Distance to high-voltage power lines and risk of childhood leukemia:An analysis of confounding by and interaction with other potential risk factors

We investigated whether there is an interaction between distance from residence at birth to nearest power line and domestic radon and traffic-related air pollution, respectively, in relation to childhood leukemia risk. Further, we investigated whether adjusting for potential confounders alters the association between distance to nearest power line and childhood leukemia. We included 1024 cases aged <15, diagnosed with leukemia during 1968-1991, from the Danish Cancer Registry and 2048 controls randomly selected from the Danish childhood population and individually matched by gender and year of birth. We used geographical information systems to determine the distance between residence at birth and the nearest 132-400 kV overhead power line. Concentrations of domestic radon and traffic-related air pollution (NOx at the front door) were estimated using validated models. We found a statistically significant interaction between distance to nearest power line and domestic radon regarding risk of childhood leukemia (p = 0.01) when using the median radon level as cut-off point but not when using the 75th percentile (p = 0.90). We found no evidence of an interaction between distance to nearest power line and traffic-related air pollution (p = 0.73). We found almost no change in the estimated association between distance to power line and risk of childhood leukemia when adjusting for socioeconomic status of the municipality, urbanization, maternal age, birth order, domestic radon and traffic-related air pollution. The statistically significant interaction between distance to nearest power line and domestic radon was based on few exposed cases and controls and sensitive to the choice of exposure categorization and might, therefore, be due to chance

A comparison of procedures for structural learning of biological networks

Over the past years, microarray technologies have produced a tremendous amount of gene expression data. The availability of these data has motivated researchers to assess genes function and to gain a deeper understanding of the cellular processes, using network theory as tool for the analysis. An elegant framework for modeling and inferring network structures in biological systems is provided by graphical models. They allow the stochastic description of network associations and dependence structures in complex highly structured data. However, typically gene expression data set includes a large number of variables but only few samples making standard graphical model theories inapplicable. The issues presented by genetic data have led to further extend the theory of graphical models to allow their applications in this area. The main aim of this thesis is the comparison of recent procedures, which estimate sparse concentration matrices and learn the structure of biological networks, through the use of both simulated and real data. The compared procedures are: G-Lasso algorithm (Friedman et al., 2008), Shrinkage estimator with empirical Bayes approach for model selection (Schafer and Strimmer, 2005a, 2005b), PC-algorithm (Kalisch and Buhlmann, 2007). When n > p, we consider also the simple frequentist approach based on MLE and t-test for model selection (see Lauritzen, 1996). Regarding the simulated data, for having a realistic simulation of the biological structures, the data have the peculiarity to reproduce few gene regulatory network structures of interest and they are generated by exploiting some properties of the Cholesky decomposition of a matrix. Concerning the real data, we consider the analysis of one of the best characterized system: Escherichia coli. A large part of its transcriptional regulatory network is known, hence it can be used as a gold-standard to assess the performance of different procedures in the comparative study.Negli ultimi anni, le tecnologie dei microarray hanno prodotto una grande quantità di dati provenienti da processi di espressione genica. La disponibilità di questi dati ha permesso ai ricercatori di poter approfondire lo studio della funzione dei diversi geni e poter acquisire una più profonda conoscenza sui processi cellulari, utilizzando come strumento di ricerca la teoria dei network. I modelli grafici risultano essere un utile strumento per la modellazione e l'analisi delle strutture dei networks derivanti da dati biologici. Infatti, questi modelli consentono di rappresentare in modo stocastico le associazioni e le strutture di dipendenza tra gli elementi di data set con struttura complessa. Tuttavia, i dati derivanti da profili di espressione genica si presentano con un elevato numero di variabili ma solo poche osservazioni rendendo, perciò, la teoria classica dei modelli grafici inapplicabile. I problemi legati all'utilizzo di dati genetici hanno portato ad estendere la teoria dei modelli grafici per consentire l'impiego di questi modelli anche in questo campo di applicazione. Lo scopo principale di questa tesi è quello di confrontare, attraverso l'utilizzo di dati simulati e reali, recenti procedure sviluppate con lo scopo di stimare matrici di concentazione sparse e ricostruire i networks biologici. Le procedure considerate per il confronto sono: l'algoritmo G-Lasso (Friedman et al., 2008), lo stimatore Shrinkage associato con l'approccio Bayes empirico per la selezione del modello (Schafer and Strimmer, 2005a, 2005b), l'algoritmo PC (Kalisch and Buhlmann, 2007). Quando n > p, consideriamo anche un semplice approccio frequentista basato sullo stimatore ML e l'utilizzo del test t per la selezione del modello (si veda Lauritzen, 1996). Per quanto riguarda i dati simulati, per avere strutture biologiche simili a quelle reali, i dati hanno la peculiarità di riprodurre alcune strutture dei network di regolazione genica e sono ottenuti sfruttando alcune proprieta’ della decomposizione di Cholesky di una matrice. Per il confronto con dati reali, sono stati utilizzati dati derivanti da uno dei sistemi maggiormente studiati: Escherichia coli. Infatti, grand parte del network di regolazione genica di questo battere è noto, quindi può essere utilizzato come riferimento per valutare il rendimento delle diverse procedure poste a confronto

