High-density SNP arrays are now available for a wide range of crop species. Despite the development of many tools for generating genetic maps, the genome position of many SNPs from these arrays is unknown. Here we propose a linkage disequilibrium (LD)-based algorithm to allocate unassigned SNPs to chromosome regions from sparse genetic maps. This algorithm was tested on sugarcane, wheat, and barley data sets. We calculated the algorithm’s efficiency by masking SNPs with known locations, then assigning their position to the map with the algorithm, and finally comparing the assigned and true positions.In the 20-fold cross-validation, the mean proportion of masked mapped SNPs that were placed by the algorithm to a chromosome was 89.53, 94.25, and 97.23% for sugarcane, wheat, and barley, respectively. Of the markers that were placed in the genome, 98.73, 96.45 and 98.53% of the SNPs were positioned on the correct chromosome. The mean correlations between known and new estimated SNP positions were 0.97, 0.98, and 0.97 for sugarcane, wheat, and barley. The LD-based algorithm was used to assign 5920 out of 21,251 unpositioned markers to the current Q208 sugarcane genetic map, representing the highest density genetic map for this species to date.Our LD-based approach can be used to accurately assign unpositioned SNPs to existing genetic maps, improving genome-wide association studies and genomic prediction in crop species with fragmented and incomplete genome assemblies. This approach will facilitate genomic-assisted breeding for many orphan crops that lack genetic and genomic resources. The rate of genetic gains in crop breeding programs can be accelerated using genomic information, either through genomic selection (GS), the use of markers linked to causal mutations of moderate to large effects discovered through genome-wide association (GWAS) if these exist, or a combination of both . IdeallySingle-nucleotide polymorphisms (SNPs) are the most common polymorphisms at the DNA level. SNPs are cost-effective and easy to genotype in a high throughput manner, making them increasingly relevant in crop/animal genetics studies such as association mapping, marker-assisted, and genomic selection. With advancements in high-throughput genotyping, high-density SNP arrays are now available as an effective genetic tool for many important crop species such as barley, sugarcane, and wheat –7. This Despite the development of many tools for generating genetic maps, the exact genome location of many SNPs from these SNP arrays is unknown. Sugarcane’s highly heterozygous and primarily autopolyploid genetics have all hampered the development of a comprehensive genetic map. Modern sugarcane cultivars present a high ~ 8–16) ploidy level with mainly random chromosome pairing and significant inbreeding depression, making it difficult to generate more traditional experimental mapping populations such as recombinant inbred lines or double haploids , 8, 9. M–16 ploidGoddard and Meuwissen  proposedInferring chromosomal positions using LD can be challenging, as LD between unlinked markers can result from factors other than physical proximity on a chromosome (linkage), such as epistatic interactions, genetic drift, selection, and mutation. In addition, admixing genetically distinct populations result in the linkage between two loci with different allele frequencies, even though they are unlinked . PopulatEstimates of LD have been used to infer the position of the unmapped markers in diploid species . Miller The main aim of this study was to i) adopt an LD-based algorithm to allocate unassigned SNPs in order to develop a method for expanding established genetic maps for several crops with complex genomes and to ii) investigate the accuracy of this approach in sugarcane, wheat and barley. To achieve this, a modified multi-point LODE approach was implemented. The algorithm’s efficiency was first investigated on a breeding population of sugarcane clones using a 20-fold cross-validation process. Test sets of 200 out of 4502 mapped SNPs of the newly developed sugarcane Q208 genetic map were positioned based on our modified LODE approach in every iteration. To validate the LD-based approach, the algorithm was also assessed in hexaploid wheat diversity panel, including elite varieties with a genome size estimated at 17 Gb and a structured nested association mapping population for barley, a self-pollinated and one of the largest diploid genomes (haploid genome size 5.3 Gb). Finally, the algorithm was used to assign 5920 out of 21,251 unassigned SNPs (MAF > 0.01) to the current Q208 sugarcane genetic map. This updated Q208 sugarcane genetic map is available to the sugarcane research community.r2 (= 0.1) is in the 75th percentile  for sugarcane, whereas it is in the 90th and 95th percentiles for wheat and barley. Sugarcane exhibited a high LD level, and LD decay was relatively slow compared to wheat and barley , the mean efficiency rate significantly decreased compared to a cutoff of r2 = 0.1 for sugarcane and barley  was used to attempt to assign the remaining 21,251 unpositioned SNPs with MAF > 0.01 to the Q208 genetic map. As a result, 5920 unpositioned markers on the Q208 genetic map were successfully allocated to the existing linkage groups, resulting in a total of 10,387 SNPs with MAF > 0.01 on the extended genetic map produced with our LD-based approach.As a result of high throughput genotyping, approximately 25,753 high polymorphic markers with MAF (≥ 0.01) are available in a large population of approximately 3006 elite clones from the Australian sugarcane breeding program run by Sugar Research Australia. However, only 4502 markers were mapped on the newly developed Q208 genetic map (CSIRO unpublished data). Therefore, the LD-based approach  were estimated as a measure of LD while allelic dosage was taken into account. In population genetic studies of the highbush blueberry population, the effects of diploid and tetraploid marker genotyping calling approaches were examined. LD and population structure were found to be consistent independent of the ploidy model [Yang et al.  used higdy model . The samdy model . This suThe effectiveness of the LD-based procedure for placing unassigned SNPs is determined by the magnitude of LD in the population. The extent of LD and its decay with genetic distance determines the mapping resolution with this approach and further helps determine the appropriate number of SNP markers required in association mapping studies. Moreover, both genomic and marker-assisted selection also exploits LD between markers and QTL , 25. Sugr2 > 0.4) to position the mapped bovine SNPs. Applying such a threshold in our study would have reduced efficiency to 63.14% in sugarcane (MAF > 0.01) and 87.9 and 79.7% in wheat and barley (MAF > 0.1), respectively. In comparison to using threshold (r2 > 0.1), Khatkar et al. [r2 > 0.4). In addition, a higher efficiency rate was observed in wheat and barley than in sugarcane, which might be because we only used SNPs with MAF > 0.1 in both species and because fewer markers had known positions in our sugarcane data set compared to the wheat and barley data sets.Miller et al.  used LD r et al.  also recr2 > 0.1) because of the high efficiency and comparable accuracy rate. However, despite the high efficiency rate, only 5920 unpositioned markers were assigned to the genetic map. This might be because only 17% of mapped single-dosage markers were used as an anchor to assign the remaining 83% of unmapped markers. The low-density anchoring to the sugarcane genome can occur due to uneven marker coverage across the genetic map, as demonstrated in all previously reported maps, whether generated via selfing or biparental crosses [Finally, we used the LD-based algorithm on sugarcane unpositioned SNPs (MAF > 0.01) by setting a threshold  , was useAn international diversity set of 460 hexaploid wheat accession, including elite varieties, landraces, and experimental lines from different geographic backgrounds, was genotyped using a 90 K SNP wheat genotyping array (Illumina Inc.) . For thiThe multi-parent nested association mapping (NAM) lines utilised in the validation study were derived from crossing the three Australian reference varieties Commander, Compass, and La Trobe to donor parents, which are elite breeding lines from the Northern Region Barley Breeding Program . The NAMr2 statistics (square of the correlation coefficient) for all pair-wise combinations of SNPs in each of the crop species mentioned above using a function st.calc.ld (ld.measure = “r2”) implemented in the R package “SelectionTools” version 19.3 (population-genetics.uni-giessen.de/ ~ software /). The option “ld.measure = “r2” assume that the input data is in the correct (known) gametic phase and estimates the correlation between two variables, coded as 0, 1, and 2, which indicate the number of alternative alleles at each SNP, based on genotype allele count (without phasing). For a large number of genotypes, however, the squared correlation based on genotypic allele counts is equivalent to the r2 calculated from haplotype frequencies [Estimates of LD were obtained as quencies , 37. Anoquencies .Unlike plant species with simpler genomes like barley and wheat, segregation occurs in sugarcane within the first generation of a progeny generated through biparental crosses. As a result, genetic mapping has been restricted to single-dose markers. In this approach, a copy (dose) of a particular marker is present in either one or both parents, resulting in a 1:1 or 3:1 (presence: absence) ratio in the mapping population , 38, 39.r2 values against the genetic distance (cM) between markers. The R package ggplot2 was used to visualise the results, including a locally estimated scatterplot smoothing (LOESS) line [r2 values, the critical r2 value for all crop species in this study was set to 0.1, which refers to the minimum threshold for a significant association between two loci. The LD decay over genetic distance was determined as the mean distance associated with an empirical threshold of r2 = 0.1. The analysis was run with a range of different r2 thresholds.The decay of LD over genetic distance was investigated by plotting pair-wise intra-chromosomal SS) line . After ar2 between the unmapped and all mapped SNPs was estimated.There are two main steps in the algorithm Fig.  used in r2 (> 0.1) estimates were identified, and if both mapped SNPs were on the same chromosome, the unassigned SNP was allocated to the same linkage group (or chromosome). To test the algorithm accuracy, groups of SNPs with an actual map position in each species had their position masked and were considered unassigned. Then, each “unassigned” SNP that could be allocated to a chromosome was positioned within a chromosome using a weighted average of mapped markers’ position, where the weight was determined by the LD estimates of the unpositioned SNP with mapped markers. The algorithm’s performance was assessed in terms of “Efficiency,” defined as the percentage of “masked” SNPs assigned to a chromosome, “Accuracy” as the percentage of SNPs allocated to the correct chromosome, and “Precision” as the difference in the distance between the known and assigned positions.For each unassigned SNP, the previously mapped SNPs with the first and second highest The algorithm was evaluated on various sizes of test sets of masked SNPs in sugarcane (~ 200), wheat (~ 900), and barley (~ 130) using random 20-fold cross-validation in which 20 random non-overlapping sets of masked SNPs were selected. All mapped SNPs were divided into 20 groups at random, with each unique group of SNPs masked (“unmapped”) and the remaining groups of SNPs classified as “mapped”.


