Bemisia tabaci Mediterranean  that influence host fitness by altering stylet probing behavior. We investigated the probing behavior of B. tabaci MED infected with the facultative endosymbiont Candidatus Cardinium hertigii ). We generated genetically similar Cardinium-infected (C*+) and uninfected (C-) clonal sublines and analyzed the probing behavior of newly emerged adult on cotton , Gossypium hirsutum L., using electropenetrography (EPG). The C- subline demonstrated a longer duration of E2 (2.81-fold) and more events of E2 (2.22-fold) than the C*+ subline, indicating a greater level of sustained ingestion of plant phloem. These findings provide insight into the fitness costs (fitness of a particular genotype is lower than the average fitness of the population) of the Cardinium-infected B. tabaci.Facultative endosymbionts can affect the growth, physiology, and behavior of their arthropod hosts. There are several endosymbionts in the invasive whitefly  Wolbachia infection can negatively influence mosquito blood-ingesting ability (Acyrthosiphon pisum Mordvilko (Hemiptera: Aphididae)  . UnderstBemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae), is an invasive species that causes substantial losses in crops worldwide. It damages tomato, cucumber, eggplant, and collards  has replaced B. tabaci Middle East Asia Minor 1 , which was first introduced into China in 2003. It has now become the dominant species throughout China  and secondary symbionts (S-symbionts) and each has an important ecological and evolutionary role. The P-symbiont, Portiera is confined to bacteriocytes and is essential for the survival and development of the host insect , Hamiltonella, Fritschea, Arsenophonus, and Rickettsia, can manipulate various physiological characteristics of their hosts, and differ among cryptic species  is a maternally inherited, facultative symbiont in B. tabaci MED (Cardinium infection (<17.3%) occurs in B. tabaci MED. Cardinium-infected (C+) has less competitive ability and fitness than Cardinium-uninfected (C-) , represented by waveforms (B. tabaci biotypes. Cardinium-uninfected (C-) and Cardinium-infected (C+) sublines of B. tabaci MED showed differences in probing behaviors. The total duration of probes  after the 1st E of the C+ subline was significantly lower than that of the C- subline  is an electronic technique used to record the real-time probing behavior of phloem sap-ingesting insects, such as aveforms . This teaveforms . Jiang e subline . This inCardinium is harmful to the probing behavior of B. tabaci. Therefore, we studied the differences in probing behaviors between Cardinium-uninfected (C-) and Cardinium-infected (C*+) B. tabaci MED sublines, after eliminating the influence of different genetic backgrounds. We reared different pairs of C- and C+ whiteflies in the laboratory, as well as an introgression subline of Cardinium-infected (C*+) whiteflies from Shandong Province. The probing behaviors of C- and C*+ whiteflies on cotton plants were examined using EPG, because cotton is an important crop in Shandong Province. Our results supported our hypothesis. They provide information about the mechanisms involved in the fitness decrease in Cardinium-infected B. tabaci MED’s, and provide a reference for studying the interaction of plants and insects.We hypothesized, based on the research of Gossypium hirsutum L., cv. ‘Lu-Mian-Yan 21’ produced at the Shandong Cotton Research Center , was used in the probing behavior study. The plants (2–3 true leaf stage) were grown in isolated screen cages  in plant rearing chambers under controlled environmental conditions  and were watered every 5–7 d.Cotton, Bemisia tabaci MED whiteflies were initially collected from Shouguang, Shandong Province in August 2017. These whiteflies were reared on cotton plants in isolated screen cages in climatic chambers under controlled conditions . Approximately 200 field-collected whiteflies were transferred onto a cotton seedling  until B. tabaci had laid eggs. Then the progeny were collected, and the parent and five offspring were frozen and scored for Cardinium infection using PCR. The primer used to detect Cardinium was CFB-F (5′-GCGGTGTAAAATGAGCGTG-3′) and CFB-R (5′-ACCTMTTCTTAACTCAAGCCT-3′) (B. tabaci into Cardinium-uninfected (C-) and Cardinium-infected (C+), and reared them in different mesh cages. In this way, C- and C+ sublines were established from Shandong origins. Tests proved that all C- and C+ sublines were infected with the obligate nutritional symbiont Portiera and the facultative symbionts Rickettsia and Hamiltonella (unpublished data). The Cardinium of each subline was monitored every 30 d by sampling 20 adults as described previously (seedling . BemisiaGCCT-3′) . These weviously .- and C+ sublines, an introgression backcrossing scheme was conducted in the laboratory to minimize genetic differences and homogenize the nuclear backgrounds of both whitefly sublines in the hosts  and stored in a 0. 2-ml centrifuge tube at −20°C. In order to unify the experiment, this experiment uniformly uses the female B. tabaci. After that, B. tabaci DNA was extracted and used as a template for PCR amplification to detect the endosymbiotic bacteria (B. tabaci MED infected with endosymbiotic bacteria was used as the positive control. The primers and PCR used to detect Cardinium were the same as above. The PCR products were electrophoresed on an agarose gel (1.0%) with negative and positive controls and visualized using a gel imager . The C*+ subline had clear bands, while the C- subline had no bands.bacteria . Sterile9-Ohm input resistance system . Twenty-eight successful EPG recordings were obtained for each of the C*+ and C- sublines of B. tabaci MED. Cotton plants, insects, and EPG head stage amplifiers were placed into electrically grounded Faraday cages to shield the setup from external electrical noise. Before recording, a newly emerged female (exactly 24 h old) whitefly, was picked randomly either from the C*+ or C- population, starved for 20 min, and placed in a frozen glass dish (4 cm in diameter). Then, a 1.5-cm long gold wire (12.5 μm diameter) was quickly attached to the whitefly dorsum by applying a droplet of water-based silver glue for about 10 s until the glue dried. The wired whitefly was connected to the DC Giga-8 head-stage amplifier, and then placed on the abaxial side of a cotton leaf. EPG waveform signals were digitized with a DI710-UL analog-to-digital converter , and waveforms were acquired using software the PROBE 3.4. The EPG was adjusted before each experimental run to make the signal fit within the voltage range on the computer screen. Each B. tabaci was provided one cotton plant to record continuously with EPG for 6 h for each recording. The experimental design is shown in The stylet probing behaviors of whiteflies on cotton plants were characterized by EPG using a direct-current electropenetrograph (DC-EPG) with a 10The EPG waveforms were categorized as previously described ,b. Five U test analysis was used if the data did not conform to normal distribution. The variables were selected based on their biological significance . These variables were conventional variables recorded in the EPG of piercing and sucking insects because they reflect the entire probing process of insects.Data were analyzed by the Shapiro–Wilk test  to certify that they followed a normal distribution. If the data were normally distributed, an independent sample t-test was used to analyze the differences. A Mann–Whitney Cardinium-uninfected B. tabaci MED (C-) subline was more conducive to probing than was the Cardinium-infected subline (C*+) (Cardinium-uninfected (C-) and the Cardinium-infected (C*+) B. tabaci MED sublines in the non-phloem phase (10 EPG variables) on cotton  . HoweverCardinium on probing behavior during the phloem phase of B. tabaci MED on cotton  and the number of E2    were theCardinium on the probing behavior of B. tabaci MED after eliminating the genetic background of B. tabaci.The EPG technique helps analyze the otherwise-invisible insect stylet penetration activities within plant tissues. EPG technology has been used to study the behavior or physiology of sap-sucking insects (mainly Hemiptera) on host plants , 2020. WBemisia tabaci MED ingests phloem sap to obtain nutrients, such as amino acids and sugars (Cardinium-uninfected (C-) subline in the phloem phase were longer than those of the Cardinium-infected (C*+) subline  of Drosphila (Wolbachia adversely affects mosquito probing behavior (B. tabaci Cardinium-infected (C+) and Cardinium-uninfected (C-) sublines may be related to the difference in fitness . The results of B. tabaci could have shown more natural movement and probing behaviors, and the number of B. tabaci that reached the phloem of cotton may have increased. Whether these results would be consistent with the results of this experiment requires further experimentation. However, we speculate that our phloem ingestion results would be enhanced by better wire, and perhaps further significant differences would be found in potential drops and other aspects of the non-phloem phase.That said, it is also possible that the gold wires we used had a negative effect on our whiteflies. Even the thinnest gold wires are highly encumbering for tiny whiteflies, and this encumbrance affects their recorded stylet probing behaviors. Most whitefly EPG studies use gold wire, as in our study, probably because it is relatively inexpensive and easy to purchase and to use. However, Cardinium on the probing behavior of B. tabaci MED on the premise of eliminating the genetic background of the test insects. Cardinium interfered with the probing behavior of B. tabaci MED. Differences in probing behavior of the B. tabaci MED C*+ subline and C- subline were caused by the presence of Cardinium. We suggest that other methods (such as gene fluorescence quantification and RNAi) be combined to verify the relationship between Cardinium and B. tabaci MED, and to analyze the fitness differences between whiteflies.This experiment explored the effect of - subline ingested phloem more readily than the C*+ subline. This indicated the influence of Cardinium on the stylet probing behavior of B. tabaci MED. Understanding whitefly behavior with or without symbiotic bacteria may help improve whitefly pest management.This study provides information on the effects of symbiotic bacteria on whitefly feeding behavior. After eliminating genetic differences between whitefly sublines, the uninfected C


