Fine mapping of interspecific secondary CSSL populations revealed key regulators for grain weight at qTGW3.1 locus from Oryza nivara.

Grain weight (GW) is the most important stable trait that directly contributes to crop yield in case of cereals. A total of 105 backcross introgression lines (BC2F10 BILs) derived from Swarna/O. nivara IRGC81848 (NPS) and 90 BILs from Swarna/O. nivara IRGC81832 (NPK) were evaluated for thousand-grain weight (TGW) across four years (wet seasons 2014, 2015, 2016 and 2018) and chromosome segment substitution lines (CSSLs) were selected. From significant pair- wise mean comparison with Swarna, a total of 77 positively and 29 negatively significant NPS lines and 62 positively and 29 negatively significant NPK lines were identified. In all 4 years, 14 NPS lines and 9 NPK lines were positively significant and one-line NPS69 (IET22161) was negatively significant for TGW over Swarna consistently. NPS lines and NPK lines were genotyped using 111 and 140 polymorphic SSRs respectively. Quantitative trait locus (QTL) mapping using ICIM v4.2 software showed 13 QTLs for TGW in NPS. Three major effect QTLs qTGW2.1, qTGW8.1 and qTGW11.1 were identified in NPS for two or more years with PVE ranging from 8 to 14%. Likewise, 10 QTLs were identified in NPK and including two major effect QTL qTGW3.1 and qTGW12.1 with 6 to 32% PVE. In all QTLs, O. nivara alleles increased TGW. These consistent QTLs are very suitable for fine mapping and functional analysis of grain weight. Further in this study, CSSLs NPS1 (10-2S) and NPK61 (158 K) with significantly higher grain weight than the recurrent parent, Swarna cv. Oryza sativa were selected from each population and secondary F2 mapping populations were developed. Using Bulked Segregant QTL sequencing, a grain weight QTL, designated as qTGW3.1 was fine mapped from the cross between NPK61 and Swarna. This QTL explained 48% (logarithm of odds = 32.2) of the phenotypic variations and was fine mapped to a 31 kb interval using recombinant analysis. GRAS transcription factor gene (OS03go103400) involved in plant growth and development located at this genomic locus might be the candidate gene for qTGW3.1. The results of this study will help in further functional studies and improving the knowledge related to the molecular mechanism of grain weight in Oryza and lays a solid foundation for the breeding for high yield. Supplementary Information: The online version contains supplementary material available at 10.1007/s12298-024-01483-0.

Heritability estimates and genome-wide association study of methane emission traits in Nellore cattle.

The objectives of the present study were to estimate the heritability for daily methane emission (CH4) and residual daily methane emission (CH4res) in Nellore cattle, as well as to perform genome-wide association studies (GWAS) to identify genomic regions and candidate genes influencing the genetic variation of CH4 and CH4res. Methane emission phenotypes of 743 Nellore animals belonging to 3 breeding programs were evaluated. CH4 was measured using the sulfur hexafluoride (SF6) tracer technique (which involves an SF6 permeation tube introduced into the rumen, and an appropriate apparatus on each animal), and CH4res was obtained as the difference between observed CH4 and CH4 adjusted for dry matter intake. A total of 6,252 genotyped individuals were used for genomic analyses. Data were analyzed with a univariate animal model by the single-step GBLUP method using the average information restricted maximum likelihood (AIREML) algorithm. The effects of single nucleotide polymorphisms (SNPs) were obtained using a single-step GWAS approach. Candidate genes were identified based on genomic windows associated with quantitative trait loci (QTLs) related to the 2 traits. Annotation of QTLs and identification of candidate genes were based on the initial and final coordinates of each genomic window considering the bovine genome ARS-UCD1.2 assembly. Heritability estimates were of moderate to high magnitude, being 0.42 ±â€…0.09 for CH4 and 0.21 ±â€…0.09 for CH4res, indicating that these traits will respond rapidly to genetic selection. GWAS revealed 11 and 15 SNPs that were significantly associated (P < 10-6) with genetic variation of CH4 and CH4res, respectively. QTLs associated with feed efficiency, residual feed intake, body weight, and height overlapped with significant markers for the traits evaluated. Ten candidate genes were present in the regions of significant SNPs; 3 were associated with CH4 and 7 with CH4res. The identified genes are related to different functions such as modulation of the rumen microbiota, fatty acid production, and lipid metabolism. CH4 and CH4res presented sufficient genetic variation and may respond rapidly to selection. Therefore, these traits can be included in animal breeding programs aimed at reducing enteric methane emissions across generations.

