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

Leveraging genomic prediction to surpass current yield gains in spring barley

KEY MESSAGE

Genetic gain in Nordic spring barley varieties was estimated to 1.07% per year. Additionally, genomic predictive ability for yield was 0.61 in a population of breeding lines. Barley is one of the most important crops in Europe and meeting the growing demand for food and feed requires continuous increase in yield. Genomic prediction (GP) has the potential to be a cost-efficient tool in breeding for complex traits; however, the rate of yield improvement in current barley varieties is unknown. This study therefore investigated historical and current genetic gains in spring barley and how accounting for row-type population stratification in a breeding population influences GP results. The genetic gain in yield was estimated using historical data from field trials from 2014 to 2022, with 22-60 market varieties grown yearly. The genetic gain was estimated to 1.07% per year for all varieties, serving as a reference point for future breeding progress. To analyse the potential of using GP in spring barley a population of 375 breeding lines of two-row and six-row barley were tested in multi-environment trials in 2019-2022. The genetic diversity of the row-types was examined and used as a factor in the predictions, and the potential to predict untested locations using yield data from other locations was explored. This resulted in an overall predictive ability of 0.61 for yield (kg/ha), with 0.57 and 0.19 for the separate two-row and the six-row breeding lines, respectively. Together this displays the potential of implementing GP in breeding programs and the genetic gain in spring barley market varieties developed through GP will help in quantifying the benefit of GP over conventional breeding in the future.

Exploring natural genetic variation in photosynthesis-related traits of barley in the field

Optimizing photosynthesis is considered an important strategy for improving crop yields to ensure food security. To evaluate the potential of using photosynthesis-related parameters in crop breeding programs, we measured chlorophyll fluorescence along with growth-related and morphological traits of 23 barley inbred lines across different developmental stages in field conditions. The photosynthesis-related parameters were highly variable, changing with light intensity and developmental progression of plants. Yet, the variation in photosystem II quantum yield observed among the inbred lines in the field largely reflected the variation in CO2 assimilation properties in controlled climate chamber conditions, confirming that the chlorophyll fluorescence-based technique can provide proxy parameters of photosynthesis to explore genetic variation under field conditions. Heritability (H2) of the photosynthesis-related parameters in the field ranged from 0.16 for the quantum yield of non-photochemical quenching to 0.78 for the fraction of open photosystem II center. Two parameters, the maximum photosystem II efficiency in the light-adapted state (H2=0.58) and the total non-photochemical quenching (H2=0.53), showed significant positive and negative correlations, respectively, with yield-related traits (dry weight per plant and net straw weight) in the barley inbred lines. These results indicate the possibility of improving crop yield through optimizing photosynthetic light use efficiency by conventional breeding programs.

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.

Methylomes as key features for predicting recombination in some plant species

Knowing how chromosome recombination works is essential for plant breeding. It enables the design of crosses between different varieties to combine desirable traits and create new ones. This is because the meiotic crossovers between homologous chromatids are not purely random, and various strategies have been developed to describe and predict such exchange events. Recent studies have used methylation data to predict chromosomal recombination in rice using machine learning models. This approach proved successful due to the presence of a positive correlation between the CHH context cytosine methylation and recombination rates in rice chromosomes. This paper assesses the question if methylation can be used to predict recombination in four plant species: Arabidopsis, maize, sorghum, and tomato. The results indicate a positive association between CHH context methylation and recombination rates in certain plant species, with varying degrees of strength in their relationships. The CG and CHG methylation contexts show negative correlation with recombination. Methylation data was key effectively in predicting recombination in sorghum and tomato, with a mean determination coefficient of 0.65 ± 0.11 and 0.76 ± 0.05, respectively. In addition, the mean correlation values between predicted and experimental recombination rates were 0.83 ± 0.06 for sorghum and 0.90 ± 0.05 for tomato, confirming the significance of methylomes in both monocotyledonous and dicotyledonous species. The predictions for Arabidopsis and maize were not as accurate, likely due to the comparatively weaker relationships between methylation contexts and recombination, in contrast to sorghum and tomato, where stronger associations were observed. To enhance the accuracy of predictions, further evaluations using data sets closely related to each other might prove beneficial. In general, this methylome-based method holds great potential as a reliable strategy for predicting recombination rates in various plant species, offering valuable insights to breeders in their quest to develop novel and improved varieties.

