The asymptotic distribution of the isotonic regression estimator over a general countable pre-ordered set

We study the isotonic regression estimator over a general countable pre-ordered set. We obtain the limiting distribution of the estimator and study its properties. It is proved that, under some general assumptions, the limiting distribution of the isotonized estimator is given by the concatenation of the separate isotonic regressions of the certain subvectors of an unrestrecred estimator's asymptotic distribution. Also, we show that the isotonization preserves the rate of convergence of the underlying estimator. We apply these results to the problems of estimation of a bimonotone regression function and estimation of a bimonotone probability mass function

Monotone spectral density estimation

We propose two estimators of a monotone spectral density, that are based on the periodogram. These are the isotonic regression of the periodogram and the isotonic regression of the log-periodogram. We derive pointwise limit distribution results for the proposed estimators for short memory linear processes and long memory Gaussian processes and also that the estimators are rate optimal.Comment: Published in at http://dx.doi.org/10.1214/10-AOS804 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Chemogenetic fingerprinting by analysis of cellular growth dynamics

<p>Abstract</p> <p>Background</p> <p>A fundamental goal in chemical biology is the elucidation of on- and off-target effects of drugs and biocides. To this aim chemogenetic screens that quantify drug induced changes in cellular fitness, typically taken as changes in composite growth, is commonly applied.</p> <p>Results</p> <p>Using the model organism <it>Saccharomyces cerevisiae </it>we here report that resolving cellular growth dynamics into its individual components, growth lag, growth rate and growth efficiency, increases the predictive power of chemogenetic screens. Both in terms of drug-drug and gene-drug interactions did the individual growth variables capture distinct and only partially overlapping aspects of cell physiology. In fact, the impact on cellular growth dynamics represented functionally distinct chemical fingerprints.</p> <p>Discussion</p> <p>Our findings suggest that the resolution and quantification of all facets of growth increases the informational and interpretational output of chemogenetic screening. Hence, by facilitating a physiologically more complete analysis of gene-drug and drug-drug interactions the here reported results may simplify the assignment of mode-of-action to orphan bioactive compounds.</p

Variation in GYS1 interacts with exercise and gender to predict cardiovascular mortality

