Bos indicus) such as Sahiwal, Gir, Rathi, Tharparkar, Red Sindhi and Kankrej of India. A high percentage of reads (94.53%) were mapped to the Bos taurus (ARS-UCD1.2) reference genome assembly. Following filtration criteria, a total of 84,027 high quality SNPs were identified across the genome of 6 cattle breeds with the highest number of SNPs observed in Gir , followed by Red Sindhi , Kankrej , Sahiwal (8956), Tharparkar (7356) and Rathi (7068). Most of these SNPs were distributed in the intronic regions (53.87%) followed by intergenic regions (34.94%) while only 1.23% were located in the exonic regions. Together with analysis of nucleotide diversity (π = 0.373), Tajima’s D , observed heterozygosity (HO ranging from 0.464 to 0.551), inbreeding coefficient (FIS ranging from − 0.253 to 0.0513) suggested for the presence of sufficient within breed diversity in the 6 major milch breeds of India. The phylogenetic based structuring, principal component and admixture analysis revealed genetic distinctness as well as purity of almost all of the 6 cattle breeds. Overall, our strategy has successfully identified thousands of high-quality genome wide SNPs that will further enrich the Bos indicus representation basic information about genetic diversity and structure of 6 major Indian milch cattle breeds which should have implications for better management and conservation of valuable indicine cattle diversity.The present investigation aimed to identify genome wide SNPs and to carry out diversity and population structure study using ddRAD-seq based genotyping of 58 individuals of six indigenous milch cattle breeds ( Bos indicus cattle breeds of the world1. The Indian zebu cattle (Bos Indicus) is believed to have originated from wild aurochs Bos primigenius nomadicus3 and studies based on mitochondrial DNA markers analysis indicated that Bos indicus separated from Bos taurus between 110,000 and 850,000 years ago5. Worldwide, I1 and I2 are the two major mitochondrial DNA (mtDNA) haplogroups have been reported for Bos indicus. The I1 which is predominant haplogroup is believed to have originated from India–Pakistan, whereas haplogroup I2 has a complex diversity pattern making it difficult to resolve its origin8. Although recent findings have identified a new sub-haplogroup I1a, in the I1 haplogroup within the Bos indicus lineage9. On the other hand, the Y chromosome diversity found in Bos indicus cattle is characterized by a single haplogroup Y3, in contrast to two different haplogroups Y1 and Y2 found in Bos taurus. In addition, two distinct sub-haplogroups within each of the Y2 (Y2a and Y2b) and Y3 (Y3a and Y3b) haplogroups have been identified. The Y3 haplogroup was observed to be inimitable to Bos indicus and findings have shown that the sub-haplogroup Y3a dominated the cattle from South China, whereas the sub-haplotype Y3b was found in the Bos indicus breeds of Indian origin10. With a population of 192.49 million cattle, India has 13.1% of world’s cattle population11. Further, India holds the first rank in milk production in the world with a total production of 198.4 million tons of milk production during 2019–202012. The Indian zebu cattle is an important member of the Bovidae family and is a major resource for milk and drought power in the Indian subcontinent. At present there are 53 well defined Indigenous cattle breeds in India which can be differentiated as dairy, dual or draft purpose breeds on the basis of their utility. The dairy cattle breeds on the average produced more than 1600 kg of milk per lactation, the dual-purpose breeds yield about 150–500 kg per lactation while the draft breeds are mainly used for agricultural work. The major dairy breeds of India include Gir (GIC), Rathi (RAC), Red Sindhi (RSC), Sahiwal (SAC) and Tharparkar (THC), the dual-purpose breeds comprise of Badri, Belahi, Deoni, Gaolao, Hariana, Kankrej, Konkani, Ladakhi, Malnad Gidda, Mewati, Ongole while the remaining breeds are classified as draft breeds.The Indian subcontinent is home to a mega diverse Bos indicus) are well adapted to withstand the harsh climate and still perform efficiently. Besides, these are suited to low input production system with lower maintenance and management requirements. However, under the present production system, which is mostly focusing on increased milk production, the population size of indigenous cattle is in declining phase due to (1) modernization of agriculture and (2) cross breeding with exotic breeds to maximize the overall production and economic profit. The negligence of superior characters of Indian cattle like adaption to diverse climate and survival on low input system over production is resulting in loss of breeds or overall genetic diversity. Hence mitigation measures to characterize and conserve genetic diversity is key to evade further loss of important gene/gene pool and loss in the variability, which is very important for achieving higher genetic gain in economic traits of the indigenous dairy cattle breeds. In depth characterization and evaluation of genetic diversity among cattle breeds is of great importance to ensure long-term genetic improvement, facilitate rapid adaptation to changing climate and for efficient management and conservation of animal genetic resources14.The Indian native cattle breeds  approaches has received worldwide attention due to their capacity to identify genome-wide variations at relatively low cost. Genome wide SNPs based diversity and population structure analysis using ddRAD have been carried out in different livestock species like buffalo, yak, horse and camel19. ddRAD being a restriction digestion based reduced representation Next Generation Sequencing method fragments a target genome with both frequent and rare cutting restriction enzymes and such a strategy minimizes the hassles of uninformative and repetitive sequences, sequence assembly and SNP calling that accompanies with Whole genome sequencing (WGS). Evidently, ddRAD has been employed for the discovery of species-specific genome wide SNPs in economic, production and adaptation traits related candidate genes22. Furthermore, reduced representation methods based on whole genome sequencing of single individuals solve the problem of ascertainment bias23. Previously, array-based SNP chips has been widely used in genetic studies of livestock, including genome-wide association studies (GWAS)25, selection signature studies27, diversity and population structure analysis30. However, SNP chips commonly include SNPs that were previously discovered by DNA sequencing. These SNPs may not be geographically representative and tend to be at higher frequency than random SNPs and most importantly impair identification of casual mutations. Hence, population genetic parameters such as diversity, population structure and recombination estimates may be biased31. Therefore, in this study we have applied ddRAD sequencing approach, to overcome ascertainment biasness for discovering genome wide SNPs and undertake diversity studies in indicine cattle.Genome-wide studies focused on population genetics, phylogeography and conservation biology have been greatly facilitated by quick advances in high-throughput sequencing technologiesConsidering the valuable contribution of the native dairy cattle in supporting the livelihood of many Indians for many generations, few efforts have been made to evaluate the genetic diversity and relationship in Indian cattle using genome wide SNPs. Comprehensive characterization on within-and between-breed genetic diversity of the Indian native cattle breeds to facilitate an effective and rational management is lacking. Exploration of the genetic diversity including population structure and admixture can expedite appropriate conservation programs. Deep and thorough understanding of indigenous genes/gene pool will help to understand the mechanism underlying important functional traits and help to meet the future production demands of the local people. The present investigation was undertaken to identify genome wide SNPs and assess the within and between breed genomic diversity and establish breed relationships and to assess their population structure.Bos taurus (ARS-UCD1.2) reference assembly  reference genome. With respect to their distribution in the genome, a large number of annotated SNPs were predicted to be in the intronic region  followed by intergenic regions . There were only 948 SNPs (1.23%) that were distributed in the exonic regions. Further, there were 3497 SNPs (4.55%) located within the 5 Kb region upstream and 3661 SNPs (4.77%) in the downstream of transcription start site. The analysis also resulted in 93 SNPs (0.121%) located in 5’UTR, 293 SNPs (0.38%) in 3’UTR region. A total of 8 SNPs (0.01%) were predicted to cause premature stop codon were also identified , moderate impact , and low impact, . Majority of the SNPs  were identified as modifier  and 1,676,710 (24.6%) were intronic. There were 230,365 (3.4%) SNPs located within 5 kb upstream and 197,827 (2.9%) in downstream of a transcription start site; 12,428 SNPs were located in the 5′ UTR and 2613 in the 3′ UTR. A total of 4356 SNPs were located in splice sites of 2966 genes: 142 were in splice-donor sites, 142 were splice-acceptor sites and 4072 were within the region of the splice site. We identified 45,776 SNPs affecting the coding sequences of 11,538 genes. There were 221 SNPs predicted to cause premature stop codon and 17 to cause gain in coding sequence. The numbers of SNPs predicted to be non-synonymous were 20,828. The numbers of intergenic SNPs were 4,639,873 (68.1%) and 1,676,710 (24.6%) were intronic. There were 230,365 (3.4%) SNPs located within 5 kb upstream and 197,827 (2.9%) in downstream of a transcription start site; 12,428 SNPs were located in the 5′ UTR and 2613 in the 3′ UTR. A total of 4356 SNPs were located in splice sites of 2966 genes: 142 were in splice-donor sites, 142 were splice-acceptor sites and 4072 were within the region of the splice site. The numbers of intergenic SNPs were 4,639,873 (68.1%) and 1,676,710 (24.6%) were intronic. There were 230,365 (3.4%) SNPs located within 5 kb upstream and 197,827 (2.9%) in downstream of a transcription start site; 12,428 SNPs were located in the 5′ UTR and 2613 in the 3′ UTR. A total of 4356 SNPs were located in splice sites of 2966 genes: 142 were in splice-donor sites, 142 were splice-acceptor sites and 4072 were within the region of the splice site. We identified 45,776 SNPs affecting the coding sequences of 11,538 genes. There were 221 SNPs predicted to cause premature stop codon and 17 to cause gain in coding sequence. The numbers of SNPs predicted to be non-synonymous were 20,828. The numbers of intergenic SNPs were 4,639,873 (68.1%) and 1,676,710 (24.6%) were intronic. There were 230,365 (3.4%) SNPs located within 5 kb upstream and 197,827 (2.9%) in downstream of a transcription start site; 12,428 SNPs were located in the 5′ UTR and 2613 in the 3' UTR. A total of 4,356 SNPs were located in splice sites of 2966 genes: 142 were in splice-donor sites, 142 were splice-acceptor sites and 4072 were within the region of the splice site. We identified 45,776 SNPs affecting the coding sequences of 11,538 genes. There were 221 SNPs predicted to cause premature stop codon and 17 to cause gain in coding sequence. The numbers of SNPs predicted to be non-synonymous were 20,828.The nucleotide diversity (π) was highest in THC (π = 0.458) followed by RSC π = 0.364), SAC (π = 0.363), GIC (π = 0.356), KAC (π = 0.348) and RAC (π = 0.347). The mean nucleotide diversity value was 0.373  values ranged from 0.464 to 0.551 while the expected heterozygosity (HE) ranged from 0.448 to 0.535. The highest observed heterozygosity values were observed in THC (HO = 0.551) followed by RAC (HO = 0.523), RSC (HO = 0.5184), SAC (HO = 0.5180), GIC (HO = 0.499) and KAC (HO = 0.464) (Table IS (inbreeding coefficient) ranges from -0.253 in THC to 0.0513 in KAC. The FIS estimate amongst the six cattle breeds was highest in THC (FIS = − 0.253) followed by RAC (FIS = − 0.105), whereas the lowest FIS estimate was observed in KAC (FIS = 0.0513) followed by GIC (FIS = − 0.00063). The overall FIS analysis revealed excess of heterozygosity for all the cattle breeds except for KAC (Table IS estimates indicated presence of sufficient diversity within the six cattle breeds.The observed heterozygosity (HST) ranged from 0.2840 to 0.3905, indicating sufficient between breed diversity. The highest divergence was observed between RAC-SAC pair (FST = 0.3905), followed by RSC-RAC breed pair (FST = 0.3790), RSC-SAC breed pair (FST = 0.3751). The least divergence was observed for KAC-THC breed pair (FST = 0.2840) (Table The genetic differentiation on the basis of fixation index (F0) Table .Table 5PThe admixture analysis was carried out by partitioning the genome of each individual into a predefined cluster. The analysis was performed at K = 3, 4, 5 and 6 Fig. . The indThe PCA based analysis also clustered 6 cattle breeds separately and reinforces the fact that these are distinct cattle breeds  cattle breeds. Geographical isolation over time has built up a plethora of genetic types/breeds but the magnitude of genetic differentiation has not been well quantified. Genetic variability in