LIN5, ALMT9, AAT1, CXE1, and LoxC. The aim of this study was to identify any association of these genes with trait variation and to describe the genetic diversity at these loci in the red-fruited tomato clade comprised of the wild ancestor Solanum pimpinellifolium, the semi-domesticated species Solanum lycopersicum cerasiforme and early domesticated Solanum lycopersicum. High genetic diversity was observed at these five loci, including novel haplotypes that could be incorporated into breeding programs to improve fruit quality of modern tomatoes. Using newly available high-quality genome assemblies, we assayed each gene for potential functional causative polymorphisms and resolved a duplication at the LoxC locus found in several wild and semi-domesticated accessions which caused lower accumulation of lipid derived volatiles. In addition, we explored gene expression of the five genes in nine phylogenetically diverse tomato accessions. In general, the expression patterns of these genes increased during fruit ripening but diverged between accessions without clear relationship between expression and metabolite levels.Fruit flavor is defined as the perception of the food by the olfactory and gustatory systems, and is one of the main determinants of fruit quality. Tomato flavor is largely determined by the balance of sugars, acids and volatile compounds. Several genes controlling the levels of these metabolites in tomato fruit have been cloned, including  Solanum lycopersicum var. lycopersicum) flavor is determined by the right balance of sugars and organic acids, as well as a range of volatile organic compounds, the latter detected primarily by olfaction . These studies have identified hundreds of loci involved in the production of multiple compounds, which paved the way for a targeted molecular breeding approach to recover the flavor in modern tomatoes . Severalolatiles . Specifiolatiles  have oftolatiles .Solanum pimpinellifolium (SP) gave rise to Solanum lycopersicum var. cerasiforme (SLC) in South America from which cultivated tomato Solanum lycopersicum var. lycopersicum (SLL) eventually arose in Mexico . The read quality of raw sequencing data was evaluated using FastQC2. Low quality reads (read length less than 20) and adapter sequences were trimmed with the tool Trimmomatic  were detected using aligned BAM files and its corresponding splitter and discordant files using “lumpyexpress” function of LUMPY . The resThe same pipeline was employed to analyze a subset of cultivated accessions representative of the genetic diversity within heirloom and modern varieties, previously sequenced . The seqp-Value from Shapiro test < 0.01) were normalized using quantile normalization. Genome-wide kinship matrix was calculated based on SNPs using the Centered IBS method, to generate the Hapmap files in TASSEL 5.2.44  by a SNP was approximated subtracting the likelihood ratio-based R2 of the model with the SNP and the likelihood ratio-based R2 of the model without the SNP  heatmaps were generated using LDBlockShow 1.33  using meVCFtools . The resCXE1 was absent in the ITAG4.1 gene model4, we used the FGENESH  was estimated per subpopulation using exclusively SNPs within each gene and flanking sequences (3 kb upstream and 1 kb downstream). The quality thresholds were the same as described before . We classified the SNPs as non-synonymous (resulting in protein changes), synonymous (silent mutations in coding sequence), and non-coding  by following SnpEff annotation . Then weLoxC was duplicated in SP accessions, we used the new high-quality assembly of PAS014479, a SP-PER accession from the Varitome collection that carries the two paralogs  of PAS014479 to itself and generated a dot-plot to identify identical sequence matches using MUMmer . Strand-specific RNA-Seq libraries were constructed using an established protocol  or in volatile production affecting smell  (We compiled a list of known genes that affect tomato flavor . For eacnd LoxC) . The othnd LoxC) . In addiLIN5- The simple sugars, glucose and fructose, are among the most important metabolites in tomato as higher levels contribute to high consumer liking , one SNP in the coding region resulting in a missense mutation from asparagine to aspartate at position 366 (SL4.0ch09:3510682) and 20 variants that mapped 4 to 7 kb downstream  and three in the second intron , two resulted in missense mutations from serine to glycine at amino acid position 94 and from valine to glycine at position 211, respectively. The SVs were three deletions of 445 bp, 3.3 and 4.8 kb and one duplication of 7.0 kb .At the llection . The varllection . The PVEd tomato . A signiLoxC- Lipid-derived volatiles are also significantly associated with consumer liking as they contribute to flavor intensity  and covering the LIN5 gene. A total of 228 variants were identified at the locus  were identified  and the remaining eight were INDELs (ranging from 1 to 14 bp)  . Eight me groups . ClusterCXE1 locus, the distribution of actual levels was skewed toward 0, with ∼50% of the accessions showing less than 1 ng/g of the volatile  was found in all SLL, SLC-MEX, and SLC-SM as well as subsets from the other subpopulations . Only onAAT1 locus encompassed 167 variants including 128 SNPs, 37 INDELs (ranging from 1 to 59 bp) and two SVs . When selecting the accessions from Cluster VI for V and VI . These as III-VI .LoxC, read mapping indicated an unusual high level of apparent heterozygosity in SP accessions and we sought to explore that first  and did not appear to encode another paralog of LoxC since no gene model was predicted. To check whether this duplication was correlated with heterozygosity signal, we analyzed the alignments of a subset of representative accessions using PAS014479 as the reference. The reference genome and accessions with a similar haplotype at this locus, e.g., BGV007990, carried a deletion of ∼15 kb immediately upstream LoxC in accordance with the duplication coordinates, while the apparent heterozygous accessions, e.g., BGV006370, lacked the deletion , Z-3-hexen-1-ol content varied within each group, with a range from 0.01 to 70.61 and 0.14-98.77 ng/g when the duplication was present and absent, respectively. Therefore, additional genetic variation at the locus was likely responsible for the phenotypic variation found within the groups. We performed the association mapping at the locus using the subset of accessions containing exclusively LoxC and obtained seven significant SNPs , which may suggest positive selection on non-synonymous mutations with beneficial impact.For all five genes, non-coding regions carried the highest proportion of genetic diversity, as expected . SynonymLoxC, the number of polymorphisms found in cultivated tomato was lower than in the Varitome collection  was not found in cultivated tomato. Five accessions carried the alternate allele of the two associated variants from this cluster, but in combination with other polymorphisms. For ALMT9, the desirable haplotype associated with lowest malate content (Cluster VI) was present in both the Varitome collection and cultivated tomato. For CXE1, the best haplotype was difficult to discern. One of the likely beneficial haplotypes in CXE1 (Cluster VI) was found in cultivated tomato. For AAT1, the best haplotypes (Clusters III and VI) were absent from cultivated tomato; only one accession from Tunisia carried a likely beneficial haplotype. For LoxC, three haplotypes were associated with higher levels of Z-3-hexen-1-ol  and only Cluster VI haplotype was present in cultivated tomato.We hypothesized that some potentially valuable haplotypes may have been left behind during domestication and improvement of tomato. To test whether novel haplotypes conferring superior flavor found in the Varitome collection were absent in cultivated tomato, we selected a representative subset of cultivated accessions for which sufficiently high-quality sequencing data were publicly available. As expected, for all genes except llection . FurtherHaplotype analyses showed that SLL had no unique haplotypes. Hence, the haplotypes of flavor genes that characterize cultivated tomato appeared to have come from standing genetic variation present in ancestral populations. Novel mutations in flavor genes rarely appeared during domestication according to the results at these five genes. Since only certain haplotypes were selected and those were now nearly fixed in cultivated tomato, SLC accessions from South and Central America continues to be a good source of improved haplotypes at these loci.P-value for each gene for identifying differential expression based on a cut-off value of 0.05  carried the 3-bp INDEL in the promoter described before as likely causative  and CXE1  were categorized as differentially expressed genes in linear modeling analysis. In most cases, the expression of AAT1 and CXE1 was equally high; for example, BGV008189 showed the highest expression for AAT1 and also one of the highest for CXE1. However, in the SP accessions BGV007151 and BGV006370, expression of AAT1 was low, limiting the synthesis of isobutyl acetate, whereas expression of CXE1 was high, further enhancing the degradation of the limited amount of the volatile. The two accessions that showed high CXE1 expression in ripe fruit showed medium to low isobutyl acetate content, which fits the hypothesis of these esters to be catalyzed at a high rate. Four SLC contained in Cluster VI showed lower CXE1 expression on average, yet the metabolite content was variable within the group. AAT1 expression was lower (<500 RKPM) in the two SP accessions, from Clusters I and II, than in accessions from Cluster VI, the most common haplotype (∼1000 RKPM).For e fruits . HoweverLoxC were variable across accessions as indicated by a P-value of 1.22 - 10–18, although the dynamics were similar. In most of them, the expression was low at flower and young fruit, increased gradually until it peaked at breaker and then slightly reduced in ripe red fruits  were also associated with low malate content and could be used in breeding programs for improved flavor.For ariation . This smariation . The 3-bariation . None ofCXE1 are proposed to increase expression in red fruited tomato compared to the green fruited S. pennellii , implying that these paralogs arose millions of years ago. In addition, a QTL mapping study using a RIL population derived from a cross with NC EBR-1 (only reference LoxC copy) and LA2093 (incomplete LoxC and LoxC-SP copies) found increases in multiple lipid-derived volatiles and apocarotenoids controlled by the NC EBR-1 haplotype  seems to be adequate for high lipid-derived volatile content  or representing novel genes. Our collection would be an excellent material to discover new flavor genes through genetic mapping approaches.SRP150040, SRA: SRP045767, and SRA: SRP094624.The datasets analyzed for this study can be found in NCBI, accession numbers SRA: LP and EvdK conceived the study. LP, MS, MA, NT, YiZ, YoZ, and HR performed the experiments and data analyses. DT generated the metabolic data. YW generated the RNA-seq data. AF, AC, ZF, and MCS provided advice and resources. LP and EvdK drafted the original manuscript. All authors reviewed and agreed to the published version of 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. The reviewer GD declared a past co-authorship with one of the authors AF and the reviewer CS declared a past co-authorship with several of the authors AF and DT to the handling editor.