Genetic selection designed to reduce the amount of enteric methane emission from livestock is a mitigation strategy to ensure more sustainable production over generations since genetic gains are cumulative. Brazil is a large producer of beef, and the Nellore breed (Bos taurus indicus) plays a very important role in this production. There are a few studies evaluating genetic and genomic aspects of enteric methane emission in Nellore cattle. The objectives of the present study were to estimate the heritability of daily methane emission (CH4) and residual daily methane emission (CH4res) in Nellore cattle, as well as to identify genomic regions and candidate genes associated with genetic variation of these traits. The heritability estimates for CH4 and CH4res were of moderate to high magnitude (0.42 ±â€…0.09 and 0.21 ±â€…0.09, respectively). Genome-wide association analyses revealed new loci associated with methane emission in Nellore cattle on chromosomes 5, 11, 17, and 20, where 10 candidate genes were identified, 3 for CH4 and 7 for CH4res. The 2 traits possess sufficient genetic variability to be included as selection criteria in breeding programs.

Genome-wide association study of trace elements in maize kernels.

Maize (Zea mays L.), a staple food and significant economic crop, is enriched with riboflavin, micronutrients and other compounds that are beneficial for human health. As emphasis on the nutritional quality of crops increases maize research has expanded to focus on both yield and quality. This study exploreed the genetic factors influencing micronutrient levels in maize kernels through a comprehensive genome-wide association study (GWAS). We utilized a diverse panel of 244 inbred maize lines and approximately 3 million single nucleotide polymorphisms (SNPs) to investigate the accumulation of essential and trace elements including cadmium (Cd), cobalt (Co), copper (Cu), nickel (Ni), selenium (Se) and zinc (Zn). Our analysis identified 842 quantitative trait loci (QTLs), with 12 QTLs shared across multiple elements and pinpointed 524 potential genes within a 100 kb radius of these QTLs. Notably ZmHMA3 has emerged as a key candidate gene previously reported to influence the Cd accumulation. We highlighted ten pivotal genes associated with trace element transport including those encoding heavy metal ATPases, MYB transcription factors, ABC transporters and other crucial proteins involved in metal handling. Additionally, haplotype analysis revealed that eight inbred linesaccumulated relatively high levels of beneficial elements while harmful elements were minimized. These findings elucidate the genetic mechanisms underlying trace element accumulation in maize kernels and provide a foundation for the breeding of nutritionally enhanced maize varieties.

The Molecular Mechanism of Cold-Stress Tolerance: Cold Responsive Genes and Their Mechanisms in Rice (Oryza sativa L.).

Rice (Oryza sativa L.) production is highly susceptible to temperature fluctuations, which can significantly reduce plant growth and development at different developmental stages, resulting in a dramatic loss of grain yield. Over the past century, substantial efforts have been undertaken to investigate the physiological, biochemical, and molecular mechanisms of cold stress tolerance in rice. This review aims to provide a comprehensive overview of the recent developments and trends in this field. We summarized the previous advancements and methodologies used for identifying cold-responsive genes and the molecular mechanisms of cold tolerance in rice. Integration of new technologies has significantly improved studies in this era, facilitating the identification of essential genes, QTLs, and molecular modules in rice. These findings have accelerated the molecular breeding of cold-resistant rice varieties. In addition, functional genomics, including the investigation of natural variations in alleles and artificially developed mutants, is emerging as an exciting new approach to investigating cold tolerance. Looking ahead, it is imperative for scientists to evaluate the collective impacts of these novel genes to develop rice cultivars resilient to global climate change.

