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- Næss, Sigrid, et al.
(författare)
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Refinement of the MHC risk map in a scandinavian primary sclerosing cholangitis population
- 2014
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 9:12
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Tidskriftsartikel (refereegranskat)abstract
- Genetic variants within the major histocompatibility complex (MHC) represent the strongest genetic susceptibility factors for primary sclerosing cholangitis (PSC). Identifying the causal variants within this genetic complex represents a major challenge due to strong linkage disequilibrium and an overall high physical density of candidate variants. We aimed to refine the MHC association in a geographically restricted PSC patient panel.
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- Hov, J. R., et al.
(författare)
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Electrostatic Modifications of the Human Leukocyte Antigen-DR P9 Peptide-Binding Pocket and Susceptibility to Primary Sclerosing Cholangitis
- 2011
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Ingår i: Hepatology. - : Ovid Technologies (Wolters Kluwer Health). - 0270-9139 .- 1527-3350. ; 53:6, s. 1967-1976
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Tidskriftsartikel (refereegranskat)abstract
- The strongest genetic risk factors for primary sclerosing cholangitis (PSC) are found in the human leukocyte antigen (HLA) complex at chromosome 6p21. Genes in the HLA class II region encode molecules that present antigen to T lymphocytes. Polymorphisms in these genes are associated with most autoimmune diseases, most likely because they contribute to the specificity of immune responses. The aim of this study was to analyze the structure and electrostatic properties of the peptide-binding groove of HLA-DR in relation to PSC. Thus, four-digit resolution HLA-DRB1 genotyping was performed in 356 PSC patients and 366 healthy controls. Sequence information was used to assign which amino acids were encoded at all polymorphic positions. In stepwise logistic regressions, variations at residues 37 and 86 were independently associated with PSC (P = 1.2 x 10(-32) and P = 1.8 x 10(-22) in single-residue models, respectively). Three-dimensional modeling was performed to explore the effect of these key residues on the HLA-DR molecule. This analysis indicated that residue 37 was a major determinant of the electrostatic properties of pocket P9 of the peptide-binding groove. Asparagine at residue 37, which was associated with PSC, induced a positive charge in pocket P9. Tyrosine, which protected against PSC, induced a negative charge in this pocket. Consistent with the statistical observations, variation at residue 86 also indirectly influenced the electrostatic properties of this pocket. DRB1*13:01, which was PSC-associated, had a positive P9 pocket and DRB1*13:02, protective against PSC, had a negative P9 pocket. Conclusion: The results suggest that in patients with PSC, residues 37 and 86 of the HLA-DR beta chain critically influence the electrostatic properties of pocket P9 and thereby the range of peptides presented. (HEPATOLOGY 2011;53:1967-1976)
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- Olsson, Marita, 1965
(författare)
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EM Estimation in Phase Type Models
- 1995
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis consists of four articles whose theme in common is the class of phase type distributions. In the first article an EM algorithm is presented to estimate the parameters of a phase type distribution of fixed order. Also, it is shown that the algorithm can be used to approximate other continuous distributions by phase type distributions. In article number two, the EM algorithm is adjusted to handle fitting of phase type distributions to samples containing right censored and/or interval censored observations. The third article deals with approximations of standard errors of identifiable functions (e.g. the distribution function at a fixed point) of a fitted phase type distribution. Standard error approximations are calculated both by using asymptotic theory and by using the jackknife technique. The two methods are compared and evaluated by simulations in several examples. The last article presents a parametric model for estimation of the relapse time of a disease in certain clinical trials. The model is a special phase type model where the state space of the underlying Markov process is split into two parts; the first set of states represents the patient still being healthy, a transition to a second set of states takes place when the patient get a relapse, and a transition to an absorbing state represents the patient getting symptoms of the disease.
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