Prediction of Cacao (Theobroma cacao) Resistance to Moniliophthora spp. Diseases via Genome-Wide Association Analysis and Genomic Selection

State of the Art of the Molecular Biology of the Interaction between Cocoa and Witches’ Broom Disease: A Systematic Review

Significant scientific advances to elucidate the Moniliophthora perniciosa pathosystem have been achieved in recent years, but the molecular biology of this pathogen-host interaction is still a field with many unanswered questions. In order to present insights at the molecular level, we present the first systematic review on the theme. All told, 1118 studies were extracted from public databases. Of these, 109 were eligible for the review, based on the inclusion and exclusion criteria. The results indicated that understanding the transition from the biotrophic-necrotrophic phase of the fungus is crucial for control of the disease. Proteins with strong biotechnological potential or that can be targets for pathosystem intervention were identified, but studies regarding possible applications are still limited. The studies identified revealed important genes in the M. perniciosa-host interaction and efficient molecular markers in the search for genetic variability and sources of resistance, with Theobroma cacao being the most common host. An arsenal of effectors already identified and not explored in the pathosystem were highlighted. This systematic review contributes to the understanding of the pathosystem at the molecular level, offering new insights and proposing different paths for the development of new strategies to control witches’ broom disease.

Characterization of the microbiota dynamics associated with Moniliophthora roreri, causal agent of cocoa frosty pod rot disease, reveals new viral species

Introduction

Theobroma cacao, the cocoa tree, is a target for pathogens, such as fungi from the genera Phytophthora, Moniliophthora, Colletotrichum, Ceratocystis, among others. Some cacao pathogens are restricted to specific regions of the world, such as the Cacao swollen shoot virus (CSSV) in West African countries, while others are expanding geographically, such as Moniliophthora roreri in the Americas. M. roreri is one of the most threatening cacao pathogens since it directly attacks the cacao pods driving a significant reduction in production, and therefore economic losses. Despite its importance, the knowledge about the microenvironment of this pathogen and the cocoa pods is still poorly characterized.

Methods

Herein we performed RNA sequencing of spores in differential stages of culture in a medium supplemented with cacao pod extract and mycelium collected of the susceptible variety ICT 7121 naturally infected by the pathogen to evaluate the diversity and transcriptional activity of microorganisms associated with the in vitro sporulation of M. roreri.

Results

Our data revealed a great variety of fungi and bacteria associated with M. roreri, with an exceptional diversity of individuals from the genus Trichoderma sp. Interestingly, the dynamics of microorganisms from different kingdoms varied proportionally, suggesting they are somehow affected by M. roreri culture time. We also identified three sequences similar to viral genomes from the Narnaviridae family, posteriorly confirmed by phylogenetic analysis as members of the genus Narnavirus. Screening of M. roreri public datasets indicated the virus sequences circulating in samples from Ecuador, suggesting a wide spread of these elements. Of note, we did not identify traces of the viral sequences in the M. roreri genome or DNA sequencing, restricting the possibility of these sequences representing endogenized elements.

Discussion

To the best of our knowledge, this is the first report of viruses infecting the fungus of the genus Moniliophthora and only the third description of viruses that are able to parasite elements from the Marasmiaceae family.

Genomic selection for morphological and yield-related traits using genome-wide SNPs in oil palm

Oil palm is the most important oil crop worldwide. Colombia is the fourth largest producer, primarily relying on production from interspecific hybrids, derived from crosses between Elaeis oleifera and Elaeis guineensis (OxG). However, conventional breeding can take up to 20 years to generate a new variety. Therefore, reducing the breeding cycle while improving the genetic gain for complex traits is desirable. Genomic selection (GS) is an approach with the potential to achieve this goal. In this study, we evaluated 431 F1 interspecific hybrids (OxG) and 444 backcrosses (BC1) for morphological and yield-related traits. Genomic predictions were performed with the G-BLUP model using three different population datasets for training the model: the same population (TRN1), the other population (TRN2), and both populations (TRN1+2). Higher multi-family prediction accuracies were obtained for foliar area (0.3 in OxG) and trunk height (0.47 in BC1) when the model was trained with TRN1. Single-family prediction accuracies were lower in the OxG compared to BC1 families for traits such as trunk diameter, trunk height, bunch number, and yield using TRN1. Conversely, lower prediction accuracies were obtained for most traits when the model was trained using TRN2 (< 0.1). Multi-trait models showed a substantial increase of the predictions for traits such as yield (0.22 for OxG and 0.44 for BC1), because of the genetic correlations between traits. The results herein highlighted the potential of GS for parental selection in OxG and BC1 populations, but further studies are required to improve the models to select individuals by their genetic value.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01341-5.

