Lise Bellanger

Testing the burden of rare variation in arrhythmia-susceptibility genes provides new insights into molecular diagnosis for Brugada syndrome

The Brugada syndrome (BrS) is a rare heritable cardiac arrhythmia disorder associated with ventricular fibrillation and sudden cardiac death. Mutations in the SCN5A gene have been causally related to BrS in 20-30% of cases. Twenty other genes have been described as involved in BrS, but their overall contribution to disease prevalence is still unclear. This study aims to estimate the burden of rare coding variation in arrhythmia-susceptibility genes among a large group of patients with BrS. We have developed a custom kit to capture and sequence the coding regions of 45 previously reported arrhythmia-susceptibility genes and applied this kit to 167 index cases presenting with a Brugada pattern on the electrocardiogram as well as 167 individuals aged over 65-year old and showing no history of cardiac arrhythmia. By applying burden tests, a significant enrichment in rare coding variation (with a minor allele frequency below 0.1%) was observed only for SCN5A, with rare coding variants carried by 20.4% of cases with BrS versus 2.4% of control individuals (P = 1.4 × 10(-7)). No significant enrichment was observed for any other arrhythmia-susceptibility gene, including SCN10A and CACNA1C. These results indicate that, except for SCN5A, rare coding variation in previously reported arrhythmia-susceptibility genes do not contribute significantly to the occurrence of BrS in a population with European ancestry. Extreme caution should thus be taken when interpreting genetic variation in molecular diagnostic setting, since rare coding variants were observed in a similar extent among cases versus controls, for most previously reported BrS-susceptibility genes.

Lead contamination in French children's homes and environment

Lead in homes is a well-known source of childhood lead exposure, which is still of concern due to the health effects of low lead doses. This study aims to describe lead contamination in the homes of children aged 6 months to 6 years in France (without overseas). Between October 2008 and August 2009, 484 housing units were investigated. Lead in tap water and total and leachable lead levels from floor dust, outdoor soils and paint chips were measured. X-ray fluorescence measurements were carried out on non-metallic and metallic substrates. Nationwide results are provided. The indoor floor dust lead (PbD) geometric mean (GM) was 8.8 μg/m² (0.8 μg/ft²) and 6.8 μg/m² (0.6 μg/ft²) for total and leachable lead respectively; 0.21% of homes had an indoor PbD loading above 430.5 μg/m² (40 μg/ft²). The outdoor play area concentration GM was 33.5 mg/kg and 21.7 mg/kg in total and leachable lead respectively; 1.4% of concentrations were higher than or equal to 400 mg/kg. Outdoor floor PbD GM was 44.4 μg/m² (4.1 μg/ft²) that was approximately 3.2 times higher than the GM of indoor PbD. Lead-based paint (LBP) was present in 25% of dwellings, LBP on only non-metallic substrates was present in 19% of homes and on metallic substrates in 10% of dwellings. The GM of lead concentrations in tap water was below 1 μg/L; 58% of concentrations were lower than 1 μg/L and 2.9% were higher than or equal to 10 μg/L. The age cut-off for homes with lead would be 1974 for paint and 1993 for indoor floor dust. This study provides, for the first time, a look at the state of lead contamination to which children are exposed in French housing. Moreover, it provides policy makers an estimate of the number of French dwellings sheltering children where abatement should be conducted.

Relationships between concentrations of trace metals in wheat grains and soil

In order to reduce the amount of trace metals such as cadmium in human food, it is useful to predict the trace metal composition of cereal grains from well-chosen topsoil variables. Statistical relationships between soil properties and metal concentration in plant organs have already been studied. However, such studies involved only a few common soil properties such as pH, organic carbon content and cation exchange capacity. Here, we compared metal contents in grain and in topsoil samples. 198 samples of grains of winter wheat were collected from paired topsoil and crop surveys in the northern half of France. The soils belong to 18 contrasting pedogeological families. Grain and topsoil samples were analysed for Cd, Cr, Cu, Ni, Pb, Zn, Fe, Mg and Mn. For soil samples, three pools of metal were determined: total concentration by HF digestion, and two available pools by partial extraction using either diethylenetriamine pentaacetic acid (DTPA) or NH4NO3. Canonical correlation analysis including multiple linear regression was used to study relationships between soil and grain data. Our findings show the occurrence of six significant and independent relations between the topsoil variables and the grain variables. The adjustment quality of those relations is measured by six coefficients equivalent to individual determination coefficients. The maximum coefficient of 0.79 is higher than any individual ones. Our findings show in particular an excellent regression model for grain Cd with a small number of topsoil variables, thus allowing an accurate prediction for winter wheat grains. Practically, farmers can use the prediction model to increase soil pH and by growing a less metal-accumulating cultivar upon detection of a threat. The strengths of our study are the following: (1) we studied a very large number of sites, including soils with very diverse parent materials, soil-forming processes and geochemical properties; (2) we measured and tested a large number of variables, including in particular available metal fractions; and (3) we applied unusual statistical methods such as canonical correlation analysis.

