TUFT1) gene is important in the development and mineralization of dental enamel. The study aimed to identify potential functionally deleterious non-synonymous SNPs (nsSNPs) in the TUFT1 gene by using different in silico tools. The deleterious missense SNPs were identified from SIFT, PolyPhen-2, PROVEAN, SNPs & GO, PANTHER, and SNAP2. The stabilization, conservation, and three-dimensional modeling of mutant proteins were analyzed by I-Mutant 3.0, Consurf, and Project HOPE, respectively. The protein–protein interaction using STRING, GeneMANIA for gene–gene interaction, and DynaMut for evaluating the impact of the mutation on protein stability, conformation, and flexibility.Tuftelin 1  out of 304 were found to have high-risk damaging effects using six in silico toolsTUFT1 gene. This will aid in the development of candidate deleterious markers as a potential target for disease diagnosis and therapeutic interventions.Our study predicted eight functional SNPs that had detrimental effects on the structure and function of the  The genotypic and phenotypic variation between individuals arises through genetic mutation. The genetic variation provides the diversity within and across populations. The source variance in a genome known as single-nucleotide polymorphism (SNP) is the most abundant genetic variation in the human genome . They caTUFT1 is expressed in the morula, embryonic stem cells, and soft tissues, such as brain neurons, testis, suprarenal gland, liver, kidneys, and tumor cells [TUFT1 expression induced by human HepG2 and MCF-7 cell lines when treated with 1% O2 in the hypoxic environment causes tumorigenesis [TUFT1, an acidic protein highly conserved and located on chromosome 1q21-31 with 13 exons and a phosphorylated glycoprotein of 390 amino acids, was initially discovered and sequenced from a complementary DNA library enriched in bovine ameloblasts. They are involved in the development and maturation of extracellular enamel which leads to the mineralization of the epithelial tissue of the vertebrate teeth . It is aor cells , 8. It iTUFT1 by mutation analysis associated with high caries experience in Turkish samples [TUFT1 is involved in the enamel [TUFT1 can be able to influence its expression and functions. This study aims towards examining the potential effect of nsSNPS in TUFT1 protein using a computational approach and screening deleterious nsSNPs by in silico method for further analysis.A study reported one nonsynonymous mutation in exon 1 of  samples . Previoue enamel . The TUFTUFT1 gene were obtained from the National Center for Biotechnology Information (NCBI) dbSNP database (http://www.ncbi.nlm.nih.gov/snp/).the). SNPs of TUFT1 were also retrieved from the ENSEMBL database. The TUFT1 protein primary sequence (UniProt accession number: Q9NNX1) was retrieved from the UniProt database.nsSNPs of the TUFT1 gene were analyzed using the following bioinformatics tools: SIFT and PolyPhen-2 were used to predict the deleterious nsSNPs. To increase the accuracy of the in silico approaches and for prioritizing deleterious nsSNPs, nsSNPs that were found to be deleterious by SIFT and PolyPhen-2 were further analyzed by PROVEAN, SNPs & GO, PANTHER, and SNAP2 tools.The effects of nsSNPs on the https://sift.bii.a-star.edu.sg/) [SIFT (edu.sg/)  is a powPolyPhen-2 (http:// genetics.bwh.harvard.edu/pph2/)  is an onhttp://provean.jcvi.org/index.php) [The biological impact of an amino acid substitution on a protein was predicted using the PROVEAN software tool (dex.php) . It predhttp://snps.biofold.org/snps-and-go/snps-and-go.html) [The disease relationship with the studied SNPs was analyzed using this online web server (go.html) . The reshttps://www.pantherdb.org/tools) [PANTHER (g/tools)  was usedhttps://rostlab.org/services/snap2web) [TUFT1 was provided as the input query.SNAP2 (nap2web)  predictshttp://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi) [I-Mutant server (3.0.cgi)  calculathttps://consurf.tau.ac.il) [TUFT1 was given as the input for identifying the evolutionary conservation of the predicted deleterious nsSNPs.The evolutionary conservation of amino acid positions in a protein molecule was predicted by the Consurf server (u.ac.il)  based onhttp://bioinf.cs.ucl.ac.uk/psipred/) [E value cutoff of 0.01 gives sensitivity and selectivity of domain boundary prediction.The PSIPRED workbench (sipred/)  makes avhttp://www.cmbi.ru.nl/hope/home) [Project HOPE web server (pe/home)  was usedhttps://string-db.org/) [A pre-computed database STRING (db.org/)  was usedhttps://genemania.org/) [TUFT1 gene.GeneMANIA (ia.org/)  is a webhttps://alphafold.ebi.ac.uk/) [The 3D structure was predicted using an artificial intelligence system, AlphaFold (.ac.uk/) , 24 whichttp://biosig.unimelb.edu.au/dynamut/) [Vib).The structure-based tool DynaMut (ynamut/)  was usedynamut/) , SDM [27ynamut/)  as well TUFT1 gene has a total of 10,860 SNPs, out of which we selected 304 missense nsSNPs for our investigation. Using various in silico prediction tools, we analyzed the deleterious nsSNPs and compared their scores with each tool. Various SNPs of TUFT1 were predicted using the variant effector predictor of ENSEMBLE as shown in Fig. The SNP database in NCBI contains both synonymous and non-synonymous polymorphisms. A total of 304 nsSNPs were selected for SIFT analysis. According to SIFT, the considered deleterious nsSNPs score is 0.05 or below. Among the 304 nsSNPs, 95 nsSNPs were predicted as damaging by SIFT tool whereas the remaining nsSNPs were predicted as “tolerated.”TUFT1 were considered for further analysis with other in silico tools.The deleterious nsSNPs filtered through the SIFT server were then subjected to the Polyphen server. Out of the 95 nsSNPs, 15 were considered to be “PROBABLY DAMAGING” with a score of 0.9–1, and 24 were observed as “POSSIBLY DAMAGING” with a score of 0.4–0.8. To increase the accuracy of the prediction, the results of both SIFT and Polyphen were combined and these deleterious SNPs of The 15 nsSNPs determined by SIFT and Polyphen were subjected to PROVEAN, SNPs&GO, and PANTHER software tools, respectively. Using PROVEAN prediction, 9 nsSNPS were found to be deleterious based on a default threshold score. According to SNPs&GO, 8 nsSNPs were associated with diseases. Moreover, via the PANTHER software tool, 2 nsSNPs were predicted as probably damaging and 13 were probably benign. The SNAP2 tool predicted 4 neutral nsSNPs and 11 nsSNPs were diseases associated. Deleterious and disease-related nsSNPs were investigated further by at least five in silico software. Finally, eight nsSNPs  were identified as the most deleterious and are shown in Table I-Mutant 3.0 analysis of the nsSNPs revealed that six of the eight deleterious nsSNPs decreased the stability of the TUFT1 protein as shown by its score, which was < 0 for for every mutation. Table TUFT1 were recognized by the ConSurf server  AMBN (Ameloblastin), RABGAP1 (RabGTPase-activating protein 1), ENAM (Enamelin), AMELX (Amelogenin), RABGAP1L (RabGTPase-activating protein 1-like), MMP20 , SMC6 , DHX15 (Pre-mRNA-splicing factor ATP-dependent RNA helicase), ALOX5AP (Arachidonate 5-lipoxygenase-activating protein) Fig. . Except TUFT1 gene. GeneMANIA revealed that 11 genes had physical interactions, 8 genes co-localize, 1 in pathway interactions, and 2 genes shared a protein domain with TUFT1.Figure An individual residue confidence score (pLDDT) between 0 and 100 is generated by the AlphaFold algorithm. The majority of the 3D structural region corresponds to α-helical domains and has extremely high confidence (pLDDT > 90). The remaining components of the model are depicted as unresolved loops with low (70 > pLDDT > 50) and extremely low (pLDDT 50) scores Fig. .Fig. 6AlVib ENCoM values, indicating enhanced molecular flexibility except for R117W. The prediction from the above server is given in Table The DynaMut server was used to evaluate the predicted interatomic interactions of eight harmful nsSNPs that were chosen from upstream analyses. The DynaMut server showed the predictions of the ΔΔG and Δ vibrational entropy energy by ENCoM between the mutant and wild-type. According to the predicted DynaMut ΔΔG values, R117W, H289Q, and Q107R were stabilizing the TUFT1 protein when compared to the wild type. The ΔΔG SDM value decreased in E242A, K182N, and K123N when compared to other mutants, and ΔΔS ENCoM showed destabilizing effect for all mutants. Amino acid alterations were detected for all the variants from ΔΔSTUFT1 using various in silico methods.Genetic differences between individuals can influence therapeutic response and drug-induced adverse effects in addition to disease susceptibility. Studying the effects of functional exonic SNPs in proteins correlated with the disease can help in developing new drugs to reverse the consequences of such mutations in the population. The current study predicted the consequences of nsSNPs of TUFT1 gene were initially determined using sequence-based methods such as SIFT and POLYPHEN and those predicted as deleterious were validated using PROVEAN, SNPs&GO, and PANTHER. The SNPs&GO gives the prediction of both PHD-SNP and PANTHER in addition. Differentiating the scores of all the in silico tools, E242A, R303W, K182N, K123N, R117W, H289Q, R203W, and Q107R, were found to be highly deleterious. Screening the 304 nsSNPs through six in silico tools, eight highly damaging nsSNPs were identified. These eight deleterious nsSNPs include rs4994616, rs148582735, rs149655288, rs149655288, rs150612239, rs369673392, rs370920800, and rs374164451.The nsSNPs of the The biological mechanism in protein, such as stability or folding, is generally controlled by conserved residues . EnzymatTUFT1. The substituted amino acids have various physiochemical characteristics that could damage the structure of the TUFT1 protein. The change in mass and charge of a protein have an impact on the spatial and temporal patterns of protein–protein interactions. The difference in charge by the mutation could cause the mutant residues and their nearby residues to repel one another [TUFT1 gene.Findings from the Project Hope software have given important details on the potential consequences of missense SNPs of  another . As pred another . The funWith high accuracy, AlphaFold predicts 3D protein structures and generates a predicted local distance difference test (pLDDT) on a range from 0 to 100 that measures confidence for each residue . Based oTUFT1 gene polymorphism using various in silico tools. In summary, it can be suggested that these eight SNPs  may affect the TUFT1 protein functions since they are found to be both structurally and functionally deleterious. Accordingly, prioritizing such SNPs for further analysis can be done by systemically analyzing their effects through these types of comprehensive studies. To confirm the deleterious variants of TUFT1, further laboratory analysis and in vivo studies are recommended.Our study explored the TUFT1, and the variants are likely to have an effect on the protein structure and/or function. Further wet lab data and genome association studies are needed to confirm the functional variants to consider as candidate markers in causing oral/dental diseases related to TUFT1 for diagnosis and therapeutic interventions.Our in silico SNP study identified eight potential high-risk deleterious nsSNPs of


