Abstract
BackgroundIn 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 FindingsHere 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.SignificanceThe 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.
Highlights
Data utilized in genetic association studies of rare diseases are usually ascertained by initially recruiting individuals with the phenotype of interest from some background population
Predictive probabilities of type 1 diabetes mellitus (T1D) free survival are shown in Figure 2 for some high risk human leucocyte antigen (HLA) DRB1 genotypes and in Figure 3 for a set of selected haplotypes
As could be expected from many earlier studies, the heterozygous genotype DR3/DR4 at the DRB1 locus was associated with the lowest predictive probability of the 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
Summary
Data utilized in genetic association studies of rare diseases are usually ascertained by initially recruiting individuals with the phenotype of interest from some background population. After this initial study phase, it is possible to gain information about the relatives of the recruited subjects Such an ascertainment procedure is usually taken into account in the statistical analysis of familial data by constructing either a retrospective or prospective likelihood expression, which conditions on the ascertainment event [1]. 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
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