Copy number variations affecting growth curve parameters in a crossbred chicken population.

Copy number variations (CNVs) are key structural variations in the genome and may contribute to phenotypic differences. In this study, we used a F2 chicken population created from reciprocal crossing between fast-growing Arian broiler line and Urmia native chickens. The chickens were genotyped by 60 K SNP BeadChip, and PennCNV algorithm was used to detect genome-wide CNVs. The growth curve parameters of W0, k, L, Wf, Wi, ti and average GR were used as phenotypic data. The association between CNV and growth curve parameters was carried out using the CNVRanger R/Bioconductor package. Five CNV regions (CNVRs) were chosen for the validation experiment using qPCR. Gene enrichment analysis was done using WebGestalt. The STRING database was used to search for significant pathways. The results identified 966 CNVs and 600 CNVRs including 468 gains, 67 losses, and 65 both events on autosomal chromosomes. Validation of the CNVRs obtained from the qPCR assay were 79 % consistent with the prediction by PennCNV. A total of 43 significant CNVs were obtained for the seven growth curve parameters. The 416 genes annotated for significant CNVs. Six genes out of 416 genes were most related to growth curve parameters. These genes were LCP2, Dock2, CD80, CYFIP1, NIPA1 and NIPA2. Some of these genes in their biological process were associated with the growth, reproduction and development of cells or organs that ultimately lead to the growth of the body. The results of the study could pave the way for better understanding the molecular process of CNVs and growth curve parameters in birds.

Mapping and candidate gene analysis of QTLs for grain shape in a rice chromosome segment substitution line Z485 and breeding of SSSLs.

Grain shape is one of the most important factors that affects rice yield. Cloning novel grain shape genes and analyzing their genetic mechanisms are crucial for high yield breeding. In this study, a slender grain CSSL-Z485 with 3-segments substitution in the genetic background of Nipponbare was constructed in rice. Cytological analysis showed that the longer grain length of Z485 was related to the increase in glume cell numbers, while the narrower grain width was associated with the decrease in cell width. Three grain shape-related quantitative trait locus (QTLs), including qGL12, qGW12, and qRLW12, were identified through the F2 population constructed from a cross between Nipponbare and Z485. Furthermore, four single segment substitution lines (SSSLs, S1-S4) carrying the target QTLs were dissected from Z485 by MAS. Finally, three candidate genes of qGL12 for grain length and qGW12 for grain width located in S3 were confirmed by DNA sequencing, RT-qPCR, and protein structure prediction. Specifically, candidate gene 1 encodes a ubiquitin family protein, while candidate genes 2 and 3 encode zinc finger proteins. The results provide valuable germplasm resources for cloning novel grain shape genes and molecular breeding by design. Supplementary information: The online version contains supplementary material available at 10.1007/s11032-024-01480-x.

RNA-Seq Bulked Segregant Analysis of an Exotic B. napus ssp. napobrassica (Rutabaga) F2 Population Reveals Novel QTLs for Breeding Clubroot-Resistant Canola.

In this study, a rutabaga (Brassica napus ssp. napobrassica) donor parent FGRA106, which exhibited broad-spectrum resistance to 17 isolates representing 16 pathotypes of Plasmodiophora brassicae, was used in genetic crosses with the susceptible spring-type canola (B. napus ssp. napus) accession FG769. The F2 plants derived from a clubroot-resistant F1 plant were screened against three P. brassicae isolates representing pathotypes 3A, 3D, and 3H. Chi-square (χ2) goodness-of-fit tests indicated that the F2 plants inherited two major clubroot resistance genes from the CR donor FGRA106. The total RNA from plants resistant (R) and susceptible (S) to each pathotype were pooled and subjected to bulked segregant RNA-sequencing (BSR-Seq). The analysis of gene expression profiles identified 431, 67, and 98 differentially expressed genes (DEGs) between the R and S bulks. The variant calling method indicated a total of 12 (7 major + 5 minor) QTLs across seven chromosomes. The seven major QTLs included: BnaA5P3A.CRX1.1, BnaC1P3H.CRX1.2, and BnaC7P3A.CRX1.1 on chromosomes A05, C01, and C07, respectively; and BnaA8P3D.CRX1.1, BnaA8P3D.RCr91.2/BnaA8P3H.RCr91.2, BnaA8P3H.Crr11.3/BnaA8P3D.Crr11.3, and BnaA8P3D.qBrCR381.4 on chromosome A08. A total of 16 of the DEGs were located in the major QTL regions, 13 of which were on chromosome C07. The molecular data suggested that clubroot resistance in FGRA106 may be controlled by major and minor genes on both the A and C genomes, which are deployed in different combinations to confer resistance to the different isolates. This study provides valuable germplasm for the breeding of clubroot-resistant B. napus cultivars in Western Canada.

