Genomic prediction in Brassica napus: evaluating the benefit of imputed whole-genome sequencing data.

Advances in sequencing technology allow whole plant genomes to be sequenced with high quality. Combining genotypic and phenotypic data in genomic prediction helps breeders to select crossing partners in partially phenotyped populations. In plant breeding programs, the cost of sequencing entire breeding populations still exceeds available genotyping budgets. Hence, the method for genotyping is still mainly single nucleotide polymorphism (SNP) arrays; however, arrays are unable to assess the entire genome- and population-wide diversity. A compromise involves genotyping the entire population using an SNP array and a subset of the population with whole-genome sequencing. Both datasets can then be used to impute markers from whole-genome sequencing onto the entire population. Here, we evaluate whether imputation of whole-genome sequencing data enhances genomic predictions, using data from a nested association mapping population of rapeseed (Brassica napus). Employing two cross-validation schemes that mimic scenarios for the prediction of close and distant relatives, we show that imputed marker data do not significantly improve prediction accuracy, likely due to redundancy in relationship estimates and imputation errors. In simulation studies, only small improvements were observed, further corroborating the findings. We conclude that SNP arrays are already equipped with the information that is added by imputation through relationship and linkage disequilibrium.

haploMAGIC: Accurate phasing and detection of recombination in multiparental populations despite genotyping errors.

Recombination is a key mechanism in breeding for promoting genetic variability. Multiparental populations constitute an excellent platform for precise genotype phasing, identification of genome-wide crossovers, estimation of recombination frequencies and construction of recombination maps. Here, we introduce haploMAGIC, a pipeline to detect crossovers in multiparental populations with single-nucleotide polymorphism (SNP) data by exploiting the pedigree relationships for accurate genotype phasing and inference of grandparental haplotypes. haploMAGIC applies filtering to prevent false positive crossovers due to genotyping errors, a common problem in high-throughput SNP analysis of complex plant genomes. Hence it discards haploblocks not reaching a specified minimum number of informative alleles. A performance analysis using populations simulated with AlphaSimR revealed that haploMAGIC improves upon existing methods of crossover detection in terms of recall and precision, most notably when genotyping error rates are high. Furthermore, we constructed recombination maps using haploMAGIC with high-resolution genotype data from two large multi-parental populations of winter rapeseed (Brassica napus). The results demonstrate the applicability of the pipeline in real-world scenarios and showed good correlations in recombination frequency compared with alternative software. Therefore, we propose haploMAGIC as an accurate tool at crossover detection with multiparental populations that shows robustness against genotyping errors.

Simulation-based establishment of base pools for a hybrid breeding program in winter rapeseed.

KEY MESSAGE: Simulation planned pre-breeding can increase the efficiency of starting a hybrid breeding program. Starting a hybrid breeding program commonly comprises a grouping of the initial germplasm in two pools and subsequent selection on general combining ability. Investigations on pre-breeding steps before starting the selection on general combining ability are not available. Our goals were (1) to use computer simulations on the basis of DNA markers and testcross data to plan crosses that separate genetically two initial germplasm pools of rapeseed, (2) to carry out the planned crosses, and (3) to verify experimentally the pool separation as well as the increase in testcross performance. We designed a crossing program consisting of four cycles of recombination. In each cycle, the experimentally generated material was used to plan the subsequent crossing cycle with computer simulations. After finishing the crossing program, the initially overlapping pools were clearly separated in principal coordinate plots. Doubled haploid lines derived from the material of crossing cycles 1 and 2 showed an increase in relative testcross performance for yield of about 5% per cycle. We conclude that simulation-designed pre-breeding crossing schemes, that were carried out before the general combining ability-based selection of a newly started hybrid breeding program, can save time and resources, and in addition conserve more of the initial genetic variation than a direct start of a hybrid breeding program with general combining ability-based selection.

Integrated multi-omics analyses and genome-wide association studies reveal prime candidate genes of metabolic and vegetative growth variation in canola.

