Publications by authors named "Kristy Hobson"

9 Publications

  • Page 1 of 1

Evidence for the Application of Emerging Technologies to Accelerate Crop Improvement - A Collaborative Pipeline to Introgress Herbicide Tolerance Into Chickpea.

Front Plant Sci 2021 3;12:779122. Epub 2021 Dec 3.

Tamworth Agricultural Institute, New South Wales Department of Primary Industries, Tamworth, NSW, Australia.

Accelerating genetic gain in crop improvement is required to ensure improved yield and yield stability under increasingly challenging climatic conditions. This case study demonstrates the effective confluence of innovative breeding technologies within a collaborative breeding framework to develop and rapidly introgress imidazolinone Group 2 herbicide tolerance into an adapted Australian chickpea genetic background. A well-adapted, high-yielding desi cultivar PBA HatTrick was treated with ethyl methanesulfonate to generate mutations in the () gene. After 2 years of field screening with imidazolinone herbicide across >20 ha and controlled environment progeny screening, two selections were identified which exhibited putative herbicide tolerance. Both selections contained the same single amino acid substitution, from alanine to valine at position 205 (AV) in the AHAS1 protein, and KASP™ markers were developed to discriminate between tolerant and intolerant genotypes. A pipeline combining conventional crossing and F production with accelerated single seed descent from F and marker-assisted selection at F rapidly introgressed the herbicide tolerance trait from one of the mutant selections, D15PAHI002, into PBA Seamer, a desi cultivar adapted to Australian cropping areas. Field evaluation of the derivatives of the D15PAHI002 × PBA Seamer cross was analyzed using a factor analytic mixed model statistical approach designed to accommodate low seed numbers resulting from accelerated single seed descent. To further accelerate trait introgression, field evaluation trials were undertaken concurrent with crop safety testing trials. In 2020, 4 years after the initial cross, an advanced line selection CBA2061, bearing acetohydroxyacid synthase (AHAS) inhibitor tolerance and agronomic and disease resistance traits comparable to parent PBA Seamer, was entered into Australian National Variety Trials as a precursor to cultivar registration. The combination of cross-institutional collaboration and the application of novel pre-breeding platforms and statistical technologies facilitated a 3-year saving compared to a traditional breeding approach. This breeding pipeline can be used as a model to accelerate genetic gain in other self-pollinating species, particularly food legumes.
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http://dx.doi.org/10.3389/fpls.2021.779122DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8678039PMC
December 2021

Genomic prediction of preliminary yield trials in chickpea: Effect of functional annotation of SNPs and environment.

Plant Genome 2021 Nov 16:e20166. Epub 2021 Nov 16.

School of Agriculture, Food and Wine, The Univ. of Adelaide, Adelaide, SA, 5064, Australia.

Achieving yield potential in chickpea (Cicer arietinum L.) is limited by many constraints that include biotic and abiotic stresses. Combining next-generation sequencing technology with advanced statistical modeling has the potential to increase genetic gain efficiently. Whole genome resequencing data was obtained from 315 advanced chickpea breeding lines from the Australian chickpea breeding program resulting in more than 298,000 single nucleotide polymorphisms (SNPs) discovered. Analysis of population structure revealed a distinct group of breeding lines with many alleles that are absent from recently released Australian cultivars. Genome-wide association studies (GWAS) using these Australian breeding lines identified 20 SNPs significantly associated with grain yield in multiple field environments. A reduced level of nucleotide diversity and extended linkage disequilibrium suggested that some regions in these chickpea genomes may have been through selective breeding for yield or other traits. A large introgression segment that introduced from C. echinospermum for phytophthora root rot resistance was identified on chromosome 6, yet it also has unintended consequences of reducing yield due to linkage drag. We further investigated the effect of genotype by environment interaction on genomic prediction of yield. We found that the training set had better prediction accuracy when phenotyped under conditions relevant to the targeted environments. We also investigated the effect of SNP functional annotation on prediction accuracy using different subsets of SNPs based on their genomic locations: regulatory regions, exome, and alternative splice sites. Compared with the whole SNP dataset, a subset of SNPs did not significantly decrease prediction accuracy for grain yield despite consisting of a smaller number of SNPs.
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http://dx.doi.org/10.1002/tpg2.20166DOI Listing
November 2021

The genetics of vigour-related traits in chickpea (Cicer arietinum L.): insights from genomic data.

Theor Appl Genet 2022 Jan 13;135(1):107-124. Epub 2021 Oct 13.