PySmooth: a Python tool for the removal and correction of genotyping errors.

In genetic mapping studies involving many individuals, genome-wide markers such as single nucleotide polymorphisms (SNPs) can be detected using different methods. However, it comes with some errors. Some SNPs associated with diseases can be in regions encoding long noncoding RNAs (lncRNAs). Therefore, identifying the errors in genotype file and correcting them is crucial for accurate genetic mapping studies. We develop a Python tool called PySmooth, that offers an easy-to-use command line interface for the removal and correction of genotyping errors. PySmooth uses the approach of a previous tool called SMOOTH with some modifications. It inputs a genotype file, detects errors and corrects them. PySmooth provides additional features such as imputing missing data, better user-friendly usage, generates summary and visualization files, has flexible parameters, and handles more genotype codes. AVAILABILITY AND IMPLEMENTATION: PySmooth is available at https://github.com/lncRNAAddict/PySmooth .

QTL mapping for the flag leaf-related traits using RILs derived from Trititrigia germplasm line SN304 and wheat cultivar Yannong15 in multiple environments.

BACKGROUND: Developing and enriching genetic resources plays important role in the crop improvement. The flag leaf affects plant architecture and contributes to the grain yield of wheat (Triticum aestivum L.). The genetic improvement of flag leaf traits faces problems such as a limited genetic basis. Among the various genetic resources of wheat, Thinopyrum intermedium has been utilized as a valuable resource in genetic improvement due to its disease resistance, large spikes, large leaves, and multiple flowers. In this study, a recombinant inbred line (RIL) population was derived from common wheat Yannong15 and wheat-Th. intermedium introgression line SN304 was used to identify the quantitative trait loci (QTL) for flag leaf-related traits. RESULTS: QTL mapping was performed for flag leaf length (FLL), flag leaf width (FLW) and flag leaf area (FLA). A total of 77 QTLs were detected, and among these, 51 QTLs with positive alleles were contributed by SN304. Fourteen major QTLs for flag leaf traits were detected on chromosomes 2B, 3B, 4B, and 2D. Additionally, 28 QTLs and 8 QTLs for flag leaf-related traits were detected in low-phosphorus and drought environments, respectively. Based on major QTLs of positive alleles from SN304, we identified a pair of double-ended anchor primers mapped on chromosome 2B and amplified a specific band of Th. intermedium in SN304. Moreover, there was a major colocated QTL on chromosome 2B, called QFll/Flw/Fla-2B, which was delimited to a physical interval of approximately 2.9 Mb and contained 20 candidate genes. Through gene sequence and expression analysis, four candidate genes associated with flag leaf formation and growth in the QTL interval were identified. CONCLUSION: These results promote the fine mapping of QFll/Flw/Fla-2B, which have pleiotropic effects, and will facilitate the identification of candidate genes for flag leaf-related traits. Additionally, this work provides a theoretical basis for the application of Th. intermedium in wheat breeding.

Mapping of Candidate Genes for Nitrogen Uptake and Utilization in Japonica Rice at Seedling Stage.

