An iterative algorithm for joint covariate and random effect selection in mixed effects models.

We consider joint selection of fixed and random effects in general mixed-effects models. The interpretation of estimated mixed-effects models is challenging since changing the structure of one set of effects can lead to different choices of important covariates in the model. We propose a stepwise selection algorithm to perform simultaneous selection of the fixed and random effects. It is based on Bayesian Information criteria whose penalties are adapted to mixed-effects models. The proposed procedure performs model selection in both linear and nonlinear models. It should be used in the low-dimension setting where the number of ovariates and the number of random effects are moderate with respect to the total number of observations. The performance of the algorithm is assessed via a simulation study, which includes also a comparative study with alternatives when available in the literature. The use of the method is illustrated in the clinical study of an antibiotic agent kinetics.

Multinomial Logistic Model for Coinfection Diagnosis Between Arbovirus and Malaria in Kedougou.

In tropical regions, populations continue to suffer morbidity and mortality from malaria and arboviral diseases. In Kedougou (Senegal), these illnesses are all endemic due to the climate and its geographical position. The co-circulation of malaria parasites and arboviruses can explain the observation of coinfected cases. Indeed there is strong resemblance in symptoms between these diseases making problematic targeted medical care of coinfected cases. This is due to the fact that the origin of illness is not obviously known. Some cases could be immunized against one or the other of the pathogens, immunity typically acquired with factors like age and exposure as usual for endemic area. Thus, coinfection needs to be better diagnosed. Using data collected from patients in Kedougou region, from 2009 to 2013, we adjusted a multinomial logistic model and selected relevant variables in explaining coinfection status. We observed specific sets of variables explaining each of the diseases exclusively and the coinfection. We tested the independence between arboviral and malaria infections and derived coinfection probabilities from the model fitting. In case of a coinfection probability greater than a threshold value to be calibrated on the data, long duration of illness and age are mostly indicative of arboviral disease while high body temperature and presence of nausea or vomiting symptoms during the rainy season are mostly indicative of malaria disease.

A Dual Model for Prioritizing Cancer Mutations in the Non-coding Genome Based on Germline and Somatic Events.

We address here the issue of prioritizing non-coding mutations in the tumoral genome. To this aim, we created two independent computational models. The first (germline) model estimates purifying selection based on population SNP data. The second (somatic) model estimates tumor mutation density based on whole genome tumor sequencing. We show that each model reflects a different set of constraints acting either on the normal or tumor genome, and we identify the specific genome features that most contribute to these constraints. Importantly, we show that the somatic mutation model carries independent functional information that can be used to narrow down the non-coding regions that may be relevant to cancer progression. On this basis, we identify positions in non-coding RNAs and the non-coding parts of mRNAs that are both under purifying selection in the germline and protected from mutation in tumors, thus introducing a new strategy for future detection of cancer driver elements in the expressed non-coding genome.

High-resolution linkage map for two honeybee chromosomes: the hotspot quest.

Meiotic recombination is a fundamental process ensuring proper disjunction of homologous chromosomes and allele shuffling in successive generations. In many species, this cellular mechanism occurs heterogeneously along chromosomes and mostly concentrates in tiny fragments called recombination hotspots. Specific DNA motifs have been shown to initiate recombination in these hotspots in mammals, fission yeast and drosophila. The aim of this study was to check whether recombination also occurs in a heterogeneous fashion in the highly recombinogenic honeybee genome and whether this heterogeneity can be connected with specific DNA motifs. We completed a previous picture drawn from a routine genetic map built with an average resolution of 93 kb. We focused on the two smallest honeybee chromosomes to increase the resolution and even zoomed at very high resolution (3.6 kb) on a fragment of 300 kb. Recombination rates measured in these fragments were placed in relation with occurrence of 30 previously described motifs through a Poisson regression model. A selection procedure suitable for correlated variables was applied to keep significant motifs. These fine and ultra-fine mappings show that recombination rate is significantly heterogeneous although poorly contrasted between high and low recombination rate, contrarily to most model species. We show that recombination rate is probably associated with the DNA methylation state. Moreover, three motifs (CGCA, GCCGC and CCAAT) are good candidates of signals promoting recombination. Their influence is however moderate, doubling at most the recombination rate. This discovery extends the way to recombination dissection in insects.

Influence function for robust phylogenetic reconstructions.

Based on the computation of the influence function, a tool to measure the impact of each piece of sampled data on the statistical inference of a parameter, we propose to analyze the support of the maximum-likelihood (ML) tree for each site. We provide a new tool for filtering data sets (nucleotides, amino acids, and others) in the context of ML phylogenetic reconstructions. Because different sites support different phylogenic topologies in different ways, outlier sites, that is, sites with a very negative influence value, are important: they can drastically change the topology resulting from the statistical inference. Therefore, these outlier sites must be clearly identified and their effects accounted for before drawing biological conclusions from the inferred tree. A matrix containing 158 fungal terminals all belonging to Chytridiomycota, Zygomycota, and Glomeromycota is analyzed. We show that removing the strongest outlier from the analysis strikingly modifies the ML topology, with a loss of as many as 20% of the internal nodes. As a result, estimating the topology on the filtered data set results in a topology with enhanced bootstrap support. From this analysis, the polyphyletic status of the fungal phyla Chytridiomycota and Zygomycota is reinforced, suggesting the necessity of revisiting the systematics of these fungal groups. We show the ability of influence function to produce new evolution hypotheses.