Red sea bream, a popular fish resource in Korea and Japan, is being bred in fish farms of the two countries. It is hypothesized that the genomes of red sea bream are influenced by decades of artificial selection. This study investigates the impact of artificial selection on genomes of red sea bream. Whole genome sequencing was conducted for 40 samples of red sea bream either from Ehime, Nagasaki and Tongyeong fish farms or from the wild. Population stratification based on whole genome data was investigated and the genomic regions of fish farm populations under selection were identified using XP-EHH and relative nucleotide diversity. Gene ontology analysis revealed that different functions were enriched in different fish farms. In conclusion, this study highlights the difference between independently cultured red sea bream populations by showing that influence of artificial selection acted upon completely different genes related to different functions including metabolic and developmental processes. Pagrus major, is a valuable aquaculture fish in Korea and Japan. Being an important fish resource in the two countries, red sea bream has been bred in aquacultures since 1980s in Korea1 and 1960s in Japan2. The aquaculture production of red sea bream in 2016 was 33,876 tons and 67,200 tons in Korea and Japan, respectively. To optimize the productivity of red sea bream, breeding programs have been established in Japanese fish farms for selecting and breeding offspring with better productivity traits such as higher growth rate2. Although not as systematic as in Japanese aquacultures, artificial selection is also being practiced in Korean fish farm based on measurable phenotypic traits including the weight and growth rate. As a result, several studies reported that the growth rate of breeding lines in Korean and Japanese fish farms have significantly increased3. However, the improved productivity of the red sea bream comes with the cost of reduced genetic diversity. The issue on breeding programs and the reduced genetic diversity has been previously discussed in other studies5; however, these studies did not make in-depth discussion on the impact of artificial selection on red sea bream genomes. To elucidate further on genetics of red sea bream and its relations to the selected traits, further analysis based on whole genome population data is necessary.Red sea bream, 7. A number of statistics can be used to detect these signatures including cross-population extended haplotype (XP-EHH)8, nucleotide diversity and cross-population composite likelihood ratio (XP-CLR)9. Using combinations of these statistics may allow us to detect genomic regions under selection more reliably11.Red sea bream in the fish farms are exposed to intense artificial selection, which creates specific signatures or so-called ‘selective sweep’ in population genomes. Provided there is a superior allele in a population, the frequency of the beneficial allele within the population increases rapidly. This also results in increased frequency of hitchhiking variants near the beneficial allele, creating selective sweep signature12. Regarding this fish, a previous study performed whole genome sequencing on selection lines and wild-type samples and performed selective sweep analysis to detect many genes under selection related to growth, immune system, response to chemical, stress and drug12. Previous study investigating artificial selection in Asian seabass revealed selection signature in the prolactin gene and its association with the growth traits13. In case of channel catfish, another previous study performed selective sweep analysis and identified genes including hypoxia inducible factor 1b (HIF1b) and ATP-binding cassette sub-family B member 5 (ABCB5) which are related to productivity traits14. As such, the genes under selection varies for different species and we expect these markers to be different in red sea bream as well. Apart from monitoring decreased genetic diversity, discovery of genes showing signatures of selection may also facilitate marker-assisted selection which can lead to further improvement in productivity in aquaculture.In case of other species being cultured in aquaculture, previous studies on artificial selection revealed various signatures of selection. Tilapia which is native cichlid fish from Middle East and Africa, exists in various commercial lines due to breeding programThe aim of this publication is to investigate population genomes of red sea bream from various fish farms in Korea and Japan, and to detect genomic regions with selective sweep signature in each fish farm. Red sea bream populations were collected from three different fish farms (1 Korean and 2 Japanese farms) and pair-wise comparisons were made against a wild-type population collected from Korea using the statistics mentioned above. To our knowledge, this is the first study to perform population analysis on whole genome sequencing data of red sea bream.The geographical location and the representative samples of each population were presented in Fig. http://marua-suisan.co.jp/) with improved growth and disease resistance. The samples from Nagasaki fish farm originates from a strain developed by Nichimo marinculture (http://www.nichimo-mariculture.co.jp/) which also selected for fish with improved growth trait. Finally, the breeding strategy is again similar for Tongyeong population which selected for fish that are showing improved growth trait1. Comparison to the non-selected offspring of red sea bream showed that the developed line has higher weight gain and growth rate1.Red sea bream from Ehime fish farm composed of a strain developed by Marua Suisan  were identified for intergenic region. Many of the SNPs also were located upstream (19.0%), downstream (18.7%) of genes, or introns (9.4%). Among SNPs located in the protein coding region, 187,427 (0.811%) were synonymous variant while 102,402 (0.443%) were missense variant.The whole genome sequence data of 40 red sea breams were generated to ~10 × ~371Gbp), trimmed and aligned against Pagrus major 0.1 genome71Gbp, trPrior to discovery of the signature of artificial selection, population stratification of red sea bream was investigated based on the genome-wide SNP data. Principal component analysis (PCA) visualized the sample distribution in the two-dimension scatter plot of PC1 and PC2, without prior knowledge on the population. Four different clusters were observed. The red sea bream individuals from the same population were distributed in the same cluster without any exception Fig. . AccordiThe population genomic structure was investigated through admixture analysis with different number of ancestries, K ranging from 2 to 8 to determine the number of distinct ancestries of red sea bream population. As a result, we observed minimum cross-validation (CV) error at K = 2 . The regions with significantly high scores in only XP-EHH and relative nucleotide diversity statistics were considered as strong candidate regions; the result of XP-CLR could not be used due to extremely low XP-CLR statistics  identified by intersecting XP-EHH and relative nucleotide diversity score. The significantly enriched GO terms identified using Panther are presented in Fig. To investigate the function of candidate regions under selection, we performed gene ontology (GO) analysis using PantherAs a result of GO term analysis on cellular component, we found that the genes with selective sweep signature were associated with different cellular components in different fish farms Fig. . SimilarWe define highly differentiating genotypes of a fish farm population as the genotypes that appear in significantly higher or lower frequency to that of wild-type population. For the three fish farm populations, pairwise comparison with the wild-type population was performed for the entire SNP loci to find highly differentiating genotypes at the respective positions. As a result, we identified 2,446,630, 2,325,736 and 1,622,269 loci with highly differentiating genotypes  in Ehime, Nagasaki and Tongyeong populations, respectively. Comparing these loci with the candidate selective sweep regions , we found 1,407, 420 and 1,133 overlapping loci in 43, 34 and 28 genes in Ehime, Nagasaki and Tongyeong populations, respectively Table . Among o1 and 1960 s2. As an important fish resource in the two countries, many researches on red sea bream focused on improving its productivity or resistance to diseases; however, not as many genetic studies have been made on red sea bream. Among few previous studies, one study investigated genetic variability of inbred populations and showed that genetic variability has not dropped significantly4. On the other hand, another study admits that the genetic structure of red sea breams reared in captivity differ from the wild populations and further expressed concerns over genetic diversity of local populations perturbed by the large-scale hatchery releases5; however, these studies were limited to microsatellite markers and could not make in-depth discussion on the impact of breeding programs on red sea bream genomes. Not much is understood about what impacts decades of inbreeding might have had on genomes and phenomes of red sea bream. Further studies on whole genome data may provide deeper insight into the current status of red sea bream genomes and its relation to phenotypic traits. The current study analysed the red sea bream genomes based on whole genome population data. 40 red sea bream genomes collected from three different fish farms  were investigated to reveal the population structure of fish farm populations and discover genes under selection.Red sea bream has been bred in Korean and Japanese aquacultures since 1980s21 as the fish can freely move along these areas. Consequently, selective sweep analysis was performed as we planned. The XP-EHH scores in the two Japanese populations were higher than that of Tongyeong . The map of Korea and Japan shown in Fig. The genomic DNA (~200 ng) from individual sample was extracted from the fin tissues and fragmented into smaller piece of ~500 bp, using Covaris M220 . Indexed shotgun paired-end libraries for the fragmented DNA (~500 bp) were prepared, using TruSeq Nano DNA Library Prep Kit , following the standard Illumina sample-preparation protocol. For the genomic DNA, end repair, A-tailing and adapter ligation (~125 bp adapter) were performed followed by gel-based size selection for 550~650 bp and amplification with 8 cycles of PCR. Size distribution of the DNA was investigated by Agilent 2100 Bioanalyzer (Agilent Technologies). Finally, DNA sequencing was conducted using Illumina HiSeq2500 sequencer (2 × 150 bp paired-end reads were generated).15. The function of protein coding genes in this genome was predicted by performing BLASTp alignment against Swiss-Prot database, three close fish genomes  and Danio rerio genome obtained from Ensembl database release-9233. BLAST hits with e-value < 10E-5 and bit score > 50 were used for the annotation, resulting in annotation of 21,341 genes (Table The genome assembly of Pagrus major 0.1 which was assembled in our previous study was usedes Table . With thhttp://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/). The adapter, low quality base (Q score < 20), read sequences with length < 75b and unpaired read sequences were removed via Trimmomatic-0.3634. Remaining high quality reads were aligned against the red sea bream reference genome Pagrus major 0.1, using Bowtie2-2.3.3.135. The mapped reads were grouped and duplicated reads were filtered out using Picard 2.9.2 (http://picard.sourceforge.net). Local realignment was performed to correct errors caused by insertions and deletions (indels) using Genome-AnalysisTK-3.3-0 (GATK)36. Unifiedgenotyper of GATK was used to discover variants. Criteria used for the variant calling include: 1) count of reads that have mapped quality 0 (MQ0) < 4 and (MQ0/DP) = < 0.1, 2) variant call confidence (QUAL) = > 30, 3) QUAL score normalized by allele depth (QD) = > 5.0 and 4) strand bias score from Fisher’s Exact test (FS) = < 200. The high quality SNPs identified were used to recalibrate base quality. The second round of variant calling was conducted with the recalibrated data resulting in 11,891,478 SNPs and 2,150,618 indels. The effect of each SNP was predicted using SnpEff16. Imputation of the missing genotypes and phasing were performed using beagle 