Pyrenestes finches are unique among birds in showing a non-sex-determined polymorphism in bill size and are considered a textbook example of disruptive selection. Morphs breed randomly with respect to bill size, and differ in diet and feeding performance relative to seed hardness. Previous breeding experiments are consistent with the polymorphism being controlled by a single genetic factor. Here, we use genome-wide pooled sequencing to explore the underlying genetic basis of bill morphology and identify a single candidate region. Targeted resequencing reveals extensive linkage disequilibrium across a 300 Kb region containing the insulin-like growth factor 1 (IGF1) gene, with a single 5-million-year-old haplotype associating with phenotypic dominance of the large-billed morph. We find no genetic similarities controlling bill size in the well-studied Darwin’s finches (Geospiza). Our results show how a single genetic factor may control bill size and provide a foundation for future studies to examine this phenomenon within and among avian species. Pyrenestes finches have a bill size polymorphism thought to be maintained by disruptive selection. Here, the authors identify a single candidate region, including insulin-like growth factor 1, differentiating small and large bill size morphs and a wider region differentiating the mega-billed morph. Despite the ubiquity of phenotypic polymorphisms in natural populations2, there are few instances in which the ecological and evolutionary factors that produce and maintain them are well understood, and still fewer where the genetic bases have been identified5. Resource polymorphisms, defined as intraspecific phenotypic variation associated with niche partitioning, can be under strong selection and are prime candidates for investigating the genetic, ecological, and evolutionary factors maintaining variation in natural populations and may represent important steps in speciation7.How phenotypic variation arises and is maintained in natural populations is a fundamental question in evolutionary biologyPyrenestes ostrinus), a seed-eating estrildid finch common to the rainforests of Equatorial Africa, displays dramatic variation in bill size11 , and large and mega morphs specializing on increasingly hard-seeded species . Disruptive selection affecting bill size primarily targets lower mandible width, which is related to the performance of cracking seeds of differing hardness19. Analysis of pedigreed crosses show that beak size differences in small and large morphs are due to a single, diallelic autosomal locus where the large-billed phenotype is dominant11. Beak size differences in small and large morphs have been shown to be due to differences in postnatal craniofacial development however, beak size differences do not scale with body size9. In contrast, the transition to the mega phenotype involves concomitant increases in both beak and body size9, suggesting that the evolution of the mega phenotype involves more complex developmental processes that affect multiple traits20. The genetic basis of the mega morph is unknown.The black-bellied seedcracker  and targeted resequencing to investigate the genetic basis of this classic resource polymorphism. We identify a single candidate region underlying bill size differences in small and large morphs, confirming earlier work that suggested this trait is controlled by a single locus. In contrast, the mega morph is associated with more extensive variation on the same chromosome. Our results provide insights into how genetic variation contributes to discrete resource polymorphisms, and how they evolve and are maintained in natural populations.P. ostrinus morphs to identify candidate regions associated with bill morphology. We used pairwise comparisons among morphs to test two hypotheses: (1) that the differences between the small-billed and large-billed morphs are due to a single genetic locus, as predicted by breeding studies11, and2 that the mega-billed phenotype is the product of a series of stepwise mutations: from an ancestral small-billed phenotype, to a large-billed phenotype, and ultimately to mega-billed phenotype. To test these hypotheses, we obtained a minimum depth of 20-fold sequence coverage per morph  from which we identified 6,262,908 SNPs genome-wide that were sequenced in at least two morphs and passed filters for allele frequency and FST estimation.We performed genome-wide Pool-seq on 20 small-billed, 20 large-billed, and 21 mega-billed FST scan with 200 bp windows across the genome showed strong differentiation between small-billed and large-billed morphs in a single, approximately 300 Kb region on zebra finch chromosome 1A (hereafter referred to as TGU1A)  windows between small- and large-billed morphs, 13 are on TGU1A with 12 spanning the 300 Kb candidate region  between the small and large morphs. Over 60% of these sites  are concordant with the candidate region identified by the FST scan, spanning 301,630 bp . The remaining highly divergent sites were distributed across other chromosomes .A sliding-window 1A) Fig. . Of 14 tFST windows in comparisons involving the mega-billed morph   genome-wide windows found in comparing the small-billed and mega-billed morphs, 1464 (99%) are on TGU1A. Similarly, 1088 of 1108 (98%) high-divergence windows between the large-billed and mega-billed morphs are on TGU1A. While small-billed morphs have significantly more high-divergence windows than large-billed morphs in comparisons with the mega-billed , the high-divergence windows do not fully overlap between comparisons . We found similar patterns in the number of fixed differences between non-mega and mega-billed morphs, with nearly all fixed differences located on TGU1A (Supplementary Table P < 2.2 × 10−16) between the small- and mega-billed morphs (2270 of 2274 variants) than large- and mega-billed morphs (1647 of 1655 variants). However, only 1341 fixed differences are shared between the two comparisons .As with the comparison between the small-billed and large-billed morphs, TGU1A was enriched in high-FST scan identified two additional, highly divergent regions downstream on the same chromosome .While the comparison between the small-billed and mega-billed morph identified slightly more divergent sites than the large- and mega-billed comparison, the overall patterns of divergence between mega- and both non-mega-billed morphs were similar Fig. . In addiCCDC91, DDX11, IGF1, KLHL42, PTHLH, NUP37, and WASHC3) are significantly enriched in private alleles . We predicted effects of each genetic variant using Ensembl’s Variant Effect Predictor (VEP)21. Predictions on all 1341 mega-specific private alleles suggest that most are modifiers, which are either non-coding variants or variants that affect non-coding genes , with genes CCDC91, DDX11, IGF1, and WASHC3 carrying the majority of modifier SNPs   = inf)   with individual observed heterozygosity. Individuals group into three clusters on the first two PCs: small-billed individuals with low heterozygosity ; large-billed individuals with low heterozygosity, hereafter referred to as the homozygous subgroup ; and large-billed individuals with high heterozygosity, hereafter referred to as the heterozygous subgroup . The large-billed homozygous subgroup has marginally, but significantly larger bills than the heterozygous subgroup (one-tailed t-test P = 0.0067) . Sliding-window analysis shows that the heterozygous large-billed subgroup individuals have consistently higher HO through nearly the entire candidate region . The targeted region contains the ~300 Kb candidate region, flanked by approximately 100 Kb on either side . This region is gene-poor, with only 10 annotated genes Fig. . After fons Fig. , with 1533) Fig.  while PC67) Fig. . The larion Fig. . Thus, tAdmixture22 model predicts two distinct genetic clusters (K = 2) in small-billed and large-billed morphs within the candidate region . The remaining two small-billed individuals (birds S7 and S8) are admixed but have majority membership in the small-billed cluster . These individuals also have the largest LMW of the small morphs. Mixed assignments were more common in the large morphs, with individuals from the heterozygous subgroup assigned 50% membership to each cluster, consistent with the high observed heterozygosity in these individuals. Thus, individuals cluster by phenotype when using SNPs from the candidate region.The most strongly supported P < 0.001) associated with bill size in a quantitative association analysis , and are candidate genes for influencing bill morphology   and additionally displayed characteristics of divergent haplotypes associated with bill size in small- and large-billed morphs. Namely, PCAs of each cluster clearly separated individuals into three genotypic groups along the first axis, which explains >99% of the total genetic variation at the component SNPs  . Given this strong LD, and that all four clusters are concordant in their genotypic patterns and association with phenotype, we pooled them into a single large cluster of 972 SNPs. This pooled cluster delineates a single extended region of high LD that spans 319.3 Kb  and includes the entire candidate region  and are flanked by regions of lower LD  . A similar and inverse trend was noted with the large-billed haplotype I and LMW . Further, we identified five recombination events on the phased haplotypes that produces six recombinant blocks in two small-billed and four large-billed individuals  . These recombination blocks align closely with the high LD SNP clusters identified in the LDna analysis , PAH , PARPBP , PMCH , and WASHC3 . IGF1 and PMCH are significantly enriched in variants when accounting for gene size  to estimate the age of the large-billed haplotype. From the 200,000 simulated replicates, we derived a posterior distribution from the 200 simulations with the smallest Euclidean distance of summary statistics  to the data. A comparison of the prior distribution with the posterior distribution suggests that the summary statistics are highly informative about the equilibrium frequency and the age of the allele  allele are not expected to share any polymorphisms. However, regions flanking an inversion are expected to experience free recombination with many shared polymorphisms between the ancestral and derived inversion background. We performed three analyses to determine whether the large-bill haplotype is the result of an inversion: (1) read-based analysis of genetic variation using paired-end sequence data to assess shared polymorphism and predict breakpoints; (2) PCR validation of predicted inversion breakpoints; and (3) sequence comparison of the small-billed and large-billed alleles. The read-based analysis showed that the candidate region is depleted of shared polymorphisms between the small- and large-billed haplotypes  produced 21,919 contigs, of which 1706 were greater than 1Kb in length. We additionally collected genome-wide, long-read sequence data from the same individual for scaffolding. Of 7.35 Gb total output, we retained 297 reads that spanned the target region . We used these long reads to scaffold all assembled contigs greater than 1 Kb. The final scaffold assembly consisted of 1688 contigs with a total length of 3,373,765 bp (N50 = 1762 bp) and was not improved by any combination of the alternative algorithms tested. We aligned all scaffolds greater than 5Kb against the candidate region in the zebra finch for a comparison with the small-billed allele. Four scaffolds, including the two largest in the assembly and with a total length of 488,349 bp, mapped successfully to the zebra finch reference  and spanned the putative breakpoints predicted by the read-based genetic diversity analysis. In sum, sequence comparisons of the small- and large-billed alleles show no evidence of an inversion . In a PCA, the first axis explains 48.4% of the variation and separates individuals by beak morphology, with the small-billed and mega-billed morphs at the extremes and the large-billed morphs in the middle. The second PC (1.9% of variation explained) separates the large-billed morphs from the others, with the two large subgroups (homozygous and heterozygous) clustering separately  to determine whether alleles associated with bill morphology in P. ostrinus were segregating or fixed within the highly-studied Geospiza system. We mapped sequence reads from 10 individuals across three Geospiza species and capture sequence reads from 12 small- and 12 large-billed P. ostrinus to the G. fortis reference genome (Supplementary Table n = 9911) mapped to scaffold 10, which we infer to be the homologous region to the candidate region. We restricted our survey to scaffold 10. Of these sites, after excluding three fuliginosa and three magnirostris individuals with missing data > 50% on scaffold 10, missing data was highest for G. magnirostris . A PCA of sites mapping