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Key message: Rice breeding programs based on pedigree schemes can use a genomic model trained with data from their working collection to predict performances of progenies produced through rapid generation advancement. Abstract: So far, most potential applications of genomic prediction in plant improvement have been explored using cross validation approaches. This is the first empirical study to evaluate the accuracy of genomic prediction of the performances of progenies in a typical rice breeding program. Using a cross validation approach, we first analyzed the effects of marker selection and statistical methods on the accuracy of prediction of three different heritability traits in a reference population (RP) of 284 inbred accessions. Next, we investigated the size and the degree of relatedness with the progeny population (PP) of sub-sets of the RP that maximize the accuracy of prediction of phenotype across generations, i.e., for 97 F-F lines derived from biparental crosses between 31 accessions of the RP. The extent of linkage disequilibrium was high ( r = 0.2 at 0.80 Mb in RP and at 1.1 Mb in PP). Consequently, average marker density above one per 22 kb did not improve the accuracy of predictions in the RP. The accuracy of progeny prediction varied greatly depending on the composition of the training set, the trait, LD and minor allele frequency. The highest accuracy achieved for each trait exceeded 0.50 and was only slightly below the accuracy achieved by cross validation in the RP. Our results thus show that relatively high accuracy (0.41-0.54) can be achieved using only a rather small share of the RP, most related to the PP, as the training set. The practical implications of these results for rice breeding programs are discussed. [ABSTRACT FROM AUTHOR]

Maritime pine provides essential ecosystem services in the south-western Mediterranean basin, where it covers around 4 million ha. Its scattered distribution over a range of environmental conditions makes it an ideal forest tree species for studies of local adaptation and evolutionary responses to climatic change. Highly multiplexed single nucleotide polymorphism ( SNP) genotyping arrays are increasingly used to study genetic variation in living organisms and for practical applications in plant and animal breeding and genetic resource conservation. We developed a 9k Illumina Infinium SNP array and genotyped maritime pine trees from (i) a three-generation inbred ( F2) pedigree, (ii) the French breeding population and (iii) natural populations from Portugal and the French Atlantic coast. A large proportion of the exploitable SNPs (2052/8410, i.e. 24.4%) segregated in the mapping population and could be mapped, providing the densest ever gene-based linkage map for this species. Based on 5016 SNPs, natural and breeding populations from the French gene pool exhibited similar level of genetic diversity. Population genetics and structure analyses based on 3981 SNP markers common to the Portuguese and French gene pools revealed high levels of differentiation, leading to the identification of a set of highly differentiated SNPs that could be used for seed provenance certification. Finally, we discuss how the validated SNPs could facilitate the identification of ecologically and economically relevant genes in this species, improving our understanding of the demography and selective forces shaping its natural genetic diversity, and providing support for new breeding strategies. [ABSTRACT FROM AUTHOR]

Introduction: Genetic improvement in rice increased yield potential and improved varieties for farmers over the last decades. However, the demand for rice is growing while its cultivation faces challenges posed by climate change. To address these challenges, rice breeding programs need to adopt efficient breeding strategies to provide a steady increase in the rate of genetic gain for major traits. The International Rice Research Institute (IRRI) breeding program has evolved over time to implement faster and more efficient breeding techniques such as rapid generation advance (RGA) and genomic selection (GS). Simulation experiments support data-driven optimization of the breeding program toward the desired rate of genetic gain for key traits., Methods: This study used stochastic simulations to compare breeding schemes with different cycle times. The objective was to assess the impact of different genomic selection strategies on medium- and long-term genetic gain. Four genomic selection schemes were simulated, representing the past approaches (5 years recycling), current schemes (3 years recycling), and two options for the future schemes (both with 2 years recycling)., Results: The 2-Year within-cohort prediction scheme showed a significant increase in genetic gain in the medium-term horizon. Specifically, it resulted in a 22%, 24%, and 27% increase over the current scheme in the zero, intermediate, and high genotype-by-environment interaction (GEI) contexts, respectively. On the other hand, the 2-Year scheme based on between-cohort prediction was more efficient in the long term, but only in the absence of GEI. Consistent with our expectations, the shortest breeding schemes showed an increase in genetic gain and faster depletion of genetic variance compared to the current scheme., Discussion: These results suggest that higher rates of genetic gain are achievable in the breeding program by further reducing the cycle time and adjusting the target population of environments. However, more attention is needed regarding the crossing strategy to use genetic variance optimally., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Seck, Prakash, Covarrubias-Pazaran, Gueye, Diédhiou, Bhosale, Kadaru and Bartholomé.)

