A multi-SNP association test for complex diseases incorporating an optimal P-value threshold algorithm in nuclear...

Area of Science:

• Genetics and Genomics
• Statistical Genetics
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) identify single nucleotide polymorphisms (SNPs) linked to complex diseases.
• Complex diseases involve multiple genes, necessitating methods that analyze joint SNP effects for increased power.
• Selecting informative subsets of SNPs from biologically defined sets is crucial for multi-SNP analysis.

Purpose of the Study:

• To develop a novel statistical test for multi-SNP association analysis in family-based GWAS.
• To enhance the power of gene-based and pathway-based association studies.
• To provide an effective tool for secondary analysis of existing GWAS datasets.

Main Methods:

• Developed the Optimal P-value Threshold Pedigree Disequilibrium Test (OPTPDT) for general nuclear families.
• Utilized a variable p-value threshold algorithm to select optimal SNP subsets.
• Employed permutation testing for significance assessment and simulations for validation.

Main Results:

• OPTPDT demonstrated correct Type I error rates in simulations.
• Power studies indicated OPTPDT outperforms existing methods like PLINK set-based tests, multi-SNP FBAT, and GATES.
• Applied to an autism GWAS dataset, OPTPDT identified MACROD2-AS1 with genome-wide significance (p=2.5×10⁻⁶).

Conclusions:

• OPTPDT is a statistically valid and powerful method for gene-based and pathway association analysis.
• The method is well-suited for secondary analysis of GWAS data to detect joint SNP effects.
• OPTPDT offers improved power for identifying genetic associations with complex diseases.