Ability of Genomic Prediction to Bi-Parent-Derived Breeding Population Using Public Data for Soybean Oil and Protein Content.

Genomic selection (GS) is a marker-based selection method used to improve the genetic gain of quantitative traits in plant breeding. A large number of breeding datasets are available in the soybean database, and the application of these public datasets in GS will improve breeding efficiency and reduce time and cost. However, the most important problem to be solved is how to improve the ability of across-population prediction. The objectives of this study were to perform genomic prediction (GP) and estimate the prediction ability (PA) for seed oil and protein contents in soybean using available public datasets to predict breeding populations in current, ongoing breeding programs. In this study, six public datasets of USDA GRIN soybean germplasm accessions with available phenotypic data of seed oil and protein contents from different experimental populations and their genotypic data of single-nucleotide polymorphisms (SNPs) were used to perform GP and to predict a bi-parent-derived breeding population in our experiment. The average PA was 0.55 and 0.50 for seed oil and protein contents within the bi-parents population according to the within-population prediction; and 0.45 for oil and 0.39 for protein content when the six USDA populations were combined and employed as training sets to predict the bi-parent-derived population. The results showed that four USDA-cultivated populations can be used as a training set individually or combined to predict oil and protein contents in GS when using 800 or more USDA germplasm accessions as a training set. The smaller the genetic distance between training population and testing population, the higher the PA. The PA increased as the population size increased. In across-population prediction, no significant difference was observed in PA for oil and protein content among different models. The PA increased as the SNP number increased until a marker set consisted of 10,000 SNPs. This study provides reasonable suggestions and methods for breeders to utilize public datasets for GS. It will aid breeders in developing GS-assisted breeding strategies to develop elite soybean cultivars with high oil and protein contents.

De novo genome assembly of a high-protein soybean variety HJ117.

OBJECTIVES: Soybean is an important feed and oil crop in the world due to its high protein and oil content. China has a collection of more than 43,000 soybean germplasm resources, which provides a rich genetic diversity for soybean breeding. However, the rich genetic diversity poses great challenges to the genetic improvement of soybean. This study reports on the de novo genome assembly of HJ117, a soybean variety with high protein content of 52.99%. These data will prove to be valuable resources for further soybean quality improvement research, and will aid in the elucidation of regulatory mechanisms underlying soybean protein content. DATA DESCRIPTION: We generated a contiguous reference genome of 1041.94 Mb for HJ117 using a combination of Illumina short reads (23.38 Gb) and PacBio long reads (25.58 Gb), with high-quality sequence coverage of approximately 22.44× and 24.55×, respectively. HJ117 was developed through backcross breeding, using Jidou 12 as the recurrent parent and Chamoshidou as the donor parent. The assembly was further assisted by 114.5 Gb Hi-C data (109.9×), resulting in a contig N50 of 19.32 Mb and scaffold N50 of 51.43 Mb. Notably, Core Eukaryotic Genes Mapping Approach (CEGMA) assessment and Benchmarking Universal Single-Copy Orthologs (BUSCO) assessment results indicated that most core eukaryotic genes (97.18%) and genes in the BUSCO dataset (99.4%) were identified, and 96.44% of the genomic sequences were anchored onto twenty pseudochromosomes.

Fe, N, S co-doped carbon network derived from acetate-modified Fe-ZIF-8 for oxygen reduction reaction.

In this work, potassium acetate (KAc) was added during the synthesis of a Zn-Fe based metal-organic framework (Fe-ZIF-8) to increase the fixed amount of Fe while simultaneously enhancing the number of pores. Electrospinning was utilized to embed KAc-modified Fe-ZIF-8 (Fe-ZIF-8-Ac) into the polyacrylonitrile nanofiber mesh, to obtain a network composite (Fe@NC-Ac) with hierarchical porous structure. Fe@NC-Ac was co-pyrolyzed with thiourea, resulting in Fe, N, S co-doped carbon electrocatalyst. The electrochemical tests indicated that the prepared catalyst displayed relatively remarkable oxygen reduction reaction (ORR) catalytic activity, with an onset potential (Eonset) of 1.08 V (vs. reversible hydrogen electrode, RHE) and a half-wave potential (E1/2) of 0.94 V, both higher than those of the commercial Pt/C (Eonset = 0.95 V and E1/2 = 0.84 V), respectively. Assembled into Zn-air batteries, the optimized catalyst exhibited higher open circuit voltage (1.698 V) and peak power density (90 mW cm-2) than those of the commercial 20 wt% Pt/C (1.402 V and 80 mW cm-2), respectively. This work provided a straightforward manufacturing strategy for the design of hierarchical porous carbon-based ORR catalysts with desirable performance.