Over half of the world's arable land is acidic, which constrains cereal production. In South America, different rice-growing regions (Cerrado in Brazil and Llanos in Colombia and Venezuela) are particularly affected due to high aluminum toxicity levels. For this reason, efforts have been made to breed for tolerance to aluminum toxicity using synthetic populations. The breeding program of CIAT-CIRAD is a good example of the use of recurrent selection to increase productivity for the Llanos in Colombia. In this study, we evaluated the performance of genomic prediction models to optimize the breeding scheme by hastening the development of an improved synthetic population and elite lines. We characterized 334 families at the S0:4 generation in two conditions. One condition was the control, managed with liming, while the other had high aluminum toxicity. Four traits were considered: days to flowering (FL), plant height (PH), grain yield (YLD), and zinc concentration in the polished grain (ZN). The population presented a high tolerance to aluminum toxicity, with more than 72% of the families showing a higher yield under aluminum conditions. The performance of the families under the aluminum toxicity condition was predicted using four different models: a single-environment model and three multi-environment models. The multi-environment models differed in the way they integrated genotype-by-environment interactions. The best predictive abilities were achieved using multi-environment models: 0.67 for FL, 0.60 for PH, 0.53 for YLD, and 0.65 for ZN. The gain of multi-environment over single-environment models ranged from 71% for YLD to 430% for FL. The selection of the best-performing families based on multi-trait indices, including the four traits mentioned above, facilitated the identification of suitable families for recombination. This information will be used to develop a new cycle of recurrent selection through genomic selection., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Bartholomé et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Genetic improvement is crucial for ensuring food security globally. Indeed, plant breeding has contributed significantly to increasing the productivity of major crops, including rice, over the last century. Evaluating the efficiency of breeding strategies necessitates a quantification of this progress. One approach involves assessing the genetic gain achieved through breeding programs based on quantitative traits. This study aims to provide a theoretical understanding of genetic gain, summarize the major results of genetic gain studies in rice breeding, and suggest ways of improving breeding program strategies and future studies on genetic gain. To achieve this, we present the concept of genetic gain and the essential aspects of its estimation. We also provide an extensive literature review of genetic gain studies in rice (Oryza sativa L.) breeding programs to understand the advances made to date. We reviewed 29 studies conducted between 1999 and 2023, covering different regions, traits, periods, and estimation methods. The genetic gain for grain yield, in particular, showed significant variation, ranging from 1.5 to 167.6 kg/ha/year, with a mean value of 36.3 kg/ha/year. This translated into a rate of genetic gain for grain yield ranging from 0.1% to over 3.0%. The impact of multi-trait selection on grain yield was clarified by studies that reported genetic gains for other traits, such as plant height, days to flowering, and grain quality. These findings reveal that while breeding programs have achieved significant gains, further improvements are necessary to meet the growing demand for rice. We also highlight the limitations of these studies, which hinder accurate estimations of genetic gain. In conclusion, we offer suggestions for improving the estimation of genetic gain based on quantitative genetic principles and computer simulations to optimize rice breeding strategies., (© 2023. The Author(s).)

Genomic selection is a worthy breeding method to improve genetic gain in recurrent selection breeding schemes. The integration of multi-generation and multi-location information could significantly improve genomic prediction models in the context of shuttle breeding. The Cirad-CIAT upland rice breeding program applies recurrent genomic selection and seeks to optimize the scheme to increase genetic gain while reducing phenotyping efforts. We used a synthetic population (PCT27) of which S 0 plants were all genotyped and advanced by selfing and bulk seed harvest to the S 0:2 , S 0:3 , and S 0:4 generations. The PCT27 was then divided into two sets. The S 0:2 and S 0:3 progenies for PCT27A and the S 0:4 progenies for PCT27B were phenotyped in two locations: Santa Rosa the target selection location, within the upland rice growing area, and Palmira, the surrogate location, far from the upland rice growing area but easier for experimentation. While the calibration used either one of the two sets phenotyped in one or two locations, the validation population was only the PCT27B phenotyped in Santa Rosa. Five scenarios of genomic prediction and 24 models were performed and compared. Training the prediction model with the PCT27B phenotyped in Santa Rosa resulted in predictive abilities ranging from 0.19 for grain zinc concentration to 0.30 for grain yield. Expanding the training set with the inclusion of the PCT27A resulted in greater predictive abilities for all traits but grain yield, with increases from 5% for plant height to 61% for grain zinc concentration. Models with the PCT27B phenotyped in two locations resulted in higher prediction accuracy when the models assumed no genotype-by-environment (G × E) interaction for flowering (0.38) and grain zinc concentration (0.27). For plant height, the model assuming a single G × E variance provided higher accuracy (0.28). The gain in predictive ability for grain yield was the greatest (0.25) when environment-specific variance deviation effect for G × E was considered. While the best scenario was specific to each trait, the results indicated that the gain in predictive ability provided by the multi-location and multi-generation calibration was low. Yet, this approach could lead to increased selection intensity, acceleration of the breeding cycle, and a sizable economic advantage for the program., (© 2023. Springer Science+Business Media, LLC, part of Springer Nature.)

