The AMNOG regulation, introduced in 2011 in Germany, changed the game for new drugs. Now, the industry is required to submit a dossier to the GBA  to show additional benefit. After granting the magnitude of the additional benefit by the GBA, the manufacturer is entitled to negotiate the reimbursement price with the GKV-SV . The reimbursement price is defined as a discount on the drug price at launch. As the price or discount negotiations between the manufacturers and the GKV-SV takes place behind closed doors, the factors influencing the results of the negotiation are not known.The aim of this evaluation is to identify factors influencing the results of the AMNOG price negotiation process.The analysis was based on a dataset containing detailed information on all assessments until the end of 2015. A descriptive analysis was followed by an econometric analysis of various potential factors .p = 0.030) and non-orphan drugs (p = 0.015). Smaller population size, no deviation from recommended appropriate comparative therapy and the incorporation of HRQoL-data were associated with a lower discount on the price at launch. However, neither a uni- nor the multivariate linear regression showed enough power to predict the final discount.Until December 2015, manufacturers and the GKV-SV finalized 96 negotiations in 193 therapeutic areas, based on assessment conducted by the GBA. The GBA has granted an additional benefit to 100/193 drug innovations. Negotiated discount was significantly higher for those drugs without additional benefit (Although the AMNOG regulation implemented binding and strict rules for the benefit assessment itself, the outcome of the discount negotiations are still unpredictable. Obviously, negotiation tactics, the current political situation and soft factors seem to play a more influential role for the outcome of the negotiations than the five hard and known factors analyzed in this study. Further research is needed to evaluate additional factors. Arzneimittelmarkt-Neuordnungsgesetz, AMNOG) became effective in 2011. Upon market registration, pharmaceutical companies are obliged by law to submit a dossier to the Federal Joint Committee  in order to prove a patient-relevant medical benefit in mortality, morbidity, and health-related quality of life (HRQoL). The GBA is the decision-making body of the joint self-governing board of stakeholders in healthcare  in the German Statutory Health Insurance . The methodological basis of the underlying assessment and the uncertainties regarding outcomes and study results is in accordance with the principles of evidence-based medicine. The Institute for Quality and Efficiency in Health Care  publishes its own methods on assessments in a specific method paper including key elements on how assessments are to be carried out  was significantly higher than discount for an “additional benefit” (n = 100) 19.98 %  (p = 0.030). One assessment resulted in a “major additional benefit” (13.64 %) and one in “additional harm potential” (9.00 %). Overall, two indication classes showed significant results for the mean discount compared to the others: “Diseases of the Respiratory System” and “Diseases of the Nervous System”. They present the minimum and the maximum mean discount when comparing discounts per indication class  led to an average discount of 22.14 %  compared to a discount of 23.81 %  in assessments without identical rating (n = 69). When renouncing a dossier submission (n = 5) the negotiated average discount was 31.51 %  vs. 23.84 %  with dossier submission and the GBA granted “no additional benefit” (n = 86).Manufacturers’ own rating of the additional benefit was different from the rating of the additional benefit by IQWiG and/or GBA in 95/193 assessments and often overestimated. An identical rating between IQWiG and GBA . When comparing orphan drugs only with non-orphans with additional benefit (71/193) 20.67 %  the difference was not significant (p = 0.256). Table Mean discount for orphan drugs (29/193) was 18.31 %  compared to non-orphan drugs 164/193) 22.60 %  (/193 22.6n = 29) the additional benefit is preset by law independent from the chosen ACT. Table For orphan drugs (The mean discount did not significantly differ compared to those where the manufacturer followed the recommendation and no noticeable differences were seen regarding indication groups Table .n = 30), the manufacturer chose the more costly ACT in 19 assessments. Annual cost of the new compound ranged from 94–8100 % compared to ACT. Mean discount was 21.72 %  compared to those without incorporated HRQoL-data 22.87 % , n) Table .Table 6In = 9) was significant (Appendix p = 0.020). The model explained 16.3 % of the variations.The univariate regression provided significant results for factor “Rating” and “Indication”, which significantly correlate with the dependent variable Appendix . For theAppendix . There wThe present study analyses possible factors influencing the final negotiated discount after AMNOG-assessment. Therefore, results of this analysis should be interpreted as a trend and starting point for further research.Of the analyzed 193 assessments, 91 rated with “no additional benefit”. This is nearly half of all assessments, whereas for orphan drugs (29/193) the law presets an additional benefit. As expected, a proven additional benefit influences the final discount positively. However, the rating itself seems not to fully “reflect” the expected discount. For example, 0 % given for a rating with minor additional benefit and 78.02 % for a rating with no additional benefit. Discounted prices negotiated based on the early benefit assessment show that the new system has resulted in substantial rebates within a wide range of results. However, free pricing for the first year seems not to impose a structurally negative impact .The number of target populations assessed did not show any significant differences on final discount. For size of target population, there was only a hint that a smaller target population leads to lower discounts. This might reflect the circumstance that discounts for orphan drugs were lower, compared to non-orphan drugs.Orphan drugs need to be treated differently due to the circumstance that the law presets their additional benefit. When separating orphan drugs, mean discount for orphan drugs was not significantly different to those with additional benefit and non-orphan drug status.If the manufacturer does not deviate from GBA recommendations on the ACT, discounts seem to be smaller. When deviating from recommendation, assessments were granted with “no additional benefit” if no adequate reason existed. For half of all assessments, the GBA recommended more than one ACT. If the manufacturer chosed the more costly ACT, it had no significant impact on discounts.The acceptance and consideration of HRQoL-data within the GBA appraisal tends to influence the final discount positively. In the future, measured quality of life data and other patient-reported outcomes might play a greater role in early benefit assessment . For exaR2 = 0.163 the regression model explained only a small proportion of total variation . Hence, 92 subgroups assessed with “no additional benefit” within 96 assessments totally changes the picture. When taken “no additional benefit” as the highest rating granted, the number of assessments is 29 [When evaluating the discount by assessment level  and the wide spread of negotiated discounts limit our analysis. Due to the limited group size, all significant results need to be interpreted with caution.While the early benefit assessment and appraisal of clinical evidence itself is a very clear and transparent procedure, no official algorithm exists for a prediction of levels of negotiated price discounts , 14. ThiResult of the GBA-assessmentPrice as set by the manufacturerAnnual costs of comparable therapiesSome hard criteria for the discount negotiation are mandatory and pre-set:That leads to the presumption that the negotiation by itself is a “black box” with uncertain outcome and perhaps contrary to the interests of the individuals insured in the GKV . This seFrom an economic standpoint, there are two approaches to explain price building for pharmaceuticals and for the assessment of functionality as well as appropriateness of negotiation criteria. The first approach calculates the price of a new pharmaceutical based on research and development costs (plus X for innovation). The second approach measures the willingness-to-pay in dependence of patient preferences of the society (assessing the properties and advantages of a product). Pareto-optimum might be reached when full information about preferences and willingness-to-pay and low transaction costs exist . In realUnder highly specific assumptions, it is possible to internalize occurring external effects with bilateral negotiations and reach pareto-efficient negotiation results. However, manufacturers do not negotiate with patients directly. GKV-SV acts on behalf of the sickness funds and representative for patients (insured community). Physicians influence the distribution of new drugs when prescribing drugs and there is a need for a reserve capacity of health services that has to be offered by the government independent of the actual demand.In addition, monetization of the additional benefit by the arbitration board would also require the measurement of patients’ willingness-to-pay. Preferences and the willingness-to-pay of subjects (patients) are not known , 16 and Most other European countries focus more on cost-effectiveness approaches. For instance, the British National Institute for Health and Care Excellence (NICE), besides evaluating the “clinical effectiveness” of a new technology, also performs cost-effectiveness assessments. Cost-effectiveness approaches are also established in several other European countries such as Sweden, the Netherlands, Austria, and Poland , 19. HenIn the United States, no organization performs drug assessments similar to those in Europe, although the relevance of a comparative effectiveness research has increased with the introduction of the American Recovery and Reinvestment Act of 2009 .Discounts in Germany are based on early benefit assessment. Evaluations of the society’s willingness-to-pay and the welfare maximizing price cap are not relevant . Of courManufacturers value this process in a different way. On the one hand, incentives for the development of innovative drugs could be lower due to (sometimes misleading) assessments. One year after launch, they are no longer able to achieve monopolistic prices for their innovative products. On the other hand, when negotiating individual discounts it is possible to focus on individual product properties so that the final discount prevents the abuse of the monopolistic position .As a final point, the negotiation procedure ties up public resources and may lead to overwhelming bureaucracy costs because of delayed negotiations and the inability to find a price (when going to the arbitration court). This raises the question what the real amount of budget impact and cost savings is .Negotiations alone seem not to be the appropriate way to find a price optimum for new drugs. With the influence factors assessed in this study, the results are not properly predictable. Each negotiation process starts under new conditions. With respect to the negotiated discounts, there is a potential risk that the aim of a mere cost reduction becomes dominant with significant spillover effects worldwide.Decision-analytic assessments might help to fill potential gaps when planning different price strategies. For that reason, cost-benefit-assessment might be an essential tool for negotiations. Cost-benefit-assessments conducted by the manufacturer and/or payer at the time of dossier submission may help to give a first impression about the adequacy of the true value for a new drug.