Nitrogen (N) is one of the essential nutrients for the growth and development of crops. The adequate application of N not only increases the yield of crops but also improves the quality of agricultural products, but the excessive application of N can cause many adverse effects on ecology and the environment. In this study, genome-wide association analysis (GWAS) was performed under low- and high-N conditions based on 788,396 SNPs and phenotypic traits relevant to N uptake and utilization (N content and N accumulation). A total of 75 QTLs were obtained using GWAS, which contained 811 genes. Of 811 genes, 281 genes showed different haplotypes, and 40 genes had significant phenotypic differences among different haplotypes. Of these 40 genes, 5 differentially expressed genes (Os01g0159250, Os02g0618200, Os02g0618400, Os02g0630300, and Os06g0619000) were finally identified as the more valuable candidate genes based on the transcriptome data sequenced from Longjing31 (low-N-tolerant variety) and Songjing 10 (low-N-sensitive variety) under low- and high-N treatments. These new findings enrich the genetic resources for N uptake and utilization in rice, as well as lay a theoretical foundation for improving the efficiency of N uptake and utilization in rice.

Recent advances in plant translational genomics for crop improvement.

The growing population, climate change, and limited agricultural resources put enormous pressure on agricultural systems. A plateau in crop yields is occurring and extreme weather events and urbanization threaten the livelihood of farmers. It is imperative that immediate attention is paid to addressing the increasing food demand, ensuring resilience against emerging threats, and meeting the demand for more nutritious, safer food. Under uncertain conditions, it is essential to expand genetic diversity and discover novel crop varieties or variations to develop higher and more stable yields. Genomics plays a significant role in developing abundant and nutrient-dense food crops. An alternative to traditional breeding approach, translational genomics is able to improve breeding programs in a more efficient and precise manner by translating genomic concepts into practical tools. Crop breeding based on genomics offers potential solutions to overcome the limitations of conventional breeding methods, including improved crop varieties that provide more nutritional value and are protected from biotic and abiotic stresses. Genetic markers, such as SNPs and ESTs, contribute to the discovery of QTLs controlling agronomic traits and stress tolerance. In order to meet the growing demand for food, there is a need to incorporate QTLs into breeding programs using marker-assisted selection/breeding and transgenic technologies. This chapter primarily focuses on the recent advances that are made in translational genomics for crop improvement and various omics techniques including transcriptomics, metagenomics, pangenomics, single cell omics etc. Numerous genome editing techniques including CRISPR Cas technology and their applications in crop improvement had been discussed.

Quality Tolerance Limits: A General Guidance for Parameter Selection and Threshold Setting.

The past years have sharpened the industry's understanding of a Quality by Design (QbD) approach toward clinical trials. Using QbD encourages designing quality into a trial during the planning phase. The identification of Critical to Quality (CtQs) factors and specifically Critical Data and Processes (CD&Ps) is key to such a risk-based monitoring approach. A variable that allows monitoring the evolution of risk regarding the CD&Ps is called a Quality Tolerance Limit (QTL) parameter. These parameters are linked to the scientific question(s) of a trial and may identify the issues that can jeopardize the integrity of trial endpoints. This paper focuses on defining what QTL parameters are and providing general guidance on setting thresholds for these parameters allowing for the derivation of an acceptable range of the risk.

Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variations.

Quantifying the temporal or longitudinal growth dynamics of crops in diverse environmental conditions is crucial for understanding plant development, requiring further modeling techniques. In this study, we analyzed the growth patterns of two different maize (Zea mays L.) populations using high-throughput phenotyping with a maize population consisting of 515 recombinant inbred lines (RILs) grown in Texas and a hybrid population containing 1090 hybrids grown in Missouri. Two models, Gaussian peak and functional principal component analysis (FPCA), were employed to study the Normalized Green-Red Difference Index (NGRDI) scores. The Gaussian peak model showed strong correlations (c. 0.94 for RILs and c. 0.97 for hybrids) between modeled and non-modeled temporal trajectories. Functional principal component analysis differentiated NGRDI trajectories in RILs under different conditions, capturing substantial variability (75%, 20%, and 5% for RILs; 88% and 12% for hybrids). By comparing these models with conventional BLUP values, common quantitative trait loci (QTLs) were identified, containing candidate genes of brd1, pin11, zcn8 and rap2. The harmony between these loci's additive effects and growing degree days, as well as the differentiation of RIL haplotypes across growth stages, underscores the significant interplay of these loci in driving plant development. These findings contribute to advancing understanding of plant-environment interactions and have implications for crop improvement strategies.