4.137.Base quality of sequenced data was visualized using fastQC prior to re-sequencing analysis  effect was mitigated by incorporating filtering step. SNPs with pair-wise R2 values > 0.1 with any other SNPs within sliding window of 50 SNPs were filtered out using plink39, resulting in 996,004 SNPs. The ancestry fractions of individual red sea bream samples were estimated using ADMIXTURE40 with K ranging from 2 to 8 and bootstrap of 1,000. Maximum likelihood phylogenetic tree was reconstructed based on the genome-wide SNP data. SNPhylo software package41 was used with the bootstrap of 1,000. FigTree program was used for visualization of the tree.Population structure was examined based on the genome-wide SNP data. Principal component analysis (PCA) implemented in SNPRelate package8. Whereas, XP-CLR measures multi-locus allele frequency differentiation between two populations9. According to a previous study, both XP-EHH and XP-CLR can show high performance in case when there is low migration between two populations and XP-CLR is generally one of the best performing methods among the methods being compared42. Finally, relative nucleotide diversity compares the nucleotide diversity between two different populations. The three methods measure different signals  to find strong candidates of selective sweep.Different methods  were used to identify selective sweep regions. XP-EHH compares the extended haplotypes between two populations8 statistics (http://hgdp.uchicago.edu/Software/) implemented in SELSCAN43. The red sea bream genome was divided into 50 kb bin, and for each bin, the highest XP-EHH score was used as the representative score. Based on the observed score, normalized score was calculated, from which empirical p-value was computed. Empirical p-value of XP-EHH score assigned for each bin <0.05 was considered significant. The nucleotide diversity was calculated across non-overlapping 50 kb bins using VCFtools44. The relative nucleotide diversity was calculated as (pi wild type)/(pi fish farms). Note that higher value of relative nucleotide diversity represents reduced nucleotide diversity in the fish farm population. Similar to XP-EHH, empirical p-value of relative nucleotide diversity <0.05 was considered significant.For each of the 3 fish farm populations, pair-wise comparison against Korean wild type population was made using XP-EHHhttp://genetics.med.harvard.edu/reich/Reich_Lab/Software.html). The XP-CLR statistics was calculated across the non-overlapping bin of 50 kb. The maximum number of SNPs allowed within each bin was 600. The two SNPs with correlation level (R2) > 0.95 were down-weighted to prevent use of redundant SNPs during computation of composite likelihood9. The top 1% XP-CLR value were considered as the signature of selective sweep.Additionally, pair-wise comparison of composite likelihood was performed using the source code for XP-CLR statistics available in , chi-square test (codominant model) or Cochran-Armitage test (Cochran-Armitage model) was performed to identify highly differentiating genotypes of the fish farm populations compared to the wild-type. SNPs located in the genic regions, upstream or downstream of within 5 kb from the genes, showing p-value of below 0.05 under at least one of the models were considered as highly differentiating genotypes of the respective fish farm population. The polymorphic loci across the regions near the highly differentiating genotypes were visualized using ComplexHeatmap46.Genotype-differentiation of fish farm populations was identified using snpSiftAdditional file 1S4S5S5-2S5-3S6S7


Pinus radiata. In the absence of full reference genomes for large megagenomes such as in conifers, RNA sequencing in a range of genotypes and tissue types, offers a rich source of genetic markers for downstream application. We compared nine different assembler and variant calling software combinations in a single transcriptomic library and found that Single Nucleotide Polymorphism (SNPs) discovery could vary by as much as an order of magnitude . The assembler with the best realignment of the packages trialled, Trinity, in combination with several variant callers was then applied to a much larger multi-genotype, multi-tissue transcriptome and identified 683,135 in silico SNPs across a predicted 449,951 exons when mapped to the Pinus taeda ver 1.01e reference.There is a wide diversity of bioinformatic tools available for the assembly of next generation sequence and subsequence variant calling to identify genetic markers at scale. Integration of genomics tools such as genomic selection, association studies, pedigree analysis and analysis of genetic diversity, into operational breeding is a goal for New Zealand’s most widely planted exotic tree species,  Pinus radiata D.Don) is New Zealand’s most widely planted exotic forestry species   performed sequencing on the RNA extracted from all trees (Trees 1–8). RNA quality (18S and 26S ratios) and RNA concentration were determined using the RNA 6000 LabChip in an Agilent 2100 Bioanalyzer . Thereafter, samples progressed through sequencing on the next-generation Illumina Hiseq 2000 sequencing platform . The total number of reads and RNA input metrics are summarised in Trinity v r2013-01-25 [All programmes and software used in this analysis are listed in 13-01-25 . Dataset13-01-25 .To evaluate a different variant calling workflows, different software for the various steps were tested in a range of combinations . For theSAMtools (Sequence Alignment/Map) was used to reformat the outputs and identify sequence variants. All defaults were used, with the following exceptions: 1) to reduce the mapping quality for reads with a high number of mismatches, the coefficient to reduce the mapping quality of reads that had a higher number of mismatches was set to 50 as recommended for BWA alignments, 2) Bayesian inference was used to call variants using maximum-likelihood inference for the priors, 3) genotypes were called at the variant site, and 4) for areas of high coverage, , variants with a read depth greater than 100 were removed, as there is a known problem assembling abundantly expressed genes .https://broadinstitute.github.io/picard/) was user to reorder files prior to running the GATK variant calling tool.Default parameters were also used for GATK, however, some file reformatting was required. For BWA alignment files, SAMtools was first used to merge, sort and convert outputs into binary sequence alignment/map format (BAM) prior to performing local realignments in GATK. Picard . Therefore, to identify SNPs that were variable between genotypes, the raw reads from each genotype were mapped to the multi-genotype transcriptome using Bowtie2 version 2.1.0 , and polPinus taeda ver 1.01e genome [Neves et al 2013  reportede genome  using the genome .After SNP discovery via the mapping of raw reads back to the assemblies, additional filtering was performed to increase the likelihood of detecting biologically real SNPs and not sequencing or alignment errors. Firstly, SNPs had to be located more than 10 bp from the edge of contig. Secondly, read depth at SNP locations had to be greater than or equal to 10. Thirdly, as each genotype was from a diploid individual, heterozygous SNPs within an individual should display approximate allele frequencies of 0.5 across all reads; we widened our criteria to allow minor allele frequencies of no less than 0.25 within an individual.www.ncbi.nlm.nih.gov/bioproject/482145).A total of 18 transcriptome libraries were sequenced , and assPinus taeda ver 1.01e genome assembly, 144,007 (74%) of the radiata contigs aligned to the P. taeda genome. This enabled the prediction of 449,951 putative exons, corresponding to 46,342 P. taeda scaffolds.The 8 individual genotypes, previously assembled independently, were combined into a large “multi-genotype radiata transcriptome” containing 194,299 contigs, ranging in size from 201 bp to 16,575 bp, with an N50 of 1,434. When mapped to the denovo assembly. However, many of the raw ‘clean’ reads could not be remapped onto the unigene sequence, suggesting a problem with this approach. The best result was achieved with Mosaik (combination 9), although only 36% of the raw reads remapped to the unigene sequence. This unexpectedly low rate of realignment meant that a high number of polymorphisms were likely missed. Therefore, the decision was made to perform a new assembly using Trinity, and a combined transcriptomic library contig set was created for Tree 1, comprising 137,228 different contigs sequences, ranging in length from 201 to 9,175 bp.Three different combinations of global and/or local alignment, followed by variant calling  to 93% (BWA and Mosaik). For this reason, no further analyses were performed using the SOAPdenovo assembly, and all subsequent reporting of SNPs were identified solely from the Trinity assemblies. SNPs were regarded as high confidence when all of the following criteria were satisfied: 1) more than 10 bases from the edge of the unigene, 2) allele frequency between 0.25 and 0.75, 3) a read depth of 10 or more sequences at that SNP position, and 4) at least 60 bases clear of other polymorphic features on at least one side of the SNP.Variant calling was undertaken using all nine different combinations of global alignment, and/or local alignment, and SNP identification software packages  for the The number of high confidence SNPs  ranged fSNPs predicted multiple times using different software package combinations were expected have a higher likelihood of being a true polymorphic event, therefore, we aimed to distinguish SNPs common to multiple pipelines from those predicted in only one pipeline. Using a pair-wise combination approach, each pipeline was compared to the others . PipelinA total of 164,145 different SNPs were predicted across all pipelines, with only six SNPs predicted in all 9 software combinations . There wFor SNP discovery within each of the genotypes, we selected the three best SNP prediction tools identified in the pilot study: rtg-GA, GATK and SAMtools . Using iFor the final round of SNP discovery within the multi-genotype Trinity v 2.0  assemblywww.affymetrix.com) [in silico SNP predictions became less critical with these alternative genotyping platforms as preselected SNP markers were not required. However, the prediction of high confidence SNPs, described here, did influence the selection of genomic sites to target for an exome capture GBS assay.SNPs identified from tissue-specific transcriptomes can be an ideal resource  for candrix.com) , 33. Durrix.com) , or targrix.com)  became mWe tested a number of sequence assembly software packages, including Trinity, SOAPdenovo, and Velvet/Oases , 49 , and highlights the extent of variation among SNP calling pipelines, with 54% of the 164,145 SNPs discovered in the Tree 1 pilot study being unique to a single pipeline. A similar study comparing SNP discovery pipelines in Antarctic fur seals  showed tThere are two processes that current NGS algorithms can employ in the discovery of polymorphic loci: (a) global and local sequence alignment, and (b) polymorphism detection. The Burrows-Wheeler alignment (BWA) can be used to assemble short sequence reads  then SAMin silico SNP markers and 449,951 exome templates, the first large-scale SNP resource reported for this species. In addition, the P. radiata multi-genotype transcriptome assembly is proving to be a valuable resource and being utilised in multiple downstream projects, including facilitating the assembly of a P. radiata reference genome, various gene discovery programmes, pedigree reconstruction and genomic selection.The purpose of this work was to evaluate bioinformatic workflows and combinations of software for identification of polymorphic loci and the development of a resource for a number of genomic tools for the radiata pine industry, with a requirement for varying SNP densities. A large, complex genome and incomplete reference resources precluded whole genome resequencing for SNP marker discovery in radiata pine. Nine different pipelines applied to a single pilot transcriptome identified SNPs at a rate the ranged over an order of magnitude. However, the utilisation of transcriptomic RNA sequencing in combination with several variant calling pipelines and quality filtering, has identified 683,135 S1 Table(DOCX)Click here for additional data file.S1 File(TGZ)Click here for additional data file.


