Musa textilis Née) is an economically important fiber crop in the Philippines. Its economic potential, however, is hampered by biotic and abiotic stresses, which are exacerbated by insufficient genomic resources for varietal identification vital for crop improvement. To address these gaps, this study aimed to discover genome-wide polymorphisms among abaca cultivars and other Musa species and analyze their potential as genetic marker resources. This was achieved through whole-genome Illumina resequencing of abaca cultivars and variant calling using BCFtools, followed by genetic diversity and phylogenetic analyses. A total of 20,590,381 high-quality single-nucleotide polymorphisms (SNP) and DNA insertions/deletions (InDels) were mined across 16 abaca cultivars. Filtering based on linkage disequilibrium (LD) yielded 130,768 SNPs and 13,620 InDels, accounting for 0.396 ± 0.106 and 0.431 ± 0.111 of gene diversity across these cultivars. LD-pruned polymorphisms across abaca, M. troglodytarum, M. acuminata and M. balbisiana enabled genetic differentiation within abaca and across the four Musa spp. Phylogenetic analysis revealed the registered varieties Abuab and Inosa to accumulate a significant number of mutations, eliciting further studies linking mutations to their advantageous phenotypes. Overall, this study pioneered in producing marker resources in abaca based on genome-wide polymorphisms vital for varietal authentication and comparative genotyping with the more studied Musa spp.Abaca ( Musa textilis Née) is a fiber crop endemic to the Philippines [Musa tex 51, Tinawagan Puti and Abuab are recommended for cultivation; Linawaan, Laylay and Inosa in the Visayas; and Bongolanon, Maguindanao and Tangongon in Mindanao [Abaca  and DNA insertions/deletions (InDels), which are the top two most abundant DNA polymorphisms/variants in the plant genome ,17.SNPs are the smallest unit of genetic differences and are the most abundant sequence variations in the genomes of most organisms . They haInDels, which are the second most abundant genetic variation next to SNPs, are another group of markers of choice due to their codominant inheritance, wide genome distribution and multiallelic nature . CompareM. textilis is very rich, as evidenced by the calculated Shannon’s Diversity Index of 0.68 [Musa species, but the identification of varieties within M. textilis was not performed. Aside from varietal identification, banana-based SSR markers associated with abaca bunchy top virus (ABTV) resistance have also been used to screen potential resistance to said virus among 57 accessions [As of this date, only two studies ,24 have  of 0.68 . The afo of 0.68  during acessions . Since tcessions .To facilitate the uncovering of true genome-wide polymorphisms that will be extremely important resources for abaca varietal identification and efficient breeding programs, the declining cost and increasing accessibility of next-generation sequencing (NGS) can be taken advantage of. NGS allows the generation of huge datasets containing genome-wide variants across individuals, hence providing a highly abundant resource of genetic markers for varietal identification and comparative genotyping across individuals, populations or species ,29. A reMusa and analyze their potential as genetic marker resources through genetic diversity and phylogenetic analyses.Recently, the abaca genome has been assembled , thus prM. textilis and among Musa spp.Specifically, this study aimed at (1) whole-genome resequencing of 11 abaca varieties and accessions, (2) mapping of sequence reads to the abaca reference genome, (3) mining and analysis of SNPs and InDels and (4) determining genetic variation and evolutionary relationships within Nine varieties housed at the Mindoro Fiber Experimental Station and Seedbank (MFESS), a PhilFIDA germplasm collection located in Socorro, Oriental Mindoro, Philippines, were used for the determination of genome-wide variants in abaca in the form of SNPs and InDels. The varieties are Abuab, Inosa, Kutay-kutay, Laylay, Tangongon, Hagbayanon, Luno, Socorro and Tinawagang Puti (T-puti). The Abuab, Inosa and Tangongon varieties are among the only three abaca varieties that are currently registered under the National Seed Industry Council (NSIC) of the Philippines. NSIC registration indicates that these varieties qualified for the criteria set for commercially propagated varieties, such that they must: (1) yield at least 800 kg/hectare dried fiber, (2) have a minimum fiber recovery of 1.5%, (3) be currently planted commercially at the event of registration and (4) have been reported and described technically in reputable publications . MoreoveDNAs were extracted from all samples using the SEPa Plant DNA Isolation Reagent Kit . For next-generation sequencing, libraries were prepared using the Nextera DNA Library Prep Kit , and PE 150 sequencing was performed using the Illumina Hiseq 4000 platform .To ensure the quality of bases used for variant calling, the program Trimmomatic  RRID:SCRThe quality-filtered sequence reads were mapped to a polished version of the Abuab reference genome  using thThe bamqc command by the Qualimap tool v.2.2.2-dev  RRID:SCRTo serve as additional sources of polymorphisms, quality-filtered reads previously produced for the Ihalas accession  were incM. acuminata, M. balbisiana and M. troglodytarum were also included in the mapping step to determine the polymorphisms present and evolutionary relationships between M. textilis and the other Musa spp. These will be important resources and information for identification of polymorphisms associated with disease resistance and advantageous agronomic traits. A summary of all sequence reads mapped to the reference genome is shown in Sequence reads from Raw variants between the reference genome and each of the varieties were called through BCFtools version 1.15.1 RRID:SCR_005227 mpileup and call -vc commands using the sorted BAM files as input . The mpiThe VCF files generated from the variant calling step were indexed and merged into one VCF file using the BCFtools commands index and merge, respectively. The SNPs and InDels were distributed into two separate VCF files using VCFtools version 0.1.16  RRID:SCRTo filter high-quality variants, the VCF files were used as input for VCFtools. The parameters --min-alleles 2 and --max-alleles 2 were used to select only biallelic variants, while the --minQ 40 parameter was used to select only the variants with a mapping quality higher than 40.http://pngu.mgh.harvard.edu/purcell/plink/, accessed on 11 November 2022) [2 > 0.2. The r2 [i and pBj, respectively, and the haplotype frequency of AiBj is represented by After quality filtering in VCFtools, the variants were further filtered using the PLINK 2.0 software (er 2022)  RRID:SCRer 2022) . As a co. The r2  refers tby pAiBj ,47.Pruned variants were further filtered using the parameters --geno 0.10 and --maf 0.05. The former filters out all variants with missing call rates exceeding 10% and the latter filters out all variants with minor allele frequency (MAF) below 5%, respectively. This step yielded high-quality genome-wide SNPs and InDels.He); minor allele frequency (MAF) and polymorphism information content (PIC) were also calculated. He provides information on the likelihood of particular loci being heterozygous; hence, He is a fundamental metric for estimating the genetic diversity in a population in terms of gene diversity at a locus [He is calculated using the following formula:I represents the distinct number of alleles at a particular locus and Pi represents the frequency of allele I within the population [The retained high-quality polymorphisms were analyzed for their informativeness using metrics such as heterozygosity  of each locus was calculated. Phylogenetic analysis was conducted using the Poppr package through a neighbor-joining algorithm [For downstream genetic variation and phylogenetic analyses, the VCF files produced after variant pruning were preprocessed using the vcfR package  RRID:SCRlgorithm  with 100lgorithm .The extracted DNA from the nine abaca varieties and two additional accessions, namely ‘Luno Green’ and Samoro, were of sufficient purity, with A260/A280 ratios ranging from 2.161 to 3.056; A260/A230 ratios ranging from 0.373 to 0.738; and DNA concentration ranging from 266.50 to 641.35 ng/μL . The DNAWhole-genome resequencing through the Illumina Hiseq platform generated a total of 646,842,305 sequences, ranging from 37,019,179 to 83,388,873 sequences across the varieties, and equivalent to a total of 1,053,306,114 sequence reads . The reaMusa reads [M. textilis  as well as for other diploid relatives, namely, M. acuminata , M. balbisiana  and M. troglodytarum  [The quality-filtered sequence reads were used for mapping to the polished version (through the PILON v1.22 program) of the reference genome . This stsa reads  for M. t2x = 20) ,59. The Musa sequence reads [M. balbisiana, 18.4×), SRR9850640  and SRR9850641 . Mean MQ values across these accessions also ranged between 17.9–37.1.Mapping of the ce reads  to the aM. textilis and among Musa spp., variant calling was performed using the SAMtools and BCFtools programs. Variant calling produced a VCF file for polymorphisms within M. textilis containing 19,189,434 SNPs and 1,400,947 InDels  was produced and contained 34,643,663 and 1,933,417 high-quality and biallelic SNPs and InDels, respectively.Among these, the number of high-quality (MQ > 40) and biallelic SNPs and InDels was 15,410,778 and 1,109,789, respectively. Another VCF containing polymorphisms between and among M. textilis and 34,253,534 high-quality polymorphisms among Musa spp. were found to be correlated in terms of linkage (r2 > 0.2). To avoid capturing loci with redundant genetic information, these polymorphisms were pruned, retaining 635,945 SNPs and 84,711 InDels within M. textilis, and 2,130,711 SNPs and 192,835 InDels among Musa spp. To ensure the accuracy of downstream multivariate and phylogenetic analysis, only loci with at most 10% missing genotypes were selected, retaining 130,768 SNPs and 13,620 InDels within M. textilis and 31,244 SNPs and 577 InDels among Musa spp.Despite the huge number of polymorphisms, a total of 15,799,911 high-quality polymorphisms within M. textilis was also discovered, consisting of 2.5% and 10.9% of the total SNPs and InDels, respectively  comprise most of the multiallelic InDels within M. textilis, and 142,145 triallelic InDels  comprising most of the multiallelic InDels among Musa spp.  and transversions (Tv) among the mined high-quality and pruned SNPs within versions .M. textilis species, the Ts/Tv ratio was calculated to be 4.76 .To compare this dataset with existing literature, SNPs were mined within R8989638 . The minR8989638  for M. aM. textilis, and 31,244 SNPs and 577 InDels among Musa spp. in The retained 130,768 SNPs and 13,620 InDels within The genome-wide variation between abaca varieties and accessions was visualized through multivariate analysis by PCA .The Abuab, Inosa and ‘Luno Green’ varieties were observed to have coinciding and indistinguishable points in PCA, while the Luno and Hagbayanon varieties were observed to have partially coinciding points. The rest of the samples have points that are clearly distinguishable from each other in PCA.He and MAF across the 130,768 SNPs and 13,620 InDels revealed quantitative estimates of the genetic diversity among the abaca varieties and accessions  tree was also generated using Hamming distance (through the bitwise.dist function) for calculation of pairwise genetic distances, i.e., the proportion of dissimilar loci between varieties/accessions  Figure .The NJ tree generated using the genetic distances generated for the SNP genotypes grouped the varieties and accessions into three main monophyletic groups. The accessions , namely,Musa spp. distinguished M. textilis and M. troglodytarum species, and distinguished each of the Musa accessions, indicating the resolving power of InDels for genetic differentiation  .The persisting problems of destructive abaca diseases and abiotic stresses and the slow development of abaca breeding programs for disease resistance and climate resilience, aggravated by confusions in the true identity of abaca varieties and unavailability of abaca genome-based genetic marker resources, impede the Philippine abaca industry.Musa, this resequencing approach has been previously employed [Musa itinerans species by mining genome-wide variation using sequencing data with 15.5× coverage. This approach was employed in another study (through resequencing of at least 20× coverage) for multiple Musa species to generate and analyze genomic resources for polymorphism detection usable for studying Musa evolution, diversity and breeding [The whole-genome resequencing approach is a useful method for identification of genome-wide polymorphisms vital for plant genetic resources management . While aemployed  for detebreeding .Musa spp. That are important for addressing the scarcity of genetic information and resources vital for efficient abaca breeding programs. Specifically, these are the lack of abaca genome-based genetic markers usable for accurate identification of abaca varieties used for mass cultivation and as breeding materials, and the lack of genetic marker resources for identification of polymorphisms