Improving plant performance in salinity-prone conditions is a significant challenge in breeding programs. Genomic selection is currently integrated into many plant breeding programs as a tool for increasing selection intensity and precision for complex traits and for reducing breeding cycle length. A rice reference panel (RP) of 241 Oryza sativa L. japonica accessions genotyped with 20,255 SNPs grown in control and mild salinity stress conditions was evaluated at the vegetative stage for eight morphological traits and ion mass fractions (Na and K). Weak to strong genotype-by-condition interactions were found for the traits considered. Cross-validation showed that the predictive ability of genomic prediction methods ranged from 0.25 to 0.64 for multi-environment models with morphological traits and from 0.05 to 0.40 for indices of stress response and ion mass fractions. The performances of a breeding population (BP) comprising 393 japonica accessions were predicted with models trained on the RP. For validation of the predictive performances of the models, a subset of 41 accessions was selected from the BP and phenotyped under the same experimental conditions as the RP. The predictive abilities estimated on this subset ranged from 0.00 to 0.66 for the multi-environment models, depending on the traits, and were strongly correlated with the predictive abilities on cross-validation in the RP in salt condition (r = 0.69). We show here that genomic selection is efficient for predicting the salt stress tolerance of breeding lines. Genomic selection could improve the efficiency of rice breeding strategies for salinity-prone environments., Competing Interests: The authors declare that they have no competing interests., (Copyright: © 2023 Bartholomé et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

One of the main challenges of breeding programs is to identify superior genotypes from a large number of candidates. By gradually increasing the frequency of favorable alleles in the breeding population, recurrent selection improves the population mean for target traits, increasing the chance to identify promising genotypes. In rice, population improvement through recurrent selection has been used very little to date, except in Latin America. At Embrapa (Brazilian Agricultural Research Corporation), the upland rice breeding program is conducted in two phases: population improvement followed by product development. In this study, the CNA6 population, evaluated over five cycles (3 to 7) of selection, including 20 field trials, was used to assess the realized genetic gain. A high rate of genetic gain was observed for grain yield, at 215 kg.ha -1 per cycle or 67.8 kg.ha -1 per year (3.08%). The CNA6 population outperformed the controls only for the last cycle, with a yield difference of 1128 kg.ha -1 . An analysis of the product development pipeline, based on 29 advanced yield trials with lines derived from cycles 3 to 6, showed that lines derived from the CNA6 population had high grain yield, but did not outperform the controls. These results demonstrate that the application of recurrent selection to a breeding population with sufficient genetic variability can result in significant genetic gains for quantitative traits, such as grain yield. The integration of this strategy into a two-phase breeding program also makes it possible to increase quantitative traits while selecting for other traits of interest., (© 2023. The Author(s), under exclusive licence to The Genetics Society.)

Background: Assessing the performance of elite lines in target environments is essential for breeding programs to select the most relevant genotypes. One of the main complexities in this task resides in accounting for the genotype by environment interactions. Genomic prediction models that integrate information from multi-environment trials and environmental covariates can be efficient tools in this context. The objective of this study was to assess the predictive ability of different genomic prediction models to optimize the use of multi-environment information. We used 111 elite breeding lines representing the diversity of the international rice research institute breeding program for irrigated ecosystems. The lines were evaluated for three traits (days to flowering, plant height, and grain yield) in 15 environments in Asia and Africa and genotyped with 882 SNP markers. We evaluated the efficiency of genomic prediction to predict untested environments using seven multi-environment models and three cross-validation scenarios., Results: The elite lines were found to belong to the indica group and more specifically the indica-1B subgroup which gathered improved material originating from the Green Revolution. Phenotypic correlations between environments were high for days to flowering and plant height (33% and 54% of pairwise correlation greater than 0.5) but low for grain yield (lower than 0.2 in most cases). Clustering analyses based on environmental covariates separated Asia's and Africa's environments into different clusters or subclusters. The predictive abilities ranged from 0.06 to 0.79 for days to flowering, 0.25-0.88 for plant height, and - 0.29-0.62 for grain yield. We found that models integrating genotype-by-environment interaction effects did not perform significantly better than models integrating only main effects (genotypes and environment or environmental covariates). The different cross-validation scenarios showed that, in most cases, the use of all available environments gave better results than a subset., Conclusion: Multi-environment genomic prediction models with main effects were sufficient for accurate phenotypic prediction of elite lines in targeted environments. These results will help refine the testing strategy to update the genomic prediction models to improve predictive ability., (© 2023. The Author(s).)

Estimating genetic trends using historical data is an important parameter to check the success of the breeding programs. The estimated genetic trends can act as a guideline to target the appropriate breeding strategies and optimize the breeding program for improved genetic gains. In this study, 17 years of historical data from IRRI's rice drought breeding program was used to estimate the genetic trends and assess the breeding program's success. We also identified top-performing lines based on grain yield breeding values as an elite panel for implementing future population improvement-based breeding schemes. A two-stage approach of pedigree-based mixed model analysis was used to analyze the data and extract the breeding values and estimate the genetic trends for grain yield under non-stress, drought, and in combined data of non-stress and drought. Lower grain yield values were observed in all the drought trials. Heritability for grain yield estimates ranged between 0.20 and 0.94 under the drought trials and 0.43-0.83 under non-stress trials. Under non-stress conditions, the genetic gain of 0.21% (10.22 kg/ha/year) for genotypes and 0.17% (7.90 kg/ha/year) for checks was observed. The genetic trend under drought conditions exhibited a positive trend with the genetic gain of 0.13% (2.29 kg/ha/year) for genotypes and 0.55% (9.52 kg/ha/year) for checks. For combined analysis showed a genetic gain of 0.27% (8.32 kg/ha/year) for genotypes and 0.60% (13.69 kg/ha/year) for checks was observed. For elite panel selection, 200 promising lines were selected based on higher breeding values for grain yield and prediction accuracy of > 0.40. The breeding values of the 200 genotypes formulating the core panel ranged between 2366.17 and 4622.59 (kg/ha). A positive genetic rate was observed under all the three conditions; however, the rate of increase was lower than the required rate of 1.5% genetic gain. We propose a recurrent selection breeding strategy within the elite population with the integration of modern tools and technologies to boost the genetic gains in IRRI's drought breeding program. The elite breeding panel identified in this study forms an easily available and highly enriched genetic resource for future recurrent selection programs to boost the genetic gains., (© 2022. The Author(s).)

Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context., (© 2022. The Author(s).)

Population breeding through recurrent selection is based on the repetition of evaluation and recombination among best-selected individuals. In this type of breeding strategy, early evaluation of selection candidates combined with genomic prediction could substantially shorten the breeding cycle length, thus increasing the rate of genetic gain. The objective of this study was to optimize early genomic prediction in an upland rice (Oryza sativa L.) synthetic population improved through recurrent selection via shuttle breeding in two sites. To this end, we used genomic prediction on 334 S0 genotypes evaluated with early generation progeny testing (S0:2 and S0:3) across two sites. Four traits were measured (plant height, days to flowering, grain yield, and grain zinc concentration) and the predictive ability was assessed for the target site. For days to flowering and plant height, which correlate well among sites (0.51-0.62), an increase of up to 0.4 in predictive ability was observed when the model was trained using the two sites. For grain zinc concentration, adding the phenotype of the predicted lines in the nontarget site to the model improved the predictive ability (0.51 with two-site and 0.31 with single-site model), whereas for grain yield the gain was less (0.42 with two-site and 0.35 with single-site calibration). Through these results, we found a good opportunity to optimize the genomic recurrent selection scheme and maximize the use of resources by performing early progeny testing in two sites for traits with best expression and/or relevance in each specific environment., (© The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America.)

Rice genetic improvement is a key component of achieving and maintaining food security in Asia and Africa in the face of growing populations and climate change. In this effort, the International Rice Research Institute (IRRI) continues to play a critical role in creating and disseminating rice varieties with higher productivity. Due to increasing demand for rice, especially in Africa, there is a strong need to accelerate the rate of genetic improvement for grain yield. In an effort to identify and characterize the elite breeding pool of IRRI's irrigated rice breeding program, we analyzed 102 historical yield trials conducted in the Philippines during the period 2012-2016 and representing 15,286 breeding lines (including released varieties). A mixed model approach based on the pedigree relationship matrix was used to estimate breeding values for grain yield, which ranged from 2.12 to 6.27 t·ha -1 . The rate of genetic gain for grain yield was estimated at 8.75 kg·ha -1  year -1 (0.23%) for crosses made in the period from 1964 to 2014. Reducing the data to only IRRI released varieties, the rate doubled to 17.36 kg·ha -1  year -1 (0.46%). Regressed against breeding cycle the rate of gain for grain yield was 185 kg·ha -1  cycle -1 (4.95%). We selected 72 top performing lines based on breeding values for grain yield to create an elite core panel (ECP) representing the genetic diversity in the breeding program with the highest heritable yield values from which new products can be derived. The ECP closely aligns with the indica 1B sub-group of Oryza sativa that includes most modern varieties for irrigated systems. Agronomic performance of the ECP under multiple environments in Asia and Africa confirmed its high yield potential. We found that the rate of genetic gain for grain yield found in this study was limited primarily by long cycle times and the direct introduction of non-improved material into the elite pool. Consequently, the current breeding scheme for irrigated rice at IRRI is based on rapid recurrent selection among highly elite lines. In this context, the ECP constitutes an important resource for IRRI and NAREs breeders to carefully characterize and manage that elite diversity., (© 2021. The Author(s).)

The pulse of the tree (diurnal cycle of stem radius fluctuations) has been widely studied as a way of analyzing tree responses to the environment, including the phenotypic plasticity of tree-water relationships in particular. However, the genetic basis of this daily phenotype and its interplay with the environment remain largely unexplored. We characterized the genetic and environmental determinants of this response, by monitoring daily stem radius fluctuation (dSRF) on 210 trees from a Eucalyptus urophylla × E. grandis full-sib family over 2 years. The dSRF signal was broken down into hydraulic capacitance, assessed as the daily amplitude of shrinkage (DA), and net growth, estimated as the change in maximum radius between two consecutive days (ΔR). The environmental determinants of these two traits were clearly different: DA was positively correlated with atmospheric variables relating to water demand, while ΔR was associated with soil water content. The heritability for these two traits ranged from low to moderate over time, revealing a time-dependent or environment-dependent complex genetic determinism. We identified 686 and 384 daily quantitative trait loci (QTL) representing 32 and 31 QTL regions for DA and ΔR, respectively. The identification of gene networks underlying the 27 major genomics regions for both traits generated additional hypotheses concerning the biological mechanisms involved in response to water demand and supply. This study highlights that environmentally induced changes in daily stem radius fluctuation are genetically controlled in trees and suggests that these daily responses integrated over time shape the genetic architecture of mature traits., (© 2020 Society for Experimental Biology and John Wiley & Sons Ltd.)