FST values revealed the lowest genetic distance between Murrah and Nili-Ravi (0.0022), while the highest between Surti and Pandharpuri (0.030). Principal component analysis and structure analysis unveiled the differentiation of Surti, Pandharpuri, and Jaffarabadi in first two principal components and at K = 4, respectively, while remaining breeds were grouped together as a separate single cluster and admixed. Murrah and Mehsana showed early linkage disequilibrium (LD) decay, while Surti breed showed late decay. In LD blocks to quantitative trait locis (QTLs) concordance analysis, 4.65% of concordance was observed with 873 LD blocks overlapped with 2,330 QTLs. Overall, total 4,090 markers were identified from all LD blocks for six types of traits. Results of this study indicated that these single-nucleotide polymorphism (SNP) markers could differentiate phenotypically distinct breeds like Surti, Pandharpuri, and Jaffarabadi but not others. So, there is a need to develop SNP chip based on SNP markers identified by sequence information of local breeds.India is considered as the home tract of some of the best buffalo breeds. However, the genetic structure of the Indian river buffalo is poorly understood. Hence, there is a need to characterize the populations and understand the genetic structure of various buffalo breeds for selection and to design breeding strategies. In this study, we have analyzed genetic variability and population structure of seven buffalo breeds from their respective geographical regions using Axiom Buffalo Genotyping Array. Diversity, as measured by expected heterozygosity, ranged from 0.364 in Surti to 0.384 in Murrah breed, and pair-wise  The importance of genetic diversity in livestock is directly related to the need for genetic improvement of economically important traits as well as to facilitate rapid adaptation to potential changes as per breeding goals. Population structure, and unusual levels of shared ancestry, can potentially cause problems with genome-wide association studies. The analysis of a large number of SNPs across the genome will reveal aspects of the population genetic structure, including evidence of adaptive selection across the genome . DomestiRiver buffalo domestication is likely to have occurred around 6,300 years before present in north-western India . India iGenetic analysis is facilitated by genotyping polymorphic genetic loci, also called as genetic variants or markers. SNPs are the most common type of genetic variants, consisting of single-nucleotide differences between two individuals at a particular site in the DNA sequence. Assessing genetic biodiversity and population structure of minor breeds through the information provided by neutral molecular markers, such as SNPs and microsatellites, allows the determination of their extinction risk and to design strategies for their management, conservation, and maintenance of genetic variation for continuous genetic improvement . The rivA total of 295 female buffaloes from seven breeds were used in this study . All aniDNA was extracted using Qiagen QIAamp Blood DNA kit  as per manufacturer’s instructions. DNA quantity and quality were checked using Nanodrop  and agarose gel electrophoresis, respectively. SNP genotyping was carried out using commercially available Axiom Buffalo Genotyping Array (90 K) designed with 123,040 probes on Gene Titan MC  instrument at a commercial laboratory . It was designed based on SNPs discovered from Mediterranean, Murrah, Jaffarabadi, and Nili-Ravi breeds of buffaloes. In the designed array, there are 123,040 probes, which include 89,988 probes for SNPs, while other probes are for quality control (QC) and gender calling.P-value <0.00001; Only SNPs mapped to autosomal chromosomes were used in this study. Data were filtered with PLINK v1.07  based onP-values for significance test for Hardy-Weinberg Equilibrium, obtained for each loci. Linkage disequilibrium (LD) was calculated using PLINK and r2 values were calculated for all SNP pairs that were located less than 1,000 SNPs apart and falling under 10 Mb distance windows. Furthermore, SNPs were binned with bin size of 10,000 bases distance, and average r2 value of each bin was plotted against median distance value ggplot2 v2.2.1 package in R v3.3. Pair-wise FST values and the associated 95% confidence intervals were calculated using the Hierfstat package  was calculated using SNeP v1.1  with parBos taurus) QTL_UMD_3.1.1 was used as a reference for the analysis, containing the information regarding six types of the traits: milk traits; health traits; production traits; reproduction traits; exterior traits; and meat and carcass traits. The QTL files were intersected with the files of LD blocks using Bedtools v2.26.0  and expected heterozygosity (HE) in all breeds did not differ and ranged from 0.3719 (Pandharpuri) to 0.3864 (Murrah) and 0.3643 (Surti) to 0.3846 (Murrah), respectively (FIS was observed for Murrah (−0.0046) followed by Mehsana (−0.0070), while comparative higher values were observed in Surti (−0.0314) followed by Banni (−0.0270).Highest number of SNPs with alternate allele frequency between 0.3 and 0.4 was observed in all studied buffalo breeds except Surti . Highestectively . The lowFST values showed least genetic distance between Murrah and Nili-Ravi (0.00221) followed by Murrah and Mehsana (0.00402), while highest genetic distance was observed between Surti and Pandharpuri (0.03097) followed by Surti and Banni . For K = 3, total three breed groups were identified  of purebred individuals with more than 80% of Q-value. Likewise, Surti breed has 19 purebred individuals with negligible admixture with other breeds.Furthermore, relatedness between breeds and the significance of the existence of subpopulations was investigated by model-based unsupervised clustering using entified ; clusterentified . So, K =t levels . Three Jr2 value in Surti (from 0.412 to 0.175) followed by Banni , while the least number of blocks on chromosome 28 with 19 blocks . OverallOut of 1,144 LD blocks , 436 , 368 , 326 , 345 , 104 (426 markers), and 81 LD blocks (338 markers) overlapped with QTLs for traits, such as milk production , productwww.nddb.coop) and Central Institute for Research on Buffalo (India), (cirb.res.in) respectively. Surprisingly, no particular pattern was observed linking phenotypic data (literature-based QTLs of milk fat and body weight) with trait-specific marker-based separation.Furthermore, analysis was also performed based on markers overlapping with QTLs of milk fat percentage (143 markers) and body weight (315 markers) using phenotypic recorded data from National Dairy Development Board (India)   although using the reference of ssembly) . The difssembly) . Therefotellites  and usintellites .FST indicated that a pair of Surti and Pandharpuri population has greater genetic distance than other pairs. Pair-wise FST between these buffalo breeds was significantly different from zero (P < 0.05). Genetic differentiation (pair-wise FST) indices observed in the present study are sufficient to explain the fact that these buffalo breeds are geographically well separated from each other, and we had reported a similar observation in our previous study among Western-Central Indian cattle breeds  based clustering between Mehsana with Jaffarabadi and Surti with Pandharpuri  2 and on BTA 23 and concluded a major role of BTA 23 for susceptibility to bovine tuberculosis.In the concordance analysis of exterior traits, majorly the QTLs were associated with udder traits . This information of genotypes could be used to associate phenotypes and perform the selection. Based on the above results, we can assume that exterior traits are less important for the association of QTL with LD block or haplotypes due to the insufficient size of QTL and low proportion of concordant QTL with LD blocks. HE and HO, showed the absence of genetic differentiation while FIS was able to differentiate Banni and Surti animals. Furthermore, FST was able to differentiate the buffaloes as per their respective geographical locations. The levels of SNPs variation in this study could be insufficient to differentiate the other local breed except Pandharpuri, Surti, and Jaffarabadi , so there is a need to develop SNP chip based on SNP markers identified by sequence information of local buffalo breeds. LD block QTLs concordance study could explore a new window for genomic selection in animals and, therefore, the development of new SNP chip based on information of buffalo genome and buffalo-specific genetic technologies is warranted.The study of population structure analysis in Indian buffaloes based on SNPs revealed that the distribution of SNP markers across the buffalo genome of all breeds studied was almost similar. Genetic parameters, such as txab033_suppl_Supplementary_FiguresClick here for additional data file.txab033_suppl_Supplementary_TablesClick here for additional data file.


