Haliotis midae is one of the most valuable commercial abalone species in the world, but is highly vulnerable, due to exploitation, habitat destruction and predation. In order to preserve wild and cultured stocks, genetic management and improvement of the species has become crucial. Fundamental to this is the availability and employment of molecular markers, such as microsatellites and single nucleotide (SNPs). Transcriptome sequences generated through sequencing-by-synthesis technology were utilized for the in vitro and in silico identification of 505 putative SNPs from a total of 316 selected contigs. A subset of 234 SNPs were further validated and characterized in wild and cultured abalone using two Illumina GoldenGate genotyping assays. Combined with VeraCode technology, this genotyping platform yielded a 65%–69% conversion rate (percentage polymorphic markers) with a global genotyping success rate of 76%–85% and provided a viable means for validating SNP markers in a non-model species. The utility of 31 of the validated SNPs in population structure analysis was confirmed, while a large number of SNPs (174) were shown to be informative and are, thus, good candidates for linkage map construction. The non-synonymous SNPs (50) located in coding regions of genes that showed similarities with known proteins will also be useful for genetic applications, such as the marker-assisted selection of genes of relevance to abalone aquaculture. Haliotis midae, is a highly valuable marine resource and one of almost 30 commercially viable abalone species found worldwide [H. midae using various enrichment and in silico techniques [Gadus morhua) [Patinopecten yessoensis) [Salmo salar) [Ictalurus punctatus) [Haliotis discus hannai) [The South African abalone, orldwide . The Souorldwide . In ordechniques –7. Althochniques . The focchniques ,9. Thesechniques . The inc morhua) ,12, Japasoensis) , Atlantio salar) ,15, Channctatus)  and, als hannai) ,18.H. midae, as in many other non-model organisms, no complete genome map is currently available. Transcriptomic data (in the form of expressed sequence tags (ESTs)), however, serve as a viable source for SNP discovery [In the case of iscovery ,19–21, fiscovery . Despiteiscovery .in vitro or in silico approaches [In vitro detection of SNPs involves re-sequencing of targeted ESTs to identify nucleotide variations, whereas in silico detection refers to the use of bioinformatic pipelines to identify polymorphisms [Single nucleotide polymorphism isolation procedures have improved greatly, due to advances in DNA sequence technology, and, at present, most commonly include the utilization of whole genome or transcriptome next generation sequencing (NGS) data. The NGS platforms expedite sequence data generation by increasing the production of sequence data to several thousand megabase pairs (Mb) ,25, and proaches . In vitrorphisms ,31. LargCurrently, various high-throughput genotyping platforms exist, but only a few are suitable for medium-throughput genotyping, which is preferred for non-model species . In thisH. midae following two bioinformatic pipelines. The EST data formed the basis for in vitro and in silico SNP discovery. Primer efficiency and genotyping success was evaluated by characterization of two GoldenGate assays in individuals from wild and cultured H. midae populations. Downstream applications of successfully genotyped SNPs were also assessed for both GoldenGate assays, and these results form part of an integrative research effort with regards to genetic characterization and improvement of South African abalone. The 48-plex GoldenGate assay (Plex-48) was employed to assess population differentiation between six H. midae populations, and a second 192-plex GoldenGate assay (Plex-192) was used to illustrate and test the utility of SNP markers in conjunction with microsatellite markers for linkage map construction in eight H. midae full-sib families.In this study, we focused on the development of SNPs from transcriptome data previously described for de novo assembly was 22,761, with an average length of 260 bp and an average contig coverage of 400 reads/contig. For the in vitro SNP detection via re-sequencing of 58 selected contigs, a total of 66% of the optimized PCR amplicons yielded trace quality adequate for putative SNP discovery. The in vitro primer success rate (primers that amplified successfully) observed correlated well with similar studies on aquaculture species, including that of the Eastern oyster (Crassostrea virginica) (69%) [Hyriopsis cumingii) (58%) [Haliotis discus hannai) (67.3%) [in vitro approach [Crassostrea gigas) also showed a marked (~30%) increase in primer success rate, by designing primers where the reverse primer is situated in the 3′ untranslated region (UTR) [in silico detection approach using the SNP detection module in CLC Genomics Workbench, 958 assembled contigs containing 3645 putative SNPs were identified. Following the in vitro and in silico detection approaches, a subset of 505 putative SNPs  was selected. This amounted to approximately one SNP every 550 bp, which is a much lower SNP frequency than previously obtained in H. midae  ratio of 1.71, while 253 (63%) transitions and 145 (36%) transversions for the in silico SNPs, representing a ts:tv ratio of 1.74  (69%) , Pearl mi) (58%)  and Paci (67.3%) . The genapproach ,38. A ston (UTR) . A simil~150 bp; ,38). Thi~150 bp; , but no  of 1.74 . AlthougH. midae ,12, a hiH. midae .in vitro: 50%) and (4) via the CLC workbench approach (in silico: 25%). The loci included in Plex-192 were all detected using the CLC workbench SNP detection utility. It was found that the majority (57%) of SNPs that failed to genotype were identified from microsatellite flanking regions. This failure could be due to the hyper-variability of microsatellite flanking regions and, consequently, the possibility that primers are binding in variable regions [in silico depends mostly on the species, the representative sample used for generating data, as well as the quality of the sequencing data [in silico markers from Plex-48 and 23.7% of the markers from Plex-192 failed to genotype, this method proved to be a more time- and cost-effective means of isolating SNPs in a non-model species.Due to the various SNP isolation methods used in this study, it was necessary to evaluate the different approaches in terms of marker performance. The SNPs evaluated in Plex-48 were isolated in four different ways: (1) directly from a cDNA library via a targeted EST approach (8%), (2) from microsatellite flanking regions (17%), (3) from EST contigs and re-sequencing  genotyping success rate of 85.4% (41 SNPs) and 76.3% (142 SNPs) was obtained for Plex-48 and Plex-192, respectively. Of the successfully genotyped loci, a total of 159 SNPs were found to be polymorphic and 24 were monomorphic . These rIn this study, only SNPs with a functionality score ≥0.75 were selected for validation to ensure a high genotyping success rate, but despite this, 14.6% (7 from Plex-48) and 22.9% (44 from Plex-192) still failed to cluster and were considered genotyping failures. Previous studies found that the inclusion of SNPs with functionality scores ≤0.6 reduced the overall success rate of such markers significantly ,46. In PIctalurus spp.