Bovine tuberculosis (bTB) infection in cattle is a significant economic concern in many countries, with annual costs to the UK and Irish governments of approximately €190 million and €63 million, respectively, for bTB control. The existence of host additive and non-additive genetic components to bTB susceptibility has been established.Two approaches i.e. single-SNP (single nucleotide polymorphism) regression and a Bayesian method were applied to genome-wide association studies (GWAS) using high-density SNP genotypes  from 841 dairy artificial insemination (AI) sires. Deregressed estimated breeding values for bTB susceptibility were used as the quantitative dependent variable. Network analysis was performed using the quantitative trait loci (QTL) that were identified as significant in the single-SNP regression and Bayesian analyses separately. In addition, an identity-by-descent analysis was performed on a subset of the most prolific sires in the dataset that showed contrasting prevalences of bTB infection in daughters.−5, Bayes factor >10) across all analyses. Sires with the minor allele  for this QTL on BTA23 had estimated breeding values that conferred a greater susceptibility to bTB infection than those that were homozygous for the major allele. Imputation of the regions that flank this QTL on BTA23 to full sequence indicated that the most significant associations were located within introns of the FKBP5 gene.A significant QTL region was identified on BTA23  contains supplementary material, which is available to authorized users. Mycobacterium bovis) is an important infectious disease, that affects primarily cattle. It is an OIE  listed disease (https://www.oie.int), with global annual losses to agriculture of €2 billion [Bovine tuberculosis , which shares some epidemiological similarities with bTB [Both within-breed –7 and bewith bTB , range fwith bTB –12.Bos taurus chromosome) was putatively associated with bTB susceptibility [PTPRT on BTA13 and MYO3B on BTA2; however, the SNPs that were identified as significantly associated with bTB susceptibility by Finlay et al. [toll-like receptor genes on BTA6.Although several genome-wide association studies (GWAS) have been undertaken on Johne’s disease in cattle –15, onlytibility ; (2) Bertibility  carried y et al.  were noty et al.  and vicey et al.  used an M. tuberculosis) have also been performed on human populations [NRAMP1, IFNG, NOS2A, MBL, VDR and four toll-like receptor genes [M. tuberculosis [sst1 gene that is involved in the immune response of macrophages to TB [NRAMP1 (SLC11A) was identified as a susceptibility locus in both cattle and humans [Several GWAS on susceptibility to human tuberculosis . High-density genotypes of dairy bulls (n = 841) used for artificial insemination (AI) were associated with estimated breeding values (EBV) for bTB susceptibility that had been calculated from epidemiological information on 105,914 daughters. This information was used in two distinct GWAS approaches and a haplotype association approach, to search for QTL associated with bTB susceptibility in dairy cattle.Animal Care and Use Committee approval was not obtained for this study because all data were from the pre-existing database infrastructure operated by the Irish Cattle Breeding Federation database .Individual animal EBV for bTB susceptibility were calculated using an animal linear mixed model in ASREML . DetailsR is a diagonal matrix containing one divided by, one minus the animal’s reliability from its progeny, A is the numerator relationship matrix among animals and Sire EBV from the animal model were deregressed as:Illumina high-density (HD) SNP genotypes  were available on 770 Holstein–Friesian bulls. All animals had a genotype call rate greater than 95 %. When genotypes were available for both sire and son(s), the proportion of Mendelian inconsistencies between sire-son pairs was determined to confirm true parentage. A total of 3554 autosomal SNPs that displayed more than 2 % Mendelian inconsistencies were discarded. The genotypes of both the sire and son were set to missing for the remaining autosomal SNPs for which sporadic Mendelian inconsistencies existed.−8) from the Hardy–Weinberg equilibrium were excluded. After editing, 597,144 autosomal SNPs remained for the analyses. Missing genotypes were imputed using Beagle [A total of 1574 SNPs with an Illumina GenTrain score less than 0.55, 40,934 non-autosomal SNPs and duplicate SNPs on the Illumina high-density array were also discarded. Of the remaining SNPs, 17,273 with a call rate less than 95 %, 83,833 SNPs with a minor allele frequency less than 0.02 and SNPs that deviated  were available on 5313 Holstein–Friesian dairy bulls; 639 of these animals also had HD genotypes. Animal and SNP editing criteria were as previously described and 45,239 SNPs remained after editing. Genotypes were imputed to HD for each chromosome separately using BEAGLE –30 , \docum−5 were considered to be genome-wide significant based on a false discovery rate of 1 % [pqa2, where p was the major allele frequency, q the minor allele frequency and a the allele substitution effect.SNP effects for bTB susceptibility were estimated using mixed models implemented in WOMBAT , 33. Weie of 1 % . The genEstimation of SNP effects for bTB susceptibility using a Bayesian model was implemented in the GenSel software package . Weightefi is the number of times SNP i was included in the model, π is the posterior probability that SNP i is not associated with bTB susceptibility, and π1 is the posterior probability that SNP i is associated with bTB susceptibility (represented as 1 − π). Bayes factors greater than 3.2, 10 and 100 are considered as ‘substantially’, ‘strongly’ and ‘decisively’ associated with a trait, respectively [A BayesB algorithm, as described by Meuwissen et al. , was subectively . The pro−5 or a Bayes factor greater than 10. Pairwise LD, as measured by r2 between all SNPs within 5 Mb upstream and downstream of significant SNPs, was calculated using PLINK [2 was set to 0.5 or more and the SNPs that were furthest upstream and downstream of a significant SNP with this level of LD were used to indicate the beginning and end of a QTL. QTL that overlapped with one another were combined into a single QTL. These QTL were then investigated for the presence of genes within these regions.QTL regions of interest were defined based on linkage disequilibrium (LD) around each SNP that had a genome-wide association significance level of P < 1 × 10ng PLINK . Within Genes that are present in QTL regions can be used to construct a biological network from available biological network databases, such as InnateDB . Such nen*(n − 1)/2n), where n is the number of animals within each group. The proportions of genome IBD per segment for the case group were normalized against the proportions of genome IBD per locus for the control group as follows:X is the case–case IBD proportions and Y is the control–control IBD proportion, per segment.A subset of prolific AI sires (i.e. with more than 50 daughters in more than 10 herds) that had been identified by Richardson et al. , 29 and −5, Bayes factor >10 and normalized proportion of genome IBD >0.5) across all three analyses were imputed from HD to full sequence. Imputation was performed with BEAGLE [3-Mb sequence windows that flanked the significant QTL regions . A QQ-plot of the observed against expected SNP P values is in Fig. −6 with lengths ranging from 0.6 to 183 kb. The most significant (P < 1 × 10−8) SNP, BovineHD1400020824, was located on BTA14 and accounted for 0.089 % of the genetic variance; no QTL region could be identified around this SNP since it was not in strong LD with flanking SNPs. SNP BovineHD1400020824 was not located within a gene and the closest gene (RUNX1T1) was about 1 Mb away. The second most significant (P < 1 × 10−8) SNP, BovineHD0100019801, was located on BTA1 and accounted for 0.092 % of the genetic variance for bTB susceptibility. SNP BovineHD0100019801 was located within a 12-kb QTL region and within intron 11 of the KALRN gene. Its major allele A was associated with increased resistance to bTB infection, and homozygous animals for this allele had a mean EBV of −0.099 , whereas homozygous animals for the minor allele (MAF = 0.33) had a mean EBV of −0.0120 .Associations between each SNP and bTB susceptibility based on the single-SNP regression approach are illustrated in Fig. AP3B1 on BTA10 and FKBP5 on BTA23), have a known role in immune response. The major T allele at the SNP Hapmap50596-BTA-121389  that is located within the region on BTA23 was associated with increased resistance to bTB. Homozygous animals for the major allele of this SNP were on average more resistant to bTB infection with a mean EBV of −0.0909  whereas homozygous animals for the minor allele (MAF = 0.1361) had a mean EBV of 0.0778 .In the single-SNP regression analysis, we identified four genes Table  that ovePID Biocarta 4144) as the most significantly overrepresented pathway (P < 1 × 10−4). The 10 most significantly represented pathways from the single-SNP regression analysis are in Table Network analysis based on the results from the single-SNP regression analysis provided a list of networks that contained 292 genes, with “Antigen processing and presentation” , and homozygous animals for the minor allele (MAF = 0.3234) had a mean EBV of −0.0191 .SNP BovineHD1700021001 that had the highest Bayes factor  was located on BTA17 and accounted for 0.0018 % of the additive genetic variance. This SNP was located within a 3-kb QTL region and within intron 3 of the PTN on BTA4, AP3B1 on BTA10, HSF1on BTA14, SHARPIN on BTA14, TRAF4 on BTA20 and FKBP5 on BTA23.We identified 40 genes that overlapped with or were within the 57 QTL regions detected Table . Six of −4) overrepresented pathways. Several pathways associated with immunity were identified, including TNF alpha , adipocytokine signalling pathway , and Fas signalling pathway . The ten most significant pathways from the Bayesian approach are in Table Genes within 242 QTL defined from SNPs with a Bayes factor higher than 10 were considered for pathway analysis, which returned more than 350 statistically (P < 1 × 10Eighteen prolific sires in the test group (i.e. sires with more than 50 daughters in more than 10 herds) were previously identified by Richardson et al. [−5) with both approaches .The Bayesian and single-SNP regression approaches that were applied to GWAS data identified 17 SNPs that were significant  were located within introns 1, 2, 5 or 8 of the FKBP5 gene (including the two most significant SNPs (P < 4 × 10−6) located at positions 9590819 and 9591806) and the other two SNPs were located within intron 8 of the MAPK14 gene. Furthermore, a distinct peak of SNPs with strong P values was detected near the significant SNPs located in the FKBP5 gene , to effectively control infection in known infected herds, and to limit the spread of infection between herds and regions. These strategies are increasingly associated with active programmes to manage infection in known wildlife reservoirs. Other strategies are currently being investigated including increased host resistance through focused breeding practices  or the uIn a single-SNP regression analysis, each SNP is included and tested for significance individually in the model. In the Bayesian approach, all SNPs are fitted simultaneously within the same model. Among the significant SNPs that were identified in the single-SNP regression and Bayesian approaches, 17 common SNPs were found Table . Howevertoll-like receptor gene cluster on BTA6, which we did not replicate either here. Finally, the genomic regions that we found to be associated with bTB susceptibility differed from those documented to be associated with paratuberculosis in cattle [Results from the current study do not corroborate those of Finlay et al. , who iden cattle –15, whicFKBP5 and MAPK14. The FKBP5 gene encodes a protein from the immunophillin protein family, a family of proteins that is often targeted by immunosuppressant drugs. FKBP5 is a cis–trans prolyl isomerase that binds to the immunosuppressants FK506 and rapamycin [FKBP5 is also involved in the TNF alpha/NF-kB signalling pathway, which is a major pathway involved in host immune response to disease and other harmful stressors, and is highly expressed in T-lymphocytes [FKBP5 gene and were imputed to full sequence were located in introns, which is consistent with previously described associations between introns and disease traits [FKBP5  that were detected by the single-SNP regression analysis were either located within genes that were not directly involved in immune response , or in QTL regions that do not contain genes . The most significant SNP found in the Bayesian analysis  was identified within the RNF185gene that has no known role in immune response. However, because this SNP is strongly associated with bTB susceptibility, this region