We applied a modified BSA method to assessed genome-wide homozygous and heterozygous linkage patterns in the Chinese Wagyu Beef Cattle F2/F3 mixed population. Two overlapped regions from F2 and F3 populations on autosomes were found with high-density heterozygote alleles between high and low intramuscular fat groups. Regions from 24.8 M ~ 29.6 M of chromosome 23 were identified as most significantly correlated to the intramuscular fat in our samples. We also identified other 4 potential loci on chromosomes 5, 9, 15, and 21 correlated with Intramuscular fat. This study provided a novel low-cost method for QTLs mapping and identify molecular markers of phenotypical changes in a small mixed population. Bovine are one of the most important economic animals in the world. It serves the community by supplying milk, meat, leather, and nutrition to the human body. After years of development, Bovine has led to different breeds with a variety of phenotypes in the different geographical regions. Natural selection has led to an increase in cattle diversity. At the same time, artificial selection has greatly been implemented to maximize the economic benefits of cattle. Many breeds have been successfully achieved to accommodate the demands of specific traits. This study introduced the frozen sperm of Japanese Wagyu cattle hybridized with Qinchuan and Luxi cattle in order to improve the potential of the quality of meat.1. IMF content has become one of the most important indexes for meat quality evaluation2. It affects the meat tenderness, hydraulic power, shearing force, flavor, and juiciness3. To impove the IMF in Chinese beef cattle and understand the regulation mechanism, the researches on IMF have become a hot topic in recent years.Intramuscular fat (IMF) of meat is distributed in the form of white marbling, commonly known as marbling, and is one of the important indicators for meat quality. The richer the marbling, the higher the IMF content of meat and the better meat quality. IMF deposition and distribution are regulated by heredity, age and epigenetics. The word “marbling” is defined by the amount and distribution of visible IMF that accumulated within the muscle and muscle fiber bundles. It is an important economic trait in all cattle and stands out particularly in the Japanese cattleQinchuan and Luxi cattle are the local breeds in China. They are both steer, less intramuscular fat deposition However, in comparison with Japanese wagyu cattle, Qinchuan and Luxi cattle have smaller transection area of the longissimus dorsi muscle and lower IMF content. Nevertheless, there is one disadvantage of Qinchuan cattle, IMF deposits require longer feefing times (40 month). Japanese wagyu cattle are now being brought in to cross-breed with local Chinese cattle in the hope of improving the quality of meat. A significant improvement was found in meat quality in filial generation. The hypothesis that the majority of IMF content in these offspring should be located in between Qinchuan and purebred Wagyu cattle, and some cattle could exhibit the extreme phenotypes on both ends. Due to the limitation of GWAS, it was difficult to locate the genes associated with IMF. Therefore, the potential SNPs in Chinese hybrid waygu cattle were analyzed by BSA.4. BSA has a significant contribution in understanding animal genetics5, genomics, crop breeding, and improve productivity8. BSA analysis with high-throughput sequencing is an attractive tool to most scholars. Incomplete dominance, also known as semi-dominance, is the recessive genes that can express in the heterozygotes' progeny. Coat color has been researched as semi-dominant inheritance in cattle9.Bulked Segregant Analysis (BSA) is a method of rapid gene mapping for target traits inindividuals with extreme phenotypes in hybrid populations. The whole genome was sequenced on the two sample pools with extreme traits and the DNA fragments between the two mixed pools were detected to be the candidate regions. In recent years, BSA has been widely adopted for DNA and RNA analysis and has been successful used in genemapping and single nucleotide polymorphism(SNP) mappingIn this study, a modified BSA-based method was applied for IMF QTLs mapping. Both F2 and F3 cross-population of Luxi, Qinchuan and Wagyu were used for bulk allele frequency difference analysis. Several QTLs were found correlated to extreme high and low IMF. 5 Mb of chromosome 23 was identified as the most significant QTLs of IMF in two paired bulk (both F2 and F3) sequencing. There were six highly expressed fat genes and two highly expressed muscle genes in this region.In total, 186 samples of Chinese wagyu beef cattle  were taken from three different farms in China . The background information of samples is shown in Table Cattles were hybridized for four generations according to Fig. Due to the limitation of cattle farms hybridization program, it is very difficult to take experimental samples from one single farm, therefore three farms were selected. The source of wagyu beef sperm came from Beijing Dairy Cow Center (BDCC). The animal use protocol listed below has been reviewed and approved by the Animal Ethical and Welfare Committee (AEWC). NO. IACUC-NXU1014, and all cattle were electric shocked before slaughter, Electric shocks can make cattle painless when they are slaughtered, this test conform to the requirements of American Veterinary Medical Association (AVMA) Guidelines. Samples collected in this experiment have been given permission from respective farms in China from where of Chinese Wagyu Beef Cattle. I've provided three statements to confirm that permission was obtained from respective farms in China from where the of Chinese wagyu beef cattle. Three statements are uploaded in the Supplementary Materials and Related Files section.Phenotype determination was obtained after acid excretion and segmentation. In short, 100 g longissimus forsi were collected, and then intramuscular fat (IMF) content was determined according to the national fat content determination standard. The intramuscular fat content was determined following the guideline of Chinese national standard GB/T 9695.7–2008 in the booklet “Determination of Total Fat in Meat and Meat Products”. The crude fat content was determined by Soxhlet method.A total of 2 µg genomic DNA (35 ng/ml) was fragmented into 300 bp using the Bioruptor UCD-200 sonicator  for each sample. Library preparation was constructed using the Kapa Hyper DNA library prep kit for the Illumina platform. Fragmented DNA was end-repaired with an end-repair enzyme and a deoxyadenosine was added to the 3’ ends of the fragments. Kapa barcoded DNA and Kapa indexed adapters were ligated to the sample libraries. The adapter-ligated libraries were selected for an average insert size of 300 bp using next-generation sequencing cleanup and size selection kit  according to the manufacturer’s protocols. The quality of libraries was assessed using the Bioanalyzer 4200 . The libraries were then quantified by qRT-PCR and then sequenced were performed by Illumina Nova-seq platform to generate 150-bp paired-end reads.10 to remove low-quality reads and adapters. BWA (v0.7.16)11 mem (-M -a) module was applied to align the high-quality reads to ARS-UCD1.2 cattle genome with default parameters. SAMtools (v1.9)12 were used to sort BAM files, remove read of PCR duplications and for sample alignment statistics. “BaseRecalibrator” and “ApplyBQSR” module of GATK (v4.0.10.1) were used for base quality score recalibration. “HaplotypeCaller” module with minimum-mapping-quality 30 for each sample GVCF. After that, raw cohort VCF was worked out with modules “CombineGVCFs” and “GenotypeGVCFs”. Bcftools13 (v1.9) was performed as variants quality filtering with "QUAL >  = 100". To annotate variants, ensemble gene annotations base on ARS-UCD1.2 were used for the database which was created by snpEff (v4.3T)14. The brief workflow of variants calling and annotation was shown as blue boxes in Fig. Raw sequence reads were processed using Fastpv0.12.4).410 to rhttps://github.com/YinLiLin/CMplot) with 1 M bp as windows size. Variance analysis of allele’s frequency was flowed by varBScore15, windows size was set as 10 SNP and step size was 5 SNP. Power AFD was modified by ED algorithm and inherited the power 4 to reduce the background noise7.The allele frequency of each population was extracted from the VCF file by bcftools query module. DFScore defined as the variance of allele frequency in a sliding window. When the allele frequency in a window obeys the frequency expectation of two bulk samples or the variance in each bulk sample close to or equal to zero, we treat it as the best window. The benefit of this calculation is that we can more accurately screen segments that meet genetic expectations. For instance, assumed the data is from an F2 population, the allele frequency expectation of dominant bulk should be 1/3 or 2/3 and the allele frequency expectation of recessiveness bulk should be 0 or 1. Similarly, if the data were generated with homozygous samples in the two extreme phenotype bulks, the allele frequency expectation of the two bulks will be either 0 or 1. The benefit of this calculation is that we can more accurately screen segments that meet genetic expectations. SNP density was plot by R package CMPlot . In total, 112 muscle and fat tissues of cattle RNA-seq datasets were downloaded from NCBI and processed to the FASTQ format using the NCBI sratoolkit (version 2.9.6). Then low-quality reads and adapters were removed using the Fastq program 10. Kallisto (version 0.45.0)16 was used to quantized gene expression. Ensembl genome bos_taurus.ARS-UCD1.2 and gene annotation version 100 were used as references gene. Gene expression heatmap was generated by R package “pheatmap” (version 1.0.12) with gene TPM.Gene expression conversion and general expression summary in annotated tissues was obtained on the cattle gene expression database from Bgee  were sequenced with an average of 10X coverage with reference genome per sample. Total 1075G raw sequencing data were obtained. Cohort variants calling was performed with GATK4 best-practice pipelines. SNP and InDel were worked out with reference bos_taurus.ARS-UCD1.2 for all samples. To apply BSA-based QTL mapping, samples from the same generation population were extracted into a sub VCF. Three paired-bulk datasets  were used for allele frequency analysis in the next step.Calculate the SNP density with the allele frequency difference between the two paired-bulks was setting up with the filter premaster as AF =  < 0.45 or AF >  = 0.55. 1 Mb was set as slide window length on the genome to evaluate the SNP density with a difference in frequency of each allele. The highest density AFD SNP was 43/Mb of F2 bulks and 55/Mb of F3 bulks. There were 4 chromosomes in the F2 population and 5 chromosomes in F3 had AFD-SNP density greater than 30/Mb. Among them, chromosome 7 appeared in both groups, however, the specific segments were inconsistent. Chromosome 23 also appeared in the two populations, and the segments were very close to each other. AFD-SNP appeared in the same segment on chromosome 19 in F2 and F3. Also, F3 had a high density of SNPs in the segment of 322 M in chromosome 27  analysis based on SNP sites with AFD >  = 0.3 to amplify the ratio of site allele frequency differences. After visualazed the power4(AFD) with CMPlot  > 1). 46 genes were identified with AFD SNP between two group. Six genes  were found high expression in fat and two genes (ENSBTAG0000013919 and ENSBTAG0000037605) were found highly expressed in muscle (max log2(TMP) > 8)  gene is located on the quantitative trait loci affecting IMF content and encodes key factors in the metabolic pathway19. Another research for body height, two significant Runs of homozygosity(ROHs) were found at BTA23 and BTA7, but fat coverage at BTA2820. The possible reason is that the population combinations are completely different and different genetic background generate different regulatory mechanism. Quantitative traits, such as IMF content, are influenced by interaction between genotype and environment, nutrition also has an effect on the population. From the mothed point of view, Traditional SNP GWAS provides limited genetic information. To increase the sensitivity of region/segments with SNP linkage in our F2/F3 populations, we used the haplotypes which contained 10 adjacent SNPs as a window, and calculated individual SNP frequency based on the sliding window. In general, chromosome recombination and exchange occur randomly and independently and can be very different in each individual. The sliding windows that were significant associated with the population may be related to our target traits.In particular, marbling is the primary factor for determining meat quality and price. The IMF content is positively correlated with marbling score (MS). Some of which have been previously reported MS was also localized on Chr. 237,16. By mixing the samples, it can minimize the costs of next-generation sequencing. On the contrary, we sequenced all individuals with extreme traits. The overall data of the same phenotype is later mixed to ensure the consistency and coverage of sequencing. Our data suggested that sequencing individual samples may be a better option to ensure data consistency and sequencing depth.In the classical BSA analysis, researchers usually first mix individuals with the same traits in equal amounts of DNA for next-generation sequencing and allele frequency analysisThis study provided a BSA-based QTL mapping method to analyze the genetic marker of IMF in the different generation (F2/F3) cross populations between Luxi, Qinchuan and Wagyu. Following Mendelian's law of inheritance, the allele frequencies of the hybrid populations were also being investigated, and those AFD may be associated with the phenotypic trait of IMF which had a potentially high economical value of cattle. Multiple potential QTL loci were found. For better association analysis and marker validation, it is recommended that more sequence of extreme traits is needed to obtain better resolution.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.


