NIR-responsive CN-Pt-GEM hydrogel induces necroptosis and immunotherapeutic responses prevent postoperative recurrence and wound infection in lung carcinoma.

BACKGROUND: Cancer recurrence following surgical resection is a major cause of treatment failure. Finding effective methods to prevent postoperative recurrence and wound infection is an important component of successful surgery. With the development of new nanotechnology, more treatment options have been provided for postoperative adjuvant therapy. This study presents an innovative hydrogel system that stimulates tumoricidal immunity after surgical resection of non-small cell lung cancer (NSCLC) and prevents cancer relapse. RESULTS: The hydrogel system is based on the excellent photothermal conversion performance of single-atom platinum (CN-Pt) along with the delivery and release of the chemotherapy drug, gemcitabine (GEM). The system is coated onto the wound surface after tumor removal with subsequent near-infrared (NIR) photothermal therapy, which efficiently induces necroptosis of residual cancer cells, amplifies the levels of damage-associated molecular patterns (DAMPs), and increases the number of M1 macrophages. The significantly higher levels of phagocytic macrophages enhance tumor immunogenicity and sensitize cancer cells to CD8 + T-cell immunity to control postoperative recurrence, which has been verified using an animal model of postoperative lung cancer recurrence. The CN-Pt-GEM-hydrogel with NIR can also inhibit postoperative wound infection. CONCLUSIONS: These findings introduce an alternative strategy for supplementing antitumor immunity in patients undergoing resection of NSCLC tumors. The CN-Pt-GEM-hydrogel with the NIR system also exhibits good biosafety and may be adaptable for clinical application in relation to tumor resection surgery, wound tissue filling, infection prevention, and recurrence prevention.

The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review.

Roots are essential for acquiring water and nutrients to sustain and support plant growth and anchorage. However, they have been studied less than the aboveground traits in phenotyping and plant breeding until recent decades. In modern times, root properties such as morphology and root system architecture (RSA) have been recognized as increasingly important traits for creating more and higher quality food in the "Second Green Revolution". To address the paucity in RSA and other root research, new technologies are being investigated to fill the increasing demand to improve plants via root traits and overcome currently stagnated genetic progress in stable yields. Artificial intelligence (AI) is now a cutting-edge technology proving to be highly successful in many applications, such as crop science and genetic research to improve crop traits. A burgeoning field in crop science is the application of AI to high-resolution imagery in analyses that aim to answer questions related to crops and to better and more speedily breed desired plant traits such as RSA into new cultivars. This review is a synopsis concerning the origins, applications, challenges, and future directions of RSA research regarding image analyses using AI.

Preharvest Sprouting in Quinoa: A New Screening Method Adapted to Panicles and GWAS Components.

The introduction of quinoa into new growing regions and environments is of interest to farmers, consumers, and stakeholders around the world. Many plant breeding programs have already started to adapt quinoa to the environmental and agronomic conditions of their local fields. Formal quinoa breeding efforts in Washington State started in 2010, led by Professor Kevin Murphy out of Washington State University. Preharvest sprouting appeared as the primary obstacle to increased production in the coastal regions of the Pacific Northwest. Preharvest sprouting (PHS) is the undesirable sprouting of seeds that occurs before harvest, is triggered by rain or humid conditions, and is responsible for yield losses and lower nutrition in cereal grains. PHS has been extensively studied in wheat, barley, and rice, but there are limited reports for quinoa, partly because it has only recently emerged as a problem. This study aimed to better understand PHS in quinoa by adapting a PHS screening method commonly used in cereals. This involved carrying out panicle-wetting tests and developing a scoring scale specific for panicles to quantify sprouting. Assessment of the trait was performed in a diversity panel (N = 336), and the resulting phenotypes were used to create PHS tolerance rankings and undertake a GWAS analysis (n = 279). Our findings indicate that PHS occurred at varying degrees across a subset of the quinoa germplasm tested and that it is possible to access PHS tolerance from natural sources. Ultimately, these genotypes can be used as parental lines in future breeding programs aiming to incorporate tolerance to PHS.