T. aestivum L.) varieties with a high grain protein content (GPC) due to its greater milling and cooking quality, and improved grain price. Here, we used a genome-wide association study (GWAS) to map single nucleotide polymorphisms (SNPs) associated with GPC in 93 spring bread wheat varieties developed by eight Russian Breeding Centers. The varieties were evaluated for GPC, grain weight per spike (GWS), and thousand-kernel weight (TKW) at six environments, and genotyped with 9351 polymorphic SNPs and two SNPs associated with the NAM-A1 gene. GPC varied from 9.8 to 20.0%, depending on the genotype and environment. Nearly 52% of the genotypes had a GPC > 14.5%, which is the threshold value for entry into high-class wheat varieties. Broad-sense heritability for GPC was moderate (0.42), which is due to the significant effect of environment and genotype × environment interactions. GWAS performed on mean GPC evaluated across six environments identified eleven significant marker-trait associations, of which nine were physically mapped on chromosome 6A. Screening of wheat varieties for allelic variants of the NAM-A1 gene indicated that 60% of the varieties contained the NAM-A1c allele, followed by 33% for NAM-A1d, and 5% for NAM-A1a alleles. Varieties with the NAM-A1d allele showed significantly (p < 0.01) smaller GPC than those with NAM-A1c and NAM-A1a. However, no significant differences between NAM-A1 alleles were observed for both GWS and TKW.Most modern breeding programs aim to develop wheat ( Triticum aestivum L.) plays a key role in most countries worldwide, as a major source of plant protein. According to the FAO data, the area of the worldwide arable land occupied by wheat varieties in recent years is 219 million hectares, of which five major producers and exporters of wheat  account for 50% of the total area (https://www.fao.org/faostat/ (accessed on 28 December 2021)). Although worldwide annual wheat production has increased over the years, reaching 761 million tons in 2021, the demand for wheat has also constantly increased [Wheat  is the most important gene that is located on the short arm of chromosome 6B [Gpc-B1 allele on GPC in common wheat is much less than in tetraploid wheat, this gene is characterized by a stable contribution to the phenotypic variation of the trait under various environments [Of the numerous QTLs associated with GPC, osome 6B ,18,19,20ronments . Howeverronments ,22,23. GNAM-B1 gene has been reported in a few commercial wheat cultivars. For example, screening of 196 common wheat varieties from the INRA core collection showed that only five of them carry the wild-type allele of NAM-B1 [NAM-B1 allele. Other NAM genes have been identified in hexaploid wheat, which includes NAM-A1 and NAM-D1 that are located on chromosomes 6A and 6D, respectively. It is assumed that NAM-A1 performs a similar function as NAM-B1 [NAM-A1 gene-specific markers.The f NAM-B1 . Anotherf NAM-B1  screeneds NAM-B1 ,26,27,28p < 0.001) greater (15.2 ± 0.09) than in Field-1 (14.3 ± 0.1), but both TKW and GWS in Field-2 were significantly smaller than in Field-1  interaction for GPC . GPC was negatively correlated with both GWS and TKW, but the correlation coefficients were low .Analysis of variance (ANOVA) showed significant differences among the 93 genotypes, six environments, and genotype × environment  marker-trait associations (MTAs) for 50 SNPs (QGpc.icg-6A.1), 23.4 Mb, 61.0–61.2 Mb (QGpc.icg-6A.2), 103.7–107.1 Mb (QGpc.icg-6A.3), and 569.9–597.0 Mb (QGpc.icg-6A.4). Candidate gene search using the physical interval of the four QTLs summarized in QGpc.icg-6A.1, 5 genes for QGpc.icg-6A.2, 38 genes for QGpc.icg-6A.3, and 2 genes for QGpc.icg-6A.4 (GWAS performed using the mixed linear model (MLM) and GPC data of individual environments showed significant , QGpc.icg-6A.2 , QGpc.icg-6A.3 , and QGpc.icg-6A.4   C of mark1369_53) D. A posiphysical  map of cNAM-A1 gene [NAM-A1a, NAM-A1c, and NAM-A1d, which were observed in 5.3%, 60.2%, and 33.3% of the 93 varieties, respectively  that were previously reported to detect the allelic composition of the -A1 gene . SNP1 is-A1 gene , the allectively . The NAMNAM-A1b allele was not found. Based on the comparative analysis of GPC in varieties containing different allelic variants of the NAM-A1 gene, genotypes with the NAM-A1d allele showed a significantly (p < 0.01) smaller GPC than those with NAM-A1a and NAM-A1c  consisted of a cluster of 30 SNPs between 63 and 83 Mb on chromosome 6A that explained 2.7–4.8% of phenotypic variation. In the present study, two of the eleven SNPs associated with GPC fell within this range QGpc.icg-6A.2  identified in the present study  that were significantly associated with the GPC recorded in each environment . Each siicg-6A.2 . It shouu et al. ; and (2)osome 6A  reportednt study . Our resT. aestivum genes (TraesCS6A02G132700 (6A:104531253-104533070) is one of the candidate genes that encodes a CBS-containing protein, which is expressed predominantly in the developing grain; this gene is slightly or not at all expressed in the other plant tissues [TraesCS6A02G132700 gene most likely plays a role in grain protein content. In addition to chromosome 6A, we also detected a significant SNP on the short arm of 4A (Ku_c3891_395). According to several studies, chromosome 4A may contain GPC-associated loci, although most QTLs were reported on the long arm of this chromosome [Candidate gene search using the physical interval of the four QTLs summarized in um genes . TraesCS tissues . Since p4A Ku_c381_395. Acromosome ,39.The power of QTL detection depends on several factors, including population size, marker density, population structure, the genetic architecture of the trait, and trait heritability ,44. Our NAM-A1a, NAM-A1c, and NAM-A1d haplotypes in the Russian spring wheat was 5.3%, 60.2%, and 33.3%, respectively  allele. At the same time, 72% of the elite European varieties had a NAM-A1d (T/Del) allele as compared with just 0.3% for the NAM-A1b. Among 51 wheat varieties of the Australian selection, NAM-A1a was most common, but NAM-A1b was detected only in one variety [NAM-A1a allele in the genome of early maturing Australian varieties. On the contrary, the NAM-A1d and NAM-A1b were common in the Pakistan wheat varieties [The proportion of ectively . It shouing date ,49. Corm variety . The dat variety ,51 confiarieties .T. durum, T. dicoccum, T. dicoccoides, T. timopheevii). Four out of six wheat varieties contained NAM-A1d. Among the wild relatives, the most common haplotype was NAM-A1c; NAM-A1b was not identified among the studied genotypes. Combined across all six environments, we found higher mean GPC among varieties with the NAM-A1a than both NAM-A1c and NAM-A1d, which was consistent with the data of Cormier et al. [NAM-A1a haplotypes and higher GPC for non-NAM-A1a haplotypes.Orlovskaya et al.  studied r et al. . Studiesr et al. ,51, on tPlant material included 93 spring wheat varieties and advanced breeding lines (hereafter referred to as varieties) developed by eight Russian Breeding Centers and adapted for cultivation in the Siberian region of the Russian Federation. The list of varieties and their origin is presented in Grain protein content was determined using infrared express analyzer OmegAnalyzer G  using 10 g seeds in triplicate according to the manufacturer’s instructions. The mean grain weight per spike and the 1000-kernel weight were calculated from 25 plants of each variety.In 2016, the weather was dry with monthly temperatures in May, June, and July, on average, 4 °C higher than the long-term values. The total precipitation (140 mm) that fell during the heading and grain-filling period in 2016 was almost 40% smaller than the long-term average values (220.0 mm). Total precipitation during the growing season of 2017 was 271.0 mm, which is 10% more than long-term average values (220.0 mm). In 2019, the amount of precipitation (205.0 mm) was comparable to the long-term. The average monthly air temperature in 2017 and 2019 did not differ from the average long-term values. The growing season of 2018 was characterized by low temperatures in May  and high waterlogging in May–June, compared with other seasons. Therefore, the most favorable weather for yield was in 2017 and 2019.2) across all environments was calculated as:G×E interaction, and residual (error) variance, respectively, while Env and Rep are the number of environments and the number of replicates within each environment, respectively.The phenotype data analyses were performed using R. The two-way ANOVA was performed to determine the significance of differences among the genotypes and the environments using genotypes as a fixed effect, and environments and replications as random effects. Pearson’s correlation coefficients (r) were calculated to explore the association among GPC, grain weight per spike, and thousand-kernel weight. The significance of differences between the mean values of the two sample sets was determined using the Student’s t-test. The broad-sense heritability (Hwww.traitgenetics.com (accessed on 28 December 2021)) that consisted of 13,006 SNP markers that were mapped in the wheat genome [Genomic DNA was isolated from 5–7-day-old seedlings using a modified sodium bisulfite protocol, as described by Kiseleva et al. . DNA purt genome ,32. SNP t genome , while tt genome  on the ft genome .p-values < 0.001 after adjustment for multiple testing using the Benjamini–Hochberg method [Triticum aestivum genome (https://plants.ensembl.org/index.html (accessed on 28 December 2021)).Quantile–quantile and Manhattan plots were generated using the R package “GWASTools” . Signifig method . Linkageg method . LD decag method . Chromosg method  and consg method . The staNAM-A1 gene sequences. The PCR products were separated on 2.0% agarose gel, the target bands were excised from the gel and purified using QIAquick® Gel Extraction Kit . SNP detection was performed by the Sanger sequencing method. PCR products were directly sequenced using a BigDye Terminator v. 3.1 kit  with primers 512-R (5′-TGCTAGCTATACCGTGCGAT-3′) and 1191-F (5′-GGACGTACCATCAACACCAT-3′) reported by Orlovskaya et al. [NAM-A1 gene, respectively. Sequencing products were analyzed at the Collective Use Center “Genomika” of the SB RAS.A pair of previously reported primers, NAMA1F (5′-TAGCTAGCTTGCTAGGGGGAAC-3′) and NAMA1R (5′-CAACTACTGGCTACACTTGCAAA-3′) , were usa et al.  to detecNAM-A1 gene using two SNPs showed that 95% of varieties contained either the NAM-A1c (60%) or NAM-A1d (33%) alleles, with the NAM-A1a allele accounting for the remaining 5%. The results presented here provide researchers with valuable data for selecting parental lines for new breeding starts, and understanding the SNPs that map close to the NAM-A1 gene. However, the physical information (chromosomes and their positions) presented in this paper is based on IWGSC RefSeq v1.0, which may be different from the latest version (IWGSC RefSeq v2.1).Most of the Russian spring wheat varieties had a grain protein content greater than the 14.5% for entry into high-class wheat varieties . Genome-wide association analysis conducted on the mean GPC of the six environments identified significant MTAs on chromosomes 4A and 6A, but most SNPs were mapped at five regions on 6A. Each significant SNP explained between 18.7% and 27.7% of the GPC averaged across the six environments, which may have been upward biased to the relatively small population size. Haplotype screening of the