Exotic pathogens cause severe damage in natural populations in the absence of coevolutionary dynamics with their hosts. However, some resistance to such pathogens may occur in naive populations. The objective of this study was to investigate the genetics of this so-called 'exapted' resistance to two pathogens of Asian origin (Erysiphe alphitoides and Phytophthora cinnamomi) in European oak. Host-pathogen compatibility was assessed by recording infection success and pathogen growth in a full-sib family of Quercus robur under controlled and natural conditions. Two high-resolution genetic maps anchored on the reference genome were used to study the genetic architecture of resistance and to identify positional candidate genes. Two genomic regions, each containing six strong and stable quantitative trait loci (QTLs) accounting for 12-19% of the phenotypic variation, were mainly associated with E. alphitoides infection. Candidate genes, especially genes encoding receptor-like-kinases and galactinol synthases, were identified in these regions. The three QTLs associated with P. cinnamomi infection did not colocate with QTLs found for E. alphitoides. These findings provide evidence that exapted resistance to E. alphitoides and P. cinnamomi is present in Q. robur and suggest that the underlying molecular mechanisms involve genes encoding proteins with extracellular signaling functions., (© 2019 The Authors. New Phytologist © 2019 New Phytologist Trust.)

Developing high yielding rice varieties that are tolerant to drought stress is crucial for the sustainable livelihood of rice farmers in rainfed rice cropping ecosystems. Genomic selection (GS) promises to be an effective breeding option for these complex traits. We evaluated the effectiveness of two rather new options in the implementation of GS: trait and environment-specific marker selection and the use of multi-environment prediction models. A reference population of 280 rainfed lowland accessions endowed with 215k SNP markers data was phenotyped under a favorable and two managed drought environments. Trait-specific SNP subsets (28k) were selected for each trait under each environment, using results of GWAS performed with the complete genotype dataset. Performances of single-environment and multi-environment genomic prediction models were compared using kernel regression based methods (GBLUP and RKHS) under two cross validation scenarios: availability (CV2) or not (CV1) of phenotypic data for the validation set, in one of the environments. Trait-specific marker selection strategy achieved predictive ability (PA) of genomic prediction up to 22% higher than markers selected on the bases of neutral linkage disequilibrium (LD). Tolerance to drought stress was up to 32% better predicted by multi-environment models (especially RKHS based models) under CV2 strategy. Under the less favorable CV1 strategy, the multi-environment models achieved similar PA than the single-environment predictions. We also showed that reasonable PA could be obtained with as few as 3,000 SNP markers, even in a population of low LD extent, provided marker selection is based on pairwise LD. The implications of these findings for breeding for drought tolerance are discussed. The most resource sparing option would be accurate phenotyping of the reference population in a favorable environment and under a managed drought, while the candidate population would be phenotyped only under one of those environments., Competing Interests: The authors have declared that no competing interests exist.

Modern rice cultivars are adapted to a range of environmental conditions and human preferences. At the root of this diversity is a marked genetic structure, owing to multiple foundation events. Admixture and recurrent introgression from wild sources have played upon this base to produce the myriad adaptations existing today. Genome-wide studies bring support to this idea, but understanding the history and nature of particular genetic adaptations requires the identification of specific patterns of genetic exchange. In this study, we explore the patterns of haplotype similarity along the genomes of a subset of rice cultivars available in the 3,000 Rice Genomes data set. We begin by establishing a custom method of classification based on a combination of dimensionality reduction and kernel density estimation. Through simulations, the behavior of this classifier is studied under scenarios of varying genetic divergence, admixture, and alien introgression. Finally, the method is applied to local haplotypes along the genome of a Core set of Asian Landraces. Taking the Japonica, Indica, and cAus groups as references, we find evidence of reciprocal introgressions covering 2.6% of reference genomes on average. Structured signals of introgression among reference accessions are discussed. We extend the analysis to elucidate the genetic structure of the group circum-Basmati: we delimit regions of Japonica, cAus, and Indica origin, as well as regions outlier to these groups (13% on average). Finally, the approach used highlights regions of partial to complete loss of structure that can be attributed to selective pressures during domestication., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Developing rice varieties adapted to alternate wetting and drying water management is crucial for the sustainability of irrigated rice cropping systems. Here we report the first study exploring the feasibility of breeding rice for adaptation to alternate wetting and drying using genomic prediction methods that account for genotype by environment interactions. Two breeding populations (a reference panel of 284 accessions and a progeny population of 97 advanced lines) were evaluated under alternate wetting and drying and continuous flooding management systems. The predictive ability of genomic prediction for response variables (index of relative performance and the slope of the joint regression) and for multi-environment genomic prediction models were compared. For the three traits considered (days to flowering, panicle weight and nitrogen-balance index), significant genotype by environment interactions were observed in both populations. In cross validation, predictive ability for the index was on average lower (0.31) than that of the slope of the joint regression (0.64) whatever the trait considered. Similar results were found for progeny validation. Both cross-validation and progeny validation experiments showed that the performance of multi-environment models predicting unobserved phenotypes of untested entrees was similar to the performance of single environment models with differences in predictive ability ranging from -6-4% depending on the trait and on the statistical model concerned. The predictive ability of multi-environment models predicting unobserved phenotypes of entrees evaluated under both water management systems outperformed single environment models by an average of 30%. Practical implications for breeding rice for adaptation to alternate wetting and drying system are discussed., (Copyright © 2018 Ben Hassen et al.)