—69%; [Pinus pinaster—66.9%; [Ictalurus spp.—59.8%; [Picea glauca—69.2%) and black spruce (Picea mariana—77.1%) [The success of any genotyping method is reflected in what is referred to as the conversion rate and the global success rate. The former considers only the polymorphic markers, whereas the global success rate considers all the markers (monomorphic and polymorphic) that were successfully typed within the sample group . In thispp.—69%; ) and marr—66.9%; . The con.—59.8%; ) and thea—77.1%) . The cona—77.1%) . FactorsH. tuberculata coccinea [Final confirmation of the success of the current genotyping assays is the significantly lower number of monomorphic SNPs  that was found in comparison to other aquaculture species . Interescoccinea . These mE-value), expected variants and SNP position and the effect for SNPs of Plex-48 and Plex-192 are shown in Functional annotation of the contigs utilized in the compilation of the GoldenGate assays (Plex-48 and Plex-192) indicated that the majority of the sequences showed significant similarity to the genes of interest: 88% Plex-48,  and 79% The majority of hits were classified in the Mollusca (47%) and Chordata (22%) phyla. The group Mollusca was further divided into Gastropoda and Bivalvia, with twice as many significant hits classed as Gastropoda. A total of 31 contigs could not be annotated, and the position and functional effect for four Plex-48 SNPs (8.33%) and 30 Plex-192 SNPs (16.13%) could, therefore, not be determined. For the two assays combined, 34.61% of the SNPs were located in the 3′ untranslated regions (UTRs), while 50.85% of the SNPs were in coding regions. Further analysis of functional changes revealed that 69 of the total SNPs were synonymous and 50 were non-synonymous, of which the latter accounted for over 20% of the SNPs developed in this study and could be considered as functionally important changes in the corresponding proteins [o) and unbiased expected heterozygosity (He) values ranged from 0.003 to 0.788 and 0.003 to 0.497, respectively, for Plex-48 and from 0.083 to 0.750 and 0.083 to 0.522 for Plex-192. For Plex-48, 15 SNP loci deviated significantly from Hardy-Weinberg equilibrium (HWE) (p < 0.05), while only 12 SNPs were not in accordance with HWE for Plex-192. The low to moderate levels of heterozygosity along with the average MAF (18.1%) observed in the majority of the SNPs applied in this study were comparable to reports in, for example, Pacific abalone, H. discus hannai [Scophthalmus maximus [f = −0.188) was observed for all the Plex-48 SNPs and most of the SNPs of Plex-192.All SNP loci showed two alleles and were in agreement with those originally observed before validation , while fs hannai ,18 and t maximus . AlthougF-statistics), as well as multivariate analysis (FCA). Pairwise FST (θ) values ranged from 0.006 to 0.199, with significance (p < 0.05) over all but one of the population pairs tested. Results indicated limited genetic differentiation between the three wild populations, while the relatively high values (FST = 0.090–0.140) obtained between wild and cultured populations indicated considerable genetic differentiation between wild and cultured abalone. Factorial correspondence analysis was performed to obtain a three-dimensional view of the genetic relationship between the six different populations. The first factor accounted for 43.39% of the genetic variation and the second factor for 24.02%. Most evident from the FCA plots was the tight clustering of the three wild populations and the noticeable genetic distinctness of each of the three cultured populations (FST) was evident between cultured and wild populations, but was most striking between the three cultured full-sib families. The differentiation between wild and cultured abalone was in accordance with what has recently been found in H. midae based on microsatellite markers [FST > 0.15) could be attributed to the highly heterozygous nature of the wild-caught parents, leading to an increase in the probability of the resulting F1 progenies receiving two different alleles at each locus, inflating the genetic distinctness of the respective families. With regards to the wild populations that included abalone from the West , South (Witsand) and East coast (Riet Point) of South Africa, FCA results corroborated the summary statistics in that the only statistically unsupported pairwise FST value was found between the wild populations of Witsand and Saldanha Bay. Bester-van der Merwe et al. [H. midae, but that the primary barrier to gene flow was around the Cape-Agulhas area. Saldanha Bay is situated west of Cape-Agulhas and Witsand and Saldanha Bay, east of the proposed barrier. The level of population differentiation and pattern of gene flow depicted by the SNPs in this study are therefore in full agreement with the population structure previously suggested for wild H. midae populations. The population differentiation was also supported by the molecular analysis of variance (AMOVA) results, with significant differentiation amongst populations within groups  and within populations .Applying the 31 polymorphic markers of Plex-48, population differentiation between three wild and three commercial populations was inferred based on summary statistics  was exhibited for family DS_1. Only a very small number of SNPs  showed distortion of segregation after correction for multiple tests, and most of the markers were distorted in one family only. Consequently, a high percentage of the SNPs (118) developed in this study were found to be informative markers (heterozygous in both or at least one of the parents) and could be considered as candidates for linkage map construction . However alleles . Accordide novo assembly of the H. midae transcriptome have been described in detail by Franchini et al. [de novo using the CLC Genomics Workbench v4.0 software , and sequence annotation was performed using Blast2GO 2.4.4 [Samples and methods for sequencing and i et al. . In brieGO 2.4.4 . In bothGO 2.4.4 ), Gene OGO 2.4.4 ) and theGO 2.4.4 –59).in vitro identification of SNPs, annotated contigs resulting from the first bioinformatic analysis were screened manually to identify contigs with significant hits  against genes with known functions using the BLASTN algorithm (BLAST) [E-value < 1.3 × 10−19) were selected and facilitated the design of 97 primer pairs with BatchPrimer3 [H. midae individuals in 10 μL reaction volumes containing 20 ng genomic DNA, 200 μM dNTPs, 2.0 mM MgCl2, 2.0 pmol of each primer and 0.25 U GoTaq® Flexi DNA polymerase . Thermal cycling was conducted on the Gene-Amp System 2700 thermal cycler , and conditions consisted of an initial denaturing step of 95 °C for 5 min, followed by 35 cycles of 94 °C for 30 s, Tm (specific for each primer pair) for 45 s, 72 °C for 45 s and a final elongation step of 10 min at 72 °C. Assessment of PCR amplification was conducted through 2% agarose gel electrophoresis. PCR amplicons were purified, quantified and Sanger