To better understand the genetic architecture of egg production, we measured traits including age at first egg (AFE), weekly egg number (EN) from onset of laying eggs to 80 weeks which was divided into five stage  based on egg production curve and total egg number across the whole laying period . Then we performed genome-wide association studies (GWAS) in 1078 Rhode Island Red hens using a linear mixed modelPRLHR that may affect AFE through regulating oxytocin secretion in chickens. Sixteen genome-wide significant SNPs associated with EN3 were in a strong linkage disequilibrium (LD) region spanning from 117.87 Mb to 118.36 Mb on GGA1 and the most significant SNP (rs315777735) accounted for 3.57% of phenotypic variance. Genes POLA1, PDK3, PRDX4 and APOO identified by annotating sixteen genome-wide significant SNPs can be considered as candidates associated with EN3. Unfortunately, our study did not find any candidate gene for the total egg number.Estimates of pedigree and SNP-based genetic parameter showed that AFE and EN1 exhibited high heritability , while the h2 for EN in other stages varied from low (0.07 ± 0.04) to moderate (0.24 ± 0.07) magnitude. Subsequently, seven univariate GWAS for AFE and ENs were carried out independently, from which a total of 161 candidate SNPs located on GGA1, GGA2, GGA5, GGA6, GGA9 and GGA24 were identified. Thirteen SNP located on GGA6 were associated with AFE and an interesting gene Findings in our study could provide promising genes and SNP markers to improve egg production performance based on marker-assisted breeding selection, while further functional validation is still needed in other populations.The online version of this article (10.1186/s12863-019-0771-7) contains supplementary material, which is available to authorized users. Egg production is the most important trait in layers as it directly affects economic benefits of poultry farmers . Therefohttps://www.animalgenome.org/cgi-bin/QTLdb/GG/index) have been reported to be associated with age at first egg, egg number and egg production rate in chickens [ODZ2, GRB14, GTF2A1 and CLSPN etc. [Until now, egg production has acquired considerable improvement by the conventional selection method. However, the conventional breeding approaches could not completely eliminate the environmental effects which unavoidably results in inaccuracy of heritability estimates , 3. Ananchickens –14. Althchickens , 16. A fchickens . A new rSPN etc. –22. MostIn our research, we employed the commercial 600 K SNP array to identify the genomic regions and candidate genes associated with age at first egg and egg numbers in a pure line population derived from Rhode Island Red using genome-wide association study (GWAS) that could potentially accelerate the genetic improvement of egg production.Descriptive statistics of AFE and ENs across the whole laying period are shown in Table Estimates of SNP-based heritability as well as genetic and phenotypic correlations between AFE and ENs are displayed in Table P value < 3.17E-05) located on GGA1, GGA2, GGA5, GGA6, GGA9 and GGA24 were identified  and were located within a 0.49 Mb region that spans from 117.87 Mb to 118.36 Mb on GGA1 , homeodomain interacting protein kinase 3 (HIPK3), RAB11 family interacting protein 2 (RAB11FIP2), Family with sequence similarity 204 member A (FAM204A), prolactin releasing hormone receptor (PRLHR) and Neurotrimin (NTM).All the candidate genes associated with AFE were displayed in Table (POLA1), while the other SNPs were near to the candidate genes Pyruvate dehydrogenase kinase 3 (PDK3), Peroxiredoxin 4 (PRDX4), apolipoprotein O (APOO) and acyl-CoA thioesterase 9 (ACOT9).The phenotypic variance of EN3 explained by the most genome-wide significant SNP (rs315777735) were estimated as 3.57% using the GCTA software. To detect promising EN3-associated genes, detailed information of sixteen genome-wide significant SNPs were annotated using the online VEP tool Table . The SNPEgg production is an important economic trait. So far, many studies have focused on the genetic determinants of AFE and EN in chicken, and most of them were based on an F2 crossed population or cross-section in a specific laying period. In earlier work, Goraga et al. 2011) and Wolc et al. 2014) reported that some QTLs on chromosomes 4, 5, 7 and 17 had an influence on EN  reported. Yuan et11 and WoThe descriptive statistics Table  reveal thttps://www.animalgenome.org/cgi-bin/QTLdb/GG/index) [(PRLHR) gene also known as PrRPR in chickens. Previous studies reported that PrRP and its structurally related peptide (C-RFa) may play distinct roles in controlling feed intake and pituitary functions in chicks [PrRP participates in many important physiological processes to influence sexual maturity, including gonadotropin-releasing hormone, vasopressin and oxytocin secretion [PRLHR gene has an indirect effect on AFE and further validation is required in multiple populations.We carried out seven univariate genome-wide association analysis for AFE and ENs independently. Previous QTL mapping and genome-wide association studies reported some QTLs or SNPs on chromosome 1, 2, 3, 4, 5, 7, 11, 13, 20, 24 and Z were significantly associated with AFE (G/index) , 19, 27.n chicks . In addiecretion –31. We sP value < 1.58E-06) and were located in a strong LD region (0.49 Mb) on GGA1  is one of the four PDK isoenzymes, which negatively regulates the activity of pyruvate dehydrogenase complex (PDC) by reversible phosphorylation [PDK3 exhibits tissue-specific expression in testes, brain, kidney and pancreatic islets of adult [PRDX4 is a 2-cysteine peroxiredoxin that is a major component of the endoplasmic reticulum (ER) oxidative protein folding pathway [PRDX4 oxidative activity acts as a sensor to directly couple neuronal differentiation with redox environments in the ER [APOO) around the significant SNPs was also detected, which was a new member of the apolipoprotein family. Previous studies reported that APOO participated in fatty acid and lipid metabolism and regulated the production of lipoprotein through hormones in the chicken [APOO is a pleiotropic gene, which affects not only the fatty deposition, but also the egg number at a specific stage in chickens. However, our study did not find a gene that affecting the egg number all the time.Univariate tests in egg numbers at different stages detected a total of 155 SNPs located on GGA1, GGA2, GGA6 and GGA9, only sixteen of which associated with EN3 were genome-wide significant  identified by annotating sixteen genome-wide significant SNPs can be considered as candidates associated with EN. Findings in our research could better understand the genetic basis of egg production, while further functional validation is still needed in other populations.In conclusion, the GWAS performed in this study demonstrates that AFE is highly heritable and negatively correlated with ENs which have relatively low heritability. A total of 1078 Rhode Island Red hens from Beijing Huadu Yukou Poultry Breeding Co., Ltd. were used in our study. The egg production and quality of this pure line has been artificially selected over ten generations. We only utilized the data from the last generation (G11) in the study, and the population was produced by crossing 92 sires and 801 dams (One sire mating 8–12 hens). Blood samples were collected from brachial veins using the standard procedure of the breeding program, which was approved by the Animal Welfare Committee of China Agricultural University.https://www.r-project.org/). Because the transformed data has the lower standard deviation.All birds had pedigree and were housed in individual cages of the same condition. The age at first egg (AFE) and weekly egg production from onset of laying eggs to 80 weeks of age for each bird were recorded. We divided the whole laying period into 5 stages based on the egg production curve  were filtered out through the PLINK package [Genomic DNA was extracted from 1.5-mL blood samples using DNeasy 96 Blood & Tissue Kits . A total of 1078 hens were genotyped with the Chicken 600 K SNP array   was implemented to evaluate the population stratification using the PLINK package . We prunY denotes the vector of phenotypic values of AFE and ENs for n samples; W is a matrix of covariates ; α is the corresponding coefficients of the fixed effects; x is a vector of SNP genotypes ; β is the effect sizes of SNPs for the phenotypes; u is a vector of random polygenic effects with u ~ MVNn where λ represents the ratio between the two variance components and τ−1 denotes the variance of the residual errors, K is a known kinship relatedness matrix; ε is a matrix of errors. We applied the Wald statistical test to evaluate the null hypothesis that the SNP effect sizes for all phenotypes are zero, H0:β = 0 for each SNP versus H1:β ≠ 0. For each SNP in turn, GEMMA acquires either the maximum likelihood estimate or restricted maximum likelihood estimate of λ, and outputs the corresponding P value.In this expression, P-values based on the number of linkage disequilibrium blocks and the independent SNP markers [P values of suggestive and genome-wide significance were calculated as 3.17E-05  and 1.58E-06 , respectively.Considering the over-conservation of 5% Bonferroni correction method, we adjusted the threshold of genome-wide significant  markers . Therefohttps://cran.r-project.org/web/packages/gap/). Genomic inflation factor (GIF) was calculated using the “GenABEL” package in the R software to judge the extent of false positive signals [The Manhattan and quantile-quantile (Q-Q) plots for AFE and EN were performed using the “gap” package in the R  [We performed linkage disequilibrium (LD) analysis to further characterize causative regions associated with AFE and ENs traits by applying the solid spine algorithm in the Haploview v4.2 software . Annotatols/VEP) .1 is the n × 1 vector of all 1’s,μis population means (fixed effect), Z is a incidence matrix of random effect (n × 1 vector), and a and e denote the additive effect and random residual error, respectively.The pedigree-based hereditability values for AFE and ENs were estimated using a multi-trait general animal model method in the DMU v6.0 software . The mod2snp) [Moreover, the SNP-based heritability (h2snp)  and pair2snp) . Besides2snp) .Additional file 1:Table S1. The whole information of genome-wide significant and suggestively significant SNPs for AFE and ENs using genome-wide association studies. (XLSX 21 kb)Additional file 2:Figure S1. Manhattan plots for egg numbers at different stages. (PDF 537 kb)Additional file 3:Figure S2. The plot of egg production curve in this population. (JPG 109 kb)Additional file 4:Table S2. Basic information of SNP markers on a physical map after quality control. (XLSX 11 kb)