to G. fortis scaffold 10 revealed substantial genotypic divergence between P. ostrinus and Geospiza species, suggesting a divergent underlying genetic architecture determining bill morphology  shows all Geospiza species clustering together, separate from the large- and small-billed P. ostrinus . Our results are remarkably consistent with an earlier pedigree analysis11 that suggested a dominant large-billed allele at a single locus controls bill size differences between small and large morphs. We identify this locus as a 300Kb gene-poor region of low recombination on TGU1A . We further present evidence that insulin-like growth factor 1 (IGF1), which falls within this 300 Kb candidate region, is likely important in determining bill size differences between small- and large-billed morphs. In contrast, our analysis suggests that the third, mega-billed morph is the result of a more expansive set of genetic changes spanning a larger region on TGU1A.We provide a genome-level view of the mechanisms underlying a bill size polymorphism in the black-bellied seedcracker  driving the transition from small to large morph should fall in recombination blocks 3, 4, or 5 . The annotated genes in this region are IGF1, PMCH, and PARPBP. Due to the lack of known functional relevance of PMCH and PARPBP  and a paucity of modifying variants associated with them, we focus our discussion on IGF1.Using high-throughput sequencing of small- and large-billed individuals, we identified a single 300 Kb gene-poor region on chromosome TGU1A segregating with bill morphology. Targeted resequencing revealed extensive linkage and strong divergence between haplotypes associated with the small and large morphs. Both haplotypes segregate within the population, and as expected, the genotypic patterns indicate that the large-billed allele is dominant to the small-billed allele. However, dominance does not appear to be complete, as heterozygous large individuals  are on average smaller than the homozygous large-billed individuals. Additionally, individuals carrying recombinant haplotypes often scale in bill size according to the amount of the large haplotype carried. For example, the candidate region in recombinant individuals S7 and S8 contains 17 and 9% large haplotype, respectively, and these individuals have the largest bills of the small morphs. This suggests perhaps that there are multiple variants throughout the candidate region affecting bill size in 32. We further used a coalescence approach to estimate the molecular age of the derived haplotype associated with the large-billed morphology to be at least 5 million years old. One possible mechanism that could maintain the observed extensive LD is a chromosomal inversion, which suppresses local recombination . Chromosomal inversions have been identified as responsible for phenotypic variants in some taxa . We collected long-read sequence data and surveyed the candidate region to identify possible breakpoints and a signal of genetic variation that might indicate a possible inversion, with subsequent PCR amplification to validate potential breakpoints. We found no evidence for an inversion underlying the bill size polymorphism. Although we cannot definitively reject the hypothesis that there is an inversion, other possible mechanisms  could explain the origin of the large-bill haplotype.The persistence of these divergent haplotypes with extensive LD is likely due to the region’s low recombination rate, which appears to be phylogenetically conservedIGF1, which has a well-known role in body size scaling in many taxa  and skeletal development (reviewed in ref. 44). Further, IGF1 is regulated by multiple genes and their proteins and encodes a highly flexible pleiotropic secretory polypeptide hormone47. Thus, it would not be surprising for IGF1’s regulatory elements to play important roles in shaping craniofacial morphology and scaling.Of the variants characterizing the extended haplotypes associated with bill morphology, 43% are non-coding and predicted to be modifiers, with the rest falling in intergenic regions. Nearly half of these modifying variants are located upstream of IGF1 activity are known to be responsible for dramatic changes in size and shape of target tissues . For example, in domestic dogs a single haplotype of IGF1’s promoter is associated with small body size and is nearly absent in larger breeds41. Of course, dog breeds are maintained via selective breeding, which is not the case in P. ostrinus, where the bill size polymorphism follows Mendelian segregation within randomly mating populations. In the case of P. ostrinus, the association of genetic variation at IGF1 resides in a region of phylogenetically conserved low recombination rates, which maintains a high level of LD within the haplotype associated with bill size.Changes in IGF1 plays a major role in determining avian bill morphology. Genetic mapping studies of Darwin’s finches (Geospiza) and great tits (Parus major) have associated genetic and regulatory variation of genes with known roles in craniofacial morphology to bill size and shape54, but have not implicated IGF1. While niche partitioning of food resources has driven the evolution of bill morphology in both Geospiza and P. ostrinus, there are critical differences between these two systems. Most obvious is that morphological variation in bill size and shape in Geospiza typically distinguish different species56 with restricted gene flow; in contrast, the bill polymorphism in P. ostrinus is maintained within randomly-mating populations17. The single locus controlling bill size in small and large morphs of P. ostrinus contrasts with the apparent polygenic control of bill size and shape that characterizes interspecific variation in Geospiza. To more fully assess whether our candidate region in P. ostrinus also contributes to bill phenotypic variation in Geospiza, we compared allelic variation in our candidate region with the homologous region in several Geospiza species. We find that the alleles associated with P. ostrinus bill variation do not segregate with Geospiza bill morphologies.To our knowledge this is first study to suggest that 58. Megas are distributed only in drier ecotone regions that characterize the transition between rainforest and savanna, where the extremely hard seeds of the sedge Scleria racemosa occur. We found evidence of selective sweeps in regions of TGU1A that are strongly divergent between the mega-billed and non-mega-billed morphs, consistent with positive resource-based selection driving the evolution of the mega-billed morph, likely due to the abundance of S. racemosa. In contrast, we do not find a signature of selective sweeps in the candidate region for the small- and large-billed morphs, which are subject to disruptive selection where both soft (S. goossensii) and hard (S. verrucosa) seeds occur and the extremely hard seeded S. racemosa is absent. In megas, we identified additional variants downstream of the candidate region, suggesting that the genetic basis for this extreme phenotype is more complex than in small and large morphs. Although mega-bills were sampled from multiple populations, we found very low levels of genome-wide genetic divergence  that indicates geographic population structure is unlikely a contributing factor to bill differences.In contrast to the small-billed and large-billed morphs that display Mendelian inheritance, we find that the mega-billed morph is under more complex control. The mega-billed morphs occur both geographically separate and together with the other bill morphs. All morphs interbreed where they are found togetherP. ostrinus, which differ in bill size but little in body size, have a simple genetic basis likely involving differential regulation of IGF1. The evolutionary transition to the mega-billed morph, which is larger in both bill and body size, involves changes across a more extensive region on the same chromosome but does not seem to have resulted from a simple stepwise evolution from the large-bill morph. Additional research will be necessary to more fully understand the genetic basis of the bill size polymorphism in P. ostrinus, especially with respect to the mega-billed morph. Further, given the importance of IGF1 and its role in biological scaling, regulatory and transcriptional variation would be a fruitful area of future work to explore the specific evolution of scaling. Differences among related bird species are frequently characterized by beak size, a trait typically highly correlated with feeding, performance and adaptive divergence among populations and species59. Thus, the results of this research underline the importance of future work to understand the role of IGF1 in the evolution of avian bill size and the diversification of birds more broadly.We have shown that bill size differences between small- and large-billed morphs of P. ostrinus individuals captured between 1986 and 2007 from the same population in Ndibi, Cameroon and from 21 mega-billed individuals from four different populations in Cameroon58 (Supplementary Data 57. DNA was quantified using a Qubit 2.0 Fluorometer and checked on a 2% agarose gel for degradation. We constructed Pool-seq libraries for paired-end sequencing (2 × 100 nt) on an Illumina HiSeq 4000 using the TruSeq DNA PCR-Free LT kit Set A (Illumina), tagging each bill morph with a unique barcode. Briefly, DNA was fragmented with a Bioruptor NGS sonicator (Diagenode) to an average insert size of 500 bp (5 cycles of 30 s on and 90 s off on the high setting), ends were repaired with adenine on the 3′ ends, followed by ligation of dual adapters, and selection of 300 bp fragments with Agencourt AMPure XP beads (Beckman Coulter). Library quality was assessed with an Agilent 2100 BioAnalyzer (Agilent) and concentrations were estimated with a Qubit 2.0 Fluorometer. Libraries were then standardized to 10 nM, and all libraries were pooled into two lanes at UC Berkeley Genomics Core.We performed Pool-seq to identify candidate regions associated with bill size in all three morphs. By barcoding the phenotype rather than the individual, Pool-seq is a cost-effective method for estimating allele frequencies. We prepared genomic DNA using QIAamp DNA mini kits (Qiagen) from 20 small-billed and 20 large-billed adult stampy60. Stampy is useful for mapping reads from a species that is highly divergent to a reference genome60. We used PoPoolation261 to calculate differences in allele frequency (module SNP-frequency-diff.pl) and pairwise genetic differentiation (module fst-sliding.pl) among the three morphs. Allele frequency differences were called only for sites with a minimum minor allele depth of 2, an overall minimum depth of 10, and a maximum depth of 40. FST estimation was in 200 bp windows with a 200 bp step size with the same coverage requirements and discarding windows with insufficient coverage along their entire length. We additionally used the mpileup function in SAMtools63 for variant calling, filtering to retain only bi-allelic SNPs with a minimum depth of 10x and a minimum mapping quality score of 20, and excluding SNPs located within predicted indels. We identified SNPs with a difference in allele frequency between small and large morphs greater than 0.9 to establish the bounds of the candidate region.For demultiplexing, we retained sequences with no more than two mismatches to that of the expected index. All reads were trimmed using CLC Genomics Workbench, using the Illumina TruSeq adapter sequences searched on both strands and removing reads that were < 20 bases and of low quality . Reads were mapped to the zebra finch reference genome (taeGut2) using default parameters in Variant Effect Predictor (VEP)21. To identify genes that carry an excess of mega-billed private alleles relative to random expectation, we performed a bootstrap resampling with 10,000 replicates. Each bootstrap replicate consisted of random draws with replacement of genes from all genes represented in the dataset, with the probability of selecting each gene weighted by its total length. This yielded a null distribution of the number of expected mega-billed private alleles for each gene. P-values were calculated per gene as the proportion of null replicates that were greater than the number of observed mega-private alleles.To identify potential candidate genes underlying the mega-billed phenotype, we identified SNPs with fixed differences between mega and non-mega  morphs . We assessed the putative effects of the mega-private alleles on gene function with Ensembl’s Pool-hmm64, which uses patterns of allele frequencies in Pool-seq data to predict the most likely of three possible states  for each polymorphic site. We analyzed each morph separately, with theta (-t) set at the default of 0.005 and the per-site probability of transition among states (-k) at 10-8. We applied a strict threshold of a reported posterior