Improving the use of plant genetic resources to sustain breeding programs' efficiency.

Genetic progress of crop plants is required to face human population growth and guarantee production stability in increasingly unstable environmental conditions. Breeding is accompanied by a loss in genetic diversity, which hinders sustainable genetic gain. Methodologies based on molecular marker information have been developed to manage diversity and proved effective in increasing long-term genetic gain. However, with realistic plant breeding population sizes, diversity depletion in closed programs appears ineluctable, calling for the introduction of relevant diversity donors. Although maintained with significant efforts, genetic resource collections remain underutilized, due to a large performance gap with elite germplasm. Bridging populations created by crossing genetic resources to elite lines prior to introduction into elite programs can manage this gap efficiently. To improve this strategy, we explored with simulations different genomic prediction and genetic diversity management options for a global program involving a bridging and an elite component. We analyzed the dynamics of quantitative trait loci fixation and followed the fate of allele donors after their introduction into the breeding program. Allocating 25% of total experimental resources to create a bridging component appears highly beneficial. We showed that potential diversity donors should be selected based on their phenotype rather than genomic predictions calibrated with the ongoing breeding program. We recommend incorporating improved donors into the elite program using a global calibration of the genomic prediction model and optimal cross selection maintaining a constant diversity. These approaches use efficiently genetic resources to sustain genetic gain and maintain neutral diversity, improving the flexibility to address future breeding objectives.

Portability of genomic predictions trained on sparse factorial designs across two maize silage breeding cycles.

KEY MESSAGE: We validated the efficiency of genomic predictions calibrated on sparse factorial training sets to predict the next generation of hybrids and tested different strategies for updating predictions along generations. Genomic selection offers new prospects for revisiting hybrid breeding schemes by replacing extensive phenotyping of individuals with genomic predictions. Finding the ideal design for training genomic prediction models is still an open question. Previous studies have shown promising predictive abilities using sparse factorial instead of tester-based training sets to predict single-cross hybrids from the same generation. This study aims to further investigate the use of factorials and their optimization to predict line general combining abilities (GCAs) and hybrid values across breeding cycles. It relies on two breeding cycles of a maize reciprocal genomic selection scheme involving multiparental connected reciprocal populations from flint and dent complementary heterotic groups selected for silage performances. Selection based on genomic predictions trained on a factorial design resulted in a significant genetic gain for dry matter yield in the new generation. Results confirmed the efficiency of sparse factorial training sets to predict candidate line GCAs and hybrid values across breeding cycles. Compared to a previous study based on the first generation, the advantage of factorial over tester training sets appeared lower across generations. Updating factorial training sets by adding single-cross hybrids between selected lines from the previous generation or a random subset of hybrids from the new generation both improved predictive abilities. The CDmean criterion helped determine the set of single-crosses to phenotype to update the training set efficiently. Our results validated the efficiency of sparse factorial designs for calibrating hybrid genomic prediction experimentally and showed the benefit of updating it along generations.

High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions.

KEY MESSAGE: Transcriptomics and proteomics information collected on a platform can predict additive and non-additive effects for platform traits and additive effects for field traits. The effects of climate change in the form of drought, heat stress, and irregular seasonal changes threaten global crop production. The ability of multi-omics data, such as transcripts and proteins, to reflect a plant's response to such climatic factors can be capitalized in prediction models to maximize crop improvement. Implementing multi-omics characterization in field evaluations is challenging due to high costs. It is, however, possible to do it on reference genotypes in controlled conditions. Using omics measured on a platform, we tested different multi-omics-based prediction approaches, using a high dimensional linear mixed model (MegaLMM) to predict genotypes for platform traits and agronomic field traits in a panel of 244 maize hybrids. We considered two prediction scenarios: in the first one, new hybrids are predicted (CV-NH), and in the second one, partially observed hybrids are predicted (CV-POH). For both scenarios, all hybrids were characterized for omics on the platform. We observed that omics can predict both additive and non-additive genetic effects for the platform traits, resulting in much higher predictive abilities than GBLUP. It highlights their efficiency in capturing regulatory processes in relation to growth conditions. For the field traits, we observed that the additive components of omics only slightly improved predictive abilities for predicting new hybrids (CV-NH, model MegaGAO) and for predicting partially observed hybrids (CV-POH, model GAOxW-BLUP) in comparison to GBLUP. We conclude that measuring the omics in the fields would be of considerable interest in predicting productivity if the costs of omics drop significantly.

