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The African buffalo (Syncerus caffer) is a wild bovid with a historical distribution across much of sub-Saharan Africa. Genomic analysis can provide insights into the evolutionary history of the species, and the key selective pressures shaping populations, including assessment of population level differentiation, population fragmentation, and population genetic structure. In this study we generated the highest quality de novo genome assembly (2.65 Gb, scaffold N50 69.17 Mb) of African buffalo to date, and sequenced a further 195 genomes from across the species distribution. Principal component and admixture analyses provided little support for the currently described four subspecies. Estimating Effective Migration Surfaces analysis suggested that geographical barriers have played a significant role in shaping gene flow and the population structure. Estimated effective population sizes indicated a substantial drop occurring in all populations 5-10,000 years ago, coinciding with the increase in human populations. Finally, signatures of selection were enriched for key genes associated with the immune response, suggesting infectious disease exert a substantial selective pressure upon the African buffalo. These findings have important implications for understanding bovid evolution, buffalo conservation and population management.

Introduction: Predicting the performance (yield or other integrative traits) of cultivated plants is complex because it involves not only estimating the genetic value of the candidates to selection, the interactions between the genotype and the environment (GxE) but also the epistatic interactions between genomic regions for a given trait, and the interactions between the traits contributing to the integrative trait. Classical Genomic Prediction (GP) models mostly account for additive effects and are not suitable to estimate non-additive effects such as epistasis. Therefore, the use of machine learning and deep learning methods has been previously proposed to model those non-linear effects. Methods: In this study, we propose a type of Artificial Neural Network (ANN) called Convolutional Neural Network (CNN) and compare it to two classical GP regression methods for their ability to predict an integrative trait of sorghum: aboveground fresh weight accumulation. We also suggest that the use of a crop growth model (CGM) can enhance predictions of integrative traits by decomposing them into more heritable intermediate traits. Results: The results show that CNN outperformed both LASSO and Bayes C methods in accuracy, suggesting that CNN are better suited to predict integrative traits. Furthermore, the predictive ability of the combined CGM-GP approach surpassed that of GP without the CGM integration, irrespective of the regression method used. Discussion: These results are consistent with recent works aiming to develop Genome-to-Phenotype models and advocate for the use of non-linear prediction methods, and the use of combined CGM-GP to enhance the prediction of crop performances.

Using a combination of short- and long-reads sequencing, we were able to sequence the complete mitochondrial genome of the invasive 'New Zealand flatworm' Arthurdendyus triangulatus (Geoplanidae, Rhynchodeminae, Caenoplanini) and its two complete paralogous nuclear rRNA gene clusters. The mitogenome has a total length of 20,309 bp and contains repetitions that includes two types of tandem-repeats that could not be solved by short-reads sequencing. We also sequenced for the first time the mitogenomes of four species of Caenoplana (Caenoplanini). A maximum likelihood phylogeny associated A. triangulatus with the other Caenoplanini but Parakontikia ventrolineata and Australopacifica atrata were rejected from the Caenoplanini and associated instead with the Rhynchodemini, with Platydemus manokwari. It was found that the mitogenomes of all species of the subfamily Rhynchodeminae share several unusual structural features, including a very long cox2 gene. This is the first time that the complete paralogous rRNA clusters, which differ in length, sequence and seemingly number of copies, were obtained for a Geoplanidae.

Social insects, particularly honey bees, have exceptionally high genomic frequencies of genetic recombination. This phenomenon and underlying mechanisms are poorly understood. To characterise the patterns of crossovers and gene conversion in the honey bee genome, a recombination map of 187 honey bee brothers was generated by whole-genome resequencing. Recombination events were heterogeneously distributed without many true hotspots. The tract lengths between phase shifts were bimodally distributed, indicating distinct crossover and gene conversion events. While crossovers predominantly occurred in G/C-rich regions and seemed to cause G/C enrichment, the gene conversions were found predominantly in A/T-rich regions. The nucleotide composition of sequences involved in gene conversions that were associated with or distant from crossovers corresponded to the differences between crossovers and gene conversions. These combined results suggest two types of DNA double-strand break repair during honey bee meiosis: non-canonical homologous recombination, leading to gene conversion and A/T enrichment of the genome, and the canonical homologous recombination based on completed double Holliday Junctions, which can result in gene conversion or crossover and is associated with G/C bias. This G/C bias may be selected for to balance the A/T-rich base composition of eusocial hymenopteran genomes. The lack of evidence for a preference of the canonical homologous recombination for double-strand break repair suggests that the high genomic recombination rate of honey bees is mainly the consequence of a high rate of double-strand breaks, which could in turn result from the life history of honey bees and their A/T-rich genome.