probability of inf, reflecting the lowest P-value detectable with this method, to identify high-confidence selective sweep regions.We additionally estimated the posterior probability of selective sweeps in each bill morph on TGU1A with FST scan identified a single region of high divergence on TGU1A that putatively underlies bill size differences between small and large morphs (see Results section). To refine the bounds of this candidate region, we used a heuristic metric for identifying divergent allele frequencies relative to the phenotype, referred to here as a delta value (Δ). Delta values were estimated as the absolute difference of the reference allele frequency between the small-billed and large-billed P. ostrinus. To identify highly divergent SNPs, we filtered to retain SNPs with high delta values (Δ ≥ 0.9) for a more conservative threshold.The window-based cutadapt65 with the same parameters as implemented in CLC (described above) and mapped to the reference zebra finch genome using stampy. PCR duplicates were removed using Picard tools (http://broadinstitute.github.io/picard). We used ANGSD66 to call SNP genotypes with a minimum depth of 30-fold sequence coverage, a minimum mapping quality of 20, and a minimum variant quality of 60. All downstream analysis contained only the SNPs and sequence that mapped within the coordinates of the candidate and flanking regions in taeGut2. We used gene annotations from taeGut2 downloaded from Ensembl BioMart67 to annotate each SNP.We identified a single ~300 Kb candidate region on chromosome TGU1A (hereafter the candidate region) that was enriched with highly divergent alleles between small and large morphs. To obtain individual-level genotypic data to discover individual variants associated with bill morphology, we designed baits to target and enrich genomic libraries for this candidate region. Due to the known Mendelian inheritance for small- and large-billed morphs, we selected a subset of 12 large- and 12 small-billed individuals from the Pool-seq samples for targeted enrichment using 80mer baits designed and conducted by MYcroarray©. The target region contains the 301,630 bp candidate region, and is additionally flanked on both sides by ~100 Kb for a total enrichment of 499,630 bp  Fig. . MYcroarHO) for the resequenced 12 small- and 12 large-billed morphs using PLINK v1.968, implementing a sliding window approach  to visualize the mean values per window. A principal component analysis (PCA) was conducted using flashPCA69 on SNP genotypes. We analyzed the SNP genotypes for genetic structure with the program Admixture22, using the --cv flag for a cross-validation evaluation of the fit of each genetic partition (K). We used PLINK to associate genetic variation with the quantitative trait LMW. This method estimates the regression coefficient (ß) and the significance value from the Wald Test, also called a Wald Chi-Squared test, which tests if ß at a site is non-zero.We estimated observed heterozygosity (HO > 50%), and a low minor allele frequency (MAF < 20%). We estimated pairwise LD for SNPs within the candidate region as the correlation coefficient r2 using the LD function in the R package genetics70. Patterns of LD revealed that SNPs are a mixture of strongly correlated sites interspersed with sites that appear statistically independent (see Results). To extract and characterize these strongly correlated sites, we used a network analytic approach implemented in LDna23. By treating network vertices as loci, and edges connecting vertices as LD between pairs of loci, LDna identifies clusters of SNPs that are in stronger LD with each other than they are to the rest of the network, and that cannot be subdivided into smaller groups. Cluster delimitation in LDna is controlled by two user-defined parameters: |E|min, the minimum number of edges required above an LD threshold; and ϕ, a constant that defines the stringency for considering a cluster to be a statistical outlier. Following the recommendation by Kemppainen et al. (ref. 23), we tested |E|min values between 1 and 10% of the total number of loci, and tested ϕ values ranging from 3 (least stringent) to 7 (most stringent). Outlier clusters  are referred to as single-outlier clusters in LDna terminology; for simplicity, we refer to them as outlier clusters.To investigate patterns of LD within the candidate region associated with bill morphology, we calculated pairwise LD among SNPs from the targeted resequencing data. SNPs were excluded from LD analysis if a site had > 10% missing data, high observed heterozygosity, . The resulting phased haplotypes were polarized based on the number of haplotypes carried that were derived from the large haplogroup. We used these haplotype counts  per phased site as a multi-locus diploid genotype to conduct a quantitative association test in PLINK across the candidate region. To explore relationships among haplotypes, we constructed and viewed two types of unrooted haplotype trees, neighbor joining and median network joining trees, in SplitsTree using default parameters72. We additionally used VEP to annotate the inferred functional and phenotypic impact of SNP variants. To identify genes that carried an excess of variants while controlling for gene size, we performed a bootstrap resampling as above: 10,000 replicates of random draws of each gene, with genes weighted by their total length.To assess haplotype structure across the candidate region to determine if a single haplotype carries a candidate variant associated with bill morphology, we used 73, using the coalescent simulator msms74. These simulations assume no recombination between the derived and the ancestral haplotype, supported by the phylogenetically conserved low rate of recombination in this region. We assumed an unscaled mutation rate75 of 2.2 × 10-9 and a generation time of 5 years76.The high level of divergence between the large-bill and small-bill haplotypes seems incompatible with a neutral model and suggests that the derived haplotype associated with the dominantly inherited large-billed morphology is old and under selection. We estimated the age of the derived haplotype assuming long-term balancing selection. To this end, we first simulated 200,000 replicates of a heterozygous advantage model where trajectories of a selected allele start with a single copy and over time converge to an equilibrium frequency defined by the fitness of each genotype. The simulated trajectories have three parameters: (1) the equilibrium frequency, (2) the heterozygous fitness advantage, and (3) the age of the allele. We randomly sampled these parameters from uniform prior distributions with a range of 50–1000 for the scaled heterozygous selection coefficient, 0.2–0.8 for the equilibrium allele frequency, and 0–5 for the age of the allele in coalescent units. Then we simulated population genetic variation data under the structured coalescent model conditional on the simulated trajectoriesWe estimate the age of the derived haplotype using an approximate Bayesian computation (ABC) approach. We computed three summary statistics from the simulated data: (1) nucleotide diversity within the ancestral haplotype, (2) nucleotide diversity within the derived haplotype, and (3) average divergence between the ancestral and the derived haplotype. To compute the same statistics from the data, we selected individuals homozygous for the small haplotype A  or homozygous for the large haplotype I . These individuals do not have any recombinant haplotypes in the 300 Kb candidate region , and a second that is fully contained within the putative inversion to serve as an internal control . The primers were designed based on the zebra finch reference genome, using the LBP coordinates predicted in the read-based analysis of genetic variation described above. If the large-billed allele is inverted, the LBP primers should amplify only the small-billed allele, while the internal control will amplify in all samples. The LBP and Internal primers were amplified with 1.5 µl of DNA (average 82 ng) with 5 µl of Multiplex PCR Kit master mix (Qiagen), 1 µl of each of the primer pairs at 2 µM, and 2.5 µl of molecular grade water. Cycling conditions for the LBP primers were: 95 °C for 15 min, 45 cycles of , 72 °C for 10 min, and hold at 4 °C. Cycling conditions for the Internal primers were: 95 °C for 15 min, 40 cycles of , 60 °C for 30 min, and hold at 4 °C. All PCR products were visualized on a 1% agarose gel with a Routine 100 bp low scale DNA ladder (Fisher Scientific). For the LBP primer pair, in silico PCR based on the zebra finch reference genome predicts an amplicon size of 201 bp in small-billed individuals only, while the Internal primer pair should yield an amplicon size of 463 bp for all individuals.LDna analysis as homozygous for the large-billed allele, on six PacBio SMRT cells at the UC Davis DNA Technologies Core facility. We filtered these reads by aligning to the zebra finch (taeGut 3.2.4) using BWA-MEM78 with default settings, and only retained reads that mapped to the candidate region (TGU1A: 54.9–55.5 Mb). We then assembled the single-end and paired-end Illumina reads from the targeted resequencing effort for the same individual into contigs using SPAdes79. All contigs larger than 1 Kb were scaffolded using the filtered PacBio reads with SSPACE-LongRead80. For comparison, we constructed additional assemblies using three alternative algorithms: (1) BLASR81 for read mapping to the zebra finch reference; (2) performing a hybrid assembly using SPAdes with both Illumina and PacBio reads; and (3) using CAP382 to perform a meta-assembly with the assembled contigs. To compare the assembled large-billed allele with the small-billed allele and assess for inversions, we mapped all large-billed scaffolds > 5 Kb to the candidate region in the zebra finch reference and generated a dot plot in Gepard83.To compare the sequences of the small- and large-bill alleles, we generated a draft assembly of the non-wildtype allele carried by large-billed individuals. We sequenced a sample of high molecular weight genomic DNA from a single large-billed individual (L5), previously identified in the P. ostrinus are also commonly found to influence bill size and shape in other bird species, we conducted a comparative analysis of a region homologous in Darwin’s finches (Geospiza) genome. If the candidate region of high LD and the genes contained within have a conserved function in shaping bill morphology, we expect that to find that previous genome-wide scans would have identified this region and subset of same genes. With the possibility that this chromosomal region and IGF1 have remained undetected in previous studies, and if genetic variation in this candidate region is preserved independent of phylogeny, we expect genetic similarities within the candidate region would resolve bill morphology and not species histories. To determine if any alleles associated with bill morphology in P. ostrinus are also segregating or fixed within the highly-studied Geospiza species, we obtained publically available paired-end (2 × 100 nt) whole-genome sequence data for four individuals from G. fuliginosa , two G. fortis (medium ground finch), and four G. magnirostris (large ground finch)25  for each phenotype using PLINK v1.9 and conducted a PCA using flashPCA on SNP genotypes. To assess haplotype patterns within this targeted 500 Kb region of interest, we phased SNP genotypes using SHAPEIT following methods described in the main text . We constructed and viewed unrooted  neighbor-joining (NJ) haplotype networks in SplitsTree.We collected additional targeted resequencing data on 21 mega-billed adult birds (LMW = 19 m6 ± 0.9 mm) for the candidate region on TGU1A. We collected and processed sequence data following methods detailed above, combining all bill morphs for subsequent analyses. We used Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