A transformer-based genomic prediction method fused with knowledge-guided module.

Genomic prediction (GP) uses single nucleotide polymorphisms (SNPs) to establish associations between markers and phenotypes. Selection of early individuals by genomic estimated breeding value shortens the generation interval and speeds up the breeding process. Recently, methods based on deep learning (DL) have gained great attention in the field of GP. In this study, we explore the application of Transformer-based structures to GP and develop a novel deep-learning model named GPformer. GPformer obtains a global view by gleaning beneficial information from all relevant SNPs regardless of the physical distance between SNPs. Comprehensive experimental results on five different crop datasets show that GPformer outperforms ridge regression-based linear unbiased prediction (RR-BLUP), support vector regression (SVR), light gradient boosting machine (LightGBM) and deep neural network genomic prediction (DNNGP) in terms of mean absolute error, Pearson's correlation coefficient and the proposed metric consistent index. Furthermore, we introduce a knowledge-guided module (KGM) to extract genome-wide association studies-based information, which is fused into GPformer as prior knowledge. KGM is very flexible and can be plugged into any DL network. Ablation studies of KGM on three datasets illustrate the efficiency of KGM adequately. Moreover, GPformer is robust and stable to hyperparameters and can generalize to each phenotype of every dataset, which is suitable for practical application scenarios.

From Sheep Track to Motorway: Supramolecular-Mediated 2D Nanofluidic Channels for Ultrafast Water Transport.

Atomic thick 2D materials hold great potential as building blocks to construct highly permeable membranes, yet the permeability of laminar 2D material membranes is still limited by their irregularity sheep track-like interlayer channels. Herein, a supramolecular-mediated strategy to induce the regular assembly of high-throughput 2D nanofluidic channels based on host-guest interactions is proposed. Inspired by the characteristics of motorways, supramolecular-mediated ultrathin 2D membranes with broad and continuous regular water transport channels are successfully constructed using graphene oxide (GO) as an example. The prepared membrane achieves an ultrahigh water permeability (369.94 LMH bar-1 ) more than six times higher than that of the original membranes while maintaining dye rejection above 98.5%, which outperforms the reported 2D membranes. Characterization and simulation results show that the introduction of hyaluronate-grafted ß-cyclodextrin not only expands the interlayer channels of GO membranes but also enables the membranes to operate stably under harsh conditions with the help of host-guest interactions. This universal supramolecular assembly strategy provides new opportunities for the preparation of 2D membranes with high separation performance and reliable and stable nanofluidic channels.

Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Oryza sativa L.).

Accurately identifying varieties with targeted agronomic traits was thought to contribute to genetic selection and accelerate rice breeding progress. Genomic selection (GS) is a promising technique that uses markers covering the whole genome to predict the genomic-estimated breeding values (GEBV), with the ability to select before phenotypes are measured. To choose the appropriate GS models for breeding work, we analyzed the predictability of nine agronomic traits measured from a population of 459 diverse rice varieties. By the comparison of eight representative GS models, we found that the prediction accuracies ranged from 0.407 to 0.896, with reproducing kernel Hilbert space (RKHS) having the highest predictive ability in most traits. Further results demonstrated the predictivity of GS is altered by several factors. Moreover, we assessed the method of integrating genome-wide association study (GWAS) into various GS models. The predictabilities of GS combined peak-associated markers generated from six different GWAS models were significantly different; a recommendation of Mixed Linear Model (MLM)-RKHS was given for the GWAS-GS-integrated prediction. Finally, based on the above result, we experimented with applying the P-values obtained from optimal GWAS models into ridge regression best linear unbiased prediction (rrBLUP), which benefited the low predictive traits in rice. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-023-01423-y.

Environmental ammonia analysis based on exclusive nitrification by nitrifying biofilm screened from natural bioresource.