Oaks are an important part of our natural and cultural heritage. Not only are they ubiquitous in our most common landscapes 1 but they have also supplied human societies with invaluable services, including food and shelter, since prehistoric times 2 . With 450 species spread throughout Asia, Europe and America 3 , oaks constitute a critical global renewable resource. The longevity of oaks (several hundred years) probably underlies their emblematic cultural and historical importance. Such long-lived sessile organisms must persist in the face of a wide range of abiotic and biotic threats over their lifespans. We investigated the genomic features associated with such a long lifespan by sequencing, assembling and annotating the oak genome. We then used the growing number of whole-genome sequences for plants (including tree and herbaceous species) to investigate the parallel evolution of genomic characteristics potentially underpinning tree longevity. A further consequence of the long lifespan of trees is their accumulation of somatic mutations during mitotic divisions of stem cells present in the shoot apical meristems. Empirical 4 and modelling 5 approaches have shown that intra-organismal genetic heterogeneity can be selected for 6 and provides direct fitness benefits in the arms race with short-lived pests and pathogens through a patchwork of intra-organismal phenotypes 7 . However, there is no clear proof that large-statured trees consist of a genetic mosaic of clonally distinct cell lineages within and between branches. Through this case study of oak, we demonstrate the accumulation and transmission of somatic mutations and the expansion of disease-resistance gene families in trees.

Key Message: Rice breeding programs based on pedigree schemes can use a genomic model trained with data from their working collection to predict performances of progenies produced through rapid generation advancement. So far, most potential applications of genomic prediction in plant improvement have been explored using cross validation approaches. This is the first empirical study to evaluate the accuracy of genomic prediction of the performances of progenies in a typical rice breeding program. Using a cross validation approach, we first analyzed the effects of marker selection and statistical methods on the accuracy of prediction of three different heritability traits in a reference population (RP) of 284 inbred accessions. Next, we investigated the size and the degree of relatedness with the progeny population (PP) of sub-sets of the RP that maximize the accuracy of prediction of phenotype across generations, i.e., for 97 F 5 -F 7 lines derived from biparental crosses between 31 accessions of the RP. The extent of linkage disequilibrium was high (r 2  = 0.2 at 0.80 Mb in RP and at 1.1 Mb in PP). Consequently, average marker density above one per 22 kb did not improve the accuracy of predictions in the RP. The accuracy of progeny prediction varied greatly depending on the composition of the training set, the trait, LD and minor allele frequency. The highest accuracy achieved for each trait exceeded 0.50 and was only slightly below the accuracy achieved by cross validation in the RP. Our results thus show that relatively high accuracy (0.41-0.54) can be achieved using only a rather small share of the RP, most related to the PP, as the training set. The practical implications of these results for rice breeding programs are discussed.

Background: Increasing our understanding of the genetic architecture of complex traits, through analyses of genotype-phenotype associations and of the genes/polymorphisms accounting for trait variation, is crucial, to improve the integration of molecular markers into forest tree breeding. In this study, two full-sib families and one breeding population of maritime pine were used to identify quantitative trait loci (QTLs) for height growth and stem straightness, through linkage analysis (LA) and linkage disequilibrium (LD) mapping approaches., Results: The populations used for LA consisted of two unrelated three-generation full-sib families (n = 197 and n = 477). These populations were assessed for height growth or stem straightness and genotyped for 248 and 217 markers, respectively. The population used for LD mapping consisted of 661 founders of the first and second generations of the breeding program. This population was phenotyped for the same traits and genotyped for 2,498 single-nucleotide polymorphism (SNP) markers corresponding to 1,652 gene loci. The gene-based reference genetic map of maritime pine was used to localize and compare the QTLs detected by the two approaches, for both traits. LA identified three QTLs for stem straightness and two QTLs for height growth. The LD study yielded seven significant associations (P ≤ 0.001): four for stem straightness and three for height growth. No colocalisation was found between QTLs identified by LA and SNPs detected by LD mapping for the same trait., Conclusions: This study provides the first comparison of LA and LD mapping approaches in maritime pine, highlighting the complementary nature of these two approaches for deciphering the genetic architecture of two mandatory traits of the breeding program., Competing Interests: The authors have declared that no competing interests exist. The commercial affiliation of IL to HelixVenture does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background: Genomic selection (GS) is a promising approach for decreasing breeding cycle length in forest trees. Assessment of progeny performance and of the prediction accuracy of GS models over generations is therefore a key issue., Results: A reference population of maritime pine (Pinus pinaster) with an estimated effective inbreeding population size (status number) of 25 was first selected with simulated data. This reference population (n = 818) covered three generations (G0, G1 and G2) and was genotyped with 4436 single-nucleotide polymorphism (SNP) markers. We evaluated the effects on prediction accuracy of both the relatedness between the calibration and validation sets and validation on the basis of progeny performance. Pedigree-based (best linear unbiased prediction, ABLUP) and marker-based (genomic BLUP and Bayesian LASSO) models were used to predict breeding values for three different traits: circumference, height and stem straightness. On average, the ABLUP model outperformed genomic prediction models, with a maximum difference in prediction accuracies of 0.12, depending on the trait and the validation method. A mean difference in prediction accuracy of 0.17 was found between validation methods differing in terms of relatedness. Including the progenitors in the calibration set reduced this difference in prediction accuracy to 0.03. When only genotypes from the G0 and G1 generations were used in the calibration set and genotypes from G2 were used in the validation set (progeny validation), prediction accuracies ranged from 0.70 to 0.85., Conclusions: This study suggests that the training of prediction models on parental populations can predict the genetic merit of the progeny with high accuracy: an encouraging result for the implementation of GS in the maritime pine breeding program.