Genome-Wide Association Analysis Unravels New Quantitative Trait Loci (QTLs) for Eight Lodging Resistance Constituent Traits in Rice (Oryza sativa L.).

Lodging poses a significant challenge to rice yield, prompting the need to identify elite alleles for lodging resistance traits to improve cultivated rice varieties. In this study, a natural population of 518 rice accessions was examined to identify elite alleles associated with plant height (PH), stem diameter (SD), stem anti-thrust (AT/S), and various internode lengths (first (FirINL), second (SecINL), third (ThirINL), fourth (ForINL), and fifth (FifINL) internode lengths). A total of 262 SSR markers linked to these traits were uncovered through association mapping in two environmental conditions. Phenotypic evaluations revealed striking differences among cultivars, and genetic diversity assessments showed polymorphisms across the accessions. Favorable alleles were identified for PH, SD, AT/S, and one to five internode lengths, with specific alleles displaying considerable effects. Noteworthy alleles include RM6811-160 bp on chromosome 6 (which reduces PH) and RM161-145 bp on chromosome 5 (which increases SD). The study identified a total of 42 novel QTLs. Specifically, seven QTLs were identified for PH, four for SD, five for AT/S, five for FirINL, six for SecINL, five for ThirINL, six for ForINL, and four for FifINL. QTLs qAT/S-2, qPH2.1, qForINL2.1, and qFifINL exhibited the most significant phenotypic variance (PVE) of 3.99% for the stem lodging trait. AT/S, PH, ForINL, and FifINL had additive effects of 5.31 kPa, 5.42 cm, 4.27 cm, and 4.27 cm, respectively, offering insights into eight distinct cross-combinations for enhancing each trait. This research suggests the potential for crossbreeding superior parents based on stacked alleles, promising improved rice cultivars with enhanced lodging resistance to meet market demands.

Genetic Dissection of Panicle Morphology Traits in Super High-Yield Hybrid Rice Chaoyou 1000.

The morphological characteristics of the rice panicle play a pivotal role in influencing yield. In our research, we employed F2 and F2:3 populations derived from the high-yielding hybrid rice variety Chaoyou 1000. We screened 123 pairs of molecular markers, which were available, to construct the genetic linkage map. Subsequently, we assessed the panicle morphology traits of F2 populations in Lingshui County, Hainan Province, in 2017, and F2:3 populations in Hangzhou City, Zhejiang Province, in 2018. These two locations represent two types of ecology. Hangzhou's climate is characterized by high temperatures and humidity, while Lingshui's climate is characterized by a tropical monsoon climate. In total, 33 QTLs were identified, with eight of these being newly discovered, and two of them were consistently detected in two distinct environments. We identified fourteen QTL-by-environment interactions (QEs), which collectively explained 4.93% to 59.95% of the phenotypic variation. While most of the detected QTLs are consistent with the results of previous tests, the novel-detected QTLs will lay the foundation for rice yield increase and molecular breeding.

Uncovering the Genomic Regions Associated with Yield Maintenance in Rice Under Drought Stress Using an Integrated Meta-Analysis Approach.

