A flexible Bayesian framework for modeling haplotype association with disease, allowing for dominance effects of the underlying causative variants - PubMed (original) (raw)
DAG representing the model underlying the likelihood, , given by equation (3). The gray nodes represent observed data and estimated relative haplotype frequencies, obtained via implementation of the EM algorithm. The likelihood depends on a number of model parameters,
θ
—including the baseline risk of disease (μ), covariate-regression coefficients (
γ
), and genetic effects (
β
)—of causative variants at the functional polymorphism(s). To evaluate the likelihood, I allow for the correlation between marker-SNP haplotypes (
H
) and genotypes (Z) at the functional polymorphism(s), by means of a Bayesian partition model. The model is parameterized in terms of the number of clusters of haplotypes (K), the cluster centers (
C
), and the probability (φ) that haplotypes within each cluster carry a causative variant at the functional polymorphism(s). The parameters,
θ
, depend on the underlying model of association (
ℳ
) of disease with marker SNPs. Under the null model,
_M_0
, the genetic effects are zero, and there is a single cluster of haplotypes. Under the alternative model of association,
_M_1
, I allow for dominance effects of the causative variants at the functional polymorphism(s), and there are at least two clusters in the partition of haplotypes.