should be further investigated and might provide more insight into the host genetic susceptibility to bTB. Moreover, while the QTL region that harbored the second most significant SNP in the Bayesian analysis  contained two genes (HSF1 and SHARPIN) that are involved in immune response, it is also associated with the DGAT1 gene which is known to play a key role in milk fat production [However, it must be noted that although the QTL region that contains the −5, Bayes factor >10) in both the Bayesian and single-SNP regression approaches contained nine of the 17 significant SNPs common to both approaches, but it was not detected in the IBD approach. Although, to date, no relevant genes were identified within this QTL, because of this large number of significant SNPs that are shared between the Bayesian and single-SNP regression approaches, it is an interesting candidate for further investigation.A QTL region on BTA1 that was estimated as significant (P < 1 × 10M. bovis replicates within the lysosomes of the host macrophages, which suggests that an impaired antigen processing and presentation pathway could allow M. bovis to survive longer in the host, leading macrophages to be less readily degraded, thus increasing the host’s susceptibility to infection.Identification of TB susceptibility genes in humans has been documented to be more difficult compared to that for other infections, even with large sample sizes . This isFKBP5 gene, which is involved in immune response. However, it must be noted that this association signal on BTA23 was not the most significant in any one of the single analyses and that the power of analysis was not yet sufficient to identify clear associations between SNPs and bTB susceptibility. Aside from this observation, our study constitutes another step towards the identification of the genetic mechanisms that underlie the elusive bTB susceptibility trait.Due to the likely polygenic nature of bTB susceptibility, several approaches applied to GWAS were conducted to identify novel associations that would otherwise not be identified by using a single-SNP approach. A candidate region on BTA23 was identified and imputation of this region to sequence data pinpointed the association signal in the introns of the


Mangifera indica L., a member of the family Anacardiaceae: Kensington Pride (KP), Irwin (IW) and Nam Doc Mai (NDM) and to determine associations with gene expression and mango flavonoid profiles.Mango fruits contain a broad spectrum of phenolic compounds which impart potential health benefits; their biosynthesis is catalysed by enzymes in the phenylpropanoid-flavonoid (PF) pathway. The aim of this study was to reveal the variability in genes involved in the PF pathway in three different mango varieties Populus trichocarpa) and grape of the Vitaceae family (Vitis vinifera), was revealed through phylogenetic analysis of PF pathway genes. We discovered 145 SNPs in total within coding sequences with an average frequency of one SNP every 316 bp. Variety IW had the highest SNP frequency (one SNP every 258 bp) while KP and NDM had similar frequencies . The position in the PF pathway appeared to influence the extent of genetic diversity of the encoded enzymes. The entry point enzymes phenylalanine lyase (PAL), cinnamate 4-mono-oxygenase (C4H) and chalcone synthase (CHS) had low levels of SNP diversity in their coding sequences, whereas anthocyanidin reductase (ANR) showed the highest SNP frequency followed by flavonoid 3’-hydroxylase (F3’H). Quantitative PCR revealed characteristic patterns of gene expression that differed between mango peel and flesh, and between varieties.A close evolutionary relationship between mango genes and those from the woody species poplar of the Salicaceae family  in those extracts reflects the rate-limiting role of ANR on ECG biosynthesis in mango.The online version of this article (doi:10.1186/s12864-015-1784-x) contains supplementary material, which is available to authorized users. Arabidopsis thaliana (Family: Brassicaceae) and several temperate fruit species including grape, apple, berry and olive fruits [The PF pathway in fruits and vegetables is of great interest because it leads to compounds that exhibit important health benefits, including antioxidant, antibacterial, anti-inflammatory and anticarcinogenic effects . This pae fruits –8. Mangoe fruits  and a goe fruits  and the e fruits . A compae fruits . A parale fruits . The tote fruits .Mango fruit studies have previously largely focused on the ripening process –16, volaArabidopsis thaliana [Zea mays) (Family: Poaceae) [Hordeum vulgare) (Family: Poaceae) [The level of nucleotide sequence variation, the position of the gene products in the pathway and the expression levels of genes all contribute to the abundance and chemical diversity of bioactive compounds . The mosthaliana , 28 and Poaceae)  and barlPoaceae) .www.genome.jp/kegg/) and the presence of selected flavonoids in our mango extracts [O-gallate [In this paper, we report the sequence variability of genes involved in the PF biosynthesis of mangoes of different genetic origin and the relative levels of gene expression in order to assess the potential role of these genes in governing accumulation of flavonoid compounds in mango fruits. Our study was aimed at investigating health benefits of consuming mango fruit; for this reason, we studied the expression of mango genes and associated bioactive polyphenolic compounds at the mature ripe fruit stage. Previously, the Mango Genomics Initiative of the Queensland Department of Agriculture and Fisheries generated ~25,000 expressed sequence tags (ESTs) from cDNA libraries of KP root, leaf, flower and fruit tissues as well as from IW leaf tissue  which enextracts , we also-gallate , 33 , 4-coumarate:CoA ligase (4CL), CHS1, CHS2, flavanone 3-hydroxylase , F3’H, dihydroflavonol 4-reductases (DFR), anthocyanidin synthase (ANS) and ANR with annotations matching enzymes in the PF pathway leading to ECG and mangiferin biosynthesis were identified. This information was used to design PCR strategies for the amplification of the coding regions of these genes using cDNA prepared from KP, IW and NDM fruit. All individual sequences have been deposited in GenBank  as closest neighbour [Phylogenetic comparisons of the translated consensus sequences of mango PF pathway proteins against corresponding sequences of a selected plant reference protein set, available through GenBank, revealed that the two closest neighbours of mango were eighbour .Fig. 2NeP. trichocarpa) (one SNP per 229 bp) [Glycine max) (Family: Fabaceae) (one SNP every 504 – 609 bp) [Zea mays) or grape (V. vinifera) (approximately one SNP every 69 bp) [Multiple sequence alignments showed that the PF pathway genes of the three mango varieties tested have similar lengths, and high sequence identity with a range of 95.7–99.9 %, implying conservation of each gene sequence between varieties. There were no deletions or insertions in any of the amplified gene fragments and no mutations that introduced premature stop codon that would generate a truncated protein. However, there was variation in sequences between the three mango varieties studied. Overall, we detected 145 polymorphic sites across the entire 46,560 bp of nucleotide sequence of the 12 PF biosynthetic genes analysed. Of these, 84 were synonymous SNPs or silent changes and 61 were non-synonymous SNPs, which would lead to changes in the encoded amino acid. The IW variety had approximately 30 % more SNPs than the other two varieties although IW and KP had the same number of non-synonymous SNPs. The overall frequency of SNPs in the three mango varieties tested was one in every 316 bp. Variety IW had the highest SNP frequency (one SNP every 258 bp) while KP and NDM had similar frequencies . This SNP frequency is close to that reported in coding regions in black cottonwood ( 229 bp) , higher y 69 bp) , 38.Table The sequence positions of identified mutations and the alleles present in the three mango varieties are presented in Table We used quantitative, reverse-transcription PCR (RT-PCR) to assess potential correlations between the expression of PF genes and the differences in chemical profiles of KP, IW and NDM. The expression patterns of the phenylpropanoid biosynthetic genes  and genes involved in the biosynthesis of various flavonoids  were examined in mango peel and flesh of ripe fruits with actin as an endogenous reference gene.P < 0.01) higher than in flesh when expression was normalized to actin housekeeping gene  mutant restored PA synthesis in seeds [Previous studies of gene expression in temperate fruits reported two peaks of flavonoid activity during fruit ripening, one in green fruit and the other in nearly ripe fruit –46. In oin seeds . During in seeds . Howeverin seeds , 54. In in seeds . ANR is P. trichocarpa and V. vinifera. This study has determined, for the first time, the SNP frequency in coding sequences of PF biosynthetic genes of KP, IW and NDM mangoes. This study established a positive relationship between total phenolic content and expression levels of PAL, C4H and CHS in mango fruit. The high expression of anthocyanin-related gene, ANR in KP and NDM peel compared to IW peel may explain the presence of ECG in KP and NDM peels, but its absence in IW peel. Further study could examine the possible association of the identified SNPs with expression differences. This combined knowledge will facilitate future breeding and selection of new mango varieties possessing tailored bioactive compound profiles with properties beneficial to health.Our study shows that it is possible to discover candidate genes for many mango PF biosynthetic pathway enzymes through use of a mango fruit EST library. Phylogenetic analysis of pathway genes revealed a close evolutionary relationship between mango, KP, IW and NDM were provided by Dr Ian Bally from the Department of Agriculture and Fisheries' Southedge Research Station, Queensland, Australia and left to rest at 20 °C until they reached consumption ripeness as assessed by the sprung test  with somg for 10 min at 4 °C. Total RNA was precipitated by addition of 1 volume of 10 M LiCl to the supernatant. The mixture was incubated at 4 °C overnight and RNA was selectively pelleted by centrifugation at 10,000 g for 40 min at 4 °C. The pellet was resuspended in 5 mL of 70 % ethanol and the mixture was centrifuged at 10,000 g for 5 min at 4 °C, dried and resuspended in DEPC–treated water. RNA concentrations were determined using a NanoDrop® ND-1000 Spectrophotometer (Thermo Scientific), and the quality was confirmed on 1.2 % agarose gel using TAE buffer. RNA samples were treated with TURBO™ DNase (Ambion) in accordance with the manufacturer’s instruction and stored at −80 °C. About 1 μg of total RNA was reverse-transcribed by use of Superscript III First-strand Synthesis Mix (Invitrogen) with oligo (dT)20 primer in accordance with the manufacturer’s protocol.Total RNA was extracted using the cetyltrimethyl ammonium bromide (CTAB) method described by Chang et al.  with somhttp://mango.qfab.org), a proprietary database containing nucleotide sequence, phenotypic, and sensory information for mango. Sequence contig assemblies were used as initial source for analyses. Full-length EST sequences were obtained using M13 Forward (−20) and Reverse primers. Sanger sequencing was performed using Applied Biosystems 3730xI capillary sequencers by the Australian Genome Research Facility . Nucleotide sequences from three colonies each were analysed using the Geneious v.5.1  [http://blast.ncbi.nlm.nih.gov/Blast.cgi) [e-value ≤10−5) to confirm identities. All sequences were aligned using Geneious default values, with pair-wise parameters set at gap opening penalty 10, gap extension penalty 0.1 and multiple alignment parameters set at gap opening penalty of 10, gap extension 0.2. Sequences were deposited in GenBank with accession numbers shown in Table Information on selected biosynthesis genes in mango Fig.  was obtaZealand) . The amiast.cgi)  with defPrimers were designed based on the open reading frames of all mango candidate genes identified from ESTs with default settings on the Primer feature in Geneious . PrimersAmino acid sequences were aligned in Geneious using global alignment with default parameters . Searche−ΔCt [http://mapman.gabipd.org, version 3.5.1) [Real-time RT-PCR was done in a RotorGene RG-6000 Thermal Cycler (Qiagen) to determine relative gene expression levels in peel and flesh of KP, IW and NDM fruits with actin (GenBank accession number HQ830244) as an endogenous reference gene , 60 with−ΔCt –63. Chann 3.5.1) .Statistical analysis was done with GraphPad PRISM 5 . Significance was determined using one-way analysis of variance with the Tukey’s test for all pairwise multiple comparisons for normally distributed data of equal variance. Standard deviations of ΔCt values were calculated from measurements performed in triplicate.