The inference of biological relations between individuals is fundamental to understanding past human societies. Caregiving, resource sharing and sexual behaviours are often mediated by biological kinship and yet the identification and interpretation of kin relationships in prehistoric human groups is difficult. In recent years, the advent of archaeogenetic techniques have offered a fresh approach, and when combined with more traditional osteological and interpretive archaeological methods, allows for improved interpretation of the burial practices, cultural behaviours, and societal stratification in ancient societies. Although archaeogenetic techniques are developing at pace, questions remain as to their accuracy, particularly when applied to the low coverage datasets that results from the sequencing of DNA derived from highly degraded ancient material.The performance of six of the most commonly used kinship identifcation software methods was explored at a range of low and ultra low genome coverages. An asymmetrical response was observed across packages, with decreased genome coverage resulting in differences in both direction and degree of change of calculated kinship scores and thus pairwise relatedness estimates are dependant on both package used and genome coverage. Methods reliant upon genotype likelihoods methods  show a decreased level of prediction at coverage below 1x, although were consistent in the particular relationships identified at these coverages when compared to the pseudohaploid reliant methods tested . The three pseudohaploid methods show predictive potential at coverages as low as 0.05x, although the accuracy of the relationships identified is questionable given the increase in the number of relationships identifIed at the low coverage (type I errors).Two pseudohaploid methods (READ and Kennett 2017) show relatively consistent inference of kin relationships at low coverage (0.5x), with READ only showing a significant performance drop off at ultralow coverages (< 0.2x). More generally, our results reveal asymmetrical kinship classifications in some software packages even at high coverages, highlighting the importance of applying multiple methods to authenticate kin relationships in ancient material, along with the continuing need to develop laboratory methods that maximise data output for downstream analyses.The online version contains supplementary material available at 10.1186/s12864-023-09198-4. Over the past two decades, ancient DNA studies have made outstanding contributions to the understanding of historical human migrations that occurred on both a regional and continental scale –5. HowevThe most common method of identifying biological pairwise relatedness in genetic studies is the calculation of the kinship coefficient (nt (IBD)  and whosmentclasspt{minimant (IBD) . Notablynt (IBD) . To addrThe majority of ancient DNA kinship estimation software packages calculate a pairwise e genome . This ale genome . The sece genome . Such meDespite developments in ancient DNA recovery, datasets are still typically low in both data quantity and quality, resulting in low or even ultra-low genome coverage data . This problem is typically not a consideration during the development of aDNA specific kinship estimation software packages, with the focus instead on data of intermediate (2x and over) genome coverage.Here, we compare the performance of six relatedness-inference software methods explicitly designed for ancient DNA data . All statistical tests reported in the main text have been performed on a combined dataset containing the results of analysis on the three ancient WGS datasets. Results for each dataset can be found in the supplemental information (Table S1.1-1.14), but mirror patterns seen in the combined WGS datasets. The mean genome coverage for all sequence data was calculated using coverage command in samtools v1.12  coverage level that are consistent with those identified at maximum coverage level / total # of relationships identified at [x] coverage level.Consistency = # relationships identified at [x] coverage level that are consistent with those identified at maximum coverage level / total # of relationships identified at maximum coverage level.False positives were defined as the number of specific relationships at [x] coverage not identified at maximum coverage in the modern dataset. False negatives were defined as the number of specific relationships at maximum coverage not identified at [x] coverage.To identify how consistent each package was across coverage levels, measures to determine the accuracy and consistency of each packages were calculated across all datasets  with data visualisation performed in R version 4.1.1 . All assBelow is the link to the electronic supplementary material.Additional file 1: Identifying biological kinship in ancient datasets of ultra-low coverage: a comparison of aDNA specific software packages. Figure S1. The effect of low coverage data on biological relationship calculations using the R0, R1 and KING ratio method (3). Figure S2. Variation in the number of pairwise relationships identified between packages and across coverages. Figure S3. The consistency and accuracy (as defined in the methods) at all coverage levels for each kinship calculation method. Additional file 2: Table S1.1. Results from all datasets using program lcMLkin and autosomal sites from the 1240k SNP panel. Table S1.2. Results from all datasets using program NGS relate and all shared autosomal sites. Table S1.3. Results from all datasets using program NGSrelate and autosomal sites on from the 1240k SNP panel. Table S1.4. Results from all datasets using program NGSremix and autosomal sites on the 1240k SNP panel. Table S1.5. Results from all datasets using program TKGWV2 and site from the 1240k SNP panel. Table S1.6. Results from all datasets using program TKGWV2 and autosomal sites from the panel of 22M SNP sites as described in the text. Table S1.7. Results from all datasets using program READ and autosomal sites from the 1240k SNP panel. Table S1.8. Results from all datasets using the Kennett method and autosomal sites from the 1240k SNP panel. Table S1.9. Mantel tests for all packages and datasets. Table S1.10. Accuracy, consistency and false positive calculations for the Koszyce/Schroeder dataset. Table S1.11. Accuracy, consistency and false positive calculations for the UP/Sikora dataset. Table S1.12. Accuracy, consistency and false positive calculations for the Viking/Willeslev dataset. Table S1.13. Accuracy, consistency and false positive calculations for the three WGS datasets. Table S1.14. Accuracy, consistency and false positive calculations for the modern/Fula dataset. Table S1.15. Coverages for all downsampled datafiles as calculated using samtools.


Biopsy sampling of pre-implantation embryos has been described since 1968. However, it was only after 2010, with the advancement of molecular biology techniques such as whole genomic amplification and SNP Chips, that next-generation sequencing became commercially available for bovine embryos. It is now possible to make decisions about which embryos to transfer not only based on recipients’ availability or embryo morphology but also on genomic estimates. This technology can be implemented for a wide spectrum of applications in livestock. In this review, we discuss the use of embryo biopsy for genomic selection and share our experience with Gir and Girolando Brazilian breeding programs, as well as future goals for implementing it in Brazilian bovine  Embryo biopsy involves manipulating embryos to extract samples. It is a well-established practice in veterinary medicine  with sevSignificant progress has been made in molecular biology techniques and equipment in recent decades, enabling the analysis of large amounts of molecular markers at a relatively low cost requiring lower DNA input in chips containing thousands of SNP (single nucleotide polymorphisms) of interest . Global Combining assisted reproduction techniques in cattle with genomic selection confers sophistication and has the potential to increase efficiency of animal production. In vitro production of embryos can maximize the number of offspring from genetically superior animals, and the use of genomic estimates for embryo selection enables targeted animal production. Embryo genomic evaluation allows to estimate the productive potential of individuals since their embryonic stage and embryos with high added value can be frozen and traded in straws or transferred to recipients. Economic and environmental losses resulting from the production and breeding of low-value animals, such as males in herds intended for milk production, animals carrying genetic anomalies, and even animals with performance below the minimum acceptable for a particular group, can be avoided. This practice can save embryos recipients, as the breeder can choose which embryo will be transferred based on genomic breeding value and reduces the exchange of recipients between properties and the resulting logistical and sanitary problems.Genomic selection was introduced in the milking Gir and Girolando breeding programs by a collaboration between Embrapa and breeders’ associations in 2017. Our current challenge now is to enable the genomic selection of embryos throw biopsy samples in these breeding programs, in which three main technological challenges were identified: (i) offering simple and effective protocols to enable the diffusion of biopsy technology to small laboratories, (ii) associating the optimized biopsy protocol to whole genome amplification and genotyping using appropriate SNP chips, and (iii) ensuring that the entire process can be applied to Gir and Girolando databases, validating genomic estimated breeding values by comparing them with the values of born animals. We discuss these topics below., biopsied samples from human  has been used since 1989 to identify the sex of embryos  or speciIn cattle, the first studies using karyotyping have estimated that 13.7%  of in viRecently, PGT-A human-like approaches have been applied to bovine embryos, such as the use of Karyomapping  . Using SAn approach that should be mentioned is the use of cell-free DNA from embryo culture medium for analysis. In 2016, it was proven that DNA released by human blastocysts into the culture medium (cfDNA - cell-free DNA) is useful for estimating the chromosomal content of embryos, with a high correlation with data from trophectoderm (TE) biopsy analysis to detect aneuploidy . A recenPerforming biopsies on blastocyst-stage embryos has many advantages over other approaches described for earlier stage embryos , includiAnother possible approach is the use of a microblade, also known as a handmade biopsy . In thisSeveral studies confirm the safety of biopsy for bovine embryos derived from both superovulation and in vitro fertilization. Although some embryos may be lost during the biopsy, those that survive the procedure and reexpand have good pregnancy rates (26.37 to 71%), equivalent to non-biopsied embryos in each paper .Embryo biopsy does not affect cryosurvival. Many studies in mouse and human embryos have demonstrated similar results, and biopsy coupled with vitrification has been followed in medical practice aiming to perform PGT before embryo transfer in human patients  Studies The size of the biopsy, the embryo stage, and the embryo quality can impact the survival of the embryo and the quality of the sample obtained for