Genome-wide association studies (GWAS) identified thousands of genetic loci associated with complex plant traits, including many traits of agronomical importance. However, functional interpretation of GWAS results remains challenging because of large candidate regions due to linkage disequilibrium. High-throughput omics technologies, such as genomics, transcriptomics, proteomics and metabolomics open new avenues for integrative systems biological analyses and help to nominate systems information supported (prime) candidate genes. In the present study, we capitalise on a diverse canola population with 477 spring-type lines which was previously analysed by high-throughput phenotyping of growth-related traits and by RNA sequencing and metabolite profiling for multi-omics-based hybrid performance prediction. We deepened the phenotypic data analysis, now providing 123 time-resolved image-based traits, to gain insight into the complex relations during early vegetative growth and reanalysed the transcriptome data based on the latest Darmor-bzh v10 genome assembly. Genome-wide association testing revealed 61 298 robust quantitative trait loci (QTL) including 187 metabolite QTL, 56814 expression QTL and 4297 phenotypic QTL, many clustered in pronounced hotspots. Combining information about QTL colocalisation across omics layers and correlations between omics features allowed us to discover prime candidate genes for metabolic and vegetative growth variation. Prioritised candidate genes for early biomass accumulation include A06p05760.1_BnaDAR (PIAL1), A10p16280.1_BnaDAR, C07p48260.1_BnaDAR (PRL1) and C07p48510.1_BnaDAR (CLPR4). Moreover, we observed unequal effects of the Brassica A and C subgenomes on early biomass production.

Stacked ensembles on basis of parentage information can predict hybrid performance with an accuracy comparable to marker-based GBLUP.

Testcross factorials in newly established hybrid breeding programs are often highly unbalanced, incomplete, and characterized by predominance of special combining ability (SCA) over general combining ability (GCA). This results in a low efficiency of GCA-based selection. Machine learning algorithms might improve prediction of hybrid performance in such testcross factorials, as they have been successfully applied to find complex underlying patterns in sparse data. Our objective was to compare the prediction accuracy of machine learning algorithms to that of GCA-based prediction and genomic best linear unbiased prediction (GBLUP) in six unbalanced incomplete factorials from hybrid breeding programs of rapeseed, wheat, and corn. We investigated a range of machine learning algorithms with three different types of predictor variables: (a) information on parentage of hybrids, (b) in addition hybrid performance of crosses of the parental lines with other crossing partners, and (c) genotypic marker data. In two highly incomplete and unbalanced factorials from rapeseed, in which the SCA variance contributed considerably to the genetic variance, stacked ensembles of gradient boosting machines based on parentage information outperformed GCA prediction. The stacked ensembles increased prediction accuracy from 0.39 to 0.45, and from 0.48 to 0.54 compared to GCA prediction. The prediction accuracy reached by stacked ensembles without marker data reached values comparable to those of GBLUP that requires marker data. We conclude that hybrid prediction with stacked ensembles of gradient boosting machines based on parentage information is a promising approach that is worth further investigations with other data sets in which SCA variance is high.

Reduced glucosinolate content in oilseed rape (Brassica napus L.) by random mutagenesis of BnMYB28 and BnCYP79F1 genes.

The presence of anti-nutritive compounds like glucosinolates (GSLs) in the rapeseed meal severely restricts its utilization as animal feed. Therefore, reducing the GSL content to < 18 µmol/g dry weight in the seeds is a major breeding target. While candidate genes involved in the biosynthesis of GSLs have been described in rapeseed, comprehensive functional analyses are missing. By knocking out the aliphatic GSL biosynthesis genes BnMYB28 and BnCYP79F1 encoding an R2R3 MYB transcription factor and a cytochrome P450 enzyme, respectively, we aimed to reduce the seed GSL content in rapeseed. After expression analyses on single paralogs, we used an ethyl methanesulfonate (EMS) treated population of the inbred winter rapeseed 'Express617' to detect functional mutations in the two gene families. Our results provide the first functional analysis by knock-out for the two GSL biosynthesis genes in winter rapeseed. We demonstrate that independent knock-out mutants of the two genes possessed significantly reduced seed aliphatic GSLs, primarily progoitrin. Compared to the wildtype Express617 control plants (36.3 µmol/g DW), progoitrin levels were decreased by 55.3% and 32.4% in functional mutants of BnMYB28 (16.20 µmol/g DW) and BnCYP79F1 (24.5 µmol/g DW), respectively. Our study provides a strong basis for breeding rapeseed with improved meal quality in the future.

