Based on the CTD database, PPARG, ADAM12, IL6, SMAD3, and TIMP2 were identified to interact with selenium, sodium selenite, and T-2 toxin. KBD was found to be significantly associated with rs12629751 of PPARG , rs1871054 of ADAM12 , rs1800796 of IL6 , rs6494629 of SMAD3 , and rs4789936 of TIMP2 . Immunohistochemistry verified significantly upregulated PPARG, ADAM12, SMAD3, and TIMP2 in KBD compared with OA and normal controls (p < 0.05). Genetic polymorphisms of PPARG, ADAM12, SMAD3, and TIMP2 may contribute to the risk of KBD. These genes could promote the pathogenesis of KBD by disturbing ECM homeostasis.The mechanism of environmental factors in Kashin–Beck disease (KBD) remains unknown. We aimed to identify single nucleotide polymorphisms (SNPs) and protein alterations of selenium- and T-2 toxin–responsive genes to provide new evidence of chondrocytic damage in KBD. This study sampled the cubital venous blood of 258 subjects including 129 sex-matched KBD patients and 129 healthy controls for SNP detection. We applied an additive model, a dominant model, and a recessive model to identify significant SNPs. We then used the Comparative Toxicogenomics Database (CTD) to select selenium- and T-2 toxin–responsive genes with the candidate SNP loci. Finally, immunohistochemistry was applied to verify the protein expression of candidate genes in knee cartilage obtained from 15 subjects including 5 KBD, 5 osteoarthritis (OA), and 5 healthy controls. Forty-nine SNPs were genotyped in the current study. The C allele of rs6494629 was less frequent in KBD than in the controls (OR = 0.63,  Moreover, new cases have been diagnosed in Tibet recently (Kashin–Beck disease (KBD) is an endemic osteoarthropathy distributed throughout North Korea, Siberia, Japan, and China. In the 21st century, this condition has been most prevalent in China. Endemic areas encompass 13 provinces (or city/autonomous regions) and 379 counties from the northeast to the southwest. Monitoring data reported in the 2018 China Health Statistical Yearbook documents 535,878 patients, including 8,540 juvenile cases and more than 104 million residents at risk (recently . The mairecently  have beerecently . Howeverrecently . The aetrecently .Single nucleotide polymorphisms (SNPs) typically indicate disease susceptibility. Recently, a number of studies have suggested that SNPs play a crucial role in revealing the pathogenesis of osteochondral diseases such as OA  and KBD In this study, we selected 49 SNPs of 41 genes to perform SNP genotyping in 129 KBD and 129 normal controls. A Sequenom MassARRAY®SNP analysis was used to detect the associations between KBD and the 49 candidate SNPs. Immunohistochemistry was used to verify the distribution and protein expression of five candidate genes that interacted with selenium, sodium selenite, and T-2 toxin. Our results identify potential SNP biomarkers of selenium deficiency– and T-2 toxin–responsive genes to help reveal the pathogenesis of KBD.All subjects provided informed consent  for sample collection. The study protocol was approved by the ethics committee of Xi’an Jiaotong University .Diagnosis and degree classification of juvenile and adult KBD patients were strictly applied according to the national criteria of KBD in China [WS/T 207-2010]. Patients with OA were strictly diagnosed according to the Modified Outerbridge Classification. All subjects were diagnosed with KBD if they manifested X-ray alterations such as defects and sclerosis on the bone end of phalanges combined with compression changes of the calcaneus and talus and enlarged/deformed limb joints . KBD subjects were excluded if they were suffering or had previously suffered from any other osteoarticular diseases such as osteoarthritis, rheumatoid arthritis, gout, or skeletal fluorosis. Patients with any type of macrosomia, disorder of osteochondrodysplasia, chronic disease , or autoimmune disease were excluded if they had accepted any treatments that could affect the skeletal system within the last 6 months. All healthy controls lacked musculoskeletal pathologies or recent injuries and were selected from the same endemic areas as the KBD cases. All subjects were of Chinese Han lineage from Tongde and Guide Counties in Qinghai Province. The subjects were summoned to the local CDC and township health center for sample collection. Using the inclusion and exclusion criteria described above, we selected 129 patients with KBD and 129 healthy controls for this study. The articular cartilage samples from KBD and OA patients were collected from individuals who underwent arthroplasty of the knee. Healthy controls were obtained from patients who had suffered trauma or amputation due to an accident.For SNP detection, approximately 5–7 ml of venous blood was collected from 258 subjects . The samples were rested for 5–10 min. First, the samples were centrifuged at 1,500×g for 5 min, and the supernatant was collected. The samples were then centrifuged at 12,000×g for 10 min, and the supernatant was collected. The resulting serum was then frozen in liquid nitrogen overnight and preserved at −80°C until testing. None of the samples was thawed more than twice before being analyzed.Because the incidence of KBD has decreased dramatically and there are almost no new juvenile cases, it is extremely difficult to collect juvenile samples for verification. Moreover, the adult samples used here were typical KBD cases that were strictly screened. In addition, the OA patients were all adults. For these reasons, we chose only adult KBD patients for immunohistochemistry (IHC) verification. We included 15 adult subjects  who had undergone arthroplasty of the knee or had died due to accidents or other diseases (not related to osteochondrosis) in Shaanxi Province to verify the protein expression of candidate genes in articular cartilage using immunohistochemistry. The knee joint samples were collected with the informed consent of patients and their families.2 > 0.8 . First, 258 SNP loci of 96 corresponding genes were collected from previous publications. Of these, 49 SNP loci of 41 corresponding genes that were differentially expressed in KBD or OA articular cartilage and/or peripheral blood mononuclear cells were selected for verification. These genes were mainly functional in KBD-associated pathological changes such as apoptosis, extracellular matrix metabolism, aggrecan, collagen, selenoprotein synthesis, and signal transduction. The selection process of candidate SNP loci and their corresponding genes is shown in detail in the research design (We determined the SNP loci reported in the literatures . For unph design .PCR amplification primers and single-base extension primers for the tested SNP loci were designed using Sequenom’s (United States) Genotyping Tools and MassARRAY Assay Design software and submitted to Biotech for synthesis.DNA was extracted from blood samples using a Wizard Genomic DNA Purification Kit . DNA quantification and quality testing were determined by spectrophotometry and agarose gel electrophoresis, respectively. Qualified DNA was adjusted to 50 ng/μl, transferred to a 384-well plate, and stored at −20°C.Sequenom MassARRAY SNP detection was applied to reflect the base difference caused by SNP polymorphisms using the difference in molecular weight. Using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), the molecular weight of the elongated product was determined and processed using MassArray TYPER 4.0 software. The analysis was conducted in cooperation with CapitalBio Corporation .http://ctdbase.org/) provides manually curated information about chemical–gene/protein interactions and chemical–disease and gene–disease relationships mainly based on publications. These data are integrated with functional and pathway data to assist in developing hypotheses regarding the mechanisms of environmentally influenced diseases. This database was used to explore whether selenium, sodium selenite, and/or T-2 toxin interact with a corresponding gene to a candidate SNP locus.The Comparative Toxicogenomics Database  and the subchondral bone, were harvested from the lateral tibial plateau 1 h after operation. Cartilage tissues were fixed with 4% (w/v) paraformaldehyde for 24 h immediately after acquisition and decalcified in 10% (w/v) ethylenediaminetetraacetic acid disodium salt (·EDTA-2Na) for 2–3 weeks. The samples were dehydrated in an alcohol series, cleared in xylene, and embedded in paraffin wax. Paraffin sections were cut into 5-µm sections, mounted on slides, and stored at room temperature until use for staining. The paraffin-embedded sections were baked at 65°C for 1 h, deparaffinized with xylene, and then rehydrated in decreasing concentrations of ethanol. Endogenous peroxidase activity was blocked with 0.3% (w/v) hydrogen peroxide for 10 min at room temperature, and then the sections were washed with 1×PBS. The sections were then incubated in a 10 mol/L urea solution diluted with water at 37°C for 20 min and washed with 1×PBS. The sections were then treated with 2 mg/ml hyaluronidase at pH 5.0 and 37°C for 30 min. Blocking with 5% (w/v) goat serum for 20 min at room temperature was followed by the addition of anti-PPARG , anti-ADAM12 , anti-SMAD3 , and anti-TIMP2  primary antibodies overnight at 4°C with IgG as a negative control. The slides were removed and held at room temperature for 30 min and then washed three times with PBS. Next, secondary antibodies were added at 37°C for 20 min. After washing, SABC reagent  was added, and the slides were incubated at 37°C for 20 min. Finally, DAB (Abcam) staining, haematoxylin slight counterstaining, and neutral balsam fixation were performed. Instead of the primary antibody, PBS was used in each experiment as a negative control. Chondrocytes with brown granules in the nucleus and the cytoplasm were considered to be positive for the target protein.For the SNP polymorphisms of candidate genes in KBD, PLINK 1.90 was used to perform an association analysis based on an additive model, a dominant model, and a recessive model to identify significant SNPs associated with the disease. Specifically, for an SNP at a certain locus, whether a genotype increased the risk of disease was considered. For example, in the GT mutation, G is less prevalent and T is prevalent. When GG, GT, and TT differ in diseases, we designated this as an additive model . The analysis of (GG + GT) vs. TT represents the dominant model of G. The analysis of GG vs. (TT + GT) represents the recessive model of G. In most cases, the less prevalent nucleotide is the disease onset SNP, which invokes the referred dominant model or recessive model. The results of each model can be divided into two parts, adjusted covariates and unadjusted covariates, depending on the presence of uncorrected confounding factors, such as age and sex.For immunohistochemistry, a German semiquantitative scoring system was used to evaluate the staining. The staining intensity and area extent were evaluated separately with a 0 for no staining, one for weak staining, two for moderate staining, and three for strong staining. In addition, the percentage of staining was given a score of 0 (<5%), 1 (5–25%), 2 (25–50%), 3 (51–75%), or 4 (>75%). These two scores were multiplied to obtain the final score for comparison.t-test was applied to determine the difference in continuous variables between two groups. A chi-square test was applied to determine the difference in categorical variables between two groups. A p value <0.05 indicates a statistically significant difference.SPSS18.0 was used for the comparative analyses. A Student’s p = 0.081). There