Wheat was one of the crops domesticated in the Fertile Crescent region approximately 10,000 years ago. Despite undergoing recent polyploidization, hull-to-free-thresh transition events, and domestication bottlenecks, wheat is now grown in over 130 countries and accounts for a quarter of the world’s cereal production. The main reason for its widespread success is its broad genetic diversity that allows it to thrive in different environments. To trace historical selection and hybridization signatures, genome scans were performed on two datasets: approximately 113K SNPs from 921 predominantly bread wheat accessions and approximately 110K SNPs from about 400 wheat accessions representing all ploidy levels. To identify environmental factors associated with the loci, a genome–environment association (GEA) was also performed. The genome scans on both datasets identified a highly differentiated region on chromosome 4A where accessions in the first dataset were dichotomized into a group (n = 691), comprising nearly all cultivars, wild emmer, and most landraces, and a second group (n = 230), dominated by landraces and spelt accessions. The grouping of cultivars is likely linked to their potential ancestor, bread wheat cv. Norin-10. The 4A region harbored important genes involved in adaptations to environmental conditions. The GEA detected loci associated with latitude and temperature. The genetic signatures detected in this study provide insight into the historical selection and hybridization events in the wheat genome that shaped its current genetic structure and facilitated its success in a wide spectrum of environmental conditions. The genome scans and GEA approaches applied in this study can help in screening the germplasm housed in gene banks for breeding, and for conservation purposes. Domestication is one of the major bottlenecks in crops that can result in a drastic reduction in genetic diversity compared to the wild gene pool . The losTriticum sp.) is believed to have originated and been domesticated approximately 10,000 years ago in the Fertile Crescent region, alongside other founder crops [Wheat , is incongruent with the short history of its triple bottleneck events: polyploidy, domestication, and transition from the hulled to the free threshing type [Aegilops tauschii  and T. turgidum ssp. dicoccum, which resulted in the emergence of hexaploidy wheat [The success of wheat, and particularly that of what is commonly known as bread wheat , domesticated emmer (T. turgidum ssp. dicoccum) plus durum (T. turgidum ssp. durum) and bread (T. aestivum) wheat, respectively. Hexaploid wheat accounts for nearly 95% of currently cultivated wheat, while the remaining fraction is mostly durum wheat [T. monococcum ssp. boeoticum) and wild emmer (T. turgidum ssp. dicoccoides), respectively. The modern tetraploid, durum wheat, was derived from domesticated emmer wheat (T. turgidum ssp. dicoccum) via selection [Cultivated wheat exists in three ploidy levels, with seven base chromosomes: diploid , tetraploid  and hexaploid . These levels are represented by einkorn  and genome–environment association (GEA) analyses using exome capture  and wholn = 921), (n = 347), , , and the = 347), , subsets = 347), c–f. The  = 347), g,h. Most = 347), g. There The clustering pattern was similar to the overall population, even though most accessions from the New World  were not represented in the landrace subset. The accessions in the OLDWP clustered together and occupied a separate clade in the NJ tree h. The MEintra = 0.50) and inter-individual (Dinter = 0.61) comparisons were both observed in the subpopulation where Australian accessions mixed with others (AUSb). In the landrace subset, the largest differentiation (FST = 0.24) was between FSV and the SAS, while the smallest were observed for SAS, followed by OLDWP (inter = 0.66) in the landrace subset.The largest variation (FST = 0.34) in the overall exome capture population (n = 921) was computed between subpopulations dominated by collections from the FSV and the OLDWP. These two subpopulations also had the first and second highest variations with most other subpopulations . The FSVby OLDWP . DespiteGenome scans on both the main and landrace subsets of the exome capture datasets revealed a highly differentiated genomic region on chromosome 4A spanning an approximately 174–465 Mb region . Both geThe genome scan on the whole-genome dataset magnificently discriminated the readily identified large region on chromosome 4A , which wTo dissect the genetic variation within the highly differentiated chromosome 4A region, phylogenetic and PC analyses were performed based solely on the SNPs within the outlier region of the exome dataset. Both analyses dichotomized the main and the landrace subset populations into group 1 (G1) and group 2 (G2) c–h. To op < 1 × 10−750) without missing SNPs in the highly differentiated 4A region, divided the genotypes into eight haplotypes where a haplotype was considered if present in at least five individuals. Congruent to the population structure of this region, the haplotypes were also dichotomized in the two previously defined groups, where G1 was dominated by haplotype 1 (Hap1) and G2 by haplotype 2 (Hap2). In G1, 560 individuals had Hap1, while in G2, 115 individuals had Hap2; together, these two main haplotypes represented 675 of the 921 accessions of the overall population  . Of these, five were associated with latitude, including three on chromosome 7A, and the remaining two on 6B and 7B . The twohreshold b. This lhreshold . The SNPhreshold . However makers) c.The significant loci, whether represented by large regions of co-located significant loci or regions surrounding discrete significant loci identified by genome scans, harbored genes potentially important for the adaptation of the crop to different environmental conditions . The higTraesCS2A02G337900 and TraesCS2A02G339200, which are predicted to encode AGAMOUS-LIKE NITRATE REGULATED 1 (ANR1) and RIBOSOMAL PROTEIN L34 (RPL34), respectively. The 7B:221_480 Mb locus contained TraesCS7B02G184800, predicted to encode an RNA polymerase sigma factor (SIGA). TraesCS4A02G157100, located in the 4A region, was also predicted to encode a SIGA, which is an important gene in adaptation to different environmental conditions  and CYTOKININ-RESPONSIVE GROWTH REGULATOR (CKG) proteins, respectively , were found within the vicinity of the 7A:690367177 locus  and a pectinlyase-like superfamily protein (ADPG2) involved in floral physiology and seed dormancy, respectively  and Phi- (PhiPT) analyses. Both FST and PhiPT analyses showed significant (p < 0.001) differentiation between wild emmer and hexaploid wheat landraces. The hexaploid cultivars and nearly all the durum accessions were undifferentiated from the wild emmer accessions. In fact, these latter two had the lowest differentiation M (FST = PhiPT = 0) with wild emmer, based on both analyses (T. aestivum L. ssp. sphaerococcum), Tibetan semi-wild (T. aestivum ssp. tibetanum), Georgian wheat , Xinjiang wheat ((T. aestivum ssp. petropavlovskyi/T. petropavlovskyi), Persian wheat (T. turgidum ssp. carthlicum) and Vavilovii wheat (T. vavilovii), had similarly low FST and PhiPT (FST = PhiPT = 0) with hexaploid cultivars . The highest intra-accession gene diversity was computed for spelt wheat , followed by macha wheat . The highest inter-accession diversity was computed for hexaploid wheat landraces (MSD = ~0.9), followed by Yunan wheat .Based on mean square allele size difference (MSD) of the most significant 5000 SNPs of the 4A region, it appeared that the Xinjiang wheat lacked both intra- and inter-accession gene diversity. Indian dwarf and Vavilovii wheats had the next lowest gene diversity, with MSD of 4.10 × 10n levels . Durum wK = 5; T. urartu and T. monococcum clades and the clade that grouped the rest of the wheat types  in this study did not perfectly follow the geographic origin of the germplasm, which is inconsistent with previous studies that reported strong geographic differentiation among wheat populations [The genetic structure of both populations  and the landrace subset (n = 347) populations in this region of chromosome 4A indicate a major event on the chromosome and also in wheat genome evolution. More interestingly, the dichotomy of both population sets into a group dominated by OLDWP and one containing all remaining accessions invites further investigation of this separation event.Studies have already reported the uniqueness of chromosome 4A, which is attributed to the translocations it received from chromosomes 5A and 7B, and further rearrangements that it underwent ,45. SuchTraesCS4A02G157100 in the 4A and TraesCS7B02G184800 in the 7B signature regions were both predicted to encode an RNA polymerase sigma factor (SIGA). The SIGA factor regulates the circadian clock in wheat [RETINOBLASTOMA-RELATED (RBR1) gene in this region may also play a role in regulating biological processes, including DNA repair and hypersensitivity associated with the adaptation of plants to different environmental stresses, such as salinity, temperature, and heavy metals [The chromosome 4A signature region has a high gene density and contains many genes with potential to affect major target traits of adaptations to environmental conditions, and, as such, may have been subjected to high selection pressure. He et al.  previousin wheat , a vitalin wheat ,48,49. Iin wheat ,51. The y metals . The RBRy metals ,54.CALCINEURIN B–LIKE (CBLs) genes interacting with PROTEIN KINASE (CIPK). The CIPKs play pivotal roles in ion homeostasis, in responses to diverse abiotic stresses, including drought and extreme temperature conditions, and in regulating several biological processes that involve the calcium ion [The 4A signature region also harbored multiple cium ion ,56. The cium ion  can leadcium ion ,59,60.AGAMOUS LIKE 30 (AGL30) at this locus also has an important role in pollen development [TraesCS4A02G182900, which is predicted to encode the SALT-INDUCED ZINC FINGER PROTEIN2 (SIZ2) has been shown to play a role in tolerance to drought [CIPK, AGLs, BRIs, and SIZs) in many critical biological processes renders them not only candidates for natural selection, but also candidates with potential to influence important agronomic traits, such as yield. As such, they could have been the object of anthropogenic (improvement) selections.The elopment  and in telopment ,63. The elopment ,65, inclelopment , and in elopment . The AGLelopment . The preelopment . Furtherelopment ,71,72,73 drought  and heav drought , as well drought . The invTraesCS5A02G292900 