Genomic evidence for climate-linked diversity loss and increased vulnerability of wild barley spanning 28 years of climate warming

The information on how plant populations respond genetically to climate warming is scarce. Here, landscape genomic and machine learning approaches were integrated to assess genetic response of 10 wild barley (Hordeum vulgare ssp. spontaneum; WB) populations in the past and future, using whole genomic sequencing (WGS) data. The WB populations were sampled in 1980 and again in 2008. Phylogeny of accessions was roughly in conformity with sampling sites, which accompanied by admixture/introgressions. The 28-y climate warming resulted in decreased genetic diversity, increased selection pressure, and an increase in deleterious single nucleotide polymorphism (dSNP) numbers, heterozygous deleterious and total deleterious burdens for WB. Genome-environment associations identified some candidate genes belonging to peroxidase family (HORVU2Hr1G057450, HORVU4Hr1G052060 and HORVU4Hr1G057210) and heat shock protein 70 family (HORVU2Hr1G112630). The gene HORVU2Hr1G120170 identified by selective sweep analysis was under strong selection during the climate warming of the 28-y, and its derived haplotypes were fixed by WB when faced with the 28-y increasingly severe environment. Temperature variables were found to be more important than precipitation variables in influencing genomic variation, with an eco-physiological index gdd5 (growing degree-days at the baseline threshold temperature of 5 °C) being the most important determinant. Gradient forest modelling revealed higher predicted genomic vulnerability in Sede Boqer under future climate scenarios at 2041-2070 and 2071-2100. Additionally, estimates of effective population size (Ne) tracing back to 250 years indicated a forward decline in all populations over time. Our assessment about past genetic response and future vulnerability of WB under climate warming is crucial for informing conservation efforts for wild cereals and rational use strategies.

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.

Major chromosome 5H haplotype switch structures the European two-rowed spring barley germplasm of the past 190 years

KEY MESSAGE

Selection over 70 years has led to almost complete fixation of a haplotype spanning ~ 250 Mbp of chomosome 5H in European two-rowed spring barleys, possibly originating from North Africa. Plant breeding and selection have shaped the genetic composition of modern crops over the past decades and centuries and have led to great improvements in agronomic and quality traits. Knowledge of the genetic composition of breeding germplasm is essential to make informed decisions in breeding programs. In this study, we characterized the structure and composition of 209 barley cultivars representative of the European two-rowed spring barley germplasm of the past 190 years. Utilizing high-density SNP marker data, we identified a distinct centromeric haplotype spanning a ~ 250 Mbp large region on chromosome 5H which likely was first introduced into the European breeding germplasm in the early to mid-twentieth century and has been non-recombining and under strong positive selection over the past 70 years. Almost all cultivars in our panel that were released after 2000 carry this new haplotype, suggesting that this region carries one or several genes conferring highly beneficial traits. Using the global barley collection of the German Federal ex situ gene bank at IPK Gatersleben, we found the new haplotype at high frequencies in six-rowed spring-type landraces from Northern Africa, from which it may have been introduced into modern European barley germplasm via southern European landraces. The presence of a 250 Mbp genomic region characterized by lack of recombination and high levels of fixation in modern barley germplasm has substantial implications for the genetic diversity of the modern barley germplasm and for barley breeding.

Genomic features of meiotic crossovers in diploid potato

Meiotic recombination plays an important role in genome evolution and crop improvement. Potato (Solanum tuberosum L.) is the most important tuber crop in the world, but research about meiotic recombination in potato is limited. Here, we resequenced 2163 F₂ clones derived from five different genetic backgrounds and identified 41 945 meiotic crossovers. Some recombination suppression in euchromatin regions was associated with large structural variants. We also detected five shared crossover hotspots. The number of crossovers in each F₂ individual from the accession Upotato 1 varied from 9 to 27, with an average of 15.5, 78.25% of which were mapped within 5 kb of their presumed location. We show that 57.1% of the crossovers occurred in gene regions, with poly-A/T, poly-AG, AT-rich, and CCN repeats enriched in the crossover intervals. The recombination rate is positively related with gene density, SNP density, Class II transposon, and negatively related with GC density, repeat sequence density and Class I transposon. This study deepens our understanding of meiotic crossovers in potato and provides useful information for diploid potato breeding.

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.

Accurate recombination estimation from pooled genotyping and sequencing: a case study on barley

Sexual reproduction involves meiotic recombination and the creation of crossing over between homologous chromosomes, which leads to new allele combinations. We present a new approach that uses the allele frequency differences and the physical distance of neighboring polymorphisms to estimate the recombination rate from pool genotyping or sequencing. This allows a considerable cost reduction compared to conventional mapping based on genotyping or sequencing data of single individuals. We evaluated the approach based on computer simulations at various genotyping depths and population sizes as well as applied it to experimental data of 45 barley populations, comprising 4182 RIL. High correlations between the recombination rates from this new pool genetic mapping approach and conventional mapping in simulated and experimental barley populations were observed. The proposed method therefore provides a reliable genetic map position and recombination rate estimation in defined genomic windows.

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).