Estimation of Transmission Probabilities in Families Ascertained through a Proband with Variable Age-at-Onset Disease: Application to the HLA A, B and DR Loci in Finnish Families with Type 1 Diabetes

Hakulinen²,

Näsänen³

et al. 2004

Hum Hered

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Objectives: We explored the properties of the long-term survivor model (LTS) in the genetic association studies and studied allelic and haplotypic associations between the age at onset and partially latent susceptibility of type 1 diabetes (T1DM) and Human Leucocyte Antigen (HLA) A, B and DR loci. Methods: The authors applied the long-term survivor model (LTS) for sibships collected in a population-based registry during a calendar time period. The method uses sibs that could not become probands and includes the proband’s age at onset during the recruitment period. Association between the candidate gene and the partially latent susceptibility is modeled with logistic regression and the age at onset with a two-parameter gamma distribution, where a scale parameter depends on the candidate genotypes. We also performed a simulation study of nuclear families to compare the power of the likelihood ratio tests of the genetic association based on the LTS model with those obtained using family-based association method (FBAT) and bias of the case-pseudo control design. In addition, we analysed allele and haplotype associations between HLA A, B and DR loci (IDDM1) with T1DM, using population-based ascertainment of 705 sibships with complete HLA information. Results: A simulation study showed that the estimates of the genetic association using an ascertainment-corrected LTS model are virtually unbiased and that the relative risk estimates obtained from case-pseudo control design (TDT) are negatively biased. In the analysis of the Finnish T1DM families we found that only B62 (p < 0.05) is positively significantly associated with susceptibility after adjusting for the haplotype effects. Five alleles were significantly associated with age at onset (B8 and DR3, p < 0.01; A2, B60 and DR6, p < 0.05). No significant three-locus haplotype associations with the susceptibility were found, but A3B18DR4 (p < 0.001) haplotype was associated with older age at onset than average. Conclusions: Estimates of genetic relative risk obtained from the case-pseudo control design are negatively biased and the prospective LTS model is an appropriate choice, when there are non-susceptible subjects in the population with variable age at onset. Based on the analysis of T1DM, we conclude that there are gene(s) in the HLA region that are associated with susceptibility and/or age at onset of T1DM, and this should be taken into account in future studies.

Section: Introductioncontrasting

confidence: 63%

Section: Long-term Survival Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Class I and II HLA Genes Are Associated with Susceptibility and Age at Onset in Finnish Families with Type 1 Diabetes

Hakulinen²,

Näsänen³

et al. 2004

Hum Hered

Self Cite

“…We did not apply any available statistical methods for possible correction of ascertainment problem because either conditional likelihood methods are developed for different phenotypes than variable age at onset [Burton, 2003] or they are not directly applicable to the variable age-at-onset disease with population (registry)-based recruitment . Ascertainment correction for population-based T1D registry data with variable age at onset by Pitkaniemi et al [2000] lead to substantial loss of data and thus the analysis would suffer from insufficient power. There are also interesting proposals to use computer intensive methods [Burton, 2003;Clayton, 2003] or recently proposed different forms of conditional likelihoods [Ginsburg et al, 2003;Carayol and Bonaiti-Pellie, 2004].…”

Section: Discussionmentioning

confidence: 99%

Genetic random effects model for family data with long‐term survivors: analysis of diabetic nephropathy in type 1 diabetes

Moltchanova²,

Haapala

et al. 2007

Genetic Epidemiology

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A shared and additive genetic variance component-long-term survivor (LTS) model for familial aggregation studies of complex diseases with variable age-at-onset phenotype and non-susceptible subjects in the study cohort is proposed. LTS has been used from the early 1970s, especially in epidemiological studies of cancer. The LTS model utilizes information on the age at onset (survival) distribution to make inference on partially latent susceptibility. Bayesian modeling with uninformative priors is used and estimates of the posterior distribution of age at onset and susceptibility parameters of interest have been obtained using Bayesian Markov chain Monte Carlo (MCMC) methods with OpenBugs program. A simulation study confirms that we obtain posterior estimates of the model parameters on shared and genetic variance components of age at onset and susceptibility with good coverage rates. Further, we analyze familial aggregation of diabetic nephropathy (DN) in large Finnish cohort of 528 sibships with type 1 diabetes (T1D). According to the variance components estimated a substantial familial variation in the susceptibility to DN exist among families, while time to DN is less influenced by shared familial factors.

“…Such an extreme form of transmission distortion seems biologically and empirically [35] very unlikely, given current knowledge of Type 1 diabetes genes and their effects. Therefore, it is evident that biologically reasonable transmission distortion alone, with penetrances as defined for example DR4 carrier and non-carrier genotypes, can explain only a small part of the rapid increase in the incidence of Type 1 diabetes observed in Finland.…”

Section: Discussionmentioning

confidence: 99%

Increasing incidence of Type 1 diabetes – role for genes?

Onkamo

Tuomilehto³

et al. 2004

BMC Genet

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BackgroundThe incidence of Type 1 diabetes (T1DM) is increasing fast in many populations. The reasons for this are not known, although an increase in the penetrance of the diabetes-associated alleles, through changes in the environment, might be the most plausible mechanism. After the introduction of insulin treatment in 1930s, an increase in the pool of genetically susceptible individuals has been suggested to contribute to the increase in the incidence of Type 1 diabetes.ResultsTo explore this hypothesis, the authors formulate a simple population genetic model for the incidence change driven by non-Mendelian transmission of a single susceptibility factor, either allele(s) or haplotype(s). A Poisson mixture model is used to model the observed number of cases. Model parameters were estimated by maximizing the log-likelihood function. Based on the Finnish incidence data 1965–1996 the point estimate of the transmission probability was 0.998. Given our current knowledge of the penetrance of the most diabetic gene variants in the HLA region and their transmission probabilities, this value is exceedingly unrealistic.ConclusionsAs a consequence, non-Mendelian transmission of diabetic allele(s)/haplotype(s) if present, could explain only a small part of the increase in incidence in Finland. Hence, the importance of other, probably environmental factors modifying the disease incidence is emphasized.