So far, few chickpea (Cicerarietinum) germplasm accessions have been characterized at the genome sequence level2. Here we present a detailed map of variation in 3,171 cultivated and 195 wild accessions to provide publicly available resources for chickpea genomics research and breeding. We constructed a chickpea pan-genome to describe genomic diversity across cultivated chickpea and its wild progenitor accessions. A divergence tree using genes present in around 80% of individuals in one species allowed us to estimate the divergence of Cicer over the last 21 million years. Our analysis found chromosomal segments and genes that show signatures of selection during domestication, migration and improvement. The chromosomal locations of deleterious mutations responsible for limited genetic diversity and decreased fitness were identified in elite germplasm. We identified superior haplotypes for improvement-related traits in landraces that can be introgressed into elite breeding lines through haplotype-based breeding, and found targets for purging deleterious alleles through genomics-assisted breeding and/or gene editing. Finally, we propose three crop breeding strategies based on genomic prediction to enhance crop productivity for 16 traits while avoiding the erosion of genetic diversity through optimal contribution selection (OCS)-based pre-breeding. The predicted performance for 100-seed weight, an important yield-related trait, increased by up to 23% and 12% with OCS- and haplotype-based genomic approaches, respectively.Zero hunger and good health could be realized by 2030 through effective conservation, characterization and utilization of germplasm resources Whole-genome sequencing of 3,171 cultivated and 195 wild chickpea accessions is used to construct a chickpea pan-genome, providing insight into chickpea evolution and enabling breeding strategies that could improve crop productivity. Chickpea (Cicer arietinum L.) production ranks third among pulses, and chickpea is cultivated in more than 50 countries, especially in South Asia and sub-Saharan Africa. As it is an important source of protein, dietary fibre and micronutrients, chickpea is key to nutritional security. More than 80,000 chickpea germplasm accessions are being conserved in 30 genebanks across the world4, but only a few have been used for chickpea improvement2.Pulses are an important crop commodity providing protein for human health. Worldwide pulse productivity has been stagnant for the last five decades, contributing to low per-capita availability of these foods and high levels of malnutrition in developing countries5; how this diversity has been shaped by the genetic bottlenecks associated with domestication6 and by the effects of selective breeding7; and, finally, how we can link this genetic variation to phenotypic diversity2 for breeding applications. Haplotype maps developed using whole-genome sequencing (WGS) data have helped to determine the percentage of the constrained genome and detect deleterious mutations that can be purged for accelerated breeding9. Furthermore, sequencing and genotyping of a germplasm collection allows better conservation and management in genebanks10.Germplasm sequencing efforts in some crop plants have provided insights into the global distribution of genetic variationC. arietinum), rare allele burden and fitness loss in chickpea. We propose three genomic breeding approaches—haplotype-based breeding, genomic prediction and OCS—for developing tailor-made high-yielding and climate-resilient chickpea varieties.On the basis of WGS of 3,366 chickpea germplasm accessions, we report here a rich map of the genetic variation in chickpea. We provide a chickpea pan-genome and offer insights into species divergence, the migration of the cultigen (11 identified 3.94 million and 19.57 million single-nucleotide polymorphisms (SNPs) in 3,171 cultivated and 195 wild accessions, respectively  and C. reticulatum  accessions  using an iterative mapping and assembly approach by combining the CDC Frontier reference genome, an additional 2.93 Mb from a desi genome ICC 4958)95812, 3.11. Gene ontology (GO) annotations identified genes that encode response to oxidative stress, response to stimulus, heat shock protein, cellular response to acidic pH and response to cold  were identified, of which 1,582 were to our knowledge novel compared to previously reported genes 16 Fig. . N50, a C. reticulatum (22) accessions with a coverage of greater than 10× were analysed to discover structural variations, including insertions , deletions , inversions , intra-chromosomal translocations (417) and inter-chromosomal translocations  in cultivated and 287,854 insertions, 67,351 deletions, 58,070 inversions, 446 intra-chromosomal translocations and 2,066 inter-chromosomal translocations among C. reticulatum accessions as compared to the CDC Frontier genome11  and 209 gene-loss CNVs incultivated accessions, and 643 gene-gain and 247 gene-loss CNVs in C. reticulatum accessions  and 8 and C. e11 Fig. . More stCicer species analysed here, single-copy genes identified using ‘fabales’ genes from the BUSCO17 database were used to carry out homologue-based gene annotation in preliminary genome assemblies, the CDC Frontier11 and Medicago truncatula18. Using these single-copy genes, Cicer cuneatum was estimated to have diverged from other Cicer species around 21.4 (19.6–22.8) million years ago (Ma)  Ma, which is higher than previous estimates and might be influenced by: (i) wild accessions conserved at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) representing only some populations of these species, when recent work has shown that only some C. echinospermum populations are cross-compatible with C. arietinum; and (ii) introgression from C. echinospermum into cultivated chickpea, which is widespread in Australian and North American breeding lines, and is also likely to have occurred in International Center for Agricultural Research in the Dry Areas (ICARDA) lines.To understand speciation and estimate species divergence time in the eight C. reticulatum and one C. echinospermum , whereas cluster IVb included all C. echinospermum and one C. reticulatum . Similarly, one Cicer pinnatifidum  was grouped with the Cicer bijugum accessions in cluster II, and one C. bijugum  was grouped with C. pinnatifidum accessions in cluster I. These are two cross-compatible species. Spontaneous hybridization might have occurred in nature. In terms of post-species divergence, a homologue (Ca_25684) of SHATTERPROOF2 ), which is responsible for seed dispersal, was analysed for haplotypic variation  , among accessions from Central Asia (4.74 × 10−4) and South Asia (3.62 × 10−4) (Supplementary Data 2. Pairwise fixation index (FST) estimations further supported these findings  relative to cultivated accessions  from East Africa was grouped with wild chickpea  versus landraces , landraces versus breeding lines  and breeding lines versus cultivars  that might have undergone selection during domestication and breeding  between landraces versus breeding lines and breeding lines versus cultivars. Furthermore, we identified a total of 37 unique potential genes in these 36 regions that may have a role in the adaptation of chickpea during migration to different environments by regulating flowering time and plant growth (Supplementary Data FLP2 (flower development and vegetative to reproductive phase transition of meristem), LRP1 (root growth), PIP5KL1  and MYB12 (flavonoid biosynthesis) are some key genes we pinpointed that are critical for plant growth, metabolic pathways and adaptation in changing environments.To detect selection sweeps, we pinpointed 18 fragments in cultivated chickpea using the composite likelihood ratio (CLR)  score (less than 0.05) and GERP (greater than 2), 10,616 non-synonymous SNPs were identified as candidate deleterious mutations  abundance of deleterious alleles in the wild progenitor (C. reticulatum) than in cultivated accessions   analysis to identify 29 Mb 8.36%) genomic regions as evolutionarily constrained (GERP score of greater than 0), indicating purifying selection  values was bimodal. Higher predicted progeny index values were observed in kabuli as compared with desi. However, marked improvements were predicted in desi and kabuli, from candidate parents to predicted progenies 25; (ii) implementation of the WhoGEM approach26; and (iii) a haplotype-based approach for estimating local GEBVs27.We used different subsets of SNPs and phenotyping data on 16 traits across 12 combinations of year and location, following 3 genomic prediction approaches: (i) interaction of marker and environment covariates , 531,457 (G2) and 754,576 (G3) SNPs, and phenotyping data for 9 traits on 2,980 genotypes, were used to understand the variability explained within the groups and environments . Prediction accuracies of the full model ranged from 0.25 to 0.91  developed from cultivated draft genomes12 and the C. reticulatum genome13, together with WGS data on cultivated and C. reticulatum accessions, provided insights into gene content variation across cultivated chickpea and its wild progenitor.Our study reports global polymorphisms in chickpea by sequencing 3,366 germplasm accessions . This analysis brings greater resolution to our understanding of the within-species diversity of 28 and nuclear ribosomal DNA29 have been conducted to investigate the evolution and domestication of Cicer species in the past, their resolution was limited. Here, by using WGS data for a large number of individuals, we estimated the divergence time between chickpea and its closest progenitor species. Our study also provides opportunities to rectify misclassifications of accessions to the correct species and to determine whether chickpea seeds preserved in archaeological sites were wild or cultivated.Although some studies based on chloroplast DNACicer species. Although selection and recombination have successfully purged many deleterious alleles, the current collection of breeding lines and cultivars still contains substantial genetic loads that affect crop fitness. Here, we have identified deleterious alleles for purging through genome-informed breeding and/or gene editing.We identified selective sweeps and candidate genes under domestication and breeding that were responsible for reducing genetic diversity in the cultivated genepool. Most importantly, our study analysed genetic loads in 30. Furthermore, we estimated prediction accuracies for agronomic traits using three genomic prediction approaches and provided a case study for 100SW, demonstrating that genomic prediction approaches have great potential for enhancing crop productivity. We suggest using haplotype mining and genomic prediction approaches in chickpea and other crops to provide climate resilience and improved nutrition to meet future worldwide demand.We identified numerous superior haplotypes for improvement-related traits in landraces, and used the concept of superior haplotypes by comparing the yield of the released varieties carrying superior versus regular haplotypes for yield-related traitsCicer accessions from a global composite collection4 using the HiSeq2500 at the Center of Excellence in Genomics and Systems Biology, ICRISAT. By including sequence data of 399 lines from an earlier study2, we analysed 3,366 accessions  altogether  Set-A: only SNPs with <30% missing call, and biallelic calls, and (ii) Set-B: SNPs with less than 30% missing calls, biallelic calls, and LD-pruned using PLINK33 v.1.90 (“--indep-pairphase 50 10 0.2” parameter). Set-B SNPs were only used to depict the population genetic structure.We aligned sequencing data from the 3,366 chickpea accessions to the reference genome of CDC Frontier34 of GATK v3.8.1. We defined ‘private alleles’ as those present in at least four accessions within a population and absent in other populations, and ‘population-enriched alleles’ as those present in a given population (≥20%) and less frequent in other populations5 (≤2%).To determine the private and population-specific SNPs, the frequency of alleles within a given population was