Euphytica最新文献

The traits of shoot growth habit differ between sweetpotato (Ipomoea batatas) and its wild ancestor (Ipomoea trifida). In general, sweetpotatoes have thick stems without twining, while I. trifida have slender twining stems. Anatomical observation in this study showed that this difference is caused by the difference in the size and number of cells between the stems of sweetpotato and those of I. trifida. To reveal the genetic basis of the difference in shoot phenotype, F₁ progeny were produced by crossing sweetpotato (Konaishin) and I. trifida (K123-11), and the G-statistic method of bulked-segregant analysis was used to investigate stem-twining ability as a representative trait of shoot growth habit. As a result, a major quantitative trait locus (qSgh) related to shoot growth was successfully detected at 12.37–14.12 Mb in Chr13 of the reference genome. Genotyping F₁ individuals using a PCR-based SNP marker designed for qSgh supported the results of bulked-segregant analysis and further suggested that qSgh had a dosage effect on stem diameter. Based on these results, we propose that the G-statistic method is an effective approach for bulked-segregant analysis in polyploid species, including sweetpotato. Additionally, some candidate genes in qSgh were found by comparative analysis of the genome and transcriptome between sweetpotato and I. trifida. At least two of these, Iba_chr13aCG7290 and Iba_chr13cCG9960, are likely involved in radial growth of the stem in sweetpotato. The results of this study provide new insight into the transition of shoot phenotype from I. trifida to sweetpotato.

For nonseed crops reliant on roots, leaves, and stems, breeding bolting-tolerant varieties is vital, and understanding the genetic mechanism aids effective selection. In sugar beet (Beta vulgaris), which accumulates sugar in roots, sequencing variations of BvBTC1, the master gene controlling annual and biennial life cycles, is associated with bolting tolerance, but the effects have not been demonstrated. We conducted quantitative trait loci (QTL) analysis on two generations (F_2:3 and F_5:6) from diverse bolting-tolerant crosses. Over 4 years, using phenotypic and mainly amplified fragment length polymorphism-based genotypic data, we identified two consistent QTLs: qB2 and qB6. These loci, detected regardless of the survey year or generation, were found to be crucial for enhancing sugar beet’s bolting tolerance. qB2 on chromosome 2 exhibited the highest phenotypic variance (PVE; 41.9–66.6%) and was attributed to BvBTC1 based on mapping and gene function. On chromosome 6, qB6 (PVE 7.8–23.7%) was located near bolting-related genes, such as Bv_22330_orky and BvFL1, but the gene responsible for qB6 remains unclear owing to map information limitations. Overall, the key QTL qB2 and qB6 hold promise for advancing bolting tolerance in sugar beet, offering valuable insights for targeted breeding efforts.

Genomic prediction in Coffee breeding has shown good potential in predictive ability (PA), genetic gains and reduction of the selection cycle time. It is known that the cost of genotyping was prohibitive for many species, and their value is associated with the density markers panel used. The use of optimize marker density panel may reduce the genotyping cost and improve the PA. We aimed to evaluate the trade-off between density marker panels size and the PA for eight agronomic traits in Coffea canephora using machine learning algorithms. These approaches were compared with BLASSO method. The used data consisted of 165 genotypes of C. canephora genotyped with 14,387 SNP markers. The plants were phenotyped for vegetative vigor (Vig), rust (Rus) and cercosporiose incidence (Cer), fruit maturation time (Mat), fruit size (FS), plant height (PH), diameter of the canopy projection (DC) and yield (Y). Twelve different density marker panels were used. The common trend observed in the analysis shows an increase of the PA as the number of markers decreases, having a peak when used between 500 and 1,000 markers. Comparing the best and the worse results (full SNP panel density) for each trait, some had an improvement around of 100% (PH: 0.35–0.77; Cer: 0.40–0.84; DC: 0.39–0.82; Rus: 0.39–0.83, Vig: 0.40–0.77), the other showed an improvement more than 340% (Mat: 0.12–0.60; Y: 0.14–0.61; FS: 0.07–0.60). The results of the current study indicate that the reduction of the number of markers can improve the selection of individuals at a lower cost.

