Theoretical and Applied Genetics最新文献

Keymessage: In this first QTL mapping study of embryo size in barley, novel and stable QTL were identified and candidate genes underlying a significant locus independent of kernel size were identified based on orthologous analysis and comparison of the whole-genome assemblies for both parental genotypes of the mapping population. Embryo, also known as germ, in cereal grains plays a crucial role in plant development. The embryo accounts for only a small portion of grain weight but it is rich in nutrients. Larger embryo translates to a more nutritious grain and larger store of energy reserves, which can benefit seed germination and seedling establishment. However, reports on quantitative trait loci (QTL) affecting embryo size in barley is rare. To understand the genetic basis of embryo size in barley, a population consisting of 201 F9 recombination inbred lines (RILs) was assessed in four environments. Three regions affecting various characteristics of embryo size including embryo length (EL), embryo width (EW) and embryo area (EA) were consistently identified. They located on chromosomes 2H, 4H and 7H, respectively. Among them, the QTL on 7H was not significantly affected by kernel size. Phenotypic variances explained by this QTL for EL, EW and EA were 11.8%, 9.3% and 12.7%, respectively. Taken advantage of the available genomic assemblies of the two parental genotypes, candidate genes for this locus on 7H were identified. In addition, significant correlations between embryo size and early vigour and kernel traits were detected. To our knowledge, the present study is for the first time reporting QTL conferring embryo size by directly measuring the characteristics as quantitative trait in barley, which would broaden our understanding of the genetic basis of barley embryo size and offer valuable targets for future breeding programmes.

Key message: QTL mapping of two RIL populations in multiple environments revealed a consistent QTL for bristle length, and combined with RNA-seq, a potential candidate gene influencing bristle length was identified. Foxtail millet bristles play a vital role in increasing yields and preventing bird damage. However, there is currently limited research on the molecular regulatory mechanisms underlying foxtail millet bristle formation, which constrains the genetic improvement and breeding of new foxtail millet varieties. This study leveraged genetic linkage maps from two populations: the published RYRIL population (Hongjiugu × Yugu 18) with 1420 bins and the newly established YYRIL population (Huangruangu × Yugu 18) with 542 bins. We identified 17 QTLs associated with bristle length, explaining 1.76-47.37% of the phenotypic variation. Among these, 6 were multi-environment QTLs, and 11 were environment-specific QTLs. Notably, qBL-1-1 and qBL-3-2 were detected in both populations, and exhibited epistasis interactions. By analyzing genotypic data from the RYRIL population and its parents, we identified two lines with significant variation in bristle length at the qBL-1-1 locus, designated CM3 (short) and CM4 (long). RNA-seq during the flowering phase identified 1812 differentially expressed genes (DEGs). Thirty-three DEGs were identified within 6 multi-environment QTL regions, and the RNA-seq results were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Within the qBL-1-1 region, Seita.1G325800 is predicted to be a key candidate gene controlling foxtail millet bristle length. These findings provide preliminary insights into the genetic basis of bristle development and lay a foundation for the genetic improvement of foxtail millet bristle length.

Key message: Genomic selection using white clover multi-year-multi-site data showed predicted genetic gains through integrating among-half-sibling-family phenotypic selection and within-family genomic selection were up to 89% greater than half-sibling-family phenotypic selection alone. Genomic selection, an effective breeding tool used widely in plants and animals for improving low-heritability traits, has only recently been applied to forages. We explored the feasibility of implementing genomic selection in white clover (Trifolium repens L.), a key forage legume which has shown limited genetic improvement in dry matter yield (DMY) and persistence traits. We used data from a training population comprising 200 half-sibling (HS) families evaluated in a cattle-grazed field trial across three years and two locations. Combining phenotype and genotyping-by-sequencing (GBS) data, we assessed different two-stage genomic prediction models, including KGD-GBLUP developed for low-depth GBS data, on DMY, growth score, leaf size and stolon traits. Predictive abilities were similar among the models, ranging from -0.17 to 0.44 across traits, and remained stable for most traits when reducing model input to 100-120 HS families and 5500 markers, suggesting genomic selection is viable with fewer resources. Incorporating a correlated trait with a primary trait in multi-trait prediction models increased predictive ability by 28-124%. Deterministic modelling showed integrating among-HS-family phenotypic selection and within-family genomic selection at different selection pressures estimated up to 89% DMY genetic gain compared to phenotypic selection alone, despite a modest predictive ability of 0.3. This study demonstrates the potential benefits of combining genomic and phenotypic selection to boost genetic gains in white clover. Using cost-effective GBS paired with a prediction model optimized for low read-depth data, the approach can achieve prediction accuracies comparable to traditional models, providing a viable path for implementing genomic selection in white clover.

