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).

Phenomic selection in wheat breeding: prediction of the genotype-by-environment interaction in multi-environment breeding trials.

KEY MESSAGE: Phenomic prediction of wheat grain yield and heading date in different multi-environmental trial scenarios is accurate. Modelling the genotype-by-environment interaction effect using phenomic data is a potentially low-cost complement to genomic prediction. The performance of wheat cultivars in multi-environmental trials (MET) is difficult to predict because of the genotype-by-environment interactions (G × E). Phenomic selection is supposed to be efficient for modelling the G × E effect because it accounts for non-additive effects. Here, phenomic data are near-infrared (NIR) spectra obtained from plant material. While phenomic selection has recently been shown to accurately predict wheat grain yield in single environments, its accuracy needs to be investigated for MET. We used four datasets from two winter wheat breeding programs to test and compare the predictive abilities of phenomic and genomic models for grain yield and heading date in different MET scenarios. We also compared different methods to model the G × E using different covariance matrices based on spectra. On average, phenomic and genomic prediction abilities are similar in all different MET scenarios. Better predictive abilities were obtained when G × E effects were modelled with NIR spectra than without them, and it was better to use all the spectra of all genotypes in all environments for modelling the G × E. To facilitate the implementation of phenomic prediction, we tested MET designs where the NIR spectra were measured only on the genotype-environment combinations phenotyped for the target trait. Missing spectra were predicted with a weighted multivariate ridge regression. Intermediate predictive abilities for grain yield were obtained in a sparse testing scenario and for new genotypes, which shows that phenomic selection is an efficient and practicable prediction method for dealing with G × E.

Analyzing the Economic Effectiveness of Genomic Selection Relative to Conventional Breeding Approaches.

Comparing the economic efficiency of alternative strategies for breeding requires to compare the genetic gain obtained with breeding schemes that represent the same total investment. In this chapter, we present a generic method to assess this economic efficiency for alternative breeding schemes. After presenting the baseline framework and the necessity of comparing breeding schemes with equivalent total investment, we propose one illustrative example on wheat breeding. In this application, we compare the use of conventional breeding and genomic selection. With this example, we explain the requirements and the different steps to implement this method. At last, we discuss several extensions of the baseline model.

Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection.

KEY MESSAGE: Phenomic selection is a promising alternative or complement to genomic selection in wheat breeding. Models combining spectra from different environments maximise the predictive ability of grain yield and heading date of wheat breeding lines. Phenomic selection (PS) is a recent breeding approach similar to genomic selection (GS) except that genotyping is replaced by near-infrared (NIR) spectroscopy. PS can potentially account for non-additive effects and has the major advantage of being low cost and high throughput. Factors influencing GS predictive abilities have been intensively studied, but little is known about PS. We tested and compared the abilities of PS and GS to predict grain yield and heading date from several datasets of bread wheat lines corresponding to the first or second years of trial evaluation from two breeding companies and one research institute in France. We evaluated several factors affecting PS predictive abilities including the possibility of combining spectra collected in different environments. A simple H-BLUP model predicted both traits with prediction ability from 0.26 to 0.62 and with an efficient computation time. Our results showed that the environments in which lines are grown had a crucial impact on predictive ability based on the spectra acquired and was specific to the trait considered. Models combining NIR spectra from different environments were the best PS models and were at least as accurate as GS in most of the datasets. Furthermore, a GH-BLUP model combining genotyping and NIR spectra was the best model of all (prediction ability from 0.31 to 0.73). We demonstrated also that as for GS, the size and the composition of the training set have a crucial impact on predictive ability. PS could therefore replace or complement GS for efficient wheat breeding programs.

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.

A genomics resource for genetics, physiology, and breeding of West African sorghum.