indigenous breeds is a major concern considering the necessity of preserving what may be a precious and irreplaceable richness developed as the results of complex interactions between the genotype and the environment. Hence molecular information is crucial for preserving genetic diversity as well as preventing undesirable loss of alleles. In this study genetic diversity and population structure of 6 major Indian milk cattle breeds was estimated using large number of genome wide SNPs generated through ddRAD sequencing.The Indian subcontinent is bestowed with immense richness of zebu (Bos taurus reference genome (ARS-UCD1.2). The total number of SNPs identified in the present study varies when compared to previous reports. Gurgul et al.32 reported 8065 high-confidence SNPs in 48 individuals of different taurine cattle breeds by single enzyme restriction digestion GBS approach. Likewise, by using the same approach Malik et al.33 identified 1,07,488 SNPs in 24 animals belonging to seven Indian cattle breeds viz., Gangatiri, Hariana, Kankrej, Ongole, Sahiwal, Siri and Tharparkar. Furthermore, De Donato et al.34 identified 63,697 SNPs in 47 animals from both taurine and indicine breeds using a single enzyme GBS method. On the other hand, by using 2 enzyme GBS protocol, Brouard et al.35 reported a total of 2,72,103 variants in 48 Canadian dairy cattle. Similarly, RAD sequencing was used to identify 2,38,725 and 84,854 high-confidence SNPs in Sichuan and Liangshan indigenous cattle breeds of China, respectively37. Recently, studies carried out using ddRAD in Indian native cattle breed Sahiwal reported a total of 258,231 genome-wide SNPs with a minimum read depth of 2, 10, 232,570 SNPs at read depth 5 and 193,803 SNPs were identified respectively21. The number of high confidence SNPs identified in the present study and other previous studies could be attributed to the levels and stringency of filtering parameters applied while calling for SNPs.In the present study, an average of 2.2 million reads per animal were obtained with a high mapping rate of 94.53% to 39. Large numbers of SNPs detected were found to be in the intronic and intergenic regions, which was similarly observed in previous studies22. Annotation of SNPs in this study also revealed that the G/A and C/T substituted genotypes were mostly found, whereas AT genotype was the least. This observation was similar to the studies carried out by Kumar et al.11 and Wang et al.36.The average Ts/Tv ratio was found to be 2.53. The observed Ts/Tv ratio in the present study was similar to many other reduced representation sequencing studies carried out in yak, buffaloes and cattle37. Furthermore, the nucleotide diversity in the studied cattle breeds was also comparatively high when compared to the nucleotide diversity reported for Eastern Finn cattle, Western Finn cattle, and Yakutian cattle with π values of 1.559 × 10–3, 1.512 × 10–3, and 1.728 × 10–3, respectively40. Similarly, other taurine breeds such as Braunvieh cattle of Switzerland also showed relatively lower nucleotide diversity41. The results of nucleotide diversity strongly suggest that the 6 major milch breed of India are maintaining sufficient degree of within breed genetic variation.The nucleotide diversity (π) with an overall value of 0.373 was significantly higher in the 6 Indian native milch breeds when compared to the mean nucleotide diversity (π = 0.18 and π = 0.227) reported for Chinese cattle using RAD sequencing26 On the contrary the positive Tajima’s D values detected for Gir and Sahiwal indicates signals of balancing selection in these breeds. Similar observation was also reported whereby a lower selection signals in 7 Indian native breeds such as Gir, Hariana, Kankrej Ongole, Red Sindhi, Sahiwal and Tharpakar using 50 K bovine SNPchip data was identified22.The negative Tajima’s D values observed for the 4 Indian cattle breeds viz., RSC, RAC, KAC and THC except for GIC and SAC signifies the population size expansion and presence of an excess of rare alleles. Further, the negative Tajima’s D values also signifies the occurrence of recent positive selection in these Indian native cattle breeds. This observation was also consistent with selection signals detected in some of the other SNP array-based studies involving Indian native breedsO) and expected heterozygosity (HE) values for the 6 cattle breeds ranged from 0.464 to 0.551 (HO) and 0.448 to 0.535 (HE), respectively. The maximum heterozygosity value was observed in THC (HO = 0.551) followed by RAC (HO = 0.523), RSC (HO = 0.5184), SAC (HO = 0.5180), GIC (HO = 0.499), while the lowest level of heterozygosity was observed in KAC (HO = 0.464). The diversity estimates in the present study was much higher to what has been reported in seven taurine and indicine cattle breeds (0.064 to 0.197) from the US and Africa using GBS approach34. In addition, lower heterozygosity value (0.22) was also reported in Chinese cattle using restriction site-associated DNA sequencing (RADSeq)37. Further low diversity values were also reported by Malik et al.33 for two Indian Bos indicus cattle breeds; Sahiwal (HO = 0.084) and Kankrej (HO = 0.086) along with Bos taurus Holstein Frisian cattle33 using GBS approach. The explanation for lower heterozygosity in aforesaid mentioned studies could be due to use of single enzyme digestion in RADSeq and GBS approaches.The observed  as high FIS estimates is linked to high degree of inbreeding. The high negative FIS values obtained in the study are similar to the study carried out by Strucken et al. (2021)28 in 13 Indian cattle breeds using 777 k SNP BovineHD Beadchip with exception for Sahiwal where indications of inbreeding are observed.The overall FIS estimate. However, the overall FIS estimates observed are in contrast to those identified for Indian native cattle by microsatellite markers44 who reported high FIS values. The depression in the FIS estimate in the present investigation demonstrates the presence of heterozygote excess in the Indian native cattle breeds.Sahiwal is an important and by far the best milch cattle breed of India, hence the race for rearing pure line animals with desired economic traits could have resulted in the slight increase in the FST) values that ranged from 0.2840 to 0.3905 suggested moderate to substantial genetic differentiation across the 6 cattle breeds. The maximum divergence was observed between RAC-SAC pair (FST = 0.3905), followed by RSC-RAC breed pair (FST = 0.3790), RSC-SAC breed pair (FST = 0.3751). The least divergence was observed for KAC-THC breed pair (FST = 0.2840). In few other studies based on bovine 50 K and 770 K SNP chips, the authors have reported relatively lower FST values for Indian cattle breeds29. The overall high between breed genetic differentiation in the present investigation might be attributed to the fact that the blood samples were obtained from individuals that are true to the breed and the studied populations are genetically distinct from each other.The fixation index  of Gir, Sahiwal, Hariana, Ongole, Kangyam, into their respective breeds.The admixture analysis showed that all most all the 6 native cattle breeds have maintained their genetic purity with little traces of admixturing. At subpopulations K = 6, though few individuals of KAC and SAC were observed to have little admixturing from THC. The shared and common geographical area of these breeds and lack of pedigree information in the field conditions might have contributed to small extent of admixturing. Interestingly, at K = 6, a shared ancestry was observed between THC and RSC. Although in previous studies, no admixture analysis was carried out in both Tharparkar and Red Sindhi, our observation of genetic closeness between these breeds can be unraveled by further analysis as close geographical origins exists between the two breeds. Overall the admixture analysis in the present study was in agreement with one of our previous microsatellites based genotyping studies where in high proportions of individuals of native cattle have been assigned to their respective breeds45.The genetic separation of these 6 native cattle breeds was also supported by principal component analysis. Except one individual of KAC, all the animals of GIC, KAC and SAC were grouped as per their breed affiliations and widely separated from each other. Similarly, the individuals of THC, RAC and RSC were also grouped as per their breed but were placed closed to each other. The phylogenetic, admixture and PCA analysis thus suggested substantial between breed genetic distinctness of the 6 major milch breeds of India. The outcome of the present study has lot of similarity with many other previous reports on Indian native cattle published either using microsatellite or SNP chip markersBos indicus genome database for future exploitation in diversity and genotype: phenotype association studies. Further, the genome wide SNP data set has provided a strong clue that each of the 6 major milch cattle breeds of India has sufficient within breed diversity. The between breed analysis along with phylogenetic, admixture and PCA analysis showed high level of genetic distinctness and purity of each of the 6 cattle breeds. In future similar approach could be extended to rest of the native cattle breeds (Bos indicus) to define the population structure along with their evolutionary relationships. Further, as the native Indian cattle breeds are known for better milk quality, heat tolerance and disease resistance, therefore such data set could also be exploited to understand the signatures of selection with respect to these traits. Such information will be quite helpful to realize the potential of these tropically adapted native germplasm especially in the era of climate change and global warming.The present study has shown the utility of ddRAD sequencing strategy in identifying thousands of high-quality SNPs in native milch cattle breeds of India. The study has also provided an opportunity to establish a robust methodology as well as bioinformatics pipeline to generate and characterize genome wide SNPs. The SNPs derived from genome of native cattle could also help to enrich the 46 followed by purification through RNAse treatment and Qiaquick Nuclease Removal Kit  to eliminate any RNA related impurities. The quality of DNA was checked on agarose gel (1%) electrophoresis, and the quantity of DNA was measured using a Nanodrop Spectrophotometer (Nanodrop ND-1000).To identify the genome wide SNPs, the blood samples of 58 unrelated animals belonging to Gir GIC, n = 12), Sahiwal , Kankrej , Rathi , Red Sindhi , and Tharparkar  cattle breeds were collected by visiting their respective breeding tracts. However, the samples of Red Sindhi animals were collected from Hosur farm of Krishnagiri district of Tamil Nadu state as this breed is only available in organized cattle farms. The blood samples were collected as per the guidelines of Institutional Animal Ethics Committee (IAEC). Further, all the details related to animals’ experiments were as per the ARRIVE guidelines and all the procedures were approved by the animal ethics committee of ICAR-NBAGR, Karnal. The geographical and ecological distribution of the cattle breeds is shown in Fig. 2, Sahiwain-silico simulation using SimRAD package47. Briefly, 0.3–0.6 μg of genomic DNA of each animal was digested with the optimized restriction enzyme set. After digestion each end of digested fragment was ligated to EcoRI-specific P1 and the MseI-specific P2, barcoded adapters with a T4 DNA Ligase . The ligation reaction consisted of overnight incubation (> 12 h) at room temperature (approx. 