Assessing the potential of genetic resource introduction into elite germplasm: a collaborative multiparental population for flint maize.

KEY MESSAGE: Implementing a collaborative pre-breeding multi-parental population efficiently identifies promising donor x elite pairs to enrich the flint maize elite germplasm. Genetic diversity is crucial for maintaining genetic gains and ensuring breeding programs' long-term success. In a closed breeding program, selection inevitably leads to a loss of genetic diversity. While managing diversity can delay this loss, introducing external sources of diversity is necessary to bring back favorable genetic variation. Genetic resources exhibit greater diversity than elite materials, but their lower performance levels hinder their use. This is the case for European flint maize, for which elite germplasm has incorporated only a limited portion of the diversity available in landraces. To enrich the diversity of this elite genetic pool, we established an original cooperative maize bridging population that involves crosses between private elite materials and diversity donors to create improved genotypes that will facilitate the incorporation of original favorable variations. Twenty donor × elite BC1S2 families were created and phenotyped for hybrid value for yield related traits. Crosses showed contrasted means and variances and therefore contrasted potential in terms of selection as measured by their usefulness criterion (UC). Average expected mean performance gain over the initial elite material was 5%. The most promising donor for each elite line was identified. Results also suggest that one more generation, i.e., 3 in total, of crossing to the elite is required to fully exploit the potential of a donor. Altogether, our results support the usefulness of incorporating genetic resources into elite flint maize. They call for further effort to create fixed diversity donors and identify those most suitable for each elite program.

Predicting worsening heart failure hospitalizations in patients with implantable cardioverter defibrillators: is it all about alerts? A pooled analysis of nine trials.

AIMS: To predict worsening heart failure hospitalizations (WHFHs) in patients with implantable defibrillators and remote monitoring, the HeartInsight algorithm (Biotronik, Berlin, Germany) calculates a heart failure (HF) score combining seven physiologic parameters: 24 h heart rate (HR), nocturnal HR, HR variability, atrial tachyarrhythmia, ventricular extrasystoles, patient activity, and thoracic impedance. We compared temporal trends of the HF score and its components 12 weeks before a WHFH with 12-week trends in patients without WHFH, to assess whether trends indicate deteriorating HF regardless of alert status. METHODS AND RESULTS: Data from nine clinical trials were pooled, including 2050 patients with a defibrillator capable of atrial sensing, ejection fraction ≤ 35%, NYHA class II/III, no long-standing atrial fibrillation, and 369 WHFH from 259 patients. The mean HF score was higher in the WHFH group than in the no WHFH group (42.3 ± 26.1 vs. 30.7 ± 20.6, P < 0.001) already at the beginning of 12 weeks. The mean HF score further increased to 51.6 ± 26.8 until WHFH (+22% vs. no WHFH group, P = 0.003). As compared to the no WHFH group, the algorithm components either were already higher 12 weeks before WHFH (24 h HR, HR variability, thoracic impedance) or significantly increased until WHFH (nocturnal HR, atrial tachyarrhythmia, ventricular extrasystoles, patient activity). CONCLUSION: The HF score was significantly higher at, and further increased during 12 weeks before WHFH, as compared to the no WHFH group, with seven components showing different behaviour and contribution. Temporal trends of HF score may serve as a quantitative estimate of HF condition and evolution prior to WHFH.

Identifying QTLs involved in hybrid performance and heterotic group complementarity: new GWAS models applied to factorial and admixed diallel maize hybrid panels.

KEY MESSAGE: An original GWAS model integrating the ancestry of alleles was proposed and allowed the detection of background specific additive and dominance QTLs involved in heterotic group complementarity and hybrid performance. Maize genetic diversity is structured into genetic groups selected and improved relative to each other. This process increases group complementarity and differentiation over time and ensures that the hybrids produced from inter-group crosses exhibit high performances and heterosis. To identify loci involved in hybrid performance and heterotic group complementarity, we introduced an original association study model that disentangles allelic effects from the heterotic group origin of the alleles and compared it with a conventional additive/dominance model. This new model was applied on a factorial between Dent and Flint lines and a diallel between Dent-Flint admixed lines with two different layers of analysis: within each environment and in a multiple-environment context. We identified several strong additive QTLs for all traits, including some well-known additive QTLs for flowering time (in the region of Vgt1/2 on chromosome 8). Yield trait displayed significant non-additive effects in the diallel panel. Most of the detected Yield QTLs exhibited overdominance or, more likely, pseudo-overdominance effects. Apparent overdominance at these QTLs contributed to a part of the genetic group complementarity. The comparison between environments revealed a higher stability of additive QTL effects than non-additive ones. Several QTLs showed variations of effects according to the local heterotic group origin. We also revealed large chromosomic regions that display genetic group origin effects. Altogether, our results illustrate how admixed panels combined with dedicated GWAS modeling allow the identification of new QTLs that could not be revealed by a classical hybrid panel analyzed with traditional modeling.

Dynamic changes in nocturnal heart rate predict short-term cardiovascular events in patients using the wearable cardioverter-defibrillator: from the WEARIT-France cohort study.

AIMS: While elevated resting heart rate measured at a single point of time has been associated with cardiovascular outcomes, utility of continuous monitoring of nocturnal heart rate (NHR) has never been evaluated. We hypothesized that dynamic NHR changes may predict, at short term, impending cardiovascular events in patients equipped with a wearable cardioverter-defibrillator (WCD). METHODS AND RESULTS: The WEARIT-France prospective cohort study enrolled heart failure patients with WCD between 2014 and 2018. Night-time was defined as midnight to 7 a.m. NHR initial trajectories were classified into four categories based on mean NHR in the first week (High/Low) and NHR evolution over the second week (Up/Down) of WCD use. The primary endpoint was a composite of cardiovascular death and heart failure hospitalization. A total of 1013 [61 (interquartile range, IQR 53-68) years, 16% women, left ventricular ejection fraction 26% (IQR 22-30)] were included. During a median WCD wear duration of 68 (IQR 44-90) days, 58 patients (6%) experienced 69 events. After considering potential confounders, High-Up NHR trajectory was significantly associated with the primary endpoint compared to Low-Down [adjusted hazard ratio (HR) 6.08, 95% confidence interval (CI) 2.56-14.45, P < 0.001]. Additionally, a rise of >5 bpm in weekly average NHR from the preceding week was associated with 2.5 higher composite event risk (HR 2.51, 95% CI 1.22-5.18, P = 0.012) as well as total mortality (HR 11.21, 95% CI 3.55-35.37, P < 0.001) and cardiovascular hospitalization (HR 2.70, 95% CI 1.51-4.82, P < 0.001). CONCLUSION: Dynamic monitoring of NHR may allow timely identification of impending cardiovascular events, with the potential for 'pre-emptive' action. REGISTRATION NUMBER: Clinical Trials.gov Identifier: NCT03319160.

Disentangling group specific QTL allele effects from genetic background epistasis using admixed individuals in GWAS: An application to maize flowering.

When handling a structured population in association mapping, group-specific allele effects may be observed at quantitative trait loci (QTLs) for several reasons: (i) a different linkage disequilibrium (LD) between SNPs and QTLs across groups, (ii) group-specific genetic mutations in QTL regions, and/or (iii) epistatic interactions between QTLs and other loci that have differentiated allele frequencies between groups. We present here a new genome-wide association (GWAS) approach to identify QTLs exhibiting such group-specific allele effects. We developed genetic materials including admixed progeny from different genetic groups with known genome-wide ancestries (local admixture). A dedicated statistical methodology was developed to analyze pure and admixed individuals jointly, allowing one to disentangle the factors causing the heterogeneity of allele effects across groups. This approach was applied to maize by developing an inbred "Flint-Dent" panel including admixed individuals that was evaluated for flowering time. Several associations were detected revealing a wide range of configurations of allele effects, both at known flowering QTLs (Vgt1, Vgt2 and Vgt3) and new loci. We found several QTLs whose effect depended on the group ancestry of alleles while others interacted with the genetic background. Our GWAS approach provides useful information on the stability of QTL effects across genetic groups and can be applied to a wide range of species.

Genomic prediction of hybrid performance: comparison of the efficiency of factorial and tester designs used as training sets in a multiparental connected reciprocal design for maize silage.

KEY MESSAGE: Calibrating a genomic selection model on a sparse factorial design rather than on tester designs is advantageous for some traits, and equivalent for others. In maize breeding, the selection of the candidate inbred lines is based on topcross evaluations using a limited number of testers. Then, a subset of single-crosses between these selected lines is evaluated to identify the best hybrid combinations. Genomic selection enables the prediction of all possible single-crosses between candidate lines but raises the question of defining the best training set design. Previous simulation results have shown the potential of using a sparse factorial design instead of tester designs as the training set. To validate this result, a 363 hybrid factorial design was obtained by crossing 90 dent and flint inbred lines from six segregating families. Two tester designs were also obtained by crossing the same inbred lines to two testers of the opposite group. These designs were evaluated for silage in eight environments and used to predict independent performances of a 951 hybrid factorial design. At a same number of hybrids and lines, the factorial design was as efficient as the tester designs, and, for some traits, outperformed them. All available designs were used as both training and validation set to evaluate their efficiency. When the objective was to predict single-crosses between untested lines, we showed an advantage of increasing the number of lines involved in the training set, by (1) allocating each of them to a different tester for the tester design, or (2) reducing the number of hybrids per line for the factorial design. Our results confirm the potential of sparse factorial designs for genomic hybrid breeding.

Common gardens in teosintes reveal the establishment of a syndrome of adaptation to altitude.

In plants, local adaptation across species range is frequent. Yet, much has to be discovered on its environmental drivers, the underlying functional traits and their molecular determinants. Genome scans are popular to uncover outlier loci potentially involved in the genetic architecture of local adaptation, however links between outliers and phenotypic variation are rarely addressed. Here we focused on adaptation of teosinte populations along two elevation gradients in Mexico that display continuous environmental changes at a short geographical scale. We used two common gardens, and phenotyped 18 traits in 1664 plants from 11 populations of annual teosintes. In parallel, we genotyped these plants for 38 microsatellite markers as well as for 171 outlier single nucleotide polymorphisms (SNPs) that displayed excess of allele differentiation between pairs of lowland and highland populations and/or correlation with environmental variables. Our results revealed that phenotypic differentiation at 10 out of the 18 traits was driven by local selection. Trait covariation along the elevation gradient indicated that adaptation to altitude results from the assembly of multiple co-adapted traits into a complex syndrome: as elevation increases, plants flower earlier, produce less tillers, display lower stomata density and carry larger, longer and heavier grains. The proportion of outlier SNPs associating with phenotypic variation, however, largely depended on whether we considered a neutral structure with 5 genetic groups (73.7%) or 11 populations (13.5%), indicating that population stratification greatly affected our results. Finally, chromosomal inversions were enriched for both SNPs whose allele frequencies shifted along elevation as well as phenotypically-associated SNPs. Altogether, our results are consistent with the establishment of an altitudinal syndrome promoted by local selective forces in teosinte populations in spite of detectable gene flow. Because elevation mimics climate change through space, SNPs that we found underlying phenotypic variation at adaptive traits may be relevant for future maize breeding.

Wearable cardioverter-defibrillator in patients with a transient risk of sudden cardiac death: the WEARIT-France cohort study.

AIMS: We aimed to provide contemporary real-world data on wearable cardioverter-defibrillator (WCD) use, not only in terms of effectiveness and safety but also compliance and acceptability. METHODS AND RESULTS: Across 88 French centres, the WEARIT-France study enrolled retrospectively patients who used the WCD between May 2014 and December 2016, and prospectively all patients equipped for WCD therapy between January 2017 and March 2018. All patients received systematic education session through a standardized programme across France at the time of initiation of WCD therapy and were systematically enrolled in the LifeVest Network remote services. Overall, 1157 patients were included (mean age 60 ± 12 years, 16% women; 46% prospectively): 82.1% with ischaemic cardiomyopathy, 10.3% after implantable cardioverter-defibrillator explant, and 7.6% before heart transplantation. Median WCD usage period was 62 (37-97) days. Median daily wear time of WCD was 23.4 (22.2-23.8) h. In multivariate analysis, younger age was associated with lower compliance [adjusted odds ratio (OR) 0.97, 95% confidence interval (CI) 0.95-0.99, P < 0.01]. A total of 18 participants (1.6%) received at least one appropriate shock, giving an incidence of appropriate therapy of 7.2 per 100 patient-years. Patient-response button allowed the shock to be aborted in 35.7% of well-tolerated sustained ventricular arrhythmias and in 95.4% of inappropriate ventricular arrhythmia detection, finally resulting in an inappropriate therapy in eight patients (0.7%). CONCLUSION: Our real-life findings reinforce previous studies on the efficacy and safety of the WCD in the setting of transient high-risk group in selected patients. Moreover, they emphasize the fact that when prescribed appropriately, in concert with adequate patient education and dedicated follow-up using specific remote monitoring system, compliance with WCD is high and the device well-tolerated by the patient.

Optimized breeding strategies to harness genetic resources with different performance levels.

BACKGROUND: The narrow genetic base of elite germplasm compromises long-term genetic gain and increases the vulnerability to biotic and abiotic stresses in unpredictable environmental conditions. Therefore, an efficient strategy is required to broaden the genetic base of commercial breeding programs while not compromising short-term variety release. Optimal cross selection aims at identifying the optimal set of crosses that balances the expected genetic value and diversity. We propose to consider genomic selection and optimal cross selection to recurrently improve genetic resources (i.e. pre-breeding), to bridge the improved genetic resources with elites (i.e. bridging), and to manage introductions into the elite breeding population. Optimal cross selection is particularly adapted to jointly identify bridging, introduction and elite crosses to ensure an overall consistency of the genetic base broadening strategy. RESULTS: We compared simulated breeding programs introducing donors with different performance levels, directly or indirectly after bridging. We also evaluated the effect of the training set composition on the success of introductions. We observed that with recurrent introductions of improved donors, it is possible to maintain the genetic diversity and increase mid- and long-term performances with only limited penalty at short-term. Considering a bridging step yielded significantly higher mid- and long-term genetic gain when introducing low performing donors. The results also suggested to consider marker effects estimated with a broad training population including donor by elite and elite by elite progeny to identify bridging, introduction and elite crosses. CONCLUSION: Results of this study provide guidelines on how to harness polygenic variation present in genetic resources to broaden elite germplasm.

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.

Genetic basis of phenotypic plasticity and genotype × environment interactions in a multi-parental tomato population.

Deciphering the genetic basis of phenotypic plasticity and genotype × environment interactions (G×E) is of primary importance for plant breeding in the context of global climate change. Tomato (Solanum lycopersicum) is a widely cultivated crop that can grow in different geographical habitats and that displays a great capacity for expressing phenotypic plasticity. We used a multi-parental advanced generation intercross (MAGIC) tomato population to explore G×E and plasticity for multiple traits measured in a multi-environment trial (MET) comprising optimal cultural conditions together with water deficit, salinity, and heat stress over 12 environments. Substantial G×E was observed for all the traits measured. Different plasticity parameters were estimated by employing Finlay-Wilkinson and factorial regression models and these were used together with genotypic means for quantitative trait loci (QTL) mapping analyses. In addition, mixed linear models were also used to investigate the presence of QTL × environment interactions. The results highlighted a complex genetic architecture of tomato plasticity and G×E. Candidate genes that might be involved in the occurrence of G×E are proposed, paving the way for functional characterization of stress response genes in tomato and for breeding climate-adapted cultivars.

Genomic prediction with a maize collaborative panel: identification of genetic resources to enrich elite breeding programs.

KEY MESSAGE: Collaborative diversity panels and genomic prediction seem relevant to identify and harness genetic resources for polygenic trait-specific enrichment of elite germplasms. In plant breeding, genetic diversity is important to maintain the pace of genetic gain and the ability to respond to new challenges in a context of climatic and social expectation changes. Many genetic resources are accessible to breeders but cannot all be considered for broadening the genetic diversity of elite germplasm. This study presents the use of genomic predictions trained on a collaborative diversity panel, which assembles genetic resources and elite lines, to identify resources to enrich an elite germplasm. A maize collaborative panel (386 lines) was considered to estimate genome-wide marker effects. Relevant predictive abilities (0.40-0.55) were observed on a large population of private elite materials, which supported the interest of such a collaborative panel for diversity management perspectives. Grain-yield estimated marker effects were used to select a donor that best complements an elite recipient at individual loci or haplotype segments, or that is expected to give the best-performing progeny with the elite. Among existing and new criteria that were compared, some gave more weight to the donor-elite complementarity than to the donor value, and appeared more adapted to long-term objective. We extended this approach to the selection of a set of donors complementing an elite population. We defined a crossing plan between identified donors and elite recipients. Our results illustrated how collaborative projects based on diversity panels including both public resources and elite germplasm can contribute to a better characterization of genetic resources in view of their use to enrich elite germplasm.

Genomic selection efficiency and a priori estimation of accuracy in a structured dent maize panel.

KEY MESSAGE: Population structure affects genomic selection efficiency as well as the ability to forecast accuracy using standard GBLUP. Genomic prediction models usually assume that the individuals used for calibration belong to the same population as those to be predicted. Most of the a priori indicators of precision, such as the coefficient of determination (CD), were derived from those same models. But genetic structure is a common feature in plant species, and it may impact genomic selection efficiency and the ability to forecast prediction accuracy. We investigated the impact of genetic structure in a dent maize panel ("Amaizing Dent") using different scenarios including within- or across-group predictions. For a given training set size, the best accuracies were achieved when predicting individuals using a model calibrated on the same genetic group. Nevertheless, a diverse training set representing all the groups had a certain predictive efficiency for all the validation sets, and adding extra-group individuals was almost always beneficial. It underlines the potential of such a generic training set for dent maize genomic selection applications. Alternative prediction models, taking genetic structure explicitly into account, did not improve the prediction accuracy compared to GBLUP. We also investigated the ability of different indicators of precision to forecast accuracy in the within- or across-group scenarios. There was a global encouraging trend of the CD to differentiate scenarios, although there were specific combinations of target populations and traits where the efficiency of this indicator proved to be null. One hypothesis to explain such erratic performances is the impact of genetic structure through group-specific allele diversity at QTLs rather than group-specific allele effects.

Quantitative trait loci mapping in hybrids between Dent and Flint maize multiparental populations reveals group-specific QTL for silage quality traits with variable pleiotropic effects on yield.

KEY MESSAGE: Silage quality traits of maize hybrids between the Dent and Flint heterotic groups mostly involved QTL specific of each parental group, some of them showing unfavorable pleiotropic effects on yield. Maize (Zea mays L.) is commonly used as silage for cattle feeding in Northern Europe. In addition to biomass production, improving whole-plant digestibility is a major breeding objective. To identify loci involved in the general (GCA, parental values) and specific combining ability (SCA, cross-specific value) components of hybrid value, we analyzed an incomplete factorial design of 951 hybrids obtained by crossing inbred lines issued from two multiparental connected populations, each specific to one of the heterotic groups used for silage in Europe ("Dent" and "Flint"). Inbred lines were genotyped for approximately 20K single nucleotide polymorphisms, and hybrids were phenotyped in eight environments for seven silage quality traits measured by near-infrared spectroscopy, biomass yield and precocity (partly analyzed in a previous study). We estimated variance components for GCA and SCA and their interaction with environment. We performed QTL detection using different models adapted to this hybrid population. Strong family effects and a predominance of GCA components compared to SCA were found for all traits. In total, 230 QTL were detected, with only two showing SCA effects significant at the whole-genome level. More than 80% of GCA QTL were specific of one heterotic group. QTL explained individually less than 5% of the phenotypic variance. QTL co-localizations and correlation between QTL effects of quality and productivity traits suggest at least partial pleiotropic effects. This work opens new prospects for improving maize hybrid performances for both biomass productivity and quality accounting for complementarities between heterotic groups.

Assessment of breeding programs sustainability: application of phenotypic and genomic indicators to a North European grain maize program.