Genome-wide SNP genotyping as a simple and practical tool to accelerate the development of inbred lines in outbred tree species: An example in cacao (Theobroma cacao L.)

Cacao is a globally important crop with a long history of domestication and selective breeding. Despite the increased use of elite clones by cacao farmers, worldwide plantations are established mainly using hybrid progeny material derived from heterozygous parents, therefore displaying high tree-to-tree variability. The deliberate development of hybrids from advanced inbred lines produced by successive generations of self-pollination has not yet been fully considered in cacao breeding. This is largely due to the self-incompatibility of the species, the long generation cycles (3–5 years) and the extensive trial areas needed to accomplish the endeavor. We propose a simple and accessible approach to develop inbred lines based on accelerating the buildup of homozygosity based on regular selfing assisted by genome-wide SNP genotyping. In this study we genotyped 90 clones from the Brazilian CEPEC´s germplasm collection and 49 inbred offspring of six S₁ or S₂ cacao families derived from self-pollinating clones CCN-51, PS-13.19, TSH-1188 and SIAL-169. A set of 3,380 SNPs distributed across the cacao genome were interrogated on the EMBRAPA multi-species 65k Infinium chip. The 90 cacao clones showed considerable variation in genome-wide SNP homozygosity (mean 0.727± 0.182) and 19 of them with homozygosity ≥90%. By assessing the increase in homozygosity across two generations of self-pollinations, SNP data revealed the wide variability in homozygosity within and between S₁ and S₂ families. Even in small families (<10 sibs), individuals were identified with up to ~1.5 standard deviations above the family mean homozygosity. From baseline homozygosities of 0.476 and 0.454, offspring with homozygosities of 0.862 and 0.879 were recovered for clones TSH-1188 and CCN-51 respectively, in only two generations of selfing (81–93% increase). SNP marker assisted monitoring and selection of inbred individuals can be a practical tool to optimize and accelerate the development of inbred lines of outbred tree species. This approach will allow a faster and more accurate exploitation of hybrid breeding strategies in cacao improvement programs and potentially in other perennial fruit and forest trees.

Genome-Wide SNP Markers Accelerate Perennial Forest Tree Breeding Rate for Disease Resistance through Marker-Assisted and Genome-Wide Selection

The ecological and economic importance of forest trees is evident and their survival is necessary to provide the raw materials needed for wood and paper industries, to preserve the diversity of associated animal and plant species, to protect water and soil, and to regulate climate. Forest trees are threatened by anthropogenic factors and biotic and abiotic stresses. Various diseases, including those caused by fungal pathogens, are one of the main threats to forest trees that lead to their dieback. Genomics and transcriptomics studies using next-generation sequencing (NGS) methods can help reveal the architecture of resistance to various diseases and exploit natural genetic diversity to select elite genotypes with high resistance to diseases. In the last two decades, QTL mapping studies led to the identification of QTLs related to disease resistance traits and gene families and transcription factors involved in them, including NB-LRR, WRKY, bZIP and MYB. On the other hand, due to the limitation of recombination events in traditional QTL mapping in families derived from bi-parental crosses, genome-wide association studies (GWAS) that are based on linkage disequilibrium (LD) in unstructured populations overcame these limitations and were able to narrow down QTLs to single genes through genotyping of many individuals using high-throughput markers. Association and QTL mapping studies, by identifying markers closely linked to the target trait, are the prerequisite for marker-assisted selection (MAS) and reduce the breeding period in perennial forest trees. The genomic selection (GS) method uses the information on all markers across the whole genome, regardless of their significance for development of a predictive model for the performance of individuals in relation to a specific trait. GS studies also increase gain per unit of time and dramatically increase the speed of breeding programs. This review article is focused on the progress achieved in the field of dissecting forest tree disease resistance architecture through GWAS and QTL mapping studies. Finally, the merit of methods such as GS in accelerating forest tree breeding programs is also discussed.