Peach is a genetic and genomic model within the Rosaceae and will provide a template for the use of this method with other members of this family. Our F2 mapping population of 57 genotypes segregates for bloom time (BD) and chilling requirement (CR) and we have extensively phenotyped this population. The population derives from a selfed F1 progeny of a cross between ‘Hakuho’ (high CR) and ‘UFGold’ (low CR). We were able to successfully employ GBS and the TASSEL GBS pipeline without modification of the original methodology using the ApeKI restriction enzyme and multiplexing at an equivalent of 96 samples per Illumina HiSeq 2000 lane. We obtained hundreds of SNP markers which were then used to construct a genetic linkage map and identify quantitative trait loci (QTL) for BD and CR.Low-cost, high throughput genotyping methods are crucial to marker discovery and marker-assisted breeding efforts, but have not been available for many ‘specialty crops’ such as fruit and nut trees. Here we apply the Genotyping-By-Sequencing (GBS) method developed for cereals to the discovery of single nucleotide polymorphisms (SNPs) in a peach F Prunus persica (L) Batsch]  , 4. SuccChilling requirement (CR) refers to the minimum duration of cold exposure required before dormant buds will bloom in response to bud break-inducing conditions . CR contPrunus, we genotyped a F2 peach mapping population segregating for CR and BD for which we have multiple years of phenotypic data. Here we present the successful use of GBS in peach and the identification of multiple QTLs for CR and BD in our F2 population.CR and BD are known to be quantitative genetic traits whose phenotypes vary widely among peach cultivars , 9. Sinc2 population of 57 genotypes was developed at the USDA-ARS Southern Fruit and Tree Nut Research Laboratory  by crossing the high chill requirement cultivar Hakuho with the low chill requirement cultivar UFGold and selfing its F1 hybrid. The female grandparent ‘Hakuho’ is a white-fleshed commercial peach cultivar originating from Japan that requires approximately 900 chilling hours for spring bloom [A peach Fng bloom  that req2 seeds were stratified, germinated and planted in a greenhouse in fall 2003, then transplanted in spring 2004 to Clemson University’s Musser Fruit Research Center . Replicate clones of each tree were produced by rooting current-year stem cuttings during the 2005 growing season and replicate plantings were established on their own roots at the Musser Fruit Research Center in spring 2006. Replicates of each grandparent were also included in these plantings, although the F1 tree was no longer available. ‘Hakuho’ was propagated from the original tree used in the cross. Twenty ‘UF Gold’ trees were obtained from a commercial nursery grafted onto ‘Nemaguard’ rootstock.F2 individuals and grandparents was scored in the original 2004 planting each spring from 2006 through 2012. Observations of bloom progression were made every 2–3 days from the onset of floral bud break. Bloom date was recorded as the number of days from January 1st of each year.Bloom date was visually scored as the date at which 50% of the floral buds on an individual tree reached full bloom stage. Bloom date of all F2 population and grandparents was determined in two successive winters (2008/2009 and 2009/2010) using the procedures detailed in our previous publication [Chilling requirement of the Flication .On each sampling date three branches  were harvested from each of three replicate trees from each genotype. Branch cuttings were brought to a 25°C greenhouse at Clemson University, recut under water, placed in a 1% Floralife solution  and maintained under a 16 hour photoperiod. Branches were recut and fresh solution supplied after seven days. At 14 d, % floral bud break was scored on all cuttings. A genotype’s CR was considered to be fulfilled when 50% of the flower buds on all cuttings had opened sufficiently for the petals to be visible.2 individuals and grandparents using the Dellaporta et al. miniprep method [Genomic DNA for SSR marker screening was isolated as described previously . Genomicp method . DNA quaPrunus species were screened for polymorphism in the F2 mapping population. Markers from all eight major peach linkage groups were selected based on known locations on the T x E Prunus reference map [et al. [et al. [A set of 370 simple sequence repeat (SSR) markers from ence map  and the ence map . SSR scr [et al. . SSR ori [et al. .et al. [ApeKI-associated DNA fragments for sequencing on the Illumina HiSeq® 2000 platform. Ninety-six double-stranded forward adaptors each with a unique barcode and a single common double-stranded reverse adaptor were created from a set of 194 single-stranded oligonucleotides . Each adaptor contained a three base overhang for ligation with ApeKI digested DNA. The ApeKI compatible barcode set was that published in Elshire et al. [We followed the ‘Genotyping By Sequencing’ (GBS) method of Elshire et al.  to gener2 individuals were run once. Ninety-six barcoded samples were run on a single Illumina lane, including some samples from a separate experiment. Samples were re-sequenced if less thant 100,000 reads with identifiable barcodes were obtained from the initial run.Adaptors were ligated to restriction-digested DNA from F2 individuals and grandparents following the methods described in [Preliminary inspection of the raw fastq files showed low recovery of five expected barcodes. Using Homertools  and basiwww.maizegenetics.net) at Cornell University [Sequenced reads were processed using default parameters of the TASSEL 3.0 and 4.0 GBS pipeline obtained from the Maize Genetics and Diversity Lab .SNP data were converted to the ‘a, h, b’ codes with the female ‘Hakuho’ grandparent conferring the ‘a’ genotype. Because the F2 population was performed using JoinMap® 4.1 [2 test in JoinMap. Markers with significant segregation distortion (p<0.01) were excluded if surrounding markers did not also show significant segregation distortion of a similar direction and magnitude.Genetic mapping of the FMap® 4.1 . Highly Map® 4.1 , 14, 17.QTLs were detected with MapQTL® 6 . Genome-Illumina sequencing of 63 pooled, barcoded samples  generated approximately 1.5 million single-end 100 bp reads per sample. The final mean read number per genotype was 1,843,261  after merging genotypes with multiple barcodes and resequencing samples with low initial read numbers.The TASSEL GBS pipeline initially identified 9,998 SNP loci distributed across all major scaffolds of the peach genome. Prior to the creation of a linkage map, data were filtered to remove SNPs with low read support. Within a genotype, data for individual SNPs were retained only if they possessed a minimum read depth of five and a minimum quality score of 98. After depth and quality filtering, SNPs for which data were missing in more than five genotypes were removed from further analysis. SNPs were also removed in grandparental genotype data were missing, if both grandparents shared a heterozygous genotype or if the minor allele frequency was less than 0.20. These filtering procedures resulted in 410 final SNP loci.2 individuals. Deeper sequencing of the grandparents  resulted in the identification of 4,833 final SNPs, approximately ten times more SNPs than were identified in the analysis of the F2 population. It should be noted that the decreased number of samples in this analysis (two grandparents vs. 57 F2s) results in fewer SNPs being discarded due to frequency of missing data across all individuals. The distribution of these grandparental SNPs on the physical map is shown in To estimate the effect of sequencing depth on SNP identification, we included the grandparental genotypes in triplicate in the sequencing pool . This increased the combined sequencing depth of the grandparents threefold relative to the F2 population. SSRs were combined with SNP markers identified from the TASSEL GBS pipeline and used to create a genetic linkage map. Following removal of SNPs with highly similar segregation or locally discontinuous segregation distortion, a linkage map was calculated using 201 SNPs and 33 SSR markers with an average intermarker distance of 2.85 cM. The resulting linkage map comprised eight linkage groups and a total map distance of 666.1 centiMorgans  and bloom date (BD). CR ranged from as little as 300–400 chilling hours to >1100 chilling hours  to as long as 37 days (2007). Duration of the interval between earliest and latest bloom was reflective of year to year variation in chilling accumulation. Interruptions of chilling accumulation by warm periods promoted earlier bloom of low chill genotypes while consistent cold weather compressed bloom of all genotypes into a shorter interval  was generated by GoldenGatearacters . A seconaracters . Our pooaracters . Convers2 population across the two years of CR phenotyping and seven years of BD observation. Fewer QTLs were observed for CR than for BD. This discrepancy is consistent with the role of CR as a major, but not exclusive, determinant of BD. Additionally, more frequent BD observations allowed finer discrimination between genotypes than did the <10 bins of chill hour bins into which F2 genotypes were partitioned. 2010 and 2011 had notably fewer BD QTLs of all of the years . All other years had three or four QTL. The reduced number of QTL in these two years likely resulted from the relatively compact population variation in bloom in 2010 and 2011  with horizontal marks indicating position of SNPs between grandparental genotypes.(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S1 File(PDF)Click here for additional data file.S2 File(XLSX)Click here for additional data file.