In silico analysis identified bZIP transcription factors genes responsive to abiotic stress in Alfalfa (Medicago sativa L.).

BACKGROUND: Alfalfa (Medicago sativa L.) is the most cultivated forage legume around the world. Under a variety of growing conditions, forage yield in alfalfa is stymied by biotic and abiotic stresses including heat, salt, drought, and disease. Given the sessile nature of plants, they use strategies including, but not limited to, differential gene expression to respond to environmental cues. Transcription factors control the expression of genes that contribute to or enable tolerance and survival during periods of stress. Basic-leucine zipper (bZIP) transcription factors have been demonstrated to play a critical role in regulating plant growth and development as well as mediate the responses to abiotic stress in several species, including Arabidopsis thaliana, Oryza sativa, Lotus japonicus and Medicago truncatula. However, there is little information about bZIP transcription factors in cultivated alfalfa. RESULT: In the present study, 237 bZIP genes were identified in alfalfa from publicly available sequencing data. Multiple sequence alignments showed the presence of intact bZIP motifs in the identified sequences. Based on previous phylogenetic analyses in A. thaliana, alfalfa bZIPs were similarly divided and fell into 10 groups. The physico-chemical properties, motif analysis and phylogenetic study of the alfalfa bZIPs revealed high specificity within groups. The differential expression of alfalfa bZIPs in a suite of tissues indicates that bZIP genes are specifically expressed at different developmental stages in alfalfa. Similarly, expression analysis in response to ABA, cold, drought and salt stresses, indicates that a subset of bZIP genes are also differentially expressed and likely play a role in abiotic stress signaling and/or tolerance. RT-qPCR analysis on selected genes further verified these differential expression patterns. CONCLUSIONS: Taken together, this work provides a framework for the future study of bZIPs in alfalfa and presents candidate bZIPs involved in stress-response signaling.

Evolutionary genomics of climatic adaptation and resilience to climate change in alfalfa.

Given the escalating impact of climate change on agriculture and food security, gaining insights into the evolutionary dynamics of climatic adaptation and uncovering climate-adapted variation can empower the breeding of climate-resilient crops to face future climate change. Alfalfa (Medicago sativa subsp. sativa), the queen of forages, shows remarkable adaptability across diverse global environments, making it an excellent model for investigating species responses to climate change. In this study, we performed population genomic analyses using genome resequencing data from 702 accessions of 24 Medicago species to unravel alfalfa's climatic adaptation and genetic susceptibility to future climate change. We found that interspecific genetic exchange has contributed to the gene pool of alfalfa, particularly enriching defense and stress-response genes. Intersubspecific introgression between M. sativa subsp. falcata (subsp. falcata) and alfalfa not only aids alfalfa's climatic adaptation but also introduces genetic burden. A total of 1671 genes were associated with climatic adaptation, and 5.7% of them were introgressions from subsp. falcata. By integrating climate-associated variants and climate data, we identified populations that are vulnerable to future climate change, particularly in higher latitudes of the Northern Hemisphere. These findings serve as a clarion call for targeted conservation initiatives and breeding efforts. We also identified pre-adaptive populations that demonstrate heightened resilience to climate fluctuations, illuminating a pathway for future breeding strategies. Collectively, this study enhances our understanding about the local adaptation mechanisms of alfalfa and facilitates the breeding of climate-resilient alfalfa cultivars, contributing to effective agricultural strategies for facing future climate change.

Cabozantinib prevents AGEs-induced degradation of type 2 collagen and aggrecan in human chondrocytes.