Genomic selection in tropical perennial crops and plantation trees: a review

To overcome the multiple challenges currently faced by agriculture, such as climate change and soil deterioration, more efficient plant breeding strategies are required. Genomic selection (GS) is crucial for the genetic improvement of quantitative traits, as it can increase selection intensity, shorten the generation interval, and improve selection accuracy for traits that are difficult to phenotype. Tropical perennial crops and plantation trees are of major economic importance and have consequently been the subject of many GS articles. In this review, we discuss the factors that affect GS accuracy (statistical models, linkage disequilibrium, information concerning markers, relatedness between training and target populations, the size of the training population, and trait heritability) and the genetic gain expected in these species. The impact of GS will be particularly strong in tropical perennial crops and plantation trees as they have long breeding cycles and constrained selection intensity. Future GS prospects are also discussed. High-throughput phenotyping will allow constructing of large training populations and implementing of phenomic selection. Optimized modeling is needed for longitudinal traits and multi-environment trials. The use of multi-omics, haploblocks, and structural variants will enable going beyond single-locus genotype data. Innovative statistical approaches, like artificial neural networks, are expected to efficiently handle the increasing amounts of heterogeneous multi-scale data. Targeted recombinations on sites identified from profiles of marker effects have the potential to further increase genetic gain. GS can also aid re-domestication and introgression breeding. Finally, GS consortia will play an important role in making the best of these opportunities.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01326-4.

Resequencing worldwide spinach germplasm for identification of field resistance QTLs to downy mildew and assessment of genomic selection methods

Downy mildew, commercially the most important disease of spinach, is caused by the obligate oomycete Peronospora effusa. In the past two decades, new pathogen races have repeatedly overcome the resistance used in newly released cultivars, urging the need for more durable resistance. Commercial spinach cultivars are bred with major R genes to impart resistance to downy mildew pathogens and are effective against some pathogen races/isolates. This work aimed to evaluate the worldwide USDA spinach germplasm collections and commercial cultivars for resistance to downy mildew pathogen in the field condition under natural inoculum pressure and conduct genome wide association analysis (GWAS) to identify resistance-associated genomic regions (alleles). Another objective was to evaluate the prediction accuracy (PA) using several genomic prediction (GP) methods to assess the potential implementation of genomic selection (GS) to improve spinach breeding for resistance to downy mildew pathogen. More than four hundred diverse spinach genotypes comprising USDA germplasm accessions and commercial cultivars were evaluated for resistance to downy mildew pathogen between 2017-2019 in Salinas Valley, California and Yuma, Arizona. GWAS was performed using single nucleotide polymorphism (SNP) markers identified via whole genome resequencing (WGR) in GAPIT and TASSEL programs; detected 14, 12, 5, and 10 significantly associated SNP markers with the resistance from four tested environments, respectively; and the QTL alleles were detected at the previously reported region of chromosome 3 in three of the four experiments. In parallel, PA was assessed using six GP models and seven unique marker datasets for field resistance to downy mildew pathogen across four tested environments. The results suggest the suitability of GS to improve field resistance to downy mildew pathogen. The QTL, SNP markers, and PA estimates provide new information in spinach breeding to select resistant plants and breeding lines through marker-assisted selection (MAS) and GS, eventually helping to accumulate beneficial alleles for durable disease resistance.

Unravelling cucumber resistance to several viruses via genome-wide association studies highlighted resistance hotspots and new QTLs

The mapping and introduction of sustainable resistance to viruses in crops is a major challenge in modern breeding, especially regarding vegetables. We hence assembled a panel of cucumber elite lines and landraces from different horticultural groups for testing with six virus species. We mapped 18 quantitative trait loci (QTL) with a multiloci genome wide association studies (GWAS), some of which have already been described in the literature. We detected two resistance hotspots, one on chromosome 5 for resistance to the cucumber mosaic virus (CMV), cucumber vein yellowing virus (CVYV), cucumber green mottle mosaic virus (CGMMV) and watermelon mosaic virus (WMV), colocalizing with the RDR1 gene, and another on chromosome 6 for resistance to the zucchini yellowing mosaic virus (ZYMV) and papaya ringspot virus (PRSV) close to the putative VPS4 gene location. We observed clear structuring of resistance among horticultural groups due to plant virus coevolution and modern breeding which have impacted linkage disequilibrium (LD) in resistance QTLs. The inclusion of genetic structure in GWAS models enhanced the GWAS accuracy in this study. The dissection of resistance hotspots by local LD and haplotype construction helped gain insight into the panel's resistance introduction history. ZYMV and CMV resistance were both introduced from different donors in the panel, resulting in multiple resistant haplotypes at same locus for ZYMV, and in multiple resistant QTLs for CMV.