The antioxidant capacity of six HMV produced using traditional methods and the physicochemical characterization were measured in different vinegar production steps throughout a double spontaneous fermentation process, i.e., without any addition of yeasts or acetic acid bacteria. Their antioxidant capacity was compared with seven fruit commercial vinegars (ComV). Furthermore, the antioxidant capacity was independently measured with the TEAC  assay, aiming at correlating with the electrochemical experimental data. Obtained results from both methods, the electrochemical and TEAC assays, interestingly indicated that all HMV have at least 10 times higher antioxidant activity than ComV. Furthermore, the large range of values for antioxidant capacity in samples of commercial vinegars from apples attested the importance of the raw material quality and technological procedures. The positive correlation between total phenolic content and antioxidant capacity measured by the two type of assays indicated that rose hip homemade vinegar (HMV5) has the highest antioxidant capacity. In contrast, the lowest levels of phenolic compounds and antioxidant capacity were found in apple and persimmon homemade vinegars  which indicated that the type of fruit is crucial towards the production of high-quality vinegars. In this way, the use of traditional processes for the production of fruit vinegars proved to be very promising in terms of producing differentiated vinegars and, concomitantly, reaching high levels of health-promoting antioxidant capacities.In the current study, the antioxidant activity of traditional homemade fruit vinegars (HMV) was estimated by measuring the rate of homogeneous redox reaction with 2,2′-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid radical cation (ABTS Fruit vinegars contain high concentrations of polyphenolic compounds and their final quality depends mainly on the raw material used as a substrate, acidification process, fermentation and ageing procedures employed during manufacturing  solution. The two solutions were mixed and allowed to stand in the dark at room temperature for a minimum of 12 h in order to produce the ABTS radical cation. The ABTS•+ solution was diluted in a ratio of 1:9  with ddH2O to an absorbance, spectrophotometrically determined, of 0.8 at 735 nm wavelength  , dry matter  and antioxidant capacity throughout time in the vinegar samples under scrutiny—HMV samples during fermentation (or intermediate samples) and in the final products of both HMV and ComV—one-way analysis of variance (ANOVA) was used by significance level of all statistical analyses set at α = 0.05. The only studied factor was the fermentation time—more specifically the intermediate samples and final vinegars, i.e., 2, 4, 6, 8, 10, 24, 31 and 41 days of fermentation and at 60 days, for the 6 types of HMV and the final products for the 7 types of ComV. The one-way ANOVA compares the mean values of pH, total acidity, percentage of alcohol, dry matter and antioxidant activity measured through the concentration of ABTS•+ by cyclic voltammetry and TEAC assay in order to determine whether there is statistical evidence that the associated population means are significantly different. When the F-test led to significant differences (at α = 0.05), the one-way ANOVA using Tukey’s post-hoc tests was used to compare differences in the mean values (in HMV and ComV samples) between groups of the variable time (fermentation time and in storage time after fermentation). An α-value of 0.05 was used as a reference for the F- and post-hoc tests.For examination of the dependent variables pH value, total acidity (g/L), content of alcohol . A. A33]. Ap = 0.001) for the sample of persimmon vinegar (HMV6). Indeed, the significant (p < 0.05) lowest and highest levels of TA were obtained in samples of HMV6 and HMV1, respectively.As well known, the decrease of ethanol content usually indicates the completion of the alcoholic fermentation by yeasts and the concomitant initiation of acetic fermentation by AAB , causingp < 0.05), i.e., the highest negative slope of pH values within time  or even some carbon acid  and possibly also due to some but low AAB activity (with release of acetic acid). Nevertheless, such a decrease is not as significant as the one obtained by the acetic acid (pKa = 4.75) production, since the acidification capacity is much higher .,60.59,60 d—i.e., O, 2000)  recommenO, 2000) , the conIn conclusion, homemade fruit vinegars made by consecutive spontaneous alcohol and acetic acid fermentations need to be optimized, to yield acceptable levels of ethanol and acetic acid based on the referential values from Codex Alimentarius  . Having The significantly higher pH value of rosehip vinegar (pH = 3.4) can be attributed to the presence of vitamin C and organic acids . The lowAcetobacter and yeasts [In fact, it is well recognized in the literature the likelihood to positively influence the antioxidant capacity of these vinegars by using different selected strains of d yeasts . Such exd yeasts ,62.Overall, analysis of the antioxidant capacity revealed that traditional homemade vinegars produced by spontaneous fermentations contained successfully higher values of antioxidants in comparison to commercial vinegars . AccordiRosa species by Koczka et al. (2018) [R. spinosissima was characterized by the highest phenolic content. Significantly lower values were found both for R. canina and R. rugosa which showed similar values. Significantly, the lowest TPC level was measured for R. gallica. In the water extract of R. canina, TPC level proved to be significantly higher in the samples from Koczka (2018) [O-glucoside on the other hand, were detected in raspberry extracts while fermented vinegars were characterized by the lowest concentration and the smallest diversity of flavonoids. [p-coumaric acid [p-coumaric acid. Within the flavonols, phloridzin and phloretin were detected as major compounds in apple vinegars [The total phenolic content (TPC) was determined from water and ethanolic extracts from hips of selected . (2018) . Accordia (2018) . The resa (2018) . Their sa (2018) , relativvonoids. ,62. Otheric acid , six phevinegars .This study aimed to compare the production of fruit vinegars by spontaneous fermentation, with the possibility to examine the effect of the fruit matrix on the chemical characteristics of the final vinegars as well as in the antioxidant capacity determined by two different analytical methodologies: cyclic voltammetry and TEAC assay. The results from total phenolic compounds were in strong correlation with the antioxidant capacity of homemade vinegars. The highest amount of total phenolic content  and the highest value for antioxidant activity determined by the two analytical techniques was detected in rose hip homemade vinegar HMV5. In addition, the antioxidant capacity of commercial vinegars produced, both by traditional and industrial methodologies, was assessed and compared with the homemade fruit vinegars.In general, the results for apple and raspberry vinegars interestingly indicated that homemade vinegars had more than 10 times higher antioxidant activity in comparison to commercial vinegars. Furthermore, results from our study indicated a large range of antioxidant capacity levels for different commercial vinegars made from apples. The ratio of each variable between apple commercial vinegars ComVA5 and ComVA3 (28.4) indicated the factors quality of raw material as well as the biotechnological processing as the most contributors for the antioxidant capacity in commercial vinegar from the same fruit. Analyzing the cyclic voltammograms, recorded for each vinegar separately, one could conclude that redox mediator ABTS undertakes electrochemical reaction with antioxidants presented in the vinegars. In the presence of a higher concentration of antioxidant compounds, the electrolytic cell recorded an increase in the intensity of the anodic peak currents and a decrease in the intensity of the peak cathode current. The results from the antioxidant capacity of TEAC assay and cyclic voltammetry were almost identical, and positive correlations found between both techniques indicated that the homemade vinegar from rose hip (HMV5) had the highest antioxidant capacity. The lowest amount of phenolic compounds in apple and persimmon vinegars resulted in the lowest antioxidant capacity which led us to the conclusion that the type of the fruit is a limitation factor for high-quality vinegars. Based on this study, the use of cyclic voltammetry is recommended as simple and robust analytical technique for the determination of the antioxidant activity of homemade and commercial vinegars.