Direct access to millions of mutations by whole genome sequencing of an oilseed rape mutant population.

Induced mutations are an essential source of genetic variation in plant breeding. Ethyl methanesulfonate (EMS) mutagenesis has been frequently applied, and mutants have been detected by phenotypic or genotypic screening of large populations. In the present study, a rapeseed M2 population was derived from M1 parent cultivar 'Express' treated with EMS. Whole genomes were sequenced from fourfold (4×) pools of 1988 M2 plants representing 497 M2 families. Detected mutations were not evenly distributed and displayed distinct patterns across the 19 chromosomes with lower mutation rates towards the ends. Mutation frequencies ranged from 32/Mb to 48/Mb. On average, 284 442 single nucleotide polymorphisms (SNPs) per M2 DNA pool were found resulting from EMS mutagenesis. 55% of the SNPs were C → T and G → A transitions, characteristic for EMS induced ('canonical') mutations, whereas the remaining SNPs were 'non-canonical' transitions (15%) or transversions (30%). Additionally, we detected 88 725 high confidence insertions and deletions per pool. On average, each M2 plant carried 39 120 canonical mutations, corresponding to a frequency of one mutation per 23.6 kb. Approximately 82% of such mutations were located either 5 kb upstream or downstream (56%) of gene coding regions or within intergenic regions (26%). The remaining 18% were located within regions coding for genes. All mutations detected by whole genome sequencing could be verified by comparison with known mutations. Furthermore, all sequences are accessible via the online tool 'EMSBrassica' (http://www.emsbrassica.plantbreeding.uni-kiel.de), which enables direct identification of mutations in any target sequence. The sequence resource described here will further add value for functional gene studies in rapeseed breeding.

The Brassica napus seed microbiota is cultivar-specific and transmitted via paternal breeding lines.

Seed microbiota influence germination and plant health and have the potential to improve crop performance, but the factors that determine their structure and functions are still not fully understood. Here, we analysed the impact of plant-related and external factors on seed endophyte communities of 10 different oilseed rape (Brassica napus L.) cultivars from 26 field sites across Europe. All seed lots harboured a high abundance and diversity of endophytes, which were dominated by six genera: Ralstonia, Serratia, Enterobacter, Pseudomonas, Pantoea, and Sphingomonas. The cultivar was the main factor explaining the variations in bacterial diversity, abundance and composition. In addition, the latter was significantly influenced by diverse biotic and abiotic factors, for example host germination rates and disease resistance against Plasmodiophora brassicae. A set of bacterial biomarkers was identified to discriminate between characteristics of the seeds, for example Sphingomonas for improved germination and Brevundimonas for disease resistance. Application of a Bayesian community approach suggested vertical transmission of seed endophytes, where the paternal parent plays a major role and might even determine the germination performance of the offspring. This study contributes to the understanding of seed microbiome assembly and underlines the potential of the microbiome to be implemented in crop breeding and biocontrol programmes.

Two wrongs make a right: heat stress reversion of a male-sterile Brassica napus line.

