Intraspecific variation of recombination rate in maize

Characterization of adaptation mechanisms in sorghum using a multireference back-cross nested association mapping design and envirotyping

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.

Genetic Diversity within a Collection of Italian Maize Inbred Lines: A Resource for Maize Genomics and Breeding

Genetic diversity is fundamental for studying the complex architecture of the traits of agronomic importance, controlled by major and minor loci. Moreover, well-characterized germplasm collections are essential tools for dissecting and analyzing genetic and phenotypic diversity in crops. A panel of 360 entries, a subset of a larger collection maintained within the GenBank at CREA Bergamo, which includes the inbreds derived from traditional Italian maize open-pollinated (OP) varieties and advanced breeding ones (Elite Inbreds), was analyzed to identify SNP markers using the tGBS® genotyping-by-sequencing technology. A total of 797,368 SNPs were found during the initial analysis. Imputation and filtering processes were carried out based on the percentage of missing data, redundant markers, and rarest allele frequencies, resulting in a final dataset of 15,872 SNP markers for which a physical map position was identified. Using this dataset, the inbred panel was characterized for linkage disequilibrium (LD), genetic diversity, population structure, and genetic relationships. LD decay at a genome-wide level indicates that the collection is a suitable resource for association mapping. Population structure analyses, which were carried out with different clustering methods, showed stable grouping statistics for four groups, broadly corresponding to 'Insubria', 'Microsperma', and 'Scagliolino' genotypes, with a fourth group composed prevalently of elite accessions derived from Italian and US breeding programs. Based on these results, the CREA Italian maize collection, genetically characterized in this study, can be considered an important tool for the mapping and characterization of useful traits and associated loci/alleles, to be used in maize breeding programs.

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.

Comparison of genomic-enabled cross selection criteria for the improvement of inbred line breeding populations

A crucial step in inbred plant breeding is the choice of mating design to derive high-performing inbred varieties while also maintaining a competitive breeding population to secure sufficient genetic gain in future generations. In practice, the mating design usually relies on crosses involving the best parental inbred lines to ensure high mean progeny performance. This excludes crosses involving lower performing but more complementary parents in terms of favorable alleles. We predicted the ability of crosses to produce putative outstanding progenies (high mean and high variance progeny distribution) using genomic prediction models. This study compared the benefits and drawbacks of 7 genomic cross selection criteria (CSC) in terms of genetic gain for 1 trait and genetic diversity in the next generation. Six CSC were already published, and we propose an improved CSC that can estimate the proportion of progeny above a threshold defined for the whole mating plan. We simulated mating designs optimized using different CSC. The 835 elite parents came from a real breeding program and were evaluated between 2000 and 2016. We applied constraints on parental contributions and genetic similarities between selected parents according to usual breeder practices. Our results showed that CSC based on progeny variance estimation increased the genetic value of superior progenies by up to 5% in the next generation compared to CSC based on the progeny mean estimation (i.e. parental genetic values) alone. It also increased the genetic gain (up to 4%) and/or maintained more genetic diversity at QTLs (up to 4% more genic variance when the marker effects were perfectly estimated).

Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets

In modern plant breeding, genomic selection is becoming the gold standard for selection of superior genotypes. The basis for genomic prediction models is a set of phenotyped lines along with their genotypic profile. With high marker density and linkage disequilibrium (LD) between markers, genotype data in breeding populations tends to exhibit considerable redundancy. Therefore, interest is growing in the use of haplotype blocks to overcome redundancy by summarizing co-inherited features. Moreover, haplotype blocks can help to capture local epistasis caused by interacting loci. Here, we compared genomic prediction methods that either used single SNPs or haplotype blocks with regards to their prediction accuracy for important traits in crop datasets. We used four published datasets from canola, maize, wheat and soybean. Different approaches to construct haplotype blocks were compared, including blocks based on LD, physical distance, number of adjacent markers and the algorithms implemented in the software "Haploview" and "HaploBlocker". The tested prediction methods included Genomic Best Linear Unbiased Prediction (GBLUP), Extended GBLUP to account for additive by additive epistasis (EGBLUP), Bayesian LASSO and Reproducing Kernel Hilbert Space (RKHS) regression. We found improved prediction accuracy in some traits when using haplotype blocks compared to SNP-based predictions, however the magnitude of improvement was very trait- and model-specific. Especially in settings with low marker density, haplotype blocks can improve genomic prediction accuracy. In most cases, physically large haplotype blocks yielded a strong decrease in prediction accuracy. Especially when prediction accuracy varies greatly across different prediction models, prediction based on haplotype blocks can improve prediction accuracy of underperforming models. However, there is no "best" method to build haplotype blocks, since prediction accuracy varied considerably across methods and traits. Hence, criteria used to define haplotype blocks should not be viewed as fixed biological parameters, but rather as hyperparameters that need to be adjusted for every dataset.

Identification of Candidate QTLs and Genes for Ear Diameter by Multi-Parent Population in Maize

Ear diameter (ED) is a critical component of grain yield (GY) in maize (Zea mays L.). Studying the genetic basis of ED in maize is of great significance in enhancing maize GY. Against this backdrop, this study was framed to (1) map the ED-related quantitative trait locus (QTL) and SNPs associated with ED; and (2) identify putative functional genes that may affect ED in maize. To accomplish this, an elite maize inbred line, Ye107, which belongs to the Reid heterotic group, was used as a common parent and crossed with seven elite inbred lines from three different heterotic groups (Suwan1, Reid, and nonReid) that exhibited abundant genetic variation in ED. This led to the construction of a multi-parent population consisting of 1215 F₇ recombinant inbred lines (F₇RILs). A genome-wide association study (GWAS) and linkage analysis were then conducted for the multi-parent population using 264,694 high-quality SNPs generated via the genotyping-by-sequencing method. Our study identified a total of 11 SNPs that were significantly associated with ED through the GWAS, and three QTLs were revealed by the linkage analysis for ED. The major QTL on chromosome 1 was co-identified in the region by the GWAS at SNP_143985532. SNP_143985532, located upstream of the Zm00001d030559 gene, encodes a callose synthase that is expressed in various tissues, with the highest expression level in the maize ear primordium. Haplotype analysis indicated that the haplotype B (allele AA) of Zm00001d030559 was positively correlated with ED. The candidate genes and SNPs identified in this study provide crucial insights for future studies on the genetic mechanism of maize ED formation, cloning of ED-related genes, and genetic improvement of ED. These results may help develop important genetic resources for enhancing maize yield through marker-assisted breeding.

Deep haplotype analyses of target-site resistance locus ACCase in blackgrass enabled by pool-based amplicon sequencing

Rapid adaptation of weeds to herbicide applications in agriculture through resistance development is a widespread phenomenon. In particular, the grass Alopecurus myosuroides is an extremely problematic weed in cereal crops with the potential to manifest resistance in only a few generations. Target-site resistances (TSRs), with their strong phenotypic response, play an important role in this rapid adaptive response. Recently, using PacBio's long-read amplicon sequencing technology in hundreds of individuals, we were able to decipher the genomic context in which TSR mutations occur. However, sequencing individual amplicons are costly and time-consuming, thus impractical to implement for other resistance loci or applications. Alternatively, pool-based approaches overcome these limitations and provide reliable allele frequencies, although at the expense of not preserving haplotype information. In this proof-of-concept study, we sequenced with PacBio High Fidelity (HiFi) reads long-range amplicons (13.2 kb), encompassing the entire ACCase gene in pools of over 100 individuals, and resolved them into haplotypes using the clustering algorithm PacBio amplicon analysis (pbaa), a new application for pools in plants and other organisms. From these amplicon pools, we were able to recover most haplotypes from previously sequenced individuals of the same population. In addition, we analysed new pools from a Germany-wide collection of A. myosuroides populations and found that TSR mutations originating from soft sweeps of independent origin were common. Forward-in-time simulations indicate that TSR haplotypes will persist for decades even at relatively low frequencies and without selection, highlighting the importance of accurate measurement of TSR haplotype prevalence for weed management.

