Utilizing identity-by-descent probabilities for genetic fine-mapping in population based samples, via spatial smoothing of haplotype effects

Linda Hartman, Ola Hssjer and Keith Humphreys

Computational Statistics & Data Analysis, 2009, vol. 53, issue 5, 1802-1817

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. From a population genetic perspective the use of an identity-by-descent based similarity metric is theoretically motivated. This approach is then compared to other more intuitively motivated models and other similarity measures based on identity-by-state, suggested in the literature.

Date: 2009
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