The purpose of this study was to investigate a single step genome-wide association study (ssGWAS) for identifying genomic regions affecting reproductive traits in Landrace and Large White pigs.The traits included the number of pigs weaned per sow per year (PWSY), the number of litters per sow per year (LSY), pigs weaned per litters (PWL), born alive per litters (BAL), non-productive day (NPD) and wean to conception interval per litters (W2CL). A total of 321 animals (140 Landrace and 181 Large White pigs) were genotyped with the Illumina Porcine SNP 60k BeadChip, containing 61,177 single nucleotide polymorphisms (SNPs), while multiple traits single-step genomic BLUP method was used to calculate variances of 5 SNP windows for 11,048 Landrace and 13,985 Large White data records.The outcome of ssGWAS on the reproductive traits identified twenty-five and twenty-two SNPs associated with reproductive traits in Landrace and Large White, respectively. Three known genes were identified to be candidate genes in Landrace pigs including retinol binding protein 7, and ubiquitination factor E4B genes for PWL, BAL, W2CL, and PWSY and one gene, solute carrier organic anion transporter family member 6A1, for LSY and NPD. Meanwhile, five genes were identified to be candidate genes in Large White, two of which, aldehyde dehydrogenase 1 family member A3 and leucine rich repeat kinase 1, associated with all of six reproduction traits and three genes; retrotransposon Gag like 4, transient receptor potential cation channel subfamily C member 5, and LHFPL tetraspan subfamily member 1 for five traits except W2CL.The genomic regions identified in this study provided a start-up point for marker assisted selection and estimating genomic breeding values for improving reproductive traits in commercial pig populations. The genetic improvement is one approach to improve reproductive performance. However, the reproduction traits are characteristic of low heritability and difficult using conventional selection method to improve. The conventional selection method may provide a lower accuracy, in comparison to the whole genome selection . In the Institutional Animal Care and Use Committee (IACUC) approval was not obtained for this study because the data were obtained from an existing database on pig breeding.The data used for this study collected from Thailand commercial herds; extracted from the SowTracker (Version 3.4.7) reproductive data management software. The number of animals considered in the analyses was different for each trait because of the lack of data for some animals. A total of 11,048 Landrace and 13,985 Large White pigs were collected data records with 13,351 and 16,731 pedigree records respectively. These sows were raised in six farms. The reproduction traits included PWSY, LSY, NPD, W2CL, BAL, and PWL were recorded to a maximum of 10 parities between 2006 through 2015. PWSY was calculated as litter per sow per year multiplied by pigs weaned per litter; LSY was calculated as (the number days of gestating divided by 115 days) divided by (the number of days in the breeding herd divided by 365 days); W2CL, BAL, and PWL were collected on every litter and divided by total litters; the NPD was calculated as 365 minuses productive days multiplied by LSY, productive days was the total number of days that all gilts and sows were either gestating or lactating. Then checks the normal distribution before basic statistical analyses such as mean, standard deviation and coefficient of variation.Used 321 animals; 140 Landrace and 181 Large White pigs were genotyped with the Illumina Porcine SNP 60k BeadChip, contained 61,177 SNPs. The quality control for genotypes SNPs of each breed following criteria was: SNP call rates <0.90, genotype call rates <0.90, minor allele frequencies <0.05, Monomorphic and checks parent-progeny Mendelian conflicts were selected for further analysis. A total of 129 Landrace and 175 Large White pigs with a total of 47,590 and 47,865 SNPs respectively were available for the genome-wide association analyses in this study.The genome-wide association analysis was estimated by using single-step genomic BLUP (ssGBLUP) . GWAS byβ is a vector of fixed effects. The fixed factors used in this study were last farrow-month, last farrow-year, last parity and farm, u is a vector of additive genetic effects, which was assumed to be distributed Where y represented a vector of observations , −1) was replaced by H−1 that combines the pedigree and genomic information −1âg, where û is a vector of SNP marker effects, and âg is the animal effects of genotyped animals.Convert GEBVs to SNP effects  was calculated as described by Wang et al [ai is the genetic value of the i-th region that consists of consecutive 5 SNPs, Zj is a vector of the gene content of the j-th SNP for all individuals, and ûj is marker effect of the j-th SNP within the i-th region.Where, In this study used 5 SNP window because it has been reported by Beissinnger et al  that bothttps://www.genecards.org/) for identification of biology function of the associated genes. In this study used HGNC gene symbols referenced from the HUGO Gene Nomenclature Committee. If the genes were not found in these regions, then it was considered flanking regions about 2.0 Mb upstream or downstream of QTL regions to possibly represent the locus [http://www.animalgenome.org/cgi-bin/QTLdb/SS/index) [The consecutive SNPs which explained 1% or more than of genetic variance were selected as SNPs that considered to associated with reproduction traits. These regions were used to determine possible putative QTL or candidate genes based on the regions those within the gene. Gene search was carried out by using the Sus scrofa Build 11.1 assembly database release on August 2017 and GeneCards (S/index) .This study is preliminary of research using genomic information to apply for field data in commercial pigs of Thailand and need to beware of using small data size. However, we use ssGWAS because this approach utilizes all available information jointly in one step and has been validated using field data which more precise estimates of variance components by including non-genotyped animals if the number of genotyped animals is limited .Heritabilities calculated from the variance components are shown in Sus scrofa chromosome (SSC)1 (left) to X and unmapped (right). In total, there were 47,590 and 47,865 regions in Landrace and Large White, respectively.The ssGWAS results of the 6 measured traits of Landrace and Large White were shown in http://www.animalgenome.org/cgi-bin/QTLdb/SS/index).Genomic regions were found to be associated with reproduction traits for Landrace and Large White pigs in RBP7) and ubiquitination factor E4B (UBE4B) gene located on SSC6 for PWL (3.36%), BAL (3.06%) W2CL (5.69%) and PWSY (1.67%); solute carrier organic anion transporter family member 6A1 (SLCO6A1) gene located on SSC2 for LSY (2.05%) and NPD (2.20%). From the results, some regions and genes showed a significant associated with more than one trait within a breed. It means that the presence of a variant may affect the multiple traits. For instance, the region that strongly associated with PWL, BAL, W2CL, and PWSY was rs81320475. The RBP7 and UBE4B gene have highly associated with four traits were PWL, BAL, W2CL, and PWSY.For Landrace pigs, the QTL regions were identified on SSC 2, 6, 14, and X. A total 60 regions were significantly associated (SNP windows that explained more than 1% of genetic variance) with reproduction traits for all six traits which were included in 11, 14, 13, 8, 6, and 8 regions for PWL, BAL, W2CL, LSY, PWSY and NPD, respectively. When the consideration of overlapped of QTL regions from all traits, it was found that a total of 25 SNPs was associated with reproduction traits. The candidate genes which had the highest of genetic variance of 5 adjacent SNPs for each trait were retinol binding protein 7  associated with reproduction traits in Landrace pigs . All of n testis  and has n testis .RBP7 is a member of the retinol binding protein family. The retinoids play roles in vision, growth, reproduction, and cellular differentiation beginning in early development [RBP7 gene has high expression in fat and higher expression in the endometrium on day 15 of estrous cycle compared to the pregnancy of day 15 [RBP7 plays a role in regulating peroxisome proliferator-activated receptor gamma transcriptional activity and that adiponectin (AdipoQ) might be a potential downstream target of RBP7 in the endothelium.The elopment . This gef day 15 . Accordif day 15  reportedUBE4B gene located on SSC6, which associated with PWL, BAL, W2CL, and PWSY. The vertebrate, UBE4B gene is also known as ubiquitin fusion degradation (UFD2a), homolog in yeast. In 2005, Kaneko-Oshikawa et al [UFD2a or deletion of ubiquitin enzymes effected on embryo-lethal and apoptosis in the heart. According to Zage et al [UBE4B overexpression reduced neuroblastoma tumor cell proliferation which neuroblastoma is a type of cancer that found in an embryo or fetus in human.The wa et al  showed tALDH1A3) gene located on SSC1 for PWL (4.27%); leucine rich repeat kinase 1 (LRRK1) gene located on SSC1 for BAL (4.86%) and W2CL (3.66%); retrotransposon Gag like 4 (RTL4) located on SSCX for LSY (1.64%) and PWSY (1.86%). Finally, an uncharacterized gene (ENSSSCG00000022384) or the known gene was RTL4 gene found on SSCX (1.57 and 1.44% respectively) was the highest percentage of genetic variance for NPD. This result indicated that some regions showed significant associated with more than one trait within a breed. It means that the presence of a variant may affect multiple traits. For examples, the region that strongly associated with PWL, BAL, W2CL and PWSY was rs80830052. The ALDH1A3 and LRRK1 gene have highly associated with all reproduction traits.For Large White pigs, a total of 69 putative QTL regions with in the significant regions (SNP windows explained more than 1% of genetic variance) were included 10, 11, 11, 11, 10, and 16 regions for PWL, BAL, W2CL, LSY, PWSY, and NPD respectively. When consideration of overlapped of QTL regions from all traits, it was found that a total of 22 SNPs was associated with reproduction traits. The candidate genes have the highest of genetic variance of 5 adjacent SNPs for each trait were aldehyde dehydrogenase 1 family member A3  family is the important enzyme for the aldehyde metabolism which plays an important role in embryo formation and development, cell proliferation and differentiation [ALDH1A3 is primarily responsible for oxidizing all-trans retinal to retinoic acid (RA) and active derivative of vitamin A (retinol) [ALDHLA3 knockout in mouse suppresses RA synthesis, vitamin A-deficient fetuses and cause malformations restricted to ocular and nasal regions, which is responsible for respiratory distress and death at birth [RBP7 and ALDH1A3 may enhance the growth and proliferation of muscle cells, possibly early in embryonic development, and contribute to the phenotypic expression of high feed efficiency through stimulation of the Jnk pathway [It was found that a total of 9 genes  associated with reproduction traits in Large White pigs . There antiation . ALDH1A3retinol) ,22, whicat birth . Moreove pathway . The infLRRK1 are large multidomain proteins containing kinase, GTPase and multiple protein-protein interaction domains which play a role in the regulation of bone mass in human mutation of LRRK1 [The of LRRK1  and leadRTL4 gene or mammalian retrotransposon transcripts also called Sushi-ichi-related retrotransposon homologue 11/zinc finger CCHC domain-containing 16 (SIRH11/ZCCHC16), located on SSCX. It is expressed in the brain, kidney, testis, and ovary in adult mice [SIRH11/ZCCHC16 gene leads to abnormal behaviors related to cognition which, including attention, impulsivity and working memory, possibly via the noradrenergic system [The ult mice  but undeult mice . The delc system .TRPC5) gene, plays an important role in maintaining blood pressure stability [TRPC5 also associated with the weight of the biceps brachii muscle, which related to leg weakness in pigs [Transient receptor potential (TRP) channels play fundamental roles in sensory biology. The short transient receptor potential channel 5  is a member of the lipoma HMGIC fusion partner (LHFP) gene family. It is expressed widely in all tissues, especially high in lung, thymus, skeleton muscle, colon, and ovary [LHFPL tetraspan subfamily member 1  to estimate genomic value for ssGWAS analysis which classical GWAS use only information from genotyped animals. However, the ssGWAS still weakness is cannot provide the p-value for each SNPs. Although the p-value can use the normalizing each SNP solution to a t-like statistical analysis; nonetheless, it difficult to apply to multiple SNPs and the future research may provide the level of significance.