were more juveniles (n = 90) than adults (n = 39) in the KBD group, whereas the opposite situation existed in the control group , dominant , and recessive  models after adjustment for age and sex. SNP rs6494629 of SMAD3 was significantly associated with KBD under the additive  and dominant  models. SNP rs3775296 of TLR-3  was significantly associated with KBD under the dominant model. SNP rs1800796 of IL6  and SNP rs1871054 of ADAM12  were significantly associated with KBD under the dominant model before and after adjustment for age and sex. SNP rs4789936 of TIMP2  was significantly associated with KBD under the recessive model after adjustment for age and sex  were found to interact with selenium, sodium selenite, and T-2 toxin. These chemicals could affect the expression of the above genes at both the gene and protein levels to participate in KBD-associated pathogenic processes such as apoptosis, changes to the extracellular matrix, and ROS reactions. Details of the interactions are summarized in p = 0.016, p = 0.034). This protein was also significantly upregulated in OA compared with the controls (p = 0.041) and showed slight but insignificant upregulation in the middle and deep zones compared with KBD. TIMP2 was significantly upregulated in all zones of KBD compared with OA and the normal control and upregulated in the middle zone of OA compared with the normal control (p = 0.026). The genes ADAM12 (p = 0.002) and SMAD3 (p = 0.036) were significantly upregulated in all zones of KBD compared with OA and the normal control.Immunohistochemistry was used to verify the distribution and protein expression of PPARG, ADAM12, SMAD3, and TIMP2 in adult articular cartilage obtained from KBD and OA patients and normal controls. The gene PPARG was barely expressed in normal controls but significantly upregulated in KBD, particularly in the superficial zone and the deep zone (Gene–environment interactions have been widely used in studying the aetiology and pathogenesis of complex diseases  and havep = 0.0176). Therefore, the upregulation of PPARG might contribute to KBD cartilage damage. Moreover, the treatment of KBD by injecting hyaluronic acid into the knee joint is effective in alleviating arthritic pain and morning stiffness, hence promoting patient quality of life (The gene PPARG (peroxisome proliferator activated receptor gamma), also known as PPARγ, is involved in cell apoptosis and necessary in cartilage differentiation . However of life . Sodium  of life . This coThe gene ADAM12  encodes a member of the matrix metalloproteinases and is involved in skeletal growth and development . It is cSMAD3 (SMAD Family Member 3) is a protein-encoding gene. The SMAD3 protein transmits signals from the cell surface to the nucleus and functions in the TGF-β signaling pathway, which is critical in the proliferation, differentiation, migration, and apoptosis as well as extracellular matrix (ECM) synthesis and degradation . SMAD3 pvia inactivation of wnt/β-catenin signaling  maintains extracellular balance. The SNP rs4789936 on TIMP-2 was observed to decrease the risk of alcohol-induced osteonecrosis of the femoral head (ONFH) in the Chinese Han population under allele, dominant, overdominant, and log-additive models after adjusting for age and sex . Choi etignaling . Compareignaling . TherefoThe results were obtained based on a literature review, Sequenom MassARRAY SNP detection, CTD database query, and IHC verification. However, due to the dramatically decreased incidence of KBD in recent years, the sample size for SNP analysis is relatively small, which indicates that we may have missed other SNP loci indicative of susceptibility to KBD as affected by low selenium and T-2 toxin.The evidence suggests that SNPs and upregulated expression of low selenium– and T-2 toxin–responsive genes, including PPARG, ADAM12, SMAD3, and TIMP2, could participate in the pathogenesis of KBD by disturbing ECM homeostasis. The functions of these genes appear to be linked through the TGF-β and wnt/β-catenin pathways, which needs further investigation.


Coffea arabica L. is an economically important agricultural crop and the most popular beverage worldwide. As a perennial crop with recalcitrant seed, conservation of the genetic resources of coffee can be achieved through the complementary approach of in-situ and ex-situ field genebank. In Ethiopia, a large collection of C. arabica L. germplasm is preserved in field gene banks. Here, we report the whole-genome resequencing of 90 accessions from Choche germplasm bank representing garden and forest-based coffee production systems using Illumina sequencing technology.C. arabica L. reference genome. A total of 11.08 million variants were identified, among which 9.74 million (87.9%) were SNPs (Single nucleotide polymorphisms) and 1.34 million (12.1%) were InDels. In all accessions, genomic variants were unevenly distributed across the coffee genome. The phylogenetic analysis using the SNP markers displayed distinct groups.The genome sequencing generated 6.41 billion paired-end reads, with a mean of 71.19 million reads per sample. More than 93% of the clean reads were mapped onto the Resequencing of the coffee accessions has allowed identification of genetic markers, such as SNPs and InDels. The SNPs discovered in this study might contribute to the variation in important pathways of genes for important agronomic traits such as caffeine content, yield, disease, and pest in coffee. Moreover, the genome resequencing data and the genetic markers identified from 90 accessions provide insight into the genetic variation of the coffee germplasm and facilitate a broad range of genetic studies.The online version contains supplementary material available at 10.1186/s12870-022-03449-4. Coffea [Coffea arabica L. and C. canephora Pierre [C. canephora and C. eugenioides [C. arabica L. is valued for its superior beverage quality [Coffee (Rubiaceae) is an important agricultural crop and is mainly grown as a cash crop in several tropical countries , 2. PresCoffea , the pria Pierre . Arabicaenioides . The culenioides . Besides quality –11 and aCoffea arabica L. is the only coffee species cultivated and exported from Ethiopia [C. arabica L. gene pool, the germplasm found in the country is valuable [C. arabica L. is threatened by various factors that could affect the genetic base for the future breeding program [Ethiopia . It is tEthiopia  and servEthiopia –14. The Ethiopia –18. PresEthiopia . Hence, valuable , and couvaluable –22. Notavaluable , 23. Spevaluable . Despite program . The los program , climate program , 26, and program .Presently, global ex-situ coffee germplasm conservation programs have been implemented in various countries including Ethiopia . Coffee Currently, NGS technology has enabled the identification of genetic variation in germplasm collections . ParticuC. arabica L., studies with SNP markers are scarce and a small number of SNP markers are available for this species [C. arabica L. by whole-genome resequencing. The genomic data and SNP generated in this study could be of great relevance for undertaking various genetic studies.SNP represent single nucleotide change in DNA sequence and are considered the most abundant form of genetic variation , 40. Pre species . This stC. arabica L. accessions that have been maintained at the Choche germplasm bank of the Ethiopian Biodiversity Institute  were used in this study. Genomic DNA was isolated from Silica gel dried leaf material of the 90 accessions originally collected from the garden and forest-based coffee production systems using MagicMag Genomic DNA Micro Kit . The quantity and quality of isolated DNA were checked and analyzed with the NanoDrop2000 spectrophotometer  and 1.0% agarose gel electrophoresis, respectively. Detailed information about each accession included in this study is shown in Additional file The Paired-end libraries with approximately 350 bp insert sizes were constructed from 1 μg of genomic DNA from each accession using Illumina TruSeq or Nextera  kits according to the Illumina manufacturer’s specifications. Whole-genome resequencing was performed for 90 coffee accessions at the Beijing Genomics Institute  using the Illumina Hiseq 2500 Platform .C. arabica L. reference genome using a burrows wheeler aligner (BWA) with default parameters [http://broadinstitute.github.io/picard/) to remove duplicate reads. The Illumina sequencing reads of each accession is deposited under accession number from SRR17316330 to SRR17316419.The filtering of raw reads was accomplished using the FASTQC (version 0.11.3) program. The clean reads were aligned onto the rameters . SAMtoolrameters . Then, tThe mapped reads were used to detect variants (SNP and InDels) using the Genome Analysis Toolkit (version 3.6) software . The annC. arabica L. was performed with the Illumina sequencing platform. The genome sequencing generated 6.41billion paired-end reads, with a mean of 71.19 million reads per sample. After removing low-quality reads, high-quality reads of each accession were mapped onto the C. arabica L. reference genome using a BWA aligner. The percentage of reads mapped onto the reference genome varied from 88.93 to 98.11%. The detailed resequencing information was provided in Additional file Whole-genome resequencing of 90 accessions of The high-quality sequences were used for the identification of SNP, and a total of 9.74 million SNPs were identified in all the 90 coffee accessions. We used the SnpEff  program Based on the nucleotide substitutions, the SNPs identified in the coffee genome were classified into two classes, namely, transitions (A/G and C/T) and transversions . The total transitions and transversions detected were 204,665,968 and 103,693,961, with a transitions/transversions (Ts/Tv) ratio of 1.97. The transition frequency of C/T was more than G/A. The transversion frequency of C/A was higher, similarly within transitions; the C/T transition was higher in number , contained six accessions, of which two were collected from the northwest, while four were collected from the southwest. Cluster II (Purple) consisted of accessions collected from the southwest and the north part of the country. Cluster III (Blue) mainly consisted of accessions sampled from the southwest parts of the country i.e., GUG5, YCG2 and GNG4 sampled from the country’s north, south and southeastern parts, clustered together. Almost all of the south and southeast accession were clustered in group IV (Green). The garden coffee accessions collected from similar geographical areas consistently clustered together in the same group . Only, GNG, GUG, YCG, SAG, GMG and WEG garden coffee samples showed exceptional grouping patterns (Fig. We inferred the phylogenetic relationships of the 90 accessions of C. arabica L. nuclear, mitochondrial and chloroplast genomes yielded a total of 9.74 million SNPs and 1.34 million InDels across 90 accessions. These variants were heterogeneously distributed across the eleven chromosomes of C. arabica L. Such uneven dispersal of variants has also been reported in other plant species such as in Solanum melongena and C. canephora [Presently, whole-genome resequencing is the most convenient approach for genome-wide SNP identification. It provides information of great relevance for crop genetics and breeding . Besidesanephora , 48.C. arabica L. Among these, the Kyoto Encyclopedia of Genes and Genome pathways analysis discovered 11 SNPs that have direct associations with genes encoding enzymes involved in caffeine biosynthesis pathways. These studies demonstrated the importance of SNP in identifying the genetic