Osteoarthritis (OA) is a joint degenerative disease commonly observed in the old population, lacks effective therapeutic methods, and markedly impacts the normal lives of patients. Degradation of extracellular matrix (ECM) is reported to participate in OA development, which is a potential target for treating OA. Cabozantinib is an inhibitor of tyrosine kinases and is recently claimed with suppressive properties against inflammation. Herein, the protective function of Cabozantinib on advanced glycation end products (AGEs)-induced damages to chondrocytes was tested. SW1353 chondrocytes were stimulated with 100 µg/ml AGEs with or without 10 and 20 µM Cabozantinib for 24 h. Signally increased reactive oxygen species (ROS) levels, declined reduced glutathione (GSH) levels, and elevated release of inflammatory cytokines were observed in AGEs-stimulated SW1353 chondrocytes, which were markedly reversed by Cabozantinib. Moreover, the notably reduced type II collagen and aggrecan levels, and increased matrix metalloproteinase-13 (MMP-13) and A Disintegrin and Metalloproteinase with Thrombospondin Motifs-5 (ADAMTS-5) levels in AGEs-stimulated SW1353 chondrocytes were largely rescued by Cabozantinib. The downregulated Sry-type high-mobility-group box 9 (SOX-9) observed in AGEs-stimulated SW1353 chondrocytes was abolished by Cabozantinib. Furthermore, the impact of Cabozantinib on type II collagen and aggrecan levels in AGEs-treated SW1353 chondrocytes was abrogated by silencing SOX-9. Collectively, Cabozantinib prevented AGEs-induced degradation of type 2 collagen and aggrecan in human chondrocytes by mediating SOX-9.

Clinical outcomes of robotic-assisted and manual total hip arthroplasty in the same patient: A case report.

BACKGROUND: Total hip arthroplasty (THA) is an effective treatment for advanced osteonecrosis of the femoral head, which can significantly relieve pain and improve patients' quality of life. Robotic-assisted THA enhances the accuracy and stability of THA surgery and achieves better clinical outcomes than manual THA. CASE SUMMARY: We report the clinical outcomes of robotic-assisted THA and manual THA in the same patient with osteonecrosis of the femoral head. A 49-year-old male patient attended our hospital due to more than 3 years of pain in both hip joints. The left hip was treated with robotic-assisted THA. The patient underwent manual THA of the right hip 3 mo after robotic-assisted THA. We obtained postoperative radiograph parameters, Harris hip score and forgotten joint score of the patient 1 year after surgery. CONCLUSION: Compared with manual THA, the patient's left hip felt better 1 year after robotic-assisted THA. Robotic-assisted THA resulted in a better Harris hip score and forgotten joint score than manual THA in the same patient with osteonecrosis of the femoral head.

An iRGD-conjugated photothermal therapy-responsive gold nanoparticle system carrying siCDK7 induces necroptosis and immunotherapeutic responses in lung adenocarcinoma.

Although immunotherapy has improved the clinical treatment of lung adenocarcinoma (LUAD), many tumors have poor responses to immunotherapy. In this study, we confirmed that high expression of Cyclin-Dependent Kinase 7 (CDK7) promoted an immunosuppressive macrophage phenotype and macrophage infiltration in LUAD. Thus, we have developed an internalizing-RGD (iRGD)-conjugated gold nanoparticle (AuNP) system which carries siCDK7 to activate the antitumor immune response. The iRGD-conjugated AuNP/siCDK7 system exhibited good tumor targeting performance and photothermal effects. The AuNP/siCDK7 system with excellent biosafety exerted a significant photothermal antitumor effect by inducing tumor cell necroptosis. Furthermore, the AuNP/siCDK7 system ameliorated the immunosuppressive microenvironment and enhanced the efficacy of anti-PD-1 treatment by increasing CD8+ T cell infiltration and decreasing M2 macrophage infiltration. Hence, this iRGD-conjugated AuNP/siCDK7 system is a potential treatment strategy for lung adenocarcinoma, which exerts its effects by triggering tumor cell necroptosis and immunotherapeutic responses.

Joint analysis of phenotype-effect-generation identifies loci associated with grain quality traits in rice hybrids.