Male-sterile lines play important roles in plant breeding to obtain hybrid vigour. The male sterility Lembke (MSL) system is a thermosensitive genic male sterility system of Brassica napus and is one of the main systems used in European rapeseed breeding. Interestingly, the MSL system shows high similarity to the 9012AB breeding system from China, including the ability to revert to fertile in high temperature conditions. Here we demonstrate that the MSL system is regulated by the same restorer of fertility gene BnaC9-Tic40 as the 9012AB system, which is related to the translocon at the inner envelope membrane of chloroplasts 40 (TIC40) from Arabidopsis. The male sterility gene of the MSL system was also identified to encode a chloroplast-localized protein which we call BnChimera; this gene shows high sequence similarity to the sterility gene previously described for the 9012AB system. For the first time, a direct protein interaction between BnaC9-Tic40 and the BnChimera could be demonstrated. In addition, we identify the corresponding amino acids that mediate this interaction and suggest how BnaC9-Tic40 acts as the restorer of fertility. Using an RNA-seq approach, the effects of heat treatment on the male fertility restoration of the C545 MSL system line were investigated. These data demonstrate that many pollen developmental pathways are affected by higher temperatures. It is hypothesized that heat stress reverses the male sterility via a combination of slower production of cell wall precursors in plastids and a slower flower development, which ultimately results in fertile pollen. The potential breeding applications of these results are discussed regarding the use of the MSL system in producing thermotolerant fertile plants.

Multi-omics-based prediction of hybrid performance in canola.

KEY MESSAGE: Complementing or replacing genetic markers with transcriptomic data and use of reproducing kernel Hilbert space regression based on Gaussian kernels increases hybrid prediction accuracies for complex agronomic traits in canola. In plant breeding, hybrids gained particular importance due to heterosis, the superior performance of offspring compared to their inbred parents. Since the development of new top performing hybrids requires labour-intensive and costly breeding programmes, including testing of large numbers of experimental hybrids, the prediction of hybrid performance is of utmost interest to plant breeders. In this study, we tested the effectiveness of hybrid prediction models in spring-type oilseed rape (Brassica napus L./canola) employing different omics profiles, individually and in combination. To this end, a population of 950 F1 hybrids was evaluated for seed yield and six other agronomically relevant traits in commercial field trials at several locations throughout Europe. A subset of these hybrids was also evaluated in a climatized glasshouse regarding early biomass production. For each of the 477 parental rapeseed lines, 13,201 single nucleotide polymorphisms (SNPs), 154 primary metabolites, and 19,479 transcripts were determined and used as predictive variables. Both, SNP markers and transcripts, effectively predict hybrid performance using (genomic) best linear unbiased prediction models (gBLUP). Compared to models using pure genetic markers, models incorporating transcriptome data resulted in significantly higher prediction accuracies for five out of seven agronomic traits, indicating that transcripts carry important information beyond genomic data. Notably, reproducing kernel Hilbert space regression based on Gaussian kernels significantly exceeded the predictive abilities of gBLUP models for six of the seven agronomic traits, demonstrating its potential for implementation in future canola breeding programmes.

Chromosome-Scale Assembly of Winter Oilseed Rape Brassica napus.

Rapeseed (Brassica napus), the second most important oilseed crop globally, originated from an interspecific hybridization between B. rapa and B. oleracea. After this genome collision, B. napus underwent extensive genome restructuring, via homoeologous chromosome exchanges, resulting in widespread segmental deletions and duplications. Illicit pairing among genetically similar homoeologous chromosomes during meiosis is common in recent allopolyploids like B. napus, and post-polyploidization restructuring compounds the difficulties of assembling a complex polyploid plant genome. Specifically, genomic rearrangements between highly similar chromosomes are challenging to detect due to the limitation of sequencing read length and ambiguous alignment of reads. Recent advances in long read sequencing technologies provide promising new opportunities to unravel the genome complexities of B. napus by encompassing breakpoints of genomic rearrangements with high specificity. Moreover, recent evidence revealed ongoing genomic exchanges in natural B. napus, highlighting the need for multiple reference genomes to capture structural variants between accessions. Here we report the first long-read genome assembly of a winter B. napus cultivar. We sequenced the German winter oilseed rape accession 'Express 617' using 54.5x of long reads. Short reads, linked reads, optical map data and high-density genetic maps were used to further correct and scaffold the assembly to form pseudochromosomes. The assembled Express 617 genome provides another valuable resource for Brassica genomics in understanding the genetic consequences of polyploidization, crop domestication, and breeding of recently-formed crop species.