Recently, rice breeding program has shown increased interests on the pigmented rice varieties due to their benefits to human health. However, the genetic variation of pigmented rice varieties is still scarce and remains unexplored. Hence, we performed genome-wide SNP analysis from the genome resequencing of four Malaysian pigmented rice varieties, representing two black and two red rice varieties. The genome of four pigmented varieties was mapped against Nipponbare reference genome sequences, and 1.9 million SNPs were discovered. Of these, 622 SNPs with polymorphic sites were identified in 258 protein-coding genes related to metabolism, stress response, and transporter. Comparative analysis of 622 SNPs with polymorphic sites against six rice SNP datasets from the Ensembl Plants variation database was performed, and 70 SNPs were identified as novel SNPs. Analysis of SNPs in the flavonoid biosynthetic genes revealed 40 nonsynonymous SNPs, which has potential as molecular markers for rice seed colour identification. The highlighted SNPs in this study show effort in producing valuable genomic resources for application in the rice breeding program, towards the genetic improvement of new and improved pigmented rice varieties. Oryza sativa L.) is the most crucial staple food crops in Asian countries. The most consumed rice is white rice, which resulted from the white pericarp. The coloured pericarp such as black, red, and brown has become more popular. Coloured pericarp accumulates secondary metabolites such as flavonoid, anthocyanin, and proanthocyanidin and usually are associated as potent antioxidants. Previous study has found that food sources with high antioxidant properties can lower the risk of chronic diseases such as type II diabetes, cardiovascular disease, and cancers [Rice  that involved in the catalysis activity of dihydroflavonol to leucoanthocyanidin [Rd alone has no phenotype change. Rc is also known as domestication gene [Kala4, a transcription factor in basic helix-loop-helix (bHLH) family, is involved in black rice pigmentation [Kala4 causes the upregulation of LDOX in pericarp, accumulates the anthocyanidin, and produces black pericarp [Previous efforts have been performed to elucidate the genetic basis of black and red rice varieties –4. In recyanidin , 3. Howeion gene  and has ion gene –8. Kala4To further investigate the genetic basis of pigmented rice varieties, many efforts have been performed using omics technologies and bioinformatics. For instance, several studies on the phytochemical diversity of the coloured or pigmented rice from landraces, varieties, and wild relatives have been widely conducted using a metabolomics approach to reveal their antioxidant properties and variabilities –14. PrevMolecular marker is a DNA fragment with phenotypic expression that is associated with a certain location within the genome . SeveralSNP is defined as a single base difference in DNA sequence and the most common type of genetic variation to distinguish individuals . The abuHere, we report the genome-wide SNP analysis on the whole genome resequencing of two black rice varieties  and two red rice varieties (MRM16 and MRQ100). Bali is a landrace rice variety, while Pulut Hitam 9 (PH9), MRM16, and MRQ100 are modern rice varieties. All of them were from indica subspecies. These four varieties were chosen due to their nutritional trait that was enriched with antioxidant properties .  FigureWe mined the SNPs from the genomes of four pigmented Malaysian rice varieties to search for the SNPs with polymorphic sites and candidate SNPs associated with the flavonoid biosynthetic genes. Additionally, we have identified 70 novel SNPs after comparing with SNP data from Ensembl Plants variation , comprisPlant materials consisted of four pigmented rice varieties from Malaysian, i.e., Bali, PH9, MRM16, and MRQ100. Four varieties were selected based on (a) the presence of high antioxidant contents and (b) released variety. Seeds of Bali, PH9, MRM16, and MRQ100 were obtained from MARDI Seberang Perai, Penang, Malaysia. Seeds were sterilized, incubated at 42°C overnight, and soaked in water for two days before being placed onto wet tissues or directly sowed into the soil.Total DNA of each variety was extracted from leaves of two-week-old germinated seedling using Mutou et al.'s protocol  and SigmThe pair-end sequencing reads from Bali, PH9, MRM16, and MRQ100 with the read length of 150 bp at each end were aligned with Nipponbare genome sequences  using BuWe followed the GATK best-practices pipeline for SNP calling . This SNSnpEff  version http://www.pantherdb.org) with FDR cutoff selected at ≤0.05. Gene Ontology database for Oryza sativa was selected for this analysis.Gene ontology enrichment analysis of genes containing 622 SNPs with polymorphic sites was performed using PANTHER (protein annotation through evolutionary relationship) classification system  (http://Oryza sativa gene identification (OsID) using R scripts.The flavonoid biosynthetic genes (FBGs) were obtained from the similarity and bibliomic search. The list of FBGs is provided in the Supplementary ndica and japonica varieties might be a contributing factor that caused the highest mapped rate. Genome sequencing of Bali, PH9, MRM16, and MRQ100 has produced 101.71, 99.98, 98.76, and 99.99 million reads, respectively. The average read lengths of 2 × 150 bp were generated with 30× depth of sequencing. This 30× depth of sequencing was chosen as it provides sufficient coverage in identifying high-quality genetic variations such as SNP, single-nucleotide variation (SNV), and insertion-deletion (InDel) . TherefoOryza sativa Ensembl Plants variation database as of October 2017 , followed by chromosome 1 (80) and chromosome 2 (80). These values demonstrate the random distribution of SNPs with polymorphic sites within the 12 rice chromosomes. Interestingly, 70/10% of the SNPs with polymorphic sites were novel SNPs based on the comparison against ber 2017 . The SNP studies –64. ThisThe annotation of SNPs in four pigmented rice varieties has revealed that most of the SNPs were located in the intergenic region  while fewer SNPs are located within the genic region  . This fiAnalysis of the SNP differences between rice varieties showed that MRM16 165,124) has a higher number of SNPs in the genic region whereas PH9  has the least number of SNPs in the genic region. High number of SNPs in the genic region of MRM16 suggested the introgression, and recombination have occurred through human-guided artificial selection during rice breeding activity. Previous studies by Sang et al. and Tatarinova et al. suggested that artificial selection in developing modern rice varieties has shaped the present of SNP frequency and gene pool in the rice genome [,124 has Functional annotation analysis was performed to explore the effect of 662 SNPs with polymorphic sites on gene function. SNPs with polymorphic sites in the genic region will be valuable if associated with phenotypic expression or important agronomical trait . EnrichmGO:0009987 (cellular process) and GO:0008152 (metabolic process) were assigned for all genes that carry the SNPs with polymorphic sites in Bali, PH9, MRM16, and MRQ100 varieties suggesting their involvement in various physiological functions. Cellular process plays essential roles in cell communication while the metabolic process involved in the anabolism and catabolism of biosynthesis pathway. In the molecular function category, the SNPs with polymorphic sites were assigned to the binding function  and catalytic activity suggesting their possible involvement in the formation of molecule and enzymatic activities related to abiotic stress , severalThe biological interpretation of genes in the SNPs with polymorphic sites was further examined using the information obtained from the Reactome pathway analysis . In totaFunctional annotation of the SNPs with polymorphic sites was further conducted using Pfam analysis on the 23 nonsynonymous SNPs (nsSNPs). Usually, nonsynonymous SNPs can affect the function of a gene to encode for the right protein, hence will affect its function. 13 nsSNPs were assigned into several functional gene classifications such as metabolism, stress response, and transporter and 10 nonsynonymous SNPs were assigned to the domain of unknown function (DUF). Parida et al. discovered the involvement of Os01g0128000, Os07g0117000, Os09g0314200, Os10g0371100, and Os11g0539000 genes in plant resistance, pathogenesis, and abiotic stress mechanism . Our anaBesides, four nsSNPs were also identified in four transcription factor families such as Myb-like DNA-binding domain (Os01g0128000), AP2 domain (Os10g0371100), IQ calmodulin-binding motif (Os07g0562800), and SWItch/Sucrose Non-Fermentable (SWI/SNF2) family N-terminal domain (Os08g0180300). Interestingly, Os01g0128000 that encodes for the Myb-like DNA-binding domain has been identified to be involved in the uptake and higher accumulation of phosphate (Pi) . In partOn the contrary, not much study has been conducted to confirm the function of Os10g0371100 that encodes for the ethylene-responsive transcription factor (ERF) domain or AP2/ERF domain. However, Os10g0371100 is predicted to be involved in plant growth and development either as an activator or a repressor in the expression of stress-responsive genes that are related to the abiotic stress responses . SimilarKala4 gene activates LBG to produce anthocyanin whilst Rc gene activates DFR to produce proanthocyanidin. Rc is unable to regulate the production of proanthocyanidin alone; instead, it requires the presence of Rd gene which encodes DFR to activate the accumulation of proanthocyanidin.Pigmented rice is significantly associated with higher antioxidant content due to the presence of anthocyanin and proanthocyanidin. The production of these secondary metabolites is controlled by a set of flavonoid biosynthetic genes such as DFR, LAR, ANR, UGT, and LDOX, which lead to the production of anthocyanin and proanthocyanidin. The difference between anthocyanin and proanthocyanidin synthesis is the inclusion of the catalysed enzymes LAR and ANR for proanthocyanidin, while catalysis of LDOX for anthocyanin. Besides, Kala4, and Rc. R2R3-MYB (Os06g0205100) due to their role in activating the DFR gene in the upstream biosynthesis [Kala4 (Os04g0557500) encodes for a basic helix-loop-helix (bHLH) transcription factor, which plays a role in activating the LDOX gene in the regulation of black pigmentation [Rc (Os07g0211500) has previously been shown as an activator for Rd (Os01g0633500) in the production of red pigmentation [In this study, a total of 99 flavonoid biosynthetic genes (FBGs) were selected from Nipponbare genome using similarity and bibliomic search –81. Suppynthesis , 85. KalA total of 1649 genic SNPs were found in the flavonoid biosynthetic genes, and 511 SNPs were identified in the genes related to the general phenylpropanoid, 463 SNPs in EBGs and 675 SNPs in LBGs . A high Interestingly, ten genic SNPs associated with UGT (Os02g0589400) were identified in this analysis. A previous study has reported that one SNP was strongly associated with UGT (Os02g0589400) and was suggested as a metabolite quantitative trait loci (mQTL) for antioxidant trait . UDP-gluTwo genic SNPs associated with UGT (Os01g0736300) at position 30712175 (chr01_30712175) and 30713739 (chr01_30713739) have been identified and were found as SNPs in the untranslated (UTR) region and CDS, respectively. This finding suggests that the mutation in the UGT can be used as potential genetic markers for the accumulation of antioxidant properties in the pigmented rice varieties as Dong et al. found that a mutation in Os01g0736300 was associated with 7-0-glycosylated flavonoids . FurtherRc (Os07g0211500), 38 mutations in R2R3-MYB genes, and 92 mutations in Kala4 (Os04g0557500). In comparison to the number of SNPs in the structural genes, fewer SNPs were found in the transcription factor, and this finding suggests that the character of the transcription factors are highly conserved compared to other classes of genes [In addition, there were 160 genic SNPs found in the transcription factor genes, i.e., 30 mutations in of genes . In concof genes , 87.This study also investigated the distribution of genic SNPs in four pigmented rice varieties. A total of 448, 420, 491, and 459 genic SNPs were identified in Bali, PH9, MRM16, and MRQ100, respectively . Of thesSNPs are considered unique if they are present in one variety but absent in the other three varieties  were identified at positions 26199225, 26199416, 26199448, and 26199529 in UGT (Os05g0527000), respectively, and one nsSNP (b_UGT_2) which occurred at position 10479849 in UGT Os06g0288300) . Os05g05300 . Os0Genomic variation among these four pigmented rice varieties provides a resource for genetic variability as well as generating new allelic variants towards the development of new and improved pigmented rice varieties. However, SNP validation must be conducted using a genotyping platform. This genome-wide gene-based SNP marker identification can provide a solution for breeders to effectively screen diverse accessions or interspecific hybrid breeding program for the genetic improvement in pigmented rice varieties.Extensive bioinformatic analysis on next-generation sequencing (NGS) data has contributed to the identification of a high number of SNPs. From this study, the candidate SNPs associated with the essential functional genes and SNPs with polymorphic sites provide important insights into the genetic basis of four Malaysian pigmented rice varieties. Therefore, a genotyping experiment can be conducted on these SNPs for validation before progressing into genetic diversity study, cultivar identification, and marker-assisted selection (MAS), towards the development of new and improved pigmented rice varieties.


