Abstract
BackgroundThe etiology of multifactorial human diseases involves complex interactions between numerous environmental factors and alleles of many genes. Efficient statistical tools are demanded in identifying the genetic and environmental variants that affect the risk of disease development. This paper introduces a retrospective polytomous logistic regression model to measure both the main and interaction effects in genetic association studies of human discrete and continuous complex traits. In this model, combinations of genotypes at two interacting loci or of environmental exposure and genotypes at one locus are treated as nominal outcomes of which the proportions are modeled as a function of the disease trait assigning both main and interaction effects and with no assumption of normality in the trait distribution. Performance of our method in detecting interaction effect is compared with that of the case-only model.ResultsResults from our simulation study indicate that our retrospective model exhibits high power in capturing even relatively small effect with reasonable sample sizes. Application of our method to data from an association study on the catalase -262C/T promoter polymorphism and aging phenotypes detected significant main and interaction effects for age-group and allele T on individual's cognitive functioning and produced consistent results in estimating the interaction effect as compared with the popular case-only model.ConclusionThe retrospective polytomous logistic regression model can be used as a convenient tool for assessing both main and interaction effects in genetic association studies of human multifactorial diseases involving genetic and non-genetic factors as well as categorical or continuous traits.
Highlights
The etiology of multifactorial human diseases involves complex interactions between numerous environmental factors and alleles of many genes
The model identified a highly significant effect of age-group that is negatively correlated with individual's cognitive function (RRR = 0.630, p-value = 0.001)
It is interesting to see that, the overall effect of allele T reduces carrier's cognitive score, the interaction effect indicates that the effect of the allele is agedependent which means that the T allele conveys beneficial effect that improves carries' cognitive performances at old ages
Summary
The etiology of multifactorial human diseases involves complex interactions between numerous environmental factors and alleles of many genes. This paper introduces a retrospective polytomous logistic regression model to measure both the main and interaction effects in genetic association studies of human discrete and continuous complex traits. The case-control design, a retrospective design by nature, has been popular in establishing the genetic associations in single locus and haplotype analyses [2] as well as in assessing gene-environment interactions [3,4]. This approach has been extended to handle both dichotomous and continuous traits by introducing the retrospective logistic regression model [5] that treats alleles or genotypes as dependent variables. The same idea has been used for single locus analysis in both unmatched and matched case-control studies [7] and for haplotype analysis [8]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 call