How Population Structure Impacts Genomic Selection Accuracy in Cross-Validation: Implications for Practical Breeding.

Over the last two decades, the application of genomic selection has been extensively studied in various crop species, and it has become a common practice to report prediction accuracies using cross validation. However, genomic prediction accuracies obtained from random cross validation can be strongly inflated due to population or family structure, a characteristic shared by many breeding populations. An understanding of the effect of population and family structure on prediction accuracy is essential for the successful application of genomic selection in plant breeding programs. The objective of this study was to make this effect and its implications for practical breeding programs comprehensible for breeders and scientists with a limited background in quantitative genetics and genomic selection theory. We, therefore, compared genomic prediction accuracies obtained from different random cross validation approaches and within-family prediction in three different prediction scenarios. We used a highly structured population of 940 Brassica napus hybrids coming from 46 testcross families and two subpopulations. Our demonstrations show how genomic prediction accuracies obtained from among-family predictions in random cross validation and within-family predictions capture different measures of prediction accuracy. While among-family prediction accuracy measures prediction accuracy of both the parent average component and the Mendelian sampling term, within-family prediction only measures how accurately the Mendelian sampling term can be predicted. With this paper we aim to foster a critical approach to different measures of genomic prediction accuracy and a careful analysis of values observed in genomic selection experiments and reported in literature.

Strong temporal dynamics of QTL action on plant growth progression revealed through high-throughput phenotyping in canola.

A major challenge of plant biology is to unravel the genetic basis of complex traits. We took advantage of recent technical advances in high-throughput phenotyping in conjunction with genome-wide association studies to elucidate genotype-phenotype relationships at high temporal resolution. A diverse Brassica napus population from a commercial breeding programme was analysed by automated non-invasive phenotyping. Time-resolved data for early growth-related traits, including estimated biovolume, projected leaf area, early plant height and colour uniformity, were established and complemented by fresh and dry weight biomass. Genome-wide SNP array data provided the framework for genome-wide association analyses. Using time point data and relative growth rates, multiple robust main effect marker-trait associations for biomass and related traits were detected. Candidate genes involved in meristem development, cell wall modification and transcriptional regulation were detected. Our results demonstrate that early plant growth is a highly complex trait governed by several medium and many small effect loci, most of which act only during short phases. These observations highlight the importance of taking the temporal patterns of QTL/allele actions into account and emphasize the need for detailed time-resolved analyses to effectively unravel the complex and stage-specific contributions of genes affecting growth processes that operate at different developmental phases.

Genome-wide haplotype analysis improves trait predictions in Brassica napus hybrids.

Combining ability is crucial for parent selection in crop hybrid breeding. Many studies have attempted to provide reliable and quick methods to identify genome regions in parental lines correlating with improved hybrid performance. The local haplotype patterns surrounding densely spaced DNA markers include a large amount of genetic information, and analysis of the relationships between haplotypes and hybrid performance can provide insight into the underlying genome regions which might contribute to enhancing combining ability. Here, we generated 24,403 single-copy, genome-wide SNP loci and calculated the general combining ability (GCA) of 950 hybrids from a diverse panel of 475 pollinators of spring-type canola inbred lines crossed with two testers for days to flowering (DTF) and seed glucosinolate content (GSL). We performed a genome-wide analysis of the haplotypes and detected eight and seven haplotype regions that were significantly associated with the GCA values for DTF and seed GSL, respectively. Additionally, two haplotype blocks containing orthologs of flowering time genes FLOWERING LOCUS T (FT) and FLOWERING LOCUS C (FLC) on chromosome A02 showed additive epistatic interactions influencing flowering time. Moreover, two homoeologous haplotype regions on chromosomes A02 and C02 corresponded to major quantitative trait loci (QTL) for GSL which showed additive effects related to reduction of seed GSL in F1 hybrids. Our study showed that haplotype analysis has the potential to substantially improve the efficiency of hybrid breeding programs.

