The genetic architecture of soybean photothermal adaptation to high latitudes.

Soybean is a major plant protein source for both human food and animal feed, but to meet global demands as well as a trend towards regional production, soybean cultivation needs to be expanded to higher latitudes. In this study, we developed a large diversity panel consisting of 1503 early-maturing soybean lines and used genome-wide association mapping to dissect the genetic architecture underlying two crucial adaptation traits, flowering time and maturity. This revealed several known maturity loci, E1, E2, E3, and E4, and the growth habit locus Dt2 as causal candidate loci, and also a novel putative causal locus, GmFRL1, encoding a homolog of the vernalization pathway gene FRIGIDA-like 1. In addition, the scan for quantitative trait locus (QTL)-by-environment interactions identified GmAPETALA1d as a candidate gene for a QTL with environment-dependent reversed allelic effects. The polymorphisms of these candidate genes were identified using whole-genome resequencing data of 338 soybeans, which also revealed a novel E4 variant, e4-par, carried by 11 lines, with nine of them originating from Central Europe. Collectively, our results illustrate how combinations of QTL and their interactions with the environment facilitate the photothermal adaptation of soybean to regions far beyond its center of origin.

The performance of phenomic selection depends on the genetic architecture of the target trait.

KEY MESSAGE: The phenomic predictive ability depends on the genetic architecture of the target trait, being high for complex traits and low for traits with major QTL. Genomic selection is a powerful tool to assist breeding of complex traits, but a limitation is the costs required for genotyping. Recently, phenomic selection has been suggested, which uses spectral data instead of molecular markers as predictors. It was shown to be competitive with genomic prediction, as it achieved predictive abilities as high or even higher than its genomic counterpart. The objective of this study was to evaluate the performance of phenomic prediction for triticale and the dependency of the predictive ability on the genetic architecture of the target trait. We found that for traits with a complex genetic architecture, like grain yield, phenomic prediction with NIRS data as predictors achieved high predictive abilities and performed better than genomic prediction. By contrast, for mono- or oligogenic traits, for example, yellow rust, marker-based approaches achieved high predictive abilities, while those of phenomic prediction were very low. Compared with molecular markers, the predictive ability obtained using NIRS data was more robust to varying degrees of genetic relatedness between the training and prediction set. Moreover, for grain yield, smaller training sets were required to achieve a similar predictive ability for phenomic prediction than for genomic prediction. In addition, our results illustrate the potential of using field-based spectral data for phenomic prediction. Overall, our result confirmed phenomic prediction as an efficient approach to improve the selection gain for complex traits in plant breeding.

Speed breeding short-day crops by LED-controlled light schemes.

KEY MESSAGE: A simple and rapid speed breeding system was developed for short-day crops that enables up to five generations per year using LED lighting systems that allow very specific adjustments regarding light intensity and quality. Plant breeding is a key element for future agricultural production that needs to cope with a growing human population and climate change. However, the process of developing suitable cultivars is time-consuming, not least because of the long generation times of crops. Recently, speed breeding has been introduced for long-day crops, but a similar protocol for short-day crops is lacking to date. In this study, we present a speed breeding protocol based on light-emitting diodes (LEDs) that allow to modify light quality, and exemplarily demonstrate its effectiveness for the short-day crops soybean (Glycine max), rice (Oryza sativa) and amaranth (Amaranthus spp.). Adjusting the photoperiod to 10 h and using a blue-light enriched, far-red-deprived light spectrum facilitated the growth of short and sturdy soybean plants that flowered ~ 23 days after sowing and matured within 77 days, thus allowing up to five generations per year. In rice and amaranth, flowering was achieved ~ 60 and ~ 35 days after sowing, respectively. Interestingly, the use of far-red light advanced flowering by 10 and 20 days in some amaranth and rice genotypes, respectively, but had no impact on flowering in soybeans, highlighting the importance of light quality for speed breeding protocols. Taken together, our short-day crops' speed breeding protocol enables several generations per year using crop-specific LED-based lighting regimes, without the need of tissue culture tools such as embryo rescue. Moreover, this approach can be readily applied to a multi-storey 96-cell tray-based system to integrate speed breeding with genomics, toward a higher improvement rate in breeding.

