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

Werner Hartman, Linda LU ; Hossjer, Ola and Humphreys, Keith (2009) In Computational Statistics & Data Analysis 53(5). p.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... (More)
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. (C) 2008 Elsevier B.V. All rights reserved. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
TRAIT LINKAGE ANALYSIS, CLADISTIC-ANALYSIS, GENERAL PEDIGREES, DISEASE, GENES, ASSOCIATIONS
in
Computational Statistics & Data Analysis
volume
53
issue
5
pages
1802 - 1817
publisher
Elsevier
external identifiers
  • wos:000264751000026
  • scopus:60349094237
ISSN
0167-9473
DOI
10.1016/j.csda.2008.05.029
language
English
LU publication?
yes
id
f087996e-da18-4e46-adbe-1a7a56cdf2d1 (old id 2799130)
date added to LUP
2012-06-19 11:34:08
date last changed
2017-01-01 04:43:23
@article{f087996e-da18-4e46-adbe-1a7a56cdf2d1,
  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. (C) 2008 Elsevier B.V. All rights reserved.},
  author       = {Werner Hartman, Linda and Hossjer, Ola and Humphreys, Keith},
  issn         = {0167-9473},
  keyword      = {TRAIT LINKAGE ANALYSIS,CLADISTIC-ANALYSIS,GENERAL PEDIGREES,DISEASE,GENES,ASSOCIATIONS},
  language     = {eng},
  number       = {5},
  pages        = {1802--1817},
  publisher    = {Elsevier},
  series       = {Computational Statistics & Data Analysis},
  title        = {Utilizing identity-by-descent probabilities for genetic fine-mapping in population based samples, via spatial smoothing of haplotype effects},
  url          = {http://dx.doi.org/10.1016/j.csda.2008.05.029},
  volume       = {53},
  year         = {2009},
}