Hidden Effects of Seed Quality Breeding on Germination in Oilseed Rape (Brassica napus L.).

Intense selection for specific seed qualities in winter oilseed rape breeding has had an inadvertent negative influence on seed germination performance. In a panel of 215 diverse winter oilseed rape varieties spanning over 50 years of breeding progress in winter-type rapeseed, we found that low seed erucic acid content and reduced seed glucosinolate content were significantly related with prolonged germination time. Genome-wide association mapping revealed that this relationship is caused by linkage drag between important loci for seed quality and germination traits. One QTL for mean germination time on chromosome A09 co-localized with significant but minor QTL for both seed erucic acid and seed glucosinolate content. This suggested either potential pleiotropy or close linkage of minor factors influencing all three traits. Therefore, a reduction in germination performance may be due to inadvertent co-selection of genetic variants associated with 00 seed quality that have a negative influence on germination. Our results suggest that marker-assisted selection of positive alleles for mean germination time within the modern quality pool can help breeders to maintain maximal germination capacity in new 00-quality oilseed rape cultivars.

Genome-wide regression models considering general and specific combining ability predict hybrid performance in oilseed rape with similar accuracy regardless of trait architecture.

KEY MESSAGE: Genomic prediction using the Brassica 60 k genotyping array is efficient in oilseed rape hybrids. Prediction accuracy is more dependent on trait complexity than on the prediction model. In oilseed rape breeding programs, performance prediction of parental combinations is of fundamental importance. Due to the phenomenon of heterosis, per se performance is not a reliable indicator for F1-hybrid performance, and selection of well-paired parents requires the testing of large quantities of hybrid combinations in extensive field trials. However, the number of potential hybrids, in general, dramatically exceeds breeding capacity and budget. Integration of genomic selection (GS) could substantially increase the number of potential combinations that can be evaluated. GS models can be used to predict the performance of untested individuals based only on their genotypic profiles, using marker effects previously predicted in a training population. This allows for a preselection of promising genotypes, enabling a more efficient allocation of resources. In this study, we evaluated the usefulness of the Illumina Brassica 60 k SNP array for genomic prediction and compared three alternative approaches based on a homoscedastic ridge regression BLUP and three Bayesian prediction models that considered general and specific combining ability (GCA and SCA, respectively). A total of 448 hybrids were produced in a commercial breeding program from unbalanced crosses between 220 paternal doubled haploid lines and five male-sterile testers. Predictive ability was evaluated for seven agronomic traits. We demonstrate that the Brassica 60 k genotyping array is an adequate and highly valuable platform to implement genomic prediction of hybrid performance in oilseed rape. Furthermore, we present first insights into the application of established statistical models for prediction of important agronomical traits with contrasting patterns of polygenic control.

Genomic Prediction of Testcross Performance in Canola (Brassica napus).

Genomic selection (GS) is a modern breeding approach where genome-wide single-nucleotide polymorphism (SNP) marker profiles are simultaneously used to estimate performance of untested genotypes. In this study, the potential of genomic selection methods to predict testcross performance for hybrid canola breeding was applied for various agronomic traits based on genome-wide marker profiles. A total of 475 genetically diverse spring-type canola pollinator lines were genotyped at 24,403 single-copy, genome-wide SNP loci. In parallel, the 950 F1 testcross combinations between the pollinators and two representative testers were evaluated for a number of important agronomic traits including seedling emergence, days to flowering, lodging, oil yield and seed yield along with essential seed quality characters including seed oil content and seed glucosinolate content. A ridge-regression best linear unbiased prediction (RR-BLUP) model was applied in combination with 500 cross-validations for each trait to predict testcross performance, both across the whole population as well as within individual subpopulations or clusters, based solely on SNP profiles. Subpopulations were determined using multidimensional scaling and K-means clustering. Genomic prediction accuracy across the whole population was highest for seed oil content (0.81) followed by oil yield (0.75) and lowest for seedling emergence (0.29). For seed yieId, seed glucosinolate, lodging resistance and days to onset of flowering (DTF), prediction accuracies were 0.45, 0.61, 0.39 and 0.56, respectively. Prediction accuracies could be increased for some traits by treating subpopulations separately; a strategy which only led to moderate improvements for some traits with low heritability, like seedling emergence. No useful or consistent increase in accuracy was obtained by inclusion of a population substructure covariate in the model. Testcross performance prediction using genome-wide SNP markers shows considerable potential for pre-selection of promising hybrid combinations prior to resource-intensive field testing over multiple locations and years.

