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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...
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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Regionally Smoothed Meta-Analysis Methods for GWAS Datasets.

Ferdouse Begum1, Monir H Sharker2, Stephanie L Sherman3

  • 1Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.

Genetic Epidemiology
|December 29, 2015
PubMed
Summary

This study introduces a new regionally smoothed meta-analysis method to improve the detection of genetic effects in genome-wide association studies. The method enhances statistical power by smoothing signals across nearby genetic markers, especially when combining diverse cohorts.

Keywords:
GWAS meta-analysissimulationsliding-windowwindow-based method

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) are crucial for identifying disease-associated genes.
  • Meta-analysis of multiple cohorts is often required to achieve statistical significance in GWAS.
  • Challenges in meta-analysis include heterogeneous study designs, differing genotyping platforms, limited SNP overlap, and unavailability of raw data for imputation.

Purpose of the Study:

  • To develop and evaluate a novel meta-analysis approach that enhances statistical power in GWAS.
  • To address the issue of discordant association signals across different cohorts due to population-specific linkage disequilibrium or environmental factors.
  • To improve the detection of true genetic associations when only summary statistics from genotyped single nucleotide polymorphisms (SNPs) are available.

Main Methods:

  • Development of regionally smoothed meta-analysis methods.
  • Application and comparison of these methods using both simulated and real genetic data.
  • Evaluation of performance in scenarios with limited SNP overlap and no raw data for imputation.

Main Results:

  • The proposed regionally smoothed meta-analysis methods demonstrate improved power to detect genetic associations compared to traditional methods.
  • The smoothing approach effectively integrates signals from nearby markers, mitigating issues caused by differing peak SNP locations across cohorts.
  • Performance was validated on both simulated datasets and real-world GWAS data.

Conclusions:

  • Regionally smoothed meta-analysis is a powerful tool for increasing the sensitivity of GWAS, particularly when combining data from diverse or incomplete cohorts.
  • This method offers a robust solution for meta-analysis when imputation is not feasible and SNP sets vary.
  • The findings suggest a valuable enhancement for genetic association studies aiming to identify complex disease genes.