VDR (22 tag SNPs) and RXRG (23 tag SNPs) genes on metabolic outcomes such as body mass index, waist circumference, waist-hip ratio (WHR), high- and low-density lipoprotein (LDL) cholesterols, serum triglycerides, systolic and diastolic blood pressures and glycated haemoglobin in the 1958 British Birth Cohort . We used Multifactor- dimensionality reduction (MDR) program as a non-parametric test to examine for potential interactions between the VDR and RXRG gene polymorphisms in the 1958BC. We used the data from Northern Finland Birth Cohort 1966  and Twins UK  to replicate our initial findings from 1958BC.Low vitamin D status has been shown to be a risk factor for several metabolic traits such as obesity, diabetes and cardiovascular disease. The biological actions of 1, 25-dihydroxyvitamin D, are mediated through the vitamin D receptor (VDR), which heterodimerizes with retinoid X receptor, gamma (RXRG). Hence, we examined the potential interactions between the tagging polymorphisms in the interaction >0.17).After Bonferroni correction, the joint-likelihood ratio test suggested interactions on serum triglycerides (4 SNP - SNP pairs), LDL cholesterol (2 SNP - SNP pairs) and WHR (1 SNP - SNP pair) in the 1958BC. MDR permutation model testing analysis showed one two-way and one three-way interaction to be statistically significant on serum triglycerides in the 1958BC. In meta-analysis of results from two replication cohorts , none of the interactions remained after correction for multiple testing (PVDR and RXRG genes on metabolic outcomes; however, further replication studies on large samples are needed to confirm our findings.Our results did not provide strong evidence for interactions between allelic variations in  Low vitamin D status has become a major public health problem due to its associations with several chronic diseases such as diabetes and card2D), the genomic actions of which are mediated through the ligand-activated transcription factor, vitamin D receptor (VDR) to replicate the initial findings from the 1958BC.Detailed description of the 1958 British birth cohort (1958BC) has been published previously, and forThe Northern Finland Birth Cohort of 1966 (NFBC66) comprises a total of 12,058 live-births to mothers living in the two northern‒most provinces of Finland, who were invited to participate if they had expected delivery dates during 1966. At age The Twins registry in St. Thomas' Hospital, King's College London recruited a total sample of 11,000 identical and non-identical, mostly female Caucasian, twins from across the UK through national media campaigns. Their a2. Blood pressure was measured in a seated position, after 5 min rest, using an Omron 705CP automated sphygmomanometer with a large cuff for participants with a mid-upper arm circumference ≥32 cm; the measurement was repeated three times, and blood pressure was determined as the average of successful measurements.Weight and standing height, at 45 years of age, were measured without shoes and in light clothing by a trained nurse using standardized protocols and equipment; waist circumference was measured by the nurse midway between the costal margin and iliac crest. BMI was calculated as weight (kg)/height (m)Venous blood samples were drawn without prior fasting and posted to the collaborating laboratory. Glycosylated haemoglobin (HbA1c) was assayed using high-performance liquid chromatography standardized to the Diabetes Control and Complications Trial. TriglycHeight and body weight were measured using a standardized height measure and scale. The participants were asked to fast overnight before a blood sample was taken. Serum HDL cholesterol and triglycerides were determined by enzymatic methods using a Hitachi 911 Clinical Chemistry Analyzer (Boehringer Mannheim). Serum LDL was calculated by the Friedewald formula if the serum TG level was <354 mg/dl; if the triglyceride level was <354 mg/dl, LDL was determined by precipitating LD-lipoproteins with heparin and measuring cholesterol in the liquid phase and subtracting it from total cholesterol.Weight and standing height were measured without shoes and in light clothing by a trained nurse. Blood sample collection for determination of fasting lipids was drawn from most subjects after a minimum 8-h overnight fast. Serum was stored at −45°C until analyzed using a Cobas Fara machine . A colourimetric enzymatic method was used to determine total cholesterol, triglycerides and HDL cholesterol levels. The latter was measured after precipitation from chylomicron, LDL and VLDL particles by magnesium and dextran sulphate.VDR and RXRG genes were chosen using the genotype data from the International HapMap collected in individuals of Northern and Western European ancestry (CEU) . The Haploview software V3.3 (http://www.broadinstitute.org/haploview/haploview-downloads) was used to assess the linkage disequilibrium (LD) structure between SNPs, there were only 22 VDR and 23 RXRG tag SNPs.Tag SNPs for een SNPs. Tagger  studied-18 beforGenome-wide data for the 1958BC were obtained through two sub-studies, both using the 1958BC participants as population controls. The first sub-study included 3000 DNA samples randomly selected as part of the Welcome Trust Case Control Consortium (WTCCC2) and genotyped on the Affymetrix 6.0 platform. IMPUTE For NFBC, genomic DNA was extracted from whole blood using standard methods. All DNA samples for the Illumina Infinium 370cnvDuo array were prepared for genotyping by the Broad Institute Biological Sample Repository (BSP). The 1000 Genome imputation was carried out for the NFBC66 samples using IMPUTE2.−6, assessed in a set of unrelated samples; (v) observed pairwise IBD probabilities (samples excluded if the IBD threshold was less than 0.30) suggestive of sample identity errors.Genotyping of the TwinsUK dataset was done with a combination of Illumina arrays . The normalised intensity data for each of the three arrays were pooled separately (with 1 M-Duo and 1.2 M Duo 1 M pooled together). For each dataset, the Illluminus calling algorithm was used to assign genotypes in the pooled data. No calls were assigned if an individual's most likely genotype was called with less than a posterior probability threshold of 0.95. Prior to merging, pairwise comparison was performed among the three datasets. Further exclusion of SNPs and samples was done to avoid spurious genotyping effects, identified as follows: (i) concordance at duplicate samples <1% ; (ii) concordance at duplicate SNPs <1% ; (iii) visual inspection of QQ plots for logistic regression applied to all pairwise dataset comparisons; (iv) Hardy-Weinberg p-value <10VDR and RXRG tag SNPs on the following outcomes: BMI, WC, WHR, SBP, DBP, HDL and LDL cholesterol, serum triglycerides and HbA1c. The Friedewald equation was used to calculate LDL cholesterol levels in subjects with triglycerides ≤4.52 mmol/L. All analyses were carried out using STATA, version 12, except for the analyses in Twins UK, where STATA, version 10 was used.The power to detect SNP-SNP interactions in the 1958BC  was calculated using the Quanto software (version 1.2.4). The power to detect SNP-SNP interactions for a standard normal outcome was calculated with different combinations of minor allele frequency (MAF) and an interaction beta of up to 0.25 . There was 80% power to detect an interaction β as small as 0.08 when both SNPs had a MAF of 0.40. However, when both SNPs had a MAF of 0.1, there was 80% power to detect interaction effect sizes only as small as 0.22  showed a significant association with WHR (adjusted for BMI) and LDL, respectively. None of the VDR or other SNPs from RXRG was associated with any of the metabolic traits , after correction for multiple testing, only two 31, afterIn the 1958BC, the joint LRT in linear regression analyses showed significant SNP-SNP interactions on serum triglycerides (4 SNP-SNP pairs), LDL cholesterol (2 SNP-SNP pairs) and WHR (1 SNP-SNP pair) after correction for multiple testing  and one three-way  interaction on serum triglycerides .MDR program was used as an additional method (non-parametric approach) to test for the potential interactions (one-way to four-way) between VDR SNP rs3847987 and the RXRG SNP rs10918172 on serum triglycerides was borderline , none of the interactions identified from the joint LRT were significant after correction for multiple testing   Table .VDR and RXRG tag SNPs on metabolic traits. VDR is the key nuclear receptor for the active hormonal form of vitamin D  and there is some previous evidence for its role in obesity- and diabetes- related traits and metabolic traits in the NFBC66 and Twins UK.Click here for filea: Association between the Retinoid X Receptor- gamma polymorphisms and metabolic traits in the 1958 British Birth Cohort; b: Association between the Retinoid X Receptor- gamma polymorphisms [that showed a significant association (uncorrected for multiple testing) in the 1958BC] and metabolic traits in the NFBC66 and Twins UK.Click here for fileSNP-SNP interactions in the 1958 British Birth Cohort based on one degree of freedom interaction test.Click here for file


Both genome-wide association (GWA) studies and genomic selection depend on the level of non-random association of alleles at different loci, i.e. linkage disequilibrium (LD), across the genome. Therefore, characterizing LD is of fundamental importance to implement both approaches. In this study, using a 60K single nucleotide polymorphism (SNP) panel, we estimated LD and haplotype structure in crossbred broiler chickens and their component pure lines  and calculated the consistency of LD between these populations.r2) between adjacent SNPs across the chicken autosomes studied here ranged from 0.34 to 0.40 in the pure lines but was only 0.24 in the crossbred populations, with 28.4% of adjacent SNP pairs having an r2 higher than 0.3. Compared with the pure lines, the crossbred populations consistently showed a lower level of LD, smaller haploblock sizes and lower haplotype homozygosity on macro-, intermediate and micro-chromosomes. Furthermore, correlations of LD between markers at short distances (0 to 10 kb) were high between crossbred and pure lines (0.83 to 0.94).The average level of LD (measured by Our results suggest that using crossbred populations instead of pure lines can be advantageous for high-resolution QTL (quantitative trait loci) mapping in GWA studies and to achieve good persistence of accuracy of genomic breeding values over generations in genomic selection. These results also provide useful information for the design and implementation of GWA studies and genomic selection using crossbred populations.The online version of this article (doi:10.1186/s12711-015-0098-4) contains supplementary material, which is available to authorized users. Genome-wide association (GWA) studies and genomic selection are currently widely applied in animal genetics research and animal breeding programs. Using thousands of genetic markers, the ultimate goal of most GWA studies is to find causal polymorphisms that affect a phenotype , whereasProgress in next-generation sequencing and high-density single nucleotide polymorphism (SNP) genotyping technologies offer unprecedented opportunities for detecting causal polymorphisms or achieving high accuracies of prediction in genomic selection -12. HoweIn this study, our aim was to characterize the consistency of LD and differences in LD between crossbred and their component purebred populations. As for previous studies on LD in layer  and villA total of 2844 individuals were genotyped using the Illumina 60K chicken SNP array . All genAll 2341 genotyped crossbred chickens were produced by a three-way cross of B × [C × D], in which males of line B were mated with CD crossbred females, which is a crossbred female product produced by a two-way C × D cross, in which males of line C were mated with females of line D. Crossbred individuals were sampled from two broiler chicken populations: (i) broiler chickens from the field (end product meat birds), which will be referred to as field crossbred chickens  and (ii) broiler chickens from a pedigree house , which will be referred to as pedigree crossbred chickens   population.Gallus gallus 4.0 UCSC, May 2012). Within each pure line and crossbred population, we discarded SNPs with a call rate less than 90%, Mendelian inconsistency greater than 0.001 and minor allele frequency (MAF) less than 0.05. Also, SNPs that strongly deviated from Hardy–Weinberg equilibrium  in the pure lines were discarded, as well as SNPs on chromosome 16 and two linkage groups because there were too few SNPs in the 60K SNP panel for these chromosomes.The Illumina 60K SNP chip contains 57636 SNPs . In thisDetermining haplotype phase and frequency is necessary to estimate LD and can provide useful information about breed-specific haplotypes and the history of artificial selection. We used BEAGLE (Version 3.3.2)  to inferIn theory, haplotype homozygosity is