The Control of the Crossover Localization in Allium

Meiotic crossovers/chiasmata are not randomly distributed and strictly controlled. The mechanisms behind crossover (CO) patterning remain largely unknown. In Allium cepa, as in the vast majority of plants and animals, COs predominantly occur in the distal 2/3 of the chromosome arm, while in Allium fistulosum they are strictly localized in the proximal region. We investigated the factors that may contribute to the pattern of COs in A. cepa, A. fistulosum and their F1 diploid (2n = 2x = 8C + 8F) and F1 triploid (2n = 3x = 16F + 8C) hybrids. The genome structure of F1 hybrids was confirmed using genomic in situ hybridization (GISH). The analysis of bivalents in the pollen mother cells (PMCs) of the F1 triploid hybrid showed a significant shift in the localization of COs to the distal and interstitial regions. In F1 diploid hybrid, the COs localization was predominantly the same as that of the A. cepa parent. We found no differences in the assembly and disassembly of ASY1 and ZYP1 in PMCs between A. cepa and A. fistulosum, while F1 diploid hybrid showed a delay in chromosome pairing and a partial absence of synapsis in paired chromosomes. Immunolabeling of MLH1 (class I COs) and MUS81 (class II COs) proteins showed a significant difference in the class I/II CO ratio between A. fistulosum (50%:50%) and A. cepa (73%:27%). The MLH1:MUS81 ratio at the homeologous synapsis of F1 diploid hybrid (70%:30%) was the most similar to that of the A. cepa parent. F1 triploid hybrid at the A. fistulosum homologous synapsis showed a significant increase in MLH1:MUS81 ratio (60%:40%) compared to the A. fistulosum parent. The results suggest possible genetic control of CO localization. Other factors affecting the distribution of COs are discussed.

Fine mapping of meiotic crossovers in Brassica oleracea reveals patterns and variations depending on direction and combination of crosses

Meiotic recombination is crucial for assuring proper segregation of parental chromosomes and generation of novel allelic combinations. As this process is tightly regulated, identifying factors influencing rate, and distribution of meiotic crossovers (COs) is of major importance, notably for plant breeding programs. However, high-resolution recombination maps are sparse in most crops including the Brassica genus and knowledge about intraspecific variation and sex differences is lacking. Here, we report fine-scale resolution recombination landscapes for 10 female and 10 male crosses in Brassica oleracea, by analyzing progenies of five large four-way-cross populations from two reciprocally crossed F1s per population. Parents are highly diverse inbred lines representing major crops, including broccoli, cauliflower, cabbage, kohlrabi, and kale. We produced approximately 4.56T Illumina data from 1248 progenies and identified 15 353 CO across the 10 reciprocal crosses, 51.13% of which being mapped to <10 kb. We revealed fairly similar Mb-scale recombination landscapes among all cross combinations and between the sexes, and provided evidence that these landscapes are largely independent of sequence divergence. We evidenced strong influence of gene density and large structural variations on CO formation in B. oleracea. Moreover, we found extensive variations in CO number depending on the direction and combination of the initial parents crossed with, for the first time, a striking interdependency between these factors. These data improve our current knowledge on meiotic recombination and are important for Brassica breeders.

Crossover patterning in plants

KEY MESSAGE

Chromatin state, and dynamic loading of pro-crossover protein HEI10 at recombination intermediates shape meiotic chromosome patterning in plants. Meiosis is the basis of sexual reproduction, and its basic progression is conserved across eukaryote kingdoms. A key feature of meiosis is the formation of crossovers which result in the reciprocal exchange of segments of maternal and paternal chromosomes. This exchange generates chromosomes with new combinations of alleles, increasing the efficiency of both natural and artificial selection. Crossovers also form a physical link between homologous chromosomes at metaphase I which is critical for accurate chromosome segregation and fertility. The patterning of crossovers along the length of chromosomes is a highly regulated process, and our current understanding of its regulation forms the focus of this review. At the global scale, crossover patterning in plants is largely governed by the classically observed phenomena of crossover interference, crossover homeostasis and the obligatory crossover which regulate the total number of crossovers and their relative spacing. The molecular actors behind these phenomena have long remained obscure, but recent studies in plants implicate HEI10 and ZYP1 as key players in their coordination. In addition to these broad forces, a wealth of recent studies has highlighted how genomic and epigenomic features shape crossover formation at both chromosomal and local scales, revealing that crossovers are primarily located in open chromatin associated with gene promoters and terminators with low nucleosome occupancy.

Global dissection of the recombination landscape in soybean using a high-density 600K SoySNP array

Recombination is crucial for crop breeding because it can break linkage drag and generate novel allele combinations. However, the high-resolution recombination landscape and its driving forces in soybean are largely unknown. Here, we constructed eight recombinant inbred line (RIL) populations and genotyped individual lines using the high-density 600K SoySNP array, which yielded a high-resolution recombination map with 5636 recombination sites at a resolution of 1.37 kb. The recombination rate was negatively correlated with transposable element density and GC content but positively correlated with gene density. Interestingly, we found that meiotic recombination was enriched at the promoters of active genes. Further investigations revealed that chromatin accessibility and active epigenetic modifications promoted recombination. Our findings provide important insights into the control of homologous recombination and thus will increase our ability to accelerate soybean breeding by manipulating meiotic recombination rate.

Dissecting the Meiotic Recombination Patterns in a Brassica napus Double Haploid Population Using 60K SNP Array

Meiotic recombination not only maintains the stability of the chromosome structure but also creates genetic variations for adapting to changeable environments. A better understanding of the mechanism of crossover (CO) patterns at the population level is useful for crop improvement. However, there are limited cost-effective and universal methods to detect the recombination frequency at the population level in Brassica napus. Here, the Brassica 60K Illumina Infinium SNP array (Brassica 60K array) was used to systematically study the recombination landscape in a double haploid (DH) population of B. napus. It was found that COs were unevenly distributed across the whole genome, and a higher frequency of COs existed at the distal ends of each chromosome. A considerable number of genes (more than 30%) in the CO hot regions were associated with plant defense and regulation. In most tissues, the average gene expression level in the hot regions (CO frequency of greater than 2 cM/Mb) was significantly higher than that in the regions with a CO frequency of less than 1 cM/Mb. In addition, a bin map was constructed with 1995 recombination bins. For seed oil content, Bin 1131 to 1134, Bin 1308 to 1311, Bin 1864 to 1869, and Bin 2184 to 2230 were identified on chromosomes A08, A09, C03, and C06, respectively, which could explain 8.5%, 17.3%, 8.6%, and 3.9% of the phenotypic variation. These results could not only deepen our understanding of meiotic recombination in B. napus at the population level, and provide useful information for rapeseed breeding in the future, but also provided a reference for studying CO frequency in other species.

Integrated consensus genetic map and genomic scaffold re-ordering of oil palm (Elaeis guineensis) genome

A high-quality reference genome is an important resource that can help decipher the genetic basis of traits in combination with linkage or association analyses. The publicly available oil palm draft genome sequence of AVROS pisifera (EG5) accounts for 1.535 Gb of the 1.8 Gb oil palm genome. However, the assemblies are fragmented, and the earlier assembly only had 43% of the sequences placed on pseudo-chromosomes. By integrating a number of SNP and SSR-based genetic maps, a consensus map (AM_EG5.1), comprising of 828.243 Mb genomic scaffolds anchored to 16 pseudo-chromosomes, was generated. This accounted for 54% of the genome assembly, which is a significant improvement to the original assembly. The total length of N50 scaffolds anchored to the pseudo-chromosomes increased by ∼18% compared to the previous assembly. A total of 139 quantitative trait loci for agronomically important quantitative traits, sourced from literature, were successfully mapped on the new pseudo-chromosomes. The improved assembly could also be used as a reference to identify potential errors in placement of specific markers in the linkage groups of the genetic maps used to assemble the consensus map. The 3422 unique markers from five genetic maps, anchored to the pseudo-chromosomes of AM_EG5.1, are an important resource that can be used preferentially to either construct new maps or fill gaps in existing genetic maps. Synteny analysis further revealed that the AM_EG5.1 had high collinearity with the date palm genome cultivar 'Barhee BC4' and shared most of its segmental duplications. This improved chromosomal-level genome is a valuable resource for genetic research in oil palm.