basis of traits of interest. The resequencing efforts of [Capsicum annuum. Furthermore, [The variant identified by the resequencing study could be used for various studies including marker-assisted selection, genome-wide association studies, phylogenetic and diversity analyses . SNPs alforts of , identifhermore,  highlighhermore, . Thus, tPhenotypic variations in crop plants are the result of variation at the DNA level . SNP is The variant effects that might affect the protein-coding sequences include: synonymous/non-synonymous amino acid replacement, start codon gains or losses, stop codon gains or losses . In thisCoffea spp. into five distinct groups. Further, Silvestrini et al. [C. arabica L. [SNP markers have been employed for analyzing the phylogenetic relationships and differences between genotypes . In thisi et al.  reportedabica L. , 17, 63.Most of the garden coffee accessions collected from the north parts of the country are grouped with southwest forest-based accessions. Whereas, the majority of garden coffee accessions sampled from southern parts of the country are grouped with southeast forest-based accession. Delsuc et al.  reportedC. arabica L. varieties also found the grouping of varieties into different clusters regardless of their geographic origin. The clustering pattern found in this study could indicate the presence of a high level of genetic diversity within coffee accessions sampled from the same geographic origin. Furthermore, the garden coffee accessions sampled from the neighboring regions were consistently grouped with few exceptions (Fig. C. arabica L. germplasm found in Ethiopia has a broad genetic base, and is valuable in developing varieties that could sustain global coffee production.A recent study by Benti et al.  on the Eons Fig. . This miThe availability of reference genomes and the continuous improvements of genetic data analysis methods are fostering resequencing studies. In this study, we performed the resequencing of coffee accessions, which has allowed the identification of genetic markers, such as SNPs and InDels. The SNPs discovered in this study might contribute to the variation in important pathways of genes for important agronomic traits such as caffeine content, yield, disease, and pest in coffee. Moreover, the genome resequencing data and the genetic markers identified from 90 accessions provide insight into the genetic variation of the coffee germplasm and facilitate a broad range of genetic studies.Additional file 1: Table S1. The details of the analyzed 90 accession of C. arabica L.Additional file 2: Table S2. Summary of the C. arabica L. whole-genome sequencing data.Additional file 3: Table S3. Amino acid changes identified by SnpEff software. Rows are reference amino acids and columns are changed amino acids. E.g., Row 'A' column 'E' indicates how many 'A' amino acids have been replaced by 'E' amino acids.Additional file 4: Table S4. Summary count of SNPs with effects on the genome.


Cicer arietinum L.) production. Early flowering genotypes escape terminal drought, and the increase in seed size compensates for yield losses arising from terminal drought. A MutMap population for early flowering and large seed size was developed by crossing the mutant line ICC4958-M3-2828 with wild-type ICC 4958. Based on the phenotyping of MutMap population, extreme bulks for days to flowering and 100-seed weight were sequenced using Hi-Seq2500 at 10X coverage. On aligning 47.41 million filtered reads to the CDC Frontier reference genome, 31.41 million reads were mapped and 332,395 single nucleotide polymorphisms (SNPs) were called. A reference genome assembly for ICC 4958 was developed replacing these SNPs in particular positions of the CDC Frontier genome. SNPs specific for each mutant bulk ranged from 3,993 to 5,771. We report a single unique genomic region on Ca6 (between 9.76 and 12.96 Mb) harboring 31, 22, 17, and 32 SNPs with a peak of SNP index = 1 for low bulk for flowering time, high bulk for flowering time, high bulk for 100-seed weight, and low bulk for 100-seed weight, respectively. Among these, 22 SNPs are present in 20 candidate genes and had a moderate allelic impact on the genes. Two markers, Ca6EF10509893 for early flowering and Ca6HSDW10099486 for 100-seed weight, were developed and validated using the candidate SNPs. Thus, the associated genes, candidate SNPs, and markers developed in this study are useful for breeding chickpea varieties that mitigate yield losses under drought stress.Globally terminal drought is one of the major constraints to chickpea ( Cicer arietinum L.) is the second most important annual grain legume crop predominantly cultivated on residual soil moisture in the arid and semi-arid areas of the world. Global annual cultivation of chickpea is over 14.56 million ha with a total production of 14.77 million tons (–1) through symbiosis , medium-duration , and long-duration (Northern India) environments. In general, chickpea matures in a wide time frame of 80–180 days. However, in 66% of chickpea-growing areas, the available crop-growing season is about 80–120 days as they are exposed to abiotic stresses such as drought and heat toward the grain-filling stage. A major shift in the chickpea area (about 3 million ha) from Northern India  to Southern India  has been observed during the past four decades. As a result, no major boost in the total production of chickpea has been substantiated.Terminal drought is considered as one of the most important constraints to chickpea production, and almost 40–50% yield losses were observed globally see . The num2 progeny derived from crosses made between candidate mutants and the parental line  technologies facilitated understanding of the genetics of complex traits at a faster pace in cereals and legumes . In the tal line . This netal line .Here, we report for the first time in chickpea, deployment of the MutMap approach that enabled us to rapidly identify genes and single nucleotide polymorphisms (SNPs) associated with early flowering and seed size. In addition, we also report the development and validation of markers that can be used for selection in chickpea breeding programs for improving these traits.To identify phenotypically distinct mutant lines for early flowering and larger seed size, a set of 100 mutant lines from a TILLING  population developed through ethyl methanesulfonate (EMS) mutagenesis of desi chickpea genotype ICC 4958 (unpublished ICRISAT) was phenotyped for these traits at ICRISAT .ICC 4958 is a drought-tolerant accession available from the ICRISAT germplasm collection. It was collected from Jabalpur, Madhya Pradesh, India, in 1973, and it was among the over 1,500 germplasm accessions screened for drought resistance at ICRISAT Center between 1978 and 1983. It is being used as a donor parent for introgressing drought tolerance-related traits and that produces high yields in low productivity, short-duration, terminal drought-prone environments, e.g., those in peninsular India .1s were selfed to produce F2 seeds. These F2 seeds were sown in the field during crop season 2017–2018 at ICRISAT. The F2 population was scored for days to flowering (DF) and 100-seed weight (SDW).A set of 45 simple sequence repeat (SSR) markers distributed equally across the genomes was used to identify the genetic similarity among the selected lines. SSR genotyping was performed as described earlier . PCR pro2 individuals using the NucleoSpin Plant II kit . An equimolar concentration of DNA from 15 F2 plants with high phenotypic values was pooled together as high bulk, and similarly, DNA from low phenotypic values was pooled together as low bulk. Thus, four extreme bulks, two for each trait, were prepared for WGRS along with wild-type parent ICC 4958 separately. About 5 μg of the pooled DNA was used for the preparation of a sequencing library of average insert size 200–500 bp, according to the protocol for the Paired-End DNA Sample PrepKit . The library was sequenced to 10X of genome coverage with the Illumina HiSeq 2500 platform .Genomic DNA was extracted from the leaves of FInitially, a reference-based sequence of the ICC 4958 wild type was generated by aligning the sequence data generated to the CDC Frontier reference genome  as descr2 progeny were aligned to the ICC 4958 reference sequence, and SNPs were scored as homozygous SNPs (with SNP index ≥ 0.9) and heterozygous SNPs (with SNP index ≥ 0.3 and < 0.9). We further excluded common SNPs shared by at least two mutant lines as well as G→A or C→T transitions (as they are most frequent in EMS mutagenesis). After identifying the genomic regions harboring a cluster of SNPs with an SNP index of 1, we relaxed the condition of the filter to consider all SNPs  in the region as candidate SNPs for the causal mutation. SNP index plot regression lines were obtained by averaging SNP indices from a moving window of five consecutive SNPs and shifting the window one SNP at a time. The x-axis value of each averaged SNP index was set at a midpoint between the first and the fifth SNP.Paired-end sequence reads of bulked DNA of mutant F1. A total of 82 desi chickpea genotypes (47 for early flowering and 48 for seed size) were selected for marker validation. PCR was carried out in a 5 μl volume containing 10 ng of DNA, 1X buffer, 200 μM dNTP, 2.5 mM MgCl2, 1–5 picomole forward and reverse primers, and 0.1 U of Taq polymerase. PCR was performed using Perkin Elmer 384-well Thermal cyclers  and involved a touchdown PCR. Touch down PCR cycles involved initial denaturation at 94°C for 5 min followed by 10 cycles of denaturation at 94°C for 20 s, 60°C for 30 s, 72°C for 30 s that decreases 1°C per cycle; then 35 cycles of 94°C for 20 s, at an optimized annealing temperature of each primer pair (51–58°C) for 30 s, 72°C for 30 s; final extension of 72°C for 20 min, and hold at 4–10°C forever. The PCR products were checked on 1.2% agarose gel containing 0.5 μl/10 ml ethidium bromide (10 mg/ml) with a 100-bp DNA ladder by running it at a constant voltage of 90 V for 25 min. The amplification was visualized under UV illumination using the Uvi-Tech gel documentation system .The candidate SNPs with a SNP index = 1 were targeted for designing allele-specific markers. WASP, a web-based tool, was used for designing allele-specific primers 1. A tota1 seeds were harvested by crossing ICC4958-M3-2828 and ICC 4958 wild type from July to September 2017 in the greenhouse at ICRISAT. During the crop season 2017–2018, F1s were advanced to F2 and a total of 204 F2 seeds were harvested.Based on phenotyping of 100 mutant lines from the TILLING population, 25 lines that were phenotypically distinct from the wild-type ICC 4958 for flowering time and seed size were identified. In order to identify a mutant line that is > 95% similar to ICC 4958 wild type at the genome level, 25 lines along with wild type were genotyped using 25 SSR markers that are equally distributed across the genome . Based o2 plants were phenotyped for early flowering and 100-seed weight during the crop season 2018–2019 in the field. The MutMap population had high phenotypic variability for both flowering time and seed size (R = −0.13) was observed among these traits  and late flowering  was combined. Similarly, we also combined DNA from 15 F2 progeny that displayed high 100-seed weight  and low 100-seed weight  and subjected them to whole-genome sequencing using the Illumina HiSeq2500 platform  between the amplified and non-amplified genotypes based on their phenotypic values for Ca6EF10509893 and Ca6HSDW10099486 can be visualized in Of 102 candidate SNPs on Ca6, between 9.76 and 12.96 Mb, 74 