LAUSR

Background Associations between haplotypes and quantitative traits provide valuable information about the genetic basis of complex human diseases. Haplotypes also provide an effective way to deal with untyped SNPs. Two major challenges arise in haplotype-based association analysis of family data. First, haplotypes may not be inferred with certainty from genotype data. Second, the trait values within a family tend to be correlated because of common genetic and environmental factors. Results To address these challenges, we present an efficient likelihood-based approach to analyzing associations of quantitative traits with haplotypes or untyped SNPs. This approach properly accounts for within-family trait correlations and can handle general pedigrees with arbitrary patterns of missing genotypes. We characterize the genetic effects on the quantitative trait by a linear regression model with random effects and develop efficient likelihood-based inference procedures. Extensive simulation studies are conducted to examine the performance of the proposed methods. An application to family data from the Childhood Asthma Management Program Ancillary Genetic Study is provided. A computer program is freely available. Conclusions Results from extensive simulation studies show that the proposed methods for testing the haplotype effects on quantitative traits have correct type I error rates and are more powerful than some existing methods.