Fillet yield (FY) and harvest weight (HW) are economically important traits in Nile tilapia production. Genetic improvement of these traits, especially for FY, are lacking, due to the absence of efficient methods to measure the traits without sacrificing fish and the use of information from relatives to selection. However, genomic information could be used by genomic selection to improve traits that are difficult to measure directly in selection candidates, as in the case of FY. The objectives of this study were: (i) to perform genome-wide association studies (GWAS) to dissect the genetic architecture of FY and HW, (ii) to evaluate the accuracy of genotype imputation and (iii) to assess the accuracy of genomic selection using true and imputed low-density (LD) single nucleotide polymorphism (SNP) panels to determine a cost-effective strategy for practical implementation of genomic information in tilapia breeding programs. The data set consisted of 5,866 phenotyped animals and 1,238 genotyped animals  using a 50K SNP panel. The GWAS were performed using all genotyped and phenotyped animals. The genotyped imputation was performed from LD panels  to high-density panel (HD), using information from parents and 20% of offspring in the reference set and the remaining 80% in the validation set. In addition, we tested the accuracy of genomic selection using true and imputed genotypes comparing the accuracy obtained from pedigree-based best linear unbiased prediction (PBLUP) and genomic predictions. The results from GWAS supports evidence of the polygenic nature of FY and HW. The accuracy of imputation ranged from 0.90 to 0.98 for LD0.5K and LD3K, respectively. The accuracy of genomic prediction outperformed the estimated breeding value from PBLUP. The use of imputation for genomic selection resulted in an increased relative accuracy independent of the trait and LD panel analyzed. The present results suggest that genotype imputation could be a cost-effective strategy for genomic selection in Nile tilapia breeding programs. Investment in selective breeding programs generates economic return because genetic selection is aimed at improving productivity of important economic traits that result in permanent, cumulative and sustainable changes in a farm’s population over generations of selection . In a sie.g., genetic gains for body weight ranged from 7 to 20% per generation  are considered key traits in the breeding goal for Nile tilapia genetic improvement  using a dense SNP panel and selection candidates using a LD SNP panel, and then imputing (predicting) missing genotypes from the lower to the HD SNP panel  to perform a genome-wide association study to dissect the genetic architecture and identify molecular markers for growth and fillet yield; (ii) to evaluate the accuracy of genotype imputation as a cost-effective strategy for genotyping, and (iii) to assess the accuracy of genomic selection for growth and fillet yield using true and imputed SNP genotypes in farmed Nile tilapia. To our knowledge, this is the first study evaluating the incorporation of true and imputed dense genotypes for the implementation of genomic predictions in farmed Nile tilapia.The Nile tilapia population used in the current study belong to a breeding nucleus established by Aquacorporación Internacional group (GACI) in Costa Rica. The origin of the population is described in detail by −6), Minor Allele Frequency (MAF < 0.05) and genotyping call-rate for SNP and samples < 0.95.Genomic DNA was extracted from fin clip samples from 108 parents (45 sires and 63 dams) and 1,364 offspring from year-class 2017. Samples were then genotyped using a 50K SNP Illumina BeadChip, which is described in detail by We performed the GWAS to dissect the genetic architecture and to identify regions of the Nile tilapia genome containing SNPs with important effects on FY and HW. We used the weighted single step genomic best linear unbiased prediction (wssGBLUP) method (−1 is replaced by matrix H−1 (The wssGBLUP is similar to the pedigree-based BLUP (PBLUP) method except for the use of a combined genomic and pedigree relationship. The kinship matrix ABasic Local Alignment Search Tool) of the SNP probes against the last version of the Oreochromis niloticus reference genome , 1,000 (LD1K) and 3,000 (LD3K). The SNPs from the LD panels were selected using the option–indep-pairwise of Plink v1.9 software  as a reference set (HD panel) and 80% of offspring (n = 904) were used as the validation set (LD panel). The assignment of the offspring to the reference and validation sets was random, and five replicates were used each time. In addition, we used pedigree information available for all individuals for imputation , consisting of eight generations of selection. Imputation of genotypes was performed using the FImpute v2.2 software  and the true 32K SNPs . Briefly, all genotyped animals (n = 904) with phenotypes were randomly divided into five exclusive training sets  which were used to estimate the SNP effects; the remaining animals were used as validation sets , for which their phenotypes were masked and their performance was predicted based on the marker effects. This fivefold cross validation was replicated five times for each SNP panel density and the results are presented as a mean for all replications.We used two scenarios to assess prediction accuracies using a fivefold cross validation. The first scenario used the true genotyped LD panels  and represented the same −1 instead of H−1 in ssGBLUP.We used the BLUPF90 family of programs  in the vPrediction accuracies were calculated in the validation sets using the following equation:The total additive genetic variance  and the other half as selection candidates (SC). All animals descend from a fixed number of parents  (Table S2). We evaluated four different scenarios: scenario A: all animals  were genotyped using a HD panel; and, scenario B, C and D, in which all parents and 20% of RP were genotyped using a HD SNP panel and the remaining animals were genotyped using a 3K, 1K or 0.5K LD panel, respectively. The genotyping cost was calculated assuming prices of U$50, $25, $20 and $10 per sample for HD (50K), 3K, 1K, and 0.5K, respectively. In addition, for scenario A, we assumed a price reduction of 10% in each increase of 1,000 animals genotyped using a HD panel, which resulted in prices ranging from $50 to $26.60 per sample for genotyping 4,150 and 10,150 animals, respectively. The results of the genotyping cost evaluation (Table S3) are presented as terms of cost reduction (%) comparing scenario A to other scenarios.https://figshare.com/s/9b265a22b7e138c5a839). Furthermore, Supplementary Figures  and Supplementary Tables (Table S1-S3) are available at Figshare: https://figshare.com/s/9b265a22b7e138c5a839.All raw phenotypic and genotypic data used in the current study can be found at Figshare public repository  and the average HW was 878 g (SD = 254 g) for phenotyped fish .Out of the initial 1,364 individuals and 43,272 SNPs which were effectively genotyped, a total of 1,130 animals and 32,306 SNPs (32K) passed in the quality control. The MAF < 0.05 parameter excluded the highest number of SNPs , whereas HWE and genotyped call-rate excluded 1,982 and 4,205 SNPs, respectively.For both FY and HW the additive genetic variance and heritability were slightly higher when using genomic information compared to the pedigree-based method. For instance, heritability values using ssGBLUP were 0.21 and 0.36 for FY and HW, respectively. For PBLUP heritability for FY and HW was estimated to be 0.21 to 0.31, respectively . AdditioManhattan plots for the proportion of genetic variance explained by each 20-SNP window for FY and HW are shown in UTP6), Ras-related protein (Rab31) and Follistatin-related protein (FLRG or FSTL3), located in chromosome 06, 18 and 23, respectively. For HW we identified the genes Natriuretic Peptide Receptor 1 (NPR1) and Exostosin Like Glycosyltransferase 3 (EXTL3) located in chromosome 22 and 15, respectively.The full list of genes located within the top five 20-SNPs windows associated with FW and HW is shown in Table S1. Some candidate genes found within the top five most important windows have been suggested to be involved with growth-related traits in previous studies. For FY we identified genes U3 small nucleolar RNA-associated protein 6 homolog . Interestingly, due to value adjustment when increasing the number of samples to genotype, for scenario B and a number of RP + SC equal to 10,150, it is more cost efficient to genotype all animals using a HD panel than genotyping a proportion of animals using LD panel.We found moderate heritability values for FY and HW which agrees with previous estimates calculated using pedigree-based methods  gene .Although, it is out of the scope of the present study to discuss in detail the putative genes involved in FY and HW, we found it worthy to mention some of the most biologically relevant candidates that may be worthy of functional validation. For instance, the FR) gene , an impoactivity . Rebhan NPR1 is an essential regulator of systemic acquired resistance, conferred immunity to broad-spectrum of pathogens . Our results indicate that genomic prediction accuracies using imputed genotypes were always higher than those obtained using true LD genotypes and equal or slightly lower than using true 32K genotypes . For HW The extra costs to genotyping animals represents a key limiting factor for the practical implementation of genomic selection in freshwater fish species. One of the main objectives of this study was to test genotyping imputation as an alternative to reduce the costs of genotyping for genomic selection in Nile tilapia breeding programs. The cost to genotype depends on the density of the SNP panel, genotyping technology used and number of samples. In general, the price ranges from USD $5 to USD $75 for low- (0.5K) and high-density panels (50K), respectively. We did a cost evaluation, that resulted in a reduction of cost ranging from 45 to 69% , assuming different sizes of a breeding population and a strategy aimed to genotyping a proportion of animals using a HD panel and the remaining animals using a 0.5K panel. These values represent the direct savings on genotyping fewer animals using a HD panel and support the results suggested by The GWAS indicated a polygenic architecture for fillet yield and harvest weight, with some markers explaining a small proportion of genetic variance; indicating that the implementation of marker assisted selection could not be successfully applied for these traits in the present Nile tilapia population. In contrast, the use of genomic selection could increase the response to selection and improve genetic progress. The use of genotype imputation can reduce genotyping costs and allow the implementation of genomic selection in Nile tilapia breeding programs.


