Theoretical and Applied Genetics最新文献

Key message: BrCYP71 encoding multifunctional oxidase was mapped using BSA-Seq and linkage analysis, and its function in stay-green of pak choi was verified through Arabidopsis heterologous transgenic experiment. Stay-green refers to the phenomenon that plant leaves remain green during senescence and even after death, which is of great significance for improving the commerciality of leafy vegetables during storage or transportation and extending their shelf life. In this study, we identified a stay-green mutant of pak choi and named it nye2. Genetic analysis showed that the stay-green trait was controlled by a recessive nuclear gene. We obtained a 550 kb candidate region on chromosome A03 using BSA-Seq and linkage analysis. In this interval, BraA03g049920.3.5C, named BrCYP71, was identified as a candidate gene using sequence variation analysis. BrCYP71 is an ortholog of Arabidopsis AT4G13290, which encodes a multifunctional oxidase. A 4 bp insertion from T to TGATC in the first exon of BrCPY71 in the mutant led to the formation of a stop codon, TAA. Ectopic overexpression of BrCYP71 in Arabidopsis cyp71 could restored the wild-type phenotype. These results indicate that BrCYP71 contributes to the stay-green of nye2. The expression levels of chlorophyll catabolism-related genes in nye2 were significantly reduced compared to those in the wild-type, suggesting that BrCPY71 affected chlorophyll catabolism. Our achievement provides a novel genetic resource for breeding the stay-green varieties of Brassica rapa.

Cotton is an important crop for fiber production, but the genetic basis underlying key agronomic traits, such as fiber quality and flowering days, remains complex. While machine learning (ML) has shown great potential in uncovering the genetic architecture of complex traits in other crops, its application in cotton has been limited. Here, we applied five machine learning models-AdaBoost, Gradient Boosting Regressor, LightGBM, Random Forest, and XGBoost-to identify loci associated with fiber quality and flowering days in cotton. We compared two SNP dataset down-sampling methods for model training and found that selecting SNPs with an Fscale value greater than 0 outperformed randomly selected SNPs in terms of model accuracy. We further performed machine learning quantitative trait loci (mlQTLs) analysis for 13 traits related to fiber quality and flowering days. These mlQTLs were then compared to those identified through genome-wide association studies (GWAS), revealing that the machine learning approach not only confirmed known loci but also identified novel QTLs. Additionally, we evaluated the effect of population size on model accuracy and found that larger population sizes resulted in better predictive performance. Finally, we proposed candidate genes for the identified mlQTLs, including two argonaute 5 proteins, Gh_A09G104100 and Gh_A09G104400, for the FL3/FS2 locus, as well as GhFLA17 and Syntaxin-121 (Gh_D09G143700) for the FSD09_2/FED09_2 locus. Our findings demonstrate the efficacy of machine learning in enhancing the identification of genetic loci in cotton, providing valuable insights for improving cotton breeding strategies.

Complex traits influenced by multiple genes pose challenges for marker-assisted selection (MAS) in breeding. Genomic selection (GS) is a promising strategy for achieving higher genetic gains in quantitative traits by stacking favorable alleles into elite cultivars. Resistance to Fusarium oxysporum f. sp. niveum (Fon) race 2 in watermelon is a polygenic trait with moderate heritability. This study evaluated GS as an additional approach to quantitative trait loci (QTL) analysis/marker-assisted selection (MAS) for enhancing Fon race 2 resistance in elite watermelon cultivars. Objectives were to: (1) assess the accuracy of genomic prediction (GP) models for predicting Fon race 2 resistance in a F_2:3 versus a recombinant inbred line (RIL) population, (2) rank and select families in each population based on genomic estimated breeding values (GEBVs) for developing testing populations, and (3) determined how many of the most superior families based on GEBV also have all QTL associated with Fon race 2 resistance. GBS-SNP data from genotyping-by-sequencing (GBS) for two populations were used, and parental line genome sequences were used as references. The GBLUP and random forest outperformed the other three parametric (GBLUP, Bayes B, Bayes LASSO) and three nonparametric AI (random forest, SVM linear, and SVM radial) models, with correlations of 0.48 and 0.68 in the F_2:3 and RIL population, respectively. Selection intensities (SI) of 10%, 20%, and 30% showed that superior families with highest GEBV can also comprise all QTL associated with Fon race 2 resistance, highlighting GP efficacy in improving elite watermelon cultivars with polygenic traits of disease resistance.

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.