21 °C) and heat deactivation of the enzyme at 65 °C for 10 min. In order to eliminate unincorporated adapters and small DNA fragments, ligation reactions were purified using with 0.8X volume of Agencourt AMPure XP SPRI magnetic beads . A unique combination of the dual-indexed barcodes was attached to purified fragments with 14 cycles of PCR. Indexed PCR products were pooled in equal volumes and size selected using Agencourt AMPure XP SPRI magnetic beads. The amplification protocol involved initial denaturation at 95 °C for 3 min; 25 cycles of denaturation at 95 °C for 30 s, annealing at 55 °C for 30 s, and extension at 72 °C for 30 s; followed by a final extension at 72 °C for 5 min.For DNA library preparation, each sample was digested with two Restriction enzymes (REs); a 6 cutter EcoR1 (G/AATTC) and a 4 cutter Mse1 (T/TAA)  as determined by FGBS20H000717-1a, FGBS20H000718-1a which included all the samples of different cattle breeds were constructed for sequencing on Illumina HiSeq™ 2000 sequencing platform. The concentration of each library was checked using Qubit® 2.0 fluorometer . Each library was diluted to 1 ng/ul and the insert size was assessed and quantified using the Agilent high-sensitivity DNA kit in a 2100 Bioanalyzer . Quantitative real-time PCR (qPCR) was performed to detect the effective concentration of each library. Finally, the libraries with appropriate insert size and effective concentration of more than 2 nM were sequenced on Illumina HiSeq™ 2000 and more than 100 bp end reads were generated.In the present study, a total of 2 sequencing libraries viz., 48. The raw reads with less than Q20 were removed from the data set using PRINSEQ software49. Both the adaptors were removed using Cutadapt 1.1550. Finally, the filtered reads with read length of 144 bp were retained for subsequent analysis. The filtered reads were aligned to Bos taurus reference genome ARS-UCD1.2 using Bowtie2 tool52. The aligned SAM files were converted to BAM files using SAMtools53 and subsequently sorted using Picard tool. All the duplicate reads were flagged and tagged using MarkDuplicatesWithMateCigar module of Picard tools 54. The reads were re-calibrated using GATK-BQSR tool with default parameters54. In order to discover genome wide SNPs in each animal, GATK Haplotypecaller was run in ERC GVCF mode54. The breed wise cohort GVCF file was created by combining all the individual GVCF files using CombinedGVFs of GATK tool. The cohort GVCF files were converted to VCF using GenotypeGVCF command of GATK tool. The insertions and deletions (INDELS) were discarded using GATKSelect Variants. The SNPs were annotated with reference to 1000 Bull Genome data using BCFTools 55. After annotation, all those SNPs located on X, Y chromosomes as well as mitochondrial DNA were removed using VCFtools56 and only those SNPs which are present in autosomes were retained for further analysis. The SNPs were also filtered at a minimum read depth level of 2, 5 and 10 (RD) and SNPs identified at RD of 5 were further filtered having minimum quality score of GQ30 using VCFtools56. Finally, three rounds of filtering for minor allele frequency (MAF < 0.05), missing genotypes (0.8), and HWE deviation (P < 0.001) was carried out using PLINK 1.9 57 to retain the high-quality SNPs for downstream analysis.The raw paired-end FASTQ sequencing files were quality checked using FASTQC software58. The VCF file and annotation data of the Bos taurus reference genome were used to partitioned the SNPs as per their genomic location such as exonic, intronic, upstream/downstream regions, splicing sites and intergenic regions. The SNPs were also categorized based on their functional impact on protein coding genes such as high, moderate, modifier, missense, nonsense and silent.The high-quality SNPs identified in each breed was annotated using SnpEff Ver. 4.3 software59 software by selecting 500-SNP sliding window with step size of 100-SNP. VCF tools was employed to calculate observed heterozygosity (HO), expected heterozygosity (HE), inbreeding co-efficient (FIS) and Wright's FST estimates. For phylogenetic relationship, amongst the studied cattle populations, TASSEL software (v. 5.0)59 and phangorn60 softwares available in R language were used. A bootstrap value of 100 was used to draw the tree based on UPGMA and Neighbor joining (NJ) algorithms. The SNPs that were in strong Linkage Disequilibrium (LD) (r2 > 0.5) in a 5000-Kb sliding windows with 50 SNPs were pruned using PLINK v.1.957. The admixture analysis was performed using the pruned SNPs data by applying the ADMIXTOOLS of admixr-R package61. The admixture analysis was performed by assuming different numbers of sub-populations K = 6 in order to identify the optimal number of ancestral populations by detecting the lowest value of cross-validation error. Similarly, the Principal component analysis (PCA) was performed using pruned data by employing “adegent” software and the results were plotted using ggplot.Nucleotide diversity (π), TajimaD in each of the 6 cattle breeds was computed using TASSEL software (v. 5.0)All the experimental procedure was done in accordance with the ARRIVE guidelines and regulations of Institutional Animal Ethics Committee (IAEC), ICAR-National Bureau of Animal Genetic Resources (ICAR-NBAGR), Karnal, Haryana, India.Supplementary Information.


Phytophthora infestans is one of the most challenging fungal diseases to manage in tomato plants (Solanum lycopersicum L.). Toward managing the disease, conventional breeding has successfully introgressed genetic loci conferring disease resistance from various wild relatives of tomato into commercial varieties. The cataloging of disease-associated SNP markers and a deeper understanding of disease-resistance mechanisms are needed to keep up with the demand for commercial varieties resistant against emerging pathogen strains. To this end, we performed transcriptome sequencing to evaluate the gene expression dynamics of tomato varieties, resistant and susceptible to Phytophthora infection. Further integrating the transcriptome dataset with large-scale public genomic data of varieties with known disease phenotypes, a panel of single nucleotide polymorphism (SNP) markers correlated with disease resistance was identified. These SNPs were then validated on 31 lines with contrasting phenotypes for late blight. The identified SNPs are located on genes coding for a putative cysteine-rich transmembrane module (CYSTM), Solyc09g098310, and a nucleotide-binding site–leucine-rich repeat protein, Solyc09g098100, close to the well-studied Ph-3 resistance locus known to have a role in plant immunity against fungal infections. The panel of SNPs generated by this study using transcriptome sequencing showing correlation with disease resistance across a broad set of plant material can be used as markers for molecular screening in tomato breeding.Late blight, caused by oomycetes  Solanum lycopersicum L., one of the most consumed fruit, has emerged to be an integral part of the global diet in recent centuries [Phytophthora infestans, a fungal pathogen causing the disease late blight, is amongst the most difficult diseases to manage in tomato plants [enturies . Oomyceto plants . The diso plants . Moreoveo plants . The info plants .Solanum relatives into commercial tomato varieties has been the major source of resistance. The most prominent of these resistant genes (R gene) loci are Ph-1,2,3,5-1,5-2 from Solanum pimpinellifolium [Ph-4,3 from Solanum habrochaites [Phytophthora species. A combination of Ph-2 and Ph-3 has been one of the most successful introgressions into breeding and commercial lines like NC1 CELBR, NC2 CELBR, Mountain Magic, and Mountain Merit that have resulted [Over the decades, strategies to manage the disease have majorly focused on the use of fungicides and selective resistance breeding . More relifolium ,7 and Phochaites ,9. Theseresulted . Regardlresulted .Another direction of conferring resistance to late blight is by developing strategies to exploit the use of microRNAs (miRNAs). Previous studies by Luan et al. ,11 demonPhytophthora Egyptian isolate. While resistance-associated molecular markers have been reported [To achieve such molecular breeding strategies and track the resistance loci, single nucleotide polymorphism (SNP) markers have proven to be extremely helpful . With adreported ,15, therPhytophthora infestans in resistant and susceptible breeding lines, followed by the discovery of SNPs in disease-responsive genes dysregulated upon infection between resistant and susceptible lines. The selected SNP markers were additionally correlated primarily with all publicly available sequencing datasets of tomato plants with contrasting disease phenotypes and finally validated on a test dataset of secondary lines with known disease phenotypes. Thirty-nine candidate SNPs discriminating the sequenced resistant and susceptible lines in our dataset were screened across publicly deposited lines with known phenotypes and eight SNPs from two genes were finally validated by correlating with disease phenotype on a broader germplasm. The set of validated SNP assays generated by this study can serve as a valuable resource for the tomato breeding community.Along with the discovery of markers for breeding, it is also important to understand the disease dynamics and the host response in different resistant and susceptible lines. In this context, the aim of this study was to identify SNP markers in functional genes associated with disease resistance and validate them across a range of plant material. This was primarily achieved using an RNA sequencing-guided approach to understand the transcriptomic changes upon infection by Phytophthora infestans genome showed the presence of Phytophthora infestans only in infected susceptible plants  consisting of Solanum lycopersicum and Solanum pimpinellifolium. Interestingly, out of 39 identified SNP variants, 16 SNPs were found to be present in more than two resistant varieties and absent in all susceptible varieties. This analysis resulted in a confident set of 16 SNPs for large-scale experimental validation. , identified Solyc09g098310 as putative a cysteine-rich transmembrane (CYSTM1) family protein and Solyc09g098100 as topovirus resistance protein C with maximum identity followed by a putative late blight resistance protein homolog R1A-3. Domain analysis using InterProScan showed the presence of a cysteine-rich and transmembrane domain in Solyc09g098310 and a nucleotide-binding domain and a leucine-rich repeat domain (NLB-LRR) domain in Solyc09g098100 b. The anIn any host-pathogen study, understanding the genetic basis of resistance and tracking them in the population has been a fundamental strategy. Considering that tomato breeding is extremely fast-paced with a minimum of two to three growing cycles per year, the monitoring of host defense and pathogen evolution through molecular markers becomes even more pertinent for resulting yield and trait improvements. Furthermore, with the increasing pressure from aggressive pathogen strains, there is a huge demand for molecular markers identified in plants able to resist current pathogen strains .Phytophthora infestans. The major changes indicated a shift by upregulation of immune response pathways in susceptible plants upon infection, whereas resistant plants had an overall active immune response even during pre-infection as represented by cluster D, In this study, we used RNA sequencing to understand the transcriptomic differences between resistant and susceptible tomato lines upon infection with Previous studies to identify SNP markers for late blight resistance in tomatoes have employed a genome-wide association study (GWAS) with DNA sequencing data including whole genome resequencing (WGRS) and reduced genome sequencing techniques like ddRAD seq . As RNA Ph-3 [Phytophthora was further strengthened by the interaction sites of sly-miR482d-3p [Phytophthora and other fungal infections. The expression pattern showing upregulation of Solyc09g098100 and the presence of salicylic acid (SA) response element in the promoter region could indicate a salicylic acid-mediated response in the resistant plants. While multiple pieces of evidence in our study suggest the involvement of Solyc09g098100 in resistance, further studies are required to explore the role of the gene and associated polymorphisms to understand the underlying molecular mechanism and its regulation.Our analysis and wide validation resulted in the identification of seven SNPs belonging to two genes. A C to T mutation at position 72672774 on chromosome 9 belongs to the gene (Solyc09g098310/LOC101258900), annotated as a putative cysteine-rich transmembrane (CYSTM) family protein. The role of CYSTMs has not been well understood and studies so far have indicated their role in plant stress responses by preventing ROS damage they are a component of the salicylic acid response pathway ,20. SomePh-3 . The putR482d-3p , sly-miRR482d-3p  and sly-R482d-3p , which aPhytophthora infection in tomato plants. The selection of SNPs was achieved not only by comparing RNA-sequencing data of our plant material but also by integrating a large set of publicly available sequencing datasets with known phenotypes under Phytophthora infection. The association of the SNPs identified with maximal confidence was validated on additional plant material with contrasting phenotypes. The identified SNPs in this study are located on two pertinent genes (Solyc09g098310 and Solyc09g098100) with a putative role in plant defense. In addition to serving as routine assays for marker-assisted selection, these SNPs can also be functional targets for creating edited tomato varieties resistant to late blight. In conclusion, we identified and validated a set of SNPs in disease-responsive genes under Phytophthora infestans were selected for this study. The resistant line was also characterized by the presence of Ph-2 and Ph-3 late blight-resistant loci. Selected resistant and susceptible lines were grown in pots under uniform conditions. Upon reaching 60 days, the leaves were sprayed with a suspension of P. infestans isolates from the Po Valley . Sporangia were collected from infected tomato leaves. To prepare a sporangial suspension for experimental inoculations, lesions were washed in 10 mL of sterile water. The concentration of the resulting sporangial suspension was determined by pipetting 4 µL of suspension onto a glass slide. The number of sporangia was calculated under a microscope. To achieve a final concentration of 10,000 sporangia mL−1, the suspension was diluted with sterile water and then chilled for 1 h at 4 °C before spraying. Inoculated and non-inoculated plants were kept in separate transparent boxes and placed in a controlled growth chamber with a 12 h light/dark cycle at 21 °C and 97–99% relative humidity.The plant material used in