across Musa spp. that are associated with agronomically and economically important traits. The current study utilized the resequencing approach in an attempt to capture genetic diversity and relationships among abaca cultivars and among The first steps undertaken were the resequencing of 11 abaca varieties and accessions at a minimum of 20× coverage and mapping the reads to the abaca reference genome . The calMusa sequence reads [Mapping of the published ce reads  to the ace reads  producedM. textilis, consisting of 93.2% SNPs and 6.8% InDels, and 47,580,541 variants among M. textilis, M. acuminata, M. balbisiana and M. troglodytarum, consisting of 94.8% SNPs and 5.2% InDels. This number of polymorphisms among Musa spp. is significantly higher compared to those within M. textilis polymorphisms owing to the higher expected interspecific  genetic diversity than infraspecific  genetic diversity. Among the mined polymorphisms, the high-quality (MQ > 40) SNPs and InDels were selected, and accounted for 80.3% and 79.2% of their respective total number within M. textilis, and 76.7% and 78.3% of their respective total number among the Musa spp. Even though mean MQ values around 17 were regarded as acceptable based on the equivalent mismapping probability, filtering of individual polymorphisms was based on more stringent MQ values to ensure optimal accuracy of these mined polymorphisms.Mining of genome-wide SNPs and InDels was achieved using a bioinformatics pipeline featuring SAMtools and BCFtools. This step produced 20,590,381 variants within M. textilis have MQ values within the range of 57.5–60.0 , owing to their supposedly biallelic nature . The perlic loci , enable M. textilis (4.76) than among Musa spp. (1.38). Transitions are single-base mutations resulting in the nucleotide with the same number of rings as the wild type , while transversions occur as a result of mutation of the wildtype nucleotide base into a base with different number of rings  [Musa spp., there is a higher rate of transversion; this could be another source of phenotypic differences among the Musa spp.Further analysis of the SNPs revealed the transition/transversion ratio to be higher within utation) . Since tutation) . Since SMusa spp. than within the M. textilis species contributed to the genetic differentiation across the four Musa spp., and these findings can serve as a basis for further research on the phenotypic implications of these genetic variations in Musa.The current study thus suggests that the higher percentage of multiallelic loci and higher transversion rate among the four M. textilis and among Musa spp. PCA of the SNP genotypes within M. textilis revealed a high similarity between the Abuab reference and the resequenced Abuab variety. It also enabled differentiation among abaca varieties based on their SNP profiles, except for two sets of varieties in which their SNP profiles were projected by coinciding points  of the high-quality polymorphisms that were pruned for linkage disequilibrium revealed genetic variation within g points a. The ovg points b. Calculg points , both ofHe for each locus provided information on genetic diversity among the M. textilis varieties and accession. In comparison with the only published literature on abaca SNPs, i.e., the banana genome-based SNP markers cross-species adapted into abaca [He than the cross-species markers. This therefore indicates the higher capability of the abaca genome-based SNPs mined in this study to capture genetic variation within the M. textilis species compared to the cross-species markers. The mean MAF calculations, which are also genetic diversity metrics since they measure the frequency of the second most common allele in the population [M. textilis species, and thus are usable for genetic differentiation applications such as varietal identification.Calculation of to abaca , this cupulation , were alpulation . These mM. textilis, a neighbor-joining tree generation approach was conducted. The calculated pairwise genetic distances among the abaca varieties and accessions are listed in To elucidate phylogenetic relationships within Musa spp., UPGMA analysis was performed. Within M. textilis, the Ihalas was observed to be the abaca accession closest to M. troglodytarum, and the clusters that were formed were in agreement with M. balbisiana, were widely documented to possess degrees of tolerance to viral diseases, e.g., [Both the NJ tree for the SNP and InDel loci clustered together the pairs Samoro and Socorro, Luno and Hagbayanon and Tangongon and Tputi b. The brs, e.g., . These dMusa spp., and then the insertion/deletion events in Musa spp. and within M. textilis. These findings, together with the homozygosity data in Since SNPs are the most abundant genetic variation and are significantly more abundant than InDels , the phyM. textilis species and between Musa textilis, M. acuminata, M. balbisiana and M. troglodytarum. This is further supported by the elucidated genome-wide variations, genetic diversity and evolutionary relationships among abaca varieties and other Musa spp. While markers for abaca genetics studies were developed in the past few years, these markers were based on the banana genome. This study, hence, is the first report on genome-wide polymorphisms across abaca varieties based on the genome of abaca itself. Consequently, this is a pioneering work elucidating the genome-wide variation and evolutionary information across abaca varieties and among the four Musa species.Overall, this study successfully mined genome-wide SNPs and InDels in abaca, which served as highly important resources for genetic marker discovery and downstream applications in genetic resources management such as varietal identification. Their potential as genetic marker resources is exemplified by their capability to detect variation within the The genetic markers with sufficient diversity and polymorphism detection capability generated in this study will be impactful for the development of abaca genome-based assays for routine identification of abaca varieties and accessions. The mined polymorphisms among abaca and the more extensively studied banana will be important resources for identifying genes (through comparative genotyping) that may be important for abaca crop improvement and climate change adaptability. Altogether, these will lead to an efficient and effective abaca breeding program that will highly benefit the Philippine abaca industry and its global stakeholders. Musa polymorphisms identified in this study to enable the discovery of polymorphisms in the widely studied banana species that are linked to important traits. Since phylogenetic analysis using the genome-wide SNPs revealed the Abuab and Inosa varieties to have experienced a significant number of mutations, further characterization studies of these two varieties are recommended. This will identify agronomically important traits that resulted from accumulation of possibly advantageous characteristics.For future studies, more phenotypically associated markers will need to be mined from the between-


Combining the whole genome sequence (n = 19 bulls) and the cows’ BovineHD BeadChip allowed imputing genotypes for these mutations that were verified by Sanger sequencing, whereas, an error rate of 11.6% and 10.7% were encountered for HRM and TaqMan, respectively. We show that this error rate seriously affected the linkage-disequilibrium analysis that supported this gene candidacy over other BTA14 gene candidates. Thus, imputation produced superior genotypes and should also be regarded as a method of choice to validate the reliability of the genotypes obtained by other methodologies that are prone to genotyping errors due to technical conditions. These results support the view that RFI is a complex trait and that searching for the causative sequence variation underlying cattle RFI should await the development of statistical methods suitable to handle additive and epistatic interactions.In dairy cattle, identifying polymorphisms that contribute to complex economical traits such as residual feed intake (RFI) is challenging and demands accurate genotyping. In this study, we compared imputed genotypes ( In dairy cattle, identifying polymorphisms that contribute to complex economical traits remains a challenge as gene effects fit an infinitesimal distribution, whereas, within breeds, the high levels of linkage disequilibrium (LD) often result in several significant effects being in complete LD ,2. This Following quantitative-trait-loci (QTL) linkage-based studies, generally practiced as genome-wide association studies (GWAS), the identification of a causative polymorphism is achieved by targeted resequencing of small QTL intervals or by analysis of candidate mutations documented by whole-genome sequence (WGS) projects . The cauFABP4) has a large effect on this trait [FABP4 gene to RFI is smaller than has been previously estimated [Being a statistical methodology, genotype imputation predicts single nucleotide polymorphism (SNP) genotypes based on observed haplotypes in a reference population . Using ris trait ,12. We dstimated ,12, compA total of 192 Israeli Holstein multiparous dairy cows from the Agricultural Research Organization (ARO) dairy herd  were used in the present study of which 114 cows were used in previous studies . The RFIBos taurus ARS-UCD1.2) [https://github.com/lh3/bwa, accessed on 13 February 2023). Next, polymerase-chain reaction (PCR) duplicates were removed using the Picard tool v2.20.2; , and the curated BAM files were coordinated, sorted, and indexed using the Picard algorithm. Variant calling was carried out with the Genome Analysis Tool Kit v4.1.6.0  . Reads w.0 (GATK ) as reco.0  following the manufacturer’s protocol. A total of 192 cows were genotyped on the BovineHD BeadChip (777K SNPs), Illumina Inc., San Diego, CA, USA) by GeneSeek . Using the Linux awk command, genetic markers along BTA14 were selected in the analyzed windows  with a maximal Pearson correlation coefficient of 0.71.From the joint VCF file of 19 bulls with WGS data, all likely functional variants  and their genotype in each of the sires were obtained from +/− 1 Mb flanking the marker with the lowest probability of accepting the null hypothesis of no effect. A total of 18 likely functional SNPs were retained for further analysis. Assuming linkage disequilibrium between the BovineHD SNP associated with RFI and the hypothetical causative variant, both are expected to co-segregate. Therefore, the numeric representation of their genotypes  would correlate. The numeric representation was as follows: homozygote for the reference allele , the heterozygote , and the non-reference homozygote . We thus calculated the Pearson correlation coefficient between the vector of the likely functional SNP genotypes, to the vector of the significant SNP marker genotype. These steps pointed out one SNP in the gene zinc-fingers and homeoboxes 2  of 10 SNPs detected in gene FABP4, of which FABP4_4 is capable of encoding a conservative substitution of hydrophobic amino acids [FABP4 locus indicated segregation for all 17 examined SNPs and the accuracy of the determined genotypes  software  representing three possible zygosity states , which was also present in two other haplotypes that had no effect  have previously displayed different genotypes for these SNPs in the same cows , respectively. Thus, current results show a modest effect and ability to explain variance  related individuals with WGS data were enough to yield accurate genotyping imputation, whereas, the HRM and TaqMan methods had an error rate of over 10%. Genotyping error rates as low as 3% and even 1% are thought to have serious effects on LD analyses [FABP4 SNPs on RFI was as expected of the infinitesimal model for highly complex traits involving numerous additive and epistatic interactions such as the scenario that has been previously described for RFI [FABP4 and were able to locate another missense mutation in the highly conserved homeobox gene ZHX2. In mice, ZHX2 is a transcription factor with demonstrated centrality in liver metabolism, including lipid accumulation, enhanced inflammation, and hepatic fibrosis [FABP4 missense mutation. Altogether, this suggests that attributing the effect of BTA14 on RFI to FABP4 [p ≤ 0.05 would result in random cases being overestimated as having large effects. Genotyping errors can be generated even with high-quality standards [Sanger sequencing results indicated that the imputed genotypes were all correct and that few , this mto FABP4 ,12 was otandards . Our stutandards , and Taqtandards .Our case of a re-evaluation of genotyping methodologies used to study the RFI of dairy cattle demonstrated that genotyping by imputation based on WGS and BeadChip data produced superior results compared to other methods that are prone to genotyping errors due to technical conditions. Thus, genotyping by imputation should also be regarded as a method of choice to validate the reliability of the genotypes obtained by other methodologies. The results support the view that RFI is a complex trait and that searching for the causative sequence variation that underlies RFI should await the development of statistical methods that would be able to handle additive and epistatic interactions of more than two loci that influence parametric trait values.