determined using VariantsToTable35 v.3.29 with the parameter “-MaxDist 1000”. Nucleotide diversity (π) was calculated from a 100-kb sliding window with a 10-kb step using VCFtools36 v.0.1.13. The average of all valid windows was considered the population genetic diversity. The fixation index (FST) was calculated from 100-kb non-overlapping windows using VCFtools. The global weighted FST was used to measure the differentiation of populations.LD decay was determined using the software PopLDdecay11  together with ICC 495837 (a desi genome sequence), a C. reticulatum genome13, and de-novo-assembled sequences from 3,171 cultivated and 28 C. reticulatum accessions were used to guide the assembly of the chickpea pan-genome using a conservative approach38. Following the alignment of reads from each accession to the reference, unmapped and dangling mapped read pairs were extracted using SAMTools39 v.1.2 based on the FLAG field. The extracted reads were de-novo-assembled using MEGAHIT40 v.1.2.9 with default parameters. To identify possible redundancies among assembled contigs that were already present in the foundation genome, the assembled contigs were aligned to the foundation genome using NUCmer41 v.4.0.0beta2 with the parameters “-l 20 -c 65” and the alignments with length ≥ 500 bp and identity of greater than 80% were extracted to be added into the intermediate pan-genome. The processes were performed one by one: ICC 4958, de-novo-assembled sequences from 3,171 cultivated accessions, the C. reticulatum genome, and de-novo-assembled sequences from 28 C. reticulatum accessions. Further, to identify redundancy among the ‘novel’ sequences, all-versus-all alignment was performed using CD-HIT42 v.4.81. The same process was performed for the next iteration until no sequence was left. Finally, we removed the potential containments from vectors, bacteria, viruses, animals, fungi and organelle sequences using BLASTN43 v.2.2.31 to the corresponding NT databases and obtained the final pan-genome. As a result, the CDC Frontier genome11 and novel assembled sequences were combined to construct the chickpea pan-genome.The chickpea draft genome of CDC FrontierC. reticulatum accessions (with sequence depth of greater than 10×) were used to identify structural variations against the reference genome of CDC Frontier11, such as large insertions, deletions, inversions, and intra- and inter-chromosomal translocations. The insertions, deletions and inversions were identified using a dual calling strategy through BreakDancer44 v.1.1.2 and Pindel45 v.0.2.5b9. First, BreakDancer was used to detect structural variations with parameter “-q 20 -y 20 -r 1”. Secondly, the output of BreakDancer was used as an input for Pindel using the parameter “-x 4 -breakdancer” to increase the sensitivity and specificity. To merge the results from BreakDancer and Pindel, two structural variants with a distance between the two breakpoints of less than 100 bp were considered the same structural variation and merged. Owing to the inability of Pindel to detect intra- and inter-chromosomal translocations, only BreakDancer was used for their analysis. Furthermore, a structural variation was considered if it was present in at least 5% of the individuals in a given population.A total of 2,258 cultivated and 22 http://bioinfo-out.curie.fr/projects/freec/src/gccount.tar.gz) with parameter “window = 1000 step = 1000” to normalize non-uniform read coverage of genomic position. Then, Control-FREEC46 v.11.0 was used to detect CNVs in 1-kb non-overlapping windows (bins) with parameter “ploidy = 2 window = 1000 step = 1000 mateOrientation=FR” for each high-depth individual (sequencing depth > 10X). Next, the sample-level copy numbers were combined to produce a matrix of copy numbers for each bin at the cohort level. To further reduce false positives, we filtered out the bins with a CNV rate of less than 1%. The affected genes were identified by the presence of overlapping regions with CNVs.For CNVs, we first generated a GC-content profile using gccount , which contains 5,366 single-copy orthologues to predict the genes for 195 wild species accessions, CDC Frontier genome11 and M. truncatula genome18 (as outgroup) using GeneWise47 v.2.4.1 with the parameters “-both -sum -genesf”. On the basis of the gene annotations of 195 wild species accessions, only one sample with the longest average coding sequence (CDS) length was chosen for each wild species. The CDS sequences of single-copy genes in seven wild species, CDC Frontier and M. truncatula were extracted. For each single-copy family, multiple sequence alignment was performed using MUSCLE48 v.3.8.31 with default parameters and poorly aligned and divergent regions were eliminated using Gblocks49 v.0.91b with the parameter “-t=c”. The aligned matrix from each single-copy family was combined to construct the super aligned matrix. The maximum likelihood tree was constructed using RAxML50 v.8.2.12 with parameters “-f a -x 12345 -p 12345 -# 1000 -m GTRCATX”. Finally, divergence time was estimated by MCMCTree51 v.4.4 with three time-calibration points  from the literature54.For divergence time estimation, 195 wild species accessions were assembled individually using MEGAHIT55 v.3.6.To assess the relatedness among 195 wild accessions and 3,171 cultivated lines, the genetic distance matrix based on identity by state (IBS) was calculated through PLINK v1.90 with the parameter “--distance 1-ibs” using LD-pruned SNPs (--indep-pairwise 50 10 0.2) present on pseudomolecules. On the basis of the distance matrix, neighbour-joining phylogenetic trees were then constructed using ‘neighbor’ in PHYLIP56 v.7.2.0 with default parameters on LD-pruned SNPs present on pseudomolecules. PCA results were plotted using the R package ‘rworldmap’ (ref. 57).A PCA was undertaken to study the relatedness and clustering among cultivated chickpea accessions. The top 20 principal components (PCs) of the variance-standardized relationship matrix were estimated using EIGENSOFTFST) were calculated in a 10-kb/2-kb sliding window using VCFtools v.0.1.13. A range of pairwise FST was calculated in the same combinations as for the ROD calculations. Tajima’s D was calculated using VCFtools (“--TajimaD 100000”) in 100-kb non-overlapping windows. A window was considered a selection window in the upper 90% of the population’s empirical distribution for ROD and FST statistics, along with a negative Tajima’s D value (less than −2). Genes located on the selection windows were identified, and functional enrichment of the KEGG pathway (v.87.0) and GO term for these candidate genes was conducted using the Fisher’s exact test with false discovery rate correction using EnrichmentPipeline58 (https://sourceforge.net/projects/enrichmentpipeline/).To characterize variation among populations, population differentiation statistics . To avoid potential bias in the estimates owing to the long run of homozygosity, we filtered out homozygous regions longer than 5 kb in the 150 samples. For each of the 20 estimations, we used 5 different combinations of distinguished lineages, as suggested previously59. We then calculated the median of the 20 independent estimates for each time point.For determining population size histories and split times, the SMC++ programme61 on chromosomes Ca1 to Ca8. To keep calculation time and resource into reasonable burdens while staying conservative in pointing genomic regions as being likely to be under positive selection, 2 random sub-samples of 251 landraces, proportional to 2,439 landraces for each geographical region, were considered. The analysis computes in each sub-sample a CLR for each SNP along the genome. We used a grid value of 10,000 for each chromosome, corresponding roughly to computing a CLR ratio every 9 kb. We considered the highest 1% CLR values for each sample and kept them as candidate SNPs for positive selection of the positions detected in both samples. Owing to linkage disequilibrium, a high CLR value detected on an SNP can result from selection acting on a nearby gene. Therefore, we computed a list of intervals that are likely to be targeted by selection from the list of SNPs detected under selection, without pointing to particular SNPs but including all SNPs within 10 kb of each other.SweeD (v.3.3.1) analysis was performed as previously21 v.2.0.0. Putative deleterious mutations were identified with a SIFT score of less than 0.05. The Medicago genome was used as an outgroup to identify the derived alleles in the chickpea genome. Mutation burden was computed by counting the number of derived deleterious alleles present in constrained regions of the genome in each genotype as described before8.Effect of nucleotide variations on protein function was predicted with SIFT 4GP value of 3.16 × 10−7 was used to consider the MTA as significant.Genome-wide association study (GWAS) analysis was performed using 3.94 million genome-wide SNPs and phenotypic data generated on 16 traits for 2 seasons and 6 locations. Only biallelic SNPs in cultivated genotypes were used in the GWAS analysis. Furthermore, the filtration was done with a minor allele frequency (MAF) cut-off of 0.05, missing rate cut-off of 0.8 and heterozygosity rate of 0.1. Marker trait association (MTA) analysis was then performed using a mixed linear model with the filtered HapMap file and phenotyping data. The first three PCs were used to control the population structure. The Manhattan plots and QQ plots were generated from the GWAS results. A 62 v.1.19.09.04.For haplotype analysis, we retained a SNP set for 3,171 cultivated chickpea lines according to the following criteria: (i) MAF > 0.001; and (ii) proportion of missing calls per SNP < 30%. The haplotypes present within trait-associated genes were examined and only homozygous calls were considered for haplotype analysis. The identified haplotypes were visualized in Flapjack63. Haplotype-wise means for 100SW, days to flowering (DF) and YPP were compared to define superior haplotypes. Duncan’s multiple range test was used for statistical significance.For the haplo–pheno analysis, haplotypes carrying only one genotype were removed from the analysis. The accessions were categorized on the basis of haplotype groups, and together with phenotypic data, superior haplotypes were identifiedhttp://matesel.une.edu.au) to generate an optimized mating design within desi, kabuli and intermediate types. The relative emphasis on the mean index versus co-ancestry was set by choosing the target degrees on the response surface24. We chose a target of 60 degrees to minimize the increase in population co-ancestry (maximize population genetic diversity) while achieving an acceptable rate of genetic gain. As this study aimed to maintain a diverse pre-breeding pool while making economic improvements, we followed the conservative approach for ‘evolving gene banks’ (ref. 23).We used GEBV from the genomic prediction section for key production traits ) to generate a genomic relationship matrix based on 754,576 SNPs. We used the breeding program implementation platform MateSel v.6.3  with a bonus price for large seeds (when average GEBV for 100SW over 9 sites exceeded the average for kabuli of +5.9 g) and earliness (average GEBV for DF and DM over 9 sites < 0 days). The base price for chickpea was assumed to be US$400 per tonne, and YPP was converted to an equivalent grain yield value per hectare by assuming that the mean YPP of 18 g per plant is equivalent to 1.8 tonnes per hectare. The index was also adjusted for a price bonus for large seeds and earliness as follows. The starting values for GEBV for 100SW are low in desi candidates (mean −4.0 g) and high in kabuli candidates (mean +5.9 g). Hence, the starting value for a price bonus for 100SW begins at GEBV + 5.9 g, and there is no bonus below this value. The price bonus per gram (GEBV 100SW > 5.9 g) is US$35 per gram, which is added to the base price. Similarly, a bonus was provided in price per tonne for GEBV earliness (average of GEBV DF and GEBV DM). The average GEBV earliness in the desi group was −1.6 days, and in the kabuli group was +2.4 days. The starting