defined as the likelihood of randomly sampling two identical haplotypes from a population, which is calculated as the sum of squares of haplotype frequencies . Based or using the equation below and used its square, r2, to measure LD between marker pairs that are separated by less than 5 Mb on each chromosome:We calculated rij is the correlation between alleles at SNP i  and alleles at SNP j ; f(MN) is the observed frequency of haplotype MN, which can be simply obtained from the phasing results; and f(M), f(m), f(N) and f(n) are observed frequencies of alleles M, m, N and n, respectively , which means that this line contributed 50% of genetic material to the autosomes of crossbred individuals; whereas the female lines C and D were expected to contribute each 25%. As for female lines, the correlation of r was greater between line D and the crossbred populations than between line C and the crossbred populations, which could be due to a greater correlation of r between lines D and B than between lines C and B.The correlation of r can reflect the relative degree of similarities and divergences between purebred animals. In a large-scale genome-wide survey of SNP variation in cattle breeds, the correlation of r declined as the divergence between breeds increased [r between female lines was slightly but consistently higher than that between female and male lines, which suggests that the two female lines may share a more similar genetic background than each of these lines does with the male line. This is consistent with the fact that male and female lines originated from different breeds, i.e. the male line from Cornish, a meat type breed, and the female lines from dual-purpose breeds. Furthermore, since selection goals of male and female lines are different, this may have contributed to the similarity between female lines and to the divergence between the female lines and the male line. Unlike the male line, which was selected primarily for growth-related traits, the female lines were selected for both reproductive and growth traits.The correlation of ncreased . In our vs. Dutch red-and-white Holstein-Friesian and New Zealand Friesian vs. Zealand Jersey [r ranged from 0.73 to 0.82 for distances between markers less than 10 kb, even on macro-chromosomes. It is likely that the genetic diversity of the chicken lines in our study was greater (average Fst > 0.20) than that of both the cattle and pig breeds used in the aforementioned studies. Another possible explanation for this difference is the overall higher recombination rates per unit of physical distance on chicken chromosomes compared with the average ratio of 1 cM/1 Mb in mammalian livestock animals.Using markers on chromosomes 1 and 4, Andreescu et al.  estimated Jersey . In our r were very high (>0.99) for distances between markers less than 50 kb between the crossbred and the combined BCD populations, and decayed gradually as the distance between markers increased, but still remained high (0.87 to 0.93) for markers that were about 5 Mb apart  is remarkably high. Compared with the pure lines, the crossbred populations showed a considerably lower level of LD and a smaller haploblock size, which suggests that using crossbred animals as a reference population can be an advantage for high-resolution mapping of causal polymorphisms in GWA studies and to achieve better persistence of the accuracy of genomic estimated breeding values over generations in genomic selection programs. However, our results also suggest that a higher SNP density, particularly on micro-chromosomes, is necessary to take full advantage of crossbred populations in GWA studies or in genomic selection programs. Finally, our results prove that LD for short and long distances between markers and haplotype phase for short distances between markers in a crossbred population can be predicted with very high accuracy using genotype information of its parental pure lines.


K = 7) that were representative of different breeding programs in Pakistan, in addition to local landraces. Forty four stable marker-trait associations (MTAs) with -log p > 4 were identified for nine yield-related traits. Nine multi-trait MTAs were found on chromosomes 1AL, 1BS, 2AL, 2BS, 2BL, 4BL, 5BL, 6AL, and 6BL, and those on 5BL and 6AL were stable across two seasons. Gene annotation and syntey identified that 14 trait-associated SNPs were linked to genes having significant importance in plant development. Favorable alleles for days to heading (DH), plant height (PH), thousand grain weight (TGW), and grain yield (GY) showed minor additive effects and their frequencies were slightly higher in cultivars released after 2000. However, no selection pressure on any favorable allele was identified. These genomic regions identified have historically contributed to achieve yield gains from 2.63 million tons in 1947 to 25.7 million tons in 2015. Future breeding strategies can be devised to initiate marker assisted breeding to accumulate these favorable alleles of SNPs associated with yield-related traits to increase grain yield. Additionally, in silico identification of 454-contigs corresponding to MTAs will facilitate fine mapping and subsequent cloning of candidate genes and functional marker development.Genome-wide association studies (GWAS) were undertaken to identify SNP markers associated with yield and yield-related traits in 123 Pakistani historical wheat cultivars evaluated during 2011–2014 seasons under rainfed field conditions. The population was genotyped by using high-density Illumina iSelect 90K single nucleotide polymorphism (SNP) assay, and finally 14,960 high quality SNPs were used in GWAS. Population structure examined using 1000 unlinked markers identified seven subpopulations ( Historically, breeding has contributed to radical increases in cereal production, particularly by the use of dwarf genes and exploitation of heterosis. However, grain production must increase by 70% from present levels to meet projected world food requirements by 2050 , grain numbers per spike (GN), thousand grain weight (TGW), grain yield (GY), biological yield (BY), and harvest index (HI). DH were recorded on 50% spike emergence, and DM were recorded when 50% of spikes showed total loss of green color. PH was measured after physiological maturity by measuring the distance between the stem base and the top of the spike excluding awns. SN was scored in 1 m sample and then transformed to spike number per m2 (SN). GN was calculated from the mean of 30 randomly selected spikes in each plot. After harvest, TGW was measured by weighing duplicates of 500 kernels from each plot. Grain yield (GY) was determined as the weight of grain harvested per unit area (g m−2). BY was estimated as the weight of the above ground biomass for whole plots at 72 h after harvest, and then HI was calculated.Nine phenotypic traits were evaluated, including days to heading (DH), days to maturity (DM), plant height (PH), spike number (SN)  and co-efficients of correlation were calculated for all traits.k = 1 to 15 was assessed using 100,000 burn-in iterations followed by 500,000 recorded Markov-Chain iterations. To estimate the sampling variance (robustness) of inferred population structure, 10 independent runs were carried out for each k. K was estimated using an ad-hoc statistic ΔK based on the rate of change in log probability of data between successive values  and P < 0.001 was determined for each chromosome to compare the degree of LD among genes.Population structure was estimated with 1000 unlinked SNP markers using STRUCTURE software 2.3.3, which implements a model-based Bayesian cluster analysis  procedure was used, where the genotypes, replications, rows, and columns were considered as fixed in the model for each environment. MLM was also used to check statistically significant differences among the genotypes for each trait, whereas the Best Linear Unbiased Predictions (BLUPs) and variance components were acquired for all traits. The unified mixed model approach (Q+K model) of Yu and Buckler  was applhttps://www.blast2go.com/), and SNPs were further annotated using the TriAnnot Pipeline in the International Wheat Genome Sequencing Pipeline.The flanking sequences of SNPs associated with traits were obtained from the Wheat 90K SNP array database . Sequences of SNP markers linked with yield traits (henceforth: MTA) were BLASTed against wheat contigs of using a described previously methodology  were then aligned against the wheat contigs and simultaneously against the identified MTA of all the traits. The criteria for alignment were escalated by considering three parameters reported in Salse et al. , e et al. , that isP < 0.01). ANOVA showed significant differences (P < 0.01) among genotypes for most of the traits (Table −2. DH in 2013–14 (107.9 days) was longer than in 2011–12 and 2012–13 . However, BY was lowest in the third year (691.2 g m−2) due to lack of rain at the early growth stage. PH was significantly higher in 2013–14 (93.3 cm), while TGW was higher in year 2012–13 , BY(r = 0.85), and HI (r = 0.73) and (Table r = 0.14), but PH was significantly correlated with TGW (r = 0.35) and BY (r = 0.24). Other correlations were non-significant or significant with very small r-value.GY was positively correlated with TGW  were monomorphic and 19,810 (23.9%) had poor quality, thus were excluded from the analysis. The remaining 60,594 (69.1%) were further reduced to 14,960 (18.4%) by eliminating markers with minor allele frequencies  and minimum counts of 80%. SNP dataset can be requested for future research via email.A genetic framework map of all 21 wheat chromosomes was constructed using the 14,960 polymorphic SNPs projected onto the consensus map , among which the D genome had the highest proportion of marker pairs in LD (41%), followed by B (35%) and A (25%) genome. The estimated average r2 for unlinked loci was 0.024. Chromosome 6D had the maximum number of marker pair in LD (73%), followed by 2D (48%) and 1B (46%). LD decreased with increasing genetic distance between marker loci. In all three cases, the r2 declined within 12 cM below critical values. In GWAS, there are two ways to account for confounding effect of population structure: (i) statistically accounting population structure, and (ii) carefully selection of the association mapping panel to reduce the range of phenology. We employed first strategy because the number of cultivars released in Pakistan were not sufficient for further selection based on phenology.The number of subgroups (K) were estimated based on the rate of change in the log probability of data between successive e Figure . This inP >3 using MLM; however, only 44 MTAs with -log P >4 were considered to avoid false positive impact of alleles  of 44 MTA sequences were assessed using BLAST2GO v.3 software and SNPs were further annotated using the TriAnnot Pipeline. Twenty (54.5%) SNPs were annotated as present within genes  in pre-2000 compared to post-2000 cultivars, respectively  appeared in same sub-populations along with Pakistani cultivars. Secondly, the population structure clearly dissected the cultivars released for irrigated and rainfed environments, indicating different genetic backgrounds used in developing cultivars for these two regions. 1BL.1RS translocation lines from CIMMYT are important breeding source which adapted very well in rainfed environments of Pakistan. Earlier studies reported that 1BL.1RS lines had higher root and shoot dry weight under drought treatments and an increased root/shoot ratio  are more stable across environments and have higher heritabilities  gene in the gibberellin metabolic pathway affecting PH  and TGW (TaGW2-6A and TaCKX6-D1) deployed in Pakistani wheat cultivars (Rasheed et al., The overall superiority of post-2000 cultivars highlights the considerable progress made during the past decade, and the significant improvement achieved for important economic traits like DH, PH, TGW, and GY. There were minor additive relationships between numbers of favorable alleles and agronomic traits, indicating that genetic improvement of these traits were independent and therefore can be combined. Many of the identified MTAs seem to involve genes that are components of networks driven by the prevailing environmental plasticity and show little improvement in phenotypic stability. The results are also interesting in that none of the favorable alleles has been a breeding target during cutlivar development. This is in contrast to favorable alleles of functional genes for adaptability (GWAS identified chromosomal regions associated with yield traits in historical germplasm deployed in Pakistan. Annotation and comparative genomics assigned 14 of those MTAs to different metabolic functions which have implications to combat stressed environments. These MTAs can be important targets for marker-based breeding and fine mapping of functional genes after further validation.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.