EDN3 and a few other genes) and diluting genes (including PMEL and a few other genes). Muzzle colour was mainly determined by MC1R gene markers. Piebaldism was mainly associated with KIT gene markers. Stature was associated with BTA6 markers upstream of the NCAPG–LCORL genes. Teat defects were associated with TBX3/TBX5, MCC and LGR5 genes. Overall, the identified genomic regions not only can be directly used in selection plans in the Reggiana breed, but also contribute to clarifying the genetic mechanisms involved in determining exterior traits in cattle.Autochthonous cattle breeds constitute important reservoirs of genetic diversity. Reggiana is an Italian local cattle breed reared in the north of Italy for the production of a mono‐breed Parmigiano–Reggiano cheese. Reggiana cattle usually have a classical solid red coat colour and pale muzzle. As part of the strategies designed for the sustainable conservation of this genetic resource, we investigated at the genome‐wise level the within‐breed detected variability of three pigmentation‐related traits , stature, presence/absence and number of supernumerary teats and teat length. A total of 1776 Reggiana cattle (about two‐thirds of the extant breed population) were genotyped with the GeneSeek GGP Bovine 150k SNP array and single‐marker and haplotype‐based GWASs were carried out. The results indicated that two main groups of genetic factors affect the intensity of red coat colour: darkening genes (including  Autochthonous breeds, available in all main livestock species, constitute important reservoirs of genetic diversity. These breeds are, in general, well adapted to the production systems in which they have been developed. Breeds can usually be distinguished by associated inheritable exterior phenotypes that could be considered in many cases breed‐specific traits and, for that reason, they might be fixed or close to the fixation in the breed population. Some degrees of within‐population variability could, however, exist for some of these descriptors, mainly in local breeds, owing to recent admixture with other populations or relaxed selection pressures towards standard forms, or because some degrees of variability are allowed in the population. As such, within‐breed phenotypic variability can be useful to identify markers associated with different expression of exterior features that could help, in turn, to understand the genetic architecture of these traits and then to apply genomic information to facilitate the fixation of standardised phenotypes in the targeted breeds (Schiavo Vacche Rosse) created to refer to the characteristic typical red coat colour of the Reggiana cattle , with a high density of farms that raise this breed in the province of Reggio Emilia (from which its name comes), in the well‐known worldwide Protected Designation of Origin Parmigiano–Reggiano cheese production area. Milk of this breed is almost exclusively utilised to produce a mono‐breed Parmigiano–Reggiano cheese, that is labelled with a brand name  gene  determines the classical red coat colour (fromentino) of this breed , some Reggiana animals not fully matching the classical exterior descriptors of the breed can emerge sometimes over generations and some phenotypic variability is present in the population. Therefore, a phenotyping programme embracing a large fraction of the breed population has been established, with the aim of monitoring the level of compliance of the Reggiana population to the Herd Book standard and subsequently planning actions to introduce breed‐specific exterior features in the selection goals of this population and reduce the frequency of morphological defects. The programme is part of the strategies that have been designed for the sustainable conservation of this genetic resource and it is also planned to use genetic markers to achieve its aims.Genome‐wide analyses using SNP arrays have been conducted in Reggiana cattle to identify breed‐informative markers that could distinguish Reggiana animals from those of other cosmopolitan breeds , for the description of the body structure and related purpose of the animals (stature) and teat‐related traits that are directly or indirectly involved in udder health and milking adaptability (supernumerary teats and teat length). The results identified new candidate genes and confirmed the role of some additional genes that have been already indicated to affect the same or similar traits. The obtained information not only can be directly used in selection plans in the Reggiana breed but also contributes to clarifying the genetic mechanisms involved in determining exterior traits in cattle, including novel aspects related to pigmentation in A total of 1796 Reggiana cattle, born over the years 2000–2019, were included in this study. The exterior traits of animals, recorded by trained personnel, were the following Table ; (ii) muplink 1.9 software . All animals were genotyped with the GeneSeek GGP Bovine 150k Array . Standard genotyping protocols, based on the supplier’s recommendations, were used. shapeit v.2  of 201 estimated with snep v.1.1  analysis that pointed out a rapid decline of LD such that r2 ≈ 0.1 at 100 kb. Haplotypes were treated as bi‐allelic markers and genotypes were coded as NN, NH and HH genotypes  in the subsequent analyses. A regular ped file was obtained with plink 1.9 software, filtering out haplotypes presenting a MAF less than or equal to 0.02. The final datasets consisted of a total of 1776 animals and 403 307 haplotypes and 49 994 haploblocks. Each block contained an average number of haplotypes (±standard deviation) equal to 8 ± 4 . Haplotypes included an average number of SNPs equal to 5 ± 3 . This was expected considering the genome size and the DNA marker distribution across the genome. The haplotype structure of regions of interest was further evaluated using haploview software  is a vector containing the phenotype for the nth animal, W (n × k) is a covariate matrix and α is the k‐dimensional vector of covariates effects, x (n × 1) is the vector containing genotypes for the ith DNA marker, β is the additive fixed effect of the ith DNA marker (SNP or haplotype), g≈N is a multivariate Gaussian polygenic effect, with covariance matrix proportional to the relatedness matrix K (n × n), and e≈N is a multivariate Gaussian vector of uncorrelated residuals. The assessment of the association between each DNA marker/haplotype and the targeted phenotype was obtained by testing the null hypothesis H0:β = 0. Significance was obtained using the Wald test. All of the models were fitted with gemma v.0.98 (Zhou & Stephens K as a centred genomic matrix. Two K matrices were produced: one from SNPs and another from haplotypes. Details about the linear mixed effect models are reported in Table P = 5.0 × 10−6 was applied to identify suggestively associated markers or haplotypes. gemma was used to estimate the genomic (chip) heritability (hG2). Genomic control inflation factors (λGC) were computed in r. QQ and Manhattan plots were generated in r using the qqman package  or a dark red coat colour (10.7%). Another pigmentation‐related trait was defined according to the three observed muzzle colour classes indicated in Fig. Phenotyping of the investigated cattle was obtained for three pigmentation‐related traits Fig. . Considen. 206, 18.5%), two  or three  supernumerary teats . All animals had at least four nipples and about 31% of the cattle had one  is reported in Tables Data were treated with different models as described in Table Estimated genomic heritability from single‐marker analysis Table  ranged fThe GWASs that included three different classes of red coat colour grades and different comparisons were designed to potentially dissect the genetic architecture affecting the colour intensity. The obtained results indicated that there might be two main general groups of factors underlying this trait: some factors that darken the basic red colour  and some others that might lighten or dilute the regular red grade .P = 1.32 × 10−15 and P = 1.52 × 10−14 respectively . This marker is located at nucleotide position 57037937, close to the region of the endothelin 3 (EDN3) gene . The two most significant haplotype windows in all three GWASs  whely Table . These rly Table , even ifSs Table  perfectlP = 1.90 × 10−9 and 7.10 × 10−9 for the two analyses respectively). This SNP is a splice region variant in the LEM domain containing 3 (LEMD3) gene, whose known functions are not directly related to pigmentation. However, two other genes located in this region [WNT inhibitory factor 1 (WIF1) gene (positions: 48687578–48779884 bp) and TBC1 domain family member 30 (TBC1D30) gene (positions: 48952181–49035846 bp)], which encode for factors involved in pigmentation processes, have been associated with hair colour in humans . The results were confirmed by the haplotype‐based analyses Tables  & S2. Nopremelanosome protein  gene (Tables P = 6.72 × 10−8 and P = 2.98 × 10−7 respectively). This marker  is determined by a three‐nucleotide in‐frame deletion in exon 1 of PMEL, which eliminates the leucine residue at position 19 of the ENSBTAP00000005250.4 encoded protein sequence (p. Leu19del). This mutation has already been associated with a diluted coat colour phenotype in other cattle populations  gene has been reported to contribute to the variability of human hair colour  gene (positions 35611321–35666918 bp) is located, which encodes for an ion channel specifically expressed and acting in epidermal melanocytes  the light red class was compared with the other two classes grouped together Fig.  and whenP = 1.41 × 10−29) on BTA18 within the melanocortin 1 receptor (MC1R) gene  have been already described to affect coat colour in many species, including cattle  from the other alleles at this gene  = 0.95, f(E) = 0.05; genotype frequencies: f(ee) = 0.905, f(Ee) = 0.090, f(EE) = 0.005; where E indicates all other alleles that are different from e]. The single marker GWASs with some models  showed another significant SNP on BTA18 but at about 3 Mb upstream from the MC1R gene and the haplotype analysis identified a few additional significant haplotype windows that were just upstream MC1R (<1 Mb from it) or at about 10 Mb downstream  identified a