Toxocara canis is quite closely related to Ascaris suum but its biology is more complex, involving a phase of arrested development (diapause or hypobiosis) in tissues as well as transplacental and transmammary transmission routes. In the present study, we explored and compared dauer-like signalling pathways of T. canis and A. suum to infer which components in these pathways might associate with, or regulate, this added complexity in T. canis.Caenorhabditis elegans, we bioinformatically inferred and compared components of dauer-like signalling pathways in T. canis and A. suum using genomic and transcriptomic data sets. In these two ascaridoids, we also explored endogenous dafachronic acids (DAs), which are known to be critical in regulating larval developmental processes in C. elegans and other nematodes, by liquid chromatography-mass spectrometry (LC-MS).Guided by information for C. elegans dauer signalling genes were identified in T. canis (n = 55) and A. suum (n = 51), inferring the presence of a dauer-like signalling pathway in both species. Comparisons showed clear differences between C. elegans and these ascaridoids as well as between T. canis and A. suum, particularly in the transforming growth factor-β (TGF-β) and insulin-like signalling pathways. Specifically, in both A. suum and T. canis, there was a paucity of genes encoding SMAD transcription factor-related protein  and insulin/insulin-like peptide  homologues, suggesting an evolution and adaptation of the signalling pathway in these parasites. In T. canis, there were more orthologues coding for homologues of antagonist insulin-like peptides (Tc-ins-1 and Tc-ins-18), an insulin receptor substrate (Tc-ist-1) and a serine/threonine kinase (Tc-akt-1) than in A. suum, suggesting potentiated functional roles for these molecules in regulating larval diapause and reactivation. A relatively conserved machinery was proposed for DA synthesis in the two ascaridoids, and endogenous Δ4- and Δ7-DAs were detected in them by LC-MS analysis. Differential transcription analysis between T. canis and A. suum suggests that ins-17 and ins-18 homologues are specifically involved in regulating development and migration in T. canis larvae in host tissues.Orthologues of T. canis. Elucidating the functional roles of these molecules might contribute to the discovery of novel anthelmintic targets in ascaridoids.The findings of this study provide a basis for functional explorations of insulin-like peptides, signalling hormones (i.e. DAs) and related nuclear receptors, proposed to link to development and/or parasite-host interactions in The online version of this article (10.1186/s13071-018-3265-y) contains supplementary material, which is available to authorized users. Toxocara canis is an important pathogen of both animal and human health importance worldwide  in C. elegans were available from published information [n = 182) and functional information were obtained from WormBase (WS261) [C. elegans protein sequences (using BLAT v.35 and tblastn v.2.5.1) against the genome and transcriptome assemblies of T. canis and A. suum. In addition, Pfam, PANTHER and SUPERFAMILY conserved domain architectures (using InterProScan v.5.15.54) [de novo-assembled transcripts of these two ascaridoid species, using an established approach [e-value: ≤ 10-5) to proteins of C. elegans (BioProject PRJNA13758.WS261) to verify their identity.Genes  . Homolog5.15.54) , 43 of tapproach . PredictT. canis and A. suum using the program BLAT v.35 [e-value: ≤ 10-5; sequence similarity: ≥ 30%) [Homologous sequences were manually curated using a recently described workflow . In brieLAT v.35 . TranscrLAT v.35 . Full-leLAT v.35 . Gene prLAT v.35 . Gene or: ≥ 30%) .C. elegans using blastp  to infer their identity. For inferred insulin-like peptides, conserved patterns and motifs were searched using the programs Pratt v.2.1 [e-value: ≤ 10-5; sequence similarity: ≥ 50%).Sequence similarities of the classified orthologues were compared by pairwise alignment using EMBOSS Needle  . Domain tt v.2.1  and MEMEtt v.2.1 . The relT. canis and of A. suum [Available RNA-seq reads from egg, and first- (L1), second- (L2), third-  and fourth-stage larvae and/or adults of  A. suum –36 were  A. suum , 54. LevT. canis and A. suum, lipids were extracted from four individual male and four individual female adults using an established lipid extraction method [g for 10 min at room temperature (24 °C). The organic phase was retained, dried and resuspended in methanol for subsequent mass spectrometric analysis using an Orbitrap Fusion Lumos mass spectrometer coupled to an Ultimate 3000 UHPLC . Commercially available dafachronic acids (25S)-Δ7-DA and (25S)-Δ4-DA (exact mass: 413.3061)  were used as reference standards for the identification of endogenous DAs.For each n method , 56. In C. elegans, we identified 55 and 51 orthologues encoding signalling molecules in T. canis and A. suum, respectively  and relate mainly to less TGF-β and insulin/insulin-like signalling components in the ascaridoids. Specifically, orthologues inferred to represent SMAD transcription factors , SKI family transcriptional co-repressor (daf-5), insulin (daf-28), insulin-like peptides , serine/threonine-protein kinase (akt-2), bZip transcription factor (skn-1), 14-3-3 protein (par-5) and iron/manganese superoxide dismutase (sod-3) were not detected in either T. canis or A. suum, whereas two orthologues encoding heat-shock protein 90 (daf-21) were identified in both species  in T. canis compared with A. suum  and Tc-daf-12 (n = 18), than for A. suum  and one conserved sequence pattern  were discovered  to Ce-INS-1, Ce-INS-12, Ce-INS-17, Ce-INS-18 or Ce-INS-32   and A. suum (n = 2)  among C. elegans, T. canis and A. suum , respectively   were detected by LC-MS in the female and male adults of ely Fig. b. Specifuum Fig. . For bot05) Fig. c.T. canis , Tc-asna-1 and Tc-ins-18 , and Tc-daf-9 , transcription was higher in the L3 than the adult stage , As-asna-1 and As-daf-2 , and As-daf-9   stages of T. canis and compared them with those of A. suum using a bioinformatic strategy for gene curation and classification. We compared these components, proposed the biosynthetic pathways of DA and investigated developmental transcription of identified orthologues.Based on current genomic and transcriptomic datasets, we identified orthologues of the dauer signalling genes in T. canis and A. suum. Although dauer signalling gene homologues had been identified previously in a range of parasitic nematodes, including A. suum and T. canis [Ce-ins-1 and Ce-dhs-16 in both T. canis and A. suum, which were not reported previously [Ce-bra-1 homologues had been identified [T. canis or A. suum. Specifically, orthologues of Ce-ftt-2 and Ce-par-5 (encoding 14-3-3 proteins) were classified based on reciprocal BLAST searches and OrthoMCL grouping. Thus, the accurate identification and classification of orthologues provided a basis for reliable comparative studies.The identification of key dauer-associated signalling gene orthologues implies dauer-like signalling pathways in T. canis , ortholoT. canis . Specifieviously . In addientified , the ortC. elegans, the reduced number of genes coding for SMAD-related transcription factors and insulin/insulin-like peptides in T. canis and A. suum suggest divergences in both TGF-β and insulin-like signalling between the free-living and the two parasitic nematodes. Specifically, orthologues of Ce-daf-3, Ce-daf-5, Ce-daf-8 and Ce-daf-14 were not detected in T. canis or A. suum, suggesting a uniqueness in TGF-β signalling in these two species (clade III), as suggested previously by other workers for other parasitic nematodes such as Trichinella spiralis and Trichuris suis (clade I) [ins-1, ins-17 and ins-18, no orthologue of any of the other insulin/insulin-like peptide-coding genes (n = 37) of C. elegans (clade V) was found, implying a contraction of the gene family representing these signalling molecules in the two ascaridoids (clade III) studied here. Reduced numbers of genes encoding insulin-like peptides have been reported also in other parasitic nematodes, including Strongyloides stercoralis (clade IV) [The present findings indicated a divergent evolution and adaptation of the dauer-like signalling pathway among the three nematodes studied. Compared with clade I) , 57. In lade IV) .T. canis and A. suum might relate to differences in biology and/or developmental regulation. Although the dauer-like signalling pathways of T. canis and A. suum were similar, the increased number of orthologues in the former species indicates a distinction in TGF-β and insulin-like signalling  predicted for T. canis might also be involved in regulating larval development, but this proposal needs to be assessed. Secondly, the two Tc-ist-1 paralogues likely encode insulin receptor substrates, which function together with phosphoinositide 3-kinase (PI3K) adaptor/regulatory subunit to potentiate dauer-associated daf-2/insulin-like signalling, as known for C. elegans [Tc-akt-1 orthologues encode serine/threonine kinases Akt/protein kinase B, which play a role in antagonising the fork head transcription factor DAF-16 when activated by phospholipid products from PI3K [A. suum, it appears that T. canis produces more antagonist molecules (INS-1 and INS-18) which may trigger a stronger inhibitive effect on the fork head transcription factor DAF-16 (via phosphorylation of a serine/threonine kinase) to enable arrested development, particularly when migrating through and/or encysting in host tissues. In addition, the relatively high numbers of transcript isoforms for Tc-akt-1 (n = 5) and Tc-daf-12 (n = 18) might indicate rapid adaptive expression and/or plastic functional roles, similar to those of daf-16 reported for C. elegans [Tc-egl-4 paralogues were identified in T. canis, their functions are unclear, because orthologues coding for SMAD-related transcription factors [T. canis larvae. Significant progress has been made through the development of a functional genomics platform for A. suum [T. canis, in order to explore the functional roles of signalling molecules in this species.Distinctiveness in signalling between ing Fig. d which m elegans , 59. The elegans . Thirdlyrom PI3K . Therefo elegans . However factors  were not A. suum , which sT. canis and A. suum, we also analysed transcription profiles across developmental stages, employing RNA-sequence data for pooled worms. The consistently high messenger RNA levels of daf-21 and ftt-2 suggest key roles for these genes and their products in larval development, supported, to some extent, by information for C. elegans [gpa-3, daf-11 and tax-4 , asna-1  and daf-9  among developmental stages, particularly L3 stages in eggs, liver and lung, might suggest plastic, but crucial roles in regulating developmental processes and/or host-parasite interactions. In addition, differential transcriptional levels of the genes ins-17 and ins-18 in the L3 stage between T. canis and A. suum suggest a distinctive signalling mechanism in the host animal, since Ce-ins-17 and Ce-ins-18 promote dauer formation in C. elegans [To investigate dauer-like signalling pathway components in  elegans . By cont elegans , 65. Thi elegans , 66. ThiC. elegans implied relative conservation in the biosynthetic machinery for DA between T. canis and A. suum. The orthologues of Ce-ncr-1, Ce-daf-36, Ce-dhs-16, Ce-emb-8 and Ce-daf-9 likely function in the trafficking, catalysation and modification of cholesterol destined for the biosynthesis of DAs, known to be critical for regulating larval development in C. elegans and S. stercoralis [T. canis and A. suum. Interestingly, although Δ4- and Δ7-DAs are isomers, differential abundance in the adult stages of T. canis and A. suum might suggest functional distinctions in specific biological