Modelling haplotypes with respect to reference cohort variation graphs

Abstract

Motivation: Current statistical models of haplotypes are limited to panels of haplotypes whose genetic variation can be represented by arrays of values at linearly ordered bi-or multiallelic loci. These methods cannot model structural variants or variants that nest or overlap. Results: A variation graph is a mathematical structure that can encode arbitrarily complex genetic variation. We present the first haplotype model that operates on a variation graph-embedded population reference cohort. We describe an algorithm to calculate the likelihood that a haplotype arose from this cohort through recombinations and demonstrate time complexity linear in haplotype length and sublinear in population size. We furthermore demonstrate a method of rapidly calculating likelihoods for related haplotypes. We describe mathematical extensions to allow modelling of mutations. This work is an important incremental step for clinical genomics and genetic epidemiology since it is the first haplotype model which can represent all sorts of variation in the population.