at Chr5A, 502614628, whose Arabidopsis ortholog is AT5G50915, is predicted to function as a CYTOKININ-RESPONSIVE GROWTH REGULATOR (CKG). This gene can be a key player in the adaptation of plants, including wheat, to both biotic and abiotic stresses [TraesCS5A02G292700 (Arabidopsis ortholog AT3G12300), encodes ciliary protein BUG22 , which is involved in the biogenesis of motile organelles, such as centriole cilia and flagella [The stresses  via biosstresses ,79. The flagella . Althougflagella ,82,83, wflagella , which aflagella , in extrflagella ,87,88. Aflagella .TraesCS6A02G286600LC at 6A:233244464 hints at the importance of this locus in adaptation to diverse stress conditions [The presence of the predicted cytochrome P450 gene nditions ,90 and mnditions . This prTraesCS7A02G162400 (Arabidopsis ortholog AT1G48270) at 7A: 118327401, is predicted to encode a guanine nucleotide-binding protein G-PROTEIN-COUPLED RECEPTOR 1 (GCR1), which regulates flowering time and seed dormancy, these being typical adaptive responses to environmental conditions, such as seed dormancy break and early flowering [TraesCS7A02G162500, which is orthologous to AT2G41850, is predicted to encode a POLYGALACTURONASE (PG) that is involved in floral and foliar abscissions [The loci associated with latitude can be attributed to differential factors, such as light photoperiod and temperature. For example, lowering . The GCRlowering , includilowering  and biotlowering ,95. The lowering ,96. The cissions . Organ acissions ,99,100.TraesCS2A02G243800 and TraesCS2A02G248700, associated with temperature, at Chr2A:344449220 and Chr2A:372230509, respectively, are predicted to encode proteins that mediate responses to heat. While the former is predicted to encode Hsp70-Hsp90 organizing protein 1, that regulates heat shock protein chaperones Hsp70 and Hsp90 [AT1G56260 , a gene that protects against heat-induced telomere truncation and protects against other damage caused by heat [The genes, nd Hsp90 , the lat by heat .The observed low genetic differentiation of the hexaploid cultivars and the durum wheats with the wild emmer wheats in the 4A divergent region is consistent with He et al. , who intThe deviance of many wheat landraces from the cultivars, emmer and most other wheat types, coupled with the close genetic relationship of these landraces with spelt and macha wheats, triggers the question as to the chronology of the events that led to the dichotomized separation of wheats in this 4A region. This signature region may indicate the history of spelt wheat and its potential contribution in the bread wheat hybridizations ,104,105.Mrg28 locus, which is an improvement selection target for heading date [Mrg18 locus, that harbors multiple disease resistant genes [Aberrantly differentiated genomic regions are a frequent occurrence in highly-bred- crops, including cereals. These regions usually harbor major adaptive genes and are subjected to both improvement and natural selections. For instance, the SUB1 locus in rice is known for its high level differentiation and contains a gene conferring submergence tolerance . Bekele ing date , and thent genes ,113.https://triticeaetoolbox.org/wheat (accessed on 11 August 2022) [Triticum species and other non-domesticated Triticum from a primary gene pool, such as T. monococcum or T. turgidum ssp dicoccoides, were downloaded from Genome Variation Map  [T. aestivum) wheat. The two datasets were processed separately. For both datasets, SNPs on each chromosome were filtered using a 90% call rate. The filtered SNPs from all chromosomes were merged into a single variant call file (VCF) and further filtered to a maximum of 5% missing calls. Individuals missing more than 10% of the SNPs after consolidation of the SNPs from all chromosomes were omitted. The filtered quality-controlled exome capture-derived dataset contained around 113K SNPs and 921 individuals. The whole genome resequencing dataset represented different species, all three ploidy levels  and genomes . As such, different SNP subsets were generated for downstream analyses. These are described below.In this study we used two SNP datasets. First, approximately 8.7M SNPs, obtained from exome capture data of 1026 wheat accessions comprising landraces, breeding materials, and cultivars, were downloaded, along with the passport data of the accessions, from st 2022) . We focuhttp://qgis.osgeo.org, accessed on 1 September 2022; https://power.larc.nasa.gov, accessed on 22 September 2022). These data, and latitudinal information provided within the passport data of the accessions of this collection [Using the information of the passport data, 347 landraces, selected from the 921 accessions of the exome dataset, were used for downstream analyses to shed light on the historical selection events and eco-geographic adaptation signatures across the habitats of the crop. The final 921 accessions were collected from 96 countries, of which 80 were also the origin of at least one landrace. The germplasms’ countries of origin were positioned on the world map. along with the corresponding annual temperature, using the Quantum Geographic Information System (QGIS) from the Open-Source Geospatial Foundation Project QGIS 3.28 (n = 921) and landrace subset (n = 347) populations of the exome capture dataset. Following the Cattle rule [n = 921) and its landrace subset (n = 347) using ADMIXTURE v1.3 with default parameters [Q ≥ 0.5) at the selected K were assigned to the same group (subpopulation). Individuals with <50% ancestral coefficient were also assigned to the subpopulation where they had the highest ancestral proportion. Following the assignment of all individuals to their appropriate group, the ancestral coefficients were summarized into a structure plot using Tess3 in R [To define the appropriate number of ancestral subpopulations, the eigenvalues of each individual were estimated for the first 30 PCs and visualized in scree plots using PCADAPT in R , separattle rule , as suggtle rule , the PC rameters . Subpopuss3 in R .Principal component analysis (PCA) was also performed by setting the number of PCs equal to the number of the subpopulations (K). The first three PCs, along with the percent of variation that each explained, were visualized in a scatter plot using ggplot2 in R. To gain insights into the pattern of clustering without predefining the subpopulations and to hint at the evolutionary events over time, a neighbor-joining (NJ) phylogenetic analysis was performed on the entire population sets from the exome dataset. TASSEL 5.2  was empln = 921) and the landrace (n = 347) subsets, where the number of PCs was equal to the number of subpopulations. To further trace potential adaptive alleles to specific environmental conditions, individuals with known geographic coordinates of the collection site were selected and a GEA analysis was performed using the latent factor mixed model (LFMM2) [https://power.larc.nasa.gov, accessed on 22 September 2022) described above. For genome scans from PCADAPT, genomic regions with strongly differentiated outlier loci were considered without defining a threshold. For GEA, the significance threshold was set to the Benjamini–Hochberg false discovery rate (FDR) [p-values with FDR < 0.05 threshold were considered to be significantly associated. To visualize the overall results, −log10 p-values were displayed in Manhattan plots with a threshold mark based on the Bonferroni multiple test correction at α/n, where α < 0.05 and n = total number of SNPs used for the association analysis.To trace genomic regions that had undergone selection and/or that indicated adaptive signatures, a PC-based genome scan was performed using PCADAPT v4 implemented in R ,122 for  (LFMM2) , based ote (FDR)  at α = Fhttps://www.wheatgmap.org, accessed on 4 October 2022). Where a big chunk of genome region peaked up, all genes within that region were downloaded. The extracted genes’ physical positions matched the coordinates in all wheat genes downloaded from the joint Genome Institute database . The genes’ functions and the traits affected were further explored using the published literature and a variety of databases, such as ensemblplants  and knetminer T. aestivum . The number of consistent reports and the likelihood of association of a gene with an important adaptive trait was evaluated, based on its predicted function, the strength of the evidence and on the number of reports demonstrating its causal effect.To identify potential genes linked to the differentiated regions and loci identified by PCADAPT and GEA, all genes within 200 kb upstream and downstream of the detected loci, and their predicted functional annotations, were extracted from the WheatGmap database (p-value from the genome scan analysis were used to compute FST, PhiPT, and individual ancestral coefficients for the whole genome dataset comprising all ploidy levels and all available wheat types, including cultivars, landraces, other ecotypes and wild relatives. To overcome the sample size differences, population differentiation analyses, including FST and PhiPT, were performed with 10,000 permutations. In addition, a population differentiation analysis based on a maximum likelihood statistical significance G-test was performed after 10,000 Markov chains and 5000 iterations using R package genepop [The genome scans from both population sets of the exome capture dataset identified the uniquely differentiated region on chromosome 4A (see  genepop .The public availability of data in repositories, combined with statistical models and computing abilities, provide opportunities for scientists to pose new hypotheses, deepen analysis and explore new research avenues to address evolution, breeding and conservation in plants and animals. In the present study, genome scan and genome–environment association identified new and previously reported genomic regions in wheat genomes indicative of historical hybridization and evolutionary events and adaptation signatures of the crop across its habitats. Further analyses to confirm the source of introgressions previously postulated to be of wild relative origins is warranted because our results suggest that cultivated wheat ecotypes are a more likely source. Investigations of other genomic regions previously reported as introgressions from the wild are of interest to determine the extent of such potential phenomena and their impacts on wheat adaptation. These considerations within the pedigrees of current cultivars would enable us to revise their genetic potential towards the development of better adapted varieties.