peak of highly significant markers  gene whose function has not been completely characterised yet. A few other genes of the ADAMTS family are however involved in pigmentation defects or in melanoblast survival  gene and marks a region that also includes two genes  that have been associated with iris heterochromicity in humans  at position 12671675 bp Fig. . This SNv‐kit Hardy‐Zuckerman 4 feline sarcoma viral oncogene homolog (KIT) gene, which has already been described to be involved in spotted patterns in cattle  gene (position: 87427184–87706738 bp). Members of the ADAMTS protein family participate in diverse morphogenetic processes during embryonic development and connective tissue maintenance, and a few of them have been involved in melanocyte development and melanoblast migration  gene (position: 92951310–93291875 bp). FRAS1 is an extracellular matrix protein, which is involved in regulating membrane adhesion in the epidermal basement during embryonic development  gene (position: 2765576–3145422 bp) which encodes a member of the importin‐β superfamily of nuclear transport receptors that are involved in diverse biological processes including cell growth, differentiation and development  gene (position: 18673820–19567204 bp) that was reported to be a candidate gene for pigmentation in humans , which encodes an interacting transcription factor involved in melanocyte development  was also detected by single‐marker analysis Tables . These rnon‐SMC condensin I complex subunit G (NCAPG) and the ligand dependent nuclear receptor corepressor like (LCORL) genes (positions: 37277323–37378124 and 37380296–37557106 bp respectively). These genes have already been reported to be associated with stature and body size in cattle and several other species  were, however, located upstream of these genes, within the close family with sequence similarity 184 member B (FAM184B) and leucine aminopeptidase 3 (LAP3) genes . Figure Only one significant peak, located on BTA6, concordant with both single‐marker and haplotype‐based analyses, was detected Fig.  & S1. Thpleomorphic adenoma gene 1 (PLAG1) and Coiled‐Coil–Helix–Coiled‐Coil–Helix Domain Containing 7 (CHCHD7) genes, with the most significant SNP  located at position 23.3 Mb. Inclusion of the significant BTA6 SNPs in the mixed model nullified its effect and did not disclose any other important genomic regions. Other suggestively associated haplotypes (Tables P = 1.40 × 10−7), in the dispatched RND transporter family member 1 (DISP1) gene region and on BTA3:105.85–106.00 Mb (P = 4.56 × 10−6) in the RLF zinc finger (RLF) gene region, which have both been associated with human height  gene that encodes a tumour suppressor factor which is thought to negatively regulate cell cycle progression. A minor peak of association  where the T‐Box Transcription Factor 3 (TBX3) and T‐Box Transcription Factor 5 (TBX5) genes are located. A few other suggestively associated haplotypes were identified on BTA3, BTA7 and BTA29 (Table The genome scans for supernumerary teats (absence vs. presence of supernumerary teats) identified only one suggestively significant SNP  on BTA10  gene. Haplotype‐based analyses identified a second close region on BTA8 (109.25–109.35 Mb) and another region on BTA2:69.15–69.35 Mb.Only suggestively significant SNPs and haplotypes were identified for teat length. Markers and haplotypes identified a suggestively associated region for this trait on BTA8:106.95–107.55 Mb Fig  & S1 whiDespite the small population size of the Reggiana cattle, the monitoring of exterior traits revealed some phenotypic variability within this breed for three pigmentation‐related traits , a body structure‐related trait (stature) and two udder morphometric‐related traits (supernumerary teats and teat length). This phenotypic variability, which can in part be genetically explained, might have been originally derived by the ancestral founder population of this breed or could be subsequently acquired over the years through some unrecorded admixture with other cattle populations, mainly in the past decades. This variability was exploited through the first GWASs carried out thus far in this autochthonous cattle breed that included almost two‐thirds of the extant population in terms of number of cows involved.fromentino coat colour. As far as we know, the darkening factors acting on a recessive red background  have not been explored in detail in cattle, probably owing to the low phenotypic variability present in other red cattle populations and breeds and the difficulties in establishing different classes from the basic colour. The subjective approach that we used, which relied on trained technical personnel, was however effective in defining different groups of animals, as demonstrated by the results obtained from the GWAS that relied on the phenotypes retrieved in this way. The main significant peak that we identified in the analyses that compared the dark animals with all other cows pointed out EDN3 as the candidate gene involved in the darkening of red coat colour. Several in vitro studies using quail and mouse embryos have reported that EDN3 affects the melanocyte lineage population by increasing their number in a dose‐dependent manner  that is responsible for the Dilution coat colour locus, already described in some breeds by classical genetic studies and molecular investigations, which have reported a few mutated dominant or partially dominant alleles over the wt allele at this main locus (Searle CD (Charolais type), caused by the PMEL missense mutation c.64G>A  reported by several studies , partially associated with a congenital hypotrichosis in cattle (commonly referred to as rat‐tail syndrome) and suggested to be caused by the combination of two PMEL mutations – the three base deletion in exon 1, p.Leu19del (rs385468954), and another missense mutation at codon 612, p.A612E , reported to have the p.Leu19del but partially characterised for other mutations , always in a heterozygous condition. The p.Leu19del allele could have been introgressed into Reggiana through crossbreeding with Simmental cattle, which probably happened in the past. In the Reggiana breed, however, no reports have suggested the presence of the rat‐tail defect that, however, might not be completely associated with mutations in the PMEL gene , referred to with the symbol E, segregating in the breed population. The phenotypic expression of E allele(s) might be only evident at the muzzle colour level as, at the coat level, E might confer a reddish/brownish colouration that could be similar to the classical fromentino red colour of the breed. The effect of E allele(s) could be modified by other loci, as described above, that might determine the observed grades of red, confusing the phenotypic assignment at different Extension genotypes. The three coat colour classes discussed above, however, were not associated with MC1R variability, as no significant signals were reported on BTA18 in the GWASs that were carried out for coat colour intensity. It will be interesting to evaluate which of the other MC1R alleles already described in brown/red cattle breeds . Despite the large effect of the MC1R gene, other genetic factors are involved in determining the black muzzle colour in Reggiana cattle as significant SNPs and haplotypes have been identified in another 11 genome regions. The intermediate muzzle colour class (grey) appeared to be determined by other genetic mechanisms, separated by those determining the black muzzle colour. Only one significant region on BTA3 was detected in the GWASs that compared pink vs. grey muzzle groups of cattle. Additional studies with a larger number of animals would be needed to complete the dissection of this pigmentation‐related trait that appears to be more complex than expected.The pink or pale muzzle (the standard muzzle colour reported by the Reggiana Herd Book) was clearly associated with the KIT gene, which is a well‐known determinant of piebaldism in cattle  whose functions could be directly or indirectly involved in the migration and development processes of melanoblasts and melanocytes, which are absent in the white regions of the body. Spotted cattle emerge sporadically in Reggiana from sires and dams that had a solid red coat colour without any spotted patterns, suggesting that piebaldism might be a quantitative trait determined by several genetic factors in combination, most of which are recessive  are any abnormal teats in addition to the usual four teats that correspond to the four usual functional mammary glands. Supernumerary teats are an undesired and heritable trait in cattle , which was similar to that of other investigated traits in this study. This might suggest that no major genes affect this quantitative trait, as it is also possible to deduce from the several previous studies that investigated this trait in other cattle breeds  of this coat colour darkening locus in Reggiana and eventually in other cattle breeds. Some of the results on coat colour and muzzle colour can be directly implemented to select against undesired phenotypes that are not allowed by the Herd Book standard of the Reggiana breed. Other results that might need to be confirmed could be useful to start selection programmes to improve udder conformation and related milking performances and redefine the body structure of the Reggiana animals.This study demonstrated that, by exploiting variability within an autochthonous cattle genetic resource, it was possible to obtain interesting results on associated markers that contributed to clarifying the genetic mechanisms of several exterior traits in cattle. It will be useful to explore variability in the The authors declare they do not have any conflict of interests.Figure S1 Q–Q plots of the single‐marker GWASs carried out in the Reggiana cattle breed.Figure S2 Q–Q plots of the haplotype‐based GWASs carried out in the Reggiana cattle breed.Click here for additional data file.Table S1 SNPs with P < 5.00 × 10−6 in GWASs for the investigated traits and defects in the Reggiana cattle breed.Click here for additional data file.Table S2 Haplotypes with P < 5.00 × 10−6 in GWASs for the investigated traits and defects in the Reggiana cattle breed.Click here for additional data file.


