Utilizing IdentityByDescent Probabilities for Genetic Finemapping in
Population Based Samples, via Spatial Smoothing of Haplotype Effects
Computational Statistics and Data Analysis
Linda Hartman, Keith Humphreys and Ola Hössjer
Centre for Mathematical Sciences
Mathematical Statistics
Lund Institute of Technology,
Lund University,
2007
ISSN 14039338

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 identitybydescent based similarity
metric. We compare this approach to other more intuitively motivated models
and similarity measures based on identitybystate, suggested in the literature



Key words:

genetic association analysis, spatial smoothing, Generalized Linear Mixed
Model, population genetics, IdentityByDescent (IBD), IdentityByState
(IBS), Single Nucleotide Polymorphism (SNP)
