Theoretical and Applied Genetics最新文献

Key messages: We investigate a method of extracting and fitting synthetic environmental covariates and pedigree information in multilocation trial data analysis to predict genotype performances in untested locations. Plant breeding trials are usually conducted across multiple testing locations to predict genotype performances in the targeted population of environments. The predictive accuracy can be increased by the use of adequate statistical models. We compared linear mixed models with and without synthetic covariates (SCs) and pedigree information under the identity, the diagonal and the factor-analytic variance-covariance structures of the genotype-by-location interactions. A comparison was made to evaluate the accuracy of different models in predicting genotype performances in untested locations using the mean squared error of predicted differences (MSEPD) and the Spearman rank correlation between predicted and adjusted means. A multi-environmental trial (MET) dataset evaluated for yield performance in the dry lowland sorghum (Sorghum bicolor (L.) Moench) breeding program of Ethiopia was used. For validating our models, we followed a leave-one-location-out cross-validation strategy. A total of 65 environmental covariates (ECs) obtained from the sorghum test locations were considered. The SCs were extracted from the ECs using multivariate partial least squares analysis and subsequently fitted in the linear mixed model. Then, the model was extended accounting for pedigree information. According to the MSEPD, models accounting for SC improve predictive accuracy of genotype performances in the three of the variance-covariance structures compared to others without SC. The rank correlation was also higher for the model with the SC. When the SC was fitted, the rank correlation was 0.58 for the factor analytic, 0.51 for the diagonal and 0.46 for the identity variance-covariance structures. Our approach indicates improvement in predictive accuracy with SC in the context of genotype-by-location interactions of a sorghum breeding in Ethiopia.

Key message: De novo genotyping in potato using methylation-sensitive GBS discovers SNPs largely confined to genic or gene-associated regions and displays enhanced effectiveness in estimating LD decay rates, population structure and detecting GWAS associations over 'fixed' SNP genotyping platform. Study also reports the genetic architectures including robust sequence-tagged marker-trait associations for sixteen important potato traits potentially carrying higher transferability across a wider range of germplasm. This study deploys recent advancements in polyploid analytical approaches to perform complex trait analyses in cultivated tetraploid potato. The study employs a 'fixed' SNP Infinium array platform and a 'flexible and open' genome complexity reduction-based sequencing method (GBS, genotyping-by-sequencing) to perform genome-wide association studies (GWAS) for several key potato traits including the assessment of population structure and linkage disequilibrium (LD) in the studied population. GBS SNPs discovered here were largely confined (~ 90%) to genic or gene-associated regions of the genome demonstrating the utility of using a methylation-sensitive restriction enzyme (PstI) for library construction. As compared to Infinium array SNPs, GBS SNPs displayed enhanced effectiveness in estimating LD decay rates and discriminating population subgroups. GWAS using a combined set of 30,363 SNPs identified 189 unique QTL marker-trait associations (QTL-MTAs) covering all studied traits. The majority of the QTL-MTAs were from GBS SNPs potentially illustrating the effectiveness of marker-dense de novo genotyping platforms in overcoming ascertainment bias and providing a more accurate correction for different levels of relatedness in GWAS models. GWAS also detected QTL 'hotspots' for several traits at previously known as well as newly identified genomic locations. Due to the current study exploiting genome-wide genotyping and de novo SNP discovery simultaneously on a large tetraploid panel representing a greater diversity of the cultivated potato gene pool, the reported sequence-tagged MTAs are likely to have higher transferability across a wider range of potato germplasm and increased utility for expediting genomics-assisted breeding for the several complex traits studied.