value for a price bonus for earliness begins at average GEBV 0 days; there is a bonus for negative values of US$10 per day added to the base price and no bonus for positive values.25, three models, a basic model (E + L) with main effects of environments (E) and lines (L), a model (E + L + G) including the main effects of markers, and a genomic by environment interaction model (E + L + G + GE) were used. Three different SNP datasets  were used as a genomic matrix (G), post-conventional quality controls on missing values (<20%) and MAF (>0.05). Phenotyping data for nine traits across 12 different year × location combinations were used. The Pearson’s correlation coefficient between observed phenotype and predicted genomic breeding value was used to estimate the accuracy of genomic prediction. Three different random cross-validation (CV) schemes, CV1 , CV2  and CV0 (predicts an unobserved environment using the remaining environments as a training set) were used. CV1 and CV2 with fivefold cross-validation were implemented to generate the training and testing sets, and the prediction accuracy was assessed for each testing set. The permutation of the five subsets led to five possible training and validation datasets. This procedure was repeated 20 times, and 100 runs were performed for each trait–environment combination on each population. The same partition was used for the analysis of all the GS models. For CV0, each environment was predicted using the remaining environments. For fitting the GS models, the R package Bayesian Generalized Linear Regression (BGLR)64 v.1.0.7 was used.As described previously65 v.1.3.0 between K = 19 and K = 30 to identify the most likely number of admixture components. To confirm the admixture value, another method, DAPC , was used. The optimal number of admixture components in the WhoGEM method was obtained by comparing the predicted and recorded locations  and fixed to K = 23.For WhoGEM analysis, 1,318 accessions with the validated geographical location were selected and used as a reference dataset. The SNP dataset was filtered for missing (>0.1) and MAF (<0.01) and used for a detailed search with ADMIXTUREA general linear model explored the relationships between the phenotypes and admixture components, and land types. A forward–backward algorithm was used to reduce the set of predictors to the most significant ones. The model is fitted on the whole dataset, and the significant factors are identified and conserved. A negative control ) is also fitted to the data. The models were fitted on the whole dataset, and the significant factors were identified and conserved.A test of WhoGEM significance is given by a likelihood ratio test comparing the WhoGEM-based model and the environment-only-based model. The performances of the three models  are then evaluated using 100–300 replicates of a fivefold cross-validation scheme.27.The SNP set was filtered, first by excluding all markers with more than two called alleles, missing (>10%) and MAF (<5%). A subset of 124,833 (20%) of 2.4 million high-quality SNPs were randomly selected to reduce the computational load in further analyses. Those SNPs were used to construct LD blocks and estimate local GEBVs for haplotypes of those LD blocks. Details on the method used to calculate local GEBVs for haplotypes of LD blocks are described in a previous report66) v.4.6.0 to predict marker effects for seven agronomic traits, then summed up the predicted allelic effects of each observed haplotype for all genome-wide LD blocks. Finally, we estimated variances among local GEBVs for haplotypes within each LD block to highlight regions in the genome showing molecular variation linked to observed phenotypic variation for the agronomic traits measured in the field trials.We also ran a ridge-regression best linear unbiased prediction (BLUP) model in the R-package rrBLUP (ref.Further information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-021-04066-1.Supplementary InformationThis file contains Supplementary Notes and References – see contents page for details.Reporting SummarySupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10


Aulacaspis tubercularis is an invasive pest that causes important damage to mango crops in Southern Spain. The use of integrated management strategies  is recommended for its effective and sustainable management. Among cultural control measures, fruit bagging technology is being widely used in some mango production regions prior to harvest to protect fruit from diseases, pests, and mechanical damage. However, despite the proven efficacy of bagging, its usefulness has still not been proved to control A. tubercularis infestations on mango fruits. In this study, we evaluated not only the mango bagging efficacy on A. tubercularis control but also its effects on the development and quality of bagged fruits. We tested two commercial types of bags (a yellow satin paper bag and a white muslin cloth bag) commonly used to cover several other fruits. Both bags were effective in reducing the pest incidence and damage caused by A. tubercularis when mango fruits were bagged before the scales migrated to them. Our findings indicate that the use of bags for the pre-harvest protection of mango fruits is feasible, and it may improve their development (weight and size) and quality (color and soluble solids).The white mango scale Aulacaspis tubercularis Newstead (Hemiptera: Diaspididae) is the main pest of mango, Mangifera indica L., in Spain, causing significant economic losses by aesthetic damage that reduce the commercial value of fruit. Bagging fruit with two commercial bags (a yellow satin paper and a white muslin cloth bag) was evaluated for control of A. tubercularis in two organic mango orchards during the 2020 cropping season in pursuit of the development of a mango IPM program to produce pest-free and residue-free fruits. Results from fruit damage evaluations at harvest showed that bagging significantly reduced pest incidence and fruit damage compared with non-bagged plots. Of the two bags evaluated, white muslin cloth bag provided higher levels of fruit protection from A. tubercularis damage, reducing the non-commercial fruit percentage by up to 93.42%. Fruit quality assessment indicated that weight and size of bagged fruit were significantly higher than the non-bagged. Paper-bagged mangoes showed higher whiteness and yellowness compared to the other treatments. Soluble solids content (ºBrix) was higher in paper-bagged fruit than all other treatment plots. The results from this study indicate that pre-harvest fruit bagging is effective at controlling A. tubercularis and should be integrated into an IPM program for Spanish mango production. Mangifera indica L.) is a tropical fruit tree native to Southeast Asia belonging to the Anacardiaceae family, which is grown commercially in tropical and subtropical regions of the world [Mango . This armored scale is currently the main pest in the Spanish mango orchards and one of the most important pests of this crop worldwide in tropical and even subtropical regions [A. tubercularis causes conspicuous pink blemishes on the epidermis of the ripe mango fruits [A. tubercularis can cause important economic losses in the Spanish organic mango orchards, which may exceed 40% in late-ripening cultivars [A. tubercularis is mainly carried out through the mango tree post-harvest pruning and the repeated application of a limited number of authorized insecticides during the crop cycle, which may increase the risk of resistance development, cause a negative impact on beneficial insects and the environment, and generate pesticide residues that make the marketing of the mango fruits difficult [A. tubercularis in the Spanish mango orchards [Until a few years ago, Spanish mango cultivation was characterized by a low presence of problems caused by pests and diseases ,8. Howevo fruits ,11, whico fruits ,13. Someultivars . Controlifficult . Thus, dorchards ,15.Pre-harvest fruit bagging is a traditional Asian physical protection method commonly applied to many fruits ,17. ThisBactrocera dorsalis (Hendel) (Diptera: Tephritidae), were significantly reduced up to 100% by covering the fruits with double-layer brown paper bags at least 30 days prior to harvest [Anastrepha obliqua (Macquart) (Diptera: Tephritidae), 90% control of anthracnose (Colletotrichum gloeosporioides Penz.) in ripe fruit, and a considerable reduction of damage caused by Capnoium spp. [Sternochetus mangiferae (Fabricius) (Coleoptera: Curculionidae), and some scale and mealybugs in South Africa [A. tubercularis or other species of Coccoidea. In this sense, this physical protection method may be an appropriate non-chemical strategy to incorporate into existing IPM programs for mango in Southern Spain, provided that bags are easy to set up and economically feasible, and fruit protection from scales is similar to that of chemical control.Several studies have reported the bagging technique as a successful control measure against fruit flies in commercial mango orchards ,27,28,29 harvest ,29. In Mium spp. . Baggingh Africa  in ThailA. tubercularis and thus reduce economic losses and to evaluate physiological effects of bagging on fruit such as size and weight, skin color, internal flesh color, and total soluble solids content. Of course, mango bagging could also contribute to reducing other pests, either present as some species of coccids or even not yet present in the Southern Spain groves, which probably will colonize this area.Thus, the main objective of this study was to investigate the field efficacy of pre-harvest mango bagging to avoid damage by Oidium mangiferae Berthet). The presence of A. tubercularis had been recorded in both orchards for several years before the study. Trees were maintained following the cultural practices routinely employed in this mango cultivation area.Studies were conducted in two organic mango orchards located in Algarrobo  during the fruiting season of 2020. The first orchard  contained fifteen years old Osteen cv. mango trees, which were planted at a spacing of 4 m between rows and 3 m between trees. The second orchard  contained ten years old Sensation cv. mango trees with a plantation framework of 4 m × 3 m. Both mango orchards were drip-irrigated, and no pesticides were sprayed during the sampling period, except some fungicides authorized in organic production (sulfur) to control the powdery mildew  measuring 35.0 cm by 23.5 cm and similar to those used in the grapes industry in Spain. This bag is made of virgin cellulose and satin on the outside, opened at both ends, and has a thickness of approximately 5 µm. In our case, one end of the bag was stapled to prevent insect access. The second one was a white muslin cloth bag  measuring 30.0 cm in length by 24.5 cm in width, such as those used to cover several fruits in China.A. tubercularis, were selected and randomly assigned to the following treatments: T1 = yellow paper bag, T2 = white cloth bag, and T3 = non-bagged (control). Four fruits per tree for each treatment were used. In both orchards, uniformly grown fruits were bagged when they were 7–10 cm long or approximately 45 days (28 July) after fruit set. The bags were distributed uniformly around each canopy and at all heights. Bags were secured around the fruit peduncle with a thin wire or drawstring embedded along one edge of the bag. Mature fruit were harvested 71 and 91 days (7 and 27 October) after bagging for the first and second orchards, respectively. All mango fruits from each treatment and orchard were harvested. Bags were removed immediately after harvest, and fruits were labeled. The harvested fruits of each treatment were kept separately and brought to the laboratory the same day for further observations.A completely randomized design with three treatments and twelve single tree replications was performed. In each orchard, twelve mango fruits per tree, without presence of A. tubercularis was evaluated at harvest. All selected fruit from the treatments remaining on trees at harvest were assessed. Fruits were classified as free of damage (clean) or damaged