Phenomic and genomic prediction of yield on multiple locations in winter wheat.

Genomic selection has recently become an established part of breeding strategies in cereals. However, a limitation of linear genomic prediction models for complex traits such as yield is that these are unable to accommodate Genotype by Environment effects, which are commonly observed over trials on multiple locations. In this study, we investigated how this environmental variation can be captured by the collection of a large number of phenomic markers using high-throughput field phenotyping and whether it can increase GS prediction accuracy. For this purpose, 44 winter wheat (Triticum aestivum L.) elite populations, comprising 2,994 lines, were grown on two sites over 2 years, to approximate the size of trials in a practical breeding programme. At various growth stages, remote sensing data from multi- and hyperspectral cameras, as well as traditional ground-based visual crop assessment scores, were collected with approximately 100 different data variables collected per plot. The predictive power for grain yield was tested for the various data types, with or without genome-wide marker data sets. Models using phenomic traits alone had a greater predictive value (R2 = 0.39-0.47) than genomic data (approximately R2 = 0.1). The average improvement in predictive power by combining trait and marker data was 6%-12% over the best phenomic-only model, and performed best when data from one full location was used to predict the yield on an entire second location. The results suggest that genetic gain in breeding programmes can be increased by utilisation of large numbers of phenotypic variables using remote sensing in field trials, although at what stage of the breeding cycle phenomic selection could be most profitably applied remains to be answered.

Phasing and imputation of single nucleotide polymorphism data of missing parents of biparental plant populations.

This paper presents an extension to a heuristic method for phasing and imputation of genotypes of descendants in biparental populations so that it can phase and impute genotypes of parents that are ungenotyped or partially genotyped. The imputed genotypes of the parent are used to impute low-density (Ld) genotyped descendants to high density (Hd). The extension was implemented as part of the AlphaPlantImpute software and works in three steps. First, it identifies whether a parent has no or Ld genotypes and identifies its relatives that have Hd genotypes. Second, using the Hd genotypes of relatives, it determines whether the parent is homozygous or heterozygous for a given locus. Third, it phases heterozygous positions of the parent by matching haplotypes to its relatives. We measured the accuracy (correlation between true and imputed genotypes) of imputing parent genotypes in simulated biparental populations from different scenarios. We tested the imputation accuracy of the missing parent's descendants using the true genotype of the parent and compared this with using the imputed genotypes of the parent. Across all scenarios, the imputation accuracy of a parent was >0.98 and did not drop below ∼0.96. The imputation accuracy of a parent was always higher when it was inbred than outbred. Including ancestors of the parent at Hd, increasing the number of crosses and the number of Hd descendants increased the imputation accuracy. The high imputation accuracy achieved for the parent translated to little or no impact on the imputation accuracy of its descendants.

Septoria Leaf Blotch and Reduced Nitrogen Availability Alter WRKY Transcription Factor Expression in a Codependent Manner.

A major cause of yield loss in wheat worldwide is the fungal pathogen Zymoseptoria tritici, a hemibiotrophic fungus which causes Septoria leaf blotch, the most destructive wheat disease in Europe. Resistance in commercial wheat varieties is poor, however, a link between reduced nitrogen availability and increased Septoria tolerance has been observed. We have shown that Septoria load is not affected by nitrogen, whilst the fungus is in its first, symptomless stage of growth. This suggests that a link between nitrogen and Septoria is only present during the necrotrophic phase of Septoria infection. Quantitative real-time PCR data demonstrated that WRKYs, a superfamily of plant-specific transcription factors, are differentially expressed in response to both reduced nitrogen and Septoria. WRKY39 was downregulated over 30-fold in response to necrotrophic stage Septoria, whilst changes in the expression of WRKY68a during the late biotrophic phase were dependent on the concentration of nitrogen under which wheat is grown. WRKY68a may therefore mediate a link between nitrogen and Septoria. The potential remains to identify key regulators in the link between nitrogen and Septoria, and as such, elucidate molecular markers for wheat breeding, or targets for molecular-based breeding approaches.

Characterisation of barley landraces from Syria and Jordan for resistance to rhynchosporium and identification of diagnostic markers for Rrs1Rh4.

KEY MESSAGE: Diagnostic markers for Rrs1Rh4 have been identified by testing for associations between SNPs within the Rrs1 interval in 150 barley genotypes and their resistance to Rhynchosporium commune isolates recognised by lines containing Rrs1. Rhynchosporium or barley scald, caused by the destructive fungal pathogen Rhynchosporium commune, is one of the most economically important diseases of barley in the world. Barley landraces from Syria and Jordan demonstrated high resistance to rhynchosporium in the field. Genotyping of a wide range of barley cultivars and landraces, including known sources of different Rrs1 genes/alleles, across the Rrs1 interval, followed by association analysis of this genotypic data with resistance phenotypes to R. commune isolates recognised by Rrs1, allowed the identification of diagnostic markers for Rrs1Rh4. These markers are specific to Rrs1Rh4 and do not detect other Rrs1 genes/alleles. The Rrs1Rh4 diagnostic markers represent a resource that can be exploited by breeders for the sustainable deployment of varietal resistance in new cultivars. Thirteen out of the 55 most resistant Syrian and Jordanian landraces were shown to contain markers specific to Rrs1Rh4. One of these lines came from Jordan, with the remaining 12 lines from different locations in Syria. One of the Syrian landraces containing Rrs1Rh4 was also shown to have Rrs2. The remaining landraces that performed well against rhynchosporium in the field are likely to contain other resistance genes and represent an important novel resource yet to be exploited by European breeders.

