Estimating the age-at-onset function using life-table methods

Hereditary ovarian cancer. Heterogeneity in age at diagnosis

An unknown fraction of the ovarian cancer burden occurs in women with a family history indicative of a putative autosomal dominantly inherited cancer susceptibility syndrome. The results from a five-generation, extended, hereditary breast-ovarian cancer kindred are described 10 years after it was initially ascertained. Significantly more cancers were observed in high-risk family members during this decade than were expected (P less than 0.001). The age of ovarian cancer diagnosis was studied in additional ovarian cancer-prone families of three types: site-specific ovarian cancer syndrome, the breast-ovarian cancer syndrome, and Lynch syndrome II. The age of onset in each of the three sets was significantly (P less than 0.001) earlier than the general population mean of 59, and there were significant differences in the age of onset (P = 0.050) among these three cohorts. Ovarian cancer histology was similar to that of patients with negative family histories. There may be clinically significant heterogeneity in the age at diagnosis of ovarian cancer among these ovarian cancer-prone syndromes. This has important implications for understanding its natural history and targeting surveillance-management strategies.

A time-dependent logistic hazard function for modeling variable age of onset in analysis of familial diseases

The paper presents an extension of the regressive logistic models proposed by Bonney [Biometrics 42:611-625, 1986], to address the problems of variable age-of-onset and time-dependent covariates in analysis of familial diseases. This goal is achieved by using failure time data analysis methods, and partitioning the time of follow up in K mutually exclusive intervals. The conditional probability of being affected within the kth interval (k = 1...K) given not affected before represents the hazard function in this discrete formulation. A logistic model is used to specify a regression relationship between this hazard function and a set of explanatory variables including genotype, phenotypes of ancestors, and other covariates which can be time dependent. The probability that a given person either becomes affected within the kth interval (i.e., interval k includes age of onset of the person) or remains unaffected by the end of the kth interval (i.e., interval k includes age at examination of the person) are derived from the general results of failure time data analysis and used for the likelihood formulation. This proposed approach can be used in any genetic segregation and linkage analysis in which a penetrance function needs to be defined. Application of the method to familial leprosy data leads to results consistent with our previous analysis performed using the unified mixed model [Abel and Demenais, Am J Hum Genet 42:256-266, 1988], i.e., the presence of a recessive major gene controlling susceptibility to leprosy. Furthermore, a simulation study shows the capability of the new model to detect major gene effects and to provide accurate parameter estimates in a situation of complete ascertainment.

A genetic linkage study of familial breast-ovarian cancer

Linkage analysis was conducted in 17 families identified by the familial occurrence of breast and ovarian cancer using a series of 17 serologic and biochemical markers. Lod scores suggestive of linkage of breast/ovarian cancer susceptibility to the RH blood group locus on chromosome 1p were obtained. When the presence of fibrocystic disease of the breast in a first-degree relative of an affected family member was added as an indicator of susceptibility, the evidence for linkage increased. No evidence of linkage to GPT or ABO, both previously suggested to be linked to breast cancer susceptibility, was seen in this study.

Using survival methods to estimate age-at-onset distributions for genetic diseases with an application to Huntington disease

Two important considerations in genetic disease are the risk of inheriting the gene for a disease and the age-at-onset distribution among those inheriting it. This paper describes the use of survival data analytic methods to estimate these entities. When a sample consists of children and/or siblings of affected individuals, standard life-table methods such as Kaplan-Meier techniques can be applied. The desired estimators are functions of the Kaplan-Meier estimator and their properties are derived using the invariance principle and delta methods. A discussion of the underlying assumptions and estimators of standard errors and mean onset age are given. In an application to Huntington disease, the risk (+/- SE) of inheriting the gene for disease among children of a singly affected parent is estimated to be 0.499 +/- 0.020 and the estimated mean onset age (+/- SE) is 45.6 +/- 0.7.

Modeling the age-of-onset function in segregation analysis: a causal scheme for leprosy

Several methods have been proposed to take into account the variable age of onset of a disease in genetic analysis. A different approach is presented from an etiological point of view. To illustrate the method, we used leprosy, an infectious disease with a variable age of onset depending on both the time of contamination with the bacillus and the latency of the disease; the role of a major gene in the susceptibility to this disease has been recently detected. The age-of-onset function was modeled to account for the two temporal processes: contamination event and incubation period. For genetic analysis, this function was combined with the probability of being susceptible to the disease, which was expressed by the use of regressive models. To test this new approach, ten sets of 500 nuclear families were simulated considering different hypotheses of contamination risks, which were either constant or dependent on contacts with contagious leprosy patients, and varying the extent to which the disease is heritable. Analyses of these data using two versions of the model indicate that the model can detect familial correlations in variable age of onset and discriminate between the different simulated effects.