Transcriptomic and presence/absence variation in the barley genome assessed from multi-tissue mRNA sequencing and their power to predict phenotypic traits

Genetic mapping reveals new loci and alleles for flowering time and plant height using the double round-robin population of barley

Flowering time and plant height are two critical determinants of yield potential in barley (Hordeum vulgare). Despite their role in plant physiological regulation, a complete overview of the genetic complexity of flowering time and plant height regulation in barley is still lacking. Using a double round-robin population originated from the crossings of 23 diverse parental inbred lines, we aimed to determine the variance components in the regulation of flowering time and plant height in barley as well as to identify new genetic variants by single and multi-population QTL analyses and allele mining. Despite similar genotypic variance, we observed higher environmental variance components for plant height than flowering time. Furthermore, we detected new QTLs for flowering time and plant height. Finally, we identified a new functional allelic variant of the main regulatory gene Ppd-H1. Our results show that the genetic architecture of flowering time and plant height might be more complex than reported earlier and that a number of undetected, small effect, or low-frequency genetic variants underlie the control of these two traits.

Affordable, accurate and unbiased RNA sequencing by manual library miniaturization: A case study in barley

We present an easy-to-reproduce manual miniaturized full-length RNA sequencing (RNAseq) library preparation workflow that does not require the upfront investment in expensive lab equipment or long setup times. With minimal adjustments to an established commercial protocol, we were able to manually miniaturize the RNAseq library preparation by a factor of up to 1:8. This led to cost savings for miniaturized library preparation of up to 86.1% compared to the gold standard. The resulting data were the basis of a rigorous quality control analysis that inspected: sequencing quality metrics, gene body coverage, raw read duplications, alignment statistics, read pair duplications, detected transcripts and sequence variants. We also included a deep dive data analysis identifying rRNA contamination and suggested ways to circumvent these. In the end, we could not find any indication of biases or inaccuracies caused by the RNAseq library miniaturization. The variance in detected transcripts was minimal and not influenced by the miniaturization level. Our results suggest that the workflow is highly reproducible and the sequence data suitable for downstream analyses such as differential gene expression analysis or variant calling.

Comparative gene retention analysis in barley, wild emmer, and bread wheat pangenome lines reveals factors affecting gene retention following gene duplication

BACKGROUND

Gene duplication is a prevalent phenomenon and a major driving force underlying genome evolution. The process leading to the fixation of gene duplicates following duplication is critical to understand how genome evolves but remains fragmentally understood. Most previous studies on gene retention are based on gene duplicate analyses in single reference genome. No population-based comparative gene retention analysis has been performed to date.

RESULTS

Taking advantage of recently published genomic data in Triticeae, we dissected a divergent homogentisate phytyltransferase (HPT2) lineage caught in the middle stage of gene fixation following duplication. The presence/absence of HPT2 in barley (diploid), wild emmer (tetraploid), and bread wheat (hexaploid) pangenome lines appears to be associated with gene dosage constraint and environmental adaption. Based on these observations, we adopted a phylogeny-based orthology inference approach and performed comparative gene retention analyses across barley, wild emmer, and bread wheat. This led to the identification of 326 HPT2-pattern-like genes at whole genome scale, representing a pool of gene duplicates in the middle stage of gene fixation. Majority of these HPT2-pattern-like genes were identified as small-scale duplicates, such as dispersed, tandem, and proximal duplications. Natural selection analyses showed that HPT2-pattern-like genes have experienced relaxed selection pressure, which is generally accompanied with partial positive selection and transcriptional divergence. Functional enrichment analyses showed that HPT2-pattern-like genes are over-represented with molecular-binding and defense response functions, supporting the potential role of environmental adaption during gene retention. We also observed that gene duplicates from larger gene family are more likely to be lost, implying a gene dosage constraint effect. Further comparative gene retention analysis in barley and bread wheat pangenome lines revealed combined effects of species-specific selection and gene dosage constraint.

CONCLUSIONS

Comparative gene retention analyses at the population level support gene dosage constraint, environmental adaption, and species-specific selection as three factors that may affect gene retention following gene duplication. Our findings shed light on the evolutionary process leading to the retention of newly formed gene duplicates and will greatly improve our understanding on genome evolution via duplication.