South Australian Research and Development Institute, Hartley Grove, Urrbrae, SA, Australia.

Key Message: QTL controlling vigour and related traits were identified in a chickpea RIL population and validated in diverse sets of germplasm. Robust KASP markers were developed for marker-assisted selection. To understand the genetic constitution of vigour in chickpea (Cicer arietinum L.), genomic data from a bi-parental population and multiple diversity panels were used to identify QTL, sequence-level haplotypes and genetic markers associated with vigour-related traits in Australian environments. Using 182 Recombinant Inbred Lines (RILs) derived from a cross between two desi varieties, Rupali and Genesis836, vigour QTL independent of flowering time were identified on chromosomes (Ca) 1, 3 and 4 with genotypic variance explained (GVE) ranging from 7.1 to 28.8%. Haplotype analysis, association analysis and graphical genotyping of whole-genome re-sequencing data of two diversity panels consisting of Australian and Indian genotypes and an ICRISAT Chickpea Reference Set revealed a deletion in the FTa1-FTa2-FTc gene cluster of Ca3 significantly associated with vigour and flowering time. Across the RIL population and diversity panels, the impact of the deletion was consistent for vigour but not flowering time. Vigour-related QTL on Ca4 co-located with a QTL for seed size in Rupali/Genesis836 (GVE = 61.3%). Using SNPs from this region, we developed and validated gene-based KASP markers across different panels. Two markers were developed for a gene on Ca1, myo -inositol monophosphatase (CaIMP), previously proposed to control seed size, seed germination and seedling growth in chickpea. While associated with vigour in the diversity panels, neither the markers nor broader haplotype linked to CaIMP was polymorphic in Rupali/Genesis836. Importantly, vigour appears to be controlled by different sets of QTL across time and with components which are independent from phenology.
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http://dx.doi.org/10.1007/s00122-021-03954-4DOI Listing
January 2022

Modelling the effects of cold temperature during the reproductive stage on the yield of chickpea (Cicer arietinum L.).

Int J Biometeorol 2022 Jan 5;66(1):111-125. Epub 2021 Oct 5.

NSW Department of Primary Industries, 4 Marsden Park Road, Calala, NSW, 2340, Australia.

During the reproductive stage, chilling temperatures and frost reduce the yield of chickpea and limit its adaptation. The adverse effects of chilling temperature and frost in terms of the threshold temperatures, impact of cold duration, and genotype-by-environment-by-management interactions are not well quantified. Crop growth models that predict flowering time and yield under diverse climates can identify combinations of cultivars and sowing time to reduce frost risk in target environments. The Agricultural Production Systems Simulator (APSIM-chickpea) model uses daily temperatures to model basic crop growth but does not include penalties for either frost damage or cold temperatures during flowering and podding stages. Regression analysis overcame this limitation of the model for chickpea crops grown at 95 locations in Australia using 70 years of historic data incorporating three cultivars and three sowing times (early, mid, and late). We modified model parameters to include the effect of soil water on thermal time calculations, which significantly improved the prediction of flowering time. Simulated data, and data from field experiments grown in Australia (2013 to 2019), showed robust predictions for flowering time (n = 29; R = 0.97), and grain yield (n = 22; R = 0.63-0.70). In addition, we identified threshold cold temperatures that significantly affected predicted yield, and combinations of locations, variety, and sowing time where the overlap between peak cold temperatures and peak flowering was minimal. Our results showed that frost and/or cold temperature-induced yield losses are a major limitation in some unexpected Australian locations, e.g., inland, subtropical latitudes in Queensland. Intermediate sowing maximise yield, as it avoids cold temperature, late heat, and drought stresses potentially limiting yield in early and late sowing respectively.
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http://dx.doi.org/10.1007/s00484-021-02197-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8727402PMC
January 2022

Current population structure and pathogenicity patterns of in Australia.

Microb Genom 2021 07;7(7)

Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, QLD 4111, Australia.