Biofilm-based biological nitrification is widely used for ammonia removal, while hasn't been explored for ammonia analysis. The stumbling block is the coexist of nitrifying and heterotrophic microbes in real environment resulting in non-specific sensing. Herein, an exclusive ammonia sensing nitrifying biofilm was screened from natural bioresource, and a bioreaction-detection system for the on-line analysis of environmental ammonia based on biological nitrification was reported. The nitrifying microbes were aggregated into a nitrifying biofilm through a result-oriented bioresource enrichment strategy. The predominant nitrifying population and progressive surface reaction in the plug flow bioreactor led to the exclusive and exhaustive ammonia biodegradation for the establishment of a novel analytical method. The on-line ammonia monitoring prototype achieved complete biodegradation for determining ammonium nitrogen within 5 min and showed exceptional reliability in long-term real sample measurements without frequent calibration. This work offers a low-threshold natural screening paradigm for developing sustainable bioresource-based analytical technologies.

Transcriptome mapping related genes encoding PR1 protein involved in necrotic symptoms to soybean mosaic virus infection.

Necrosis caused by soybean mosaic virus (SMV) has not been specifically distinguished from susceptible symptoms. The molecular mechanism for the occurrence of necrosis is largely overlooked in soybean genetic research. Field evaluation reveals that SMV disease seriously influences soybean production as indicated by decreasing 22.4% ~ 77.0% and 8.8% ~ 17.0% of yield and quality production, respectively. To expand molecular mechanism behind necrotic reactions, transcriptomic data obtained from the asymptomatic, mosaic, and necrotic pools were assessed. Compared between asymptomatic and mosaic plants, 1689 and 1752 up- and down-regulated differentially expressed genes (DEGs) were specifically found in necrotic plants. Interestingly, the top five enriched pathways with up-regulated DEGs were highly related to the process of the stress response, whereas the top three enriched pathways with down-regulated DEGs were highly related to the process of photosynthesis, demonstrating that defense systems are extensively activated, while the photosynthesis systems were severely destroyed. Further, results of the phylogenetic tree based on gene expression pattern and an amino acid sequence and validation experiments discovered three PR1 genes, Glyma.15G062400, Glyma.15G062500, and Glyma.15G062700, which were especially expressed in necrotic leaves. Meanwhile, exogenous salicylic acid (SA) but not methyl jasmonate (MeJA) could induce the three PR1 gene expressions on healthy leaves. Contrastingly, exogenous SA obviously decreased the expression level of Glyma.15G062400, Glyma.15G062500, and concentration of SMV, but increased Glyma.15G062700 expression in necrotic leaves. These results showed that GmPR1 is associated with the development of SMV-induced necrotic symptoms in soybean. Glyma.15G062400, Glyma.15G062500, and Glyma.15G062700 is up-regulated in necrotic leaves at the transcriptional levels, which will greatly facilitate a better understanding of the mechanism behind necrosis caused by SMV disease. Supplementary information: The online version contains supplementary material available at 10.1007/s11032-022-01351-3.

Development of SNP marker panels for genotyping by target sequencing (GBTS) and its application in soybean.

A high-throughput genotyping platform with customized flexibility, high genotyping accuracy, and low cost is critical for marker-assisted selection and genetic mapping in soybean. Three assay panels were selected from the SoySNP50K, 40K, 20K, and 10K arrays, containing 41,541, 20,748, and 9670 SNP markers, respectively, for genotyping by target sequencing (GBTS). Fifteen representative accessions were used to assess the accuracy and consistency of the SNP alleles identified by the SNP panels and sequencing platform. The SNP alleles were 99.87% identical between technical replicates and 98.86% identical between the 40K SNP GBTS panel and 10× resequencing analysis. The GBTS method was also accurate in the sense that the genotypic dataset of the 15 representative accessions correctly revealed the pedigree of the accessions, and the biparental progeny datasets correctly constructed the linkage maps of the SNPs. The 10K panel was also used to genotype two parent-derived populations and analyze QTLs controlling 100-seed weight, resulting in the identification of the stable associated genetic locus Locus_OSW_06 on chromosome 06. The markers flanking the QTL explained 7.05% and 9.83% of the phenotypic variation, respectively. Compared with GBS and DNA chips, the 40K, 20K, and 10K panels reduced costs by 5.07% and 58.28%, 21.44% and 65.48%, and 35.74% and 71.76%, respectively. Low-cost genotyping panels could facilitate soybean germplasm assessment, genetic linkage map construction, QTL identification, and genomic selection. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-023-01372-6.

In situ and self-adaptive BOD bioreaction sensing system based on environmentally domesticated microbial populations.