The soybean experiment '1000 Gardens': a case study of citizen science for research, education, and beyond.

KEY MESSAGE: Citizen science, an approach that includes normal citizens in scientific research, holds great potential also for plant sciences and breeding and can be a powerful research tool to complement traditional approaches. Citizen science is an approach that includes normal citizens in scientific research, but has so far not been exploited by the various disciplines in plant sciences. Moreover, global threats challenge human well-being and science can provide solutions, but needs to leave the ivory tower in the mind of the broader public. In 2016, we performed the '1000 Gardens-the soybean experiment' citizen science project, that aimed at finding citizens in Germany who would grow soybean lines in their own gardens and evaluate them for a range of traits related to adaptation and agronomic performance. Here, we describe details of this project, i.e. the recruitment, performance, and compliance of the citizen scientists. A total of 2492 citizen scientists volunteered for the project, but through the high media coverage a much broader audience than just the participants was reached. Our 1000 Gardens project was successful in collecting a scientifically unique data set with heritabilities ranging up to 0.60 for maturity date or 0.69 for plant height. Our results suggest that the citizen science approach holds great potential also for plant sciences and can be a powerful research tool to complement traditional approaches. Our project was also successful in raising public awareness about the importance of plant breeding and in communicating key messages on the manifold benefits of legumes for a sustainable agriculture to a broader public. Thus, citizen science appears as a promising avenue to demonstrate the value of breeding and science to the general public by including normal citizens in scientific research.

Cadmium concentrations in German soybeans are elevated in conurbations and in regions dominated by mining and the metal industry.

BACKGROUND: Cadmium (Cd) is a mobile heavy metal and soybeans accumulate more of this element compared to other grain crops. Because vegan and vegetarian lifestyles are increasingly popular, plant-based diets should be free from contaminants. Cd minimization has recently been identified as a breeding aim in addition to protein and oil levels. It is also important to consider the regional variability of Cd in crops. RESULTS: A large sample collection (n = 602) was subjected to Cd analyses. Soybeans were grown using a standard protocol by private gardeners in a citizen science program. Some 12.5% of samples exceeded European Union limits, although Cd concentrations and exceedances showed regional variation around the national mean (0.13 mg kg-1 ). Contamination was higher in densely populated regions, possibly as a result of the long-term emissions of heavy metal loaded dusts from industry, traffic and energy production. Cd concentrations were also elevated in regions once characterized by mining and heavy industry. Values were low overall in rural areas and agricultural regions despite potential inputs via phosphate fertilizers. CONCLUSION: Grain Cd levels are highly variable across Germany. Soybean farming may be problematic in regions with a long history of mining and heavy metal deposition. © 2018 Society of Chemical Industry.

Copy number variations of CBF genes at the Fr-A2 locus are essential components of winter hardiness in wheat.

Winter hardiness is important for the adaptation of wheat to the harsh winter conditions in temperate regions and is thus also an important breeding goal. Here, we employed a panel of 407 European winter wheat cultivars to dissect the genetic architecture of winter hardiness. We show that copy number variation (CNV) of CBF (C-repeat Binding Factor) genes at the Fr-A2 locus is the essential component for winter survival, with CBF-A14 CNV being the most likely causal polymorphism, accounting for 24.3% of the genotypic variance. Genome-wide association mapping identified several markers in the Fr-A2 chromosomal region, which even after accounting for the effects of CBF-A14 copy number explained approximately 15% of the genotypic variance. This suggests that additional, as yet undiscovered, polymorphisms are present at the Fr-A2 locus. Furthermore, CNV of Vrn-A1 explained an additional 3.0% of the genotypic variance. The allele frequencies of all loci associated with winter hardiness were found to show geographic patterns consistent with their role in adaptation. Collectively, our results from the candidate gene analysis, association mapping and genome-wide prediction show that winter hardiness in wheat is a quantitative trait, but with a major contribution of the Fr-A2 locus.