sequenced using the BigDye® Terminator v3 Cycle Sequencing kit (Applied Biosystems) and the ABI PRISM® 3100 DNA automated sequencer (Applied Biosystems). DNA sequence chromatograms were individually analyzed using Sequence Scanner v1.0 (Applied Biosystems) to determine sequence quality. Multiple alignments for sequences of each primer pair were carried out using ClustalW v1.4 [For the  (BLAST) . A set ohPrimer3 . Twenty alW v1.4 , implemealW v1.4 . Sequencin silico identification of SNPs, the SNP detection module in CLC Genomics Workbench was used to screen 22,761 assembled contigs following the second bioinformatic pipeline. The parameters that were set for putative SNP calling included a minimum quality score of 20, minimum coverage of 80 and a minor allele frequency (MAF) >10%. Of these, 139 SNP-containing contigs were selected for further analysis of 400 putative SNPs. To ensure reliable primer design, the flanking sequences of putative SNPs were checked to ensure that there were no other polymorphisms within 60 bp of the SNPs. Contigs containing the predicted SNPs were then subjected to BLAST similarity searches using Blast2GO.For the in vitro SNPs developed in the first bioinformatic analysis, 12 in vitro SNPs previously developed by Bester et al. (2008) and Rhode (2010) and 12 novel in silico SNPs identified by means of the second bioinformatic pipeline using CLC Genomics Workbench. The in silico SNPs represented a test panel to determine the efficiency and performance of markers identified with the CLC workbench. For Plex-192, 186 SNPs from the second bioinformatic pipeline and six SNP loci from Plex-48 (positive SNP controls) were included. For both assays, sequences containing putative SNPs were submitted to the Illumina Assay Design Tool to determine the functionality and primer designability score for each SNP locus. Scores of 0.5–1.0 are required for a high quality assay, and only loci with a final score ≥0.75 were considered for genotyping.SNP characterization using the GoldenGate genotyping assay was conducted in two separate multiplex assays, further referred to as Plex-48 and Plex-192. The SNPs selected for Plex-48 were comprised of 24 Samples for Plex-48 included individuals from three wild abalone populations from the coast of South Africa , as well as parents and offspring of four linkage mapping families collected from two commercial abalone farms . The sam® BigDye Terminator v3.1 Cycle Sequencing kit in the forward direction. Genotypes acquired from the Sanger sequencing were manually compared to those generated by the Genotyping Module. For one of the cultured populations, DS_2, the majority (87.6%) of the population could not be genotyped, due to technical difficulties, and were excluded from further analysis.Genotyping was achieved with the Illumina GoldenGate genotyping assay according to the manufacturer’s specifications. Genotyping was conducted at the National Health Laboratory Services (NHLS) facility at the University of the Witwatersrand, South Africa. The GenomeStudio™ Genotyping Module v1.0 was employed for data analysis to infer genotypes and allele frequencies for each locus. Data quality assessment was done with the default No-Call  parameters defined by Illumina, a GenTrain  value of 0.25 for Plex-48 and 0.45 for Plex-192 and an individual call rate of 0.85 for Plex-48 and 0.80 for Plex-192. The more stringent GenTrain value used for Plex-192 was due to a lack of clustering taking place at values below 0.45, which led to the exclusion of those markers. Individuals that failed genotyping and SNPs that illustrated ambiguous clustering were omitted prior to further analysis. As an additional means of validation, nine randomly selected SNPs were sequenced in 30 individuals using the ABI PRISMAll the ESTs and contigs screened for SNPs in this study were subjected to BLASTX similarity searches against the NCBI protein database using Blast2GO. Functionally annotated contigs with the highest sequence similarity were used to identify the strand and reading frame orientation. The contig sequences containing SNPs within coding regions, or, alternatively, the corresponding 120 bp flanking sequences, were translated with the EMBOSS transeq utility for all six possible reading frames. The resulting amino acid sequences for alternative alleles at each SNP locus were compared to determine whether SNPs were synonymous or non-synonymous (Tables S1 and S2).Fis, were obtained using GENEPOP 4.0 [All successfully genotyped SNPs were further evaluated for the level of polymorphism observed in a subset of individuals that represented wild abalone samples. For Plex-48, this included all the wild populations and the parents of the four full-sib families, while for Plex-192, only the 12 parents  of the six mapping families were included, as they were the only individuals of Plex-192 that could be considered wild abalone. Genetic diversity estimates, such as minor allele frequency, observed and expected heterozygosity and locus-specific EPOP 4.0 . Hardy-WFST estimator (θ) of Weir and Cockerham [versus cultured populations.In order to test for population differentiation between wild and cultured populations (including offspring), the polymorphic markers from Plex-48 were employed to determine the pairwise ockerham  in GENETockerham . Signifip < 0.05) and subjected to linkage analysis using JOINMAP® 4.1 [et al. [Selected progenies of all six mapping families  included in this study were used to evaluate conformance to Mendelian segregation for all the polymorphic SNPs (234). Offspring ranged from 70 individuals for Family H to 103 for Family DS_1. SNP markers were tested for segregation distortion from expected Mendelian ratios with the chi square goodness-of-fit test (MAP® 4.1  to creat [et al. .Haliotis midae, since transcriptome sequencing in non-model species promises greater representation of the functional characteristics of these species [H. midae, since previously, these types of applications were addressed mainly using microsatellite markers [H. midae. The Illumina GoldenGate assay was equally successful in testing the utility of these markers in population differentiation inference, as well as in the saturation of a preliminary linkage map for H. midae, both of which are very important in the genetic management of this South African mollusk. In the current study, contigs for SNP development were selected based on a vast selection of genes of relevance, ranging from cellular processes to stress response, which is potentially also linked to important traits in aquaculture species, such as disease resistance and growth. Further analysis of these markers, specifically focusing on the functional SNPs identified in this study, can be directed to target genes of interest, such as those regulating immune response and environmental adaptation, as well as gene expression studies to facilitate a better understanding of this species’ genome.The major objective of this study was to investigate the utility of ESTs generated by Illumina sequencing-by-synthesis for the development of SNPs in the abalone,  species ,68,69. T markers ,5,55,70.