Puccinia graminis f. sp. tritici Eriks. is an important disease of common wheat globally. The production and cultivation of genetically resistant cultivars are one of the most successful and environmentally friendly ways to protect wheat against fungal pathogens. Seedling screening and genome-wide association study (GWAS) were used to determine the genetic diversity of wheat genotypes obtained on stem rust resistance loci. At the seedling stage, the reaction of the common stem rust race QFCSC in Nebraska was measured in a set of 212 genotypes from F3:6 lines. The results indicated that 184 genotypes (86.8%) had different degrees of resistance to this common race. While 28 genotypes (13.2%) were susceptible to stem rust. A set of 11,911 single-nucleotide polymorphism (SNP) markers was used to perform GWAS which detected 84 significant marker-trait associations (MTAs) with SNPs located on chromosomes 1B, 2A, 2B, 7B and an unknown chromosome. Promising high linkage disequilibrium (LD) genomic regions were found in all chromosomes except 2B which suggested they include candidate genes controlling stem rust resistance. Highly significant LD was found among these 59 significant SNPs on chromosome 2A and 12 significant SNPs with an unknown chromosomal position. The LD analysis between SNPs located on 2A and Sr38 gene reveal high significant LD genomic regions which was previously reported. To select the most promising stem rust resistant genotypes, a new approach was suggested based on four criteria including, phenotypic selection, number of resistant allele(s), the genetic distance among the selected parents, and number of the different resistant allele(s) in the candidate crosses. As a result, 23 genotypes were considered as the most suitable parents for crossing to produce highly resistant stem rust genotypes against the QFCSC.Stem rust caused by  Puccinia graminis f. sp. tritici (Pgt) Erikss and Henning, has been devastating to wheat (Triticum aestivum L.) through many decades of production especially during the 1950s in the United States(Berberis vulgaris L.) eradication program , identifying stable sources of resistance, reducing potential new races in the population, and monitoring potential new races of the pathogen through a global network  is useful to confirm the phenotypic selection (Oryza sativa L.) and barley (Hordeum vulgare L.) by  and end-use quality . In thiselection . Moreoveelection . Identifelection in markere L.) by .38 and Sr24 genes.In a recent study, the plant materials (hereafter referred to as DUP 2017) represent part of the preliminary yield trial of Nebraska winter wheat breeding program that is used for releasing new wheat cultivars. The DUP2017 genotypes were evaluated for grain yield in nine U.S.A. locations and high yielding genotypes were found . MoreoveSr38 and Sr24 genes in the population, 4) select the most promising stem rust resistance genotypes to be used in future breeding programs.The objectives of this study were to: 1) screen a nursery of 212 Nebraska winter wheat genotypes for their resistance to stem rust race QFCSC, the common race in the United States, 2) identify SNP markers associated with stem rust resistance using GWAS, 3) screen the presence of 3:6 lines  were selected for this study. As mentioned previously, DUP2017 is the preliminary yield trial and the lines are developed from of 800–1,000 crosses among elite Nebraska adapted and cultivars from Great Plains states  were previously collected from naturally infected field-grown wheat, then increased in the greenhouse using a highly susceptible cultivar (McNair 701). Two hundred and twelve genotypes and four check cultivars were evaluated in a randomized complete block design with four replicates: two at the plant pathology greenhouses, University of Nebraska Lincoln, UNL and the other two at the USDA-ARS at Kansas State University (KSU).The reaction to stem rust race QFCSC was evaluated at the seedling stage using 212 F−1 in 3 ml Soltrol 170 mineral oil) (Sigma-Aldrich Corp.) according to  using a 0–4 scale . CategorSr24, Sr38, Sr31, and Sr1RSAmigo, so SSR and STS markers for these genes were screened for those genotypes  as described in . The extenotypes . Also, genotypes . The GBSenotypes . The refenotypes .i, ej are the main effects of genotypes and replications, respectively; geij is genotype × replications interaction of genotype i with replicate j. The random effects were assigned to genotypes whereas the fixed effects were assigned to replications. Broad-sense heritability was calculated asg is the genotypic variation and Vp is phenotypic variation.The analysis of variance for stem rust resistance was performed with PLABSTAT software . The dat3:6 Nebraska winter wheat was performed using the criteria described in  and genotypic data (SMA) that link to known stem rust resistance gene especially  V. 3.25 .GWAS for stem rust resistance was performed using 11,991 SNPs markers after filtration of minor allele frequencies (MAF<0.05) were removed and excluding all heterozygous loci which were calculated as missing values. The phenotypic means for both traits and SNPs were subjected to association analysis using a mixed linear model (MLM) in TASSEL v5.2.40 software .p-value. Marker–trait associations were considered significant at false discovery rate (FDR) at 5% significance level. For each regression model, the SNP markers were ranked from smallest to largest p-values. A conservative, close approach to previous studies (R2) was determined using TASSEL v5.2.40. Manhattan plots for stem rust were visualized using FarmCPU package (r2) was estimated using TASSEL 5.0 between each pair of SNPs located on the same chromosome. The LD heatmap was visualized using ‘LDheatmap’ R package  means squares for genotypes (G), indicating the presence of considerable differences among genotypes for resistance to stem rust (data not shown) as would be expected in a breeding program that selects for stem rust resistance (H2) for stem rust scores was 0.78. Out of the 212 genotypes, 184 (86.8%) had different degrees of resistance to this common race with a range extending from very high resistance scored as 0 (9 genotypes) to moderate resistance scored as 5 (23 genotypes). Only 28 genotypes  were susceptible to stem rust with a score ranging from susceptible with a score ranging from 5–6 (six genotypes) to highly susceptible (9) scored as 8 (two genotypes). All genotypes with a score of 0 or one were considered highly resistant to stem rust. A set of 65 highly resistant genotypes (IT < 1) were selected for further genetic investigation. The stem rust resistance of each genotype is presented in The analysis of variance revealed highly significant (sistance . In the Population structure (PS) analysis of DUP2017 was previously analyzed by . The resvia Manhattan plot in p-values comparing the uniform distribution of the expected−log10 p-value to the observed−log10 p-value for stem rust resistance trait showed that the MLM fitted the data well (The mixed linear model (MLM) was used to test the genetic association between the 11,911 SNPs and the stem rust-resistance scores of all tested genotypes. The results of GWAS revealed 84 SNPs located on four chromosomes: 1B (4 SNPs), 2A (59 SNPs), 2B (1 SNP), 7B (8 SNPs) and 12 SNPs that were not mapped to a known chromosome (Unknown chromosome). The summary of GWAS results is presented in ata well .R2) ranged from 6.04% (S1B_561712520) to 25.61% (in both markers S2A_13028312 and S2A_13028321). The effects of alleles associated with decreased susceptibility to stem rust ranged from -2.83 (G) in both markers, S2A_13028312 and S2A_13028321, to -1.42 (T), S1B_547524267. Notably, the highest allele effects which decreased the stem rust symptoms were accounted for alleles found on 2A chromosome and those that belonged to the unknown chromosomal position.The phenotypic variation explained by each SNP marker (r2) was estimated between each pair of SNPs located on the same chromosome . All the 12 SNPs markers located on unknown chromosomal position had significant LD indicating that they are all linked (GR 4). For unknown positional SNP markers, LD was tested between this group and SNPs located on each of the known chromosome GR to determine the most likely chromosome which the SNPs on an unknown chromosome belong to. The 12 unknown SNPs were in a high LD with the 59 SNPs located on 2A chromosome  and Sr24 (3D). Based on the STS marker of the two genes (Sr24#12 and VENTRIUP-LN2), a percentage of 55.7% of the genotypes contained Sr38 marker in their genome (118 genotypes) while only 52 genotypes with a percentage of 24.5% contained Sr24 resistance gene  was 1.15 while it was 4.07 for the group not possessing the Sr38 gene (58 genotypes). Thirty-six genotypes were heterozygous/heterogenous for the presence of Sr38 gene. It was observed that some resistant genotypes (low stem rust score) did not have the markers associated with Sr38 gene indicating the presence of other gene(s). The linkage disequilibrium was performed again between the STS marker VENTRIUP-LN2 (Sr38 gene marker) and all SNPs located in GR 3 (71 SNPs) (r2 ∼ 0.90) between the SNP markers and STS marker.Based on the pedigrees of the tested genotypes, some stem rust resistance genes were strongly expected such as nce gene . The res71 SNPs) . The resThree criteria were considered to determine the most promising stem rust-resistant genotypes as candidate parents for a future cross to increase stem rust resistance. These criteria were based upon the following:First, phenotypic selection in which all highly resistant genotypes with stem rust score of 0 or one were selected. As a result, 65 genotypes were identified and included in the second stage.Sr38 gene except NE17670. Therefore, it was necessary to examine the genetic distance between the NE17670 and the 22 genotypes. Consequently, the 23 genotypes were included in the third stage Third, population structure and the genetic distance matrix (GD) among the selected genotypes.Second, the presence of resistant alleles in each genotype was determined . The numvia dendrogram cluster analysis in Sr38 gene except