Species-wide genome sequence and nucleotide polymorphisms from the model allopolyploid plant Brassica napus.

Brassica napus (oilseed rape, canola) is one of the world's most important sources of vegetable oil for human nutrition and biofuel, and also a model species for studies investigating the evolutionary consequences of polyploidisation. Strong bottlenecks during its recent origin from interspecific hybridisation, and subsequently through intensive artificial selection, have severely depleted the genetic diversity available for breeding. On the other hand, high-throughput genome profiling technologies today provide unprecedented scope to identify, characterise and utilise genetic diversity in primary and secondary crop gene pools. Such methods also enable implementation of genomic selection strategies to accelerate breeding progress. The key prerequisite is availability of high-quality sequence data and identification of high-quality, genome-wide sequence polymorphisms representing relevant gene pools. We present comprehensive genome resequencing data from a panel of 52 highly diverse natural and synthetic B. napus accessions, along with a stringently selected panel of 4.3 million high-confidence, genome-wide SNPs. The data is of great interest for genomics-assisted breeding and for evolutionary studies on the origins and consequences in allopolyploidisation in plants.

Heterotic Haplotype Capture: precision breeding for hybrid performance.

The need to improve hybrid performance, abiotic stress tolerance, and disease resistance without compromising seed quality makes the targeted capture of untapped diversity a major objective for crop breeders. Here we introduce the concept of Heterotic Haplotype Capture (HHC), in which genome sequence imputation is used to trace novel heterozygous chromosome blocks contributing to hybrid performance in large, structured populations of interrelated F1 hybrids containing interesting new diversity for breeding.

Genome-wide association mapping unravels the genetic control of seed germination and vigor in Brassica napus.

Rapid and uniform seed germination is a crucial prerequisite for crop establishment and high yield levels in crop production. A disclosure of genetic factors contributing to adequate seed vigor would help to further increase yield potential and stability. Here we carried out a genome-wide association study in order to define genomic regions influencing seed germination and early seedling growth in oilseed rape (Brassica napus L.). A population of 248 genetically diverse winter-type B. napus accessions was genotyped with the Brassica 60k SNP Illumina genotyping array. Automated high-throughput in vitro phenotyping provided extensive data for multiple traits related to germination and early vigor, such as germination speed, absolute germination rate and radicle elongation. The data obtained indicate that seed germination and radicle growth are strongly environmentally dependent, but could nevertheless be substantially improved by genomic-based breeding. Conditions during seed production and storage were shown to have a profound effect on seed vigor, and a variable manifestation of seed dormancy appears to contribute to differences in germination performance in B. napus. Several promising positional and functional candidate genes could be identified within the genomic regions associated with germination speed, absolute germination rate, radicle growth and thousand seed weight. These include B. napus orthologs of the Arabidopsis thaliana genes SNOWY COTYLEDON 1 (SCO1), ARABIDOPSIS TWO-COMPONENT RESPONSE REGULATOR (ARR4), and ARGINYL-t-RNA PROTEIN TRANSFERASE 1 (ATE1), which have been shown previously to play a role in seed germination and seedling growth in A. thaliana.