Indica accessions from 37 countries for textural attributes, which includes adhesiveness (ADH), hardness (HRD), springiness (SPR), and cohesiveness (COH) as well as amylose content (AC). A set of 147,692 high quality SNPs resulting from genotyping data of 700K high Density Rice Array (HDRA) derived from the Indica diversity panels of 218 lines were retained for marker-trait associations of textural attributes using single-locus (SL) genome wide association studies (GWAS) which resulted in identifying hotspot on chromosome 6 for AC and ADH attributes. Four independent multi-locus approaches (ML-GWAS) including FASTmrEMMA, pLARmEB, mrMLM, and ISIS_EM-BLASSO were implemented to dissect additional loci of major/minor effects influencing the rice texture and to overcome limitations of SL-based GWAS approach. In total 224 significant quantitative trait nucleotide (QTNs) were identified using ML-GWAS, of which 97 were validated with at least two out of the four multi-locus methods. The GWAS results were in accordance with the very significant negative correlation (r = −0.83) observed between AC and ADH, and the significant correlation exhibited by AC (r < 0.4) with HRD, SPR, and COH. The novel haplotypes and putative candidate genes influencing textural properties beyond AC will be a useful resource for deployment into the marker assisted program to capture consumer preferences influencing rice texture and palatability.The textural attributes of cooked rice determine palatability and consumer acceptance. Henceforth, understanding the underlying genetic basis is pivotal for the genetic improvement of preferred textural attributes in breeding programs. We characterized diverse set of 236  Texture is an important attribute of consumer's acceptance criteria and thus quality evaluation of texture is a critical step in breeding. High range of phenotypic textural variability exist in rice which are described as sticky, soft, dry, firm, and hard. These textural attributes appeals to be of interest to different segment of rice consumers across the globe , gel consistency (GC), and gelatinization temperature (GT)  has become the state-of-art method to link genotypic variation to corresponding differences in phenotype, with the aim of dissecting the genetic basis of complex trait in various crops   and brown rice was milled through Grainman 60-230-60-2AT instrument,  to produce white milled rice samples. A portion from each sample corresponding to 100 unbroken grains was used for texture profile analyses (TPA) and the rest of samples were ground to a fine powder using Cyclone Sample Mill 3010-030, Udy Corp, USA. The resulted homogenized rice flour was further used for estimating amylose content (AC).2). The absorbance of the sample's containing K-I2 complex was estimated at 620 nm wavelength and subsequently, AC was quantified with standards by plotted against the standard curve.AC determination was based on iodine colorimetric reaction using method of ISO 6647 , ADH (Negative force area under the first bite), COH (A2/A1), and SPR (T2/T1) were obtained and processed using Exponent Lite Software (version 3.0.5.0). ADH was recorded as negative numbers to indicate the direction of the probe's movement. Hence, adhesiveness values were reported in absolute values. Texture experiments were conducted in triplicate with three biological replications. For further details, refer Supplementary Note Twenty-five whole polished rice grains per sample of accession were washed thrice, soaked for 30 min in Milli-Q water 1 mL) for 15 min in a test tube. The samples were heated to boiling point for 20 min and kept at 50°C prior to avoid retrogradation. Textural parameters of the cooked rice  were analyzed according to the method described by Lyon et al.  with mod mL for 1A 700K high Density Rice Array (HDRA) SNP genotyping set developed by an Affymetrix Custom Gene Chip Array from a SNP discovery dataset  = 6.47; P = 0.05/147692] was used as a threshold p-value. Nevertheless, since few loci have surpassed this threshold, significant SNPs above suggestive line at p-value of utmost 1e-5 were extracted as set of significant SNPs identified from SL-GWAS approach. Linkage Disequilibrium (LD)-plot and beta-effects of SNPs were plotted using combination of Haploview  size of 20 SNPs was used as parameter to run the multi-locus association using algorithms mrMLM and FASTmrMLM. A threshold criterion of LOD of 3 and above was used to get the final set of significant SNPs. In house perl script was used to identify the overlapping genes with significant LOD score SNPs. SNPEff  was implemented to identify the boxplot with significant phenotypic value at the significant level of P ≤ 0.05.Four different methods namely FASTmrEMMA , which supports the fact that low amylose rice accessions tends to be stickier . Moreover, we did not observe significant correlation between AC with other textural attributes viz. COH and SPR. Instead, we observed a positive correlation within 3 textural attributes such as COH, HRD, and SPR  ≥ 5] with texture-related traits. Among them the prominent peak on chromosome 6 associated with highly heritable traits AC (h2 = 0.86) and ADH (h2 = 0.86)  = 6.39. HRD is another highly heritable trait (h2 = 0.82), a significant region  was detected on chromosome 8  GWAS was performed using EMMAx  model approach was followed utilizing four independent methods namely FASTmrEMMA  gene  and SPR . Additional SNPs associated with ADH was identified on chromosome 1 (LOC_Os01g17402) with a highly significant SNP of splice variant in Cyclin B1-3 and on chromosome 5 , identified by all four multi-locus methods  distinguished different AC and ADH classes of phenotypes , chromosome 4 , chromosome 5 , chromosome 7 , chromosome 9 , chromosome 10 , and chromosome 11  , which is linked negatively with ADH trait with a beta value of −0.42 identified using SL-GWAS and confirmed using two independent methods of ML-GWAS with LOD scores of 3.2 and 6.4. In addition, a total 11 QTNs were identified for ADH but not linked to AC, which are identified from at least two independent methods of ML-GWAS  Table . Among t0) Table .2 value. The major effect QTN affecting AC namely snp_06_19437296 has a non-synonymous nucleotide change (C>T) in the candidate gene LOC_Os06g33360 lead to amino acid change  , many of them were turned out to be minor effect QTNs with low rIndica germplasm with intermediate hardness and its alternative haplotype TCCG representing lines depicted higher hardness value . Likewise, additional QTNs representing missense mutations such as snp_05_12361761 leading to amino acid change  in candidate LOC_Os05g21010 and snp_12_1204320 (Gly>Asp) was detected in LOC_Os12g03160, associated with HRD trait  validated using ML-GWAS approach Table . Additior2 12.05) and SPR (LOD 4.95 with r2 2.88).For COH, we observed significant snp_10_13693521 located downstream of LOC_Os10g26370 validated through ISIS_EM-BLASSO, FASTmrEMMA multi-locus GWAS methods with LOD score value of 3.7 and 5.7 (Table Kyota Encyclopedia of Genes and Genomes (KEGG) analysis was conducted to identify the functional categories across all the QTNs identified. A total of 40 candidate genes with functional information were mined, of which 17 were involved in genetic information processing, whereas the rest of the candidate genes were involved in other cellular and metabolic processes Figure .Texture of cooked rice plays a pivotal role in consumer acceptability; henceforth researchers continuously develop strategies to predict texture of cooked rice. AC is widely explored to capture the diversity of rice quality . Previous studies reported that low AC value with higher amylopectin content increase the stickiness in cooked rice , reflecting the consistency across both seasons . Thus the defined genetic region will be an added value. Furthermore, notably AC and ADH showed high degree of correlation and overlapping genetic region influenced by both traits. Likewise, HRD (h2 = 0.82) and COH (h2 = 0.68), showed higher heritable values from the RDP than that observed in previous studies dealing with biparental mapping populations    . An important hot spot QTLs on chromosome 6 where QTNs for ADH co-localized with QTNs for AC were identified. Furthermore, a fine mapped genetic region on chromosome 8 affecting HRD was identified. The use of different models increased the number of variations captured across the diverse germplasm lines. Multi-locus model using different methods could overcome the limitations of single-locus analysis. Furthermore, this integrative approach has enabled the identification of novel small and large effect putative potential candidate genes and diagnostic haplotypes, which subsequently on validation, potentially be deployed in breeding to improve rice texture.In summary, our findings addressed the underlying genetic basis of rice texture attributes. We found considerable phenotypic variations in texture attributes  among the 218 NS conceptualized the research work, designed and supervised all experiments, wrote and edited the manuscript. GM conducted GWAS and haplotype analyses, generated figures, and created an in-house functional annotation pipeline. SB and EM interpreted the data, prepared excel sheets, and developed the first draft of the manuscript. CD and RC conducted texture profile experiments, developed TPA methodology and processed the raw data. CL conducted texture profile experiments from different seasons for the contrasting haplotype lines.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.


Bemisia tabaci is a major leaf feeding insect pest to pepper (Capsicum annuum), causing serious damage to pepper growth and yield. It is particularly important to study the mechanism of pepper resistance to B. tabaci, and to breed and promote the varieties of pepper resistant to B. tabaci. However, very limited molecular mechanism is available about how plants perceive and defend themselves from the destructive pest. Proteome technologies have provided an idea method for studying plant physiological processes in response to B. tabaci.The B. tabaci feeding for 48 h to explore the defense mechanisms of pepper resistance to B. tabaci. The proteomic differences between both genotypes were compared using isobaric tag for relative and absolute quantification (iTRAQ). The quantitative data were validated by parallel reaction monitoring (PRM). The results showed that 37 differential abundance proteins (DAPs) were identified in the RG (resistant genotype), while 17 DAPs were identified in the SG (susceptible genotype) at 48 h after B. tabaci feeding. 77 DAPs were identified when comparing RG with SG without feeding. The DAP functions were determined for the classification of the pathways, mainly involved in redox regulation, stress response, protein metabolism, lipid metabolism and carbon metabolism. Some candidate DAPs are closely related to B. tabaci resistance such as annexin D4-like (ANN4), calreticulin-3 (CRT3), heme-binding protein 2-like (HBP1), acidic endochitinase pcht28-like (PR3) and lipoxygenase 2 (LOX2).Here, a highly resistant genotype and a highly susceptible genotype were exposed to B. tabaci interaction, provides novel insights into the molecular mechanism underlying the response of plant to B. tabaci, and identifies some candidate proteins against B. tabaci attack.Taken together, this study indicates complex resistance-related events in The online version of this article (10.1186/s12870-019-1849-0) contains supplementary material, which is available to authorized users. Bemisia tabaci (Gennadius) is one of the most widely distributed agricultural pests that may be harmful to many arable and horticultural crops such as Solanaceae, Cucurbitaceae and Cruciferae. Pepper (Capsicum annuum), an important vegetable type in China, is one of the most serious hosts of B. tabaci. B. tabaci complex contains more than 24 morphologically indistinguishable biotypes [B. tabaci contains at least 34 putative species [B. tabaci biotype B has been introduced into at least 54 countries from its origin in the Middle East-Asia Minor region and become a world-wide invasive and destructive whitefly species. In China, B. tabaci was first recorded in the late 1940s, but was not recorded as the major agricultural pest until the introduction of B. tabaci biotype B in the mid-1990s [biotypes . PhylogeB. tabaci not only damages plants by sucking vital sap from the phloem tissue but also causes indirect damage by vectoring many kinds of viruses and by promoting the growth of saprophytic fungi on the leaves [B. tabaci are leaf deformation and honeydew secretion. The control of B. tabaci has been relied heavily on the synthetic insecticides, but the pesticide resistance has been developed in many parts of the world. For example, B. tabaci field populations were found highly resistant to imidacloprid and thiamethoxam, both in Israel, Spain, Crete, and China [B. tabaci is to study the resistant mechanism of host-plants, explore resistant genes, and breed for durable host-plant resistance [B. tabaci, and to breed and promote the varieties of pepper resistant to B. tabaci.e leaves , 5. The nd China . Plant–ind China . A promisistance , 7. TherB. tabaci induces salicylic acid (SA) defenses and suppresses effectual jasmonic acid (JA) defenses in Arabidopsis [B. tabaci causes JA levels increase initially and decline within days, whereas the expression of SA-regulated genes was gradually increased [B. tabaci is known to induce specific genes such as WFI1 in tomato and SLW1 in squash [B. tabaci biotype B feeding [B. tabaci and showed that MPK-WRKY-JA and ethylene (ET) pathways might regulate cotton defenses to B. tabaci [Similar to pathogen-plant interaction, bidopsis . In tomancreased , 10 Feedn squash . Tomato  feeding . Recentl. tabaci .B. tabaci remain poorly defined. So far, most of these studies focus on nucleic acid level, but fewer have studied the actual protein. Proteome technologies provide an idea method for studying plant physiological processes. Recently, the proteome changes of Arabidopsis thaliana leaves infested by B. tabaci were reported using two-dimensional electrophoresis and mass spectrometry [B. tabaci interaction, two genotypes were identified exhibiting different B. tabaci susceptibilities, one that was a highly resistant genotype (termed RG) and another that was a highly susceptible genotype (termed SG), and the proteomic differences between both genotypes