"Background. The muscle glycogen synthase gene (GYS1) has been associated with type 2 diabetes (T2D), the metabolic syndrome (MetS), male myocardial infarction and a defective increase in muscle glycogen synthase protein in response to exercise. We addressed the questions whether polymorphism in GYS1 can predict cardiovascular (CV) mortality in a high-risk population, if this risk is influenced by gender or physical activity, and if the association is independent of genetic variation in nearby apolipoprotein E gene (APOE). Methodology/Principal Findings. Polymorphisms in GYS1 (XbaIC>T) and APOE (-219G>T, epsilon 2/epsilon 3/epsilon 4) were genotyped in 4,654 subjects participating in the Botnia T2D-family study and followed for a median of eight years. Mortality analyses were performed using Cox proportional-hazards regression. During the follow-up period, 749 individuals died, 409 due to CV causes. In males the GYS1 XbaI T-allele (hazard ratio (HR) 1.9 [1.2-2.9]), T2D (2.5 [1.7-3.8]), earlier CV events (1.7 [1.2-2.5]), physical inactivity (1.9 [1.2-2.9]) and smoking (1.5 [1.0-2.3]) predicted CV mortality. The GYS1 XbaI T-allele predicted CV mortality particularly in physically active males (HR 1.7 [1.3-2.0]). Association of GYS1 with CV mortality was independent of APOE (219TT/epsilon 4), which by its own exerted an effect on CV mortality risk in females (2.9 [1.9-4.4]). Other independent predictors of CV mortality in females were fasting plasma glucose (1.2 [1.1-1.2]), high body mass index (BMI) (1.0 [1.0-1.1]), hypertension (1.9 [1.2-3.1]), earlier CV events (1.9 [1.3-2.8]) and physical inactivity (1.9 [1.2-2.8]). Conclusions/Significance. Polymorphisms in GYS1 and APOE predict CV mortality in T2D families in a gender-specific fashion and independently of each other. Physical exercise seems to unmask the effect associated with the GYS1 polymorphism, rendering carriers of the variant allele less susceptible to the protective effect of exercise on the risk of CV death, which finding could be compatible with a previous demonstration of defective increase in the glycogen synthase protein in carriers of this polymorphism.""Background. The muscle glycogen synthase gene (GYS1) has been associated with type 2 diabetes (T2D), the metabolic syndrome (MetS), male myocardial infarction and a defective increase in muscle glycogen synthase protein in response to exercise. We addressed the questions whether polymorphism in GYS1 can predict cardiovascular (CV) mortality in a high-risk population, if this risk is influenced by gender or physical activity, and if the association is independent of genetic variation in nearby apolipoprotein E gene (APOE). Methodology/Principal Findings. Polymorphisms in GYS1 (XbaIC>T) and APOE (-219G>T, epsilon 2/epsilon 3/epsilon 4) were genotyped in 4,654 subjects participating in the Botnia T2D-family study and followed for a median of eight years. Mortality analyses were performed using Cox proportional-hazards regression. During the follow-up period, 749 individuals died, 409 due to CV causes. In males the GYS1 XbaI T-allele (hazard ratio (HR) 1.9 [1.2-2.9]), T2D (2.5 [1.7-3.8]), earlier CV events (1.7 [1.2-2.5]), physical inactivity (1.9 [1.2-2.9]) and smoking (1.5 [1.0-2.3]) predicted CV mortality. The GYS1 XbaI T-allele predicted CV mortality particularly in physically active males (HR 1.7 [1.3-2.0]). Association of GYS1 with CV mortality was independent of APOE (219TT/epsilon 4), which by its own exerted an effect on CV mortality risk in females (2.9 [1.9-4.4]). Other independent predictors of CV mortality in females were fasting plasma glucose (1.2 [1.1-1.2]), high body mass index (BMI) (1.0 [1.0-1.1]), hypertension (1.9 [1.2-3.1]), earlier CV events (1.9 [1.3-2.8]) and physical inactivity (1.9 [1.2-2.8]). Conclusions/Significance. Polymorphisms in GYS1 and APOE predict CV mortality in T2D families in a gender-specific fashion and independently of each other. Physical exercise seems to unmask the effect associated with the GYS1 polymorphism, rendering carriers of the variant allele less susceptible to the protective effect of exercise on the risk of CV death, which finding could be compatible with a previous demonstration of defective increase in the glycogen synthase protein in carriers of this polymorphism.""Background. The muscle glycogen synthase gene (GYS1) has been associated with type 2 diabetes (T2D), the metabolic syndrome (MetS), male myocardial infarction and a defective increase in muscle glycogen synthase protein in response to exercise. We addressed the questions whether polymorphism in GYS1 can predict cardiovascular (CV) mortality in a high-risk population, if this risk is influenced by gender or physical activity, and if the association is independent of genetic variation in nearby apolipoprotein E gene (APOE). Methodology/Principal Findings. Polymorphisms in GYS1 (XbaIC>T) and APOE (-219G>T, epsilon 2/epsilon 3/epsilon 4) were genotyped in 4,654 subjects participating in the Botnia T2D-family study and followed for a median of eight years. Mortality analyses were performed using Cox proportional-hazards regression. During the follow-up period, 749 individuals died, 409 due to CV causes. In males the GYS1 XbaI T-allele (hazard ratio (HR) 1.9 [1.2-2.9]), T2D (2.5 [1.7-3.8]), earlier CV events (1.7 [1.2-2.5]), physical inactivity (1.9 [1.2-2.9]) and smoking (1.5 [1.0-2.3]) predicted CV mortality. The GYS1 XbaI T-allele predicted CV mortality particularly in physically active males (HR 1.7 [1.3-2.0]). Association of GYS1 with CV mortality was independent of APOE (219TT/epsilon 4), which by its own exerted an effect on CV mortality risk in females (2.9 [1.9-4.4]). Other independent predictors of CV mortality in females were fasting plasma glucose (1.2 [1.1-1.2]), high body mass index (BMI) (1.0 [1.0-1.1]), hypertension (1.9 [1.2-3.1]), earlier CV events (1.9 [1.3-2.8]) and physical inactivity (1.9 [1.2-2.8]). Conclusions/Significance. Polymorphisms in GYS1 and APOE predict CV mortality in T2D families in a gender-specific fashion and independently of each other. Physical exercise seems to unmask the effect associated with the GYS1 polymorphism, rendering carriers of the variant allele less susceptible to the protective effect of exercise on the risk of CV death, which finding could be compatible with a previous demonstration of defective increase in the glycogen synthase protein in carriers of this polymorphism."Peer reviewe