Prediction accuracies of estimated breeding values for economically important traits are expected to benefit from genomic information. Single nucleotide polymorphism (SNP) panels used in genomic prediction are increasing in density, but the Markov Chain Monte Carlo (MCMC) estimation of SNP effects can be quite time consuming or slow to converge when a large number of SNPs are fitted simultaneously in a linear mixed model. Here we present an EM algorithm (termed “fastBayesA”) without MCMC. This fastBayesA approach treats the variances of SNP effects as missing data and uses a joint posterior mode of effects compared to the commonly used BayesA which bases predictions on posterior means of effects. In each EM iteration, SNP effects are predicted as a linear combination of best linear unbiased predictions of breeding values from a mixed linear animal model that incorporates a weighted marker-based realized relationship matrix. Method fastBayesA converges after a few iterations to a joint posterior mode of SNP effects under the BayesA model. When applied to simulated quantitative traits with a range of genetic architectures, fastBayesA is shown to predict GEBV as accurately as BayesA but with less computing effort per SNP than BayesA. Method fastBayesA can be used as a computationally efficient substitute for BayesA, especially when an increasing number of markers bring unreasonable computational burden or slow convergence to MCMC approaches. Genomic prediction of breeding values for economically important traits of farm animals based on high-density genome-wide SNP genotypes is typically performed in two steps Currently, two classes of methods are used to overcome the over-parameterization problem of linear models used for genomic prediction when relating a lesser number of phenotypes to a larger number of SNP genotypes. The first is best linear unbiased prediction of SNP effects from a linear mixed model in which random SNP effects are assumed to be independently and identically distributed as zero-mean normal random variables with a common effect variance (ridge regression) Several non-MCMC algorithms have been proposed to improve computational efficiency for linear models with differential shrinkage of SNP effects and/or with variable selection. VanRaden In this study we formulate a principled EM algorithm (termed “fastBayesA”) that converges to a joint posterior mode of SNP effects under the BayesA model. By applying the method to simulated datasets with contrasting sizes and genetic architectures, fastBayesA is shown to predict GEBV as accurately as BayesA but with less computing effort per SNP than BayesA. The latter will become more important as SNP densities increase to that provided by individual DNA sequence.marginally normally distributed, whereas BayesA assumes a scaled inverse Chi-square prior distribution for effect variances with scale parameter conditional on its variance, i.e.,The linear mixed model for phenotypes based on GBLUP isThe calculation strategy to develop fastBayesA follows Strandén and Garrick We use the above relationships to develop an EM algorithm for BayesA by treating the effect variance of each SNP as missing data. In the E-step, the conditional expectation of the logarithm of the joint probability of The M-step of the algorithm is to maximize the above expectation with respect to Prediction of breeding values and computational efficiency of fastBayesA were compared to other methods by applying to simulated phenotypes and SNP genotypes of pedigreed populations. The initial generation comprised a population of effective size Two datasets were generated for the comparison of alternative methods in terms of prediction accuracy of GEBV (Dataset A) and computing time (Dataset B). Dataset A includes four scenarios of different genetic architectures and Dataset B varies in training size and genome length. The scenarios used in each dataset are summarized in Each chromosome was True breeding values were obtained by summing up all QTL effects for a given individual. In Dataset A, normal random errors with mean For the analysis of the simulated datasets using the Bayesian methods, the degrees of freedom of the prior distribution for effect variance and residual variance was For Bayesian methods, the MCMC was run for Eight scenarios of contrasting heritability, number of QTL and distribution of QTL variance were simulated to represent a range of genetic architectures. The average correlation and regression coefficient of TBV on GEBV in the first validation generation from 50 replicates are shown in As the number of QTL increased from Bias in the prediction of GEBV is shown by the deviation of regression coefficients from To study the optimizing property of fastBayesA, accuracies of GEBV in the five validation generations were calculated at each EM iteration until the convergence criterion was achieved. Computational efficiency of different methods was compared in relation to training population size and size of SNP panels. Results are in In this study, a fast EM algorithm fastBayesA was developed for genomic selection without MCMC. The method is non-stochastic, but only approximates BayesA estimates of marker effects and GEBV because it uses a joint posterior mode of effects rather than the posterior means used in BayesA. Compared with MCMC-based Bayesian methods on the simulated datasets, fastBayesA was shown to have similar prediction accuracy to BayesA but less computational effort per SNP than BayesA.An EM algorithm with the marginal distribution of SNP effects modeled as a The computational efficiency of fastBayesA is sensitive to the number of individuals in training since construction and inversion of the realized relationship matrix is computationally expensive. For datasets with a large number of training individuals, the faster Jacobi iteration as in VanRaden Both in BayesA and fastBayesA, inferences are based on the same posterior distribution that may not be unimodal, and both methods have to be used with caution. In BayesA the posterior mean is used to estimate SNP effects, and when the marginal posterior distribution for SNP effect is multimodal, the MCMC sampler will tend to stay within the neighborhood of a local mode and fail to visit other modes that are distant from this one Method fastBayesA results in similar prediction accuracy as BayesA because of their identical modeling of SNP effects. Any differences in accuracy are due to the fact that the joint posterior mode of SNP effects used in fastBayesA can be quite different from the posterior means used in BayesA. In The regression coefficient of TBV on GEBV was smaller than Each single step of fastBayesA can be regarded as BLUP of breeding values based on a weighted marker-derived relationship matrix. The realized relationship between each pair of individuals not only incorporates information of genome fragments that are IBS or IBD given high density SNP genotypes but also incorporates information about genetic architecture by allowing differing sizes of contributions of each SNP to the overall genetic variance. The relationship matrix used here is similar to the trait-specific relationship matrix in the heuristic TA-BLUP of Zhang et al. Method fastBayesA inherits the main advantages that GBLUP possesses and which MCMC-based methods lack. First, animals that have not been genotyped can be included in the model through pedigree relationship using single-step approach by Legarra et al. In conclusion, a fast EM algorithm fastBayesA is shown to approach BayesA estimates of marker effects without requiring MCMC. Simulation studies showed that fastBayesA has similar accuracy to BayesA under a range of genetic architectures. Method fastBayesA can be an appropriate substitute for BayesA for datasets with large numbers of markers or for pedigreed population with ungenotyped animals.Appendix S1Expectation of the reciprocal of a scaled inverse Chi-square random variable.(PDF)Click here for additional data file.Appendix S2Estimation equations for parameters from fastBayesA.(PDF)Click here for additional data file.


