Statistical sampling of missing environmental variables improves biophysical genomic prediction in wheat

KEY MESSAGE

The integration of genomic prediction with crop growth models enabled the estimation of missing environmental variables which improved the prediction accuracy of grain yield. Since the invention of whole-genome prediction (WGP) more than two decades ago, breeding programmes have established extensive reference populations that are cultivated under diverse environmental conditions. The introduction of the CGM-WGP model, which integrates crop growth models (CGM) with WGP, has expanded the applications of WGP to the prediction of unphenotyped traits in untested environments, including future climates. However, CGMs require multiple seasonal environmental records, unlike WGP, which makes CGM-WGP less accurate when applied to historical reference populations that lack crucial environmental inputs. Here, we investigated the ability of CGM-WGP to approximate missing environmental variables to improve prediction accuracy. Two environmental variables in a wheat CGM, initial soil water content (InitlSoilWCont) and initial nitrate profile, were sampled from different normal distributions separately or jointly in each iteration within the CGM-WGP algorithm. Our results showed that sampling InitlSoilWCont alone gave the best results and improved the prediction accuracy of grain number by 0.07, yield by 0.06 and protein content by 0.03. When using the sampled InitlSoilWCont values as an input for the traditional CGM, the average narrow-sense heritability of the genotype-specific parameters (GSPs) improved by 0.05, with GNSlope, PreAnthRes, and VernSen showing the greatest improvements. Moreover, the root mean square of errors for grain number and yield was reduced by about 7% for CGM and 31% for CGM-WGP when using the sampled InitlSoilWCont values. Our results demonstrate the advantage of sampling missing environmental variables in CGM-WGP to improve prediction accuracy and increase the size of the reference population by enabling the utilisation of historical data that are missing environmental records.

Genomic selection for target traits in the Australian lentil breeding program

Genomic selection (GS) uses associations between markers and phenotypes to predict the breeding values of individuals. It can be applied early in the breeding cycle to reduce the cross-to-cross generation interval and thereby increase genetic gain per unit of time. The development of cost-effective, high-throughput genotyping platforms has revolutionized plant breeding programs by enabling the implementation of GS at the scale required to achieve impact. As a result, GS is becoming routine in plant breeding, even in minor crops such as pulses. Here we examined 2,081 breeding lines from Agriculture Victoria's national lentil breeding program for a range of target traits including grain yield, ascochyta blight resistance, botrytis grey mould resistance, salinity and boron stress tolerance, 100-grain weight, seed size index and protein content. A broad range of narrow-sense heritabilities was observed across these traits (0.24-0.66). Genomic prediction models were developed based on 64,781 genome-wide SNPs using Bayesian methodology and genomic estimated breeding values (GEBVs) were calculated. Forward cross-validation was applied to examine the prediction accuracy of GS for these targeted traits. The accuracy of GEBVs was consistently higher (0.34-0.83) than BLUP estimated breeding values (EBVs) (0.22-0.54), indicating a higher expected rate of genetic gain with GS. GS-led parental selection using early generation breeding materials also resulted in higher genetic gain compared to BLUP-based selection performed using later generation breeding lines. Our results show that implementing GS in lentil breeding will fast track the development of high-yielding cultivars with increased resistance to biotic and abiotic stresses, as well as improved seed quality traits.

Integrating biophysical crop growth models and whole genome prediction for their mutual benefit: a case study in wheat phenology

Running crop growth models (CGM) coupled with whole genome prediction (WGP) as a CGM-WGP model introduces environmental information to WGP and genomic relatedness information to the genotype-specific parameters modelled through CGMs. Previous studies have primarily used CGM-WGP to infer prediction accuracy without exploring its potential to enhance CGM and WGP. Here, we implemented a heading and maturity date wheat phenology model within a CGM-WGP framework and compared it with CGM and WGP. The CGM-WGP resulted in more heritable genotype-specific parameters with more biologically realistic correlation structures between genotype-specific parameters and phenology traits compared with CGM-modelled genotype-specific parameters that reflected the correlation of measured phenotypes. Another advantage of CGM-WGP is the ability to infer accurate prediction with much smaller and less diverse reference data compared with that required for CGM. A genome-wide association analysis linked the genotype-specific parameters from the CGM-WGP model to nine significant phenology loci including Vrn-A1 and the three PPD1 genes, which were not detected for CGM-modelled genotype-specific parameters. Selection on genotype-specific parameters could be simpler than on observed phenotypes. For example, thermal time traits are theoretically more independent candidates, compared with the highly correlated heading and maturity dates, which could be used to achieve an environment-specific optimal flowering period. CGM-WGP combines the advantages of CGM and WGP to predict more accurate phenotypes for new genotypes under alternative or future environmental conditions.

Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes

Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher throughput and nondestructive testing technologies, such as near-infrared (NIR), could enable early-stage testing and effective selection of these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on prediction accuracy in genomic best linear unbiased prediction (GBLUP) of adding NIR-predicted secondary traits for six end-product quality traits (crumb yellowness, water absorption, texture hardness, flour yield, grain protein, flour swelling volume). Bread wheat lines (1,400-1,900) were measured across 8 years (2012-2019) for six end-product quality traits with standard laboratory assays and with NIR, which were combined to generate predicted data for approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. End-product and NIR phenotypes were genetically correlated (0.5-0.83, except for flour swelling volume 0.19). Prediction accuracies of end-product traits ranged between 0.28 and 0.64 and increased by 30% through the inclusion of NIR-predicted data compared to single-trait analysis. There was a high correlation between the multi-trait prediction accuracy and genetic correlations between end-product and NIR-predicted data (0.69-0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracy at a level that enables selection for end-product quality traits early in the breeding cycle.

Genomic prediction and selection response for grain yield in safflower

In plant breeding programs, multiple traits are recorded in each trial, and the traits are often correlated. Correlated traits can be incorporated into genomic selection models, especially for traits with low heritability, to improve prediction accuracy. In this study, we investigated the genetic correlation between important agronomic traits in safflower. We observed the moderate genetic correlations between grain yield (GY) and plant height (PH, 0.272–0.531), and low correlations between grain yield and days to flowering (DF, −0.157–0.201). A 4%–20% prediction accuracy improvement for grain yield was achieved when plant height was included in both training and validation sets with multivariate models. We further explored the selection responses for grain yield by selecting the top 20% of lines based on different selection indices. Selection responses for grain yield varied across sites. Simultaneous selection for grain yield and seed oil content (OL) showed positive gains across all sites with equal weights for both grain yield and oil content. Combining g×E interaction into genomic selection (GS) led to more balanced selection responses across sites. In conclusion, genomic selection is a valuable breeding tool for breeding high grain yield, oil content, and highly adaptable safflower varieties.

Using genomic prediction with crop growth models enables the prediction of associated traits in wheat

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.

Benefit of Introgression Depends on Level of Genetic Trait Variation in Cereal Breeding Programmes

We investigated the benefit from introgression of external lines into a cereal breeding programme and strategies that accelerated introgression of the favourable alleles while minimising linkage drag using stochastic computer simulation. We simulated genomic selection for disease resistance and grain yield in two environments with a high level of genotype-by-environment interaction (G × E) for the latter trait, using genomic data of a historical barley breeding programme as the base generation. Two populations (existing and external) were created from this base population with different allele frequencies for few (N = 10) major and many (N ~ 990) minor simulated disease quantitative trait loci (QTL). The major disease QTL only existed in the external population and lines from the external population were introgressed into the existing population which had minor disease QTL with low, medium and high allele frequencies. The study revealed that the benefit of introgression depended on the level of genetic variation for the target trait in the existing cereal breeding programme. Introgression of external resources into the existing population was beneficial only when the existing population lacked variation in disease resistance or when minor disease QTL were already at medium or high frequency. When minor disease QTL were at low frequencies, no extra genetic gain was achieved from introgression. More benefit in the disease trait was obtained from the introgression if the major disease QTL had larger effect sizes, more selection emphasis was applied on disease resistance, or more external lines were introgressed. While our strategies to increase introgression of major disease QTL were generally successful, most were not able to completely avoid negative impacts on selection for grain yield with the only exception being when major introgression QTL effects were very large. Breeding programmes are advised to carefully consider the level of genetic variation in a trait available in their breeding programme before deciding to introgress germplasms.

