Utilizing Identity-By-Descent Probabilities for Genetic Fine-mapping in Population Based Samples, via Spatial Smoothing of Haplotype Effects Computational Statistics and Data Analysis

Linda Hartman, Keith Humphreys and Ola Hössjer

Abstract:: Genetic fine mapping can be performed by exploiting the notion that haplotypes that are structurally similar in the neighbourhood of a disease predisposing locus are more likely to harbour the same susceptibility allele. Within the framework of Generalized Linear Mixed Models this can be formalized using spatial smoothing models, i.e.inducing a covariance structure for the haplotype risk parameters, such that risks associated with structurally similar haplotypes are dependent. In a Bayesian procedure a local similarity measure is calculated for each update of the presumed disease locus. Thus, the disease locus is searched as the place where the similarity structure produces risk parameters that can best discriminate between cases and controls.
: We describe an approach which takes a population genetics perspective to theoretically motivate the use of an identity-by-descent based similarity metric. We compare this approach to other more intuitively motivated models and similarity measures based on identity-by-state, suggested in the literature

Key words:: genetic association analysis, spatial smoothing, Generalized Linear Mixed Model, population genetics, Identity-By-Descent (IBD), Identity-By-State (IBS), Single Nucleotide Polymorphism (SNP)