candidate SNPs were targeted to design primer pairs using WASP (see text footnote 1). A total of 12 allele-specific primer pairs were designed, and the primer sequence information is provided in Early flowering and seed size are the two important traits in chickpea as short-duration cultivars can escape terminal drought and high/low-temperature stresses, and enhanced seed size increases the yield to compensate for yield loss due to drought stress. Although early flowering accessions of desi and kabuli types have been identified from germplasm collections  and supeThe development of climate-resilient cultivars will make small-holder agriculture profitable in the anticipated climate change scenarios. In addition to the integration of multidisciplinary approaches in breeding, the adoption of a 5Gs breeding approach will accelerate genetic gains as well as meet the future demands of nutritious food , 2020. I2 plants selected for a phenotype of interest. Although the MutMap method was initially considered for mapping of monogenic recessive (mutations) gene-controlled traits, it is now possible to map dominant mutations through progeny testing and bulking of homozygous dominant F2 individuals. In total 3,32,395 SNPs were used to develop a consensus reference genome sequence by replacing them at a particular position on the CDC Frontier genome. The sliding window analysis was applied to identify the trait linked SNPs with a SNP index value = 1. The extreme bulks sequenced reads were aligned to a consensus reference genome through a sliding window approach (moving averages). The SNPs in the sequenced F2 population were in heterozygous state, show a 1:1 segregation pattern, 50% SNPs were mutant type, and the remaining 50% SNPs were wild type represented by a SNP index value of 0.5. If the F2 population SNPs are in homozygous condition, then these are linked to the mutant phenotype . A SNP index value of 1 or near to 1 indicates the causal mutant SNP linked to the trait of interest, whereas a value of 0.5 indicates SNP not linked to the trait. The SNPs possess a SNP index value of 1 or near to 1 can be successfully targeted for marker development that can potentially be used in breeding. The number of SNPs among the bulks ranged from 3,993 (EF pool) to 5,771 (HSDW pool). Nevertheless, we identified only 102 candidate SNPs with a SNP index = 1. Interestingly, a genomic region harboring the candidate SNPs for all four bulks was on Ca6. In previous reports on chickpea, major QTLs for flowering time were reported on CaLG04 in the genomic region referred to as “QTL-hotspot.” Nevertheless, major QTLs corresponding to flowering time genes efl-1 from ICCV 96029, efl-3 from BGD 132, and efl-4 from ICC 16641 were mapped on CaLG04, CaLG08, and CaLG06, respectively (QTL-hotspot” was reported on Ca4 from 9.1 to 16.1 Mb (efl1 and efl2 genes mapped by QTL-hotspot” were determined using the primer sequence of flanking markers NCPGR21 and GAA47 using blastn -task “blastn-short.” The marker for efl1 (NCPGR21) was found to be present inside the “QTL-hotspot” at ∼10 Mb on Ca4, whereas the marker for efl2 (GAA47) was found present ∼818.1kb upstream of the “QTL-hotspot” on the genome at ∼8.3 Mb on Ca4. Further, the flowering time genes are distributed throughout the genome and are dependent on the genetic background. Genome-wide distribution of flowering time genes is not uncommon and was recently also reported in cucurbits (We developed a MutMap population by crossing ICC 4958-M3-2828 to ICC 4958 (wild type) to identify the candidate genes and causal SNPs for early flowering and large seed size. A previous MutMap approach and its variants were successfully deployed to localize the position of genes for agronomically important genes in cereals such as rice , sorghumectively . This inectively . The “QT 16.1 Mb . The relucurbits .2+ signals and are reported to be involved in flowering time  approaches and enables the identification of candidate genes and causal SNPs. In the present study, we report 102 candidate SNPs in 22 candidate genes. The candidate genes identified in this study are involved in early flowering as well as enhanced seed size. Further, we also report the development and validation of markers for use with chickpea. Testing of these markers on a large and diverse panel of genotypes will be required prior to use in breeding programs for improving these traits.https://www.ncbi.nlm.nih.gov/bioproject/PRJNA715624.The datasets presented in this study can be found at: MT conceived the project and secured funding. PM performed the experiments and prepared the first draft. BPM developed the MutMap population. DK, PB, and AWK performed the bioinformatics analysis. RP performed the experiments and phenotyped the population. UCJ contributed germplasm for marker validation. RKV, AC, and PS contributed the resources. MT and RKV wrote the review, edited manuscript and finalized the manuscript. All authors read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Ne) of the Central European collection and assessed changes in diversity over time, which revealed a dramatic ~ 97% genetic erosion between 1955 and 2015.Historical wheat landraces are rich sources of genetic diversity offering untapped reservoirs for broadening the genetic base of modern varieties. Using a 20K SNP array, we investigated the accessible genetic diversity in a Central European bread wheat landrace collection with great drought, heat stress tolerance and higher tillering capacity. We discovered distinct differences in the number of average polymorphisms between landraces and modern wheat cultivars, and identified a set of novel rare alleles present at low frequencies in the landrace collection. The detected polymorphisms were unevenly distributed along the wheat genome, and polymorphic markers co-localized with genes of great agronomic importance. The geographical distribution of the inferred Bayesian clustering revealed six genetically homogenous ancestral groups among the collection, where the Central European core bared an admixed background originating from four ancestral groups. We evaluated the effective population sizes ( Triticum aestivum L., AABBDD, 2n = 6x = 42) is traditionally one of the main food sources of humankind and modern cultivars provide 15% of calories consumed every day1. Despite its global impact on food security, domesticated wheat faces critical challenges generated by the changing climate. Climate change manifested in increased temperatures, drought or alteration in rainfall frequency and intensity is already affecting agriculture, posing a further barrier to efficient wheat production2.Wheat  are responsible for the better adaptation to changing climatic conditions. Population genetic studies provided extensive evidence for the greater genetic variation in bread wheat landraces and highlighted them as excellent sources of unaccustomed alleles potentially useful for modern breedingRht-B1b, Rht-D1b) from the Japanese cultivar ‘Norin 10’ during the ‘green revolution’ led to spectacular increases in yield11. ‘Norin 10’ inherited these genes from the landrace ‘Shiro Daruma’. Another example is the old Hungarian wheat cultivar ‘Bánkúti 1201’ developed in the first half of the 20th century, which contributed to the significant diversity and several unique alleles of modern Hungarian wheat cultivars, known for their improved bread making quality parameters14.There are clear examples of how exploiting landraces to introduce novel yield or drought characteristics into modern wheat, have resulted in global economic impacts. For instance, the introduction of dwarfing genes  Tápiószele, Hungary. This important legacy of Hungarian wheat breeding represents a vast and largely untapped source of genetic diversity. These wheat landraces are generally tall, prone to lodging and collectively considered to be highly adaptable to the agro-ecological conditions of Central Europe. They have excellent drought and heat stress tolerance and stronger tillering ability under low nutrition input farming conditions18. By using SNP genotyping, many lines can be cost-efficiently screened at an early stage making it possible to design more effective breeding programs. Such arrays and genotype calling algorithms can successfully identify SNPs across a broad range of wheat as it was demonstrated in populations with hexaploid- or tetraploid backgrounds, from landraces to modern varieties20. When SNPs are chosen from a limited panel, the ascertainment bias for chip data is introduced21. While SNP arrays are susceptible to such bias due to preselection of SNPs in small populations21, their low computing needs for downstream data processing, high call frequency, low error rate, and simplicity of use make them a desirable genotyping instrument. To prevent SNP ascertainment bias, whole genome sequencing should be utilized ideally. However, since small sample sizes affect allele frequency distributions, one may never be entirely free of SNP ascertainment bias22. The study by Chu et al.23 examined the influence of ascertainment bias on array-based SNP markers, which resulted in an underestimate of molecular diversity among wheat populations. Their study revealed that the marker system had a minor effect on the overall image of population structure and the accuracy of genome-wide predictions. They also noted that uncommon markers significantly improved prediction performance; this, along with the assumption that new diversity would most likely be uncommon, implies that minor allele frequencies should be carefully considered when designing a pre-breeding program. Other studies implied that ascertainment bias can also be corrected by incorporating maximum likelihood methods24, Bayesian estimations25 or haplotype statistics1.Modern marker-assisted selection (MAS) programs are based on genetic markers, but their use in wheat breeding was encumbered for a long time by the large genome size and the presence of three homoeologous genomes (ABD). The level of genetic diversity within a population can, however, be measured by single nucleotide polymorphism (SNP) genotyping arrays that offer the anticipated impetus to accelerate wheat breeding26.High-power Genome-Wide Association Studies (GWAS) are also based on information gained from high-throughput SNP genotyping. GWAS enables the identification of markers linked to agronomically important genes and their transfer via MAS from landraces into modern cultivars. For GWAS analysis the population structure needs to be investigated to avoid false positive associations between phenotypes and markersHere we explored the genetic structure, effective population size and the available genetic diversity in the Central and Eastern European bread wheat landraces by using a 20K SNP array. We then compared these landraces to modern wheat cultivars and to gain insight to changes in diversity over time.H) of 0.78, polymorphic information content (PIC) of 0.70, and had a discriminatory power (D) of 0.31 among all investigated accessions. Due to the possibility of ascertainment bias caused by the marker type and selection, we inferred haplotype blocks and matching alleles throughout the genome using chip-based markers27. This was accomplished by partitioning the bread wheat genome into 1984 regions with limited evidence of recombination and just a few common haplotypes. The average number of haplotype blocks per chromosome was 94.48, ranging from 5 to 178 and it was correlated with chromosome size (Supplementary Table Hd)28 was 0.46, similarly to the findings of Balfourier et al.1 using 4,506 accessions and 280,226 