Some of the important genes underlying the novel MTAs were OsFBA1,OsFBL7, and mTERF which are known to be associated with salinity tolerance in crops. These MTAs pave way for combining salinity tolerance with high yield in rice genotypes through molecular breeding.Rice germplasm is a rich resource for discovering genes associated with salt tolerance. In the current study, a set of 96 accessions were evaluated for seedling stage salinity tolerance and its component traits. Significant phenotypic variation was observed among the genotypes for all the measured traits and eleven accessions with high level of salt tolerance at seedling stage were identified. The germplasm set comprised of three sub-populations and genome-wide association study (GWAS) identified a total of 23 marker–trait associations (MTAs) for traits studied. These MTAs were located on rice chromosomes 1, 2, 5, 6, 7, 9, and 12 and explained the trait phenotypic variances ranging from 13.98 to 29.88 %. Twenty-one MTAs identified in this study were located either in or near the previously reported quantitative trait loci (QTLs), while two MTAs namely, qSDW2.1 and qSNC5 were novel. A total of 18 and 13 putative annotated candidate genes were identified in a genomic region spanning ~200 kb around the MTAs  Oryza sativa L.) is cultivated in ~114 countries with an annual production of ~755 million tons [viz., sodium chloride, potassium chloride, magnesium chloride, sodium sulphate, magnesium sulphate, calcium sulphate, and sodium carbonate are high in saline soils [+ and Cl− are the major ions in saline soils responsible for causing injury to various biochemical and metabolic processes apart from inducing physiological drought stress in plants [−1 and for every dSm−1 increase, the estimated yield drop is 12% [−1, the yield reduction has been estimated up to 50% [Rice  ranged from 12.80 cm (Pusa 1301) to 67.35 cm (Seond Basmati), with an average of 35.53 cm, while the average root length (RL) ranged from 3.75 cm (CO-51) to 24.20 cm (Tompha Khau) with an average of 12.44 cm . The ave+ and K+ content in both roots and shoots  ranged from 0.01 (Pusa 1301) to 0.62 g (PMK-1) with an average of 0.22 g, and root dry weight (RDW) ranged from 0.01 g (PNR381) to 0.09 g (CSR27) with an average of 0.03 g. Considerable variation between different tolerant groups was observed for Nad shoots .+ concentration (RNC) and shoot Na+ concentration (SNC) was 1.30 and 1.56 mmol/g, which ranged from 0.53 mmol/g (chandana) to 2.29 mmol/g  and 0.46 mmol/g (Shah Pasand) to 3.72 mmol/g (ASD 19), respectively (+ concentration was seen in highly susceptible groups than others. The average root K+ concentration (RKC) and shoot K+ concentration (SKC) was 0.80 and 0.92 mmol/g, ranging from 0.16 mmol/g (Mahanadi) to 1.82 (Samanta) mmol/g and 0.24 mmol/g (Improved Shambha Mashuri) to 1.96 mmol/g (CSR23), respectively (+/K+ (RNK) ranged from 0.41 (Samanta) to 10.5 (Mahanadi) with an average of 2.43, while shoot Na+/K+ (SNK) ranged from 0.26 (Shah Pasand) to 8.85  with an average of 2.64 , and SNK+ (0.78) were significantly positive. Na+ and Na+/K+ was significantly negatively correlated with length, fresh and dry weight of root and shoot, while K+ was significantly positively associated with all the root and shoot morphological parameters studied.For understanding the physiological traits that best define seedling stage salinity tolerance, correlation between the traits was generated . STS ShoA set of 96 germplasm lines in the current study was subjected to population structure analysis. Based on Evanno plot, ΔK value was highest for the model parameter K = 3 a. Therefp-values showed less deviation from the expected p-values.A total of 23 MTAs were identified for twelve traits in the current study. Out of 23 MTAs, 21 MTAs were located either in or near the previously reported QTLs/MTAs associated with seedling stage salinity tolerance and two MTAs were novel. These MTAs are located on rice chromosomes 1, 2, 5, 6, 7, 9, and 12 and explained the trait phenotypic variance ranging from 13.98% to 29.88% . ManhattFor SL and RL, one common MTA (SNP-AX-95920196) was identified on chromosome 2 and explained the trait phenotypic variance of 15.11% and 19.26%, respectively. For SFW and SEW, three common MTAs  were identified on chromosome 2, 7, and 9 and explained the phenotypic variance ranging from 13.98% to 15.43 %. One MTA (SNP-AX-95921620) was identified for RFW on chromosome 2 with phenotypic variance 14.64 %. For SDW, a total of three MTAs  were identified on chromosome 2 and 12 and explained 25.51%, 22.52%, and 20.24% of phenotypic variance, respectively. For RDW, two MTAs (SNP AX-95956901 and SNP AX-95929366) were identified on chromosome 6 and 7 and explained the trait phenotypic variance of 22.05% and 22.62%, respectively.For RNC, two MTAs (SNP-AX-95921298 and SNP- AX-95920628) were identified on chromosome 2, and explained 18.17% and 15.15% of phenotypic variance. For SNC, three MTAs  were identified on chromosome 1, 2, and 5 and explained the trait phenotypic variance of 16.26%, 16.64%, and 18.08%, respectively. For SKC, two MTAs (SNP-AX-95940587 and SNP-AX-95937335) were identified on chromosome 1 and 6 and explained 16.24% and 14.33% of phenotypic variance. For RNK and SNK, one MTAs each was identified on chromosome 1 (SNPAX-95918556 and SNP-AX-95940642), explaining the trait phenotypic variance of 25.11 and 29.88%, respectively.silico search for annotated gene MSU-RAP database led to identification of 18 and 13 putative candidate genes in ~200 Kb genomic region of identified novel MTAs, qSDW2.1 and qSNC5, respectively . Furthermore, significant negative correlation between Na+ ion concentration of root and shoot with seedling weight, length, fresh weight, and dry weight of root and shoot was observed. Reduced uptake of sodium while increasing the uptake of potassium is one of the critical salt tolerance mechanisms [+, there is significant decrease in chlorophyll concentration which limits the photosynthetic capacity of salt-sensitive plants, leading to chlorosis and reduced growth of seedlings [+ uptake, high K+ uptake and low Na+/K+ ratio with salt tolerance was formerly reported in many studies [SKC1 gene from Nona Bokra regulates Na+/K+ homeostasis in the shoot under salt stress conditions [+ and low Na+/K+ were identified  accumulated less Nachanisms ,60,61,62eedlings ,20,63. T studies ,62,64. Tnditions . In the entified  which coXa43(t) at −log10(P) value of 4 which was further validated in a bi-parental mapping population.Identifying the genomic regions governing this complex trait is of utmost importance to develop high yielding salinity tolerant rice varieties. Association mapping takes advantage of historical recombination and mutational events in order to precisely detect MTAs . However+ concentration , leaf chlorophyll content (qCHl2: RM12713-RM6318) [+/K+ ratio  [OsGMST1 (LOC_Os02g17500-10.07 Mb on chromosome 2) located near the MTA qSDW2.2 (AX-95934798-10.21 Mb) is known to induce under salinity stress and govern tolerance [OsCMO (29.34 Mb on chromosome 6) located near the MTA qRDW6 (AX-95956901-29.72 Mb) was reported to induce in response to salinity stress [+ concentration and stress evaluation score  at reproductive stage salt stress [+ concentration (qRKC2: RM13197-RM6318) [+ concentration (qSKC1:RM8094-RM10825) and shoot and root Na+/K+ ratio  [+ concentration (qSNC1: RM1287-RM10793), shoot and root K+ concentration  and root Na+/K+ ratio qRNK1: RM1287-RM10825) [In the present study, we have identified a total of 23 MTAs for 12 seedling stage salinity tolerance associated traits. These QTLs are located on rice chromosomes 1, 2, 5, 6, 7, 9, and 12 and explained the trait phenotypic variances ranging from 13.98% to 29.88%. We compared the MTAs identified in this study with previously reported QTLs related to salinity tolerance in the QTL Annotation Rice Online (Q-TARO) database and by literature survey. This comparison showed that 18 MTAs identified in this study were located either in or near the previously reported QTLs associated with seedling stage salinity tolerance . The gen-RM6318)  and germ-RM6318)  under sa-RM6318) . The MTA-RM7175)  and germ-RM7175) . The canolerance . The MTAolerance . A largey stress .The MTA t stress . The MTA-RM6318)  and germ-RM6318) . The MTA-RM1025) . The MTARM10825) .Os01g0304100 (11.26Mb on chromosome 1) located near the MTA qRNK1 (AX-95918556-11.02 Mb) encode cation chloride co-transporter which was identified as a determinant of salt tolerance in previous works [OsVTE1 (LOC_Os02g17650-10.15 Mb on chromosome 2) located near the MTA qSDW2.2 (AX-95934798-10.21 Mb), encode a rice tocopherol cyclase ortholog which was induced significantly by abiotic stresses such as high salt, drought, cold, and by the salicylic acid and abscisic acid plant hormones [The candidate gene us works . The canhormones .silico analysis revealed that the candidate gene LOC_Os02g10590 in the genomic region of MTA qSDW2.1, was annotated as peptidyl-prolyl cis-trans isomerase (FKBP-type) proteins , respectively  genes play significant role in regulating organelle gene expression. Environmental stimulus experiments revealed differential up or downregulation expression of maize mTERF genes in seedlings exposed to light/dark, salts and plant hormones, respectively, suggesting numerous important roles of maize mTERF genes in light acclimation and stress-related responses. The transcript levels of the maize mTERF12 gene, the ortholog of Arabidopsis mTERF6, also those of maize mTERF13 and mTERF28, altered after exposure to NaCl, AlCl3, or ABA in comparison to the untreated plants [mTERF gene could possibly play role in the rice seedlings in response to salinity stress and have important role in the growth, and development of rice seedling under stress conditions. These novel MTAs identified in the study may play important role in imparting salinity tolerance in rice.The putative candidate gene LOC_Os05g33500 in the genomic region of MTAs qSNC5, was annotated as  protein . Plant md plants . TherefoA set of 96 rice genotypes, consisting of landraces, cultivars and breeding lines were evaluated for seedling stage salinity tolerance under hydroponic conditions. The details of the genotypes used in the current study are presented in The experiment for evaluation of seedling stage salinity tolerance was set up under hydroponic conditions in the glass house of National Phytotron Facility, ICAR-IARI, New Delhi, India. The temperature was set to 30℃ during day and 22 °C at night with relative humidity of 65–70%. The seeds were surface sterilized with 5% sodium hypochlorite solution for 30 min and rinsed with distilled water several times. Sterilized seeds were placed on germination paper in Petri dishes and incubated for 72 h. Sterile plastic containers with styrofoam sheets were used for screening the genotypes under hydroponics. Styrofoam with 10 × 16 matrix of hole was used, where the bottom of the hole was covered by stitching nylon net to prevent seeds from falling into the nutrient solution. The plastic tray was filled with 15 liters of modified Yoshida nutrient solution  and styrOne healthy pre-germinated seed was placed in each hole of styrofoam sheet with each genotype in a row (10 holes). Each plastic tray could accommodate 14 test genotypes along with sensitive (IR29) and tolerant (FL478) checks. The entire experimental set up consisted of plastic trays with modified Yoshida nutrient solution in completely randomized design (CRD) with two replications and each genotype comprised of ten plants per replication.Fourteen days after germination, saline solution with 60 milimolar (mM) NaCl (EC of 6.9 dS/m) was added to the tray and after three days, salinity stress was raised to 120 mM (EC of 13.9 dS/m) which was maintained until final phenotypic scoring. The container was refilled with fresh nutrient solution maintaining the required salinity level and pH of 5.0 at every four days interval. On 16th day after first salinization, the genotypes were visually scored using modified standard evaluation system for rice .viz., SL, SFW, RL, RFW, SEW, SDW, and RDW were recorded. For each plant, the SL was recorded from the base of the plant to the tip of the longest leaf while RL was measured from the base of the plant to the tip of the longest root. Plants were dried in hot air oven at 80 °C for 72 h and SDW and RDW were measured using a high precision digital balance. Dried samples of shoot and root were used for assessment of Na+ and K+ ion concentration.After final scoring, three plants per genotype were rinsed three times in distilled water and data on seven traits + and K+ ion content of the root and shoot samples were determined using flame photometer as described by Yoshida et al. [3 and HClO4, 10:3). The mixture was cooled and transferred to a 50 mL volumetric flask and volume was made up to 50 mL using double distilled water. The mixture was shaken gently and filtered with Whatman number 42 filter paper and concentration of Na+ and K+ ions was estimated using Systronics Type 128 Flame Photometer . Three replicates were performed per sample and the average value of the replicate was taken. The concentration of ions was expressed in millimoles per gram of dry weight (mmol/g of dry wt).The Naa et al. . The oveDescriptive statistics and correlations between traits were worked out using R v.3.6.0. Hierarchical cluster analysis  was condTM 2000/2000 c, Thermo Fisher Scientific, DE, United States). High throughput genotyping was carried out using a “OsSNPnks” 50 K genic Affymetrix chip containing a total of 50,051 high-quality SNPs. The chip was based on single copy genes, covering all 12 rice chromosomes [Total genomic DNA was isolated from young leaves using Cetyl Trimethyl Ammonium Bromide (CTAB) method . The DNAomosomes . DNA ampomosomes .The software STRUCTURE 2.3.4  was utilAssociation mapping panel of 96 germplasm lines were genotyped using 50 K SNP chip. SNP data was filtered for minor allele frequency (MAF) ≥0.05 and maximum missing sites per SNP was fixed to <20%. After filtering, a total of 26,108 SNPs were used to detect MTAs. MTAs were identified using MLM and FarmCPU ,95 impleMorphological and physiological characterization of rice germplasm lines identified 11 genotypes tolerant to salinity stress. These lines can be utilized as donors in a breeding program for developing salt tolerant rice varieties. Furthermore, GWAS identified a total of 23 MTAs for traits associated with seedling stage salinity tolerance, of which two are novel.