Protoscoleces (PSCs) are one component of the larval stages that can interact with both definitive and intermediate hosts. Previous genomic and transcriptomic data have provided an overall snapshot of the genomics of the growth and development of this parasite. However, our understanding of how PSCs subvert the immune response of hosts and maintains metabolic adaptation remains unclear. In this study, we used Roche 454 sequencing technology and E. granulosus genome and 17,705 (66.8%) were distributed within the coding sequence (CDS) regions. Of the 2,280 ESPs predicted from the transcriptome, 138 ESPs were inferred to be involved in the metabolism of carbohydrates, while 124 ESPs were inferred to be involved in the metabolism of protein. Eleven ESPs were identified as intracellular enzymes that regulate glycolysis/gluconeogenesis (GL/GN) pathways, while a further 44 antigenic proteins, 25 molecular chaperones and four proteases were highly represented. Many proteins were also found to be significantly enriched in development-related signaling pathways, such as the TGF-β receptor pathways and insulin pathways.A large number of nonredundant sequences as unigenes were generated , of which 22,910 (86.4%) were mapped to the newly published This study provides valuable information on the metabolic adaptation of parasites to their hosts that can be used to aid the development of novel intervention targets for hydatid treatment and control. E. granulosus as a model system to study this complex host-parasite interaction by investigating the role of excretory-secretory proteins (ESPs) in the physiological adaptation of the parasite. Using Roche 454 sequencing technology and in silico secretome analysis, we predicted 2280 ESPs and analyzed their biological functions. Our analysis of the bioinformatic data suggested that ESPs are integral to the metabolism of carbohydrates and proteins within the parasite and/or hosts. We also found that ESPs are involved in mediating the immune responses of hosts and function within key development-related signaling pathways. We found 11 intracellular enzymes, 25 molecular chaperones and four proteases that were highly represented in the ESPs, in addition to 44 antigenic proteins that showed promise as candidates for vaccine or serodiagnostic development purposes. These findings provide valuable information on the mechanisms of metabolic adaptation in parasites that will aid the development of novel hydatid treatment and control targets.The successful infection establishment of parasites depends on their ability to combat their host's immune system while maintaining metabolic adaptation to their hosts. The mechanisms of these processes are not well understood. We used the protoscoleces (PSCs) of  Echinococcus granulosus, a cestode that poses a threat to public health as well as significant economic losses Cystic hydatid disease (CHD) is a serious parasitic zoonosis that is caused by the larval stages of E. granulosus has a complex developmental cycle, involving eggs, oncospheres, protoscoleces (PSCs), and adult stages. Adult parasites live in the small intestine of dogs. After sexual maturation, numerous eggs are produced by the adult parasites and are then excreted with the dog feces. Infections occur in an intermediate host, when eggs containing larvae are ingested. Hydatid cysts  develop in the internal organs (primarily in liver and lungs) of intermediate hosts. The larval stages of E. granulosus are comprised of two layers of cyst wall: cyst fluid and PSCs via the intraperitoneal inoculation with PSCs, a method that has been widely applied to drug screening and vaccine development As the only infectious form of the larval stages, PSCs can interact with both definitive and intermediate hosts. They mature into adult parasites when the hydatid cysts are ingested by the definitive host. They can also differentiate into new cysts when released into the body cavity of intermediate hosts upon cyst rupture via ESPs is the key to successful infection. Recently, we found that ESPs from adult E. granulosus could downregulate host immune responses by preventing dendritic cells (DC) from maturing, by impairing DC function and by inducing the generation of CD4+ CD25+ FoxP3+ T cells (unpublished data). Previous studies have shown that cystic fluids produced in the intermediate hosts can modulate DC differentiation and cytokine secretion E. granulosus can direct immature DCs towards the maturation of a Th2 cell response E. multilocularis larvae have been found to induce apoptosis and tolerogenic properties in DC in vitroUnderstanding the elaborate immune evasion strategies and mechanisms of physiological adaptation of the PSCs is critical to ascertain effective intervention targets to control the prevalence of the parasite. In this study, we focus on the role of excretory-secretory products (ESPs) that are released by parasites, as these compounds are exposed directly to the immune system of the hosts and are engaged at the host-parasite interface Further investigations into the mechanisms of physiological adaptation of ESPs released by PSC have been hampered due to the paucity of information regarding ESPs. Although studies have utilized proteomics to identify the constituents of ESPs in silico secretome analysis to explore the transcriptome profiles of E. granulosus PSCs and to elucidate the potential functions of the ESPs released by the parasite.In this study, we used Roche 454 sequencing technology and This study was performed in strict accordance with the recommendations provided in the Guide for the Care and Use of Laboratory Animals of the National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention. The protocol was approved by the Laboratory Animal Welfare & Ethics Committee (LAWEC), National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention (Permit Number: IPD 2011-006).Hydatid cysts were collected from the livers of a naturally infected sheep in a slaughterhouse in Qinghai, China. Cyst fluids containing PSCs were sucked out of the cysts using a sterile syringe. After natural sedimentation for 10 min, PSCs were carefully collected from the sediment of cyst fluids and washed 10 times with saline solution. We then added 2 mL of Trizol reagent  to the well-washed PSCs. After continuous mixing with a pipette, the PSCs were stored at −80°C prior to use.Echinococcus species: cytochrome coxidase subunit 1 (cox1) gene  and cytochrome b (cytb) gene . Each 25-µL reaction mixture contained 1 µL of template DNA, 12.5 µL Premix Taq® mix , l µL of 10 µM of each primer, and 9.5 µL nuclease-free water. The procedure of PCR amplification consisted of 94°C for 1 min, 30 cycles of 94°C for 30 s, 56°C for 30 s, and 72°C for 1 min, followed by 72°C for 10 min, with a final holding step at 4°C. The PCR products were directly sequenced with a Dye Terminator Cycle Sequencing Kit  and ABI 3730 DNA Analyzer .Genomic DNA from the PSCs was extracted using the DNeasy tissue kit  and used as a template for a polymerase chain reaction (PCR) et al.de novo sequence assembly software, Roche, USA) was used to remove low quality sequences and assemble the remaining sequences. Briefly, the quality score trimming filter trims back from the 3′ end of reads and was based on estimated quality scores  derived from an internal calibrated signal histogram. The error rate in a sliding window (default size of 40 bp) was calculated from the estimated quality scores and multiplied by an empirical scaling factor (default of 1.1). The window was moved leftwards until the estimated error rate in the window was <1.0% (by default). If the resulting read was less than 40 bp (default), the read was discarded and not counted . After removing low quality sequences and sequencing adaptors, the remaining sequencing reads were assembled using the Newbler 2.5.3 with the ‘extend low depth overlaps’ parameter. All of the ESTs from the Roche 454 were used to run the final assembly. The resulting isotig consensus sequences and singletons were referred as ‘unigenes’ in the following study.The total RNA was extracted from the PSCs in TRIzol reagent, and RNA quality was performed by gel electrophoresis with a 2100 BioAnalyzer . The sequencing protocol followed that described in Liao The software SOAP2 was used to map the raw sequence reads to the nonredundant sequence data E. multilocularisE. granulosusSchistosoma hematobiumS. japonicumS. mansoniUnigene sequences were compared  to public sequences available in NCBI non-reductant (Nr) and STRING databases, and to five entire genome sequences (E. granulosus PSC (EgPSC) were assigned to three gene ontology (GO) categories, including molecular function, cellular component and biological process After conceptual translation from the predicted coding domains of individual transcriptomic sequences, the functions of the potential proteins were predicted using InterProScan Excretory-secretory proteins (ESPs) were predicted according to the methods described by Garg and Ranganathan E. granulosus ESPs (EgESPs) using BLASTP S. mansoni, S. japonicum, Brugia malayi, Ancylostoma caninum, Teladorsagia circumcinta, Fasciola hepatica and Clonorchis sinesis) In addition to traditional computational approaches for ESPs prediction, we also predicted E. granulosus  and Panther (http://www.patherdb.org/) databases to identify functional groups and pathway annotations. Enrichment of KEGG pathways for genes with significant expression was calculated utilizing a classical hypergeometric distribution statistical comparison of the query gene list against all predicted E. granulosus genes. Caenorhaditis elegans pathways were used as a reference. Calculated P-values were subjected to FDR correction, with p<0.05 taken as the threshold for significance.All potential ESPs were blasted with known ESP sequences from http://www.ncbi.nlm.nih.gov/sra/?term=SRP040541).The transcriptome data is stored in Sequence Read Archive  to the E. granulosus G1 genotype referenced in GenBank , Caenorhabditis elegans  and S. mansoni . Moreover, 22,910 (86.4%) contigs were mapped to the E. granulosus genome and 17,705 (66.8%) of these were distributed within the coding sequence (CDS) region, which suggested that our results were reliable.A total of 330,188 raw reads (mean length = 411.8 bp) were generated. The data is stored in Sequence Read Archive . After trimming to remove adaptors, low quality reads and polyN tail sequences, 329,927 clean reads remained  Teladorsagia circumcincta  were also found in the ESPs of EgPSC, which strengthens the concept that parasites employ universal ESPs to mediate parasite-host interplay EgPSCs identified in this study were reliable.To validate the E. granulosus that have identified just 157 ESPs among them EgPSCs. For example, WD40 repeats