this study belongs to a collection of the Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE) at the University of Padova. Two tomato pre-breeding lines, one susceptible and one showing a high level of resistance against For validation of identified markers, a total of 121 individuals including 14 resistant and 17 susceptible breeding lines with biological replicates were used.A total of 30 mg of fresh leaf samples were processed using Tissue Lyser  with 100 µL of the lysis-binding buffer. mRNAs were directly isolated using the Dynabeads mRNA Direct Micro Kit . The quality and quantity of isolated mRNAs were checked by Agilent TapeStation 1500 . Sequencing libraries were prepared using Ion Total RNA-Seq Kit v2 (Thermo Fisher Scientific). The prepared libraries were loaded onto Ion 540 chip kit followed by sequencing using the Ion S5 GS system. Solanum lycopersicum L.) reference genome SL3.0 (http://solgenomics.net (accessed on 29 December 2022)) using bowtie2 v2.3.5.1 [Phytophthora infestans (NCBI genome: ASM14294v1) to check for the presence of the fungal pathogen. Following this, the aligned files were converted into sorted and indexed BAM files using the samtools suite. Raw read counts for genes were calculated using samtools multiBamCov and further analyzed using Deseq2 [Demultiplexed raw sequences exported as unaligned BAM files from the Ion S5 GS sequencer were converted to fastq format using samtools 1.10 . Qualityv2.3.5.1 . Unaligng Deseq2 . The norg Deseq2  to explohttp://solgenomics.net (accessed on 29 December 2022)). Variants were then filtered using the bcftools filter [S. lycopersicum L. and accessions of S. pimpinellifolium. Raw fastq files were downloaded from NCBI-SRA (https://www.ncbi.nlm.nih.gov/sra) and processed using the same pipeline as done for transcriptome for variant calling. Datasets with available variant call files were directly compared. The list of public datasets used for the study is mentioned in Aligned bam files were processed using samtools mpileup  and bcfts filter  with a cicalcu.com/stat/chisqtest.html (accessed on 29 December 2022).Variants selected after bioinformatics analysis were genotyped using rhAmp assays  on a wide range of resistant and susceptible lines including 14 resistant and 17 susceptible lines with biological replicates. DNA from the samples was extracted and purified using BioSprint 96 workstation  by the method described by Stevanato et al. 2015 . Genotyphttp:/solgenomics.net (accessed on 29 December 2022)) and subsequently with a newer version of Solanum lycopersicum L. genome annotation ITAG 4.0 (http://solgenomics.net (accessed on 29 December 2022)) for updated gene structure and annotation. NCBI protein BLAST [Arabidopsis thaliana and NCBI non-redundant database [Genes containing the validated SNPs of interest were first checked with ITAG 3.0 annotation (in BLAST  was useddatabase . Furtherdatabase . PlantCAdatabase  was useddatabase  was used


Natural and artificial selection for more than 9000 years have led to a variety of domestic pig breeds. Accurate identification of pig breeds is important for breed conservation, sustainable breeding, pork traceability, and local resource registration.http://alphaindex.zju.edu.cn/iDIGs_en/) as a comprehensive application for global pig breed identification. iDIGs allows users to (1) identify pig breeds without a reference population and (2) design small panels to discriminate several specific pig breeds.We evaluated the performance of four selectors and six classifiers for breed identification using a wide range of pig breeds (N = 91). The internal cross-validation and external independent testing showed that partial least squares regression (PLSR) was the most effective selector and partial least squares-discriminant analysis (PLS-DA) was the most powerful classifier for breed identification among many breeds. Five-fold cross-validation indicated that using PLSR as the selector and PLS-DA as the classifier to discriminate 91 pig breeds yielded 98.4% accuracy with only 3K single nucleotide polymorphisms (SNPs). We also constructed a reference dataset with 124 pig breeds and used it to develop the web tool iDIGs (In this study, we proved that breed identification among a wide range of pig breeds is feasible and we developed a web tool for such pig breed identification.The online version contains supplementary material available at 10.1186/s12711-023-00788-0. Pigs are one of the main sources of animal protein for humans and are commonly used for biomedical research. Over the past 9000 years, various pig breeds have been produced through natural and artificial selection . AccordiThe long history of pig domestication has shaped the genetic structure of pig breeds, which facilitates breed identification based on genetic information. Molecular genetic markers, such as short tandem repeats (STRs) and SNPs, can be used to identify genetic heterogeneity among breeds. For more than 30 years, the highly variable STRs have been used for breed identification in various species . HoweverCurrent studies on breed identification typically include two steps, i.e., (1) selecting breed informative SNPs and (2) fitting a classification model using these SNPs , 9. In mST), and principal component analysis (PCA) [ST to discriminate 13 pig breeds using different machine-learning methods [For selection of breed informative markers, commonly used methods are the delta method, Wright’s fixation index (Fis (PCA) . For breis (PCA) , 14, supis (PCA) , and k-nis (PCA) , with peis (PCA) . Most stis (PCA)  and a pa methods . These sAs a result of the massive application of SNPs in genomic selection and genome-wide association analysis in pigs, abundant SNP information is now publicly available from commercial SNP chips and WGS datasets for many pig breeds. These data can help develop a public database for the identification of breeds from across the globe. However, the performance of breed identification methods remains unclear when the number of breeds is very large. In addition, while it is well known that more SNPs are required to distinguish among a large number of breeds, the minimum number of SNPs to distinguish pig breeds globally is unknown.In this study, we curated data from three commercial SNP chips and a WGS dataset that represent 3605 individuals from various pig breeds worldwide. To confirm the applicability of breed identification in a wide range of pig breeds, we compared the performance of four selectors and six classifiers using internal cross-validation or an external independent dataset. We generated a reference dataset for breed identification and developed an interactive web tool, iDIGs, to perform pig breed identification by simply uploading SNP information of testing individuals. The iDIGs tool also allows users to design panels of breed informative markers for specific pig breeds without requiring additional pre-prepared data.A flowchart that depicts the general scheme of this study is in Fig. https://datadryad.org/) [https://figshare.com/) [Sus scrofa assembly, we also generated a chip reference dataset based on the 11.1 Sus scrofa assembly [Sus scrofa assembly as the reference genome.Three pig SNP chip datasets were downloaded from the Dryad Digital Repository (ad.org/)  and the re.com/) , 17. Brere.com/)  was usedre.com/)  was usedassembly . A WGS dassembly  was alsoassembly  with theST, PCA, and partial least squares regression (PLSR) were used to select breed informative SNPs. These four selectors each assign a different statistic to each SNP that reflects the heterogeneity of that SNP between breeds. The four statistics were calculated as follows:AED: the AED value for a certain SNP across all breeds was calculated as:ST: estimates of Weir and Cockerham’s global FST [Fobal FST  were calobal FST  softwareprcomp function of the R software [PCA: first, the allele frequencies of each SNP in each breed were calculated and a breed-specific allele frequency matrix was generated. Then, PCA was performed on the matrix of breed-specific allele frequencies using the software . The loaPLSR: PLSR is a supervised learning algorithm that reduces the explanatory variables to a smaller set of uncorrelated components and performs a regression on these components. This is appropriate for a regression analysis for which multicollinearity of explanatory variables is high. Several studies have shown the efficiency of PLSR for the selection of breed informative SNPs , 8. ConsTo compare the performance of different selectors, average Euclidean distance (AED), FUsing these four statistics, nine SNP panels with different densities  based on their magnitude were constructed to assess the impact of different marker densities.KNN: KNN is a non-parametric algorithm for multi-class classification. For each test individual , it calculates its distance to all the training individuals  and chooses the first k nearest training individuals. Then, it assigns the test individual with the mode of the breed labels of the k nearest training individuals. KNN classification was performed using the R package class . The parNSC: NSC classification was first introduced for the diagnosis of multiple cancer types based on DNA microarrays , using dPLS-DA: PLS-DA is a discrimination method based on PLSR and recently has become increasingly popular in the research area of metabolomics for the purpose of classifying individuals into either a case or control group based on their metabolic profile . As a vaRF: RF is an ensemble learning method that assembles results from several decision trees to avoid overfitting by a single decision tree model. For classification jobs, the output of the RF model is the mode of the predicted class from all decision trees. RF models were previously considered adequate for the selection of breed informative markers  and for SVMp and SVMr: SVM is a well-known and fast machine-learning algorithm that can be used for regression and classification . SVM useFive multi-class classification methods were used to perform breed assignment using different numbers of breed informative SNPs, including KNN, nearest shrunken centroids (NSC), partial least squares discriminant analysis (PLS-DA), RF, SVM with a polynomial kernel (SVMp), and SVM with a Gaussian radial basis function (SVMr). The model parameters for these six classifiers are summarized in Table Stratified five-fold cross-validation was used to evaluate the performance of all classification methods with the chip reference dataset by partitioning this dataset into five equally-sized subsets of individuals, while maintaining each subset to have roughly equal proportions of all breeds. To obtain reliable estimates of model performance, we performed the stratified five-fold cross-validation five times, and the average accuracy across the five times five-fold cross-validation was used as a measurement of model performance.We randomly selected eight breeds from the chip reference dataset as unknown breeds. All individuals that belonged to these unknown breeds were extracted as testing sets. For the other breeds in the chip reference dataset, we randomly extracted one third of the individuals for testing. Individuals from two crossbreds, CSLM  because of their high performance in cross-validation. PLS-DA was used to predict breed labels of test individuals with different panel densities. The regression coefficients for each breed label from PLS-DA were transformed to a probability distribution using the softmax function. In the cross-validation analyses, the breed label with the highest probability was set as the predicted label. A predefined threshold was used to identify unknown breeds and crossbreds. When the highest probability was lower than the threshold, the predicted label was set as “unknown”. To identify an adequate threshold, we tested different thresholds from 0.01 to 0.03 with increments of 0.001.Individuals whose breed labels occurred in the chip reference dataset were extracted from the WGS PHARP datasets. In total, 26,858 SNPs that were shared between the chip reference (version 11.1) and the WGS PHARP datasets were used. PCA and PLSR were chosen to select different panel densities because of their performance in cross-validation. PLS-DA was used to predict breed labels of individuals in the independent testing dataset with different panel densities, ranging from 1000 to 15,000, with increments of 1000.Breeds from PHARP with more than 10 individuals were merged with the chip dataset to produce a larger reference dataset. To generate a high-quality SNP reference dataset for use with different genotyping platforms, SNPs that overlapped between the chip and the PHARP reference dataset were retained and imputed. The R package Shiny  was usedSus scrofa assembly resulted in the loss of 1231 SNPs. Therefore, the chip reference dataset (Sus scrofa 11.1) contained genotypes on 2272 individuals and 45,743 SNPs and comprised 91 breeds, of which 41 were Chinese indigenous breeds. Breeds used in the chip reference dataset and the number of individuals for each breed are in Additional file After basic quality control of the merged dataset, we obtained genotypes on 2333 individuals and 46,974 common SNPs in the chip dataset. Forty crossbreds were removed