The double round-robin population unravels the genetic architecture of grain size in barley

Grain number, size and weight primarily determine the yield of barley. Although the genes regulating grain number are well studied in barley, the genetic loci and the causal gene for sink capacity are poorly understood. Therefore, the primary objective of our work was to dissect the genetic architecture of grain size and weight in barley. We used a multi-parent population developed from a genetic cross between 23 diverse barley inbreds in a double round-robin design. Seed size-related parameters such as grain length, grain width, grain area and thousand-grain weight were evaluated in the HvDRR population comprising 45 recombinant inbred line sub-populations. We found significant genotypic variation for all seed size characteristics, and observed 84% or higher heritability across four environments. The quantitative trait locus (QTL) detection results indicate that the genetic architecture of grain size is more complex than previously reported. In addition, both cultivars and landraces contributed positive alleles at grain size QTLs. Candidate genes identified using genome-wide variant calling data for all parental inbred lines indicated overlapping and potential novel regulators of grain size in cereals. Furthermore, our results indicated that sink capacity was the primary determinant of grain weight in barley.

Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation

Structural variants (SV) of 23 barley inbreds, detected by the best combination of SV callers based on short-read sequencing, were associated with genome-wide and gene-specific gene expression and, thus, were evaluated to predict agronomic traits. In human genetics, several studies have shown that phenotypic variation is more likely to be caused by structural variants (SV) than by single nucleotide variants. However, accurate while cost-efficient discovery of SV in complex genomes remains challenging. The objectives of our study were to (i) facilitate SV discovery studies by benchmarking SV callers and their combinations with respect to their sensitivity and precision to detect SV in the barley genome, (ii) characterize the occurrence and distribution of SV clusters in the genomes of 23 barley inbreds that are the parents of a unique resource for mapping quantitative traits, the double round robin population, (iii) quantify the association of SV clusters with transcript abundance, and (iv) evaluate the use of SV clusters for the prediction of phenotypic traits. In our computer simulations based on a sequencing coverage of 25x, a sensitivity > 70% and precision > 95% was observed for all combinations of SV types and SV length categories if the best combination of SV callers was used. We observed a significant (P < 0.05) association of gene-associated SV clusters with global gene-specific gene expression. Furthermore, about 9% of all SV clusters that were within 5 kb of a gene were significantly (P < 0.05) associated with the gene expression of the corresponding gene. The prediction ability of SV clusters was higher compared to that of single-nucleotide polymorphisms from an array across the seven studied phenotypic traits. These findings suggest the usefulness of exploiting SV information when fine mapping and cloning the causal genes underlying quantitative traits as well as the high potential of using SV clusters for the prediction of phenotypes in diverse germplasm sets.

Identification of Heat-Tolerant Genes in Non-Reference Sequences in Rice by Integrating Pan-Genome, Transcriptomics, and QTLs

The availability of large-scale genomic data resources makes it very convenient to mine and analyze genes that are related to important agricultural traits in rice. Pan-genomes have been constructed to provide insight into the genome diversity and functionality of different plants, which can be used in genome-assisted crop improvement. Thus, a pan-genome comprising all genetic elements is crucial for comprehensive variation study among the heat-resistant and -susceptible rice varieties. In this study, a rice pan-genome was firstly constructed by using 45 heat-tolerant and 15 heat-sensitive rice varieties. A total of 38,998 pan-genome genes were identified, including 37,859 genes in the reference and 1141 in the non-reference contigs. Genomic variation analysis demonstrated that a total of 76,435 SNPs were detected and identified as the heat-tolerance-related SNPs, which were specifically present in the highly heat-resistant rice cultivars and located in the genic regions or within 2 kbp upstream and downstream of the genes. Meanwhile, 3214 upregulated and 2212 downregulated genes with heat stress tolerance-related SNPs were detected in one or multiple RNA-seq datasets of rice under heat stress, among which 24 were located in the non-reference contigs of the rice pan-genome. We then mapped the DEGs with heat stress tolerance-related SNPs to the heat stress-resistant QTL regions. A total of 1677 DEGs, including 990 upregulated and 687 downregulated genes, were mapped to the 46 heat stress-resistant QTL regions, in which 2 upregulated genes with heat stress tolerance-related SNPs were identified in the non-reference sequences. This pan-genome resource is an important step towards the effective and efficient genetic improvement of heat stress resistance in rice to help meet the rapidly growing needs for improved rice productivity under different environmental stresses. These findings provide further insight into the functional validation of a number of non-reference genes and, especially, the two genes identified in the heat stress-resistant QTLs in rice.