NE17670.The genetic distance among the 23 genotypes is illustrated Crop scientists face a serious challenge of increasing productivity by controlling stresses caused by biotic and abiotic effects. Stem rust of wheat, among biotic factors, poses a continuous threat through the rapid evolution of new races. Resistant cultivars are developed and considered to be the most economical and environmentally friendly tool for disease control. The primary gene pool, including indigenous collections comprising landraces, old cultivars, and breeding lines, is considered a valuable genetic resource to provide new and sustainable resistance that can be utilized for the production of today’s high yielding cultivars . A bette3:6 lines (DUP 2017) as indicated by the analysis of variance (ANOVA). The high broad-sense heritability observed in this study indicated the reliability of data for GWAS and that selection for stem rust-resistant genotypes would be successful. The phenotypic distribution of disease response was not normally distributed which was reported also in previous studies on stem rust resistance (Significant variation was observed among a collection of 212 selected genotypes from Fsistance . In thissistance . The gensistance . Moreoven = 65) from the three subpopulations can be selected for genetic diversity and stem rust resistance for future wheat breeding  were considered a minor QTL with R2 less than 10%. Many earlier studies reported that large-effect QTLs controlling target trait have R2 of >10%  markers for further validation studies as KASP markers have more advantages over the other DNA molecular markers. While 10 markers were in common between the two studies, the other seven SNP makers in Sr38 stem rust resistance gene remains a broadly used and effective resistance gene to the QFCSC local strain  encoded to receptor like kinases (RLKs). RLKs have been discovered to play a role in both broad-spectrum, elicitor-initiated defense responses and race-specific pathogen defense as dominant resistance (R) genes. The majority of defense-related RLKs are of the leucine-rich repeat (LRR) subclass (TraesCS2A02G010200.2 (3965047–3965054 bp) which encoded to steroidogenic acute regulatory based transfer (StART)-like domain superfamily. In insect, humans, and plants, StART proteins play a variety of roles in the transport of lipid molecules  which encoded to F- Box domain proteins family. As one of the largest and most diverse plant gene families, F-box proteins are involved in many cellular processes, including cell cycle, circadian rhythms, embryogenesis, floral organ development, stress responses, and various signal transduction pathways. F-box proteins are reported to be related with the plant response against bacterial, viral and fungal pathogens  which encoded the ABC transporter domain superfamily. Plant ABC transporters are classified into several sub-families (ABCA - ABCH) and play diverse roles  and around 15 gene models were recognized . The gensubclass . TraesCSolecules . These polecules . The StAolecules . The genathogens . Also, mathogens . The gense roles . Althougse roles . ABC trase roles , and to se roles , or act se roles . In addiTraesCS2B02G057600  which encodes to MFS transporter superfamily. The MFS transporter is a member of plant defense-related proteins that could be involved in exporting the antimicrobial compounds produced by plant pathogens, the plant-generated antimicrobial compounds; and potassium which is important during plant defense reactions  was found to be associated with stem rust resistance. This SNPs located very near eactions . Antimiceactions . Accordieactions , many stSr17 on 7B chromosome  encoded cytochrome P450 Enzymes which play an important role in enhancing the resistance of several plant-fungal interactions including stem rust, powdery mildew (Blumeria graminis f. sp. tritici) and Fusarium head blight (Fusarium graminearum)  and TraesCS7B02G441700.1 (706811897) are near the gene detected in this study. These results indicated that 7B chromosome may include very important genomic regions associated with QFCSC resistance which can be used for marker-assisted selection after validating the SNPs in a different genetic background. A set of four SNPs were associated with stem rust and located on 7B detected by A GWAS analysis showed the presence of highly significant SNPs located on 7B chromosome. This set of eight SNPs located on 7B was highly LD and considered as a genomic region . Previouromosome . Howevere models . Three cinearum) . S7B_704Therefore, by comparing the position of SNPs and candidate genes detected in the study with the positions of SNPs and candidate genes reported by earlier studies, it can be concluded the most genomic regions detected in this study were novel. Bearing in mind that, all detected candidate genes found in this study have a strong relation with plant disease resistance in wheat.Sr38 gene (We also expect that many stem rust resistance genes are present in our materials due to, wide range of infection types. For instance, we have 58 genotypes with non r38 gene  the averPhenotypic selection is widely used in traditional plant breeding. However, phenotypic selection could be misleading due to epistasis and the environmental or human errors which could reduce heritability and lead to ineffective selection. Selection based on genotypic and phenotypic values together can address this challenge by truly select the most promising stem rust-resistant genotypes.To address this challenge, genotypes were selected based on three criteria as described by . FirstlySr genes associated with stem rust resistance were identified. In particular, the genomic region harboring Sr38, one of the most important resistant genes to stem rust, was found. The validated SNPs in this region can be converted to KASP markers which can be used for marker-assisted selection for Sr38. Moreover, important new SNPs especially those located on 7B chromosome were identified. These markers will need to be validated before using them in MAS, but the first step is completed. The gene annotation analysis revealed putative genes associated with fungal disease resistance. These results further support that GWAS was a powerful method to identify target alleles. Moreover, most of significant SNP detected by GWAS were considered with major effects in stem rust resistance except six SNPs with minor effects.Four LD genomic regions controlling important Finally, information from genetic diversity, population structure, and GWAS results were combined to identify the most promising wheat resistant genotypes as potential parents for future a breeding program. As a result, 23 genotypes were identified, and based on our results, we recommend NE17670 be used as a parent in future crosses as it may have other resistant genes which were not identified by GWAS.


Ricinus communis L.) is an important oil crop, which belongs to the Euphorbiaceae family. The seed oil of castor bean is currently the only commercial source of ricinoleic acid that can be used for producing about 2000 industrial products. However, it remains largely unknown regarding the origin, domestication, and the genetic basis of key traits of castor bean.Castor bean (Here we perform a de novo chromosome-level genome assembly of the wild progenitor of castor bean. By resequencing and analyzing 505 worldwide accessions, we reveal that the accessions from East Africa are the extant wild progenitors of castor bean, and the domestication occurs ~ 3200 years ago. We demonstrate that significant genetic differentiation between wild populations in Kenya and Ethiopia is associated with past climate fluctuation in the Turkana depression ~ 7000 years ago. This dramatic change in climate may have caused the genetic bottleneck in wild castor bean populations. By a genome-wide association study, combined with quantitative trait locus analysis, we identify important candidate genes associated with plant architecture and seed size.This study provides novel insights of domestication and genome evolution of castor bean, which facilitates genomics-based breeding of this important oilseed crop and potentially other tree-like crops in future. Ricinus communis L., Euphorbiaceae, 2n = 20) is an important non-food oilseed crop worldwide, with a unique seed oil profile, rich in ricinoleic acid , which has been used in industry for making lubricants, cosmetic, coatings, inks, plastics, and biodiesel [https://www.zionmarketresearch.com/report/castor-oil-market). Its seeds contain the extremely toxic protein ricin that has been used as an immunotoxin for therapeutic purposes in different cancers, was likely used by early hunters, and has been reportedly used as a weapon [Castor bean   and earlDuring broad field surveys in Kenya and Ethiopia, we therefore collected castor bean accessions with traits typical of the wild progenitor, such as dehiscent capsule, small seeds, and a tree phenotype with a single elongated trunk  from Ethiopia for genome sequencing  in diverse biological processes (including photosynthesis and oxidative phosphorylation), pathways , and some metabolites   diverged ~ 64.55 million years ago (MYA). Castor bean and Mercurialis annua clustered together, both members of subfamily Acalyphoideae, and diverged from four other members of Euphorbiaceae (subfamily Crotonoideae) ~ 48.28 MYA  with Vitis, while for H. brasiliensis and M. esculenta experienced a recent species-specific duplication , and M. esculenta (2n = 36) revealed a substantial degree of collinearity and several large collinear regions , 126 wild accessions from Kenya (WK population), and 323 domesticated accessions from the world  were used for subsequent analysis, which covers the worldwide distribution and phenotypic diversity of castor bean , group 2 mainly consisted of WK accessions (120 WK and three WE), and group 3 mainly consisted of LC accessions (316 LC and four WK) with significant mixture of landraces and cultivars  populations, with evidence of mixed ancestry in the WE group, and at K = 3, the WE, WK, and LC groups are apparent. At K = 5, further subgroups emerge, including the