Abstract
Complex diseases are generally thought to be under the influence of multiple, and possibly interacting, genes. Many association methods have been developed to identify susceptibility genes assuming a single-gene disease model, referred to as single-locus methods. Multilocus methods consider joint effects of multiple genes and environmental factors. One commonly used method for family-based association analysis is implemented in FBAT. The multifactor-dimensionality reduction method (MDR) is a multilocus method, which identifies multiple genetic loci associated with the occurrence of complex disease. Many studies of late onset complex diseases employ a discordant sib pairs design. We compared the FBAT and MDR in their ability to detect susceptibility loci using a discordant sib-pair dataset generated from the simulated data made available to participants in the Genetic Analysis Workshop 14. Using FBAT, we were able to identify the effect of one susceptibility locus. However, the finding was not statistically significant. We were not able to detect any of the interactions using this method. This is probably because the FBAT test is designed to find loci with major effects, not interactions. Using MDR, the best result we obtained identified two interactions. However, neither of these reached a level of statistical significance. This is mainly due to the heterogeneity of the disease trait and noise in the data.
Highlights
It is commonly believed that complex diseases are caused not by single genes acting alone, but by multiple genes interacting with one another
Comparison of results from Family-based association tests (FBAT) and multifactor-dimensionality reduction (MDR) All of the 29 single-nucleotide polymorphisms (SNPs) were in Hardy-Weinberg Equilibrium (HWE) (p > 0.05), so all of them are included in the analyses
Our family-based analysis using FBAT did not result in a statistically significant association with any of the susceptibility loci. This may be because the algorithm is designed to find loci with main effects, not loci interacting with one another, which was the case in the Genetic Analysis Workshop 14 (GAW14) simulation dataset
Summary
It is commonly believed that complex diseases are caused not by single genes acting alone, but by multiple genes interacting with one another. Due to the large number of single-nucleotide polymorphisms (SNPs) available in a genome-wide scan, the computational burden of testing each locus for main effects and all possible pair-wise, 3way, and even higher-order interactions is overwhelming. A more thorough statistical analysis can be performed. At this second stage, a univariate test is commonly used, which we refer to as singlelocus method. When SNPs have large interaction effects, but very small marginal effects in the population, the single-locus method will result in low power for detecting them
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