Source contributions of lead in residential floor dust and within-home variability of dust lead loading

Evidence of the impact of exposure to low levels of lead on childrens health is increasing. Residential floor dust is the assumed origin of lead exposure by young children. In this study, we estimate the contribution of different lead sources to household interior floor dust contamination. We also estimate the within-home variability of interior floor dust lead loadings. A multilevel model was developed based on data collected in a French survey in 2008-2009 (484 housing units, 1834 rooms). Missing data were handled by multiple imputation using chained equations. The intra-home correlation between interior floor Log dust lead loadings was approximately 0.6. Dust lead from the landing of an apartment, mostly originating outside the building, was the major contributor to interior floor dust lead. Secondary contributors included the lead-based paint on exterior railings, track-in of the exterior soil of the childrens play area into the dwelling, smoking inside the home, demolition of nearby old buildings and sites of pollution in the vicinity. Interior lead-based paint contaminated interior floor dust only in old and non-renovated dwellings. To reduce interior floor dust lead levels in the general population of dwellings, common areas should be maintained, and track-in from the outside should be limited as much as possible.

A Statistical Approach for Dating Archaeological Contexts

This paper describes a statistical model developing from Cor- respondence Analysis to date archaeological contexts of the city of Tours (France) and also to obtain an estimated absolute timescale. The data set used in the study is reported as a contingency table of ceramics against con- texts. But, as pottery is not intrinsically a dating indicator (a date is rarely inscribed on each piece of pottery), we estimate dates of contexts from their finds, and we use coins to attest the date of assemblages. The model-based approach uses classical tools (correspondence analysis, linear regression and resampling methods) in an iterative scheme. Archaeologists may find in the paper a useful set of known statistical methods, while statisticians can learn a way to order well known techniques. No method is new, but their gathering is characteristic of this application.

Trend in high tropospheric ozone levels. Application to paris monitoring sites

This article describes the extreme value analysis of tropospheric ozone level exceedances collected at seven monitoring sites in the Paris metropolitan area during May–September over the 14-year period 1988–2001. The purpose of the study is to establish whether observed trends over time in ozone exceedances of a high threshold are real or if they are the result of meteorological changes affecting the conditions under which ozone is generated. A non-homogeneous Poisson process (NHPP), with parameters depending on meteorological covariates, temporal trend and sites factor, is used to model regionally the exceedance times and sizes of daily maxima of ozone over a high threshold. We highlight the importance of non-linear methods to detect the non-linearities. *E-mail: lise.bellanger@math.univ.nantes.fr

Multilevel modelling of survey data: impact of the two-level weights used in the pseudolikelihood

Approaches that use the pseudolikelihood to perform multilevel modelling on survey data have been presented in the literature. To avoid biased estimates due to unequal selection probabilities, conditional weights can be introduced at each level. Less-biased estimators can also be obtained in a two-level linear model if the level-1 weights are scaled. In this paper, we studied several level-2 weights that can be introduced into the pseudolikelihood when the sampling design and the hierarchical structure of the multilevel model do not match. Two-level and three-level models were studied. The present work was motivated by a study that aims to estimate the contributions of lead sources to polluting the interior floor dust of the rooms within dwellings. We performed a simulation study using the real data collected from a French survey to achieve our objective. We conclude that it is preferable to use unweighted analyses or, at the most, to use conditional level-2 weights in a two-level or a three-level model. We state some warnings and make some recommendations.

