Statistical Analysis of Haplotypes with Traits and Covariates when Linkage Phase is Ambiguous

Routines for the analysis of indirectly measured haplotypes. The statistical methods assume that all subjects are unrelated and that haplotypes are ambiguous (due to unknown linkage phase of the genetic markers). The main functions are: haplo.em(), haplo.glm(), haplo.score(), and haplo.power(); all of which have detailed examples in the vignette.

The haplo.stats Package

Overview of haplo.stats

Routines for the analysis of indirectly measured haplotypes. The statistical methods assume that all subjects are unrelated and that haplotypes are ambiguous (due to unknown linkage phase of the genetic markers).

The main functions are described below.

haplo.em

Estimation of haplotype frequencies, and posterior probabilities of haplotype pairs for a subject, conditional on the observed marker data.

haplo.glm

GLM regression models for the regression of a trait on haplotypes, possibly including covariates and interactions. S3 methods for anova and summary have been implemented.

haplo.score

Score statistics to test associations between haplotypes and a wide variety of traits, including binary, ordinal, quantitative, and Poisson.

haplo.design

Uses as input the result from haplo.em(), and makes a design matrix for haplotype dosage, such that modeling haplotypes is similar to how it would be done within haplo.glm(), but without the iteratetively re-weighted least squares steps.

haplo.cc

Runs simple haplo.score and haplo.glm without covariates with combined results for case-control (binomial family) response.