Genome-wide association studies dissect the G × E interaction for agronomic traits in a worldwide collection of safflowers (Carthamus tinctorius L.)

Genome-wide association studies were conducted using a globally diverse safflower (Carthamus tinctorius L.) Genebank collection for grain yield (YP), days to flowering (DF), plant height (PH), 500 seed weight (SW), seed oil content (OL), and crude protein content (PR) in four environments (sites) that differed in water availability. Phenotypic variation was observed for all traits. YP exhibited low overall genetic correlations (rGoverall) across sites, while SW and OL had high rGoverall and high pairwise genetic correlations (rGij) across all pairwise sites. In total, 92 marker-trait associations (MTAs) were identified using three methods, single locus genome-wide association studies (GWAS) using a mixed linear model (MLM), the Bayesian multi-locus method (BayesR), and meta-GWAS. MTAs with large effects across all sites were detected for OL, SW, and PR, and MTAs specific for the different water stress sites were identified for all traits. Five MTAs were associated with multiple traits; 4 of 5 MTAs were variously associated with the three traits of SW, OL, and PR. This study provided insights into the phenotypic variability and genetic architecture of important safflower agronomic traits under different environments.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01295-8.

Genomic variants affecting homoeologous gene expression dosage contribute to agronomic trait variation in allopolyploid wheat

Allopolyploidy greatly expands the range of possible regulatory interactions among functionally redundant homoeologous genes. However, connection between the emerging regulatory complexity and expression and phenotypic diversity in polyploid crops remains elusive. Here, we use diverse wheat accessions to map expression quantitative trait loci (eQTL) and evaluate their effects on the population-scale variation in homoeolog expression dosage. The relative contribution of cis- and trans-eQTL to homoeolog expression variation is strongly affected by both selection and demographic events. Though trans-acting effects play major role in expression regulation, the expression dosage of homoeologs is largely influenced by cis-acting variants, which appear to be subjected to selection. The frequency and expression of homoeologous gene alleles showing strong expression dosage bias are predictive of variation in yield-related traits, and have likely been impacted by breeding for increased productivity. Our study highlights the importance of genomic variants affecting homoeolog expression dosage in shaping agronomic phenotypes and points at their potential utility for improving yield in polyploid crops.

Novel Design of Imputation-Enabled SNP Arrays for Breeding and Research Applications Supporting Multi-Species Hybridization

Array-based single nucleotide polymorphism (SNP) genotyping platforms have low genotype error and missing data rates compared to genotyping-by-sequencing technologies. However, design decisions used to create array-based SNP genotyping assays for both research and breeding applications are critical to their success. We describe a novel approach applicable to any animal or plant species for the design of cost-effective imputation-enabled SNP genotyping arrays with broad utility and demonstrate its application through the development of the Illumina Infinium Wheat Barley 40K SNP array Version 1.0. We show that the approach delivers high quality and high resolution data for wheat and barley, including when samples are jointly hybridised. The new array aims to maximally capture haplotypic diversity in globally diverse wheat and barley germplasm while minimizing ascertainment bias. Comprising mostly biallelic markers that were designed to be species-specific and single-copy, the array permits highly accurate imputation in diverse germplasm to improve the statistical power of genome-wide association studies (GWAS) and genomic selection. The SNP content captures tetraploid wheat (A- and B-genome) and Aegilops tauschii Coss. (D-genome) diversity and delineates synthetic and tetraploid wheat from other wheat, as well as tetraploid species and subgroups. The content includes SNP tagging key trait loci in wheat and barley, as well as direct connections to other genotyping platforms and legacy datasets. The utility of the array is enhanced through the web-based tool, Pretzel (https://plantinformatics.io/) which enables the content of the array to be visualized and interrogated interactively in the context of numerous genetic and genomic resources to be connected more seamlessly to research and breeding. The array is available for use by the international wheat and barley community.

Improving Selection Efficiency of Crop Breeding With Genomic Prediction Aided Sparse Phenotyping

Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a sparse phenotyping method that does not test all entries in all environments, but instead capitalizes on genomic prediction to predict missing phenotypes in additional environments without extra phenotyping expenditure. The breeders' main interest - response to selection - was directly simulated to evaluate the effectiveness of the sparse genomic phenotyping method in a wheat and a rice data set. Whether sparse phenotyping resulted in more selection response depended on the correlations of phenotypes between environments. The sparse phenotyping method consistently showed statistically significant higher responses to selection, compared to complete phenotyping, when the majority of completely phenotyped environments were negatively (wheat) or lowly positively (rice) correlated and any extension environment was highly positively correlated with any of the completely phenotyped environments. When all environments were positively correlated (wheat) or any highly positively correlated environments existed (wheat and rice), sparse phenotyping did not improved response. Our results indicate that genomics-based sparse phenotyping can improve selection response in the middle stages of crop breeding programs.

Genomic selection can accelerate the biofortification of spring wheat

Genomic selection enabled accurate prediction for the concentration of 13 nutritional element traits in wheat. Wheat biofortification is one of the most sustainable strategies to alleviate mineral deficiency in human diets. Here, we investigated the potential of genomic selection using BayesR and Bayesian ridge regression (BRR) models to predict grain yield (YLD) and the concentration of 13 nutritional elements in grains (B, Ca, Co, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P and Zn) using a population of 1470 spring wheat lines. The lines were grown in replicated field trials with two times of sowing (TOS) at 3 locations (Narrabri-NSW, all lines; Merredin-WA and Horsham-VIC, 200 core lines). Narrow-sense heritability across environments (locations/TOS) ranged from 0.09 to 0.45. Co, K, Na and Ca showed low to negative genetic correlations with other traits including YLD, while the remaining traits were negatively correlated with YLD. When all environments were included in the reference population, medium to high prediction accuracy was observed for the different traits across environments. BayesR had higher average prediction accuracy for mineral concentrations (r = 0.55) compared to BRR (r = 0.48) across all traits and environments but both methods had comparable accuracies for YLD. We also investigated the utility of one or two locations (reference locations) to predict the remaining location(s), as well as the ability of one TOS to predict the other. Under these scenarios, BayesR and BRR showed comparable performance but with lower prediction accuracy compared to the scenario of predicting reference environments for new lines. Our study demonstrates the potential of genomic selection for enriching wheat grain with nutritional elements in biofortification breeding.

Meta-analysis of genome-wide association studies reveal common loci controlling agronomic and quality traits in a wide range of normal and heat stressed environments

Several stable QTL were detected using metaGWAS analysis for different agronomic and quality traits under 26 normal and heat stressed environments. Heat stress, exacerbated by global warming, has a negative influence on wheat production worldwide and climate resilient cultivars can help mitigate these impacts. Selection decisions should therefore depend on multi-environment experiments representing a range of temperatures at critical stages of development. Here, we applied a meta-genome wide association analysis (metaGWAS) approach to detect stable QTL with significant effects across multiple environments. The metaGWAS was applied to 11 traits scored in 26 trials that were sown at optimal or late times of sowing (TOS1 and TOS2, respectively) at five locations. A total of 2571 unique wheat genotypes (13,959 genotypes across all environments) were included and the analysis conducted on TOS1, TOS2 and both times of sowing combined (TOS1&2). The germplasm was genotyped using a 90 k Infinium chip and imputed to exome sequence level, resulting in 341,195 single nucleotide polymorphisms (SNPs). The average accuracy across all imputed SNPs was high (92.4%). The three metaGWAS analyses revealed 107 QTL for the 11 traits, of which 16 were detected in all three analyses and 23 were detected in TOS1&2 only. The remaining QTL were detected in either TOS1 or TOS2 with or without TOS1&2, reflecting the complex interactions between the environments and the detected QTL. Eight QTL were associated with grain yield and seven with multiple traits. The identified QTL provide an important resource for gene enrichment and fine mapping to further understand the mechanisms of gene × environment interaction under both heat stressed and unstressed conditions.