Sheep were among the first domesticated animals, but their demographic history is little understood. Here we analyzed nuclear polymorphism and mitochondrial data (mtDNA) from ancient central and west Anatolian sheep dating from Epipaleolithic to late Neolithic, comparatively with modern-day breeds and central Asian Neolithic/Bronze Age sheep (OBI). Analyzing ancient nuclear data, we found that Anatolian Neolithic sheep (ANS) are genetically closest to present-day European breeds relative to Asian breeds, a conclusion supported by mtDNA haplogroup frequencies. In contrast, OBI showed higher genetic affinity to present-day Asian breeds. These results suggest that the east-west genetic structure observed in present-day breeds had already emerged by 6000 BCE, hinting at multiple sheep domestication episodes or early wild introgression in southwest Asia. Furthermore, we found that ANS are genetically distinct from all modern breeds. Our results suggest that European and Anatolian domestic sheep gene pools have been strongly remolded since the Neolithic. Yurtman, Özer, Yüncü et al. provide an ancient DNA data set to demonstrate the impact of human activity on the demographic history of domestic sheep. The authors demonstrate that there may have been multiple domestication events with notable changes to the gene pool of European and Anatolian sheep since the Neolithic. The wild ancestor is thought to have been the Asian mouflon , which was, by the early Holocene, distributed from west Anatolia to east Zagros. Its close relatives Urial sheep  and Argali sheep (Ovis ammon) are distributed to the east of this region 5. This is evidenced, for instance, by signs of corralling in the central Anatolian site Aşıklı Höyük8 and young male kill-off practices identified in southeast Anatolian Çayönü9 and Nevali Çori11. After 7500 BCE, young male kill-off as well as domestication-related morphological changes, such as small size, became widespread across the Fertile Crescent , as in the 7th millennium central Anatolian site of Çatalhöyük11. Following 7000 BCE, along with other elements of Neolithic lifeways, humans spread domesticated sheep to neighboring regions, including Europe, north Africa, and central Asia5.Archaeological evidence indicates that sedentary human communities were practicing sheep management already by 9000–8000 BCE in an area ranging from central Turkey to northwest Iran3 and genomic evidence13 imply a complex demographic history of domestic sheep. One notable pattern involves the high levels of genetic heterogeneity in domestic sheep. This includes multiple distinct mitochondrial DNA haplogroups found in modern breeds14, as well as high nuclear genetic diversity in sheep compared to that in some other domesticates, such as cattle or dog15. High diversity would be consistent with scenarios where domestication involved multiple centers and/or a large and heterogeneous wild population. A non-exclusive scenario would be major introgression from wild sheep into domestic flocks, a notion supported by zooarchaeological evidence3.Both zooarchaeological data16. Similarly, Asian and European sheep tend to carry higher proportions of mitochondrial DNA haplogroups A and B, respectively19  and west (European)25. Although the first domesticated sheep were likely used for their meat23 and possibly their milk26, they started to be increasingly exploited for their wool in Bronze Age southwest Asia during the 4th and 3rd millennia BCE27. Intriguingly, a comparison of DNA retroelements across modern breeds implies an expansion of SW Asian lineages estimated to date back to the Bronze Age; according to this model, SW Asian sheep with desired traits, such as fine wool, were introduced into local breeds across the globe28. A recent ancient DNA study reports evidence consistent with novel breeds being introduced into Europe during the Bronze Age, coinciding with archaeological evidence for the introduction of wool to this continent29. In later periods, export and admixture of selected sheep breeds into local stocks continued, possibly across the globe12. Indeed, using haplotype sharing information, the most recent common ancestor of domestic sheep breeds has been inferred to date back only 800 generations ago (about 3200 years30)—a result interpreted as evidence for a common origin of all domestic breeds12.At the same time, genomic analyses suggest high degrees of allele sharing across domestic sheep breeds. This has been considered as evidence for the recent spread of sheep with desired traits across the globe, especially within the last five millennia, as part of the “Secondary Products Revolution”5. Analyzing both nuclear polymorphism data and mitochondrial DNA (mtDNA) sequences in comparison with modern-day genomes as well as ancient sheep genomes from central Asia, we find support for the notions that the present-day domestic sheep population has complex origins, and that the western Eurasian sheep gene pool changed considerably since the Neolithic period.We currently lack a solid demographic history model to explain these observations: high diversity, clear genetic structure, and high degrees of haplotype sharing. What is missing is genetic data on the initial steps of domestication and characterization of the early domesticated sheep gene pool. Here, we present the first attempt to bridge this gap by studying the ancient DNA of Neolithic period sheep remains from Anatolia, which partly includes the putative zone of sheep domesticationn = 7) and early to late Pottery Neolithic (n = 173) periods  sheep (7012–4260 BCE) of Kyrgyzstan 32 , we enriched the libraries of these four individuals using hybridization capture, targeting 20,000 SNPs , where ANSi represents one of the ANS individual’s genotype based on either short or long reads, and Modern represents a modern sheep breed’s genotype. We then calculated the Pearson correlation between outgroup f3 values based on short vs. long reads, for each individual. The correlation coefficients were all positive (Spearman correlation coefficient rho = [0.12–0.68]) and nominally significant for TEP62  and Ovis vignei  as outgroups, at six loci (38%), the derived allele was carried by at least one Anatolian Neolithic sheep individual, but never among all ANS , which included 636–1020 SNPs overlapping with the Illumina OvineSNP50 Beadchip 40k SNP set. In the analyses involving f-statistics, we used Argali sheep as the outgroup, after confirming that D-statistics of the form D were all nonsignificant, which suggested that none of the modern breeds used here had received any visible Argali admixture   and southeast (SE) Europe] and eight non-European breeds  Asia, Africa, and east Mediterranean islands) generated on Illumina arrays35 analysis of modern breeds and ANS indicated similarity between ancestry components in ANS and European breeds . We observed that all four ANS individuals’ ancestry component distributions showed a higher correlation with those of European breeds than non-European breeds  , calculating the principal components on the modern breed data and projecting the four ANS and three OBI genotypes onto the space described by the first two components , where ANS1 and ANS2 denote two different ANS individuals and Modern denotes any of the 18 present-day breeds. ANS were consistently genetically closer to each other than to present-day breeds   showed that modern breeds did not show higher affinity to any ANS individual over any other ANS individual   showed no significant affinity between any pair of ANS relative to another individual    than with non-European breeds   revealed that ANS had a higher affinity to European breeds, such that 50% of 80 tests were significant in this direction and no test was significant in the reverse direction (multiple testing adjusted P < 0.05)  Fig. , indicatD-statistics , we pooled the three individuals’ data. In outgroup f3-statistics, OBI showed higher shared drift with non-European breeds (median = 0.245) than with European breeds (median = 0.234) although the difference was not significant (Mann–Whitney U test P = 0.102)  and f3 values, and found no correlation between them . Meanwhile, calculating D, we observed that OBI have significantly higher affinity to the S Asian breeds in our dataset, Deccani and Changthangi (CHA and IDC), relative to European, African, and east Mediterranean breeds  (Supplementary Data We next studied the OBI data using 02) Fig.  Sheet C.D. The two S Asian breeds (CHA and IDC) and one SW Asian  breed showed significant affinity to OBI over ANS in three out of 18 tests (multiple testing adjusted P < 0.05). Intriguingly, D-statistics involving the remaining 15 present-day breeds, including European breeds, were nonsignificant but all showed a trend of higher affinity to OBI over ANS  and D. In all comparisons performed as D, modern breeds consistently grouped with other modern breeds over ANS  , such that in 91% of comparisons modern breeds showed higher affinity to other modern breeds, at least as a trend  and D. Only c.1500 SNPs were available and none of the results were significant; still, we note that in all 18 tests ANS showed a trend of higher affinity to OBI than to modern breeds, but OBI showed higher affinity to modern breeds in 67% of the 18 tests  data. mtDNA, although only informing about the maternal lineage, is more readily available across poorly preserved samples14, and is short enough to be analyzed in ancient samples39 , reaches 26% in present-day central Anatolia, while remaining <5% in west AnatoliaOur combined analyses of genome-wide polymorphism data and mitochondrial DNA from ancient central and west Anatolian sheep provide novel insights into sheep domestication and later dynamics. First, we find that Anatolian Neolithic sheep show significantly higher affinity to present-day European breeds than to Asian, African, and east Mediterranean breeds. This pattern is observed both with respect to genome-wide polymorphism, and also mitochondrial haplogroup composition, with mtDNA haplogroup B predominating in both ANS and present-day breeds from Europe. We further observe that Neolithic and Early Bronze Age sheep from southeastern Kyrgyzstan, compared to Anatolian Neolithic sheep, display a contrasting pattern of genetic affinity: ancient Kyrgyz sheep show greater affinity to present-day Asian sheep relative to European sheep and also carry the mtDNA haplogroup A.40, whereas another section of the gene pool contributed to the OBI sample and modern-day Asian breeds.These results suggest that the east-west genetic structure observed in modern breeds had already partly emerged by 7000–6000 BCE. We hypothesize that one section of the early domestic sheep gene pool, related to our ANS sample, was brought to Europe through the spread of Neolithic domesticates of the 7th and 6th millennia11, populations of local Ovis orientalis may have been domesticated by each region’s Neolithic communities in parallel. The observed genetic structure in domestic sheep would then be reflecting the genetic structure of the local wild sheep involved. Indeed, zooarchaeological studies suggest that sheep and goat domestication likely involved multiple regions of the Fertile Crescent41, a claim recently corroborated by ancient genomes in the case of goat domestication42.One possible explanation for such an early differentiation pattern is the existence of multiple sheep domestication centers in the east and west of the Fertile Crescent, including central Anatolia. During the 9th to 8th