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- Sjölander, Arvid, et al.
(författare)
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Fine Mapping of Disease Genes Using Tagging SNPs
- 2007
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Ingår i: Annals of Human Genetics. - : Wiley. - 1469-1809 .- 0003-4800. ; 71:6, s. 815-827
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Tidskriftsartikel (refereegranskat)abstract
- We describe a haplotype clustering approach for localising a disease mutation within a fixed genomic region, which supplements tagging SNP (tSNP) information with (external) information on linkage disequilibrium. By applying our method to simulated data based on the coalescent, and on real haplotype data, we demonstrate that there are situations where significant gains can be made by incorporating tagged SNPs into the analysis. The issues we explore are important not only to these types of studies, but also to studies that select tSNPs based on (external) HapMap phase II data, and those that use genome-wide markers.
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| 5. |
- Werner Hartman, Linda
(författare)
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Bayesian modelling of spatial data using Markov random fields, with application to elemental composition of forest soil
- 2006
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Ingår i: Mathematical Geology. - : Springer Science and Business Media LLC. - 0882-8121 .- 1573-8868. ; 38:2, s. 113-133
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Tidskriftsartikel (refereegranskat)abstract
- Spatial datasets are common in the environmental sciences. In this study we suggest a hierarchical model for a spatial stochastic field. The main focus of this article is to approximate a stochastic field with a Gaussian Markov Random Field (GMRF) to exploit computational advantages of the Markov field, concerning predictions, etc. The variation of the stochastic field is modelled as a linear trend plus microvariation in the form of a GMRF defined on a lattice. To estimate model parameters we adopt a Bayesian perspective, and use Monte Carlo integration with samples from Markov Chain simulations. Our methods does not demand lattice, or near-lattice data, but are developed for a general spatial data-set, leaving the lattice to be specified by the modeller. The model selection problem that comes with the artificial grid is in this article addressed with cross-validation, but we also suggest other alternatives. From the application of the methods to a data set of elemental composition of forest soil, we obtained predictive distributions at arbitrary locations as well as estimates of model parameters.
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| 6. |
- Werner Hartman, Linda, et al.
(författare)
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Fast kriging of large data sets with Gaussian Markov random fields
- 2008
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Ingår i: Computational Statistics & Data Analysis. - : Elsevier BV. - 0167-9473. ; 52:5, s. 2331-2349
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Tidskriftsartikel (refereegranskat)abstract
- Abstract in Undeterminedpatial data sets are analysed in many scientific disciplines. Kriging, i.e. minimum mean squared error linear prediction, is probably the most widely used method of spatial prediction. Computation time and memory requirement can be an obstacle for kriging for data sets with many observations. Calculations are accelerated and memory requirements decreased by using a Gaussian Markov random field on a lattice as an approximation of a Gaussian field. The algorithms are well suited also for nonlattice data when exploiting a bilinear interpolation at nonlattice locations. (c) 2007 Elsevier B.V. All rights reserved.
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- Werner Hartman, Linda
(författare)
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Spatial Statistics and Ancestral Recombination Graphs with Applications in Gene Mapping and Geostatistics
- 2007
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis explores models and algorithms in geostatistics and gene mapping. The first part deals with the use of computationally effective lattice models for inference of data with a continuous spatial index. The fundamental idea is to approximate a Gaussian field with a Gaussian Markov random field (GMRF) on a lattice, and then to conduct a bilinear interpolation of this at non-lattice locations. The resulting model is used for spatial interpolation, both in a Bayesian approach using Markov chain Monte Carlo (MCMC), and in kriging. The second part of the thesis concerns genetic association analysis, particularly multi-locus gene mapping using case-control samples. The algorithms utilize the fact that a population based sample of haplotypes (a collection of alleles at closely linked loci on the same chromosome) mirrors the population history of shared ancestry, mutation, recombination etc. Around the disease locus chromosomes carrying the disease mutation will be more similar than chromosomes that do not carry the disease mutation (on account of increased levels of shared ancestry). Two models and corresponding algorithms for gene mapping are presented. The first explicitly models the genealogy taking the over-sampling of cases into account. Under certain model approximations, a permutation-based test for genetic association is developed that is computationally feasible, even when haplotype phase is unknown. It contends with arbitrary phenotypes and genetic models, allows for neutral mutations, and adapts to marker allele frequencies. The second model utilizes concepts and algorithms from both spatial statistics and statistical genetics. A spatial smoothing model is used for haplotypes, such that structurally similar haplotypes have risk parameters with high correlation. The disease locus is then searched as the place where a local similarity measure produces risk parameters that can discriminate between cases and controls. Different covariance structures and similarity metrics are suggested and compared.
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- Werner Hartman, Linda, et al.
(författare)
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Studenter lär varandra, erfarenheter från en grundkurs i matematisk statistik
- 2006
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Ingår i: Qvartilen. - 0283-3654. ; 21:1, s. 15-18
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Tidskriftsartikel (refereegranskat)abstract
- Går det att undvika passiva studenter som fokuserar på tentaläsning? Räcker det att låta studenterna arbeta med problemlösning, laborationer och projektarbeten, eller måste kanske undervisningen omorganiseras? Kan man få studenterna att själva ta ansvar för sin inlärning, att läsa mer i boken och att arbeta mer aktivt med materialet? På en av våra grundkurser i matematisk statistik i Lund har vi med goda resultat frångått det vanliga upplägget med föreläsningar och räkneövningar. Istället har vi sedan tre år tillämpat samarbetslärande i smågrupper, en metod som använts i varianter i åtskilliga år i USA och Australien. I Sverige har vår form av metoden utvecklats i Luleå av Andrejs Dunkels och Kerstin Vännman.
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| 9. |
- Werner Hartman, Linda, et al.
(författare)
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Utilizing identity-by-descent probabilities for genetic fine-mapping in population based samples, via spatial smoothing of haplotype effects
- 2009
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Ingår i: Computational Statistics & Data Analysis. - : Elsevier BV. - 0167-9473 .- 1872-7352. ; 53:5, s. 1802-1817
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Tidskriftsartikel (refereegranskat)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.
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