SeSAM: software for automatic construction of order-robust linkage maps

Background

Genotyping and sequencing technologies produce increasingly large numbers of genetic markers with potentially high rates of missing or erroneous data. Therefore, the construction of linkage maps is more and more complex. Moreover, the size of segregating populations remains constrained by cost issues and is less and less commensurate with the numbers of SNPs available. Thus, guaranteeing a statistically robust marker order requires that maps include only a carefully selected subset of SNPs.

Results

In this context, the SeSAM software allows automatic genetic map construction using seriation and placement approaches, to produce (1) a high-robustness framework map which includes as many markers as possible while keeping the order robustness beyond a given statistical threshold, and (2) a high-density total map including the framework plus almost all polymorphic markers. During this process, care is taken to limit the impact of genotyping errors and of missing data on mapping quality. SeSAM can be used with a wide range of biparental populations including from outcrossing species for which phases are inferred on-the-fly by maximum-likelihood during map elongation. The package also includes functions to simulate data sets, convert data formats, detect putative genotyping errors, visualize data and map quality (including graphical genotypes), and merge several maps into a consensus. SeSAM is also suitable for interactive map construction, by providing lower-level functions for 2-point and multipoint EM analyses. The software is implemented in a R package including functions in C++.

Conclusions

SeSAM is a fully automatic linkage mapping software designed to (1) produce a framework map as robust as desired by optimizing the selection of a subset of markers, and (2) produce a high-density map including almost all polymorphic markers. The software can be used with a wide range of biparental mapping populations including cases from outcrossing. SeSAM is freely available under a GNU GPL v3 license and works on Linux, Windows, and macOS platforms. It can be downloaded together with its user-manual and quick-start tutorial from ForgeMIA (SeSAM project) at https://forgemia.inra.fr/gqe-acep/sesam/-/releases

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-05045-7.

High-resolution mapping reveals hotspots and sex-biased recombination in Populus trichocarpa

Fine-scale meiotic recombination is fundamental to the outcome of natural and artificial selection. Here, dense genetic mapping and haplotype reconstruction were used to estimate recombination for a full factorial Populus trichocarpa cross of 7 males and 7 females. Genomes of the resulting 49 full-sib families (N = 829 offspring) were resequenced, and high-fidelity biallelic SNP/INDELs and pedigree information were used to ascertain allelic phase and impute progeny genotypes to recover gametic haplotypes. The 14 parental genetic maps contained 1,820 SNP/INDELs on average that covered 376.7 Mb of physical length across 19 chromosomes. Comparison of parental and progeny haplotypes allowed fine-scale demarcation of cross-over regions, where 38,846 cross-over events in 1,658 gametes were observed. Cross-over events were positively associated with gene density and negatively associated with GC content and long-terminal repeats. One of the most striking findings was higher rates of cross-overs in males in 8 out of 19 chromosomes. Regions with elevated male cross-over rates had lower gene density and GC content than windows showing no sex bias. High-resolution analysis identified 67 candidate cross-over hotspots spread throughout the genome. DNA sequence motifs enriched in these regions showed striking similarity to those of maize, Arabidopsis, and wheat. These findings, and recombination estimates, will be useful for ongoing efforts to accelerate domestication of this and other biomass feedstocks, as well as future studies investigating broader questions related to evolutionary history, perennial development, phenology, wood formation, vegetative propagation, and dioecy that cannot be studied using annual plant model systems.

Recombination landscape divergence between populations is marked by larger low-recombining regions in domesticated rye

The genomic landscape of recombination plays an essential role in evolution. Patterns of recombination are highly variable along chromosomes, between sexes, individuals, populations, and species. In many eukaryotes, recombination rates are elevated in sub-telomeric regions and drastically reduced near centromeres, resulting in large low-recombining (LR) regions. The processes of recombination are influenced by genetic factors, such as different alleles of genes involved in meiosis and chromatin structure, as well as external environmental stimuli like temperature and overall stress. In this work, we focused on the genomic landscapes of recombination in a collection of 916 rye (Secale cereale) individuals. By analysing population structure among individuals of different domestication status and geographic origin, we detected high levels of admixture, reflecting the reproductive biology of a self-incompatible, wind-pollinating grass species. We then analysed patterns of recombination in overlapping subpopulations, which revealed substantial variation in the physical size of LR regions, with a tendency for larger LR regions in domesticated subpopulations. Genome-wide association scans (GWAS) for LR region size revealed a major quantitative-trait-locus (QTL) at which, among 18 annotated genes, an ortholog of histone H4 acetyltransferase ESA1 was located. Rye individuals belonging to domesticated subpopulations showed increased synaptonemal complex length, but no difference in crossover frequency, indicating that only the recombination landscape is different. Furthermore, the genomic region harbouring rye ScESA1 showed moderate patterns of selection in domesticated subpopulations, suggesting that larger LR regions were indirectly selected for during domestication to achieve more homogeneous populations for agricultural use.

Genetic diversity of European maize landraces: Dataset on the molecular and phenotypic variation of derived doubled-haploid populations

Genetic variation is the basis of selection, evolution and breeding. Maize landraces represent a rich source of allelic diversity, but their efficient utilization in breeding and research has been hampered by their heterogeneous and heterozygous nature and insufficient information about most accessions. While molecular inventories of germplasm repositories are growing steadily, linking these data to meaningful phenotypes for quantitative traits is challenging.

Here, we present comprehensive molecular and phenotypic data for ∼1,000 doubled-haploid (DH) lines derived from three pre-selected European maize landraces. Due to their full homozygosity, the DH lines can be multiplied ad libitum and represent a powerful biological resource available to the community. The DH lines allow high-precision phenotyping in repeated experiments and reveal the full additive genetic variance of the population. The DH lines were evaluated for nine agronomically important, quantitative traits in multi-environment field trials comprising seven locations and two years. The DH populations revealed high genetic variance and high heritability for the analysed traits. The DH lines were genotyped with 600k SNP markers. After stringent quality filtering 500k markers remained for further analyses.

This is the largest resource of landrace derived DH material in maize, unprecedented in its structure and dimension. The presented data are ideal for linking molecular variation to meaningful phenotypes. They can be used for genome-wide association studies, genomic prediction, and population genetic analyses as well as for developing and testing statistical methods. All plant material is available to the community for conducting additional experiments, extending the panel of traits and environments, and for testing the landrace-derived lines in combination with other genetic material.

Building a Calibration Set for Genomic Prediction, Characteristics to Be Considered, and Optimization Approaches

The efficiency of genomic selection strongly depends on the prediction accuracy of the genetic merit of candidates. Numerous papers have shown that the composition of the calibration set is a key contributor to prediction accuracy. A poorly defined calibration set can result in low accuracies, whereas an optimized one can considerably increase accuracy compared to random sampling, for a same size. Alternatively, optimizing the calibration set can be a way of decreasing the costs of phenotyping by enabling similar levels of accuracy compared to random sampling but with fewer phenotypic units. We present here the different factors that have to be considered when designing a calibration set, and review the different criteria proposed in the literature. We classified these criteria into two groups: model-free criteria based on relatedness, and criteria derived from the linear mixed model. We introduce criteria targeting specific prediction objectives including the prediction of highly diverse panels, biparental families, or hybrids. We also review different ways of updating the calibration set, and different procedures for optimizing phenotyping experimental designs.