Genomic prediction and genomic heritability of grain yield and its related traits in a safflower genebank collection

Safflower, a minor oilseed crop, is gaining increased attention for food and industrial uses. Safflower genebank collections are an important genetic resource for crop enhancement and future breeding programs. In this study, we investigated the population structure of a safflower collection sourced from the Australian Grain Genebank and assessed the potential of genomic prediction (GP) to evaluate grain yield and related traits using single and multi-site models. Prediction accuracies (PA) of genomic best linear unbiased prediction (GBLUP) from single site models ranged from 0.21 to 0.86 for all traits examined and were consistent with estimated genomic heritability (h² ), which varied from low to moderate across traits. We generally observed a low level of genome × environment interactions (g × E). Multi-site g × E GBLUP models only improved PA for accessions with at least some phenotypes in the training set. We observed that relaxing quality filtering parameters for genotype-by-sequencing (GBS), such as missing genotype call rate, did not affect PA but upwardly biased h² estimation. Our results indicate that GP is feasible in safflower evaluation and is potentially a cost-effective tool to facilitate fast introgression of desired safflower trait variation from genebank germplasm into breeding lines.

Fine mapping of root lesion nematode (Pratylenchus thornei) resistance loci on chromosomes 6D and 2B of wheat

Resistance QTL to root lesion nematode (Pratylenchus thornei) in wheat (Triticum aestivum), QRlnt.sk-6D and QRlnt.sk-2B, were mapped to intervals of 3.5 cM/1.77 Mbp on chromosome 6D and 1.4 cM/2.19 Mbp on chromosome 2B, respectively. Candidate resistance genes were identified in the QTL regions and molecular markers developed for marker-assisted breeding. Two previously known resistance QTL for root lesion nematode (Pratylenchus thornei) in bread wheat (Triticum aestivum), QRlnt.sk-6D and QRlnt.sk-2B, were fine-mapped using a Sokoll (moderately resistant) by Krichauff (susceptible) doubled haploid (DH) population and six newly developed recombinant inbred line populations. Bulked segregation analysis with the 90K wheat SNP array identified linked SNPs which were subsequently converted to KASP assays for mapping in the DH and RIL populations. On chromosome 6D, 60 KASP and five SSR markers spanned a total genetic distance of 23.7 cM. QRlnt.sk-6D was delimited to a 3.5 cM interval, representing 1.77 Mbp in the bread wheat cv. Chinese Spring reference genome sequence and 2.29 Mbp in the Aegilops tauschii genome sequence. These intervals contained 42 and 43 gene models in the respective annotated genome sequences. On chromosome 2B, 41 KASP and 5 SSR markers produced a map spanning 19.9 cM. QRlnt.sk-2B was delimited to 1.4 cM, corresponding 3.14 Mbp in the durum wheat cv. Svevo reference sequence and 2.19 Mbp in Chinese Spring. The interval in Chinese Spring contained 56 high-confidence gene models. Intervals for both QTL contained genes with similarity to those previously reported to be involved in disease resistance, namely genes for phenylpropanoid biosynthetic pathway-related enzymes, NBS-LRR proteins and protein kinases. The potential roles of these candidate genes in P. thornei resistance are discussed. The KASP markers reported in this study could potentially be used for marker-assisted breeding of P. thornei-resistant wheat cultivars.

The Global Durum Wheat Panel (GDP): An International Platform to Identify and Exchange Beneficial Alleles

Representative, broad and diverse collections are a primary resource to dissect genetic diversity and meet pre-breeding and breeding goals through the identification of beneficial alleles for target traits. From 2,500 tetraploid wheat accessions obtained through an international collaborative effort, a Global Durum wheat Panel (GDP) of 1,011 genotypes was assembled that captured 94-97% of the original diversity. The GDP consists of a wide representation of Triticum turgidum ssp. durum modern germplasm and landraces, along with a selection of emmer and primitive tetraploid wheats to maximize diversity. GDP accessions were genotyped using the wheat iSelect 90K SNP array. Among modern durum accessions, breeding programs from Italy, France and Central Asia provided the highest level of genetic diversity, with only a moderate decrease in genetic diversity observed across nearly 50 years of breeding (1970-2018). Further, the breeding programs from Europe had the largest sets of unique alleles. LD was lower in the landraces (0.4 Mbp) than in modern germplasm (1.8 Mbp) at r ² = 0.5. ADMIXTURE analysis of modern germplasm defined a minimum of 13 distinct genetic clusters (k), which could be traced to the breeding program of origin. Chromosome regions putatively subjected to strong selection pressure were identified from fixation index (F _st ) and diversity reduction index (DRI) metrics in pairwise comparisons among decades of release and breeding programs. Clusters of putative selection sweeps (PSW) were identified as co-localized with major loci controlling phenology (Ppd and Vrn), plant height (Rht) and quality (gliadins and glutenins), underlining the role of the corresponding genes as driving elements in modern breeding. Public seed availability and deep genetic characterization of the GDP make this collection a unique and ideal resource to identify and map useful genetic diversity at loci of interest to any breeding program.

Extension of a haplotype-based genomic prediction model to manage multi-environment wheat data using environmental covariates

A multi-environment genomic prediction model incorporating environmental covariates increased the prediction accuracy of wheat grain protein content. The advantage of the haplotype-based model was dependent upon the trait of interest. The inclusion of environment covariates (EC) in genomic prediction models has the potential to precisely model environmental effects and genotype-by-environment interactions. Together with EC, a haplotype-based genomic prediction approach, which is capable of accommodating the interaction between local epistasis and environment, may increase the prediction accuracy. The main objectives of our study were to evaluate the potential of EC to portray the relationship between environments and the relevance of local epistasis modelled by haplotype-based approaches in multi-environment prediction. The results showed that among five traits: grain yield (GY), plant height, protein content, screenings percentage (SP) and thousand kernel weight, protein content exhibited a 2.1% increase in prediction accuracy when EC was used to model the environmental relationship compared to treatment of the environment as a regular random effect without a variance-covariance structure. The approach used a Gaussian kernel to characterise the relationship among environments that displayed no advantage in contrast to the use of a genomic relationship matrix. The prediction accuracies of haplotype-based approaches for SP were consistently higher than the genotype-based model when the numbers of single-nucleotide polymorphisms (SNP) in a haplotype were from three to ten. In contrast, for GY, haplotype-based models outperformed genotype-based methods when two to four SNPs were used to construct the haplotype.

Thatcher wheat line RL6149 carries Lr64 and a second leaf rust resistance gene on chromosome 1DS

The leaf rust resistance gene Lr64 in the Thatcher wheat RL6149 was mapped to chromosome 6AL with SNP and KASP markers and a second leaf rust resistance gene was mapped to chromosome 1DS. RL6149, a near-isogenic line of Thatcher wheat, carries leaf rust resistance gene Lr64 on chromosome arm 6AL. The objective of this study was to develop molecular markers that can be easily used to select wheat lines with Lr64. RL6149 was crossed with Thatcher and F₂ plants derived from a single F₁ plant were advanced to F₆ lines by single seed descent. The 100 F₇ recombinant inbred lines (RIL) were inoculated with two races of P.triticina that differed widely for virulence in order to identify resistant and susceptible RIL. Thirty RIL that differed for resistance and the parental lines were genotyped with the 90 K Infinium iSelect single nucleotide polymorphism (SNP) array to find closely linked markers with Lr64. Seven linked SNPs on chromosome arm 6AL were converted into Kompetitive Allele Specific PCR (KASP) markers that were genotyped on the 100 RIL. A genetic linkage map for the seven KASP markers spanned 19.1 cM on chromosome arm 6AL. KASP marker K-IWB59855 was tightly linked to Lr64. A second unexpected gene for leaf rust resistance also segregated in the F₇ lines. Four KASP markers that spanned 18.6 cM located the gene on chromosome 1DS. The KASP marker K-IWB38437 was tightly linked to the second leaf rust resistance gene.