Genetic Prediction of Future Type 2 Diabetes

BACKGROUND: Type 2 diabetes (T2D) is a multifactorial disease in which environmental triggers interact with genetic variants in the predisposition to the disease. A number of common variants have been associated with T2D but our knowledge of their ability to predict T2D prospectively is limited. METHODS AND FINDINGS: By using a Cox proportional hazard model, common variants in the PPARG (P12A), CAPN10 (SNP43 and 44), KCNJ11 (E23K), UCP2 (−866G>A), and IRS1 (G972R) genes were studied for their ability to predict T2D in 2,293 individuals participating in the Botnia study in Finland. After a median follow-up of 6 y, 132 (6%) persons developed T2D. The hazard ratio for risk of developing T2D was 1.7 (95% confidence interval [CI] 1.1–2.7) for the PPARG PP genotype, 1.5 (95% CI 1.0–2.2) for the CAPN10 SNP44 TT genotype, and 2.6 (95% CI 1.5–4.5) for the combination of PPARG and CAPN10 risk genotypes. In individuals with fasting plasma glucose ≥ 5.6 mmol/l and body mass index ≥ 30 kg/m(2), the hazard ratio increased to 21.2 (95% CI 8.7–51.4) for the combination of the PPARG PP and CAPN10 SNP43/44 GG/TT genotypes as compared to those with the low-risk genotypes with normal fasting plasma glucose and body mass index < 30 kg/m(2). CONCLUSION: We demonstrate in a large prospective study that variants in the PPARG and CAPN10 genes predict future T2D. Genetic testing might become a future approach to identify individuals at risk of developing T2D

Deconvolution under monotonicity assumptions

We state limit distribution results for the isotonic inverse estimator of a distribution function when the data are disturbed by a random variable with a decreasing density. Three different dependence structures are considered: independent, weak dependent and suboordinated gaussian long range dependent data

Functional central limit theorems for the Nelson–Aalen and Kaplan–Meier estimators for dependent stationary data

We derive process limit distribution results for the Nelson–Aalen estimator of a hazard function and for the Kaplan–Meier estimator of a distribution function, under different dependence assumptions. The data are assumed to be right censored observations of a stationary time series. We treat weakly dependent as well as long range dependent data, and allow for qualitative differences in the dependence for the censoring times versus the time of interest

Nonparametric Functional Estimation under Order Restrictions

This thesis consists of three papers (Papers A-C) on problems in nonparametric functional estimation, in particular density and regression function estimation and deconvolution, under order assumptions. Pointwise limit distribution results are stated for the obtained estimators, which include isotonic regression estimates, nonparametric maximum likelihood estimates of monotone densities, estimates of convex regression and density functions and deconvolution estimates. Paper A states a limit distribution formula for the greatest convex minorant mapping and its derivative, which is then applied to regression function and density function estimation under monotonicity or convexity restrictions, at points of continuity and under various smoothness assumptions on the unknown function. Also treated is isotonization of kernel estimates, with application to regression and density estimation. Paper B extends the results of Paper A to limit results at points of discontinuity of the unknown function. Paper C is concerned with deconvolution under order restrictions. Our methods give a unified approach to regression and density function estimation with order restrictions, thereby restating many previously known results as special cases as well as obtaining new results, mainly for dependent data and/or discontinuous functions