genic and intergenic SNPs. The reduced diversity reported when haplotypes are used may be due to the increased number of alleles, especially rare ones, as Balfourier et al.1 also noted. From the selected markers, 15,565 (98.46%) were polymorphic across the two collections considered together, while there were 15,121 (95.65%) and 15,357 (97.14%) polymorphic markers in landraces and modern varieties, respectively. Most of the detected markers  were polymorphic in both collections. Nucleotide diversity (π), defined as the number of nucleotide differences per site between two randomly chosen sequences, was estimated to be 2.6 × 10−3 in the entire collection. Landraces showed a greater value (2.5 × 10-3) compared to modern varieties (1.03 × 10−3) in line with previous estimates30.To compare the variability of modern elite varieties and landraces, we genotyped the two collections using a 20K array containing 17,267 SNPs to identify polymorphisms across the 21 chromosomes of hexaploid wheat. Genotyping resulted in 15,808 high quality SNPs   were found at higher frequencies (> 0.15) among the novel polymorphisms. These alleles are likely responsible for the wide adaptation of the landraces, as shown by previous studies in wheat33, maize34 and rice35.After determining that landraces contain a considerable number of polymorphisms, we were interested in investigating how many of these polymorphisms are unique to the Central European collection. We also assessed whether a geographical bias exists in this regard, resulting in some accessions being more polymorphic than others on a regional basis. Thus, the genotypic scores of Central European landraces were sequentially added to the scores of the accessions consisting of all modern varieties in order to determine the number of novel polymorphisms following Winfield et al.82 range 0–154  reference , photoperiod response genes , as well as several genes associated with disease resistance , suggesting that they might have provided the basis of selection during the breeding programs.A higher number of polymorphisms was concentrated on the homoeologous group 2 of landraces when the genetic origin of the accession was considered  in order to allow further comparison between geographical and genotypic data Fig. . Principrns Fig. , but wer38. In this respect, the first component axis explained the genomic variation found in the modern varieties, while the second component axis explained the variation mostly found in the landraces, in accordance with the ML tree. Interestingly, the Austrian landraces grouped together with the modern varieties in the upper right corner of the PCA plot  followed by three (K = 3) and six (K = 6) with the lowest variance among replicates in the Bayesian analysis. At K =3 the cluster of landraces was divided , and modern varieties were grouped together with closely related landraces (Q3)  grouped together with modern wheat varieties (Q3) in our analysis.A well-supported ML split (> 90%) can also be observed among the modern elite varieties, which divided the accessions into two major groups. This split was less prominent in the PCA, but loosely corresponded with the accessions clustering at the upper right corner of the plot and grouped closer to the first axis. The Bayesian cluster (STRUCTURE) analyses indicated a peak in the mean posterior probabilities LnP (K) at Q3) Fig. b. Betwee40. These statistics provided peaks for K values either at 2 (MedMedK and MedMeanK) or 6 (MaxMedK and MaxMeanK) and provided weaker support at 3. The K = 2 clustering divided the wheat collection into two differentiated groups, with a clear pattern of subdivision among the landraces and modern elite varieties  of each region to the inferred clusters were divided into six distinct ancestral groups. The geographical distribution of the inferred Bayesian clustering was also investigated by projecting the inferred Q scores of the ancestral grouping on a map and on PCA clustering  if they had > 0.50 membership to that group.As our uneven sampling can bias the inferences on the number of Bayesian clusters, efforts were made to have comparable numbers of accessions from all countries evened across chronological groups. We carried out subsampling by removing closely related accessions using PCA clustering, and evaluated the Bayesian analyses based on the statistics described by Puechmaille et al.Q scores ranged from 0.001 to 0.995 for accessions across the six inferred clusters  were predominant among the landraces, while Q2 and Q5 were mostly found among the modern elite varieties. Accessions with > 90% membership to ancestral group 5 were principally modern varieties, while group 2 was dominated by modern varieties from Romania and Bulgaria. This is in line with previous results of Winfield et al.20. Moreover, our ancestral group 5 possibly coincides with their ancestral group 2, mainly found in Western European varieties. The Central European landraces were shown to have admixed origins of four ancestral groups, where group 4 was the rarest—it was found in only 3% of the collection (> 0.50). However, some proportion (< 0.50) of this group was almost always found among the entire wheat collection. It had the highest proportion in a few landraces originating from the Balkans area , and it was dominantly present only in one Hungarian landrace (‘Pitvaros’). Ancestral group 6, with accessions having > 90% membership, was exclusive to Central European landraces. It was highly dominant in the Hungarian landrace group (> 0.50), while it was found to be predominant (> 0.90) in 34% of the accessions.Mean Ne) of the Central European wheat collection between 1955 and 2015 . A similarly small number (~ 30) was estimated by He et al.41 from a larger sample with exome sequencing, demonstrating the accuracy of our estimation despite sparser sampling. The results showed that the effective population size of wheat landraces kept stable at a plateau of 51.03 (95% HPD 69.53–36.85) during the 1980s until a bottleneck appeared and reduced the population size by ~ 21% , with Ne remaining constant in the 1990s. A second bottleneck occurred in the 2000s that reduced the population size by ~ 28% , dropping down to 1.72 (95% HPD 3.75–0.78) by 2015. The population size of modern wheat dramatically eroded by ~ 97% between 1955 and 2015.We estimated the changes in genetic diversity through time and calculated the effective population sizes  with the modern elite varieties stood out in our analyses. Landraces appearing in the most basal position on the ML tree  ranked together with modern wheat varieties in the Q5 ancestral group demonstrating that this line may have contribute to modern breeding.The geographical projection of the p 5 Fig. . DominanNe), which is an important population genetic parameter measuring the genetic diversity that can be maintained among the circumscribed collection. In this respect it defines the size of an ideal collection that would present the same amount of genetic drift as the collection of individuals under study; together with the mutation rate, it determines the number of alleles expected in collection. We detected a dramatic bottleneck in our investigated timeline (1955–2015), which might relate to the selection of a small number of founder lines for modern elite wheat breeding programs that relied on only a very limited number of parental lines in variety development , as major effect MTAs might have already become fixed within the modern wheat cultivars56. This limitation can be overcome by using a very diverse association panel composed of landraces and modern wheat cultivars.The unprecedented availability of large-scale genomic resources, genome-wide association studies (GWAS) are now a valuable alternative to bi-parental QTL mapping defining with high precision the genetic architecture of the quantitative traitsOur investigation revealed an unexplored diversity within the Central European wheat landrace collection and pointed out the regions under a considerable selection during modern breeding. Our study showed the potential of genebank genomics to identify allelic diversity currently missing in breeding programs.Progress in genomics has resulted in new concepts and techniques that have the potential to improve the precision and efficiency of plant breeding. Reference genome assemblies, in conjunction with germplasm high-throughput SNP genotyping or sequencing, can help identify breeding targets that might help secure future food supplies. Significant advancements in plant genome sequencing explain how the availability of such resources, along with gene editing tools is transforming trait identification and modification operations. New techniques for breeding, such as genomic selection and speed breeding, may be able to overcome some of the constraints of traditional breeding. When genetics and genomics are integrated into breeding such as genotyping by sequencing, SNP genotyping, genomic selection, gene editing, rapid generation turnover, and haplotype-based breeding, the pace of genetic gains in breeding programs is projected to accelerate. Our work focusing on the genetic diversity of Central European wheat provided valuable information for understanding the relationships between landraces and modern elite varieties. We facilitated their characterization and determined their population structure and ancestral origins. Our results could enrich breeding strategies for future crop improvement through helping breeders to develop new varieties by reducing pre-breeding activities. Our data can be used, for example, to explore selective sweeps for any specific gene or chromosome region, analyze footprints defining divergence of landraces from distinct ecologies, or identify germplasm groups conserving allelic diversity missing in current breeding programs. The genomic data and analysis tools made public with this paper can assist wheat researchers to discover and use functional diversity that may be essential for meeting these challenges.The plant material consisted of 199 Central European winter wheat landraces originating from 6 countries  and 67 modern winter wheat cultivars originating from 10 countries across the world . Part of the landrace collection originated from dissolved countries such as Yugoslavia  and Czechoslovakia (Czechia and Slovakia). The landraces were collected during 1950-60 and conserved in the Gene Bank collection of the Centre for Plant Diversity (NÖDIK) Tápiószele, Hungary. The modern wheat cultivars used in the present study were registered between 1970 and 2015. The description of the accessions is detailed in the Supplementary Table http://www.traitgenetics.com/en/), using a 20K Illumina SNP chip, which represents a subset of markers from the 90K SNP array and the 35K Axiom Wheat Genotyping Breeders’ Array57. SNPs with greater than 10% missing values and 5% heterozygosity were removed from the subsequent analysis, which left a set of 15,808 high quality SNP markers.DNA was extracted from 6 weeks old seedlings using the DNeasy Plant Mini Kit  according to the manufacturer’s instructions. SNP genotyping was performed by TraitGenetics GmbH  reference were calculated with Geneious Prime. ‘MV Ménrót’ is one of the leading cultivars in Hungary and it is used as a general standard by the National Food Chain Safety Office (Hungary) during the plant variety registration process. The position of several genes of great agronomic importance61 were plotted alongside polymorphic sites to identify co-localizing regions. Circular ideograms from the calculated values were created with shinyCircos62. General descriptive