A two-generation maritime pine (Pinus pinaster Ait.) breeding population (n=661) was genotyped using 2500 SNP markers. The extent of linkage disequilibrium and utility of genomic selection for growth and stem straightness improvement were investigated. The overall intra-chromosomal linkage disequilibrium was r(2)=0.01. Linkage disequilibrium corrected for genomic relationships derived from markers was smaller (rV(2)=0.006). Genomic BLUP, Bayesian ridge regression and Bayesian LASSO regression statistical models were used to obtain genomic estimated breeding values. Two validation methods (random sampling 50% of the population and 10% of the progeny generation as validation sets) were used with 100 replications. The average predictive ability across statistical models and validation methods was about 0.49 for stem sweep, and 0.47 and 0.43 for total height and tree diameter, respectively. The sensitivity analysis suggested that prior densities (variance explained by markers) had little or no discernible effect on posterior means (residual variance) in Bayesian prediction models. Sampling from the progeny generation for model validation increased the predictive ability of markers for tree diameter and stem sweep but not for total height. The results are promising despite low linkage disequilibrium and low marker coverage of the genome (∼1.39 markers/cM)., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

While recent advances have been gained on genome evolution in angiosperm lineages, virtually nothing is known about karyotype evolution in the other group of seed plants, the gymnosperms. Here we used high density gene-based linkage mapping to compare the karyotype structure of two families of conifers (the most abundant group of gymnosperms) separated around 290 million years ago: Pinaceae and Cupressaceae. We propose for the first time a model based on the fusion of 20 ancestral chromosomal blocks that may have shaped the modern karyotpes of Pinaceae (with n=12) and Cupressaceae (with n=11). The considerable difference in modern genome organization between these two lineages contrasts strongly with the remarkable level of synteny already reported within the Pinaceae. It also suggests a convergent evolutionary mechanism of chromosomal block shuffling that has shaped the genomes of the spermatophytes., (© The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Trees adjust their growth following forced changes in orientation to re-establish a vertical position. In angiosperms, this adjustment involves the differential regulation of vascular cambial activity between the lower (opposite wood) and upper (tension wood) sides of the leaning stem. We investigated the molecular mechanisms leading to the formation of differential wood types through a quantitative proteomic and phosphoproteomic analysis on poplar subjected to a gravitropic stimulus. We identified and quantified 675 phosphopeptides, corresponding to 468 phosphoproteins, and 3 763 nonphosphorylated peptides, corresponding to 1 155 proteins, in the differentiating xylem of straight-growing trees (control) and trees subjected to a gravitational stimulus during 8 weeks. About 1% of the peptides were specific to a wood type (straight, opposite, or tension wood). Proteins quantified in more than one type of wood were more numerous: a mixed linear model showed 389 phosphopeptides and 556 proteins to differ in abundance between tension wood and opposite wood. Twenty-one percent of the phosphoproteins identified here were described in their phosphorylated form for the first time. Our analyses revealed remarkable developmental molecular plasticity, with wood type-specific phosphorylation events, and highlighted the involvement of different proteins in the biosynthesis of cell wall components during the formation of the three types of wood.

Genetic maps are key tools in genetic research as they constitute the framework for many applications, such as quantitative trait locus analysis, and support the assembly of genome sequences. The resequencing of the two parents of a cross between Eucalyptus urophylla and Eucalyptus grandis was used to design a single nucleotide polymorphism (SNP) array of 6000 markers evenly distributed along the E. grandis genome. The genotyping of 1025 offspring enabled the construction of two high-resolution genetic maps containing 1832 and 1773 markers with an average marker interval of 0.45 and 0.5 cM for E. grandis and E. urophylla, respectively. The comparison between genetic maps and the reference genome highlighted 85% of collinear regions. A total of 43 noncollinear regions and 13 nonsynthetic regions were detected and corrected in the new genome assembly. This improved version contains 4943 scaffolds totalling 691.3 Mb of which 88.6% were captured by the 11 chromosomes. The mapping data were also used to investigate the effect of population size and number of markers on linkage mapping accuracy. This study provides the most reliable linkage maps for Eucalyptus and version 2.0 of the E. grandis genome., (© 2014 CIRAD. New Phytologist © 2014 New Phytologist Trust.)