Rapid adaptation to new environments can facilitate species invasions and range expansions. Understanding the mechanisms of adaptation used by invasive disease vectors in new regions has key implications for mitigating the prevalence and spread of vector-borne disease, although they remain relatively unexplored.Aedes aegypti mosquitoes collected from various sites in southern and central California with 25 annual topo-climate variables to investigate genome-wide signals of local adaptation among populations. Patterns of population structure, as inferred using principal components and admixture analysis, were consistent with three genetic clusters. Using various landscape genomics approaches, which all remove the confounding effects of shared ancestry on correlations between genetic and environmental variation, we identified 112 genes showing strong signals of local environmental adaptation associated with one or more topo-climate factors. Some of them have known effects in climate adaptation, such as heat-shock proteins, which shows selective sweep and recent positive selection acting on these genomic regions.Here, we integrate whole-genome sequencing data from 96 Ae. aegypti impacts the arboviral disease landscape and how such adaptation could help or hinder efforts at population control.Our results provide a genome wide perspective on the distribution of adaptive loci and lay the foundation for future work to understand how environmental adaptation in The online version contains supplementary material available at 10.1186/s12864-023-09402-5. Biological invasions, involving the introduction, establishment, and spread of species outside their native zone, present one of the main threats to biodiversity, ecosystem integrity, agriculture, fisheries, and public health; with economic costs amounting to hundreds of billions of dollars per year worldwide , 2. DuriAedes aegypti, the yellow fever mosquito, is the primary urban vector of dengue viruses worldwide is prevalent throughout the tropics and sub-tropics and is closely associated with human habitats outside its native range in Africa. The state of California has maintained an active and extensive mosquito surveillance program initiated in the early 1900s [Ae. aegypti near airports [Ae. aegypti in California were never reported prior to the summer of 2013, when they were detected in three cities in the central valley counties of Fresno and Madera and the coastal county of San Mateo [Ae. aegypti has now become established and is spreading throughout large regions of California [Ae. aegypti were presumably introduced from two independent introductions which came from the South-Central US and Southwest US/northern Mexico region specifically [Ae.aegypti to USA have also undergone behavioural and genetic changes in comparison to their ancestral African form, including the evolution of house-entering behaviour and a preference for human odour and blood-feeding [The establishment and persistence of vectors within new ecological niches poses a serious threat from emerging and endemic arboviral diseases . Dengue ly 1900s  and has airports . Confirman Mateo , 17. Sublifornia . Recent -feeding .Aedes mosquitoes [Ae. aegypti in California will improve our knowledge of evolutionary forces and processes during the invasion of disease vectors, which is crucial for advancing dynamic mitigation strategies aimed at reducing disease risk worldwide [Although it is known that the environment is a key element in driving and altering the life-history traits of squitoes –24, thersquitoes . In the orldwide , 27.Ae. aegypti from 12 geographic districts were collected across southern and central California between 2013–2017 .Raw reads were trimmed using Trimmomatic  version We called variants using Freebayes  version 2) between markers exceeded 0.2 using an R package, SNPRelate [We started by generating linkage disequilibrium (LD) pruned SNP sets as follows. We set sliding widows of size 50 (that is the number of markers used for linkage disequilibrium testing at a time) and window increments of 5 markers. For any pair of SNPs in a window we defined, the first marker of the pair was discarded when the correlation coefficient   was condA total of 25 biologically relevant topo-climate variables  via the transformation 10 log10 (BF). As a decision rule and to calculate a significance threshold for outlier identification, pseudo-observed data (POD) were employed with the same random 10 kb SNPs used for the core model, and a 1% empirical threshold was calculated for the observed Bayes factor. To produce a narrower set of outlier loci, we then followed the popular Jeffreys’ rule [5 MCMC integrations with 5 burn-in steps with 10 replicate runs. Z-scores from replicate runs were combined and adjusted using the genomic inflation factor which estimates the excess of the false discovery rate due to multiple testing, and it is defined as the ratio of the observed and the expected median of the distribution of the test statistic [BayPass package version 2.1  provideser, 2015 ) relies er, 2015  and usesys’ rule  that idetatistic . Lambda tatistic :\documenWe corrected for multiple testing by fixing the false discovery rate to 5%. Only SNPs with FDR < 5% were retained as those significantly associated with topo-climate variables.p2jk, which is the squared correlation between the jth SNP and Kth principal component) between genetic variants and specific PCs without any prior definition of populations. Assuming a chi-square distribution (degree of freedom = 1) for the squared loadings p2j1, as suggested by Luu et al. 2017 [P values for all SNPs and then estimated the FDR to generate a list of candidate SNPs showing significant associations to population structure. Only SNPs with FDR < 5% were retained as those significantly involved in local adaptation.In addition to two EAAs methods, PCAdapt was used to find loci putatively under selection pressure as they deviate from the typical distribution of the test statistic Z . Similaral. 2017 , we usedi genes), calculating p separately for each environmental variable and excluding genes with no outliers from the calculation of p . Any genes with  p values falling above this cutoff threshold were then identified as “top candidate genes” [Loci that selected as outliers by all three implemented methods, BayPass, LFMM and PCAdapt, were identified. For each gene, we counted the number of outlier SNPs (a) and the total number of SNPs (n). To identify top-candidate genes for each variable, we compared the number of outlier SNPs per gene to the 0.9999 quantile of the binomial expectation where the expected frequency of SNPs per gene is e genes” . The pose genes” .Two standard methods were further applied to search for signs of selective sweep in different groups of populations. Pairwise nucleotide diversity (π) , which iDrosophila melanogaster genome in order to potentially obtain more precise information on their functional annotation.To explore which biological processes (BP) top candidate genes are involved in, we performed a Gene Ontology (GO) and enrichment analysis for the top candidate genes we identified using topGO package in R . SignifiAe. aegypti samples and obtained, on average, over 110 million Illumina raw reads with an average sequencing depth of ~ 10X per individual covering > 85% of the reference genome. After variant calling and applying appropriate filtering, we identified a total of 1,968,198 single nucleotide polymorphisms (SNPs) with a minor allele frequency (MAF) > 3% which were subjected to downstream analysis. Supplementary Table We performed whole genome re-sequencing of all 101 Ae.a1) included samples collected from various sites in Consolidated Mosquito Abatement District in central California. The second cluster (Ae.a2) primarily included samples from other mosquito districts in central California  and the third cluster (Ae.a3) consisted of samples collected from southern California mosquito districts .We examined population structure and identified ancestral components with an autosomal marker dataset  using PCA and ADMIXTURE. We found a strong local population structure across the entire range of collections by PCA. The two-first axes  explained a large proportion of the variation, cumulatively accounting for 63.9% of the variance in SNP genotypes and three main genetic clusters were determined from this analysis  and more sensitive for identifying SNPs displaying weak association signals resulting from soft selective sweeps often involved in polygenic characters . AnalysiThe PCAdapt  method iDrosophila and AAEL019772 (FBgn0015245) and AAEL008641 (FBgn0001122) which encode heat shock proteins in Drosophila [Drosophila homologs are described in Supplementary Table We identified top candidate genes as those where an exceptional proportion of their total SNPs were outliers across all the three methods used, as explained above and in the methods section Fig. . In totaosophila  . The nucleotide diversities (π) at the selected genes were significantly below the genome-wide averages in coding regions in all three groups  that were recently introduced and established in various locations in central and southern California (Fig. Our investigation of putative signals of selection and local adaptation of Ae.aegypti were likely founded by two independent introductions which came from the south central US and southwest US/northern Mexico regions respectively. We found three major genetic clusters among 96 individuals collected from 12 geographic sites across central and southern California. Our results were consistent with a previous study by Lee et al. [Ae.aegypti distributed in California originated from multiple independent introductions from genetically distinct source populations; although the exact origin of the introductions remains uncertain and open for the future investigations.The study by Pless et al.  showed ne et al.  where thAnopheles [To find genomic regions that have been targets of natural selection, we identified SNPs that are putatively selected for and strongly associated with topo-climatic variables using various landscape genomics methods. The methods we employed substantially controlled for neutral population genetic structure such as genetic drift or gene flow. We chose outlier SNPs as those which were consistently identified by all applied methods, allowing us to eliminate stochastic variation that could affect the results. We assumed that outlier loci detected along the genome are likely to be under selection, either directly or through hitchhiking ; althougnopheles .Ae. aegypti. Our results are in accordance with previous reports identifying these abiotic variables as major predictors in Aedes distribution patterns [Ae. aegypti in larval habitats [Ae. aegypti since rainfall generates breeding grounds for adults. Unlike other mosquito species, Ae. aegypti eggs are laid above the water surface and hatch only when the water level rises and wets them [Natural selection plays a key role in shaping the available genetic variation of populations and thereby produces adaptation . By applpatterns , 25. Temhabitats . These vhabitats , 64, 65.ets them .Aedes aegypti into California would most certainly have been through some anthropogenic means which is a well-known mode of dispersal in this species. [Ae.aegypti and also a relatively limited number of generations after introduction to California. Therefore, small number of regions with signature of adaptation can be stemmed from biological limitations and not just analytical limitations [The introduction of species.  A rapid species. , 68. It itations . These lBy applying top candidate gene methods, we discovered 112 genes that contain SNPs highly associated with at least one topo-climatic variable (Supplementary Table The study of invasive adaptation and genome evolution is an emerging field that is developing rapidly and offers countless opportunities to investigate adaptive processes. Understanding the genomic basis of adaptive evolution in invasive species is important for predicting future invasion scenarios, identifying candidate genes involved in invasions, and, more generally, for understanding how populations can evolve rapidly in response to novel and changing environments.Ae. aegypti in California. Our findings help to elucidate the role of rapid evolution in the establishment and spread of invasive species. We detected evidence indicating local adaptation to various environmental conditions in populations of Ae. aegypti just a few years after its introduction into California, adaptations which may translate into a fitness advantage for specific populations.Here we used a landscape genomics approaches to identify genomic regions and candidate genes potentially involved in adaptation. The identified genes showed footprints of selection and were correlated with environmental factors that differed between sites, as expected under a scenario of environment-mediated selection in natural populations of Additional file 1: Table S1. Metadata.Additional file 2: Supplementary table 2. Environmental data collected from ClimateNA database for each sampling site.Additional file 3: Supplementary table 3. List of genes selected as top candidates by top candidate gene approach, their homologs and biological function and topo-climate variables associated with each candidate gene.Additional file 4: Supplementary Figure 1. Cross validation error plot showing the selection of K = 3 is the most well-presented population structure for this dataset. Additional file 5: Supplementary Figure 2. a. Heatmap indicating correlation among 25 topo-climate variables collected from ClimateNA. b. Principle component analysis of the topo-climate variables for 12 populations used for the association analysis, with the projection of the correlations on the first PCA plan. The full names of the topo-climate variables are available in supplementary table Additional file 6: Supplementary Figure 3. Inferred relationship among 12 populations of the Ae.aegypti represented by a. a hierarchical clustering tree derived from the matrix Ω estimated under the core model and b. a correlation plot.