by pests. Fruit was considered damaged if it exhibited signs of individual pest infestation or damage. The surface of fruit was inspected for the presence of females and male colonies of A. tubercularis, as well as external lesions and conspicuous pink blemishes on the epidermis. In our case, fruit that exceeded the threshold of four pink blemishes per piece was considered as non-commercial fruit for export and fresh consumption, following the quality criteria established by the local marketing companies [Fruit damage by ompanies ,14. The A. tubercularis was converted to proportion damaged by dividing the number damaged by the total number of fruits evaluated from each treatment plot.Subsequently, the percentages of damaged and non-commercial fruits were calculated from each treatment plot. The number of fruits damaged by a = width, b = length, c = depth, and d = circumference.Fruit development  and quality (color and soluble solids) were assessed according to Chonhenchob et al. . Size anL* a* b* values, where: L* indicates the lightness (0 = black and 100 = white); a* represents the green and red color (+a* = green and −a* = red); and b* indicates the yellow and blue (+b* = yellow and −b* = blue). The hue angle (°) and chroma were estimated using the following equations:Peel color was determined with a CR-300 Chromometer  using the CIE system. Data were expressed as Internal flesh color development stages were estimated according to the procedure described in the Mango Maturity and Ripeness Guide provided by the National Mango Board in the USA (NMB 2010), which describes five stages (1 to 5) defined by the appearance and progression of yellow to orange color, from 0 to 100% of the flesh in 20% increments starting near the pit and progressing outward. Soluble solids or sugar content was measured using a HI-96801 Refractometer , expressed as ºBrix.A. tubercularis chemical control in mango with the costs derived from the pre-harvest fruit bagging technique. For this purpose, the costs associated with the purchase of the insecticide product  and its application (labor and time spent) were considered. The price of the insecticide product was obtained from local distributors and the application rate (doses/ha) was that recommended by the product label. Finally, the frequency of treatments was that used by mango growers in the area.A full economic analysis was conducted for the first and second organic mango orchards, comparing the costs per hectare derived from an estimated Adjusted net returns were calculated based on the selling price received by growers in the area during the fruiting season of 2020 for both mango cultivars (Osteen and Sensation). Costs were estimated for all treatments, including bags and labor costs. According to Hossain et al. , adjustep ≤ 0.05) by means of the statistical software IBM® SPSS® Statistics v26.0 for Windows . All proportion data were subjected to arcsine square root transformation prior to analysis to meet the assumptions of normality and homogeneity of variances. Abbott’s formula [A. tubercularis incidence with respect to the control.All data were analyzed by analysis of variance (ANOVA) test applying the GLM procedure, and the average values were compared by Tukey’s honestly significant difference (HSD) test  and second  mango orchards. In addition, both treatments showed a significantly lower number of pink blemishes on the epidermis than the untreated control in the first  and second  mango orchards. In both orchards, there were no significant differences among two commercial fruit bags relative to the presence of A. tubercularis females, as well as conspicuous pink blemishes on the epidermis.Significant differences in the number of females and male colonies of est time . Both frA. tubercularis with respect to the untreated control in the first  and second  mango orchards. White mango scale pressure was similar among both orchards with infestation levels of 65.28% and 62.5% in control treatments for the first and second orchard, respectively. The bagging with white cloth bags provided the maximum damaged fruit reduction in the first (68.09%) and second orchards (60.00%). Consequently, both treatments showed significantly lower percentage of non-commercial fruits that the untreated control in the first  and second  mango orchards. In both mango groves, there were no significant differences among the two commercial fruit bags from the percentage of non-commercial fruits, but the white cloth bag provided the maximum reduction by 93.49% and 92.12% in the first and second orchards, respectively.Significant differences in the percentages of damaged fruits and non-commercial fruits were found among treatments . Both frF = 1.077; df = 2; p = 0.345; size: F = 0.795; df = 2; p = 0.455; sphericity: F = 0.190; df = 2; p = 0.827). However, the results from the second orchard indicate that mangoes bagged with both fruit bags were significantly higher in weight  and size  than the untreated control. In this case, the weight and size of mango fruit bagged with yellow satin paper bag were similar to the white cloth bag treatment. Additionally, fruit bagging had no significant effect on sphericity among treatments .L* a* b* values of mango peels for different bagging treatments in both orchards are shown in L* and b* values were found among treatments at the harvest time. Both fruit bags provided significantly higher lightness or whiteness  than the untreated control in the first  and second  mango orchards. In addition, all treatments showed significantly higher yellowness  than the untreated control in the first  and second  mango orchards. As compared to the other treatments, paper-bagged mangoes had the highest L* and b* values in both orchards, indicating higher whiteness and yellowness and expressing more ‘pale’ yellow as compared to the other treatments. Finally, there were no significant differences among treatments with respect to the greenness  in the first  and second  orchards. Changes in F = 0.701; df = 2; p = 0.499) .Fruit quality as measured by soluble solids content (ºBrix) was not significantly different among treatments in the first orchard (= 0.499) . HoweverA. tubercularis in both mango orchards. According to our results, the cost of treatment was lowest for the chemical control in both orchards (525 EUR/ha) and highest for the white muslin cloth bag treatment with 3766.28 EUR/ha and 6000.05 EUR/ha for the first and second orchards, respectively. However, both bags provided an increase in production and fruit quality which resulted in a higher gross return for the farmer. The value of average production per hectare, considering the different quality categories and their market prices, was highest for the white muslin cloth bag treatment with 43,048.20 EUR/ha and 58,679.25 EUR/ha for the first and second orchards, respectively. Based on the above, both bags provided the highest net return in the second orchard which resulted in an adjusted net return of 6382.68 EUR/ha and 5044.61 EUR/ha for the yellow satin paper bag and white muslin cloth bag, respectively.Aulacaspis tubercularis is a serious concern for the mango industry in Southern Spain and in the world [Icerya seychellarum (Westwood) (Hemiptera: Monophlebidae), Pulvinaria psidii Maskell, and Ceroplastes floridensis Comstock (Hemiptera: Coccidae) are increasing their populations and becoming important pests in the Southern Spanish mango groves [Ceratitis capitata (Wiedemann) (Diptera: Tephritidae), is generally not very attracted to mango fruits; however, its incidence in Southern Spain depends on the cultivar and the ripening stage at harvest [he world ,14. Otheo groves . The Med harvest  and may  harvest .A. tubercularis and other pests [A. tubercularis or pink blemishes produced by the pest and other scales in the two studied cultivars  [Anastrepha spp.) [Bactrocera spp.) [The most extended mango cultivar in Southern Spain is Osteen, which occupies 75% of the cultivated mango area and is collected at medium season. The other studied cultivar was Sensation, which is seldom planted and is collected in late season. Therefore, Sensation cv. fruits are more vulnerable to er pests . Due to ultivars . In addiultivars . Althougealybugs , weevilsgiferae) , and fruha spp.)  and Asiara spp.) ,29,37. Ora spp.) , who foura spp.)  found a ra spp.)  found a In the first of the studied orchards (Osteen cv.), we did not find differences in weight or volume between controls and bagged fruits, but in the second orchard (Sensation cv.), we found that bagged mangoes were 9.6% (cloth bags) and 11.1% (paper bags) heavier than controls but only by about 3% greater in volume, given that sphericity was not different among the treatments . These rL* and b* values were higher in bagged fruits, which showed better luminosity and higher yellowish color, in the two studied cultivars. However, hue angle (color tone) was not different between treatments, and chroma value (intensity) was only slightly lower in cloth bags and higher in paper bags with respect to control in Sensation cv. Moreover, internal flesh color was not different in Sensation cv., but bagged fruits in Osteen cv. showed a higher ripening color  contents, only paper bags in Sensation cv. showed slightly increased ºBrix values than in controls or in cloth bags . HoffmanAs seen, the literature reports different results referring to the variables treated in the present research because they may depend on the cultivar, bagging time, and the climate associated with geographic area. Generally, a good control of pests and diseases and an improvement of the appearance (including peel color) and other physical or chemical features of bagging fruits have been pointed out ,39,42,43A. tubercularis, without a decrease in fruits quality. Moreover, this technique may be used to control other well-known fruit pests such as the fruit fly C. capitata or pests with an increasing presence in Southern Spain mango groves such as I. seychellarum, P. psidii, and C. floridensis, and, in general, other future pests in groups such as scales (Coccoidea), fruit flies (Diptera), or fruit borers in Coleoptera and Lepidoptera. Sometimes, when fruit bags were removed in the field, specimens of the common earwig Forficula auricularia L. (Demaptera: Forticulidae) appeared. They use the bags for shelter [Our objective was to demonstrate that pre-harvest fruit bagging may be a viable and useful cultural control measure for  shelter , and the shelter ,46,47. T shelter . In our According to Ali et al. , fruit bFuture research dealing with bagging time setting and removing and bagging types on different cultivars and different pests will shed more light on this interesting question which has a high impact on sustainable mango management in Spain and across the world.A. tubercularis and other mango pests in Southern Spain, in addition to improving the development and quality of the fruit. However, this pest management technique requires considerably more time and labor, being an economically unprofitable practice for large-scale mango producers in Southern Spain but very cost-effective for those small-scale organic growers who expect to sell a high quality, healthy, and sustainable fruit in selected markets.This research showed that pre-harvest fruit bagging can reduce the incidence and damage caused by