millennia BCE, when evidence for sheep and goat management gradually accumulate in the zooarchaeological records3. Similar intogression patterns have been reported for pigs, cattle, and goats, supported by both zooarchaeological analysis3 as well as ancient DNA44. Wild introgression into domestic sheep stocks could also have contributed to the observed differentiation patterns.Alternative scenarios are also possible. For instance, the observed patterns could be explained by domestication of a single genetically diverse wild sheep population; following domestication this may have rapidly split into east and west populations accompanied by severe genetic bottlenecks, leading to the observed structure by the 7th millennium BCE. A single domestication event in the west (or east) Fertile Crescent, followed by rapid introgression by genetically differentiated wild sheep when early domestic populations spread to the east (or west), could also be plausible. Here it is interesting to note that in Anatolian sites dating to the early Chalcolithic , more than one millennium after the initial decrease in sheep body size, sheep body sizes again rise to wild caprine levels, a pattern that has been interpreted as a sign of wild introgressionWe note that determining the exact roles of these three non-exclusive scenarios, i.e. multiple domestication centers, rapid bottlenecks, and rapid introgression from the wild, will require further genetic sampling of ancient sheep, and in particular pre-domestic sheep populations in SW Asia.D-statistics, present-day breeds were consistently closer to other present-day breeds than to ANS. This also included present-day European breeds and is inconsistent with a scenario where ANS would be the sole ancestral population of present-day European breeds.A second, unexpected observation was that in D using only transversion SNPs, we find the same result, i.e. modern-modern affinity to the exclusion of ANS  were obtained from 6 archaeological sites in Anatolia: Pınarbaşı, Boncuklu Höyük, Tepecik-Çiftlik Höyük, and Canhasan III in central Anatolia; Ulucak Höyük and Barcın Höyük in west Anatolia  and one sample at Beta Analytic Inc.  (Table 50 were followed. All equipment and benchtops were frequently decontaminated with DNAaway (Molecular Bio-Products Inc.) or 2% bleach during the experiments. Negative controls were included at each step of the laboratory procedures. During DNA extraction two tubes of 80 mg hydroxyapatite were included as negative controls. The following controls were implemented to track contamination: one negative control was included at the beginning of all extraction sets to monitor modern contamination, and a second negative control was placed at the end of all sets to keep track of cross-contamination. In addition, the order of the samples was always kept the same throughout each extraction. For mtDNA amplification, PCR reactions were prepared in the aDNA laboratory and then immediately transferred to the post-PCR laboratory, which is located in a different building, to carry out amplification and agarose gel electrophoresis imaging. Two negative controls were included in each set of PCR amplification following the same rationale in the DNA extraction procedure. Negative controls were also included during the whole-genome library preparations. All of the negative controls were quantified with Agilent 2100 Bioanalyzer to assess contamination.All laboratory analyses were carried out in a dedicated ancient DNA laboratory at the Middle East Technical University, Ankara, Turkey. Strict measures recommended for aDNA work to prevent contamination51. A small piece of bone was cut out of cleaned bone and then ground to a fine powder with a mortar and pestle. In total, 120–200 mg of bone powder was weighed and placed in a 2-ml screw-top tube. Briefly, 1 ml extraction buffer (0.45 M EDTA and 0.25 mg/ml ProteinaseK) was added onto bone powder and incubated at 37 °C for 18 h in a rotator incubator After incubation, tubes were centrifuged and supernatants were transferred into tubes containing 13 ml binding buffer  isopropanol, 0.05% Tween-20, 90 mM sodium acetate). This mixture was filtered through Qiagen Minelute spin columns to bind and wash DNA which was followed by two consecutive elutions with 50 μl Qiagen EB buffer.Prior to DNA extraction, the outer surfaces of bone samples were cleaned by abrasion with a cutting disk attached to a Dremel tool. A new cutting disk was used for each sample. DNA was extracted twice from each sample at different times following Dabney et al.52 and sequenced these on Illumina HiSeq 4000 platform at low coverage (median c.13 million reads per library)   via in-solution capture hybridization using custom designed12, giving priority to transversions and also including mitochondrial markers and SNPs associated with putatively positively selected regions. Four 60-nucleotide-long probes were designed per SNP, summing up to a total of 80 K probes for 20 K SNPs. Two of the probes, carrying either the reference or the alternative allele, were centered on the SNP, while the other two (carrying the reference sequence) were located at each side of the targeted SNP, following the design by Haak et al.54. The 20,000 SNPs included all 8850 transversions present in the Illumina OvineSNP50 Beadchip variant set, 1237 chromosome X transitions, three mtDNA transitions, and 521 putatively functional SNPs . The remaining 9389 SNPs were randomly selected among OvineSNP50 transitions. The designed probes were produced by Arbor Biosciences Inc.For in-solution-hybridization capture, 20,000 SNPs were selected from Illumina’s OvineSNP50 Genotyping Beadchip SNPs58 and Kılınç et al.34. BAM files from shotgun and capture libraries from the same individual were combined, the residual adapter sequences in FASTQ files were removed and paired-end sequencing reads were merged using AdapterRemoval (v.2.3.1)59, with an overlap of at least 11 bp between the pairs. The merged reads were mapped to the sheep reference genome oviAri3 (Oar_v3.1), which includes 26 autosomal sequences, as well as chromosome X and the mitochondrial genome. For mapping, we used the BWA (v.0.7.15)60 “aln” algorithm. All libraries from the same individual were merged using SAMtools (v.1.9)61 “merge” command, the PCR duplicates were removed using FilterUniqueSAMCons.py52 and reads shorter than 35 base pairs were also removed. The number of mismatches was proportioned to read length and then the reads with a ratio of >10% were removed. Ancient individuals’ BAM files were trimmed from both ends by 10 bp using bamUtil (v.1.0.14)62 “trimBAM” command to avoid postmortem damage at read ends being interpreted as true variants.ANS data were processed following Günther et al.63 “liftover” tool. Of the 49,034 SNPs in the SNP chip dataset, 40,225 could be mapped to the oviAri3 genome version and were used in subsequent analyses. For genotype calling, the SAMtools (v.1.9)61 “mpileup” command was run on BAM files. The data were pseudohaploidised by randomly selecting a single read to represent the genotype58. The data belonging to the three OBI libraries were processed in the same manner as ANS data.We used the The Illumina OvineSNP50 Beadchip SNP panel for genotype calling. This list is based on oviAri1.v1 reference genome coordinates and were mapped to oviAri3 coordinates using the UCSC Genome Browser64. PMD profile graphs were generated using the PMDtools (v.0.60)64 “–deamination” parameter and DNA molecules bearing the postmortem damage signature were selected using PMDtools (v.0.60)64 with the “–threshold 3” parameter.PMD patterns at the first 60 positions at 5’ and 3’ ends of the reads that are specific to ancient DNA and not expected in modern DNA data were quantified by PMDtools (v.0.60)65 library ggplot266, using the “stat_smooth” function to construct a regression curve with the loess method, and a span value of 0.80 (fraction of points used to fit the curve) to control the amount of smoothing. The regression models were constructed for each individual separately. The X chromosome data were not included in the regression model. Confidence intervals (95%) were constructed using a t-based approximation.The molecular sex of the four individuals was estimated by comparing the number of reads mapping to autosomes and chromosome X, after duplicate removal and quality filtering. The number of mapped reads was normalized by the length of the chromosome. Libraries of XY male individuals are expected to have reads mapping to chromosome X at an intensity of 50% compared to those mapping to autosomes (relative to the chromosomes sizes), unlike XX females, which are expected to have 1:1 intensity ratios. We calculated expected values of X intensity and confidence intervals under the null hypothesis of an individual being XX, using loess regression between chromosome size and the number of reads mapped. Graphs were plotted with the R (v.3.5)31 SNP chip dataset, excluding breeds known to have undergone strong bottlenecks (e.g. Soay) or recent admixture (e.g. Creole). This modern genotype data and our ancient genotype dataset were merged using the PLINK (v.1.9)67 “mergelist” command, to produce a dataset with eighteen modern populations and four ANS samples.For comparative analyses, we chose a subset of eighteen modern sheep breeds that would be representative and relevant to our study, out of 74 worldwide breeds (SheepHapMap) included the Kijas et al.68 “smartpca” command with “lsqproject:YES” parameter Components of individuals from eighteen modern populations from the SheepHapMap project dataset were first calculated, and the four ANS individuals were projected onto the first three components. Visualization of the PCA was done by the R (v.3.5)65 library ggplot266.Principal component analyses (PCA) were conducted using EIGENSOFT (v.7.2.0)D-statistics were calculated using the AdmixTools (v.5.1)33 “qpDstat” command. Modern, ancient, and outgroup datasets were merged using the “mergelist” command; extra chromosomes of sheep not recognized by PLINK (v.1.9)67 were excluded by “exclude” command. The ‘Plink’ file format was converted into “eigenstrat” file format by the AdmixTools (v.5.1)33 “convertf” command, before the D-tests were performed. The P values were calculated from Z scores based on the block jackknife procedure33. In order to control for the false discovery rate, multiple testing corrections using the Benjamini–Hochberg method69 for each batch of tests were performed using the R (v.3.5)65 “p.adjust” function.f3-statistics were calculated using the AdmixTools (v.5.1)33 “qp3Pop” command. For calculating outgroup f3-statistics, modern and ancient datasets were merged and converted into eigenstrat file format using the same procedure applied in D-statistics. The significance of the correlation between the resulting f3 values for each pair of ancient individuals and between f3 and f3 outgroup test were calculated using the Spearman correlation test by R (v.3.5)65 “cor.test” function, the significance of the difference between values and European-ANS comparisons vs. non-European-ANS comparisons was calculated using the Mann––Whitney U test by R (v.3.5)65 “wilcox.test” function.Outgroup 35. The 18 modern sheep-breed datasets were first filtered for linkage disequilibrium using PLINK (v.1.9)67 with parameters “–indep-pairwise 50 5 0.05”. All SNPs filtered  from the modern dataset were also excluded from ancient individuals using PLINK (v.1.9)67. Cluster analysis for modern sheep breeds was carried out using