Bos taurus breed, and some Mexican breeders aim to improve it genetically. The aim was to estimate allelic and genotypic frequencies for SNPs associated with meat quality in a Mexican Romosinuano population. Four hundred ninety-six animals were genotyped using the Axiom©BovMDv3 array. Only SNPs related to meat quality in this array were studied in this analysis. The Calpain, Calpastatin, and Melanocortin-4 receptor alleles were considered. Allelic and genotypic frequencies and Hardy-Weinberg equilibrium were estimated with the PLINK software. Alleles associated with meat tenderness and higher marbling scores were found in the Romosinuano cattle population. CAPN1_4751 was not found in Hardy-Weinberg equilibrium. The rest of the markers could not be affected by selection and inbreeding. Romosinuano cattle in Mexico have similar genotypic frequencies in markers related to meat quality to Bos taurus breeds known for their meat tenderness. Breeders can choose a marker-assisted selection to improve meat quality characteristics.Romosinuano is a tropically adapted  Bos taurus breeds predominantly found in countries with temperate weather have a high-production potential but poor adaptation to the harsh tropical environment  or tropically adapted Bos taurus breed to produce more tender beef in Mexico’s tropical region.Zebu cattle are widely utilized as a genetic resource for beef production in the tropics of Mexico due to their adaptation to high temperatures and humidity  showed no differences in tenderness of meat compared with Angus and Hereford . The sampled animals were born between 2003 and 2019, including 49 males and 447 females, from four farms located in Mexico’s tropical areas. In general, genetic material is continuously exchanged among these farms, providing most of the sires, semen, and females to other breeders . Only markers in the SNP chip used could be considered.Samples of hair follicles were collected from 496 Romosinuano animals registered in the OB = FEXP). The maximum per SNP missing of 0.1 was considered the inclusion threshold.The markers in the SNP chip used were Calpain (CAPN1_316 CAPN1_530 CAPN1_4751), Calpastatin , Myostatin , thyroglobulin (TG), diacylglycerol O-acyltransferase 1 (DGAT1), and melanocortin-4 receptor (MC4R). Allele and genotypic frequencies were calculated with PLINK v1.9 software  test are presented in Table SNP genotyping assays normally give rise to a certain percentage of no-calls. Only one marker did not present Hardy-Weinberg equilibrium in the population. This method assumes that genotype frequencies should comply with HWE proportions using the chi-squared test in a large and random mating population. Many factors can cause deviation from these proportions: a genotyping error, purifying selection, copy number variation, inbreeding, or population substructure  proteins post-mortem. This gene has three markers  associated with meat tenderness. These are not causative mutations, but they are predictors of tenderness in multiple cattle breeds; the markers each explain from 0.4 to 2% of the phenotypic variation in tenderness, with the amount explained varying across breeds (McClure and McClure p < 0.05). In other studies, cattle populations also presented similar frequencies between trait allele and normal allele  and CAPN1_530 (c.1588G>A) showed a low frequency of the favorable homozygote. It has been reported that genotype CC in CAPN1_316 is associated with higher marbling scores than the other genotypes  is associated with energy homeostasis. It was also considered a positional candidate gene for final body weight, backfat thickness, and marbling score (Liu et al. Bos taurus breeds known for their meat tenderness. The majority of markers was in Hardy-Weinberg equilibrium. Breeders can choose a marker-assisted selection as a complementary tool for conventional genetic selection or genomic selection to improve meat quality characteristics.Romosinuano cattle in Mexico have similar genotypic frequencies in markers related to meat quality to


As an important reproductive hormone, melatonin plays an important role in regulating the reproductive activities of sheep and other mammals. Hu sheep is a breed favoring for meat, with prolific traits. In order to explore the relationship between melatonin and reproductive function of Hu sheep, 7,694,759 SNPs were screened out through the whole genome sequencing analysis from high and low melatonin production Hu sheep.AANAT/ASMT gene and MTNR1A/MTNR1B gene were carried out. The melatonin level of CG genotype 7,981,372 of AANAT, GA genotype 7,981,866 of ASMT and GG genotype 17,355,171 of MTNR1A were higher than the average melatonin level of 1.64 ng/mL. High melatonin Hu sheep appear to have better multiple reproductive performance.A total of 68,673 SNPs, involving in 1126 genes, were identified by ED association analysis. Correlation analysis of SNPs of AANAT/ASMT and receptor MTNR1A, respectively. Considering the positive association between melatonin production and reproductive performance in ruminants, these three SNPs can be served as the potential molecular markers for breading Hu sheep with the desirable reproductive traits.By using different methods, three SNPs which are associated with high melatonin production trait have been identified in Hu sheep. These 3 SNPs are located in melatonin synthetase The online version contains supplementary material available at 10.1186/s12864-023-09494-z. Ovis aries) is a unique sheep breed in China. This breed has the advantages of early sexual maturity, strong fertility with large litter size and easy adapts to coarse feeding materials [FecB, GDF9 and TGFBR2 were significantly associated with the fertility trait of Hu sheep [Small ruminants, particularly sheep significantly impact the daily life of a considerable part of human population socio-economically . These saterials . By analHu sheep  and thesHu sheep . SpecifiMTNR1A expression peak prior to ovulation, melatonin application could improve luteal function, increase progesterone secretion level, pregnancy rate and litter size of animals [Melatonin is an important reproductive hormone which is synthesized in many tissues and organs including the pineal gland  and repr animals . It appeSingle nucleotide polymorphism (SNP) mainly refers to DNA polymorphism at the genome level, which is characterized by large number, wide distribution and genetic stability . CurrentAANAT/ASMT as well as MTNR1A/MTNR1B. Our purpose is to identify whether SNPs among these genes are associated with melatonin production in Hu sheep.In this study, BSA method will be used to analyze the genotypes of individual Hu sheep to match up their endogenous melatonin levels. The focus will be given to the SNP polymorphisms of AANAT/ASMT, and genes of receptors, MTNR1A/MTNR1B, was also analyzed. If some of these SNPs of melatonin trait are strongly associated with reproductive performance of Hu sheep these SNPs may serve as the biomarkers for guiding the breeding selection of a new strain of Hu sheep. A total of 195 Hu sheep   in the Central Laboratory of Institute of Animal Science, Beijing, Chinese Academy of Agricultural Sciences. The Symmetry C18 column was used to separate the melatonin from the samples.Blood DNA was extracted following the instructions of the Kit (NEBNext®Ultra™DNA Library Prep Kit for Illumina®) for library construction. A sample of 1 μg genomic DNA was randomly broken into fragments < 500 bp by ultrasonic treatment (Covaris S220). End repair was performed on the fragment with End Prep Enzyme Mix, appropriate ends were added to both ends with T-A ligase, then 5 'phosphorylation and da-tailed were performed. Adaptor-ligated DNA was size-screened using the AxyPrep Mag PCR cleanup (Axygen) to obtain fragments about 410 bp. Each sample was amplified by PCR using P5 and P7 primers for 8 cycles, respectively. Both P5 and P7 primers carry bridge PCR sequences that can be annealed by flow cytometers. The P7 primer carries a six-base index for multiplexing. PCR products were cleaned using AxyPrep Mag PCR (Axygen) and validated using Agilent 2100 Bioanalyzer . Quantification was performed using Qubit 2.0 fluorometer . According to the kit instructions , libraries with different indexes were multiplexed and loaded onto the Illumina HiSeq instrument. Sequencing was performed using the 2X150 paired terminal (PE) configuration. Using HiSeq control software (HCS) + OLB + gappeline-1.6 (Illumina) for image analysis and basic invocation on HiSeq instrument.The bcf2fastq (version 2.17.1.14) software was used for image Base calling of original image Data. Pass Filter Data (PF) in FASTQ (fq) format was obtained. Clean Data was obtained by using cutadapt (version 1.9.1) software to remove junctions and low-quality sequences from Pass Filter Data. Clean data was compared to reference Genome sequences using Dragen Genome Pipline. For the resulting BAM (binary SAM file) file, Picard and GATK were used to remove duplicate data of PCR and collect the local realignment, base quality recalibration and the corrected genome alignment results. The coverage and coverage depth of the genome were calculated according to the comparison results.According to the comparison results of Clean Reads in reference genome, the Haplotype Caller module of software GATK (version 4.0.4.0) was used for mutation detection. Filtration was done using the Variant Filtration module, Filter parameters for:—filter Expression “QD < 2.0 | | MQ < 40.0 | | FS > 60.0 | | SOR > 3.0 | | MQ Rank Sum < 12.5 | | Read Pos Rank Sum < -8.0”. ANNOVAR software was used for the functional annotation of the detected gene variants. When the coverage depth of the sample at a SNP site is < 5X, the site is considered as missing and listed as NA in the table. When the sample genotype mutation frequency at a SNP site is ≥ 0.8 or ≤ 0.2, the site is a pure sum. If the mutation frequency is between 0.2 and 0.8 and each allele of the heterozygous genotype has at least 4 reads, the SNPs is considered as the heterozygous mutation; otherwise, the site is treated as deletion and listed as NA in the table. Finally, all SNPs of genotypes in the tested samples were summarized in the table for comparison.Euclidean Distance (ED) algorithm was used to search for significantly different markers between pools. In the process of ED analysis, MMAPPR software package will used to filter SNP loci of non-secondary gene and SNP loci with sequencing depth less than 10X of wild type or mutant mixed pool. If the frequency of A, T, C and G in the wild type pool is greater than or equal to 95%, it will be filtered. The calculation formula of ED method is as follows:Note: Amut is the frequency of base A in mutant mixing pool, Awt is the frequency of base A in wild type mixing pool; Cmut is the frequency of C base in mutant mixing pool and Cwt is the frequency of C base in wild type mixing pool. Gmut is the frequency of G base in mutant pool and Gwt is the frequency of G base in wild type pool. Tmut is the frequency