processes between these species. Based on the information for C. elegans and some parasitic nematodes [strm-1 gene in A. suum. Although similar components of the DA synthesis were predicted for both T. canis and A. suum, the functionality of this machinery in these species needs to be shown. By contrast, an orthologue of Ce-hsd-1, which likely functions in the biosynthesis of other DAs, was not detected in either T. canis or A. suum [T. canis [Comparisons with rcoralis –69. The ematodes , 70, the A. suum , suggestT. canis , the proT. canis , 68, 72.T. canis , 73, 74.C. elegans, T. canis and A. suum, but indicates similarity in the steroid hormone signalling pathway between the two ascaridoid nematodes. Inferring the elements of the dauer-like signalling pathway in these ascaridoids provides a basis for future explorations of the functional roles of these elements in each species using a reliable functional genomics platform. Understanding these processes could enlighten developmental processes and host-parasite interactions for these ascaridoids and facilitate the discovery of novel intervention strategies against the diseases that these parasites cause.The present study reveals distinctiveness in the TGF-β and insulin-like signalling pathways among Additional file 1:Table S1. Dauer signalling gene homologues of Toxocara canis and Ascaris suum. Table S2. Orthologous groups of dauer signalling genes in Toxocara canis and Ascaris suum inferred from Caenorhabditis elegans. Table S3. Salient information on dauer signalling genes of Toxocara canis predicted from Caenorhabditis elegans orthologues. Table S4. Salient information on dauer signalling genes of Ascaris suum predicted from Caenorhabditis elegans orthologues. Table S5. Classification of insulin-like peptide homologues in Toxocara canis and Ascaris suum. Table S6. Transcription of dauer-like signalling gene orthologues in key developmental stages of Toxocara canis and Ascaris suum. (XLSX 418 kb)


Genome wide association studies (GWAS) on residual feed intake (RFI) and its component traits including daily dry matter intake (DMI), average daily gain (ADG), and metabolic body weight (MWT) were conducted in a population of 7573 animals from multiple beef cattle breeds based on 7,853,211 imputed whole genome sequence variants. The GWAS results were used to elucidate genetic architectures of the feed efficiency related traits in beef cattle.P-value < 1.00E-05, 16, 72, 88, and 116 lead DNA variants on multiple chromosomes were significantly associated with RFI, DMI, ADG, and MWT, respectively. In addition, lead DNA variants with potentially large pleiotropic effects on DMI, ADG, and MWT were found on chromosomes 6, 14 and 20. On average, missense, 3’UTR, 5’UTR, and other regulatory region variants exhibited larger allele substitution effects in comparison to other functional classes. Intergenic and intron variants captured smaller proportions of additive genetic variance per DNA variant. Instead 3’UTR and synonymous variants explained a greater amount of genetic variance per DNA variant for all the traits examined while missense, 5’UTR and other regulatory region variants accounted for relatively more additive genetic variance per sequence variant for RFI and ADG, respectively. In total, 25 to 27 enriched cellular and molecular functions were identified with lipid metabolism and carbohydrate metabolism being the most significant for the feed efficiency traits.The DNA variant allele substitution effects approximated a bell-shaped distribution for all the traits while the distribution of additive genetic variances explained by single DNA variants followed a scaled inverse chi-squared distribution to a greater extent. With a threshold of RFI is controlled by many DNA variants with relatively small effects whereas DMI, ADG, and MWT are influenced by a few DNA variants with large effects and many DNA variants with small effects. Nucleotide polymorphisms in regulatory region and synonymous functional classes play a more important role per sequence variant in determining variation of the feed efficiency traits. The genetic architecture as revealed by the GWAS of the imputed 7,853,211 DNA variants will improve our understanding on the genetic control of feed efficiency traits in beef cattle. Improving animal meat production efficiency has become an imperative goal for the industry to achieve as the global demand for meat products continues to increase due to population growth and improved economic prosperity in the developed and developing countries. Animal meat production efficiency is primarily determined by an animal’s ability to convert consumed feed into saleable meat as feeding related cost is the single largest variable expense in animal production –3. Of meFeed efficiency can be measured in different ways –12, of wFeed efficiency is a complex trait and it is likely controlled by multiple genes involved in several physical, physiological and metabolic processes such as feed intake, digestion, body composition, tissue metabolism, activity and thermoregulation –20. ReseThe descriptive statistics of four feed efficiency related traits including mean, standard deviation, additive genetic variances (±SE), and heritability estimates (±SE) obtained based on the 50 K SNP and 7,853,211 DNA variant (or 7.8 M sequence variant) panels were shown in Table P-value < 0.005, significant P-value < 1.00E-05 and FDR < 0.10, and numbers of corresponding lead SNPs (or DNA variants) were presented in Table P-value < 0.005 detected by the 50 K SNP panel for RFI, DMI, ADG, and MWT were also identified by the 7.8 M sequence variant panel with a P-value < 0.005. The rest of the suggestive SNPs (12 or 0.1% for RFI to 39 or 0.2% for MWT) were detected by the 7.8 M sequence variant panel with a relaxed significance threshold of P-value < 2 × 0.005 = 0.01. Since all SNPs in the 50 K SNP panel were included in the 7.8 M sequence variant panel, it is expected that the SNP allele substitution effects and their significance test of P-value would be the same for both GWAS analyses if the same G matrix was used. The slight difference of P-values observed in this study is likely due to the different G matrix used in the 7.8 M sequence variant and 50 K SNP GWAS analyses. However, it is clearly shown that the 7.8 M sequence variant panel detected additional or novel significant SNPs at various significant thresholds for all the traits than the 50 K SNP panel as summarized in Table A summary of numbers of significant SNPs at the suggestive P-value < 0.005, ranging from 0.53% for RFI to 0.61% for MWT  were found to be associated with RFI, DMI, ADG, and MWT, respectively . We also obtained human orthologues of the gene to enhance the annotation. The annotation of the 7,853,211 DNA variants showed that 66.9% DNA variants were intergenic, followed by 25.3% for intronic variants, 3.6% for upstream gene variants, and 3.2% for downstream gene variants. Synonymous variants, missense variants and 3’UTR variants accounted for 0.41, 0.22 and 0.20% of the total DNA variants, respectively, and other functional classes had less than 0.1% of the total DNA variants  of the functional class and GRM constructed from DNA variants of all other functional classes simultaneously. In each run of additive genetic variance partition, the sum of the additive genetic variances captured by the two GRMs Table  was almoKoufariotis et al.  also fouP-values were slightly larger than the lead SNP  that are not included in the 7.8 M DNA variant panel.Although the intergenic region and intron variant classes had relative smaller effects on the feed efficiency traits, most of the lead SNPs with larger allele substitution effects were located within the intergenic region or intron variants Table . We alsoP-value < 0.005, we detected a range of 41,248 to 47,923 SNPs associated with RFI and its component traits on all autosomes based on the 7.8 M DNA variant GWAS as shown in the Manhattan plots  gene loci. These overlapped QTLs indicate a greater reliability of the detected significant SNPs for the traits. However, the unique QTL regions could suggest that the underlying causative DNA variants are segregating in the respective populations examined.Most of the overlapped SNPs were from QTLs identified by Seabury et al.  using acP-value < 1.00E-05 among the four traits. It was found that RFI shared only one lead SNP with DMI (rs382972340), and shared no lead SNPs with ADG or MWT , followed by gene NCAPG, which is located 293,308 to 301,284 bp from the lead SNPs. Snelling et al. [NCAPG-LCORL genes that were associated with DMI and ADG in a crossbred beef cattle population. Lindholm-Perry et al. [NCAPG-LCORL region were strongly associated with DMI and ADG, respectively. Furthermore, the differentially expressed genes in the NCAPG-LCORL region were also identified by Lindholm-Perry et al. [LCORL transcript abundance  in adipose tissue, a positive correlation between DMI and LCORL transcript abundance  and protein expression  in muscle tissue, and a positive correlation between NCAPG transcript abundance and ADG  in muscle tissue. Therefore, LCORL and NCAPG genes are considered to be candidate genes for the pleiotropic effects on the three traits. Although the lead SNPs are not in either of the genes, 121 support SNPs are within the LCORL gene and 122 support SNPs are within NCAPG. For LCORL, all the 121 support SNPs are located in the intron regions except for rs109572301 in the 3′-untranslated region. For NCAPG, 116 SNPs are located in the intronic regions, and 4 SNPs are synonymous variants and 2 SNPs are missense. The missense mutations rs109570900 and rs110251642 in NCAPG were simultaneously associated with ADG (FDR = 2.95E-07 and 2.20E-05), DMI (FDR = 1.61E-03 and 1.2E-01) and MWT (FDR = 3.03E-10 and 6.15E-06) in this study  while the lead SNP for DMI was annotated as the downstream gene variant of gene MOS (i.e. within 1997 bp). Pleomorphic adenoma gene 1 (PLAG1) regulates many target genes, including a number of growth factors such as insulin-like growth factor 2, and substantial evidence supports the gene as a regulator of growth and reproduction [MOS is a serine/threonine kinase involved in the MAPK signaling pathway and oocyte meiosis. Gene Ontology (GO) annotations related to this gene mainly focus on biological processes including transferase activity, transferring phosphorus-containing groups and protein tyrosine kinase activity. DNA variants downstream of a gene have been reported to have roles in regulating gene expression or gene translation [STC2, 5S_rRNA, NKX2–5, BNIP1, CREBRF, RPL26L1, and ERGIC1. However, the SNP rs42661323 accounted for 0.67% phenotypic variance of ADG  and is located within gene STC2 as a missense mutation. The SNP also showed significant effects on DMI  and MWT  and it explained 0.53 and 1.73% phenotypic variance for DMI and MWT, respectively . STC2 is a homologue of a glycoprotein hormone playing a role in cell proliferation in multiple cancers [STC2 gene was shown to down-regulate postnatal growth [STC2 also played a role in adiposity and obesity in nondiabetic humans [STC2 is considered as the primary positional candidate gene on BTA20 with pleiotropic effects for ADG, DMI and MWT.Other candidate QTLs that had relatively large pleiotropic effects on DMI, ADG, and MWT included a QTL region on BTA 14 and 20. On BTA 14, the three lead SNPs for the three traits are located within 32,172 bp, and ADG and MWT shared the same lead SNP rs134215421. The genes oduction . MOS is  cancers . Overexpl growth , and redl growth  in mice.c humans . TherefoARRDC3 on BTA7, accounted for 0.59% for DMI  and 0.70% for MWT   internalization and degradation. In addition, the lead SNP (rs209660822) explained 0.19% for DMI  and 0.35% for MWT   in humans [On other chromosomes, a