High-Resolution Genetic Map and QTL Analysis of Growth-Related Traits of Hevea brasiliensis Cultivated Under Suboptimal Temperature and Humidity Conditions

Genetic analysis of agronomic and physiological traits associated with latex yield revealed complex genetic bases in Hevea brasiliensis

Hevea brasiliensis, a natural rubber producing species, is widely cultivated due to its high rubber yield potential. Natural rubber is synthesised in the rubber particles of laticifers. Latex diagnosis (LD) was established to characterise the physiological state of the laticiferous system by measuring its physiological parameters, i.e., sucrose, inorganic phosphorous (Pi), thiols and total solid content (TSC). Rubber clones are often classified in three groups i.e., quick starters, medium starters and slow starters. To better understand the genetic bases of latex yield, a biparental population was generated from a cross between the quick-starter clone PB 260 and the medium-starter clone SP 217. LD was performed during the peak latex production season and used to calculate sucrose loading. The agronomic and physiological parameters associated with latex yield led to the classification of genotypes according to the rubber clonal typology and to the identification of quantitative trait loci (QTL) using a high-density map. Inorganic phosphorous content was positively associated with yield during the first year of production thus enabling identification of quick-starter clones. In addition, the LD-based clonal typology led to determine the long-term yield potential and the use of appropriate ethephon stimulation. QTL analysis successfully identified several QTLs related to yield, sucrose, Pi and TSC. One QTL related to sucrose loading was identified in the same position as the QTL for sucrose on linkage group 1. To our knowledge, this is the first study to report QTL analysis for this trait. The use of a high-density map enables the identification of genes underlying QTLs. Several putative genes underlying QTLs related to yield, sucrose and TSC were identified.

Analysis of Genetic Diversity and Resistance to Foliar Pathogens Based on Genotyping-by-Sequencing of a Para Rubber Diversity Panel and Progeny of an Interspecific Cross

Para rubber trees (Hevea brasiliensis) are the largest major source of natural rubber in the world. Its major pathogens are Phytophthora spp., Corynespora cassiicola, and Colletotrichum spp. A rubber diversity panel of 116 clones using over 12,000 single nucleotide polymorphisms (SNPs) from DArTSeq genotyping revealed clear phylogenetic differences in clones that originated from different geographical regions of the world. An integrated linkage map constructed with an F₁ progeny of 86 from an interspecific cross between H. brasiliensis and H. benthamiana using 23,978 markers [10,323 SNPs and 13,655 SilicoDArTs] spanned 3947.83 cM with 0.83 cM average marker-interval. The genome scaffolds that were anchored to the linkage map, covering 1.44 Gb of H. brasiliensis reference genome, revealed a high level of collinearity between the genetic map and reference genome. Association analysis identified 12 SNPs significantly associated with the resistance against Phytophthora, Corynespora, and Colletotrichum in six linkage groups: 2, 6, 12, 14, 17, and 18. Kompetitive Allele-Specific PCR marker assays were developed for those 12 SNPs, screened with 178 individuals, and detected clear separation between two genotypes. Within the proximity to those SNPs, 41 potentially key genes that have previously been reported to associate with plant disease resistance were predicted with high confidence.

A divide-and-conquer approach for genomic prediction in rubber tree using machine learning

Rubber tree (Hevea brasiliensis) is the main feedstock for commercial rubber; however, its long vegetative cycle has hindered the development of more productive varieties via breeding programs. With the availability of H. brasiliensis genomic data, several linkage maps with associated quantitative trait loci have been constructed and suggested as a tool for marker-assisted selection. Nonetheless, novel genomic strategies are still needed, and genomic selection (GS) may facilitate rubber tree breeding programs aimed at reducing the required cycles for performance assessment. Even though such a methodology has already been shown to be a promising tool for rubber tree breeding, increased model predictive capabilities and practical application are still needed. Here, we developed a novel machine learning-based approach for predicting rubber tree stem circumference based on molecular markers. Through a divide-and-conquer strategy, we propose a neural network prediction system with two stages: (1) subpopulation prediction and (2) phenotype estimation. This approach yielded higher accuracies than traditional statistical models in a single-environment scenario. By delivering large accuracy improvements, our methodology represents a powerful tool for use in Hevea GS strategies. Therefore, the incorporation of machine learning techniques into rubber tree GS represents an opportunity to build more robust models and optimize Hevea breeding programs.

QTL mapping for growth-related traits by constructing the first genetic linkage map in Simao pine

BACKGROUND

Simao pine is one of the primary economic tree species for resin and timber production in southwest China. The exploitation and utilization of Simao pine are constrained by the relatively lacking of genetic information. Construction a fine genetic linkage map and detecting quantitative trait locis (QTLs) for growth-related traits is a prerequisite section of Simao Pine's molecular breeding program.

RESULTS

In our study, a high-resolution Simao pine genetic map employed specific locus amplified fragment sequencing (SLAF-seq) technology and based on an F₁ pseudo-testcross population has been constructed. There were 11,544 SNPs assigned to 12 linkage groups (LGs), and the total length of the map was 2,062.85 cM with a mean distance of 0.37 cM between markers. According to the phenotypic variation analysis for three consecutive years, a total of seventeen QTLs for four traits were detected. Among 17 QTLs, there were six for plant height (Dh.16.1, Dh16.2, Dh17.1, Dh18.1-3), five for basal diameter (Dbd.17.1-5), four for needle length (Dnl17.1-3, Dnl18.1) and two for needle diameter (Dnd17.1 and Dnd18.1) respectively. These QTLs individually explained phenotypic variance from 11.0-16.3%, and the logarithm of odds (LOD) value ranged from 2.52 to 3.87.

CONCLUSIONS

In our study, a fine genetic map of Simao pine applied the technology of SLAF-seq has been constructed for the first time. Based on the map, a total of 17 QTLs for four growth-related traits were identified. It provides helpful information for genomic studies and marker-assisted selection (MAS) in Simao pine.

Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches

Hevea brasiliensis (rubber tree) is a large tree species of the Euphorbiaceae family with inestimable economic importance. Rubber tree breeding programs currently aim to improve growth and production, and the use of early genotype selection technologies can accelerate such processes, mainly with the incorporation of genomic tools, such as marker-assisted selection (MAS). However, few quantitative trait loci (QTLs) have been used successfully in MAS for complex characteristics. Recent research shows the efficiency of genome-wide association studies (GWAS) for locating QTL regions in different populations. In this way, the integration of GWAS, RNA-sequencing (RNA-Seq) methodologies, coexpression networks and enzyme networks can provide a better understanding of the molecular relationships involved in the definition of the phenotypes of interest, supplying research support for the development of appropriate genomic based strategies for breeding. In this context, this work presents the potential of using combined multiomics to decipher the mechanisms of genotype and phenotype associations involved in the growth of rubber trees. Using GWAS from a genotyping-by-sequencing (GBS) Hevea population, we were able to identify molecular markers in QTL regions with a main effect on rubber tree plant growth under constant water stress. The underlying genes were evaluated and incorporated into a gene coexpression network modelled with an assembled RNA-Seq-based transcriptome of the species, where novel gene relationships were estimated and evaluated through in silico methodologies, including an estimated enzymatic network. From all these analyses, we were able to estimate not only the main genes involved in defining the phenotype but also the interactions between a core of genes related to rubber tree growth at the transcriptional and translational levels. This work was the first to integrate multiomics analysis into the in-depth investigation of rubber tree plant growth, producing useful data for future genetic studies in the species and enhancing the efficiency of the species improvement programs.

Optimizing imputation of marker data from genotyping-by-sequencing (GBS) for genomic selection in non-model species: Rubber tree (Hevea brasiliensis) as a case study

Genotyping-by-sequencing (GBS) provides the marker density required for genomic predictions (GP). However, GBS gives a high proportion of missing SNP data which, for species without a chromosome-level genome assembly, must be imputed without knowing the SNP physical positions. Here, we compared GP accuracy with seven map-independent and two map-dependent imputation approaches, and when using all SNPs against the subset of genetically mapped SNPs. We used two rubber tree (Hevea brasiliensis) datasets with three traits. The results showed that the best imputation approaches were LinkImputeR, Beagle and FImpute. Using the genetically mapped SNPs increased GP accuracy by 4.3%. Using LinkImputeR on all the markers allowed avoiding genetic mapping, with a slight decrease in GP accuracy. LinkImputeR gave the highest level of correctly imputed genotypes and its performances were further improved by its ability to define a subset of SNPs imputed optimally. These results will contribute to the efficient implementation of genomic selection with GBS. For Hevea, GBS is promising for rubber yield improvement, with GP accuracies reaching 0.52.

Update of Genetic Linkage Map and QTL Analysis for Growth Traits in Eucommia ulmoides Oliver

Eucommia ulmoides (Tu-chung) is an economically and ecologically important tree species which has attracted worldwide attention due to its application in pharmacology, landscaping, wind sheltering and sand fixation. Molecular marker technologies can elucidate the genetic mechanism and substantially improve the breeding efficiency of E. ulmoides. The current research updated the original linkage map, and quantitative trait loci (QTL) analysis was performed on tree growth traits measured over 10 consecutive years in an E. ulmoides F1 population (“Xiaoye” × “Qinzhong No.1”). In total, 452 polymorphic markers were scored from 365 simple sequence repeat (SSR) primers, with an average of 1.24 polymorphic markers per primer combination. The integrated map was 1913.29 cM (centimorgan) long, covering 94.10% of the estimated genome and with an average marker density of 2.20 cM. A total of 869 markers were mapped into 19 major independent linkage groups. Growth-related traits measured over 10 consecutive years showed a significant correlation, and 89 hypothetical QTLs were forecasted and divided into 27 distinct loci. Three traits for tree height, ground diameter and crown diameter detected 25 QTLs (13 loci), 32 QTLs (17 loci) and 15 QTLs (10 loci), respectively. Based on BLASTX search results in the NCBI database, six candidate genes were obtained. It is important to explore the growth-related genetic mechanism and lay the foundation for the genetic improvement of E. ulmoides at the molecular level.

Genetic Mapping of Prince Rupprecht's Larch (Larix principis-rupprechtii Mayr) by Specific-Locus Amplified Fragment Sequencing

A high-density genetic linkage map is essential for plant genetics and genomics research. However, due to the deficiency of genomic data and high-quality molecular markers, no genetic map has been published for Prince Rupprecht’s larch (Larix principis-rupprechtii Mayr), a conifer species with high ecological and commercial value in northern China. In this study, 145 F1 progeny individuals from an intraspecific cross between two elite clones of L. principis-rupprechtii and their parents were employed to construct the first genetic map in this important tree species using specific-locus amplified fragment sequencing (SLAF-seq). After preprocessing, the procedure yielded 300.20 Gb of raw data containing 1501.22 M pair-end reads. A total of 324,352 SNP markers were detected and 122,785 of them were polymorphic, with a polymorphism rate of 37.86%. Ultimately, 6099 SNPs were organized into a genetic map containing 12 linkage groups, consistent with the haploid chromosome number of larch and most other species in the Pinaceae family. The linkage map spanned 2415.58 cM and covered 99.6% of the L. principis-rupprechtii genome with an average of 0.4 cM between adjacent markers. To the best of our knowledge, this map is the first reference map for L. principis-rupprechtii, as well as the densest one obtained in larch species thus far. The genome-wide SNPs and the high-resolution genetic map will provide a foundation for future quantitative trait loci mapping, map-based cloning, marker-assisted selection, comparative genomics, and genome sequence assembly for larch trees.

Genomic technologies for Hevea breeding

The commercial production of high quality natural rubber (NR) solely depends on Hevea brasiliensis Muell. Arg, (Para rubber tree) and accounts for >98% of total production worldwide. NR with its unique properties is an essential commodity for the automobile industry and its synthetic counterparts are in no way substitute to it. The rubber tree genome is very complex and plays an important role in delivering the unique properties of Hevea. But a lack of knowledge on the molecular mechanisms of rubber biosynthesis, disease resistance, etc., in elite clones of rubber still persists. Marker-assisted selection and transgenic techniques were proved to be advantageous in improving the breeding efficiency for latex yield, disease resistance, etc. The suppression subtractive hybridization (SSH), in the form of subtracted cDNA libraries and microarrays, can assist in searching the functions of expressed genes (candidate gene approach). Expressed sequence tags (ESTs) related to various metabolic aspects are well utilized to create EST banks that broadly represent the genes expressed in one tissue, such as latex cells, that assists in the study of gene function and regulation. Transcriptome analysis and gene mapping have been accomplished in Hevea at various stages. However, a selection criterion to delineate high yielding genotypes at the juvenile stage has not been accomplished so far. This is the main pit fall for rubber breeding apart from stock-scion interactions leading to yield differences among a clonally multiplied population. At least four draft genome sequences have been published on Hevea rubber, and all give different genome size and contig lengths-a comprehensive and acceptable genomic map remains unfulfilled. The progress made in molecular markers, latex biosynthesis genes, transcriptome analysis, chloroplast and mitochondrial DNA diversity, paternity identification through Breeding without Breeding (BwB), stimulated latex production and its molecular intricacies, molecular biology of tapping panel dryness, genomics for changed climates and genome mapping are discussed in this review. These information can be utilized to improvise the molecular breeding programs of Hevea in future.