of T base in mutant mixing pool and Twt is the frequency of T base in wild type mixing pool.In this project, the 4th power of the original ED served as the correlation value to eliminate background noise, and this value was used for the local polynomial regression fitting to obtain the fitting curve. The value of Median + 3SD of all locus fitting values was used as the association threshold for analysis. This association threshold was used to determine the correlation area. Thereafter, sites with mutation frequency > 0.75 and Euclidean distance > 0.5 were selected as candidate sites from the correlation area and the annotation results of ANNOVAR were also extracted from these candidate sites.http://www.geneontology.org/). for candidate genes the numbers of genes in each term were calculated to obtain the genes with a certain GO function and their statistical analysis. Then the GO entries with significantly enriched in the whole genome background were identified by hypergeometric test.Based on the GO each term mapping database  had melatonin content between 0–0.49 ng/mL, 42 (21.5%) had melatonin content between 0.5- 1.0 ng/mL and 53 (27.2%) had melatonin content above 1 ng/mL. Three individuals with highest and three individuals with lowest melatonin levels were selected for whole genome sequencing analysis   Table , which w5) Table . As showAANAT and ASMT overexpressed sheep has increased the number of their offspring [Due to the nutritional value and the rich taste, the demanding of mutton product has excessed its production globally. To increase mutton production and quality has become an urgent agenda not only for sheep husbandry but also for researchers. It appears that the traditional sheep breeding methods cannot satisfy consumers’ demanding, therefore, the animal breeding techniques have shifted from traditional cross breeding to genome selection which can directly target the traits to be selected . In the ffspring , 28. Melffspring , 30. Melffspring , 32.Jiang et al. had conducted whole gene sequencing correlation analysis on the body size of Hu sheep and found that 5 SNPs were correlated with body height traits and 4 SNPs were correlated with chest circumference traits . In thisThe 7,694,759 SNPs were obtained from 3 individuals with highest and 3 individuals with lowest melatonin productions. SNPs mainly occur in the intronic and splicing regions of the genome. A total of 68,673 SNPs were selected by ED association analysis and 1126 genes were involved in these SNPs. GO and KEGG enrichment analyses showed that the SNPs associated with melatonin traits in Hu sheep at the genome-wide level were enriched into multiple genes in multiple pathways.AANAT and ASMT are two key genes in melatonin synthesis and MTNR1A and MTNR1B are two key genes of melatonin receptors in mediation of melatonin biological functions [AANAT and 4 SNPs of ASMT in Holstein dairy cows that can significantly affect the production of melatonin [AANAT/ASMT and MTNR1A may improve the enzyme activity to directly enhance melatonin production or promote receptor function which can positively feed-back melatonin production. Since melatonin level in animals is associated with their reproductive efficiency, these three SNPs can serve as the molecular markers to bread Hu sheep with improved reproductive activity.unctions . Therefoelatonin . Here, aelatonin . The repelatonin . The 3 SAANAT/ASMT and MTNR1A were identified to directly associated with high melatonin production in the Hu sheep. Considering the positive relationship between melatonin production and reproductive performance in different ruminants, these three SNPs can be selected as the potential molecular markers for breading Hu sheep with the suitable reproductive traits.In this study, we focused on the of melatonin production ability and its association with SNPs in melatonergic system including its synthesis enzymes and receptors in Hu sheep. By use of the whole-genome sequencing analysis the 3 SNPs located in the Additional file 1.Additional file 2.Additional file 3.Additional file 4.


The cattle industry contributes to Uganda's agricultural output. It faces challenges that include theft and parentage ascertainment. These challenges can benefit from recent molecular genomics and bioinformatics technologies.We employed genomic analyses to establish potential ownership of a group of nine cattle that were being claimed by two farmers in Uganda. We investigated the genetic relationship of Ugandan cattle with regional indigenous breeds as well as exotic breeds that are currently present in Uganda. In addition, we investigated regions that are likely to be under selection in the Ugandan cattle.Hair samples were collected from seven and two animals from farmers A and B, respectively. They were genotyped for 53,218 Single Nucleotide Polymorphism markers. To establish genetic relationships between the sampled animals, we performed genomic analyses including, principal component analysis (PCA), hierarchical clustering analysis and identity by state/descent. We also performed admixture and runs of homozygosity analyses to assess the ancestry composition and identify regions potentially under selection in Ugandan cattle, respectively.The seven animals from Farmer A were genetically close to each other but showed minimal relationship with the disputed animals. The two animals from Farmer B were genetically distant from each other but showed greater similarity to four of the disputed animals. Four of the disputed animals showed great dissimilarity from the animals of both farmers. Comparison of these with the reference breeds revealed minimal European exotic genetic introgression into these animals, but rather high similarity to the Sheko. Results also revealed high homozygosity in the major histocompatibility complex regions.Our results demonstrate the use of currently available genomic tools to empirically establish the ownership of cattle; these could be scaled up as a resourceful and viable tool that could be employed to support conflict resolution where reliable livestock identification is unavailable. The cattle industry contributes significantly to agricultural output, although it faces challenges that include theft and parentage ascertainment. We used Single Nucleotide Polymorphism genomic analyses to establish potential ownership of nine cattle that were being claimed by two farmers in Uganda. Seven animals from Farmer A were genetically close to each other but showed minimal relationship with the disputed animals, while two animals from Farmer B were genetically distant from each other but showed greater similarity to the disputed animals. DNA sample of each animal was individually genotyped for 53,218 Single Nucleotide Polymorphism (SNP) markers using the commercially available Illumina BovineSNP50‐24 version 3 BeadChip . Quality control was performed on the genotype data using PLINK  and sex chromosome SNP markers (n = 1168), markers that were not assigned to any chromosome (n = 759), markers that did not show substantial variability between study individuals , those that showed significant deviation from Hardy–Weinberg equilibrium , and those whose genotyping rate was less than 95% . Using the cleaned genotype data , PLINK , Brahman (n = 49), Sheko (n = 18), Jersey (n = 42), Holstein (n = 67) and Angus (n = 47). Genotype data and breed description information for the reference animals were obtained from publicly available data from the Bovine HapMap project , a maximum of one heterozygous SNP allowed in the window (–homozyg‐window‐het 1), a maximum of five SNP with missing genotypes allowed in the window (–homozyg‐window‐missing 5), a scanning window hit rate threshold of 0.05 (–homozyg‐window‐threshold 0.05), maximum gap between two homozygous SNPs of 500 kb (–homozyg‐gap 500), a minimum density of one SNP per 60 kb in the homozygous segment (–homozyg‐density 60), minimum length of the ROH of 1000 kb (–homozyg‐kb 1000) and a minimum number of 100 consecutive SNPs per the ROH (–homozyg‐snp 100). The Ensembl biomart tool (http://www.ensembl.org/biomart/martview) was used to obtain genes located within the identified ROH regions from the ARS‐UCD1.2 bovine reference genome hosted by Ensembl genome browser (105 release). To elucidate on the collective biological functions and processes of the genes located in the identified ROH regions, functional enrichment analysis was further performed in Panther classification system  long‐horned with only one animal having short lateral horns. The animals from Farmer B were predominantly brown, adult females of which one was polled and the other long‐horned. The detailed morphological descriptions of the animals used in this study are provided in Table We obtained high‐quality genotype data with a genotyping call rate of 99.6% per sample and an average minor allele frequency of 0.18 ± 0.16. For all the 18 genotyped cattle in the current study, the sex inferred from SNP genotype data corresponded with the sex recorded at sampling or pedigree sex Table . GeneticIn agreement to these results, our IBD analyses also revealed very low IBD relationship (0–0.027) between the contested animals and Farmer A's animals ranging between 0 to 0.025, while the former showed predominantly high IBD relationships (0–0.5) with Farmer B's animals Table . It is wThe results from these genomic analyses demonstrate that indeed all the contested animals are most likely to belong to Farmer B than Farmer A. It is worth highlighting that livestock management systems in Africa especially cattle are predominantly characterised by transhumance and migratory movement to livestock production zones and grazing locations during the dry season in the search of lustrous pasture and water  of the contested animals were distant from the rest of the sampled animals and Sheko animals. For the admixture analyses, when we assumed only two ancestral populations (K = 2), the results showed that the investigated animals had both taurine and indicine proportions similar to that of the Sheko breed reference animals. Results with higher presumed ancestral populations demonstrated further that the investigated animals shared great ancestry (79.5 ± 3.26% at K = 8) with Sheko animals, as compared to other reference breeds .This study was approved by National Animal Genetics Resources Centre and Data Bank (NAGRC&DB). Ethical approval number: SVAR_IACUC/104/2022.https://publons.com/publon/10.1002/vms3.1272The peer review history for this article is available at Table S1: Morphological descriptions of the studied animals.Click here for additional data file.Table S2: Plink SNP genotype‐based sex validation (–check‐sex).Click here for additional data file.Table S3: Identity by descent (IBD) analysis results.Click here for additional data file.Table S4: Runs of homozygosity identified in the Ugandan indigenous cattle.Click here for additional data file.Table S5: Candidate genes within the identified runs of homozygosity regions.Click here for additional data file.