Identification of mega-environments in Europe and effect of allelic variation at maturity E loci on adaptation of European soybean.

Soybean cultivation holds great potential for a sustainable agriculture in Europe, but adaptation remains a central issue. In this large mega-environment (MEV) study, 75 European cultivars from five early maturity groups (MGs 000-II) were evaluated for maturity-related traits at 22 locations in 10 countries across Europe. Clustering of the locations based on phenotypic similarity revealed six MEVs in latitudinal direction and suggested several more. Analysis of maturity identified several groups of cultivars with phenotypic similarity that are optimally adapted to the different growing regions in Europe. We identified several haplotypes for the allelic variants at the E1, E2, E3 and E4 genes, with each E haplotype comprising cultivars from different MGs. Cultivars with the same E haplotype can exhibit different flowering and maturity characteristics, suggesting that the genetic control of these traits is more complex and that adaptation involves additional genetic pathways, for example temperature requirement. Taken together, our study allowed the first unified assessment of soybean-growing regions in Europe and illustrates the strong effect of photoperiod on soybean adaptation and MEV classification, as well as the effects of the E maturity loci for soybean adaptation in Europe.

Diversity analysis and genomic prediction of Sclerotinia resistance in sunflower using a new 25 K SNP genotyping array.

KEY MESSAGE: We have developed a SNP array for sunflower containing more than 25 K markers, representing single loci mostly in or near transcribed regions of the genome. The array was successfully applied to genotype a diversity panel of lines, hybrids, and mapping populations and represented well the genetic diversity of cultivated sunflower. Results of PCoA and population substructure analysis underlined the complexity of the genetic composition of current elite breeding material. The performance of this genotyping platform for genome-based prediction of phenotypes and detection of QTL with improved resolution could be demonstrated based on the re-evaluation of a population segregating for resistance to Sclerotinia midstalk rot. Given our results, the newly developed 25 K SNP array is expected to be of great utility for the most important applications in genome-based sunflower breeding and research. ABSTRACT: Genotyping with a large number of molecular markers is a prerequisite to conduct genome-based genetic analyses with high precision. Here, we report the design and performance of a 25 K SNP genotyping array for sunflower (Helianthus annuus L.). SNPs were discovered based on variant calling in de novo assembled, UniGene-based contigs of sunflower derived from whole genome sequencing and amplicon sequences originating from four and 48 inbred lines, respectively. After inclusion of publically available transcriptome-derived SNPs, in silico design of the Illumina(®) Infinium iSelect HD BeadChip yielded successful assays for 22,299 predominantly haplotype-specific SNPs. The array was validated in a sunflower diversity panel including inbred lines, open-pollinated varieties, introgression lines, landraces, recombinant inbred lines, and F2 populations. Validation provided 20,502 high-quality bi-allelic SNPs with stable cluster performance whereby each SNP marker represents a single locus mostly in or near transcribed regions of the sunflower genome. Analyses of population structure and quantitative resistance to Sclerotinia midstalk rot demonstrate that this array represents a significant improvement over currently available genomic tools for genetic diversity analyses, genome-wide marker-trait association studies, and genetic mapping in sunflower.

Genetic dynamics underlying phenotypic development of biomass yield in triticale.

BACKGROUND: The nature of dynamic traits with their phenotypic plasticity suggests that they are under the control of a dynamic genetic regulation. We employed a precision phenotyping platform to non-invasively assess biomass yield in a large mapping population of triticale at three developmental stages. RESULTS: Using multiple-line cross QTL mapping we identified QTL for each of these developmental stages which explained a considerable proportion of the genotypic variance. Some QTL were identified at each developmental stage and thus contribute to biomass yield throughout the studied developmental phases. Interestingly, we also observed QTL that were only identified for one or two of the developmental stages illustrating a temporal contribution of these QTL to the trait. In addition, epistatic QTL were detected and the epistatic interaction landscape was shown to dynamically change with developmental progression. CONCLUSIONS: In summary, our results reveal the temporal dynamics of the genetic architecture underlying biomass accumulation in triticale and emphasize the need for a temporal assessment of dynamic traits.