between K = 2 and K = 12 components. For every K value, we generated ten replicate runs, repeating the analysis using different random seed numbers. Finally, ADMIXTURE (v.1.3.0)35 “projection” functions were used with random different seeds and again ten runs were performed following, to calculate ANS individuals’ ancestral components based on those calculated from modern breeds. Pong (v.1.4.9)70 was used for visualization. For each modern sheep breed, eight individuals were randomly chosen to improve visualization.Unsupervised clustering analysis was performed using ADMIXTURE (v.1.3.0)12 had identified 31 putatively selected loci using an FST scan across global sheep breeds, comprising the most extremely differentiated 0.1% of genome-wide markers. We compared these ancient sheep genotypes with those of 18 populations of modern domestic sheep breeds , as well as with two outgroups, Argali and Vignei, to identify the ancestral state. Data for both the outgroups and the modern sheep were retrieved from the Kijas et al.31 dataset. We also considered studying putatively selected loci identified by Li et al.16 but since none of the SNPs at these loci were present in our dataset, our analysis was limited to the aforementioned 31 SNPs.Kijas et al.2, 0.25 mM dNTPs, 15 pmol of each primer, 2 ng BSA, and three unit of Applied Biosystems Amplitaq Gold 360 (5 u/µL) DNA polymerase (Fisher Scientific Inc.) Thermocycling conditions were as follows: initial denaturation at 94 °C for 10 min, followed by 60 cycles of denaturation at 94 °C for 30 s, annealing at 53 °C for 45 s, extension at 72 °C for 45 s, and a final extension at 72 °C for 5 min. PCR amplicons were run on 2% agarose gel to observe successful amplification and assess contaminations in negative controls. The 144-bp long fragment of mtDNA corresponding to the positions 15,391–15,534 on the reference mitogenome (AF01040671) was sequenced from ancient samples by using primer pairs from Cai et al.39 Sanger sequencing of the PCR products were carried out by Refgen Inc.Amplification of two sets of aDNA extracts from 180 samples for 144 bp mtDNA control region (CR) was performed in a volume of 20 µL containing, 1× Taq buffer, 2 mM MgCl72 sequence (Supplementary Table 61 “mpileup” command and BCFtools (v.1.9)61, “call” command. For haplogroup assignment based on mitogenome data, VCF format was converted to FASTA format using custom python 3 code53. The best nucleotide substitution model was determined according to the Bayesian information criterion (BIC), as TN93 + G73 using MEGA-X74. We again used MEGA-X74 to construct a neighbor-joining (NJ) tree with 1000 bootstraps.Our general strategy for haplogroup assignment was as follows: (i) In Sanger sequencing, we aimed to produce at least two 144 bp sequences per sample; (ii) for each sample, we compared the sequences to determine any discrepancies; (iii) in case of a discrepancy, we amplified and sequenced the third round, and used the consensus genotype; (iv) in samples where sufficient mitogenome data (>1500 bp coverage) was available, we also used this data to identify haplogroups, (v) in two cases (TEP03 and ULU31) where there was the discrepancy between the Sanger-based assignment and mitogenome-based assignment, which appears to arise due to convergent transitions (homoplasy) at diagnostic positions, we reported the mitogenome-based result (see Supplementary Note 65 “fisher.test” function and the effect size was estimated by Cohen’s w75 by R (v.3.5)65 “cohenW” function.The significance of pairwise comparisons of haplogroup frequency differences between ancient sheep from central Anatolia and west Anatolia with modern domestic sheep from central Anatolia (Akkaraman), west Anatolia (Sakız and Kıvırcık) as well as Asia  and Europe  were tested using Fisher’s exact by R (v.3.5)65 via RStudio (v.1.3)76. All statistical tests were two-sided and nonparametric unless otherwise stated, p-values less than 0.05 were significant. Sample sizes and the number of replicates are reported where applicable. All laboratory procedures and parameters of statistical analyses are reported in the “Methods” section.We performed all statistical analyses in (v.3.5)Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary DatasReporting Summary


Arachis hypogaea) is one of the important legume oilseed crops. Cultivated peanut has a narrow genetic base. Therefore, it is necessary to widen its genetic base and diversity for additional use. The objective of the present study was to assess the genetic diversity and population structure of 96 peanut genotypes with 9478 high-resolution SNPs identified from a 48 K ‘Axiom_Arachis’ SNP array. Korean set genotypes were also compared with a mini-core of US genotypes. These sets of genotypes were used for genetic diversity analysis. Model-based structure analysis at K = 2 indicated the presence of two subpopulations in both sets of genotypes. Phylogenetic and PCA analysis clustered these genotypes into two major groups. However, clear genotype distribution was not observed for categories of subspecies, botanical variety, or origin. The analysis also revealed that current Korean genetic resources lacked variability compared to US mini-core genotypes. These results suggest that Korean genetic resources need to be expanded by creating new allele combinations and widening the genetic pool to offer new genetic variations for Korean peanut improvement programs. High-quality SNP data generated in this study could be used for identifying varietal contaminant, QTL, and genes associated with desirable traits by performing mapping, genome-wide association studies.Cultivated peanut ( Arachis hypogaea L.) is one of the important self-pollinated leguminous and oilseed crops cultivated globally. It plays a vital role in human and animal nutrition by serving as a rich source of protein, oil, essential fatty acids, vitamins, and minerals1. The current polyploidy peanut cultivar is originated from central South America, natural hybridization between two wild diploids with A and B genome from the polyploidy peanut. Precisely, it is an allotetraploid  derived from chromosomal sets belonging to two ancestral species, namely A. duranesis and A. ipaensis 5.Peanut  and A. ipaensis (B subgenome), have been sequenced, and the draft genome sequence is publicly available, thus greatly facilitating peanut molecular breeding research13.America Archeological studies and evolutionary studies have suggested that peanut has been domesticated roughly 3500 years ago. For centuries it has been used in cultivation and breeding selection20. However, earlier studies have used a relatively limited number of peanut accessions, predominantly SSR markers for evaluations. Therefore, it is necessary to explore the genetic variation and diversity among the cultivated peanut species and the genetic resources available for peanut breeding. Although the SSR markers are co-dominant and used predominantly for detecting genetic diversity and variation among the genotypes or populations, they need allele separation that is a tedious process. Recent advances and the development of next-generation sequencing (NGS) and cost-effective approaches in recent decades have suggested that single-nucleotide polymorphic (SNP) markers are superior to SSR markers and other tools. SNP markers have been widely utilized in diversity analysis, evolutionary studies, association mapping, diagnostics, and fingerprinting, thereby contributing to the success of precise and efficient interpretation of genetic resources and improved breeding. SNPs are by far the most predominant biallelic DNA sequence variations occurring in plant genomes. They can facilitate automation and high throughput assay development22. Besides, SNP markers can reduce the error rate in allele calling and enhance the reproducibility of results across laboratories.A precise knowledge of the genetic diversity of cultivated crops is highly crucial for breeding and crop improvement. Thus, additional studies on peanut quality, quantity, and agronomic traits are needed. Several studies on the genetic diversity of cultivated peanut have been performed using different marker systems25. These methods have their advantages and shortfalls. For instance, although the GBS method is cost-effective and rapid, it cannot generate allelic data for all SNPs detected between individual genotypes and lines in a subjected population. Besides, the accuracy of this method is affected by abundant missing data. Thus, it requires a well-established setup of bioinformatics pipelines and imputing algorithms, which may not be possible for all laboratories. Conversely, SNP arrays are more expensive than GBS but having high and robust call rates are used to detect SNPs for all the genotypes/lines26. This is one of the most important prerequisites for genomic selection. It is the key to consistent genotyping of SNP loci across different individual sets in the population compared to subsequent breeding generations. Therefore, several fixed high-density SNP arrays have been developed for diverse plant species, including peanut (58 K Axiom_arachis SNP), rice (Illumina RiceSNP50), maize (MaizeSNP600K Affymetrix Axiom Maize Genotyping Array), soybean , wheat , chickpea (50 K Axiom CicerSNP Array), cowpea (Illumina 60 K iSelect BeadArray), and pigeonpea (56 K Axiom Cajanus SNP)35.Generally, DNA variation in plant genomes is detected using methods such as genotyping-by-sequencing (GBS), whole-genome sequencing (WGS), and high-density fixed arrays36. Similarly, an ‘Axiom_Arachis’ (58 K SNPs) array has been used to assess a reference set of 300 peanut germplasm based on genetic architecture37. Recently, one study has explored evolutionary relationships among 320 peanut germplasm using a tunable genotyping-by-sequencing (tGBS) method19. Therefore, the relationship between genetic resources can be determined using a high-throughput technology based on abundant SNP variations in the genome. Genetic diversity is critical for crop improvement, germplasm protection, and conservation of diversity in domesticated cultivars. Besides, novel alleles can be detected for future breeding and crop improvement programs. Since the launching of peanut breeding program during the early 80’s in Korea, cultivars have been developed based on a traditional pedigree breeding method by selecting the prominent line merely based on morphological evaluation in the field. Thus, we decided to investigate the genetic backgrounds and determine the genetic pool of peanuts in Korea for the framework of future peanut breeding programs. In addition, we attempted to introduce novel genotypes for enhancing the genetic variations of peanuts.In the recent decade, several studies have used different marker platforms including SNP array and GBS to investigate the genetic diversity and population structures of peanuts. For instance, agronomic traits related to domestication have been investigated in 158 Chinese peanut accessions via specific-locus amplified fragment sequencing (SLAF-seq)_Arachis’ 48 K SNPs array. Results reported herein represent the genetic variations among 96 peanut genotypes. Besides, the high-resolution polymorphic SNPs reported in this study can be used to distinguish different peanut cultivars based on Kompetitive Allele-Specific PCR (KASP) markers. These high-resolution polymorphic SNPs can also be used to detect genetic contaminants among the newly released cultivars, create genetic maps, map the desirable traits, and facilitate marker-assisted