Local adaptation of animals to the environment can abruptly become a burden when faced with rapid climatic changes such as those foreseen for the Italian peninsula over the next 70 years. Our study investigates the genetic structure of the Italian goat populations and links it with the environment and how genetics might evolve over the next 50 years. We used one of the largest national datasets including > 1000 goats from 33 populations across the Italian peninsula collected by the Italian Goat Consortium and genotyped with over 50 k markers. Our results showed that Italian goats can be discriminated in three groups reflective of the Italian geography and its geo-political situation preceding the country unification around two centuries ago. We leveraged the remarkable genetic and geographical diversity of the Italian goat populations and performed landscape genomics analysis to disentangle the relationship between genotype and environment, finding 64 SNPs intercepting genomic regions linked to growth, circadian rhythm, fertility, and inflammatory response. Lastly, we calculated the hypothetical future genotypic frequencies of the most relevant SNPs identified through landscape genomics to evaluate their long-term effect on the genetic structure of the Italian goat populations. Our results provide an insight into the past and the future of the Italian local goat populations, helping the institutions in defining new conservation strategy plans that could preserve their diversity and their link to local realities challenged by climate change. Among the key factors shaping genetic and phenotypic diversity there is climatic and environmental heterogeneity2, with indigenous domestic breeds showing better adaptation to local environments than highly productive breeds kept in controlled farming systems in which the effects of climatic challenges are minimized5. In this rapidly evolving situation, species that are mostly reared in marginal rural areas of the world such as goats are also more likely to be among those most affected by environmental changes6.The preservation of animal genetic diversity is fundamental to ensure food security and the development of farming communities7. This environmental richness is paired with a reservoir of genetic resources for the caprine species counting over 36 goat breeds registered by the National Goat and Sheep breeds Association (http://www.assonapa.it). Italian goat genetic resources are managed throughout diverse farming systems ranging from intensive and semi-intensive to traditional grazing and transhumance. Importantly, most of them pertain to marginal areas where they play a crucial socio-economic role, contributing to the management of landscapes, biodiversity preservation, and the production of niche traditional products8. The Italian caprine diversity and adaptation to climate has been previously investigated through genotype data analyses9. However, no previous work included breeds from the central areas of the country, which hosts several minor, local, and niche populations10. Importantly, the latter are specifically adapted to the broad range of Italian eco-climatic scenarios, making them an ideal model to investigate genetic adaptation to climate, which only few studies tried to address in the goat species13.To investigate the link between territory and genetics, we analyse the second release of the goat genotype dataset assembled by the Italian Goat Consortium, a collaboration across Italian universities that aims to enhance the understanding of the Italian goat genetic variability. This new release of the dataset, called IGC2, expands the previous version, improving the sampling coverage of the central-southern goat populations and completes the whole Italian panorama and its internal connections , increase temperature-related health problems , and cause metabolic problems (decreasing productive and reproductive performance or depressing feed intake16). Locally adapted breeds will be the hardest hit, mostly relying on grazing17.The most recent climate predictions by the Koppen-Geiger climate classification foresee hotter and drier climate across the Italian peninsula over the next 70 years19.We performed genome-wide analyses on the IGC2 dataset to investigate the genetic structure of Italian goat breeds with particular attention to the newly sampled local populations, and link the heritage and genomic structure of current populations with the present and future climatic condition of the rearing areas. Our results will help to understand their environment-driven adaptation and draw effective management plans to face climate change2 < 0.2).After filtering the initial raw dataset of 1,071 goats for poor quality genotype and related animals we obtained the dataset used for the haplotype sharing analysis, which included 980 animals and 48,396 SNPs. This dataset was further pruned for linkage disequilibrium , Admixture, and Reynolds distances).Upon the removal of 2nd-degree related individuals and animals without geographical coordinates, we retained 41,898 SNPs and 489 individuals for Landscape Genomics analyses. See Table 6. The first MDS component identified three main groups corresponding to northern Italian, central-southern Italian, and Maltese populations. The second MDS component discriminated the insular Montecristo goat . The north-western part of the country and Sardinia were part of the Sardinian kingdom, tightly connected with the French empire, whereas the north-east part (the Kingdom of Lombardy—Venetia) was under the political influence of the Austrian Empire. Central Italy was ruled by the Papal state, and southern Italy and Sicily were under the Kingdom of the two Sicilies ruled by the Borbone  and the Northern Italy breeds (yellow component), and improves the representation of the North–South gradient over previous studies on Italian goat populations6. At K = 3 it resembles the MDS plot distinguishing the central-southern Italian breeds led by the Girgentana  and the mean proportion for each breed overlap nicely with the political borders of Italy prior 1860  and Valdostana . The lowest cross-validation error was recorded at K = 20  show no haplotype exchange with the other clusters, with the exception of SAA and TER probably due to a recent introgression event. Within the Northern-Italian cluster there is a more pronounced haplotype sharing among the Lombardy breeds  than among those from the rest of the Alps. The Val Passiria (VPS) together with the Garfagnana (GAR) are the only two populations that do not exchange haplotypes at all, perhaps suggesting a geographical and/or political isolation. Populations from Central-Southern Italy (blue cluster) show large haplotype sharing within and among different clusters, possibly due to breeding and management practices as well as local geographical conditions, such as breeds from the Lazio region  have high haplotype sharing among themselves. Lastly, the populations from the isles and in particular the Maltese  and Sarda (SAR and MXS) are those that mostly shared haplotypes with all other southern breeds, probably as a consequence of their high productivity and diffusion over the territory. The green colour represents the outgroup 13, with only a minor impact on the population structure of the following 150 years of history of the country.The political subdivision of Italy preceding the unification of the country has probably contributed to maintain the ancient genetic flows from central-north Europe in the north of the country and from Africa and Spain in the south23 and LFMM24. We observed no direct overlap between the two methods. However, this is not surprising as simulation studies showed that LFMM is overall more conservative than Samβada, and the two methods tend to have marginal overlap on co-selecting the same signals, with the most significant loci detected by Samβada ignored by LFMM23. Samβada identified 252 genotypes belonging to 216 different SNPs significantly associated (FDR < 0.05) with at least one climatic variable  were performed using the climatic variables representing the current climate applying two different approaches: Samβadap-value < 0.0045) for those genes associated with “Mean diurnal range” with two genes linked to the circadian clock regulation (MAPK925) and to hair follicle formation and hair growth in Cashmere goat (NTKR326).Initially, we investigated the role in biological pathways of the 62 genes identified by Samβada  meat- and growth-related genes, (2) circadian rhythm-related genes, (3) fertility-related genes, and (4) inflammatory response genes.HADC9, which has a role in the feedback inhibition of myogenic differentiation in sheep muscle27, DLG1, that is related to adipogenesis and residual feed intake in cows28, and KLF12, which is related to the formation of preadipocytes in goats29. The second group (circadian rhythm) includes 12 genes, such as MAPK9 and EYA3, both related to melanin production and photoperiod regulation30, and KCNJ1, associated with the production of polyunsaturated fatty acid (PUFA) and feed efficiency in cattle32. The third group (fertility-related) includes 15 genes such as BTRC, whose mutations can affect spermatogenesis and mammary gland development in mice33, PRKD1, associated to age at puberty in pigs34, and DENND1A, related to anti-Mullerian hormone and superovulation in dairy cows and to polycystic ovarian syndrome in human35. Finally, the fourth group (inflammatory response) includes eight genes such as BTLA, strongly related