Sus scrofa chromosome (SSC) 4 has been previously reported to have a strong association with serum viremia and weight gain in pigs experimentally infected with the PRRS virus (PRRSV). The objective here was to identify haplotypes associated with the favorable phenotype, investigate additional genomic regions associated with host response to PRRSV, and to determine the predictive ability of genomic estimated breeding values (GEBV) based on the SSC4 region and based on the rest of the genome. Phenotypic data and 60 K SNP genotypes from eight trials of ~200 pigs from different commercial crosses were used to address these objectives.Host genetics has been shown to play a role in porcine reproductive and respiratory syndrome (PRRS), which is the most economically important disease in the swine industry. A region on Across the eight trials, heritability estimates were 0.44 and 0.29 for viral load  and weight gain to 42 days post infection (WG), respectively. Genomic regions associated with VL were identified on chromosomes 4, X, and 1. Genomic regions associated with WG were identified on chromosomes 4, 5, and 7. Apart from the SSC4 region, the regions associated with these two traits each explained less than 3% of the genetic variance. Due to the strong linkage disequilibrium in the SSC4 region, only 19 unique haplotypes were identified across all populations, of which four were associated with the favorable phenotype. Through cross-validation, accuracies of EBV based on the SSC4 region were high (0.55), while the rest of the genome had little predictive ability across populations (0.09).Traits associated with response to PRRSV infection in growing pigs are largely controlled by genomic regions with relatively small effects, with the exception of SSC4. Accuracies of EBV based on the SSC4 region were high compared to the rest of the genome. These results show that selection for the SSC4 region could potentially reduce the effects of PRRS in growing pigs, ultimately reducing the economic impact of this disease. Sus scrofa (SSC) 4 that explains a considerable amount of the total genetic variance for serum viremia and weight gain of weaned piglets following experimental infection [For decades the swine industry has been battling the economically important disease of porcine reproductive and respiratory syndrome (PRRS), caused by the PRRS virus (PRRSV) but success has been limited. Since the infection mechanisms of the virus are not fully understood, vaccine development is a challenge . There hnfection . In subsnfection . Many ofnfection ,3. The fThe objectives of the current study were to (1) re-estimate genetic parameters for host response to experimental PRRSV infection in growing pigs by including three additional, unrelated populations, (2) investigate additional genomic regions associated with weight gain and viremia in response to PRRSV challenge, (3) determine if there is a smaller informative haplotype block within the ~1 Mb region on SSC4 associated with host response to PRRSV infection, (4) determine which haplotypes within the SSC4 region are associated with the favorable effects of the QTL for viremia and weight gain, and (5) investigate accuracies of genomic estimated breeding values (GEBV) for host response to PRRS based on the SSC4 region and the rest of the genome, by using cross-validation.The Kansas State University Institutional Animal Care and Use Committee approved all experimental protocols for this study.Mycoplasma hyopneumoniae, and swine influenza virus. Upon arrival, pigs were randomly placed into pens of 10 to 15 pigs. After a 7-day acclimation period, pigs between 25 and 35 days of age (day 0), were experimentally infected intramuscularly and intranasally with 105 (TCID50) of NVSL 97–7985, a highly virulent PRRSV isolate [A detailed description of the design, data collection, and molecular techniques used in the PRRS host genetic consortium trials has been previously published . Briefly10 of PRRSV RNA copies per mL of serum. Ear tissue was collected from all pigs for DNA isolation. Tissue or genomic DNA from the sires of pigs in trials 1 through 3 and from available sires and dams for trials 4 through 8 was supplied by the breeding companies. Tissues or DNA samples were sent to GeneSeek Inc.  for genotyping with Illumina’s Porcine SNP60 BeadChip .Viremia was measured using a semi-quantitative TaqMan PCR assay for PRRSV RNA, as described in Boddicker et al. . Assay rData from eight trials of up to 200 pigs were analyzed Table . Trials Escherichia coli, Streptococcus suis, Staphylococcus aureus, and Mycoplasma hyopneumoniae.Table Details on the phenotypic traits analyzed are in Boddicker et al. ,3. BriefRecorded pedigrees (sire and dam) were checked and corrected using SNP genotypes, as described in Boddicker et al. . BrieflyHeritabilities and variances due to litter were estimated based on validated and corrected pedigree relationships, as determined from SNP genotypes  based on the number of B alleles using Illumina’s  genotype calling , αj = allele substitution effect for SNP j, and δj = indicator for whether SNP j was included (δj = 1) or excluded (δj = 0) in the model for a given iteration of the Markov chain Monte Carlo (MCMC). Pen within trial was included as a fixed factor, as opposed to a random effect in ASREML, because the current version of GenSel does not allow additional random effects. A total of 50 000 iterations were run for each analysis, with the first 5000 iterations discarded as burn-in. The probability of δi = 0 was set equal to π = 0.99. The Bayesian model was implemented using method Bayes-B [http://www.ncbi.nlm.nih.gov/nuccore?term=199 Sus%20scrofa%2C%20whole%20genome%20shotgun%20sequence, accessed November 1, 2011).where  Bayes-B . GenomicThe QTL previously identified on SSC4 included 38 SNPs, two of which had 0 called genotypes . The remParental and offspring genotypes for the 36 informative SNPs in the ~1 Mb region on SSC4 were ordered using the PHASE software , separatUsing the results from the phylogenetic tree for the reduced region, haplotypes that carried the B allele at SNP WUR10000125 were allocated to two groups based on phylogenetic distance from one another. The main effect of haplotype group was fitted as a fixed class factor in the abovementioned ASREML analysis to determine which haplotypes were associated with the desirable phenotypes of VL and WG. Due to the apparent dominance effect of the B allele, an individual had to have at least one copy of a haplotype that carried the B allele to be placed in that group. If the two groups of haplotypes that carried the B allele were not significantly different from each other but were significantly different from the group of haplotypes that carried the A allele, both B haplotypes groups were assumed to carry the favorable allele of the causative mutation. A similar procedure was used for the A haplotypes to determine whether all A haplotypes were associated with an unfavorable phenotype.Accuracy of genomic predictions across populations was evaluated by cross-validation, which involved training on one population and validating on another population. Populations were defined by trial, except for trials 1 through 3, which were considered as one population, since pigs included in those three trials were crossbreds from the same lines and breeding company. Each population was validated twice, using a ‘reduced’ and a ‘full’ training population  was predicted as:To allow predictive ability of the rest of the genome, excluding the SSC4 region, to be assessed, estimates of allele substitution effects for the training population were acquired using the additive Bayesian model described above but with genotype at SNP WUR10000125 included as a fixed class effect to account for the additive and non-additive effects of the QTL in this region on VL and WG . The stak = number of SNPs included in the prediction (58 277 SNPs), zij = genotype covariate of SNP j for animal i , and j based on analysis of the training population. To estimate the accuracy of predictions based on the SSC4 region alone, the estimates of the additive and dominance effects of SNP WUR10000125 from the GenSel analysis of the training population were used to estimate the genotypic value based on the observed genotype for SNP WUR10000125 of each individual in the validation population. Accuracy of prediction, defined as the correlation between the GEBV and the true BV, were estimated as the correlation between the GEBV and adjusted phenotypes in the validation population divided by the square root of heritability  that was identified by Boddicker et al. 5 that wa for triaThe region on SSC4 spans 1 284 081 base pairs and includes a total of 38 SNPs that are on the 60 k SNP panel, including two that are fixed and two for which no genotypes were called. Figure Table A total of 77 unique haplotypes were identified in the original ~1 Mb region across all eight trials, 11 of which carried the B allele at SNP WUR10000125 (B haplotypes). After reducing the region to 487 kb, only 19 unique haplotypes remained, of which four carried the B allele. Table Starting with haplotype 17 in Figure The results from the GWAS for VL are presented in Figure http://www.animalgenome.org/cgi-bin/QTLdb/SS/index). The region does include one reported QTL for blood pH in Meishan and Pietrain pigs infected with Sarcocystis miescheriana[A region on SSCX, which included two consecutive 1 Mb regions, accounted for 1.73% of the genetic variance across all trials for VL but only 0.02% of the genetic variance for WG Figure . There hscheriana.http://www.animalgenome.org/cgi-bin/QTLdb/SS/index). However, none of these QTL were identified under a PRRS challenge. Interesting candidate genes within 2 Mb on either side of this 1 Mb window include DBC1 (deleted in bladder cancer 1) and tumor necrosis factor (TNF) receptor-associated factor 1-like . The DBC1 gene is a tumor suppressor gene that is associated with programmed cell death [TNF receptor-associated factor 1-like gene is associated with antiviral activity [A region on SSC1 accounted for 0.70% of the genetic variance for VL across all trials and was the third highest region for VL Figure . This rell death . The TNFactivity .The results from the GWAS for WG are presented in Figure http://www.animalgenome.org/cgi-bin/QTLdb/SS/index). Interleukin-2 and interferon-gamma are both cytokines that respond to pathogen invasion of host cells. Interferon-gamma has been shown to inhibit PRRSV replication in macrophages [http://www.animalgenome.org/cgi-bin/QTLdb/SS/index).A region on SSC5 was the second highest and accounted for 2.6% of the genetic variance across all trials  genes within the swine Major Histocompatibility Complex . To date, there have been 19 reports of QTL associated with production traits in this region, 18 of which were associated with BW or average daily gain from birth to market weight . However, these QTL were identified using healthy, non-challenged pigs. The QTL identified here is for WG under PRRSV challenge and could be a new QTL; alternately one of the previously reported QTL could also be associated with WG under PRRSV challenge. Additional work is required to understand the underlying mechanism.A 1 Mb window on SSC7 (Mb 27) accounted for 1.45% of the genetic variance in WG across all trials Figure  but onlyAccuracies of predictions based on the SSC4 region and based on the whole genome including the SSC4 region, were previously reported by , using tFor GEBV based on the rest of the genome and the reduced training data, accuracies centered around zero for VL, ranging from -0.23 to +0.18 for individual validation trials, with an accuracy of 0.10 and -0.04 for the joint analysis with the full and reduced training data, respectively. For WG, accuracies based on the rest of the genome tended to be positive, ranging from -0.14 to +0.31, with an accuracy of 0.21 and 0.07 for the joint analysis with the full and reduced training data. For the full training populations, accuracies increased substantially for trials 1 and 2, except for WG for trial 2, but had little impact on the other trials. These increases in accuracy for trials 1 and 2 were as expected because the full training data included data from the same crossbred genetics as used for validation.Reasons for the generally observed poor predictive performance of GEBV across populations can be attributed to dominance, epistatis, and differences in linkage disequilibrium . AccuracTraits associated with PRRS following experimental infection in growing pigs appear to have a strong genetic component, with moderate estimates of heritability for VL and WG, along with a large QTL on SSC4. Strong LD is present in the SSC4 region. However, combining all eight trials broke some of the LD in the ~1 Mb region, resulting in a smaller haplotype. This reduced haplotype block could be useful to identify the causative mutation. A phylogenetic analysis separated the haplotypes by the allele at the SNP that explains nearly all the genetic variance for VL and WG that is contributed by the region, providing additional evidence that SNP WUR10000125 is the most informative SNP in the region  and is likely in very high LD with the causative mutation. The most common B haplotype was present in all breeds and lines represented in this study and was likely present before the segregation of the breeds. Cross-validation for the SSC4 QTL resulted in high estimates of accuracy when validating across populations for VL and WG, which can benefit selection strategies without infecting piglets with PRRSV. After accounting for the SSC4 region, the rest of the genome had little predictive ability for VL and WG across unrelated populations. Additional work is required to determine predictive ability within a population. With the effect of the SSC4 region present in all breeds and lines analyzed here, there is a good possibility that the effect is present in more populations and that genetic progress for PRRS tolerance or resistance can be made. However, additional work is needed to verify the effects of this region on challenges with different PRRSV strains and in the field.Other genomic regions associated with VL and WG were identified also but with small effects. In general, these regions will probably not play a major role in reducing the economic impact of PRRS.The genomic region identified in this study can be used for marker-assisted selection for response to PRRS in growing pigs. The implementation of selection for these markers will not make pigs completely resistant to PRRS but will increase the well-being of the animals and reduce the financial impact of PRRSV infections by limiting reductions in growth rate and other negative effects of PRRS following infection. Further work is required to investigate the effects of these QTL against different PRRSV strains and in the field where pigs are subjected to many additional environmental stressors.The authors declare that they have no competing interests.NJB conducted the statistical analyses, interpretation of results, and wrote the manuscript, AB conducted the initial haplotype analysis studies, RRRR conceived the study and led the animal infection trials and sample collection, JKL conceived the study and coordinated the handling, storage, and sample preparation for DNA, JMR helped conceive the study, coordinated data base development and helped with interpretation of analyses, JCMD coordinated and oversaw statistical analysis of the data and contributed to interpretation of results and writing the manuscript. All co-authors reviewed and contributed to development of the manuscript. All authors read and approved the final manuscript.