Artificial selection causes significant linkage disequilibrium among multiple unlinked genes in Australian wheat

Australia has one of the oldest modern wheat breeding programs worldwide although the crop was first introduced to the country in 1788. Breeders selected wheat with high adaptation to different Australian climates, while ensuring satisfactory yield and quality. This artificial selection left distinct genomic signatures that can be used to retrospectively understand breeding targets, and to detect economically important alleles. To study the effect of artificial selection on modern cultivars and cultivars released in different Australian states, we genotyped 482 Australian cultivars representing the history of wheat breeding in Australia since 1840. Computer simulation showed that 86 genomic regions were significantly affected by artificial selection. Characterization of 18 major genes known to affect wheat adaptation, yield, and quality revealed that many were affected by artificial selection and contained within regions under selection. Similarly, many reported QTL and genes for yield, quality, and adaptation were also contained in regions affected by artificial selection. These included TaCwi-A1, TaGw2-6A, Sus-2B, TaSus1-7A, TaSAP1-7A, Glu-A1, Glu-B1, Glu-B3, PinA, PinB, Ppo-D1, Psy-A1, Psy-A2, Rht-A1, Rht-B1, Ppd-D1, Vrn-A1, Vrn-B1, and Cre8. Interestingly, 17 regions affected by artificial selection were in moderate-to-high linkage disequilibrium with each other with an average r 2 value of 0.35 indicating strong simultaneous selection on specific alleles. These regions included Glu-B1, TaGw2-6A, Cre8, Ppd-D1, Rht-B1, Vrn-B1, TaSus1-7A, TaSAP1-7A, and Psy-A1 plus multiple QTL affecting wheat yield and yield components. These results highlighted the effects of the long-term artificial selection on Australian wheat germplasm and identified putative regions underlying important traits in wheat.

High-resolution mapping of rachis nodes per rachis, a critical determinant of grain yield components in wheat

Exploring large genomic data sets based on the latest reference genome assembly identifies the rice ortholog APO1 as a key candidate gene for number of rachis nodes per spike in wheat. Increasing grain yield in wheat is a key breeding objective worldwide. Several component traits contribute to grain yield with spike attributes being among the most important. In this study, we performed a genome-wide association analysis for 12 grain yield and component traits measured in field trials with contrasting agrochemical input levels in a panel of 220 hexaploid winter wheats. A highly significant, environmentally consistent QTL was detected for number of rachis nodes per rachis (NRN) on chromosome 7AL. The five most significant SNPs formed a strong linkage disequilibrium (LD) block and tagged a 2.23 Mb region. Using pairwise LD for exome SNPs located across this interval in a large worldwide hexaploid wheat collection, we reduced the genomic region for NRN to a 258 Kb interval containing four of the original SNP and six high-confidence genes. The ortholog of one (TraesCS7A01G481600) of these genes in rice was ABBERANT PANICLE ORGANIZATION1 (APO1), which is known to have significant effects on panicle attributes. The APO1 ortholog was the best candidate for NRN and was associated with a 115 bp promoter deletion and two amino acid (C47F and D384 N) changes. Using a large worldwide collection of tetraploid and hexaploid wheat, we found 12 haplotypes for the NRN QTL and evidence for positive enrichment of two haplotypes in modern germplasm. Comparison of five QTL haplotypes in Australian yield trials revealed their relative, context-dependent contribution to grain yield. Our study provides diagnostic SNPs and value propositions to support deployment of the NRN trait in wheat breeding.

Increase in coleoptile length and establishment by Lcol-A1, a genetic locus with major effect in wheat

BACKGROUND

Good establishment is important for rapid leaf area development in wheat crops. Poor establishment results in fewer, later-emerging plants, reduced leaf area and tiller number. In addition, poorly established crops suffer from increased soil moisture loss through evaporation and greater competition from weeds while fewer spikes are produced which can reduce grain yield. By protecting the emerging first leaf, the coleoptile is critical for achieving good establishment, and its length and interaction with soil physical properties determine the ability of a cultivar to emerge from depth.

RESULTS

Here we characterise a locus on chromosome 1AS, that increases coleoptile length in wheat, which we designate as Lcol-A1. We identified Lcol-A1 by bulked-segregant analysis and used a Halberd-derived population to fine map the gene to a 2 cM region, equivalent to 7 Mb on the IWGSC genome reference sequence of Chinese Spring (RefSeqv1.0). By sowing recently released cultivars and near-isogenic lines in the field at both conventional and deep sowing depths, we confirmed that Locl-A1 was associated with increased emergence from depth in the presence and absence of conventional dwarfing genes. Flanking markers IWB58229 and IWA710 were developed to assist breeders to select for long coleoptile wheats.

CONCLUSIONS

Increased coleoptile length is sought in many global wheat production areas to improve crop emergence. The identification of the gene Lcol-A1, together with tools to allow wheat breeders to track the gene, will enable improvements to be made for this important trait.

Breeding improves wheat productivity under contrasting agrochemical input levels

The world cropping area for wheat exceeds that of any other crop, and high grain yields in intensive wheat cropping systems are essential for global food security. Breeding has raised yields dramatically in high-input production systems; however, selection under optimal growth conditions is widely believed to diminish the adaptive capacity of cultivars to less optimal cropping environments. Here, we demonstrate, in a large-scale study spanning five decades of wheat breeding progress in western Europe, where grain yields are among the highest worldwide, that breeding for high performance in fact enhances cultivar performance not only under optimal production conditions but also in production systems with reduced agrochemical inputs. New cultivars incrementally accumulated genetic variants conferring favourable effects on key yield parameters, disease resistance, nutrient use efficiency, photosynthetic efficiency and grain quality. Combining beneficial, genome-wide haplotypes could help breeders to more efficiently exploit available genetic variation, optimizing future yield potential in more sustainable production systems.

Breeding improves wheat productivity under contrasting agrochemical input levels

The world cropping area for wheat exceeds that of any other crop, and high grain yields in intensive wheat cropping systems are essential for global food security. Breeding has raised yields dramatically in high-input production systems; however, selection under optimal growth conditions is widely believed to diminish the adaptive capacity of cultivars to less optimal cropping environments. Here, we demonstrate, in a large-scale study spanning five decades of wheat breeding progress in western Europe, where grain yields are among the highest worldwide, that breeding for high performance in fact enhances cultivar performance not only under optimal production conditions but also in production systems with reduced agrochemical inputs. New cultivars incrementally accumulated genetic variants conferring favourable effects on key yield parameters, disease resistance, nutrient use efficiency, photosynthetic efficiency and grain quality. Combining beneficial, genome-wide haplotypes could help breeders to more efficiently exploit available genetic variation, optimizing future yield potential in more sustainable production systems.

Mesophyll Protoplasts and PEG-Mediated Transfections: Transient Assays and Generation of Stable Transgenic Canola Plants

Plant protoplasts are derived by controlled enzymatic digestion that removes the plant cell wall without damaging the cell membrane. Protoplasts represent a true single-cell system and are useful for various biochemical and physiological studies. Protoplasts from several agriculturally important crop species can be regenerated into a fertile whole plant, extending the utility of protoplasts from transient expression assays to the generation of stable transformation events. Here we describe procedures for transient and stable transformation of leaf mesophyll protoplasts obtained from axenic shoot cultures of canola (Brassica napus). Key steps including enzymatic digestion for protoplast release, density gradient-based protoplast purification, PEG-mediated transfection, bead-type culturing (sea-plaque agarose and sodium alginate), and the recovery of putative transgenic canola plants are described. This method has been used for double-stranded DNA break-mediated genome editing and for the routine generation of stable transgenic canola events at commercial scale.