E. granulosusTo date, there have been five proteomic studies regarding EgPSC, the largest functional group represented ‘binding’ , followed by ‘catalytic activity’  for ‘molecular function’, ‘metabolic process’  and ‘cellular process’  for ‘biological process’, and ‘cell part’  and ‘cell’  for ‘cellular component’ , followed by Phagosome , Protein folding  and Chaperonin-mediated protein folding .The pathway enrichment analysis for identified ESPs was performed using KOBAS v2.0 software and more than 400 pathways were identified, of which 33 were statistically significant . The terOf the 2,280 putative ESPs, only 1,406 were mapped to known functions . These pThe interaction of pathogens with mammalian hosts leads to a variety of physiology responses that drive the adaptation of the interacting partners to their new environments and conditions E. granulosus has evolved an optimal strategy to gain energy and nutrition from its host using ESPs (via gluconeogenesis (GN) when alternative carbon sources  were available. In addition to the reversible enzymatic GL steps, several reactions are essential in the GN pathway from pyruvate via oxaloacetate to glucose: the reactions catalyzed by pyruvate carboxylase, phosphoenolpyuvate carboxykinase (PEPCK), fructose-1, 6-bisphosphatase, and glucose-6-phosphatase leading to oxaloacetate, phosphoenolpyruvate (PEP), fructose-6-phosphate, and glucose. Finally, tricarboxylic acid (TCA) enzymes, such as aconitate hydratase, succinate dehydrogenase complex, malate dehydrogenase, were identified in the TCA cycle. Other enzymes involved in carbohydrate metabolism are shown in ing ESPs . FirstlyS. mansoniCertain enzymes have been recognized to play key roles in the development of parasites. Phosphoglucose isomerase (PGI), one of glycolytic enzymes, has been found to stimulate parasite growth and the formation of novel blood vessels nearby the developing metacestode E. granulosus to regulate carbohydrate metabolism. Further work is required to identify these additional ESPs and establish their functions.To date, only five ESPs have been identified to participate in this metabolic process E. granulosus, the intermediate host produces a significant immune response that affects the growth and development of parasites Following infection with In this study, we found that 36 ESP domains were molecular chaperones , and ideE. granulosus may secrete proteases or inhibitors to digest host proteins, or to protect itself from digestion by endogenous or host-derived proteinases. In this study, 39 proteases, including serine, aspartic, metallo- and cysteine proteinases, and five inhibitors, were inferred among the set of ESPs  and CD4+CD25+FoxP3+ T cells (Treg) via the action of ESPs as many ESPs have been found to redirect host immune responses In previous experiments we demonstrated that following infection with via the induction of IL-4, which is the main cytokine responsible for Th2 differentiation Notably, cysteine proteinases have been reported to inhibit Th1 immune response EgPSC possesses many signaling pathways such as P13K-Akt, mitogen-activated protein kinase (MAPK), Wnt, calcium, HIF-1, insulin, estrogen and chemokine signaling , TGF-β and insulin signaling pathways might closely associate with the development of C. elegans can trigger an ‘alternative’ developmental pathway, and can regulate and transit the environmental stresses on the first larval stage of the parasite Brugia pahangi, Brugia malayi and Parastrongyloides trichosuriE. granulosus development and growth warrants further investigation. A recent study revealed that host insulin acts as a stimulant for parasite development within the host liver and that E. multilocularis senses the hormones of hosts through an evolutionary-conserved insulin signaling pathway, which demonstrates the importance of insulin signaling for parasite survival Studies have shown that TGF-β and insulin signaling pathways in E. granulosus species, which limits its utility. We suggest that the ESPs of EgPSCs are an excellent alternative candidate for a vaccine, as they are easy to prepare and safer for human health. More importantly, the ESPs obtained by in vitro culture have shown a 92.07% protection rate against a high dose of egg infection in sheep  CHD has a global distribution and causes high rates of morbidity and has a high socio-economic burden in several countries in silico secretome analysis, we identified 44 antigenic proteins present at high abundance in our set of ESPs  has been used for the immunodiagnosis of human CHD E. granulosus in mice Using  of ESPs . Of thes of ESPs . These aE. granulosus metacestode in both adults and PSCs E. granulosus-specific protein domain antigen B (EgAgB) family, which are well known as diagnostic targets, were undetectable in this study. This result was consistent with previous observations that little or no AgB is secreted by in vitro cultured PSCs Proteins that are present at lower levels of abundance might also be relevant as diagnostic markers or target molecules for vaccine development. In this study, these include antigen 5 (Ag5), calreticulin, calcineurin B, thioredoxin, phosphoglucomutase, fructose-bisphosphate aldolase and gp96 . Many ofE. multilocularis. Thus, interventions that utilize these molecules could also arrest parasite growth. In addition, GL enzymes could be drug targets for parasites that rely on the GL pathway for growth and development There are currently just two methods for the treatment of hydatid disease: surgery and the use of benzimidazole, both of which give unsatisfactory results. Hence, novel treatment compounds are urgently needed. In this study, we have identified several secretory drug targets for echinococcosis , includiE. granulosus are pathogenic to human, which therefore have become the research focus of CHD. Parkinson et al. [2012] first reported genes with features that reflect physiological adaptations of different parasite stages, including PSCs, and revealed abundant long non-protein coding transcripts, upregulated fermentative pathways, candidate apomucins and a set of platyhelminth-specific gene products, which greatly increased the quality and the quantity of the molecular information regarding E. granulosusE. granulosus. In this study, we focused on the transcriptome of PSCs, which is the only infective component of the larval stages. We present novel and urgently needed information regarding the components of ESPs released by PSCs and their potential roles in the metabolic adaptation of parasites to their hosts. We suggest that intracellular ESPs are essential to the metabolism of carbohydrates within their hosts and that various molecular chaperones with a high level of expression may play a role in resisting harsh host environments. We also reveal a set of antigenic ESPs that show promise as candidates for vaccine development or in the development of serodiagnostic markers. Such findings will encourage more novel strategies for the treatment and control of CHD.The larval stages of E. granulosus, and deepens our understanding of host-parasite interactions.Although the coverage of the transcriptome data in this study was not deep as the genome-wide study S1 FigureGenotype identification of E. granulosus. (A) PCR amplification. M, DNA maker; Cytb, 601 bp; Cox1, 885 bp. (B) Sequence alignment of the cytochrome b (cytb) gene. Bases that differed are marked with red boxes.(TIF)Click here for additional data file.S2 FigureLength distribution of singletons and isotigs of the EgPSCs transcriptome.(TIF)Click here for additional data file.S3 FigureGene ontology (GO) analysis of the EgPSCs transcriptome. BLASTP against SwissProt and GO mapping of identified proteins (performed with BLAST2GO) (TIF)Click here for additional data file.S4 FigureDistribution of the KOG functional categories of the proteins identified from the EgPSCs transcriptome. Percentages and numbers of proteins in each functional category are indicated in the sectors of the circle. KOG functional categories: (A) RNA processing and modification; (B) Chromatin structure and dynamics; (C) Energy production and conversion; (D) Cell cycle control, cell division, chromosome partitioning; (E) Amino acid transport and metabolism; (F) Nucleotide transport and metabolism; (G) Carbohydrate transport and metabolism; (H) Coenzyme transport and metabolism; (I) Lipid transport and metabolism; (J) Translation, ribosomal structure and biogenesis; (K) Transcription; (L) Replication, recombination and repair; (M) Cell wall/membrane/envelope biogenesis; (N) Cell motility; (O) Posttranslational modification, protein turnover, chaperones; (P) Inorganic ion transport and metabolism; (Q) Secondary metabolites biosynthesis, transport and catabolism; (R) General function prediction only; (S) Function unknown; (T) Signal transduction mechanisms; (U) Intracellular trafficking, secretion, and vesicular transport; (V) Defense mechanisms; (W) Extracellular structures; (Y) Nuclear structure; (Z) Cytoskeleton. The number of proteins in the graphic might exceed the total of predicted ESP because some were grouped in more than one functional category.(TIF)Click here for additional data file.S5 FigureGene ontology (GO) analysis of the identified ESPs from the EgPSCs transcriptome. The figure shows the number of mapped proteins identified in this study as a function of all the available GO terms of level 2 for (A) biological process, (B) cellular component, and (C) molecular function.(TIF)Click here for additional data file.S1 TableKEGG pathway analysis of the EgPSCs transcriptome sequences.(XLSX)Click here for additional data file.S2 TableValidation evaluation of the predicted ESPs from the EgPSCs transcriptome.(XLS)Click here for additional data file.S3 TableOverview of the predicted ESPs from the EgPSCs transcriptome. ESPs were conceptually translated and inferred from the coding domains of transcriptomic sequences. Domain analysis of ESPs was then carried out using InterProScan.(XLS)Click here for additional data file.S4 TableDomains associated with carbohydrate metabolism in the ESP.(XLSX)Click here for additional data file.S5 TableDomains related to post-translational modification, protein turnover, and chaperones in the ESPs.(XLSX)Click here for additional data file.S6 TableDomains of the proteases and protease inhibitors in the ESPs.(XLSX)Click here for additional data file.S7 TableOverview of the KEGG pathways involved in the predicted ESPs.(XLSX)Click here for additional data file.S8 TableThe most abundant transcripts in the ESPs of the EgPSCs based on RPKM (reads per kilobase per million reads).(XLSX)Click here for additional data file.S9 TableThe proteins that were significantly enriched in the KEGG pathways of the predicted ESPs.(XLSX)Click here for additional data file.