Local landrace and breeding germplasm is a useful source of genetic diversity for regional and global crop improvement initiatives. Sorghum (Sorghum bicolor L. Moench) in western Africa (WA) has diversified across a mosaic of cultures and end uses and along steep precipitation and photoperiod gradients. To facilitate germplasm utilization, a West African sorghum association panel (WASAP) of 756 accessions from national breeding programs of Niger, Mali, Senegal, and Togo was assembled and characterized. Genotyping-by-sequencing (GBS) was used to generate 159,101 high-quality biallelic single nucleotide polymorphisms (SNPs), with 43% in intergenic regions and 13% in genic regions. High genetic diversity was observed within the WASAP (π = .00045), only slightly less than in a global diversity panel (GDP) (π = .00055). Linkage disequilibrium (LD) decayed to background level (r2 < .1) by ∼50 kb in the WASAP. Genome-wide diversity was structured both by botanical type and by populations within botanical type with eight ancestral populations identified. Most populations were distributed across multiple countries, suggesting several potential common gene pools across the national programs. Genome-wide association studies (GWAS) of days to flowering (DFLo) and plant height (PH) revealed eight and three significant quantitative trait loci (QTL), respectively, with major height QTL at canonical height loci Dw3 and SbHT7.1. Colocalization of two of eight major flowering time QTL with flowering genes previously described in U.S. germplasm (Ma6 and SbCN8) suggests that photoperiodic flowering in West African sorghum is conditioned by both known and novel genes. This genomic resource provides a foundation for genomics-enabled breeding of climate-resilient varieties in WA.

BWGS: A R package for genomic selection and its application to a wheat breeding programme.

We developed an integrated R library called BWGS to enable easy computation of Genomic Estimates of Breeding values (GEBV) for genomic selection. BWGS, for BreedWheat Genomic selection, was developed in the framework of a cooperative private-public partnership project called Breedwheat (https://breedwheat.fr) and relies on existing R-libraries, all freely available from CRAN servers. The two main functions enable to run 1) replicated random cross validations within a training set of genotyped and phenotyped lines and 2) GEBV prediction, for a set of genotyped-only lines. Options are available for 1) missing data imputation, 2) markers and training set selection and 3) genomic prediction with 15 different methods, either parametric or semi-parametric. The usefulness and efficiency of BWGS are illustrated using a population of wheat lines from a real breeding programme. Adjusted yield data from historical trials (highly unbalanced design) were used for testing the options of BWGS. On the whole, 760 candidate lines with adjusted phenotypes and genotypes for 47 839 robust SNP were used. With a simple desktop computer, we obtained results which compared with previously published results on wheat genomic selection. As predicted by the theory, factors that are most influencing predictive ability, for a given trait of moderate heritability, are the size of the training population and a minimum number of markers for capturing every QTL information. Missing data up to 40%, if randomly distributed, do not degrade predictive ability once imputed, and up to 80% randomly distributed missing data are still acceptable once imputed with Expectation-Maximization method of package rrBLUP. It is worth noticing that selecting markers that are most associated to the trait do improve predictive ability, compared with the whole set of markers, but only when marker selection is made on the whole population. When marker selection is made only on the sampled training set, this advantage nearly disappeared, since it was clearly due to overfitting. Few differences are observed between the 15 prediction models with this dataset. Although non-parametric methods that are supposed to capture non-additive effects have slightly better predictive accuracy, differences remain small. Finally, the GEBV from the 15 prediction models are all highly correlated to each other. These results are encouraging for an efficient use of genomic selection in applied breeding programmes and BWGS is a simple and powerful toolbox to apply in breeding programmes or training activities.

Dissecting Adaptive Traits with Nested Association Mapping: Genetic Architecture of Inflorescence Morphology in Sorghum.

In the cereal crop sorghum (Sorghum bicolor) inflorescence morphology variation underlies yield variation and confers adaptation across precipitation gradients, but its genetic basis is poorly understood. We characterized the genetic architecture of sorghum inflorescence morphology using a global nested association mapping (NAM) population (2200 recombinant inbred lines) and 198,000 phenotypic observations from multi-environment trials for four inflorescence morphology traits (upper branch length, lower branch length, rachis length, and rachis diameter). Trait correlations suggest that lower and upper branch length are under somewhat independent control, while lower branch length and rachis diameter are highly pleiotropic. Joint linkage and genome-wide association mapping revealed an oligogenic architecture with 1-22 QTL per trait, each explaining 0.1-5.0% of variation across the entire NAM population. There is a significant enrichment (2.twofold) of QTL colocalizing with grass inflorescence gene homologs, notably with orthologs of maize Ramosa2 and rice Aberrant Panicle Organization1 and TAWAWA1 Still, many QTL do not colocalize with inflorescence gene homologs. In global georeferenced germplasm, allelic variation at the major inflorescence QTL is geographically patterned but only weakly associated with the gradient of annual precipitation. Comparison of NAM with diversity panel association suggests that naive association models may capture some true associations not identified by mixed linear models. Overall, the findings suggest that global inflorescence diversity in sorghum is largely controlled by oligogenic, epistatic, and pleiotropic variation in ancestral regulatory networks. The findings also provide a basis for genomics-enabled breeding of locally-adapted inflorescence morphology.

