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Introduction: In plant breeding, we often aim to improve multiple traits at once. However, without knowing the economic value of each trait, it is hard to decide which traits to focus on. This is where "desired gain selection indices" come in handy, which can yield optimal gains in each trait based on the breeder's prioritisation of desired improvements when economic weights are not available. However, they lack the ability to maximise the selection response and determine the correlation between the index and net genetic merit. Methods: Here, we report the development of an iterative desired gain selection index method that optimises the sampling of the desired gain values to achieve a targeted or a user-specified selection response for multiple traits. This targeted selection response can be constrained or unconstrained for either a subset or all the studied traits. Results: We tested the method using genomic estimated breeding values (GEBVs) for seven traits in a bread wheat (Triticum aestivum) reference breeding population comprising 3,331 lines and achieved prediction accuracies ranging between 0.29 and 0.47 across the seven traits. The indices were validated using 3,005 double haploid lines that were derived from crosses between parents selected from the reference population. We tested three user-specified response scenarios: a constrained equal weight (INDEX1), a constrained yield dominant weight (INDEX2), and an unconstrained weight (INDEX3). Our method achieved an equivalent response to the user-specified selection response when constraining a set of traits, and this response was much better than the response of the traditional desired gain selection indices method without iteration. Interestingly, when using unconstrained weight, our iterative method maximised the selection response and shifted the average GEBVs of the selection candidates towards the desired direction. Discussion: Our results show that the method is an optimal choice not only when economic weights are unavailable, but also when constraining the selection response is an unfavourable option. [ABSTRACT FROM AUTHOR]

Leaf rust caused by the pathogen Puccinia triticina (Pt) is a destructive fungal disease of wheat that occurs in almost all wheat-growing areas across the globe. Genetic resistance has proven to be the best solution to mitigate the disease. Wheat breeders are continuously seeking new diversified and durable sources of resistance to use in developing new varieties. We developed recombinant inbred line (RIL) populations from two leaf rust-resistant genotypes (Kenya Kudu and AUS12568) introduced from Kenya to identify and characterize resistance to Pt and to develop markers linked closely to the resistance that was found. Our studies detected four QTL conferring adult plant resistance (APR) to leaf rust. Two of these loci are associated with known genes, Lr46 and Lr68, residing on chromosomes 1B and 7B, respectively. The remaining two, QLrKK_2B and QLrAus12568_5A, contributed by Kenya Kudu and AUS12568 respectively, are putatively new loci for Pt resistance. Both QLrKK_2B and QLrAus12568_5A were found to interact additively with Lr46 in significantly reducing the disease severity at adult plant growth stages in the field. We further developed a suite of six closely linked markers within the QLrAus12568_5A locus and four within the QLrKK_2B region. Among these, markers sunKASP_522 and sunKASP_524, flanking QLrAus12568_5A, and sunKASP_536, distal to QLrKK_2B, were identified as the most closely linked and reliable for marker-assisted selection. The markers were validated on a selection of 64 Australian wheat varieties and found to be polymorphic and robust, allowing for clear allelic discrimination. The identified new loci and linked molecular markers will enable rapid adoption by breeders in developing wheat varieties carrying diversified and durable resistance to leaf rust. [ABSTRACT FROM AUTHOR]

BackgroundBread wheat is an allopolyploid species with a large, highly repetitive genome. To investigate the impact of selection on variants distributed among homoeologous wheat genomes and to build a foundation for understanding genotype-phenotype relationships, we performed population-scale re-sequencing of a diverse panel of wheat lines.ResultsA sample of 62 diverse lines was re-sequenced using the whole exome capture and genotyping-by-sequencing approaches. We describe the allele frequency, functional significance, and chromosomal distribution of 1.57 million single nucleotide polymorphisms and 161,719 small indels. Our results suggest that duplicated homoeologous genes are under purifying selection. We find contrasting patterns of variation and inter-variant associations among wheat genomes; this, in addition to demographic factors, could be explained by differences in the effect of directional selection on duplicated homoeologs. Only a small fraction of the homoeologous regions harboring selected variants overlapped among the wheat genomes in any given wheat line. These selected regions are enriched for loci associated with agronomic traits detected in genome-wide association studies.ConclusionsEvidence suggests that directional selection in allopolyploids rarely acted on multiple parallel advantageous mutations across homoeologous regions, likely indicating that a fitness benefit could be obtained by a mutation at any one of the homoeologs. Additional advantageous variants in other homoelogs probably either contributed little benefit, or were unavailable in populations subjected to directional selection. We hypothesize that allopolyploidy may have increased the likelihood of beneficial allele recovery by broadening the set of possible selection targets.

