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Full Likelihood Analysis of Genetic Risk with Variable Age at Onset Disease—Combining Population-Based Registry Data and Demographic Information

BACKGROUND: In genetic studies of rare complex diseases it is common to ascertain familial data from population based registries through all incident cases diagnosed during a pre-defined enrollment period. Such an ascertainment procedure is typically taken into account in the statistical analysis of...

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Autores principales: Pitkäniemi, Janne, Varvio, Sirkka-Liisa, Corander, Jukka, Lehti, Nella, Partanen, Jukka, Tuomilehto-Wolf, Eva, Tuomilehto, Jaakko, Thomas, Andrew, Arjas, Elja
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2730012/
https://www.ncbi.nlm.nih.gov/pubmed/19718441
http://dx.doi.org/10.1371/journal.pone.0006836
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author Pitkäniemi, Janne
Varvio, Sirkka-Liisa
Corander, Jukka
Lehti, Nella
Partanen, Jukka
Tuomilehto-Wolf, Eva
Tuomilehto, Jaakko
Thomas, Andrew
Arjas, Elja
author_facet Pitkäniemi, Janne
Varvio, Sirkka-Liisa
Corander, Jukka
Lehti, Nella
Partanen, Jukka
Tuomilehto-Wolf, Eva
Tuomilehto, Jaakko
Thomas, Andrew
Arjas, Elja
author_sort Pitkäniemi, Janne
collection PubMed
description BACKGROUND: In genetic studies of rare complex diseases it is common to ascertain familial data from population based registries through all incident cases diagnosed during a pre-defined enrollment period. Such an ascertainment procedure is typically taken into account in the statistical analysis of the familial data by constructing either a retrospective or prospective likelihood expression, which conditions on the ascertainment event. Both of these approaches lead to a substantial loss of valuable data. METHODOLOGY AND FINDINGS: Here we consider instead the possibilities provided by a Bayesian approach to risk analysis, which also incorporates the ascertainment procedure and reference information concerning the genetic composition of the target population to the considered statistical model. Furthermore, the proposed Bayesian hierarchical survival model does not require the considered genotype or haplotype effects be expressed as functions of corresponding allelic effects. Our modeling strategy is illustrated by a risk analysis of type 1 diabetes mellitus (T1D) in the Finnish population-based on the HLA-A, HLA-B and DRB1 human leucocyte antigen (HLA) information available for both ascertained sibships and a large number of unrelated individuals from the Finnish bone marrow donor registry. The heterozygous genotype DR3/DR4 at the DRB1 locus was associated with the lowest predictive probability of T1D free survival to the age of 15, the estimate being 0.936 (0.926; 0.945 95% credible interval) compared to the average population T1D free survival probability of 0.995. SIGNIFICANCE: The proposed statistical method can be modified to other population-based family data ascertained from a disease registry provided that the ascertainment process is well documented, and that external information concerning the sizes of birth cohorts and a suitable reference sample are available. We confirm the earlier findings from the same data concerning the HLA-DR3/4 related risks for T1D, and also provide here estimated predictive probabilities of disease free survival as a function of age.
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spelling pubmed-27300122009-08-31 Full Likelihood Analysis of Genetic Risk with Variable Age at Onset Disease—Combining Population-Based Registry Data and Demographic Information Pitkäniemi, Janne Varvio, Sirkka-Liisa Corander, Jukka Lehti, Nella Partanen, Jukka Tuomilehto-Wolf, Eva Tuomilehto, Jaakko Thomas, Andrew Arjas, Elja PLoS One Research Article BACKGROUND: In genetic studies of rare complex diseases it is common to ascertain familial data from population based registries through all incident cases diagnosed during a pre-defined enrollment period. Such an ascertainment procedure is typically taken into account in the statistical analysis of the familial data by constructing either a retrospective or prospective likelihood expression, which conditions on the ascertainment event. Both of these approaches lead to a substantial loss of valuable data. METHODOLOGY AND FINDINGS: Here we consider instead the possibilities provided by a Bayesian approach to risk analysis, which also incorporates the ascertainment procedure and reference information concerning the genetic composition of the target population to the considered statistical model. Furthermore, the proposed Bayesian hierarchical survival model does not require the considered genotype or haplotype effects be expressed as functions of corresponding allelic effects. Our modeling strategy is illustrated by a risk analysis of type 1 diabetes mellitus (T1D) in the Finnish population-based on the HLA-A, HLA-B and DRB1 human leucocyte antigen (HLA) information available for both ascertained sibships and a large number of unrelated individuals from the Finnish bone marrow donor registry. The heterozygous genotype DR3/DR4 at the DRB1 locus was associated with the lowest predictive probability of T1D free survival to the age of 15, the estimate being 0.936 (0.926; 0.945 95% credible interval) compared to the average population T1D free survival probability of 0.995. SIGNIFICANCE: The proposed statistical method can be modified to other population-based family data ascertained from a disease registry provided that the ascertainment process is well documented, and that external information concerning the sizes of birth cohorts and a suitable reference sample are available. We confirm the earlier findings from the same data concerning the HLA-DR3/4 related risks for T1D, and also provide here estimated predictive probabilities of disease free survival as a function of age. Public Library of Science 2009-08-31 /pmc/articles/PMC2730012/ /pubmed/19718441 http://dx.doi.org/10.1371/journal.pone.0006836 Text en Pitkäniemi et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Pitkäniemi, Janne
Varvio, Sirkka-Liisa
Corander, Jukka
Lehti, Nella
Partanen, Jukka
Tuomilehto-Wolf, Eva
Tuomilehto, Jaakko
Thomas, Andrew
Arjas, Elja
Full Likelihood Analysis of Genetic Risk with Variable Age at Onset Disease—Combining Population-Based Registry Data and Demographic Information
title Full Likelihood Analysis of Genetic Risk with Variable Age at Onset Disease—Combining Population-Based Registry Data and Demographic Information
title_full Full Likelihood Analysis of Genetic Risk with Variable Age at Onset Disease—Combining Population-Based Registry Data and Demographic Information
title_fullStr Full Likelihood Analysis of Genetic Risk with Variable Age at Onset Disease—Combining Population-Based Registry Data and Demographic Information
title_full_unstemmed Full Likelihood Analysis of Genetic Risk with Variable Age at Onset Disease—Combining Population-Based Registry Data and Demographic Information
title_short Full Likelihood Analysis of Genetic Risk with Variable Age at Onset Disease—Combining Population-Based Registry Data and Demographic Information
title_sort full likelihood analysis of genetic risk with variable age at onset disease—combining population-based registry data and demographic information
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2730012/
https://www.ncbi.nlm.nih.gov/pubmed/19718441
http://dx.doi.org/10.1371/journal.pone.0006836
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