Multiple-line cross QTL mapping for biomass yield and plant height in triticale (× Triticosecale Wittmack).

KEY MESSAGE: QTL mapping in multiple families identifies trait-specific and pleiotropic QTL for biomass yield and plant height in triticale. Triticale shows a broad genetic variation for biomass yield which is of interest for a range of purposes, including bioenergy. Plant height is a major contributor to biomass yield and in this study, we investigated the genetic architecture underlying biomass yield and plant height by multiple-line cross QTL mapping. We employed 647 doubled haploid lines from four mapping populations that have been evaluated in four environments and genotyped with 1710 DArT markers. Twelve QTL were identified for plant height and nine for biomass yield which cross-validated explained 59.6 and 38.2 % of the genotypic variance, respectively. A major QTL for both traits was identified on chromosome 5R which likely corresponds to the dominant dwarfing gene Ddw1. In addition, we detected epistatic QTL for plant height and biomass yield which, however, contributed only little to the genetic architecture of the traits. In conclusion, our results demonstrate the potential of genomic approaches for a knowledge-based improvement of biomass yield in triticale.

Mapping dynamic QTL for plant height in triticale.

BACKGROUND: Plant height is a prime example of a dynamic trait that changes constantly throughout adult development. In this study we utilised a large triticale mapping population, comprising 647 doubled haploid lines derived from 4 families, to phenotype for plant height by a precision phenotyping platform at multiple time points. RESULTS: Using multiple-line cross QTL mapping we identified main effect and epistatic QTL for plant height for each of the time points. Interestingly, some QTL were detected at all time points whereas others were specific to particular developmental stages. Furthermore, the contribution of the QTL to the genotypic variance of plant height also varied with time as exemplified by a major QTL identified on chromosome 6A. CONCLUSIONS: Taken together, our results in the small grain cereal triticale reveal the importance of considering temporal genetic patterns in the regulation of complex traits such as plant height.

BSTA: a targeted approach combines bulked segregant analysis with next- generation sequencing and de novo transcriptome assembly for SNP discovery in sunflower.

BACKGROUND: Sunflower belongs to the largest plant family on earth, the genomically poorly explored Compositae. Downy mildew Plasmopara halstedii (Farlow) Berlese & de Toni is one of the major diseases of cultivated sunflower (Helianthus annuus L.). In the search for new sources of downy mildew resistance, the locus Pl(ARG)on linkage group 1 (LG1) originating from H. argophyllus is promising since it confers resistance against all known races of the pathogen. However, the mapping resolution in the Pl(ARG) region is hampered by significantly suppressed recombination and by limited availability of polymorphic markers. Here we examined a strategy developed for the enrichment of molecular markers linked to this specific genomic region. We combined bulked segregant analysis (BSA) with next-generation sequencing (NGS) and de novo assembly of the sunflower transcriptome for single nucleotide polymorphism (SNP) discovery in a sequence resource combining reads originating from two sunflower species, H. annuus and H. argophyllus. RESULTS: A computational pipeline developed for SNP calling and pattern detection identified 219 candidate genes. For a proof of concept, 42 resistance gene-like sequences were subjected to experimental SNP validation. Using a high-resolution mapping population, 12 SNP markers were mapped to LG1. We successfully verified candidate sequences either co-segregating with or closely flanking Pl(ARG). CONCLUSIONS: This study is the first successful example to improve bulked segregant analysis with de novo transcriptome assembly using next generation sequencing. The BSTA pipeline we developed provides a useful guide for similar studies in other non-model organisms. Our results demonstrate this method is an efficient way to enrich molecular markers and to identify candidate genes in a specific mapping interval.