Colletotrichum spp. that has a significant impact on worldwide pepper production. Colletotrichum scovillei is the most common pathogenic anthracnose-causing species in the Republic of Korea.Anthracnose is a fungal disease caused by Capsicum chinense) accessions deposited in Korea’s National Agrobiodiversity Center were evaluated for their response against the virulent pathogens Colletotrichum acutatum isolate ‘KSCa-1’ and C. scovillei isolate ‘Hana’) in the field and in vitro methods for three consecutive years (2018 to 2020). The severity of the disease was recorded and compared between inoculation methods. Six phenotypically resistant pepper accessions were selected based on three years of disease data. All of the selected resistant pepper accessions outperformed the control resistant pepper in terms of resistance . A genome-wide association study (GWAS) was carried out to identify single nucleotide polymorphisms (SNPs) associated with anthracnose resistance. An association analysis was performed using 53,518 SNPs and the disease score of the 2020 field and in vitro experiment results. Both field and in vitro experiments revealed 25 and 32 significantly associated SNPs, respectively. These SNPs were found on all chromosomes except Ch06 and Ch07 in the field experiment, whereas in the in vitro experiment they were found on all chromosomes except Ch04 and Ch11.The resistances of 197 pepper (C. chinense accessions were selected. Additionally, in this study, significantly associated SNPs were found in a gene that codes for a protein kinase receptor, such as serine/threonine-protein kinase, and other genes that are known to be involved in disease resistance. This may strengthen the role of these genes in the development of anthracnose resistance in Capsicum spp. As a result, the SNPs discovered to be strongly linked in this study can be used to identify a potential marker for selecting pepper material resistant to anthracnose, which will assist in the development of resistant varieties.In this study, six resistant The online version contains supplementary material available at 10.1186/s12870-023-04388-4. Capsicum spp.) is one of the most popular and widely grown horticultural crops worldwide due to its variety of products, uses, and forms of consumption [Capsicum annuum L., Capsicum chinense Jacq., Capsicum baccatum L. (var. pendulum), Capsicum frutescens L., and Capsicum pubescens Ruiz et Pav [C. chinense (Habanero pepper) is diploid (2n = 24) and is predominantly self-pollinated. However, cross-pollination plays an important role in increasing the number and quality of fruits as well as facilitating higher seed production [Pepper , Colletotrichum capsici (synonym Colletotrichum dematium), Colletotrichum coccodes, Colletotrichum gloeosporioides (teleomorph Glomerella cingulata), and [C. acutatum and C. gloeosporioides are the most damaging and widely distributed anthracnose disease-causing species [Although diseases . Of thesdiseases . The genc fungus . There ata), and . C. acut species , 9.Capsicum spp.) has become a major limiting factor in pepper production, causing significant economic losses in different countries, including China, Korea, India, Indonesia, and Thailand [C. acutatum species complex stands out as the predominant pathogen responsible for the disease, infecting both immature and mature pepper fruits [Colletotrichum scovillei is the major causal agent of anthracnose of chili in Korea [Anthracnose of pepper  revealed mostly dominant resistance observed in chromosome P5 for both green and red fruit [C. annuum breeding line ‘83–168’ identified a single dominant gene responsible for resistance against C. acutatum [C. baccatum PBC80 [C. chinense and C. baccatum [Colletotrichum pathogens in pepper crops.Genetic analysis studies have provided insights into the inheritance patterns of resistance to n stages . Marker-n stages . Researcive gene . In conted fruit . Similaracutatum . Dominanum PBC80 , while dbaccatum . UnderstSeveral methods have been developed and used to select a core collection with maximum genetic diversity based on morphological characteristics and/or passport information . ApproacC. chinense accessions from different countries. These accessions were evaluated for their resistance to anthracnose both in the field and in vitro in different crop years to identify resistant germplasm. The experiment started in 2018 with the C. acutatum isolate ‘KSCa-1’. In 2019, a highly virulent and widely distributed C. scovillei isolate named ‘Hana’ was identified. Consequently, the studies conducted in 2019 and 2020 utilized the ‘Hana’ isolate. The study aims to identify SNPs associated with anthracnose resistance and the underlying genes through GWAS analysis. The findings from this study will provide valuable insights for future breeding programs, ultimately leading to the development of improved disease-resistant pepper varieties.Therefore, this experiment was conducted using a total of 197 Capsicum chinense) accessions deposited in the Republic of Korea’s National Agrobiodiversity Center were evaluated for their response against the virulent pathogen C. acutatum isolates ‘KSCa-1’ and C. scovillei isolate ‘Hana’ in field and in vitro methods for three consecutive years (2018 to 2020). Multiple genes of the isolate ‘Hana’ were sequenced and its identity as C. scovillei was confirmed as presented in the phylogenetic tree  and generated approximately 1.2 billion sum of mapped reads with an average mapping depth of 16.96× for a single accession. The summary of GBS statistics of 197 pepper germplasm summarized in Table C. chinense GWAS panels.Finally, a total of 53,518 SNPs were obtained after filtering with the combination of MAF (> 5%) and missing data (< 30%). The number of SNPs retained on all 12 chromosomes within a 1 Mb window size is presented in Fig. Capsicum cultivars relies on identifying resistance sources and understanding the genetic basis, crucial for combating this economically important disease [C. acutatum were found in the C. chinense population during field and in vitro disease evaluation methods, and their disease scores fall between 0 and 1  [Pseudomonas syringae pv. tomato, which expresses the avirulence gene avrPto [An SNP  showed a significant association with anthracnose resistance was detected within a gene responsible for encoding the receptor-like kinase protein. Receptor kinases play a vital role as receptors that bind to molecules involved in signaling pathways . These karis L.) . The stuaris L.) . Severalaris L.) , 43, Xa2aris L.) , 45, andaris L.) . Pto ence avrPto . The tobe avrPto . The stue avrPto . The rese avrPto –49. Serie avrPto , 51. Thee avrPto , 53.P. syringae 5 (RPS5) in Arabidopsis thaliana, is a well-characterized CNL protein that confers resistance to Pseudomonas pv. tomato (Pto) strain DC3000 carrying the heterologous avirulence gene avrPphB [Disease-resistant markers are primarily associated with resistance (R) proteins, the majority of which feature a domain comprising nucleotide-binding sites and carboxy-terminal leucine-rich repeat regions (NBS-LRR) –58. The  avrPphB , 61, 62.The results of the BLAST searches indicate that the SNP marker on chromosome 02  corresponds to a gene annotated as an E3 ubiquitin ligase protein. Additionally, a similar finding was reported for a significantly associated SNP marker for stripe rust in wheat on chromosome 6AL (Tdurum_contig29607_413), which also encodes an E3 ubiquitin ligase protein . While PC. acutatum isolate ‘KSCa-1’ and C. scovillei isolate ‘Hana’ that cause anthracnose on capsicum spp. A phenotypic evaluation of C. chinense against anthracnose was carried out, and six resistant accessions were identified. These accessions showed better resistance compared to the resistant control pepper . Based on the results of this experiment, the selected resistant materials can be used as a potential source of anthracnose resistance for pepper breeding and genetic studies. Using GWAS, SNPs associated with anthracnose resistance on different chromosomes and their positions in candidate genes were identified. Furthermore, the identified SNPs can be further investigated as potential markers for resistance screening of C. chinense genetic resources. As mentioned in several studies, genes that encode protein kinase receptors have been known as a source of resistance for several diseases. The presence of significantly associated SNPs in this study in a gene that encodes a protein kinase receptor, including serine/threonine-protein kinase, can be an indication of its involvement in anthracnose resistance development in Capsicum spp.This study evaluated pepper accessions collected from different countries for resistance to Capsicum chinense collected from different countries were used. This experiment was conducted for three consecutive years from 2018 to 2020. In 2018, ten plants from each accession were grown in greenhouses at the National Agrobiodiversity Center in Jeonju, and for two years from 2019 to 2020 at Hana Seed Co.