Coffea arabica, an allotetraploid hybrid of Coffea eugenioides and Coffea canephora, is the source of approximately 60% of coffee products worldwide, and its cultivated accessions have undergone several population bottlenecks. We present chromosome-level assemblies of a di-haploid C. arabica accession and modern representatives of its diploid progenitors, C. eugenioides and C. canephora. The three species exhibit largely conserved genome structures between diploid parents and descendant subgenomes, with no obvious global subgenome dominance. We find evidence for a founding polyploidy event 350,000–610,000 years ago, followed by several pre-domestication bottlenecks, resulting in narrow genetic variation. A split between wild accessions and cultivar progenitors occurred ~30.5 thousand years ago, followed by a period of migration between the two populations. Analysis of modern varieties, including lines historically introgressed with C. canephora, highlights their breeding histories and loci that may contribute to pathogen resistance, laying the groundwork for future genomics-based breeding of C. arabica.

Identifying the genetic factors impacting the adaptation of crops to environmental conditions is of key interest for conservation and selection purposes. It can be achieved using population genomics, and evolutionary or quantitative genetics. Here we present a sorghum multireference back-cross nested association mapping population composed of 3,901 lines produced by crossing 24 diverse parents to 3 elite parents from West and Central Africa-back-cross nested association mapping. The population was phenotyped in environments characterized by differences in photoperiod, rainfall pattern, temperature levels, and soil fertility. To integrate the multiparental and multi-environmental dimension of our data we proposed a new approach for quantitative trait loci (QTL) detection and parental effect estimation. We extended our model to estimate QTL effect sensitivity to environmental covariates, which facilitated the integration of envirotyping data. Our models allowed spatial projections of the QTL effects in agro-ecologies of interest. We utilized this strategy to analyze the genetic architecture of flowering time and plant height, which represents key adaptation mechanisms in environments like West Africa. Our results allowed a better characterization of well-known genomic regions influencing flowering time concerning their response to photoperiod with Ma6 and Ma1 being photoperiod-sensitive and the region of possible candidate gene Elf3 being photoperiod-insensitive. We also accessed a better understanding of plant height genetic determinism with the combined effects of phenology-dependent (Ma6) and independent (qHT7.1 and Dw3) genomic regions. Therefore, we argue that the West and Central Africa-back-cross nested association mapping and the presented analytical approach constitute unique resources to better understand adaptation in sorghum with direct application to develop climate-smart varieties.

Sugarcane, the world's most harvested crop by tonnage, has shaped global history, trade and geopolitics, and is currently responsible for 80% of sugar production worldwide1. While traditional sugarcane breeding methods have effectively generated cultivars adapted to new environments and pathogens, sugar yield improvements have recently plateaued2. The cessation of yield gains may be due to limited genetic diversity within breeding populations, long breeding cycles and the complexity of its genome, the latter preventing breeders from taking advantage of the recent explosion of whole-genome sequencing that has benefited many other crops. Thus, modern sugarcane hybrids are the last remaining major crop without a reference-quality genome. Here we take a major step towards advancing sugarcane biotechnology by generating a polyploid reference genome for R570, a typical modern cultivar derived from interspecific hybridization between the domesticated species (Saccharum officinarum) and the wild species (Saccharum spontaneum). In contrast to the existing single haplotype ('monoploid') representation of R570, our 8.7 billion base assembly contains a complete representation of unique DNA sequences across the approximately 12 chromosome copies in this polyploid genome. Using this highly contiguous genome assembly, we filled a previously unsized gap within an R570 physical genetic map to describe the likely causal genes underlying the single-copy Bru1 brown rust resistance locus. This polyploid genome assembly with fine-grain descriptions of genome architecture and molecular targets for biotechnology will help accelerate molecular and transgenic breeding and adaptation of sugarcane to future environmental conditions.

