Approaches to variant discovery for conifer transcriptome sequencing

Genomic selection for resistance to mammalian bark stripping and associated chemical compounds in radiata pine

The integration of genomic data into genetic evaluations can facilitate the rapid selection of superior genotypes and accelerate the breeding cycle in trees. In this study, 390 trees from 74 control-pollinated families were genotyped using a 36K Axiom SNP array. A total of 15,624 high-quality SNPs were used to develop genomic prediction models for mammalian bark stripping, tree height, and selected primary and secondary chemical compounds in the bark. Genetic parameters from different genomic prediction methods-single-trait best linear unbiased prediction based on a marker-based relationship matrix (genomic best linear unbiased prediction), multitrait single-step genomic best linear unbiased prediction, which integrated the marker-based and pedigree-based relationship matrices (single-step genomic best linear unbiased prediction) and the single-trait generalized ridge regression-were compared to equivalent single- or multitrait pedigree-based approaches (ABLUP). The influence of the statistical distribution of data on the genetic parameters was assessed. Results indicated that the heritability estimates were increased nearly 2-fold with genomic models compared to the equivalent pedigree-based models. Predictive accuracy of the single-step genomic best linear unbiased prediction was higher than the ABLUP for most traits. Allowing for heterogeneity in marker effects through the use of generalized ridge regression did not markedly improve predictive ability over genomic best linear unbiased prediction, arguing that most of the chemical traits are modulated by many genes with small effects. Overall, the traits with low pedigree-based heritability benefited more from genomic models compared to the traits with high pedigree-based heritability. There was no evidence that data skewness or the presence of outliers affected the genomic or pedigree-based genetic estimates.

High density linkage maps, genetic architecture, and genomic prediction of growth and wood properties in Pinus radiata

Background

The growing availability of genomic resources in radiata pine paves the way for significant advances in fundamental and applied genomic research. We constructed robust high-density linkage maps based on exome-capture genotyping in two F₁ populations, and used these populations to perform quantitative trait locus (QTL) scans, genomic prediction and quantitative analyses of genetic architecture for key traits targeted by tree improvement programmes.

Results

Our mapping approach used probabilistic error correction of the marker data, followed by an iterative approach based on stringent parameters. This approach proved highly effective in producing high-density maps with robust marker orders and realistic map lengths (1285–4674 markers per map, with sizes ranging from c. 1643–2292 cM, and mean marker intervals of 0.7–2.1 cM). Colinearity was high between parental linkage maps, although there was evidence for a large chromosomal rearrangement (affecting ~ 90 cM) in one of the parental maps. In total, 28 QTL were detected for growth (stem diameter) and wood properties (wood density and fibre properties measured by Silviscan) in the QTL discovery population, with 1–3 QTL of small to moderate effect size detected per trait in each parental map. Four of these QTL were validated in a second, unrelated F₁ population. Results from genomic prediction and analyses of genetic architecture were consistent with those from QTL scans, with wood properties generally having moderate to high genomic heritabilities and predictive abilities, as well as somewhat less complex genetic architectures, compared to growth traits.

Conclusions

Despite the economic importance of radiata pine as a plantation forest tree, robust high-density linkage maps constructed from reproducible, sequence-anchored markers have not been published to date. The maps produced in this study will be a valuable resource for several applications, including the selection of marker panels for genomic prediction and anchoring a recently completed de novo whole genome assembly. We also provide the first map-based evidence for a large genomic rearrangement in radiata pine. Finally, results from our QTL scans, genomic prediction, and genetic architecture analyses are informative about the genomic basis of variation in important phenotypic traits.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08950-6.

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.

Chasing genetic correlation breakers to stimulate population resilience to climate change

Global climate change introduces new combinations of environmental conditions, which is expected to increase stress on plants. This could affect many traits in multiple ways that are as yet unknown but will likely require the modification of existing genetic relationships among functional traits potentially involved in local adaptation. Theoretical evolutionary studies have determined that it is an advantage to have an excess of recombination events under heterogeneous environmental conditions. Our study, conducted on a population of radiata pine (Pinus radiata D. Don), was able to identify individuals that show high genetic recombination at genomic regions, which potentially include pleiotropic or collocating QTLs responsible for the studied traits, reaching a prediction accuracy of 0.80 in random cross-validation and 0.72 when whole family was removed from the training population and predicted. To identify these highly recombined individuals, a training population was constructed from correlation breakers, created through tandem selection of parents in the previous generation and their consequent mating. Although the correlation breakers showed lower observed heterogeneity possibly due to direct selection in both studied traits, the genomic regions with statistically significant differences in the linkage disequilibrium pattern showed higher level of heretozygosity, which has the effect of decomposing unfavourable genetic correlation. We propose undertaking selection of correlation breakers under current environmental conditions and using genomic predictions to increase the frequency of these ’recombined’ individuals in future plantations, ensuring the resilience of planted forests to changing climates. The increased frequency of such individuals will decrease the strength of the population-level genetic correlations among traits, increasing the opportunity for new trait combinations to be developed in the future.