Biochemical oxygen demand (BOD) is a water quality parameter of vital importance. Rapid BOD analysis methods have emerged to simplify the five-day BOD (BOD5) measurement protocol. However, their universal implementations are restricted by the tricky environmental matrix (including environmental microbes, contaminants, ionic compositions, etc.). Here, an in situ and self-adaptive BOD bioreaction sensing system consisting of a "gut-like" microfluidic coil bioreactor with self-renewed biofilm was proposed for the establishment of a rapid, resilient and reliable BOD determination method. With the spontaneous surface adhesion of environmental microbial populations, the biofilm was colonized in situ on the inner surface of the microfluidic coil bioreactor. Exploiting the environmental domestication during every real sample measurement, the biofilm was capable of self-renewal to adapt to the environmental changes and exhibited representative biodegradation behaviors. The aggregated abundant, adequate and adapted microbial populations in the BOD bioreactor rendered a total organic carbon (TOC) removal rate of 67.7% within a short hydraulic retention time of 99 s. As validated by an online BOD prototype, exceptional analytical performance was achieved in terms of reproducibility (relative standard deviation of 3.7%), survivability (inhibition by pH and metal ion interference of <20%) and accuracy (relative error of -5.9% to 9.7%). This work rediscovered the interactive effects of the environmental matrix on BOD assays and demonstrated an instructive attempt by making use of the environment to develop practical online BOD monitoring devices for water quality assessments.

Asymmetric C3-Allylation of Pyridines.

Asymmetric intermolecular C-H functionalization of pyridines at C3 is unprecedented. Herein, we report the first examples of such transformations: specifically, C3-allylation of pyridines via tandem borane and iridium catalysis. First, borane-catalyzed pyridine hydroboration generates nucleophilic dihydropyridines; then, the dihydropyridine undergoes enantioselective iridium-catalyzed allylation; and finally, oxidative aromatization with air as the oxidant gives the C3-allylated pyridine. This protocol provides direct access to C3-allylated pyridines with excellent enantioselectivity (up to >99% ee) and is suitable for late-stage functionalization of pyridine-containing drugs.

Genome-wide identification of genes encoding SWI/SNF components in soybean and the functional characterization of GmLFR1 in drought-stressed plants.

ATP-dependent SWI/SNF chromatin remodeling complexes (CRCs) are evolutionarily conserved multi-component machines that regulate transcription, replication, and genome stability in eukaryotes. SWI/SNF components play pivotal roles in development and various stress responses in plants. However, the compositions and biological functions of SWI/SNF complex subunits remain poorly understood in soybean. In this study, we used bioinformatics to identify 39 genes encoding SWI/SNF subunit distributed on the 19 chromosomes of soybean. The promoter regions of the genes were enriched with several cis-regulatory elements that are responsive to various hormones and stresses. Digital expression profiling and qRT-PCR revealed that most of the SWI/SNF subunit genes were expressed in multiple tissues of soybean and were sensitive to drought stress. Phenotypical, physiological, and molecular genetic analyses revealed that GmLFR1 (Leaf and Flower-Related1) plays a negative role in drought tolerance in soybean and Arabidopsis thaliana. Together, our findings characterize putative components of soybean SWI/SNF complex and indicate possible roles for GmLFR1 in plants under drought stress. This study offers a foundation for comprehensive analyses of soybean SWI/SNF subunit and provides mechanistic insight into the epigenetic regulation of drought tolerance in soybean.

Overexpression of CHAF1A is associated with poor prognosis, tumor immunosuppressive microenvironment and treatment resistance.

Background: As distinct marker of proliferating cells, chromatin assembly factor-1 (CAF-1) was critical in DNA replication. However, there is paucity information about the clinical significance, functions and co-expressed gene network of CHAF1A, the major subunit in CAF-1, in cancer. Methods: Bioinformatic analysis of CHAF1A and its co-expression gene network were performed using various public databases. Functional validation of CHAF1A was applied in breast cancer. Results: Overexpression of CHAF1A was found in 20 types of cancer tissues. Elevated expression of CHAF1A was positively correlated with breast cancer progression and poor patients' outcome. The analysis of co-expression gene network demonstrated CHAF1A was associated with not only cell proliferation, DNA repair, apoptosis, but cancer metabolism, immune system, and drug resistance. More importantly, higher expression of CHAF1A was positively correlated with immunosuppressive microenvironment and resistance to endocrine therapy and chemotherapy. Elevated expression of CHAF1A was confirmed in breast cancer tissues. Silencing of CHAF1A can significantly inhibit cell proliferation in MDA-MB-231 cells. Conclusion: The current work suggested that overexpression of CHAF1A can be used as diagnostic and poor prognostic biomarker of breast cancer. Higher expression of CHAF1A induced fast resistance to endocrine therapy and chemotherapy, it may be a promising therapeutic target and a biomarker to predict the sensitivity of immunotherapy in breast cancer.