The complex trait of yield is controlled by several quantitative trait loci (QTLs). Given the global water deficit issue, the development of rice varieties suitable for non-flooded cultivation holds significant importance in breeding programs. The powerful approach of Meta-QTL (MQTL) analysis can be used for the genetic dissection of complicated quantitative traits. In the current study, a comprehensive MQTL analysis was conducted to identify consistent QTL regions associated with drought tolerance and yield-related traits under water deficit conditions in rice. In total, 1087 QTLs from 134 rice populations, published between 2000 to 2021, were utilized in the analysis. Distinct MQTL analysis of the relevant traits resulted in the identification of 213 stable MQTLs. The confidence interval (CI) for the detected MQTLs was between 0.12 and 19.7 cM. The average CI of the identified MQTLs (4.68 cM) was 2.74 times narrower compared to the average CI of the initial QTLs. Interestingly, 63 MQTLs coincided with SNP peak positions detected by genome-wide association studies for yield and drought tolerance-associated traits under water deficit conditions in rice. Considering the genes located both in the QTL-overview peaks and the SNP peak positions, 19 novel candidate genes were introduced, which are associated with drought response index, plant height, panicle number, biomass, and grain yield. Moreover, an inclusive MQTL analysis was performed on all the traits to obtain "Breeding MQTLs". This analysis resulted in the identification of 96 MQTLs with a CI ranging from 0.01 to 9.0 cM. The mean CI of the obtained MQTLs (2.33 cM) was 4.66 times less than the mean CI of the original QTLs. Thirteen MQTLs fulfilling the criteria of having more than 10 initial QTLs, CI < 1 cM, and an average phenotypic variance explained greater than 10%, were designated as "Breeding MQTLs". These findings hold promise for assisting breeders in enhancing rice yield under drought stress conditions.

Grain yield trade-offs in spike-branching wheat can be mitigated by elite alleles affecting sink capacity and post-anthesis source activity.

Introducing variations in inflorescence architecture, such as the 'Miracle-Wheat' (Triticum turgidum convar. compositum (L.f.) Filat.) with a branching spike, has relevance for enhancing wheat grain yield. However, in the spike-branching genotypes, the increase in spikelet number is generally not translated into grain yield advantage because of reduced grains per spikelet and grain weight. Here, we investigated if such trade-offs might be a function of source-sink strength by using 385 recombinant inbred lines developed by intercrossing the spike-branching landrace TRI 984 and CIRNO C2008, an elite durum (T. durum L.) cultivar; they were genotyped using the 25K array. Various plant and spike architectural traits, including flag leaf, peduncle, and spike senescence rate, were phenotyped under field conditions for 2 consecutive years. On chromosome 5AL, we found a new modifier QTL for spike branching, branched headt3 (bht-A3), which was epistatic to the previously known bht-A1 locus. Besides, bht-A3 was associated with more grains per spikelet and a delay in flag leaf senescence rate. Importantly, favourable alleles, viz. bht-A3 and grain protein content (gpc-B1) that delayed senescence, are required to improve grain number and grain weight in the spike-branching genotypes. In summary, achieving a balanced source-sink relationship might minimize grain yield trade-offs in Miracle-Wheat.

QTL Mapping for Adult-Plant Resistance to Leaf Rust in Italian Wheat Cultivar Libellula.

Wheat leaf rust (Lr), which is caused by Puccinia triticina Eriks. (Pt), is one of the most important wheat diseases affecting wheat production globally. Using resistant wheat cultivars is the most economical and environmentally friendly way to control leaf rust. The Italian wheat cultivar Libellula has demonstrated good resistance to Lr in field studies. To identify the genetic basis of Lr resistance in 'Libellula', 248 F6 recombinant inbred lines from the cross 'Libellula'/'Huixianhong' was phenotyped for Lr severity in seven environments: the 2014/2015, 2016/2017, 2017/2018, and 2018/2019 cropping seasons at Baoding, Hebei Province, and the 2016/2017, 2017/2018, and 2018/2019 crop seasons at Zhoukou, Henan Province. Bulked segregant analysis and simple sequence repeat markers were then used to identify the quantitative trait loci (QTLs) for Lr adult-plant resistance in the population. Six QTLs were consequently detected and designated as QLr.hebau-1AL and QLr.hebau-1AS that were presumed to be new and QLr.hebau-1BL, QLr.hebau-3AL, QLr.hebau-4BL, and QLr.hebau-7DS that were identified at similar physical positions as previously reported QTLs. Based on chromosome positions and molecular marker tests, QLr.hebau-1BL and QLr.hebau-7DS share similar flanking markers with Lr46 and Lr34, respectively. Lr46 and Lr34 are race nonspecific adult plant resistance (APR) genes for leaf rust and stripe rust and powdery mildew. QLr.hebau-4BL showed multiple disease resistance to leaf rust, stripe rust, Fusarium head blight, and powdery mildew. The QTL identified in this study, as well as their closely linked markers, may potentially be used in marker-assisted selection in wheat breeding.