Genetic mapping and prediction of flowering time and plant height in a maize Stiff Stalk MAGIC population

The Stiff Stalk heterotic pool is a foundation of US maize seed parent germplasm and has been heavily utilized by both public and private maize breeders since its inception in the 1930's. Flowering time and plant height are critical characteristics for both inbred parents and their test crossed hybrid progeny. To study these traits, a six parent multiparent advanced generation intercross (MAGIC) population was developed including maize inbred lines B73, B84, PHB47 (B37 type), LH145 (B14 type), PHJ40 (novel early Stiff Stalk), and NKH8431 (B73/B14 type). A set of 779 doubled haploid lines were evaluated for flowering time and plant height in two field replicates in 2016 and 2017, and a subset of 689 and 561 doubled haploid lines were crossed to two testers, respectively, and evaluated as hybrids in two locations in 2018 and 2019 using an incomplete block design. Markers were derived from a Practical Haplotype Graph built from the founder whole genome assemblies and genotype-by-sequencing and exome capture-based sequencing of the population. Genetic mapping utilizing an update to R/qtl2 revealed differing profiles of significant loci for both traits between 635 of the DH lines and two sets of 570 and 471 derived hybrids. Genomic prediction was used to test the feasibility of predicting hybrid phenotypes based on the per se data. Predictive abilities were highest on direct models trained using the data they would predict (0.55 to 0.63), and indirect models trained using per se data to predict hybrid traits had slightly lower predictive abilities (0.49 to 0.55). Overall, this finding is consistent with the overlapping and non-overlapping significant QTL found within the per se and hybrid populations and suggests that selections for phenology traits can be made effectively on doubled haploid lines before hybrid data is available.

Genomic prediction of the recombination rate variation in barley - A route to highly recombinogenic genotypes

Meiotic recombination is not only fundamental to the adaptation of sexually reproducing eukaryotes in nature but increased recombination rates facilitate the combination of favourable alleles into a single haplotype in breeding programmes. The main objectives of this study were to (i) assess the extent and distribution of the recombination rate variation in cultivated barley (Hordeum vulgare L.), (ii) quantify the importance of the general and specific recombination effects, and (iii) evaluate a genomic selection approach's ability to predict the recombination rate variation. Genetic maps were created for the 45 segregating populations that were derived from crosses among 23 spring barley inbreds with origins across the world. The genome-wide recombination rate among populations ranged from 0.31 to 0.73 cM/Mbp. The crossing design used in this study allowed to separate the general recombination effects (GRE) of individual parental inbreds from the specific recombination effects (SRE) caused by the combinations of parental inbreds. The variance of the genome-wide GRE was found to be about eight times the variance of the SRE. This finding indicated that parental inbreds differ in the efficiency of their recombination machinery. The ability to predict the chromosome or genome-wide recombination rate of an inbred ranged from 0.80 to 0.85. These results suggest that a reliable screening of large genetic materials for their potential to cause a high extent of genetic recombination in their progeny is possible, allowing to systematically manipulate the recombination rate using natural variation.

Modeling allelic diversity of multiparent mapping populations affects detection of quantitative trait loci

The search for quantitative trait loci that explain complex traits such as yield and drought tolerance has been ongoing in all crops. Methods such as biparental quantitative trait loci mapping and genome-wide association studies each have their own advantages and limitations. Multiparent advanced generation intercross populations contain more recombination events and genetic diversity than biparental mapping populations and are better able to estimate effect sizes of rare alleles than association mapping populations. Here, we discuss the results of using a multiparent advanced generation intercross population of doubled haploid maize lines created from 16 diverse founders to perform quantitative trait loci mapping. We compare 3 models that assume bi-allelic, founder, and ancestral haplotype allelic states for quantitative trait loci. The 3 methods have differing power to detect quantitative trait loci for a variety of agronomic traits. Although the founder approach finds the most quantitative trait loci, all methods are able to find unique quantitative trait loci, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time quantitative trait loci, qDTA8, which contains vgt1, highlights the strengths and weaknesses of each method and suggests a potential epistatic interaction. Overall, our results reinforce the importance of considering different approaches to analyzing genotypic datasets, and shows the limitations of binary SNP data for identifying multiallelic quantitative trait loci.

Quantitative modelling of fine-scale variations in the Arabidopsis thaliana crossover landscape

In, essentially, all species where meiotic crossovers (COs) have been studied, they occur preferentially in open chromatin, typically near gene promoters and to a lesser extent, at the end of genes. Here, in the case of Arabidopsis thaliana, we unveil further trends arising when one considers contextual information, namely summarised epigenetic status, gene or intergenic region size, and degree of divergence between homologs. For instance, we find that intergenic recombination rate is reduced if those regions are less than 1.5 kb in size. Furthermore, we propose that the presence of single nucleotide polymorphisms enhances the rate of CO formation compared to when homologous sequences are identical, in agreement with previous works comparing rates in adjacent homozygous and heterozygous blocks. Lastly, by integrating these different effects, we produce a quantitative and predictive model of the recombination landscape that reproduces much of the experimental variation.

An IBD-based mixed model approach for QTL mapping in multiparental populations

The identity-by-descent (IBD)-based mixed model approach introduced in this study can detect quantitative trait loci (QTLs) referring to the parental origin and simultaneously account for multilevel relatedness of individuals within and across families. This unified approach is proved to be a powerful approach for all kinds of multiparental population (MPP) designs. Multiparental populations (MPPs) have become popular for quantitative trait loci (QTL) detection. Tools for QTL mapping in MPPs are mostly developed for specific MPPs and do not generalize well to other MPPs. We present an IBD-based mixed model approach for QTL mapping in all kinds of MPP designs, e.g., diallel, Nested Association Mapping (NAM), and Multiparental Advanced Generation Intercross (MAGIC) designs. The first step is to compute identity-by-descent (IBD) probabilities using a general Hidden Markov model framework, called reconstructing ancestry blocks bit by bit (RABBIT). Next, functions of IBD information are used as design matrices, or genetic predictors, in a mixed model approach to estimate variance components for multiallelic genetic effects associated with parents. Family-specific residual genetic effects are added, and a polygenic effect is structured by kinship relations between individuals. Case studies of simulated diallel, NAM, and MAGIC designs proved that the advanced IBD-based multi-QTL mixed model approach incorporating both kinship relations and family-specific residual variances (IBD.MQMkin_F) is robust across a variety of MPP designs and allele segregation patterns in comparison to a widely used benchmark association mapping method, and in most cases, outperformed or behaved at least as well as other tools developed for specific MPP designs in terms of mapping power and resolution. Successful analyses of real data cases confirmed the wide applicability of our IBD-based mixed model methodology.

Reap the crop wild relatives for breeding future crops

Crop wild relatives (CWRs) have provided breeders with several 'game-changing' traits or genes that have boosted crop resilience and global agricultural production. Advances in breeding and genomics have accelerated the identification of valuable CWRs for use in crop improvement. The enhanced genetic diversity of breeding pools carrying optimum combinations of favorable alleles for targeted crop-growing regions is crucial to sustain genetic gain. In parallel, growing sequence information on wild genomes in combination with precise gene-editing tools provide a fast-track route to transform CWRs into ideal future crops. Data-informed germplasm collection and management strategies together with adequate policy support will be equally important to improve access to CWRs and their sustainable use to meet food and nutrition security targets.