Fine Mapping of Lr49 Using 90K SNP Chip Array and Flow-Sorted Chromosome Sequencing in Wheat

Leaf rust, caused by Puccinia triticina, threatens global wheat production due to the constant evolution of virulent pathotypes that defeat commercially deployed all stage-resistance (ASR) genes in modern cultivars. Hence, the deployment of combinations of adult plant resistance (APR) and ASR genes in new wheat cultivars is desirable. Adult plant resistance gene Lr49 was previously mapped on the long arm of chromosome 4B of cultivar VL404 and flanked by microsatellite markers barc163 (8.1 cM) and wmc349 (10.1 cM), neither of which was sufficiently closely linked for efficient marker assisted selection. This study used high-density SNP genotyping and flow sorted chromosome sequencing to fine-map the Lr49 locus as a starting point to develop a diagnostic marker for use in breeding and to clone this gene. Marker sunKASP_21 was mapped 0.4 cM proximal to Lr49, whereas a group of markers including sunKASP_24 were placed 0.6 cM distal to this gene. Testing of the linked markers on 75 Australian and 90 European cultivars with diverse genetic backgrounds showed that sunKASP_21 was most strongly associated with Lr49. Our results also show that the Lr49 genomic region contains structural variation relative to the reference stock Chinese Spring, possibly an inverted genomic duplication, which introduces a new set of challenges for the Lr49 cloning.

Zinc finger nuclease-mediated precision genome editing of an endogenous gene in hexaploid bread wheat (Triticum aestivum) using a DNA repair template

Sequence-specific nucleases have been used to engineer targeted genome modifications in various plants. While targeted gene knockouts resulting in loss of function have been reported with relatively high rates of success, targeted gene editing using an exogenously supplied DNA repair template and site-specific transgene integration has been more challenging. Here, we report the first application of zinc finger nuclease (ZFN)-mediated, nonhomologous end-joining (NHEJ)-directed editing of a native gene in allohexaploid bread wheat to introduce, via a supplied DNA repair template, a specific single amino acid change into the coding sequence of acetohydroxyacid synthase (AHAS) to confer resistance to imidazolinone herbicides. We recovered edited wheat plants having the targeted amino acid modification in one or more AHAS homoalleles via direct selection for resistance to imazamox, an AHAS-inhibiting imidazolinone herbicide. Using a cotransformation strategy based on chemical selection for an exogenous marker, we achieved a 1.2% recovery rate of edited plants having the desired amino acid change and a 2.9% recovery of plants with targeted mutations at the AHAS locus resulting in a loss-of-function gene knockout. The latter results demonstrate a broadly applicable approach to introduce targeted modifications into native genes for nonselectable traits. All ZFN-mediated changes were faithfully transmitted to the next generation.

Characterisation and mapping of adult plant stripe rust resistance in wheat accession Aus27284

A new adult plant stripe rust resistance gene, Yr80, was identified in a common wheat landrace Aus27284. Linked markers were developed and validated for their utility in marker-assisted selection. Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is among the most important constraints to global wheat production. The identification and characterisation of new sources of host plant resistance enrich the gene pool and underpin deployment of resistance gene pyramids in new cultivars. Aus27284 exhibited resistance at the adult plant stage against predominant Pst pathotypes and was crossed with a susceptible genotype Avocet S. A recombinant inbred line (RIL) population comprising 121 lines was developed and tested in the field at three locations in 2016 and two in 2017 crop seasons. Monogenic segregation for adult plant stripe rust response was observed among the Aus27284/Avocet S RIL population and the underlying locus was temporarily designated YrAW11. Bulked-segregant analysis using the Infinium iSelect 90K SNP wheat array placed YrAW11 in chromosome 3B. Kompetitive allele specific PCR (KASP) primers were designed for the linked SNPs and YrAW11 was flanked by KASP_65624 and KASP_53292 (3 cM) proximally and KASP_53113 (4.9 cM) distally. A partial linkage map of the genomic region carrying YrAW11 comprised nine KASP and two SSR markers. The physical position of KASP markers in the pseudomolecule of chromosome 3B placed YrAW11 in the long arm and the location of markers gwm108 and gwm376 in the deletion bin 3BL2-0.22 supported this conclusion. As no other stripe rust resistance locus has been reported in chromosome 3BL, YrAW11 was formally designated Yr80. Marker KASP_ 53113 was polymorphic among 94% of 81 Australian wheat cultivars used for validation.

Zebularine treatment is associated with deletion of FT-B1 leading to an increase in spikelet number in bread wheat

The number of rachis nodes (spikelets) on a wheat spike is a component of grain yield that correlates with flowering time. The genetic basis regulating flowering in cereals is well understood, but there are reports that flowering time can be modified at a high frequency by selective breeding, suggesting that it may be regulated by both epigenetic and genetic mechanisms. We investigated the role of DNA methylation in regulating spikelet number and flowering time by treating a semi-spring wheat with the demethylating agent, Zebularine. Three lines with a heritable increase in spikelet number were identified. The molecular basis for increased spikelet number was not determined in 2 lines, but the phenotype showed non-Mendelian inheritance, suggesting that it could have an epigenetic basis. In the remaining line, the increased spikelet phenotype behaved as a Mendelian recessive trait and late flowering was associated with a deletion encompassing the floral promoter, FT-B1. Deletion of FT-B1 delayed the transition to reproductive growth, extended the duration of spike development, and increased spikelet number under different temperature regimes and photoperiod. Transiently disrupting DNA methylation can generate novel flowering behaviour in wheat, but these changes may not be sufficiently stable for use in breeding programs.

Adult Plant Leaf Rust Resistance Derived from Toropi Wheat is Conditioned by Lr78 and Three Minor QTL

Leaf rust caused by Puccinia triticina is an important disease of wheat in many regions worldwide. Durable or long-lasting leaf rust resistance has been difficult to achieve because populations of P. triticina are highly variable for virulence to race-specific resistance genes, and respond to selection by resistance genes in released wheat cultivars. The wheat cultivar Toropi, developed and grown in Brazil, was noted to have long-lasting leaf rust resistance that was effective only in adult plants. The objectives of this study were to determine the chromosome location of the leaf rust resistance genes derived from Toropi in two populations of recombinant inbred lines in a partial Thatcher wheat background. In the first population, a single gene with major effects on chromosome 5DS that mapped 2.2 centimorgans distal to IWA6289, strongly reduced leaf rust severity in all 3 years of field plot tests. This gene for adult plant leaf rust resistance was designated as Lr78. In the second population, quantitative trait loci (QTL) with small effects on chromosomes 1BL, 3BS, and 4BS were found. These QTL expressed inconsistently over 4 years of field plot tests. The adult plant leaf rust resistance derived from Toropi involved a complex combination of QTL with large and small effects.

Genetic Relationship of Stripe Rust Resistance Genes Yr34 and Yr48 in Wheat and Identification of Linked KASP Markers

The Australian continent was free from wheat stripe rust caused by Puccinia striiformis f. sp. tritici until exotic incursions occurred in 1979 and 2002. The 2002 incursion enabled the identification of a new stripe rust resistance gene (Yr34) in the advanced breeding line WAWHT2046. In this study, we developed and validated markers closely linked with Yr34, which is located in the distal region in the long arm of chromosome 5A. Four kompetitive allele-specific polymerase chain reaction (KASP) and three sequence-tagged site (STS) markers derived from the International Wheat Genome Sequencing Consortium RefSeq v1.0 scaffold-77836 cosegregated with Yr34. Markers sun711, sun712, sun725, sunKASP_109, and sunKASP_112 were shown to be suitable for marker-assisted selection in a validation panel of 71 Australian spring wheat genotypes, with the exception of cultivar Orion that carried the Yr34-linked alleles for sunKASP_109 and sunKASP_112. Markers previously reported to be linked with adult plant stripe rust resistance gene Yr48 also cosegregated with Yr34. Wheat genotypes carrying Yr34 and Yr48 produced identical haplotypes for the Yr34-linked markers identified in this study and those previously reported to be linked with Yr48. Phenotypic testing of genotypes carrying Yr34 and Yr48 showed that both genes conferred similar seedling responses to pre-2002 and post-2002 P. striiformis f. sp. tritici pathotypes. Further testing of 600 F₂ plants from a cross between WAWHT2046 and RIL143 (Yr48) with P. striiformis f. sp. tritici pathotype 134 E16A+Yr17+Yr27+ failed to reveal any susceptible segregants. Our results strongly suggest that Yr34 and Yr48 are the same gene, and that Yr48 should be considered a synonym of Yr34.