to rheumatoid arthritis36. This last gene in particular is relevant as a candidate for one of the most relevant infective diseases of goats worldwide, the Caprine Arthritis Encephalitis Virus (CAEV). This virus belongs to the Retroviriade virus family, like the human immunodeficiency virus (HIV), and has rheumatoid arthritis among its principal symptoms38. Due to the CAEV importance and the relevance of climatic factors and their change play into pathogens diffusion39, this group of genes becomes a potential candidate for studies on livestock resilience to incoming climate challenges.The first group (meat and growth) is the largest and counts 24 genes, including 40 and previously associated with continental goat group in the work of Bertolini et all 20189, and the RHOBTB1 gene that is known to be associated to meat quality in cattle41 (Supplementary Table LFMM identified four SNPs significantly associated (FDR < 0.05) with three different climatic variables , two of which intercepting NBEA, a gene located within a region involved with wool production in sheepThe data collected on the current Koppen-Geiger climate classification showed that 21 Italian breeds live in “Temperate” regions, eight in “Cold” regions , two in “Arid” regions , and one in a “Polar” region  populate “Temperate dry hot summer (Csa)” areas, one (GAR) is present in an “Arid step cold (Bsk)” area, and one (NVE) in a “Temperate without dry season hot summer (Cfa)” region. The remaining 21 breeds populate regions with a warmer and drier climate in the future , we predicted the future G allele frequency for this SNP in both groups  and consequently the expected GG genotype frequency (respectively 0.1509 for the DRY group and 0.3366 in the NOTDRY group). This simplified model suggests a future reduction of the genotype currently associated with the reference variable  in both groups. Interestingly, the gene intercepted by ARL13B interacts with RABGEF1, related to the reduction of the circadian cycle in humans according to the GenomeRNAi human phenotypes database (http://www.genomernai.org). In general, the prediction analysis identified SNPs that might go to stabilization of the frequencies or fixation  and 11 that differentiate the groups HOT/NOTHOT (two within a gene region) . Lastly, we predicted the future genotypic frequencies under the light of climate changes and foresee the directionality of changes in genotypes frequencies, an important starting point for future studies aiming at improving these analytical approaches. We infer that improved modelling approaches could deepen and perfect such results and shed light on today’s favorable genotypes for specific environmental conditions. These results will likely be instrumental in breeding schemes and genomic selection, assisting locally adapted breeds to cope with the expected climate change toward warmer and drier climates14.This new release of the Italian goat consortium dataset (IGC2)—almost three times the size of the previous iteration—fills in the gaps in terms of completeness and representativeness of the Italian caprine diversity. Our analyses overlap and expand on previous studies providing insight into the past, present, and future evolution of the populations considered. We confirm the geographic gradient of goat diversity ranging from north to southManagement and handling of the animals involved in this study were performed following the Italian and European legislation on animal welfare  and adhering to the ARRIVE ESSENTIAL 10 guidelines, where applicable. Blood samples were taken by official veterinary surgeons following the recommendations of the European directive 2010/63, without performing any actual experimental research on animals. Experimental protocol was approved by the Ethical committee of the Department of Veterinary Science of the University of Messina (code 046/2020).6. DNA samples were genotyped using the GoatSNP50 BeadChip  developed by the International Goat Genome Consortium (IGGC) at the Agrotis srl , Porto Conte Ricerche s.r.l. , and University of Palermo facilities .Blood sampling collection of the new individuals was performed using Vacutainer tubes with the K-EDTA anticoagulant, then all the samples were stored at − 20 °C until genomic DNA was extracted using a commercial kit  according to the manufacturer’s instructions6, intercepting the local diversity of several under-represented areas of the country  and identifying small, indigenous breeds never characterized before. For this work, 19 new Italian goat populations, for a total amount of 586 individuals, were sampled and added to SNP genotyping data taken from previously studies44, including seven Iranian Bezoar (Capra aegagrus) genotyped by the NEXTGEN project as outgroup for the analyses (“NEXTGEN Project” n.d.). From that, we obtained a final raw dataset consisting of 1071 goats from 33 Italian breeds and populations and one wild species, Capra aegagrus  were removed and for each pair of highly related animals (Mendelian Errors count < 100) we excluded the animal occurring in multiple pairs or having the highest missingness. Phasing and imputation of missing genotypes was performed using BEAGLE v4.146, using sliding windows of ~ 5 Mb with an overlap of ~ 2 Mb and allowing two SNPs trimming (~ 0.15 Mb). The resulting dataset was used for the haplotype sharing analysis. In order to investigate the population structure using comparable population sizes, we created a specific dataset reducing the number of individuals for each population to ≤ 30 while maintaining the overall within-population diversity, using the ‘representative.sample’ function implemented in the R package BITE v1.2.000747.The raw dataset was updated to the latest goat genome map (ARS1.2) and pruned to retain SNPs having SNP call rate > 98%, individual call rate > 95% and minor allele frequency (MAF) > 0.05. Then, we pruned loci in linkage disequilibrium (LD), removing one of each couple of SNPs having LD > 0.2 using PLINK v1.90b6Lastly, individuals with more than second-degree relatedness were identified using the—genome flag in PLINK and removed to perform the Landscape genomics analisis.48. Unsupervised clustering was calculated for K values from 2 to 35. We used fivefold cross-validation (CV) errors for each K to evaluate the optimal partitioning, and plots for each K were generated using an in-house R script. A phylogeny tree based on Reynolds Genetic distances, with 100 bootstrap replicates, was computed using a custom script. A Neighbour‐joining consensus tree was generated using PHYLIP v3.69749 and using Bezoar as an outgroup.Population structure analysis was conducted through MDS of the identity by state (IBS) distances obtained with the flag—cluster in PLINK. Maximum likelihood analysis of population structure was conducted using ADMIXTURE v1.3.0 Software50 on all the individuals that passed the initial quality check. Sliding windows size was set to of 1 Mb, reporting windows of at least 0.2 Mb and allowing 0.05 Mb overlap. We considered the shared haplotypes between two breeds as the median length of shared haplotypes among all the possible pairs of individuals belonging to the breeds considered .The proportion of haplotypes shared among breeds was determined as Identity By Descent (IBD) estimation among individuals, and calculated using RefinedIBD v4.152 to retrieve the values of 19 bioclimatic and elevation variables available from the WorldClim database53 as those referring to the time span between 1960 and 1990 as proxy for the current climate, and the estimated future values for 2070 (average for 2061–2080)  correction for multiple testing. The R function ‘p.adjust’ was used to perform corrections for multiple testing21.LGA was performed to assess the genotype/environmental variable association using Samβada v.0.5.3https://www.ensembl.org/biomart). These genes were investigated individually (https://www.genecards.org) and used as input for an enrichment analysis for pathways and ontologies using the online tool Enricher (https://amp.pharm.mssm.edu/Enrichr/).To further investigate the biology underlying the signals identified, we screened all the SNPs that resulted significantly associated with a WorldClim variable for annotated genes of interest at the exact location of each marker in the ARS1.2 goat genome version  only on those SNPs that fall within genomic regions including an annotated gene considering the mean allelic frequencies of the SNP and the mean value of the environmental variable resulted significantly associated with that SNP for each breed in each group. Finally, we calculated the future hypothetical change in allelic and genotypic frequencies only for those SNPs with a statistically significant linear regression model. For each group, we multiplied the delta between the current and the projected future value of the environmental variable associated with the SNP with the regressor of the linear model.We calculated the MAF of all the significant SNPs from LGA in each Italian breed using a custom script. We summarized the MAF in each of the four groups and performed a one-way ANOVA analysis using the R base packageAll authors declare that animal samples were obtained in compliance with local/national laws in force at the time of sampling. Genotyping data exchange was in accordance with national and international regulations.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9.Supplementary Information 10.Supplementary Information 11.Supplementary Information 12.Supplementary Information 13.