Rust diseases, including leaf rust, stripe/yellow rust, and stem rust, significantly impact wheat (Triticum aestivum L.) yields, causing substantial economic losses every year. Breeding and deployment of cultivars with genetic resistance is the most effective and sustainable approach to control these diseases. The genetic toolkit for wheat breeders to select for rust resistance has rapidly expanded with a multitude of genetic loci identified using the latest advances in genomics, mapping and cloning strategies. The goal of this review was to establish a wheat genome atlas that provides a comprehensive summary of reported loci associated with rust resistance. Our atlas provides a summary of mapped quantitative trait loci (QTL) and characterised genes for the three rusts from 170 publications over the past two decades. A total of 920 QTL or resistance genes were positioned across the 21 chromosomes of wheat based on the latest wheat reference genome (IWGSC RefSeq v2.1). Interestingly, 26 genomic regions contained multiple rust loci suggesting they could have pleiotropic effects on two or more rust diseases. We discuss a range of strategies to exploit this wealth of genetic information to efficiently utilise sources of resistance, including genomic information to stack desirable and multiple QTL to develop wheat cultivars with enhanced resistance to rust disease.

Key message: Three QTLs associated with low-temperature tolerance were identified by genome-wide association analysis, and 15 candidate genes were identified by haplotype analysis and gene expression analyses. Low temperature is a critical factor affecting the geographical distribution, growth, development, and yield of soybeans, with cold stress during seed germination leading to substantial productivity loss. In this study, an association panel comprising 260 soybean accessions was evaluated for four germination traits and four cold tolerance index traits, revealing extensive variation in cold tolerance. Genome-wide association study (GWAS) identified 10 quantitative trait nucleotides (QTNs) associated with cold tolerance, utilizing 30,799 single nucleotide polymorphisms (SNPs) and four GWAS models. Linkage disequilibrium (LD) analysis positioned these QTNs within three cold-tolerance quantitative trait loci (QTL) and, with QTL19-1, was positioned by three multi-locus models, underscoring its importance as a key QTL. Integrative haplotype analysis, supplemented by transcriptome analysis, uncovered 15 candidate genes. The haplotypes within the genes Glyma.18G044200, Glyma.18G044300, Glyma.18G044900, Glyma.18G045100, Glyma.19G222500, and Glyma.19G222600 exhibited significant phenotypic variations, with differential expression in materials with varying cold tolerance. The QTNs and candidate genes identified in this study offer substantial potential for marker-assisted selection and gene editing in breeding cold-tolerant soybeans, providing valuable insights into the genetic mechanisms underlying cold tolerance during soybean germination.

Key message: Underpinned natural variations and key genes associated with yield under different water regimes, and identified genomic signatures of genetic gain in the Indian wheat breeding program. A novel KASP marker for TKW under water stress was developed and validated. A comprehensive genome-wide association study was conducted on 300 spring wheat genotypes to elucidate the natural variations associated with grain yield and its eleven contributing traits under fully irrigated, restricted water, and simulated no water conditions. Utilizing the 35K Wheat Breeders' Array, we identified 1155 quantitative trait nucleotides (QTNs), with 207 QTNs exhibiting stability across diverse conditions. These QTNs were further delimited into 539 genomic regions using a genome-wide LD value of 3.0 Mbp, revealing pleiotropic control across traits and conditions. Sub-genome A was significantly associated with traits under irrigated conditions, while sub-genome B showed more QTNs under water stressed conditions. Favourable alleles with significantly associated QTNs were delineated, with a notable pyramiding effect for enhancing trait performance. Additionally, allele of only 921 QTNs significantly affected the population mean. Allele profiling highlighted C-306 as a most potential source of drought tolerance. Moreover, 762 genes overlapping significant QTNs were identified, narrowing down to 27 putative candidate genes overlapping 29 novel and functional SNPs expressing (≥ 0.5 tpm) relevance across various growth conditions. A new KASP assay was developed, targeting a gene TraesCS2A03G1123700 regulating thousand kernel weight under severe drought condition. Genomic selection models (GBLUP, BayesB, MxE, and R-Norm) demonstrated an average prediction accuracy of 0.06-0.58 across environments, indicating potential for trait selection. Retrospective analysis of the Indian wheat breeding program supported a genetic gain in GY at the rate of ca. 0.56% per breeding cycle, since 1960, supporting the identification of genomic signatures driving trait selection and genetic gain. These findings offer insight into improving the rate of genetic gain in wheat breeding programs globally.