and an additional 21 individuals were removed due to their inconsistent admixture profile , we produced a reference dataset with 45,743 SNPs and 3605 individuals from 124 pig breeds. The information included for each breed is in Additional file Based on the reference dataset, the web tool (iDIGs) was designed for pig breed identification-related analyses, including pig breed identification and design of SNP panels (breed informative marker selection) for the identification of specific pig breeds. The workflow of breed identification analysis is as follows. First, the PLINK binary file needs to be uploaded to the iDIGs web server and the version of the reference genome should be chosen. iDIGs renames the SNP ID to “Chr:Pos:allele1:allele2”, where “Chr” and “Pos” are the chromosome and position localizations of this SNP, respectively; “allele1” and “allele2” are the alleles that have the smaller and larger ASCII code, respectively. Next, iDIGs extracts the common SNPs between the uploaded data and the reference data, based on SNP ID. Then, SNPs in the uploaded data are recoded as the number of reference alleles. The reference allele of each SNP in the uploaded data is consistent with the reference allele used in the reference dataset. By default, iDIGs selects 5000 breed informative SNPs using PLSR. If the number of common SNPs is smaller than 5000, the marker selection procedure is skipped. iDIGs then retrains the PLS-DA model using the selected SNPs and assigns a breed label for each sample in the uploaded file.In order to design a SNP panel for the identification of specific breeds, no additional files need to be uploaded. Users need to choose the panel density and input the breeds that are targeted to be differentiated from the available 124 breeds. Here, users can input multiple breed IDs or a single breed ID. If multiple breed IDs are used, markers that are most discriminating among all input breeds are selected. When using a single breed ID, markers that can distinguish the breed from all other breeds in the reference database are selected. After obtaining the panel of breed informative markers, iDIGs automatically conducts five-fold cross-validation to evaluate the performance of the panel. Users can then judge if the number of SNPs in the panel is sufficiently large for identification of the breeds of interest.In this study, we generated a reference dataset with 124 pig breeds and built the web tool iDIGs for pig breed identification using this reference dataset. To our knowledge, this is the first breed identification study based on worldwide pig breeds, as well as the first public tool for the identification of a wide range of pig breeds. In addition to breed identification, iDIGs can also be used to authenticate the breed label of publicly available data. It is acknowledged that individuals within such datasets may possess incorrect or inaccurate breed labels, which can result in erroneous conclusions if used in genomic analyses (e.g. selection signatures). As such, it is recommended that breed labels be verified using iDIGs prior to conducting any related analyses.While previous studies on breed identification only selected tens or hundreds of markers , 34, 40,”Breed” is a dynamic concept and the genetic characteristics of a breed may change over time as a result of selection, genetic drift, and other gene flow events. Therefore, the increasing genetic distance of the test individuals from individuals from the same breed in the reference dataset will decrease prediction accuracy. This can be addressed by continuously updating the reference dataset.In iDIGs, each test individual is assigned to one of 124 breed labels, even if the true breed label is not included in our reference dataset. It was, however, not possible to include all worldwide pig breeds in our reference dataset. To overcome this shortcoming, we predefined a threshold for the model fitness of PLS-DA and reset the predicted breed as “unknown” if the probability of the predicted breed is smaller than this threshold. iDIGs uses 0.02 as the default threshold, but users can change this parameter to achieve a better trade-off between true positive and false positive rates. If the threshold is set to “NA”, iDIGs disables the function of unknown breed prediction.iDIGs also provides a function to select breed informative markers to differentiate among specific breeds. All SNPs in the reference dataset were derived from commercial SNP chips and are, therefore, easy to design and to genotype for a small panel. Building a reference dataset is the prerequisite for the design of a panel for breed identification but this is time-consuming and expensive. With iDIGs, users can directly use the reference database to select breed informative markers to design panels for most situations because it includes 124 pig breeds that were distributed across 47 sites in 24 countries. However, it is important to consider that the performance of the panels generated by iDIGs may be subject to bias due to the limited number of individuals representing certain breeds in the reference dataset. Therefore, to mitigate this potential bias, we suggest increasing the size of the SNP panel.With economic globalization, gene flow between local and foreign breeds will increase, which may threaten the purity and integrity of local breeds. Some local breeds may be crossed with commercial breeds in order to accelerate lean growth and improve carcass mass, which may disintegrate precious local genetic resources. Analysis of breed purity is referred to as genomic breed composition (GBC) analysis , 43. GBChttps://github.com/JanMiao/GBC) to compile reference data from user-provided data. The procedure for the construction of the reference database includes removing outliers, removing relatives, and selection of breed informative markers. This also allows users to perform GBC analysis with multiple algorithms based on their own reference data.Breed identification is a qualitative analysis, while GBC analysis is quantitative, which makes it more complex and requires detailed prior knowledge  of candidate individuals. Because of this, iDIGs currently does not include a function for GBC analysis with the linear model. In addition, because GBC analyses are based on allele frequencies of breeds, sample sizes in our reference database are too small for some breeds to calculate accurate allele frequencies. In addition, because natural and artificial selection can result in multiple strains of a given breed, allele frequencies calculated from individuals in public databases may be less representative for some populations. We did attempt GBC analyses for some crossbreds, but obtained no reliable results. Since we are not sure about the true pedigree of the crossbreds (which were downloaded from public databases), we did not include these results here. To eliminate the bias introduced by public data, we designed an R package  based on the reference database. iDIGs can be used to identify breed identity and to design small panels for the identification of specific pig breeds without the need to prepare a separate reference population.Additional file 1: Figure S1. Plots of admixture profiles before and after removing outliers; (a) shows the admixture profile of raw reference individuals, and (b) shows the admixture profile of the remaining individuals after removing 21 outliers.Additional file 2: Table S1. Information on the breeds included in the chip reference dataset.Additional file 3: Table S2. Information on the individuals used as crossbreds, unknown breeds, and testing pure breeds.Additional file 4: Table S3. Information on the breeds used as independent testing dataset.Additional file 5: Table S4. Information on the breeds used in the reference dataset.


Cluster and transmission analysis utilising pairwise SNP distance are increasingly used in genomic epidemiological studies. However, current methods are often challenging to install and use, and lack interactive functionalities for easy data exploration.GraphSNP is an interactive visualisation tool running in a web browser that allows users to rapidly generate pairwise SNP distance networks, investigate SNP distance distributions, identify clusters of related organisms, and reconstruct transmission routes. The functionality of GraphSNP is demonstrated using examples from recent multi-drug resistant bacterial outbreaks in healthcare settings.https://github.com/nalarbp/graphsnp. An online version of GraphSNP, including demonstration datasets, input templates, and quick start guide is available for use at https://graphsnp.fordelab.com.GraphSNP is freely available at The online version contains supplementary material available at 10.1186/s12859-023-05332-x. The number of single nucleotide polymorphisms (SNPs) as the proxy of similarity between genomes—commonly referred to as SNP distance—are increasingly used as a measure of relatedness in outbreak studies –3. SNP dCurrent tools that process and visualise SNP distance for outbreak analysis exist mainly as software libraries for programming language environments or lack the combined feature of clustering and transmission reconstruction. R packages, such as Outbreaker , Outbreagraph-based and interactive SNP distance visualisation tool for cluster and transmission analysis. GraphSNP is built as a simple, ready-to-use standalone visualisation tool to generate interactive charts, networks and trees to rapidly explore outbreak clusters and transmission using SNP distances and associated metadata. Here, we describe and demonstrate the functionality of GraphSNP using examples from previously published datasets.To fill this gap, we developed GraphSNP—A https://graphsnp.fordelab.com or use it offline through a locally-installed web server.GraphSNP implements graph models and algorithms to process and visualise SNP distances within a standalone single page application (SPA) built using React.js , AntDesiInput, Distances, Graph and Documentation  of the resulted clusters using the Kruskal’s algorithm , from krTo start using GraphSNP, users can simply drag and drop the SNP alignment (or distance matrix) and metadata file into the web page. The alignment file must contain a minimum of two fasta-formatted nucleotide sequences of equal length. Alternatively, a symmetric pairwise distance matrix can be uploaded if users precompute the distance using 3rd party tools. The metadata input file is a comma-separated value (CSV) table containing information about the sample with a column “sample_id” referring to the sample identifier that must be identical to the sequence id in the SNP alignment or distance matrix file. An additional column listing sample’s collection time  is required for transmission analysis. Once the input file is loaded, users can create interactive charts, network and trees. Static images and tables can also be downloaded, if required.The average processing time of most key functionalities is < 2 min for datasets containing < 1000 samples  and none (when using it online), providing instant availability for rapid data exploration –similar to the approach adopted by Phandango . Other fK. michiganensis dataset sample metadata was obtained from the manuscript and sequence data for each genome was downloaded from the Sequence Read Archive (SRA). A SNP alignment was created from the sequence read data using the methods described in the study [E. faecium and K. pneumoniae SNP distance matrix and metadata were created from SNP distance data and sample information provided in each study's Additional file For demonstration purposes, example datasets were created using data from three previously published studies , 20, 21..To demonstrate the utility of GraphSNP, we tested it on three previously published datasets , 20, 21.The distribution of SNP distance can illustrate the relationship between samples and sample groups. GraphSNP provides functionality to visualise these distributions rapidly and interactively. Users can select SNP distances from all samples or sample groups using a dropdown button and visualise its distribution on interactive charts Fig. . The staKlebsiella michiganensis genomes to display a minimal genetic diversity between the samples  of the resulting clusters after applying the threshold. The tree visualises clusters as compound nodes with edges representing their minimum distance to other clusters or singletons. Figure s et al. . The treKlebsiella pneumoniae . In such cases, users may be advised to use tools utilising separate processes, such as CATHAI  that useGraphSNP allows users to visualise SNP distances for investigating outbreak clusters and transmission interactively. The intuitive web application design, minimal installation and configuration steps, make it easy to use for rapid exploratory analysis. GraphSNP is freely available for both online and offline use.Project name: GraphSNPhttps://github.com/nalarbp/graphsnpProject home page: Operating system(s): Platform independentProgramming language: JavaScriptOther requirements: None for online use, a local web server for offline useLicense: MITAny restrictions to use by non-academics: NoneAdditional file 1 The Documentation page provides users with a quick start guide and examples of input files.