Population Structure of Moniliophthora perniciosa in the Main Cacao Producing Departments of Colombia

The witches' broom (Moniliophthora perniciosa) is considered as one of the main threats for cacao production and, consequently, for chocolate production worldwide. In this work, the genetic diversity and population structure of M. perniciosa were analyzed for 59 isolates collected in five departments of Colombia and using 10 microsatellite markers. Analyses revealed 35 multilocus genotypes and clonal populations structure according to linkage disequilibrium analysis. One of the objectives of this study was to determine whether populations were differentiated by geographic origin or Theobroma cacao host genotype. Analysis of molecular variance, discriminant analysis of principal components, and Bruvo genetic distance suggested that the genetic structure was driven by geographic origin and not by T. cacao genotype. The results of this study were consistent with previous findings obtained in other cocoa-producing countries. Important insights were discussed regarding the dispersal patterns of the pathogen in Colombia and the genetic change of its populations because of different environmental conditions.

Deciphering the Genetic Architecture of Plant Virus Resistance by GWAS, State of the Art and Potential Advances

Growing virus resistant varieties is a highly effective means to avoid yield loss due to infection by many types of virus. The challenge is to be able to detect resistance donors within plant species diversity and then quickly introduce alleles conferring resistance into elite genetic backgrounds. Until now, mainly monogenic forms of resistance with major effects have been introduced in crops. Polygenic resistance is harder to map and introduce in susceptible genetic backgrounds, but it is likely more durable. Genome wide association studies (GWAS) offer an opportunity to accelerate mapping of both monogenic and polygenic resistance, but have seldom been implemented and described in the plant-virus interaction context. Yet, all of the 48 plant-virus GWAS published so far have successfully mapped QTLs involved in plant virus resistance. In this review, we analyzed general and specific GWAS issues regarding plant virus resistance. We have identified and described several key steps throughout the GWAS pipeline, from diversity panel assembly to GWAS result analyses. Based on the 48 published articles, we analyzed the impact of each key step on the GWAS power and showcase several GWAS methods tailored to all types of viruses.

The BAHD Gene Family in Cacao (Theobroma cacao, Malvaceae): Genome-Wide Identification and Expression Analysis

The benzyl alcohol O-acetyl transferase, anthocyanin O-hydroxycinnamoyl transferase, N-hydroxycinnamoyl anthranilate benzoyl transferase, and deacetylvindoline 4-O-acetyltransferase (BAHD) enzymes play a critical role in regulating plant metabolites and affecting cell stability. In the present study, members of the BAHD gene family were recognized in the genome of Theobroma cacao and characterized using various bioinformatics tools. We found 27 non-redundant putative tcBAHD genes in cacao for the first time. Our findings indicate that tcBAHD genes are diverse based on sequence structure, physiochemical properties, and function. When analyzed with BAHDs of Gossypium raimondii and Corchorus capsularis clustered into four main groups. According to phylogenetic analysis, BAHD genes probably evolved drastically after their divergence. The divergence time of duplication events with purifying selection pressure was predicted to range from 1.82 to 15.50 MYA. Pocket analysis revealed that serine amino acid is more common in the binding site than other residuals, reflecting its key role in regulating the activity of tcBAHDs. Furthermore, cis-acting elements related to the responsiveness of stress and hormone, particularly ABA and MeJA, were frequently observed in the promoter region of tcBAHD genes. RNA-seq analysis further illustrated that tcBAHD13 and tcBAHD26 are involved in response to Phytophthora megakarya fungi. In conclusion, it is likely that evolutionary processes, such as duplication events, have caused high diversity in the structure and function of tcBAHD genes.

