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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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Robust methods for population stratification in genome wide association studies.

Li Liu1, Donghui Zhang, Hong Liu

  • 1Department of Biostatistics and Programming, Mail Stop 55C-305A, 55 Corporate Drive, Sanofi, Bridgewater, NJ 08807, USA. li.liu@sanofi.com

BMC Bioinformatics
|April 23, 2013
PubMed
Summary

Robust principal component analysis (PCA) with k-medoids clustering effectively corrects population stratification in genome-wide association studies (GWAS), even with subject outliers. This method improves accuracy in genetic association analyses.

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) are crucial for understanding disease and treatment response.
  • Population stratification, due to ancestry differences, can lead to spurious associations in GWAS.
  • Existing methods like PCA and linear mixed models (LMM) may not adequately correct for sample structures with outliers.

Purpose of the Study:

  • To develop and evaluate robust methods for correcting population stratification in GWAS.
  • To address the challenge of subject outliers that can compromise existing stratification correction methods.

Main Methods:

  • Proposed a novel approach combining robust PCA with k-medoids clustering.
  • Extended existing Principal Component Analysis (PCA) and Multidimensional Scaling (MDS) methodologies.
  • Compared the proposed robust methods against PCA and MDS using simulation studies.

Main Results:

  • Subject outliers significantly impact analysis results from current stratification methods.
  • The proposed robust PCA and k-medoids clustering method effectively handles population stratification in discrete and admixed populations with outliers.
  • Demonstrated superior performance of the robust methods compared to existing approaches in simulations.

Conclusions:

  • Subject outliers pose a significant challenge in GWAS analysis.
  • The developed robust methods provide superior correction for population stratification in the presence of subject outliers.
  • These findings enhance the reliability of GWAS by improving the adjustment for population structure.