Searching for epistatic interactions in nuclear families using conditional linkage analysis

Area of Science:

• Genetics
• Statistical genetics
• Bioinformatics

Background:

• Genomic screens typically use single-locus linkage analysis, which may lack power for epistatic interactions.
• Ordered subsets analysis (OSA) offers a method for conditional linkage analysis using continuous covariates.

Purpose of the Study:

• To evaluate the effectiveness of Ordered Subsets Analysis (OSA) in detecting two-locus interactions.
• To assess the performance of OSA in a simulated dataset with known epistatic effects.

Main Methods:

• Utilized OSA on the simulated Genetic Analysis Workshop 14 dataset, including nuclear families.
• Performed multipoint affected-sibling-pair (ASP) linkage analysis and then applied OSA using LOD scores as covariates.
• Investigated two methods for identifying positive results within OSA, including correction for multiple comparisons.

Main Results:

• Single-locus linkage analysis identified strong evidence for disease loci.
• OSA failed to detect the simulated two-locus interactions.
• Inflated Type I error rates were observed with initial OSA methods, necessitating a corrected alpha-level calculation.

Conclusions:

• OSA was unable to detect simulated two-locus interactions, potentially due to limitations in covariate choice or phenotypic subgroup incorporation.
• A corrected alpha-level was determined, providing a basis for future OSA applications in detecting epistasis.
• The study highlights challenges in identifying complex genetic interactions using current OSA methodologies.