Genome-wide association studies using an adaptive two-stage analysis for a case-control design

Area of Science:

• Genetics
• Statistical genetics
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) are crucial for identifying genetic variants associated with diseases.
• Traditional GWAS methods can be computationally intensive and may lack power for detecting complex genetic architectures.
• Case-control study designs are commonly employed in GWAS.

Purpose of the Study:

• To introduce and evaluate a novel adaptive two-stage (ATS) analysis for genome-wide association studies.
• To assess the performance of ATS compared to other statistical methods in identifying trait susceptibility loci.
• To demonstrate the utility of ATS in a case-control study design using simulated genetic data.

Main Methods:

• The adaptive two-stage (ATS) analysis combines the Hardy-Weinberg disequilibrium trend test (HWDTT) and the Cochran-Armitage trend test (CATT).
• Stage 1 involves screening single-nucleotide polymorphisms (SNPs) using HWDTT.
• Stage 2 involves testing a reduced set of significant SNPs from Stage 1 using CATT.

Main Results:

• The ATS analysis successfully identified genomic regions containing trait loci on chromosome 6 in the Genetic Analysis Workshop 15 simulated data set.
• The identified regions were 32447.149 kb to 32859.819 kb and around 37363.880 kb, following Bonferroni correction.
• ATS demonstrated good performance and outperformed other methods, including Fisher's exact test variations, in detecting susceptibility loci.

Conclusions:

• The proposed adaptive two-stage (ATS) analysis is an effective method for genome-wide association studies.
• ATS offers improved power and efficiency in detecting genetic variants associated with traits, particularly in case-control studies.
• This approach provides a valuable tool for identifying complex genetic architectures and susceptibility loci.