Farmers and plant breeders selected genotypes for grain production with reduced plant height, early flowering, increased grain yield, adaptation to drought, and improved resistance to lodging, diseases and pests. DNA polymorphisms that distinguish three historically important grain sorghum genotypes, BTx623, BTx642 and Tx7000, were characterized by genome sequencing, genotyping by sequencing, genetic mapping, and pedigree-based haplotype analysis. The distribution and density of DNA polymorphisms in the sequenced genomes varied widely, in part because the lines were derived through breeding and selection from diverse Kafir, Durra, and Caudatum race accessions. Genomic DNA spanning dw1 (SBI-09) and dw3 (SBI-07) had identical haplotypes due to selection for reduced height. Lower SNP density in genes located in pericentromeric regions compared with genes located in euchromatic regions is consistent with background selection in these regions of low recombination. SNP density was higher in euchromatic DNA and varied >100-fold in contiguous intervals that spanned up to 300 Kbp. The localized variation in DNA polymorphism density occurred throughout euchromatic regions where recombination is elevated, however, polymorphism density was not correlated with gene density or DNA methylation. Overall, sorghum chromosomes contain distal euchromatic regions characterized by extensive, localized variation in DNA polymorphism density, and large pericentromeric regions of low gene density, diversity, and recombination.Sorghum genotypes currently used for grain production in the United States were developed from African landraces that were imported starting in the mid-to-late 19 Genome sequencing is being used to characterize genome-wide patterns of genetic variation and inheritance, to help identify regions of genomes under selection, and to aid in the discovery of SNPs and alleles associated with quantitative trait loci (QTL) th century, followed by selection of genotypes exhibiting high grain production with reduced height, early maturity, increased grain quality, adaptation to local environments, and resistance to lodging, diseases, and pests Whole genome re-sequencing of key breeding lines can provide valuable information regarding the lineage of a cultivar and the origins of different portions of the cultivar’s genome Sorghum bicolor genome is comprised of ten chromosomes spanning ∼818 Mbp of DNA The In this study, sequences from the genomes of Tx7000 and BTx642 were obtained and aligned to the BTx623 reference sequence to aid in the identification of DNA polymorphisms. Digital Genotyping (DG) Tx7000 and BTx642 were selected for genome re-sequencing and comparison to BTx623 for several reasons. Tx7000 (Caprock), released in 1941, was an elite variety used for grain production in the United States in the 1940–50s prior to the development of hybrids NgoMIV restriction sites in the sorghum genome, were collected from many of the genotypes in the pedigrees of BTx623, Tx7000 and BTx642 The genetic relationships between BTx623, BTx642, Tx7000, and their progenitors in the U.S. grain sorghum breeding program, and other representative genotypes were analyzed using sequence information derived by Digital Genotyping, a genotyping by sequencing method developed for sorghum Map Reads to Reference function of the CLC Genomics Workbench. Approximately 190 million reads  from each genotype aligned to the BTx623 reference genome sequence, while approximately 33 million reads from each genotype did not align to the reference sequence, even when the stringency of mapping criteria was reduced. This indicates that the non-aligned reads likely originate from DNA that is not included in the reference genome sequence such as centromeric regions of the genome where the assembled sequence data in the reference genome may have limited coverage, genetic material that was not anchored to the reference genome, the mitochondrial genome, the chloroplast genome, or another source of DNA. Overall, the sequences obtained from Tx7000 and BTx642 that were aligned to the reference sequence provided a 25-fold average depth of sequence coverage across the genomes.Genome sequences from BTx642 and Tx7000 were acquired using Illumina TruSeq reagents and a HiSeq2000 sequencing system, aligned to the BTx623 reference sequence, and SNPs and INDELs that distinguish the genotypes were identified (12) derived from these parental lines were subjected to Digital Genotyping NgoMIV sites of digestion were obtained at a minimum of 5-fold depth (average depth per DG-sequence per line of ∼15X). DG-sequences obtained from BTx642 and Tx7000 that mapped uniquely to one location in the sorghum genome were compared to identify DG-markers. The sequences of 1462 DG-markers were obtained from the 90 RILs and the DG-marker information was used to construct a genetic map that spanned 1130 cM across the ten sorghum chromosomes  and small insertion-deletion polymorphisms (INDELs). The analysis identified 1.2 M SNPs and 120,969 INDELs that distinguish the Tx7000 sequence from BTx623, and 1.6 M SNPs and 152,836 INDELs that distinguish the BTx642 sequence from BTx623. Approximately 86% of the SNPs were located in intergenic DNA, with the remaining 14% in annotated genes including 7% in introns, 6% in coding regions, and 0.9% within UTRs . In codiThe distribution of SNPs and INDELs that distinguish BTx642 and Tx7000 from BTx623 was determined for each of the ten sorghum chromosomes . Some of1 derived line from a cross of IS12555 (Durra) to BTx406 followed by one backcross to IS12555 with subsequent selection for short height and early flowering  To better understand the DNA polymorphism density distributions shown in Polymorphism data from 8900 DG-markers was used to identify the origin of DNA in each of the sequenced genomes by tracing haplotypes through pedigrees to each of the sequenced genomes  [28]. Hadw1, Sb-HT9.1) dw3) Haplotype tracing through pedigrees revealed that DNA comprising the three sequenced sorghum genomes was derived during breeding primarily from six diverse sorghum accessions. All three genomes contained regions with haplotypes identical to Blackhull Kafir and Dwarf Yellow Milo (DYM) because these accessions are in the pedigrees of all three sequenced genotypes  that could be obtained if all of the NgoMIV recognition sites used to generate DG-template were unmethylated. NgoMIV is a methylation sensitive restriction enzyme; therefore DG-sequences are only generated adjacent to recognition sites that are not methylated. As expected, very few of the DG-sequences identified in silico in the pericentromeric region were recovered by deep sequencing DG-templates, indicating that a high percentage of the NgoMIV sites were methylated. The percentage of the in silico predicted DG-sequences recovered by sequencing increased in euchromatin, reaching 40–50% in DNA near the ends of SBI-02, consistent with reduced levels of DNA methylation  was analyzed at higher resolution to further characterize the basis of variation in SNP density  and 8. AThe region showing the highest SNP density in this 4 Mbp region of euchromatic DNA is centered at ∼13.25 Mbp on SBI-02. This region spans ∼180 Kbp and encodes ∼10 genes . In thisin silico) was used to assess variation in DNA methylation in the interval of SBI-02 centered at 13.2 Mbp  spanning 90 Kbp (4 genes) and 340 Kbp (∼25 genes) . Genes e13.2 Mbp . DG-sequA different region of low SNP density (0.12 SNP/Kbp) from 15.3–15.9 Mbp on SBI-02 that encodes only 1 gene per 140 Kbp