‘s experimental sites in Ansang, Korea. This experiment was triplicated. Resistant control  and susceptible control (Manitta) pepper germplasms were used. The pepper plants were grown according to the standard pepper cultivation methods of the Rural Development Administration  [C. acutatum and C. scovillei isolates were evaluated. The introduction number (IT) and geographic origin of the 197 C. chinense accessions used in this study are presented in Supplementary Table In this study, 197 pepper germplasms from , Korea) . The fieThe single spore isolation was performed following the protocol described by Oo et al.  with minC. acutatum, whereas the ‘Hana’ isolate is classified as C. scovillei  and 2020 (field and in vitro experiments). Dr. Lee [C. accutatum (KSCa-1 isolate). Isolate ‘Hana’ was identified from the pepper cultivation fields of Hana Seed Co.   at 75 °C for 3 h. Libraries for GBS were constructed according to previously described protocols [C. chinense reference genome v1.2 (http://peppergenome.snu.ac.kr/).Genomic DNA was extracted using the CTAB method  from yourotocols , 73 withC. chinense reference genome v1.2 (http://peppergenome.snu.ac.kr/). A SAM file was created, and default values were used, except for the following options: a seed length (− l) of 30, maximum differences in the seed (− k) of 1, number of threads (− t) of 16, mismatch penalty (− M) of 6, gap opening penalty (− O) of 15, and gap extension penalty (-E) of 8.Barcode sequence was used for demultiplexing, followed by adapter sequence removal and sequence quality trimming. Adapter and barcode sequences were eliminated using the software Cutadapt (version 1.8.3) . DynamicThe clean reads were mapped to the reference genome sequence. The resulting SAM files were utilized for raw SNP discovery using SAMtools (0.1.16) , and conC. chinense. v1.2, http://peppergenome.snu.ac.kr/).The SNP matrix was generated by removing the incorrectly identified SNP sites using SNP comparison between samples. SNPs were classified as homozygous (SNP read depth ≥ 90%) or heterozygous (40% ≤ SNP read depth ≤ 60%) and other SNPs, which did not meet the criteria for Homozygous or Heterozygous (Table S2). Finally, a total of 53,518 high-quality SNPs were generated for association analysis. Based on the location information of the reference genome sequence  package , which iCapsicum genome database  and the NCBI  database. Our search focused on a 200 kb region surrounding the SNP, including 100 kb on each side. The flanking sequences of the SNP were obtained from the Capsicum chinense genome database. Subsequently, we compared these sequences against the Capsicum genome database and the NCBI database to identify genes or gene regions that exhibited similarity or alignment. This comprehensive approach allowed for the efficient identification of potential candidate genes associated with the SNP of interest. We reported the physical location of the SNPs within the reference genome of C. chinense using a 1 Mb window size. Additionally, we predicted the gene positions based on the C. annum reference and the NCBI reference genome regions.For the identification of candidate genes associated with the SNP of interest, we conducted a BLAST search using the The disease assessment was summarized using Microsoft Excel. Pearson’s correlation between experiment methods and experiment years, as well as principal component analysis (PCA), were carried out using the R statistical software program (4.0.2 version).Below is the link to the electronic supplementary material.Supplementary Material 1


SCN4A, TAS1R2 and KCNG4), milk yield (GABRG1), meat quality , feed efficiency (PCDH8 and SLC26A3), immune response , heat resistance (NPFFR1 and HTR7) and genes belonging to the olfactory gene family, which may be related to adaptation to harsh environments. Tigray indigenous cattle are very diverse. Their genome-wide average nucleotide diversity ranged from 0.0035 to 0.0036. The number of heterozygous SNPs was about 0.6–0.7 times higher than homozygous ones. The within-breed average number of ROHs ranged from 777.82 to 1000.45, with the average sum of the length of ROHs ranging from 122.01 Mbp to 163.88 Mbp. The genomic inbreeding coefficients differed among animals and breeds, reaching up to 10% in some Begait and Raya animals. Tigray indigenous cattle shared a common ancestry with Asian indicine (85.6%–88.7%) and African taurine (11.3%–14.1%) cattle, with very small, if any, European taurine introgression. This study identified high within-breed genetic diversity representing an opportunity for breeding improvement programs and, also, significant novel variants that could increase the number of known cattle variants, an important contribution to the knowledge of domestic cattle genetic diversity.The Tigray region, where we found around eight per cent of the indigenous cattle population of Ethiopia, is considered as the historic centre of the country, with the ancient pre-Aksumite and Aksumite civilisations in contact with the civilisations of the Fertile Crescent and the Indian subcontinent. Here, we used whole genome sequencing data to characterise the genomic diversity, relatedness, and admixture of five cattle populations  indigenous to the Tigray region of Ethiopia. We detected 28 to 29 million SNPs and 2.7 to 2.9 million indels in each population, of which 7% of SNPs and 34% of indels were novel. Functional annotation of the variants showed around 0.01% SNPs and 0.22%–0.27% indels in coding regions. Enrichment analysis of genes overlapping missense private SNPs revealed 20 significant GO terms and KEGG pathways that were shared by or specific to breeds. They included important genes associated with morphology ( From the main domestication centres, cattle dispersed worldwide through trading and human migration routes . The lonEthiopia is a major entry gate for cattle into the African continent . It is tThe region of Tigray in the North of Ethiopia is an ancient centre of civilisations  which were in trading contacts with the ancient civilisations of the Fertile Crescent and the Indus Valley . AccordiY-chromosome markers or SNPs arrays  in the Tigray region of Northern Ethiopia . The samThe gDNA was extracted using the Qiagen DNeasy Blood and Tissue Kit  following the manufacturer’s standard procedure. The extracted gDNA samples were evaluated for their concentration and quality using a Nanodrop spectrophotometer  and 1% agarose gel electrophoresis. A minimum of 5 µg of high quality gDNA with a concentration >50 nanogram per microliter (ng/µL)  was usedThe gDNA samples were sent to the ILRI-CAAS Joint Laboratory of Livestock and Forage Genetic Resources in Beijing, P.R. China, which supervised the genome sequencing. Following the manufacturer’s specifications, a paired-end DNA library was constructed for each of the 54 samples. The gDNA was sequenced on an Illumina HiSeq X10 platform.Bos taurus, Hereford breed) using the BWA version 0.7.17  were aligned against the cattle reference genome assembly  was performed using the GATK’s BaseRecalibrator and the uniquely mapped reads for variant calling were selected using the GATK’s HaplotypeCaller . The genver150 . Finally, the variants (SNPs and indels) were classified according to their potential functions using the Ensemble Variant Effect Predictor tool , and the genes overlapping private missense variants were functionally annotated by DAVID version 6.8 (https://david.ncifcrf.gov/home.jsp). Significant Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were selected based on different criteria including p < 0.05, Bonferroni < 0.05, FDR < 0.05 and fold enrichment > 1.To compute the variant statistics , we used the VCF-stats command of VCFtools/0.1.14/Perl. The number of transition and transversion, average ratios of transitions-to-transversions (Ti/Tv), and distribution of SNPs and indels at different allelic frequencies were analysed using stats command and plot-VCF-stats of BCFtools/1.8 . Moreoveπ) was analysed for each population using VCFtools version 0.1.15 in 20 kb windows with a 10 kb sliding step (with the--window-pi 20000 --window-pi-step 10000 option) (Ho) was calculated following the command “--het” in PLINK version 1.9 (The genome-wide nucleotide diversity ( option) . The numsion 1.9 .The runs of homozygosity (ROH) were detected using PLINK version 1.9  by settiROH = LROH/LAUTO), following HOM) was also calculated for each animal using the “--het” command in PLINK version 1.9 . To calculate pca.egenvel and pca.egenvec, we used the flag “plink--pca” with a default parameter, for the first 20 principal components (PCs). Then, the proportions of variances explained by the eigenvector were computed by dividing each egenvel by the total sum of all egenvels (1–20) and expressing it as a per centage. Finally, the two first PCs were plotted against each other using the ggplot2 package in R version 3.6.1  to illusUsing the same LD-pruned dataset , the ADMIXTURE version 1.3.0 software  was usedFST) between pairs of populations . The average sequencing depth among populations ranged from 10.13 X (Erob cattle) to 10.64 X (Begait cattle). Furthermore, over 88% of the bases were covered with at least five reads, and 39%–42% were covered with at least ten reads .Variant calling and filtration combining the five Tigray cattle populations resulted in the detection of around 36 million (M) SNPs  and 3.7 M indels  . The numWe detected around 1.1 M–1.2 M indels in each individual cattle , while tExcept for Erob cattle, the number of private SNPs across individual samples ranged from 32,245 to 81,933, and the number of private indels ranged from 5,276 to 14,182 . Among EThe density of genome-wide SNPs ranged from 11.27 ± 7.69 to 11.94 ± 7.88 SNPs/kb and of indels from 1.08 ± 1.34 to 1.17 ± 1.41 indels/kb across the five Tigray cattle populations . ChromosAcross the five Tigray indigenous cattle populations, the average alternate (non-reference) allele frequencies of SNPs and indels were 0.32 and 0.28 to 0.3, respectively. The proportion of SNPs with mean alternate allele frequency (AAF) < 0.5 ranged from 77% to 79% and the proportion of SNPs with mean AAF > 0.9 was around 4%. The proportion of indels with mean AAF < 0.5 ranged from 78% to 80% . HoweverThe Ti/Tv ratio was around 2.35 . It suppThe annotation of the SNPs showed that around 59.5% of them were in the intergenic regions. Around 76% of annotated SNPs were in introns, 7% in upstream of genes, 7.2% in downstream of genes, 0.5% in 3′ untranslated region (UTR), 0.2% in 5’ UTR and 0.11% in non-coding transcript exon. The number of SNPs in the coding regions  was approximately 0.01% in all populations   Supplem, of whicAround 57%, 77%, 7.4%, 8%, 0.6%, 0.2% and 0.1% of the indels were in intergenic regions, introns, upstream of genes, downstream of genes, 3′ UTR, 5’ UTR and non-coding transcript exons, respectively. The total numbers of indels located within the coding regions  ranged from 0.22%  to 0.27%  . Among tA separate analysis of the private SNPs for each population showed 1,455, 1,809, 1,470, 1,203, and 1,701 private missense SNPs for Abergelle, Arado, Begait, Erob and Raya cattle populations, respectively; of which 97.9% (Erob) to 98.9% (Raya) were in