few significant SNPs with relatively large pleiotropic effects on DMI, ADG, and MWT were also observed. The lead SNP rs109901274), a missense variant in 74, a misP-value < 1.00E-05 and a LD window of 70 k bp from the lead SNPs. These candidate genes provide a reasonable reference to infer enriched biological functions for the feed efficiency related traits.Understanding of enriched molecular processes, pathways and gene networks associated with complex traits will help shed some light on the underlying genetic mechanisms and associated genes. These enriched molecular processes are usually inferred based on global transcriptome analyses such as microarray and RNAseq studies. Global transcriptome analyses typically target specific tissues and they focus more on differences of transcripts between groups of extreme phenotypes , 61. On The greater similarity of the top 5 enriched molecular processes between RFI and MWT, and between ADG and DMI are a reflection of more candidate genes shared between RFI and MWT, and between ADG and DMI  that consisted of AAFC Lacombe Research and Development Centre crossbreds, and commercial terminal crossbreds (TX/TXX). Descriptions of the herds/populations, cattle breeding, and management of the animals were previously described , 26, 44.0.75, where the midpoint BW was computed as the sum of the initial BW and the product of ADG multiplied by half of the days on test. Residual feed intake (RFI) in kg of DMI per day was computed as the difference between the standardized daily DMI and the expected DMI that was predicted based on animal’s ADG and MWT. The detail of GrowSafe data quality control and calculation of DMI, ADG, MWT, and RFI was described in previous reports [Phenotypic data were collected on animals from 1998 to 2006 for the Elora crossbred, from 2002 to 2015 for Kinsella Composite, from 2004 to 2015 for Angus and Charolais, and from 2008 to 2011 for PG1 and TX/TXX populations. Feed intake was measured for finishing calves (steers and heifers) using the GrowSafe system  at their respective feedlot test stations for a period of 76 to 112 days. Serial body weights (BW) in kg were measured for each animal at the beginning and end of the test and at approximately 14-day interval during the test. The daily dry matter intake (DMI) in kg was calculated as an average of dry matter intake over the test period and was further standardized to 12 MJ ME per kg dry matter for finishing steers and heifers, and 10 MJ ME per kg dry matter for replacement heifers based on the energy content of the diet. The initial BW at the start of the test and average daily gain (ADG) were derived from a linear regression of the serial BW measurements against time (day). The metabolic BW (MWT) in kg was calculated as midpoint BW reports , 69. TheP-value < 0.001) were excluded from further analyses. After SNP data editing, 33,321 SNP were retained for further analyses. Sporadic missing SNP genotypes in the SNP data set (< 0.065%) were then imputed via the population-based algorithm implemented in Beagle 3.3.2 [Animals with phenotype data from the above herds were also genotyped on bovine 50 K SNP panels under previous projects. In this study, SNP data from various data sources were combined with the same SNP format. Furthermore, to ensure that SNP genotypes from different data sources were merged correctly, alleles of each SNP were examined after merging to ensure that each SNP had only two alleles as expected. In total, 50 K SNP genotypes from 11,448 beef cattle were compiled with a SNP genotype call rate greater than 90%. SNP quality control was also conducted for each data source, where SNPs that had a minor allele frequency less than 0.05, had a missing rate larger than 0.05, or were significantly deviated from Hardy-Weinberg equilibrium test (HWT) (le 3.3.2 .N = 1162), Charolais (N = 717), Kinsella Composite (N = 1506), Elora crossbred population (N = 775), commercial crossbred PG1 (N = 1911), and commercial TX/TXX strain terminal crossbreds (N = 1502).With the 50 K SNP panel 33,321 SNPs) on 11,448 beef cattle, a principal component analysis was conducted to examine the population admixture of the Canadian beef cattle, and the breed composition of each animal was predicted using Admixture software . The pri SNPs on https://www.westgrid.ca/). A dataset of 240 animals with both genotyped 50 K SNP panel  and whole sequence data was used to evaluate the accuracy of imputation in a 5-fold cross validation, assuming that the 240 animals had no whole sequence data. The 240 individuals were randomly split into five groups. Each group or fold of animal (N = 48) was then chosen as a validation group in turn, and the rest of individuals were merged into the whole genome sequence reference population to impute WGS genotypes for all animals in the validation group. The accuracy of imputation was calculated as the average proportion of WGS variant genotypes of the animals in the validation group that were correctly imputed assuming that the real genotypes of WGS variants had no errors. In total, 38,318,974 WGS variant genotypes were imputed on all the animals and the average imputation accuracy of each chromosome was provided in  on all the animals. The 7,853,211 DNA variants included 7,497,128 SNPs and 356,083 INDELs (termed 7.8 M DNA variants or SNPs for simplicity). To facilitate comparison of the 50 K SNPs and 7.8 M DNA variants, the imputed 30,155 SNPs in the 7.8 M DNA variant panel were replaced by their actual genotypes.Imputation of genotypes from the 50 K SNP panel  to whole genome sequence variants was performed via FImpute 2.2  in a twomlma (mixed linear model association) option as implemented in the GCTA package [The GWAS analyses were performed using the  package , 76, witij is the adjusted phenotypic value of the ith animal with the jth SNP , bj is the allele substitution effect of the jth SNP, xij is the jth SNP genotype of animal i and xij is coded as 0, 1, 2 for genotypes A1A1, A1A2, and A2A2, respectively, aij is the additive polygenic effect of the ijth animal ij is the random residual effect G (GRM) that was derived as defined in Yang et al. [where yg et al. :\documeAjk is the off diagonal element of the G matrix, i.e. genome relationship of animal j and animal k, and it represents the diagonal element if j = k, xij = 0, 1, 2 are for genotypes A1A1, A1A2, and A2A2, respectively, pi is the allele frequency of A2 at locus i, M is the total number of SNPs. The G matrix was constructed with all the DNA marker genotypes in the 7.8 M DNA variant panel, i.e. the MLMi method. As a comparison, GWAS analyses were performed on the 50 K SNP panel using the same approach with the G matrix constructed based on the 30,155 SNPs.Where p and q denote the SNP allele frequency of A1 and A2, respectively; ß is the SNP allele substitution effect, and 2pqβ2 is the additive genetic variance, and S2 is the phenotypic variance.The allele substitution effect of each DNA variant (or SNP) was estimated and the significance test of SNP allele substitution effect was conducted using the GCTA package , 77–79. p-value < 0.005 were considered as suggestively significant SNPs as proposed by Benjamin et al. [P-value < 1.00E-05 (i.e.10− 5) were classified as significant SNPs based on the recommendation of The Wellcome Trust Case Control Consortium [P-value in non-decreasing order. The SNPs with the lowest P-value was identified as the lead SNPs while other SNPs within 70 k bp upstream and downstream from the lead SNP were defined as supportive SNPs. A 70 k bp window was chosen for this study as this was the chromosomal length within which a high LD phase correlation (> 0.77) was maintained across a sample of Canadian beef cattle breeds [SNPs that have a nominal n et al. , while Snsortium . In addinsortium , which ce breeds .To estimate the genomic heritability of a trait, we used the GREML-LDMS approach proposed by Yang et al. , 83 thatThe 7,853,211 DNA variants were assigned to a functional class based on their overlap with gene features described in the Ensembl database (release 81), using an updated version of the NGS-SNP annotation system . Gene fuDistribution of SNP effects was obtained by plotting SNP allele substitution effects of all the DNA variants in the 7.8 M DNA variant panel, and by plotting the additive genetic variances explained by individual DNA variant in the panel. To examine relative importance of DNA variant functional classes, SNPs were further classified into 9 broad functional classes as shown in . Ingenuity Pathway Analysis (IPA) web-based software  (IPA Spring 2019 release) was used for the functional enrichment analyses of the candidate genes identified. Briefly, for the genes with known human orthologues from Ensembl, their gene IDs were replaced with their human orthologous gene IDs, whereas for those without human orthologs their bovine gene IDs were maintained in the gene list. These Ensembl gene IDs were then used as input gene identifiers in IPA and a core analysis was performed on the genes that were mapped to the IPA knowledge base database. Molecular and cellular biological functions were considered significantly enriched if the P-value for the overlap comparison test between the input gene list and the IPA knowledge base database for a given biological function was less than 0.05. Additionally, biological processes/subfunctions interaction networks within the most significant molecular and cellular function were generated to show possible biological networks, through which the candidate genes involved in the network were identified.Genes that are located within 70 k bp upstream and downstream of the lead SNP were considered as candidate genes associated with the trait based on SNP annotation information from the UMD3.1 bovine genome assembly from Ensembl  and additive genetic variances explained by single SNPs (right) for RFI, DMI, ADG, and MWT based on 7.8 M whole genome sequence (WGS) variants; Figure S2. The enriched cellular and molecular functions (top) and cell morphology network (bottom) for RFI; Figure S3. The enriched cellular and molecular functions (top) and carbohydrate metabolism network (bottom) for DMI; Figure S4. The enriched cellular and molecular functions (top) and carbohydrate metabolism network (bottom) for ADG; Figure S5. The enriched cellular and molecular functions (top) and cellular compromise network (bottom) for MWT; Figure S6. Lipid metabolism network for RFI and its component traits DMI, ADG, and MWT; Figure S7. Common lead significant SNPs with the same IDs (a) and candidate genes (b) among RFI and its three component traits at thresholds P-value < 1.00E-05 and FDR < 0.10 based on the imputed 7.8 M DNA variant GWAS; Figure S8. Principal component analyses of Canadian beef cattle populations (N = 7573) based on the 50 K (a) and 7.8 M WGS (b) panels.Additional file 2. This file contains information of all suggestive significant SNPs/INDELs at P-value < 0.005 based on the 7.8 M sequence variant GWAS for RFI, DMI, ADG, and MWT.Additional file 3: Table S1. Accuracy of genotype imputation from Illumina 50 K SNPs to whole genome sequence (WGS) variant genotypes via HD using Fimpute 2.2; Table S2. Functional annotations of 7.8 M WGS variants along with the number of variants in each class, classification of SNP functions, percentage of WGS and 9 functional class assignments; Table S3. Functional annotation of all DNA variants  based on DNA variants of the 1000 bulls genome project; Table S4. Functional annotations of SNPs in the 50 K SNP panel after quality control along with the number of variants in each class, classification of SNP functions, percentage of WGS, and 9 functional class assignments; Table S5. List of lead SNPs that were overlapped with QTLs published in Cattle QTL database for RFI, DMI, ADG, and MWT.