LG5, a Novel Allele of EUI1, Regulates Grain Size and Flag Leaf Angle in Rice.

Grain size and flag leaf angle are two important traits that determining grain yield in rice. However, the mechanisms regulating these two traits remain largely unknown. In this study, a rice long grain 5 (lg5) mutant with a large flag leaf angle was identified, and map-based cloning revealed that a single base substitution followed by a 2 bp insertion in the LOC_Os05g40384 gene resulted in larger grains, a larger flag leaf angle, and higher plant height than the wild type. Sequence analysis revealed that lg5 is a novel allele of elongated uppermost internode-1 (EUI1), which encodes a cytochrome P450 protein. Functional complementation and overexpression tests showed that LG5 can rescue the bigger grain size and larger flag leaf angle in the Xiushui11 (XS) background. Knockdown of the LG5 transcription level by RNA interference resulted in elevated grain size and flag leaf angle in the Nipponbare (NIP) background. Morphological and cellular analyses suggested that LG5 regulated grain size and flag leaf angle by promoting cell expansion and cell proliferation. Our results provided new insight into the functions of EUI1 in rice, especially in regulating grain size and flag leaf angle, indicating a potential target for the improvement of rice breeding.

Tailored engineering of Fe3O4 and reduced graphene oxide coupled architecture to realize the full potential as electrode materials for lithium-ion batteries.

Developing advanced electrode materials with appropriate compositions and exquisite configurations is crucial in fabricating lithium-ion batteries (LIBs) with high energy density and fast charging capability plateau. Herein, a Fe3O4@reduced graphene oxide (Fe3O4@rGO) coupled architecture was rationally designed and in-situ synthesized. Monodispersed mesoporous Fe3O4 nanospheres were homogeneously formed and strongly bound on interconnected macroporous rGO frameworks to form well-defined three-dimensional (3D) hierarchical porous morphologies. This tailored Fe3O4@rGO coupled architecture fully exploited the advantages of Fe3O4 and rGO to overcome their inherent challenges, including spontaneous aggregating/excessive restacking tendency, sluggish ions diffusion/electrons transportation, and severe volume expansion/structural collapse. Benefitting from their synergistic effects, the optimized Fe3O4@rGO composite electrode exhibited an improved electrochemical reactivity, electrical conductivity, electrolyte accessibility, and structural stability. The optimized composite electrode displayed a high specific capacity of 1296.8 mA h g-1 at 0.1 A g-1 after 100 cycles, even retaining 555.1 mA h g-1 at 2 A g-1 after 2000 cycles. The electrochemical kinetics analysis revealed the predominantly pseudocapacitive behaviors of the Fe3O4@rGO heterogeneous interfaces, accounting for the excellent electrode performance. This study proposes a viable strategy for use in engineering hybrid composites with coupled architectures to optimize their potential as high-performance electrode materials for use in LIBs.

A multi-trait GWAS-based genetic association network controlling soybean architecture and seed traits.

Ideal plant architecture is essential for enhancing crop yields. Ideal soybean (Glycine max) architecture encompasses an appropriate plant height, increased node number, moderate seed weight, and compact architecture with smaller branch angles for growth under high-density planting. However, the functional genes regulating plant architecture are far not fully understood in soybean. In this study, we investigated the genetic basis of 12 agronomic traits in a panel of 496 soybean accessions with a wide geographical distribution in China. Analysis of phenotypic changes in 148 historical elite soybean varieties indicated that seed-related traits have mainly been improved over the past 60 years, with targeting plant architecture traits having the potential to further improve yields in future soybean breeding programs. In a genome-wide association study (GWAS) of 12 traits, we detected 169 significantly associated loci, of which 61 overlapped with previously reported loci and 108 new loci. By integrating the GWAS loci for different traits, we constructed a genetic association network and identified 90 loci that were associated with a single trait and 79 loci with pleiotropic effects. Of these 79 loci, 7 hub-nodes were strongly linked to at least three related agronomic traits. qHub_5, containing the previously characterized Determinate 1 (Dt1) locus, was associated not only with plant height and node number (as determined previously), but also with internode length and pod range. Furthermore, we identified qHub_7, which controls three branch angle-related traits; the candidate genes in this locus may be beneficial for breeding soybean with compact architecture. These findings provide insights into the genetic relationships among 12 important agronomic traits in soybean. In addition, these studies uncover valuable loci for further functional gene studies and will facilitate molecular design breeding of soybean architecture.