genotyping. These are important points to consider when performing embryo biopsy for genotyping.Regarding the size of the biopsy, it is important to strike a balance between the risks to embryo implantation and the quality of the sample obtained. With fewer cells, the sample could be more sensitive to errors during the pre-amplification process. Missing genotypes and allele drop out drastically increased when less than 30 cells are processed throw MDA-based techniques, but embryo viability is reduced when more than 10 cells are collected throw biopsy . Call raThe embryonic stage of blastocysts at the time of biopsy may affect the efficiency of the process, particularly for microblade biopsies. Early, regular, and expanded blastocysts have similar re-expansion rates, but after embryo transfer, pregnancy rates are increased in blastocysts that were expanded at the biopsy procedure .We noticed that expanded blastocysts with larger blastocoel cavities are easier to manipulate, resulting in a shorter procedure time and potential benefits for the structures. In early blastocysts, which were predominant on day 6 (46.1% under our conditions), it can be difficult to preserve the inner cell mass and remove cells due to their small size. Additionally, a reduction in the total number of cells is expected in biopsied embryos compared to controls (approximately 25% fewer cells in our conditions), reflecting the removal of cells for the biopsy sample and damage to surrounding cells. In this sense, an increase in apoptosis was also reported, which indicates the injury caused by the procedure possibly due to the microblade contact . FollowiEmbryo quality, as usual, affects pregnancy establishment in biopsied embryos, and grade 1 rates are higher than grade 2 .Another interesting finding concerns pregnancy losses. In a controlled study carried out at an Embrapa Dairy Cattle Experimental Farm, following transfer of non-biopsied and microblade biopsied embryos to recipients, we observed absence of pregnancy losses in biopsied embryos during the first trimester, unlike the control embryos (non-biopsied), which presented relevant losses (17.6%) . Early eThe main steps for genomic selection using embryo biopsies are summarized in Small samples have a limited amount of DNA template. A biopsy of 10-20 cells contains around 100 pg of DNA, while at least 200 ng of DNA is required for genotyping assays. Therefore, to perform genotyping from small samples, it is necessary to expand the genomic content in order to obtain sufficient DNA for commercial SNP panels.Global genome pre-amplification in embryonic samples was initially described by , and itsIn MDA method, random primers that cover the genome are employed to initiate DNA replication by polymerase. In this method, Phi29 enzyme has a displacement activity, unwinding the double helix and allowing additional primers and enzymes to access DNA at the site . The resWe conducted an experiment using Gir and Girolando embryo biopsies to evaluate the quality parameters of the MDA-amplified sample before sending it for genotyping . We founAnother point worth mentioning is the CallRate parameter (CR), obtained in return for the genotyping performed. Although very high in the case of genomic DNA samples obtained from hair or blood, this parameter is frequently reduced in biopsy samples submitted to global genome amplification. This phenomenon originates in missing genome regions, which were not properly copied in the amplification process and will be absent in the genotyping analysis, causing inconsistencies between the embryo's and the born animal's genotype  chips were developed as a high-throughput genotyping platform to genotype hundreds of thousands of SNPs across the genome. SNP chips have revolutionized the field of genomics, allowing for high-throughput genotyping of large numbers of samples at a relatively low cost. They are widely used in animal and plant breeding programs, as well as in human genetics research, and have led to significant advances in our understanding of the genetic basis of complex traits.The development of SNP chips began in the late 1990s and early 2000s when the first SNP maps were being constructed for the human genome. The first SNP chip for cattle was the Illumina BovineSNP50 BeadChip, launched in 2008, which contained approximately 54,000 SNPs selected from a pool of over 800,000 SNPs identified in the cattle genome. The chip was designed to capture genetic variation across all major beef and dairy cattle breeds, as well as other populations of cattle. The BovineSNP50 BeadChip has been widely adopted by the cattle industry and is currently used in many cattle breeding programs around the world. Illumina has released several updated versions of the chip, including the BovineHD BeadChip, which contains over 777,000 SNPs, and the BovineLD BeadChip, which contains approximately 6,000 SNPs that are highly informative for linkage disequilibrium-based analyses. These SNP chips have greatly facilitated genomic selection and other applications of genomics in cattle breeding programs.Whole-genome amplification inevitably results in a random loss of information and introduces erroneous genotypes, which poses a challenge for interpreting genomic results obtained from embryonic samples. Therefore, the accuracy of genomic information heavily relies on genotype imputation, a process where unobserved genotypes can be statistically inferred using known haplotypes in a given population. This approach increases the density of single nucleotide polymorphisms (SNPs) and expands the coverage of genotyping results . By predSome commercial breeding companies discard genotype results with call rates below 0.85 to minimize error rates in SNP genotyping and genomic estimated breeding values in European breeds . From ouAs reported , imputatTo further improve GEBV estimates in Gir embryo samples, using genotyping panels with closer association to the BovineHD panel would be beneficial, rather than relying on the Z-Chip panel. Additionally, the use of lower density panels for genotyping following imputation to high-density panels could be a cost-effective alternative for commercial applications.Genomic selection is based on the use of a high number of molecular markers spread all over the genome, together with phenotype data obtained from a reference population, to predict the phenotypic values of a non-phenotyped population. The estimation of genomic breeding values rapidly increases the genetic gain by shortening the generation interval in a given population . With thThe breeding programs for most dairy and beef breeds in Brazil take genomic information into account for genetic evaluation. Differences among these programs include parameters such as the number of genotyped animals, marker density, and evaluation models. In vitro embryo production is a consolidated tool for disseminating desirable genetics in genetic improvement programs. Therefore, nondestructive biopsy for embryo selection would be the fastest way to achieve the goals set in genetic improvement programs, reducing the generation interval and costs related to the milk industry .Over the past 35 years, our team at Embrapa Dairy Cattle, in coordination with breeders' associations (ABCGIL and Girolando), has implemented genetic improvement programs for the Gir and Girolando breeds, resulting in a doubling of milk yield. In 2017, the implementation of genomic selection, for milk production and age at first calving traits in these breeding programs, has led to a significant increase in the genetic gain by reducing the generation interval through the selection of young sires and dams.By 2018, genomic selection was definitively implemented into the Gir breeding program, resulting in the publication of the first genomic progeny testing summary for sires and dams. Since then, the number of genotypes, phenotypes, and pedigree information has significantly increased.Genomic selection has also been used in the Girolando breed to correct pedigree errors and estimate the genomic estimated breeding values (GEBVs) of young sire candidates for progeny testing since 2016. Clarifide® Girolando, a commercial genotyping tool, was developed in partnership with Embrapa Dairy Cattle, the Girolando breeders’ association, Zoetis, and CRV Lagoa. It allows for the estimation of Genomic Prediction Transmitting Ability (GPTAs) for milk production, calving interval, age at first calving, and genetic tests for hereditary diseases in dairy cattle. Monthly analyses are performed and results of progeny testing with genomic evaluation for sires and dams are published once or twice a year in the herd book.With genomic prediction tool, animals are selected before their milk production is verified with high accuracy, according to breeders' requirements. As a result, the demand for genetically superior animals, semen, and embryos has increased both domestically and internationally.The use of embryo biopsy in Gir and Girolando animals in Brazil is expected to be introduced gradually, starting with a small proportion of the animals that would be genotyped. Currently, this encompasses 100% of the Gir and Girolando bulls used in the National breeding program and 78% of registered Gir females in 2022. The Gir female percentage appears to be on the rise when compared to preceding years (74% in 2021 and 69% in 2020) . Furthermore, Brazilian breeders have demonstrated a strong inclination towards embracing technological advancements in the field of animal reproduction. This is evident from the significant increase in IVF adoption following its implementation in Brazil. According to statistics provided by ABCZ , the ratThe main benefit of embryo biopsy in cattle is genomic selection before the calf birth. Differences of up to 875kg of milk per lactation are found between sister embryos . In Gir,Expenses related to the production of animals that will be destined for disposal could be avoided. The Brazilian cost of calf production was estimated at R$1,561 in 2017 , and froEmbryo genomic selection can also have an impact on the in vitro production market embryos in Brazil, which was about 441,000 embryos produced and 435,000 embryos transferred in 2021 . It can Genomic selection before embryo transfer also represents an important step towards sustainability, as the increase in animal performance and directed production preserves environmental resources, using smaller and more productive lands in the activity. Furthermore, a smaller and more productive herd is expected to have a lower carbon footprint, as the number of animals is directly related to greenhouse gas emissions.Due to the potential gains conferred by genetic improvement programs and dairy cattle industry, as well as the Brazilian characteristics of worldwide leadership in the in vitro embryo production market, and in the exploitation of Gir and Girolando breeds and their high adaptability to dairy production systems in the tropics, we believe that genomic selection in embryos will be part of the Brazilian reproductive biotechniques portfolio in the near future. Thus, this technology may catalyze productivity increases in Brazil, with a consequent reduction in the carbon footprint of milk produced. The use of DNA recovered from media represents a promising approach in this context, if it is proven compatible with bovine IVP and genomic selection protocols.