High-Frequency Homologous Recombination Occurred Preferentially in Populus

Homologous recombination (HR), the most significant event in meiosis, has important implications for genetic diversity and evolution in organisms. Heteroduplex DNA (hDNA), the product of HR, can be captured by artificially induced chromosome doubling during the development of the embryo sac to inhibit postmeiotic segregation, subsequently, and hDNAs are directly detected using codominant simple sequence repeat (SSR) markers. In the present study, two hybrid triploid populations derived from doubling the chromosomes of the embryo sac induced by high temperature in Populus tomentosa served as starting materials. Eighty-seven, 62, and 79 SSR markers on chromosomes 01, 04, and 19, respectively, that were heterozygous in the maternal parent and different from the paternal parent were screened to detect and characterize the hDNA in P. tomentosa. The results showed that the hDNA frequency patterns on chromosomes changed slightly when the number of SSR primers increased. The highest hDNA frequency occurred at the adjacent terminal on chromosomes, which was slightly higher than those at the terminals in the two genotypic individuals, and the hDNA frequency gradually decreased as the locus-centromere distance decreased. With the increase in the number of SSR markers employed for detection, the number of recombination events (REs) detected significantly increased. In regions with high methylation or long terminal repeat (LTR) retrotransposon enrichment, the frequency of hDNA was low, and high frequencies were observed in regions with low sequence complexity and high gene density. High-frequency recombination occurring at high gene density regions strongly affected the association between molecular markers and quantitative trait loci (QTLs), which was an important factor contributing to the difficulty encountered by MAS in achieving the expected breeding results.

Megabase-scale presence-absence variation with Tripsacum origin was under selection during maize domestication and adaptation

BACKGROUND

Structural variants (SVs) significantly drive genome diversity and environmental adaptation for diverse species. Unlike the prevalent small SVs (< kilobase-scale) in higher eukaryotes, large-size SVs rarely exist in the genome, but they function as one of the key evolutionary forces for speciation and adaptation.

RESULTS

In this study, we discover and characterize several megabase-scale presence-absence variations (PAVs) in the maize genome. Surprisingly, we identify a 3.2 Mb PAV fragment that shows high integrity and is present as complete presence or absence in the natural diversity panel. This PAV is embedded within the nucleolus organizer region (NOR), where the suppressed recombination is found to maintain the PAV against the evolutionary variation. Interestingly, by analyzing the sequence of this PAV, we not only reveal the domestication trace from teosinte to modern maize, but also the footprints of its origin from Tripsacum, shedding light on a previously unknown contribution from Tripsacum to the speciation of Zea species. The functional consequence of the Tripsacum segment migration is also investigated, and environmental fitness conferred by the PAV may explain the whole segment as a selection target during maize domestication and improvement.

CONCLUSIONS

These findings provide a novel perspective that Tripsacum contributes to Zea speciation, and also instantiate a strategy for evolutionary and functional analysis of the "fossil" structure variations during genome evolution and speciation.

A Modified Meiotic Recombination in Brassica napus Largely Improves Its Breeding Efficiency

Simple Summary

The selection of varieties more resilient to disease and climate change requires generating new genetic diversity for breeding. The main mechanism for reshuffling genetic information is through the recombination of chromosomes during meiosis. We showed in oilseed rape (Brassica napus, AACC, 2n = 4x = 38), which is a natural hybrid formed from a cross between turnip (B. rapa, AA, 2n = 2x = 20) and cabbage (B. oleracea, CC, 2n = 2x = 18), that there is significantly more crossovers occurring along the entire A chromosomes in allotriploid AAC (crossbetween B. napus and B. rapa) than in diploid AA or allotetraploid AACC hybrids. We demonstrated that these allotriploid AAC hybrids are highly efficient to introduce new variability within oilseed rape varieties, notably by enabling the introduction of small genomic regions carrying genes controlling agronomically interesting traits.

Abstract

Meiotic recombination is the main tool used by breeders to generate biodiversity, allowing genetic reshuffling at each generation. It enables the accumulation of favorable alleles while purging deleterious mutations. However, this mechanism is highly regulated with the formation of one to rarely more than three crossovers, which are not randomly distributed. In this study, we showed that it is possible to modify these controls in oilseed rape (Brassica napus, AACC, 2n = 4x = 38) and that it is linked to AAC allotriploidy and not to polyploidy per se. To that purpose, we compared the frequency and the distribution of crossovers along A chromosomes from hybrids carrying exactly the same A nucleotide sequence, but presenting three different ploidy levels: AA, AAC and AACC. Genetic maps established with 202 SNPs anchored on reference genomes revealed that the crossover rate is 3.6-fold higher in the AAC allotriploid hybrids compared to AA and AACC hybrids. Using a higher SNP density, we demonstrated that smaller and numerous introgressions of B. rapa were present in AAC hybrids compared to AACC allotetraploid hybrids, with 7.6 Mb vs. 16.9 Mb on average and 21 B. rapa regions per plant vs. nine regions, respectively. Therefore, this boost of recombination is highly efficient to reduce the size of QTL carried in cold regions of the oilseed rape genome, as exemplified here for a QTL conferring blackleg resistance.

Evolution of Recombination Landscapes in Diverging Populations of Bread Wheat

Reciprocal exchanges of DNA (crossovers) that occur during meiosis are mandatory to ensure the production of fertile gametes in sexually reproducing species. They also contribute to shuffle parental alleles into new combinations thereby fueling genetic variation and evolution. However, due to biological constraints, the recombination landscape is highly heterogeneous along the genome which limits the range of allelic combinations and the adaptability of populations. An approach to better understand the constraints on the recombination process is to study how it evolved in the past. In this work, we tackled this question by constructing recombination profiles in four diverging bread wheat (Triticum aestivum L.) populations established from 371 landraces genotyped at 200,062 SNPs. We used linkage disequilibrium (LD) patterns to estimate in each population the past distribution of recombination along the genome and characterize its fine-scale heterogeneity. At the megabase scale, recombination rates derived from LD patterns were consistent with family-based estimates obtained from a population of 406 recombinant inbred lines. Among the four populations, recombination landscapes were positively correlated between each other and shared a statistically significant proportion of highly recombinant intervals. However, this comparison also highlighted that the similarity in recombination landscapes between populations was significantly decreasing with their genetic differentiation in most regions of the genome. This observation was found to be robust to SNPs ascertainment and demography and suggests a relatively rapid evolution of factors determining the fine-scale localization of recombination in bread wheat.

Nested association mapping reveals the genetic architecture of spike emergence and anthesis timing in intermediate wheatgrass

Intermediate wheatgrass (Thinopyrum intermedium) is an outcrossing, cool season grass species currently undergoing direct domestication as a perennial grain crop. Though many traits are selection targets, understanding the genetic architecture of those important for local adaptation may accelerate the domestication process. Nested association mapping (NAM) has proven useful in dissecting the genetic control of agronomic traits many crop species, but its utility in primarily outcrossing, perennial species has yet to be demonstrated. Here, we introduce an intermediate wheatgrass NAM population developed by crossing ten phenotypically divergent donor parents to an adapted common parent in a reciprocal manner, yielding 1,168 F₁ progeny from 10 families. Using genotyping by sequencing, we identified 8,003 SNP markers and developed a population-specific consensus genetic map with 3,144 markers across 21 linkage groups. Using both genomewide association mapping and linkage mapping combined across and within families, we characterized the genetic control of flowering time. In the analysis of two measures of maturity across four separate environments, we detected as many as 75 significant QTL, many of which correspond to the same regions in both analysis methods across 11 chromosomes. The results demonstrate a complex genetic control that is variable across years, locations, traits, and within families. The methods were effective at detecting previously identified QTL, as well as new QTL that align closely to the well-characterized flowering time orthologs from barley, including Ppd-H1 and Constans. Our results demonstrate the utility of the NAM population for understanding the genetic control of flowering time and its potential for application to other traits of interest.

Joint analysis of days to flowering reveals independent temperate adaptations in maize

Domesticates are an excellent model for understanding biological consequences of rapid climate change. Maize (Zea mays ssp. mays) was domesticated from a tropical grass yet is widespread across temperate regions today. We investigate the biological basis of temperate adaptation in diverse structured nested association mapping (NAM) populations from China, Europe (Dent and Flint) and the United States as well as in the Ames inbred diversity panel, using days to flowering as a proxy. Using cross-population prediction, where high prediction accuracy derives from overall genomic relatedness, shared genetic architecture, and sufficient diversity in the training population, we identify patterns in predictive ability across the five populations. To identify the source of temperate adapted alleles in these populations, we predict top associated genome-wide association study (GWAS) identified loci in a Random Forest Classifier using independent temperate-tropical North American populations based on lines selected from Hapmap3 as predictors. We find that North American populations are well predicted (AUC equals 0.89 and 0.85 for Ames and USNAM, respectively), European populations somewhat well predicted (AUC equals 0.59 and 0.67 for the Dent and Flint panels, respectively) and that the Chinese population is not predicted well at all (AUC is 0.47), suggesting an independent adaptation process for early flowering in China. Multiple adaptations for the complex trait days to flowering in maize provide hope for similar natural systems under climate change.

Gossypium tomentosum genome and interspecific ultra-dense genetic maps reveal genomic structures, recombination landscape and flowering depression in cotton

The high-quality reference-grade genome for Gossupium tomentosum can greatly promote the progress in biological research and introgression breeding for the mainly cultivated species, G. hirsutum. Here, we report a high-quality genome assembly for G. tomentosum by integrating PacBio and Hi-C technologies. Comparative genomic analysis revealed a large number of genetic variations. Two re-sequencing-based ultra-dense genetic maps were constructed which comprised 4,047,199 and 6,009,681 SNPs, 4120 and 4599 bins and covering 4126.36 cM and 4966.72 cM in the EMF₂ (F₂ from G. hirsutum × G. tomentosum) and GHF₂ (F₂ from G. hirsutum × G. barbadense). The EMF₂ exhibited lower recombination rate at the whole-genome level as compared with GHF₂. We mapped 22 and 33 QTL associated with crossover frequency and predicted Gh_MRE11 and Gh_FIGL1 as the candidate genes governing crossover in the EMF₂ and GHF₂, respectively. We identified 13 significant QTL that regulate the floral transition, and revealed that Gh_AGL18 was associated with the floral transition. Therefore, our study provides a valuable genomic resource to support a better understanding of cotton interspecific cross and recombination landscape for genetic improvement and breeding in cotton.

Enhancing backcross programs through increased recombination

Background

Introgression of a quantitative trait locus (QTL) by successive backcrosses is used to improve elite lines (recurrent parent) by introducing alleles from exotic material (donor parent). In the absence of selection, the proportion of the donor genome decreases by half at each generation. However, since selection is for the donor allele at the QTL, elimination of the donor genome around that QTL will be much slower than in the rest of the genome (i.e. linkage drag). Using markers to monitor the genome around the QTL and in the genetic background can accelerate the return to the recurrent parent genome. Successful introgression of a locus depends partly on the occurrence of crossovers at favorable positions. However, the number of crossovers per generation is limited and their distribution along the genome is heterogeneous. Recently, techniques have been developed to modify these two recombination parameters.

Results

In this paper, we assess, by simulations in the context of Brassicaceae, the effect of increased recombination on the efficiency of introgression programs by studying the decrease in linkage drag and the recovery of the recurrent genome. The simulated selection schemes begin by two generations of foreground selection and continue with one or more generations of background selection. Our results show that, when the QTL is in a region that initially lacked crossovers, an increase in recombination rate can decrease linkage drag by nearly ten-fold after the foreground selection and improves the return to the recurrent parent. However, if the QTL is in a region that is already rich in crossovers, an increase in recombination rate is detrimental.

Conclusions

Depending on the recombination rate in the region targeted for introgression, increasing it can be beneficial or detrimental. Thus, the simulations analysed in this paper help us understand how an increase in recombination rate can be beneficial. They also highlight the best methods that can be used to increase recombination rate, depending on the situation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-021-00619-0.

Grouping of genomic markers in populations with family structure

BACKGROUND

Linkage and linkage disequilibrium (LD) between genome regions cause dependencies among genomic markers. Due to family stratification in populations with non-random mating in livestock or crop, the standard measures of population LD such as [Formula: see text] may be biased. Grouping of markers according to their interdependence needs to account for the actual population structure in order to allow proper inference in genome-based evaluations.

RESULTS

Given a matrix reflecting the strength of association between markers, groups are built successively using a greedy algorithm; largest groups are built at first. As an option, a representative marker is selected for each group. We provide an implementation of the grouping approach as a new function to the R package hscovar. This package enables the calculation of the theoretical covariance between biallelic markers for half- or full-sib families and the derivation of representative markers. In case studies, we have shown that the number of groups comprising dependent markers was smaller and representative SNPs were spread more uniformly over the investigated chromosome region when the family stratification was respected compared to a population-LD approach. In a simulation study, we observed that sensitivity and specificity of a genome-based association study improved if selection of representative markers took family structure into account.

CONCLUSIONS

Chromosome segments which frequently recombine in the underlying population can be identified from the matrix of pairwise dependence between markers. Representative markers can be exploited, for instance, for dimension reduction prior to a genome-based association study or the grouping structure itself can be employed in a grouped penalization approach.

The influence of QTL allelic diversity on QTL detection in multi-parent populations: a simulation study in sugar beet

BACKGROUND

Multi-parent populations (MPPs) are important resources for studying plant genetic architecture and detecting quantitative trait loci (QTLs). In MPPs, the QTL effects can show various levels of allelic diversity, which can be an important factor influencing the detection of QTLs. In MPPs, the allelic effects can be more or less specific. They can depend on an ancestor, a parent or the combination of parents in a cross. In this paper, we evaluated the effect of QTL allelic diversity on the QTL detection power in MPPs.

RESULTS

We simulated: a) cross-specific QTLs; b) parental and ancestral QTLs; and c) bi-allelic QTLs. Inspired by a real application in sugar beet, we tested different MPP designs (diallel, chessboard, factorial, and NAM) derived from five or nine parents to explore the ability to sample genetic diversity and detect QTLs. Using a fixed total population size, the QTL detection power was larger in MPPs with fewer but larger crosses derived from a reduced number of parents. The use of a larger set of parents was useful to detect rare alleles with a large phenotypic effect. The benefit of using a larger set of parents was however conditioned on an increase of the total population size. We also determined empirical confidence intervals for QTL location to compare the resolution of different designs. For QTLs representing 6% of the phenotypic variation, using 1600 F₂ offspring individuals, we found average 95% confidence intervals over different designs of 49 and 25 cM for cross-specific and bi-allelic QTLs, respectively.

CONCLUSIONS

MPPs derived from less parents with few but large crosses generally increased the QTL detection power. Using a larger set of parents to cover a wider genetic diversity can be useful to detect QTLs with a reduced minor allele frequency when the QTL effect is large and when the total population size is increased.

Multiparental Population in Crops: Methods of Development and Dissection of Genetic Traits

Multiparental populations are located midway between association mapping that relies on germplasm collections and classic linkage analysis, based upon biparental populations. They provide several key advantages such as the possibility to include a higher number of alleles and increased level of recombination with respect to biparental populations, and more equilibrated allelic frequencies than association mapping panels. Moreover, in these populations new allele's combinations arise from recombination that may reveal transgressive phenotypes and make them a useful pre-breeding material. Here we describe the strategies for working with multiparental populations, focusing on nested association mapping populations (NAM) and multiparent advanced generation intercross populations (MAGIC). We provide details from the selection of founders, population development, and characterization to the statistical methods for genetic mapping and quantitative trait detection.

Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding

Crop populations derived from experimental crosses enable the genetic dissection of complex traits and support modern plant breeding. Among these, multi-parent populations now play a central role. By mixing and recombining the genomes of multiple founders, multi-parent populations combine many commonly sought beneficial properties of genetic mapping populations. For example, they have high power and resolution for mapping quantitative trait loci, high genetic diversity and minimal population structure. Many multi-parent populations have been constructed in crop species, and their inbred germplasm and associated phenotypic and genotypic data serve as enduring resources. Their utility has grown from being a tool for mapping quantitative trait loci to a means of providing germplasm for breeding programmes. Genomics approaches, including de novo genome assemblies and gene annotations for the population founders, have allowed the imputation of rich sequence information into the descendent population, expanding the breadth of research and breeding applications of multi-parent populations. Here, we report recent successes from crop multi-parent populations in crops. We also propose an ideal genotypic, phenotypic and germplasm 'package' that multi-parent populations should feature to optimise their use as powerful community resources for crop research, development and breeding.

Single-Cell Genomics and Epigenomics: Technologies and Applications in Plants

The development of genomics and epigenomics has allowed rapid advances in our understanding of plant biology. However, conventional bulk analysis dilutes cell-specific information by providing only average information, thereby limiting the resolution of genomic and functional genomic studies. Recent advances in single-cell sequencing technology concerning genomics and epigenomics open new avenues to dissect cell heterogeneity in multiple biological processes. Recent applications of these approaches to plants have provided exciting insights into diverse biological questions. We highlight the methodologies underlying the current techniques of single-cell genomics and epigenomics before covering their recent applications, potential significance, and future perspectives in plant biology.

Multi-parent multi-environment QTL analysis: an illustration with the EU-NAM Flint population

Multi-parent populations multi-environment QTL experiments data should be analysed jointly to estimate the QTL effect variation within the population and between environments. Commonly, QTL detection in multi-parent populations (MPPs) data measured in multiple environments (ME) is done by analyzing genotypic values 'averaged' across environments. This method ignores the environment-specific QTL (QTLxE) effects. Running separate single environment analyses is a possibility to measure QTLxE effects, but those analyses do not model the genetic covariance due to the use of the same genotype in different environments. In this paper, we propose methods to analyse MPP-ME QTL experiments using simultaneously the data from several environments and modelling the genotypic covariance. Using data from the EU-NAM Flint population, we show that these methods estimate the QTLxE effects and that they can improve the quality of the QTL detection. Those methods also have a larger inference power. For example, they can be extended to integrate environmental indices like temperature or precipitation to better understand the mechanisms behind the QTLxE effects. Therefore, our methodology allows the exploitation of the full MPP-ME data potential: to estimate QTL effect variation (a) within the MPP between sub-populations due to different genetic backgrounds and (b) between environments.

Ten Years of the Maize Nested Association Mapping Population: Impact, Limitations, and Future Directions

It has been just over a decade since the release of the maize (Zea mays) Nested Association Mapping (NAM) population. The NAM population has been and continues to be an invaluable resource for the maize genetics community and has yielded insights into the genetic architecture of complex traits. The parental lines have become some of the most well-characterized maize germplasm, and their de novo assemblies were recently made publicly available. As we enter an exciting new stage in maize genomics, this retrospective will summarize the design and intentions behind the NAM population; its application, the discoveries it has enabled, and its influence in other systems; and use the past decade of hindsight to consider whether and how it will remain useful in a new age of genomics.

Varietal variation and chromosome behaviour during meiosis in Solanum tuberosum

Naturally occurring autopolyploid species, such as the autotetraploid potato Solanum tuberosum, face a variety of challenges during meiosis. These include proper pairing, recombination and correct segregation of multiple homologous chromosomes, which can form complex multivalent configurations at metaphase I, and in turn alter allelic segregation ratios through double reduction. Here, we present a reference map of meiotic stages in diploid and tetraploid S. tuberosum using fluorescence in situ hybridisation (FISH) to differentiate individual meiotic chromosomes 1 and 2. A diploid-like behaviour at metaphase I involving bivalent configurations was predominant in all three tetraploid varieties. The crossover frequency per bivalent was significantly reduced in the tetraploids compared with a diploid variety, which likely indicates meiotic adaptation to the autotetraploid state. Nevertheless, bivalents were accompanied by a substantial frequency of multivalents, which varied by variety and by chromosome (7-48%). We identified possible sites of synaptic partner switching, leading to multivalent formation, and found potential defects in the polymerisation and/or maintenance of the synaptonemal complex in tetraploids. These findings demonstrate the rise of S. tuberosum as a model for autotetraploid meiotic recombination research and highlight constraints on meiotic chromosome configurations and chiasma frequencies as an important feature of an evolved autotetraploid meiosis.

Revisiting hybrid breeding designs using genomic predictions: simulations highlight the superiority of incomplete factorials between segregating families over topcross designs

Simulations showed that hybrid performances issued from an incomplete factorial between segregating families of two heterotic groups enable to calibrate genomic predictions of hybrid value more efficiently than tester-based designs. Genomic selection offers new opportunities to revisit hybrid breeding by replacing extensive phenotyping of hybrid combinations by genomic predictions. A key question remains to identify the best design to calibrate genomic prediction models. We proposed to use single-cross hybrids issued from an incomplete factorial design between segregating populations and compared this strategy with a conventional approach based on topcross evaluation. Two multiparental segregating populations of lines, each specific of one heterotic group, were simulated. Hybrids considered as training sets were generated using either (1) a parental line from the opposite group as tester or (2) following an incomplete factorial design. Different specific combining ability (SCA) proportions were simulated by considering different levels of group divergence and dominance effects for the simulated QTL. For the incomplete factorial design, for a same number of hybrids, we considered different numbers of parental lines and different contributions of lines (one to four) to calibration hybrids. We evaluated for different training set sizes prediction accuracies of new hybrids and genetic gains along three generations. At a given training set size, factorial design was as efficient (considering accuracy) as tester design in additive scenarios, but significantly outperformed tester design when SCA was present. The contribution number of each parental line to the incomplete factorial design had a small impact on accuracies. Our simulations confirmed experimental results and showed that calibrating models on hybrids between two multiparental populations is a cost-efficient way to perform genomic predictions in both groups, opening prospects for revisiting reciprocal recurrent selection schemes.

High-density NGS-based map construction and genetic dissection of fruit shape and rind netting in Cucumis melo

Melon is an important crop that exhibits broad variation for fruit morphology traits that are the substrate for genetic mapping efforts. In the post-genomic era, the link between genetic maps and physical genome assemblies is key for leveraging QTL mapping results for gene cloning and breeding purposes. Here, using a population of 164 melon recombinant inbred lines (RILs) that were subjected to genotyping-by-sequencing, we constructed and compared high-density sequence- and linkage-based recombination maps that were aligned to the reference melon genome. These analyses reveal the genome-wide variation in recombination frequency and highlight regions of disrupted collinearity between our population and the reference genome. The population was phenotyped over 3 years for fruit size and shape as well as rind netting. Four QTLs were detected for fruit size, and they act in an additive manner, while significant epistatic interaction was found between two neutral loci for this trait. Fruit shape displayed transgressive segregation that was explained by the action of four QTLs, contributed by alleles from both parents. The complexity of rind netting was demonstrated on a collection of 177 diverse accessions. Further dissection of netting in our RILs population, which is derived from a cross of smooth and densely netted parents, confirmed the intricacy of this trait and the involvement of major locus and several other interacting QTLs. A major netting QTL on chromosome 2 co-localized with results from two additional populations, paving the way for future study toward identification of a causative gene for this trait.

Counting on Crossovers: Controlled Recombination for Plant Breeding

Crossovers (COs), that drive genetic exchange between homologous chromosomes, are strongly biased toward subtelomeric regions in plant species. Manipulating the rate and positions of COs to increase the genetic variation accessible to breeders is a longstanding goal. Use of genome editing reagents that induce double-stranded breaks (DSBs) or modify the epigenome at desired sites of recombination, and manipulation of CO factors, are increasingly applicable approaches for achieving this goal. These strategies for 'controlled recombination' have potential to reduce the time and expense associated with traditional breeding, reveal currently inaccessible genetic diversity, and increase control over the inheritance of preferred haplotypes. Considerable challenges to address include translating knowledge from models to crop species and determining the best stages of the breeding cycle at which to control recombination.

Natural Selection Shapes Variation in Genome-wide Recombination Rate in Drosophila pseudoobscura

While recombination is widely recognized to be a key modulator of numerous evolutionary phenomena, we have a poor understanding of how recombination rate itself varies and evolves within a species. Here, we performed a comprehensive study of recombination rate (rate of meiotic crossing over) in two natural populations of Drosophila pseudoobscura from Utah and Arizona, USA. We used an amplicon sequencing approach to obtain high-quality genotypes in approximately 8,000 individual backcrossed offspring (17 mapping populations with roughly 530 individuals each), for which we then quantified crossovers. Interestingly, variation in recombination rate within and between populations largely manifested as differences in genome-wide recombination rate rather than remodeling of the local recombination landscape. Comparing populations, we discovered individuals from the Utah population displayed on average 8% higher crossover rates than the Arizona population, a statistically significant difference. Using a Q_ST-F_ST analysis, we found that this difference in crossover rate was dramatically higher than expected under neutrality, indicating that this difference may have been driven by natural selection. Finally, using a combination of short- and long-read whole-genome sequencing, we found no significant association between crossover rate and structural variation at the 200-400 kb scale. Our results demonstrate that (1) there is abundant variation in genome-wide crossover rate in natural populations, (2) at the 200-400 kb scale, recombination rate appears to vary largely genome-wide, rather than in specific intervals, and (3) interpopulation differences in recombination rate may be the result of local adaptation.

Recombination Pattern Characterization via Simulation Using Different Maize Populations

Efficient recombination is critical to both plant breeding and gene cloning. However, almost all traditional recombination studies and genetic improvements require the slow and labor-intensive population construction process, and little is known about the recombination characteristics of populations of different types, generations, and origins. Here, we provide a simple and efficient simulation method for population construction based on doubled haploid (DH) and intermated B73 × Mo17 maize (IBM) populations to predict the recombination pattern. We found that the chromosomes had 0, 1, 2, and 3 recombination events that occurred at rates of 0.16, 0.30, 0.23, and 0.15, respectively, in the DH and the recombination rate of each chromosome in the IBM population ranged from 0 to 12.1 cM per 125 kb. Based on the observed recombination parameters, we estimated the number of recombination events and constructed the linkage maps of the simulated DH and recombination inbred line (RIL) populations. These simulated populations exhibited similar recombination patterns compared with the real populations, suggesting the feasibility of this simulation approach. We then compared the recombination rates of the simulated populations of different types (DH induced or self-crossed), generations, and origins (using the 8, 16, and 32 multiparent advanced generation intercross (MAGIC) populations), and suggested a rapid and cost-effective population construction procedure for breeders and geneticists, while maintaining an optimal recombination rate. This study offers a convenient method for optimizing the population construction process and has broader implications for other crop species, thereby facilitating future population studies and genetic improvement strategies.

Haplotyping the Vitis collinear core genome with rhAmpSeq improves marker transferability in a diverse genus

Transferable DNA markers are essential for breeding and genetics. Grapevine (Vitis) breeders utilize disease resistance alleles from congeneric species ~20 million years divergent, but existing Vitis marker platforms have cross-species transfer rates as low as 2%. Here, we apply a marker strategy targeting the inferred Vitis core genome. Incorporating seven linked-read de novo assemblies and three existing assemblies, the Vitis collinear core genome is estimated to converge at 39.8 Mb (8.67% of the genome). Adding shotgun genome sequences from 40 accessions enables identification of conserved core PCR primer binding sites flanking polymorphic haplotypes with high information content. From these target regions, we develop 2,000 rhAmpSeq markers as a PCR multiplex and validate the panel in four biparental populations spanning the diversity of the Vitis genus, showing transferability increases to 91.9%. This marker development strategy should be widely applicable for genetic studies in many taxa, particularly those ~20 million years divergent.

Regulation of carbon metabolism in two maize sister lines contrasted for chilling tolerance

Maize can grow in cool temperate climates but is often exposed to spring chilling temperatures that can affect early seedling growth. Here, we used two sister double-haploid lines displaying a contrasted tolerance to chilling to identify major determinants of long-term chilling tolerance. The chilling-sensitive (CS) and the chilling-tolerant (CT) lines were grown at 14 °C day/10 °C night for 60 d. CS plants displayed a strong reduction in growth and aerial biomass compared with CT plants. Photosynthetic efficiency was affected with an increase in energy dissipation in both lines. Chilling tolerance in CT plants was associated with higher chlorophyll content, glucose-6-phosphate dehydrogenase activity, and higher sucrose to starch ratio. Few changes in cell wall composition were observed in both genotypes. There was no obvious correlation between nucleotide sugar content and cell wall polysaccharide composition. Our findings suggest that the central starch-sucrose metabolism is one major determinant of the response to low temperature, and its modulation accounts for the ability of CT plants to cope with low temperature. This modulation seemed to be linked to a strong alteration in the biosynthesis of nucleotide sugars that, at a high level, could reflect the remobilization of carbon in response to chilling.

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

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.

An ultra-high-density genetic map provides insights into genome synteny, recombination landscape and taproot skin colour in radish (Raphanus sativus L.)

High-density genetic map is a valuable tool for exploring novel genomic information, quantitative trait locus (QTL) mapping and gene discovery of economically agronomic traits in plant species. However, high-resolution genetic map applied to tag QTLs associated with important traits and to investigate genomic features underlying recombination landscape in radish (Raphanus sativus) remains largely unexplored. In this study, an ultra-high-density genetic map with 378 738 SNPs covering 1306.8 cM in nine radish linkage groups (LGs) was developed by a whole-genome sequencing-based approach. A total of 18 QTLs for 11 horticulture traits were detected. The map-based cloning data indicated that the R2R3-MYB transcription factor RsMYB90 was a crucial candidate gene determining the taproot skin colour. Comparative genomics analysis among radish, Brassica rapa and B. oleracea genome revealed several genomic rearrangements existed in the radish genome. The highly uneven distribution of recombination was observed across the nine radish chromosomes. Totally, 504 recombination hot regions (RHRs) were enriched near gene promoters and terminators. The recombination rate in RHRs was positively correlated with the density of SNPs and gene, and GC content, respectively. Functional annotation indicated that genes within RHRs were mainly involved in metabolic process and binding. Three QTLs for three traits were found in the RHRs. The results provide novel insights into the radish genome evolution and recombination landscape, and facilitate the development of effective strategies for molecular breeding by targeting and dissecting important traits in radish.

Crossover Interference: Shedding Light on the Evolution of Recombination

Through recombination, genes are freed to evolve more independently of one another, unleashing genetic variance hidden in the linkage disequilibrium that accumulates through selection combined with drift. Yet crossover numbers are evolutionarily constrained, with at least one and not many more than one crossover per bivalent in most taxa. Crossover interference, whereby a crossover reduces the probability of a neighboring crossover, contributes to this homogeneity. The mechanisms by which interference is achieved and crossovers are regulated are a major current subject of inquiry, facilitated by novel methods to visualize crossovers and to pinpoint recombination events. Here, we review patterns of crossover interference and the models built to describe this process. We then discuss the selective forces that have likely shaped interference and the regulation of crossover numbers.