Key message: Early root traits, particularly total root length, are heritable and show positive genetic correlations with biomass yield in perennial ryegrass; incorporating them into breeding programs can enhance genetic gain. Perennial ryegrass (Lolium perenne L.) is an important forage grass widely used in pastures and lawns, valued for its high nutritive value and environmental benefits. Despite its importance, genetic improvements in biomass yield have been slow, mainly due to its outbreeding nature and the challenges of improving multiple traits simultaneously. This study aims to assess the potential advantages of including early root traits in the perennial ryegrass breeding process. Root traits, including total root length (TRL) and root angle (RA) were phenotyped in a greenhouse using rhizoboxes, and genetic correlations with field yield were estimated across three European locations over two years. Bivariate models estimated significant genetic correlations of 0.40 (SE = 0.14) between TRL and field yield, and a weak but positive correlation to RA of 0.15 (SE = 0.14). Heritability estimates were 0.36 for TRL, 0.39 for RA, and 0.31 for field yield across locations. Incorporating root trait data into selection criteria can improve the efficiency of breeding programs, potentially increasing genetic gain by approximately 10%. This results highlight the potential of early root traits to refine selection criteria in perennial ryegrass breeding programs, contributing to higher yield and efficiency.

Key message: In tetraploid F1 populations, traditional segregation distortion tests often inaccurately flag SNPs due to ignoring polyploid meiosis processes and genotype uncertainty. We develop tests that account for these factors. Genotype data from tetraploid F1 populations are often collected in breeding programs for mapping and genomic selection purposes. A common quality control procedure in these groups is to compare empirical genotype frequencies against those predicted by Mendelian segregation, where SNPs detected to have segregation distortion are discarded. However, current tests for segregation distortion are insufficient in that they do not account for double reduction and preferential pairing, two meiotic processes in polyploids that naturally change gamete frequencies, leading these tests to detect segregation distortion too often. Current tests also do not account for genotype uncertainty, again leading these tests to detect segregation distortion too often. Here, we incorporate double reduction, preferential pairing, and genotype uncertainty in likelihood ratio and Bayesian tests for segregation distortion. Our methods are implemented in a user-friendly R package, menbayes. We demonstrate the superiority of our methods to those currently used in the literature on both simulations and real data.

Key message: In the present study, we identified 22 significant SNPs, eight stable QTLs and 17 potential candidate genes associated with 100-seed weight in soybean. Soybean is an economically important crop that is rich in seed oil and protein. The 100-seed weight (HSW) is a crucial yield contributing trait. This trait exhibits complex inheritance regulated by many genes and is highly sensitive to environmental factors. In this study, an integrated strategy of association mapping, QTL analysis, candidate gene and haplotype analysis was utilized to elucidate the complex genetic architecture of HSW in a panel of diverse soybean cultivars. Our study revealed 22 SNPs significantly associated with HSW through association mapping using five GWAS models across multiple environments plus a combined environment. By considering the detection of SNPs in multiple environments and GWAS models, the genomic regions of eight consistent SNPs within the ± 213.5 kb were depicted as stable QTLs. Among the eight QTLs, four, viz. qGW1.1, qGW1.2, qGW9 and qGW16, are reported here for the first time, and the other four, viz. qGW4, qGW8, qGW17 and qGW19, have been reported in previous studies. Thirty-two genes were detected as putative candidates within physical intervals of eight QTLs by in silico analysis. Twelve genes (out of total 32) showed significant differential expression patterns among the soybean accessions with contrasting HSW. Moreover, different haplotype alleles of 10 candidate genes are associated with different phenotypes of HSW. The outcome of the current investigation can be used in soybean breeding programs for producing cultivars with higher yields.

Key message: A complete set of monosomic alien addition lines of Radish-Brassica oleracea exhibiting extensive variations was generated and well characterized for their chromosome behaviors and phenotypic characteristics. Monosomic alien addition lines (MAALs) are developed through interspecific hybridization, where an alien chromosome from a relative species is introduced into the genome of the recipient plant, serving as valuable genetic resources. In this study, an allotetraploid Raphanobrassica (RRCC, 2n = 36) was created from the interspecific hybridization between radish (Raphanus sativus, RR, 2n = 18) and Brassica oleracea (CC, 2n = 18). Subsequently, this Raphanobrassica was repeatedly backcrossed with radish to generate an aneuploid population. The identification of a complete set of MAALs (RR + 1C_1-9, 2n = 19) was achieved using PCR with C chromosome-specific markers and fluorescence in situ hybridization, revealing extensive morphological variations, particularly in the shape and size of the fleshy root. A complete set of MAALs was achieved with only one chromosome from 1 to 9 linkage groups of the C genome. Compared with parental radish, most of the MAALs showed a noticeable delay in root swelling, particularly the RR-C₆ that did not exhibit obvious root swelling throughout its entire growth stage. Cytological analysis indicated that the MAAL lines containing chromosome C₈ exhibited the highest frequency of intergenomic chromosome pairings. Additionally, some introgressive radish lines derived from MAALs displayed a preference toward the donor B. oleracea or over-parent heterosis for some certain nutritional components. Overall, these MAALs serve as valuable germplasm for the genetic enhancement of radish and provide insights into the interactions between the R genome and C chromosomes.