Accelerating wheat breeding for end-use quality with multi-trait genomic predictions incorporating near infrared and nuclear magnetic resonance-derived phenotypes

Using NIR and NMR predictions of quality traits overcomes a major barrier for the application of genomic selection to accelerate improvement in grain end-use quality traits of wheat. Grain end-use quality traits are among the most important in wheat breeding. These traits are difficult to breed for, as their assays require flour quantities only obtainable late in the breeding cycle, and are expensive. These traits are therefore an ideal target for genomic selection. However, large reference populations are required for accurate genomic predictions, which are challenging to assemble for these traits for the same reasons they are challenging to breed for. Here, we use predictions of end-use quality derived from near infrared (NIR) or nuclear magnetic resonance (NMR), that require very small amounts of flour, as well as end-use quality measured by industry standard assay in a subset of accessions, in a multi-trait approach for genomic prediction. The NIR and NMR predictions were derived for 19 end-use quality traits in 398 accessions, and were then assayed in 2420 diverse wheat accessions. The accessions were grown out in multiple locations and multiple years, and were genotyped for 51208 SNP. Incorporating NIR and NMR phenotypes in the multi-trait approach increased the accuracy of genomic prediction for most quality traits. The accuracy ranged from 0 to 0.47 before the addition of the NIR/NMR data, while after these data were added, it ranged from 0 to 0.69. Genomic predictions were reasonably robust across locations and years for most traits. Using NIR and NMR predictions of quality traits overcomes a major barrier for the application of genomic selection for grain end-use quality traits in wheat breeding.

Haplotype Analysis of the Pre-harvest Sprouting Resistance Locus Phs-A1 Reveals a Causal Role of TaMKK3-A in Global Germplasm

Pre-harvest sprouting (PHS) is an important cause of quality loss in many cereal crops and is particularly prevalent and damaging in wheat. Resistance to PHS is therefore a valuable target trait in many breeding programs. The Phs-A1 locus on wheat chromosome arm 4AL has been consistently shown to account for a significant proportion of natural variation to PHS in diverse mapping populations. However, the deployment of sprouting resistance is confounded by the fact that different candidate genes, including the tandem duplicated Plasma Membrane 19 (PM19) genes and the mitogen-activated protein kinase kinase 3 (TaMKK3-A) gene, have been proposed to underlie Phs-A1. To further define the Phs-A1 locus, we constructed a physical map across this interval in hexaploid and tetraploid wheat. We established close proximity of the proposed candidate genes which are located within a 1.2 Mb interval. Genetic characterization of diverse germplasm used in previous genetic mapping studies suggests that TaMKK3-A, and not PM19, is the major gene underlying the Phs-A1 effect in European, North American, Australian and Asian germplasm. We identified the non-dormant TaMKK3-A allele at low frequencies within the A-genome diploid progenitor Triticum urartu genepool, and show an increase in the allele frequency in modern varieties. In United Kingdom varieties, the frequency of the dormant TaMKK3-A allele was significantly higher in bread-making quality varieties compared to feed and biscuit-making cultivars. Analysis of exome capture data from 58 diverse hexaploid wheat accessions identified fourteen haplotypes across the extended Phs-A1 locus and four haplotypes for TaMKK3-A. Analysis of these haplotypes in a collection of United Kingdom and Australian cultivars revealed distinct major dormant and non-dormant Phs-A1 haplotypes in each country, which were either rare or absent in the opposing germplasm set. The diagnostic markers and haplotype information reported in the study will help inform the choice of germplasm and breeding strategies for the deployment of Phs-A1 resistance into breeding germplasm.

Exome sequence genotype imputation in globally diverse hexaploid wheat accessions

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.

Detection and validation of genomic regions associated with resistance to rust diseases in a worldwide hexaploid wheat landrace collection using BayesR and mixed linear model approaches

BayesR and MLM association mapping approaches in common wheat landraces were used to identify genomic regions conferring resistance to Yr, Lr, and Sr diseases. Deployment of rust resistant cultivars is the most economically effective and environmentally friendly strategy to control rust diseases in wheat. However, the highly evolving nature of wheat rust pathogens demands continued identification, characterization, and transfer of new resistance alleles into new varieties to achieve durable rust control. In this study, we undertook genome-wide association studies (GWAS) using a mixed linear model (MLM) and the Bayesian multilocus method (BayesR) to identify QTL contributing to leaf rust (Lr), stem rust (Sr), and stripe rust (Yr) resistance. Our study included 676 pre-Green Revolution common wheat landrace accessions collected in the 1920-1930s by A.E. Watkins. We show that both methods produce similar results, although BayesR had reduced background signals, enabling clearer definition of QTL positions. For the three rust diseases, we found 5 (Lr), 14 (Yr), and 11 (Sr) SNPs significant in both methods above stringent false-discovery rate thresholds. Validation of marker-trait associations with known rust QTL from the literature and additional genotypic and phenotypic characterisation of biparental populations showed that the landraces harbour both previously mapped and potentially new genes for resistance to rust diseases. Our results demonstrate that pre-Green Revolution landraces provide a rich source of genes to increase genetic diversity for rust resistance to facilitate the development of wheat varieties with more durable rust resistance.

Genetic Diversity, Population Structure and Ancestral Origin of Australian Wheat

Since the introduction of wheat into Australia by the First Fleet settlers, germplasm from different geographical origins has been used to adapt wheat to the Australian climate through selection and breeding. In this paper, we used 482 cultivars, representing the breeding history of bread wheat in Australia since 1840, to characterize their diversity and population structure and to define the geographical ancestral background of Australian wheat germplasm. This was achieved by comparing them to a global wheat collection using in-silico chromosome painting based on SNP genotyping. The global collection involved 2,335 wheat accessions which was divided into 23 different geographical subpopulations. However, the whole set was reduced to 1,544 accessions to increase the differentiation and decrease the admixture among different global subpopulations to increase the power of the painting analysis. Our analysis revealed that the structure of Australian wheat germplasm and its geographic ancestors have changed significantly through time, especially after the Green Revolution. Before 1920, breeders used cultivars from around the world, but mainly Europe and Africa, to select potential cultivars that could tolerate Australian growing conditions. Between 1921 and 1970, a dependence on African wheat germplasm became more prevalent. Since 1970, a heavy reliance on International Maize and Wheat Improvement Center (CIMMYT) germplasm has persisted. Combining the results from linkage disequilibrium, population structure and in-silico painting revealed that the dependence on CIMMYT materials has varied among different Australian States, has shrunken the germplasm effective population size and produced larger linkage disequilibrium blocks. This study documents the evolutionary history of wheat breeding in Australia and provides an understanding for how the wheat genome has been adapted to local growing conditions. This information provides a guide for industry to assist with maintaining genetic diversity for long-term selection gains and to plan future breeding programs.