split between WK-I and WK-II, but no geographically structured subgroups were observed with the increase of K value, although K = 10 was optimal  relative to WE (1.95 × 10−3) and WK (1.81 × 10−3)  in castor bean (1.14) was quite small relative to other crops such as rice (1.25), soybean (1.58), cucumber (1.96), and tomato (2.63) [FST between populations indicates obvious genetic divergence between wild and domesticated population  but less between the WE and WK populations  than in the domesticated population (~ 64.5 kb for LC)   suggesti.63  sugg.63  suggr bean 1. was quitθπ = 1.90 × 10−3) comparable to the wild group and substantially greater than found in South Asian and Chinese groups . The potential reason for high genetic diversity in this area is that accessions in West Asia may have repeatedly received gene flow from wild castor bean in East Africa or that this represents the earliest group of domesticates, with other Asian accessions being founded from this region. We employed TreeMix to measure gene flow and migration and found, indeed, that gene flow from Ethiopia to West Asia was supported  and greater differentiation between these Eastern sites and West Asian castor bean (FST = 0.19 between Chinese and West Asian accessions and FST = 0.11 between India and West Asian accessions).Four centers of phenotypic diversity have been proposed , 11; theTaken alongside archeological evidence, our results clearly show that accessions from East Africa are the extant wild progenitors of castor bean and that domestication occurred somewhere between East Africa and West Asia, and these are the main centers of diversity. Following this, accessions were distributed throughout the world. The lack of geographically structured genomic variation in the landraces and cultivars suggests continued and multi-directional transport and/or breeding of castor bean.Ne) through time and divergence times between castor bean populations. We used unphased SNPs and a mutation rate of 6.9 × 10−9 mutations per nucleotide per year . Despite low genetic divergence between these two subgroups  that scored in the top 5% of the distribution of FST (FST > 0.299), encompassing 2647 genes  with the LC population. This was carried out using a 100-kb sliding window with a 20-kb step. In total, 326 potential selective sweeps in the top 5% of both the ROD and FST distributions were detected, making up 4.4% (14.7 Mb) of the assembled genome , diameter of the main stem (DMS), number of nodes (NN), and seed size have been selected on by humans, reflecting morphological change from a perennial woody tree to an annual semi-woody crop  that regulates seed size by repressing cell proliferation during seed development [Arabidopsis TRANSPARENT TESTA GLABRA2 (TTG2) which can regulate endosperm/seed growth by increasing integument cell elongation [GSE5/GW5 which appears to have a crucial function in determining grain width and weight in rice [ABSCISIC ACID-INSENSITIVE5 (ABI5), which regulates seed size by repressing the expression of SHORT HYPOCOTYL UNDER BLUE1 (SHB1) during early seed development [Several genes with orthologues in other species involved in the regulation of seed size were identified , and its mutant in Arabidopsis is smaller and exhibits slowed development [Arabidopsis CORD2 (CORTICAL MICROTUBULE DISORDERING2) which is required for secondary cell wall patterning in xylem vessels [For DMS, nine signals on six chromosomes  were identified Fig. d. This gbidopsis . On chroelopment . An addi vessels . Other cArabidopsis, SLY1 and 2 make up the SCF E3 ubiquitin ligase involved in DELLA protein degradation to modulate the GA signaling, and their knockout mutants exhibit a dwarfed phenotype [For PH, we detected two significant SNPs, one each on chromosomes 8 and 9 , width (SW), thickness (ST), area (SA), single seed weight (SSW), and seed oil content (SOC) in castor bean. We first performed QTL analysis using a recombinant inbred line (RIL) population previously constructed by crossing large-seeded line Rc250 with small-seeded line Rc249 . Colocalelopment . The genbidopsis . Close tbidopsis , an Arabeed size . One memeed size .Fig. 5GArabidopsis and rice [Seed weight is a critical character for yield, seed germination, and seedling fitness, therefore is a trait that humans likely selected on during domestication. We identified five SNPs significantly associated with SSW on chromosomes 1, 2, 3, and 5 , and 67 landraces and cultivars from Pakistan, India, and China. The remaining 58 accessions were kindly provided by USDA-Agricultural Research Center in Griffin, Georgia. Detailed information for each accession is listed in the Additional file The seed was sterilized and germinated in an incubator at 30 °C for 5 days. Subsequently, germinated seeds were transplanted into our experimental field in Kunming, Yunnan, China. Five individuals were planted for each accession. For phenotypic observation and subsequent GWAS, we attempted to grow all accessions in the same field in two consecutive years from 2017 to 2018. In the first year, all castor bean accessions were self-pollinated. Accessions with consistent phenotypes across individuals (mainly referring to the stem color and seed size) were then cultivated in the second year; however, ~ 35% were lost due to uncertain climatic factors. Hence, some accessions’ data is based on 1 year of cultivation, but for most, it is averaged across 2 years. Young leaf tissue was collected in the second year from one individual of each accession for subsequent genome resequencing. In addition, we downloaded WGS data of 225 castor bean lines with clear collection information from the NCBI database under SRA accession number PRJNA548999 , that displays traits typical of wild castor bean. Genomic DNA was extracted using the Plant Genomic DNA Kit , and libraries were constructed and sequenced on the NovaSeq 6000 platform. The raw reads were pre-processed to remove the adaptors and low-quality bases using fastp (version 0.20.0)  with parlength_cutoff = − 1, seed_coverage = 40, length_cutoff_pr = 12 Kb, pa_HPCdaligner_option = -v -B128 -M20 -T8, pa_daligner_option = -e0.75 -l4800 -k18 -h480 -w8 -s100, ovlp_daligner_option = -k24 -h1024 -e.96 -l2400 -s100, ovlp_HPCdaligner_option = -v -B128 -M24 -T8, falcon_sense_option = --output_multi --min_idt 0.70 --min_cov 3 --max_n_read 300. Subsequently, the contigs were phased and polished by FALCON-Unzip based on all PacBio long reads. Finally, the assembled contigs were filtered to remove potential contaminants by BLASTN against NCBI NT database with the parameters -evalue 1e-5 -best_hit_overhang 0.25 -perc_identity 0.5 -max_target_seqs 10. Finally, sequence polishing was performed with Arrow  using PacBio long reads, and then Pilon (version: 1.23) [For de novo assembly of the Rc039 genome, we used long read sequencing on the PacBio Sequel platform with two SMRT Cells. In brief, high molecular weight (HMW) DNA was used to construct a DNA library with ~ 20 kb insert size and subsequently sequenced on the PacBio Sequel sequencing platform at Shanghai OE Biotech Co., Ltd. . De novo assembly was performed with FALCON (pb-assembly version 0.3.0)  with then: 1.23)  using Ilhttps://github.com/theaidenlab/3d-dna) with the parameters -r 1 -q 10. The resulting assembly was visualized using Juicebox Assembly Tools (version 1.11.9) [The Hi-C sequencing library was constructed and sequenced (150-bp paired-end) on the NovaSeq 6000 platform. Raw reads were quality-trimmed with fastp as mentioned above and aligned to the draft genome assembly using Juicer  with def 1.11.9)  based on 1.11.9)  to close 1.11.9)  with thede-novo TE Annotator (EDTA version 1.7.0) [For repeat annotation, we adopted the Extensive n 1.7.0) , which in 1.7.0) .https://github.com/gatech-genemark/BRAKER2) with RNA-Seq data . These samples were also aligned to the genome using HISAT2 (version 2.10.2) [Hevea brasiliensis, Manihot esculenta, Ricinus communis “Hale”, Arabidopsis thaliana , Vernicia fordii , and Jatropha curcas  were used as evidence during annotation, and finally to generate a comprehensive set of protein-coding genes with a AED score [Protein-coding annotations were predicted using the MAKER (version 2.31.10)  annotati 2.10.2)  and tran 2.10.2) . The traED score . BUSCO [ED score  was usedED score  and InfeED score  by searcED score . The tRNED score .E-value ≤1e−5). Motifs and domains were annotated by searching ProDom, PRINTS, Pfam, SMRT, PANTHER, and PROSITE using InterProScan (version 5.36). Gene Ontology (GO) annotations were assigned according to the corresponding InterPro entry.Functional annotations were assigned by aligning the castor bean protein sequences to the public databases including SwissProt, TrEMBL, NR, eggNOG, and KOG databases using diamond , Vernicia fordii (downloaded from http://bigd.big.ac.cn/gsa), Mercurialis annua (downloaded from https://osf.io/a9wjb/), and Jatropha curcas  were obtained. Orthologous genes among these plant species and castor bean were identified using OrthoFinder2 (version 2.2.7) [-S diamond. Subsequently, all single copy orthologs were subjecting to multiple sequence alignment using MAFFT (version 7.407) [burnin = 1,000,000, nsample = 20,000, and sampfreq = 500, and divergence dates for Vitis vinifera (105–115 MYA), Glycine max (97–109 MYA), and Arabidopsis (75–99 MYA) obtained from Timetree (http://www.timetree.org/) were further used to calibrate the divergence time. Evolutionary analysis of gene synteny and collinearity were performed using MCScan , and syntenic gene pairs were visualized using the dotplot script in jcvi package. We used CAFE (version 4.2) [Protein sequences from ten eudicot genomes: n 2.2.7)  with then 7.407)  and poorn 7.407)  with defn 7.407)  with 100n 7.407)  with theion 4.2)  to idention 4.2) .http://broadinstitute.github.io/picard/) were used to remove PCR duplicates according to the mapping coordinates. Genetic variants including SNPs and Indels (short insertion and deletion) were detected using Genome Analysis Toolkit  [QD < 2.0, MQ < 40.0, FS > 60.0, SOR > 3.0, MQRankSum < − 12.5, ReadPosRankSum < − 8.0, and indels filtered with the parameters QD < 2.0, FS > 200.0, MQ < 40.0, SOR > 10.0, ReadPosRankSum < − 20.0. From this, we defined a core SNP set by removing SNPs with more than two alleles, > 20% missing calls and MAF < 1% which was used for subsequent analyses.Genome resequencing was carried out for 280 castor bean accessions using the same methods as above for the Illumina NovaSeq 6000 samples. Combined with the WGS data mentioned above , the clen 3.8.1)  and its According to the gene model of the Rc039 genome, genetic variants identified above (SNPs and indels) were further annotated using the SNPeff (version 4.3T) , and theJatropha curcus. Briefly, all single copy orthologs between castor bean Rc039 and J. curcas were identified using the OrthoFinder2 [J. curcus as outgroup. The phylogenetic tree was visualized using the R package ggtree [To infer the basal group of castor bean, we constructed a rooted phylogenetic tree based on 48,450 SNPs from 9063 single copy orthologs between castor bean and oFinder2  with defoFinder2  with thee ggtree .