Worldwide phylogeography and history of wheat genetic diversity.

Since its domestication in the Fertile Crescent ~8000 to 10,000 years ago, wheat has undergone a complex history of spread, adaptation, and selection. To get better insights into the wheat phylogeography and genetic diversity, we describe allele distribution through time using a set of 4506 landraces and cultivars originating from 105 different countries genotyped with a high-density single-nucleotide polymorphism array. Although the genetic structure of landraces is collinear to ancient human migration roads, we observe a reshuffling through time, related to breeding programs, with the appearance of new alleles enriched with structural variations that may be the signature of introgressions from wild relatives after 1960.

Population genomics of sorghum (Sorghum bicolor) across diverse agroclimatic zones of Niger.

Improving adaptation of staple crops in developing countries is important to ensure food security. In the West African country of Niger, the staple crop sorghum (Sorghum bicolor) is cultivated across diverse agroclimatic zones, but the genetic basis of local adaptation has not been described. The objectives of this study were to characterize the genomic diversity of sorghum from Niger and to identify genomic regions conferring local adaptation to agroclimatic zones and farmer preferences. We analyzed 516 Nigerien accessions for which local variety name, botanical race, and geographic origin were known. We discovered 144 299 single nucleotide polymorphisms (SNPs) using genotyping-by-sequencing (GBS). We performed discriminant analysis of principal components (DAPC), which identified six genetic groups, and performed a genome scan for loci with high discriminant loadings. The highest discriminant coefficients were on chromosome 9, near the putative ortholog of maize flowering time adaptation gene Vgt1. Next, we characterized differentiation among local varieties and used a genome scan of pairwise FST values to identify SNPs associated with specific local varieties. Comparison of varieties named for light- versus dark-grain identified differentiation near Tannin1, the major gene responsible for grain tannins. These findings could facilitate genomics-assisted breeding of locally adapted and farmer-preferred sorghum varieties for Niger.

Increased Power To Dissect Adaptive Traits in Global Sorghum Diversity Using a Nested Association Mapping Population.

Adaptation of domesticated species to diverse agroclimatic regions has led to abundant trait diversity. However, the resulting population structure and genetic heterogeneity confounds association mapping of adaptive traits. To address this challenge in sorghum [Sorghum bicolor (L.) Moench]-a widely adapted cereal crop-we developed a nested association mapping (NAM) population using 10 diverse global lines crossed with an elite reference line RTx430. We characterized the population of 2214 recombinant inbred lines at 90,000 SNPs using genotyping-by-sequencing. The population captures ∼70% of known global SNP variation in sorghum, and 57,411 recombination events. Notably, recombination events were four- to fivefold enriched in coding sequences and 5' untranslated regions of genes. To test the power of the NAM population for trait dissection, we conducted joint linkage mapping for two major adaptive traits, flowering time and plant height. We precisely mapped several known genes for these two traits, and identified several additional QTL. Considering all SNPs simultaneously, genetic variation accounted for 65% of flowering time variance and 75% of plant height variance. Further, we directly compared NAM to genome-wide association mapping (using panels of the same size) and found that flowering time and plant height QTL were more consistently identified with the NAM population. Finally, for simulated QTL under strong selection in diversity panels, the power of QTL detection was up to three times greater for NAM vs. association mapping with a diverse panel. These findings validate the NAM resource for trait mapping in sorghum, and demonstrate the value of NAM for dissection of adaptive traits.

Population genomics of pearl millet (Pennisetum glaucum (L.) R. Br.): Comparative analysis of global accessions and Senegalese landraces.