Status and advances in mining for blackleg (Leptosphaeria maculans) quantitative resistance (QR) in oilseed rape (Brassica napus)

KEY MESSAGE

Quantitative resistance (QR) loci discovered through genetic and genomic analyses are abundant in the Brassica napus genome, providing an opportunity for their utilization in enhancing blackleg resistance. Quantitative resistance (QR) has long been utilized to manage blackleg in Brassica napus (canola, oilseed rape), even before major resistance genes (R-genes) were extensively explored in breeding programmes. In contrast to R-gene-mediated qualitative resistance, QR reduces blackleg symptoms rather than completely eliminating the disease. As a polygenic trait, QR is controlled by numerous genes with modest effects, which exerts less pressure on the pathogen to evolve; hence, its effectiveness is more durable compared to R-gene-mediated resistance. Furthermore, combining QR with major R-genes has been shown to enhance resistance against diseases in important crops, including oilseed rape. For these reasons, there has been a renewed interest among breeders in utilizing QR in crop improvement. However, the mechanisms governing QR are largely unknown, limiting its deployment. Advances in genomics are facilitating the dissection of the genetic and molecular underpinnings of QR, resulting in the discovery of several loci and genes that can be potentially deployed to enhance blackleg resistance. Here, we summarize the efforts undertaken to identify blackleg QR loci in oilseed rape using linkage and association analysis. We update the knowledge on the possible mechanisms governing QR and the advances in searching for the underlying genes. Lastly, we lay out strategies to accelerate the genetic improvement of blackleg QR in oilseed rape using improved phenotyping approaches and genomic prediction tools.

The GASA Gene Family in Cacao (Theobroma cacao, Malvaceae): Genome Wide Identification and Expression Analysis

The gibberellic acid-stimulated Arabidopsis (GASA/GAST) gene family is widely distributed in plants and involved in various physiological and biological processes. These genes also provide resistance to abiotic and biotic stresses, including antimicrobial, antiviral, and antifungal. We are interested in characterizing the GASA gene family and determining its role in various physiological and biological process in Theobroma cacao. Here, we report 17 tcGASA genes distributed on six chromosomes in T. cacao. The gene structure, promoter region, protein structure and biochemical properties, expression, and phylogenetics of all tcGASAs were analyzed. Phylogenetic analyses divided tcGASA proteins into five groups. Among 17 tcGASA genes, nine segmentally duplicating genes were identified which formed four pairs and cluster together in phylogenetic tree. Differential expression analyses revealed that most of the tcGASA genes showed elevated expression in the seeds (cacao food), implying their role in seed development. The differential expression of tcGASAs was recorded between the tolerant and susceptible cultivars of cacao, which indicating their possible role as fungal resistant. Our findings provide new insight into the function, evolution, and regulatory system of the GASA family genes in T.cacao and may suggest new target genes for development of fungi-resistant cacao varieties in breeding programs.

The GASA Gene Family in Theobroma cacao: Genome wide Identification and Expression Analysis.. [DOI: 10.1101/2021.01.27.425041]

The gibberellic acid-stimulated Arabidopsis (GASA/GAST) gene family is widely distributed in plants. The role of the GASA gene family has been reported previously in various physiological and biological processes, such as cell division, root and seed development, stem growth, and fruit ripening. These genes also provide resistance to abiotic and biotic stresses including antimicrobial, antiviral, and antifungal. Here, we report 17 tcGASA genes in Theobroma cacao L. distributed on six chromosomes. The gene structure, promoter-region sequences, protein structure, and biochemical properties, expression, and phylogenetics of all tcGASAs were analyzed. Phylogenetic analyses divided tcGASA proteins into five groups. The nine segmentally duplicating genes form four pairs and cluster together in phylogenetic tree. Purifying selection pressure was recorded on tcGASA, including duplicated genes. Several stress/hormone-responsive cis-regulatory elements were also recognized in the promoter region of tcGASAs. Differential expression analyses revealed that most of the tcGASA genes showed elevated expression in the seeds (cacao food), implying their role in seed development. The black rod disease of genus Phytophthora caused up to 20–25% loss (700,000 metric tons) in world cacao production. The role of tcGASA genes in conferring fungal resistance was also explored based on RNAseq data against Phytophthora megakarya. The differential expression of tcGASA genes was recorded between the tolerant and susceptible cultivars of cacao plants, which were inoculated with the fungus for 24h and 72h. This differential expression indicating possible role of tcGASA genes to fungal resistant in cacao. Our findings provide new insight into the function, evolution, and regulatory system of the GASA family genes in T. cacao and provide new target genes for development of fungi-resistant cacao varieties in breeding programs.

A review of deep learning applications for genomic selection

BACKGROUND

Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years, deep learning (DL) methods have been considered in the context of genomic prediction. The DL methods are nonparametric models providing flexibility to adapt to complicated associations between data and output with the ability to adapt to very complex patterns.