OsENT1 was prioritized as the most plausible candidate gene because its expression profile was strongly negatively correlated with the seven root phenotypes. In addition to OsENT1, OsEXPA31, OsSPL14, OsDEP1, and OsDEC1 were identified as candidate genes responsible for root phenotypes using TWAS. Furthermore, a cis-eGWAS peak SNP was detected for OsDjA6, which showed the eighth strongest association with lateral root volume in the TWAS. The cis-eGWAS peak SNP for OsDjA6 was in strong linkage disequilibrium (LD) with a GWAS peak SNP on the same chromosome for lateral root volume and in perfect LD with another SNP variant in a putative cis-element at the 518 bp upstream of the gene. These candidate genes provide new insights into the molecular breeding of root system architecture.Root system architecture plays a crucial role in nutrient and water absorption during rice production. Genetic improvement of the rice root system requires elucidating its genetic control. Genome-wide association studies (GWASs) have identified genomic regions responsible for rice root phenotypes. However, candidate gene prioritization around the peak region often suffers from low statistical power and resolution. Transcriptomics enables other statistical mappings, such as transcriptome-wide association study (TWAS) and expression GWAS (eGWAS), which improve candidate gene identification by leveraging the natural variation of the expression profiles. To explore the genes responsible for root phenotypes, we conducted GWAS, TWAS, and eGWAS for 12 root phenotypes in 57 rice accessions using 427,751 single nucleotide polymorphisms (SNPs) and the expression profiles of 16,901 genes expressed in the roots. The GWAS identified three significant peaks, of which the most significant peak responsible for seven root phenotypes  was detected at 6,199,732 bp on chromosome 8. In the most significant GWAS peak region, The online version contains supplementary material available at 10.1186/s12284-023-00672-x. Oryza sativa) is an indispensable staple crop for sustainable food production in many Asian countries  can identify genomic regions for the phenotype of interest using a diversity panel sequenced and genotyped for single nucleotide polymorphisms (SNPs) or insertion/deletion variants (indels), which is less laborious and time-consuming than creating a biparental population for QTL mapping. Beginning with pioneering studies using approximately 200 rice accessions with approximately 30,000 markers for root phenotypes measured in hydroponic cultivation systems  or expression QTL (eQTL) mapping, and has been used to reveal the genomic basis of transcriptome variations in rice  was developed and recently resequenced  value was calculated and defined as the expression profile. We defined the gene as not expressed if the expression profile was lower than 1. After calculating the expression profiles, four non-WRC lines were excluded from the dataset. Finally, we excluded 39,085 genes not expressed in more than 50% WRC accessions. This generated an expression profile matrix of 16,901 genes for the 57 WRC accessions.From the read count matrix, fragments per kilobase of exon per million read (FPKM) values were calculated based on the trimmed mean of M value normalization in the {edgeR} package version 3.38.1 . Using the GATK Best Practices for germline SNP/indel discovery  for the GWAS, the statistical power to identify an association between allelic and phenotypic variations was expected to be low, particularly for SNPs with low minor allele frequency (MAF). Therefore, we applied stringent MAF-based filtering to the 57 accessions to retain SNPs with MAF > 10% using VCFtools version 0.1.16  within 100 kb by setting the step size of the sliding window to 100 variants. This pipeline generated a SNP genotype dataset comprising 427,751 SNPs for 57 WRC accessions.Considering the small sample size , aus (n = 19), indica (n = 21), and japonica (n = 10); and (iii) with six subpopulations by dividing the 10 japonica accessions into admixed-japonica (n = 3), temperate-japonica (n = 3), and tropical- japonica (n = 4). The BIC value in the mixed model was computed based on the likelihood described by Kang et al.  > 0.50 were merged as a single peak locus, assuming that those SNPs were likely to be in LD with the same causal variant.To define the peak loci, we considered the physical distance and LD among the significant SNPs for each root phenotype. First, SNPs with FDR-adjusted r2 statistic was calculated for all SNP pairs within 500 kb of the peak SNP and visualized as a heatmap using the {LDheatmap} package  were included in the model as explanatory variables without considering the interaction between the two variables.To further prioritize the candidate genes from the search interval, we tested the statistical dependence between the expression profile of the candidate genes in root samples and the genotype of the GWAS peak SNP using an analysis of variance (ANOVA). For each GWAS peak locus, the expression profile of each gene in the ± 250 kb searching interval was used as the response variable if the gene was expressed in the root. The number of alternative alleles of the GWAS peak SNP  and subpopulation . Furthermore, we investigated the annotation and literature of all genes included in the top 10 strongest associations for each phenotype so as not to miss associations that did not pass our significance threshold but were stronger than the others. Additionally, the Pearson’s correlation coefficient between the expression profile and phenotypic value was calculated for the top 10 genes using all WRC accessions, as well as for each subpopulation.TWAS is expected to complement the candidate gene search in GWAS by testing the statistical relationship between expression profiles and phenotypic values. We performed the TWAS using a method similar to that used in previous studies   SNP within 250 kb of the gene position in the eGWAS, the most significant SNP was defined as the cis-eGWAS peak SNP for the gene. As we did not detect any significant trans-eGWAS SNPs for any tested gene, we explained the method used to compare the GWAS and eGWAS results when a cis-eGWAS peak SNP was detected. We calculated the physical distance (bp) and pairwise LD (r2) between the cis-eGWAS peak SNP and the SNP with the lowest P-value on the same chromosome in the GWAS for each relevant phenotype with which the gene possessed the top 10 associations in the TWAS. If the two SNPs were closer than 250 kb and their pairwise LD was > 0.50, we considered the gene to have an overlapping peak between the eGWAS and GWAS. An overview of the analysis pipeline is provided in Additional File 2  for RL_C. The second peak SNP found at 20,665,890 bp on the same chromosome was significantly associated only with RSA_C . The third peak SNP at 17,902,506 bp on chromosome 11 was significantly associated with RSA_L  and RV_L  within 500 kb around the peak SNP did not show any obvious LD blocks  with the peak SNPs were located within 60 and 250 kb of the peak SNP, respectively  responsible for GWAS peaks. The pairwise LD (P = 1.43 × 10− 13) to the expression profile of OsENT1 gene (LOC_Os08g10450), which is located from 6,142,125 to 6,144,418 bp (approximately 55 kb from the GWAS peak SNP) and encodes an equilibrative nucleoside transporter  in all subpopulations , which putatively encodes a protein similar to dihydrolipoamide S-acetyltransferase. Besides, OsMADS23 , which encodes a stress-responsive MADS-box transcription factor and functions as a positive regulator in response to osmotic stress by regulating ABA biosynthesis  genes for the second peak SNP. The top ANOVA hit gene for the third peak SNP was LOC_Os11g31110 (P = 2.42 × 10− 3), which was annotated as a conserved hypothetical protein.Except for the most significant peak SNP, the ANOVA did not find any other candidate genes with strong significance and an interpretable annotation. The top ANOVA hit gene for the second peak SNP was P-value < 0.10 for four root phenotypes: three genes  for RD_C, one gene (LOC_Os02g54580) for RV_C and RDW, and one gene (LOC_Os12g32536) for RD_L. Among the five genes, only LOC_Os03g31480 (OsEXPA31) and LOC_Os03g02750 (OsSub25) had a gene symbol in RAP-DB  were not candidate genes according to the GWAS, but listed as promising candidates according to the results of previous studies. The first candidate gene, OsEXPA31, had the strongest association with RD_C and was one of the six significant  candidate genes according to TWAS. Although OsEXPA31 has not been functionally characterized, other α-expansin genes have been characterized for their involvement in root phenotypes, such as primary root length or root hair elongation  as well as in each subpopulation (r = 0.70–0.87), which was consistent with the results of a recent mutant-line-based study reporting that the crown roots thickened after increasing OsSPL14 expression in the roots  found in this study implies an uninvestigated function of the gene for the root architecture. Lastly, OsDEC1 was negatively associated with RL_C (r = − 0.64 in the 57 WRC accessions), which seems to be in line with the negative effect of OsDEC1 on internode elongation , OsEXPA9 (LOC_Os01g14660), OsEXPA18 (LOC_Os03g06040), OsEXPA19 (LOC_Os03g06050), and OsEXPA31 (LOC_Os03g31480). This enrichment is consistent with the known role of expansins in root growth by mediating cell wall loosening , OsPRX42 (LOC_Os03g25280), OsPRX43 (LOC_Os03g25300), OsPRX54 (LOC_Os04g34630), OsPRX68 (LOC_Os05g04450), and OsPRX102 (LOC_Os07g31610). Although peroxidases are involved in several physiological processes throughout the plant life cycle, one of their major roles is cell wall modification and loosening by regulating the reactive oxygen species level  were eventually identified as common associations detected in all three statistical mapping methods  and was located within 50 kb of the GWAS peak SNP responsible for NRT_L  with the eGWAS peak SNP  and showed a visible relationship with OsENT1 expression profile as well as with NRT_L phenotypic values .As expected from the GWAS and TWAS results, one of the two common associations was that between T_L Fig. A. We fouOsDjA6 (LOC_Os04g46390) and RV_L  on chromosome 4 was in high LD with eGWAS peak SNPs (r2 = 0.97). Moreover, 24 polymorphic variants were present from 2 kb upstream to the end of the OsDjA6 gene region in the 57 WRC accessions, of which nine variants showed high LD (r2 > 0.90) with both eGWAS and GWAS peak SNPs  located in a TATA box-like motif, whereas the other three were not located in any putative promoter motif. This SNP exhibited the same segregation pattern to the eGWAS peak SNP in the 57 WRC accessions: eight indica accessions  had alternative alleles, whereas the remaining 49 accessions had reference alleles  and two weakly significant  genomic regions associated with at least one root phenotype were identified using TWAS and GWAS, respectively. In addition, GO enrichment analysis highlighted the importance of genes related to cell wall organization and response to oxidative stress in the natural variation in RD_C. While the sample size (n = 57) was limited to detect small-effect genes or loci, our statistical analyses dissected the genetic control of root phenotypes in a diverse panel and identified the candidate genes for molecular breeding and functional genomics of root system architecture.To optimize the root system architecture in rice through molecular breeding, statistical mapping is a promising approach for identifying the candidate genes by leveraging the natural variation in root phenotypes. Thus, we used GWAS, TWAS, and eGWAS to explore the candidate genes related to the natural variation of the 12 root phenotypes using the genotypes of 424,888 SNPs and the expression profiles of 16,901 genes in 57 rice accessions. Our comprehensive statistical analyses identified OsENT1 is a candidate gene responsible for NRT_L. Both GWAS and eGWAS identified a significant peak in proximity to this gene, with the sixth strongest association for NRT_L in TWAS, and there was a SNP variant at a putative splicing site showing a high LD with the cis-eGWAS peak SNP. Based on the RiceXPro database  expression profiles in roots, particularly in the root elongation zone  encoding potential equilibrative nucleoside transporters have been identified in rice. Although the function of OsENT1 in rice is not well understood, an expression analysis in yeast cells suggested that OsENT2 plays a role as a cytokinin transporter  with that of AtENT8  were negatively associated with RD_C, of which OsEXPA31 showed the strongest association and a strong negative correlation with RD_C (r = − 0.69) in the 57 WRC accessions. The expansin proteins, discovered by McQueen-Mason et al.  transcription under low nitrogen supply  could be identified without transcriptome data. Although OsENT1 was detected within ± 250 kb region from the GWAS peak SNP, it was impossible to shed light on its involvement without testing the statistical relationship between the expression profile and phenotypic value. Similarly, TWAS and eGWAS revealed a weak association of GWAS around the cis-regulatory region of OsDjA6. Although a GWAS can identify a genomic region associated with phenotypic variations, resolving the GWAS peak to a single candidate gene is often difficult. In contrast, the gene-level associations from the TWAS enabled us to discover novel associations of genes previously characterized for a shoot phenotype, such as OsSPL14, OsDEP1, and OsDEC1, with the root phenotypes.Our results highlight the advantage of transcriptomics for candidate gene search, as none of the six candidate genes  with the peak SNP. Figure S7. Genome-wide LD decay. Figure S8. The number of overlapped genes between the top 1% associations for different root phenotypes. Figure S9. The dependence of (A) the expression profile of OsENT1 and (B) the number of lateral root tips on the SNP at a putative splicing site of OsENT1. Figure S10. The dependence of (A) the expression profile of OsDjA6 and (B) the lateral root volume on the SNP in a TATA box-like motif at an upstream of OsDjA6 in indica subpopulation