Additionally, we revealed a significant association of SAD.1 marker with the content of the linolenic unsaturated fatty acids . Moreover, SAD.1 was positively correlated with the saturated stearic acid C18:0  based on multinomial logistic regression and Bayesian networks modeling, respectively. This research work provides better understanding and characterization of the quality of Tunisian table olive and supplies a significant knowledge and data information for table olive traceability and breeding.To enhance and highlight the authentication and traceability of table olive oil, we considered the analysis of 11 Tunisian table olive cultivars based on seven SNP molecular markers  localized in six different genes. Accordingly, we assessed the potential genotype-phenotypes links between the seven SNPs, on the one hand, and the quantitative and qualitative parameters, on the other. The obtained genotypes were analyzed with computational biology tools based on bivariate analysis, multinomial logistic regression, and the Bayesian networks modeling. Obtained results showed that PAL70 SNP marker was negatively influenced by the phenol rate ( Olea europaea L.) with regard to their size, volume, taste, and other organoleptic properties that make them suitable for table consumption.In the Mediterranean basin countries, olive is one of the most important agricultural products. It is used for olive oil extraction or processed as either table olives. These latter are chosen from cultivated olive trees , calcium-binding protein, stearoyl-acyl carrier protein desaturase (SAD), and L-phenylalanine ammonia lyase. The FAD2.1 and FAD2.3 SNPs are both harboured by the FAD2 gene which is involved in the biosynthesis of highly unsaturated fatty acids (HUFA) from the precursor polyunsaturated fatty acids (PUFA)  wit FAD2, ANTHO, CALC, SAD1, SOD, and PAL70 genes, all of them being involved in fruits pomology and associated with olive oil composition and therefore easily correlatable to phenotypic characters.We considered seven SNPs  in our study; all SNPs were selected in the coding regions of MspI restriction enzyme  at 37°C overnight. This restriction enzyme recognizes the sequence AA/GG. The G-allele carrying PCR product was cleaved once by the enzyme generating two fragments (64 and 107 bp). The PCR product (476 bp) of SNP (CALC) was digested by BstZI restriction enzyme (Promega) at 50°C overnight. This restriction enzyme recognizes the sequence CC/GG. The C-allele carrying PCR product was cleaved once by the enzyme producing two fragments (316-160 bp). The two other SNPs (FAD2.1 and FAD2.3) were analyzed using PCR-RFLP. The PCR product 241bp of the SNP FAD2.1 and 240 bp of the SNP FAD2.3 were digested by BamHI restriction enzyme  and Alw26I, respectively, at 37°C overnight. The sizes of the restriction fragments of PCR product were 224 and 17 bp and 130 and 110 bp for CC genotype of FAD2.1 SNP and FAD2.3 SNP, respectively. The PCR product (330 bp) of SNP (SAD.1) was digested by TaqI restriction enzyme (Vivantis) at 65°C for 16 hours. This restriction enzyme recognizes the sequence CC/TT. The C-allele carrying PCR product was cleaved twice by the enzyme producing four fragments . The PCR product (400 bp) of the SNP (PAL70) was digested by HinfI restriction enzyme . The size of the restriction fragments of PCR product was 308, 52, and 40 bp for AA genotype of PAL70 SNP.The SNP SOD (insertion/deletion type) was genotyped by a simple polymerase chain reaction followed by revelation through agarose gel electrophoresis, whereas the other six SNPs  were genotyped by a polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method . The PCRAll digestion products were separated by electrophoresis on 3% Nusieve ethidium bromide-stained agarose gels and visualized under UV light. P values are <0.05. Thereafter, to study the relationship of the studied seven SNPs with quantitative traits, a variance multiway analysis was carried out. In addition, multinomial logistic regression was applied in order to test the associations of the seven SNPs with qualitative traits independently.The analysis of the correlation between SNP markers and the studied parameters was performed in different steps including numerous statistical methods. In the beginning, the Chi-square test was used to evaluate the differences between the classes of qualitative traits in allele and genotype frequencies. Subsequently, a student test was used for quantitative traits, to assess the significant difference between the means of genotype groups for each SNP. R software packages were used to study the association between SNP markers and quantitative and qualitative parameters. All tests were declared statistically significant whenTo draw the directed acyclic graph (DAG), we used the R language and the ‘grow shrink' algorithm. The algorithm proficiently filters links out of a full skeletal DAG, in which all nodes are primarily connected (excluding those having no relationships with others), based on tests of conditional independence between a pair of nodes given all possible subsets of the rest. Logical rules are applied to create the direction of links , so that cycles are not introduced and patterns of conditional independence are found in the data match the generated DAG. We predicted association power in the final DAG by calculating approximately the beta-coefficient for a regression for each potential causal effect in which the variable at the base of the arrow (‘cause') was considered a covariate, and the variable at the head of the arrow (‘effect') was considered the outcome or dependent variable .The observed heterozygosity for the studied SNP markers ranged from 0.363 (SAD.1) to 0.909 (FAD2.1) (0.545 average), whereas the expected heterozygosity ranged from 0.297 to 0.495 with an average of 0.492 indicating a high level of heterozygosity for all markers .Polymorphism Information Content (PIC) value was determined for the studied table olive cultivars. via L-phenylalanine deamination and is therefore associated with phenolic compounds content in olives [n olives . Fatty acid desaturase 2 gene which is involved in the biosynthesis of HUFA from PUFA precursor has been demonstrated to be associated with oleic/linoleic acid ratio content of olive oils from Tunisian olive oil cultivars [The highest discriminating power (DP) value 0.528 was shown for FAD2.3 marker. The mean value is 0.464. Using SSR markers, Reale et al. 2006)  and Muzz06  and Multivars .The allele frequencies of the seven studied SNPs revealed a dominance of all markers, except for PAL70 whose two alleles displayed similar frequencies.p values of the LRT and Ki2 tests. The results are summarized in In order to illustrate the association between quality of the table olive cultivars and gene information, we applied the likelihood ratio test (LRT). Thus, a genome-wide association was carried out to identify table olive fruit quality susceptibility alleles. We studied 7 SNPs located in 6 genes for 11 table olive samples. Then, we evaluated the p = 0.04) was significantly different. This positive association among CALC polymorphisms and C16:0 parameter suggests that the heterozygote varieties with CG genotypes produce, on average, higher levels of C16:0 than GG genotypes varieties. As shown in p = 0.039).As shown in β-sitosterol (p=0.018) quantitative parameter is proved. Similar result is generated by multinomial logistic regression (p=0.022)  .p=0.024), rate of carotene (p=0.013), and cholesterol content (p=0.048)    . The homp=0.048) . Moreovep<0.001)  and the p<0.001) , respectp = 0.042), which has a higher average for heterozygous cultivars (AG)  (p = 0.04)  . This si = 0.04) .p=0.002) was noted. A positive correlation between the total phenol content and the genotype variation for this marker (p<0.001) could also be observed, where the varieties with AA genotypes displayed the highest total phenolic content. Regarding the relationship with oxidative stability (p<0.001), the homozygous varieties AA behaved with better oil stability than the heterozygous varieties AG. Moreover, the PAL70 SNP marker is significantly associated with the chlorophyll rate (p=0.031) by using the analysis of variance , the oxidative stability , and the chlorophyll . Furthermore, PAL70 node was not influenced by the sterol level .The relationship between the PAL70 SNP and the phenol level was assessed by Bayesian networks modeling. The derived DAG (directed acyclic graph) is shown in The oxidative stability of the olive oil samples is directly influenced by the total phenol level. Besides, total phenol amount is directly influenced by the PAL70 marker. The latter plays a key role in the total phenol level of each of the olive oil varieties. This finding could be explained by the fact that PAL70 SNP is located within a gene involved in the phenolic biosynthesis , suggesting the direct effect of the PAL70 genotype variations on the percentage of total phenol for each variety. p=0.046) and with the rate of carotene (p=0.005)  and two table olive cultivars (Toffehi and Fakhari). Two significant associations of this marker were shown with the accumulation of the linolenic unsaturated fatty acids  .p=0.01) and C18:3 (p=0.043) are confirmed by multinomial logistic regression modeling .Firstly, 3 nodes were considered as represented in SAD gene is known to be associated with the transformation of the saturated stearic fatty acid C18:0 to the monounsaturated oleic fatty acid C18:1, therefore, suggesting the direct effect of the SAD.1 genotype variations on the fatty acid content .r=-0.886; p <0.001) and the oxidative stability . Besides, we reported a significant association of SAD.1 marker with the accumulation of the linolenic unsaturated fatty acid  and that SAD.1 was positively influenced by the saturated stearic acid C18:0  based on multinomial logistic regression and Bayesian networks modeling, respectively. To the best of our knowledge, this is the first work that analyses the SNP markers of Tunisian table olive oil and the quality of the oil.SNP genotyping is a valuable approach for marker assisted selection in crops. For this reason, we studied in this current work the correlations between the six SNPs and table olive oils quality parameters and their usefulness in the traceability of Tunisian table olive oil. We revealed that PAL70 SNP marker was negatively influenced by the phenol rate (