Using single nucleotide polymorphisms to investigate association between a candidate gene and disease

Area of Science:

• Genetics
• Biostatistics
• Computational Biology

Background:

• Investigating the association between genetic variations and diseases is crucial for understanding disease mechanisms.
• Single nucleotide polymorphisms (SNPs) are common genetic variations studied for their role in disease susceptibility.
• Candidate gene approaches are frequently used to identify disease-associated variants.

Purpose of the Study:

• To evaluate the pattern of disease association for single nucleotide polymorphisms (SNPs) within a candidate gene using simulated data.
• To compare the effectiveness of different study designs in detecting SNP-disease associations.
• To identify factors influencing the strength of SNP-disease associations.

Main Methods:

• Simulated genetic data was generated for a candidate gene.
• Various study designs, including unrelated and family controls, were employed.
• Association analyses were performed for individual SNPs and short haplotypes.
• The influence of SNP allele frequency and distance from the functional locus was assessed.

Main Results:

• Strong evidence of disease association was detected at the functional locus across all study designs.
• The functional polymorphism showed higher association in a "general" population compared to an "isolated" population.
• SNP allele frequency and distance from the functional polymorphism explained up to 70% of the variation in association strength.
• Some common SNPs near the functional locus exhibited no disease association, a feature partially mitigated by haplotype analysis.

Conclusions:

• Different study designs can effectively detect SNP-disease associations at functional loci.
• Population structure and SNP characteristics significantly impact observed associations.
• Haplotype analysis can provide a more nuanced understanding of SNP-disease relationships than individual SNP analysis.