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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.
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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

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BLOCK-BASED BAYESIAN EPISTASIS ASSOCIATION MAPPING WITH APPLICATION TO WTCCC TYPE 1 DIABETES DATA.

By Yu Zhang¹, Jing Zhang, Jun S Liu

¹Department of Statistics, Pennsylvania State University, 422A Thomas, University Park, Pennsylvania 16802, USA.

The Annals of Applied Statistics

|December 6, 2011

Summary

This study introduces a new Bayesian method to analyze gene interactions for complex diseases. The approach effectively identifies disease-associated genetic markers, improving our understanding of genetic risk factors.

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Area of Science:

Genetics and Genomics
Statistical Bioinformatics
Computational Biology

Background:

Complex human diseases often result from intricate interactions among multiple genes across the genome.
Whole-genome single nucleotide polymorphism (SNP) data offers potential for understanding these interactions but faces challenges due to high correlation among nearby SNPs (linkage disequilibrium, LD) and the vast number of possible interactions.

Purpose of the Study:

To develop a novel Bayesian method for simultaneously partitioning SNPs into LD-blocks and selecting disease-associated SNPs within these blocks.
To accurately identify individual and interactive SNP associations with disease while accounting for LD.
To enhance the power of detecting multi-locus associations compared to existing methods.

Main Methods:

A Bayesian approach is proposed for simultaneous LD-block partitioning and SNP selection.
The method calculates posterior probabilities for LD-block boundaries, providing accurate partitions and uncertainty measures.
It implicitly filters SNP associations driven solely by LD with disease loci within the same blocks.

Main Results:

Simulation studies demonstrated superior power in detecting multi-locus associations compared to other tested methods.
Application to type 1 diabetes data identified known associated genes (PTPN22, CTLA4, MHC, IL2RA) and novel two-way associations missed by single SNP methods.
Significant associations were concentrated in the MHC region, revealing long-distance joint associations and interactions between genes in extended MHC regions and MHC class II genes.

Conclusions:

The proposed Bayesian method effectively addresses challenges of LD in whole-genome association studies.
It accurately partitions SNPs into LD-blocks and identifies complex gene-gene interactions contributing to disease risk.
The method shows broad applicability to classification problems involving correlated discrete covariates.