Muscat Alexandria and V. vinifera L. cv. Christmas Rose. Based on 568,953 SNP markers, genome-wide association analysis revealed that 27 candidate SNP loci belonging to 18 genes were significantly associated with the concentrations of norisoprenoid components in grape berry. Among them, 13 SNPs were confirmed in a grapevine germplasm population comprising 97 varieties, including two non-synonymous mutations SNPs within the VvDXS1 and VvGGPPS genes, respectively in the isoprenoid metabolic pathway. Genotype analysis showed that the grapevine individuals with the heterozygous genotype C/T at chr5:2987350 of VvGGPPS accumulated higher average levels of 6-methyl-5-hepten-2-one and β-cyclocitral than those with the homozygous genotype C/C. Furthermore, VvGGPPS was highly expressed in individuals with high norisoprenoids concentrations. Transient overexpression of VvGGPPS in the leaves of Vitis quinquangularis and tobacco resulted in an increase in norisoprenoid concentrations. These findings indicate the importance of VvGGPPS in the genetic control of norisoprenoids in grape berries, serving as a potential molecular breeding target for aroma.Obtaining new grapevine varieties with unique aromas has been a long-standing goal of breeders. Norisoprenoids are of particular interest to wine producers and researchers, as these compounds are responsible for the important varietal aromas in wine, characterized by a complex floral and fruity smell, and are likely present in all grape varieties. However, the single-nucleotide polymorphism (SNP) loci and candidate genes genetically controlling the norisoprenoid content in grape berry remain unknown. To this end, in this study, we investigated 13 norisoprenoid traits across two years in an F It is estimated that the global vineyard area is approximately 6950 kilo-hectares, with an yield of 78,034 kilo-tonnes in 2020, ranking first in fruit production (From FAO data). Approximately 71% of this production is used for wine, 27% for fresh fruit, and 2% for raisins. More than 50 species in the Vitis genus have been recognized to date. Vitis germplasm constitutes a valuable resource for obtaining desired traits, such as increased tolerance to pathogens and improved flavor quality, through breeding programs. New grapevine varieties with unique flavors are expected to be bred to meet diverse consumption needs. During the last several decades, grape breeding worldwide has aimed at developing new varieties with disease resistance, unique flavors and better quality  as the entrance and rate-limiting enzyme  position to form β-ionone, while VvCCD4b cleaves ξ-carotene at the 9, 10  position to form geranylacetone , β-ionone (C13), 1,1,5-trimethyl-1,2-dihydronapthalene , β-cyclocitral (C10), geranylacetone (C13), 2,2,6-trimethylcyclohexanone , (E)-1- buta-1,3-diene , theaspirane (C13), vitispirane (C13), riesling acetal (C13), and MHO (C8)  . From thdel wine . β-Ionondel wine . TDN, wing wines . TCH maing wines . Vitisping wines . Other ct scents . ImportaVvDXS was confirmed, causing the substitution of lysine with an asparagine at position 284 in VvDXS, and this non-synonymous mutation increased the catalytic efficiency by altering the enzyme kinetics  identified three QTLs located close to the functional genes DXS, FPPS, and HDR associated with the linalool and geraniol aroma compounds from 96 grapevine cultivars, however, no further verification experiment has been conducted  content using solid-phase extraction (SPE) -gas chromatography-mass spectrometry (GC-MS), two genes encoding O-methyltransferases (OMTs), termed VvOMT3 (VIT_03s0038g03090) and VvOMT4 (VIT_03s0038g03080), on LG3 were identified to be associated with the IBMP level in grape berries (VvOMT3 is a key gene for IBMP biosynthesis in the grapevine.Methoxypyrazines (MPs), a class of compounds that contribute to the smell of green peppers, peas and asparagus, are usually present in traditional  berries . Based o1 population from the hybrid of the Muscat-type variety V. vinifera L. cv. Muscat Alexandria and the non-Muscat-type variety V. vinifera L. cv. Christmas Rose to explore the SNP loci and candidate genes significantly associated with the contents of norisoprenoid derivatives in grape berries using GWAS. The results were verified using a grapevine germplasm population and the transitent over-expression of target genes. The broader aim of this study was to gain an understanding of the genetic regulation of norisoprenoid compounds and provide a basis for the molecular breeding of grape aromas.However, studies on the genetic regulatory mechanism of norisoprenoids are very limited. In this study, we used an F22.11 population was derived from a cross between Vitis vinifera L. cv. Muscat Alexandria and V. vinifera L. cv. Christmas Rose. The artificial hybridization was performed twice. The first hybridization was performed in 2010, and then 305 seedlings were cultivated the following year in an experimental vineyard at the Beijing Academy of Forestry and Pomology Sciences in Beijing, China (39°58′N and 116°13′E). The second hybridization was performed in 2011, and 351 seedlings were planted in an experimental vineyard in Pinggu district in Beijing, China (40°58′N and 117°52′E) in 2012. These vines were trained into a two-wire vertical trellis system with a planting space of 0.75 m × 2.5 m, and they began to bear fruits in 2015 to 2017. Similar field management was carried out for the two vineyards.The F1 population and applied Kompetitive Allle-Specific Polymerase chain reaction (KASP) to distinguish true and false hybrids. Finally, 149 true fruit-bearing hybrid individuals were used for this study. The hybrid identification protocol included the following steps: First, the parents were genome re-sequenced to a depth of 30X. Second, seven different homozygous SNP loci between the two parents were selected. Finally, for each SNP locus, three pairs of primers were designed: two pairs of specific forward primers (F1 and F2) with the 3′ end carrying the mutation base of the SNP locus and the 5′ ends linked with FAM and a HEX sequencing adapter, and another pair of reverse primers (R) for general use. The length of each primer pair was about 150 bp , 0.08 μL MgCI2, and 2.52 μL ddH2O. The PCR procedure was as follows: heat treatment at 94°C for 15 min; 10 cycles of denaturation at 94°C for 20 s, annealing at 61–55°C and extension for 60 s, and 0.6°C reduction per cycle, followed by 30 cycles of denaturation at 94°C for 20 s, annealing at 55°C, and extension for 60 s. Genotyping of F1 individuals was conducted via fluorescence scanning at the end of the amplification. The heterozygous SNP was identified as the true F1 progeny, and the results are shown in We originally selected 183 individual plants with similar growth potential from the F2.21 hybrids were sampled for re-sequencing. The grape berries were collected for norisoprenoid compound analysis when the total soluble solids (TSS) reached approximately 16°Brix in 2017, and 10°Brix and 16°Brix for the two sampling periods in 2018 , and wild-type tobacco (Nicotiana benthamiana) was cultivated in soil in a greenhouse under a cycle of 16 h light/8 h dark at 23 °C.For transient over-expression analysis, the leaves of 2.3D-gluconic acid lactone and ground into powder in liquid nitrogen. To extract norisoprenoid, 1 g berry powder was put into a 20 mL autosampler vial with 5 mL citrate buffer  and 10 µL internal standard (1.068 g/L 4-methyl-2-pentanol) added. Then the vials were tightly capped and equilibrated at 99 °C in a thermostatic bath for 1 h. Two technical replicates of norisoprenoid compounds detection were performed per biological replicate.The norisoprenoids in grape berry was extracted according to the previously reported method with some modification . Approxivia headspace solid-phase microextraction (HS-SPME) using a CTC Combi PAL autosampler  equipped with a 2 cm DVB/CAR/PDMS 50/30 μm SPME fiber , and at 40°C for 30 min with stirring at 500 rpm. An Agilent 6890 gas chromatography coupled with an Agilent 5975C mass spectrometer was used to analyze the norisoprenoid compounds in the samples. The compound separation was achieved with an HP-INNOWAX capillary column . Norisoprenoid compounds were identified and quantified according to the previously published methods . The two parents were genome re-sequenced to a depth of 30 ×, and the true F1 hybrid individuals were genome re-sequenced to a depth of 10 × using Illumina HiSeq platform by Annoroad Genome Technology Company . Clean reads were obtained from raw reads by removing sequences with contaminated adapters, low quality, and N ratio greater than 5%. The clean reads were aligned to the V. vinifera Pinot noir PN 40024 reference genome (http://genomes.cribi.unipd.it/grape/) using the (Burrows-Wheeler Aligner) BWA v0.7.17  using the ANNOVAR software  . Then, vsoftware .2.510(5e-8) = 7.3 to screen out significant candidate SNPs.In this study, the norisoprenoid traits and SNP markers were used for genome-wide association study (GWAS). Manhattan plots were drawn to illustrate the significance and location distribution of SNPs for GWAS results. The significance threshold was set as -logyi of a trait for individual i using a regression model:In this study, we describe the measured phenotypic value μ is the overall average value; iX refers to the subgroup to which individual i belongs; β represents the environmental factor; a and d, respectively, refer to the additive effects and dominant effects of SNPs; iξ and iζ refer to the indicator vectors corresponding to the additive effects and dominant effects of SNP for individual i; iϵ is the residual error. The j-th elements of iξ and iζ are defined as:Where The regression coefficients are estimated using maximum likelihood method. The hypothetical test of a marker influence on trait is formulated as follows:0H corresponds to the simplified model and 1H correspond to the full model. The test statistic of the hypothetical test is calculated as the log-likelihood ratio (LR). Level of significance is calculated by permutation test. The p-value thresholds of all traits were calculated through 1000 iterations using the permutation test, ultimately to determine which are significant SNP loci.where 2.6http://genomes.cribi.unipd.it/grape/), and genes within 100 kb upstream and downstream of the significant SNPs were regarded as the candidate genes. And, the focus was mainly concentrated on structural genes in the isoprenoid metabolic pathway and potential transcription factors.According to the location of significant SNPs on the grape reference genome .Total RNA of grape berry was extracted using the Spectrum™ Plant Total RNA Kit . The quality and concentration of the obtained RNA were detected by agarose gel electrophoresis and NanoDrop 2000 spectrophotometer . First-strand cDNA was transcribed from total RNA using the HiScript V. quinquangularis leaves, we performed quantitative real-time PCR (qRT-PCR) using ChamQ Universal SYBR qPCR Master Mix . The PCR system is a 20 µL reaction mixture containing 2µL cDNA template, 10µL 2×ChamQ Universal SYBR qPCR Master Mix PCR, 0.8 µL primers, and 7.2 µL ddH2O. The PCR procedure was as follows: 95 °C for 30 s, followed by denaturation at 95 °C for 10 s, and annealing at 60 °C for 30 s, with a total of 40 cycles. The data were analyzed using the CFX Maestro™ software . Three replicates were performed for each sample during qRT-PCR. The Ubiquitin gene was used as the reference, and the primers used for qRT-PCR are listed in To detect gene expression