Haplotype effects on human survival: Logistic regression models applied to unphased genotype data

Haplotype based linkage disequilibrium (LD) mapping exhibits higher power than the single locus approach because it makes use of the LD information contained in the flanking markers. New statistical methods have been proposed to help to infer haplotype effects on human diseases using multi-locus genotype data collected from unrelated individuals. In this paper, we introduce a statistical procedure for measuring haplotype effects on human survival using the popular logistic regression model with haplotype based parameterizations. By modeling haplotype frequency as a function of age, our model infers haplotype effects by estimating and testing the slope parameters under different genetic mechanisms (multiplicative, dominant, or recessive). In addition, by estimating the sex-specific slope parameters, our model allows the detection of sex-specific haplotype effects or haplotype-sex interactions. As an example, we apply our model to all empirical dataset on a stress related gene, interleukin-6, to look for haplotypes that affect individual survival and for haplotype-sex interactions. We show that our logistic regression based haplotype model can be a helpful tool for researchers interested in the genetics of human aging and longevity.