STF) for each SNP was calculated between the groups. The results verified genomic regions harboring some previously proposed candidate genes for traits related to body growth, i.e., EYA2, GDF2, GDF10, MEF2B, SLC16A7, TBX15, TFAP2B, and TNNC2. Moreover, novel candidate genes were proposed with known functional implications on growth processes such as CPXM2 and LRIG3. Subsequent association analysis showed significant effects of the considered SNPs on birth and weaning weights. Results highlight the genetic diversity associated with performance traits and thus the potential to improve growth traits in the Barki sheep breed.Sheep play a critical role in the agricultural and livestock sector in Egypt. For sheep meat production, growth traits such as birth and weaning weights are very important and determine the supply and income of local farmers. The Barki sheep originates from the northeastern coastal zone of Africa, and due to its good adaptation to the harsh environmental conditions, it contributes significantly to the meat production in these semi-arid regions. This study aimed to use a genome-wide SNP panel to identify genomic regions that are diversified between groups of individuals of Egyptian Barki sheep with high and low growth performance traits. In this context, from a phenotyped population of 140 lambs of Barki sheep, 69 lambs were considered for a genome-wide scan with the Illumina OvineSNP50 V2 BeadChip. The selected lambs were grouped into divergent subsets with significantly different performance for birth weight and weaning weight. After quality control, 63 animals and 40,383 SNPs were used for analysis. The fixation index ( STF) of Weir and Cockerham (1984) is one of the most popular methods to analyze population structure in this framework. The STF approach measures genomic differentiation between two or more populations, breeds, or divergent lines and subgroups in the same breed, depending on allele frequency, and is thus able to identify highly differentiated alleles that undergo selection, while being unbiased for sample size [Breeding is one of the main drivers affecting allele frequencies, leading to genomic regions with genetic differentiation, thus contributing to the genetic diversity of livestock species . Many stple size ,11.Growth traits are very important in sheep breeding and considerably affect the resource efficiency and breeder’s profit. Growth traits, like other quantitative traits, are controlled by the complex genetic background of the animal as well as environmental factors, such as feed and herd management. Body weight gain has a moderate to high heritability ,13,14 anIn this study, a population of 140 lambs  from single births of the Egyptian Barki sheep was considered for investigation of the genomic regions and candidate genes for growth traits comprising birth weight and weaning weight. Animals were raised in the farms of the Desert Research Centre (DRC), Ministry of Agriculture, Egypt. They were kept under an intensive breeding system in semi-open yards. The lambs were from one breeding season and were offspring of 10 rams. Birth weight within 12 h after parturition and weaning weight after a lactation period of 3 months (90 days) were recorded for every lamb using an electronic scale. From birth to weaning age, the lambs were suckled only on their mother’s milk daily. Fresh water was offered two times daily to lambs ad libitum. The experiment was conducted in accordance with all ethics and animal rights (DRC), considering all regulations in conformity with the European Union Directive for the protection of experimental animals (2010/63/EU).2 > 0.5) within windows of 50 SNPs, and significant deviation from Hardy–Weinberg equilibrium at p < 10−6 were removed from the analysis. JMP Genomics software (version 9) was used for quality control. A total of 63 animals and 40,383 SNPs remained and passed the quality control. Base pair positions and names of SNP markers were updated to the latest version of the ovine genome (Oar_v3.1 accessed on 6 July 2020). SNPs not located on autosomes and lacking reference SNP (rs) identifiers were excluded. Based on the SNP data, the genomic relationship matrix of lambs was calculated using SNPRelate R package (version 1.24.0) and visualized in a principal component plot . Out of the entire population of 140 animals, 69 lambs with considerable differences in the respective growth trait were genotyped using the Illumina OvineSNP50 V2 BeadChip . The relative identity-by-descent (IBD) was calculated for all pairs of lambs. The genetic relatedness average was 0.15. The iScan Reader (Illumina) was used to image the raw signal intensities of the 53,516 SNPs on the chip, which were subsequently converted into genotypes with the GenomeStudio software (version 2.0). The samples with call rates < 90% were removed from further analysis. The SNPs with genotype call rates < 98%, minor allele frequencies (MAF) < 0.05, in high linkage disequilibrium (rent plot .t-test was used to compute the differences between the means of high and low groups. The fixation index (STF) for each SNP was calculated between the groups low birth weight (LBW)—high birth weight (HBW) and low weaning weight (LWW)—high weaning weight (HWW) by the SNPRelate R package (version 1.24.0) using the Weir and Cockerham method [STF values were Z-transformed using the following equation Z(STF) = (STF − μ STF)/ϭ ST,F where μ STF is the overall mean of STF values and ϭ STF is the standard deviation of STF values. In addition, an SNP-specific baseline STF estimate was calculated to evaluate the results of the STF analysis. This estimation was based on a random permutation of the group assignment with 10,000 iterations. Subsequently, the distribution of SNP-specific STF values was used to calculate permutation-based p-values for the intended group comparisons (LBW–HBW and LWW–HWW) using the statmod R package [p-values were adjusted. Only the SNPs having the top 0.05% of Z(STF) values were selected for further analysis. Manhattan plots of Z(STF) value for each SNP were constructed using qqman package (version 0.1.4) in R software. To identify the candidate genes, the Ensembl database was used to select the genes within 2 Mb windows around high Z(STF) SNPs [STF) SNP were considered as positional candidate genes. Functional candidate genes in the respective genomic region were identified according to their functional relation with the phenotypes, employing available gene annotations from the GeneCards  and Uniprot  databases. Kyoto Encyclopedia of Genes and Genomes  pathway enrichment analysis of the functional, positional, and closest up- and downstream-located genes based on SNPs that exceeded the threshold was performed using ClueGO plugin with the Cytoscape software (version 3.6.1) [For each of the traits, birth weight and weaning weight, the genotyped animals were divided into two subgroups, each representing the extreme phenotypes for the respective trait with significant differences between the groups . The Stum method ,22. FST  package . Using tn 3.6.1) ,26.p ≤ 0.05 was considered significant, p ≤ 0.01 highly significant, and p > 0.05 not significant.The association analysis was carried out to test the effect of the genotypes of the selected SNPs on the phenotypes of birth and weaning weight by JMP Genomics software (version 9) using the QK mixed model procedure considering the genomic relationship matrix. In addition, the statistical model included the sex of lamb as fixed effect (two levels). Descriptive statistics of birth weight and weaning weight of Barki lambs with divergent performance in the respective trait are shown in STF) values were calculated to explore genomic differences between groups using genome-wide distributed SNPs. STF) values for birth weight and highlighted genomic regions and SNPs with the highest Z(STF) values. Obtained SNPs and regions were distributed on chromosomes 1, 2, 3, 5, 6, 8, 13, 14, 16, 17, 19, 22, 23, 24, and 25 (cutoff Z(STF) ≥ 7.68, PCKS5, WDR35, LAMA5, HYDIN, FAM160A1, GPR26, PTPRM, and DNAH3. Moreover, a total of 13 functional candidate genes in the indicated genomic regions were identified, which are known to affect birth weight or are involved in the development process during the embryonic stage and after birth. These comprise TBX15, SLC16A7, LRIG3, MATN3, OSR1, CITED2, LAMA5, URI1, SUCLG2, CPXM2, LAMA1, GDF2, and GDF10. Among the vital processes to which these genes contribute is the development of body organs, such as limbs, liver, lungs, brain, skeletal system, and muscles. Except for the SNPs indicating the genomic regions harboring BRINP1, CITED2, CPXM2, GDF2, and GDF10, the marker–trait association analysis confirmed a significant relationship of the candidate loci with birth weight in the Barki lamb population  value at 99.95% of the percentile distribution, were identified (cutoff Z(STF) ≥ 6.99, ASB3, SOX5, TP53RK, DGKZ, FBLN5, PCDH15, and GLUD1. Moreover, 19 functional candidate genes were identified in the respective genomic regions for weaning weight including MEGF9, PTPRU, FABP3, MEF2B, HAPLN4, NCAN, WNT9A, WNT3A, POSTN, EYA2, MMP9, TNNC2, ACP2, LRP4, FBLN5, TFAP2B, BMPR1A, GDF2, and GDF10. Considering the SNPs indicative for these candidate genes, most of the SNPs appeared to be significantly associated with weaning weight  values. Considering birth weight and weaning weight, identified genomic regions on chromosomes 2, 3, 5, 15, 16, 18, 22, 24, and 25 were previously proposed in the sheep QTL database to be associated with some growth traits such as body weight at different ages of lambs and average daily gain [STF) values. Within the genomic region on chromosome 1 at 95.3 Mb, T-box 15 (Tbx15) appears to be the most promising functional candidate gene. Tbx15 gene is a member of T-box gene family, which encodes for transcription factors. T-box genes play a critical role in the development of different tissues and organs in vertebrates and invertebrates, such as the skeletal system during the embryonic stage [SLC16A7) gene at 159.6 Mb and the leucine-rich repeats and immunoglobulin-like domains protein 3 (LRIG3) gene at 160.4 Mb were proposed as additional promising candidates for birth weight. SLC16A7 was reported to have a role in skeletal muscle as a lactate cotransporter [LRIG3 is known to be involved in placenta and embryonic development in pigs [CPXM2) located on chromosome 22 at 42.4 Mb plays an important role in growth processes. It was reported to be one of the genes that regulate backfat thickness at different life stages in pigs [In this study, we used the Ovine SNP50 V2 BeadChip to identify the genomic differences between groups of Barki sheep that differed significantly in growth traits (birth and weaning weight). The average birth weight of Barki lambs was similar to those of lambs of the native Egyptian breeds, such as Rahmani and Ossimi breeds, with 3.73 and 3.9 kg. However, in terms of weaning weight, Rahmani and Ossimi lambs are slightly heavier than Barki lambs with average weights of 17.63 and 14.05 kg, respectively ,28. Moreily gain . A totalic stage . The expic stage ,33. Withnsporter . LRIG3 i in pigs . Moreove in pigs .MEF2B) gene was proposed as a functional gene for weaning weight. This gene encodes a member of the myocyte enhancer factor-2 family , which plays an important and critical role in cell development, embryonic development, muscle tissue growth, and development processes [MEF2B gene showed a significant correlation with some growth traits in New Ujumqin Sheep such as body weight and chest girth at 4 and 6 months [MEF2B and skeletal muscle satellite cell and reproductive traits in pigs [MEF2B region was found to be significantly associated with weaning weight. Within chromosome 13, the eyes absent homolog 2 (EYA2) and the fast skeletal muscle troponin C (TNNC2) genes were revealed as candidates for weaning weight. In Ethiopian sheep, EYA2 was proposed as a candidate for embryonic development of tendons, bones, and cartilages [TNNC2 plays a critical role in skeletal muscle contraction, modulates the Ca2+-activation characteristics of muscle fibers [TNNC2 reported a significant association with growth traits in porcine skeletal muscles [Within the genomic region on chromosome 5 at 3.1 Mb, the myocyte enhancer factor-2B  gene was proposed as a functional candidate for weaning weight. TFAP2B was documented in vertebrates and invertebrates and has a critical role during embryonic development [TFAP2B in the development of craniofacial structures, limb formation, and kidney and skin development was reported in mice [Within the region on chromosome 20 at 23.4 Mb, the transcription factor AP-2B  on chromosome 25. GDF2 and GDF10 are members of the transforming growth factor-β (TGF- β) superfamily and the bone morphogenetic protein family (BMP), also known as bone morphogenetic protein 9 and 3B  [GDF2 and GDF10 genes and weaning weight in New Zealand dual-purpose sheep [GDF10 regulates cell growth and differentiation in embryonic and adult tissues. Genetic polymorphism in the bovine GDF10 gene showed a significant effect on some body measurements in Chinese indigenous cattle [Based on the indicated genomic regions of birth weight and weaning weight, some functional candidate genes were proposed such as growth differentiation factors 2 and 10 genes  . Previouse sheep . GDF10 rs cattle . Resultss cattle ; the resCPXM2, EYA2, GDF2, GDF10, LRIG3, MEF2B, SLC16A7, TBX15, TFAP2B, and TNNC2, which deserve further investigation, due to their relation to biological processes, including metabolism, body growth, organ morphogenesis, skeletal muscle development, and cell proliferation and differentiation. Moreover, the marker–trait association analysis revealed a significant relationship of the considered SNPs to the studied traits. Our findings provide valuable information for a better understanding of the genetics of early growth-related traits and might contribute to the improvement of these traits in the Barki sheep breed.The genome-wide SNPs analysis revealed a number of genomic regions containing putative QTL for birth weight and weaning weight. The QTL regions cover a number of promising functional candidate genes such as