Key message: Phenomic selection based on parental spectra can be used to predict GCA and SCA in a sparse factorial design. Prediction approaches such as genomic selection can be game changers in hybrid breeding. They allow predicting the genetic values of hybrids without the need for their physical production. This leads to significant reductions in breeding cycle length, and so to the increase in genetic progress. However, these methods are often underutilized in breeding programs due to the substantial cost involved in genotyping thousands of candidate parental lines annually. To address this limitation, we propose a cost-effective alternative based on phenomic selection, where genotyping of parental lines is replaced by NIR spectroscopy. Standard prediction models are then applied for genomic and phenomic selection, using similarity matrices derived from either genotyping data (genomic selection) or NIR spectral data (phenomic selection). Our hypothesis is that the chemical composition of parental tissues captured by NIRS reflects the genetic similarity between parental lines. We evaluated both strategies using a sparse factorial design, whose hybrids have been phenotyped in a multi-environment trial network, and with NIR spectra acquired on the parental lines on two independent environments. Both genomic and phenomic prediction approaches demonstrated moderate-to-high predictive abilities across various cross-validation scenarios. Our results also showcase the capability of phenomic selection to predict Mendelian sampling. This study serves as a proof of concept that low-cost high-throughput phenomics of parental lines can effectively be used to predict maize hybrids in independent trials. This paves the way for widespread adoption of prediction approaches at the very first stages of hybrid breeding, benefiting both major and orphan species.

Key message: Zebularine-treated wheat uncovered a phenotype with characteristics of an epigenetically regulated trait, but major chromosomal aberrations, not DNA methylation changes, are the cause, making zebularine unsuitable for epigenetic breeding. Breeding to identify disease-resistant and climate-tolerant high-yielding wheats has led to yield increases over many years, but new hardy, higher yielding varieties are still needed to improve food security in the face of climate change. Traditional breeding to develop new cultivars of wheat is a lengthy process taking more than seven years from the initial cross to cultivar release. The speed of breeding can be enhanced by using modern technologies including high-throughput phenomics, genomic selection, and directed mutation via CRISPR. Here we test the concept of modifying gene regulation by transiently disrupting DNA methylation with the methyltransferase inhibitor, zebularine (Zeb), as a means to uncover novel phenotypes in an elite cultivar to facilitate breeding for epigenetically controlled traits. The development and architecture of the wheat inflorescence, including spikelet density, are an important component of yield, and both grain size and number have been extensively modified during domestication and breeding of wheat cultivars. We identified several Zeb-treated plants with a dominant mutation that increased spikelet density compared to the untreated controls. Our analysis showed that in addition to causing loss of DNA methylation, Zeb treatment resulted in major chromosomal abnormalities, including trisomy and the formation of a novel telocentric chromosome. We provide evidence that increased copy number of the domestication gene, Q, is the most likely cause of increased spikelet density in two Zeb-treated plants. Collateral damage to chromosomes in Zeb-treated plants suggests that this is not a viable approach to epigenetic breeding.

Key message: Genetic variation for malting quality as well as metabolomic and near-infrared features was identified. However, metabolomic and near-infrared features as additional omics-information did not improve accuracy of predicted breeding values. Significant attention has recently been given to the potential benefits of metabolomics and near-infrared spectroscopy technologies for enhancing genetic evaluation in breeding programs. In this article, we used a commercial barley breeding population phenotyped for grain yield, grain protein content, and five malting quality traits: extract yield, wort viscosity, wort color, filtering speed, and β-glucan, and aimed to: (i) investigate genetic variation and heritability of metabolomic intensities and near-infrared wavelengths originating from leaf tissue and malted grain, respectively; (ii) investigate variance components and heritabilities for genomic models including metabolomics (GOBLUP-MI) or near-infrared wavelengths (GOBLUP-NIR); and (iii) evaluate the developed models for prediction of breeding values for traits of interest. In total, 639 barley lines were genotyped using an iSelect9K-Illumina barley chip and recorded with 30,468 metabolomic intensities and 141 near-infrared wavelengths. First, we found that a significant proportion of metabolomic intensities and near-infrared wavelengths had medium to high additive genetic variances and heritabilities. Second, we observed that both GOBLUP-MI and GOBLUP-NIR, increased the proportion of estimated genetic variance for grain yield, protein, malt extract, and β-glucan compared to a genomic model (GBLUP). Finally, we assessed these models to predict accurate breeding values in fivefold and leave-one-breeding-cycle-out cross-validations, and we generally observed a similar accuracy between GBLUP and GOBLUP-MI, and a worse accuracy for GOBLUP-NIR. Despite this trend, GOBLUP-MI and GOBLUP-NIR enhanced predictive ability compared to GBLUP by 4.6 and 2.4% for grain protein in leave-one-breeding-cycle-out and grain yield in fivefold cross-validations, respectively, but differences were not significant (P-value > 0.01).