In the context of climate change, the water-use efficiency (WUE) of highly productive tree varieties, such as eucalypts, has become a major issue for breeding programmes. This study set out to dissect the genetic architecture of carbon isotope composition (δ(13) C), a proxy of WUE, across several environments. A family of Eucalyptus urophylla × E. grandis was planted in three trials and phenotyped for δ(13) C and growth traits. High-resolution genetic maps enabled us to target genomic regions underlying δ(13) C quantitative trait loci (QTLs) on the E. grandis genome. Of the 15 QTLs identified for δ(13) C, nine were stable across the environments and three displayed significant QTL-by-environment interaction, suggesting medium to high genetic determinism for this trait. Only one colocalization was found between growth and δ(13) C. Gene ontology (GO) term enrichment analysis suggested candidate genes related to foliar δ(13) C, including two involved in the regulation of stomatal movements. This study provides the first report of the genetic architecture of δ(13) C and its relation to growth in Eucalyptus. The low correlations found between the two traits at phenotypic and genetic levels suggest the possibility of improving the WUE of Eucalyptus varieties without having an impact on breeding for growth., (© 2015 CIRAD. New Phytologist © 2015 New Phytologist Trust.)

Background: Many northern-hemisphere forests are dominated by oaks. These species extend over diverse environmental conditions and are thus interesting models for studies of plant adaptation and speciation. The genomic toolbox is an important asset for exploring the functional variation associated with natural selection., Results: The assembly of previously available and newly developed long and short sequence reads for two sympatric oak species, Quercus robur and Quercus petraea, generated a comprehensive catalog of transcripts for oak. The functional annotation of 91 k contigs demonstrated the presence of a large proportion of plant genes in this unigene set. Comparisons with SwissProt accessions and five plant gene models revealed orthologous relationships, making it possible to decipher the evolution of the oak genome. In particular, it was possible to align 9.5 thousand oak coding sequences with the equivalent sequences on peach chromosomes. Finally, RNA-seq data shed new light on the gene networks underlying vegetative bud dormancy release, a key stage in development allowing plants to adapt their phenology to the environment., Conclusion: In addition to providing a vast array of expressed genes, this study generated essential information about oak genome evolution and the regulation of genes associated with vegetative bud phenology, an important adaptive traits in trees. This resource contributes to the annotation of the oak genome sequence and will provide support for forward genetics approaches aiming to link genotypes with adaptive phenotypes.

Background: The genetic basis of growth traits has been widely studied in forest trees. Quantitative trait locus (QTL) studies have highlighted the presence of both stable and unstable genomic regions accounting for biomass production with respect to tree age and genetic background, but results remain scarce regarding the interplay between QTLs and the environment. In this study, our main objective was to dissect the genetic architecture of the growth trajectory with emphasis on genotype x environment interaction by measuring primary and secondary growth covering intervals connected with environmental variations., Results: Three different trials with the same family of Eucalyptus urophylla x E. grandis hybrids (with different genotypes) were planted in the Republic of Congo, corresponding to two QTL mapping experiments and one clonal test. Height and radial growths were monitored at regular intervals from the seedling stage to five years old. The correlation between growth increments and an aridity index revealed that growth before two years old (r = 0.5; 0.69) was more responsive to changes in water availability than late growth (r = 0.39; 0.42) for both height and circumference. We found a regular increase in heritability with time for cumulative growth for both height [0.06 - 0.33] and circumference [0.06 - 0.38]. Heritabilities for incremental growth were more heterogeneous over time even if ranges of variation were similar (height [0-0.31]; circumference [0.19 to 0.48]). Within the trials, QTL analysis revealed collocations between primary and secondary growth QTLs as well as between early growth increments and final growth QTLs. Between trials, few common QTLs were detected highlighting a strong environmental effect on the genetic architecture of growth, validated by significant QTL x E interactions., Conclusion: These results suggest that early growth responses to water availability determine the genetic architecture of total growth at the mature stage and highlight the importance of considering growth as a composite trait (such as yields for annual plants) for a better understanding of its genetic bases.

Among the biological assays used for the diagnosis of systemic lupus erythematosus, the detection of anti-double strand DNA (dsDNA) antibodies is highly specific, especially at a high level. Different methods are using for the detection of this specificity, particularly enzyme-based immunoassay (EIA) kits that become widespread in autoimmunity because of several advantages like standardization, simple use and automation. We selected 80 sera in order to evaluate six commercial anti-dsDNA EIA kits and a Farr assay. The results were compared with clinical data, to appreciate both the sensitivity and the specificity of each method. EIA assays are highly sensitive, but are less specific than the Farr assay. These EIA methods should remain organized in an integrative biological step, following an evocative clinical examination. Moreover, the EIA results require confirmations with a more specific assay.