Siliquae length (SL), beak length (BL), and seeds per siliqua (SPS) are critical for oilseed Brassica crop suitability. Breeding and biotechnology focus on genetic manipulation of the crop to enhance crop productivity. To identify QTL regions and genes contributing to SL, BL, and SPS, we developed a panel of 94 BCILs (BC₂F_3-4) with segregating traits and genotyped by 192 donor genome-specific SSRs. We developed a linkage map spanning 12 linkage groups with a total length of 1349.8 cM. We detected 1 quantitative trait locus (QTL) on Chromosome 04 with the log of odd (LOD) value of 2.68 and phenotypic variance explained (PVE) of 13.88%. Two QTLs were mapped for BL with LOD and PVE values of 5.72–20.44 and 12.75–37.48%, respectively. In addition to these, a QTL for SPS was mapped on chromosome 04 with LOD and PVE values of 7.50 and 31.35%, respectively. Moreover, a total of three QTLs for SL were identified at LOD scores of 2.99 (9.93% PVE), 3.03 (3.27% PVE), and 3.01 (6.76% PVE) in the second crop season (CS-II). Additionally, one QTL for BL at LOD 10.19 (21.69% PVE) was detected along with two QTLs of SPS at LOD 6.13 and 3.08 cumulatively controlling > 40% phenotypic variations. Among all these QTLs, the QTL of BL and SPS were major and controlling > 10% phenotypic variations. A total of 17 potential genes were predicted from all the QTL regions, out of which PG-3 was found orthologous to Brassica rapa receptor protein kinase CLAVATA1 regulating the development of multiple locules. To the best of our knowledge, this is the first report on the detection of QTLs for SL, BL, and SPS in BCILs introgressed from S. alba.

Abstract

Grain yield in rice is largely determined by grain size. Grain Size 3 (GS3) is a major quantitative trait locus for grain size. The C–A natural variation in the second exon of GS3 was reported to play an important role in regulating grain length in rice. Here we evaluate GS3 alleles among 303 germplasm accessions. The GS3^A allele was predominant in xian/indica (XI) accessions, whereas geng/japonica (GJ) accessions mainly carried GS3^C. The GS3 allele affected the grain length significantly in XI, while its function was minimal in GJ, indicating that introduction of GS3 alleles might be useful to modify grain length in XI breeding programs, but not in GJ breeding. The association between GS3 alleles and seed weight was not significant in any of the individual subpopulations, suggesting that the contribution of GS3 to grain weight could be slight in terms of different subspecies. To develop an effective marker for GS3, a penta-primer amplification-refractory mutation system (PARMS) marker exploiting a single-base mutation (C–A) was developed, which entailed lower cost and less time than other available markers, and should be useful for fine marker-assisted selection of grain length in XI accessions breeding.

Grain size and grain weight contribute greatly to the final grain yield in rice. In order to identify QTLs conferring grain size and grain weight, an F₂ population derived from a cross between two indica rice accessions, Guangzhan 63-4S (GZ63-4S) and Dodda, and its derived F_2:3 population were developed, and were exploited for QTL analysis of the five related traits, namely grain length (GL), grain width (GW), length-to-width ratio (LWR), grain thickness (GT) and thousand-grain weight (TGW). A total of 36 QTLs were detected, and the number of beneficial alleles was contributed roughly equally by the two parents. Among those, 7 QTL regions were repeatedly detected in the two populations. In order to further validate effects of QTLs detected, a BC₁F₂ population derived from a backcross of a mixture of F₂ plants with GZ63-4S was developed and was exploited for QTL selection. Heterozygous regions of 3 QTLs, qGS3, qTGW6.2 and qGT7 were identified respectively, and corresponding near-isogenic lines (NILs) of each QTL were constructed with three rounds of self-crosses. In the background of NILs, qGS3 was responsible for GL, LWR, GT and TGW, qTGW6.2 was for GL and TGW, and qGT7 was for GT and TGW. These results have laid the foundation of further fine mapping work, and could be of great use in breeding and improvement of rice lines with desirable size and yield.

To meet the rising requirement for food production, by 2050, it is necessary to increase global production two-fold, necessitating the growth of new crop varieties. However, this process is time-consuming, largely determined by the crop's generation period. To address this challenge, Speed Breeding (SB) technology leverages controlled environmental conditions to accelerate plant development, allowing for the multiplication of many generations per year. SB also allows for the integration of advanced protocols such as gene editing, phenotyping, and genotyping accelerating crop improvement. SB has been effectively applied to various crops, including cereals, pulses and canola crops, producing 4–6 generations in a year. With its application to a wide range of crops and lower labor requirements than breeding methods, SB offers a practical and efficient option for crops with large populations. Speed breeding has come up as a highly efficient and potent method for rapidly developing new plant varieties. Notable successes have been achieved in crops like cereals, oilseed and vegetables where new cultivars have been developed with desirable attributes such as more protein content, disease resistance, salt tolerance and drought tolerance. Overall, speed breeding offers an accessible and transformative option for crop improvement, which will definitely help in global agricultural demand and mitigate the result of climate alteration on agriculture. This review provides a glance of SB's activities across various crops and its significance in the current context of crop improvement.

Abstract

Cassava, an important subsistence crop in tropical countries, represents the third most important source of starch worldwide. Espírito Santo (Brazil) presents predominance of family farming, where table cassava is cultivated as a food crop. The objective of the study was to evaluate the genetic diversity, estimate the genetic parameters and quantify gains with the selection of traditional cassava genotypes collected in different regions of the state of Espírito Santo, using the methodology of the mixed models (REML/BLUP). A total of 106 genotypes were evaluated in three locations. Each planting date was considered an environment, totaling six environments. The evaluated traits were: shoot height (APH), total number of tuberous roots (NR); weight of commercial roots (WCR); total root weight (TWR), marketable root weight/total root ratio (MRTR); root cortex color (RCC), root pulp color (PC) and cooking time (CT). The studied cassava genotypes presented genetic diversity and selection potential. The highest heritabilities (h² = 0.90; 0.75 and 0.75) were recorded for the traits NRT, WCR and TWR, respectively. Gains from selection were positive for all of the traits evaluated. Higher selection gains (GS = 21.77% and 20.15%) were observed for the WCR and TWR traits, considering animal and human consumption and (GS = 10.89% and 17.45%) for NRT and WCR, when intended for human consumption only. Genotypes 82, 76, 46, T3 and 2 stood out for selection purposes for animal and human consumption. Genotypes 2, 81, 69, 12 and 49 stood out for selection purposes for human food. The assessment of the diverse genotypes has uncovered a selection of superior candidates with tremendous potential for commercial crops, catering to both human and animal consumption markets.

A current challenge of genetic breeding programs is to increase grain yield and protein content and at least maintain oil content. However, evaluations of industrial traits are time and cost-consuming. Thus, achieving accurate models for classifying genotypes with better industrial technological performance based on easier and faster to measure traits, such as agronomic ones, is of paramount importance for soybean breeding programs. The objective was to classify groups of soybean genotypes to industrial technological variables based on agronomic traits measured in the field using machine learning (ML) techniques. Field experiments were carried out in two sites in a randomized block design with two replications and 206 F₂ soybean populations. Agronomic traits evaluated were: days to maturation (DM), first pod height (FPH), plant height (PH), number of branches (NB), main stem diameter (SD), mass of one hundred grains (MHG), and grain yield (GY). Industrial technological variables evaluated were oil yield, crude protein, crude fiber, and ash contents, determined by high-optical accuracy near-infrared spectroscopy (NIRS). The models tested were: support vector machine (SVM), artificial neural network (ANN), decision tree models J48 and REPTree, random forest (RF), and logistic regression (LR, used as control). A genotype clustering was performed using PCA and k-means algorithm, and then the clusters formed were used as output variables of the ML models, while the agronomic traits were used as input variables. ML techniques provided accurate models to classify soybean genotypes for more complex variables (industrial technological) based on agronomic traits. RF outperformed the other models and can be used to contribute to soybean breeding programs by classifying genotypes for industrial technological traits.

Emmer is a progenitor of bread wheat and evolved in the Levant together with the yellow rust (YR), powdery mildew (PM) fungi, and a precursor of Zymoseptoria tritici causing Septoria tritici blotch (STB). We performed a genome-wide association mapping for the three disease resistances with 143 cultivated emmer accessions in multi-environmental trials. Significant (P < 0.001) genotypic variation was found with high heritabilities for the resistances to the two biotrophs and a moderate heritability for STB resistance. For YR, PM, and STB severity nine, three, and seven marker-trait associations, respectively, were detected that were significant across all environments. Most of them were of low to moderate effect, but for PM resistance a potentially new major gene was found on chromosome 7AS. Genomic prediction abilities were high throughout for all three resistances (≥ 0.8) and decreased only slightly for YR and PM resistances when the prediction was done for the second year with the first year as training set (≥ 0.7). For STB resistance prediction ability was much lower in this scenario (0.4). Despite this, genomic selection should be advantageous given the large number of small QTLs responsible for quantitative disease resistances. A challenge for the future is to combine these multiple disease resistances with better lodging tolerance and higher grain yield.