A Likelihood Ratio Approach to Family-based Association Studies with Covariates

Efficient simulation of epistatic interactions in case-parent trios

Statistical approaches to evaluate interactions between single nucleotide polymorphisms (SNPs) and SNP-environment interactions are of great importance in genetic association studies, as susceptibility to complex disease might be related to the interaction of multiple SNPs and/or environmental factors. With these methods under active development, algorithms to simulate genomic data sets are needed to ensure proper type I error control of newly proposed methods and to compare power with existing methods. In this paper we propose an efficient method for a haplotype-based simulation of case-parent trios when the disease risk is thought to depend on possibly higher-order epistatic interactions or gene-environment interactions with binary exposures.

Importance measures for epistatic interactions in case-parent trios

Ensemble methods (such as Bagging and Random Forests) take advantage of unstable base learners (such as decision trees) to improve predictions, and offer measures of variable importance useful for variable selection. LogicFS has been proposed as such an ensemble learner for case-control studies when interactions of single nucleotide polymorphisms (SNPs) are of particular interest. LogicFS uses bootstrap samples of the data and employs the Boolean trees derived in logic regression as base learners to create ensembles of models that allow for the quantification of the contributions of epistatic interactions to the disease risk. In this article, we propose an extension of logicFS suitable for case-parent trio data, and derive an additional importance measure that is much less influenced by linkage disequilibrium between SNPs than the measure originally used in logicFS. We illustrate the performance of the novel procedure in simulation studies and in a case study of 461 case-parent trios with autistic children.

Incorporating covariates into multipoint association mapping in the case-parent design

BACKGROUND/AIMS

To improve the efficiency of disease locus localization in association mapping using case-parent designs and to assess or account for the main covariate effects and gene-covariate interaction effects, while localizing the disease locus.

METHODS

The present study extends a multipoint fine-mapping approach to incorporate covariates into the association mapping of case-parent designs through parametric and non-parametric modeling. This approach is based on the expected preferential-allele-transmission statistics for transmission from either parent to an affected child.

RESULTS

Simulation studies indicate that the efficiency in estimating the disease locus increases considerably when incorporating a covariate associated with the disease. This is especially true when the genetic effect of the disease locus is small. The proposed approach was applied to a young-onset hypertension data sample. The relative efficiency of estimating the locus of young-onset hypertension increases 110-fold after incorporating triglyceride into the association mapping while localizing the disease variant in the lipoprotein lipase gene in the non-parametric model. By incorporating the information of SNP variants into the fine-mapping, the proposed method further assesses the gene-gene interactions between the SNP and the disease locus.

CONCLUSION

With the incorporation of covariates, the proposed method cannot only improve efficiency in estimating disease loci, but can also elucidate the etiology of a complex disease.

X-LRT: a likelihood approach to estimate genetic risks and test association with X-linked markers using a case-parents design

Recently, there has been interest in family-based tests of association to identify X-chromosome genes. However, none of the approaches allow for estimation of genetic risks. We propose a likelihood approach to estimate disease-related marker relative risks and test genotype association using a case-parents design. The test uses nuclear families with a single affected proband and allows additional siblings and missing parental genotypes. Extension to a haplotype test is based on assumptions of random mating and multiplicative penetrance. We investigate power and type I error rate of the likelihood-based test, using simulated data and apply our method to marker data from the monoamine oxidase A&B genes in families with Parkinson disease. We show how efficiency with missing parental information can be improved with additional sibling genotype information. Our likelihood approach offers great flexibility for testing different penetrance relationships within and between sexes. In addition, estimation of disease-related marker relative risks provides a measure of the magnitude of X-linked genetic effects on complex disorders.

Family-based association analysis with ordered categorical phenotypes, covariates and interactions

Genetic association analyses of family-based studies with ordered categorical phenotypes are often conducted using methods either for quantitative or for binary traits, which can lead to suboptimal analyses. Here we present an alternative likelihood-based method of analysis for single nucleotide polymorphism (SNP) genotypes and ordered categorical phenotypes in nuclear families of any size. Our approach, which extends our previous work for binary phenotypes, permits straightforward inclusion of covariate, gene-gene and gene-covariate interaction terms in the likelihood, incorporates a simple model for ascertainment and allows for family-specific effects in the hypothesis test. Additionally, our method produces interpretable parameter estimates and valid confidence intervals. We assess the proposed method using simulated data, and apply it to a polymorphism in the c-reactive protein (CRP) gene typed in families collected to investigate human systemic lupus erythematosus. By including sex interactions in the analysis, we show that the polymorphism is associated with anti-nuclear autoantibody (ANA) production in females, while there appears to be no effect in males.