Fast-forward genetics by radiation hybrids to saturate the locus regulating nuclear-cytoplasmic compatibility in Triticum

The nuclear-encoded species cytoplasm specific (scs) genes control nuclear-cytoplasmic compatibility in wheat (genus Triticum). Alloplasmic cells, which have nucleus and cytoplasm derived from different species, produce vigorous and vital organisms only when the correct version of scs is present in their nucleus. In this study, bulks of in vivo radiation hybrids segregating for the scs phenotype have been genotyped by sequencing with over 1.9 million markers. The high marker saturation obtained for a critical region of chromosome 1D allowed identification of 3318 reads that mapped in close proximity of the scs. A novel in silico approach was deployed to extend these short reads to sequences of up to 70 Kb in length and identify candidate open reading frames (ORFs). Markers were developed to anchor the short contigs containing ORFs to a radiation hybrid map of 650 individuals with resolution of 288 Kb. The region containing the scs locus was narrowed to a single Bacterial Artificial Chromosome (BAC) contig of Aegilops tauschii. Its sequencing and assembly by nano-mapping allowed rapid identification of a rhomboid gene as the only ORF existing within the refined scs locus. Resequencing of this gene from multiple germplasm sources identified a single nucleotide mutation, which gives rise to a functional amino acid change. Gene expression characterization revealed that an active copy of this rhomboid exists on all homoeologous chromosomes of wheat, and depending on the specific cytoplasm each copy is preferentially expressed. Therefore, a new methodology was applied to unique genetic stocks to rapidly identify a strong candidate gene for the control of nuclear-cytoplasmic compatibility in wheat.

Mapping of a new stem rust resistance gene Sr49 in chromosome 5B of wheat

A new stem rust resistance gene Sr49 was mapped to chromosome 5BL of wheat. Usefulness of the closely linked markers sun209 and sun479 for marker-assisted selection of Sr49 was demonstrated. Landrace AUS28011 (Mahmoudi), collected from Ghardimaou, Tunisia, produced low stem rust response against Australian pathotypes of Puccinia graminis f. sp. tritici (Pgt) carrying virulence for several stem rust resistance genes deployed in modern wheat cultivars. Genetic analysis based on a Mahmoudi/Yitpi F3 population indicated the involvement of a single all-stage stem rust resistance gene and it was temporarily named SrM. Bulked segregant analysis using multiplex-ready SSR technology located SrM on the long arm of chromosome 5B. Since there is no other all-stage stem rust resistance gene located in chromosome 5BL, SrM was permanently designated Sr49. The Mahmoudi/Yitpi F3 population was enhanced to generate F6 recombinant inbred line (RIL) population for detailed mapping of Sr49 using publicly available genomic resources. Markers sun209 and sun479 flanked Sr49 at 1.5 and 0.9 cM distally and proximally, respectively. Markers sun209 and sun479 amplified PCR products different than the Sr49-linked alleles in 146 and 145 common wheat cultivars, respectively. Six and seven cultivars, respectively, carried the resistance-linked marker alleles sun209 148bp and sun479 200bp ; however, none of the cultivars carried both resistance-linked alleles. These results demonstrated the usefulness of these markers for marker-assisted selection of Sr49 in breeding programs.

Axis I disorders in adjustable gastric band patients: the relationship between psychopathology and weight loss

BACKGROUND

Bariatric surgery candidates have higher rates of co-morbid psychological illnesses than those in the general population. The effect of weight loss on these illnesses is unclear.

METHODS

This prospective observational study explored psychiatric co-morbidities and weight loss outcomes in 204 gastric banding surgery candidates. Psychiatric co-morbidities were assessed prior to surgery and 2 years post-surgery. One hundred and fifty patients (74%) completed assessments at both time points.

RESULTS

At baseline, 39.7% of the patients met the criteria for a current axis I disorder as defined by the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV). Mood disorders were the most frequent (26.5%), followed by anxiety disorders (15.2%) and binge eating disorder (13.2%). Preoperative psychopathology predicted clinical psychopathology at 2 years. No preoperative or post-operative axis I disorder was significantly related to weight loss at 2 years. The frequency of current axis I disorders decreased significantly from 39.7% preoperatively to 20% 2 years post-surgery.

CONCLUSIONS

The point prevalence of psychopathology in this sample of Australian bariatric candidates is high. Psychopathology, preoperatively and at 2 years of follow-up, was not associated with weight loss at 2 years.

Identification of a stem rust resistance locus effective against Ug99 on wheat chromosome 7AL using a RAD-Seq approach

A locus of major effect for stem rust resistance, effective against Ug99 and possibly a target of a suppressor on chromosome arm 7DL in wheat cultivar Canthatch, was mapped to 7AL. Wheat stem rust, caused by Puccinia graminis f. sp. tritici (Pgt), is responsible for major production losses around the world. The development of resistant cultivars is an effective and environmentally friendly way to manage the disease, but outbreaks can occur when new pathogen races overcome the existing host resistance genes. Ug99 (race TTKSK) and related Pgt races are virulent to the majority of existing cultivars, which presents a potential threat to global wheat production. The hexaploid wheat cultivar Canthatch has long been known to carry a suppressor of stem rust resistance on chromosome arm 7DL. Multiple "non-suppressor" mutants of Canthatch are reported to have gained resistance to Pgt races, including Ug99 (TTKSK) and related races TTKST and TTTSK. To genetically map the suppressor locus, a mapping population was developed from a cross between the susceptible cultivar Columbus, thought to possess the suppressor, and Columbus-NS766, a resistant, near-isogenic line believed to contain a mutant non-suppressor allele introgressed from Canthatch. Genetic mapping using a 9K SNP genotyping assay and restriction site-associated DNA sequencing (RAD-Seq) on bulked segregants led to the identification of markers linked to a locus of stem rust resistance. Surprisingly, genomic sequence information revealed the markers to be located on 7AL instead of 7DL, indicating that the resistance phenotype was due to a new resistance locus, rather than the inactivated suppressor. We suggest that the 7AL locus of resistance is most likely suppressed by the 7DL suppressor.

Transcriptomic analysis of wheat near-isogenic lines identifies PM19-A1 and A2 as candidates for a major dormancy QTL

Background

Next-generation sequencing technologies provide new opportunities to identify the genetic components responsible for trait variation. However, in species with large polyploid genomes, such as bread wheat, the ability to rapidly identify genes underlying quantitative trait loci (QTL) remains non-trivial. To overcome this, we introduce a novel pipeline that analyses, by RNA-sequencing, multiple near-isogenic lines segregating for a targeted QTL.

Results

We use this approach to characterize a major and widely utilized seed dormancy QTL located on chromosome 4AL. It exploits the power and mapping resolution afforded by large multi-parent mapping populations, whilst reducing complexity by using multi-allelic contrasts at the targeted QTL region. Our approach identifies two adjacent candidate genes within the QTL region belonging to the ABA-induced Wheat Plasma Membrane 19 family. One of them, PM19-A1, is highly expressed during grain maturation in dormant genotypes. The second, PM19-A2, shows changes in sequence causing several amino acid alterations between dormant and non-dormant genotypes. We confirm that PM19 genes are positive regulators of seed dormancy.

Conclusions

The efficient identification of these strong candidates demonstrates the utility of our transcriptomic pipeline for rapid QTL to gene mapping. By using this approach we are able to provide a comprehensive genetic analysis of the major source of grain dormancy in wheat. Further analysis across a diverse panel of bread and durum wheats indicates that this important dormancy QTL predates hexaploid wheat. The use of these genes by wheat breeders could assist in the elimination of pre-harvest sprouting in wheat.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0665-6) contains supplementary material, which is available to authorized users.

Attentional biases for food cues in overweight and individuals with obesity: a systematic review of the literature

Obesity rates have increased dramatically in recent decades, and it has proven difficult to treat. An attentional bias towards food cues may be implicated in the aetiology of obesity and influence cravings and food consumption. This review systematically investigated whether attentional biases to food cues exist in overweight/obese compared with healthy weight individuals. Electronic database were searched for relevant papers from inception to October 2014. Only studies reporting food-related attentional bias between either overweight (body mass index [BMI] 25.0-29.9 kg m(-2)) or obese (BMI ≥ 30) participants and healthy weight participants (BMI 18.5-24.9) were included. The findings of 19 studies were reported in this review. Results of the literature are suggestive of differences in attentional bias, with all but four studies supporting the notion of enhanced reactivity to food stimuli in overweight individuals and individuals with obesity. This support for attentional bias was observed primarily in studies that employed psychophysiological techniques (i.e. electroencephalogram, eye-tracking and functional magnetic resonance imaging). Despite the heterogeneous methodology within the featured studies, all measures of attentional bias demonstrated altered cue-reactivity in individuals with obesity. Considering the theorized implications of attentional biases on obesity pathology, researchers are encouraged to replicate flagship studies to strengthen these inferences.