statistics of the SNPs such as the heterozygosity index (H), polymorphic information content (PIC) and discriminatory power (D) was calculated with iMEC63.To determine which landraces are particularly polymorphic compared to the modern varieties, we calculated the genotypic scores for the accessions and the number of additional polymorphisms using the SNP call function of Geneious Prime  with default settings. Polymorphic sites among landraces, as well as their chromosomal positions and allele frequencies were determined through a comparison with all modern varieties, and separately with a small subset consisting of only modern varieties originating from Western Europe or the USA. The average number of nucleotide differences per site 64. Block construction was done based on the confidence interval algorithm of Gabriel et al.65 that considers LD(D’) among the markers or loci within and outside a proposed block. Principal component analysis (PCA) was used to summarize patterns of variations among the wheat accession. The analysis was carried out with Tassel v5 according to Bradbury et al.66 excluding SNPs with minor allele frequency < 0.01. Relationships among wheat varieties were also inferred using a maximum likelihood (ML) approach implanted in IQ-TREE v1.6.1267. The general time reversible (GTR + F) nucleotide evolutionary model with direct base frequency counts was chosen as best-fitting for the dataset inferred with the -TESTMERGEONLY and -AICc options in the built-in ModelFinder68. The analyses were performed using the ultrafast bootstrap approximation (UFBoot2)69 with 1000 replicates to provide relatively unbiased bootstrap estimates under mild model misspecifications reducing computing time and achieving unbiased branch supports. Unrooted trees were visualized with FigTree v1.4.470.Haplotype blocks were constructed for each chromosome separately using HAPLOVIEW 4.271. Despite being orders of magnitude slower, STRUCTURE was chosen as an analysis platform since it performs well with polyploid data and provides an unbiased inference when differentiation is weak72. We used the admixture model with an inner alpha incorporating prior pedigree-based data assuming for the relative mixed ancestry of the accessions from multiple sources. The best fitting number of assumed clusters (K) ranging from 1 to 10 was evaluated performing 10 independent Markov chain Monte Carlo (MCMC) runs of 1 × 106 iterations following a burn‐in period of 1 ×105 steps for each value of K. The best K was chosen based on the estimated membership coefficients (Q) for every individual in each cluster. The best fitting number of assumed clusters (K) was estimated using lnP(D∣K)71 and ΔK rate change in the log probability of data between consecutive K values73, as implemented in StructureSelector74. The results of STRUCTURE are greatly affected by sample size, since the program accounts for the most salient variation. Thus, we also used the metrics MedMeaK, MaxMeaK, MedMedK and MaxMedK40 derived from the posterior probability for each K across multiple MCMC replicates. Given that unbalanced sample sizes among landraces and modern varieties could hamper the recovery of genetic clusters, we subsampled our dataset in order to maintain sample sizes as even as possible using the same model assumptions and parameters. We then used CLUMPAK v1.1.275 to find the best alignment of the results across the range of K values as implemented in StructureSelector and visualized the results using STRUCTURE PLOT v276.For the analysis of population structure, a model-based Bayesian cluster analysis was performed using STRUCTURE v2.3.47 generations using BEAST v2.3.277 and were sampled every 1000 steps, with the first 1 × 106 samples discarded as burn-in. Runs were analyzed using Tracer v1.6, and convergence was verified by assessing effective sample sizes (ESS) of all parameters. Independent runs were combined in LogCombiner v2.3.2.The historical demographic changes of wheat varieties were inferred from the estimate of effective population size (Ne) over time in order to explore temporal fluctuations. This method is a non-parametric approach developed for the inference of past population size changes from genetic data built on a piecewise-linear model of Markov Chain Monte Carlo (MCMC) probability distribution sampling to reconstruct demographic history. The SNP data was loaded to BEAUTi v.2.3.3. to set parameters and specific model criteria. The years when each wheat variety was bred were extracted from archival records and were set as tip calibration points. The initially chosen best fitting GTR nucleotide substitution model was used for Ne estimations, additionally allowing for rate heterogeneity among sites by setting the Gamma Category Count to 4 and estimating the Gamma distribution shape parameter while leaving the substitution rate fixed, which allowed the clock rate to estimate the number of substitutions per site per year. Coalescent Bayesian Skyline was selected as a tree-prior, which divided the time between modern and old landraces into segments estimating Ne for each branching time in the tree. The number of parameter dimensions was set to four, allowing Ne to change three times between the root and present day. Ten Markov chains were run for 1 × 10On behalf of all co-authors, the corresponding author states that the work described is original, previously unpublished research. All the authors listed have approved the manuscript.The plant material used in our study is not regulated by CITES and IUCN. Our sampling followed national and international guidelines for germplasm collection outlined by FAO. Plant material was obtained under the permission of a Standard Material Transfer Agreement (SMTA) of the International Treaty on Plant Genetic Resources for Food and Agriculture.Supplementary Figure S1.Supplementary Figure S2.Supplementary Figure S3.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.Supplementary Table S5.Supplementary Table S6.


Carrot is a crop with a wide range of phenotypic and molecular diversity. Within cultivated carrots, the western gene pool comprises types characterized by different storage root morphology. First western carrot cultivars originated from broad-based populations. It was followed by intercrosses among plants representing early open-pollinated cultivars, combined with mass phenotypic selection for traits of interest. Selective breeding improved root uniformity and led to the development of a range of cultivars differing in root shape and size. Based on the root shape and the market use of cultivars, a dozen of market types have been distinguished. Despite their apparent phenotypic variability, several studies have suggested that western cultivated carrot germplasm was genetically non-structured.DcS-ILP markers and 2354 SNP markers were used to evaluate the structure of genetic diversity in the collection of 78 western type open-pollinated carrot cultivars, each represented by five plants. The mean percentage of polymorphic loci segregating within a cultivar varied from 31.18 to 89.25% for DcS-ILP markers and from 45.11 to 91.29% for SNP markers, revealing high levels of intra-cultivar heterogeneity, in contrast to its apparent phenotypic stability. Average inbreeding coefficient for all cultivars was negative for both DcS-ILP and SNP, whereas the overall genetic differentiation across all market classes, as measured by FST, was comparable for both marker systems. For DcS-ILPs 90–92% of total genetic variation could be attributed to the differences within the inferred clusters, whereas for SNPs the values ranged between 91 to 93%. Discriminant Analysis of Principal Components enabled the separation of eight groups cultivars depending mostly on their market type affiliation. Three groups of cultivars, i.e. Amsterdam, Chantenay and Imperator, were characterized by high homogeneity regardless of the marker system used for genotyping.Ninety-three Both marker systems used in the study enabled detection of substantial variation among carrot plants of different market types, therefore can be used in germplasm characterization and analysis of genome relationships. The presented results likely reveal the actual genetic diversity structure within the western carrot gene pool and point at possible discrepancies within the cultivars’ passport data.The online version contains supplementary material available at 10.1186/s12870-021-02980-0. D. carota [D. carota species [D. carota group is characterized by a high level of diversity and includes several D. carota subspecies; Asian wild group is less complex and comprises mostly D. carota subsp. carota. Western cultivated carrots form a numerous group characterized by high level of diversity in terms of storage root characteristics but are generally orange. Eastern cultivated carrots have more uniform root characteristics but display more variability in terms of colour, as they usually have yellow or purple roots. Further geographic structure was identified by Arbizu et al. [Cultivated carrot is a biennial root vegetable grown around the world in temperate and subtropical regions. It is an outcrossing diploid with a relatively small genome of ca. 473 Mb organized into 2n = 18 chromosomes . To date. carota –8. Popul species –11. Eurou et al. , leadingu et al.  showed cCarrot is a crop with a wide range of phenotypic and genotyping variation that might be of use to breeders. Since the seventeenth century, a lot of breeding efforts have been focused on root traits such as shape, smoothness of the root surface or root integrity , 15. FirDaucus carota Stowaway (DcSto) Miniature Inverted Repeat Transposable Element (MITE) based molecular markers (DcS-ILP) detected substantial variation among carrot plants of different origin and could be exploited in germplasm characterization and analysis of genome relationships. MITEs are non-autonomous DNA transposons requiring the presence of a related autonomous element to be a donor of a transposase inducing their transposition in trans. Global analysis of DcSto MITEs provided evidence for their recent mobility and identified a candidate autonomous element, DcMar1, as a possible source of transposase [Selective breeding improved root uniformity and led to the development of a range of cultivars differing in root shape and size. Based on the root shape and the market use of carrot cultivars, a dozen of market types  have been distinguished  Fig. 1)1). Some nsposase .Fig. 1TDcS-ILPs and SNPs, as we assumed that the former system might be capable of revealing variability which arose more recently, resulting from the transpositional activity of DcSto MITEs.In the present study, we investigated a collection of plants from a range of OP western-type carrot cultivars producing roots of different shapes and representing several varietal groups. We aimed to detect possible genetic structure underlying apparent phenotypic differences among well-established market types. We exploited and compared two codominant molecular marker systems, DcS-ILP markers and 2354 SNP markers, distributed along the nine carrot chromosomes  than for DcS-ILPs (0.253), as well as the expected heterozygosity HE (0.295 for SNPs and 0.239 for DcS-ILPs). HE estimates of the cultivars ranged from 0.115 (LC1) to 0.323 (BE7) for DcS-ILPs and from 0.174 (LO1) to 0.350 (GU3) for SNPs.  to 91.29% (SV1) for SNP markers , whereas cultivars of the Imperator market class were characterized by the highest mean within-group HO  was generally lower than HO and ranged between 0.204 (Chantenay) and 0.267 (Berlikum) for DcS-ILP and between 0.269 (Chantenay) and 0.336  for SNPs. Average inbreeding coefficient FIS for all cultivars was negative for both DcS-ILP (− 0.055) and SNP (− 0.097), again indicating high levels of intra-cultivar heterogeneity. The overall genetic differentiation across all market classes, as measured by FST, was comparable for both marker systems  to 0.332 (LC1 vs. AM1) for DcS-ILP markers, and from 0.052 (NA2 vs. NA3) to 0.323 (LC1 vs. AM1) for SNPs  than among them (29% for DcS-ILP and 32% for SNPs), further underlying the presence of significant amounts of heterogeneity within carrot OP cultivars.The FDcS-ILP genotyping was three, four or seven, with K = 4 being the most probable (ΔK = 61.498), while for SNP genotyping it was three, four and five, with K = 3 being the most probable (ΔK = 133.541). Therefore, a more detailed assessment and comparison of the genetic structure was conducted for K ranging from three to five, and for K = 7.The possible genetic structure within western cultivated carrots was inferred without any prior classification. The entire collection of 390 carrot plants was analyzed using an admixture model-based clustering  was higher for the DcS-ILP data set when K was larger than 3, as compared to the SNP data set  ≥ 0.5) as compared to E) were highest for K2 (0.40), whereas for the clusters more homogeneous with respect to the origin of cultivars - K1 and K3, they were 0.31 and 0.30, respectively  with Q values above 0.85. The seventh group (K7) consisted mostly of cultivars attributed to the Danvers and St. Valery market types. When K = 7, for four cultivars at least one the plants representing the cultivar was assigned to the different cluster than the majority, thus represented different gene pool. Within another eight cultivars at least one of the plants representing the cultivar was admixed (Q < 0.5) and therefore could not be assigned to any of the defined clusters.E between individuals within the assumed clusters were highest for K2 (0.34), whereas for the clusters more homogeneous with respect to the origin of cultivars - K1 and K3, they were of lower values  grouped numerous populations of diverse origin such as Amsterdam, Berlikum, Autumn King in group K1 or Nantes, Imperator and St. Valery in group K2. Within K1 Amsterdam cultivars were characterized by high values of Q with mean of 0.85, whereas cultivars of other types were characterized by lower values of Q, not exceeding 0.77. The overall mean Q value within K1 was 0.74 Table . Eight oDcS-ILP and SNP genotyping data. In general, the majority of total genetic variation resulted from differences between cultivars assigned to the predefined clusters. For DcS-ILPs 90–92% of total genetic variation could be attributed to the differences within the clusters, whereas for SNPs the values ranged between 91 to 93%. For K = 3 and K = 4 the percentage of total variation resulting from differences between the assumed clusters was 7% for SNPs and 8% for DcS-ILPs. When the number of the predefined clusters was increased to seven, the percentage has risen to 9 and 10% for SNPs and DcS-ILPs, respectively.For the most probable numbers of clusters, K = 3, K = 4 and K = 7 AMOVA analysis was carried out on both DcS-ILPs group 4 was the most numerous  was found to be eight for both data sets resulting in comparable results of the classification of cultivars Fig. . 65.39% For SNPs group 2 was the most numerous and comprised 19 cultivars of the Autumn King and Berlikum types together with fodder carrot  carrot , 16, 20.DcS-ILP marker system exploited in the present study might reveal genetic variability which arose more recently as DcSto MITEs show extreme insertional polymorphism within the carrot genome [DcS-ILP panel was previously demonstrated on the collection of 23 OP cultivars of western type carrot [The codominant t genome , 22. Pose carrot . The pane carrot , thus prO, on both cultivar and market class level indicated a significantly higher level of intra-cultivar genetic diversity, mainly contributing to the overall genetic diversity observed in the investigated collection of cultivars. This observation is in accordance with previous studies of Maksylewicz and Baranski [The results of the AMOVA, together with high values of HBaranski , as theyBaranski  and MaksBaranski  indicateE) among the 11 predefined market classes, whereas cultivars classified to the Berlikum, St. Valery and Imperator types were among the most heterogenous. STRUCTURE clustering led to the decrease in the most probable number of groups from 11 predefined market classes to the most probable three to five or seven clusters. Non-model DAPC grouping also indicated the lesser number of relatively homogenous groups in the examined collection of cultivars. The choice of the most probable number of clusters was more ambiguous for DcS-ILP markers as the differences in the ΔK value were relatively small, but generally the increase of the number of defined clusters resulted in lower fraction of the unassigned cultivars together with the increase of the average membership coefficient (Q) within the clusters. This tendency was reversed in the case of SNP genotyping data. The more clusters were extracted, the lower mean values of Q within the groups were observed. Nonetheless, most of the cultivars belonging to the Amsterdam and Chantenay types were always clearly separated from other varieties and characterized by the highest values of within-group Q together with the lowest within-group HE. According to the classification of carrot market types proposed by Banga [Utrecht Forcing among them. The modern Amsterdam OP cultivars are believed to be direct descendants of the Utrecht Forcing [Cultivars classified as Chantenay, Amsterdam and Paris Market types showed lower gene diversity  with cultivars of the Danvers type could be in the use of the most easily accessible parental breeding material originating from North America. It highlights possible discrepancies between the passport data attributing a cultivar to a particular market class relative to its phenotype and its actual pedigree. Ma et al. [To date, there is no evidence pointing out that any other breeding material was used to develop Amsterdam cultivars. It is in accordance with our results of STRUCTURE and DAPC clustering, indicating a relatively strong distinctiveness of Amsterdam carrots from other market types, possibly allowing preservation of its specific agricultural characteristics. The admixed nature of lande cv .. Howeveby Banga . The twobunching . The rela et al.  reportedThe aim of the current study was to detect possible genetic structure underlying phenotypic differences among market types of western carrot. We exploited two molecular marker systems, SNPs and TE insertion-derived DcS-ILPs, to provide the tool for time- and cost-efficient evaluation of larger datasets. Both marker systems enabled detection of substantial variation among carrot plants of different market types, therefore can be used in germplasm characterization and analysis of genome relationships. Both model-based STRUCTURE clustering and non-model DAPC grouping indicated the reduction in the number of relatively homogenous groups of OP cultivars in comparison with the classification based primarily on phenotypic traits. The presented results likely reveal the actual genetic diversity within the western carrot gene pool and point at possible discrepancies within the cultivars’ passport data.: Table S10). A total of 390 plants representing 78 OP western-type carrot cultivars (five plants per cultivar) of various tap root shape and market types were grown in the field in Gołębiew (Poland), in 2014 and 2016, under standard agricultural practice, optimally irrigated, fertilized and protected from pathogens. DNA was isolated from fresh young leaves using a modified CTAB protocol .Carrot open-pollinated cultivars used in this study were obtained from the Warwick Genetic Resource Unit (WGRU) , 2 (occupied insertion site) or 0 (lack of amplification). The codominant marker matrix with diploid individuals was created , number of effective alleles (Ne), observed heterozygosity (HO) and expected heterozygosity (HE) were calculated for both DcS-ILP and SNP codominant marker systems using GenAlEx 6.51 [ST was estimated using FinePop2 R package [DcS-ILP and SNP genotyping data matrices. The length of the burn-in period was set to 100,000 and the number of Markov Chain Monte Carlo (MCMC) replications after the burn-in were assigned at 500,000 for each number of clusters (K) set from 2 to 11 (the number of the predefined market classes). Five independent iterations with an admixture and correlated allele frequencies model were performed for each simulated value of K. no prior knowledge about the origin of the analyzed populations was used. The most informative K was identified using ΔK value as described by Evanno et al. [ST estimations were calculated in GenAlEx 6.51. Analysis of molecular variance (AMOVA) was also carried out on codominant genotyping distance matrices in GenAlEx 6.51 with 999 permutations. To provide an assessment of genetic diversity of the studied collection without prior assumptions on the population structure, we conducted Principal Component Analysis (PCA) followed by Discriminant Analysis of Principal Components (DAPC) using adegenet 2.1.1 R package [DcS-ILP and SNP datasets. In DAPC, the optimal numbers of retained principal components were determined using a cross-validation . 150 and 60 principal components explaining 76% (SNP) and 89% (DcS-ILP) of the total variance were retained for the DAPC analysis of SNP and DcS-ILP datasets, respectively. The number of groups was determined de novo using find.clusters function implemented in adegenet. The optimal K was selected based on the decreasing values of Bayesian Information Criterion (BIC). Individuals were then assigned to the clusters.Genetic diversity indices such as: number of alleles (NlEx 6.51 . Pairwis package . Genetic package . Bayesiao et al. . To eval package . AnalyseAdditional file 1: Figure S1. Genomic distribution of SNP and DcS-ILP markers on nine chromosomes of the carrot genome; Legend: The black vertical bars correspond to the position of SNP and DcS-ILP markers; the names of DcS-ILPs are bold maroon.Additional file 2: Table S1.DcS-ILP panel summary statistics for the analysed collection of carrot cultivars.Additional file 3: Table S2. SNP panel summary statistics for the analysed collection of carrot cultivars.Additional file 4: Table S3. Comparison of the percentage of polymorphic loci observed within analysed cultivars.Additional file 5: Table S4. Mean HO and HE estimates and pairwise FST values obtained for the predefined market classes.Additional file 6: Table S5. Pairwise cultivar estimates of FST based on 93 DcS-ILP markers.Additional file 7: Table S6. Pairwise cultivar estimates of FST based on 2354 SNP markers.Additional file 8: Table S7. Estimation of the optimum number of clusters based on Evanno’s ∆K method.Additional file 9: Table S8. Results of DAPC grouping carried out on DcS-ILP genotyping data.Additional file 10: Table S9. Results of DAPC grouping carried out on SNP genotyping data.Additional file 11: Table S10. Characteristics of the 78 carrot cultivars used in the study.Additional file 12: Table S11. Description of 93 DcS-ILP markers used for genotyping the collection of 78 OP carrot cultivars.Additional file 13: Table S12. The codominant DcS-ILP marker matrix obtained for the collection of 390 carrot plants.Additional file 14: Table S13. The codominant SNP marker matrix obtained for the collection of 390 carrot plants.