abundance in grape berries and Nicotiana benthamiana leaves, we performed the reverse-transcription PCR (RT-PCR) following published methods with some modifications  was carried out in the leaves of V. quinquangularis using published methods with some modifications  to attain the initial optical density at 600 nm (OD600) of approximately 0.2, and then cultured on a shaker of 220 rpm at 28 °C until the OD600 reached 1.0–1.2. After the centrifugation at 4 °C and 4,000 × g for 5 min, the strains were resuspended with induction buffer consisting of 2.132 g/L MES (2-(N-morpholino) ethanesulfonic acid), 2.033 g/L MgCl2·6H2O, 5 g/L sucrose, and 0.039 g/L acetosyringone (pH 5.9–6.0) to obtain the final OD600 value of 0.4. The re-suspensended solution was maintained at 25 °C for 3 h to adapt the strains to the new buffer environment. Finally, the leaves backside up were submerged into the adjusted solution and subjected to vacuum infiltration (–0.8 MPa) for 20 min. Both the control group and the overexpression group were set three replicates with two leaves per replicate. After 3 days of incubation at 25 °C in the dark, the leaves were used for gene expression analysis and norisoprenoids detection. The relative gene expression levels were analyzed via qRT-PCR and the norisoprenoid compounds were measured via HS- SPME-GC-MS as mentioned above.The transient transformation of ications . First, 2.9N. benthamiana leaves was performed using the injection method (A. tumefaciens EHA105 strain carrying the plasmid pCAMBIA 1301-VvGGPPS was inoculated into 50 mL LB liquid medium (20 mg/L rifampicin and 50 mg/L kanamycin) to attain the initial OD600 of approximately 0.2, and then cultured on a shaker of 220 rpm at 28 °C until the OD600 reached 1.0–1.2. After the centrifugation at 4 °C and 4,000 × g for 5 min, the strains were resuspended with induction buffer consisting of 11.92 g/L MES, 20.33 g/L MgCl2·6H2O, and 19.62 g/L acetosyringone (pH 5.7) to obtain the final OD600 value of 0.6–0.8. The re-suspensended solution was maintained at 25 °C for 3 h. The A. tumefaciens solution containing the pCAMBIA 1301 empty vector was used as the the control. The strain solution was injected with a syringe without a needle into the leaves of N. benthamiana of about 5 weeks of age. Subsequently, these plants were cultured in the dark for 12 h and then transferred to the greenhouse for normal culture for 3 days. The leaves were collected for gene expression analysis and determination of aroma compounds. Both the control and transient overexpression groups were injected with at least two plants, totaling four leaves per replicate, and the experiments in each group had three replicates.In contrast to the vacuum infiltration method described above, the transient overexpression in n method . The act2.10p < 0.05 (Duncan’s multiple range test or T-test). Figures were drawn using the GraphPad Prism 8.0.2 . Pearson’s correlation coefficient calculations and heatmaps were generated using the “psych” and “corrplot” packages in R software (v 3.6.2).The SPSS version 22.0 . was used for all significance analysis at 33.1trans- and cis- theaspirane, vitispirane A and B, as well as (Z)- and (E)-β-damascenone, respectively, were summed up as theaspirane, vitispirane and β-damascenone traits. In addition, the concentrations of norisoprenoid derivatives with 13 carbons and all the detected compounds, respectively, were also summed up as C13-norisoprenoid and total norisoprenoid traits. In the present study, C13-norisoprenoid derivatives included theaspirane, vitispirane, riesling acetal, TDN, TPB, β-damascenone and geranylacetone. As a result, a total of 13 norisoprenoid traits were considered in subsequent analysis . Comparing the variation amplitude of individual norisoprenoid traits between the two experimental years, it can be concluded that various norisoprenoid traits all had a wide and continuous segregation in the hybrid generation of grapevines. Furthermore, the association between different norisoprenoid traits was assessed using Pearson correlation analysis. A positive correlation was found among the traits of 6-methyl-5-hepten-2-one, geranylacetone and β-cyclocitral with the coefficients of 0.38–0.93  region of genes. Furthermore, most of the loci and candidate genes related to these three traits were identical (VvDXS1 (VIT_205s0020g02130) and VvGGPPS (VIT_205s0020g01240) at the upstream of the norisoprenoid biosynthetic pathway, lipoxygenase, VvLOX (VIT_205s0020g03170), pyruvate dehydrogenase kinase (VIT_205s0020g00760), carbohydrate kinase (VIT_205s0020g02800), and the others  and chr5:4112382 was in the CDS regions of BSD domain-containing protein (VIT_205s0020g02420).Specifically, there were 1383, 2244, and 2497 significant SNP loci individually associated with geranylacetone, MHO, and e others Table 3.β-damascenone. Of them, three loci at chr10:315045, chr10:877279, and chr10:922729, were found in the CDS regions of tryptophan synthase (VIT_210s0116g00710), endonuclease/exonuclease/phosphatase family (VIT_210s0116g01630), and prephenate dehydratase (VIT_210s0116g01670), respectively  of the carotene ϵ-monooxygenase (VIT_208s0007g04530) (ϵ-monooxygenase (VIT_208s0007g04530) may be involved in the synthesis of TPB , and the markers 15-16 (A-C) corresponded to the two SNPs within the VvDXS gene at chr5:3851898 and chr5:3854251.Considering that the 19 significant SNP loci associated with MHO, 3.31 population had only two genotypes, and there were significant differences in the compound concentration traits between the genotypes, as expected. Among the candidate loci, 10 SNPs were found in the CDS region of the genes. In particular, four loci of them were non-synonymous mutations. We further analyzed the genotypes of these 10 loci and their corresponding traits  with asparagine (N) at position 284 of VvDXS1 (VIT_205s0020g02130), due to the mutation of G to T at chr5:3854251, also elevated the levels of MHO, β-cyclocitral and geranylacetone.We focused on the genotype analysis of 27 candidate SNP loci listed in g traits S4. It wβ-cyclocitral and geranylacetone than those with genotype A/A, and the grape berries with genotype G/G at chr10:315045 displayed higher β-damascenone concentration than those with genotype C/G. Except these four SNP loci, the remaining six ones all showed synonymous mutations, but different genotypes still presented significant differences in the norisoprenoid traits  into asparagine (N) at position 33 of the BSD domain-containing protein (VIT_205s0020g02420) and glycine (G) into alanine (A) at position 28 of the tryptophan synthase (VIT_210s0116g00710). Overall, the grape berries with genotype A/G at chr5:4112382 had higher concentrations of MHO, d traits Figure 33.4β-cyclocitral, and two were related to geranylacetone, and one was related to TPB  and VvGGPPS (VIT_205s0020g01240) are involved in the isoprenoid metabolism and act at the upstream of norisoprenoid biosynthesis  were associated with the low levels of MHO, β-cyclocitral, and geranylacetone  in controlling the production of 6-methyl-5-hepten-2-one and β-cyclocitral using homologous and heterologous transient transformation systems.Based on the above results, we mainly investigated the function of Agrobacterium-mediated transient expression system in the leaves of V. quinquangularis  was clustered in the same clade as the other five protein sequences for which the subcellular localization was experimentally demonstrated  gene that are highly associated with MHO and β-cylocitral contents using a hybrid F1 population and one germplasm population. Among them, the SNP locus at chr5:2987350 was located in the CDS region of the gene, and the mutation from C to T at this locus, resulting in the substitution of glycine (G) at position 261 with arginine (R), led to an increase in the contents of MHO and β-cylocitral, eventually enhancing the total norisoprenoid content.This study identified three SNP loci within GGPPS acts at the branch point, moving toward monoterpenoid and norisoprenoid synthesis in the isoprenoid metabolism pathway. This enzyme in plants is responsible for the synthesis of GGPP. In plastids, GGPP is further catalyzed by phytoene synthase (PSY) to produce phytoene, which is then catalyzed by phytoene desaturase (PDS) and other enzymes into carotenoids, which are the precursors of norisoprenoids. Besides, GGPP also serves as a common precursor of tocopherol, abscisic acid, solanum lactone, gibberellin, plastoquinone, and chlorophyll . In shorSlGGPPS1 in tomato is responsible for GGPP synthesis in roots, and SlGGPPS2 and SlGGPPS3 are in charge of supplying GGPP to produce carotenoids and other isoprenoids in leaves and fruits , and the gene expression level are highly associated with the content of norisoprenoids  in grapes. CCD is a key enzyme that cleaves multiple carotenoids into norisopnoid derivatives. It has been known that lycopene is the precursor of MHO, ξ-carotene is the precursor of geranylacetone, and β-carotene is the common precursor of β-cyclocitral and β-ionone  gene were also identified to be significantly associated with the contents of MHO, β-cyclocitral and geranylacetone in this study. DXS is an entrance enzyme and rate-limiting enzyme in the MEP pathway that catalyzes the condensation of the initial substrates, glyceraldehyde-3-phosphate and pyruvate, into 1-deoxy-D-xylulose-5-phosphate (DXP) (VvDXS1 (VIT_205s0020g02130) gene on LG5 is highly associated with the contents of monoterpenes such as linalool, nerol and geraniol in grape berry by means of QTL analysis (VvDXS1 (VIT_205s0020g02130) results in the substitution of lysine (K) at position 284 with asparagine (N), causing a significant increase in monoterpenoids, and it is proved that this mutation improves the catalytic efficiency and thus changes the enzyme kinetics  . Five DXte (DXP) . Previouanalysis . A gain-kinetics . Interesβ-cyclocitral, and geranylacetone in both the hybrid F1 population and the gerplasm population. However, the SNP chr5:3851898 is located in the intron region of the gene, and the mutation from A to C at this locus results in the increase of MHO, β-cyclocitral and geranylacetone contents. Therefore, the mechanism by which this SNP locus generally regulates the production of norisoprenoid compounds remains unclear at the present, and further research is required in this regard.In addition, another SNP locus at chr5:3851898 was also identified to be associated with the contents of MHO, DXS and the contents of norisoprenoid derivatives. The previous research has almost concentrated on the effect of the concentration of carotenoids, rather than norisoprenoids  and carbohydrate kinase genes were also shown to be associated with the contents of norisoprenoids. Clearly, the proteins encoded by these genes are not directly involved in the norisoprenoid biosynthetic pathway. Meanwhile, the SNP loci within some genes coding for transcription factors were also identified to be related to the content of norisoprenoid compounds, including squamosa promoter-binding-like protein 1 (SPL), BSD domain-containing protein, zinc finger protein (ZFP), bZIP protein HY5, and nuclear transcription factor Y subunit B-5. There were a total of ten significant SNP loci mapped to these above seven genes. The genetic control of these SNP loci on the levels of norisoprenoids remains unclear according to the present study.Based on GWAS of the hybrid population and KASP of the germplasm population, the SNP loci within carotene 4 rhizomatous grasses Miscanthus and switchgrass , could play an important role in the salinity stress response, directly or indirectly  under accession number CRA009145. Other data supporting the findings of this study are available within the paper or its supplementary data.The genome re-sequencing datasets supporting the results of this article can be found in the  CNCB-NGDC (1 population. Z-YS and Y-QF performed the part of chemical analysis. Q-HP supervised and guided the research. Q-HP and C-QD provided the funding. All authors contributed to the article and approved the submitted version.QS and LH performed the research, data analysis and original draft preparation. QS completed the manuscript writing and editing. LS and H-YX provided hybrid F


Early maturity in spring bread wheat is highly desirable in the regions where it enables the plants to evade high temperatures and plant pathogens at the end of the growing season.2:3 population.To reveal the genetic loci responsible for the maturity time association analysis was carried out based on phenotyping for an 11-year period and high-throughput SNP genotyping of a panel of the varieties contrasting for this trait. The expression of candidate genes was verified using qPCR. The association between the SNP markers and the trait was validated using the biparental FTraesCS7B02G391800 (bZIP9) and TraesCS7B02G412200 (photosystem II reaction center). The two genes are predominantly expressed in the flag leaf while flowering. The effect of the SNPs was verified in F2:3 population and confirmed the association of the 4A, 5B and 7BL loci with the maturity time.Our data showed that under long-day conditions, the period from seedling to maturity is mostly influenced by the time from heading to maturity, rather than the heading time. The QTLs associated with the trait were located on 2A, 3B, 4A, 5B, 7A and 7B chromosomes with the 7BL locus being the most significant and promising for its SNPs accelerated the maturity time by about 9 days. Gene dissection in this locus detected a number of candidates, the best being  Spring common wheat is a grain crop whose cultivation is important for many countries. In the Russian Federation, the main territories growing spring wheat include the Volga, Southern Urals, Altai, Western and Eastern Siberia regions. These regions are located in the climatic zones having different daylength, rainfall and the sum of effective temperatures. Despite these differences, most of them are characterized by sudden changes in temperature and rainfall during sowing and harvesting, which may affect the productivity.The duration of the growing season and phenological developmental phases are important factors affecting the expression of agronomically important traits of common wheat. The duration of heading date and maturity period has been shown to affect such important traits as yield, grain protein content, protein accumulation and composition in wheat and barley . One of Ppd-D1a allele can affect not only the flowering time but also may speed up the maturity time in wheat. Moreover, several studies have identified a locus on the short arm of the 2D chromosome that is associated with the maturity time  gene might cause faster maturity  responsible for maturity time have demonstrated that the loci associated with this trait are detected in the most wheat chromosomes: 1A, 1B, 1D, 2A, 2B, 2D, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 5D, 6B, 6D, 7A, 7B, and 7D .HvELF3. The wild allele of this gene causes earlier flowering and maturity and increases the harvest index  on the 3H chromosome has also been shown to accelerate maturity (Gibberellic acid (GA)-20 oxidase has been proposed as a candidate for the sdw1 locus ; the GIGANTEA gene homologue regulating CONSTANS and FT expression; and the GmPHYA3 and GmPHYA2 phytochrome genes  have not yet been concerned with maturity in wheat, so in the present study a collection of spring wheat varieties has been investigated for their heading and maturity time to identify the putative genetic loci responsible for the maturity trait.Despite the large amount of available data, the genes known to be associated with maturity time in other species (except https://ckp.icgen.ru/plants/). The wheat varieties had been adapted to the climatic conditions of Western Siberia whose summer period is rather short, so different environmental troubles often happen at the end of the vegetation period. More detailed information about the wheat varieties’ origin is presented in In this study a collection of 92 Russian spring wheat varieties was investigated that originated from the breeding centers of seven regions such as Samara, Altai, Novosibirsk, Krasnoyarsk, Kemerovo, Tyumen, and Omsk. The varieties were maintained in the GeneAgro seed bank of Institute of Cytology and Genetics of Siberian Branch of the Russian Academy of Sciences (ICG SB RAS)  evaluating the time from seedling to maturity.The plants were cultivated in the two experimental fields of ICG SB RAS in the Novosibirsk Region . The accessions were phenotyped on both fields in 2018 and on field 1 in 2005–2014.The phenological stages were evaluated following the Zadoks classification . The hea2. The soil in both fields was leached chernozem. The humus thickness varied from 40 to 60 cm, and the humus content was 4.2%. The soil was slightly acidic (pH 6.7–6.8), with its nitrogen content being 0.34%, total phosphorus-0.30% and potassium-0.13%.The seeds were sown in the second half of May in 2 repetitions on plots of 2 m2:3 population was obtained from a cross of mid-season cultivar Obskaya 2 and early-maturing cultivar Novosibirskaya 15. The population was phenotyped in 2018 in field 1 in 2 repetitions on plots of 0.25 m2.To test the effect of identified SNPs, an Fhttp://www.pogodaiklimat.ru. The air temperature and precipitation amount during the experiments were contrasting both during one growing season and over the years. The most optimal weather to ensure high productivity was registered in 2005 and 2009, whose temperatures and precipitation amounts in these years were closest to the mean seasonal values. The moisture supply was insufficient in 2008 (166.8 mm), 2010 (135.9 mm), 2011 (153.5 mm), 2012 (102.7 mm) and 2014 (176 mm). An extremely severe drought was noted in July 2012, with 3.7 mm of rain falling during the month, which was only 6.1% of the mean seasonal value.The weather data (average daily temperature and humidity by months) provided by a meteorological station located in the Novosibirsk region at 54.90 N, 82.95 E and an altitude of 131 m were taken from Growing degree days (GDD) from seedling to maturity, were calculated according to the formula of www.traitgenetics.com).The DNA was extracted from young leaves using the modified sodium bisulfite protocol described by 2) were calculated. The model considering genotypic effects as fixed and environmental effects as random was used to calculate the best linear unbiased estimates (BLUEs) of each genotype. To check if the distribution of the traits was normal, we used the Shapiro–Wilk normality test (R function). To estimate the maturity and heading time heritability, all effects except the intercept were assumed as random, so the component variance was estimated using R package lme4  to explore the association between traits was performed using the ‘aov’ function in R. Spearman’s correlation coefficients (rage lme4  and H2 wy method .Only polymorphic markers (SNPs) were selected for subsequent analysis. The genotyping data were filtered by minor allele frequency (MAF > 0.05) and by missing data less than 5% of population. Population structure analysis was performed using the LEA algorithm (snmf function with K = 1–10) .A mixed linear model (MLM) was performed in TASSEL V5.1 to carry out the association analysis, which accounted for fixed effects of SNPs and population structure (Q) and random effects for kinship (K). Quantile-quantile and Manhattan plots were generated using the R package “GWASTools” .Linkage disequilibrium (LD) between SNP markers was calculated using the R package “genetics” . LD decaTo provide insight into the functional specification of candidate genes, they were annotated using the IWGSC 1.0 data. To assess the gene expression, the developmental time course of the common wheat cultivar Azhurnaya  depositeAccording to the results of the association study, 10 wheat varieties with different haplotypes in the QTL located on 7B chromosome were selected. The varieties considered as early and late maturing and having the respective CCGACTGACT and TAAGTGAGCT haplotypes were cultivated in the year 2020 on the experimental field of ICG SB RAS in Novosibirsk. The plants were harvested at the start of the anthesis stage GS61 . The staThe RNA was extracted using Plant RNA MiniPrep , followed by DNase treatment with a RNase-Free DNase set . The quantity and quality of the extracted RNA were assessed using a QUBIT 4 fluorometer with RNA BR and RNA IQ kits respectively . cDNA was synthesized using RevertAid First Strand cDNA Synthesis  following the manufacturer’s protocol with 2 μg of total RNA as a template and random hexamer primers . In total, 2 μl of 20-fold final cDNA dilution was used for the following analysis.The fluorescence data were collected using a QuantStudio™ 5 Real-Time PCR System  with PowerUp SYBR Green Master Mix . The measurements were performed in three technical replicates. The reaction products were checked using melting curve analysis.To detect the most applicable reference genes, the expression of seven candidate reference genes selected according to the literature  was analThe PCR efficiencies were determined using the LinReg software . The CalThe 10-year period data included information only on the days from seedling to maturity (maturity time). To reveal correlations between the heading and maturity phenological phases, the times from seedling to heading , from heading to maturity (HTMT), and the total time from seedling to maturity  were evaluated for two environmental conditions in 2018. Using these data, HT and HTMT contributions to MT were estimated.The results of this experiment demonstrated that the time from seedling to heading (HT) of the studied varieties ranged from 35 to 44 days, with a heritability of 53% for this trait. While the total time from seedling to maturity (MT) varied from 77 to 104 days . The larp-value < 2.2E−16), while HT moderately correlated with MT   .Ten-year evaluation (2005–2014) of MT duration showed that it varied from 60 to 96 days in different years . ANOVA iFive varieties  had the earliest maturity time (MT < 72 days) if compared with cv. Novosibirskaya 81, Omskaya 28, Tulaikovskaya zolotistaya, Tulaikovskaya 10, and Baganskaya 93 which matured later than the others (MT > 81 days). These varieties were subsequently used to study the expression of candidate genes as contrasting genotypes.For further analysis and minimization in assessing the environmental impact, the best linear unbiased estimates based on the data for 11 years of observation were estimated .Association mapping for MT was performed using the BLUEs of 11-year phenotype estimation of 96 wheat varieties and its results are summarized in 2 measure between significant SNPs (p < 0.001) demonstrated that tightly linked markers within a QTL on the long arm of chromosome 7B were located within a 658,219,587–680,161,741 bp interval on the physical map. Excalibur_c31707_302 was distant from other SNPs (about 21 Mb), however, it was found to be tightly linked genetically (r2 ~ 0.9–1). According to GWAS, the 7B locus negatively influenced the trait’s manifestation by reducing MT, whereas markers Kukri_c31628_571, Tdurum_contig43995_611, Excalibur_c31707_302 demonstrated the highest effect hastening the maturity by about 9 days  binding protein and methionine aminopeptidase 1 (MetAP 1) .TraesCS7B02G411800, TraesCS7B02G401600, TraesCS7B02G400600, TraesCS7B02G399700 TraesCS7B02G404300, TraesCS7B02G400500, TraesCS7B02G398700, TraesCS7B02G413400 were not expressed. Expression of three genes  was higher in early-maturing cultivars, but no significant difference in their expression level was found. On the contrary, the expression of TraesCS7B02G412200 was significantly lower in the early samples (p < 0.01)  .2:3 progeny, while the markers located on chromosomes 3B and 7A were found to be non-polymorphic according to the genotyping performed with an Illumina 15 K Wheat array. There was a significant difference in MT for wsnp_Ku_rep_c68565_67614479 and Excalibur_c53111_215 localized on chromosomes 4A and 5B, respectively  showed that the offspring MT varied from 74.5 to 91.2 days. The SNP markers mapped on chromosomes 2A, 4A, 5B, and 7B  were polectively . No sign2:3 progeny showed that the TAAG and TGAGTCA genotypes matured later compared to the CCGA and CTGACTG ones  and two haplotypes (TAAG and CCGA). Group II included seven markers  and two haplotypes (TGAGTCA and CTGACTG). Comparison of the FCTG ones . These MVRN1 and VRN2), photoperiod sensitivity genes (PPD1), light signal perception genes (PHY), many circadian rhythm genes, some microRNAs, and an integrator of most known pathways Flowering Locus T  have been studied in detail.MT includes two main stages: HT and HTMT. The genetic basis of HT in wheat has been studied quite well. Although many components of this pathway are still unknown, the key factors involved in the transition of a plant from the vegetative to the generative developmental phase such as vernalization genes  for the MT trait, so a significant contribution of the genetic component to the formation of the trait can be assumed, which was confirmed by ANOVA demonstrating a significant impact the genetic component and environment had on the maturity time. Hence, selection for this trait in our case makes sense, and the identification of the new loci and genes associated with it can improve the varieties’ adaptation.NAM1 are the only genes whose influence on the time of wheat ripening has been confirmed. It is known that the wild-type allele of the NAM-B1 gene is associated with earlier maturity, while it is almost not found in modern cultivars  detected in our panel of cultivars were unlikely to have a different effect on development.Among the Vrn-B3 gene, we identified two alleles: vrn-B3, which is the recessive variant typically found in late cultivars, and the Vrn-B3e allele, which delays heading time by 1.5 days when compared to vrn-B3.In our investigation of the Vrn-B3e does not appear to alter MT (maturity time) in spring wheat cultivars.The ANOVA results didn’t demonstrate a significant impact of the Vrn-B3 alleles on the maturity time. We also determined that under the conditions of the experiment, the main contribution to maturity time inputed the period from heading to maturity. Based on this finding, it can be suggested that In this study significant MT-associated SNPs were found on chromosomes 2A, 3B, 4A, 7A, and 7B. The most significant markers were located on the 7B chromosome. Previously, The second locus coincided with the previously detected region was positioned on the short arm of the 7A chromosome. Its MTA was associated with SNP marker Tdurum_contig20214_279 positioned in 61,816,447. The location of QMat.dms-7A.2  was assoNAM1, a maturity time gene, is expressed after flowering begins in the flag leaf. Moreover, a number of studies have demonstrated the flag leaf to be main source of assimilates during the grain-filling period, which concurs with senescence onset , bZIP25, bZIP63, and the bZIP9 protein known to regulate bZIP53, which, upon dimerization with bZIP10 or bZIP25 (both C-group bZIPs), may enhance maturity-gene expression (TraesCS7B02G391800 is ortologous to rice OsbZIP52 (Os06g0662200) that encodes a probable transcription factor that binds to the DNA specific sequence 5′-TGAGTCA-3′ found in seed storage protein gene promoters (PubMed:11133985).One of the genes showing strong expression in the flag leaf at the flowering stage was 2G391800 . Three t2G391800 . These Spression . TraesCSTraesCS7B02G391800 expression in the early and late maturing varieties, we suppose this gene to be a highly probable candidate.However, qPCR revealed no significant difference of TraesCS7B02G401600 is expressed in the flag leaf while anthesis and then in the embryo proper while the ripening stage. The gene is known as BRITTLE1 (BT1) and provides unidirectional transmembrane transport of ADP-glucose that is crucial for starch synthesis in cereal grain , TraesCS7B02G401600 (BT1/BT2) and TraesCS7B02G413400 may interact each other  is expressed in the flag leaf after the flag-leaf stage begins. It was the only gene that demonstrated significant expression difference between early- and late-maturing variety groups in our qPCR experiment. Previous studies had demonstrated that the genes for chlorophyll binding, chloroplast, chloroplast organization, photosystem I, and photosystem II were down-regulated in early senescing varieties   . Cytokin FACTOR) . And CGA filling . The dec filling . Hudson 2:3 population mapping has shown that markers wsnp_Ku_rep_c68565_67614479 (chromosome 4A) and Excalibur_c53111_215 (chromosome 5B) are significantly associated with early maturity. Group I haplotype CCGA and group II haplotype CTGACTG on 7BL have demonstrated a strong tendency to accelerate maturity time. To confirm the contribution of 7B haplotypes to the trait, it is necessary to perform experiment in additional environments. We suppose that the early-maturity QTLs identified on 4A, 5B and 7BL can be used as a valuable source for breeding and could contribute to the genetic improvement of wheat.Thus, based on the results obtained, a conclusion can be made that the most significant QTLs that reduce the duration of the period from germination to maturity are located on chromosomes 4A, 5B, and 7B, while the 7B locus has the most significant contribution to the expression of the trait, shortening the maturity time by almost 9 days. The QTL validation performed using the F10.7717/peerj.16109/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.16109/supp-2Supplemental Information 2To perform the analysis the developmental time course of the common wheat cultivar Azhurnaya deposited in Wheat Expression Browser expVIP was used. Visualization and clustering were performed using R package d3heatmap.Click here for additional data file.10.7717/peerj.16109/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.16109/supp-4Supplemental Information 4Name and detailed phenotypic information of the 92 wheat accessions.Click here for additional data file.10.7717/peerj.16109/supp-5Supplemental Information 5p-value, R2, and other statistical data.Information about markers, their position on chromosomes, Click here for additional data file.


Vigna unguiculata is an important legume crop worldwide. The subsp. sesquipedalis and unguiculata are the two major types grown; the former is mainly grown in Asia to produce fresh pods, while the latter is mainly grown in Africa to produce seeds. Here, a chromosome‐scale genome for subsp. sesquipedalis was generated by combining high‐fidelity (HiFi) long‐read sequencing with high‐throughput chromosome conformation capture (Hi‐C) technology. The genome size for all contigs and N50 were 594 and 18.5 Mb, respectively. The Hi‐C interaction map helped cluster 91% of the contigs into 11 chromosomes. Genome comparisons between subsp. sesquipedalis and unguiculata revealed extensive genomic variations, and some variations resulted in gene loss. A germplasm panel with 315 accessions of V. unguiculata was resequenced, and a genomic variation map was constructed. Population structure and phylogenetic analyses suggested that subsp. sesquipedalis originated from subsp. unguiculata. Highly differentiated genomic regions were also identified, and a number of genes functionally enriched in adaptations were located in these regions. Two traits, pod length (PL) and pod width (PW), were observed for this germplasm, and genome‐wide association analysis of these traits was performed. The quantitative trait loci (QTLs) for these two traits were identified, and their candidate genes were uncovered. Interestingly, genomic regions of PL QTLs also showed strong signals of artificial selection. Taken together, the results of this study provide novel insights into the population differentiation and genetic basis of key agricultural traits in V. unguiculata. Vigna unguiculata [L.] Walp. (2n = 2x = 22), a member of the Fabaceae family, is an annual tropical or subtropical herb grown for human food and animal feed as a green manure, hay, or silage . The Latin name Unguiculata refers to the small stalks of their petals, meaning “with a small claw” or “with a small nail”. Its common name, “cowpea”, is based on its use as fodder for cows  is mainly grown in Asia, especially in China, for the production of fresh pods. According to the molecular evidence, it is easy to predict that subsp. sesquipedalis originated from subsp. unguiculata  Hepper var. mungo], a long‐read‐based draft genome sequence was assembled with a cumulative size of ∼454.4 Mb and an N50 size of 42.88 Mb   genome of 475.64 Mb containing 26 736 protein‐coding genes with an N50 of 18.26 Mb was obtained , a 519.4 Mb sequence scaffold was generated with an N50 of 16.4 Mb  was used as plant material for genome sequencing and assembly. Previously, V. unguiculata subsp. sesquipedalis var. Xiabao II  was used for de novo genome assembly with short‐read sequencing  of a large panel of germplasms has become feasible  long‐read sequencing with a Hi‐C interaction map. Genome comparison between the subsp. sesquipedalis and unguiculata revealed extensive genomic variations, and these variations resulted in gene loss. A V. unguiculata germplasm panel with 315 accessions  was resequenced, and a genomic variation map was constructed. With further investigation of this genomic variation map, population differentiation and selection signals in genome were identified. Two traits, pod length (PL) and pod width (PW), were observed for these 278 accessions, and a GWAS was performed for both traits. The genetic basis of the two traits was uncovered, and some candidate genes for the regulation of variations in the two traits were also predicted. We provide novel insights into the population differentiation and genetic basis of key agricultural traits in V. unguiculata.Here, a high‐quality chromosome‐scale genome for V. unguiculata subsp. sesquipedalis shows long pods (above 60 cm), white flowers and variegated seeds  of s Figure . To asses Figure . The genV. unguiculata germplasm panel, in which most lines are V. unguiculata subsp. sesquipedalis, were mapped onto the A147 genome, and most of these data showed a >98% mapping rate. Second, a total of 10 samples with approximately 75 Gb of RNA‐Seq data downloaded from SRA under project ID PRJNA763044 were also mapped onto the A147 genome by using a standardized RNA‐Seq analysis pipeline, and the average mapping rate was 95.64 ± 1.4%  reads of our 4% Table . Both ty4% Table . Fourth,ab initio, homology‐based, and transcriptome‐based gene prediction for A147 was also performed. In total, 27 439 genes encoding 41 725 proteins were predicted with an average length of 5621.4 and 1468.7 bp for genes and coding DNA sequences (CDSs)  Table .V. unguiculata subsp. unguiculata line IT97K was also assembled and released  genes were lost in the genome of A147 because of SV deletions. A comprehensive genome‐wide identification of R genes in these two genomes was performed. There were 161, 48 and 255 genes in the three types of R genes with NB‐ARC, TIR and LRR1 domains in the A147 genome  is illustrated in Figure R genes were lost. Taken together, our data suggested that SVs between A147 and IT97K resulted in extensive variations in gene contents and that these variations might play major roles in the differentiation between the two subspecies in V. unguiculata. Additionally, although we could not exclude some variations identified between A147 and IT97K that were attributed to different strategies for genome assembly and annotation, most of these variations would be reliable because both genome assemblies are of high quality.Structural variations (SVs) are >50 bp genomic variations among different genomes and include deletions, insertions, duplications, contractions, inversions and translocations. With comparisons of A147 with IT97K genomes, there were 7327 SVs, including 2837, 2871, 385, 243, 95 and 516 for deletions, insertions, duplications, contractions, inversions and translocations Table , respecte Figure , respecte Figure , respects Tables  and S9. Vitis vinifera, Populus trichocarpa, Glycine max, V. angularis, V. radiata, Phaseolus vulgaris, Arabidopsis thaliana, IT97K and A147, suggested that the divergence between IT97K and A147 occurred recently  reads were mapped onto the high‐quality chromosome‐scale genome of A147, and genomic variants, including SNPs and InDels, were identified. After quality filtering, a total of 17 831 792 genomic variants were identified for these 315 V. unguiculata accessions. The top three locations for these 17 831 792 genomic variants were intergenic 13 747 834 (53.939%), upstream of genes 4 273 944 (16.679%) and downstream of gene 3 923 595 (15.394%)  Table . A total%) Table .V. unguiculata accessions was predicted by using admixture software  for all 315 accessions was approximately 300 kb. The nucleotide diversity (Pi) for subsp. unguiculata and sesquipedalis were 0.276 ± 0.158 and 0.118 ± 0.157, respectively. These data revealed that the nucleotide diversity is much higher in subsp. unguiculata than in sesquipedalis, which is also consistent with the LD decay analysis.The phylogenetic analysis also revealed that all accessions of subsp. d Figure . Pop6‐Pod Figure . Clearlys Figure . The oveV. unguiculata germplasm panel. Population structure analysis supported that a large number of subpopulations were in this germplasm panel, especially in subsp. sesquipedalis. The data also supported that subsp. unguiculata diverged first and subsp. sesquipedalis originated from subsp. unguiculata. The nucleotide diversity is much higher in subsp. unguiculata than in sesquipedalis, and this phenomenon might result in LD decay faster than that in subsp. unguiculata.Collectively, the above analyses showed that there were a large number of genomic variants in the unguiculata is mainly distributed in Africa, while subsp. sesquipedalis is mainly planted in East Asia. Distinct growth environments in these two areas might shape different genomic characteristics for these two subspecies. To prove this prediction, two parameters, the sliding window of Fst and the cross‐population composite likelihood ratio (XP‐CLR) on 11 chromosomes for the two subpopulations, were calculated. The plots for these two parameters are illustrated in Figure Fst and XP‐CLR, respectively. When gaining insight into the functions of genes located in peak regions on each chromosome, a number of genes clearly showed functions related to biotic and abiotic resistance. For example, R genes, pathogen resistance (PR) genes and heat‐ and drought‐related genes could be found on the peaks of both XP‐CLR and Fst  revealed 47 genomic variants that formed two haplotypes (Hap1 and Hap2) and three genotypes  in the 315 accessions . The average PL of the three genotypes, Hap1/Hap1, Hap1/Hap2 and Hap2/Hap2, were 54.02 ± 12.03, 50.65 and 21.35 ± 10.98 cm . These data suggested that genomic variants in the PIN1 gene showed a significant association with PL variations although the corresponding genomic region did not show a significant association in further GWAS. These data further suggested that genomic differentiation between subsp. sesquipedalis and unguiculata might be attributed to a number of trait variations between these two subspecies.Subsp. t Figure . Interest Figure . A comprns Table . There wns Table , respectcm Table , respectFst genomic regions were identified and considered highly differentiated regions. A total of 1347 genes were located in both the top 5% XP‐CLR and Fst genomic regions  model implemented in GEMMA software  and 4.63 × 10−8  after Bonferroni correction. The Manhattan plot revealed that two QTLs showed an association with PL  and 22.00 ± 5.90 (n = 30) cm . To determine the genetic variation between subsp. sesquipedalis and unguiculata in the two PL QTLs, haplotype analysis of the ten significant SNPs in Chr03:61899120‐62027427 was performed, and a total of fourteen haplotypes that were assigned as Hap1 to Hap14 were calculated  and Fst (Pop1/Pop9) were also calculated. The data revealed that the QTL at Chr03:61899120‐62027427 was located in the genomic regions that had >top 5% Fst (Pop1/Pop8) and Fst (Pop1/Pop9)  , suggesting that PW was not a challenge in selection. The GWAS showed that there were two major QTLs for PW located on Chr01 and Chr11   and evm.TU.Chr11.1814 (EXLA2) regulate the variation in PW in V. unguiculata.An additional important agricultural trait pod width (PW) was also recorded in the five successive years 2017–2021 for the 278 germplasm accessions. A comparison of the average PW between the subsp. 1 Figure . The Q–Q1 Figure . For the2 Figure . A homol2 Figure . In this) Figure . In Arabsesquipedalis A147. Although there was a chromosome genome sequence for subsp. unguiculata in a previous report  showed that V. unguiculata subsp. unguiculata is closer to the outgroup V. angularis. This result suggested that this subspecies was originally domesticated from its progenitor and that V. unguiculata subsp. sesquipedalis evolved from V. unguiculata subsp. unguiculata. Thus, our study is highly consistent with previous results . Also, we found a greater LD distance and lower genetic diversity in s Figure . Thus, tV. unguiculata subsp. sesquipedalis, while IT97K is regarded as a representative plant of V. unguiculata subsp. unguiculata. The most prominent difference was the large variation in R gene numbers between the two genomes according to our comparisons. There were 180 more R genes in the genome of IT97K, and a total of 138 R genes were lost in A147 via SV deletions , which is widely grown in China, was used as plant material for genome assembly. It was grown in the field in Hannan district, Wuhan, Hubei Province of China  with normal irrigation, and young seedlings were collected as samples for DNA extraction. Different tissues, including seedlings, young flowers, roots, young pods and young leaves, were collected for RNA preparation. A total of 278 germplasm accessions were collected and grown in the same field where A147 was grown for five successive years (2017–2021), including autumn 2017 (August 1st to November 15th), spring 2018 (April 18th to July 30th), autumn 2019 (August 1st to November 15th), spring 2021 (April 18th to July 30th) and autumn 2021 (August 1st to November 15th). The locations where these germplasm accessions were collected are listed in Table et al., et al., et al., et al., et al., et al., et al., et al., et al., For genome assembly, two types of genomic data were generated. High‐quality genomic DNA for A147 was isolated using the modified cetyltrimethylammonium bromide (CTAB) method (Porebski de novo approaches was applied for transposable element (TE) identification. Briefly, the TE database was first built with the software program RepeatModeler‐2.0.2a; then, the TEs were searched with the software program RepeatMasker v.4.0.5 . Functional enrichment was performed by using TBtools software  pipelines to assign the GO terms to each gene . Single‐copy genes were identified among eight plants, including V. vinifera, P. trichocarpa, G. max, V. angularis, V. radiata, P. vulgaris, A. thaliana, IT97K and A147, by using the OrthoFinder program , while genomes of G. max, V. angularis, V. radiata, P. vulgaris and IT97K were downloaded from the V. unguiculata database on 2022‐05‐01 (https://phytozome‐next.jgi.doe.gov/). The phylogenetic trees were constructed with the software RAxML (the model parameter was set as “PROTGAMMAAUTO”)   . The PE reads for another 37 accessions were downloaded from the SRA database in GenBank (v) Table . The rawK from 1 to 20 , setting V. unguiculata accessions were used as plant materials for the GWAS. The PL and PW were recorded in the field for five successive years. The best linear unbiased prediction (BLUP) values of PL and PW were used as trait data for the GWAS. The BLUP was conducted by using the “lme4” package implemented in R version 4.0.5 (https://www.r‐project.org/). Genomic variants, including SNPs and InDels, were used as genotypic data. Genomic variants showing a minor allele frequency (MAF) were excluded from further analysis. GEMMA  pipelines were used to perform the GWAS, setting the parameter LMM as 1 .The 278 Lei Pan, Minghui Liu, Yan Kang, Xiang Mei, Gege Hu, Chun Bao, Yu Zheng, Huixia Zhao and Chanyou Chen performed the field experiments. Nian Wang and Lei Pan conducted data analyses, manuscript writing, and editing. Lei Pan and Nian Wang supervised this project.The authors declare no conflict of interest.Figure S1 Plot of the high‐throughput chromosome conformation capture (Hi‐C) matrix for the chromosome‐scale genome of Vigna unguiculata subsp. sesquipedalis line A147.Figure S2 Synteny comparisons of the chromosome‐scale genomes for two pairs of IT97K/A147 and IT97K/V. angularis.Figure S3 Synteny comparisons of the chromosome‐scale genomes for two pairs of A147/IT97K and A147/V. radiata.Figure S4 Determination of K values for population structure analysis.Figure S5 Genetic differentiation analysis between Pop1 and Pop8.Figure S6 Genetic differentiation analysis between Pop1 and Pop9.Click here for additional data file.Table S1 Summary of sequence data used for genome assembly.Table S2 Summary of contigs assembled with different programs.Table S3 Summary of A147 pseudochromosomes.Table S4 Summary of the mapping rate for RNA‐Seq data.Table S5 Functional annotation of the A147 genome.Table S6 Genome comparisons of two subspecies in Vigna.Table S7 Structural variations (SVs) between the A147 and IT97K genomes.Table S8 Gene loss resulting from insertion and deletion of SVs in the IT97K genome.Table S9 Gene loss resulting from insertion and deletion of SVs in the IT97K genome.Table S10 The V. unguiculata germplasm and the classification of subspecies and subpopulations.Table S11 Summary of genomic variant effects by type and region.Table S12 Functions of genomic variants.Table S13 Analysis of genomic variants on PIN1 (evm.TU.Chr04.258).Table S14 Genes located in highly differentiated regions.Table S15 Gene information in the pod length QTL region Chr03:61899120‐62027427.Click here for additional data file.