Bos taurus genome. Higher numbers of polymorphic loci were revealed in European bison than in Bos taurus through the reference pipeline. We observed a possible effect of PCR duplicates on GBS data, as previously reported with the RADSeq approach. We recommend using a reference pipeline without PCR duplicates as a more efficient tool for species with low genetic diversity.The European bison is a dramatically low-diversified species, commonly analyzed using cattle-dedicated tools. Our aim was to compare two genotyping-by-sequencing (GBS) pipelines: de novo and reference pipeline, using the STACKS software and to reveal the maximum possible number of species-specific SNPs for our further project on European bison health. Therefore, we compared two genotyping-by-sequencing (GBS) pipelines: de novo (non-reference based) and a reference-based, using the STACKS software. We found a higher number of polymorphic loci from the reference pipeline in comparison to the de novo one. Next, we compared the results of the reference pipeline for the draft genome of European bison and completely annotated the Bos taurus and European bison. Genotyping by sequencing (GBS) is a useful tool for SNP genotyping in non-model organisms due to its cost effectiveness. Our results support GBS with a reference pipeline without PCR duplicates as a powerful approach for studying the population structure and genotyping data of non-model organisms. We found more polymorphic markers in the reference pipeline in comparison to the de novo pipeline. The decreased number of SNPs from the de novo pipeline could be due to the extremely low level of heterozygosity in European bison. It has been confirmed that all the de novo/Bos taurus and Bos taurus reference pipeline obtained SNPs were unique and not included in 800 K BovineHD BeadChip.The European bison is a non-model organism; thus, most of its genetic and genomic analyses have been performed using cattle-specific resources, such as BovineSNP50 BeadChip or Illumina Bovine 800 K HD Bead Chip. The problem with non-specific tools is the potential loss of evolutionary diversified information (ascertainment bias) and species-specific markers. Here, we have used a genotyping-by-sequencing (GBS) approach for genotyping 256 samples from the European bison population in Bialowieza Forest (Poland) and performed an analysis using two integrated pipelines of the STACKS software: one is de novo (without reference genome) and the other is a reference pipeline (with reference genome). Moreover, we used a reference pipeline with two different genomes, i.e.,  The advent of massively parallel high-throughput sequencing (HTS) has dramatically altered the manner in which researchers conduct their research. This is certainly true for molecular population geneticists, who now consistently have access to large genetic datasets for non-model organisms. Genotyping by sequencing (GBS) is an efficient HTS technique. It involves next-generation sequencing (NGS) based on the reduced representation sequencing (RRS) strategy to obtain genome-wide single-nucleotide polymorphisms (SNPs). GBS provides an opportunity to discover polymorphic sites across individuals. It is a flexible, high-throughput assay capable of providing a sufficient marker density for genomic selection or genome-wide association studies in any population ,2. This Moreover, GBS has been demonstrated as an effective method for genome-wide SNP discovery and genotyping, even in studies dealing with inbreeding control and genomic selection for example, in Indian cattle and Peking duck ,9. SeverGenotyping by sequencing is one of the RRS approaches, where DNA adjacent to the restriction enzyme digestion sites is extracted, followed by NGS of the resultant fragments. The data obtained from GBS are then re-assembled into loci, anchored by the restriction enzyme digestion sites ,17,18,19Previous genomic studies on the European bison (Bison bonasus) mainly utilized the genomic resources available for cattle. This approach turned out to be successful in a num- ber of studies ,27,28,29Various soft tissues  as well as blood samples from 256 European bison , collected by the Mammal Research Institute, Polish Academy of Sciences in Bialowieza between 1990 and 2016, were used as DNA sources.®, Blood & Tissue Kit (spin-column protocol), and Sherlock AX Kit , A&A Biotechnology, a procedure with DNA precipitation, Gdansk, Poland), as per the manufacturer’s guidelines. Many of the materials available were blood samples, and the DNA was obtained using the phenol–chloroform extraction method with ammonium acetate  wCleaning and demultiplexing of the raw sequencing data were performed using custom scripts at BGI lab. The sequencing output of the GBS generated a total of 1,393,551,064 raw reads. Downstream analysis was performed using STACKS version 2.41 (USA)  and VCFthttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/)  [A quality check was performed on the cleaned and demultiplexed FASTQ files using the software FastQC 0.11.9  . FastQC dge, UK) ,38. STACdge, UK) ,40.After cleaning and demultiplexing the raw data, construction of the STACKS catalogue, SNP calling, and genotype construction were performed for de novo and reference pipelines using the denovo_map.pl and ref_map.pl programs of the STACKS software.For the reference pipeline, parameter optimization was not required. However, for the de novo pipeline, parameter optimization was a crucial step to obtain conclusive results from the analysis, particularly to facilitate the recovery of more loci for low-coverage datasets such as GBS ,42. To oustacks, cstacks, and sstacks), alleles were identified from our population set as per the guidelines given in the STACKS manual [We ran a de novo pipeline using cleaned and demultiplexed fastq files. The STACKS de novo program follows several steps to obtain variants. By running the three STACKS components . Loci and SNPs were de- tected from matching stacks at each locus. A catalogue was created for all loci across all the samples with the cstacks program, using the optimized value of mismatches allowed between loci when building the catalogue. Furthermore, the sstacks program matched loci from each sample back to the catalogue. After completion of ustacks, cstacks, and sstacks, the tsv2bam program of the STACKS pipeline transposed the data and pulled in the set of paired-end reads that was linked with each assembled single-end locus.A set of identical sequences is referred to as a “stacks/putative alleles within individuals to merge them into a putative locus; “n” is the number of mismatches allowed between stacks/putative loci between individuals during construction of the catalogue; and “r” (min. samples per pop) is the minimum percentage of individuals in a population required to process a locus for that population in particular [There were four minimum parameters involved: “m” is for the minimum number of reads required for creating a stack/putative allele within individuals; “M” is the number of mismatches allowed between rticular ,38,41,43ustacks) from 2 to 7 (M2–M7) and the ‘n’ parameter (from cstacks) from 2 to 7 (n2–n7). We consistently kept the value of ‘m’ = 3 and ‘r’ = 0.80, with the rest of the parameters on default setting. ‘r’ = 0.80 signifies that a locus has to be present in a minimum of 80% of individuals.A total of 51 samples containing a maximum and minimum number of 6,333,140 and 3,054,686 reads, respectively, were used for de novo parameter optimization. The STACKS de novo program ran several times on each dataset, while varying the following parame- ters on each parse of program. We used the ‘M’ parameter  for each incre- ment in parameters. We followed the procedure published by Paris et al. (2017) for pa- rameter optimization .Based on the parameter optimization described above, the values of M and n were kept equal to 4 (described in Results) with m = 3 and r = 0.80. These values were used for the whole dataset (256 samples). These parameters were run in the de novo pipeline, both with and without PCR duplicates.Bos taurus genome version UMD 3.1 [Bos taurus UMD 3.1 genome version was the source build of 800 K BovineHD BeadChip and has the complete chromosome information, while the European bison genome is available at the scaffold level. The draft genome assembly of the European bison was composed of 29,074 scaffolds with N50 of 4.7 Mb and 2.58 Gb size [We used two different reference genomes:  UMD 3.1  and the  UMD 3.1 . Because Gb size .Bos taurus and European bison genomes. Aligned files were converted from sequence alignment map (SAM) format to sorted, indexed binary alignment map (BAM) files using SAM tools 1.9 [Burrows–Wheeler Aligner (BWA) version 0.7.17 ,47 with ools 1.9 . The “reIS) for every SNP found in a population as defined in the population map. The population haplotype statistics contained the frequency of haplotypes, genes, and haplotype diversity. The population genetics statistics summary contained population-level average summary statistics for all loci and variant (polymorphic) loci, including the average frequency of the major allele, average observed homozygosity, and average observed and expected heterozygosity in the populations. Following this, the “population” program further used to generate VCF  files for each pipeline.Several output format files generated by the integrated “population” program of STACKS. The “populations” program produced population summary statistics for both the de novo and reference pipelines. The population statistics file included the frequency of alleles, observed homozygosity, expected and observed heterozygosity, and fixation index  obtained from the “population” program were further filtered to exclude variants with MAF < 0.05, max-missing 0.5, and minGQ 15, using VCFtools (0.1.16 (C)) . We usedE of the de novo and reference pipelines with and without PCR duplicates.  using the BWA alignment tool [Bos taurus reference (2) with de novo/Bos taurus (2) and European bison references (2) with de novo/European bison (2) using Linux commands   obtained from the de novo pipeline were then aligned back to the reference genomes  .Bos taurus and Bos taurus reference obtained SNPs are species-specific and not doubled by the markers included in the 800 K BovineHD BeadChip, widely used in European bison studies. For that purpose, we compared the SNPs of the 800 K BovineHD BeadChip with markers obtained using de novo/Bos taurus and Bos taurus reference pipelines, based on their chromosome positions, using Linux commands   .The total number of raw sequences generated following high-throughput sequencing was 1,598,748,644 reads. Before demultiplexing, the number of reads per run varied from 48, 339,898 to 131,099,277, with an average of 88,819,369.1 and a median of 92,268,720 . The tothttp://www.gnuplot.info/) and found that the number of polymorphic loci increased from M2/M3 to M4/M5, after which we observed a decrease  (accessed on December 2020)  were categorically higher than those obtained from the de novo pipeline. This trend remained consistent across values obtained with and without PCR duplicates.  