Solving the grand challenge of phenotypic integration: allometry across scales

Phenotypic integration is a concept related to the cascade of trait relationships from the lowest organizational levels, i.e. genes, to the highest, i.e. whole-organism traits. However, the cause-and-effect linkages between traits are notoriously difficult to determine. In particular, we still lack a mathematical framework to model the relationships involved in the integration of phenotypic traits. Here, we argue that allometric models developed in ecology offer testable mathematical equations of trait relationships across scales. We first show that allometric relationships are pervasive in biology at different organizational scales and in different taxa. We then present mechanistic models that explain the origin of allometric relationships. In addition, we emphasized that recent studies showed that natural variation does exist for allometric parameters, suggesting a role for genetic variability, selection and evolution. Consequently, we advocate that it is time to examine the genetic determinism of allometries, as well as to question in more detail the role of genome size in subsequent scaling relationships. More broadly, a possible-but so far neglected-solution to understand phenotypic integration is to examine allometric relationships at different organizational levels (cell, tissue, organ, organism) and in contrasted species.

Stimulatory Effects of Androgens on Eel Primary Ovarian Development - from Phenotypes to Genotypes

Androgens stimulate primary ovarian development in Vertebrate. Japanese eels underwent operation to sample the pre- and post-treated ovarian tissues from the same individual. Ovarian phenotypic or genotypic data were mined in a pair. A correlation between the initial ovarian status (determined by kernel density estimation (KDE), presented as a probability density of oocyte size) and the consequence of androgen (17MT) treatment (change in ovary) has been showed. The initial ovarian status appeared to be important to influence ovarian androgenic sensitivity. The initial ovary was important to the outcomes of androgen treatments, and ePAV (expression presence-absence variation) is existing in Japanese eel by analyze DEGs; core, unique, or accessory genes were identified, the sensitivities of initial ovaries were correlated with their gene expression profiles. We speculated the importance of genetic differential expression on the variations of phenotypes by 17MT, and transcriptomic approach seems to allow extracting multiple layers of genomic data.

Genomic prediction of the recombination rate variation in barley - A route to highly recombinogenic genotypes

Meiotic recombination is not only fundamental to the adaptation of sexually reproducing eukaryotes in nature but increased recombination rates facilitate the combination of favourable alleles into a single haplotype in breeding programmes. The main objectives of this study were to (i) assess the extent and distribution of the recombination rate variation in cultivated barley (Hordeum vulgare L.), (ii) quantify the importance of the general and specific recombination effects, and (iii) evaluate a genomic selection approach's ability to predict the recombination rate variation. Genetic maps were created for the 45 segregating populations that were derived from crosses among 23 spring barley inbreds with origins across the world. The genome-wide recombination rate among populations ranged from 0.31 to 0.73 cM/Mbp. The crossing design used in this study allowed to separate the general recombination effects (GRE) of individual parental inbreds from the specific recombination effects (SRE) caused by the combinations of parental inbreds. The variance of the genome-wide GRE was found to be about eight times the variance of the SRE. This finding indicated that parental inbreds differ in the efficiency of their recombination machinery. The ability to predict the chromosome or genome-wide recombination rate of an inbred ranged from 0.80 to 0.85. These results suggest that a reliable screening of large genetic materials for their potential to cause a high extent of genetic recombination in their progeny is possible, allowing to systematically manipulate the recombination rate using natural variation.

Improvement of prediction ability by integrating multi-omic datasets in barley

Background

Genomic prediction (GP) based on single nucleotide polymorphisms (SNP) has become a broadly used tool to increase the gain of selection in plant breeding. However, using predictors that are biologically closer to the phenotypes such as transcriptome and metabolome may increase the prediction ability in GP. The objectives of this study were to (i) assess the prediction ability for three yield-related phenotypic traits using different omic datasets as single predictors compared to a SNP array, where these omic datasets included different types of sequence variants (full-SV, deleterious-dSV, and tolerant-tSV), different types of transcriptome (expression presence/absence variation-ePAV, gene expression-GE, and transcript expression-TE) sampled from two tissues, leaf and seedling, and metabolites (M); (ii) investigate the improvement in prediction ability when combining multiple omic datasets information to predict phenotypic variation in barley breeding programs; (iii) explore the predictive performance when using SV, GE, and ePAV from simulated 3’end mRNA sequencing of different lengths as predictors.

Results

The prediction ability from genomic best linear unbiased prediction (GBLUP) for the three traits using dSV information was higher than when using tSV, all SV information, or the SNP array. Any predictors from the transcriptome (GE, TE, as well as ePAV) and metabolome provided higher prediction abilities compared to the SNP array and SV on average across the three traits. In addition, some (di)-similarity existed between different omic datasets, and therefore provided complementary biological perspectives to phenotypic variation. Optimal combining the information of dSV, TE, ePAV, as well as metabolites into GP models could improve the prediction ability over that of the single predictors alone.