θπ) was determined for WE, WK, and LC population using VCFtools [FST) was calculated among different groups using the same method. To detect selective sweeps, we calculated the ROD and FST value (wild vs. cultivar) within the same sliding windows and the regions that scored in the top 5% of the ROD and FST values were defined as candidate domestication sweeps. LD decay for each population was estimated for all pairs of SNPs using PopLDdecay (version 3.4) [−MAF 0.05 −Het 0.88 −Miss 0.25 −MaxDist 300.Based on the phylogenetic analyses, we defined three groups of individuals: WE (wild accessions from Ethiopia), WK (wild accessions from Kenya), and LC (landrace and cultivated accessions from throughout the world). Nucleotide diversity (VCFtools  using a ion 3.4)  with theindep-pairwise 50 10 0.5. In total, we obtained 754,561 SNPs that were used for subsequent analyses. Population structure was performed using ADMIXTURE (version 1.3) [K) from 2 to 20 and running the cross-validation error (CV) procedure   with a bIn order to further understand population splits and mixtures in castor bean, we employed TreeMix  to constμ) for castor bean. Briefly, we identified syntenic regions between  castor bean and J. curcas genomes using LASTZ (version 1.04.03) [T = 2, C = 2, H = 2000, Y = 3400, L = 6000, and K = 2200. The number of base pair mismatch within syntenic regions was calculated that excluded those with ambiguous nucleotide and within gap region, resulting in the 34.0% sequence divergence between them. We assumed a generation time of 2 years for wild castor bean as observed in our field investigations and a divergence time of 48.8 MYA between castor bean and J. curcas (as estimated in the species tree above), giving μ = 6.9 × 10−9 mutations per nucleotide per year for castor bean, consistent with a previous average estimate for plant nuclear genes ranging from 5 × 10−9 to 7 × 10−9.We first estimated the mutation rate per nucleotide per year (1.04.03)  with thehttp://lh3lh3.users.sourceforge.net/snpable.shtml). The above substitution rate and a generation time of 2 years for wild castor bean or 1 year for cultivated castor bean were used to convert the scaled times and population sizes into real times and sizes, respectively.We employed SMC++ (version v1.15)  to inferhttp://www.ars-grin.gov/) and were manually checked to exclude duplicated and illogical sites and cultivated sample sites. We downloaded 19 climatic variables across the three periods mentioned above from the WORLDCLIM database (www.worldclim.org). We further removed four occurrence records that lacked environmental variable data. To reduce the overfitting of these bioclimatic variables on models, environmental variables with Pearson’s correlation coefficient r > 0.7 or < − 0.7 were excluded. As a result, eight environmental variables were used for subsequent analysis: Bio1 , Bio2 , Bio3 , Bio5 (max temperature of the warmest month), Bio8 (mean temperature of the wettest quarter), Bio16 (precipitation of the wettest quarter), Bio17 (precipitation of the driest quarter), and Bio19 (precipitation of the coldest quarter). Ecological niche models were performed based on the present variables using the maximum entropy in Maxent (version 3.3.3) [We employed environmental niche modeling (ENM) to study the past demographic processes and potential distribution of castor bean from the Last Glacial Maximum  to Mid-Holocene (7–5 KYA) and the present. The occurrence sites of castor bean were collected from our field investigations, records, and collection databases (n 3.3.3)  with 10 Nine agronomic traits were measured in 2017 and 2018 in our experimental field, focusing on those traits that differed between wild and domesticated castor bean. We combined the data from five plants in each of the 2 years and the mean value was used for GWAS analysis. As mentioned above, some accessions did not survive in the second year and hence 1 year of data was used. Because of the 2-year generation time for wild castor bean, we averaged the seed phenotypes of that collected from the maternal plant in the wild as well as the seed phenotypes after one season which were highly consistent. For plant architecture, we measured three traits including plant height (PH) aboveground, diameter of the main stem (DMS), and the number of nodes (NN). Seed traits, including seed length (SL), width (SW), and thickness (ST), were determined by a digital caliper. For seed area (SA), five seeds were first scanned by a scanner and the area was calculated using Adobe Photoshop software. Single seed weight (SSW) was determined as the average value of 30 seeds. The seed oil content (SOC) was measured by MQ-ONE Seed Analyzer  using NMR. For each phenotypic trait, more than five biological replicates were used in this study.P value of 2.15 × 10−8 based on a nominal level of α = 0.05. The LD blocks around GWAS signals were further evaluated by calculating r2 between SNPs using PLINK and visualized using the R package LDheatmap (version 0.99-7) [In total, 2,314,859 SNPs with MAF > 0.05 and present in the 279 phenotyped individuals we cultivated were used for GWAS. GWAS was performed using the MLM, MLMM, and FarmCPU statistical methods implemented in GAPIT (version 3.0) . The fir 0.99-7) .useKosambi = 1 sexAveraged = 1 grandparentPhase = 1. The final genetic map included 18,946 SNP markers and the total genetic length was 1244.54 cM. This genetic map was used to recalibrate and evaluate the assembly of the Rc039 genome using ALLMAPS [We reconstructed a genetic map based on recombinant inbred lines (RILs) by crossing the landrace Rc250 with large seed with the cultivar Rc249 with small seed. The GBS sequencing data from the two parents and 200 offspring were obtained from our previous study  and link ALLMAPS  with def ALLMAPS  with 218Additional file 1: Fig. S1. Photos of wild castor bean tree from East Africa. Fig. S2. Genome size estimate for wild castor bean Rc039. Fig. S3. Genome-assisted assembly and chromosome anchoring. Fig. S4. The cumulative fraction of Annotation Edit Distance (AED) scores for the assembly of the wild castor bean genome. Fig. S5. Distribution of Ks values between syntenic gene pairs among six eudicot species,including Hevea brasiliensis (Hbr), Jatropha curcas (Jcu), Manihot esculenta (Mes), Ricinus communis (Rco), Vernicia fordii (Vfor) and Vitis vinifera (Vvi). Fig. S6. A rooted phylogenetic tree of 505 worldwide castor bean accessions based on maximum likelihood with Jatropha curcas as outgroup. Fig. S7. Population structure analysis in castor bean. Fig. S8. Nucleotide diversity in castor bean populations or subgroups. Fig. S9. Inferred population splits and admixture of castor bean using TreeMix. Fig. S10. Effective population size was inferred by SMC++ based on WGS SNPs data for WE, WK and LC. Fig. S11. Two regions of the genome containing the top 5% of FST values between WK (wild Kenya) and WE (wild Ethiopia) group. Fig. S12. Histogram and boxplot of nine agricultural traits. Fig. S13. Quantile-quantile plots for nine agricultural traits by comparing the observed –log10P with expected –log10P of GWAS. Fig. S14. Correlation of five seed traits, seed length (SL), width (SW), thickness (ST), area (SA), single seed weight (SSW). The number and color in the grid indicate the Pearson’s correlation coefficient.Additional file 2: Table S1. BUSCO  evaluation of genome completeness of wild castor bean. Table S2. Classification and annotation of repetitive sequences in the wild castor bean genome. Table S3. The number and distribution per chromosome of protein-coding genes and non-coding RNAs in the wild castor bean genome. Table S4. Functional annotation of wild castor bean genome. Table S5. Genes specific to castor bean relative to other five eudicot species . Table S6. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment for genes that have undergone expansion in castor bean genome. Table S7. Detailed information of 505 castor bean lines used in this study. Table S8. Information of genome resequencing data and mapping. Table S9. Summary of single nucleotide polymorphism (SNP) and insertions and deletions (indels) among 505 castor bean accessions. Table S10. Estimation of the relative contributions of the eight environmental variables to the Maxent model for all populations. Table S11. Genes identified as potential divergence between WK and WE based FST analysis. Table S12. Gene Ontology (GO) analysis of divergent genes between WK and WE population. Table S13. Domestication-related genes identified by two methods ROD and FST analyses. Table S14. Gene Ontology (GO) analysis of domestication-related genes. Table S15. Genome-wide associated for eight yield-associated traits in castor bean (for SOC no significant SNPs were identified). Table S16. Candidate genes within the domestication sweeps overlapping with the GWAS signals. Table S17. QTL analysis for five seed traits including seed length (SL), seed width (SW), seed thickness (ST), seed oil content (SOC) and single seed weight (SSW). Table S18. Candidate genes within QTL loci  associated with seed size.Additional file 3. Review history.


