Fanconi anemia (FA) is an autosomal recessive disorder characterized by cellular hypersensitivity to DNA cross-linking agents and cancer predisposition. Recent evidence for the interactions of ataxia-telangiectasia mutated protein ATM and breast cancer susceptibility proteins BRCA1 and BRCA2 (identified as FANCD1) with other known FA proteins suggests that FA proteins have a significant role in DNA repair/recombination and cell cycle control. The International Fanconi Anemia Registry (IFAR), a prospectively collected database of FA patients, allows us the unique opportunity to analyze the natural history of this rare, clinically heterogeneous disorder in a large number of patients. Of the 754 subjects in this study, 601 (80%) experienced the onset of bone marrow failure (BMF), and 173 (23%) had a total of 199 neoplasms. Of these neoplasms, 120 (60%) were hematologic and 79 (40%) were nonhematologic. The risk of developing BMF and hematologic and nonhematologic neoplasms increased with advancing age with a 90%, 33%, and 28% cumulative incidence, respectively, by 40 years of age. Univariate analysis revealed a significantly earlier onset of BMF and poorer survival for complementation group C compared with groups A and G; however, there was no significant difference in the time to hematologic or nonhematologic neoplasm development between these groups. Multivariate analysis of overall survival time shows that FANCC mutations (P ‫؍‬ .007) and hematopoietic stem cell transplantation (P ‫؍‬ < .0001) define a poor-risk subgroup. The results of this study of patients registered in the IFAR over a 20-year period provide information that will enable better prediction of outcome and aid clinicians with decisions regarding major therapeutic modalities.

The genetic basis of bipolar disorder has long been thought to be complex, with the potential involvement of multiple genes, but methods to analyze populations with respect to this complexity have only recently become available. We have carried out a genome-wide association study of bipolar disorder by genotyping over 550 000 single-nucleotide polymorphisms (SNPs) in two independent case-control samples of European origin. The initial association screen was performed using pooled DNA, and selected SNPs were confirmed by individual genotyping. While DNA pooling reduces power to detect genetic associations, there is a substantial cost saving and gain in efficiency. A total of 88 SNPs, representing 80 different genes, met the prior criteria for replication in both samples. Effect sizes were modest: no single SNP of large effect was detected. Of 37 SNPs selected for individual genotyping, the strongest association signal was detected at a marker within the first intron of diacylglycerol kinase eta (DGKH; P = 1.5 Â 10 À8 , experiment-wide P < 0.01, OR = 1.59). This gene encodes DGKH, a key protein in the lithium-sensitive phosphatidyl inositol pathway. This first genome-wide association study of bipolar disorder shows that several genes, each of modest effect, reproducibly influence disease risk. Bipolar disorder may be a polygenic disease.

Survival analysis encompasses investigation of time to event data. In most clinical studies, estimating the cumulative incidence function (or the probability of experiencing an event by a given time) is of primary interest. When the data consist of patients who experience an event and censored individuals, a nonparametric estimate of the cumulative incidence can be obtained using the Kaplan -Meier method. Under this approach, the censoring mechanism is assumed to be noninformative. In other words, the survival time of an individual (or the time at which a subject experiences an event) is assumed to be independent of a mechanism that would cause the patient to be censored. Often times, a patient may experience an event other than the one of interest which alters the probability of experiencing the event of interest. Such events are known as competing risk events. In this setting, it would often be of interest to calculate the cumulative incidence of a specific event of interest. Any subject who does not experience the event of interest can be treated as censored. However, a patient experiencing a competing risk event is censored in an informative manner. Hence, the Kaplan -Meier estimation procedure may not be directly applicable. The cumulative incidence function for an event of interest must be calculated by appropriately accounting for the presence of competing risk events. In this paper, we illustrate nonparametric estimation of the cumulative incidence function for an event of interest in the presence of competing risk events using two published data sets. We compare the resulting estimates with those obtained using the Kaplan -Meier approach to demonstrate the importance of appropriately estimating the cumulative incidence of an event of interest in the presence of competing risk events. Survival analysis is the analysis of data measured from a specific time of origin until an event of interest or a specified endpoint (Collett, 1994). For example, in order to determine the incidence of death due to breast cancer among breast cancer patients, every patient will be followed from a baseline date (such as date of diagnosis or date of surgery) until the date of death due to breast cancer or study closing date. A patient who dies of breast cancer during the study period would be considered to have an 'event' at their date of death. A patient who is alive at the end of the study would be considered to be 'censored'. Thus, every patient provides two pieces of information: follow-up time and status (i.e., event or censored). However, a patient can experience an event different from the event of interest. For example, a breast cancer patient may die due to causes unrelated to the disease. Such events are termed competing risk events. Survival data are often summarised using the cumulative incidence function for an event. The goal of this paper is to illustrate to the clinical investigator the nonparametric estimation of the cumulative incidence for an event of interest in the presence of competing risk events. GENERA...