Quantitative trait loci involved in the reproductive success of a parasitoid wasp

Dissecting the genetic basis of intraspecific variations in life history traits is essential to understand their evolution, notably for potential biocontrol agents. Such variations are observed in the endoparasitoid Cotesia typhae (Hymenoptera: Braconidae), specialized on the pest Sesamia nonagrioides (Lepidoptera: Noctuidae). Previously, we identified two strains of C. typhae that differed significantly for life history traits on an allopatric host population. To investigate the genetic basis underlying these phenotypic differences, we used a quantitative trait locus (QTL) approach based on restriction site‐associated DNA markers. The characteristic of C. typhae reproduction allowed us generating sisters sharing almost the same genetic content, named clonal sibship. Crosses between individuals from the two strains were performed to generate F2 and F8 recombinant CSS. The genotypes of 181 clonal sibships were determined as well as the phenotypes of the corresponding 4,000 females. Informative markers were then used to build a high‐quality genetic map. These 465 markers spanned a total length of 1,300 cM and were organized in 10 linkage groups which corresponded to the number of C. typhae chromosomes. Three QTLs were detected for parasitism success and two for offspring number, while none were identified for sex ratio. The QTLs explained, respectively, 27.7% and 24.5% of the phenotypic variation observed. The gene content of the genomic intervals was investigated based on the genome of C. congregata and revealed 67 interesting candidates, as potentially involved in the studied traits, including components of the venom and of the symbiotic virus (bracovirus) shown to be necessary for parasitism success in related wasps.     

Precipitation patterns and N availability alter plant-soil microbial C and N dynamics

Plant and Soil - Contrasting nutrient-acquisition strategies would explain why species differ in their distribution in relation to soil phosphorus (P) availability, promoting diversity. However,...     

Mutation (G275E) of nAChR subunit Foα6 associated with spinetoram resistance in Australian western flower thrips,Frankliniella occidentalis (Pergande)

Molecular Biology Reports - Western flower thrips, Frankliniella occidentalis is an economically important agricultural pest. It causes damage by feeding and oviposition or indirectly by plant...     

lca_algebraic: a library bringing symbolic calculus to LCA for comprehensive sensitivity analysis

The International Journal of Life Cycle Assessment - In this paper, we present new tools to ease the analysis of the effect of variability and uncertainty on life cycle assessment (LCA) results....     

Exploiting nonsystematic covariate monitoring to broaden the scope of evidence about the causal effects of adaptive treatment strategies

In studies based on electronic health records (EHR), the frequency of covariate monitoring can vary by covariate type, across patients, and over time, which can limit the generalizability of inferences about the effects of adaptive treatment strategies. In addition, monitoring is a health intervention in itself with costs and benefits, and stakeholders may be interested in the effect of monitoring when adopting adaptive treatment strategies. This paper demonstrates how to exploit nonsystematic covariate monitoring in EHR‐based studies to both improve the generalizability of causal inferences and to evaluate the health impact of monitoring when evaluating adaptive treatment strategies. Using a real world, EHR‐based, comparative effectiveness research (CER) study of patients with type II diabetes mellitus, we illustrate how the evaluation of joint dynamic treatment and static monitoring interventions can improve CER evidence and describe two alternate estimation approaches based on inverse probability weighting (IPW). First, we demonstrate the poor performance of the standard estimator of the effects of joint treatment‐monitoring interventions, due to a large decrease in data support and concerns over finite‐sample bias from near‐violations of the positivity assumption (PA) for the monitoring process. Second, we detail an alternate IPW estimator using a no direct effect assumption. We demonstrate that this estimator can improve efficiency but at the potential cost of increase in bias from violations of the PA for the treatment process.