Crop growth models (CGM) can predict the performance of a cultivar in untested environments by sampling genotype-specific parameters. As they cannot predict the performance of new cultivars, it has been proposed to integrate CGMs with whole genome prediction (WGP) to combine the benefits of both models. Here, we used a CGM–WGP model to predict the performance of new wheat (Triticum aestivum) genotypes. The CGM was designed to predict phenology, nitrogen, and biomass traits. The CGM–WGP model simulated more heritable GSPs compared with the CGM and gave smaller errors for the observed phenotypes. The WGP model performed better when predicting yield, grain number, and grain protein content, but showed comparable performance to the CGM–WGP model for heading and physiological maturity dates. However, the CGM–WGP model was able to predict unobserved traits (for which there were no phenotypic records in the reference population). The CGM–WGP model also showed superior performance when predicting unrelated individuals that clustered separately from the reference population. Our results demonstrate new advantages for CGM–WGP modelling and suggest future efforts should focus on calibrating CGM–WGP models using high-throughput phenotypic measures that are cheaper and less laborious to collect. [ABSTRACT FROM AUTHOR]

Abstract Background Bread wheat is one of the world’s most important food crops and considerable efforts have been made to develop genomic resources for this species. This includes an on-going project by the International Wheat Genome Sequencing Consortium to assemble its large and complex genome, which is hexaploid and contains three closely related ‘homoeologous’ copies for each chromosome. This multi-national effort avoids the complications polyploidy entails for correct assembly of the genome by sequencing flow-sorted chromosome arms one at a time. Here we report on an alternate approach, a direct homoeolog-specific assembly of the expressed portion of the genome, the transcriptome. Results After assessment of the ability of various assemblers to generate homoeolog-specific assemblies, we employed a two-stage assembly process to produce a high-quality assembly of the transcriptome of hexaploid wheat from Roche-454 and Illumina GAIIx paired-end sequence reads. The assembly process made use of a rapid partitioning of expressed sequences into homoeologous clusters, followed by a parallel high-fidelity assembly of each cluster on a 1150-processor compute cloud. We assessed assembly quality through comparison to known wheat gene sequences and found that in ca. 98.5% of cases the assembly was sufficiently accurate for homoeologous triplets to be cleanly separated into either two or three separate contigs. Comparison to publicly available transcript collections suggests that the assembly covers ~75-80% of the complete transcriptome. Conclusions This work therefore describes the first homoeolog-specific sequence assembly of the wheat transcriptome and provides a reference transcriptome for future wheat research. Furthermore, our assembly methodology is transferable to other polyploid organisms.

Key message: A new leaf rust resistance gene Lr79 has been mapped in the long arm of chromosome 3B and a linked marker was identified for marker-assisted selection.Abstract: Aus26582, a durum wheat landrace from the A. E. Watkins Collection, showed seedling resistance against durum-specific and common wheat-specific Puccinia triticina (Pt) pathotypes. Genetic analysis using a recombinant inbred line (RIL) population developed from a cross between Aus26582 and the susceptible parent Bansi with Australian Pt pathotype showed digenic inheritance and the underlying loci were temporarily named LrAW2 and LrAW3. LrAW2 was located in chromosome 6BS and this study focused on characterisation of LrAW3 using RILs lacking LrAW2. LrAW3 was incorporated into the DArTseq map of Aus26582/Bansi and was located in chromosome 3BL. Markers linked with LrAW3 were developed from the chromosome survey sequence contig 3B_10474240 in which closely-linked DArTseq markers 1128708 and 3948563 were located. Although bulk segregant analysis (BSA) with the 90 K Infinium array identified 51 SNPs associated with LrAW3, only one SNP-derived KASP marker mapped close to the locus. Deletion bin mapping of LrAW3-linked markers located LrAW3 between bins 3BL11-0.85-0.90 and 3BL7-0.63. Since no other all stage leaf rust resistance gene is located in chromosome 3BL, LrAW3 represented a new locus and was designated Lr79. Marker sun786 mapped 1.8 cM distal to Lr79 and Aus26582 was null for this locus. However, the marker can be reliably scored as it also amplifies a monomorphic fragment that serves as an internal control to differentiate the null status of Aus26582 from reaction failure. This marker was validated among a set of durum and common wheat cultivars and was shown to be useful for marker-assisted selection of Lr79 at both ploidy levels. [ABSTRACT FROM AUTHOR]

Key Message: Imputing genotypes from the 90K SNP chip to exome sequence in wheat was moderately accurate. We investigated the factors that affect imputation and propose several strategies to improve accuracy. Imputing genetic marker genotypes from low to high density has been proposed as a cost-effective strategy to increase the power of downstream analyses (e.g. genome-wide association studies and genomic prediction) for a given budget. However, imputation is often imperfect and its accuracy depends on several factors. Here, we investigate the effects of reference population selection algorithms, marker density and imputation algorithms (Beagle4 and FImpute) on the accuracy of imputation from low SNP density (9K array) to the Infinium 90K single-nucleotide polymorphism (SNP) array for a collection of 837 hexaploid wheat Watkins landrace accessions. Based on these results, we then used the best performing reference selection and imputation algorithms to investigate imputation from 90K to exome sequence for a collection of 246 globally diverse wheat accessions. Accession-to-nearest-entry and genomic relationship-based methods were the best performing selection algorithms, and FImpute resulted in higher accuracy and was more efficient than Beagle4. The accuracy of imputing exome capture SNPs was comparable to imputing from 9 to 90K at approximately 0.71. This relatively low imputation accuracy is in part due to inconsistency between 90K and exome sequence formats. We also found the accuracy of imputation could be substantially improved to 0.82 when choosing an equivalent number of exome SNP, instead of 90K SNPs on the existing array, as the lower density set. We present a number of recommendations to increase the accuracy of exome imputation.