Gossypium, the cotton genus, includes ∼50 species distributed in tropical and sub-tropical regions of all continents except Europe. Here we provide a synopsis of the evolutionary history of Gossypium and domestication of the American allopolyploid species, integrating data from fundamental taxonomic investigations, biogeography, molecular genetics, phylogenetic analysis, and archaeology. These diverse sources of information provide a temporal and phylogenetic perspective on diversification among the diploids and on polyploid formation, uncover multiple previously cryptic interspecific hybridizations, clarify and contribute to the taxonomy of the genus, and offer a firm foundation for understanding parallel domestications in Mesoamerica and South America, which led to the globally important cotton crop species G. barbadense and G. hirsutum. Gossypium thus offers a testimonial example of the importance and utility of fundamental botanical discovery combined with modern technological capabilities to generate genomic insights into evolutionary history. We also review the current state of our knowledge regarding the archaeological history of cotton domestication and diffusion in the Americas, a seemingly unlikely story entailing parallel domestication origins and parallel directional selection tracing to 8,000 (G. barbadense) and 5,500 (G. hirsutum) years ago, transforming two geographically isolated wild short-day perennial shrubs having small capsules and seeds covered by short, tan-colored epidermal trichomes into modern daylength-neutral annuals bearing abundant, fine, strong white fibers. This dual domestication was followed several millennia later by unintentional and more recently intentional interspecific introgression, as the two species came into contact following their initial domestication in different hemispheres. Thus, the cycle of species divergence and biological reunion was reiterated, this time at the allopolyploid level. Understanding this evolutionary

The discovery of new variants has slowed down in recent years in epilepsy studies, despite the use of very large cohorts. Consequently, most of the heritability is still unexplained. Rare non-coding variants have been largely ignored in studies on epilepsy, although non-coding single nucleotide variants can have a significant impact on gene expression. We had access to whole genome sequencing (WGS) from 247 epilepsy patients and 377 controls. To assess the functional impact of non-coding variants, ExPecto, a deep learning algorithm was used to predict expression change in brain tissues. We compared the burden of rare non-coding deleterious variants between cases and controls. Rare non-coding highly deleterious variants were significantly enriched with Genetic Generalized Epilepsy (GGE), but not with Non-Acquired Focal Epilepsy (NAFE) or all epilepsy cases when compared with controls. In this study, we showed that rare non-coding deleterious variants are associated with epilepsy, specifically with GGE. Larger WGS epilepsy cohort will be needed to investigate those effects at a greater resolution. Nevertheless, we demonstrated the importance of studying non-coding regions in epilepsy, a disease where new discoveries are scarce.

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.

Introduction: The oriental fruit fly Bactrocera dorsalis is one of the most destructive agricultural pests worldwide, with highly debated species delimitation, origin, and global spread routes. Objectives: Our study intended to (i) resolve the taxonomic uncertainties between B. dorsalis and B. carambolae, (ii) reveal the population structure and global invasion routes of B. dorsalis across Asia, Africa, and Oceania, and (iii) identify genomic regions that are responsible for the thermal adaptation of B. dorsalis. Methods: Based on a high-quality chromosome-level reference genome assembly, we explored the population relationship using a genome-scale single nucleotide polymorphism dataset generated from the resequencing data of 487 B. dorsalis genomes and 25 B. carambolae genomes. Genome-wide association studies and silencing using RNA interference were used to identify and verify the candidate genes associated with extreme thermal stress. Results: We showed that B. dorsalis originates from the Southern India region with three independent invasion and spread routes worldwide: (i) from Northern India to Northern Southeast Asia, then to Southern Southeast Asia; (ii) from Northern India to Northern Southeast Asian, then to China and Hawaii; and (iii) from Southern India toward the African mainland, then to Madagascar, which is mainly facilitated by human activities including trade and immigration. Twenty-seven genes were identified by a genome-wide association study to be associated with 11 temperature bioclimatic variables. The Cyp6a9 gene may enhance the thermal adaptation of B. dorsalis and thus boost its invasion, which tended to be upregulated at a hardening temperature of 38 °C. Functional verification using RNA interference silencing against Cyp6a9, led to the specific decrease in Cyp6a9 expression, reducing the survival rate of dsRNA-feeding larvae exposed to extreme thermal stress of 45 °C after heat hardening treatments in B. dorsalis. Conclusion: This study pr

Genomic offset statistics predict the maladaptation of populations to rapid habitat alteration based on association of genotypes with environmental variation. Despite substantial evidence for empirical validity, genomic offset statistics have well-identified limitations, and lack a theory that would facilitate interpretations of predicted values. Here, we clarified the theoretical relationships between genomic offset statistics and unobserved fitness traits controlled by environmentally selected loci and proposed a geometric measure to predict fitness after rapid change in local environment. The predictions of our theory were verified in computer simulations and in empirical data on African pearl millet (Cenchrus americanus) obtained from a common garden experiment. Our results proposed a unified perspective on genomic offset statistics and provided a theoretical foundation necessary when considering their potential application in conservation management in the face of environmental change.