Prunus persica (L.) Batsch] is one of the most important stone fruits species in world production. Spanish peach production is currently the second largest in the world and the available cultivars in Spain includes a great source of genetic diversity with variability in fruit quality traits and postharvest disorders tolerance. In order to explore the genetic diversity and single nucleotide polymorphism (SNP)-trait associations in the Spanish germplasm, the new peach 18K SNP v2 array was used to genotype 287 accessions belonging to the two National Peach Germplasm Collections placed at the Agrifood Research and Technology Centre of Aragon (CITA) and at the Experimental Station of Aula Dei (EEAD)-CSIC. The high density of the new SNP array allowed the identification of 30 groups of synonymies, which had not been identified before using low-density markers. In addition, a possible large-scale molecular event in ‘Starcrest’, a sport of ‘Springcrest’, was detected showing a possible chromosome replacement of a 13.5 Mb region. Previous suggestions about Spanish diversification regions agreed with our genetic diversity and linkage disequilibrium (LD) decay results using high-density markers. A genome-wide association study (GWAS) detected 34 significant SNP-trait association with the type of leaf glands (TLG), fruit hairiness (FH), and flesh texture (FT). The impact of the significant SNPs was studied with SnpEff. Candidate genes encode several important family proteins involved in trichome formation and powdery mildew resistance (linked to TLG in peach). The genetic distance among cultivars obtained, together with SNP-trait associations found, provide new knowledge for marker-assisted selection and crossing approaches in peach breeding programmes.Peach [ Prunus persica (L.) Batsch] belongs to the Rosaceae family, genus Prunus (L.), and is the third most important temperate tree fruit in terms of worldwide production (Peach  and three regions in southeast Spain . Moreover, 11 accessions from Spain whose region of origin is unknown were also studied. To assure and confirm genotyping results, replicates from 37 genotypes, located in both collections, were used. The set included peach and nectarines, round and flat fruit, and yellow or white flesh   and parent–parent–child (P-P-C) (combination of the two parents with the offspring) Mendelian-inconsistent errors were checked in those individuals with known pedigree information , to provST), gST, and DJost of the total accessions, and the allelic richness (Ar), observed heterozygosity (Ho), and expected heterozygosity (He) of the populations. These data were obtained using the “basicStats” function of the DiveRsity v.1.9.90 package of R , unless historical data was available.Genetic diversity analysis was performed by measuring the fixation index . The r2 values were plotted against genetic distance using the ggplot2 R package  to select the “best” number of clusters  (melting/non-melting), fruit hairiness (FH) (hairy/glabrous), and the type of leaf gland (TLG) (globose/reniform) from the majority of the accessions used here were recorded for 3 years and were confirmed with historical records from previously published studies. FH was phenotyped by classifying fruit type from each tree as peach (hairy) or nectarine (glabrous) at harvest time. For the TLG attribute, at least 20 leaves from each tree were visually examined each year, following the UPOV guidelines for peach characterisation . FT was 1 were used as input. The SNP predicted effects were classified by impact: moderate (non-synonymous substitution), modifier (with impact on non-coding regions), low (synonymous substitution), or high (disruptive impact on the protein). Subsequently, the functional characterisation of each affected genes according SnpEff was carried using the gene list analysis tool from the PANTHER classification system . Six individuals with poor quality  were excluded and the total of SNPs were re-clustered in GenomeStudio . ASSIsT A pairwise IBS analysis was carried out to detect identical genotypes  and identify synonymies, homonymies, and sport mutations. The IBS threshold to detect clones was 0.99, using as reference the known sports of ‘Springcrest’  . The two reps of ‘Blanco Tardío_I’ were identical and, according to PLINK, clones of ‘Fantasia’ (Clone Group 4). The sample ‘Blanco Tardío_J_5373’ was different from the two reps of ‘Blanco Tardío_I_5373’ and from ‘Fantasia’, which could indicate also a clear labelling error. Although this issue was not previously observed by the gene bank curators, it has now been checked and confirmed Pedigree verifications in GenomeStudio showed only 4 out of 12 P-C and three out of six P-P-C verifications as true . The P-POur results have also confirmed ‘Carson’ as the parent of ‘Adriática’ and ‘Tebana’, being both genotypes synonymies. In addition, our results have also shown that ‘Fortuna’ is not a parent of ‘Adriática’. P-C relationship among ‘Kakamas’ and ‘Oom Sarell_5136’, ‘Merriam’, and ‘Everts’, and all the relationships of ‘Lovell’ were detected as wrong. Lastly, ‘Late Legrand’ was confirmed as parent of ‘Summergrand’ and ‘Autumn Grand’, and ‘Andross’ as child of ‘Fortuna’.r , while Navarra, the smaller one, showed the lowest value (1.405). He showed a similar pattern, being the He values in Zaragoza and Lleida, 0.255 and 0.248, respectively, and 0.175 in Navarra. Regarding Ho, Murcia showed the highest value (0.245), and Navarra the lowest one (0.113). Inbreeding coefficient determined Zaragoza as the population with the highest value (0.322), while Murcia showed a negative value (−0.108), causing a decrease in the average value (0.203)  .ST were obtained between Murcia and the rest of the populations, being Murcia and Navarra (FST = 0.159) the more differentiated ones  and the lowest ones between northern populations . DJost values reconfirmed Murcia-Navarra as the more differentiated pairwise (0.008) and, on the other side, the pairwises Huesca–Zaragoza and Lleida–Teruel obtained a value of 0.In general, pairwise analysis of genetic population differentiation showed a considerable distinction between population of Murcia (South of Spain) and the rest of the populations (North of Spain), although also Navarra (North of Spain) showed a moderate differentiation with other populations from others northern regions  . The higted ones . Most ofST value of 0.0326 indicated a low genetic differentiation among the populations, being 3.26% of the genetic variability related to interpopulation differences. DJost and gST values showed the same level of differentiation . Global inbreeding values, FIT and FIS , indicated a considerable inbreeding in the whole population.Global genetic values for the complete subset of Spanish accessions  were calculated . The FSTchooseK.py script showed K = 6 as which maximises marginal likelihood  and it was selected to describe the population structure  to 33 (Cluster 3) . SeventyK = 1 to K = 20, retaining the maximum number of PCs feasible (around 170 PCs)  . The BIC170 PCs)  showed gxvalDapc indicated 19 as the number of PCs with the lowest root mean squared error  to 47 (Cluster 1) . ClusterThe population genetic structure analysis through two different approaches allows the observation of some agreement between them. r2 values were calculated and plotted according to the genetic distance of the SNPs pairwise .The LD was studied in the final set of 167 individuals, using the high quality 11,549 SNPs. The pairwise . The avePhenotypic data from 151 to 157 genotypes (depending on the trait) out of 167 of the total set was obtained. Regarding the TLG, 118 individuals had reniform and 34 globose glands. In the case of FH, 137 accessions were hairy and 20 were glabrous . RegardiThe 34 significant SNPs detected were analysed using SnpEff, indicating 90 effects over 66 genes , 8. The The wide genetic characterisation performed using the new peach 18K SNP v2 array has shown multiple synonymies and homonymies in the Spanish peach collections not detected previously with low-density markers . The majThe different parameters, such as IBS and IBD, and specific thresholds , have been established previously to classify two individuals as identical using NGS . Here, tAccording to the pairwise IBS analysis performed in PLINK, ‘Starcrest’ showed considerable genotype differences with the rest of the components of the ‘Springcrest’ sport group (clone group 35). These differences were detected in a region of 13.5 Mb located in the upper part of chromosome 4, where ‘Starcrest’ showed 139 SNPs with a different genotype and 362 SNPs with null alleles (being located between the heterozygous and homozygous clusters in GenomeStudio™) . In addiDespite of the commented precision of the new SNP array to distinguish between ‘Starcrest’ and ‘Springcrest’, two inconsistencies were generated by the SNPs, since accessions with differences in flesh colour (yellow and white) were considered as clones [clone group 22 (‘Sudanell’ group) and 31 (‘Rojo de Tudela’–‘Tambarría’)]. In this sense, the yellow flesh is caused by the accumulation of carotenoids in the chromoplasts due to the disruption of the allele ccd4 in LG1, which prevents the degradation of carotenoids . Three mo mean was observed in this study which can be explained by the smaller size of populations in comparison with other studies . High rates of selfing in the northern Spanish populations could be the main reason of these results, showing highest FIS values. In addition, this fact could also explain the slow decay of the LD observed here. All these results are clear descriptors of traditional Spanish accessions, which were selected from seed-propagated populations and selfing was the main mating system  controlling this trait, was responsible for the absence or globose-shape phenotype. SNP_IGA_776653 was the closest SNP to the candidate gene detected by The association analysis showed several strong SNP-trait associations with the studied traits. The leaf-gland phenotype trait acquired relevance because of glands contribution in biologic control . In addiPrupe_7G125700, such gen encoded an ankyrine repeat domain (ANK) protein. This protein family has been associated to plant immunity response against different pathogens . These genes belonging to important gene families involved in trichome formation and development in other species , a MADS-box gene protein which showed high similarity with GhMADS11, a gene expressed specifically in cotton fibres (trichomes) and associated to fibre cell elongation . COBRA-LIKE family genes were associated with important roles in specific types of cell expansion and cell wall biosynthesis, finding the COBRA-LIKE 9 highly expressed in trichomes  on chromosome 5 associated previously with the pubescence . These r species . Trichom species . Modifiebidopsis , cotton bidopsis , and petbidopsis . In factongation . Two modbidopsis  and tea bidopsis . Specifibidopsis . In addirichomes . The detrichomes , may sugFinally, we identified SNPs with significant association to flesh texture (FT) at the upper part of chromosome 4, in a region at the end part of chromosome 6 and in chromosome 7. FT has been related with endopolygalacturonase activity, and several previous studies have indicated the end region of chromosome 4 as the main region on the genetic control of peach texture . Strong p-value = 4.74E−04), located at 19,820,974 bp on scaffold 4. The SNP location is closed to the position of the two SNPs with strong association to flesh texture identified by On the other hand, a SNP detected here with high association with FT was SNP_IGA_477159  modifying regions related to harvest date indicate new lines of research. Genetic diversity results have shed new light in the predominant mating system in traditional Spanish peach varieties, the two main diversification regions (North and South of Spain) and the spread of peach cultivars. GWAS carried out detected new SNPs associated to TLG, FH, and FT using Spanish germplasm not previously analysed. The SnpEff analysis allowed the detection of PPM resistance and trichome development candidate genes to be further explored in breeding programmes. Finally, the genetic distance between accessions observed here, together with the phenotypic variation observed after evaluations of these materials, could ensure the adequate and representative diversity for future use in the improvement of important traits in peach breeding.The original contributions presented in the study are included in the article/PJM-G, CC, and MM: conceptualization. PJM-G: methodology. JM-G, CC, MM, and PJM-G: investigation and writing – review and editing. PJM-G, CC, and JM-G: data curation and writing – original draft preparation. All authors contributed to the article and approved the submitted version.