after B. tabaci infestation for 48 h were compared using isobaric tag for relative and absolute quantification (iTRAQ). The results may contribute to our understanding of protein response and alteration and provide insights into the molecular mechanisms involved in response to B. tabaci infestation in plants.Despite these advances, the comprehensive molecular mechanisms underlying plant resistant to trometry . Ibrahimtrometry  showed tB. tabaci infestation. The resistant characteristics of both genotypes were investigated after B. tabaci infestation  or susceptibility (SG) to on Figs.  and 2. IB. tabaci, the proteomic profiling analysis at 48 h after B. tabaci treatment of resistant and susceptible genotypes was performed using iTRAQ. The average of the spectral identification rates of the secondary mass spectra produced by the three replicates reached 34% or more. For example, in the replicate one, a total of 397,554 spectra were obtained, in which 13,5885 spectra were matched to the known database and the spectral identification rate is 31.2%. A total of 20,102 peptides and 2756 proteins (at least two unique peptides with high confidence) were identified by iTRAQ analysis against the Uniprot database Capsicum annuum   terms by GO analysis  and RG control (RC), between SG inoculated with B. tabaci (SB) and SG control (SC), and between RC and SC. A total of 115 DAPs was identified from “RB-RC”, “SB-SC” and “RC-SC”. A venn diagram including the total 115 proteins was generated ". These specific B. tabaci responsive proteins might be important factor for resistance to B. tabaci. In the venn diagram, 4 DAPs were shared between two genotypes after B. tabaci attack, including 60S ribosomal protein L4, xyloglucan endotransglucosylase/hydrolase, acidic endochitinase pcht28-like (PR3) and monodehydroascorbate reductase 5 (MDHAR5).To examine DAPs in response to ted Fig. . The detB. tabaci infestation  annotations, the 115 differential abundance proteins (DAPs) were grouped into three major enrichment categories using Blast2GO . The GO results showed that 101 DAPs (90.99%) had been annotated into 37 functional groups, including 15 biological processes, 14 cellular components and 8 molecular function . All of the proteins in KEGG categories were shown in Additional file B. tabaci, the DAPs of “RB-RC”, “SB-SC” and “RC-SC” were analyzed in Additional file B. tabaci damage. A master table  and unique peptide(s). Therefore, the PRM detection was performed for the 8 protein only . In general, the trends in the change of the results measured by PRM and iTRAQ are basically consistent  are shown in Fig. CAT and HBP1). The low correlation coefficient of the proteome and transcriptome data has been reported previously [To further confirm the iTRAQ data, we monitored the expression patterns of the corresponding genes encoding proteins using qRT-PCR. The expression patterns of the six genes  was used to study B. tabaci resistant mechanisms using two different resistant materials.n et al.  showed tP-value < 0.05 in the average value of three replicates was classified as a physiologically significant change. A total 398 DAPs were identified in the three classes of differentially accumulated proteins of “RB-RC” (130), “SB-SC” (139) and “RC-SC” (251). To further search for key protein components or pathways for B. tabaci resistance response, a 1.50-fold or 0.67-fold change threshold with a P-value < 0.05 in at least two experiments and in the average value of three replicates were classified as a significant change. A total 115 DAPs were identified in the three classes of differentially accumulated proteins of “RB-RC” (37), “SB-SC” (17) and “RC-SC” (77). More DAPs were present in the RB than SB. The results are supported by previous studies reporting that B. tabaci infestations drive more differentially expressed genes (DEGs) in the strong resistance cultivar than sensitivity [B. tabaci, in which down-regulation > up-regulation of DEGs. The inconsistence in proteins and genes may explain the discrepancy in the translational and post-translational regulations in pepper defense against B. tabaci infection. In the present study, the function of DAP from the second method was further analyzed and discussed in the following sections. GO enrichment and KEGG pathway analysis indicated that proteins involved in processes such as oxidative stress regulation, stimulus response, linoleic acid metabolism and photosynthesis might be involved in the host plant resistance to B. tabaci infestation.Two methods were utilized to fully assess the iTRAQ differential expression data  are generated in plant tissues in response to different stresses including the interaction with herbivores . ROS areck Table .2 and H2O2, maintaining adequately low oxyradical levels. It is an important protective enzyme in plants’ cell defense systems, and is closely related to the plants’ resistances [2O2 to water and oxygen and CAT are characterized by the oxidation of various organic compounds, which have been shown to be involved in insect response in rice [B. tabaci. MDHAR, DAR and GR are the key enzyme in the ascorbate acid- glutathione (AsA-GSH) cycle [2O2 into water with AsA serving as an electronic donor. DHAR utilizes the electrons provided by GSH to reduce DHA, while DHA is previously produced from MDHA. Simultaneously, GSH is oxidized into glutathione disulfide (GSSG) by DHAR and GSSG is then reduced into GSH, catalyzed by GR. Greenbug feeding on resistant sorghum induced the expression of peroxidase and glutathione-S-transferase genes, but both up- and down-regulated different CAT genes [SOD is the first enzyme with free radicals as the substrate, catalyzing the dismutation of superoxide radicals to Oistances . In this in rice . PODs ar in rice . In thisH) cycle . AsA-GSHH) cycle . In thisAT genes .B. tabaci, which may function to scavenge excessive ROS that result from B. tabaci feeding [B. tabaci injury. Thompson and Goggin [B. tabaci triggered a differential modulation of antioxidant proteins in both genotypes. For example, B. tabaci infestation up-regulated the expression of CAT and POD, but down-regulated the expression of SOD and MDAHR in the resistant pepper.Arabidopsis MDARP and MDAR3 were also up-regulated by  feeding . Here, td Goggin  showed tB. tabaci in the SC  DC3000, through promoting stomata closure leading to the development of resistance to this bacterial pathogen [B. tabaci attack signals.Pathogenesis-related proteins (PR proteins) and defense-related proteins are specifically induced under stress conditions . Three bpathogen . PR protpathogen . These rB. tabaci were feeding on the pepper, of which the expression was significantly increased in the RB than that in the SB. ANN4 encodes Ca2+-regulated membrane-binding proteins modulating cytosolic calcium signatures. Huh et al. [AnnAt4 and AnnAt1 interact with each other in a Ca2+-dependent manner and function to regulate responses to drought and salt stress. Recently, ANN4-mediated cytosolic calcium signaling was reported to be involved in MYB30-regulating oxidative and heat stress responses in Arabidopsis [B. tabaci damage. CRT has been documented to be a Ca2+-binding molecular chaperone that facilitates the folding of newly synthesized glycoproteins and regulates the Ca2+ homeostasis in the endoplasmic reticulum (ER) lumen [CRT mutant atcrt3 is more sensitive Pseudomonas syringae pv tomato DC3000 (Pst DC3000) and water stress [B. tabaci invasion.Other stress-related proteins like annexin D4-like  and calreticulin-3  were increased in the leaves when the h et al.  providedbidopsis . In our R) lumen . It has R) lumen  Arabidopr stress , 41. TheWhen plants are attacked by insects, they produce many defense-related proteins, many of which are synthesized and then secreted to their various destinations within the cell . In termAtPDI6 was more tolerant to high concentrations of 2,4,6-Trichlorophenol (TCP) implying that AtPDI6 can be used for TCP detoxification by the way of overexpression in plants. In our experiments, two PDIs (PDIL6 A0A1U8GVQ2) and (PDIL1 A0A1U8GX36) showed higher abundance in the RC than in the SC without B. tabaci invasion. The level of PDIL1 increased more than 2-fold in the SB-SC but not in the RB-RC after B. tabaci invasion. All results suggest that the resistant peppers have strong basal defense through regulating ribosomal proteins and protein disulfide isomerases.Protein disulfide isomerase (PDI) is a member of the thioredoxin superfamily, and is involved in the progression and maturation of secretory proteins in the ER , 43. AtPB. tabaci attack in the RB but maintained no change in the SB . Arabidopsis mutants with antisense suppression of PLDα expression decreased PA and JA production. PLD has been implicated in JA/oxylipin formation in plant interaction with Botrytis cinere and virulent Pst DC3000 [B. tabaci attack, though the expression of PLDrp1 and PLDα1 were not activated by B. tabaci.Lipid-mediated signal processes are crucial for cell survival, growth and differentiation and for plant responses to biotic and abiotic cues such as salinity, pests, and pathogens . Signalit DC3000 . The funt DC3000 . Our resOsLOX plants increased endogenous levels of JA, showing reduced plant mortality when infested with the phloem-feeding brown plant hopper [Spodoptera exigua larvae [LOX2.2 overexpressing lines showed up-regulation of some other JA-regulated genes with lower aphid number, suggesting that LOX2.2 plays a role in the activation of JA-mediated responses and indicates the involvement of LOX2.2 in basic defense responses [B. tabaci in the SB, RC has a higher level of LOX2 compared with SC under normal conditions. The B. tabaci-regulated LOX2 expression was further confirmed by PRM and RT-qPCR  is a key enzyme of plant carbon metabolism due to its amphibiotic role in both the Calvin–Benson–Bassham (CBB) cycle and the oxidative pentose phosphate pathway [In addition to the herbivore-induced production of physical and chemical defenses, numerous changes in plant primary metabolism occur in response to insect herbivores . Researc pathway . InteresB. tabaci was supported by photosynthesis-related protein expressions using iTRAQ method. Previously, wheat and barley resistance to Russian wheat aphids has been associated with increased expression of photosynthesis-related genes after B. tabaci attack [B. tabaci attack, suggesting that photosynthesis and carbon metabolism might be involved in the resistance of pepper to B. tabaci.There are two conflicting views on how plants should alter photosynthesis, and thereby carbon fixation, to optimize defense. Photosynthetic activity might be promoted or be reduced by herbivory . For exai attack , 66. Phoi attack . The resMyzus persicae on Arabidopsis thaliana AtSEOR1 and AtSEOR2 mutants perform worse when compared to aphids on control plants, indicated by reduced reproduction and shortened reproduction period. However, Anstead et al. [AtSEOR1 mutant lines indicating the possible involvement of this gene in plant defense mechanism. Besides, a possible role of AtSEOR1-mediated JA and cis-12-oxo-phytodienoic acid metabolisms was observed in plant defense against phytoplasmas. PGK2, a nucleus-encoded chloroplast phosphoglycerate kinase, plays a central role in cell metabolism. PGK protein is required for efficient watermelon mosaic virus  infection in the Arabidopsis [Some other proteins were also found involved in plant defense. For example, SEOR1 is a phloem filament protein involved in plant defense . Aphids d et al.  concluded et al.  showed tbidopsis .B. tabaci attack at 48 h was first explored by iTRAQ technique. The proteomic data presented here will help us to further understand the molecular mechanisms of plant resistance to B. tabaci. It is suggested plant express more redox regulation-related proteins to deal with the oxidative damage caused by B. tabaci to improve plant tolerance. The PR3, Hsp70–1, HSP101 and JA pathway were more active in the resistant genotype, which might contribute to B. tabaci resistance in pepper. Meantime, our results support the view of increased photosynthesis (carbon metabolism) is involved in the resistant of pepper to B. tabaci. Besides, ANN4, CRT3, CEQORH and AIMI were specifically found here involved in B. tabaci–pepper interaction processes. In the future, studies on the function of specific proteins found in this study will be helpful to explore the mechanisms of host resistance to B. tabaci attack.In the present study, an overview of the protein expression profile in the pepper resistant genotype and sensitive genotype in response to Capsicum annuum) genotype xinsujiao No.15  and the genotype sujiao No.15  were acquired from the Vegetable Institute, Jiangsu Academy of Agricultural Sciences, Nanjing, China. Seeds were grown in a pest-free growth chamber at Yangzhou University, China. The plants were irrigated and fertilized according to horticultural practices but without spraying herbicides. A virus free colony of B. tabaci biotype B used for infestation was maintained on tomato in greenhouse of insectary.In this study, the pepper  in every seedling were counted. The experiments were performed in five biological replicates and repeated twice to confirm the results.For egg hatchability test, the 7th leaf stage healthy seedlings (cv. RG and SG) were selected. Each plant was covered with 60-mesh gauze and infested with five pairs of newly emerged adult B. tabaci infestation for 48 h, the infected leaves were cut and frozen immediately in liquid nitrogen, and store at − 70 °C refrigerator for further use. All experiments were performed in three biological replicates.The tested samples (cv. RG and SG) were grown to the 7th leaf stage. 