The chromosome-scale genome and the genetic resistance machinery against insect herbivores of the Mexican toloache, Datura stramonium.

Plant resistance refers to the heritable ability of plants to reduce damage caused by natural enemies, such as herbivores and pathogens, either through constitutive or induced traits like chemical compounds or trichomes. However, the genetic architecture-the number and genome location of genes that affect plant defense and the magnitude of their effects-of plant resistance to arthropod herbivores in natural populations remains poorly understood. In this study, we aimed to unveil the genetic architecture of plant resistance to insect herbivores in the annual herb Datura stramonium (Solanaceae) through quantitative trait loci mapping. We achieved this by assembling the species' genome and constructing a linkage map using an F2 progeny transplanted into natural habitats. Furthermore, we conducted differential gene expression analysis between undamaged and damaged plants caused by the primary folivore, Lema daturaphila larvae. Our genome assembly resulted in 6,109 scaffolds distributed across 12 haploid chromosomes. A single quantitative trait loci region on chromosome 3 was associated with plant resistance, spanning 0 to 5.17 cM. The explained variance by the quantitative trait loci was 8.44%. Our findings imply that the resistance mechanisms of D. stramonium are shaped by the complex interplay of multiple genes with minor effects. Protein-protein interaction networks involving genes within the quantitative trait loci region and overexpressed genes uncovered the key role of receptor-like cytoplasmic kinases in signaling and regulating tropane alkaloids and terpenoids, which serve as powerful chemical defenses against D. stramonium herbivores. The data generated in our study constitute important resources for delving into the evolution and ecology of secondary compounds mediating plant-insect interactions.

Genetic linkage analysis of stable QTLs in Gossypium hirsutum RIL population revealed function of GhCesA4 in fiber development.

INTRODUCTION: Upland cotton is an important allotetrapolyploid crop providing natural fibers for textile industry. Under the present high-level breeding and production conditions, further simultaneous improvement of fiber quality and yield is facing unprecedented challenges due to their complex negative correlations. OBJECTIVES: The study was to adequately identify quantitative trait loci (QTLs) and dissect how they orchestrate the formation of fiber quality and yield. METHODS: A high-density genetic map (HDGM) based on an intraspecific recombinant inbred line (RIL) population consisting of 231 individuals was used to identify QTLs and QTL clusters of fiber quality and yield traits. The weighted gene correlation network analysis (WGCNA) package in R software was utilized to identify WGCNA network and hub genes related to fiber development. Gene functions were verified via virus-induced gene silencing (VIGS) and clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 strategies. RESULTS: An HDGM consisting of 8045 markers was constructed spanning 4943.01 cM of cotton genome. A total of 295 QTLs were identified based on multi-environmental phenotypes. Among 139 stable QTLs, including 35 newly identified ones, seventy five were of fiber quality and 64 yield traits. A total of 33 QTL clusters harboring 74 QTLs were identified. Eleven candidate hub genes were identified via WGCNA using genes in all stable QTLs and QTL clusters. The relative expression profiles of these hub genes revealed their correlations with fiber development. VIGS and CRISPR/Cas9 edition revealed that the hub gene cellulose synthase 4 (GhCesA4, GH_D07G2262) positively regulate fiber length and fiber strength formation and negatively lint percentage. CONCLUSION: Multiple analyses demonstrate that the hub genes harbored in the QTLs orchestrate the fiber development. The hub gene GhCesA4 has opposite pleiotropic effects in regulating trait formation of fiber quality and yield. The results facilitate understanding the genetic basis of negative correlation between cotton fiber quality and yield.