Genetic improvement of grain quality is more challenging in hybrid rice than in inbred rice due to additional nonadditive effects such as dominance. Here, we describe a pipeline developed for joint analysis of phenotypes, effects, and generations (JPEG). As a demonstration, we analyze 12 grain quality traits of 113 inbred lines (male parents), five tester lines (female parents), and 565 (113×5) of their hybrids. We sequence the parents for single nucleotide polymorphisms calling and infer the genotypes of the hybrids. Genome-wide association studies with JPEG identify 128 loci associated with at least one of the 12 traits, including 44, 97, and 13 loci with additive effects, dominant effects, and both additive and dominant effects, respectively. These loci together explain more than 30% of the genetic variation in hybrid performance for each of the traits. The JEPG statistical pipeline can help to identify superior crosses for breeding rice hybrids with improved grain quality.

Application of machine learning to explore the genomic prediction accuracy of fall dormancy in autotetraploid alfalfa.

Fall dormancy (FD) is an essential trait to overcome winter damage and for alfalfa (Medicago sativa) cultivar selection. The plant regrowth height after autumn clipping is an indirect way to evaluate FD. Transcriptomics, proteomics, and quantitative trait locus mapping have revealed crucial genes correlated with FD; however, these genes cannot predict alfalfa FD very well. Here, we conducted genomic prediction of FD using whole-genome SNP markers based on machine learning-related methods, including support vector machine (SVM) regression, and regularization-related methods, such as Lasso and ridge regression. The results showed that using SVM regression with linear kernel and the top 3000 genome-wide association study (GWAS)-associated markers achieved the highest prediction accuracy for FD of 64.1%. For plant regrowth height, the prediction accuracy was 59.0% using the 3000 GWAS-associated markers and the SVM linear model. This was better than the results using whole-genome markers (25.0%). Therefore, the method we explored for alfalfa FD prediction outperformed the other models, such as Lasso and ElasticNet. The study suggests the feasibility of using machine learning to predict FD with GWAS-associated markers, and the GWAS-associated markers combined with machine learning would benefit FD-related traits as well. Application of the methodology may provide potential targets for FD selection, which would accelerate genetic research and molecular breeding of alfalfa with optimized FD.

Graph pangenome captures missing heritability and empowers tomato breeding.

Missing heritability in genome-wide association studies defines a major problem in genetic analyses of complex biological traits1,2. The solution to this problem is to identify all causal genetic variants and to measure their individual contributions3,4. Here we report a graph pangenome of tomato constructed by precisely cataloguing more than 19 million variants from 838 genomes, including 32 new reference-level genome assemblies. This graph pangenome was used for genome-wide association study analyses and heritability estimation of 20,323 gene-expression and metabolite traits. The average estimated trait heritability is 0.41 compared with 0.33 when using the single linear reference genome. This 24% increase in estimated heritability is largely due to resolving incomplete linkage disequilibrium through the inclusion of additional causal structural variants identified using the graph pangenome. Moreover, by resolving allelic and locus heterogeneity, structural variants improve the power to identify genetic factors underlying agronomically important traits leading to, for example, the identification of two new genes potentially contributing to soluble solid content. The newly identified structural variants will facilitate genetic improvement of tomato through both marker-assisted selection and genomic selection. Our study advances the understanding of the heritability of complex traits and demonstrates the power of the graph pangenome in crop breeding.

Interpretation of Manhattan Plots and Other Outputs of Genome-Wide Association Studies.

With increasing marker density, estimation of recombination rate between a marker and a causal mutation using linkage analysis becomes less important. Instead, linkage disequilibrium (LD) becomes the major indicator for gene mapping through genome-wide association studies (GWAS). In addition to the linkage between the marker and the causal mutation, many other factors may contribute to the LD, including population structure and cryptic relationships among individuals. As statistical methods and software evolve to improve statistical power and computing speed in GWAS, the corresponding outputs must also evolve to facilitate the interpretation of input data, the analytical process, and final association results. In this chapter, our descriptions focus on (1) considerations in creating a Manhattan plot displaying the strength of LD and locations of markers across a genome; (2) criteria for genome-wide significance threshold and the different appearance of Manhattan plots in single-locus and multiple-locus models; (3) exploration of population structure and kinship among individuals; (4) quantile-quantile (QQ) plot; (5) LD decay across the genome and LD between the associated markers and their neighbors; (6) exploration of individual and marker information on Manhattan and QQ plots via interactive visualization using HTML. The ultimate objective of this chapter is to help users to connect input data to GWAS outputs to balance power and false positives, and connect GWAS outputs to the selection of candidate genes using LD extent.