Ascochyta blight disease, caused by the necrotrophic fungus , is a major biotic constraint to chickpea production in Australia and worldwide. Detailed knowledge of the structure of the pathogen population and its potential to adapt to our farming practices is key to informing optimal management of the disease. This includes understanding the molecular diversity among isolates and the frequency and distribution of the isolates that have adapted to overcome host resistance across agroecologically distinct regions. Thanks to continuous monitoring efforts over the past 6 years, a comprehensive collection of isolates was collated from the major Australian chickpea production regions. To determine the molecular structure of the entire population, representative isolates from each collection year and growing region have been genetically characterized using a DArTseq genotyping-by-sequencing approach. The genotyped isolates were further phenotyped to determine their pathogenicity levels against a differential set of chickpea cultivars and genotype-phenotype associations were inferred. Overall, the Australian population displayed a far lower genetic diversity (average Nei's gene diversity of 0.047) than detected in other populations worldwide. This may be explained by the presence of a single mating-type in Australia, MAT1-2, limiting its reproduction to a clonal mode. Despite the low detected molecular diversity, clonal selection appears to have given rise to a subset of adapted isolates that are highly pathogenic on commonly employed resistance sources, and that are occurring at an increasing frequency. Among these, a cluster of genetically similar isolates was identified, with a higher proportion of highly aggressive isolates than in the general population. The discovery of distinct genetic clusters associated with high and low isolate pathogenicity forms the foundation for the development of a molecular pathotyping tool for the Australian population. Application of such a tool, along with continuous monitoring of the genetic structure of the population will provide crucial information for the screening of breeding material and integrated disease management packages.
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http://dx.doi.org/10.1099/mgen.0.000627DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8477395PMC
July 2021

Use of Contemporary Groups in the Construction of Multi-Environment Trial Datasets for Selection in Plant Breeding Programs.

Front Plant Sci 2020 2;11:623586. Epub 2021 Feb 2.

Centre for Bioinformatics and Biometrics, School of Mathematics and Applied Statistics, National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, NSW, Australia.

Plant breeding programs use multi-environment trial (MET) data to select superior lines, with the ultimate aim of increasing genetic gain. Selection accuracy can be improved with the use of advanced statistical analysis methods that employ informative models for genotype by environment interaction, include information on genetic relatedness and appropriately accommodate within-trial error variation. The gains will only be achieved, however, if the methods are applied to suitable MET datasets. In this paper we present an approach for constructing MET datasets that optimizes the information available for selection decisions. This is based on two new concepts that characterize the structure of a breeding program. The first is that of "contemporary groups," which are defined to be groups of lines that enter the initial testing stage of the breeding program in the same year. The second is that of "data bands," which are sequences of trials that correspond to the progression through stages of testing from year to year. MET datasets are then formed by combining bands of data in such a way as to trace the selection histories of lines within contemporary groups. Given a specified dataset, we use the A-optimality criterion from the model-based design literature to quantify the information for any given selection decision. We demonstrate the methods using two motivating examples from a durum and chickpea breeding program. Datasets constructed using contemporary groups and data bands are shown to be superior to other forms, in particular those that relate to a single year alone.
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http://dx.doi.org/10.3389/fpls.2020.623586DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7884452PMC
February 2021

Mapping resistance to Phytophthora root rot identifies independent loci from cultivated (Cicer arietinum L.) and wild (Cicer echinospermum P.H. Davis) chickpea.

Theor Appl Genet 2019 Apr 7;132(4):1017-1033. Epub 2018 Dec 7.

School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, PMB1, Glen Osmond, SA, 5064, Australia.

Key Message: Major QTL for Phytophthora root rot resistance have been identified in three mapping populations with independent sources of resistance contributed by C. echinospermum and C. arietinum. Phytophthora root rot (PRR) caused by the oomycete Phytophthora medicaginis is a major soil-borne disease of chickpea in Australia. With no economic in-crop control of PRR, a genetic approach to improve resistance is the most practical management option. Moderate field resistance has been incorporated in the cultivated C. arietinum variety, Yorker, and a higher level of resistance has been identified in a derivative of wild chickpea (C. echinospermum, interspecific breeding line 04067-81-2-1-1). These genotypes and two other released varieties were used to develop one intra-specific and two interspecific F-derived recombinant inbred line mapping populations for genetic analysis of resistance. The Yorker × Genesis114 (YG), Rupali × 04067-81-2-1-1 (RB) and Yorker × 04067-81-2-1-1 (YB) populations were genotyped using genotyping-by-sequencing and phenotyped for PRR under three field environments with a mixture of 10 P. medicaginis isolates. Whole-genome QTL analysis identified major QTL QRBprrsi01, QYBprrsi01, QRBprrsi03 and QYBprrsi02 for PRR resistance on chromosomes 3 and 6, in RB and YB populations, respectively, with the resistance source derived from the wild Cicer species. QTL QYGprrsi02 and QYGprrsi03 were also identified on chromosomes 5 and 6 in YG population from C. arietinum. Aligning QTL regions to the corresponding chickpea reference genome suggested that the resistance source from C. arietinum and C. echinospermum may be different. The findings from this study provide tools for marker-assisted selection in chickpea breeding and information to assist the development of populations suitable for fine-mapping of resistance loci to determine the molecular basis for PRR resistance in chickpea.
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http://dx.doi.org/10.1007/s00122-018-3256-6DOI Listing
April 2019

Evidence and Consequence of a Highly Adapted Clonal Haplotype within the Australian Population.