Haliotis discus hannai) is an expensive seafood in Asian countries and an important species in fishery and mariculture industries, generating marginal revenue for the Chinese and South Korean economies. South Korea is the second largest producer of abalones, followed by China   . In partth Korea . Two majeratures . While oeratures . Thus, geratures . Many sceratures , proteomeratures , and meteratures  analyseseratures . Heat stolyticus , affectiolyticus  as well olyticus . To our sessment . In thisGenotype data was generated for heat-resistant and -sensitive abalones from three different populations. Initially, 96 SNPs selected from the GBS dataset and those used to build the targeted Fluidigm chip were used for validation in two additional populations. These datasets were subjected to ML methods to develop a predictive model for heat-resistant and -sensitive abalone phenotypes. This dataset could be used to generate a genetic library for genome-assisted breeding for abalone.A total of 400 abalones were selected from the Genetic and Breeding Research Center, NIFS, in Geoje, South Korea, and from a commercial farm in Wando and Heanam, South Korea . The avePstI at 37°C for 2 h and heated to 65°C for 20 min to inactivate the enzyme. Six DNA libraries were constructed for GBS as described previously  was digested with 8 U of high-fidelity eviously , pooled,eviously . A summaπ) were calculated with VCFtools v. 0.1.3 . The target SNP genotyping chip was constructed with a Fluidigm 96.96 dynamic array integrated fluidic circuit (IFC) using an Adventa sample ID genotyping panel. For chip design, the primers for each SNP were selected as 100 bp of the flanking regions. Primers such as allele-specific primers (ASPs), locus-specific primers (LSPs), and specific target amplification primers (STAs) were designed using the Fluidigm SNP type assay protocol. The PCR cocktail was prepared with ASPs, LSPs, and STAs according to the manufacturer’s protocol along with the high-quality DNA prepared from each sample. The samples were loaded in a 96-well plate (12 columns x 8 rows) and subjected to the SNPtype 96X96 thermal cycling protocol with a FC1 PCR cycler to detect fluorescence by Biomark HD and processed with the SNP Trace™ Panel Analysis tool in the SNP genotyping analysis software. Genomic DNA (gDNA) quality was assessed by the concentration (ng/μLS) of each sample, which was measured with a Biotek Epoch spectrometer at 260/280 nm. All experimental protocols were performed by the TNT research service provider in Anyang, South Korea. A detailed summary is provided in To assess the classification potential of selected SNPs, 7 ML models were used: AdaBoost (AB), Bagged Tree (BT), Generalized Boosted Regression (GBR), Boosted Logistic Regression (BLR), partial least squares (PLS), Random Forest (RF), and support vector machine with Linear Kernel (SVM-LK). In this study, we generated three datasets (datasets 1–3). We developed the basic models using a pooled dataset with individual validation datasets to understand the association of SNPs with three different populations. ML was performed using the ‘train’ in function in the caret package in R software with tenfold cross validation  . Model assessments were performed using parameters such as accuracy, kappa, sensitivity, specificity, pro pred value, negative pred value, precision, recall, F1, prevalence, and balance accuracy as described previously .In total, 185.9 GB of sequence was generated by GBS from 263 samples, and 81.59% of the reads were mapped to the abalone genome . The map


CTSS gene polymorphisms and litter size was revealed by analyzing the DNA sequence and mRNA expression of CTSS in the gonadal axis of Qianbei Ma goats. In addition, bioinformatics methods were used to evaluate the effect of non-synonymous mutations on CTSS protein structure and function. CTSS was expressed in all parts of the gonadal axis of Qianbei Ma goats, with the highest expression in the uterus in the multi-lamb group and in the fallopian tube in the single-lamb group. The sequencing results showed that four SNPs in CTSS, including g.7413C → T, g.8816A → T, g.9191 T → G and g.10193G → A, were significantly correlated with litter size (p < 0.05). All four analyzed mutation sites were in strong linkage disequilibrium . Additionally, the haplotype Hap1/2 had a significantly higher frequency than the other haplotypes (p < 0.05). g.7413C → T and g.8816A → T were non-synonymous mutations. The g.7413C → T mutation resulted in the substitution of serine 161 of the CTSS protein with phenylalanine (p.S161F), and the g.8816A → T mutation resulted in the substitution of aspartate 219 with tyrosine (p.N219Y). p.S161F was highly conserved across 13 species and that p.N219Y was relatively conserved in cloven-hoofed species. Mutations at two sites changed the local conformation of the CTSS protein, reduced its stability, and affected its function and goat breed evolution. These findings confirm that CTSS affects the lambing traits of goats and provide a theoretical basis for the regulatory mechanism of CTSS in affecting litter size.Cathepsin S (CTSS) is a member of the cysteine protease family closely related to reproductive regulation in goats. However, its effect on litter size in goats remains unclear. In this study, the relationship between  HowCTSS-encoding genes in the Qianbei Ma goat population in order to understand the relationship between CTSS gene polymorphisms and lambing traits and to screen for molecular markers associated with lambing traits to guide the breeding of Qianbei Ma goats.Qianbei Ma goats are a unique goat breed raised in the Guizhou Plateau Mountain area of China. This breed is characterized by early sexual maturity, good adaptability, strong disease resistance and stable genetic performance  However,22.1The animals used in this study strictly comply with the guidelines of the Animal Welfare Committee of Guizhou University . The Qianbei Ma goats used in the study were obtained from Fuxing Herding Co Ltd., Xishui County, Guizhou Province, China. Hundred and sixty healthy Qianbei Ma ewes with similar body weights were selected and the total number of births and live births of the first, second and third fetuses of the group were recorded. Four milliliters of blood were drawn through the jugular vein into EDTA anticoagulation tubes and stored in a refrigerator at −20°C. Three singleton pregnancy and three multiple pregnancy ewes were selected from 160 Qianbei Ma goats for which reproductive data were recorded, and were euthanized by carotid artery bloodletting after electrocution. Goat gonadal axis tissue samples  were collected within 20 min of euthanasia and washed with phosphate buffered saline solution (PBS). All samples were then rapidly frozen in liquid nitrogen and subsequently transferred to a − 80°C freezer for storage.2.2Total RNA was extracted from the gonadal axis tissues of the singleton pregnancy and multiple pregnancy groups using TRIzol reagent  and a RNeasy RNA purification kit containing DNase treatment  according to the manufacturer’s instructions. DNA extraction from the collected blood was performed strictly according to the instructions of the Blood DNA Extraction Kit . The concentration and purity of the RNA and DNA were measured using an ultramicro ultraviolet spectrophotometer . The samples were tested for integrity on 1% agarose gels, and all samples were stored in a −20°C refrigerator.2.3CTSS as published in NCBI, Primer Premier 5.0  was used to design primers for amplification. Using β-actin as a fluorescent quantitative internal reference gene, primer sequences were sent to Beijing Tsingke Biotechnology Co., Ltd. for synthesis . The primer sequence information is shown in According to the RNA (accession number: XM_005677657) and DNA (accession number: NC_030810.1) sequences of goat 2.4Total PCR amplification system (20 μL): 10 μL 2× Taq PCR Master Mix , 1 μL DNA template, 1 μL each forward and reverse primers (10 μmol/L), 7 μL deionized water (ddH2O). The PCR procedure was as follows: predenaturation at 98°C for 3 min, denaturation at 98°C for 10 s, annealing at 60°C for 10 s, and extension at 72°C for 15 s. After 35 cycles, the samples were stored at 4°C. After the PCR amplification products were tested by 1% agarose gel electrophoresis to check the expected fragment size, the PCR amplification products were sent to a biological company for sequencing.The reaction system for fluorescence quantitative PCR (10 μL) contained 5 μL of 2 × UltraSYBR Mixture , 0.5 μL of cDNA, 0.5 μL each of the forward and reverse primers, and ddH2O to 10 μL. The reaction conditions were as follows , gene purity (Ho), effective allele number (Ne), and gene heterozygosity (He) were analyzed according to Chakraborty and Nei  and was significantly lower in the pituitary gland than in the ovary, hypothalamus and oviduct (p < 0.05). Analysis between the singleton pregnancy and multiple pregnancy groups showed that CTSS gene expression was significantly higher in the uterus of multiple pregnancy ewes than in the uterus of singleton pregnancy ewes (p < 0.01), and it was significantly higher in the pituitary gland of the single-lambing ewes group than in the pituitary gland of the multilambing ewes (p < 0.05); the expression of CTSS was similar among the remaining tissues, indicating that CTSS plays an important role in the regulation of lambing traits.As shown in 3.2In this study, the sizes of the PCR amplification products were consistent with the expected fragment sizes, and the bands appeared clear and bright, with no specific amplification and no obvious trailing phenomenon, confirming the good primer specificity and the ability to be used for direct sequencing .3.3CTSS (NC_030810.1) reference sequence using DNAStar software. Two SNP loci, g.7413C → T (exon 6) and g.8816A → T (exon 7), were identified in the exon 6 and 7 regions of the CTSS gene; two SNP loci, g.9191 T → G (intron 7) and g.10193G → A (intron 8), were found in the intron 7 and 8 regions. All of the four SNP loci listed above were present with two alleles and resulted in three genotypes. The sequencing chromatograms are shown in The sequencing data were aligned against the The identified sequences were aligned to GenBank reference sequences using MegAlign and compared using the Clustal W method. g.7413C → T is the non-synonymous nsSNP leading to the substitution of serine with phenylalanine, and g.8816A → T is the nsSNP leading to the substitution of aspartic acid with tyrosine; g.9191 T → G and g.10193G → A are synonymous mutations .3.42), the genotype distributions of g.7413C → T, g.8816A → T and g.10193G → A did not deviate from Hardy–Weinberg equilibrium (p > 0.05), while g.9191 T → G deviated from HWE (p < 0.05)  .CTSS ranged from 1.560 to 1.624, the heterozygosity ranged from 0.359 to 0.384, and the purity ranged from 0.616 to 0.641 .Analysis of LD was performed with the four SNPs of the CTSS gene. The results are shown in Using the SHEsis platform, 2 CTSS haplotypes were identified: Hap1 (−TTGA-) and Hap2 (-CATG-); haplotypes with frequencies <3.