Hip joint replacement surgery for idiopathic osteoarthritis aggregates in families

In order to determine whether there is a genetic component to hip or knee joint failure due to idiopathic osteoarthritis (OA), we invited patients (probands) undergoing hip or knee arthroplasty for management of idiopathic OA to provide detailed family histories regarding the prevalence of idiopathic OA requiring joint replacement in their siblings. We also invited their spouses to provide detailed family histories about their siblings to serve as a control group. In the probands, we confirmed the diagnosis of idiopathic OA using American College of Rheumatology criteria. The cohorts included the siblings of 635 probands undergoing total hip replacement, the siblings of 486 probands undergoing total knee replacement, and the siblings of 787 spouses. We compared the prevalence of arthroplasty for idiopathic OA among the siblings of the probands with that among the siblings of the spouses, and we used logistic regression to identify independent risk factors for hip and knee arthroplasty in the siblings. Familial aggregation for hip arthroplasty, but not for knee arthroplasty, was observed after controlling for age and sex, suggesting a genetic contribution to end-stage hip OA but not to end-stage knee OA. We conclude that attempts to identify genes that predispose to idiopathic OA resulting in joint failure are more likely to be successful in patients with hip OA than in those with knee OA.     

A model for the oscillatory motion of single dynein molecules

Axonemal dynein is the molecular motor responsible for the rhythmic beating of eukaryotic cilia and flagella. An individual axonemal dynein molecule is capable of both unidirectional and oscillatory motion along a microtubule (Nature 393 (1998) 711). We propose a model which links the physical motion of a two-headed dynein molecule to its ATP hydrolysis cycle, and which exhibits both processive and oscillatory behaviors. A mathematical analysis of the model is used to make experimentally testable predictions.     

Segregation analysis of non-insulin-dependent diabetes mellitus in Pima Indians: evidence for a major-gene effect.

Non-insulin-dependent diabetes mellitus (NIDDM) has a high prevalence in Pima Indians. The disorder is familial, but the extent to which genetic factors are involved in its etiology is largely unknown. Segregation analysis was used to determine whether familial aggregation of NIDDM in this population could reflect the action of a single major gene. The analysis included 2,697 subjects from 653 nuclear families in which both parents and at least one offspring had been examined in the course of a longitudinal epidemiological study. The REGTL program of the SAGE package was used to fit models in which age at onset of NIDDM is transmitted from parent to offspring under the unified model for segregation analysis. Likelihood-ratio tests were used to test hypotheses related to genetic transmission. The hypothesis of no major effect was strongly rejected (P < .01), as was that of no transmission of the major effect (P < .01). Mendelian transmission was not rejected (P = .91). Similar results were obtained when covariates for obesity and birth cohort were added to the models and when a power transformation of age at onset was estimated. A strong effect of birth cohort with earlier age at onset in the later born cohorts was observed (P < .01). The findings are consistent with the hypothesis that a major gene influences the risk for NIDDM in Pima Indians by affecting age at onset. The expression of this gene may depend on environmental factors that have become more prevalent in recent-birth cohorts.     

Linkage analysis of alpha 1-antitrypsin deficiency: lessons for complex diseases.

OBJECTIVES: Severe alpha 1-antitrypsin (A1AT) deficiency is the one proven genetic risk factor for chronic obstructive pulmonary disease (COPD). Familial aggregation has been demonstrated for COPD among individuals who do not have A1AT deficiency, but linkage analysis of COPD has not been reported. To investigate the optimal phenotype definitions and analytical methods for the linkage analysis of COPD, we examined a set of 28 A1AT- deficient families containing 155 individuals. We have used the protease inhibitor (PI) type as a genetic marker rather than a disease gene, and we have performed linkage analysis between PI type and serum A1AT level and spirometry-related phenotypes. METHODS: Linkage analysis was performed on the quantitative phenotypes forced expiratory volume at 1 s (FEV(1) as % predicted), the ratio of FEV(1) to forced vital capacity (FEV(1)/FVC as % predicted), and serum A1AT level using the variance component approach in SOLAR, the generalized estimating equation approach in RELPAL, and the model-based classical lod score method in LINKAGE. Linkage analysis with qualitative A1AT and spirometry phenotypes was performed using a model-based method (LINKAGE) and a model-free method (GENEHUNTER). Adjustments for smoking effects were investigated under each method. RESULTS: All of the methods demonstrated linkage of PI type to serum A1AT level. Interestingly, however, the other quantitative phenotypes provided only weak evidence for linkage of PI type to lung disease. Better evidence for linkage of lung disease to PI type was found using a moderate or a mild threshold for the definition of airflow obstruction. CONCLUSIONS: For linkage analysis of spirometry phenotypes in A1AT deficiency, qualitative phenotypes provided stronger evidence for linkage than quantitative phenotypes. Possible contributors to the stronger evidence for linkage to qualitative spirometry phenotypes include the ascertainment scheme and the nonnormality of the pulmonary function data in PI Z subjects. This study provides guidelines for studies of the genetics of COPD unrelated to A1AT deficiency.     

Signal inhibition by a dynamically regulated pool of monophosphorylated MAPK

Protein kinases regulate a broad array of cellular processes and do so through the phosphorylation of one or more sites within a given substrate. Many protein kinases are themselves regulated through multisite phosphorylation, and the addition or removal of phosphates can occur in a sequential (processive) or a stepwise (distributive) manner. Here we measured the relative abundance of the monophosphorylated and dual-phosphorylated forms of Fus3, a member of the mitogen-activated protein kinase (MAPK) family in yeast. We found that upon activation with pheromone, a substantial proportion of Fus3 accumulates in the monophosphorylated state. Introduction of an additional copy of Fus3 lacking either phosphorylation site leads to dampened signaling. Conversely, cells lacking the dual-specificity phosphatase (msg5Δ) or that are deficient in docking to the MAPK-scaffold (Ste5^ND) accumulate a greater proportion of dual-phosphorylated Fus3. The double mutant exhibits a synergistic, or “synthetic,” supersensitivity to pheromone. Finally, we present a predictive computational model that combines MAPK scaffold and phosphatase activities and is sufficient to account for the observed MAPK profiles. These results indicate that the monophosphorylated and dual-phosphorylated forms of the MAPK act in opposition to one another. Moreover, they reveal a new mechanism by which the MAPK scaffold acts dynamically to regulate signaling.     

Using the Optimal Robust Receiver Operating Characteristic (ROC) Curve for Predictive Genetic Tests

Summary Current ongoing genome‐wide association (GWA) studies represent a powerful approach to uncover common unknown genetic variants causing common complex diseases. The discovery of these genetic variants offers an important opportunity for early disease prediction, prevention, and individualized treatment. We describe here a method of combining multiple genetic variants for early disease prediction, based on the optimality theory of the likelihood ratio (LR). Such theory simply shows that the receiver operating characteristic (ROC) curve based on the LR has maximum performance at each cutoff point and that the area under the ROC curve so obtained is highest among that of all approaches. Through simulations and a real data application, we compared it with the commonly used logistic regression and classification tree approaches. The three approaches show similar performance if we know the underlying disease model. However, for most common diseases we have little prior knowledge of the disease model and in this situation the new method has an advantage over logistic regression and classification tree approaches. We applied the new method to the type 1 diabetes GWA data from the Wellcome Trust Case Control Consortium. Based on five single nucleotide polymorphisms, the test reaches medium level classification accuracy. With more genetic findings to be discovered in the future, we believe a predictive genetic test for type 1 diabetes can be successfully constructed and eventually implemented for clinical use.