KEY MESSAGE: We review and propose easily implemented and affordable indicators to assess the genetic diversity and the potential of a breeding population and propose solutions for its long-term management. Successful plant breeding programs rely on balanced efforts between short-term goals to develop competitive cultivars and long-term goals to improve and maintain diversity in the genetic pool. Indicators of the sustainability of response to selection in breeding pools are of key importance in this context. We reviewed and proposed sets of indicators based on temporal phenotypic and genotypic data and applied them on an early maize grain program implying two breeding pools (Dent and Flint) selected in a reciprocal manner. Both breeding populations showed a significant positive genetic gain summing up to 1.43 qx/ha/year but contrasted evolutions of genetic variance. Advances in high-throughput genotyping permitted the identification of regions of low diversity, mainly localized in pericentromeric regions. Observed changes in genetic diversity were multiple, reflecting a complex breeding system. We estimated the impact of linkage disequilibrium (LD) and of allelic diversity on the additive genetic variance at a genome-wide and chromosome-wide scale. Consistently with theoretical expectation under directional selection, we found a negative contribution of LD to genetic variance, which was unevenly distributed between chromosomes. This suggests different chromosome selection histories and underlines the interest to recombine specific chromosome regions. All three sets of indicators valorize in house data and are easy to implement in the era of genomic selection in every breeding program.

X-chromosome meiotic drive in Drosophila simulans: a QTL approach reveals the complex polygenic determinism of Paris drive suppression.

Meiotic drivers are selfish genetic elements that promote their own transmission into the gametes, which results in intragenomic conflicts. In the Paris sex-ratio system of Drosophila simulans, drivers located on the X chromosome prevent the segregation of the heterochromatic Y chromosome during meiosis II, and hence the production of Y-bearing sperm. The resulting sex-ratio bias strongly impacts population dynamics and evolution. Natural selection, which tends to restore an equal sex ratio, favors the emergence of resistant Y chromosomes and autosomal suppressors. This is the case in the Paris sex-ratio system where the drivers became cryptic in most of the natural populations of D. simulans. Here, we used a quantitative trait locus (QTL) mapping approach based on the analysis of 152 highly recombinant inbred lines (RILs) to investigate the genetic determinism of autosomal suppression. The RILs were derived from an advanced intercross between two parental lines, one showing complete autosomal suppression while the other one was sensitive to drive. The confrontation of RIL autosomes with a reference XSR chromosome allowed us to identify two QTLs on chromosome 2 and three on chromosome 3, with strong epistatic interactions. Our findings highlight the multiplicity of actors involved in this intragenomic battle over the sex ratio.

A report on the impact of remote monitoring in patients with S-ICD: Insights from a prospective registry.

INTRODUCTION: Several clinical studies have demonstrated that remote monitoring (RM) offers potential benefits in transvenous implantable cardioverter defibrillator (ICD). The potential interest of RM in subcutaneous-ICD (S-ICD) recipients has never been evaluated. The aim of this study was to evaluate the alert burden and its clinical relevance in a prospective cohort of S-ICD recipients. METHODS: We prospectively and consecutively enrolled all patients undergoing S-ICD implantation at Lille University Hospital from September 2015 to January 2017 and gave them a LATITUDE™ NXT RM system. The relevance of transmissions was assessed by the following ratio: number of transmissions leading to reaction or intervention per patient/number of transmissions per patient. RESULTS: From September 2015 to January 2017, 69 patients were enrolled with a mean follow-up of 415 ± 96.3 days. The mean age was 44.6 ± 15.6 years old, and 25% (n = 17) had ischemic cardiomyopathy. At the end of follow-up, 12% of the patients had events recorded by RM. These events were related to nine ICD shocks and eight untreated events. A total of 1,423 transmissions were collected. Most of these transmissions were patient-initiated without any event (77%, n = 1,096) or scheduled without any event (19%, n = 272). Only 3.2% ± 1.1 of the transmissions per patient led to reactions or interventions. CONCLUSION: On the basis of the current method of transmitting, S-ICD RM allowed detection of relevant events in 12% of patients but generated a high unactionable transmission burden. As a result of these findings, efforts should be made to optimize transmissions considering automatic transmissions and to focus on patient education.