BreedVision--a multi-sensor platform for non-destructive field-based phenotyping in plant breeding.

To achieve the food and energy security of an increasing World population likely to exceed nine billion by 2050 represents a major challenge for plant breeding. Our ability to measure traits under field conditions has improved little over the last decades and currently constitutes a major bottleneck in crop improvement. This work describes the development of a tractor-pulled multi-sensor phenotyping platform for small grain cereals with a focus on the technological development of the system. Various optical sensors like light curtain imaging, 3D Time-of-Flight cameras, laser distance sensors, hyperspectral imaging as well as color imaging are integrated into the system to collect spectral and morphological information of the plants. The study specifies: the mechanical design, the system architecture for data collection and data processing, the phenotyping procedure of the integrated system, results from field trials for data quality evaluation, as well as calibration results for plant height determination as a quantified example for a platform application. Repeated measurements were taken at three developmental stages of the plants in the years 2011 and 2012 employing triticale (×Triticosecale Wittmack L.) as a model species. The technical repeatability of measurement results was high for nearly all different types of sensors which confirmed the high suitability of the platform under field conditions. The developed platform constitutes a robust basis for the development and calibration of further sensor and multi-sensor fusion models to measure various agronomic traits like plant moisture content, lodging, tiller density or biomass yield, and thus, represents a major step towards widening the bottleneck of non-destructive phenotyping for crop improvement and plant genetic studies.

Do We Need to Breed for Regional Adaptation in Soybean?-Evaluation of Genotype-by-Location Interaction and Trait Stability of Soybean in Germany.

Soybean is a crop in high demand, in particular as a crucial source of plant protein. As a short-day plant, soybean is sensitive to the latitude of the growing site. Consequently, varieties that are well adapted to higher latitudes are required to expand the cultivation. In this study, we employed 50 soybean genotypes to perform a multi-location trial at seven locations across Germany in 2021. Two environmental target regions were determined following the latitude of the locations. Adaptation and trait stability of seed yield and protein content across all locations were evaluated using Genotype plus Genotype-by-Environment (GGE) biplots and Shukla's stability variance. We found a moderate level of crossing-over type genotype-by-location interaction across all locations. Within the environmental target regions, the genotype-by-location interaction could be minimised. Despite the positive correlation (R = 0.59) of seed yield between the environmental target regions and the same best-performing genotype, the genotype rankings differed in part substantially. In conclusion, we found that soybean can be grown at a wide range of latitudes across Germany. However, the performance of genotypes differed between the northern and southern locations, with an 18.8% higher mean yield in the south. This in combination with the observed rank changes of high-performing genotypes between both environmental target regions suggests that selection targeted towards environments in northern Germany could improve soybean breeding for those higher latitude regions.

Generation of high oleic acid sunflower lines using gamma radiation mutagenesis and high-throughput fatty acid profiling.

Sunflower (Helianthus annuus L.) is the second most important oil seed crop in Europe. The seeds are used as confection seeds and, more importantly, to generate an edible vegetable oil, which in normal varieties is rich in the polyunsaturated fatty acid linoleic acid. Linoleic acid is biosynthesized from oleic acid through activity of the oleate desaturase FATTY ACID DESATURASE 2 (FAD2), which in seeds is encoded by FAD2-1, a gene that's present in single copy in sunflowers. Defective FAD2-1 expression enriches oleic acid, yielding the high oleic (HO) acid trait, which is of great interest in oil seed crops, since HO oil bears benefits for both food and non-food applications. Chemical mutagenesis has previously been used to generate sunflower mutants with reduced FAD2-1 expression and here it was aimed to produce further genetic material in which FAD2-1 activity is lost and the HO trait is stably expressed. For this purpose, a sunflower mutant population was created using gamma irradiation and screened for fad2-1 mutants with a newly developed HPLC-based fatty-acid profiling system that's suitable for high-throughput analyses. With this approach fad2-1 knock-out mutants could be isolated, which stably hyper-accumulate oleic acid in concentrations of 85-90% of the total fatty acid pool. The genetic nature of these new sunflower lines was characterized and will facilitate marker development, for the rapid introgression of the trait into elite sunflower breeding material.