Bct gene region to a 1.7-Mb interval on chromosome Pv07 using genome-wide association study (GWAS) analysis. Recombinant lines from the SnAP were used to further narrow the Bct region to a 58.0-kb interval. A missense SNP (S07_2970381) in candidate gene Phvul.007G036300 Exonuclease V (EXO5) was identified as the most likely causal mutation, and it was the most significant SNP detected by GWAS in a dry bean population (DBP) naturally infected by the CTV-Worland (Wor) strain. Tm-shift assay markers developed for SNP S07_2970381 and two linked SNPs, S07_2970276 and S07_2966197, were useful for tracking different origins of the Bct EXO5 candidate gene resistance to CTV in common bean. The three SNPs identified four haplotypes, with haplotype 3-1 (Haplo3-1) of Middle American origin associated with the highest levels of CTV resistance. This SNP-haplotype assay will enable breeders to track resistance sources and to develop cultivars with better CTV resistance.Beet curly top virus (BCTV), which is synonymous with curly top virus (CTV), causes significant yield loss in common bean (snap and dry beans) cultivars and several other important crops. Common bean cultivars have been found to be resistant to CTV, but screening for resistance is challenging due to the cyclical nature of epidemics and spotty feeding by the leafhopper that vectors the virus. We used an SNP dataset for the Snap Bean Association Panel (SnAP) agro-inoculated with CTV-Logan (CA/Logan) strain to locate the  Phaseolus vulgaris L.) and affects many other crops, including sugar beet (Beta vulgaris L.), pepper (Capsicum annuum L.), and tomato (Solanum lycopersicum L.) (Curtovirus within the family Geminiviridae and is transmitted in a persistent manner by the beet leafhopper (Circulifer tenellus B.). Among the 11 strains of BCTV described in the literature, Colorado (CO) and Worland (Wor) have been two of the more commonly isolated strains from plants and beet leafhoppers in recent studies in the western US. In contrast, the California/Logan (CA/Logan) strain has recently only been found in Idaho (Sinapis arvensis L.), Russian thistle , and kochia (Kochia scoparia L. Schrad.) (Beet curly top virus (BCTV) can cause severe yield loss in susceptible cultivars of snap and dry beans  . BCTV isin Idaho  and has in Idaho . EndemicSchrad.) .Bct allele identified by Bct locus, derived from the landrace ‘Jatu Rong’ (G122), was associated with a minor-effect quantitative trait locus (QTL) conferring resistance against CTV in the G122/’Taylor Horticulture’ dry bean RIL population  (BCTV [synonymous with curly top virus (CTV)] causes symptoms in common bean, including leaf curling, chlorosis, and severe plant stunting . Early spulation . The sams (BDMV) .Bct allele, is difficult because field-based screening is unreliable due to the cyclical nature of epidemics. Additionally, there is a lack of uniform disease pressure during an epidemic because of spotty feeding by the leafhopper, as common bean is a non-preferred food source  or false negatives . In addition, it was demonstrated that ‘SAS8.1550’ had reduced diagnostic value in snap bean genotypes derived primarily from the Middle American gene pool  enhance the efficiency and reliability of MAS for Bct in snap and dry bean germplasm.Our research objectives aimed to i) acquire a comprehensive understanding of the genetic basis of the http://arsftfbean.uprm.edu/beancap/). The SBDP was used in a diversity analysis of snap bean (The Snap Bean Association Panel (SnAP) is comprised of 378 historic-to-modern cultivars that span both fresh market and processing types, including large sieve, small sieve, wax, bush blue lake, Kentucky, Romano, pole, and others. The SnAP includes public and private industry-released snap bean cultivars predominantly from the United States, some landrace cultivars, and a few breeding lines. The SnAP has recently been used for Genome-Wide Association Studies (GWAS) of herbicide tolerance  and resinap bean  and for nap bean . In addihttps://npgsweb.ars-grin.gov/gringlobal/search). An additional 289 SnAP accessions were phenotyped for CTV disease reaction using an infectious CA/Logan clone of CTV and an agro-inoculation screening procedure in a controlled growth room environment at Harris Moran Seed Company in Sun Prairie, Wisconsin. The CTV CA/Logan strain was cloned into the binary vector pBIN19 in Agrobacterium tumefaciens strain GV3111 (Ti plasmid pTiB6S3SE) obtained originally from Dr. Drake Stenger. This unpublished infectious clone had increased infectivity for agro-inoculation of common bean compared to earlier-developed clones  database, cultivar registration articles, and the Germplasm Resources Information Network , and each pot contained two plants. The plants were covered and placed in a dark growth chamber for 24 h after inoculation. After 24 h, they were maintained in a growth chamber with a 14-/10-h light cycle and a temperature range of 18°C–27°C. The seedlings were transplanted into 1-gallon pots (two plants/pot) 2–6 days post-inoculation. The accessions were tested in subsets of approximately 30 accessions, in 4-day intervals, in a growth room. Each subset included two pots with two plants, each of the susceptible ‘Caprice’ and resistant ‘Hystyle’ cultivars as controls. Six accessions did not germinate, including ‘Angers’, ‘Processor’, ‘RH13’, ‘Serin’, ‘Golden Rod’, and ‘Sundial’. Disease reactions were recorded 17–24 days post-inoculation (dpi) as resistant (no visible symptoms), susceptible , or intermediate resistant (with fewer and less severe symptoms) .2O, 1 µl of total nucleic acid extract, and 0.1 µl GoTaq DNA polymerase . Thermal cycler conditions consisted of 95°C for 2 min, followed by 40 cycles of 95°C for 1 min, 54°C for 1 min, and 72°C for 1 min, and a final extension step of 72°C for 5 min. Amplicons were visualized on a 1.5% agarose gel with ethidium bromide staining.The DBP accessions were sown during the summer of 2022 in two replications at the Washington State University (WSU) Research Station—Roza Unit (46°17′43.6ʺN 119°43′48.0ʺW) in Prosser, WA, USA, where CTV is endemic. The experimental design was a randomized complete block, and each plot consisted of four rows of 3-m row length, with a spacing of 0.56 m between rows. To determine the CTV strain(s) causing infection in the trial, leaf tissue was collected from 1 asymptomatic plant and 16 symptomatic plants during the early pod-fill R3 growth stage. The tissue was extracted using a modified Dellaporta protocol  and analDisease reaction was rated 60 days after planting on a scale from 1 to 9, where 1 = no symptoms and 0% of plants infected, 5 = moderate disease symptoms and up to 30% of plants infected, and 9 = severe disease symptoms with ≥80% of the plants dead or dying . Trait dhttps://phytozome-next.jgi.doe.gov/info/Pvulgaris_v2_1) using Bowtie2 v2.4.1  and diluted to 10 ng/μl with nuclease-free water. Multiplexed genotyping-by-sequencing (GBS) libraries were generated through digestion with ApeKI following the protocol and with barcodes detailed in 2 v2.4.1 . The ali2 v2.4.1 .Phaseolus vulgaris v2.1 as reference genome  implemented in the package fastPHASE  pipeline  with G19e genome . MaximumastPHASE . AnnotatFor the DBP accessions, the leaf tissue was collected from individual plants grown in the USDA-ARS greenhouses at Prosser, WA. Genomic DNA was isolated from 20 mg of the leaf tissue using a Qiagen DNeasy 96 Plant Kit  and sent to the US Department of Agriculture Agricultural Research Service (USDA-ARS), Soybean Genetics and Improvement Laboratory, Beltsville, MD, for the 11,292 SNPs BARC Illumina chip assay . The SNP2 threshold and the –indep-pairwise function in Plink v2.0 (https://www.cog-genomics.org/plink/2.0/), with a sliding window of 50 kb shifting by five bases. In addition, two dry bean lines, ‘BAT 93’ (representing the Middle American gene pool) and ‘Jalo EPP 558’ (representing the Andean gene pool), were added to assess the structure and diversity in SnAP. The sequence data for ‘BAT 93’ and ‘Jalo EPP 558’ was obtained by GBS, as described above.The SNP data sets used for population structure analysis in SnAP and DBP were adjusted by linkage disequilibrium (LD) pruning using a 0.5 rThe pruned set of SNPs with <20% missing values and a MAF > 0.01 were retained. Population structure was estimated using a Bayesian Markov chain Monte Carlo model (MCMC) implemented in STRUCTURE 2.3 software . Ten runGWAS analysis was conducted by mixed linear model (MLM) using the genome association and prediction integrated tool (GAPIT) . An idenBct region were identified using the gene models from G19833 v2.1 common bean reference genome. Selected candidate genes were amplified and sequenced in a subset of 14 snap and 14 dry bean genotypes from Andean and Middle American gene pools.Candidate genes annotated within the http://bioinfo.ut.ee/primer3-0.4.0/). The PCR volume was 25 µl, containing 1× Taq buffer, 1.8 mM of MgCl2, 0.4 mM of dNTPs mix , 0.25 µM of each primer (forward and reverse), 1 U of Taq polymerase , and 25 ng of genomic DNA. The amplification conditions were 2 min at 95°C, followed by 38 cycles at 94°C for 20 s, 57°C for 30 s, and 72°C for 90 s, and a final extension at 72°C for 5 min, performed in a PCR Eppendorf Mastercycler . PCR products were visualized by gel electrophoresis on 2% (w/v) agarose. Sanger sequencing from PCR products obtained was performed using the services of Eurofins MWG Operon . Sequencing data were analyzed using Geneious 9.1.8 , 0.15 μM of each primer , 1× EvaGreen® dye, and 0.1 μl of Taq polymerase (Promega) under the following thermal profile: 94°C for 2 min, then 38 cycles of denaturation at 92°C for 20 s, annealing for 20 s (the temperature was specific to each primer trio), and extension at 72°C for 20 s, and final extension at 72°C for 5 min. Melting point analysis for allele determination of the PCR products was performed with a fluorescence-detecting thermocycler . EvaGreen Fluorescent detection was performed for 1 min at 95°C, and the melting curve step ramped from 65°C to 95°C in increments of 1°C every 20 s.Tm-shift SNP markers were amplified by PCR on an Eppendorf Mastercycler . The PCR volume was 20 μl containing 20 ng of genomic DNA, 1× Taq buffer, 1.5 mM of MgCltrimmed-means one-way ANOVA using the ggbetweenstats function in the R-Package