Front Plant Sci 2017 16;8:1029. Epub 2017 Jun 16.

Environmental Futures Research Institute, School of Natural Sciences, Griffith University, NathanQLD, Australia.

The Australian (Pass.) Labr. (syn. ) population has low genotypic diversity with only one mating type detected to date, potentially precluding substantial evolution through recombination. However, a large diversity in aggressiveness exists. In an effort to better understand the risk from selective adaptation to currently used resistance sources and chemical control strategies, the population was examined in detail. For this, a total of 598 isolates were quasi-hierarchically sampled between 2013 and 2015 across all major Australian chickpea growing regions and commonly grown host genotypes. Although a large number of haplotypes were identified (66) through short sequence repeat (SSR) genotyping, overall low gene diversity ( = 0.066) and genotypic diversity ( = 0.57) was detected. Almost 70% of the isolates assessed were of a single dominant haplotype (ARH01). Disease screening on a differential host set, including three commonly deployed resistance sources, revealed distinct aggressiveness among the isolates, with 17% of all isolates identified as highly aggressive. Almost 75% of these were of the ARH01 haplotype. A similar pattern was observed at the host level, with 46% of all isolates collected from the commonly grown host genotype Genesis090 (classified as "resistant" during the term of collection) identified as highly aggressive. Of these, 63% belonged to the ARH01 haplotype. In conclusion, the ARH01 haplotype represents a significant risk to the Australian chickpea industry, being not only widely adapted to the diverse agro-geographical environments of the Australian chickpea growing regions, but also containing a disproportionately large number of aggressive isolates, indicating fitness to survive and replicate on the best resistance sources in the Australian germplasm.
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http://dx.doi.org/10.3389/fpls.2017.01029DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5472848PMC
June 2017

Genome Analysis Identified Novel Candidate Genes for Ascochyta Blight Resistance in Chickpea Using Whole Genome Re-sequencing Data.

Front Plant Sci 2017 17;8:359. Epub 2017 Mar 17.

School of Agriculture, Food and Wine, University of Adelaide, AdelaideSA, Australia; South Australian Research and Development Institute, UrrbraeSA, Australia.

Ascochyta blight (AB) is a fungal disease that can significantly reduce chickpea production in Australia and other regions of the world. In this study, 69 chickpea genotypes were sequenced using whole genome re-sequencing (WGRS) methods. They included 48 Australian varieties differing in their resistance ranking to AB, 16 advanced breeding lines from the Australian chickpea breeding program, four landraces, and one accession representing the wild chickpea species . More than 800,000 single nucleotide polymorphisms (SNPs) were identified. Population structure analysis revealed relatively narrow genetic diversity amongst recently released Australian varieties and two groups of varieties separated by the level of AB resistance. Several regions of the chickpea genome were under positive selection based on Tajima's test. Both Fst genome- scan and genome-wide association studies (GWAS) identified a 100 kb region (AB4.1) on chromosome 4 that was significantly associated with AB resistance. The AB4.1 region co-located to a large QTL interval of 7 Mb∼30 Mb identified previously in three different mapping populations which were genotyped at relatively low density with SSR or SNP markers. The AB4.1 region was validated by GWAS in an additional collection of 132 advanced breeding lines from the Australian chickpea breeding program, genotyped with approximately 144,000 SNPs. The reduced level of nucleotide diversity and long extent of linkage disequilibrium also suggested the AB4.1 region may have gone through selective sweeps probably caused by selection of the AB resistance trait in breeding. In total, 12 predicted genes were located in the AB4.1 QTL region, including those annotated as: NBS-LRR receptor-like kinase, wall-associated kinase, zinc finger protein, and serine/threonine protein kinases. One significant SNP located in the conserved catalytic domain of a NBS-LRR receptor-like kinase led to amino acid substitution. Transcriptional analysis using qPCR showed that some predicted genes were significantly induced in resistant lines after inoculation compared to non-inoculated plants. This study demonstrates the power of combining WGRS data with relatively simple traits to rapidly develop "functional makers" for marker-assisted selection and genomic selection.
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http://dx.doi.org/10.3389/fpls.2017.00359DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5355423PMC
March 2017
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