% were not involved in the analysis. Hap2 had the highest frequency, accounting for 68.5% of all haplotypes, followed by Hap1 with 17.0% . Random 3.6CTSS gene SNP loci combined with the number of lambs produced in litters 1–3 of Qianbei Ma ewes was performed, and the results are shown in p < 0.05).Association analysis of the p < 0.05) and that the Hap1/1 genotype was not significantly correlated with litter size (p > 0.05).Association analysis of diploidy with the number of lambs born in litters 1–3 revealed that the frequency of the Hap1/2 genotype was significantly higher than that of the Hap2/2 genotype in the third litter  and the g.8816A → T mutation results in the substitution of aspartic acid by tyrosine (p.N219Y). We analyzed the sequence conservation, function, stability, secondary structure and tertiary structure of CTSS proteins for mutant proteins.The g.7413C → T mutation in the 3.7.1The conserved p.S161F and p.N219Y sites in the CTSS amino acid sequence were analyzed using Cluster Omega online software, and the results are shown in 3.7.2The effect of two nsSNPs on protein function was predicted to be neutral using PhD-SNP prediction software, with g.7413C > T scoring 3 on a scale of 0–9 and g.8816A > T scoring 2 on the same scale. According to SNAP2, is the SNPs are neutral, with scores of −38 and − 63, respectively, on a scale of −100 to 100.3.7.3Analysis using I-Mutant 2.0 and MuPro showed that the p.S161F mutation increased the stability of the protein, the p.N219Y mutation decreased the stability of the protein, and the two mutations together reduced the stability of the CTSS protein –8.3.7.4The effects of p.S161F and p.N219Y mutations on the secondary structure of CTSS in goats were analyzed using Sopma, and the results are shown in 3.7.5The tertiary structure of a protein is closely related to its function. We used AlphaFold2 to compare the 3D models of wild-type and mutant CTSS proteins at the p.S161F and p.N219Y loci, The model has 100% of amino acid residues in the reasonable region, and the protein structure obtained by the construction has high reliability and can be used as a template for subsequent studies . The con4CTSS polymorphisms are associated with acute atherosclerotic cerebral infarction , while that of g.9191 T → G deviated from HWE. Further analysis revealed that all loci were moderately polymorphic (0.295 < PIC <0.310), which may be due to long-term artificial and natural selection . Correlation analysis showed that g.7413C → T, g.8816A → T, g.9191 T → G and g.10193G → A were all associated with litter size. In addition, the third litter number of lambs with Hap1/2 diploid (CTATTGGA) was significantly higher than that with Hap2/2 diploid (CCAATTGG). Therefore, CTSS expression may be closely related to the number of litters produced by goats. In the SNP analysis, we identified the g.7413C → T mutation in the CTSS gene as leading to the substitution of serine by phenylalanine at site 161 (p.S161F) and the g.8816A → T mutation as leading to the substitution of aspartic acid by tyrosine at site 219 (p.N219Y) of the CTSS protein. To elucidate the effect of this non-synonymous mutation on CTSS protein function, bioinformatics analysis revealed that compared with homologs in 12 other species, the p.S161F mutation was highly conserved in all 13 species, and p.N219Y was relatively conserved in even-toed ungulates and less conserved in other species . The mutation had a neutral effect on protein function; but, interestingly, the p.S161F mutation increased the stability of the protein, and the p.N219Y mutation decreased the stability of the protein. It has been shown that missense mutations that increase protein stability may also alter their function, that more stable proteins are more evolved and that mutations at the p.S161F and p.N219Y loci are consistent with speciation . The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent was obtained from the owners for the participation of their animals in this study.YZ: Conceptualization, Data curation, Methodology, Software, Validation, Writing – original draft, Writing – review & editing. XC: Conceptualization, Funding acquisition, Methodology, Project administration, Writing – review & editing. YR: Methodology, Software, Writing – review & editing. JC: Investigation, Writing – review & editing. WT: Investigation, Validation, Writing – review & editing. QJ: Validation, Writing – review & editing. KF: Writing – review & editing. WG: Review, Editing & supervision.


MiRWP as the gene that causes polyembryony in mango. There is a strong correlation between a specific insertion in the gene’s promoter region and altered expression in flowers and developing fruitlets, inducing multiple embryos. The MiRWP gene is an ortholog of CitRWP that causes polyembryony in citrus. Based on the data, we speculate that promoter insertion events, which occurred independently in citrus and mango, induced nucellar embryogenesis. The results suggest convergent evolution of polyembryony in the two species. Further work is required to demonstrate the utility of these genes (mango and citrus) in other biological systems as a tool for the clonal production of other crops.Sexual reproduction in plants is the main pathway for creating new genetic combinations in modern agriculture. In heterozygous plants, after the identification of a plant with desired traits, vegetative propagation (cloning) is the primary path to create genetically uniform plants. Another natural plant mechanism that creates genetically uniform plants (clones) is apomixis. In fruit crops like citrus and mango, sporophytic apomixis results in polyembryony, where seeds contain multiple embryos, one of which is sexually originated and the others are vegetative clones of the parent mother tree. Utilizing the mango genome and genetic analysis of a diverse germplasm collection, we identified  Sexual reproduction is the main pathway for the creation of genetic variations. Plant meiosis generates new combinations of genetic material through recombination and chromosome segregation , creatinAnother asexual reproductive strategy that is naturally found in some angiosperm is apomixis. Apomixis is defined as asexual reproduction through seeds that leads to the production of clonal progeny whose genotype is identical to that of the mother plant . ApomixiApomixis occurs in at least 80 families (12%) and 300 genera (1.8%) of angiosperms , but besMango, like citrus, has both monoembryonic and polyembryonic seed types , 19. ManCitRWP gene was identified as causing polyembryony [CitRWP allele as a result of a miniature inverted-repeat transposable element (MITE) insertion leads to polyembryony [Arabidopsis. Overexpression of one of these genes promotes ectopic embryogenesis in somatic tissues [RWP orthologs in citrus as the causal gene of polyembryony.The polyembryonic trait segregates in both mango and citrus, which are phylogenetically related, as a single dominant Mendelian trait , 19. Theembryony . Genes f tissues , supportMangifera indica) is a true diploid by its genetics and cytogenetics. The mango genome size is ~400–440 Mbps [Mango developed from two centers of origin in South East Asia and North East India . Mango i440 Mbps , 33. It 440 Mbps , 33.Currently, little is known about the genes involved in mango polyembryony. In our previous efforts, we mapped the polyembryony locus to a region of mango chromosome 7 in two mapping populations   and two monoembryonic cultivars (‘Shelly’ and ‘Omer’) . Closed www.fluidigm.com). In this analysis, most accessions were either from the mono or the poly haplotype, in correlation with their phenotype  showed that Manin07g005330.1 encodes an RWP domain containing protein, similar to the Arabidopsis RKD family of proteins, which serve as regulators of egg-cell-related genes, and that has strong homology to the CitRWP gene which causes polyembryony in citrus. Based on these data and gene expression patterns (shown later), we conclude that the Manin07g005330.1 allele is the possible cause of polyembryony in mango. We renamed the gene MiRWP.In our previous study , we mapphenotype . Howeverhenotype . We defiMiRWP was performed using quantitative RT-PCR in a few monoembryonic and polyembryonic mango cultivars (MiRWP was higher in the early stages of seed fruit development (ovule size of 1–5 mm) compared to other tested tissues and higher in comparison to monoembryonic ). These data suggest that MiRWP is acting following fertilization, as there is a correlation between MiRWP expression and the polyembryonic phenotype.Expression profiling of ultivars  during fultivars . We specMiRWP in polyembryonic accessions; TBLASTN searches [CitRWP (GenBank accession: XP_006474671.1) and MiRWP. Also, 1.3 kb of the promoter of KP is presented in MiRWP in TA and KP identified two SNPs in the coding region in agreement with the NGS data  but was not an exact match . The fragment appeared to be derived from part of the coding region of a hypothetic RF1 protein (YP_010485880.1) with an unassigned function. Subsequent homology-based searches with the M. odorata chloroplast genome revealed that chloroplast DNA insertions were widespread throughout the chromosomes of the monoembryonic M. indica variety ‘Alphonso’ (−20) from different parts of the M. odorata chloroplast genome (covering 70% of the chloroplast genome).The 3.6-kb chloroplast insert identified in the lphonso’ . In ‘AlpCitrus and Mangifera , a whole-genome duplication (WGD) occurred in the Mangifera genera [Citrus maxima and M. indica has been conserved in 484 blocks retaining 15330 genes. The collinear blocks covered 362 Mbp of the pomelo genome corresponding to 62 Mbp of the mango genome . The collinear block on chromosome 5 went through an inversion. Based on the maximum parsimony principle, this block is more recent, probably as the WGD outcome. Within the synteny block in chromosome 7, the MiRWP orthologue (Manin07g005330.1) obtained an insertion in the promoter region  (The data we accumulated  demonstr Saveoy) . The homMangifera lineages. In the citrus lineage, the MITE was inserted after the divergence of citrus and Mangifera genera; otherwise, the MITE would be found in both CitRWP and MiRWP orthologues. The insertion in the MiRWP promoter is of chloroplast origin which occurred after a whole-genome duplication that the mango genome went through. Otherwise, the same element had been found in the MiRWP paralog. The independent events converged into enhanced gene expression, which caused the polyembryony trait.Two independent insertion events occurred in the citrus and Convergent evolution may appear in different forms and usually indicates adaptation . The conArabidopsis orthologue to induce somatic embryos support this idea. The challenges ahead include applications to induce embryos in other species with the mango and citrus genes or their homologs and defining the suitable tissues/promoters to test for the creation of polyembryonic seeds. This approach can open a new way to create cloned plants through seeds.Besides its interesting biology, sporophytic apomixis is a very valuable trait for agriculture as a method to duplicate identical copies of plants through seeds. Most crops do not possess this trait. The similar mechanism for polyembryony occurring in two different plant species, mango and citrus, suggests a common and universal mechanism that may be transferable to other species, possibly creating a new horizon for novel propagation in other plants. The dominant phenotype of the citrus and mango alleles and the ability of their Seeds from mature fruits (at least five fruits/accession) were collected from germplasm collections in Israel and Australia. The hard shell was carefully opened. Accessions were defined as “monoembryonic” when only a single embryo was detected. Accessions were defined as “polyembryonic” if, at least in some of the fruit, multiple embryos were detected. These tests were repeated over at least two seasons. The accession phenotypes are presented in For embryological studies, young fruitlets and ovaries at various developmental stages from four mango cultivars were collected: ‘13-1’ and ‘Sabre’ (polyembryony); ‘Shelly’ and ‘Omer’ (monoembryonic). Samples of closed and open flowers, and fruitlets at sizes of 0.3 mm, 0.5 mm, and 1 cm from each poly- and monoembryonic mango cultivar  were fixhttps://gatk.broadinstitute.org/hc/en-us). Only sites with DP (read depth) higher than 20 and MAF  higher than 0.05 were further analyzed.The genomic DNA of 15 mango accessions  was extrKP DNA was extracted by BGI Genomics, Shenzhen, China. DNA libraries with 170, 200, 500, and 800 bp inserts and mate-paired libraries with 2000, 5000, 10 000, and 20 000 bp inserts were constructed for sequencing on the Illumina HiSeq4000 platform according to the manufacturer’s protocols . Also, 125 bp paired-end reads were generated from the 170, 200, 500, 800, 2000, and 5000 bp libraries and 50 bp mate-paired reads were generated from the 10 000 and 20 000 bp libraries. Assembly was done using SOAPdenovo2 and SSPACE2.0. Dovetail Genomics Chicago library construction and HiRise scaffolding were used to improve the initial assembly. DNA was extracted from KP leaves using a CTAB-based method with magnetic beads . A Chicawww.fluidigm.com). The SNP-type assays were used to explore the genetic variation in the germplasm collection using 96 × 96 arrays utilizing the Fluidigm EP1 device (A subset of the genomic variation on chromosome 7 around the previously identified polyembryony locus (3100998–5841341 bp ) was ext1 device .–ΔΔCT) was calculated.Total RNA was isolated from various developmental stages of the flower (open and closed flower), embryo developmental stages (1-mm ovules and 5-mm ovules), and young leaf  using anMIRWP gene and 1.3 kb promoter (positions 4596985–4599982 TA genome), PCR and sequencing were performed on genomic DNA of two accessions, TA (monoembryonic) and KP (polyembryonic). The full sequence identified a few SNPs and two major structural differences between these two accessions. A heterozygous 64-bp duplication around −714 bp exists in KP but not in TA. Another difference is the heterozygous chloroplast DNA insertion around −1020 that exists in KP and not in TA. The sequence of the gene and promoter area of KP is presented in To validate the results from the KP assembly comparison to the TA genome primers that were designed based on the TA genome draft  Supplem on the gMangifera scaffolds, chromosomes, and plastid genomes were identified using BLASTN [M. odorata (NC_066470.1) were retrieved from the GenBank database [Homologous regions between various g BLASTN . Completdatabase . ‘Alphondatabase .C. maxima and their genomic position (gff file) was downloaded from the Citrus Genome Database (http://www.citrusgenomedb.org/Citrus_maxima/C.maxima_Hzau_v1_genome/annotation/) and the set of M. indica together with their genomic position (gff file) from MangoBase (https://mangobase.org/easy_gdb/index.php). A BLAST was runoff one set against the other. The outcome was applied as an input to MCScanX [Whole-genome proteins set of  MCScanX . Protein MCScanX .C. maxima proteome [M. indica proteome [The sequences of proteome  and the proteome  were incproteome . The cutWeb_Material_uhad227Click here for additional data file.