Genome-wide evaluation of genetic diversity and linkage disequilibrium in winter and spring triticale (x Triticosecale Wittmack).

BACKGROUND: Recent advances in genotyping with high-density markers nowadays enable genome-wide genomic analyses in crops. A detailed characterisation of the population structure and linkage disequilibrium (LD) is essential for the application of genomic approaches and consequently for knowledge-based breeding. In this study we used the triticale-specific DArT array to analyze population structure, genetic diversity, and LD in a worldwide set of 161 winter and spring triticale lines. RESULTS: The principal coordinate analysis revealed that the first principal coordinate divides the triticale population into two clusters according to their growth habit. The density distributions of the first ten principal coordinates revealed that several show a distribution indicative of population structure. In addition, we observed relatedness within growth habits which was higher among the spring types than among the winter types. The genome-wide analysis of polymorphic information content (PIC) showed that the PIC is variable among and along chromosomes and that especially the R genome of spring types possesses a reduced genetic diversity. We also found that several chromosomes showed regions of high genetic distance between the two growth habits, indicative of divergent selection. Regarding linkage disequilibrium, the A and B genomes showed a similar LD of 0.24 for closely linked markers and a decay within approximately 12 cM. LD in the R genome was lower with 0.19 and decayed within a shorter map distance of approximately 5 cM. The extent of LD was generally higher for the spring types compared to the winter types. In addition, we observed strong variability of LD along the chromosomes. CONCLUSIONS: Our results confirm winter and spring growth habit are the major contributors to population structure in triticale, and a family structure exists in both growth types. The specific patterns of genetic diversity observed within these types, such as the low diversity on some rye chromosomes of spring habits, provide a basis for targeted broadening of the available breeding germplasm. In addition, the genome-wide analysis of the extent and the pattern of LD will assist scientists and breeders alike in the implementation and the interpretation of association mapping in triticale.

Unraveling the potential of phenomic selection within and among diverse breeding material of maize (Zea mays L.).

Genomic selection is a well-investigated approach that facilitates and supports selection decisions for complex traits and has meanwhile become a standard tool in modern plant breeding. Phenomic selection has only recently been suggested and uses the same statistical procedures to predict the targeted traits but replaces marker data with near-infrared spectroscopy data. It may represent an attractive low-cost, high-throughput alternative but has not been sufficiently studied until now. Here, we used 400 genotypes of maize (Zea mays L.) comprising elite lines of the Flint and Dent heterotic pools as well as 6 Flint landraces, which were phenotyped in multienvironment trials for anthesis-silking-interval, early vigor, final plant height, grain dry matter content, grain yield, and phosphorus concentration in the maize kernels, to compare the predictive abilities of genomic as well as phenomic prediction under different scenarios. We found that both approaches generally achieved comparable predictive abilities within material groups. However, phenomic prediction was less affected by population structure and performed better than its genomic counterpart for predictions among diverse groups of breeding material. We therefore conclude that phenomic prediction is a promising tool for practical breeding, for instance when working with unknown and rather diverse germplasm. Moreover, it may make the highly monopolized sector of plant breeding more accessible also for low-tech institutions by combining well established, widely available, and cost-efficient spectral phenotyping with the statistical procedures elaborated for genomic prediction - while achieving similar or even better results than with marker data.

Can we abandon phosphorus starter fertilizer in maize? Results from a diverse panel of elite and doubled haploid landrace lines of maize (Zea mays L.).