MAIN BODY

We review the applications of deep learning (DL) methods in genomic selection (GS) to obtain a meta-picture of GS performance and highlight how these tools can help solve challenging plant breeding problems. We also provide general guidance for the effective use of DL methods including the fundamentals of DL and the requirements for its appropriate use. We discuss the pros and cons of this technique compared to traditional genomic prediction approaches as well as the current trends in DL applications.

CONCLUSIONS

The main requirement for using DL is the quality and sufficiently large training data. Although, based on current literature GS in plant and animal breeding we did not find clear superiority of DL in terms of prediction power compared to conventional genome based prediction models. Nevertheless, there are clear evidences that DL algorithms capture nonlinear patterns more efficiently than conventional genome based. Deep learning algorithms are able to integrate data from different sources as is usually needed in GS assisted breeding and it shows the ability for improving prediction accuracy for large plant breeding data. It is important to apply DL to large training-testing data sets.

Genome-Wide Association Study Reveals Novel Candidate Genes Associated with Productivity and Disease Resistance to Moniliophthora spp. in Cacao (Theobroma cacao L.)

Cacao (Theobroma cacao L.), the source of chocolate, is one of the most important commodity products worldwide that helps improve the economic livelihood of farmers. Diseases like frosty pod rot caused by Moniliophthora roreri and witches’ broom caused by Moniliophthora perniciosa limit the cacao productivity, this can be solved by using resistant varieties. In the current study, we sequenced 229 cacao accessions using genotyping-by-sequencing to examine the genetic diversity and population structure employing 9,003 and 8,131 single nucleotide polymorphisms recovered by mapping against two cacao genomes (Criollo B97-61/B2 v2 and Matina 1-6 v1.1). In the phenotypic evaluation, three promising accessions for productivity and 10 with good tolerance to the frosty pod rot and witches’ broom diseases were found. A genome-wide association study was performed on 102 accessions, discovering two genes associated with productivity and seven to disease resistance. The results enriched the knowledge of the genetic regions associated with important cacao traits that can have significant implications for conservation and breeding strategies like marker-assisted selection.

Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform

Genetic studies increasingly rely on high-throughput phenotyping, but the resulting longitudinal data pose analytical challenges. We used canopy height data from an automated field phenotyping platform to compare several approaches to scanning for quantitative trait loci (QTLs) and performing genomic prediction in a wheat recombinant inbred line mapping population based on up to 26 sampled time points (TPs). We detected four persistent QTLs (i.e. expressed for most of the growing season), with both empirical and simulation analyses demonstrating superior statistical power of detecting such QTLs through functional mapping approaches compared with conventional individual TP analyses. In contrast, even very simple individual TP approaches (e.g. interval mapping) had superior detection power for transient QTLs (i.e. expressed during very short periods). Using spline-smoothed phenotypic data resulted in improved genomic predictive abilities (5-8% higher than individual TP prediction), while the effect of including significant QTLs in prediction models was relatively minor (<1-4% improvement). Finally, although QTL detection power and predictive ability generally increased with the number of TPs analysed, gains beyond five or 10 TPs chosen based on phenological information had little practical significance. These results will inform the development of an integrated, semi-automated analytical pipeline, which will be more broadly applicable to similar data sets in wheat and other crops.

Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform

Genetic studies increasingly rely on high-throughput phenotyping, but the resulting longitudinal data pose analytical challenges. We used canopy height data from an automated field phenotyping platform to compare several approaches to scanning for quantitative trait loci (QTLs) and performing genomic prediction in a wheat recombinant inbred line mapping population based on up to 26 sampled time points (TPs). We detected four persistent QTLs (i.e. expressed for most of the growing season), with both empirical and simulation analyses demonstrating superior statistical power of detecting such QTLs through functional mapping approaches compared with conventional individual TP analyses. In contrast, even very simple individual TP approaches (e.g. interval mapping) had superior detection power for transient QTLs (i.e. expressed during very short periods). Using spline-smoothed phenotypic data resulted in improved genomic predictive abilities (5-8% higher than individual TP prediction), while the effect of including significant QTLs in prediction models was relatively minor (<1-4% improvement). Finally, although QTL detection power and predictive ability generally increased with the number of TPs analysed, gains beyond five or 10 TPs chosen based on phenological information had little practical significance. These results will inform the development of an integrated, semi-automated analytical pipeline, which will be more broadly applicable to similar data sets in wheat and other crops.