Genomics-Enabled Management of Genetic Resources in Radiata Pine

Traditional tree improvement is cumbersome and costly. Our main objective was to assess the extent to which genomic data can currently accelerate and improve decision making in this field. We used diameter at breast height (DBH) and wood density (WD) data for 4430 tree genotypes and single-nucleotide polymorphism (SNP) data for 2446 tree genotypes. Pedigree reconstruction was performed using a combination of maximum likelihood parentage assignment and matching based on identity-by-state (IBS) similarity. In addition, we used best linear unbiased prediction (BLUP) methods to predict phenotypes using SNP markers (GBLUP), recorded pedigree information (ABLUP), and single-step “blended” BLUP (HBLUP) combining SNP and pedigree information. We substantially improved the accuracy of pedigree records, resolving the inconsistent parental information of 506 tree genotypes. This led to substantially increased predictive ability (i.e., by up to 87%) in HBLUP analyses compared to a baseline from ABLUP. Genomic prediction was possible across populations and within previously untested families with moderately large training populations (N = 800–1200 tree genotypes) and using as few as 2000–5000 SNP markers. HBLUP was generally more effective than traditional ABLUP approaches, particularly after dealing appropriately with pedigree uncertainties. Our study provides evidence that genome-wide marker data can significantly enhance tree improvement. The operational implementation of genomic selection has started in radiata pine breeding in New Zealand, but further reductions in DNA extraction and genotyping costs may be required to realise the full potential of this approach.

Development and Validation of a 36K SNP Array for Radiata Pine (Pinus radiata D.Don)

Radiata pine (Pinus radiata D.Don) is one of the world’s most domesticated pines and a key economic species in New Zealand. Thus, the development of genomic resources for radiata pine has been a high priority for both research and commercial breeding. Leveraging off a previously developed exome capture panel, we tested the performance of 438,744 single nucleotide polymorphisms (SNPs) on a screening array (NZPRAD01) and then selected 36,285 SNPs for a final genotyping array (NZPRAD02). These SNPs aligned to 15,372 scaffolds from the Pinus taeda L. v. 1.01e assembly, and 20,039 contigs from the radiata pine transcriptome assembly. The genotyping array was tested on more than 8000 samples, including material from archival progenitors, current breeding trials, nursery material, clonal lines, and material from Australia. Our analyses indicate that the array is performing well, with sample call rates greater than 98% and a sample reproducibility of 99.9%. Genotyping in two linkage mapping families indicated that the SNPs are well distributed across the 12 linkage groups. Using genotypic data from this array, we were also able to differentiate representatives of the five recognized provenances of radiata pine, Año Nuevo, Monterey, Cambria, Cedros and Guadalupe. Furthermore, principal component analysis of genotyped trees revealed clear patterns of population structure, with the primary axis of variation driven by provenance ancestry and the secondary axis reflecting breeding activities. This represents the first commercial use of genomics in a radiata pine breeding program.

Analysis of the transcriptome of the needles and bark of Pinus radiata induced by bark stripping and methyl jasmonate

BACKGROUND

Plants are attacked by diverse insect and mammalian herbivores and respond with different physical and chemical defences. Transcriptional changes underlie these phenotypic changes. Simulated herbivory has been used to study the transcriptional and other early regulation events of these plant responses. In this study, constitutive and induced transcriptional responses to artificial bark stripping are compared in the needles and the bark of Pinus radiata to the responses from application of the plant stressor, methyl jasmonate. The time progression of the responses was assessed over a 4-week period.

RESULTS

Of the 6312 unique transcripts studied, 86.6% were differentially expressed between the needles and the bark prior to treatment. The most abundant constitutive transcripts were related to defence and photosynthesis and their expression did not differ between the needles and the bark. While no differential expression of transcripts were detected in the needles following bark stripping, in the bark this treatment caused an up-regulation and down-regulation of genes associated with primary and secondary metabolism. Methyl jasmonate treatment caused differential expression of transcripts in both the bark and the needles, with individual genes related to primary metabolism more responsive than those associated with secondary metabolism. The up-regulation of genes related to sugar break-down and the repression of genes related with photosynthesis, following both treatments was consistent with the strong down-regulation of sugars that has been observed in the same population. Relative to the control, the treatments caused a differential expression of genes involved in signalling, photosynthesis, carbohydrate and lipid metabolism as well as defence and water stress. However, non-overlapping transcripts were detected between the needles and the bark, between treatments and at different times of assessment. Methyl jasmonate induced more transcriptional responses in the bark than bark stripping, although the peak of expression following both treatments was detected 7 days post treatment application. The effects of bark stripping were localised, and no systemic changes were detected in the needles.

CONCLUSION

There are constitutive and induced differences in the needle and bark transcriptome of Pinus radiata. Some expression responses to bark stripping may differ from other biotic and abiotic stresses, which contributes to the understanding of plant molecular responses to diverse stresses. Whether the gene expression changes are heritable and how they differ between resistant and susceptible families identified in earlier studies needs further investigation.

Genetic Variation in Drought-Tolerance Traits and Their Relationships to Growth in Pinus radiata D. Don Under Water Stress

The selection of drought-tolerant genotypes is globally recognized as an effective strategy to maintain the growth and survival of commercial tree species exposed to future drought periods. New genomic selection tools that reduce the time of progeny trials are required to substitute traditional tree breeding programs. We investigated the genetic variation of water stress tolerance in New Zealand-grown Pinus radiata D. Don using 622 commercially-used genotypes from 63 families. We used quantitative pedigree-based (Genomic Best Linear Unbiased Prediction or ABLUP) and genomic-based (Genomic Best Linear Unbiased Prediction or GBLUP) approaches to examine the heritability estimates associated with water stress tolerance in P. radiata. Tree seedling growth traits, foliar carbon isotope composition (δ13C), and dark-adapted chlorophyll fluorescence (Y) were monitored before, during and after 10 months of water stress. Height growth showed a constant and moderate heritability level, while the heritability estimate for diameter growth and δ13C decreased with water stress. In contrast, chlorophyll fluorescence exhibited low heritability after 5 and 10 months of water stress. The GBLUP approach provided less breeding value accuracy than ABLUP, however, the relative selection efficiency of GBLUP was greater compared with ABLUP selection techniques. Although there was no significant relationship directly between δ13C and Y, the genetic correlations were significant and stronger for GBLUP. The positive genetic correlations between δ13C and tree biomass traits under water stress indicated that intraspecific variation in δ13C was likely driven by differences in the genotype's photosynthetic capacity. The results show that foliar δ13C can predict P. radiata genotype tolerance to water stress using ABLUP and GBLUP approaches and that such approaches can provide a faster screening and selection of drought-tolerant genotypes for forestry breeding programs.

Mass spectrometry-based forest tree metabolomics

Research in forest tree species has advanced slowly when compared with other agricultural crops and model organisms, mainly due to the long-life cycles, large genome sizes, and lack of genomic tools. Additionally, trees are complex matrices, and the presence of interferents (e.g., oleoresins and cellulose) challenges the analysis of tree tissues with mass spectrometry (MS)-based analytical platforms. In this review, advances in MS-based forest tree metabolomics are discussed. Given their economic and ecological significance, particular focus is given to Pinus, Quercus, and Eucalyptus forest tree species to better understand their metabolite responses to abiotic and biotic stresses in the current climate change scenario. Furthermore, MS-based metabolomics technologies produce large and complex datasets that require expertize to adequately manage, process, analyze, and store the data in dedicated repositories. To ensure that the full potential of forest tree metabolomics data are translated into new knowledge, these data should comply with the FAIR principles (i.e., Findable, Accessible, Interoperable, and Re-usable). It is essential that adequate standards are implemented to annotate metadata from forest tree metabolomics studies as is already required by many science and governmental agencies and some major scientific publishers. © 2019 John Wiley & Sons Ltd. Mass Spec Rev 40:126-157, 2021.

Multiple-genotypes transcriptional analysis revealed candidates genes and nucleotide variants for improvement of quality characteristics in tea (Camellia sinensis (L.) O. Kuntze)

Tea quality is a polygenic trait that exhibits tremendous genetic variability due to accumulation of array of secondary metabolites. To elucidate global molecular insights controlling quality attributes, metabolite profiling and transcriptome sequencing of twelve diverse tea cultivars was performed in tea shoots harvested during quality season. RP-HPLC-DAD analysis of quality parameters revealed significant difference in catechins, theanine and caffeine contents. Transcriptome sequencing resulted into 50,107 non-redundant transcripts with functional annotations of 81.6% (40,847) of the transcripts. Interestingly, 2872 differentially expressed transcripts exhibited significant enrichment in 38 pathways (FDR ≤ 0.05) including secondary metabolism, amino acid and carbon metabolism. Thirty-eight key candidates reportedly involved in biosynthesis of fatty acid derived volatiles, volatile terpenes, glycoside hydrolysis and key quality related pathways (flavonoid, caffeine and theanine-biosynthesis) were highly expressed in catechins-rich tea cultivars. Furthermore, enrichment of candidates involved in flavonoid biosynthesis, transcriptional regulation, volatile terpene and biosynthesis of fatty acid derived volatile in Protein-Protein Interactome network revealed well-coordinated regulation of quality characteristics in tea. Additionally, ascertainment of 23,649 non-synonymous SNPs and validation of candidate SNPs present in quality related genes suggests their potential utility in genome-wide mapping and marker development for expediting breeding of elite compound-rich tea cultivars.

Marker Selection in Multivariate Genomic Prediction Improves Accuracy of Low Heritability Traits

Multivariate analysis using mixed models allows for the exploration of genetic correlations between traits. Additionally, the transition to a genomic based approach is simplified by substituting classic pedigrees with a marker-based relationship matrix. It also enables the investigation of correlated responses to selection, trait integration and modularity in different kinds of populations. This study investigated a strategy for the construction of a marker-based relationship matrix that prioritized markers using Partial Least Squares. The efficiency of this strategy was found to depend on the correlation structure between investigated traits. In terms of accuracy, we found no benefit of this strategy compared with the all-marker-based multivariate model for the primary trait of diameter at breast height (DBH) in a radiata pine (Pinus radiata) population, possibly due to the presence of strong and well-estimated correlation with other highly heritable traits. Conversely, we did see benefit in a shining gum (Eucalyptus nitens) population, where the primary trait had low or only moderate genetic correlation with other low/moderately heritable traits. Marker selection in multivariate analysis can therefore be an efficient strategy to improve prediction accuracy for low heritability traits due to improved precision in poorly estimated low/moderate genetic correlations. Additionally, our study identified the genetic diversity as a factor contributing to the efficiency of marker selection in multivariate approaches due to higher precision of genetic correlation estimates.

Effect of trait's expression level on single-step genomic evaluation of resistance to Dothistroma needle blight

BACKGROUND

Many conifer breeding programs are paying increasing attention to breeding for resistance to needle disease due to the increasing importance of climate change. Phenotyping of traits related to resistance has many biological and temporal constraints that can often confound the ability to achieve reliable phenotypes and consequently, reliable genetic progress. The development of next generation sequencing platforms has also enabled implementation of genomic approaches in species lacking robust reference genomes. Genomic selection is, therefore, a promising strategy to overcome the constraints of needle disease phenotyping.

RESULTS

We found high accuracy in the prediction of genomic breeding values in the disease-related traits that were well characterized, reaching 0.975 for genotyped individuals and 0.587 for non-genotyped individuals. This compared well with pedigree-based accuracies of up to 0.746. Surprisingly, poorly phenotyped disease traits also showed very high accuracy in terms of correlation of predicted genomic breeding values with pedigree-based counterparts. However, this was likely caused by the fact that both were clustered around the population mean, while deviations from the population mean caused by genetic effects did not appear to be well described. Caution should therefore be taken with the interpretation of results in poorly phenotyped traits.

CONCLUSIONS

Implementation of genomic selection in this test population of Pinus radiata resulted in a relatively high prediction accuracy of needle loss due to Dothistroma septosporum compared with a pedigree-based approach. Using genomics to avoid biological/temporal constraints where phenotyping is reliable appears promising. Unsurprisingly, reliable phenotyping, resulting in good heritability estimates, is a fundamental requirement for the development of a reliable prediction model. Furthermore, our results are also specific to the single pathogen mating-type that is present in New Zealand, and may change with future incursion of other pathogen varieties. There is no doubt, however, that once a robust genomic prediction model is built, it will be invaluable to not only select for host tolerance, but for other economically important traits simultaneously. This tool will thus future-proof our forests by mitigating the risk of disease outbreaks induced by future changes in climate.