The importance of phosphorus (P) in agriculture contrasts with the negative environmental impact and the limited resources worldwide. Reducing P fertilizer application by utilizing more efficient genotypes is a promising way to address these issues. To approach this, a large panel of maize (Zea mays L.) comprising each 100 Flint and Dent elite lines and 199 doubled haploid lines from six landraces was assessed in multi-environment field trials with and without the application of P starter fertilizer. The treatment comparison showed that omitting the starter fertilizer can significantly affect traits in early plant development but had no effect on grain yield. Young maize plants provided with additional P showed an increased biomass, faster growth and superior vigor, which, however, was only the case under environmental conditions considered stressful for maize cultivation. Importantly, though the genotype-by-treatment interaction variance was comparably small, there is genotypic variation for this response that can be utilized in breeding. The comparison of elite and doubled haploid landrace lines revealed a superior agronomic performance of elite material but also potentially valuable variation for early traits in the landrace doubled haploid lines. In conclusion, our results illustrate that breeding for P efficient maize cultivars is possible towards a reduction of P fertilizer in a more sustainable agriculture.

Detection of segregation distortion loci in triticale (x Triticosecale Wittmack) based on a high-density DArT marker consensus genetic linkage map.

BACKGROUND: Triticale is adapted to a wide range of abiotic stress conditions, is an important high-quality feed stock and produces similar grain yield but more biomass compared to other crops. Modern genomic approaches aimed at enhancing breeding progress in cereals require high-quality genetic linkage maps. Consensus maps are genetic maps that are created by a joint analysis of the data from several segregating populations and different approaches are available for their construction. The phenomenon that alleles at a locus deviate from the Mendelian expectation has been defined as segregation distortion. The study of segregation distortion is of particular interest in doubled haploid (DH) populations due to the selection pressure exerted on the plants during the process of their establishment. RESULTS: The final consensus map, constructed out of six segregating populations derived from nine parental lines, incorporated 2555 DArT markers mapped to 2602 loci (1929 unique). The map spanned 2309.9 cM with an average number of 123.9 loci per chromosome and an average marker density of one unique locus every 1.2 cM. The R genome showed the highest marker coverage followed by the B genome and the A genome. In general, locus order was well maintained between the consensus linkage map and the component maps. However, we observed several groups of loci for which the colinearity was slightly uneven. Among the 2602 loci mapped on the consensus map, 886 showed distorted segregation in at least one of the individual mapping populations. In several DH populations derived by androgenesis, we found chromosomes (2B, 3B, 1R, 2R, 4R and 7R) containing regions where markers exhibited a distorted segregation pattern. In addition, we observed evidence for segregation distortion between pairs of loci caused either by a predominance of parental or recombinant genotypes. CONCLUSIONS: We have constructed a reliable, high-density DArT marker consensus genetic linkage map as a basis for genomic approaches in triticale research and breeding, for example for multiple-line cross QTL mapping experiments. The results of our study exemplify the tremendous impact of different DH production techniques on allele frequencies and segregation distortion covering whole chromosomes.

Training set design in genomic prediction with multiple biparental families.

Genomic selection is a powerful tool to reduce the cycle length and enhance the genetic gain of complex traits in plant breeding. However, questions remain about the optimum design and composition of the training set. In this study, we used 944 soybean [Glycine max (L.) Merr.] recombinant inbred lines from eight families derived through a partial-diallel mating design among five parental lines. The cross-validated prediction accuracies for the six traits seed yield, 1,000-seed weight, protein yield, plant height, protein content, and oil content were high, ranging from 0.79 to 0.87. We investigated among-family predictions, making use of the special mating design with different degrees of relatedness among families. Generally, the prediction accuracy decreased from full-sibs to half-sib families to unrelated families. However, half-sib and unrelated families also showed substantial variation in their prediction accuracy for a given family, which appeared to be caused at least in part by the shared segregation of quantitative trait loci in both the training and prediction sets. Combining several half-sib families in composite training sets generally led to an increase in the prediction accuracy compared with the best family alone. The prediction accuracy increased with the size of the training set, but for comparable prediction accuracy, substantially more half-sibs were required than full-sibs. Collectively, our results highlight the potential of genomic selection for soybean breeding and, in a broader context, illustrate the importance of the targeted design of the training set.