catalogue loci (ii) assembled loci, (iii) polymorphic loci, (iv) SNPs before filtering, and (v) filtered SNPs . The numbers of catalogue loci, assembled loci, and polymorphic loci along with SNPs (before filtering) obtained using the reference pipeline  was also higher for the European bison reference pipeline  and the de novo pipeline (run for European bison sequencing data). Using the “population” program of STACKS (see methods), we deduced the percentage of polymorphic loci (%Poly.Loci) and the average observed and expected heterozygosity along with the average observed homozygosity for every locus, as well as several other parameters  obtained using the de novo/European bison pipeline, only four were common in the de novo/European bison and European bison reference pipelines and 62 were unique in the de novo/European bison.The results of the comparison of filtered VCF files from the de novo/Glycine max) and discovered that four reference pipelines  produced a higher number of SNPs than either of the two de novo pipelines (STACKS and UNEAK) tested did [This study compares de novo and reference pipelines to acquire the highest possible number of species-specific SNPs from a non-model organism. By comparing the results based on the analysis of both pipelines, we found more polymorphic markers in the reference pipeline as compared to the de novo pipeline. Our findings are in agreement with those of Torkamaneh et al. (2016), who performed a comparison of different SNP-calling pipelines on soybean  conducted a comparison between different SNP-calling pipelines on Ponderosa pine and found that two reference pipelines (TASSEL-GBS V2 and STACKS) identified more SNPs than either of two pipelines (UNEAK and STACKS) . Shafer However, by contrast, Paris et al. (2017)  recovereWe also expected to obtain a higher number of SNPs from the de novo pipeline approach to enable a thorough genome-wide association study (GWAS) on the European bison. However, the results we obtained showed that using only the de novo pipeline approach in genomic analyses of a non-model species with low genetic variability might not be enough to achieve a sufficient number of SNPs for further use. Paris et al. (2017)  also expThe number of SNPs after filtering obtained using both attempts (de novo and reference) is substantially lower than before filtering and when compared to cattle dedicated tools ,25. NeveBos taurus). The FIS values close to zero indicate that the populations are in Hardy–Weinberg equilibrium. For each pipeline, we obtained comparable but dramatically low values of genetic diversity parameters, either with or without PCR duplicates. The expected and observed heterozygosity estimated in this study turned out to be far below the values reported previously for European bison [an bison ,52. We aBos taurus); thus we tested if any of the SNP markers we achieved using both methods (de novo/Bos taurus and Bos taurus references) had been included in the 800 K BovineHD BeadChip. It has been confirmed that all the de novo/Bos taurus and Bos taurus reference pipeline-obtained SNPs are unique. Their uniqueness is of importance as it means they have never been applied in previous GWAS studies on the European bison. We will thus be able to use them in future, expanded GWAS on susceptibility to one of the diseases that affects the species.We used the 800 K BovineHD BeadChip in this study, which we used in numerous previous projects. Over 99% of its markers were mapped to the UMD 3 bovine genome assembly (Our study shows that PCR duplicates in GBS analyses cause problems comparable to those in RADSeq processing. Their removal, as expected, enhanced the percentage of polymorphic sites detected and the values of expected heterozygosity.Comparing the performance of de novo and reference STACKS pipelines for GBS data, we were intrigued to find that reference pipelines were able to detect a higher number of variants as compared to the de novo ones. Based on these results, for specific SNP detection using GBS processing, we recommend the use of specific genome assembly or a closely related reference genome in a less genetically variable species. If a de novo approach must be used, such as in the absence of a reference genome, we suggest using the standard STACKS as the most appropriate software for GBS.In this project, the de novo attempt turnd out to be least effective as to the number of achieved SNPs, but all of them were species-specific and unique.


To be comprehensive, GWAS can require identifying and genotyping hundreds of thousands to millions of genome‐wide genetic markers in large panels of accessions  seven different enzymes individually or in combination . Four of these  generated ≤0.5M fragments and two (MseI and NlaIII) were predicted to generate >2M fragments within the defined size range. Of these different enzyme combinations, only BfaI generated an attractive number of fragments (1.4M) with what seemed to be an approximately uniform size distribution of fragments  to significantly increase the density of detectable markers, using soybean as a test case. As it is a complexity‐reduction method, GBS relies on sequencing the optimal number of fragments falling within a specific size range. Our objective in designing HD‐GBS was to select the best combination of restriction enzymes in view of generating ~1 million fragments of 100–800 bp. To guide us in this process, we used the DepthFinder tool  protocol . These reads were demultiplexed to yield an average of 3.3M PE reads per sample, on average Figure  and procet al., Very large SNP catalogues (>500K SNPs) are now more and more common for GWAS studies in many crop species but, typically, this can only be achieved via WGRS and this is usually relatively costly compared with lower‐coverage approaches such as SNP arrays  or GBS (up to 200K) in soybean. Although affordable, the latter genotyping technologies do not yield a sufficient number of SNPs to ensure the capture of all haplotypes across the entire genome. Extremely low‐depth sequencing (<1×) such as Skim‐Seq (Happ Skim-Seq@0.5x (645K), Skim-Seq@0.2x (301K), Std‐GBS (98K), SoySNP50K (41K) and HD‐GBS (823K) , ) Figure . The Ski, et al. . The dra, et al. . HD‐GBS  et al., . We alsoet al., 2) between imputed and known genotypes) ranged between 88% and 97% for common variants  >0.1), with Skim‐Seq@1x and HD‐GBS achieving the same and highest degree of accuracy (97%) for allele frequencies between 0.1 and 0.8 (Figure All low‐cost approaches will require the imputation of missing genotypes and/or untyped variants from an extensively genotyped reference panel. The quality of imputation is highly dependent on the number variants in the lower‐density genotypic dataset (Browning 8 Figure . Within In conclusion, our results demonstrate that HD‐GBS provides an extremely low‐cost method for obtaining an ultra‐dense panel of markers enabling high‐quality imputation of untyped variants from a reference panel.All authors declare no conflict of interest.DT and FB conceived the project and contributed to writing the manuscript. DLH provided DNA samples. DT and BB prepared DNA libraries and performed sequencing. DT and JL carried out data analysis.


NRAP gene represents a unique example of a young amino acid residue convergent change shared with at least 16 species of hibernating/cold-adapted mammals from six distinct phylogenetic orders. This suggests a convergent evolution event along the mammalian phylogenetic tree and fast fixation in a single isolated cattle population exposed to a harsh climate.Native cattle breeds represent an important cultural heritage. They are a reservoir of genetic variation useful for properly responding to agriculture needs in the light of ongoing climate changes. Evolutionary processes that occur in response to extreme environmental conditions could also be better understood using adapted local populations. Herein, different evolutionary histories of the world northernmost native cattle breeds from Russia were investigated. They highlighted Kholmogory as a typical taurine cattle, whereas Yakut cattle separated from European taurines approximately 5,000 years ago and contain numerous ancestral and some novel genetic variants allowing their adaptation to harsh conditions of living above the Polar Circle. Scans for selection signatures pointed to several common gene pathways related to adaptation to harsh climates in both breeds. But genes affected by selection from these pathways were mostly different. A Yakut cattle breed-specific missense mutation in a highly conserved  Bos subspecies, Bos taurus and B. indicus (B. taurus (taurine) and B. indicus (indicine) populations  resulted in a total of 1,667.6 Gb of filtered sequence data. The read mapping rate against the reference genome for Kholmogory cattle was 99.6%, whereas for the Yakut cattle, it was 99.5%, resulting in an average sequencing coverage of 11.7× and 11.2× for the Kholmogory and Yakut samples, respectively . The totTo identify historical relationships and admixture between the Yakut cattle and other cattle breeds and species with potential historical admixture, SNPs from additional 113 individuals, representing indicine  cattle and five Bovinae species , were combined with the four breed SNP sets, resulting in a total of 193 individuals  online aWe estimated demographic history using two distinct approaches: coalescent-based SMC++  and ordiTo understand demographic histories of the Kholmogory and Yakut cattle breeds we focused to the three breeds with largest sample data. GADMA returned a quite realistic demographic population model of Yakut, Kholmogory, and Holstein divergence E. The esf3 statistics calculated on population triples using the Yakut cattle as a target, various indicine cattle breeds and five related species as source populations did not result in a significant Z-score  results, however, suggested admixture between the Yakut and indicine cattle as well as with taurine breeds (The Yakut cattle were likely formed in Asia and demonstrated signatures of possible introgression from indicine cattle . To iden Z-score  online. e breeds  online.Btau6) amino acids matched the amino acid preferably found in the orthologous position of other animals, whereas for the 19/30 positions, the Yakut cattle amino acid was overrepresented in other animal proteins. These observations coupled with the TreeMix and f3 statistics results support the hypothesis that most of the high-frequency Yakut cattle SNPs could represent alleles eliminated from the European taurine cattle. For the final check, we looked for the presence of these 48 missense SNPs in the non-European taurine breeds and found 37/48 Yakut cattle alleles in Hanwoo, 46/48 in the Chinese, and 42/48 in the African (N’Dama) taurine breeds confirming our hypothesis that these variants could be present in the ancestral taurine genome.To investigate reasons for these conflicting results we extracted four sets of biallelic SNPs with high derived allele frequencies in the Yakut, Kholmogory, Holstein, and Hanwoo genomes but with low frequency of the same allele in the other three breeds . The number of such SNPs was 13,257 in the Yakut cattle but much lower in the other breeds: 758 for Hanwoo, 103 for Kholmogory, and 311 for Holstein. Of the 13,257 Yakut SNPs, the majority  overlapped the Yakut cattle genome intervals identified as “indicine-like” by the RFMix analysis. To look into the evolutionary history of the high-frequency Yakut alleles, we focused on all 48 missense mutations present in the 13,257 SNP set ; fig. 3.We detected only one Yakut cattle missense derived allele not found in the indicine, taurine cattle, or other Bovinae species, in the NRAP protein. The fact that all 48 SNPs represent alleles