Effect of trait's expression level on single-step genomic evaluation of resistance to Dothistroma needle blight

BACKGROUND

Many conifer breeding programs are paying increasing attention to breeding for resistance to needle disease due to the increasing importance of climate change. Phenotyping of traits related to resistance has many biological and temporal constraints that can often confound the ability to achieve reliable phenotypes and consequently, reliable genetic progress. The development of next generation sequencing platforms has also enabled implementation of genomic approaches in species lacking robust reference genomes. Genomic selection is, therefore, a promising strategy to overcome the constraints of needle disease phenotyping.

RESULTS

We found high accuracy in the prediction of genomic breeding values in the disease-related traits that were well characterized, reaching 0.975 for genotyped individuals and 0.587 for non-genotyped individuals. This compared well with pedigree-based accuracies of up to 0.746. Surprisingly, poorly phenotyped disease traits also showed very high accuracy in terms of correlation of predicted genomic breeding values with pedigree-based counterparts. However, this was likely caused by the fact that both were clustered around the population mean, while deviations from the population mean caused by genetic effects did not appear to be well described. Caution should therefore be taken with the interpretation of results in poorly phenotyped traits.

CONCLUSIONS

Implementation of genomic selection in this test population of Pinus radiata resulted in a relatively high prediction accuracy of needle loss due to Dothistroma septosporum compared with a pedigree-based approach. Using genomics to avoid biological/temporal constraints where phenotyping is reliable appears promising. Unsurprisingly, reliable phenotyping, resulting in good heritability estimates, is a fundamental requirement for the development of a reliable prediction model. Furthermore, our results are also specific to the single pathogen mating-type that is present in New Zealand, and may change with future incursion of other pathogen varieties. There is no doubt, however, that once a robust genomic prediction model is built, it will be invaluable to not only select for host tolerance, but for other economically important traits simultaneously. This tool will thus future-proof our forests by mitigating the risk of disease outbreaks induced by future changes in climate.

Reduced Virulence of an Introduced Forest Pathogen over 50 Years

Pathogen incursions are a major impediment for global forest health. How pathogens and forest trees coexist over time, without pathogens simply killing their long-lived hosts, is a critical but unanswered question. The Dothistroma Needle Blight pathogen Dothistroma septosporum was introduced into New Zealand in the 1960s and remains a low-diversity, asexual population, providing a unique opportunity to analyze the evolution of a forest pathogen. Isolates of D. septosporum collected from commercial pine forests over 50 years were compared at whole-genome and phenotype levels. Limited genome diversity and increased diversification among recent isolates support the premise of a single introduction event. Isolates from the 1960s show significantly elevated virulence against Pinus radiata seedlings and produce higher levels of the virulence factor dothistromin compared to isolates collected in the 1990s and 2000s. However, later isolates have no increased tolerance to copper, used in fungicide treatments of infested forests and traditionally assumed to be a strong selection pressure. The isolated New Zealand population of this forest pathogen therefore appears to have become less virulent over time, likely in part to maintain the viability of its long-lived host. This finding has broad implications for forest health and highlights the benefits of long-term pathogen surveys.

A high-density exome capture genotype-by-sequencing panel for forestry breeding in Pinus radiata

Development of genome-wide resources for application in genomic selection or genome-wide association studies, in the absence of full reference genomes, present a challenge to the forestry industry, where longer breeding cycles could benefit from the accelerated selection possible through marker-based breeding value predictions. In particular, large conifer megagenomes require a strategy to reduce complexity, whilst ensuring genome-wide coverage is achieved. Using a transcriptome-based reference template, we have successfully developed a high density exome capture genotype-by-sequencing panel for radiata pine (Pinus radiata D.Don), capable of capturing in excess of 80,000 single nucleotide polymorphism (SNP) markers with a minor allele frequency above 0.03 in the population tested. This represents approximately 29,000 gene models from a core set of 48,914 probes. A set of 704 SNP markers capable of pedigree reconstruction and differentiating individual genotypes were tested within two full-sib mapping populations. While as few as 70 markers could reconstruct parentage in almost all cases, the impact of missing genotypes was noticeable in several offspring. Therefore, 60 sets of 110 randomly selected SNP markers were compared for both parentage reconstruction and clone differentiation. The performance in parentage reconstruction showed little variation over 60 iterations. However, there was notable variation in discriminatory power between closely related individuals, indicating a higher density SNP marker panel may be required to elucidate hidden relationships in complex pedigrees.