A haplotype map of allohexaploid wheat reveals distinct patterns of selection on homoeologous genomes

Background

Bread 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.

Results

A 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.

Conclusions

Evidence 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.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0606-4) contains supplementary material, which is available to authorized users.

Genomic prediction for rust resistance in diverse wheat landraces

We have demonstrated that genomic selection in diverse wheat landraces for resistance to leaf, stem and strip rust is possible, as genomic breeding values were moderately accurate. Markers with large effects in the Bayesian analysis confirmed many known genes, while also discovering many previously uncharacterised genome regions associated with rust scores. Genomic selection, where selection decisions are based on genomic estimated breeding values (GEBVs) derived from genome-wide DNA markers, could accelerate genetic progress in plant breeding. In this study, we assessed the accuracy of GEBVs for rust resistance in 206 hexaploid wheat (Triticum aestivum) landraces from the Watkins collection of phenotypically diverse wheat genotypes from 32 countries. The landraces were genotyped for 5,568 SNPs using an Illumina iSelect 9 K bead chip assay and phenotyped for field-based leaf rust (Lr), stem rust (Sr) and stripe rust (Yr) responses across multiple years. Genomic Best Linear Unbiased Prediction (GBLUP) and a Bayesian Regression method (BayesR) were used to predict GEBVs. Based on fivefold cross-validation, the accuracy of genomic prediction averaged across years was 0.35, 0.27 and 0.44 for Lr, Sr and Yr using GBLUP and 0.33, 0.38 and 0.30 for Lr, Sr and Yr using BayesR, respectively. Inclusion of PCR-predicted genotypes for known rust resistance genes increased accuracy more substantially when the marker was diagnostic (Lr34/Sr57/Yr18) for the presence-absence of the gene rather than just linked (Sr2). Investigation of the impact of genetic relatedness between validation and reference lines on accuracy of genomic prediction showed that accuracy will be higher when each validation line had at least one close relationship to the reference lines. Overall, the prediction accuracies achieved in this study are encouraging, and confirm the feasibility of genomic selection in wheat. In several instances, estimated marker effects were confirmed by published literature and results of mapping experiments using Watkins accessions.

QTL for resistance to root lesion nematode (Pratylenchus thornei) from a synthetic hexaploid wheat source

A whole genome average interval mapping approach identified eight QTL associated with P. thornei resistance in a DH population from a cross between the synthetic-derived wheat Sokoll and cultivar Krichauff. Pratylenchus thornei are migratory nematodes that feed and reproduce within the wheat root cortex, causing cell death (lesions) resulting in severe yield reductions globally. Genotypic selection using molecular markers closely linked to Pratylenchus resistance genes will accelerate the development of new resistant cultivars by reducing the need for laborious and expensive resistance phenotyping. A doubled haploid wheat population (150 lines) from a cross between the synthetic-derived cultivar Sokoll (P. thornei resistant) and cultivar Krichauff (P. thornei moderately susceptible) was used to identify quantitative trait loci (QTL) associated with P. thornei resistance. The resistance identified in the glasshouse was validated in a field trial. A genetic map was constructed using Diversity Array Technology and the QTL regions identified were further targeted with simple sequence repeat (SSR) and single-nucleotide polymorphism (SNP) markers. Six significant and two suggestive P. thornei resistance QTL were detected using a whole genome average interval mapping approach. Three QTL were identified on chromosome 2B, two on chromosome 6D, and a single QTL on each of chromosomes 2A, 2D and 5D. The QTL on chromosomes 2BS and 6DS mapped to locations previously identified to be associated with Pratylenchus resistance. Together, the QTL on 2B (QRlnt.sk-2B.1-2B.3) and 6D (QRlnt.sk-6D.1 and 6D.2) explained 30 and 48 % of the genotypic variation, respectively. Flanking PCR-based markers based on SSRs and SNPs were developed for the major QTL on 2B and 6D and provide a cost-effective high-throughput tool for marker-assisted breeding of wheat with improved P. thornei resistance.

An independent validation association study of carcass quality, shear force, intramuscular fat percentage and omega-3 polyunsaturated fatty acid content with gene markers in Australian lamb

Previous association studies revealed several single nucleotide polymorphisms (SNPs) that explained the observed phenotypic variation for meat tenderness and long-chain omega-3 polyunsaturated fatty acid (PUFA) content of Australian lamb. To confirm the validity of these associated SNPs at predicting meat tenderness and omega-3 PUFA content, an independent validation study was designed. The OvineSNP50 genotypes of these animals were used to impute the 192 SNP Meat Quality Research (MQR) panel genotypes on nearly 6200 animals from the Cooperative Research Centre for Sheep Industry Innovation Information Nucleus Flock and Sheep Genomics Falkiner Memorial Field Station flock. Association analysis revealed numerous SNP from the 192 SNP MQR panel that were associated with carcass quality - fat depth at the C-site and eye muscle depth; shear force at day 1 and day 5 after slaughter (SF1 and SF5); and omega-3 PUFA content at P<0.01. However, 1 SNP was independently validated for SF5 (i.e. CAST_101781475). The magnitude of the effect of each significant SNP and the relative allele frequencies across Merino-, Maternal- and Terminal-sired progeny was determined. The independently validated SNP for SF5 and the associated SNP with omega-3 PUFA content will accelerate efforts to improve these phenotypic traits in Australian lamb.

Comparative analysis of syntenic genes in grass genomes reveals accelerated rates of gene structure and coding sequence evolution in polyploid wheat

Cycles of whole-genome duplication (WGD) and diploidization are hallmarks of eukaryotic genome evolution and speciation. Polyploid wheat (Triticum aestivum) has had a massive increase in genome size largely due to recent WGDs. How these processes may impact the dynamics of gene evolution was studied by comparing the patterns of gene structure changes, alternative splicing (AS), and codon substitution rates among wheat and model grass genomes. In orthologous gene sets, significantly more acquired and lost exonic sequences were detected in wheat than in model grasses. In wheat, 35% of these gene structure rearrangements resulted in frame-shift mutations and premature termination codons. An increased codon mutation rate in the wheat lineage compared with Brachypodium distachyon was found for 17% of orthologs. The discovery of premature termination codons in 38% of expressed genes was consistent with ongoing pseudogenization of the wheat genome. The rates of AS within the individual wheat subgenomes (21%-25%) were similar to diploid plants. However, we uncovered a high level of AS pattern divergence between the duplicated homeologous copies of genes. Our results are consistent with the accelerated accumulation of AS isoforms, nonsynonymous mutations, and gene structure rearrangements in the wheat lineage, likely due to genetic redundancy created by WGDs. Whereas these processes mostly contribute to the degeneration of a duplicated genome and its diploidization, they have the potential to facilitate the origin of new functional variations, which, upon selection in the evolutionary lineage, may play an important role in the origin of novel traits.

Inheritance and molecular mapping of a gene conferring seedling resistance against Puccinia hordei in the barley cultivar Ricardo

Genetic studies were undertaken to determine the inheritance and genomic location of uncharacterised seedling resistance to leaf rust, caused by Puccinia hordei, in the barley cultivar Ricardo. The resistance was shown to be conferred by a single dominant gene, which was tentatively designated RphRic. Bulk segregant analysis (BSA) and genetic mapping of an F(3) mapping population using multiplex-ready SSR genotyping and Illumina GoldenGate SNP assay located RphRic in chromosome 4H. Given that this is the first gene for leaf rust resistance mapped on chromosome 4H, it was designated Rph21. The presence of an additional gene, Rph2, in Ricardo, was confirmed by the test of allelism. The seedling gene Rph21 has shown effectiveness against all Australian pathotypes of P. hordei tested since at least 1992 and hence represents a new and useful source of resistance to this pathogen.

Transcriptome-scale homoeolog-specific transcript assemblies of bread wheat

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 GAII_x 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.