Utilizing identity-by-descent probabilities for genetic fine-mapping in population based samples, via spatial smoothing of haplotype effects
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2799130
- author
- Werner Hartman, Linda LU ; Hossjer, Ola and Humphreys, Keith
- organization
- publishing date
- 2009
- 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
- 2016-04-01 11:58:48
- date last changed
- 2022-04-25 12:53:17
@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}}, keywords = {{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}}, doi = {{10.1016/j.csda.2008.05.029}}, volume = {{53}}, year = {{2009}}, }