coding regions while the remaining ones overlapped with splice regions. Of the missense SNPs in the coding regions, 33.1%, 34.4%, 52.7%, 64.8% and 58.1% had deleterious effects in Abergelle, Arado, Begait, Erob and Raya cattle populations, respectively .p < 0.05, Bonferroni < 0.05, FDR < 0.05 and fold enrichment > 1) GO terms of biological process (BP), cellular component (CC), molecular function (MF) and KEGG pathways in Abergelle, Arado, Begait, Erob and Raya cattle populations, respectively  were olfactory receptor activity (GO:0004984), olfactory transduction (bta04740) and odorant binding (GO:0005549). These were common to all five Tigray cattle populations.Functional enrichment analysis of the genes overlapping private SNPs in coding regions identified 16, 12, 16, 6 and 10 significant  to 17% (Abergelle cattle) of the genes were associated with olfactory receptor activity, 13% (Erob cattle) to 18% (Abergelle cattle) with olfactory transduction, and 3% (Erob cattle) to 6% (Abergelle cattle) with odorant binding .OR4F73, OR1L21, OR5AN1, OR9S29, OR9M1D, OR2H20, OR4X16, OR5AK29, OR6C4, OR8B1AU and OR9S40, were commonly enriched in the three shared GO terms. Six GO terms  with related molecular functions were enriched in four populations . Interestingly, two significant GO terms of the cellular component associated with an integral component of the plasma membrane (GO:0005887) and the intrinsic component of the plasma membrane (GO:0031226) were only enriched in Begait cattle. Two GO terms of the cellular component (GO:0005578) related with proteinaceous extracellular matrix and extracellular matrix component (GO:0044420) were significantly enriched only in the Erob cattle .Eleven genes, including π) ranged from 3.5 ± 1.77 × 10−3 (Raya) to 3.57 ± 1.76 × 10−3 (Arado) (Ho) was the highest in Arado cattle (0.302 ± 0.010) but the lowest in Raya cattle (0.278 ± 0.016) (The average genome-wide nucleotide diversity ( (Arado) . Besides (Arado) . At the  (Arado) . The mea± 0.016) .We calculated ROH for the five Tigray cattle populations and the reference breeds. The average number and length of ROH segments varied considerably within and among breeds . The witThe number of ROH across length categories  varied among breeds. ROH in the length category of 0.1–0.25 Mbp accounted for 90%–92% of the total ROH. For the length categories >0.25–0.5 Mbp and > 0.5–1 Mbp, ROH frequencies were about 7%–10% and 0.2%–0.4%, respectively . ROH > 1ROH = 0.047 ± 0.004 and FHOM = 0.043 ± 0.035) but the highest in Raya cattle (FROH = 0.064 ± 0.025 and FHOM = 0.07 ± 0.054) (ROH = 0.103 and FHOM = 0.107) and three Raya cattle  showed strong genomic inbreeding values based on both methods  . One Beg methods .The PCA showed the presence of six potential clusters of populations . PC1 andPC1 and PC2 for the five Tigray cattle populations and other Ethiopian cattle representing three cattle groups of African zebu (Ethiopian Boran), African sanga (Afar) and African zenga (Fogera and Horro) jointly accounted for 15.34% of the total variation, of which the Begait and Erob cattle were separated from the other populations . The PC1As indicated by the lowest cross-validation error (0.51) , the admFST for the Tigray cattle populations ranged from 0.07 to 0.08 with Asian Zebu, 0.084 to 0.108 with Ankole (African sanga originating from Uganda), 0.236 to 0.264 with N’Dama (African taurine), 0.328 to 0.36 with Muturu (African taurine) and 0.300 to 0.335 with the European taurine cattle (Angus and Holstein)  for Begait and Erob cattle from Ethiopian Boran, Fogera and Horro cattle than for Abergelle, Arado and Raya cattle (FST around 0.01) (olstein) . Within nd 0.01) .FST values among the Tigray cattle populations and the Tigray cattle populations against other cattle breeds  showed two main genetic clades: One comprising the taurine group with two sub-clusters African and European) and another including the Asian zebu and other non-taurine African origin breeds (including the Tigray cattle populations). This was consistent with the PCA and admixture analysis results. Further, the Neighbor-Net tree based on the pairwise FST values , heterozygosity (Ho), runs of homozygosity (ROH) and genomic inbreeding coefficient to evaluate the intra-population genetic diversity. The pairwise population differentiation (FST) and relationship based on PCA and admixture analysis were employed to assess the inter-population differentiation and relationship among the Tigray cattle and between the Tigray cattle and other reference cattle groups .We report the first whole-genome-sequence-based characterisation of the genetic diversity, relatedness and admixture of cattle populations indigenous to the Ethiopia’s Tigray region. We used abundance, distribution and functional description of SNPs and indels, genome-wide nucleotide diversity  within and across the Tigray cattle populations illustrated their rich genetic diversity. Also, we found a substantial number of novel variants (SNPs and indels) in the Tigray cattle , indicatWe observed private variants in each Tigray cattle population. Though they only represented around 1.5% (Erob cattle) to 2.1% (Arado cattle) of the total variants in respective populations, they might serve as important diagnostic markers. A small proportion of these SNPs (around 0.23%–0.32%) were missense variants, of which the majority were located in coding regions (97.9%–98.9%), while a few (1.1%–2.1%) were in splice regions. Amongst these SNPs, one-third to two-thirds had a predicted deleterious effect.Further analysis of all private missense variants identified several GO terms and KEGG pathways shared by different populations or to be population specific. The two most significant GO terms of the molecular function (the olfactory receptor activity and odorant binding) and one most significant KEGG pathway (olfactory transduction) present in the five Tigray cattle populations were associated with olfaction or odour recognition. An efficient olfactory reception is an important fitness mechanisms essential for adaptation, including food and water search behaviour and reproduction . Odour rSCN4A  and its subtype intrinsic component of the plasma membrane (GO:0031226) were only found in Begait cattle. In these GO terms, several important genes were found to be associated with morphology, production, reproduction, feed efficiency, immune response and environmental adaptation. For example, SCN4A  and TAS1 , von Willebrand factor C domain containing 2 (VWC2) and laminin subunit gamma 1 (LAMC1) were important in these GO terms. The MMRN2 is associated with a meat quality trait called meat juiciness , amoebiasis and toxoplasmosis in cattle. Moreover, LAMC1 was also shown to be relevant to temperature range in cattle (Two significant GO terms of the cellular component (GO:0005578∼proteinaceous extracellular matrix and GO:0044420∼extracellular matrix component) were explicitly enriched in Erob cattle. Genes such as multimerin 2 (uiciness . Similarf cattle . VWC2 wa in pigs . LAMC1 wn cattle .CELSR1), gamma-aminobutyric acid type A receptor rho3 subunit (GABRR3), plexin A2 (PLXNA2) and toll-like receptor 6 (TLR6) were identified in Abergelle, Arado and Raya cattle, while the gene macrophage stimulating 1 receptor (MST1R) was overrepresented in the six GO terms . CELSR1  (PLXNA2) (GABRR3) , in which a few genes such as cadherin EGF LAG seven-pass G-type receptor 1  , cattle (PLXNA2)  and fert(GABRR3)  were alsIn our previous morphological study of the same Tigray cattle populations , we showπ) in all Tigray cattle, comparable with the values observed in Asian indicine cattle but higher than those in taurine cattle  (π = 2.9 × 10−3 for Jiaxian Red) cattle (Ho), an important indicator of genetic variability in domestic animals (There is a high genome-wide nucleotide diversity (olstein)  and indi) cattle . Similar animals , rangingHistoric factors associated with the arrival and admixture of cattle in the Horn of Africa, including the Tigray region, likely shaped today’s genome diversity of the Tigray cattle. As an ancient centre of civilisation, the Tigray region probably witnessed the early arrival of taurine cattle, followed by late introductions of indicine cattle in several migration waves, which continuously enriched the genomic landscape of the Tigray cattle. While our results indicated a large proportion of indicine background in Tigray cattle (around 90%), we still found a proportion of African taurine ancestry in their genomes. We may reasonably hypothesise that the rich genetic variation present in modern Tigray cattle is a legacy of multiple introductions, admixture and dispersion of cattle across the Horn of Africa.We compared the ROH distribution pattern across the Tigray cattle populations and between the Tigray cattle and other reference cattle groups included in this study and found that all the Tigray cattle showed different patterns of ROH as compared to Asian zebu, African taurine (N’Dama and Muturu), African sanga (Ankole), African zebu (Kenana) and European-taurine (Angus and Holstein) cattle. As expected from their high genomic diversity, the number and cumulative length of ROH were smaller in the Tigray cattle compared with previous reports for taurine cattle  and indiROH), is generally considered to have no consequence on an individual’s fitness , possibly resulting from consanguineous mating. So, these two populations may need special attention to maintain their within-population genetic diversity. The admixture analysis confirmed that the Tigray cattle have a common main indicine ancestry, followed by a low African taurine and a rather limited European taurine ancestry. With high within-population genetic diversity, the Tigray cattle represent an important indigenous genetic resource for breeding improvement to enhance their productivity , while maintaining their environmental adaptability. All the Tigray cattle populations shared highly significant GO and pathway terms associated with sensory perception of smell with overrepresented genes in the olfactory family, which may be relevant to their adaptation to their harsh environments.