Genomic selection (GS) is an effective method for the genetic improvement of complex traits in plants and animals. Optimization approaches could be used in conjunction with GS to further increase its efficiency and to limit inbreeding, which can increase faster with GS. Mate selection (MS) typically uses a metaheuristic optimization algorithm, simulated annealing, to optimize the selection of individuals and their matings. However, in species with long breeding cycles, this cannot be studied empirically. Here, we investigated this aspect with forward genetic simulations on a high-performance computing cluster and massively parallel computing, considering the oil palm hybrid breeding example. We compared MS and simple methods of inbreeding management (limitation of the number of individuals selected per family, prohibition of self-fertilization and combination of these two methods), in terms of parental inbreeding and genetic progress over four generations of genomic selection and phenotypic selection. The results showed that, compared to the conventional method without optimization, MS could lead to significant decreases in inbreeding and increases in annual genetic progress, with the magnitude of the effect depending on MS parameters and breeding scenarios. The optimal solution retained by MS differed by five breeding characteristics from the conventional solution: selected individuals covering a broader range of genetic values, fewer individuals selected per full-sib family, decreased percentage of selfings, selfings preferentially made on the best individuals and unbalanced number of crosses among selected individuals, with the better an individual, the higher the number of times he is mated. Stronger slowing-down in inbreeding could be achieved with other methods but they were associated with a decreased genetic progress. We recommend that breeders use MS, with preliminary analyses to identify the proper parameters to reach the goals of the breeding program in t

Background: Musa beccarii (Musaceae) is a banana species native to Borneo, sometimes grown as an ornamental plant. The basic chromosome number of Musa species is x = 7, 10, or 11; however, M. beccarii has a basic chromosome number of x = 9 (2n = 2x = 18), which is the same basic chromosome number of species in the sister genera Ensete and Musella. Musa beccarii is in the section Callimusa, which is sister to the section Musa. We generated a high-quality chromosome-scale genome assembly of M. beccarii to better understand the evolution and diversity of genomes within the family Musaceae. Findings: The M. beccarii genome was assembled by long-read and Hi-C sequencing, and genes were annotated using both long Iso-seq and short RNA-seq reads. The size of M. beccarii was the largest among all known Musaceae assemblies (∼570 Mbp) due to the expansion of transposable elements and increased 45S ribosomal DNA sites. By synteny analysis, we detected extensive genome-wide chromosome fusions and fissions between M. beccarii and the other Musa and Ensete species, far beyond those expected from differences in chromosome number. Within Musaceae, M. beccarii showed a reduced number of terpenoid synthase genes, which are related to chemical defense, and enrichment in lipid metabolism genes linked to the physical defense of the cell wall. Furthermore, type III polyketide synthase was the most abundant biosynthetic gene cluster (BGC) in M. beccarii. BGCs were not conserved in Musaceae genomes. Conclusions: The genome assembly of M. beccarii is the first chromosome-scale genome assembly in the Callimusa section in Musa, which provides an important genetic resource that aids our understanding of the evolution of Musaceae genomes and enhances our knowledge of the pangenome.

Of American origin, a wide diversity of Xylella fastidiosa strains belonging to different subspecies have been reported in Europe since 2013 and its discovery in Italian olive groves. Strains from the subspecies multiplex (ST6 and ST7) were first identified in France in 2015 in urban and natural areas. To trace back the most probable scenario of introduction in France, the molecular evolution rate of this subspecies was estimated at 3.2165 × 10-7 substitutions per site per year, based on heterochronous genome sequences collected worldwide. This rate allowed the dating of the divergence between French and American strains in 1987 for ST6 and in 1971 for ST7. The development of a new VNTR-13 scheme allowed tracing the spread of the bacterium in France, hypothesizing an American origin. Our results suggest that both sequence types were initially introduced and spread in Provence-Alpes-Côte d'Azur (PACA); then they were introduced in Corsica in two waves from the PACA bridgehead populations.

Humans have had a major influence on the dissemination of crops beyond their native range, thereby offering new hybridization opportunities. Characterizing admixed genomes with mosaic origins generates valuable insight into the adaptive history of crops and the impact on current varietal diversity. We applied the ELAI tool—an efficient local ancestry inference method based on a two-layer hidden Markov model to track segments of wild origin in cultivated accessions in the case of multiway admixtures. Source populations—which may actually be limited and partially admixed—must be generally specified when using such inference models. We thus developed a framework to identify local ancestry with admixed source populations. Using sequencing data for wild and cultivated Coffea canephora (commonly called Robusta), our approach was found to be highly efficient and accurate on simulated hybrids. Application of the method to assess elite Robusta varieties from Vietnam led to the identification of an accession derived from a likely backcross between two genetic groups from the Congo Basin and the western coastal region of Central Africa. Admixtures resulting from crop hybridization and diffusion could thus lead to the generation of elite high-yielding varieties. Our methods should be widely applicable to gain insight into the role of hybridization during plant and animal evolutionary history.