Circular RNAs (circRNAs), a class of non-coding RNA molecules, are recognized for their unique functions; however, their responses to herbicide stress in Brassica napus remain unclear. In this study, the role of circRNAs in response to herbicide treatment was investigated in two rapeseed cultivars: MH33, which confers non-target-site resistance (NTSR), and EM28, which exhibits target-site resistance (TSR). The genome-wide circRNA profiles of herbicide-stressed and non-stressed seedlings were analyzed. The findings indicate that NTSR seedlings exhibited a greater abundance of circRNAs, shorter lengths of circRNAs and their parent genes, and more diverse functions of parent genes compared with TSR seedlings. Compared to normal-growth plants, the herbicide-stressed group exhibited similar trends in the number of circRNAs, functions of parent genes, and differentially expressed circRNAs as observed in NTSR seedlings. In addition, a greater number of circRNAs that function as competing microRNA (miRNA) sponges were identified in the herbicide stress and NTSR groups compared to the normal-growth and TSR groups, respectively. The differentially expressed circRNAs were validated by qPCR. The differntially expressed circRNA-miRNA networks were predicted, and the mRNAs targeted by these miRNAs were annotated. Our results suggest that circRNAs play a crucial role in responding to herbicide stress, exhibiting distinct responses between NTSR and TSR in rapeseed. These findings offer valuable insights into the mechanisms underlying herbicide resistance in rapeseed.

Key message: Transcriptomics and proteomics information collected on a platform can predict additive and non-additive effects for platform traits and additive effects for field traits. The effects of climate change in the form of drought, heat stress, and irregular seasonal changes threaten global crop production. The ability of multi-omics data, such as transcripts and proteins, to reflect a plant's response to such climatic factors can be capitalized in prediction models to maximize crop improvement. Implementing multi-omics characterization in field evaluations is challenging due to high costs. It is, however, possible to do it on reference genotypes in controlled conditions. Using omics measured on a platform, we tested different multi-omics-based prediction approaches, using a high dimensional linear mixed model (MegaLMM) to predict genotypes for platform traits and agronomic field traits in a panel of 244 maize hybrids. We considered two prediction scenarios: in the first one, new hybrids are predicted (CV-NH), and in the second one, partially observed hybrids are predicted (CV-POH). For both scenarios, all hybrids were characterized for omics on the platform. We observed that omics can predict both additive and non-additive genetic effects for the platform traits, resulting in much higher predictive abilities than GBLUP. It highlights their efficiency in capturing regulatory processes in relation to growth conditions. For the field traits, we observed that the additive components of omics only slightly improved predictive abilities for predicting new hybrids (CV-NH, model MegaGAO) and for predicting partially observed hybrids (CV-POH, model GAOxW-BLUP) in comparison to GBLUP. We conclude that measuring the omics in the fields would be of considerable interest in predicting productivity if the costs of omics drop significantly.

