Deletions of specific exons of FHOD3 detected by next-generation-sequencing are associated with hypertrophic cardiomyopathy

Despite new strategies, such as evaluating deep intronic variants and new genes in whole-genome-sequencing studies, the diagnostic yield of genetic testing in hypertrophic cardiomyopathy (HCM) is still around 50%. FHOD3 has emerged as a novel disease-causing gene for this phenotype, but the relevance and clinical implication of copy-number-variations (CNVs) have not been determined. In this study, CNVs were evaluated using a comparative depth-of-coverage strategy by NGS in 5493 hypertrophic cardiomyopathy probands and 2973 disease-controls. We detected three symmetrical deletions in FHOD3 that involved exons 15 and 16 in three HCM families (no CNVs were detected in the control group). These exons are part of the diaphanous inhibitory domain of FHOD3 protein, considered a cluster of mutations for HCM. The clinical characteristics of the affected carriers were consistent with those reported in FHOD3 in previous studies. This study highlights the importance of performing CNV analysis systematically in NGS genetic testing panels for HCM, and reinforce the relevance of the FHOD3 gene in the disease. This article is protected by copyright. All rights reserved

Nonparametric estimation of conditional transition probabilities in a non-Markov illness-death model

One important goal in multi-state modeling is the estimation of transition probabilities. In longitudinal medical studies these quantities are particularly of interest since they allow for long-term predictions of the process. In recent years signi ficant contributions have been made regarding this topic. However, most of the approaches assume independent censoring and do not account for the influence of covariates. The goal of the paper is to introduce feasible estimation methods for the transition probabilities in an illness-death model conditionally on current or past covariate measures. All approaches are evaluated through a simulation study, leading to a comparison of two di erent estimators. The proposed methods are illustrated using real a colon cancer data set.This research was nanced by FEDER Funds through Programa Operacional Factores de Competitividade COMPETE and by Portuguese Funds through FCT - Funda ção para a Cência e a Tecnologia, within Projects Est-C/MAT/UI0013/2011 and PTDC/MAT/104879/2008. We also acknowledge nancial support from the project Grants MTM2008-03129 and MTM2011-23204 (FEDER support included) of the Spanish Ministerio de Ciencia e Innovaci on and 10PXIB300068PR of the Xunta de Galicia. Partial support from a grant from the US National Security Agency (H98230-11-1-0168) is greatly appreciated

A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests

<p>Abstract</p> <p>Background</p> <p>The detection of true significant cases under multiple testing is becoming a fundamental issue when analyzing high-dimensional biological data. Unfortunately, known multitest adjustments reduce their statistical power as the number of tests increase. We propose a new multitest adjustment, based on a sequential goodness of fit metatest (SGoF), which increases its statistical power with the number of tests. The method is compared with Bonferroni and FDR-based alternatives by simulating a multitest context via two different kinds of tests: 1) one-sample t-test, and 2) homogeneity G-test.</p> <p>Results</p> <p>It is shown that SGoF behaves especially well with small sample sizes when 1) the alternative hypothesis is weakly to moderately deviated from the null model, 2) there are widespread effects through the family of tests, and 3) the number of tests is large.</p> <p>Conclusion</p> <p>Therefore, SGoF should become an important tool for multitest adjustment when working with high-dimensional biological data.</p

Estimation in a Competing Risks Proportional Hazards Model Under Length-biased Sampling With Censoring

International audienceWhat population does the sample represent? The answer to this question is of crucial importance when estimating a survivor function in duration studies. As is well-known, in a stationary population, survival data obtained from a cross-sectional sample taken from the population at time $t_0$ represents not the target density $f(t)$ but its length-biased version proportional to $tf(t)$, for $t>0$. The problem of estimating survivor function from such length-biased samples becomes more complex, and interesting, in presence of competing risks and censoring. This paper lays out a sampling scheme related to a mixed Poisson process and develops nonparametric estimators of the survivor function of the target population assuming that the two independent competing risks have proportional hazards. Two cases are considered: with and without independent consoring before length biased sampling. In each case, the weak convergence of the process generated by the proposed estimator is proved. A well-known study of the duration in power for political leaders is used to illustrate our results. Finally, a simulation study is carried out in order to assess the finite sample behaviour of our estimators

Xylem and leaf functional adjustments to drought in Pinus sylvestris and Quercus pyrenaica at their elevational boundary

Climatic scenarios for the Mediterranean region forecast increasing frequency and intensity of drought events. Consequently, a reduction in Pinus sylvestris L. distribution range is projected within the region, with this species being outcompeted at lower elevations by more drought-tolerant taxa such as Quercus pyrenaica Willd. The functional response of these species to the projected shifts in water availability will partially determine their performance and, thus, their competitive success under these changing climatic conditions. We studied how the cambial and leaf phenology and xylem anatomy of these two species responded to a 3-year rainfall exclusion experiment set at their elevational boundary in Central Spain. Additionally, P. sylvestris leaf gas exchange, water potential and carbon isotope content response to the treatment were measured. Likewise, we assessed inter-annual variability in the studied functional traits under control and rainfall exclusion conditions. Prolonged exposure to drier conditions did not affect the onset of xylogenesis in either of the studied species, whereas xylem formation ceased 1-3 weeks earlier in P. sylvestris. The rainfall exclusion had, however, no effect on leaf phenology on either species, which suggests that cambial phenology is more sensitive to drought than leaf phenology. P. sylvestris formed fewer, but larger tracheids under dry conditions and reduced the proportion of latewood in the tree ring. On the other hand, Q. pyrenaica did not suffer earlywood hydraulic diameter changes under rainfall exclusion, but experienced a cumulative reduction in latewood width, which could ultimately challenge its hydraulic performance. The phenological and anatomical response of the studied species to drought is consistent with a shift in resource allocation under drought stress from xylem to other sinks. Additionally, the tighter stomatal control and higher intrinsic water use efficiency observed in drought-stressed P. sylvestris may eventually limit carbon uptake in this species. Our results suggest that both species are potentially vulnerable to the forecasted increase in drought stress, although P. sylvestris might experience a higher risk of drought-induced decline at its low elevational limit

Nonparametric estimation of a conditional distribution from length-biased data

Cross-sectional sampling, Left-truncation, Regression, Unemployment duration,

Estimation of the bivariate distribution function for censored gap times

First published online: 12 Dec 2014In many medical studies, patients may experience several events during follow-up. The times between consecutive events (gap times) are often of interest and lead to problems that have received much attention recently. In this work we consider the estimation of the bivariate distribution function for censored gap times. Some related problems such as the estimation of the marginal distribution of the second gap time and the conditional distribution are also discussed. In this paper we introduce a nonparametric estimator of the bivariate distribution function based on Bayes' theorem and Kaplan-Meier survival function and explore the behavior of the four estimators through simulations. Real data illustration is included.Ana Moreira acknowledges financial support by grant SFRH/BD/62284/2009 of the Portuguese Ministry of Science, Technology and Higher Education. This research was also financed by FEDER Funds through Programa Operacional Factores de Competitividade COMPETE and by Portuguese Funds through FCT - Fundação para a Ciência e a Tecnologia, within Projects PTDC/MAT/104879 and PEst-OE/MAT/UI0013/2014