Statistics, Bioinformatics and Registry

There has been much recent interest in systems biology for investigating the structure of gene regulatory systems. One popular approach is by network analysis with Gaussian graphical models (GGMs), which are statistical models associated with undirected graphs, where vertices of the graph represent genes and edges indicate regulatory interactions. Gene expression microarray data allow us to observe the amount of mRNA simultaneously for a large number of genes p under different experimental conditions n, where p is usually much larger than n prohibiting the use of standard methods. In this paper we assess and compare the performance of a number of procedures that have been specifically designed to address this large p – small n issue: G–Lasso estimation (Friedman et al., 2008), Neighbourhood selection (Meinshausen and Bühlmann, 2006), shrinkage estimation using empirical Bayes for model selection (Schäfer and Strimmer, 2005), and PC-algorithm (Kalisch and Bühlmann, 2007). We found that all approaches performed poorly on the benchmark E.coli network. Hence we systematically studied their ability to detect specific recurring regulatory patterns, called network motifs, that are interesting from a biological point of view. We conclude that all methods have difficulty detecting hubs, but the PC-algorithm is most promising. 1

Risk of Stroke in Migraineurs Using Triptans. Associations with Age, Sex, Stroke Severity and Subtype

AbstractBackgroundIdentifying migraineurs by triptan utilization we studied risk for stroke in migraineurs compared to the general population.MethodsA cohort study including all citizens 25–80years of age in Denmark 2003–2011 was conducted. All persons prescribed triptans, and all those hospitalized for a first stroke were identified in the Danish Registries. Information on stroke severity/subtype and cardiovascular risk factors was available for stroke patients.FindingsOf the 49,711 patients hospitalized for a first stroke, 1084 were migraineurs using triptans. Adjusting for age, sex, income, and educational level, risk for stroke was higher among migraineurs in respect to all strokes (RR 1.07; CI 1.01–1.14) and ischemic strokes (RR 1.07; CI 1.00–1.14). Risk for hemorrhagic stroke was increased but only in women (RR 1.41; CI 1.11–1.79). Risk was for mild strokes (RR 1.31; CI 1.16–1.48) while risk for severe strokes was lower among migraineurs (RR 0.77; CI 0.65–0.91). Risk was age-related; highest among women 25–45years (RR≈1.7). Risk was unrelated to numbers of dispensations.InterpretationMigraineurs identified by triptan utilization had higher risk for stroke. Strokes were minor and cardiovascular risk factors were less prevalent pointing to a migraine-specific etiology of stroke different from that of thromboembolism