Root systems can display variable genetic architectures leading to nutrient foraging or improving abiotic stress tolerance. Breeding for new soybean varieties with efficient root systems has tremendous potential in enhancing resource use efficiency and plant adaptation for challenging climates. In this study, root related traits were analyzed in a panel of 260 spring soybean with genome-wide association study (GWAS). Genotyping was done with specific locus amplified fragment sequencing (SLAF-seq), and five GWAS models  were used for analysis. A total of 179,960 highly consistent SNP markers distributed over the entire genome with an inter-marker distance of 2.36 kb was used for GWAS analysis. Overall, 27 significant SNPs with a phenotypic contribution ranging from 20 to 72% and distributed on chromosomes 2, 6, 8, 9, 13, 16 and 18 were identified and two of them were found to be associated with multiple root-related traits. Based on the linkage disequilibrium (LD) distance of 9.5 kb for the different chromosomes, 11 root and shoot regulating genes were detected based on LD region of a maximum 55-bp and phenotypic contribution greater than 22%. Expression analysis revealed an association between expression levels of those genes and the degree of root branching number. The current study provides new insights into the genetic architecture of soybean roots, and the underlying SNPs/genes could be critical for future breeding of high-efficient root system in soybean. Glycine max L.) is the third worldwide cultivated crop that plays a significant role in food and industrial production  roots that spread out throughout the soil, also referred to as secondary roots that emerge from the taproot, and tertiary roots that originate from lateral roots and take up the nutrients and water from soil . Over thRoots, the below ground part of the plant which is obscured from direct observation, received nearly no direct thought for selection and breeding. Thus, root characteristics are rarely considered as selection criteria , and theSatt315-I locus) controlling various seedling shoot and root traits including tap root length, lateral root number, shoot length, and shoot dry weight. Recently, using an inter-specific mapping population comprising 184 F7:8 RILs grown in a cone system, Due to the challenge in achieving reliable root trait data from the field, characterizing crops like soybean with improved root system characteristics in the field remains still a major defy to current plant biology and, root phenotyping studies are commonly conducted under controlled environments . BesidesBrassica napus, etc. (Genome-wide association study (GWAS) is an influential technique that helps researchers to determine that polymorphisms are associated with a specific trait in hundreds or thousands of individuals in the genome, enabling repeated SNPs to be checked against specific traits. Today, the latest progress in sequencing technologies and bioinformatics approaches has rendered GWAS an efficient way compared to conventional QTL mapping methods . GWAS isus, etc. . HoweverThe present study aims to (i) examine phenotypic variation of root and shoot related traits in the spring soybean germplasm; (ii) perform GWAS to detect candidate genes and SNPs associated with root and shoot related traits; and (iii) check the expression level of potential candidate genes associated with root and shoot development across stages.In this study, a panel of 260 spring soybean germplasm was used, collected from different sites that cover the major distribution areas in China . A green1 (The root and shoot traits were investigated in three vegetative stages VC (after cotyledons and unifoliate leaves have fully Expanded), V1 (Fully developed leaves at unifoliolate nodes) and V2 (Fully developed trifoliate leaf at node above the unifoliolate nodes)  correspo1 . Five ro1 . If data2) was estimated for each variable from variance estimates using the following equation  with repeated values for all the above-mentioned traits at each stage in the natural population of spring soybean was carried out to assess the significance of variability amongst the soybean germplasm. The Pearson correlation coefficients among traits and across stages were analyzed to determine the relationship between seedling traits. Based on entry mean, the broad-sense heritability  technique (slightly modified), as described by in silico digestion prediction using the following criteria: To obtain more than 309244 SLAF tags per genome, combinations of restriction enzymes were examined and selected using the following criteria in the silico digestion prediction (1) The proportion restriction fragments in the repeat sequence was as low as possible; (2) The distribution of enzyme fragments in the genome was as uniform as possible; (3) The complementarity between the length of enzyme fragment and the specific reference genome; and (4) The number of SLAF tags met the expected number. Based on these criteria, RsaI and HaeIII  were selected as restriction enzymes. To obtain the SLAF tags, DNA from each soybean accession was digested, followed by fragment end reparation, dual-index paired-end adapter ligation, PCR amplification, and target fragment selection for SLAF library construction (Oryza sativa ssp. Japonica) data genome (374.30 MB) (download address3) was used as a control. Subsequently, the SLAF-seq was performed with an Illumina HiseqTM 2500  at the Biomarker Technologies Corporation.4 To obtain the reads for each sample, the raw SLAF-seq data was analyzed using Dual-Index software (Q = −10 × log10(P)); suggesting a 0.1% risk of error and therefore 99.9% confidence). Ultimately, based on the sequence similarity, all SLAF paired-end sample reads were grouped via BLAT software (In order to acquire more than 309244 SLAF tags (defined as an enzyme fragment sequence of 364–464 bp) per genome, restriction enzyme combinations were tested and selected using truction . To evalsoftware . After fsoftware . Polymorsoftware .https://www.broadinstitute.org/gatk/guide/best-practices.php. To ensure the reliability of SNPs identified using GATK, SAMtools also was used to detect SNPs with reference to SNP loci were identified based on the polymorphic SLAF tags information using primarily GATK . Based oQ matrix) was analyzed using Admixture software  was set in from 1 to 15. The cluster results were cross-validated and the optimum number of subgroups was determined according to the valley value of the cross-validation error rate  > 4 (control threshold) and −log10 (P) > 5 (suggestive threshold) were considered as significant for SNP-trait association. Considering the complexity of root traits, markers that passed the threshold score or above the threshold −log10(P) was held to be significantly associated with the target trait.To find out the underlying SNPs or genes associated with root and shoot related traits in a diverse soybean panel, a genome-wide association study was conducted. The association analysis was performed based on the phenotypic dataset and the highly consistent population SNPs . Using the adjusted means for observations on each accession, we used five different statistical GWAS models, GLM, MLM, CMLM of TASSEL 5.0 software , FaST-LMct−△△ method  and four lines with low branching number   were selt method . All Priethod (5 .P < 0.001) among varieties for all the traits evaluated at the three stages (2) of root and shoot related traits was estimated, and the values ranged from 27% for AD to 86% for SL, indicating that the observed variation in root related traits is genetically controlled in greenhouse conditions. The Pearson correlation coefficients of all ten seedling root and shoot related traits were also analyzed at each stage, and most of the traits displayed significant positive correlations with similar tendencies at all stages  as well as between RV and SA (r > 0.86) at all stages  was 85.65%. The averages Q30 and GC content were 94.01 and 39.87%, respectively. The number of tags was recorded on the 20 soybean chromosomes . BioinfoK-values from Admixture analysis, the 260 genotypes were subdivided into ten subgroups  originated from two ancestral lines  error rate and al lines . These ral lines . Howeveral lines . Collectr2 = 0.3 .10(P) ≥ 4.0, and these SNP positions were distributed in nine chromosomes, 2, 4, 6, 8, 9, 13, 16, 18, and 20  = 7.97, R2 = 72%), was associated with RDW/SDW and was detected across three different methods  at VC stage. The second most important SNP found on Chromosome 13, location 40092616 (−log10(P) = 5.89, R2 = 34%) was associated with SDW and was detected across two different methods (GLM and MLM) at V2 stage. In regards of SNPs controlling multiple traits, two significant SNPs  = 4.64, 5.15, respectively) were found to be substantially associated with five specific seedling root and shoot traits namely AD, RDW, RV, TPB and SDW  in order to control false positives. According to the results of GWAS, a total of 27 unique SNP markers were detected which exhibited significant associations with root and shoot morphological traits at the critical threshold of −log, and 20 . Minor a, and 20 . We furt, and 20 . The FaS, and 20 . Besides, and 20 . Interes and SDW . Individ and SDW . The rem and SDW . AdditioR2 > 22%) were obtained as putative candidate genes for root and shoot development in the spring soybean panel. The candidate genes were Glyma.09G179600, Glyma.08G060300, Glyma.16G138900, Glyma.16G208400, Glyma.09G153400, Glyma.13G303800, Glyma.08G060600, Glyma.16G173300, Glyma.02G113900, Glyma.06G148800, and Glyma.18G094200  ≥ 4). Many SNPs found in this analysis were not recorded in earlier studies. This might be clarified by the variance in the genetic background as well as the environmental factors that influence root related traits expression and, ultimately, plant development.Understanding the mechanism of root related traits in crops like soybean through SNPs and genes investigation could be the most powerful and successful strategies to develop high-quality root cultivars via marker-assisted selection. Many studies have been performed to elucidate the genetic foundation and underlying root genetic function in soybean, and several root-related QTLs have been reported in various populations using various environments . NeverthIn the present research, besides the most commonly used models, three GWAS models including CMLM, FaST-LMM, and EMMAX were used to improve the accuracy of the results. As GLM uses only Q population structure information while MLM uses Q + K, due to the strict screening requirements, CMLM and MLM may increase GWAS analysis accuracy. Many researchers have reported that GLM’s stringency and accuracy are weaker compared to the MLM . FurtherqMRL6, a QTL for main root length linked to the marker locus Sat_153 (10.52 cM), which was earlier detected in a similar study at V2 stage  linked to marker Satt557 (5.55 cM)  . SNP ChrM)  (qHW4 . SNPs chM) . All The detected SNPs showed individual phenotypic contribution rates greater than 20% and were significantly associated with SDW (7), SA (6), RV (3), TRL (2), BN (2), RDW/SDW (2), TPB (1), RDW (1), and SL (1). However, two major SNPs were found to be associated with multiple traits including AD, RDW, SDW, and TPB in one side and RV, SDW, TPB in the other. Based on the LD region of no more than 55-bp and SNPs with phenotypic contribution rate greater than 22%, eleven promising root and shoot regulating genes viz. Glyma.09G179600, Glyma.08G060300, Glyma.16G138900, Glyma.16G208400, Glyma.09G153400, Glyma.13G303800, Glyma.08G060600, Glyma.16G173300, Glyma.02G113900, Glyma.06G148800, and Glyma.18G094200 overlapping with SNPs on chromosomes 2, 6, 8, 9, 13, 16, and 18 were detected and verified for expression level. The evaluated genes were shown to serve as positive regulators for root branching number in the spring soybean panel, which could play a crucial role in marker-assisted selection programs. Thus, SNPs and underlying genes discovered in the current study provide a key basis for revealing the complex genetic mechanism of root development and genetic enhancement of soybean root, as well as supporting the breeding of high-efficiency soybean root system varieties in the future.https://www.ncbi.nlm.nih.gov/bioproject/PRJNA681350.The original contributions presented in the study are publicly available. This data can be found here: AM drafted the original manuscript. AAM reviewed and edited the manuscript. AM and AAM conducted the experiments and performed the analysis. JQ, QZ, and YD performed the phenotyping. GA and NA conducted formal analysis. PW supervised the experiments. All authors have read and agreed the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.