different from the reference genome (Hereford) suggests that they are likely to be absent from both the dairy and beef European taurines. Indeed, we found only 331 SNPs for which the Yakut cattle had a high-frequency allele identical to the reference Hereford genome, whereas Kholmogory and Holstein had high-frequency derived alleles. None of these 331 SNPs were missense mutations.We used the whole set of 13,257 high-frequency Yakut cattle SNPs to identify putative regions of the ancestral taurine genome with haplotypes that are present in high frequency in the Yakut cattle but are absent/nearly absent from the European taurines. Among the 13,257 SNPs, we found 263 alleles present in the Yakut cattle only, 4,866 alleles found in a low frequency (<10%) in the European taurines and 8,128 alleles absent from the European taurine set. Of the 8,128 SNPs, 7,546 (92.83%) were found in indicine breeds; however, only 166 (2.00%) of them were indicine-specific, whereas 5,760 (70.87%) were also present in at least one additional Bovinae genome, 2,018 (24.82%) additional SNPs were present in either the African or the Chinese taurines, and 184 (2.26%) were shared with Hanwoo only  online. response to stimulus”  and “metabolic process”  among others , heat shock proteins (HSPA1A and HSPA1L), and the insulin receptor . A total of 1,639 (6.7%) genes were assigned to the ancestral and 19,205 (78.2%) genes to the European taurine segments. GO enrichment analysis of the genes in ancestral segments revealed the term “g others  online. or INSR;  online. SPTBN5 containing 21 missense mutations. Not surprisingly, the region containing the SPTBN5 also demonstrated the highest FST value between the Yakut cattle and all taurine breeds from the Run 7 of the 1000 Bull Genome project. When all breeds of the Run 7 were contrasted against the Yakut cattle, SPTBN5 was still on top of the list , 12 were homozygous for the derived allele whereas seven were heterozygous. We checked this position in the complete 1000 Bull Genome Run 7 data set . The frequency of the reference (G) allele was 0.995, whereas the frequency of the derived (T) allele was 0.005. The only four additional (to our Yakut cattle samples) animals which contained the same derived allele were four Yakut cattle samples obtained from the NCBI SRA archive, suggesting that this genetic variant is Yakut cattle-specific.The R2 = 0.77, P-value = 2.72e-06, α = 0.00011) between the presence of the glutamine  variant and cold adaptation, hibernation or ability to enter torpor (The nebulin-related-anchoring protein (NRAP) is a highly evolutionary conserved actin-binding cytoskeletal protein expressed exclusively in the skeletal and cardiac muscles, and is involved in myofibrillar assembly and force transmission in the heart . Our phyr torpor . The four torpor , whereasNRAP mutation into the Yakut cattle genome from other Bos species, we performed search for this variant in 22 ancient DNA samples from Siberia . Resequencing of these samples did not identify the missense G > T change in the position chr26: 34,131,393, observed in the Yakut cattle. In addition, we checked two historical samples: one of the Yakut cattle (94 years old) and one of the extinct Siberian cattle breed (>100 years old). We identified the G > T change in the Yakut sample only. These results confirm that the H100Q change is indeed Yakut cattle-specific and is unlikely to originate from a historical or recent introgression into their genome.To investigate possibilities of historical introgression of the NRAP is differentially expressed in the skeletal muscle and heart before and during hibernation   contains a three-allelic (G/A/T) SNP (rs1891762) with a rare T allele leading to the H100Q change  and A (0.8955) > G (0.1045) change being synonymous. During DCM, pumping ability of the left ventricle of the heart is affected leading to issues with pumping blood out of the heart  ; human putations . Intereshe heart . The humq-value < 0.01; N/dS in the Yakut and Kholmogory breeds, respectively, when focusing on SNPs with a likely effect on the whole breed  in the Kholmogory cattle.We identified 286 regions under selection in the Yakut and 219 in the Kholmogory cattle breeds using the HapFLK analyses , which determine MN and Ss blood groups in humans  and integration of energy metabolism ; myosin, actin-binding, microfilament motor activity, actin filament binding, etc. (enrichment score 5.14) in the dN/dS set ; laminin interactions , assembly of collagen fibrils and other multimeric structures , collagen formation , etc. (An important organ contributing to thermogenesis is the brown adipose tissue . In the gene set . This waN/dS set  online.  species . Another1), etc.  online.Gene enrichment for Yakut SNP set absent or in low frequency in the European taurine cattle. To understand probable contribution of the alleles, absent from European taurines, we looked for GO enrichment in the 13,257 SNPs with high frequency in the Yakut cattle. The actin cytoskeleton  category was overrepresented in the set of SNPs that were found at low frequency (≤10%) in the European taurines, whereas the response to stimulus and metabolic process categories were enriched  in the set of SNPs absent in the European taurine breeds , the Reactome pathway Acyl chain remodeling of PG . Interestingly, the genes containing Yakut-specific SNPs were enriched in biological regulation  agrees with Payne and Hodges  who conc aurochs  until thdiseases . Our datB. primigenius alleles shared with other Bos species. Another possible scenario which could potentially explain high genetic diversity of Yakut cattle at these positions is an ancient introgression with the East Asian aurochs (B. primigenius) that lived in the East Asian region during the arrival of taurine cattle together with several other Bovinae species, including yak, banteng, and gaur  compared with other taurine breeds, was shown to be under positive selection in at least three Arctic and Antarctic mammals including the wooly mammoth , lack this variant as well indicates it could form as late as approximately 800 years ago in Yakutia followed by a very fast frequency increase in the Yakut cattle. While absent in cattle breeds and phylogenetically close Bovinae species, the exact same amino acid change has formed independently at least six times in mammalian evolution, in species lowering their metabolism in response to cold, including hibernating bats and lemurs, cold adapted deep-diving species, like walrus and sea lions. Interestingly, a potential contribution of a highly evolutionary conserved NRAP protein  are required. Alternatively, it is possible that some additional functional changes could affect either regulatory gene regions or be represented by a different type of mutations not covered in this work, for example, by indels. Genes with the highest dN/dS selection signals in the Yakut cattle included genes involved in the cytoskeleton stabilization which plays an important role in the hibernating mammals during return from torpor to euthermy  + 0.5*P(IBD = 1)) of relatedness using plink (–genome) software among all the individuals for each breed separately. The majority of animals of Kholmogory breed were unrelated and for Yakut were third-degree relatives or unrelated (B. grunniens), and 15 of other Bos species  assembly  was downloaded from NCBI.To choose animals for whole-genome resequencing (20 Yakut and 20 Kholmogory), we utilized 26 Yakut and 39 Kholmogory DNA samples previously genotyped on a 150 K SNP array . After the QC and linkage-disequilibrium pruning, we calculated pairwise PI_HAT measure . Referehttp://broadinstitute.github.io/picard/, last accessed March 24, 2021). Next, we performed base quality score recalibration (using cattle known variants: dbSNP148). We followed the best practice guidelines  Z-score from the Wilcoxon rank sum test of alternative vs. reference read MQs (MQRankSum) < −12.5, and 5) Z-score from the Wilcoxon rank sum test of alternative vs. reference read position bias (ReadPosRankSum) < −8. The thresholds for these parameters were adopted from GATK Best Practices  with Ensembl v. 94 . SNPs weease 103 .We derived a subset of SNPs from the four breeds’ VCF files with the following characteristics: 1) high frequency of alternative (nonreference) allele in Yakut cattle  and 2) SNPs present in at least 25% of the four-breed sample set. NGS annotation pipeline  was usedTo calculate historical demography and separation time between pairs of populations, we used two complementary approaches: SMC++  and GADMSMC++ uses a sequential Markovian Coalescent approach to infer demographic history of separate populations and pairwise time of divergence between them on the unphased samples. The drawback of this method is an inability to implement migration into the model. For the SMC++ we analyzed demographic history using the cubic spline approach with mutation rate per bp per generation = 1.2e-8.The approach implemented in the GADMA software is based on the comparisons of observed and modeled joint allele frequency spectrums of populations  and alloWe applied a robust forward–backward algorithm implemented in RFMix  to screeR script implemented in TreeMix. Finally, in order to provide further support for a past admixture between populations, we calculated f3 and D statistics using ADMIXTOOLS (v. 5.1) with default parameters  where a negative value mean implies that X is admixed with populations close to A and B. We considered negative statistics with Z-score values below 2 as significant signals of admixture.We conducted TreeMix v. 1.12  analysesrameters . We calcD statistics using qpDstat. D statistics of the form D  to test the null hypothesis of the unrooted tree topology , ) was used. A positive value indicates that either A and X, or B and Y share more drift than expected under the null hypothesis. We quote D statistics as the Z score computed using default block jackknife parameters.We computed We performed a genome scan for selective sweeps within the groups of phylogenetically closely related breeds: Yakut and Hanwoo and Kholmogory and Holstein using haplotype-based statistics hapFLK . Due to -K 10 for the Yakut–Hanwoo, and -K 20 for Kholmogory–Holstein groups. The expected maximum number of iterations was set to 30 for both groups. We applied midpoint rooting to all sets of breeds.The hapFLK method takes the haplotype structure of the population into account. What was important for our data set is that this method can account for population bottlenecks and migration. Reynolds distances and a kinship matrix were calculated by the hapFLK program v. 1.4 . For theP-values was performed assuming that the selected regions represent only a small fraction of the genome .For hapFLK, the calculation of raw e genome . The genoworkers . This ha testing . To idenN/dS estimates were obtained for the SNPs found in any of the four cattle breeds  using the dNdScv .Whole-genome de dNdScv . The bacng dN/dS . The calFST) between biallelic SNPs identified in our Yakut or Kholmogory cattle samples as part of their analysis by the 1000 Bull Genome Project Run 7 against the reference cattle ARS-USD1.2 genome was calculated against: 1) the set of pure taurine breeds  as identified from the structure analysis reported by vcftools   was usedProtein sequences for several species and their alignments were retrieved from the Bos and Bison from the archaeological sites of Western and Eastern Siberia  were investigated for the presence of G > T mutation at position chr26: 34,131,393. In addition, DNA from two sculls of the Yakut cattle  and the Siberian cattle  provided by the Museum of Livestock  were investigated for mutation at the same position. All ancient bone specimens were subjected to UV irradiation; 1 cm2 of bone was crushed into powder in a metal mortar. Ancient DNA extraction was carried as described previously .Twenty-two bone specimens of ancient representatives of the genus eviously  and histeviously . Primersv. 2.4.2 . А 72-bpMolecular Biology and Evolution online.msab078_Supplementary_DataClick here for additional data file.