Clovamide, a Hydroxycinnamic Acid Amide, Is a Resistance Factor Against Phytophthora spp. in Theobroma cacao

The hydroxycinnamic acid amides (HCAAs) are a diverse group of plant-specialized phenylpropanoid metabolites distributed widely in the plant kingdom and are known to be involved in tolerance to abiotic and biotic stress. The HCAA clovamide is reported in a small number of distantly related species. To explore the contribution of specialized metabolites to disease resistance in cacao (Theobroma cacao L., chocolate tree), we performed untargeted metabolomics using liquid chromatography - tandem mass spectrometry (LC-MS/MS) and compared the basal metabolite profiles in leaves of two cacao genotypes with contrasting levels of susceptibility to Phytophthora spp. Leaves of the tolerant genotype 'Scavina 6' ('Sca6') were found to accumulate dramatically higher levels of clovamide and several other HCAAs compared to the susceptible 'Imperial College Selection 1' ('ICS1'). Clovamide was the most abundant metabolite in 'Sca6' leaf extracts based on MS signal, and was up to 58-fold higher in 'Sca6' than in 'ICS1'. In vitro assays demonstrated that clovamide inhibits growth of three pathogens of cacao in the genus Phytophthora, is a substrate for cacao polyphenol oxidase, and is a contributor to enzymatic browning. Furthermore, clovamide inhibited proteinase and pectinase in vitro, activities associated with defense in plant-pathogen interactions. Fruit epidermal peels from both genotypes contained substantial amounts of clovamide, but two sulfated HCAAs were present at high abundance exclusively in 'Sca6' suggesting a potential functional role of these compounds. The potential to breed cacao with increased HCAAs for improved agricultural performance is discussed.

Resistant and susceptible cacao genotypes exhibit defense gene polymorphism and unique early responses to Phytophthora megakarya inoculation

Key genes potentially involved in cacao disease resistance were identified by transcriptomic analysis of important cacao cultivars. Defense gene polymorphisms were identified which could contribute to pathogen recognition capacity. Cacao suffers significant annual losses to the water mold Phytophthora spp. (Oomycetes). In West Africa, P. megakarya poses a major threat to farmer livelihood and the stability of cocoa production. As part of a long-term goal to define key disease resistance genes in cacao, here we use a transcriptomic analysis of the disease-resistant cacao clone SCA6 and the susceptible clone NA32 to characterize basal differences in gene expression, early responses to infection, and polymorphisms in defense genes. Gene expression measurements by RNA-seq along a time course revealed the strongest transcriptomic response 24 h after inoculation in the resistant genotype. We observed strong regulation of several pathogenesis-related genes, pattern recognition receptors, and resistance genes, which could be critical for the ability of SCA6 to combat infection. These classes of genes also showed differences in basal expression between the two genotypes prior to infection, suggesting that prophylactic expression of defense-associated genes could contribute to SCA6's broad-spectrum disease resistance. Finally, we analyzed polymorphism in a set of defense-associated receptors, identifying coding variants between SCA6 and NA32 which could contribute to unique capacities for pathogen recognition. This work is an important step toward characterizing genetic differences underlying a successful defense response in cacao.

Evaluation of Genomic Prediction for Pasmo Resistance in Flax

Pasmo (Septoria linicola) is a fungal disease causing major losses in seed yield and quality and stem fibre quality in flax. Pasmo resistance (PR) is quantitative and has low heritability. To improve PR breeding efficiency, the accuracy of genomic prediction (GP) was evaluated using a diverse worldwide core collection of 370 accessions. Four marker sets, including three defined by 500, 134 and 67 previously identified quantitative trait loci (QTL) and one of 52,347 PR-correlated genome-wide single nucleotide polymorphisms, were used to build ridge regression best linear unbiased prediction (RR-BLUP) models using pasmo severity (PS) data collected from field experiments performed during five consecutive years. With five-fold random cross-validation, GP accuracy as high as 0.92 was obtained from the models using the 500 QTL when the average PS was used as the training dataset. GP accuracy increased with training population size, reaching values >0.9 with training population size greater than 185. Linear regression of the observed PS with the number of positive-effect QTL in accessions provided an alternative GP approach with an accuracy of 0.86. The results demonstrate the GP models based on marker information from all identified QTL and the 5-year PS average is highly effective for PR prediction.