BACKGROUND: Pearl millet is a staple food for people in arid and semi-arid regions of Africa and South Asia due to its high drought tolerance and nutritional qualities. A better understanding of the genomic diversity and population structure of pearl millet germplasm is needed to support germplasm conservation and genetic improvement of this crop. Here we characterized two pearl millet diversity panels, (i) a set of global accessions from Africa, Asia, and the America, and (ii) a collection of landraces from multiple agro-ecological zones in Senegal. RESULTS: We identified 83,875 single nucleotide polymorphisms (SNPs) in 500 pearl millet accessions, comprised of 252 global accessions and 248 Senegalese landraces, using genotyping by sequencing (GBS) of PstI-MspI reduced representation libraries. We used these SNPs to characterize genomic diversity and population structure among the accessions. The Senegalese landraces had the highest levels of genetic diversity (π), while accessions from southern Africa and Asia showed lower diversity levels. Principal component analyses and ancestry estimation indicated clear population structure between the Senegalese landraces and the global accessions, and among countries in the global accessions. In contrast, little population structure was observed across in the Senegalese landraces collections. We ordered SNPs on the pearl millet genetic map and observed much faster linkage disequilibrium (LD) decay in Senegalese landraces compared to global accessions. A comparison of pearl millet GBS linkage map with the foxtail millet (Setaria italica) and sorghum (Sorghum bicolor) genomes indicated extensive regions of synteny, as well as some large-scale rearrangements in the pearl millet lineage. CONCLUSIONS: We identified 83,875 SNPs as a genomic resource for pearl millet improvement. The high genetic diversity in Senegal relative to other regions of Africa and Asia supports a West African origin of this crop, followed by wide diffusion. The rapid LD decay and lack of confounding population structure along agro-ecological zones in Senegalese pearl millet will facilitate future association mapping studies. Comparative population genomics will provide insights into panicoid crop evolution and support improvement of these climate-resilient crops.

Adaptation of maize to temperate climates: mid-density genome-wide association genetics and diversity patterns reveal key genomic regions, with a major contribution of the Vgt2 (ZCN8) locus.

The migration of maize from tropical to temperate climates was accompanied by a dramatic evolution in flowering time. To gain insight into the genetic architecture of this adaptive trait, we conducted a 50K SNP-based genome-wide association and diversity investigation on a panel of tropical and temperate American and European representatives. Eighteen genomic regions were associated with flowering time. The number of early alleles cumulated along these regions was highly correlated with flowering time. Polymorphism in the vicinity of the ZCN8 gene, which is the closest maize homologue to Arabidopsis major flowering time (FT) gene, had the strongest effect. This polymorphism is in the vicinity of the causal factor of Vgt2 QTL. Diversity was lower, whereas differentiation and LD were higher for associated loci compared to the rest of the genome, which is consistent with selection acting on flowering time during maize migration. Selection tests also revealed supplementary loci that were highly differentiated among groups and not associated with flowering time in our panel, whereas they were in other linkage-based studies. This suggests that allele fixation led to a lack of statistical power when structure and relatedness were taken into account in a linear mixed model. Complementary designs and analysis methods are necessary to unravel the architecture of complex traits. Based on linkage disequilibrium (LD) estimates corrected for population structure, we concluded that the number of SNPs genotyped should be at least doubled to capture all QTLs contributing to the genetic architecture of polygenic traits in this panel. These results show that maize flowering time is controlled by numerous QTLs of small additive effect and that strong polygenic selection occurred under cool climatic conditions. They should contribute to more efficient genomic predictions of flowering time and facilitate the dissemination of diverse maize genetic resources under a wide range of environments.

Massive sorghum collection genotyped with SSR markers to enhance use of global genetic resources.

Large ex situ collections require approaches for sampling manageable amounts of germplasm for in-depth characterization and use. We present here a large diversity survey in sorghum with 3367 accessions and 41 reference nuclear SSR markers. Of 19 alleles on average per locus, the largest numbers of alleles were concentrated in central and eastern Africa. Cultivated sorghum appeared structured according to geographic regions and race within region. A total of 13 groups of variable size were distinguished. The peripheral groups in western Africa, southern Africa and eastern Asia were the most homogeneous and clearly differentiated. Except for Kafir, there was little correspondence between races and marker-based groups. Bicolor, Caudatum, Durra and Guinea types were each dispersed in three groups or more. Races should therefore better be referred to as morphotypes. Wild and weedy accessions were very diverse and scattered among cultivated samples, reinforcing the idea that large gene-flow exists between the different compartments. Our study provides an entry to global sorghum germplasm collections. Our reference marker kit can serve to aggregate additional studies and enhance international collaboration. We propose a core reference set in order to facilitate integrated phenotyping experiments towards refined functional understanding of sorghum diversity.

An initial assessment of linkage disequilibrium (LD) in coffee trees: LD patterns in groups of Coffea canephora Pierre using microsatellite analysis.