ggstatsplot. Yuen’s trimmed means test was implemented using Robust p-value fitting method using the ggbetweenstats function in the R-Package ggstatsplot ; resistant or intermediate resistance (18 accessions); or resistant, intermediate resistant, or susceptible responses (15 accessions). The disease reactions for 83 SnAP accessions, compiled from various public information sources, were categorized as 42 resistant, 32 susceptible, and 9 intermediate resistant to CTV. For upstream analysis, the qualitative reactions of each accession were transformed into a quantitative score, with resistant being coded as 1, susceptible as 0, and intermediate resistant as 2  under natural field infection. Based on the BLUP values for CTV reaction, 43 accessions were resistant (disease score between 1 to 4), 33 were tolerant (between 4.1 to 6.9), and 14 were susceptible (between 7 to 9) to the Wor strain 60 days after planting , using a pruned set of 1,833 SNPs, a likelihood of K=2 was estimated, indicating the presence of two sub-populations belonging to the Andean and Middle American gene pools, as confirmed by gene-pool checks ‘Jalo EEP 558’ and ‘BAT 93’ , using an LD-pruned set of 974 SNP markers, and K=2 was estimated .p = 1.7E−36), which was associated with resistance and intermediate resistance to CTV (Bct gene that was identified by GT) that conditions resistance to CTV found in the dry bean landrace G122 named ‘Jatu Rong’ (PI 163120) from India  and 2.79–3.60 Mb (p = 1.89.17) on Pv07, respectively (p =1.12E−45), as expected for a single major allele  from Bct region with a high p-value (p =1.84E−07)  for the Bct region based on the Andean G19833 common bean v2.1 gene models  and Phvul.L001619, which functions as a mediator of RNA polymerase II transcription subunit, showed homology with gene models from other plant species involved in the replication of geminiviruses  sequencing of the three candidate genes revealed 29 variants, 28 SNPs, and 1 InDel among a subset of 28 snap and dry bean genotypes from diverse backgrounds  protein of the sequenced ORF for gene Phvul.007G036300 did not uncover any disruptions in the EXO5 domain. Nevertheless, the missense SNP variant located in Pv07: 2,970,381 bases involves a transition of Adenine (A) to Thymine (T) base, resulting in radical replacement of a polar, uncharged amino acid, glutamine (Q), with a non-polar, hydrophobic amino acid, leucine (L), at position 279 in the EXO5 domain  (The p.Q279L) Table 2.Bct candidate gene Phvul.007G036300 in the 28 sequenced genotypes showed one cluster for susceptibility and two distinct clusters for resistance/intermediate resistance to CTV , which exhibited polymorphism between resistant and susceptible genotypes to CTV. Additionally, two Tm-shift assays were developed to distinguish introgression haplotypes of the Bct region from Andean and Middle American gene pools. The first assay, named S07_2970276, targeted the missense SNP variant Pv07: 2,970,276 bases within Phvul.007G036300 gene and differentiated genotypes from Andean and Middle American gene pools. The second assay, named S07_2966197, focuses on another missense SNP variant, Pv07: 2,966,197 bases, found in the Phvul.007G36200 gene model by A Tm-shift assay named S07_2970381 was specifically designed to detect the missense SNP Pv07: 2,970,381 in the The S07_2970381 marker exhibited a 100% correlation with the CTV phenotype observed in SnAP. For the 378 SnAP accessions, 238 (63.0%) showed resistance or intermediate resistance to the CTV CA/Logan strain and possessed the resistance allele of the S07_2970381 marker, and 100 (26.5%) were susceptible and possessed the susceptible allele of the S07_2970381 marker, while 33 (8.7%) were segregating for CTV reaction, and 7 (1.8%) had missing data (For the DBP (88 accessions) infected with the Wor strain, a correlation of 99% was observed between the S07_2970381 SNP marker and the disease score. There were 42 accessions with resistant disease scores ≤ 4.0, which possessed the resistant allele of the S07_2970381 SNP marker, and 13 with susceptible disease scores from 7.0 to 9.0, which possessed the susceptible allele  with the S07_2970381 and S07_2970276 SNP markers in the SnAP and DBP. In fact, ‘SAS8.1550’ and S07_2970276 markers were 100% identical in both bean panels.A comparison of genotypes for the S07_2970381 SNP marker and the ‘SAS8Bct region. Four haplotypes were identified: Haplo1 with AGT sequence for the S07_2966197 (A/G), S07_2970276 (G/C), and S07_2970381 (T/A) SNPs, respectively; Haplo2 with AGA sequence; and Haplo3 divided into two sub-haplotypes, Haplo3-1 with ACA sequence and Haplo3-2 with GCA sequence, based on polymorphism for S07_2966197 in Middle American accessions.The SnAP and DBP were assayed for the S07_2970381, S07_2970276, and S07_2966197 markers to identify haplotypes for the In the SnAP Table 4,pHolm-adjusted p-values, showed that the susceptible Haplo1 was significantly different from the three resistant haplotypes (p < 0.0001), while the only significant difference among resistant haplotypes mean scores was between Haplo3-1, which was more resistant than Haplo3-2 (p = 0.05)  in candidate gene Phvul.007G036300 (Exonuclease V) was identified as the most likely causal mutation conditioning Bct resistance. Moreover, GWAS of the DBP identified the S07_2970381 SNP variant as the only significant SNP for BCTV reaction in the field.The Arabidopsis homolog AT5G60370, which encodes an Exonuclease V-Like (EXOVL) protein, exhibited 43% sequence identity  with the Phvul.007G036300 Bct candidate gene. EXOVL has been reported to play critical roles in biological processes underlying morphological traits in A. thaliana . This protein is capable of degrading single-stranded DNA (ssDNA) from the 5′-end and plays a critical role in mitochondrial maintenance  from the SnAP, DBP, and additional survey of 57 dry bean genotypes clearly co-segregated with alleles of the S07_2970381 SNP marker. SVS-0863 dry bean with a tolerant disease score in the field but lacking the resistant marker allele is the only minor exception. The ‘Beluga’ dry bean with similar field tolerance but lacking the marker allele for resistance was later found to be susceptible to CTV via agro-inoculation.The Tm-shift assay marker developed for SNP S07_2970381 will be useful for tracking CTV resistance conferred by the Bct EXO5 candidate gene is nearly ubiquitous in the Middle American gene pool as 98% (140/143) Middle American snap (including admixtures) and dry bean genotypes exhibited CTV resistance (including intermediate resistance and tolerance) and possessed the resistance allele for the EXO5 candidate gene mutation (S07_2970381 SNP). Conversely, only 65% (214/327) of Andean (including admixtures) snap and dry bean genotypes tested were resistant to CTV and possessed the resistance allele for the marker. In addition, all 113 Andean snap (including admixtures) and dry bean genotypes susceptible to CTV lacked the resistance allele for the Bct EXO5 candidate gene marker. Finally, only three Middle American snap bean accessions were susceptible to CTV, and they lacked the resistance allele for the marker.The .1550’, and S07_2970276 and ‘SAS8.1550’ markers, together with distinct haplotypes for combinations of the S07_2966197, S07_2970276, and S07_2970381 SNP markers in the SnAP and DBP, suggest different origins of the Bct candidate allele. The susceptible Andean beans all possess the same haplotype (Haplo1) for the three SNP markers S07_2966197, S07_2970276, and S07_2970381 in the Bct region. Haplo2 represents a small set of Andean genotypes with resistance to CTV. Among these genotypes, there are a few Andean dry bean landrace cultivars, including IJR  and G122 from India, Canadian Wonder (ADP_010) and Kijivu (ADP_033) from East Africa, Pompadour B from the Caribbean, and California LRK, with Haplo2, which suggests that this haplotype and associated Bct resistance is of Andean origin. This ‘Haplo2’ Bct resistance source was unlikely to have been purposely used in breeding for CTV resistance. All the dry bean genotypes in the DBP with Haplo2 represent mostly breeding lines that had never been screened for CTV reaction prior to this study, and their pedigrees reveal the use of Canadian Wonder, IJR, and Kijivu landrace cultivars as parents. Moreover, only 5 of 177 CTV-resistant snap bean accessions possessed Haplo2.Resistance to CTV in snap bean was purposely initiated from crosses with Middle American dry bean genotypes like ‘Burtner’ navy bean in 1936 and small red (~ ‘Common Red Mexican’) beans during the 1950s  and dry bean (45%) genotypes in this study. Many of the dry bean genotypes with Haplo3-1 come from breeding programs that were actively selected for CTV resistance.The differences in percentage correlation between S07_2970381 and ‘SAS8he 1950s . Haplo3-Bct resistance. The mean disease score (3.5) for the Haplo3-1 dry bean genotypes indicates a significantly (p = 0.05) higher level of resistance than the mean score (4.4) for Haplo3-2 genotypes. Resistance levels for snap beans with Haplo2, Haplo3-1, or Haplo3-2 were undiscernible because only qualitative data existed for CTV reaction.Haplo3-2, on the other hand, is represented by older great northern bean cultivars  and more modern race Durango cultivars  with upright architecture obtained from race Mesoamerica introgressions. Few, if any, of the dry bean genotypes with Haplo3-2 were developed by breeding programs with active or passive selection for Bct EXO5 candidate gene should also include SNP markers S07_2966197 and S07_2970276 to monitor the different haplotypes and corresponding associations that they may have with quantitative levels of resistance observed by phenotyping. Meanwhile, based on information from this study, our recommendation is to use Haplo3-1 for MAS of CTV resistance.In light of these findings, breeding programs using S07_2970381 for MAS for CTV resistance conferred by the The original contributions presented in the study are included in the article/AS-G, JH, and PM conceived and designed the experiments. AS-G and JH implemented statistic and bioinformatic analyses. DG, AT, and CS performed and conducted phenotyping strategies. QS performed SNP chip assay genotyping. KG conducted virus detection in the field. All authors contributed to the article and approved the submitted version.