DoEstRare: A statistical test to identify local enrichments in rare genomic variants associated with disease

Next-generation sequencing technologies made it possible to assay the effect of rare variants on complex diseases. As an extension of the “common disease-common variant” paradigm, rare variant studies are necessary to get a more complete insight into the genetic architecture of human traits. Association studies of these rare variations show new challenges in terms of statistical analysis. Due to their low frequency, rare variants must be tested by groups. This approach is then hindered by the fact that an unknown proportion of the variants could be neutral. The risk level of a rare variation may be determined by its impact but also by its position in the protein sequence. More generally, the molecular mechanisms underlying the disease architecture may involve specific protein domains or inter-genic regulatory regions. While a large variety of methods are optimizing functionality weights for each single marker, few evaluate variant position differences between cases and controls. Here, we propose a test called DoEstRare, which aims to simultaneously detect clusters of disease risk variants and global allele frequency differences in genomic regions. This test estimates, for cases and controls, variant position densities in the genetic region by a kernel method, weighted by a function of allele frequencies. We compared DoEstRare with previously published strategies through simulation studies as well as re-analysis of real datasets. Based on simulation under various scenarios, DoEstRare was the sole to consistently show highest performance, in terms of type I error and power both when variants were clustered or not. DoEstRare was also applied to Brugada syndrome and early-onset Alzheimer’s disease data and provided complementary results to other existing tests. DoEstRare, by integrating variant position information, gives new opportunities to explain disease susceptibility. DoEstRare is implemented in a user-friendly R package.Next-generation sequencing technologies made it possible to assay the effect of rare variants on complex diseases. As an extension of the “common disease-common variant” paradigm, rare variant studies are necessary to get a more complete insight into the genetic architecture of human traits. Association studies of these rare variations show new challenges in terms of statistical analysis. Due to their low frequency, rare variants must be tested by groups. This approach is then hindered by the fact that an unknown proportion of the variants could be neutral. The risk level of a rare variation may be determined by its impact but also by its position in the protein sequence. More generally, the molecular mechanisms underlying the disease architecture may involve specific protein domains or inter-genic regulatory regions. While a large variety of methods are optimizing functionality weights for each single marker, few evaluate variant position differences between cases and controls. Here, we propose a test called DoEstRare, which aims to simultaneously detect clusters of disease risk variants and global allele frequency differences in genomic regions. This test estimates, for cases and controls, variant position densities in the genetic region by a kernel method, weighted by a function of allele frequencies. We compared DoEstRare with previously published strategies through simulation studies as well as re-analysis of real datasets. Based on simulation under various scenarios, DoEstRare was the sole to consistently show highest performance, in terms of type I error and power both when variants were clustered or not. DoEstRare was also applied to Brugada syndrome and early-onset Alzheimer’s disease data and provided complementary results to other existing tests. DoEstRare, by integrating variant position information, gives new opportunities to explain disease susceptibility. DoEstRare is implemented in a user-friendly R package.

On Comparison of Clustering Methods for Pharmacoepidemiological Data.

The high consumption of psychotropic drugs is a public health problem. Rigorous statistical methods are needed to identify consumption characteristics in post-marketing phase. Agglomerative hierarchical clustering (AHC) and latent class analysis (LCA) can both provide clusters of subjects with similar characteristics. The objective of this study was to compare these two methods in pharmacoepidemiology, on several criteria: number of clusters, concordance, interpretation, and stability over time. From a dataset on bromazepam consumption, the two methods present a good concordance. AHC is a very stable method and it provides homogeneous classes. LCA is an inferential approach and seems to allow identifying more accurately extreme deviant behavior.

High-level exceedances of non-stationary processes and irregular sets

Abstract At first, we show the convergence to a compound Poisson process of the high-level exceedances point process N n ( B ) = ∑ m n ∈ B 1 { ξ m > u n } 1 A ( m ) , where ξ is a stationary and weakly dependent process, U n grows to infinity with n in a suitable way and A ⊂ N satisfies certain geometrical condition, that includes as particular examples, sets where the size of the border is negligible, periodic sets and level sets (i.e., random sets of the form { m ∈ N : ξ m (ω) ∈ B }, with B a Borel set) of a process ξ that satisfies some ergodic properties. At second, we apply this result to non-stationary processes of the form X m = ϕ(ξ m , Y m ) , where ξ and Y are independent, ξ is stationary and weakly dependent, Y is non-stationary and satisfies certain ergodic conditions, and ϕ is a suitable function: we obtain that the high-level exceedances process of X converges to a compound Poisson process.