Performing Genome-Wide Association Studies with Multiple Models Using GAPIT.

Genome-wide association study (GWAS) is based on the linkage disequilibrium (LD) between phenotypes and genetic markers covering the whole genome. Besides the genetic linkage between the genetic markers and the causal mutations, many other factors contribute to the LD, including selection and nonrandom mating formatting population structure. Many methods have been developed with accompany of corresponding software such as multiple loci mixed model (MLMM). There are software packages that implement multiple methods to reduce the learning curve. One of them is the Genomic Association and Prediction Integrated Tool (GAPIT), which implemented eight models including GLM (General Linear Model), Mixed Linear Model (MLM), Compressed MLM, MLMM, SUPER (Settlement of mixed linear models Under Progressively Exclusive Relationship), FarmCPU (Fixed and random model Circulating Probability Unification), and BLINK (Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway). Besides the availability of multiple models, GAPIT provides comprehensive functions for data quality control, data visualization, and publication-ready quality graphic outputs, such as Manhattan plots in rectangle and circle formats, quantile-quantile (QQ) plots, principal component plots, scatter plot of minor allele frequency against GWAS signals, plots of LD between associated markers and the adjacent markers. GAPIT developers and users established a community through the GAPIT forum ( https://groups.google.com/g/gapit-forum ) with over 600 members for asking questions, making comments, and sharing experiences. In this chapter, we detail the GAPIT functions, input data frame, output files, and example codes for each GWAS model. We also interpret parameters, functional algorithms, and modules of GAPIT implementation.

VTag: a semi-supervised pipeline for tracking pig activity with a single top-view camera.

Precision livestock farming has become an important research focus with the rising demand of meat production in the swine industry. Currently, the farming practice is widely conducted by the technology of computer vision (CV), which automates monitoring pig activity solely based on video recordings. Automation is fulfilled by deriving imagery features that can guide CV systems to recognize animals' body contours, positions, and behavioral categories. Nevertheless, the performance of the CV systems is sensitive to the quality of imagery features. When the CV system is deployed in a variable environment, its performance may decrease as the features are not generalized enough under different illumination conditions. Moreover, most CV systems are established by supervised learning, in which intensive effort in labeling ground truths for the training process is required. Hence, a semi-supervised pipeline, VTag, is developed in this study. The pipeline focuses on long-term tracking of pig activity without requesting any pre-labeled video but a few human supervisions to build a CV system. The pipeline can be rapidly deployed as only one top-view RGB camera is needed for the tracking task. Additionally, the pipeline was released as a software tool with a friendly graphical interface available to general users. Among the presented datasets, the average tracking error was 17.99 cm. Besides, with the prediction results, the pig moving distance per unit time can be estimated for activity studies. Finally, as the motion is monitored, a heat map showing spatial hot spots visited by the pigs can be useful guidance for farming management. The presented pipeline saves massive laborious work in preparing training dataset. The rapid deployment of the tracking system paves the way for pig behavior monitoring.