Massively parallel sequencing following hybridisation enrichment provides new opportunities to obtain genetic data for various types of forensic testing and has proven successful on modern as well as degraded and ancient DNA. A customisable forensic intelligence panel that targeted 124 SNP markers  was used to examine biogeographic ancestry, phenotype and sex and Y-lineage in samples from different ethnic populations of Pakistan including Pothwari, Gilgit, Baloach, Pathan, Kashmiri and Siraiki. Targeted sequencing and computational data analysis pipeline allowed filtering of variants across the targeted loci. Study samples showed an admixture between East Asian and European ancestry. Eye colour was predicted accurately based on the highest p-value giving overall prediction accuracy of 92.8%. Predictions were consistent with reported hair colour for all samples, using the combined highest p-value approach and step-wise model incorporating probability thresholds for light or dark shade. Y-SNPs were successfully recovered only from male samples which indicates the ability of this method to identify biological sex and allow inference of Y-haplogroup. Our results demonstrate practicality of using hybridisation enrichment and MPS to aid in human intelligence gathering and will open many insights into forensic research in South Asia. In forensic investigations, massively parallel sequencing (MPS) with the ability to genotype multiple markers in various biological samples in a single assay with small DNA amount, delivers the potential to enhance human identification and forensic intelligence gathering. It also provides benefits in a number of areas such as admixture analysis, solving complex paternal/maternal cases. leading to an increase in the performance and cost/time-effectiveness of sensitive legal cases . IdentifThe potential of single nucleotide polymorphisms (SNPs) to be utilized as genetic markers has made them enormously popular especially in the field of forensic DNA analysis because of various qualities they possess such as automation ability, small fragment length and frequency in the genome . SNPs arFor forensic identification purposes targeted enrichment combined with massively parallel sequencing has been explored recently which targets mtDNA and nuclear SNPs , 6. CommThe aim of the present study is to explore the implementation of emerging target enrichment and massively parallel sequencing technologies to genotype forensically relevant SNPs in samples from different ethnic populations of Pakistan. We used a customized 124-SNP forensic intelligence panel that offered a combined suite of phenotype, biogeographic ancestry and Y-chromosome (Y-chr) SNPs for comprehensive biological profiling.A step by step workflow for the experimental lab work is presented in Blood samples were collected from twenty-eight unrelated healthy male and female individuals belonging to different ethnic populations  of Pakistan. Written acceptance was obtained from all donors with approval from the Institutional Bioethics Committee (IBC) No. #BEC-FBS-QAU2018-4. Donors had a self-declared ancestry, sex, and different combinations of eye and hair colour. DNA was extracted using a PureLinkTM Genomic DNA kit  following the manufacturer’s protocol.Genomic DNA was sheared, converted into truncated Illumina libraries and enriched (via hybridisation to 5’ biotinylated 120-mer DNA oligonucleotides (xGen Lockdown Probes) as described by Bardan (2019) . A totalEnriched DNA for all 28 samples were combined into a single pool at 5nM concentration prior to paired end sequencing using Illumina MiSeq V2 with read length 2x150 base-pairs (300 cycles).After sequencing of samples, reads were filtered according to the standard Illumina protocol at AGRF  to remove low-quality clusters, and de-multiplex by index. The raw Illumina reads were refined using the PaleoMix v1.0.1 pipeline of Schubert et al. (2014) . Dual inFor prediction of hair and eye colour in the study samples, 23 SNPs were analyzed using the prediction model from the HIrisPlex  DNA Phenhttp://www.phylotree.org/Y). Geographical affiliation was assigned based on the classifications and frequencies defined in previous studies [A SNP profile was generated for each individual against thirty-five Y-chr SNPs to identify biological sex. For males the Y haplogroup was defined according to diagnostic ancestral and derived SNPs in PhyloTreeY described by Van Oven et al, 2014 : http:// studies , 33, 34.For biogeographic ancestry (BGA) assignment of each target sample, 67 ancestry informative SNPs from each sample genotype were compared to a reference population data set consisting of genotypes from 368 individuals belonging to different regions i.e. 99 individuals from African population (AFR), 89 from East Asian population (EAS), 88 European (EUR), 64 Native American (AMR), and 28 from Oceanian (OCE) populations. Genotypes of reference population were collected from the 1000 Genomes Project Consortium and Stanford University HGDP-CEPH  datasetshttps://doi.org/10.25909/17469443.v1 [DNA was successfully extracted from the samples and after fragmentation, DNA libraries were constructed for each sample prior to hybridisation enrichment and MPS. All 125 SNP markers of the custom enrichment panel were retrieved from twenty-eight samples without recovering any SNP data for negative controls. This SNP dataset is deposited in repository Figshare and can be found at 69443.v1 .All the 35 Y-chromosome SNPs were recovered from all twenty-one male samples. No Y-chr SNPs were called for any of the female samples. Genotype data for all samples has been provided in From each of the twenty-eight DNA samples, all phenotype SNPs were obtained successfully. The HIrisPlex correctly predicted eye colour for reported blue and brown eye colours as summarized in From each of the twenty-eight samples, all 67 biogeographic ancestry SNPs were obtained successfully. All likelihood ratios were at least 1 billion times more likely EUR one population over any of the other four populations, with the exception of K3 and P12 . In PCA Human identification is a complex process that is important for social and legal reasons. In forensic investigations, MPS can enhance the potential of human identification and help resolve mixture complexities . For SNPPredicting physical characteristics from DNA as a biological source termed as forensic DNA phenotyping has gained popularity within forensics due to the potential intelligence information it can provide , 41. ThiAll 67 biogeographic-ancestry SNPs were successfully retrieved from all twenty-eight samples under study. Comparative study for the target sample’s SNP genotype data versus available reference population data showed that all likelihood ratios were at least 1 billion times more likely one population over any of the other four populations, with the exception of samples K3 and P12. Use of some additional SNPs especially enlightening for pairwise differentiation of east and south Asia’s populations will boost the ability of the panel to differentiate between these populations. Moreover, the reference dataset used for comparison included 89 individuals from EAS population which were JPT: Japanese in Tokyo, hence it is the only representation for EAS group. Inclusion of population genotype data from various countries and ethnic groups of Asia especially Pakistan and neighbouring countries for representation of east and south Asian population groups in the reference dataset could improve final predicted results and clearer biogeography-ancestry estimation. Snipper has also limitation in accurately accounting for admixture, hence it can be concluded that samples under study showed an admixture between EAS and EUR ancestry.Research has been dedicated for many years on the human Y- chromosome and its variation analysis especially targeting YSNPs. This effort resulted in establishing a well-defined Y chromosome phylogeny. The rise of MPS approaches in recent times is facilitating the discovery of new YSNPs which are in turn increasing resolution to discriminate between closely related Y-haplotypes. The Y-chromosome being haploid and largely non-recombining in nature, is widely used as a marker in many disciplines including forensics research , 50, expWhole genome sequencing is the only method that allows the simultaneous detection of all types of variations within a genome. In a single assay, a wide range of applications can be examined with the downstream analysis providing information about targets that need close examination. But as reads with bad quality were dropped prior to analysis, and whole genome approach yields less coverage in comparison to targeted approach which sequence only loci of interests. Targeted approach identifies those variants that get skipped as a result of whole genome sequencing . It elimIn an attempt to analyze various marker types together in one analytical workflow for forensic human intelligence information, a novel customisable hybridisation enrichment forensic intelligence panel has been used in the present research which provided new avenues and opened many insights to forensic human identification. This panel facilitates a technical approach that permits the possibility of using customisable SNP marker sets relevant to the question under study for hybridisation enrichment prior to MPS. The panel has distinguished biogeographic ancestry of each study sample between five major continental populations by successfully targeting 67 ancestry informative markers. Y-chr SNP analysis helped in sex determination and assigning haplogroups. Retrieval and analysis of externally visible characteristics (EVCs) such as eyes and hair colour has been achieved by targeting genomes with 23 phenotype markers and HIrisPlex phenotyping tool results match well with previously established success rates. SNPs that are helpful for prediction of more external physical traits, SNPs for biogeography lineage prediction or any additional SNPs that can facilitate in forensic research can be used as individual or in combined customisable panel to facilitate advanced outcomes. An example is recent introduction of HIrisPlex-S system that covers additional 17 SNPs in its panel that facilitates prediction of skin colour along with hair and eyes. The overarching objective of the present research was to explore and use the latest techniques to increase the likelihood of drawing inferences regarding phenotype and lineage from modern human DNA for forensic investigations in Pakistan.S1 File(DOCX)Click here for additional data file.S2 File(DOCX)Click here for additional data file.S3 FileDataset shows genotypes of study and reference samples for 67 biogeographic SNPs. First row indicates number of samples, Total number of SNPs, number of populations, rs-IDs for SNPs, respectively column wise.(DOCX)Click here for additional data file.