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Triticum monococcum, germplasms hitherto not thought to have been used in modern wheat breeding, are already found in many commercial hexaploid wheats. The full data set which is useful for genomic and gene function studies and wheat breeding is available at https://rrescloud.rothamsted.ac.uk/index.php/s/DMCFDu5iAGTl50u/authenticate.A data set of promoter and 5′UTR sequences of homoeo‐alleles of 459 wheat genes that contribute to agriculturally important traits in 95 ancestral and commercial wheat cultivars is presented here. The high‐stringency myBaits technology used made individual capture of homoeo‐allele promoters possible, which is reported here for the first time. Promoters of most genes are remarkably conserved across the 83 hexaploid cultivars used with <7 haplotypes per promoter and 21% being identical to the reference Chinese Spring. InDels and many high‐confidence SNPs are located within predicted plant transcription factor binding sites, potentially changing gene expression. Most haplotypes found in the Watkins landraces and a few haplotypes found in  Thereet al., et al., et al., et al., et al., et al., A fully annotated, high‐quality sequence assembly of the large and complex hexaploid wheat genome , IWGSCrefseq_v1.0 was used  within the genes underlying the trait, as well as the environmental factors and gene‐by‐environment interactions. However, accumulating evidence suggests that mutations within regulatory regions may be equally important in generation of significant phenotypic differences  located within 1700 nucleotides upstream of the CDS of 459 wheat genes, associated with agriculturally important traits, in ancestral, synthetic, historic and modern wheat genotypes (Allen Triticum aestivum) cultivars including CS, 15 wheat landraces (T. aestivum) from the A. E. Watkins collection , eight T. monococcum  accessions , Aegilops tauschii , Ae. speltoides (ASP)  and the wild species Ae. peregrina (APG)  . 459 wheat genes of interest with a total of 1273 unique homoeo‐allele sequences were chosen for sequence capture and detailed analyses Table . The disU) Table , Data S2A myBaits (hereafter referred to as baits) capture technology developed by Daicel Arbor Biosciences was utilized to retrieve and sequence the specific promoter sequences of interest. To ensure the highly specific capture of promoters of individual homoeo‐alleles in wheat, a proprietary stringent workflow using RNA baits was chosen. In total, 17  745 unique baits were designed and manufactured to target 1700 bp of sequences located upstream of the annotated start codon of each of the 1273 homoeo‐alleles. For 71% of the promoters, there was >50% cover with highest stringency baits Figure . This exT. monococcum accession MDR308), except for Watkins 239 which for unknown reason(s) failed through the capture procedure. Total number of SNPs and InDels (≤20 bp) for each cultivar, ranging from 3536 – 242 384 SNPs and 381 – 15 116 InDels across the 95 accessions, are shown in Table −1 for CS  to 15.1 kbp−1 for the tetraploid grass ASP. The slight variation in polymorphism frequency between individual cultivars is shown in Figure T. monococcum accessions (average 14.1 ± 0.9 kbp−1), ASP (15.1 kbp−1) and APG (12.0 kbp−1) have significantly higher polymorphism frequencies  reflecting their distant relatedness/similarity to hexaploid wheat. The average frequency for hexaploid cultivars (including Watkins landraces) was found to be 1.9 ± 0.4 kbp−1, and only Sears Synthetic stands out with a ˜2x higher frequency of 4.7 kbp−1. However, this is again as expected due to the synthetic origin including foreign DNA introgression into this cultivar. These calculated values agree very well with our other analyses described below.In total, 3.15 Mbp of genome aligned sequencing data (collapsed to 1× coverage) was generated from the captured CS sequences. Captured sequences for individual cultivars ranged from 1.46 Mbp (cv. Crusoe) to 9.81 Mbp  ranged from 10‐ to 1115‐fold for the three diploid species, from 10‐ to 233‐fold for the two tetraploid species, and from 10‐ to 119‐fold for the hexaploid wheat CS (averages shown in Table n = 908), the total sequencing length obtained and the proportions of captured promoter and 5′UTR (the target sequence) as well as any exon and intron sequences were then determined. While the target sequence was usually 1700 bp, for 63 genes the target sequence was enlarged to take account of alternate transcriptional start sites. The total sequence lengths recovered from CS ranged from 629 bp for gene TraesCS3D02G113600/ T2‐14  to 4980 bp for TraesCS3D02G043500/ T2‐9 , with a median value of 1993 ± 568 bp , 326 genes had the complete homoeologue set (ABD), 69 genes had two homoeologues  and 20 were singletons present only in one sub‐genome (Table T. monococcum (AmAm) accessions but not from Ae. tauschii (DD), ASP or APG  which could contribute to cross‐hybridization  with however more frequent capture for the A and D‐subgenome  corresponding to reduced similarity to the CS genome  and T4‐57  are shown in Figure To determine the extent of homoeologue‐specific sequence capture, captured data were compared from the included control species (described above). The data presented in Figure G Figure , and thiG Figure . For cv.on Table . It is ie Figure . It is aTriticum sp. and Aegilops sp. accessions included, strongly suggesting that this is a transposable element albeit with very limited mobility because this sequence was found in only 29 other locations in the CS genome, and on only 16 of the 21 chromosomes. However, the low copy number per se does not rule this sequence out as a MITE, because even single copy number MITEs have been reported in plants  of the homoeologous genes in CS wheat in some cases clearly revealed insertions within one or more of the homoeologue promoters. For example, the alignment of the promoters of the three homoeo‐alleles of the gene T4‐57 revealed a 151 bp insertion in the promoter of the D sub‐genome located homoeologue Figure . This seet al., Triticum sp. and Aegilops sp. genotypes.To accelerate wheat improvement through breeding, haplotype mapping is frequently used for investigating genetic pedigrees and to identify blocks of linked alleles that are likely to be inherited together in genetic diversity panels as well as to identify genomic regions that contain novel sequence segments derived from other wheat genotypes and / or acquired through wider introgression breeding  the lengths and depths of captured sequences for promoters and CDSs Data , (ii) thRht1 (T9‐23) where haplotypes A3 (TraesCS4A02G271000), B6 (TraesCS4B02G043100) and D6 (TraesCS4D02G040400) are unique to SS  for most of the 908 investigated promoter sequences. Haplotypes are grouped as ‘shared’ if at least two hexaploid cultivars show the same haplotype, the rest are referred to as ‘unique’ (singletons) within this set of cultivars  found in the Watkins landrace W199 was also present in two commercial cultivars, Bobwhite and Apogee, while haplotypes B2, D2 and D3 were unique to individual Watkins landraces W199, W209 and W624, respectively. Interestingly, for most analysed genes the different haplotypes found in Watkins landraces are clearly related with a core of identical SNPs plus/ minus a few others  were not present in the Watkins landraces  identity with the CS IWGSC_refseq_v1.0 genome (0 SNPs) as a haplotype, as well as (ii) missing genes where neither promoter nor CDS sequences were captured from individual cultivars. Details of which cultivars have which gene missing are included in Data et al., et al., Of the 45 missing genes in cv. Hobbit, 34 genes reside on chromosome arm 7BS in the CS genome. In fact, these 34 genes comprise all genes included in this project residing on 7BS and these are spread evenly across the entire chromosome arm, while all genes residing on 7BL are also present in cv. Hobbit Figure . This stT. monococcum, Ae. tauschii and T. durum  based on the presence of identical haplotypes in these species and hexaploid cultivars   and subsequently entered into commercial cultivars. Furthermore, among the 30 commercial cultivars sharing this haplotype, it is noteworthy that 27 of these are related by pedigree and only 3 cultivars show no relationship to any of the other 27  can be distinguished from A1 only by the presence/absence of just 1 or 2 SNPs , 6AS (5 genes) and 7AS (5 genes), in all three cases very close to the telomeric end of these chromosome arms. Foreign introgression events are more likely to have occurred towards the telomeres  this only equates to 0.9 SNPs per promoter (polymorphism frequency of 0.6 kbp−1) which matches completely with the calculated homozygous polymorphism frequency of 0.6 kbp−1  where unexpectedly SNPs occurred compared to the IWGSC refseq_v1.0 genome assembly. However, 21% of these genes only have a single SNP in the promoter while 62% of promoters contained less than 5 SNPs across the whole target sequences and haplotypes with more than 10 SNPs were rare Data . In tota−1 Table . This co−1 Table .et al., The large wheat genome harbours a very high percentage of transposable elements (TEs), miniature inverted‐repeat transposable elements (MITEs) and other types of repeated sequences (The IWGSC For biotic stress (trait 4) genes, all 17 large deletions (compared to IWGSC_refseq_v1.0) were identified as (part of) named TEs Figure . Five ofT. durum cv. Kronos, but not in any commercial hexaploid wheat cultivars. Accession W733 shows a unique pattern, in that it contains a smaller deletion  within the region spanned by del1 (haplotype B7)  or to the full or partial sequence in del1 upstream of del3 (n = 187 full length) in the T4‐31B promoter and elsewhere in the genome, reinforcing the chimeric nature of the del1 sequence. The sequence corresponding to del2 only maps to the three homoeologues of this gene. Most haplotypes found in Watkins landraces share many identical SNPs with just one or two additional or missing ones, but this is also true for the unique haplotype B10 for USU‐Apogee (AP) which has only one missing SNP compared to the haplotype B2 in Watkins landrace W141 (red arrow). The complete absence of captured sequence for W777 shows that this gene is missing in this Watkins landrace (haplotype B8) while the unique absence of promoter sequence in W199 (haplotype B3) suggests either a long deletion or complete replacement with a different sequence, most likely another transposable element.Details of the promoter capture of the WRKY transcription factor gene TraesCS6B02G175100/ T4‐31B are shown in Figure ) Figure . Subsequ) Figure . This sh) Figure . We did We investigated whether any of the identified SNPs resided within recognized plant transcription factor binding sites (TFBS), and if the small InDels contained or corresponded to TFBS. For individual SNPs, this could result in the gain or loss of potential TFBS, whereas cultivars containing the small deletions would have lost any TFBS contained within. This in turn may lead to changes in homoeologue‐specific gene expression. Typical examples for both scenarios in biotic stress genes are shown in Figure Stb6 locus, residing on chromosome 3A, confers resistance to one of Europe's most important fungal pathogens, Zymoseptoria tritici, which causes Septoria tritici leaf blotch disease. Homoeologues of Stb6 are not present on the B or D sub‐genomes  genotypes and the Ae. peregrina (SvSvUU) genome. The expected and observed absence of coverage for Ae. tauschii reconfirms the specificity of the baits used, because Stb6 is present on 3A and no homoeologues are present in either the D or B sub‐genomes  or D genomes, Ae. speltoides (ASP) and Ae. tauschii accession ENT‐228 (ENT), respectively , Kronos (KR), Svevo, Zavitan and T. spelta . Importantly, these data confirm the accuracy of the promoter sequence capture analysis pipeline employed in this study.The low level of polymorphism of the a Figure . Moreovee Figure  that is During completion of this study, the updated Chinese Spring reference genome, CS_refseq_v2.0, was released by IWGSC. We have therefore subsequently compared both the target