The assignment of an individual to the true population of origin using a low-panel of discriminant SNP markers is one of the most important applications of genomic data for practical use. The aim of this study was to evaluate the potential of different Artificial Neural Networks (ANNs) approaches consisting Deep Neural Networks (DNN), Garson and Olden methods for feature selection of informative SNP markers from high-throughput genotyping data, that would be able to trace the true breed of unknown samples. The total of 795 animals from 37 breeds, genotyped by using the Illumina SNP 50k Bead chip were used in the current study and principal component analysis (PCA), log-likelihood ratios (LLR) and Neighbor-Joining (NJ) were applied to assess the performance of different assignment methods. The results revealed that the DNN, Garson, and Olden methods are able to assign individuals to true populations with 4270, 4937, and 7999 SNP markers, respectively. The PCA was used to determine how the animals allocated to the groups using all genotyped markers available on 50k Bead chip and the subset of SNP markers identified with different methods. The results indicated that all SNP panels are able to assign individuals into their true breeds. The success percentage of genetic assignment for different methods assessed by different levels of LLR showed that the success rate of 70% in the analysis was obtained by three methods with the number of markers of 110, 208, and 178 tags for DNN, Garson, and Olden methods, respectively. Also the results showed that DNN performed better than other two approaches by achieving 93% accuracy at the most stringent threshold. Finally, the identified SNPs were successfully used in independent out-group breeds consisting 120 individuals from eight breeds and the results indicated that these markers are able to correctly allocate all unknown samples to true population of origin. Furthermore, the NJ tree of allele-sharing distances on the validation dataset showed that the DNN has a high potential for feature selection. In general, the results of this study indicated that the DNN technique represents an efficient strategy for selecting a reduced pool of highly discriminant markers for assigning individuals to the true population of origin. Single nucleotide polymorphism (SNP) is a new type of marker that includes many important characteristics for evaluating animals2, crops3, and human population structure4. At present, genomic data plays a critical role in a variety of biological contexts due to its numerous advantages. However, the curse of dimensionality  is a major limitation to their ability for practical applications. The lack of complete pedigrees and misidentification of parents affects the accuracy of genetic evaluations, and consequently, the efficiency of breeding programs. Identification of the discriminant SNP(s) process is one of the most appealing opportunities to exploit genomic data, for practical use, including determining the population of origin for unknown individuals2. Many researchers have widely investigated discriminant SNP(s) and genetic diversity8. Researchers can use such SNP markers for developing a cheap customized panel to trace the breeds. Furthermore, the SNP(s) can provide a reliable solution for the traceability of breed-specific branded products9.DNA probes and sequences are two important indices in gaining a deep understanding of the evolution process, and the amount of DNA sequence data is rapidly increasing10. The same condition is true for biological data11, especially SNP markers. In various areas of breeding, we are always looking for SNP markers with enormous effects. Now, we import the issue to machine learning, especially the neural network approach. In genetics, this process is also known as Tag SNP Selection Problem (TSSP)12.In feature selection, researchers seek to identify key variables and eliminate annoying (or noisy) variables13. Researchers have argued the shortcomings of ANN, including the complexity of analysis, computational cost, and time consumption. However, we must mention that ANN’s high prediction accuracy compensates its drawbacks to a great extent. Deep Neural Networks (DNN) have been employed to analyze biological data15. They have many applications in feature abstraction and selection17. DNNs were able to construct many biological prediction models18, but their power of feature selection had been ignored for individual discrimination.Mimicking the behavior of the biological brain in the nerve system is the base of Artificial Neural Networks (ANNs), which are the information processing tools20. Fernández, et al.21 have indicated that ANNs were suitable to be used in fields of time series data for weekly milk prediction and clustering individuals in goat flocks. Ince and Sofu22 modeled data with ANN for the prediction of the sheep milk yield by using the back-propagation algorithm.The ANNs have recently been applied as a powerful statistical modeling technique for many areas of different biological data, especially in the animal sciencesFor feature selection (FS) based on ANN, a comparison was made in this study to discriminate among different horse breeds as well as to assign new individuals to their breed. Statistically, in the analysis of GWAS, all SNPs act separately and conduct the research with significant results. The consequence of this analysis obtains the identification of significant SNP markers, but the relationships between them are ignored. While the network approach is more reliable and logical monitoring all SNPs simultaneously leads to better results efficiently.23. Unlike most studies, Olden, et al.24 examined the performance of the Garson algorithm in the variable selection on simulated data, and have found that it has the lowest efficiency compared with other studied algorithms. Ibrahim25 showed that the Olden and Garson methods had the weakest results. The results of Fischer26 revealed that the Garson algorithm has a higher degree of stability in modeling non-linear relationships. Additionally, other studies have used the Garson and Olden algorithms, which are only applicable to ANN with a single hidden layer.To obtain the best results, allele dosage has been applied to ANNs, which is a completely unbiased estimation. The Garson (weights) algorithm illustrates behavioral instability in the analysis, which can be considered a weaknessTo the best of our knowledge, researchers had not investigated the potential of feature selection by ANN approaches for assigning individuals in horse breeds. We have analyzed the ANN’s potential to characterise, whether ANNs can be used as a tool for tackling the curse of dimensionality of SNP(s) data. We attempted to compare the DNN alongside a brief description of Garson and Olden methods to gain the relative importance of variables (SNP markers). While the DNN is a multiple hidden layer ANN, the two mentioned methods are compatible with a single hidden layer. This paper is one of the first studies to determine the discriminant SNP(s) on a large scale by using the sophisticated methods of ANN approaches. We have conducted this study intending to find distinct SNP markers to reduce the dimensions of the SNP panels as well as comparing different variable selection methods such as Garson and Olden through the ANN approach.25. Many studies have compared different FS methods29. The selection criteria for the variables in the DNN structure were the absolute value of the first hidden layer connection weights that they assumed as the regression coefficient. According to the DNN procedure, 4270 SNP markers had been selected for the rest of the analysis. The Garson and Olden algorithms led to a selection of 4937 and 7999 SNP markers for further analysis, respectively. The reason for choosing a more significant number of SNP tags for the Olden algorithm is the low transparency of the PCA plot. We must have mentioned that increasing the number of tags did not increase transparency anymore, this could be due to no linear relationship between SNPs number and PCA plot transparency. Moreover, the absolute increase of markers did not include a useful index for improvement unless the marker allele frequencies were different across subpopulations.In the current research, we have used the three feature selection (FS) methods namely Olden, Garson, and DNN. Neural networks are commonly referred to as powerful and efficient statistical modeling techniques by various researchersAfter the selection process of SNP markers, all SNP markers were sorted based on the calculated coefficient. The 460 top-rank SNP of each approach was selected, and all sub-SNP sets were compared to each other to find the common markers Table . Table 1Regarding Table 24 revealed that the Olden method was the best overall methodology for processing and identifying the variable importance in the neural network, especially when the inputs had a weak or strong correlation with output. Fischer26 compared the Olden and Garson methods and reported that the results obtained by the Garson method are preferable and more stable than those obtained by the Olden method for nonlinear relationships. Findings from his study have shown that ranks obtained by the Garson approach may be more reliable than the Olden method, especially when those ranks are used for modeling nonlinear data such as positive and negative quadratics and interactive data. The results of these studies indicated that the Olden (Connection weights) method had an excellent performance for different assumptions and, Garson (Weights), as the ancestor of the weighted methods, had a various behavior in these studies.In general, most of the studies have widely used the Olden and Garson approaches. The results of Olden, et al.25, and some others have used simulated data where the data have mostly contained the linear28 or semi-linear relationship27. However, the DNN approach could raise the performance and efficiency of the artificial neural network in circumstances where a large number of input variables  have confronted the system.All mentioned studies used the simulated or ecological data in which the maximum input variables were less than 20 variables. At first glance, both Olden and Garson’s algorithms used the input-hidden and hidden-output connection weights for calculating the importance of variables. The linear regression modeling habe been used as a control method on the real datasets for evaluating the input's significance in some studies30. The actual coefficients of SNP markers have been obtained step by step according to the original PCA plot, which is according to the numerical analysis in mathematics. In other words, after choosing a new coefficient, the PCA plot was drawn, and the breed distinction was compared with the main PCA plot created by 50K SNP markers panel  was applied to determine how the animals were allocated to the groupsnel Fig. . After mThe results indicated an excellent performance of PCA in distinct individuals into separated groups. PCA analysis has identified two subpopulations of Thoroughbred, (TB_UK & TB_US), as one breed, and a similar result was obtained for Standardbred (STBDNor & STBDUS) too. In Fig. 31 approach. Although one particular breed (Shire) had at least one failure assignment by each method. In general, all three feature selection methods assigned most of the individuals to the right population. It resulted in a 9% reduction in the potential of the assignment procedure. Two individuals in the Shire breed failed in all subsets. Red arrows indicate these individuals in Fig. We have estimated the likelihood of assigning 795 individual genotypes to their known origins (or breeds) by the Paetkau, et al.With the analysis of assignment and concerning values of LLR, obtained results showed that one failure was recognized as Belgian breed by three methods, and the other one was known as different breeds like Paint, Quarter, Swiss