The effects of training population design on genomic prediction accuracy in wheat.

Genomic selection offers several routes for increasing the genetic gain or efficiency of plant breeding programmes. In various species of livestock, there is empirical evidence of increased rates of genetic gain from the use of genomic selection to target different aspects of the breeder's equation. Accurate predictions of genomic breeding value are central to this, and the design of training sets is in turn central to achieving sufficient levels of accuracy. In summary, small numbers of close relatives and very large numbers of distant relatives are expected to enable predictions with higher accuracy. To quantify the effect of some of the properties of training sets on the accuracy of genomic selection in crops, we performed an extensive field-based winter wheat trial. In summary, this trial involved the construction of 44 F2:4 bi- and tri-parental populations, from which 2992 lines were grown on four field locations and yield was measured. For each line, genotype data were generated for 25 K segregating SNP markers. The overall heritability of yield was estimated to 0.65, and estimates within individual families ranged between 0.10 and 0.85. Genomic prediction accuracies of yield BLUEs were 0.125-0.127 using two different cross-validation approaches and generally increased with training set size. Using related crosses in training and validation sets generally resulted in higher prediction accuracies than using unrelated crosses. The results of this study emphasise the importance of the training panel design in relation to the genetic material to which the resulting prediction model is to be applied.

A heuristic method for fast and accurate phasing and imputation of single-nucleotide polymorphism data in bi-parental plant populations.

Key message New fast and accurate method for phasing and imputation of SNP chip genotypes within diploid bi-parental plant populations. This paper presents a new heuristic method for phasing and imputation of genomic data in diploid plant species. Our method, called AlphaPlantImpute, explicitly leverages features of plant breeding programmes to maximise the accuracy of imputation. The features are a small number of parents, which can be inbred and usually have high-density genomic data, and few recombinations separating parents and focal individuals genotyped at low density (i.e. descendants that are the imputation targets). AlphaPlantImpute works roughly in three steps. First, it identifies informative low-density genotype markers in parents. Second, it tracks the inheritance of parental alleles and haplotypes to focal individuals at informative markers. Finally, it uses this low-density information as anchor points to impute focal individuals to high density. We tested the imputation accuracy of AlphaPlantImpute in simulated bi-parental populations across different scenarios. We also compared its accuracy to existing software called PlantImpute. In general, AlphaPlantImpute had better or equal imputation accuracy as PlantImpute. The computational time and memory requirements of AlphaPlantImpute were tiny compared to PlantImpute. For example, accuracy of imputation was 0.96 for a scenario where both parents were inbred and genotyped at 25,000 markers per chromosome and a focal F2 individual was genotyped with 50 markers per chromosome. The maximum memory requirement for this scenario was 0.08 GB and took 37 s to complete.

Measuring Meiotic Crossovers via Multi-Locus Genotyping of Single Pollen Grains in Barley.

The detection of meiotic crossovers in crop plants currently relies on scoring DNA markers in a segregating population or cytological visualization. We investigated the feasibility of using flow-sorted haploid nuclei, Phi29 DNA polymerase-based whole-genome-amplification (WGA) and multi-locus KASP-genotyping to measure meiotic crossovers in individual barley pollen grains. To demonstrate the proof of concept, we used 24 gene-based physically mapped single nucleotide polymorphisms to genotype the WGA products of 50 single pollen nuclei. The number of crossovers per chromosome, recombination frequencies along chromosome 3H and segregation distortion were analysed and compared to a doubled haploid (DH) population of the same genotype. The number of crossovers and chromosome wide recombination frequencies show that this approach is able to produce results that resemble those obtained from other methods in a biologically meaningful way. Only the segregation distortion was found to be lower in the pollen population than in DH plants.

DNA profiling and characterization of animal cell lines.

The history of the culture of animal cell lines is littered with published and much unpublished experience with cell lines that have become switched, mislabelled, or cross-contaminated during laboratory handling. To deliver valid and good quality research and to avoid waste of time and resources on such rogue lines, it is vital to perform some kind of qualification for the provenance of cell lines used in research and particularly in the development of biomedical products. DNA profiling provides a valuable tool to compare different sources of the same cells and, where original material or tissue is available, to confirm the correct identity of a cell line. This chapter provides a review of some of the most useful techniques to test the identity of cells in the cell culture laboratory and gives methods which have been used in the authentication of cell lines.

Establishment of the 1st International Genetic Reference Panel for Factor V Leiden, human gDNA.

Forty-one laboratories participated in an international collaborative study to assess the suitability of a panel of three genomic DNA samples as the 1st International Genetic Reference Panel for the Factor V Leiden (FVL) variant. The code numbers of the materials were 03/254 (FV wild type), 03/260 (FVL homozygote) and 03/248 (FVL heterozygote). The participants evaluated the panel against their in-house controls which were known patient samples and commercial controls. In total, 859 genotype tests were carried out on the panel, with an error rate of 0.7%. The errors were not related to specific samples of the panel or to any specific techniques. The findings of this study have indicated that this panel is suitable to be used as a reference material for genotyping of factor V Leiden. It was therefore recommended that the three genomic DNA samples be established as the 1st International Genetic Reference Panel for Factor V Leiden, Human gDNA, 04/224. This recommendation was approved by the Scientific and Standardization Committee (SSC) of the ISTH (International Society on Thrombosis and Haemostasis) in June 2004 and the Expert Committee on Biological Standardization (ECBS) of the World Health Organization (WHO) in November 2004.