Collecting detailed measurements of animals through cameras has become an important focus with the rising demand for meat production in the swine industry. Currently, researchers use computational approaches to train models to recognize pig morphological features and monitor pig behaviors automatically. Though little human effort is needed after model training, current solutions require a large amount of pre-selected images for the training process, and the expensive preparation work is difficult for many farms to implement such practice. Hence, a pipeline, VTag, is presented to address these challenges in our study. With few supervisions, VTag can automatically track positions of multiple pigs from one single top-view RGB camera. No pre-labeled images are required to establish a robust pig tracking system. Additionally, the pipeline was released as a software tool with a friendly graphical user interface, that is easy to learn for general users. Among the presented datasets, the average tracking error is 17.99 cm, which is shorter than one-third of the pig body length in the study. The estimated pig activity from VTag can serve as useful farming guidance. The presented strategy saves massive laborious work in preparing training datasets and setting up monitoring environments. The rapid deployment of the tracking system paves the way for pig behavior monitoring.

As the number falls, alternatives to the Hagberg-Perten falling number method: A review.

This review examines the application, limitations, and potential alternatives to the Hagberg-Perten falling number (FN) method used in the global wheat industry for detecting the risk of poor end-product quality mainly due to starch degradation by the enzyme α-amylase. By viscometry, the FN test indirectly detects the presence of α-amylase, the primary enzyme that digests starch. Elevated α-amylase results in low FN and damages wheat product quality resulting in cakes that fall, and sticky bread and noodles. Low FN can occur from preharvest sprouting (PHS) and late maturity α-amylase (LMA). Moist or rainy conditions before harvest cause PHS on the mother plant. Continuously cool or fluctuating temperatures during the grain filling stage cause LMA. Due to the expression of additional hydrolytic enzymes, PHS has a stronger negative impact than LMA. Wheat grain with low FN/high α-amylase results in serious losses for farmers, traders, millers, and bakers worldwide. Although blending of low FN grain with sound wheat may be used as a means of moving affected grain through the marketplace, care must be taken to avoid grain lots from falling below contract-specified FN. A large amount of sound wheat can be ruined if mixed with a small amount of sprouted wheat. The FN method is widely employed to detect α-amylase after harvest. However, it has several limitations, including sampling variability, high cost, labor intensiveness, the destructive nature of the test, and an inability to differentiate between LMA and PHS. Faster, cheaper, and more accurate alternatives could improve breeding for resistance to PHS and LMA and could preserve the value of wheat grain by avoiding inadvertent mixing of high- and low-FN grain by enabling testing at more stages of the value stream including at harvest, delivery, transport, storage, and milling. Alternatives to the FN method explored here include the Rapid Visco Analyzer, enzyme assays, immunoassays, near-infrared spectroscopy, and hyperspectral imaging.

Detection of Breeding-Relevant Fruit Cracking and Fruit Firmness Quantitative Trait Loci in Sweet Cherry via Pedigree-Based and Genome-Wide Association Approaches.

Breeding for decreased fruit cracking incidence and increased fruit firmness in sweet cherry creates an attractive alternative to variable results from cultural management practices. DNA-informed breeding increases its efficiency, yet upstream research is needed to identify the genomic regions associated with the trait variation of a breeding-relevant magnitude, as well as to identify the parental sources of favorable alleles. The objectives of this research were to identify the quantitative trait loci (QTLs) associated with fruit cracking incidence and firmness, estimate the effects of single nucleotide polymorphism (SNP) haplotypes at the detected QTLs, and identify the ancestral source(s) of functional haplotypes. Fruit cracking incidence and firmness were evaluated for multiple years on 259 unselected seedlings representing 22 important breeding parents. Phenotypic data, in conjunction with genome-wide genotypic data from the RosBREED cherry 6K SNP array, were used in the QTL analysis performed via Pedigree-Based Analysis using the FlexQTL™ software, supplemented by a Genome-Wide Association Study using the BLINK software. Haplotype analysis was conducted on the QTLs to identify the functional SNP haplotypes and estimate their phenotypic effects, and the haplotypes were tracked through the pedigree. Four QTLs (two per trait) were consistent across the years and/or both analysis methods and validated the previously reported QTLs. qCrack-LG1.1m (the label given to a consistent QTL for cracking incidence on chromosome 1) explained 2-15.1% of the phenotypic variance, while qCrack-LG5.1m, qFirm-LG1.2m, and qFirm-LG3.2m explained 7.6-13.8, 8.8-21.8, and 1.7-10.1% of the phenotypic variance, respectively. At each QTL, at least two SNP haplotypes had significant effects and were considered putative functional SNP haplotypes. Putative low-cracking SNP haplotypes were tracked to an unnamed parent of 'Emperor Francis' and 'Schmidt' and unnamed parents of 'Napoleon' and 'Hedelfingen,' among others, and putative high-firmness haplotypes were tracked to an unnamed parent of 'Emperor Francis' and 'Schmidt,' an unnamed grandparent of 'Black Republican,' 'Rube,' and an unknown parent of 'Napoleon.' These four stable QTLs can now be targeted for DNA test development, with the goal of translating information discovered here into usable tools to aid in breeding decisions.

