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International audience; Key message: A novel multi-environment competition individual-tree mixed model resulted in better fit, and greater individual narrow- and broad-sense heritabilities than the model without competition, notably for traits showing competition. In multi-environment tests, the proposed model would allow tree breeders to select genotypes with the best performance in both additive direct and competition breeding values, increasing forest productivity. Context: Genetic merit of trees is known to be affected and interact with local competition effects as well as changes across environmental conditions. Recent studies showed that competition genetic effects can affect the genetic variance and bias the tree breeding values, and its covariance with direct breeding values has been variable across traits. Aims: The present paper extends a mixed-model methodology to the problem of accounting for competition in a multi-environment set of forest genetic trials and exploring its impact on genetic variances as well as the multi-environment genetic correlation. Methods: The proposed model is illustrated using data from two full-sib trials of Pinus elliottii var. elliottii × Pinus caribaea var. hondurensis F1. Dispersion parameters and (co)variance of total breeding values were estimated for diameter at breast height, total tree height, and stem straightness at age 10. Results: For traits showing competition effects (diameter at breast height and total tree height), the proposed multi-environment competition model gave better fit than the simpler model. Accounting for competition increased the direct additive variance, reduced the residual variances, and did not change significantly the across-site additive genetic correlation. However, for diameter at breast height, top 5% best genetic rankings showed differences. Conclusion: When traits are strongly affected by inter-tree competition, the use of the proposed model in multi-environment analyses can efficiently identify the phenomenon with general benefits in the fitting of genetic components and open the door to select on the basis of competitiveness.

International audience; The identification of gene-environment interactions in relation to risk of human diseases has been challenging. One difficulty has been that measurement error in the exposure can lead to massive reductions in the power of the test, as well as in bias toward the null in the interaction effect estimates. Leveraging previous work on linear discriminant analysis, we develop a new test of interaction between genetic variants and a continuous exposure that mitigates these detrimental impacts of exposure measurement error in ExG testing by reversing the role of exposure and the diseases status in the fitted model, thus transforming the analysis to standard linear regression. Through simulation studies, we show that the proposed approach is valid in the presence of classical exposure measurement error as well as when there is correlation between the exposure and the genetic variant. Simulations also demonstrated that the reverse test has greater power compared to logistic regression. Finally, we confirmed that our approach eliminates bias from exposure measurement error in estimation. Computing times are reduced by as much as fivefold in this new approach. For illustrative purposes, we applied the new approach to an ExGWAS study of interactions with alcohol and body mass index among 1,145 cases with invasive breast cancer and 1,142 controls from the Cancer Genetic Markers of Susceptibility study.

International audience; Many different environmental factors (nutrients, pollutants, chemicals, physical activity, lifestyle, physical and mental stress) can modulate epigenetic markers in the developing and adult organism. Epigenetics, in turn, can cause and is associated with several neurodegenerative and aging-dependent human diseases. Alzheimer's disease certainly represents one of the most relevant neurodegenerative disorders due to its incidence and its huge socio-economic impact. Therefore, it is easy to understand why recent literature focuses on the epigenetic modifications associated with Alzheimer's disease and other neurodegenerative disorders. One of the most intriguing and, at the same time, worrying evidence is that even "mild" environmental factors (such as behavioral or physical stress) as well as the under-threshold exposure to pollutants and chemicals, can be effective. Finally, even mild nutrients disequilibria can result in long-lasting and functional alterations of many epigenetic markers, although they don't have an immediate acute effect. Therefore, we will probably have to re-define the current risk threshold for many factors, molecules and stresses. Among the many different environmental factors affecting the epigenome, nutrition represents one of the most investigated fields; the reasons are probably that each person interacts with nutrients and that, in turn, nutrients can modulate at molecular level the epigenetic biochemical pathways. The role that nutrition can exert in modulating epigenetic modifications in Alzheimer's disease will be discussed with particular emphasis on the role of B vitamins and DNA methylation.