50 adult insects were collected and released onto the 5th leaf and closed with a small ventilate pocket  were individually ground to powder in liquid nitrogen, and incubated in lysis buffer  containing 1 mM PMSF and 2 mM EDTA  for 5 min, then 10 mM DTT  were add to the sample. The samples were sonicated for 15 min and centrifuged at 4 °C, 13,000×g for 20 min. The supernatant was transferred to a new tube and mixed with 4 volumes of precooled alkylation at − 20 °C overnight. After centrifugation, the protein pellets were air-dried and resuspended in 8 M urea/100 mM TEAB (pH 8.0). Protein samples were reduced with 10 mM DTT at 56 °C for 30 min, and alkylated with 50 mM iodoacetamide (IAM) for 30 min in the dark.The design of proteomic study is shown in Fig. B. tabaci treatment of two pepper genotypes were labeled respectively as Additional file After diluted 5 times with 100 mM TEAB, 100 μg of proteins from each sample were used for tryptic digestion. Trypsin  was added at an enzyme-protein ratio of 1:30 (w/w), and digested at 37 °C overnight. The digested peptides were acidified using equal volume of 0.1% formic acid (FA) solution and desalted with Strata-X C18 column. The acidified enzymatic hydrolysate was injected to column for three times, then washed the column with solvent A (0.1% FA in 5% ACN) twice, the peptides were eluted 1 ml with solvent B (0.1% FA in 80% ACN). Finally, peptides were lyophilized and reconstituted in 20 ul 0.5 M TEAB for peptides labeling with iTRAQ 4-plex kits  according manufacturer’s protocol: Control and − 1 onto a C18 analytical column  over a 90 min gradient. The two mobile phases were buffer A (2% acetonitrile/0.1% formic acid/98% H2O) and buffer B (98% acetonitrile/0.1% formic acid/2% H2O). For IDA (information dependent acquisition), survey scans were acquired in 250 ms and 30 production scans were collected in 100 ms per scan. MS1 spectra were collected in the range 350–1500 m·z− 1, and MS2 spectra were collected in the range of 100–1500 m·z− 1. Precursor ions were excluded from reselection for 15 s.Each fraction was dissolved in 30 μl of 2% acetonitrile and analyzed using Triple TOF 5600+ mass spectrometer coupled with the Eksigent nanoLC System . 5 ml of peptide sample was loaded onto a C18 trap column , and eluted at 300 nL·minC. annuum protein database . Biological modifications were selected as ID focus. Bias Correction and Background Correction was checked for protein quantification and normalization. All identified proteins had an Unused Protscore of > 1.3 (which corresponds to proteins identified with > 95% confidence), as calculated by the software and a global false discovery rate (FDR) of ≤1% determined at the protein level by the PSPEP algorithm. To be considered as being differentially expressed, proteins were required to have a p value of ≤0.05, as calculated by the software. For three biological replicates, the ratio of median expression between Case and Control was defined as fold changes. Statistical significance of the difference in the levels of expression of proteins between samples to be compared was determined by student’s t-test (two-tailed and unpaired) to correct for multiple hypothesis testing. For protein abundance ratios measured using iTRAQ, a 1.50-fold or 0.67-fold change threshold in the average value of three replicates with P-value < 0.05 in at least two experiments were classified as a significant change.Protein identification and quantification were performed by a search against the UniProt https://github.com/wegnerce/taxomias). The biological and functional properties of all the identified proteins were mapped with Gene Ontology (GO) Terms (http://geneontology.org/). The functional classification of the proteins using Clusters of Orthologous Groups of Proteins System (http://www.ncbi.nlm.nih.gov/COG/) was performed. All of the identified proteins were mapped to a pathway enrichment in the Kyoto Encyclopedia of Genes and Genomes (KEGG).In this study, the functional annotations of all differential abundance proteins (DAPs) were performed by using a localized Blast2go v2.6 against program against the NCBInr plant database . Protein extraction and tryptic digestion were performed in the same way as in the iTRAQ experiment. MS data acquisition was first performed in DDA mode to obtain MS/MS spectra for the 40 most abundant precursor ions following each survey MS1 scan in each cycle. Protein Pilot software was used to identify proteins, and the database searching results were brought into Skyline software for spectra library building. Target proteins for PRM validation were imported to the software Skyline, and the peptides for protein quantification were selected according to the ion signals in spectra library. A list of associated peptides containing m/z values and retention times was exported from Skyline, and imported to MS control software Analyst for PRM acquisition method construction. PRM method was run against the biological samples of interest, evaluated and refined to develop the highest quality assay. Data collection of each sample was performed using the final PRM acquisition method on the qTOF mass spectrometer, where each precursor ion was selected by the quadrupole, fragmented, and then all fragment ions were quantified in the TOF mass analyzer. To eliminate protein carryover, a “blank” needed to be run between adjacent samples for column washing. Data processing was done in Skyline, and the quantification results were manually inspected for each peptide of the targeted proteins.Actin-97 and phosphoenolpyruvate carboxylase 2 were set as internal reference gene. The full-length cDNA sequence of the corresponding gene was obtained from NCBI based on the interested protein information, and primers  Co., Ltd), and reverse transcribed cDNA from equal amounts (1.0 μg) of total RNA using the ReverTra Ace qPCR RT Kit (Toyobo life science Co.). The reactions were performed using SuperReal PreMix Plus kit (Toyobo life science Co.) according to the protocol. The thermal cycler was performed as follows: 1 cycle of 95 °C 1 min; followed by 40 cycles of 95 °C, 15 s and 60 °C, 30 s. Relative gene expression was calculated by the 2-ΔΔCT method. The experiment was repeated three times.To investigate the correlation between transcript and protein level and confirm response proteins in the iTRAQ and PRM data set, 6 different expressional proteins were selected for further qRT-PCR analysis. Additional file 1:Figure S1. KEGG pathway of photosynthesis (ko00195). PsBo, A0A1U8FJN4/ A0A1U8FZN5; PsBp, A0A1U8EAE0; PsB27, A0A1U8FGM0; PsaD, A0A1U8GVK4; PetA, A0A1U8FRH4, PetH, A0A1U8FRH4; gamma, A0A1U8FUM0, b, A0A1U8GUM8 (TIF 4719 kb)Additional file 2:Figure S2. KEGG pathway of carbon fixation in photosynthetic organisms (ko00710). 4.1.2.13, A0A1U8FHQ4; 2.2.1.1, O78327/ A0A1U8FNB3; 3.1.3.11, A0A1U8GDS4; 2.7.2.3, A0A1U8HDS6; 3.1.3.37, A0A1U8GZ15; 1.1.1.40, A0A1U8E7W8 (TIF 1471 kb)Additional file 3:Figure S3. The controlled container. 50 B. tabaci adult insects were collected and released onto the 5th leaf and closed with a small pocket. (TIF 7155 kb)Additional file 4:Table S1. Information statistics of total protein identified (XLSX 9 kb)Additional file 5:Table S2. Gene ontology (GO) analysis of annotated proteins (XLSX 11 kb)Additional file 6:Table S3. GO biological process enrichment DAPs (XLSX 9 kb)Additional file 7:Table S4. GO molecular function enrichment of DAPs (XLSX 114 kb)Additional file 8:Table S5. GO cellular component enrichment of DAPs (XLSX 46 kb)Additional file 9:Table S6. Pathway enrichment of RB-RC (XLSX 17 kb)Additional file 10:Table S7. Number of DAPs obtained by different analytical methods (XLSX 23 kb)Additional file 11:Table S8. Parrelism between samples and iTRAQ labeling (XLSX 12 kb)Additional file 12:Table S9. Primers for qRT-PCR (XLSX 12 kb)


Croton conduplicatus , a medicinal plant native to Brazil. Antinociceptive activity was investigated by measuring the nociception induced by acetic acid, formalin, hot plate and carrageenan. A docking study was performed with the major constituents of the EO . The EO reduced nociceptive behavior at all doses tested in the acetic acid-induced nociception test (p < 0.05). The same was observed in both phases (neurogenic and inflammatory) of the formalin test. When the hot-plate test was conducted, the EO (50 mg/kg) extended the latency time after 60 min of treatment. The EO also reduced leukocyte migration at all doses, suggesting that its antinociceptive effect involves both central and peripheral mechanisms. Pretreatment with glibenclamide and atropine reversed the antinociceptive effect of the EO on the formalin test, suggesting the involvement of KATP channels and muscarinic receptors. The docking study revealed a satisfactory interaction profile between the major components of the EO and the different muscarinic receptor subtypes . These results corroborate the medicinal use of C. conduplicatus in folk medicine.Medicinal plants have been widely used in the treatment of chronic pain. In this study, we describe the antinociceptive effect of the essential oil from  Pain is a complex experience involving noxious stimuli and emotional processing. It is a pathological consequence of various disorders, but which has a protective role representing, in many cases, the only symptom for the diagnosis of diseases . In contPain commonly involves central and peripheral mechanisms, which makes it very difficult to choose an appropriated pharmacological therapy. Although highly effective, central analgesics are usually not disassociated with important adverse effects, such as dependence, respiratory depression, and constipation ,8. FurthCroton conduplicatus Kunth. (Euphorbiaceae) is a Brazilian medicinal plant, endemic of the Caatinga biome and popularly known as “quebra-faca” oct-3-yl hydroxy(diphenyl)acetate [(R)-(−)-3-Quinuclidinyl benzilate (QNB)] co-crystalized with muscarinic receptor M2 and -7-{[hydroxy(dithiophen-2-yl)acetyl]oxy}-9,9-dimethyl-3-oxa-9-azoniatricyclononane (tiotropium) co-crystalized with muscarinic receptor M3 and M4, were used in docking performance for energy evaluation. Binding energies of camphor, caryophyllene oxide, and (E)-caryophyllene, as well as co-crystalized ligands are show in The structure of ligands (3E)-caryophyllene 13.72%, and caryophyllene oxide 13.15%) and one monoterpene (camphor 8.25%) as the major chemical constituents of the EO  and (E)-caryophyllene (4.66–7.80%) also appear among the main constituents of the essential oil oct-3-yl hydroxy(diphenyl)acetate (PDB ID 3UON) [http://www.rcsb.org/pdb/home/home.do). The structure of the majority compounds camphor, caryophyllene oxide, and (E)-caryophyllene were submitted to molecular docking using the Molegro Virtual Docker, v. 6.0.1 (MVD) [The structure of Muscarinic receptors M2 in complex with (3ID 3UON) , M3 in cID 3UON) , and M4 ID 3UON)  were dow.1 (MVD) . All of .1 (MVD) . Dockingp < 0.05. All analysis was performed with GraphPad Prism 6.0 software .All data obtained were presented as mean ± standard error of the mean (SEM) and the statistical significance was determined using an analysis of variance (ANOVA) followed by Tukey′s test. Values were considered significantly different when C. conduplicatus (the EO), an aromatic plant native to Brazil, used as a natural analgesic. In addition to support the popular use of this species, the results indicated that the EO acts through central and peripheral mechanisms, possibly involving KATP channels and muscarinic receptors.This study described, for the first time, the antinociceptive effect of the essential oil of the stem-barks from