Genome Assembly of Alfalfa Cultivar Zhongmu-4 and Identification of SNPs Associated with Agronomic Traits.

Alfalfa (Medicago sativa L.) is the most important legume forage crop worldwide with high nutritional value and yield. For a long time, the breeding of alfalfa was hampered by lacking reliable information on the autotetraploid genome and molecular markers linked to important agronomic traits. We herein reported the de novo assembly of the allele-aware chromosome-level genome of Zhongmu-4, a cultivar widely cultivated in China, and a comprehensive database of genomic variations based on resequencing of 220 germplasms. Approximate 2.74 Gb contigs (N50 of 2.06 Mb), accounting for 88.39% of the estimated genome, were assembled, and 2.56 Gb contigs were anchored to 32 pseudo-chromosomes. A total of 34,922 allelic genes were identified from the allele-aware genome. We observed the expansion of gene families, especially those related to the nitrogen metabolism, and the increase of repetitive elements including transposable elements, which probably resulted in the increase of Zhongmu-4 genome compared with Medicago truncatula. Population structure analysis revealed that the accessions from Asia and South America had relatively lower genetic diversity than those from Europe, suggesting that geography may influence alfalfa genetic divergence during local adaption. Genome-wide association studies identified 101 single nucleotide polymorphisms (SNPs) associated with 27 agronomic traits. Two candidate genes were predicted to be correlated with fall dormancy and salt response. We believe that the allele-aware chromosome-level genome sequence of Zhongmu-4 combined with the resequencing data of the diverse alfalfa germplasms will facilitate genetic research and genomics-assisted breeding in variety improvement of alfalfa.

A new strategy for using historical imbalanced yield data to conduct genome-wide association studies and develop genomic prediction models for wheat breeding.

Using imbalanced historical yield data to predict performance and select new lines is an arduous breeding task. Genome-wide association studies (GWAS) and high throughput genotyping based on sequencing techniques can increase prediction accuracy. An association mapping panel of 227 Texas elite (TXE) wheat breeding lines was used for GWAS and a training population to develop prediction models for grain yield selection. An imbalanced set of yield data collected from 102 environments (year-by-location) over 10 years, through testing yield in 40-66 lines each year at 6-14 locations with 38-41 lines repeated in the test in any two consecutive years, was used. Based on correlations among data from different environments within two adjacent years and heritability estimated in each environment, yield data from 87 environments were selected and assigned to two correlation-based groups. The yield best linear unbiased estimation (BLUE) from each group, along with reaction to greenbug and Hessian fly in each line, was used for GWAS to reveal genomic regions associated with yield and insect resistance. A total of 74 genomic regions were associated with grain yield and two of them were commonly detected in both correlation-based groups. Greenbug resistance in TXE lines was mainly controlled by Gb3 on chromosome 7DL in addition to two novel regions on 3DL and 6DS, and Hessian fly resistance was conferred by the region on 1AS. Genomic prediction models developed in two correlation-based groups were validated using a set of 105 new advanced breeding lines and the model from correlation-based group G2 was more reliable for prediction. This research not only identified genomic regions associated with yield and insect resistance but also established the method of using historical imbalanced breeding data to develop a genomic prediction model for crop improvement. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-022-01287-8.