesity-related traits, including body mass index (BMI), lipids, and height.
  • Validated associations for many identified genes with relevant phenotypes in the Hybrid Mouse Diversity Panel.
  • Demonstrated the utility of integrating genotype, gene expression, and phenotype data.

Conclusions:

  • The developed transcriptome-wide association study (TWAS) approach is effective for discovering genes underlying complex traits.
  • This integrative strategy provides valuable insights into the genetic architecture of obesity and related phenotypes.
  • The findings highlight the importance of cis-regulated gene expression in complex trait variation.