Background: Somatic embryogenesis (SE) is one of the most promising processes for large-scale dissemination of elite varieties. However, for many plant species, optimizing SE protocols still relies on a trial and error approach. We report the first global scale transcriptome profiling performed at all developmental stages of SE in coffee to unravel the mechanisms that regulate cell fate and totipotency. Results: RNA-seq of 48 samples (12 developmental stages × 4 biological replicates) generated 90 million high quality reads per sample, approximately 74% of which were uniquely mapped to the Arabica genome. First, the statistical analysis of transcript data clearly grouped SE developmental stages into seven important phases (Leaf, Dedifferentiation, Primary callus, Embryogenic callus, Embryogenic cell clusters, Redifferentiation and Embryo) enabling the identification of six key developmental phase switches, which are strategic for the overall biological efficiency of embryo regeneration. Differential gene expression and functional analysis showed that genes encoding transcription factors, stress-related genes, metabolism-related genes and hormone signaling-related genes were significantly enriched. Second, the standard environmental drivers used to control SE, i.e. light, growth regulators and cell density, were clearly perceived at the molecular level at different developmental stages. Third, expression profiles of auxin-related genes, transcription factor-related genes and secondary metabolism-related genes were analyzed during SE. Gene co-expression networks were also inferred. Auxin-related genes were upregulated during dedifferentiation and redifferentiation while transcription factor-related genes were switched on from the embryogenic callus and onward. Secondary metabolism-related genes were switched off during dedifferentiation and switched back on at the onset of redifferentiation. Secondary metabolites and endogenous IAA content were tightly linked with the

The Caulimoviridae is a family of double-stranded DNA viruses that infect plants. The genomes of most vascular plants contain endogenous caulimovirids (ECVs), a class of repetitive DNA elements that is abundant in some plant genomes, resulting from the integration of viral DNA in the chromosomes of germline cells during episodes of infection that have sometimes occurred millions of years ago. In this review, we reflect on 25 years of research on ECVs that has shown that members of the Caulimoviridae have occupied an unprecedented range of ecological niches over time and shed light on their diversity and macroevolution. We highlight gaps in knowledge and prospects of future research fueled by increased access to plant genome sequence data and new tools for genome annotation for addressing the extent, impact, and role of ECVs on plant biology and the origin and evolutionary trajectories of the Caulimoviridae.

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 a

The ithomiine butterflies (Nymphalidae: Danainae) represent the largest known radiation of Müllerian mimetic butterflies. They dominate by number the mimetic butterfly communities, which include species such as the iconic neotropical Heliconius genus. Recent studies on the ecology and genetics of speciation in Ithomiini have suggested that sexual pheromones, colour pattern and perhaps hostplant could drive reproductive isolation. However, no reference genome was available for Ithomiini, which has hindered further exploration on the genetic architecture of these candidate traits, and more generally on the genomic patterns of divergence. Here, we generated high-quality, chromosome-scale genome assemblies for two Melinaea species, M. marsaeus and M. menophilus, and a draft genome of the species Ithomia salapia. We obtained genomes with a size ranging from 396 to 503 Mb across the three species and scaffold N50 of 40.5 and 23.2 Mb for the two chromosome-scale assemblies. Using collinearity analyses we identified massive rearrangements between the two closely related Melinaea species. An annotation of transposable elements and gene content was performed, as well as a specialist annotation to target chemosensory genes, which is crucial for host plant detection and mate recognition in mimetic species. A comparative genomic approach revealed independent gene expansions in ithomiines and particularly in gustatory receptor genes. These first three genomes of ithomiine mimetic butterflies constitute a valuable addition and a welcome comparison to existing biological models such as Heliconius, and will enable further understanding of the mechanisms of adaptation in butterflies.

Different fungal species of the Pleosporaceae family infect rice, causing similar symptoms. Reference genomic sequences are useful tools to study the evolution of these species and to develop accurate molecular diagnostic tools. Here, we report the complete genome sequences of Bipolaris bicolor, Curvularia hawaiiensis, Curvularia spicifera, and Exserohilum rostratum.