molecular breeding selection.Thus, the objective of this study was to genotype and interpret the genetic diversities among 96 peanut genotypes, including (varieties and germplasm) widely found in Korea and across the globe using the ‘Axiomhypogaea, 49 var. vulgaris, and 3 var. fastigiata , Republic of Korea Table . These gta Table . The pla38.A total 96 genotypes were grown in the National Institute of Crop Science, RDA, R. Korea. Fresh and young leaves collected from 15-day seedlings were almost uniform with the same age for all plants. High-quality genomic DNA extraction was performed using a Quigene DNA kit  with minor modifications. DNA quality and quantity were assessed as reported previouslyArachis’ 48 K SNP array39 containing highly informative genome-wide SNPs  analysis, the ‘Best Practices’ workflow was used to select the desired samples that passed the QC test. Further, genotype calls were generated using the ‘Sample QC’ workflow, followed by the ‘Genotyping’ workflow for genotyping on the imported cell files . Finally, the ‘Summary Only’ workflow was performed to generate a summary and retrieve SNP data. SNPs with low call rates were removed with selection criteria of DishQC (DQC ) > 0.75 and call rate > 90%. Only high-quality SNPs were selected for further analysis.High-quality DNA of 96 genotypes were used to generate genotyping data with the ‘Axiom_Ps Table , S4. The40 and TASSEL version 5.2.39 software with the default setting.SNP data were used to elucidate the genetic diversity and the genetic relationship among individual genotypes. In addition, SNP information of public domain US mini-core genotypes was downloaded and merged with the Korean set of genotypes. These sets were then merged and designated as Merge set genotypes to assess the genetic diversity. Genetic diversity parameters such as allele frequency, heterozygosity, and polymorphic information index (PIC) were measured using the PowerMarker version 3.2541. We used K = 1 to 10 to investigate the number of populations (K). Evolution genetic clusters were estimated from the replication runs with the burn-in period up to 500,000 and the Markov chain Monte Carlo (MCMC) to repetitions at each K value. In the STRUCTURE, LnP(D) representing the maximum likelihood can be obtained based on the K value. It is generally considered as an optimal number of a subpopulation42.Population structure analysis was conducted using a Bayesian clustering method implemented in STRUCTURE 2.3.4 softwarehttps://tassel.bitbucket.io/) using identity-by-state (IBS) matrix data. Pairwise IBS allele sharing was performed using PLINK V1.90 based on multidimensional scaling (MDS) plot41.Principal component analysis (PCA) was conducted using TASSEL version 5.2.39 software (http://www.megasoftware.net/.) with the neighbor-joining method with the following parameters: a test of phylogeny, bootstrap method; no. of bootstrap replications, 1000; model/method, maximum composite likelihood; substitutions to include, d: Transitions + Transversions; and Gaps/missing data treatment, pairwise deletion. A cluster was manually divided into subclades based on the hierarchically clustered genotype profiles.Phylogenetic analysis were performed to obtain insight into the genetic diversity of peanut genotypes. Meanwhile, a phylogenetic tree was constructed using MEGA software (http://cmpg.unibe.ch/software/arlequin35) to estimate the pairwise genetic distance (FST) for the subpopulations and to calculate the genetic variation between and within populations.To examine the molecular variance among and within the populations, an analysis of molecular variance (AMOVA) was performed. Both Korean set and Merge set of peanut genotypes were further analyzed using Arlequin ver. 3.5.2  around the SNP position Table . ValidatArachis’ 48 K SNP array. A total of 47,837 SNPs were initially obtained for these 96 Korean peanut genotypes based on SNP calling , A. ipaensis (52.0%), and Tifruner (8.0%) . Further SNP genotype data of US mini core were filtered based on the shared or common Affymetrix ID (SNP site) between (US-mini core) and Korean set (96 genotypes) used for the analysis as the merge set  Fig. B. After 0%) Fig. A. Similaet Table  and S2.Fis) of both sets (Korean and Merged) showed a high inbreeding coefficient  of 0.837, a polymorphic information content (PIC) of 0.375. The expected heterozygosity (He) values ranged from 0.020 to 0.500  values ranged from 0.00 to 0.979 . The genetic diversity of the Merge set showed a MAF of 0.998, and a PIC of 0.375, the He values ranged from 0.005 to 0.499  (Table https://tassel.bitbucket.io/). The genetic distance ranged from 0.42 to 0.99, with an average value of (0.90) for the Korean set  from diverse parts of the world  dominated with 8 genotypes from Israel and 6 genotypes from India. The remaining genotypes were from different parts of the world  of the Korean set of genotypes based on high-resolution SNP data. Scatter plots of the principal components displayed a variation of 29.92% for PC1 and 39.74% for PC2 Fig. A. These The AMOVA results showed a population variation of 10.34% whereas individual samples within the population for the Korean set showed a difference of 89.66% Table . LikewisBased on Axiom SNP array data, we identified a few SNPs that could distinguish Korean origin peanut genotypes n = 17) which are currently in use in the Korean peanut breeding program. These selected SNPs were used to develop the KASP primers. Further, all the designated genotypes (n = 17) were genotyped using these KASP markers to validate the results. Results of genotyping analysis yielded expected results as shown in Fig.  which ar46. Recent developments in peanut genomic research, especially after the availability of its reference genome7 and different SNP arrays39 for high throughput genotyping of peanuts, provide a great opportunity for accessing different genetic resources at the genetic level and for advancing the breeding program via multiple strategies.Advances in the genomic research of peanut are limited and advanced molecular techniques are underutilized to manage available germplasm and landraces. However, the elucidation of genetic relationships and genetic diversity among the germplasm or other breeding lines can lead to the precise use of genetic resources for crop improvement and for designing the breeding programA. duranensis (A genome) and A. ipaensis (B genome) as described previously37. Furthermore, pseudomolecule-reference genome-wise distribution of SNP arrays and genome-wise polymorphic SNPs were identified  were analyzed using 9947 high-resolution SNPs. At the same time, data related to mini-core US 111 genotypes were extracted from the public domain and combined with the Korean set of peanut genotypes to generate a Merge set. This Merged set was also used in the analysis of genetic diversity and population structure based on the 4448 polymorphic SNPs.. Results of genetic diversity analysis for the Korean set of genotypes showed FST values that suggested the genotypes in the Korean data set were more closely related. Moreover, Korean sets of genotypes seem to have a mixed lineage from parents that may belong to different geographical origins or gene pools.SNP-based genotyping is widely used due to its accuracy, abundance, and high throughput. In this study, initially, a pseudomolecule-reference genome was identified based on the genomes of two diploid subspecies of cultivated peanut, ied Fig. . This anFST values of the Merge data set suggested that the US mini-core genotypes more diverse compared to Korean genotypes. Moreover, we observed a significant difference between the Korean data set and the mini-core data set  of Korean set of genotype was slightly higher than merged set , determines the probability that two alleles at a given locus in an individual are equal by lineage from the shared ancestors of the two paternities. These results demonstrate that the mini core consisting of merged data have slightly less inbreeding compared to the Korean alone data set. Thus, it could be considered that the genetic diversity for Korean set peanut genotypes was low . Particularly, the Korean set of peanut genotypes (n = 96) were categorized into two or three  groups  were divided into two major clusters (CI and CII). CII was manually subdivided into CIIa and CIIb clades Fig. . These KMoreover, the clusters derived from both sets of genotypes were further validated by PCA Fig. . ResultsOverall, this study suggests that the high inbreeding coefficient and less variability in tested Korean set of genotypes. This might be instigated due to the self-pollination and repetitive selection of these peanut lines over the years during cultivar development and breeding process, which might have consequently reduced the genetic diversity. Therefore, higher similarity and low genetic variability were observed in tested Korean set of genotypes of the current study. This observation is in line with the pollination system and the history of peanut cultivation.55. Thus, we developed KASP marker assays to validate and distinguish important pre-breeding genotypes using a unique SNP set that showed high-resolution calls on the Axiom 48 K SNP assay and polymorphism between selected genotypes. Although the number of markers used for validation was small, the viability and usefulness of these markers were analyzed by selecting fewer SNPs to distinguish Korean origin peanut genotypes (n = 17). Our results were similar to those using Axiom array genotyping, thus confirming and validating results of SNP array (Table 56.The increasing number of individuals and the routine use of population studies in genetics and breeding programs require flexibility and precision in genotyping methods. KASP marker assay is one such genotyping method that has emerged recently. It offers several advantages such as flexibility, robustness, multiplexing, cost-effectiveness, and rapid genotyping of small to large populations with hundreds of markersay Table . ValidatResults of this study showed a low genetic variation in Korean genotypes that might be caused by domestication, intentional selection, reduced population and genetic drift relation with the same gene pool, and breeding for some desirable traits. In the present study, genotype distribution was not found with the category of subspecies, botanical variety, or origin. However, this study revealed that the US mini-core set of genotypes were more diverse and with a negative or lower inbreeding coefficient than the Korean set of genotypes. This suggests that the Korean breeding scheme needs to widen the genetic base of breeding material and create new alleles or gene pool combinations by diversifying the current Korean breeding resources and increasing the breeding population. Besides, some genetically diverse genotypes might be useful for creating new trait combinations, developing a mapping population with desirable traits, improving crops, and generating new cultivars via appropriate breeding approaches. The present study also provides a number of high-resolution polymorphic SNP markers distributed in A and B subgenomes of peanut subspecies. They can facilitate the development of a new marker set for differentiation of varieties. Furthermore, these SNPs can be utilized to develop SNP-based genetic map, mapping applications, background selection, and substantial molecular breeding applications in the Korean peanut breeding program.Supplementary Figure.Supplementary Information.Supplementary Tables.