BACKGROUND: A reciprocal recurrent selection program has been under way for the Coffea canephora coffee tree for approximately thirty years in the Ivory Coast. Association genetics would help to speed up this program by more rapidly selecting zones of interest in the genome. However, prior to any such studies, the linkage disequilibrium (LD) needs to be assessed between the markers on the genome. These data are essential for guiding association studies. RESULTS: This article describes the first results of an LD assessment in a coffee tree species. Guinean and Congolese breeding populations of C. canephora have been used for this work, with the goal of identifying ways of using these populations in association genetics. We identified changes in the LD along the genome within the different C. canephora diversity groups. In the different diversity groups studied, the LD was variable. Some diversity groups displayed disequilibria over long distances (up to 25 cM), whereas others had disequilibria not exceeding 1 cM. We also discovered a fine structure within the Guinean group. CONCLUSIONS: Given these results, association studies can be used within the species C. canephora. The coffee recurrent selection scheme being implemented in the Ivory Coast can thus be optimized. Lastly, our results could be used to improve C. arabica because one of its parents is closely related to C. canephora.

Genetic structure, linkage disequilibrium and signature of selection in Sorghum: lessons from physically anchored DArT markers.

Population structure, extent of linkage disequilibrium (LD) as well as signatures of selection were investigated in sorghum using a core sample representative of worldwide diversity. A total of 177 accessions were genotyped with 1122 informative physically anchored DArT markers. The properties of DArTs to describe sorghum genetic structure were compared to those of SSRs and of previously published RFLP markers. Model-based (STRUCTURE software) and Neighbor-Joining diversity analyses led to the identification of 6 groups and confirmed previous evolutionary hypotheses. Results were globally consistent between the different marker systems. However, DArTs appeared more robust in terms of data resolution and bayesian group assignment. Whole genome linkage disequilibrium as measured by mean r(2) decreased from 0.18 (between 0 to 10 kb) to 0.03 (between 100 kb to 1 Mb), stabilizing at 0.03 after 1 Mb. Effects on LD estimations of sample size and genetic structure were tested using i. random sampling, ii. the Maximum Length SubTree algorithm (MLST), and iii. structure groups. Optimizing population composition by the MLST reduced the biases in small samples and seemed to be an efficient way of selecting samples to make the best use of LD as a genome mapping approach in structured populations. These results also suggested that more than 100,000 markers may be required to perform genome-wide association studies in collections covering worldwide sorghum diversity. Analysis of DArT markers differentiation between the identified genetic groups pointed out outlier loci potentially linked to genes controlling traits of interest, including disease resistance genes for which evidence of selection had already been reported. In addition, evidence of selection near a homologous locus of FAR1 concurred with sorghum phenotypic diversity for sensitivity to photoperiod.

Flowering time in maize: linkage and epistasis at a major effect locus.

In a previous study, we identified a candidate fragment length polymorphism associated with flowering time variation after seven generations of selection for flowering time, starting from the maize inbred line F252. Here, we characterized the candidate region and identified underlying polymorphisms. Then, we combined QTL mapping, association mapping, and developmental characterization to dissect the genetic mechanisms responsible for the phenotypic variation. The candidate region contained the Eukaryotic Initiation Factor (eIF-4A) and revealed a high level of sequence and structural variation beyond the 3'-UTR of eIF-4A, including several insertions of truncated transposable elements. Using a biallelic single-nucleotide polymorphism (SNP) (C/T) in the candidate region, we confirmed its association with flowering time variation in a panel of 317 maize inbred lines. However, while the T allele was correlated with late flowering time within the F252 genetic background, it was correlated with early flowering time in the association panel with pervasive interactions between allelic variation and the genetic background, pointing to underlying epistasis. We also detected pleiotropic effects of the candidate polymorphism on various traits including flowering time, plant height, and leaf number. Finally, we were able to break down the correlation between flowering time and leaf number in the progeny of a heterozygote (C/T) within the F252 background consistent with causal loci in linkage disequilibrium. We therefore propose that both a cluster of tightly linked genes and epistasis contribute to the phenotypic variation for flowering time.

Genetic diversity, structure, gene flow and evolutionary relationships within the Sorghum bicolor wild-weedy-crop complex in a western African region.

Gene flow between domesticated plants and their wild relatives is one of the major evolutionary processes acting to shape their structure of genetic diversity. Earlier literature, in the 1970s, reported on the interfertility and the sympatry of wild, weedy and cultivated sorghum belonging to the species Sorghum bicolor in most regions of sub-Saharan Africa. However, only a few recent surveys have addressed the geographical and ecological distribution of sorghum wild relatives and their genetic structure. These features are poorly documented, especially in western Africa, a centre of diversity for this crop. We report here on an exhaustive in situ collection of wild, weedy and cultivated sorghum assembled in Mali and in Guinea. The extent and pattern of genetic diversity were assessed with 15 SSRs within the cultivated pool (455 accessions), the wild pool (91 wild and weedy forms) and between them. F (ST) and R (ST) statistics, distance-based trees, Bayesian clustering methods, as well as isolation by distance models, were used to infer evolutionary relationships within the wild-weedy-crop complex. Firstly, our analyses highlighted a strong racial structure of genetic diversity within cultivated sorghum (F (ST) = 0.40). Secondly, clustering analyses highlighted the introgressed nature of most of the wild and weedy sorghum and grouped them into two eco-geographical groups. Such closeness between wild and crop sorghum could be the result of both sorghum's domestication history and preferential post-domestication crop-to-wild gene flow enhanced by farmers' practices. Finally, isolation by distance analyses showed strong spatial genetic structure within each pool, due to spatially limited dispersal, and suggested consequent gene flow between the wild and the crop pools, also supported by R (ST) analyses. Our findings thus revealed important features for the collection, conservation and biosafety of domesticated and wild sorghum in their centre of diversity.

A consensus genetic map of sorghum that integrates multiple component maps and high-throughput Diversity Array Technology (DArT) markers.

BACKGROUND: Sorghum genome mapping based on DNA markers began in the early 1990s and numerous genetic linkage maps of sorghum have been published in the last decade, based initially on RFLP markers with more recent maps including AFLPs and SSRs and very recently, Diversity Array Technology (DArT) markers. It is essential to integrate the rapidly growing body of genetic linkage data produced through DArT with the multiple genetic linkage maps for sorghum generated through other marker technologies. Here, we report on the colinearity of six independent sorghum component maps and on the integration of these component maps into a single reference resource that contains commonly utilized SSRs, AFLPs, and high-throughput DArT markers. RESULTS: The six component maps were constructed using the MultiPoint software. The lengths of the resulting maps varied between 910 and 1528 cM. The order of the 498 markers that segregated in more than one population was highly consistent between the six individual mapping data sets. The framework consensus map was constructed using a "Neighbours" approach and contained 251 integrated bridge markers on the 10 sorghum chromosomes spanning 1355.4 cM with an average density of one marker every 5.4 cM, and were used for the projection of the remaining markers. In total, the sorghum consensus map consisted of a total of 1997 markers mapped to 2029 unique loci (1190 DArT loci and 839 other loci) spanning 1603.5 cM and with an average marker density of 1 marker/0.79 cM. In addition, 35 multicopy markers were identified. On average, each chromosome on the consensus map contained 203 markers of which 58.6% were DArT markers. Non-random patterns of DNA marker distribution were observed, with some clear marker-dense regions and some marker-rare regions. CONCLUSION: The final consensus map has allowed us to map a larger number of markers than possible in any individual map, to obtain a more complete coverage of the sorghum genome and to fill a number of gaps on individual maps. In addition to overall general consistency of marker order across individual component maps, good agreement in overall distances between common marker pairs across the component maps used in this study was determined, using a difference ratio calculation. The obtained consensus map can be used as a reference resource for genetic studies in different genetic backgrounds, in addition to providing a framework for transferring genetic information between different marker technologies and for integrating DArT markers with other genomic resources. DArT markers represent an affordable, high throughput marker system with great utility in molecular breeding programs, especially in crops such as sorghum where SNP arrays are not publicly available.

Mild hypothermia improves transient gene expression yields several fold in Chinese hamster ovary cells.

Large-scale transient gene expression (TGE) in mammalian cells is a rapid method to generate recombinant proteins, but the volumetric productivity for secreted proteins is still more than an order of magnitude lower than the yields typically achieved with recombinant cell lines. Here transient recombinant protein production in Chinese hamster ovary cells transfected with linear 25 kDa polyethylenimine was significantly enhanced by incubation of the cells at temperatures ranging from 29 to 33 degrees C after DNA delivery. With this approach, transient recombinant antibody yields of 60-80 mg/L were achieved within 6 days of transfection. The increase in TGE correlated with the accumulation of cells in the G1 phase of the cell cycle, increased cell size, higher cell viability, higher steady-state levels of transgene mRNA, reduced consumption of nutrients, and decreased accumulation of waste products. The enhancement of TGE was not vector-dependent, but the presence of the woodchuck hepatitis virus post-transcriptional regulatory element in the 3' untranslated region of the transgene mRNA increased transient recombinant antibody expression more than 3-fold at 31 degrees C as compared to expression at 37 degrees C. The yields achieved by the low-temperature enhancement of TGE in CHO cells makes this technology feasible for the rapid production of gram amounts of secreted recombinant proteins at large scale (up to 100 L).