sequence similarity as well as the relative positions of all genes included in this project residing on one chromosome, Chr3A, between refseq_v1.0 used for this study and refseq_v2.0. This showed that 54 of the 57 genes (95%) have identical target sequences upstream of the ATG start site in both reference genomes. Of the remaining three genes, two have 99% homology (a single  nucleotide deletion (TraesCS3A02G105500) and a 9 bp insertion (TraesCS3A02G129000) in refseq_v2.0) while the third is still 93% identical  and is the only gene to contain a significant number of changes. Furthermore, the relative location of virtually all included genes on Chr3A has changed only slightly, with the exception of TraesCS3A02G311100 (T1‐4) which resides on 3AS in refseq_v2.0 compared to 3AL in refseq_v1.0, but the target sequence of this gene is again identical in both reference genomes  is available within the ENA BioProject PRJEB45647.The high‐quality data set presented here allows for the first time detailed analysis of individual homoeologue promoters of wheat genes across the three sub‐genomes. The high‐stringency capture used permitted high‐confidence SNPs and InDels to be analysed within these individual homoeologue promoters. This should contribute directly to greater insight into the variance of homoeologue‐specific gene expression both within one species and across a wide variety of wheats and related species. In addition, these data are already being employed by UK wheat breeders and wheat researchers to generate high‐confidence KASP markers for a wide range of trait genes.T. monococcum lines). These data can be used to provide new insights in numerous fundamental research projects and to enhance the knowledge associated with emerging wheat genetic resources . The high specificity of the baits capture, which considerably simplified the subsequent data handling and analyses, was only achieved because a highest stringency approach was taken for the design and use of all the baits. This made individual capture of homoeologue promoter and 5′UTR sequences at high sequencing depths routinely possible. Also, we found that complete capture of the target sequences could be achieved with only a few well‐spaced baits, reducing the design and costs of similar capture experiments.In this study, at a modest cost, a highly flexible experimental approach, hitherto only applied to exome analysis, was devised which now provides a wealth of comparative promoter and 5′UTR polymorphism data for a large cohort of UK elite hexaploid cultivars as well as a range of wheat accessions and species important for wheat improvement  of genes of interest or considerably longer sequences within a single capture experiment for the same cost. Finally, the technical approach used in this study also successfully permitted the calling of absent sequences within the promoters and absent genes in individual cultivars, even to the point that a nullisomic cultivar (Hobbit) could be identified. Likewise, entire promoters with large numbers of polymorphisms for individual homoeologues from non T. aestivum species were captured and sequenced to high depth. These important observations and reported findings would allow researchers to explore very diverse germplasm collections using the same experimental approach with a high level of confidence.We report here, for the first time, highly specific individual capture and detailed analysis of homoeo‐allele promoters for a great diversity of functional wheat genes. This success was only possible because of the high‐stringency and high masking approach used when designing the baits . This strategy also significantly reduces the time required to complete the bioinformatic alignment of the captured sequences to the CS reference genome and allows the calling of high‐confidence homozygous SNPs. Surprisingly, this level of bait stringency did not compromise our ability to capture sequences at a high read depth even from the non T. aestivum lines from the CIMMYT breeding programme   and refseq_v2.0 (released subsequently) for 54 of the 57 Chr3A genes included in this study demonstrates again the extremely high quality of the IWGSC CS refseq_v1.0 genome and strongly suggests that similar identities would be found on the other wheat chromosomes. Therefore, the analyses and results reported here using CS refseq_v1.0 would be expected to be either very close or identical in refseq_v2.0.The freely available complete data set generated here will allow researchers to examine specific genes of interest directly and should in particular contribute to gene regulation studies because the low number of SNPs and InDels in the promoters should accelerate confirmation and / or discovery of TFBSs.et al., et al., T. monococcum , whose genome is related but not identical to the A sub‐genome of durum and bread wheat, and which possess desirable new traits for wheat improvement  that contributed the D sub‐genome of hexaploid wheat, Ae. speltoides  whose diploid genome is related to the B sub‐genome of hexaploid wheat and the tetraploid wild species Ae. peregrina . These controls were included to be able to determine the specificity of the technology used in capturing homoeo‐alleles, and in the case of the reference CS genome to determine the overall accuracy of the sequencing methodology – ideally no SNPs should appear in the captured sequences of CS relative to the CS reference to which all reads were mapped.A collaborative approach was taken for the selection of the 96 wheat genotypes Table . In totahttps://www.jic.ac.uk/research‐impact/germplasm‐resource‐unit; https://www.seedstor.ac.uk). Seed stocks for most of the T. monococcum genotypes originally came from The Vavilov Institute, St Petersburg, Russia  at the John Innes Centre (https://norgenbiotek.com/product/plantfungi‐dna‐isolation‐kit) and DNA integrity and concentrations confirmed by 0.8% agarose gel electrophoresis and Qubit fluorescent dye measurements. All seedlings of the winter wheat accessions selected for DNA extraction were then transferred into vernalization conditions for 8 weeks. Either post‐vernalization or when the seedlings of the spring wheat varieties were at the 3‐leaf stage each plant was transferred singly into a 1.5 L pot containing Rothamsted prescription mix compost with fertilizers added when required. Each plant was individually bagged prior to anthesis until full grain maturation. All mature ears were photographed before storage.Seeds were pre‐germinated on moist filter paper for 3 days at room temperature and then transferred to Levington F2S seed & modular compost in P40 trays. Leaf tip samples (5 cm in length) were taken at the 2‐leaf stage from each seedling for DNA preparation. Only a single plant for each of the 96 genotypes was selected for DNA extraction. Genomic DNA was extracted from young leaf material with NorGen Plant / Fungus DNA Isolation kits  and then screened against the IWGSC RefSeq_v1.0 for specificity. Probes with multiple strong predicted hybridization sites and/or that were 25% or more soft‐masked were then removed. This reduced the original probe candidate list by more than 50%, leaving a final 17745 surviving probe sequences that were subsequently synthesized as part of a myBaits‐1 kit with Daicel Arbor Biosciences. These 17745 high‐stringency baits were targeting 1700 bp of sequences located upstream of the annotated start codon of each of the 1273 homoeo‐alleles. For 63 genes, the target sequence was enlarged to take into account alternate transcriptional start sites . For 34 genes, only 5′UTR sequence baits were designed because these genes have very large predicted 5′UTRs (up to 5 kbp). Furthermore, for 33 genes the 1700 bp target sequence had to be reduced because of large stretches of unidentified nucleotides (Ns) upstream in the reference sequence (down to a minimum of 854 bp for gene TraesCS5B02G175800/ T2‐39). Short stretches of Ns within the target sequence were randomly assigned nucleotides using the standard proprietary Daicel Arbor Biosciences algorithms. These nucleotides are shown as small letters in the bait sequences Data .The myReads team at Daicel Arbor Biosciences first sonicated the DNA extracts using a QSonica Q800R sonicator and subsequently size‐selected the sheared material to 400–600 bp lengths. Then, they converted up to 80% of the size‐selected material (between 18 and 500 ng) to dual‐indexed TruSeq‐style Illumina sequencing libraries, each with unique combinations of dual 8 bp indexes, using 6 cycles of indexing amplification. Then, 500 ng of each library (with one exception: 81 ng of library for sample ‘Watkins 239’) was enriched with the custom myBaits‐1 kit following manual version 4.01, with 10 cycles of post‐capture amplification. They then constructed two pools of 48 enriched libraries with equal mass contribution per library and submitted these for sequencing on a HiSeq 2500 instrument using PE100 chemistry at a third party provider. FASTQs were post‐processed and demultiplexed by both index sequences and subsequently taken to analysis.et al., No trimming of reads took place. The captured sequences were mapped to the CS genome reference (IWGSC_refseq_v1.0). Within Galaxy  and Variant Call Format (VCF) files were downloaded from Galaxy and used for subsequent visualization and analysis using the IGV (Integrative Genome Viewer) software, initially. All BAM/VCF files generated for this project will be made available upon full publication of the manuscript together with the full genome (161010_Chinese_Spring_v1.0_pseudomolecules_parts.fasta) and the second version (1.1) of the gene annotation file used (IWGSC_v1.1_HCLC_parts_genome.gff3). The latest version of IGV can be downloaded from et al., https://github.com/cardinalb/helium‐docs/wiki).Pedigrees were viewed using the Helium software .For comparison of the potential introgression events on chromosome arms 5AL, 6AS and 7AS as found in this study, available cultivars were checked using the CerealDB Putative Introgression Plotter (http://www.softberry.com/berry.phtml?topic=nsitep&group=programs&subgroup=promoter). Concerning individual SNPs, the sequence was selected in IGV ±5 bp surrounding the SNP and both the 11 bp sequence for the wild type and SNP version were searched. For this analysis, the search results were filtered to include only 100% matches of recognized plant TFBS  software online (https://www.geneious.com/). Specifically for the Stb6 analyses, multiple sequences alignment was carried out using ClustalW in Geneious.The Geneious bioinformatics platform was used for the comparison of homoeologue sequences using various alignment tools (https://botserv2.uzh.ch/kelldata/trep‐db/index.html) and CLARITE_CLARIrepeatwheat databases.To search for transposable elements, all the large deletions were compared using BLASTN against the TREP  commercial varieties sharing the T. monococcum MDR037 haplotype A1 for gene TraesCS5A02G558200 (T5‐10) and (b) pedigrees of cultivars used in relation to Chinese Spring.Click here for additional data file.Figure S5 Cultivars with missing genes.Click here for additional data file.Figure S6 Physical locations of genes with potential T. monococcum introgression.Click here for additional data file.Figure S7 SNP diversity and occurrence observed in the control Chinese Spring accession.Click here for additional data file.Figure S8 Larger deletions observed in Biotic Stress (Trait 4) gene promoters.Click here for additional data file.Figure S9 Alignments of recently fully sequenced wheat genomes for Stb6.Click here for additional data file.Table S1 The 96 wheat cultivars/accessions included in this study.Table S2 Total numbers of mapped sequences, SNPs, InDels and homozygous polymorphisms frequency for each cultivar.Table S3 Cultivar distribution of Trait 4 promoter large deletions.Click here for additional data file.Data S1 List of all 1273 homoeologues including IWGSCrefseq1.1 gene IDs, complete target sequences and individual details for all baits used.Click here for additional data file.Data S2 Details for all cultivars.Click here for additional data file.Data S3 Sequencing lengths, depths and homeologue specificity.Click here for additional data file.Data S4 Hexaploid haplotypes.Click here for additional data file.Data S5 Shared haplotypes between “ancestral” and hexaploids.Click here for additional data file.Data S6 Deletions, TFBS.Click here for additional data file.Data S7 Genes on Chr3A refseq1.0 vs 2.0.Click here for additional data file.