Key message: Genotyping-by-sequencing of 723 worldwide cucumber genetic resources revealed that cucumbers were dispersed eastward via at least three distinct routes, one to Southeast Asia and two from different directions to East Asia. The cucumber (Cucumis sativus) is an economically important vegetable crop cultivated and consumed worldwide. Despite its popularity, the manner in which cucumbers were dispersed from their origin in South Asia to the rest of the world, particularly to the east, remains a mystery due to the lack of written records. In this study, we performed genotyping-by-sequencing (GBS) on 723 worldwide cucumber accessions, mainly deposited in the Japanese National Agriculture and Food Research Organization (NARO) Genebank, to characterize their genetic diversity, relationships, and population structure. Analyses based on over 60,000 genome-wide single-nucleotide polymorphisms identified by GBS revealed clear genetic differentiation between Southeast and East Asian populations, suggesting that they reached their respective region independently, not progressively. A deeper investigation of the East Asian population identified two subpopulations with different fruit characteristics, supporting the traditional classification of East Asian cucumbers into two types thought to have been introduced by independent routes. Finally, we developed a core collection of 100 accessions representing at least 93.2% of the genetic diversity present in the entire collection. The genetic relationships and population structure, their associations with geographic distribution and phenotypic traits, and the core collection presented in this study are valuable resources for elucidating the dispersal history and promoting the efficient use and management of genetic resources for research and breeding in cucumber.

Phosphorus (P) is an essential element for plant growth, and its deficiency can cause decreased crop yield. This study systematically evaluated the low-phosphate (Pi) response traits in a large population at maturity and seedling stages, and explored candidate genes and their interrelationships with specific traits. The results revealed a greater sensitivity of seedling maize to low-Pi stress compared to that at maturity stage. The phenotypic response patterns to low-Pi stress at different stages were independent. Chlorophyll content was found to be a potential indicator for screening low-Pi-tolerant materials in the field. A total of 2900 and 1446 significantly associated genes at the maturity and seedling stages were identified, respectively. Among these genes, 972 were uniquely associated with maturity traits, while 330 were specifically detected at the seedling stage under low-Pi stress. Moreover, 768 and 733 genes were specifically associated with index values (low-Pi trait/normal-Pi trait) at maturity and seedling stage, respectively. Genetic network diagrams showed that the low-Pi response gene Zm00001d022226 was specifically associated with multiple primary P-related traits under low-Pi conditions. A total of 963 out of 2966 genes specifically associated with traits under low-Pi conditions or index values were found to be induced by low-Pi stress. Notably, ZmSPX4.1 and ZmSPX2 were sharply up-regulated in response to low-Pi stress across different lines or tissues. These findings advance our understanding of maize's response to low-Pi stress at different developmental stages, shedding light on the genes and pathways implicated in this response.

Key message: Genetic editing of grain size genes quickly improves three-line hybrid rice parents to increase the appearance quality and yield of hybrid rice. Grain size affects rice yield and quality. In this study, we used CRISPR/Cas9 to edit the grain size gene GW8 in the maintainer line WaitaiB (WTB) and restorer line Guanghui998 (GH998). The new slender sterile line WTEA (gw8) was obtained in the BC₂F₁ generation by transferring the grain mutation of the maintainer plant to the corresponding sterile line WantaiA (WTA, GW8) in the T₁ generation. Two slender restorer lines, GH998E1 (gw8(II)) and GH998E2 (gw8(I)), were obtained in T₁ generation. In the early stage, new sterile and restorer lines in grain mutations were created by targeted editing of GS3, TGW3, and GW8 genes. These parental lines were mated to detect the impact of grain-type mutations on hybrid rice yield and quality. Mutations in gs3, gw8, and tgw3 had a minimal impact on agronomic traits except the grain size and thousand-grain weight. The decrease in grain width in the combination mainly came from gw8/gw8, gs3/gs3 increased the grain length, gs3/gs3-gw8/gw8 had a more significant effect on the grain length, and gs3/gs3-gw8/gw8(I) contributed more to grain length than gs3/gs3-gw8/gw8(II). The heterozygous TGW3/tgw3 may not significantly increase grain length. Electron microscopy revealed that the low-chalky slender-grain variety had a cylindrical grain shape, a uniform distribution of endosperm cells, and tightly arranged starch grains. Quantitative fluorescence analysis of endospermdevelopment-related genes showed that the combination of slender grain hybrid rice caused by gs3 and gw8 mutations promoted endosperm development and improved appearance quality. An appropriate grain size mutation resulted in hybrid rice varieties with high yield and quality.