Abstract
A family-based association study design is not only able to localize causative genes more precisely than linkage analysis, but it also helps explain the genetic mechanism underlying the trait under study. Therefore, it can be used to follow up an initial linkage scan. For an association study of binary traits in general pedigrees, we propose a logistic mixture model that regresses the trait value on the genotypic values of markers under investigation and other covariates such as environmental factors. We first tested both the validity and power of the new model by simulating nuclear families inheriting a simple Mendelian trait. It is powerful when the correct disease model is specified and shows much loss of power when the dominance of a model is inversely specified, i.e., a dominant model is wrongly specified as recessive or vice versa. We then applied the new model to the Genetic Analysis Workshop (GAW) 15 simulation data to test the performance of the model when adjusting for covariates in the case of complex traits. Adjusting for the covariate that interacts with disease loci improves the power to detect association. The simplest version of the model only takes monogenic inheritance into account, but analysis of the GAW simulation data shows that even this simple model can be powerful for complex traits.
Highlights
Linkage analysis is a useful tool for the initial exploration of complex diseases, but is limited in its ability to localize the loci potentially segregating for disease susceptibility
We first tested both the validity and power of this new model by simulating nuclear families inheriting a simple Mendelian trait of monogenic inheritance. We describe this initial study briefly before applying the new model to the Genetic Analysis Workshop 15 (GAW15) simulated complex trait data; in particular, we examined the performance of the model when adjusting for covariates in the case of a complex trait, as this could provide information on familial residual correlations due to common environmental sharing
The new method is illustrated here on the full likelihood function of a pedigree, which is usually ascertained according to the phenotypes of probands instead of random sampling, and so, without appropriate ascertainment correction, the parameter estimates may be biased; this does not affect validity for testing the significance of association as a generalized linear model, though proper parameter inference can be made only when correcting for ascertainment or building the model on a conditional likelihood [12,13]
Summary
Linkage analysis is a useful tool for the initial exploration of complex diseases, but is limited in its ability to localize the loci potentially segregating for disease susceptibility. Association analysis, which directly tests the association between a trait and marker alleles, can more precisely localize causative genes For this purpose a family-based association study design can be used to follow up an initial linkage scan. It can help explain the genetic mechanism underlying the trait because extended pedigrees provide more genetic information than a random sample consisting of the same number of individuals. In this paper we propose a logistic mixture model for an association study of binary traits in general pedigrees We first tested both the validity and power of this new model by simulating nuclear families inheriting a simple Mendelian trait of monogenic inheritance. We describe this initial study briefly before applying the new model to the Genetic Analysis Workshop 15 (GAW15) simulated complex trait data; in particular, we examined the performance of the model when adjusting for covariates in the case of a complex trait, as this could provide information on familial residual correlations due to common environmental sharing
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