Genetic variation and marker-trait association affect the genomic selection prediction accuracy of soybean protein and oil content.

Introduction: Genomic selection (GS) is a potential breeding approach for soybean improvement. Methods: In this study, GS was performed on soybean protein and oil content using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) based on 1,007 soybean accessions. The SoySNP50K SNP dataset of the accessions was obtained from the USDA-ARS, Beltsville, MD lab, and the protein and oil content of the accessions were obtained from GRIN. Results: Our results showed that the prediction accuracy of oil content was higher than that of protein content. When the training population size was 100, the prediction accuracies for protein content and oil content were 0.60 and 0.79, respectively. The prediction accuracy increased with the size of the training population. Training populations with similar phenotype or with close genetic relationships to the prediction population exhibited better prediction accuracy. A greatest prediction accuracy for both protein and oil content was observed when approximately 3,000 markers with -log10(P) greater than 1 were included. Discussion: This information will help improve GS efficiency and facilitate the application of GS.

Porous materials formed by four self-construction processes.

Multiple self-construction behavior of cyclic oligoesters is described. Rigid braces and elastic hinges are periodically incorporated into these cyclomers, which enables these rings to form various topological frameworks, such as holes, caves or cages with different sizes and shapes, through self-folding. Among them, the cave-type cyclomer self-assembles into nanotunnels and then forms porous materials via self-packing of these tunnels. This discovery provides a new perspective for the construction of novel materials aided by multiple supramolecular effects. In this work, the simplest rigid brace components and ones with soft hinges were chosen to construct cyclomers to confirm the supramolecular strategy.

QTL and candidate genes for heterophylly in soybean based on two populations of recombinant inbred lines.

Heterophylly, the existence of different leaf shapes and sizes on the same plant, has been observed in many flowering plant species. Yet, the genetic characteristics and genetic basis of heterophylly in soybean remain unknown. Here, two populations of recombinant inbred lines (RILs) with distinctly different leaf shapes were used to identify loci controlling heterophylly in two environments. The ratio of apical leaf shape (LSUP) to basal leaf shape (LSDOWN) at the reproductive growth stage (RLS) was used as a parameter for classifying heterophylly. A total of eight QTL were detected for RLS between the two populations and four of them were stably identified in both environments. Among them, qRLS20 had the largest effect in the JS population, with a maximum LOD value of 46.9 explaining up to 47.2% of phenotypic variance. This locus was located in the same genomic region as the basal leaf shape QTL qLSDOWN20 on chromosome 20. The locus qRLS19 had the largest effect in the JJ population, with a maximum LOD value of 15.2 explaining up to 27.0% of phenotypic variance. This locus was located in the same genomic region as the apical leaf shape QTL qLSUP19 on chromosome 19. Four candidate genes for heterophylly were identified based on sequence differences among the three parents of the two mapping populations, RT-qPCR analysis, and gene functional annotation analysis. The QTL and candidate genes detected in this study lay a foundation for further understanding the genetic mechanism of heterophylly and are invaluable in marker-assisted breeding.

Genomic insight into genetic changes and shaping of major inbred rice cultivars in China.

The annual planting area of major inbred rice (Oryza sativa) cultivars reach more than half of the total annual planting area of inbred rice cultivars in China. However, how the major inbred rice cultivars changed during decades of genetic improvement and why they can be prevalently cultivated in China remains unclear. Here, we investigated the underlying genetic changes of major inbred cultivars and the contributions of landraces and introduced cultivars during the improvement by resequencing a collection of 439 rice accessions including major inbred cultivars, landraces, and introduced cultivars. The results showed that landraces were the main genetic contribution sources of major inbred Xian (Indica) cultivars, while introduced cultivars were that of major inbred Geng (Japonica) cultivars. Selection scans and haplotype frequency analysis shed light on the reflections of some well-known genes in rice improvement, and breeders had different preferences for the Xian's and Geng's breeding. Six candidate regions associated with agronomic traits were identified by genome-wide association mapping, five of which were under positive selection in rice improvement. Our study provides a comprehensive insight into the development of major inbred rice cultivars and lays the foundation for genomics-based breeding in rice.