DUSP8) and insulin-like growth factor 2 (IGF2) genes and to explore their effects on inosine-5′-monophosphate (IMP), inosine, and hypoxanthine contents in Korean native chicken -red-brown line (KNC-R Line).This study aimed to identify the single-nucleotide polymorphisms (SNPs) in the dual-specificity phosphatase 8  in DUSP8 gene and two SNPs (rs315806609A/G and rs313810945T/C) in IGF2 gene were used for genotyping by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and KASP methods, respectively. The Two-way analysis of variance of the R program was used to associate DUSP8 and IGF2 genotypes with nucleotide contents in KNC-R chickens.A total sample of 284  and 230  aged of 10 weeks old KNC-R line was used for genotyping of IGF2 gene (rs315806609A/G and rs313810945T/C) was also polymorphic and had three genotypes per SNP, including GG, AG, and AA for the SNP rs315806609A/G and genotypes: CC, CT, and TT for the SNP rs313810945T/C. Association resulted into a strong significant association (p<0.01) with IMP, inosine, and hypoxanthine. Moreover, the significant effect of sex (p<0.05) on nucleotide content was also observed.The DUSP8 (rs313443014 C>T) was polymorphic in KNC-R line and showed three genotypes: CC, CT, and TT. The DUSP8 and IGF2 genes might be used as genetic markers in the selection and production of chickens with highly flavored meat.The SNPs in the  Meat has long been consumed as food in various cultures –3. ChickTo satisfy current meat preferences among consumers, which are based on quality, safety, and flavor, the National Institute of Animal Science (NIAS) of South Korea has attempted to develop a new high-quality KNC breed. The KNC-R line was selected for its higher meat quality, body weight, and contents of flavor-related compounds such as inosine 5′-monophosphate (IMP) compared to other KNC lines . IMP is Flavor is among the most important meat quality parameters influencing consumer acceptability ,19–23. NDUSP8) and insulin-like growth factor 2 (IGF2) as candidate genes influencing the nucleotide content in chickens [Unpublished manuscript]. To the best of our knowledge, this was the first study to explore the effects of DUSP8 and IGF2 genes on the content of IMP, inosine, as well as hypoxanthine in Korean native chickens.The identification of candidate genes influencing meat quality traits such as flavor is a crucial step in the genetic improvement of the polygenic traits ,28. The Dual-specificity phosphatases (DUSPs) dephosphorylate the residues of both phosphoserine/threonine and phosphotyrosine on mitogen-activated protein kinases (MAPKs) –34. MAPKIGF2 is a polypeptide hormone similar to insulin that performs many functions related to growth and the utilization of amino acids and glucose –42. In hDUSP8 gene has six exons and five introns (ENSGALG00000006647), whereas the IGF2 gene has three exons and two introns (ENSGALG00000035282). Both DUSP8 and IGF2 are involved in glucose uptake; glucose provides energy in the form of ATP, which undergoes further degradation to produce IMP, followed by inosine and hypoxanthine [DUSP8 and IGF2 genes and to explore their effects on the IMP, inosine, and hypoxanthine contents in KNC-R chickens.In chickens, DUSP8 and IGF2 have been mapped to chromosome 5. The xanthine ,45. We hThe protocol of this study was approved by the Animal Ethics Committee of Chungnam National University (202209A-CNU-141).ad libitum. Genomic DNA was extracted from 284 chickens  to examine SNPs in the DUSP8 gene and from 230 chickens  to examine SNPs in the IGF2 gene. All chickens were slaughtered at 10 weeks of age, and were not fed for 12 hours but had access to water before slaughter, and were killed by conventional neck cut followed by bleeding, feather removing, and eviscerating in accordance with ethical guidelines. Breast meats were obtained from each carcass. Finally, a 100-g sample was collected from the breast of each chicken for nucleotide analysis.KNC-R chickens were obtained from the NIAS at the poultry research institute in Pyeongchang, South Korea, and maintained under consistent management and feeding conditions. Water was provided Blood samples were collected from KNC-R chickens by the NIAS, transported to animal molecular genetics laboratory at Chungnam National University, and stored at −80°C until DNA extraction. A PrimePrep Genomic DNA Extraction Kit  was used to extract genomic DNA. The quality of genomic DNA was checked using spectrophotometry . DNA stocks were diluted with deionized distilled water to produce a working concentration of 25 ng/μL and stored at −20°C.DUSP8 gene containing the synonymous SNP of interest , and three pairs of primers were used to amplify all three exons of the IGF2 gene. The primers used in this study were designed using the primer-BLAST tool (https://www.ncbi.nlm.nih.gov/tools/primer-blast) and synthesized by Bioneer Corp. . All primers used in this study are listed in To identify SNPs, a pair of primers was designed to amplify the fragment (406 bp) of the chicken DUSP8 gene), and extension at 72°C for 60 s; with a final extension at 72°C for 10 min (Polymerase chain reaction (PCR) was performed in a 20-μL volume containing 2 μL of genomic DNA (25 ng/μL of chicken DNA), 1 μL each of the forward and reverse primers, 10 μL of HS Prime Taq Premix (2×) , and 6 μL of deionized distilled water. The conditions were: initial denaturation at 95°C for 3 min followed by 35 cycles of denaturation at 95°C for 30 s, annealing at 63°C for 45 s  and DNA quality and concentration were assessed via spectrophotometry . Sequencing was performed by Bioneer Corp. (Korea). In IGF2 gene, one SNP was discovered in intron 1  and a synonymous SNP was discovered in exon 3   ; both SNDUSP8 gene of the KNC-R line. We found six SNPs, including, one missense mutation  and five synonymous SNPs . The synonymous SNP  of DUSP8 was selected for amplification and genotyping. This SNP  of DUSP8 gene is found in the coding sequence (exon 1) leading to the alanine (A), an amino acid used as energy source and is converted into glucose by the liver during the intensive exercise[The next-generation sequencing (NGS) data for the KNCs was provided by the National Agricultural Biotechnology Information Center (NABIC), Jeonju, Korea to identify SNPs in the DUSP8 and IGF2 genes, respectively. For DUSP8, the restriction enzyme MspI was selected using NEBcutter2 software (https://nc2.neb.com/NEBcutter2/) to digest the amplicon (406 bp). The volume of the digestion mixture was 20 μL, containing 15 μL of PCR product, 0.4 μL of restriction enzyme (MspI), 2 μL of 10× CutSmart buffer, and 2.6 μL of deionized distilled water. The mixture was incubated at 37°C, for 12 h. Visualization of the DUSP8 genotypes was performed using a UV transilluminator following 3% agarose gel electrophoresis at 120 V for 30 min. For IGF2 genotyping, SNP target-specific primers were prepared for the KASP genotyping assay (The PCR restriction fragment length polymorphism (PCR-RFLP) and Kompetitive allele-specific PCR (KASP) methods were used to genotype the ng assay . The KASng assay  were valIMP, inosine, and hypoxanthine contents were analyzed using nuclear magnetic resonance (NMR) as described previously . BrieflyDUSP8 and IGF2 genes were calculated after genotyping. The genotype frequency was calculated as follows [Genotype and allele frequencies of the  follows :The allele frequency was calculated as follows :ii is the iith genotype frequency, xi is the ith allele frequency, nii is the number of samples with the iith genotype, nij is the number of samples with the ijth genotype, and N is the total number of samples.where xHardy-Weinberg equilibrium (HWE) was calculated as follows :where O and E are the total numbers of observed and expected genotypes, respectively, and N is the total number of samples.Yi,j,k = μ+αi+βj+(αβ)i,j+ɛi,j,kWe used R v4.2.1 software  to perfoi,j,k is the observation on nucleotide content, μ is the population mean for nucleotide content, αi is the genotype effect, βj is the sex effect, (αβ)i,j is the interaction effect between genotype and sex, and ɛi,j,k is the residual error.Where YTukey’s honest significant difference (HSD) test was used to compare mean values among the genotypes and the p-values (p<0.05) were considered to be statistically significant.IGF2 gene, the Sanger sequencing results were analyzed using Bioedit [IGF2 gene were based on a reference sequence from the Ensembl database . Two SNPs were identified, including one intron variant and one synonymous mutation  in DUSP8 was located in exon 1 of chicken chromosome 5. The 406 bp fragment  of the DUSP8 gene was successfully amplified and its PCR product was digested using MspI, resulting in three genotypes: CC (two fragments of 189 and 217 bp), CT , and TT (an uncut fragment of 406 bp) (Genotyping of the  406 bp) . These r 406 bp) .DUSP8 gene, the C allele was the most frequent in the KNC-R line (66%) followed by the T allele (34%) in the total samples. The CT heterozygous genotype was the most frequent (58%), followed by the CC (37%) and TT (5%) genotypes for the total samples  in the KNC-R line were in HWE  of the IGF2 gene in the KNC-R line (We detected a highly significant association (p<0.01) between IMP, inosine, and hypoxanthine contents and SNP rs313443014 (C>T) in exon 1 of the C-R line .We detected a significant association (p<0.05) between sex and the IMP and hypoxanthine contents in the KNC-R line. Females had a higher IMP content than males, whereas males had a higher hypoxanthine content than females .DUSP8 and IGF2 genes were previously investigated through a GWAS as candidate genes for altering nucleotide contents. Both DUSP8 and IGF2 are polymorphic genes in the KNC-R line. DUSP8 has been described as a gatekeeper for glucose disposal to cells [In this study, we investigated the breeding potential of KNC-R chickens, which have a higher IMP content than other KNC lines . The DUSto cells ; this glto cells ,35,45. Lto cells –42. IMP to cells ,52. Inosto cells ,17.DUSP8 gene resulted in three genotypes . Our results show that SNP of the DUSP8 gene  in the KNC-R line deviated from HWE for the population A and this influenced the total sample. This deviation from HWE might be attributed to the selection, which lead to the formation of the five lines of KNCs [Genotyping of the  of KNCs . Large p of KNCs ,54,55. O2 tests showed that both SNPs  of the IGF2 gene were in HWE (p<0.05) in the analyzed population. This may be attributed to the absence of evolutionary pressures such as mutation, selection, genetic drift, and gene flow in the population under consideration [The χderation ,54,55. TDUSP8 and IGF2 genes that are associated with nucleotide content variation in chickens is highly beneficial as these genotypes may be used in selection and breeding programs to improve chicken meat flavor. In future research, the results of this study should be validated using larger sample sizes and different KNC lines, as well as in different chicken breeds.Our study also detected effects of sex on nucleotide contents. Female chickens had a higher IMP content than male chickens, whereas males had a higher hypoxanthine content. These results are consistent with those of a previous study that reported a higher IMP content in females than in males . HoweverDUSP8 and IGF2 genes of the KNC-R line that influence chicken meat nucleotide contents. The heterozygous (CT) genotype of a synonymous SNP  of the DUSP8 gene had a higher IMP content, whereas the homozygous genotype (CC) had higher inosine and hypoxanthine contents. For IGF2, a heterozygous (AG) genotype of the SNP rs315806609 (A/G) and a CT genotype of the SNP rs313810945 (T/C) had higher IMP contents than homozygous genotypes, which had higher inosine and hypoxanthine contents. Female KNC-R chickens had a higher IMP content than males, whereas males had a higher hypoxanthine content than females. Therefore, the SNP rs313443014 (C>T) of the DUSP8 gene and the SNPs rs315806609 (A/G) and rs313810945 (T/C) of the IGF2 gene may be used as genetic markers in selection and breeding programs to improve the flavor of KNC-R chicken meat.In this study, we identified SNPs among the