Conclusions

The use of integrated omic datasets in GP model is highly recommended. Furthermore, we evaluated a cost-effective approach generating 3’end mRNA sequencing with transcriptome data extracted from seedling without losing prediction ability in comparison to the full-length mRNA sequencing, paving the path for the use of such prediction methods in commercial breeding programs.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-022-08337-7).

Investigating the Transcriptomic and Expression Presence-Absence Variation Exist in Japanese Eel (Anguilla japonica), a Primitive Teleost

The pan-genome was defined as the complete gene set across strains, and it is built upon genes displaying presence-absence variations (PAVs); the pan-transcriptome is defined by recalling the pan-genome. Indeed, a PAV is reflected from the expression presence-absence variation (ePAV). In this study, treated with androgen, eels, which are a primitive fish from the basal lineage of Teleost, with different ovarian developments were chosen and submitted to RAN-sequencing. Transcriptomes were the assembly against eel genome scaffolds; a pair was the unit (the same eel before and after treatment) to analyze DEGs (differentially expressed genes); the core, unique, or accessory genes were identified, and the list of DEGs was analyzed to investigate ePAV. The results suggest that there was ePAV in Japanese eel, and the ePAV of eel was analyzed by pathway enrichment. These results signify the importance of genetic differential expression on the variations of phenotypes by androgen, and a transcriptomic approach appears to enable extracting multiple layers of genomic data.

Identification and expression analysis of miRNAs in germination and seedling growth of Tibetan hulless barley

Germination and seedling growth are crucial for plant development and agricultural production. While, the regulatory mechanisms during these processes in Tibetan hulless barley (Hordeum vulgare L. var. nudum) are not well understood. Given the regulatory roles of microRNAs (miRNAs) in crop plants and the irreplaceability of barley in the highland area of China, we herein presented a genome-wide survey of miRNAs to reveal a potential regulatory network in the early developmental stages of two Tibetan hulless barleys, from which a total of 156 miRNAs was identified including 35 known and 121 novel ones. Six of the identified novel miRNAs were further experimentally validated. According to the evolutionary analysis, miR156, miR166, miR168, and miR171 were conserved across Tibetan hulless barleys and eight other seed plants. Expression profiles of ten known miRNAs showed that they were involved in phytohormone signaling, carbohydrate and lipid metabolism, as well as juvenile-adult transition during barley development. Moreover, a total of 1280 genes targeted by 101 miRNAs were predicted from both barley libraries. Three genes (PLN03212, MATE eukaryotic, and GRAS) were validated via the RNA ligase-mediated 5'-rapid amplification of cDNA ends (RLM-5' RACE) to be the targets of hvu-miR159a, hvu-miR166a, and hvu-miR171-3p, respectively. Based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of putative targets, the most abundant pathways were related to "metabolism". These results revealed that miRNA-target pairs participating in the regulation of multigene expression and the embryonic development of Tibetan hulless barleys were controlled by complex mechanisms involving the concordant expression of different miRNAs and feedback loops among miRNAs as well as their targets. The study provides insight into the regulatory network of barley miRNAs for better understanding of miRNA functions during germination and seedling growth.

Merging Genomics and Transcriptomics for Predicting Fusarium Head Blight Resistance in Wheat

Genomic selection with genome-wide distributed molecular markers has evolved into a well-implemented tool in many breeding programs during the last decade. The resistance against Fusarium head blight (FHB) in wheat is probably one of the most thoroughly studied systems within this framework. Aside from the genome, other biological strata like the transcriptome have likewise shown some potential in predictive breeding strategies but have not yet been investigated for the FHB-wheat pathosystem. The aims of this study were thus to compare the potential of genomic with transcriptomic prediction, and to assess the merit of blending incomplete transcriptomic with complete genomic data by the single-step method. A substantial advantage of gene expression data over molecular markers has been observed for the prediction of FHB resistance in the studied diversity panel of breeding lines and released cultivars. An increase in prediction ability was likewise found for the single-step predictions, although this can mostly be attributed to an increased accuracy among the RNA-sequenced genotypes. The usage of transcriptomics can thus be seen as a complement to already established predictive breeding pipelines with pedigree and genomic data, particularly when more cost-efficient multiplexing techniques for RNA-sequencing will become more accessible in the future.