warmblood, and Thoroughbred-US. By using three methods, the first individual has 97.30% accuracy to be assigned to the correct race (Shire). By DNN, and Olden approaches, the second individual also had 91.89% accuracy for being appointed into the right breed. For further explanation, these failures might be due to hybrid or crossbreeding parentage. There were two Shire individuals in the center of the PCA plot Fig. ; the assAs shown in Fig. We have accurately calculated the percentages of individuals and correct assignments for different numbers of SNP markers. Testing the performance of each approach has been done at four different levels of LLR analysis. We found that DNN performed better than the other two approaches by achieving 93% accuracy at the most stringent threshold (LLR > 4) , Wilkinson, et al.38 worked for assigning individuals in 17 Bovine breeds.The results revealed that the DNN outperformed other methods with fewer SNP markers. Generally, about 500 discriminant SNP markers enabled us to assign new individuals to the right groups using different ways. There are some issues related to the comparison of results in this study with other ones. First, many previous studies used another type of marker with only a limited number of tagsWe have used a separate dataset to test the model. Firstly, we have applied the PCA analysis to find the relationship among the breeds like the training dataset Fig. .Figure 4In Fig. The results of this section revealed that all three artificial neural networks had an excellent performance. The Garson method with a minimum number of markers (fifteen) had a 60% accuracy, which may be due to the low number of animals and the distinction between the source in the test data, because there are significant differences between the countries of Switzerland, France, and England (the continent of Europe) and the countries of the Middle East and the Americas (Asian and American continents).By using one dataset, there is a possibility to observe a negligible amount of kinship relationships. Because all individuals are sampled from one herd, kinship relationships are practically inevitable in the research. Therefore, using new data from other sources reduces the probability of kinship among individuals. If unknown or novel information is introduced to the desired network, the least errors will get. Previously obtained results of the network were reliable enough for DNN to infer the right class of novel information precisely. In this case (DNN), the system undoubtedly possesses much power and much success in correctly determining the essential features.For a better understanding, we have used the Neighbor-Joining tree of allele-sharing distances on the validation dataset. Neighbor-Joining analysis performs better than PCA analysis on topics such as breed-level differentiation, the intermingling of breeds, outliers, genetic isolation, etc. First, we have analyzed whole genomic data  to show the breed-level differentiation in validation data Fig. .Then, the Neighbor-Joining analysis was done for each obtained dataset . Artificial neural networks (ANNs) will receive a great deal of attention in the various scientific fields, given that they are powerful statistical modeling techniques. However, in an attempt to provide useful insights into the contributions of the input (independent) variables in the prediction process, they have been labeled as the “black box” technique. As mentioned earlier, many published studies had been conducted to clarify the interpretation of the connection between the neurons in ANN.By comparing the results, the Garson and Olden procedures only work with a single hidden layer and single output unit, while multiple layer networks (DNN) do not suffer these limitations. Regarding log-likelihood ratio (LLR) for the individual assignment, the obtained results by this research revealed that ANN’s feature selection methods could be used for genomic data, especially for dimension reduction by DNNs. This finding solves the most critical issue for genetics researchers in dealing with the considerable dimension of data. Researchers can use DNN in the field of animal sciences because of the high performance of breed discriminants. Researchers in the field of genetics and breeding are seeking to reduce the number of biomarkers to find a link between the observed phenotype and these markers.The result of this study showed that the DNN has a high potential for feature selection in genomic data along with more flexibility in the application of ANNs in the field of animal sciences. Results also showed that using the connection weight of the first hidden layer in a DN Network provides the possibility to reach a high optimum level of accuracy for ranking and selecting the variables (SNP(s)). Another conclusion of this research is that the most critical weights for output values of every variable in a DN Network are the weights in the first hidden layer because all connected loads of the next layers are functions of the first layer's connected load. If three analyzes of PCA, LLR, and Neighbor-Joining achieve the desirable results, we will get the real discriminative features.It is necessary to point out that the results of this study shed some lights on the using of DN Networks  in genetics and breeding. Feature selection in the genetic field particularly on SNP markers is in the infancy period. The computation time will be reduced significantly. It should also be noted that the DNN network is increasing computing time but it was decreasing the error rate significantly. It can open a new opportunity to extend human insights.Finally, we think that this will be a fruitful approach to the study of existing domestic populations, such as inferior local breeds and strains in developing countries. In general, the present paper highlighted the importance of variable selection from the varying point of view, including the socio-economic perspective .7 have already described the comprehensive description and necessary details of data mining. In summary, Table 40.A total of 795 animals from 37 breeds of horse populations were genotyped by using the Illumina SNP 50k Bead chip . Petersen et al.Moreover, raw predictor variable data (SNP matrix) is used as the input variable in ANN. It is assumed that each of these markers represents a mathematical variable that can only hold 3 inputs .41. Data preprocessing included extracting common SNP markers between panels of 50K and 2M. This process resulted in the identification of 32K markers, and 14K of these markers remained after quality control  for further analysis.To assess the performance of the ANN methods, learning and evaluation were performed using two separate datasets, respectively. The testing dataset contains 120 individuals from eight breeds (Table 22. Neurons and their connections create a specific network architecture such as multilayer perceptron (MLP), self-organizing map (SOM), etc.13. In terms of genomic data analysis, we used two types of ANN architecture. The first one is a feed-forward multilayer perceptron (DNN) with two hidden layers, and the second one is a standard single hidden layer (ANN) with a back-propagation algorithm for the weight adjustments43. In Figure 44 and Neural Net Tools45 packages were applied by R software (version 3.4.0)46 to select informative and unique SNP markers that are within each breed. The mentioned algorithms (Garson and Olden) have been utilized by ANN to detect the relative importance of variables for the breed diversity characterization.Artificial neural networks represent complex structures that are generated by fundamental units (elements) called neurons29 have proposed the usage of a set of neural networks instead of a single one. In contrast to29 in the present work, the high-density SNP chip was partitioned into the sub-datasets with the same dimension and were used as input to identify the discriminant SNP(s).The large dimension of the SNP-panel leads to a stack overflow error in the computing process. De Oña and Garrido47 and has also been modified by Goh48. It was used to identify the relative importance of input variables by the calculated weights within connections in a supervised neural network. The Garson approach indicates relative importance values as the absolute magnitude ranging from zero to one (0-1). Olden and Jackson49 had proposed connection weights, also known as the Olden approach that has been used in this research.Weights (Garson approach), had been described by Garson50. The optimal number of nodes in the first and second hidden layers detected 40 and 38 nodes after testing a range of combinations. Finally, ANN with Garson and Olden algorithms contained 40 nodes in the hidden layer.For the DNN approach, the ANN with two hidden layers was used to identify the discriminant SNP(s) within breeds. Many combinations exist for selecting the number of nodes in the hidden layer12. We considered the SNP markers to retain a direct relationship with the horse breeds.  equation have a significant effect on the output variable, the absolute maximum weight obtained by DNN led to the selection of SNP markers that caused the diversity of the breeds.We have used the final fitted weights of the neural network for selecting the genetic markers. In the DNN approach, we assumed there was a linear relationship between the variable and the responseFigure By considering Eq. , it is a52. The method of Paetkau, et al.31 has been used for the assignment analysis (as had been described by38), and it had high effectiveness on individual assignment when high levels of genetic differentiation between reference populations existed52. It is noteworthy that the SNP markers were applied instead of the microsatellites. We have calculated the log-likelihood ratios (LLR) to accurately assess the performance of the assignment procedure. The log-likelihood ratios (LLR) will be calculated by comparing the probability of an individual assigned to its real population to the probability of it assigned to another population ; the University of Kentucky IACUC; the University College Dublin, Animal Research Ethics Committee; Swiss Law on Animal Protection and Welfare; the Ethical Board of the University of Helsinki; the Animal Health Trust Clinical Research Ethics Committee; Norwegian Animal Research Authority; UK Home Office License; and the Lower Saxon state veterinary office).Testing Data-set: DNA samples were previously collected with approval from the Animal Care and Use Committees at the respective institutions. All animal work was performed in accordance and with approval from international and national governing bodies at the institutions where the samples were collected ; University of California, Davis Institutional Animal Care and Use Committee (protocol #17491); University of Kentucky Institutional Animal Care and Use Committee (IACUC); Ethics Committee for Animal Experiments in Uppsala, Sweden (Number C121/14); Institutional animal care and use committee at Cornell University (protocol 2008-0121); University of California, Davis IACUC 19205; Hebrew University’s approval number AG-23476-07; Institutional Animal Care and Use Committee (IACUC), the Lower Saxony state veterinary office- registration number 11A 160/7221.3-2.1-015/11, 8.84-02.05.20.12.066; University of Sydney Animal Ethics Committee: AEC APPROVAL NUMBER: N00/9-2009/3/5109; permit no. BE75/16, veterinary service of the Canton of Bern; Institutional ethics committee of the University of Veterinary Medicine Vienna Good Scientific Practice guidelines and national legislation; Italian Ministry of Agricultural, Food and Forestry Policies (Mipaaf); Ethical Committee of the Canton of Bern ) No commercial animals were used in this study. Written informed client consent describing the purpose and duration of the study, procedures, potential risks and benefits and containing study contact information were obtained from private owners.Supplementary Information.