Recently, the consumption of high-quality meat has increased. Meat flavor is the factor that most significantly contributes to meat quality, making meat flavor an important performance trait in the chicken industry. Although chicken breeding has achieved fast-growing and high-quantity meat production so far, it is still necessary to improve the meat flavor of chicken through selection. Nucleotide-related compounds generate tastes such as umami and primarily determine meat flavor. In this study, for the first time, we identified candidate genes regulating nucleotide-related compounds included in chicken breast meat using a genome-wide association study. These findings will promote the selection of chicken to produce more tasty meat.p < 0.05) associated with all three traits. The trait-related candidate genes located in this significant genomic region were investigated through performing a functional enrichment analysis and protein–protein interaction (PPI) database search. We found six candidate genes related to the function that possibly affected the content of nucleotide-related compounds in the muscle, namely, the TNNT3 and TNNT2 genes that regulate muscle contractions; the INS, IGF2, and DUSP8 genes associated with insulin sensitivity; and the C5NT1AL gene that is presumably related to the nucleotide metabolism process. This study is the first of its kind to find candidate genes associated with the content of all three types of nucleotide-related compounds in chicken meat using GWAS. The candidate genes identified in this study can be used for genomic selection to breed better-quality chickens in the future.Meat flavor is an important factor that influences the palatability of chicken meat. Inosine 5′-monophosphate (IMP), inosine, and hypoxanthine are nucleic acids that serve as taste-active compounds, mainly enhancing flavor in muscle tissue. For this study, we performed a genome-wide association study (GWAS) using a mixed linear model to identify single-nucleotide polymorphisms (SNPs) that are significantly associated with changes in the contents of the nucleotide-related compounds of breast meat in the Korean native chicken (KNC) population. The genomic region on chicken chromosome 5 containing an SNP (rs316338889) was significantly ( Meat consumption has increased worldwide over the past century  largely Taste and flavor, regarded as the most important traits of meat quality, are influenced by nucleotide-related compounds such as inosine 5′-monophosphate (IMP), which is the predominant nucleotide in meat . IMP is Similar to many other meat quality traits, meat flavor is regulated by numerous genes ; howeverKorean native chicken (KNC) is a purebred indigenous chicken population in Korea. Based on the plumage color, KNCs are categorized into five lines: white (KNC-W), black (KNC-B), yellow-brown (KNC-Y), gray-brown (KNC-G), and red-brown (KNC-R) ,28,29,30AMPD1 encodes adenosine monophosphate (AMP) deaminase, which is the enzyme that generates IMP from AMP in chicken [BMPR2 and PTEN, which are involved in IMP metabolism and muscle development, was identified in a gene expression study regarding chicken [Previous studies on cattle, pigs, and chickens have used various methods to identify multiple genes related to IMP ,37,38,39 chicken . Additio chicken . However chicken . For thiIn this study, we used KNC-R line chickens maintained as a pure line at Poultry Research Institute in NIAS  in Korea. The experimental chickens were selected from each generation out of four generations (1 generation is 52 weeks) to make up 637 birds in total. The number of birds and genders in each generation used for this experiment are described in 2/10 heads) in the windowless chicken house of the Poultry Research Institute from hatch to 10 weeks of age. In the growth stage, the chickens were fed a starter diet  from hatch up until 6 weeks of age and a grower diet  from 7 to 10 weeks of age. The feed and water were provided ad libitum. Feed and the environmental conditions  applied to the chickens were maintained consistently.All chickens of each generation were hatched on the same day. The chickens were raised in battery cages  and Animal Ethics Committee of Chungnam National University .At 10 weeks of age, the experimental birds were fainted via COp-value for normalized data among the methods was chosen. For outlier trimming, values that were larger or smaller than three standard deviations from the mean value of the data were removed. Finally, 635 IMP and inosine contents and 637 hypoxanthine contents were filtered and used in an association test. R software v 3.6.3 was used for preprocessing.For phenotype data collection, we used nuclear magnetic resonance spectroscopy to measure the IMP, inosine, and hypoxanthine contents in the breast meat samples . The meap-value < 10−6 were filtered out prior to analysis. Finally, 44,842 variants remained and were used for the association test.A commercial kit  was used to extract DNA from the blood samples. Using an Illumina chicken 60 K SNP chip , genotype data containing 57,636 SNPs were generated from the extracted DNA samples. Using PLINK1.9 v 1.90b5.2 software , SNPs wip-value to identify significantly associated SNPs. To visualize the results, the trend of marker significance was summarized in a Manhattan plot. We defined a significantly associated genomic region of 1 Mb centered around the position of the most significant SNP and then used that region for functional annotation analysis.Before conducting the GWAS, we constructed the genetic relationship metrics of 637 birds from the KNC-R population using a genome-wide complex trait analysis (GCTA)  tool kitp < 0.05) GO terms and KEGG pathways were reported. All annotation procedures were performed using the Gallus gallus database. Additional literature investigations for each gene function were also conducted. To investigate gene function, the interactions of proteins encoded by each gene with other proteins were analyzed using the STRING v 11.5  database with default settings.The genes located in the significant genomic region were retrieved from GRCg6a 106 version in the Ensembl genome database using BioMart . These gp > 0.05), but other traits did not exhibit a normal distribution according to the Shapiro–Wilk test. Inosine had a heritability of >0.5; IMP and hypoxanthine had heritability values of approximately 0.25 and 0.24, respectively.Therefore, the phenotypic values of IMP, inosine, and hypoxanthine were transformed using log, square root, and cube scaling, respectively. After transformation, inosine values exhibited a normal distribution (p < 0.05) for the inosine trait. All significant SNPs were located on chromosome 5 (10.7–18.8 Mb). Among the significant SNPs, 23 markers had a p-value < 10−5. The most significant SNP was rs316338889 (p = 5.83 × 10−27), which was located at 14,370,082 bp on chromosome 5. With respect to IMP, 17 SNPs were significant (adjusted p < 0.05). Two SNPs had a p-value < 10−5, including rs316338889, which was the most significant SNP (p = 1.62 × 10−16). Sixteen markers of seventeen significant SNPs in IMP were also common among the significant SNPs in inosine. In the GWAS results regarding hypoxanthine content, only one SNP (rs316338889) had a higher significance (adjusted p < 0.05) than the threshold; its p-value was 1.53 × 10−9. Notably, the rs316338889 SNP was significant in all three traits as the lead SNP, but none of the other genomic regions showed significance in the GWAS results. Manhattan plots and detailed GWAS results for each SNP are shown in ASCL2 gene region to the TOLLIP gene region.Based on the Bonferroni-corrected genome-wide significance threshold, 46 SNPs were significant  GO terms in the molecular function category, 11 terms in the biological process category, and 12 terms in the cellular component category. Only GO terms related to the target traits are listed in INS and IGF2 genes. There were 11 GO terms related to the muscle fiber and contraction process, which was enriched in the TNNT3 and TNNI2 genes. None of the KEGG pathways were significantly enriched in genes in the significant genomic region. Analysis of genes forming a network with genes in the protein–protein interaction (PPI) database revealed a gene network containing C5NT1AL ; we inferred that this gene was related to the contents of nucleotide-related compounds. This network consisted of 11 proteins  that were functionally clustered with high confidence and an interaction score >0.9. The groups of proteins the featured C5NT1AL in the network were enriched in the metabolic (gga01100), purine metabolism (gga00230), and pyrimidine metabolism (gga00240) pathways in the KEGG database. Additionally, groups of proteins were enriched in the GO terms of adenosine metabolic process (GO:0046085), nucleoside phosphate catabolic process (GO:1901292), and nucleotide metabolic process (GO:0009117), which were included in the biological process category of the GO database , may have contributed to the differences observed in this study regarding the contents of IMP and its degradation products.Based on the GWAS results, we defined a 1-Mb significant genomic region on chicken chromosome 5 that was associated with the contents of nucleotide-related compounds, then obtained a list of 16 protein-coding genes located in that region. GO analysis involving these genes yielded 25 GO terms that were enriched in five genes . Among tle fiber . TNNT3 aectively ,49,50. Tectively . Previouectively ,52,53 haectively . In chicectively . TherefoINS and IGF2 genes. The INS gene, encoding insulin, is reportedly related to muscle fiber density and growth traits in chickens [IGF2 gene, encoding an insulin-like growth factor, reportedly induces glucose uptake in vertebrates and plays a major role in glucose metabolism [DUSP8 gene, which possessed the most significant SNP in this study, participates in the regulation of glucose tolerance and insulin sensitivity [INS, IGF2, and DUSP8 genes likely have major effects on the content of nucleotide-related compounds in the muscle of KNC-R chickens.The GO analysis results included terms related to hormone activity that were enriched in the chickens ,56. Insuchickens . The IGFtabolism ,59. In tsitivity . Importasitivity ,61,62,63C5NT1AL gene in the significant genomic region encodes a cytosolic 5′-nucleotidase 1A-like enzyme that remains uncharacterized. According to the KEGG pathway database, the C5NT1AL gene is involved in a nucleotide metabolism pathway (gga01232) that acts on nucleoside monophosphate; this pathway contributes to the degradation of IMP to inosine. Specifically, along with NT5E and NT5C1A, the C5NT1AL gene is registered in the KEGG enzyme entry (EC no. 3.1.3.5) that describes the reaction by which IMP is dephosphorylated into inosine. Thus, C5NT1AL may be a 5′-nucleotidase that participates in the regulation of IMP metabolism [C5NT1AL gene function and its effect on the traits . Moreover, integrated studies should be conducted to characterize the transcriptome and confirm the functions of the encoded proteins.TNNT3, TNNI2, INS, IGF2, DUSP8, and C5NT1AL. These genes showed a possible association with ATP regulation and nucleic acid catabolism. The results of this study will be useful for farmers breeding KNC, enabling them to produce high-quality meat in the future.The flavor of chicken meat is influenced by IMP and its degradation products (inosine and hypoxanthine). We conducted a GWAS and detected a genomic region on chromosome 5 that had a significant effect on the contents of nucleotide-related compounds in breast meat. We identified the following candidate genes via functional annotation: