Prunus avium L. (Rosaceae), are gaining importance due to their perenniallity and nutritional attributes beneficial for human health. Interestingly, sweet cherry cultivars exhibit a wide range of phenotypic diversity in important agronomic traits, such as flowering time and defense reactions against pathogens. In this study, whole-genome resequencing (WGRS) was employed to characterize genetic variation, population structure and allelic variants in a panel of 20 sweet cherry and one wild cherry genotypes, embodying the majority of cultivated Greek germplasm and a representative of a local wild cherry elite phenotype. The 21 genotypes were sequenced in an average depth of coverage of 33.91×. and effective mapping depth, to the genomic reference sequence of ‘Satonishiki’ cultivar, between 22.21× to 36.62×. Discriminant analysis of principal components (DAPC) with SNPs revealed two clusters of genotypes. There was a rapid linkage disequilibrium decay, as the majority of SNP pairs with r2 in near complete disequilibrium (>0.8) were found at physical distances less than 10 kb. Functional analysis of the variants showed that the genomic ratio of non-synonymous/synonymous (dN/dS) changes was 1.78. The higher dN frequency in the Greek cohort of sweet cherry could be the result of artificial selection pressure imposed by breeding, in combination with the vegetative propagation of domesticated cultivars through grafting. The majority of SNPs with high impact , were identified in genes involved in flowering time, dormancy and defense reactions against pathogens, providing promising resources for future breeding programs. Our study has established the foundation for further large scale characterization of sweet cherry germplasm, enabling breeders to incorporate diverse germplasm and allelic variants to fine tune flowering and maturity time and disease resistance in sweet cherry cultivars.Sweet cherries,  Prunus avium L. (Rosaceae), is a fruit crop with growing agronomic and economical importance. For instance, in the past decade, production acreage of sweet cherries in Greece has been increased by 4000 ha, representing more than a 50% increase, while production has also increased by roughly the same percentage to an average of 73,000 tons per year in 2016  . Sweet cherry consumption has potential preventative benefits against (among others) Alzheimer’s, cancer, and inflammation related diseases1. Due to the economic importance of sweet cherry, its potential benefit for human health and its advancing position in Greek agriculture, it is vital to provide research support that would maintain global competitiveness for local growers2.3. The reason for the development of genetic variability in sweet cherry cultivars which originate from Black and Caspian Seas is their adaptation in the context of spreading. The deeper study and characterization of sweet cherry genetic diversity as well as the identification of genes controlling traits of interest will be a key factor for sweet cherry breeding. Greece contains high rates of genetic diversity, although many of local traditional landraces have been lost over years of evolution2. Moreover, in various other countries, a narrow genetic bottleneck has been observed, in modern cultivars4. In Greece, various studies of molecular diversity, of modern cherry varieties, have been conducted using SSR markers, revealing their extensive genetic basis, a valuable finding that has been extensively used in breeding programs5. Genomic characterization of sweet cherry germplasm, has been enriched with tools, such as the sweet and sour cherry 6 K array6. Whole Genome Re-Sequencing and other such techniques are the springboard for the discovery of allelic and genomic richness.Sweet cherries exhibit important phenotypic variation in fruit size, shape, color, sugar content, flowering time and other agronomic traits such as defense reactions against pathogens7, plum8, mei9, and peach10 has been studied with the recently developed, Whole-genome re-sequencing (WGRS) technique. In order to better understand the genetic basis of plant variation and to incorporate this knowledge into breeding programs, the exploitation of valuable sequences continuously provided in public databases, is of the utmost importance.Identifying allelic variation has gone a step further with the introduction of re-sequencing strategies. Deeper sequencing of genotypes of numerous species has been achieved with the use of high-throughput Next-Generation Sequencing technologies for genome-wide analysis. The genetic background of various fruit-tree species, including citrus11. Perennials, including fruit trees, have been studied to a much smaller scale compared to annual plants. In order to evolve breeding and ensure global food security, the exploitation of genomic resources and phenotypic diversity of perennial taxa is of paramount importance12.The genetic basis of phenotypic diversity has become even more enlightened by the use of genomic resources, such as the development of single nucleotide polymorphism (SNP) maps of genomes. Furthermore, a variety of polymorphisms in underlying loci has been recognized13 in conjunction with the release of chloroplast and mitochondrial genome sequences15 have enabled new discoveries, including the identification of the SI locus16.The recent release of the first version of sweet cherry genome assembly of ‘Satonishiki’ cultivar by Shirasawa et al.2 while this collection is complemented by the presence of an elite local wild cherry genotype. The sequence analysis focused on genomic regions associated with propitious variation, such as deletions, substitutions and duplications, providing the first comprehensive catalog of molecular variation in this species, which could be helpful for explaining divergence/similarity among different variants.In this article, we present analyses of whole genome re-sequencing of 20 sweet-cherry cultivars and one wild-cherry genotype. The 20 Greek genotypes span the traditional range of sweet cherry cultivation and represent the majority of variation (>95%) in Greece2. The elite wild cherry genotype was obtained from the Wild Cherry Gene Bank, located in the Xyloupolis Forest Nursery of the Hellenic Forest Service in Greece. The 20 sweet cherry accessions are traditional Greek cultivars, whereas the wild cherry one, was used as an outgroup. We predefined four groups as ‘Breeding line’, ‘Landrace’, ‘Modern cultivar’ and ‘Wild’ (Supplementary File 17. Genomic DNA was isolated from a pool of young leaf samples of five plants per genotype using NucleoSpin Plant II kit (Macherey-Nagel).Twenty cultivated sweet-cherry genotypes were selected  to generate of approximately 300 bp library insert size. Quantity and quality control of the libraries were carried out with Qubit dsDNA HS Assay kit (ThermoFisher Scientific) and Agilent 2100 Bioanalyzer System , respectively. High-quality DNA libraries were sequenced with the BGISEQ-500 platform (BGI-Tianjin) with read lengths of 100 bp.The raw sequencing data generated from the BGI platform were filtered for adapter contamination, low-quality reads, duplicated reads and short reads (length < 35 bp). This filtering produced the high quality ‘final raw data’ with an average sequencing depth of 33.91×.P. avium genome (version 1.0.a1) (https://www.rosaceae.org/species/prunus_avium/genome_v1.0.a1) reference genome using BWA (v0.7.12)18. Then the Sequence Alignment/Map (SAM) files were sorted using the Picard software (http://picard.sourceforge.net). GATK genome analysis toolkit  was employed to call SNPs and InDels across all 21 genotypes simultaneously by using standard hard filtering parameters of HaplotypeCaller and SelectVariant, respectively19.The high quality paired-end sequencing reads (100 bp) from each genotype were mapped to the P. avium Genome v1.0.a1 using snpEff software20, and SNPs were categorized into intergenic upstream or downstream regions and introns or exons. SNPs in coding exons were further classified as synonymous or non-synonymous SNPs. InDels in exons were grouped according to whether they occur a frameshift.SNP annotation was performed based on the ST) between each group were calculated by using VCFtools (v0.1.14) with a 20 kb sliding window across the P. avium Genome v1.0.a1 reference genome.The 21 genotypes were categorized in 4 groups “Wild”, “Landraces”, “Breeding lines” and “Modern cultivars”  implemented to the R package, Adegenet23. The optimal number of clusters was calculated using the Bayesian Information Criterion (BIC) as described by Jombard et al.23. Lastly, gene flow among populations was estimated by calculating the FST for pairwise comparisons for all populations using the StAMPP package in R and hierarchical clustering using the ward.d2 method24.Population genetic structure was assessed using the following methods. Initially, Nei’s distance was calculated and a neighbor-joining dendrogram of the 21 genotypes was build using StAMPPhttp://breakdancer.sourceforge.net/), SOAPcnv25, DELLY26 and LUMPY27 were used for structure variants (SVs) and copy number variants (CNVs) calling. First, alignment of sequences against the P. avium genome was done using bwa-mem pipeline. Then, DELY and LUMPY were used for CNV detection and each.vcf file was validated using “vcf-validator” tool of vcftools28. Intergation and filtering of DELLY and LUMPY output was realized using the “methodsMerge” command of the “intansv” package in R with default parameters29. The CNV detection was performed by separating DNA sequences into fragments according to the sequencing depth of bases and the alignment results. Then, the P-value was calculated for each fragment to estimate its probability to be a CNV. The fragments that passed the criteria  were identified as CNVs.BreakDancer  model23 implemented in R (v. 3.3.3)24. Background linkage disequilibrium (BLD) was estimated by bootstrapping; 1000 replications were performed, and on each replication, r2 was calculated among 1000 randomly selected SNPs. The BLD value was chosen as the upper value of the 95% confidence interval of the r2 distribution.Potential linkage disequilibrium was detected by using PLINK. Pairwise LD decay was calculated for four groups of populations (a) the entire population of the 21 cultivars, (b) the ‘Breeding line” group, (c) the ‘Landrace’ group and (d) the ‘Modern cultivar’ group.30, thirty three candidate genes involved in the regulation of flowering time were selected. Additionally, the VCF-BED intersect tool31 was employed in order to retrieve the SNPs and InDels variations that were mapped to the 119 annotated disease resistance genes, as well as the 16 pathogenesis-related genes, which play pivotal roles in the defense reactions against pathogens in the reference genome. Genetic variability of the candidate genes was explored across the different accessions and the potential effect of the genetic changes was studied, using snpEff v.4.320, by annotating each SNP, based on their predicted effect on the candidate genes.Based on the report of Sánchez-Pérez et al.33.Genome sequencing of the 21 sweet cherry genotypes yielded 204.26 Gb of high quality clean data with an average sequence depth of 33.91x and effective mapping depth, to the genomic reference sequence of ‘Satonishiki’ cultivar, between 22.21× to 36.62× , 452,544 small insertions or deletions (indels), 5,677 copy number variations (CNVs) and 6607 presence absence variations (PAVs) across 21 sweet cherry genotypes, was identified  for the SNPs in the coding regions was 1.78. For crops or species that are propagated clonally, synonymous SNPs are outnumbered by non-synonymous SNPs, while the opposite is more commonly met in wild species34. However, this value is much higher to the values reported for pigeon pea (1.1835), tomato (1.2334), peach (1.0636), Chinese plum (1.3037) and grapevine (1.1732). The higher dN frequency in our population of sweet cherry may possibly be the result of the artificial selection pressure imposed by breeding in combination with the vegetative propagation through grafting of domesticated cultivars.Paired-end sequencing reads were mapped to the genomic reference sequence of ed Table . The culed Table . Most ofed Table . These f13 WGRS study on six sweet cherry cultivars, has identified a lower number of sequence variants , SNPs  and insertions/deletions , indicating higher genome wide variations in the present study, probably due to the larger number of genotypes.We produced a unified catalog of SVs called by at least two of these four bioinformatics tools and these are described in Table 38 in 429 chickpea accessions. Moreover, among larger InDels of ≥4-nt, the total number of deletions was slightly higher than insertions  were of a single nucleotide only. Di- and tri-nucleotide insertions and deletions accounted for 11.02% and 7.4% of the total InDels, respectively  between SNPs decays to 50% of its maximum at 5 kb and 90% of its maximum at 55 kb  are found at physical distances less than 10 kb . The lower LD extensions oberved in sweet cherry (P. avium) compared to the LD observed in peach (P. persica)  with SNPs revealed a clear separation of distinct genetic clusters. Membership probabilities, interpreted as proximities of individuals to different clusters23, showed that genome-wide SNP markers achieved unambiguous separation of all groups  tree methods, group the genotypes according to their genealogy Fig. . Similarups Fig. .Fig. 4PoK = 2) based on the Discriminant Analysis of Principal Components (DAPC) . The highest genetic distance and Fst values were observed between the breeding lines group and the wild species Table . On the −3) among the four predefined groups , but still higher than other fruit-tree species such as grape42 and apple43 that lack domestication bottlenecks, indicating an effect of the artificial selection on the sweet cherry genomes.The wild progenitor of Greek cultivated sweet cherry cultivars, showed the highest nucleotide diversity  and “breeding” (π = 1.46 × 10−3) cultivars share the same level of genetic diversity, suggesting a similar genetic background of the current cultivated sweet cherry cultivars, that retained the genetic diversity during their traits’ improvement. However, the positive Tajima’s D values , the Polycomb group (PcG) protein, the Embryonic Flower gene (EMF2), FLOWERING LOCUS C (FLC), MADS‐box transcription factor SUPPRESSOR OF OVEREXPRESSION OF CO 1 (SOC1), FRIGIDA (FRI) and Far-red-impaired responsive protein (FAR1)44. The dataset of SNPs found in each gene is summarized in Supplementary File A set of 13 candidate genes were selected, based on previous QTL studies, that are involved in flowering time and bud dormancy among various sweet cherry cultivars and other well-studied model plants such as Arabidopsis thaliana (see ref. 30 and references therein). More than 60 genes have been identified to control flowering time in Arabidopsis45. Among them, SNPs present in genes including CONSTANS (CO), FLOWERING LOCUS C (FLC), VERNALIZATION INSENSITIVE 3, PHYTOCHROME D, GIBBERELLIN, etc. or coding region deletions as in FRIGIDA gene, result in significantly different phenotypes for flowering time. Dominant alleles of FRI generate late flowering phenotypes while FRI mutants are early flowering48 identified ParSOC1 as a candidate that could be involved in the regulation of dormancy break of vegetative shoots in apricot. Loss-of-function mutations in the EMF genes result in direct flowering in Arabidopsis, bypassing vegetative shoot growth49.The majority of knowledge on the genetic basis of flowering time is obtained from studies on In the present study, no high-impact mutations were found in the flowering time T genes (FT), but some moderate variants in these genes  were detected  (Pavsc0000257.1). Dormancy-associated MADS-box (DAM) gene expression has been implicated in the establishment and maintenance of endodormancy. DAM6 genes are upregulated during growth cessation and downregulated by cold exposure during winterAccording to the results obtained from this analysis, some cultivars clustered according to their flowering time Fig. . For insRPM1, RPP13, RGA2 homologs) which were previously predicted and being functionally annotated as disease resistance genes in the sweet cherry genome according to Shirasawa et al.13. These genes are usually clustered in close physical mapping and underlying QTLs have been previously described as involved in defense reactions against numerous plant pathogens51.In this study, we selected a set of 119 defense-related genes . The SNPs distribution  among the 21 genotypes and according to their annotation effect is depicted in Fig. R NBS-LRR genes, have stop-codon gain and loss SNP mutations in the coding sequences (CDS) with high impact  occurred putatively affecting their ligand-binding specificities through positive selection3. Thus, the results of this WGRS analysis, that showed variation in highly abundant SNPs and InDels occuring in NLR genes confirm that these NLR receptors are prone to variation among the sweet cherry accessions. All these findings allow us to assign these NSB-LRR proteins as the foremost surveillance mechanism against rapidly evolving pathogens, and providing breeders with effective genomics tools to speed-up the development of sweet cherry varieties with more durable resistance.In order to have a more deciphering view, the 21 sweet cherry genotypes were hierarchically clustered by using all the high impact mutations, both SNPs and InDels, in the 57 nes Fig. . The “Tr53. Most of the PR genes in sweet cherry genome belong to PR-1 and PR-4 families.We also selected the functionally annotated PATHOGENESIS-RELATED (PR) proteins which encode plant species-specific genes induced in response to infection with fungi, bacteria or viruses in order to estimate the variation upon them across the 21 accessions. These genes have been associated with basic-scale defenses through coordinated interactions within signaling pathways leading at the establishment of systemic acquired resistance (SAR) as well as induced systemic resistance (ISR)PR genes across the sweet cherry accessions  were found to map in two genes .Our results showed that 65 SNPs and 20 InDels variations was observed among the The majority of these 65 SNPs were intron variants (46 in their number), eight were 3′ and 5′ prime UTR variants and 11 were synonymous, missense and splice-region variants (Supplementary Files PR genes in comparison with the disease resistance NLR receptors (NBS-LRR-containing genes), as PR genes comprise the basal defense and they are more conservative in structure, under rather purifying selection and less abundant in numbers across plant genomes. Therefore, our results indicate that NLR genes are promising resources for breeding broad-spectrum resistance as it was previously also mentioned55.Overall, as it was expected, we found fewer variations among the 21 sweet cherry accessions for This is the first report of whole genome genetic variation characterization between sweet cherry cultivars. By WGRS we have captured the majority of genetic variation (>95%) of Greece’s cultivated germplasm. A high degree of heterozygosity and potentially functional variation was revealed, as indicated by the high nonsynonymous-to-synonymous substitution ratio. Most of the genotypes were clustered according to their geographic region of origin, with some exceptions, which indicates potential movement of the germplasm across regions.The evaluation at whole genome level of cultivated and wild germplasm is important for the identification of allelic variations with phenotypic effects. We have discovered numerous high impact allelic variants on flowering and diseases resistance genes. Further characterization of the precise impact of such variants on flowering time and defense reactions against pathogens will enable their implementation in molecular breeding programs to fine tune flowering, maturity time and diseases resistance in cherry cultivars. Hence, our study has established the foundation for further large scale characterization of sweet cherry germplasm, aiming at enabling breeders to use diverse germplasm and allelic variants towards developing improved cherry varieties with increased productivity.Additional information R1.Supplementary Figure 2Supplementary Figure 3Supplementary Figure 4Supplementary Figure 1Supplementary File 1Dataset 1Supplementary File 3Supplementary File 4Supplementary File 5Supplementary File 6Supplementary File 7Supplementary File 8Supplementary File 9Supplementary File 10Supplementary File 11


Cyclospora cayetanensis is an intestinal parasite responsible for the diarrheal illness, cyclosporiasis. Molecular genotyping, using targeted amplicon sequencing, provides a complementary tool for outbreak investigations, especially when epidemiological data are insufficient for linking cases and identifying clusters. The goal of this study was to identify candidate genotyping markers using a novel workflow for detection of segregating single nucleotide polymorphisms (SNPs) in C. cayetanensis genomes. Four whole C. cayetanensis genomes were compared using this workflow and four candidate markers were selected for evaluation of their genotyping utility by PCR and Sanger sequencing. These four markers covered 13 SNPs and resolved parasites from 57 stool specimens, differentiating C. cayetanensis into 19 new unique genotypes. Cyclospora cayetanensis, identified as a cause of food-borne diarrheal illness in the early 1990s, is routinely linked to sporadic cases and annual, seasonal outbreaks of cyclosporiasis [C. cayetanensis. Currently, there is no method to propagate this parasite for routine laboratory study [C. cayetanensis in the laboratory, the parasite must be obtained from infected patients’ stool and purified [C. cayetanensis, once the parasite is obtained, has also been difficult. The structure of the thick oocyst wall has required specialized extraction methods to obtain DNA fragment lengths sufficient for WGS, while a notably large genome (~44 MB) [The coccidian parasite poriasis . In the poriasis . Epidemiporiasis , 17. Chary study . To studpurified , which ipurified , which i(~44 MB)  has madeC. cayetanensis [Recent advances in whole genome sequencing of tanensis , 23, 28 tanensis , 13, 16,tanensis . The firtanensis . The sectanensis  and hightanensis . Nascimetanensis . A thirdC. cayetanensis outbreak samples. Thus, the goal of this study was to develop a new workflow to identify additional SNPs in the nuclear genome of C. cayetanensis, and subsequently, provide additional markers for genotyping.These genotyping methods show promise; however, none have yet been adopted for routine use due to their limited ability to fully resolve the diverse and complex nature of cyclosporiasis outbreak clusters. Additional markers may be required to further improve these methods and capture the genetic variability between de novo assembled into draft C. cayetanensis genome assembly using SPAdes v3.11.1 [de novo assembled contigs were aligned by BLASTN to mitochondria and apicoplast sequences of C. cayetanensis; only contigs of nuclear DNA origin were included in the final genome assembly.Raw Illumina sequencing reads from eight whole genomes known as CDC:HCNY16:01 (Accession: GCA_001305735.1), CDC:TX69:14 (Accession: GCA_002019455.1), CDC:HCRI001:97 (Accession: GCA_002019905.1), CDC:HCGM01:97 (Accession: GCA_002019465.1), CDC:HCDC004_96 (Accession: GCA_003945175.1), CHN_HEN01 (Accession: GCA_000769155.2), CDC:HCNP016_97 (Accession: GCA_003945145.1) and CDC:HCJK001:14 (Accession: GCA_002019475.1) were used for the workflow development and marker generation. Quality of the sequencing reads was evaluated by FASTQC v0.11.7 . The hum v3.11.1 . Additiohttps://www.biorxiv.org/content/10.1101/2019.12.31.891218v2. GeneMark-EP+ utilizes cross-species protein splice alignments generated by ProSplign [C. cayetanensis genomes was made by the Blast2Go algorithm [Annotation of protein coding genes in nuclear DNA was performed by the GeneMark-EP+ gene finding tool, roSplign  to a genroSplign . Proteinlgorithm .C. cayetanensis genomes and to identify syntenic regions and positions of SNPs. To increase the reliability of SNP calling, base calling quality was calculated for each base in the assembly. All the reads were aligned by the STAR algorithm to the assembled genomic sequences. Each base was characterized by read coverage and frequency of dominant base call.A Mauve algorithm  was usedSupplementary materials). Additional filtering criteria required i) single copy protein-coding genes; ii) genes with significant similarity (at least 70% identity) to homologous Apicomplexa proteins as detected by BLAST search; iii) syntenic genomic regions present in all analyzed strains; iv) SNPs with 99% dominant base and minimum read coverage 20; and v) regions with at least three SNPs within 400 nucleotide span (regardless of exon borders). The resulting list was searched for genes that had SNPs in a single isolate, e.g. CDC:HCNY16:01, with no SNP present in the other three isolates CDC:TX69:14, CDC:HCRI001:97 and CDC:HCGM01:97. The identified candidate genes, with at least 70% of their protein products to known Apicomplexa proteins, were then ranked by the number of observed SNPs. The highest ranked candidate in each of the four genomes was selected for further analysis. Primers for these four regions were designed using Primer3 [To further narrow down the marker search space, only four genomes with highest read coverage and, arguably, with higher quality of assembly were selected  by US State health departments for research purposes. They were received unpreserved, suspended in non-nutritive media  or preserved in alcohol-based fixatives , and used in accordance with the Human Research Protection Office in the Center for Global Health, Centers for Disease Control and Prevention, “Use of coded specimens for Cyclospora genomics research” (2014-107). The presence of oocysts was confirmed by epifluorescence microscopy.The four chosen markers were evaluated using 93 g for three minutes with phosphate-buffered saline (pH 7.2) and diluted to form a thick slurry. Nucleic acid was extracted using the UNEX-based method [Samples were washed free of preservative through one to three rounds of centrifugation at 2500 ×d method  and subj® PCR and DNA Cleanup Kit  and sequenced on an ABI PRISM® 3130xl Genetic Analyzer  in both directions using the PCR primers and the BigDye Terminator V3.1 chemistry . The DyeEx 2.0 Spin Kit  was used to remove unincorporated dyes before sequencing .The PCR products were purified using MonarchDNA sequences were visualized and analyzed within Geneious v 11.1.2 . Identification of underlying haplotypes for each marker gene was performed as described previously  to visuaC. cayetanensis specific gene prediction parameters was determined by training of the GeneMark-EP+ gene prediction algorithm on the CDC:HCNY16:01 isolate genome. The protein mapping pipeline, a part of GeneMark-EP+, was executed for all the assemblies. Final gene prediction was completed using the C. cayetanensis specific parameters together with isolate specific protein hints to predict genes. More than 80,000 SNP positions were detected using the Mauve genome alignment algorithm. Application of the filtering criteria produced a set of 485 genes candidates. These 485 genes were narrowed down to regions that had SNPs in a single isolate with no SNP present in any of the other three isolates. In each genome, at least five such genes were identified. After the candidate genes were ranked by the number of observed SNPs and similarity score to the known Apicomplexa proteins, the highest ranked candidate in each of the four genomes was selected for further analysis. This search resulted in four marker regions that uniquely identified genomes of four isolates .The set of C. cayetanensis-positive stool specimens collected during 2013, 2014, 2015, and 2017. Of the 93 specimens available for testing, 84 were tested with CDS-1, 83 with CDS-2, 73 with CDS-3, and 78 with CDS-4. Successful amplification and sequencing for all four targets combined was accomplished in 57 of the stool specimens, with 114 from CDS-1, 104 from CDS-2, 86 from CDS-3, and 109 from CDS-4. Individual marker sequencing success rates were 61% for CDS-1, 77% for CDS-2, 75% for CDS-3, and 74% for CDS-4 . Sequence information and SNP locations for each haplotype can be found in Supplementary materials. Representative nucleotide sequences for each marker genes’ haplotypes were deposited into GenBank. Accession numbers are as follows: MN367319, MN367320, MN367321, MN367322, MN367323, MN367324, MN367325, MN367326, and MN367327.The four chosen markers  were evaluated by testing A presence-absence table was generated of all four marker haplotypes present in each sample, with a 1 if the haplotype was present and a 0 if absent. If a sample had a true double peak, indicating a mixed haplotype infection, the sample had a 1 recorded for both haplotypes in that marker. To aid in visualizing the relationship between specimens, Bray-Curtis dissimilarity values were calculated from the presence-absence table of haplotypes and plotted  using a C. cayetanensis positive stool specimens collected in the United States from 2013 to 2017. At least two unique haplotypes were detected for each marker, with three haplotypes detected at the CDS-3 locus  Initiative.The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the Centers for Disease Control and Prevention.https://www.parasite-journal.org/10.1051/parasite/2020022/olm.Supplementary materials are available as “Supplemental.xlsx” at Tab (Haplotypes): This table lists the DNA sequences for all haplotypes of all four markers, with primer binding sites and SNP sites highlighted.Tab (Sequence Data Preparation): Genome information, including size, number of contigs, and number of protein coding genes present in each of the four genomes used for the workflow.Tab (CDS-1): Sequence of whole marker gene found for the GM genome and its associated SNP coordinates.Tab (CDS-2): Sequence of whole marker gene found for the NY genome and its associated SNP coordinates.Tab (CDS-3): Sequence of whole marker gene found for the TX genome and its associated SNP coordinates.Tab (CDS-4): Sequence of whole marker gene found for the RI genome and its associated SNP coordinates.Tab (Haplotype presence-absence): Table including the presence or absence of each haplotype for all 57 specimens sequenced.


Linkage and linkage disequilibrium (LD) between genome regions cause dependencies among genomic markers. Due to family stratification in populations with non-random mating in livestock or crop, the standard measures of population LD such as Given a matrix reflecting the strength of association between markers, groups are built successively using a greedy algorithm; largest groups are built at first. As an option, a representative marker is selected for each group. We provide an implementation of the grouping approach as a new function to the R package hscovar. This package enables the calculation of the theoretical covariance between biallelic markers for half- or full-sib families and the derivation of representative markers. In case studies, we have shown that the number of groups comprising dependent markers was smaller and representative SNPs were spread more uniformly over the investigated chromosome region when the family stratification was respected compared to a population-LD approach. In a simulation study, we observed that sensitivity and specificity of a genome-based association study improved if selection of representative markers took family structure into account.Chromosome segments which frequently recombine in the underlying population can be identified from the matrix of pairwise dependence between markers. Representative markers can be exploited, for instance, for dimension reduction prior to a genome-based association study or the grouping structure itself can be employed in a grouped penalization approach. Genomic markers are an invaluable source for characterizing genetic variety and to elucidate the relationship between genetic and phenotypic variation in breeding populations. Dependencies among genomic markers are caused by linkage and linkage disequilibrium (LD) between genome regions. Though this condition complicates investigations on which genetic variants are truly associated with trait expression , dependeument}r2  is a natEspecially in situations of ultra-dense panels of single nucleotide polymorphisms (SNPs), it is often sufficient to investigate representative SNPs (“tagSNPs”) out of each cluster. This subset can help identifying trait-associated genome regions in genome-wide association studies and allows comparing genome characteristics between ethnics/species/breeds . As theThe objective of this paper is to exploit the family structure of a population for specifying groups of associated markers. We generalize the grouping approach of Carlson et al.  in orderThe dependence between pairs of SNPs, each with two alleles A and B, can be expressed in terms of a covariance or correlation matrix. It has already been shown in the literature how to calculate the theoretical covariance between markers in a population consisting of half-sib families . It requR. The idea is that SNPs which are associated to each other are assigned to groups. Groups are built one after the other—the largest group at first. For b-th group is identified by searching for the SNP that has most occurrences of absolute correlation to other SNPs larger than a given threshold t. More precisely, let b-th group, and a tagSNP is selected from this group. SNP c has strong correlation with any other SNP in b. A next round of iteration is started using t. Similar to [t approaching 1), each SNP builds a single group, yielding a complexity of this algorithm of p the total number of SNPs. This approach is implemented as function tagSNP in the R package hscovar. A graphical representation of this algorithm is shown in Fig. We generalized the strategy of Carlson et al.  for binnentclass1pt{minimak, l with allele frequencies It is an obvious choice to compare the family approach with a population-LD approach. Such an approach requires the population frequency of the different haplotypes  index  which meX with dimensions X and the vector y were centered within family and scaled to obtain an empirical variance of one. The residual errors were assumed to be independently and normally distributed. For convenience, no other effects were assumed. We used the R package asreml version 3.0 [k-th SNP effect was tested by a t-like test statistic as in [Selecting a representative set of markers is a natural tool for dimension reduction prior to genomic evaluations in order to reduce the impact of multicollinearity among predictor variables. Representative SNPs capture cumulative effects of the corresponding LD blocks on trait expression. We investigated a SNP-BLUP approach, which is widely used in genomic evaluations , and thsion 3.0  to estimmentclass2pt{minimThe data sets used for studying dependencies between SNP markers differed in SNP density and family structure. They covered a range of mean inter-marker distances from 0.003 cM to 0.23 cM. The case study of mouse data was based on low-density genotypes of full-sib families; progeny and parents were genotyped. The case study of cattle data comprised medium-density genotypes of half-sib families with genotyped progeny only. Furthermore, medium-density SNP data were available for full-sib families in maize. High-density genotype data of half-sib families were generated in simulations. For evaluation, the SNP-BLUP approach was applied to simulated data only. Unless otherwise stated, computations were done using R version 4.0.3  and \dochttps://wp.cs.ucl.ac.uk/outbredmice/heterogeneous-stock-mice/. We investigated chromosome 17 because it harbors the highly recombining MHC region which affects several immunological traits [Genotype data of a heterogeneous stock of mice were available from l traits . The chrhttps://dx.doi.org/10.22000/280. The data comprised 50K SNP-chip data of five half-sib families with Genotype data of Holstein cattle were available from RADAR on 2.0.2 . Maternaon 2.0.2 . HoweverRaw marker data were available from NCBI GEO database under Accession Number GSE50558, accompanied with physical coordinates corresponding to the genome assembly B73. The data set contained two maize panels, Flint and Dent, for which about 50K SNPs have been assessed in order to estimate recombination activity in different maize populations . We arbiThe simulation study resembled the population structure of a dairy cattle population. The setup of simulation design is fully described in . Brieflyn 0.13.0 . SNP allhttps://www.ncbi.nlm.nih.gov/assembly/GCF_000001635.18/). This was caused by high variation of parental haplotypes. Though the total number of groups was smaller with the family approach than with the population-LD approach (83 vs. 98), the number of large groups, i.e., with group size For the mouse data consisting of 138 genotyped full-sib families, the population-LD approach exposed a wide region (13.82–21.13 Mbp) that had a strong association with the entire chromosome 17 shown as a red band in Fig. R. A clear distinction of regions with particularly high interdependence was not possible for any of the approaches . Other The extent of dependence among genomic markers is affected by the underlying population structure. Representative markers can be selected more efficiently if the corresponding matrix of pairwise dependencies takes this structure into account. The correlation matrix for half- or full-sib families highlights regions of high dependence between markers more precisely than the population-LD matrix. Additionally, it reveals regions of positive or negative association among markers.We contributed a new function tagSNP to the R package hscovar which is suited to samples from livestock and crop populations with typical family stratification. The covariance matrix can be set up in a piecewise manner, either separately for each chromosome or based on other meaningful information. The resulting grouping structure can be exploited in genome-based evaluations to handle the problem of high multicollinearity between markers.Additional file 1. Given the population structure, half-sib families or full-sib families, the covariance matrix is analytically retrieved. Its computation using the R package hscovar is shown.Additional file 2. Raw mouse data are processed and the matrix of correlation between markers is derived.Additional file 3. Cattle data are processed and the matrix of correlation between markers is derived.Additional file 4. Raw maize data are processed and the matrix of correlation between markers is derived.Additional file 5. With this script, genotype and phenotype data of half-sib families are simulated and a genome-based association analysis is carried out.Additional file 6. Plots of correlation matrices and population-LD matrices are produced based on results with Additional files 2–5.Additional file 7. Number of groups, number of groups with at least three SNPs and Calinski-Harabasz index for different simulation scenarios and varying threshold.


Scophthalmus maximus) using two SNP datasets from a Northeastern Atlantic population  and a domestic broodstock . Twenty-six families (∼ 40 offspring per family) from this broodstock and three SNP datasets applying differing filtering criteria were used to adjust ROH calling parameters. The best-fitted genomic inbreeding estimate (FROH) was obtained by the sum of ROH longer than 1 Mb, called using a 21,615 SNP panel, a sliding window of 37 SNPs and one heterozygous SNP per window allowed. These parameters were used to obtain the ROHi distribution in the domestic and wild populations . Regions with higher and lower genetic diversity within each population were obtained using sliding windows of 37 SNPs. Furthermore, those regions were mapped in the turbot genome against previously reported genetic markers associated with QTL (Quantitative Trait Loci) and outlier loci for domestic or natural selection to identify putative selective sweeps. Out of the 319 and 278 windows surpassing the suggestive pooled heterozygosity thresholds (ZHp) in the wild and domestic population, respectively, 78 and 54 were retained under more restrictive ZHp criteria. A total of 116 suggestive windows (representing 19 genomic regions) were linked to either QTL for production traits, or outliers for divergent or balancing selection. Twenty-four of them (representing 3 genomic regions) were retained under stricter ZHp thresholds. Eleven QTL/outlier markers were exclusively found in suggestive regions of the domestic broodstock, 7 in the wild population and one in both populations; one (broodstock) and two (wild) of those were found in significant regions retained under more restrictive ZHp criteria in the broodstock and the wild population, respectively. Genome mining and functional enrichment within regions associated with selective sweeps disclosed relevant genes and pathways related to aquaculture target traits, including growth and immune-related pathways, metabolism and response to hypoxia, which showcases how this genome atlas of genetic diversity can be a valuable resource to look for candidate genes related to natural or artificial selection in turbot populations.Massive genotyping of single nucleotide polymorphisms (SNP) has opened opportunities for analyzing the way in which selection shapes genomes. Artificial or natural selection usually leaves genomic signatures associated with selective sweeps around the responsible locus. Strong selective sweeps are most often identified either by lower genetic diversity than the genomic average and/or islands of runs of homozygosity (ROHi). Here, we conducted an analysis of selective sweeps in turbot ( Artificial selection has been accomplished by humans to domesticate species with desirable properties, in order to reach more profitable phenotypes. Despite its differences with natural selection, both fit to the same general principle: selective pressure is indirectly applied on specific genomic regions where genes controlling relevant traits are found, thus modifying the allelic frequencies in the target population . The maiA strong selective pressure leads to an increase in the frequencies of favorable allelic variants and, consequently, to a decrease of genetic variation in the gene/s under selection. Moreover, due to the physical association of that locus with nearby loci (genetic linkage), genetic variants of loci in the vicinity are also suffering a decrease of genetic variability through hitch-hiking effects, thus being deviated from the expected proportions under neutrality . AccordiIn most studies, only strong selective sweeps  have been described . Soft seThere are several methods to identify selective sweeps through a genomic screening using large numbers of markers, such as SNP (Single Nucleotide Polymorphism) loci. Two main approaches have been particularly employed to identify signatures of selection . One of Furthermore, genome-wide ROH analysis is a useful tool to identify genomic signatures of inbreeding. Inbreeding reduces genetic diversity within individuals, leading to the appearance of long ROH genome stretches. The longer the ROH, the more recently the consanguineous event has occurred, since recombination would have had fewer opportunities to break up those ROH . ROH anaSelective sweeps have been detected in various domestic species such as goat , cattle Turbot breeding programs started in the 1990s and are currently in the 5th generation of selection . DomestiROH), and secondly the ROH distribution and FROH in the wild and domestic turbot samples; (ii) to co-localize previously reported QTL and outlier markers associated with productive traits and adaptive variability with genomic regions of high and low genetic diversity suggestive of selection in wild and domestic turbot; and (iii) to explore gene functions and pathways associated with these regions through preliminary genome mining and functional annotation analyses.The objectives of this study are: (i) to conduct a genome-wide characterization of ROH and genetic diversity in wild and domestic turbot using a large number of SNP loci; obtaining firstly meaningful computational conditions to accurately call ROH and estimate the genomic inbreeding coefficient . Previous reports suggested that this is a panmictic population not introgressed from hatchery turbot releases that were carried out in the past in the North Sea . EnvironPst1 and Msp1 restriction enzymes were used for targeting RAD-tags followed by an additional agarose gel selection between 350 and 500 bp. After a PCR amplification phase (12 cycles), the library was purified with AMPure beads. The quality was controlled on a bioanalyzer using the high sensitivity DNA reagent (Agilent Technologies). The final library was then sequenced on one lane of Illumina HiSeq4000 at the BGI sequencing platform.DNA was extracted using the DNeasy blood tissue kit following provider protocol (Qiagen), while library preparation was carried out following a modified version of the ddRAD paired-end protocol from process_radtags pipeline .The pipeline for SNP calling, filtering and genotyping developed for domestic turbot by pipeline . ProcessFiltered reads were mapped against the latest version of the turbot reference genome . As some deviations between the genomic inbreeding coefficient calculated from ROH (FROH) and the θIBD were expected, families were grouped by their θIBD status in five classes: unrelated (0 < θIBD < 0.0076), second cousin (0.0076 < θIBD < 0.038), first cousin (0.038 < θIBD < 0.0937), half-sibling (0.0937 < θIBD < 0.1872) and full-sibling relatives (θIBD > 0.1872).Identity by descent estimates for each couple in the 26 domestic families analyzed were calculated using PLINK’s –genome flag . PLINK eThe observational approach implemented by PLINK v1.9 was used to call ROHs. The simplicity of this approach allows efficient execution on data from different array platforms or sequencing technologies , b. TestDifferent PLINK conditions were used to obtain ROH from the three QC datasets. In order to search for the most meaningful conditions for accurate ROH calling, the minimum number of SNPs that a ROH is required to have (–homozyg-snp), the required minimum density to consider a ROH (–homozyg-density) and the number of SNPs that the sliding window must have (-homozyg-window-snp), ROHs were set up to change from 24 to 40 SNPs for each iteration. Moreover, the number of heterozygous SNPs allowed in a window (–homozyg-window-het) was set up to 0  and 1  for each SNP-per-window iteration. The other parameters were constant for all iterations .ROH was calculated for every PLINK condition tested as the total sum of ROH divided by the total length of the genome, under the lack of sex chromosome heteromorphism in turbot  were obtained for each family and for both populations analyzed, the broodstock and the wild ones. In order to search for ROH islands a sliding window of 50 kb was used. In every 50 kb genomic window the number of individuals in ROH was obtained. To know if a specific genomic window had a significant enrichment of ROH across the population a binomial test with The selective sweep screening was performed using the SNP datasets for both the broodstock  and the wild populations. Both datasets were analyzed using a 37 SNP sliding window, previously set up as the best fitted for ROH-calling (see Results and Discussion), and 1 SNP sliding at a time. For each window, pooled heterozygosity (Hp) , 2012, aAeromonas salmonicida, Philasterides dicentrarchi and VHSV) and outlier loci related to adaptive variation previously reported in domestic and wild turbot , respectively. Given that identification of extreme diversity regions of heterogeneous physical length may be too restrictive , less std turbot , in orded turbot .in silico analysis was conducted using the Functional Annotation Clustering tool implemented in DAVID (P < 0.05). The analysis was performed on overlapping regions between ROH islands (ROHi) and/or low-high genetic diversity regions taking as reference functional QTL markers and/or outlier loci.To identify the underlying biological functions of the candidate selective sweeps, the boundaries of selected candidate regions were used to retrieve gene lists from Ensembl-BioMart using the updated version of the turbot genome assembly . The in in DAVID , b consiIn this study, broad panels of SNPs derived from genotyping by sequencing protocols were genotyped , 4, usinprocess_radtags pipeline. Reference mapping was conducted through the alignment of sequence reads with the current turbot genome assembly . However, only a total of 28,790 SNP loci were considered, since only one SNP was retained per RAD-tag. After deleting monomorphic SNPs (generated after the coverage filter), a final set of 25,681 SNP loci was obtained  and the medium  QC criteria when 1 heterozygous SNP per sliding window is allowed, while large differences are detected with the very relaxed QC criterion , 1 heterozygote SNP allowed per window and ROH longer than 1 Mb are applied. The size of a ROH is influenced by different factors such as recombination or linkage disequilibrium  on Fg to IBD . ConsideIBD , non-inbred families showed higher differences compared with more inbred ones. Thus, when non-inbred families are considered (IBD estimates of each family’s parents show extremely higher differences with the median of the FROH box-plot than in more inbred families (ROH (for ROH > 1 Mb) of the offspring correlates the best (R2 =0.78) with their parent’s θIBD when using the medium QC criterion, a PLINK’s sliding window of 37 SNP and 1 heterozygous SNP allowed.However, there is not a perfect correlation between θnsidered , the θIBfamilies . The disfamilies . Even wiLarge differences were found in ROH distribution between the wild and domestic turbot populations either considering the parental broodstock or additionally including all offspring. This is in agreement with the presence of inbred breeders in the broodstock according to kinship estimates . In the ROH estimation), followed by 4 < ROH < 8 Mb  represented about 1.28% (319 windows) and 1.34% (278 windows) of the total sum of windows in the wild and broodstock populations, respectively . WindowsMore restrictive ZHp criteria (ZHp ≥ 3.0 and ZHp ≤ −3.0) represented about 0.31% (78 windows) and 0.26% (54 windows) of the total number of windows in the wild and broodstock populations, respectively . WindowsGenomic regions in the vicinity of loci subjected to directional selection showed specific signatures that facilitate the identification of selective sweeps . ConversAn integrative genome atlas of GD in the broodstock, including the ROHi and low/high-GD regions detected, was developed in this study. Different genomic stretches were highlighted due to the presence of low-/high-GD, ROHi and/or QTL/adaptive-associated markers. Another atlas for low-/high-GD regions in the wild turbot population was also obtained, although no ROHi were found. Both genomic atlases were anchored to the turbot genome sequence  and are fgf6 and lep, and their association with growth was further validated in families from different turbot breeding programs (P. dicentrarichii resistance in turbot (Sma-USC38) was identified on C20. Functional enrichment in immune-related genes, such as tfe3, mst1r, myb and fos was previously documented  and phosphate compound metabolic processes . Hypoxia tolerance, when interacting with water temperature and salinity, has a major impact on fish physiology, including growth and immune response in aquaculture conditions  being identified as a target of natural selection in human and Great Apes  and wild turbot populations . One marn humans . Mdh2 caelection . The otheat Apes . An inteeat Apes . This reTwo SNP datasets with similar marker density from a domestic and a wild turbot population were used to characterize the genome-wide distribution of runs of homozygosity (ROH) and genetic diversity (GD). After fitting ROH-calling parameters, large differences were found in ROH distribution between the broodstock and wild turbot populations, bringing insights into the history of domestication and selection in this species. The study provides useful information to estimate genomic inbreeding and identify selective sweeps in turbot. Genomic signatures of selection were identified by checking the overlapping distribution of ROHi and extreme genetic diversity regions across the genome, along with the presence of functional markers associated with QTL or adaptive variability in turbot. Genome mining and functional analysis on some selected regions provided candidate genes and pathways potentially explaining the action of selection in turbot populations. The atlases of genome diversity in broodstock and wild turbot populations in this study represent useful information for implementing further genomic research and breeding applications in turbot.All datasets generated for this study are included in the The animal experimental procedures carried out in this study were approved and conducted in strict accordance with the Directive 2010/63/EU of the European Parliament and of the Council of 22 September 2010 on the protection of animals used for scientific purposes.OA participated in data collection, data analysis and interpretation, and drafting the manucript. FC participated in data analysis and interpretation. AC participated in data collection, data analysis and interpretation. AL, JH-H, and DB participated in data collection and critical revision of the manucript. CB and PM participated in the conception, drafting and critical revision of the manucript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The present study focused upon identification of genome-wide SNPs through the reduced representation approach and to study the genomic divergence of the Indian yak populations. A total of 80 samples belonging to Arunachali yak (N = 20), Himachali yak (N = 20), Ladakhi yak (N = 20) and Sikkimi yak (N = 20) of India were used in the study. The results of the study revealed a total of 579575 high quality SNPs along with 50319 INDELs in the Indian yaks. The observed heterozygosity was found to be high in Himachali yak, followed by Arunachali yak, Ladakhi yak and Sikkimi yaks. The Sikkimi yaks was found to be genetically distant, followed by Ladakhi yaks which was observed to have some few individuals from Arunachali and Himachali yaks. Arunachali and Himachali yaks are found to get clustered together and are genetically similar. The study provides evidence about the genomic diversity in the Indian yak populations and information generated in the present study may help to formulate a suitable breeding plan for endangered Indian yaks. Moreover, the unique yak populations identified in the study will further help to focus attention for future characterization and prioritization of the animals for conservation purposes through the ddRAD approach. Peophagus grunniens) is a unique bovine species living in the difficult terrains, which appeared some two million years ago and provides the indigenous people with meat, milk, butter, cheese, wool, fiber, leather, fuel and travel requirements. Yaks have got adapted to very high altitude regions where the oxygen content is very low and the genes responsible for the adaptation to hypoxic conditions has already been reported1. Indian yaks are classified into Arunachali yak, Himachali yak, Ladakhi yak and Sikkimi yak based upon the geographical area inhabitation and so far, only Arunachali yak has been described as a breed in India. The population of yaks in India as per the 19th Livestock census is 0.077 million2. In case of yaks, which are reared under high altitudes, data recording is almost nil or not being followed. Moreover, only few bulls are being maintained by the Brokpas or farmers who raise yaks which lead to the high level of inbreeding. Use of genome-wide SNPs to study the genetic diversity will have a clear advantage over use of only few microsatellite markers. SNPs are nucleotide variations in the DNA sequence of individuals of a population and are the most abundant molecular marker in the genome. However, the introduction of single nucleotide polymorphisms arrays made the collection of genome-wide marker data feasible at a fairly affordable costs3. The SNPs used in the array may not be geographically representative that results in ascertainment bias and inherently excludes detection of rare or population-specific variants3, a major source of information for both population history and genotype-phenotype association. This may impair estimation of population parameters like diversity, population subdivision, recombination and the identification of causal mutations. The recent advances in DNA sequencing technologies have facilitated the development of more efficient and cost effective strategies that allow simultaneous discovery and genotyping of SNPs in multiple individuals. The cost for genotyping 48 samples using Illumina Bovine SNP HD DNA Analysis is Rs 15476804 whereas for the double digestion restriction-associated DNA (ddRAD) Sequencing of 24 samples is Rs 3600005. The recent ddRAD method have advantages over other SNP genotyping platforms as it reduces the ascertainment bias and can be developed easily for genotyping the SNPs of predetermined area over many samples in cost effective manner. The SNPs genotyped by this method could be used for studying genetic diversity, genetic introgression6, QTL mapping, genomic selection8, genome-wide association studies (GWAS)9, population studies and identification of selective sweeps10. Hence, in the present study, genome-wide SNPs identified through the reduced representation approach (ddRAD) were used to study genomic diversity as well the genetic polymorphism in the candidate genes responsible for high altitude adaptation in the Indian yak populations.Yak . A total of 95.4% of QC processed reads were mapped to the reference genome. Of the total aligned reads 49.17% were uniquely aligned to the reference genome. The QC processed quality reads covered 9.43% of the reference genome.The QC processed good quality reads were aligned with Yak genome , RD5 (Read Depth5)and RD10 (Read Depth10) in the different Indian yak populations are presented in Table The high quality SNPs identified against yak reference genome at read depth ≥10 and quality ≥30 were annotated using the SnpEff software. Based on the sequence ontology terms, 425292 of SNPs were present in the intronic region; 429945 of SNPs in transcript region; 661221 of SNPs in intergenic region and 6532 of SNPs in exonic region (Table s) and 3329790 transversions (tv) were identified. The ts/tv ratio in the present study was found to be 2.36.The SNPs identified in the present study were found to have varying impacts and 215 SNPs were found to have high impact, 3388 SNPs have low impact, 2804 SNPs have moderate impact, and 1710724 SNPs have impact as modifier. The SNPs were also found to have number of effects by functional class and were found to be missense (2829 SNPs), nonsense (124 SNPs) and silent (2808 SNPs). The single base pair change in the DNA that differs from the usual base at a position has been studied .The candidate genes for high altitude adaptation were selected from studies in cattle and yakIS) ranged from 0.01 to 0.02 IS) rangeAMOVA analysis revealed the SNP variations among yak populations (3.47%); within breed 97.48%) as well among populations within groups is −0.95%  out of 30 SSRs amplified the yak genomic DNA and only 23 SSRs were found to be polymorphic in the Indian yak populations. In the present study, ddRAD approach for studying the genetic diversity in yak has been attempted for the first time and has been found to be a cost effective and efficient method to assess the genetic diversity in the livestock species.Non-availability of recommended SSRs by FAO as well SNP chip for yaks make a constraint to study the genetic diversity as well to proceed for estimating the genomic breeding value and genomic selection. Previously, in order to assess the diversity of Indian yak population, FAO recommended cattle SSRs were used. In the studyBased upon the estimated genetic distance in the Indian yaks, Sikkimi yaks was found to be genetically distant, followed by Ladakhi yaks. Arunachali and Himachali yaks were found to get clustered together and observed to be genetically similar. Private alleles are present maximum in Sikkimi yak followed by Ladakhi yak which makes the Sikkimi yak population unique among the other populations. The genetic distance between the different yak populations was found to be in decreasing order in Sikkimi-Arunachali; Sikkimi-Himachali; Sikkimi-Ladakhi; Ladakhi-Arunachli; Ladakhi-Himachali and Arunachali-Himachali yak populations.16. ITPR1 gene is target of HIF-2α and is involved in high altitude adaptation mechanisms12. HIF-1α regulates the oxygen homeostasis in cells and also regulates glycolysis18. NOS2 gene is expressed during the high altitude hypoxia and may aid in improved pulmonary capacity in high altitude19. GLUT1 like glucose transporters are responsible for anaerobic metabolism12. The SNPs identified in the candidate genes for high altitude adaptation need to be explored further in large population for its role in the adaptation process.The low level of genomic divergence between the yak populations indicates it’s origin from the same ancestors. Moreover, the low genetic differentiation among the groups may be because of the gene flow due to the movement of animals across different geographical locations. The existing genomic divergence may be due to the adaptation of the animals over years and also may be due to the geographical isolation of the animals. Higher molecular variation (97.48%) within the Indian yak populations may partly be due to lack of selection pressure applied and large effective population size that can be corroborated with the higher genetic diversity within the population. In the present study, genome-wide SNPs identified in the Indian yaks has been structurally and functionally annotated (Supplementary dataset 1). The candidate genes for high altitude adaptation were reported to be responsible for adaptation to hypoxia, anaerobic metabolism, iron homeostasis and pulmonary hypertension in various species. The EPAS1/HIF-2α which is a transcription factor that belong to hypoxia-inducible factor signaling pathway was found to be involved high altitude adaptation in through its role in response to hypoxia, erythropoiesis, iron homeostasis, pulmonary hypertension20, 4.66% in chickens21 and 13% in ducks22. Through the ddRAD approach, the unique yak populations identified in the study will help for future characterization and prioritization of animals for conservation purposes.The range of estimated heterozygosity (both observed and expected) were found to be less in ddRAD when compared to microsatellites, which gives a wider range. In ddRAD, use of methylation sensitive restriction enzymes removes the repetitive sequences which may result in lower estimate of genomic diversity. Moreover the SNPs identified in the coding regions as well in the UTR regions may also result in lower estimate of genomic diversity. In case of microsatellites the higher estimate may be because of it’s location in the intronic regions where lot of repetitive sequences are present and have higher number of variations than the coding regions. Higher amount of variations in the intronic regions usually results in more variations in the allele size in the populations which usually gives a higher estimate of genetic diversity. Being a reduced representation approach of the whole genome in which the repetitive sequences are removed, the ddRAD approach has a genome coverage of 9.43% in the present study which is supported by the genome coverage of 13.4–14.8% in buffaloes23 and the quality of the genomic DNA was checked by agarose gel electrophoresis and quantity by the digital nanophotometer. Optical density (OD) was also determined as the ratio of OD260 and OD280 in each sample. The samples meeting the required QC parameters of more than 100 ng/ul concentration and having a OD260 by OD280 ratio of 1.7–1.9 was considered for library preparation using the standard protocols described for ddRAD24. The double digestion of genomic DNA (1 microgram) was carried out using Sphl and Mluc1 restriction enzymes and cleanup of the digested product was carried out using Ampure beads. Ligation of P1 (Barcoded) and P2 adaptors were done using T4 DNA ligase. Size selection of the product was done after 2% agarose gel electrophoresis. The PCR amplification was carried out to enrich and add the Illumina specific adapters and flow cell annealing sequences. Then final pooling and sequencing was carried out on the illumina Hiseq. 2000 and 100 bp paired end reads were generated.The animals for blood collection were randomly selected from the breeding tract of the different Indian yak populations. The blood sampling of animals were performed in accordance with the relevant guidelines and regulations as approved by Institutional Animal Ethics Committee (IAEC) of National Bureau of Animal Genetics Resources (NBAGR), Karnal. A total of 80 blood samples were collected . Genomic DNA from the blood samples were isolated using Phenol-Chloroform method25. The Illumina 5′ and 3′ adapter sequences were also removed. The reads were processed in STACKS26 for finding the RAD loci and to retain the reads with minimum phred score of 15. The index of the reference genome were build using Bowtie2–2.3.3.127. The QC processed reads were aligned with yak genome28  using Bowtie 2–2.3.3.1. During the alignment, the parameters were set to very sensitive local paired end alignment and the maximum fragment length including gaps as 1000 bp. Samtools/bcftools version 1.629 were used for further processing of the reads and to call the variants. The variants identified were filtered for minimum read depth ; maximum read depth (D ≤ 1000) and base quality score (Q ≥ 30) using vcfutils varFilter of Samtools. In order to identify the extent of genome coverage by the reads obtained by reduced representation approach, SAMtool was used. High quality SNPs identified in the present study were annotated both structurally and functionally with the help of SnpEff 30 and SnpSift31. In SnpEff, the effect of SNPs were identified once the data has been downloaded and database has been built. The program loads the binary database and builds a data structure called “interval forest” to perform an efficient interval search. The effect of SNPs were grouped according to the region as exonic, intronic, upstream and downstream regions, splicing sites and intergenic regions. SNPs in the coding region were further grouped into missense, nonsense and silent SNPs. For gene wise annotation, the candidate genes for high altitude adaptation33 were collected. The genomic location of the candidate genes were drawn from the NCBI database and the SNPs present in that region were captured from the annotated VCF file using VCFtools34.The raw reads were demultiplexed using custom perl scripts to obtain sample specific reads. Up to one mismatch was allowed to demultiplex the sample data. The low quality bases and regions showing base bias at the start or end and was removed from the reads using Prinseq lite version 0.20.435. The observed and expected heterozygosities as well the level of inbreeding coefficient were estimated. Analysis of Molecular Variance (AMOVA) was also calculated using Arlequin software36 (ver 3.5.2.2).The SNPs observed to be in common for all the four populations were used for estimating the genetic distance (proportion of loci that were different) and genomic diversity using poppr packageAuthors mentioned the name of the service providers solely for providing accurate information and does not imply any recommendation.Supplementary Dataset 1.


Mineral nutrients play a crucial role in the biochemical and physiological functions of biological systems. The enhancement of seed mineral content via genetic improvement is considered as the most promising and cost-effective approach compared alternative means for meeting the dietary needs. The overall objective of this study was to perform a GWAS of mineral content  in seeds of a core set of 137 soybean lines that are representative of the diversity of early maturing soybeans cultivated in Canada (maturity groups 000-II).This panel of 137 soybean lines was grown in five environments  and the seed mineral content was measured using a portable x-ray fluorescence (XRF) spectrometer. The association analyses were carried out using three statistical models and a set of 2.2 million SNPs obtained from a combined dataset of genotyping-by-sequencing and whole-genome sequencing. Eight QTLs significantly associated with the Ca, K, P and S content were identified by at least two of the three statistical models used (in two environments) contributing each from 17 to 31% of the phenotypic variation. A strong reproducibility of the effect of seven out these eight QTLs was observed in three other environments. In total, three candidate genes were identified involved in transport and assimilation of these mineral elements.There have been very few GWAS studies to identify QTLs associated with the mineral element content of soybean seeds. In addition to being new, the QTLs identified in this study and candidate genes will be useful for the genetic improvement of soybean nutritional quality through marker-assisted selection. Moreover, this study also provides details on the range of phenotypic variation encountered within the Canadian soybean germplasm. Soybean is utilized for a wide array of food, feed, and industrial purposes, making it one of the most versatile grain crops grown. In fact, soybean is an important source of protein, oils and carbohydrates, as well as other beneficial nutrients such as mineral elements which affect end-use traits of both the oil and protein fractions as well as the quality characteristics of seed used to plant succeeding crops . The avaThe enhancement of seed mineral nutrient content via genetic improvement is considered as the most promising and cost-effective approach to ensure that the dietary needs of consumers are met. As breeding for any trait rests on the existence of phenotypic variability, this requires the identification of cultivars with useful genetic variability for grain minerals and understanding the genetic architecture of these seed traits .Since the concepts of plant nutrition were founded, much effort has been put into developing methods and tools for quantitative measurement of the elemental composition of living organisms . Spectro2:3 and 157 F2:4 lines. King et al. [2:4 lines. More recently, Ramamurthy et al. [A limited number of studies have aimed to determine the genetic architecture governing the accumulation of mineral elements in seeds and concluded these traits to be most likely controlled by many genes , 14. Zhag et al.  reportedy et al.  reportedIn the context of an incomplete and often inconsistent identification of QTLs controlling the accumulation of mineral elements in the soybean seed , especiaP <  0.001) and ranged from 0.91 (Ca) to 0.94 (P). These results demonstrated that the ED-XDF was appropriate for the quantification of Ca, P and K content in soybean seeds.To validate our chosen analytical method , thirty samples were analyzed by both ED-XRF and flame atomic absorption spectroscopy (FAAS) for Ca and K and by spectrophotometry for P content. As can be seen in Fig. The concentrations of Ca, K, P and S on a set of 137 soybean lines grown on two sites (two replicates/site) in 2013 were estimated using an ED-XRF device. The frequency distributions exhibited an approximately normal distribution and appeared to be quantitatively inherited  on phenotypic variation for all traits except for Ca where the genotypic effect was the sole significant source of variation. No significant genotype x environment interactions were observed for any of the traits. The observed phenotypic values were significantly (p <  0.001) correlated between the two experimental sites, with correlations ranging between 0.75 and 0.98. The seed content in the different minerals also proved to be correlated . All such pairwise comparisons were statistically significant (p <  0.05) and the highest correlations were observed between K and S  as well as between P and K .As illustrated in Table The lines of the association panel were initially genotyped via a GBS approach that yielded a total of 56 K high-quality SNPs. In a second step, a reference panel of 4.3 M SNPs was used to perform missing loci imputation onto the original set of GBS-derived SNPs. After removing InDels, markers with a MAF <  0.05 and heterozygosity > 0.1, a total of 2.18 M SNPs were retained, offering an average marker density of 1 SNP every 435 bases across the entire genome. The physical distribution of these 2.18 M SNPs across the soybean 20 chromosomes is illustrated in Fig. k) was 7  at the most extreme values .To discover chromosomal regions that contribute to the phenotypic variation, we used three analytical tools to measure marker-trait associations: FarmCPU, CMLM and MLMM. As shown in the quantile-quantile (QQ) plots Fig. , all thrp-values of these QTLs ranged from 1.35E-06 to 2.84E-21 for Ca, from 1.89E-05 to 8.05E-19 for K, from 1.17E-06 to 3.61E-12 for P and from 1.75E-05 to 6.63E-15 for S content.The results of these association analyses are presented as Manhattan plots for FarmCPU, CMLM and MLMM in Fig. 2) ranged from 20 to 21% for Ca, from 17 to 31% for K, 22% for P and from 18 to 23% for S. The magnitude of allelic effects varied between 0.06 to 0.07 mg/g, 0.30 to 0.57 mg/g, 0.30 mg/g and 0.15 to 0.46 mg/g for Ca, K, P and S, respectively. The genetic variance (additive) explained as the narrow-sense heritability (h2) was 41% for Ca, 82% for K, 78% for P and 93% for S.In total, among these 32 QTLs, eight QTLs were co-identified by at least two models Fig.  and the To verify the stability of each of the eight QTLs detected by at least two models, data from three additional trials were obtained. Overall, across the three new environments, seven QTLs were validated in at least two environments Fig. . Of the To more deeply explore variants in these robust QTLs, we extracted all SNPs falling within the haplotype blocks surrounding the seven most robust QTLs from the larger catalogue of 2.2 M SNPs. These were merged with the pruned data (243 K) set to perform the GWAS with three models again. In six of these seven instances, stronger association signals were observed and the physical distance between the previous and the new peak SNP ranged from 1 to 311 kb (Table S4), but always resided within the same haplotype block.Based on the GWAS results, we investigated the genes annotated in the soybean genome in order to identify putative candidate genes from loci significantly associated with each trait. To establish a list of candidate genes, we focused only on those residing within a region delimited by the left-most and right-most flanking markers that were in perfect LD (D’ = 1) with the peak SNP for the seven QTLs described above. These genomic regions (ranging in size between 32 and 360 kb) were extracted from Wm82.a2.v1 and the GO annotations of genes residing within these regions was examined Table . An examTo identify a candidate gene, we looked for genes that met either of the two following criteria: 1) genes annotated as being involved in the transport of the given mineral element and expressed in roots, shoots or leaves or 2) genes annotated as being involved in the uptake, translocation, and/or homoeostasis of the element of interest and mainly expressed in seeds. In total, three promising candidate genes involved either in the transport or assimilation of these mineral elements were identified. We first discovered Glyma.06G046000 (132 kb upstream of the peak SNP in Ca_#3), Glyma.10G020000 (222 kb downstream of the peak SNP in K_#3). These two genes were both annotated as being involved in transport and expressed in roots tips and roots hairs. In addition, Glyma.06G046000 was expressed in young leaves, flowers, main roots, pods as well as in seeds Fig. b and 4d.To determine if genetic  variation within or overlapping the candidate gene could constitute causal variants, we examined a catalogue of such variation established from the whole-genome sequencing data available for a subset of 56 lines. No structural variant (> 51 bp) was identified as overlapping in full or in part with these three candidate genes. As for nucleotide variants, a total of 18 SNPs were found within the coding regions of two genes (one within Glyma.06G046000 and 17 within Glyma.20G151500). All of these variants were predicted as a having a “modifier” or “low” impact on protein function. It is therefore unlikely that the observed phenotypic variation is due to a loss of function of these candidate genes.P <  0.05) were observed between the haplotypes identified for each candidate gene. For example, among the four haplotypes  identified for Glyma.06G046000, the seven lines carrying haplotype B exhibited a significantly different Ca content compared to the 130 other lines carrying haplotypes A, C or D. Similarly, a small group of five accessions carrying haplotype C at Glyma.10G020000 had a higher K content than the other accessions carrying one of the four other haplotypes. In the case of Glyma.20G151500, it was the more frequent haplotype A (n = 99) that exhibited a significantly higher S content than the other four haplotypes. These results support that each candidate gene is highly promising as they each contributed to the phenotypic variation.To provide more insight into the involvement of these candidate genes in the observed phenotypic variation, a haplotype analysis was performed. As presented in Fig. Across the two environments used to perform the original discovery of marker-trait associations, the seed contents for all the elements  were normally distributed and suggested that they are quantitatively inherited. The phenotypic variation in this study ranged from 1.6 to 2.4 mg/g for Ca content, 17 to 21 mg/g for K content, 4.5 to 6.5 mg/g for P content and 3.5 to 5.5 mg/g for S content on a dry-weight basis. In previous studies, different ranges have been reported. Otaka et al.  and Homu2 > 0.80) for all traits. Such relatively high broad-sense heritability suggested that the phenotype was largely determined by the genotypic effect [2 = 0.48 to 0.93) [In our study, the broad-sense heritability estimated across the two environments was high (Hc effect . Similarto 0.93) , 16. In to 0.93)  .A total of 32 QTLs associated with the Ca, K, P and S content were identified (Table S2) and eight of these were detected by at least two models. More importantly, seven of these eight QTLs proved extremely robust as they could be successfully confirmed as impacting mineral element content in three additional trials. In previous GWAS studies, Ziegler et al.  and NingThe QTLs described in this work generally explained a fairly substantial portion of the phenotypic variance (18–31%). The phenotypic variance explained by previously reported QTLs varied from 2 to 18%. Interestingly, one of our QTLs was found to impact both K and P content. This shared QTL for K and P content is not surprising given the high degree of positive correlation between these two elements. Similarly, Dhanapal et al.  also repIn order to compare our results with previously identified QTL regions, we queried the previous QTLs against the SoyBase genome browser and defined their physical position. None of the seven robust QTLs identified in this study coincided with previously reported QTL intervals identified either in family-based mapping or GWAS. Thus, the current QTLs can be considered novel. This absence of overlap between the QTLs identified through this work and those reported previously may reflect the fact that these traits are determined by different genes in the experimental materials used in the different mapping experiments.+ potassium transporter and its ortholog in A. thaliana (AT4G13420.1) encodes a high affinity K+ transporter 5 (HAK5). In rice, Yang et al. [+ into roots and its transport to the aerial parts of the plant. Two paralogs of our candidate gene (Glyma.02 g154100 and Glyma.07 g042500) were functionally characterized as being involved in the root uptake of K+ in soybean [As mentioned above, we focused our attention on transport-related genes that were also expressed in roots, shoots or leaves and genes annotated to be involved in nutrient uptake, translocation, and/or homoeostasis mainly expressed in seeds. For K content, a transport-related gene (Glyma.10G020000) was identified underlying QTL K_#3 on Gm 10. This gene was annotated as a Kg et al.  demonstr soybean , 24. Int soybean , QTLs foA. thaliana  [A thaliana cDNA encoding ASA1 successfully complemented a Saccharomyces cerevisiae ATP sulfurylase mutant (met3), thereby restoring both methionine heterotrophy and sulfate transport [For S content, we identified a gene (Glyma.20G151500) that codes for ATP sulfurylase 1, the first enzyme known to be involved in the sulfate assimilation pathway in 1; ASA1) . An A thransport . Intriguransport   that encodes a chloroplast envelope Ca2+-ATPase which is known to bind the calmodulin that leads to activation of a Ca2+ pump [+-ATPases are enzymes that actively transport Ca2+ in eukaryotic cells [Finally, for Ca content, the candidate gene Glyma.06G046000 was annotated as a calcium transporting ATPase involved in calcium transport. This gene is orthologous to an ic cells  and are ic cells .Compared to previous studies, the high density of markers used in this study has contributed to the reproducible detection of several new loci associated with the content of mineral elements in soybean seeds. In addition to providing details on the range of phenotypic variation encountered within the Canadian soybean germplasm for mineral elements content in the seeds, this study also provided more information on the genetic architecture underlying their accumulation. The markers and genes identified in this study will be useful for the genetic improvement of soybeans through marker-assisted selection.A set of 137 of early maturing soybean lines , was selected from a larger group of 304 accessions based on the analysis of population structure as described in Sonah et al [2SO4 on reference materials ) supplied by the National Institute of Standards and Technology (NIST) whose S content was known. The values thus obtained served as baselines to calibrate the standard curve of our ED-XRF device (Niton XL3t955 GOLDD). To determine the accuracy and reliability of our ED-XRF measurements, thirty seed samples were analyzed by both ED-XRF and wet chemistry for their concentration in Ca, K and P. no wet chemistry validation was necessary for S content.Calibration of the energy-dispersive X-ray fluorescence (ED-XRF) spectrometer was achieved by an empirical calibration approach  in whichFor each sample, 10 g of whole seeds were ground using a grinder (Foss A/S: Cyclotec™ 1093 Sample Mill). A 0.3-g sample of homogenous fine powder from each line was pressed using a stainless-steel pellet die in a hydraulic pellet press (Carver 4350.L) to produce compact 13-mm pellets (~ 0.2 mm thick). The pellets were stored until the measurement of Ca, K, P and S content by the ED-XRF.H2 across environments was calculated as follows:Descriptive statistics, genotypic variance, environment and genotype by environment effects as well as correlation analysis between these mineral contents were performed using an R package « lmer ». To combine information from different environments, best linear unbiased predictions (BLUPs) were calculated using the restricted maximum likelihood in META-R . The broApeK1 was used to produce a single 192-plex GBS library  that was sequenced on a single lane of an Illumina HiSeq2000 sequencer. Approximately 203 million 100-bp single-end reads were obtained for the entire population of 137 lines. The Fast-GBS pipeline [A total of ~ 203 million 100-bp Illumina HiSeq2000 single-end reads derived from sequencing 192-plex GBS libraries were available for the 137 lines (as detailed previously ). Brieflpipeline  and the pipeline  were usepipeline . The accpipeline  and foun2 > 0.5) catalogue of 14 K SNPs obtained using Plink v1.9 [k) was set from 1 to 12 with 3 independent itterations. The number of subpopulations (k) was determined using a python script (“choosek”) implemented in fastSTRUCTURE. In addition to fastSTRUCTURE, two different methods were used to infer population structure: (i) a consensus phylogenetic tree computed with the Tamura-Nei model with a boostrapping based on 2000 iterations, implemented in MEGA7 [For the population structure analysis, we used a pruned (rink v1.9 . The Bayink v1.9  was usedin MEGA7  and (ii)in MEGA7 .2 > 0.9) and the BLUP values for each trait. CMLM was used for single-locus GWAS while MLMM and FarmCPU were used for multi-locus GWAS. The genetic relatedness between the lines conveyed through the kinship matrix (K) and the population structure matrix (Q) estimated through fastSTRUCTURE were used to control for false positive associations. The threshold of significance of marker-trait associations for the three models  was an adjusted p-value with the false discovery rate (FDR) set at ≤0.05, as per the Benjamini and Hochberg procedure [p-value was reported. We chose to report and investigate only co-detected QTLs, ones that were detected by at least two models for each trait. For the identification of candidate genes, genomic regions of interest surrounding a peak SNP were defined as extending between the left-most and right-most flanking markers that were in perfect LD (D’ = 1) with the peak SNP.Genome-wide association between markers and the phenotypes was assessed in GAPIT using a pruned catalogue of 243 K SNPs  using the same set of 137 lines. Marker-trait associations were tested using a t-test. The population was divided into two groups according to the allelic class at the peak SNP. We performed a t-test between the mean phenotypic values of the two groups. The threshold of significance for marker-trait associations was adjusted for multiple tests . The significance test was assessed using a t-test function implemented in R version 3.5.1 according to the equation described in : Y = μ +www.bar.utoronto.ca) for soybean.By using a data mining algorithm , all genAltered transcripts resulting from potential loss-of-function (LOF) alleles among the list of candidate genes were investigated by inspecting the catalogue of structural variants reported by . For LOFTo further support the involvement of candidate genes in the observed phenotypic variation, a gene-centric haplotype (GCH) approach was used to identify polymorphic markers that defined the haplotypes inside the candidate genes using HaplotypeMiner  and the Additional file 1. Table S1: Phenotypic correlation between the seeds minerals content. Table S2: The 32 reported QTLs associated with Ca, K, P and S content in seeds. Table S3: The t-test results. Table S4: Summary of localized GWAS results. Table S5: The full list of all genes residing in the significant haplotype blocks.Additional file 2 Fig. S1: Distribution of the 2.18 M SNPs in the soybean genome. Fig. S2: Quantile-quantile (Q-Q) plot of p-values from the association study. Fig. S3: The boxplot and p-values for t-test result. Fig. S4: Identification of candidate genes underlying QTLs. Fig. S5: Candidate gene haplotypes and their phenotypic contrast.


Bemisia tabaci MED/Candidatus Portiera aleyrodidarum (hereafter Portiera). As such, the roles of dTTP on both sides of the symbiosis system were investigated in B. tabaci MED/Portiera. Dietary RNA interference (RNAi) showed that suppressing dTTP production significantly reduced the density of Portiera, significantly repressed the expression levels of horizontally transferred essential amino acid (EAA) synthesis-related genes, and significantly decreased the reproduction of B. tabaci MED adults as well as the hatchability of their offspring. Our results revealed the regulatory role of dTTP in B. tabaci MED/Portiera and showed that dTTP synthesis-related genes could be potential targets for controlling B. tabaci as well as other sucking pests.Deoxythymidine triphosphate (dTTP) is essential for DNA synthesis and cellular growth in all organisms. Here, genetic capacity analysis of the pyrimidine pathway in insects and their symbionts revealed that dTTP is a kind of metabolic input in several host insect/obligate symbiont symbiosis systems, including  Portiera relies on host-provided phosphoenolpyruvic acid, erythrose-4P, pyruvate and 5-phosphoribosyl diphosphate for synthesizing EAA (Acyrthosiphon pisum/Buchnera aphidicola (hereafter Buchnera) symbiosis system  and thymidine kinase (BtTK), were identified and cloned. By the silencing of these two genes, the influences of blocking dTTP production on Portiera density and EAA biosynthesis were investigated. Furthermore, the possible involvement of these two genes in B. tabaci MED management was also investigated.The genomes of many sucking insects and those of their obligate symbionts are currently available, so global views on various metabolic interactions, such as metabolic inputs, in insect/obligate symbiont symbiosis systems are feasible . In thisB. tabaci, Portiera, and other selected species  were also used as queries to search against both predicted proteomes  and genome assembly sequences  of selected species (via BLAST against the nr (NCBI) database.For genetic capacity analysis of the pyrimidine pathway, genes that are involved in these processes were identified as follows. Initially, predicted protein sets of  species . In the Bemisia tabaci MED was collected from fields in Beijing, China, in 2009. Since then, the B. tabaci MED insects have been reared on cotton (Gossypium herbaceum L. cv. Zhongmian 49) in a glasshouse , and the concentration of each RNA sample was adjusted to 1 μg/μL.Total RNA of each sample was extracted by using TRIzol reagent  following the manufacturer’s instructions. The integrity of the extracted RNA was checked with 1% agarose gel electrophoresis. The quality of the extracted RNA was then measured B. tabaci MED whole body was used to prepare cDNA with a PrimeScript II First strand cDNA Synthesis Kit . Gene-specific full-length primers  was used as a reference gene . Gene-specific primers were then designed  was used as a negative control. Before performing the RNAi assay, dsRNA for BtTS, BtTK and EGFP was prepared using a T7 RiboMAX Express RNAi system   in feeding chambers. Each treatment involved six feeding chambers, and 70 newly emerged B. tabaci MED adults were introduced into each feeding chamber. The feeding chambers were then incubated in an environmental chamber at 25°C under a photoperiod of 14 L:10 D and a relative humidity (RH) of 70%.The RNAi constructs were generated and applied as previously described , and dsRB. tabaci MED adults was recorded. The surviving B. tabaci MED adults from each replication were then collected and divided into two groups. The first group contained 25 B. tabaci MED adults that were used to determine the RNAi silencing efficiency and the effects of silenced targeted genes on the expression of EAA biosynthesis-related genes. Thirty of the other surviving B. tabaci MED adults constituted the second group and were used to determine the Portiera density of each whitefly individual. Before determining the Portiera density of the individual insects, genomic DNA of each individual insect was extracted as previously described  using 2 × SuperReal PreMix Plus . The protocol for the density of Portiera determination was the same as that described previously  of the whiteflies was used as an internal standard for normalization. All the primer pairs used are listed in After silencing for 2 and 4 days, the mortality of escribed . The deneviously . The relB. tabaci MED females, BtTS and BtTK of B. tabaci MED females were first knocked down via RNAi as described above. At 2 days after the induction of silencing, five B. tabaci MED females were released into one clip cages on health cotton plants. Three days after the release of the females, the number of eggs that were deposited on the leaves within the clip cages were counted. Later, egg hatchability was calculated. Each clip cage was considered one biological replicate, and each treatment had 20 replicates.To assess the effects of suppressing dTTP production on p < 0.05).SPSS version 23.0  was used for statistical analysis. Differences among treatments were evaluated by one-way ANOVA in conjunction with Tukey’s test , stink bugs (labeled Pantoea) and tsetse flies (labeled Wigglesworthia) encode the whole set of genes for uridine monophosphate biosynthesis. No selected obligate symbiont was found to encode a complete set of genes for the pyrimidine salvage pathway and pyrimidine degradation  and 4 days , the expression levels of BtTS and BtTK significantly decreased  and 4 days  significantly reduced the density of Portiera  and 4 days (p = 0.033). Knocking down BtTS  and BtTK  did not cause a significant lethal effect , suppressing BtTK expression more significantly reduced hatchability than did suppressing BtTS expression (p < 0.0001; The effects of silencing BtTS and BtTK, were then identified and cloned. Later, the influences of blocking dTTP production on Portiera and B. tabaci MED were investigated. Our results showed that suppressing dTTP production greatly repressed EAA biosynthesis, significantly decreased Portiera density and caused a serious decline in B. tabaci MED.Here, a genetic capacity survey revealed that many obligate symbionts required their insects’ hosts to supply dTTP. Two dTTP synthesis-related genes, Portiera were not able to produce dTTP, while their host insects retained such capacities. In view of the great demand for dTTP across whole life cycles, our study revealed that, except for EAA biosynthesis, dTTP is another kind of metabolic input in several insect/obligate symbiont symbiosis systems.Because of genomic decay, obligate symbionts rely on their hosts to supply metabolic inputs for synthesizing essential nutrients . To dateA. pisum/Buchnera symbiosis systems (Portiera. Given that those HTGs compensated for the gene loss of Portiera and that the majority of reactions of EAA biosynthesis were still afforded by Portiera (BtTS and BtTK slowed the EAA biosynthesis of Portiera. Taken together, our results indicated that dTTP is applied as a regulator to control obligate symbionts in the B. tabaci MED/Portiera symbiosis system.The metabolic input cystathionine was previously reported to determine methionine production in  systems . It has  systems . Here, oPortiera , our obsB. tabaci MED adults and the hatchability of their offspring. The supply of enough EAAs is essential for insect reproduction (Portiera supplies EAAs for B. tabaci MED (Portiera as well as EAA biosynthesis. As B. tabaci causes severe unfitness, BtTS and BtTK were suggested to be potential targets for symbiont-targeted B. tabaci MED management. Antibiotics have also been suggested to serve as novel pesticides for symbiont-targeted B. tabaci MED management (B. tabaci MED, the release of high concentrations of antibiotics was shown to take a heavy toll on the environment, such as influencing the structures and activities of microbes in the environment (B. tabaci MED and was not detrimental to environmental microbes, it seems that managing B. tabaci MED by silencing dTTP synthesis-related genes such as BtTS and BtTK is more friendly to the environment than managing B. tabaci MED by using antibiotics.In addition, we also observed that blocking dTTP production significantly decreased the reproduction of oduction . EAAs aloduction . As Portbaci MED , the negnagement . Though ironment . As sileB. tabaci MED, other sucking insects such as psyllids and mealybugs are also important agricultural pests and harbor obligate symbionts (Portiera were potential targets for symbiont-targeted B. tabaci MED management. Interestingly, we also found that obligate symbionts of psyllids and mealybugs lack the capacities for biosynthesising dTTP. The results indicated that those symbionts may also require their hosts to supply dTTP. As dTTP is essential for cellular growth that is required throughout the whole life cycle, it is probable that genes involved in dTTP production could also serve as molecular targets for psyllid and mealy bug management.In addition to ymbionts . Since oymbionts  and cannymbionts , these oymbionts . Here, wPortiera and B. tabaci MED. Our results indicated that two dTTP synthetic genes, BtTS and BtTK, could be used as molecular targets for B. tabaci MED management. The study demonstrated a regulatory mechanism in the MED/Portiera system and likely revealed new molecular targets for whitefly pests and even management of other sucking insects.In summary, we showed that suppressing dTTP production caused a series of negative effects on both All datasets presented in this study are included in the article/ZY and YZ designed the research. YZ and CG conceived the experiments. YZ, YH, and JX analyzed the data. ZY and JZ drafted the manuscript. WX, ZG, XY, WX, SW, QW, and YZ revised and finalized the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.