was also examined in greater detail. The highly conserved gene encoding glutamate decarboxylase (Sb02g010470) was located in the middle of this region of low SNP density. Analysis of DNA methylation patterns revealed by differential DG-sequence recovery showed that most of the DG-digestion sites across this region were methylated, with the exception of sites near to or within the genes Sb02g010450, Sb02g010480, and Sb02g010500 (data not shown).Sorghum bicolor and the discovery of ∼2.8 million SNPs and ∼300,000 INDELS that distinguish one or both of these genotypes from the reference genotype BTx623. DNA polymorphisms were predicted to result in ∼25,000 amino acid differences and ∼250 alterations in the positions of stop codons that could affect protein function in each genotype compared with BTx623. Comparison of re-sequenced sweet sorghum genomes to BTx623 also revealed high levels of genic polymorphism that could be associated with differences in traits in sweet sorghums compared with the grain sorghum BTx623 This study reports whole-genome re-sequencing of the Tx7000 and BTx642 genotypes of Arabidopsis thaliana ecotypes and among diverse genotypes of maize and rice dw1 and dw3 loci, which confer the short plant stature typical of grain sorghum Comparison of the three sequenced genomes, all of which were derived primarily from six diverse sorghum accessions during breeding, revealed greater than 100-fold variation in the density of DNA polymorphisms in different regions of sorghum chromosomes. Similar variation in SNP density has been found in larger-scale studies of Drosophila melanogaster, where ‘background selection’, or the elimination of deleterious mutations in regions of low recombination, was shown to reduce variation in these regions Significant variation in the distribution of SNP and INDEL density across chromosomes was present after accounting for haplotype structure and regions of DNA that originated from the same founder lines or sorghum races. In general, higher SNP and INDEL densities were present in euchromatic chromosomal regions located closer to the ends of chromosomes compared with heterochromatic pericentromeric DNA consistent with results from genotyping by sequencing analysis of 971 sorghum accessions Extensive variation in SNP and INDEL density distribution was found in euchromatic DNA on every chromosome. Regions of very high SNP density (∼40–80 SNP/Kbp) alternated with regions of medium or low SNP density (<0.5 SNP/Kbp) of varying size. In one 4 Mbp region on SBI-02, there were two regions of high SNP/INDEL frequency, three regions of low SNP/INDEL density, and one extended region of intermediate SNP/INDEL density. A 600 Kbp region on SBI-02 (15.3–15.9 Mbp) of low SNP and INDEL density was gene poor and showed a high level of DNA methylation, similar to heterochromatic DNA in pericentromeric regions. This region of low SNP and INDEL density could correspond to a localized domain of heterochromatin within a larger region of euchromatic DNA, as observed in cytogenetic analysis of sorghum chromosomes Indica and Japonica rice genotypes NgoMIV used for Digital Genotyping showed that low DNA methylation was highly correlated with DNA located in or near genes. Since gene density within and flanking the regions of high SNP density were similar, DNA methylation was not highly correlated with SNP density, except in regions of low gene density and low SNP density associated with heterochromatin.Euchromatic DNA also contained regions of relatively high gene density that showed very different SNP and INDEL densities. When regions of the genome differing in haplotype were compared, SNP densities in genic and intergenic DNA increased and decreased in parallel across the regions of high SNP density, consistent with regional differences in mutation rate and/or selection and hitchhiking which affect these regions The variable distribution of SNP density in euchromatic regions observed in sorghum has been reported in several other plant genomes Tx7000 and BTx642 seedlings were grown in a growth chamber (14 hr light/10 hr dark) under hydroponic conditions in 1x Hoagland’s solution. To minimize plastid DNA contamination of nuclear DNA, root tissue was used for genomic DNA isolation. Root tissue was collected eight days after seedling germination. The tissue was washed in distilled water and surface sterilized for five minutes in a solution containing 2.1% (v/v) sodium hypochlorite, 0.25% (v/v) Tween-20 and 1% (w/v) NaCl Map Reads to Reference function of CLC Genomics Workbench 4.9 . Mismatch costs were set to 2, and insertion and deletion costs set to 3. Reads were required to align for at least 50% of their length, with similarity higher than 70%. Reads that could not be uniquely aligned were discarded. SNP and INDEL detection were performed using the SNP Detection and DIP Detection functions of CLC Genomics Workbench 4.9, respectively, on the merged paired-end and single-end read alignments. Minimum coverage for a variant call was established at 5, and maximum was set at 150. The very few heterozygous loci found in these genotypes were not included in the analysis. Allele frequency over 75% for a given locus was considered sufficient to call that locus homozygous. Large INDELs were identified as sequences with zero read depth in either BTx642 or Tx7000 spanning sequences in BTx623. Annotations for the sorghum genome were downloaded from Phytozome (version Sbi1.4), and were used to determine the number of coding variants present in the two genotypes sequenced.The BTx623 sorghum genome was used for mapping and variant detection, and was downloaded from Phytozome (version Sbicolor-79) A BLAST database was created using the consensus sequences derived from the alignment of sequence reads from BTx642 and Tx7000 to BTx623. DG-marker sequences  that were validated by genetic mapping were compared with this database using the blastn program integrated into CLC Genomics Workbench, with an e-value maximum of 1e-10, mismatch cost of 2, gap initiation cost of 5 and gap extension cost of 1. Marker sequences from Tx7000  and BTx642  aligned perfectly to their respective consensus sequences. Sequences that did not align perfectly with validated DG-sequences contained insertions or deletions that were not detected during analyses of read alignments. All SNPs within DG-marker sequences were correctly identified.Genetic markers used to determine lineage were identified and analyzed by Digital Genotyping as described The evolutionary history of sorghum genotypes was inferred using the UPGMA method Figure S1Genetic map based on the BTx642 x Tx7000 RIL population and marker information derived from Digital Genotyping. The genetic map is based on information derived from 1462 DG markers scored in 90 RILs resulting in a genetic map of 1130 cM spanning the ten sorghum chromosomes (1–10). Genetic map distances are shown to the left of each linkage group/chromosome.(TIF)Click here for additional data file.Figure S2Comparison of SNP density distributions from independent sequence assemblies of two genotypes. DNA from Tx7000 and BTx642 were independently sequenced and reassembled across SBI-02 from 75–76 Mbp using the BTx623 reference sequence. DNA spanning this region of SBI-02 in both genotypes has the same haplotype as BTx3197. SNP density analyzed in 600 bp windows of Tx7000/BTx623 (upper) and BTx642/Tx7000 (lower) generated similar SNP distribution profiles.(TIF)Click here for additional data file.


Populus trichocarpa (black cottonwood) and P. balsamifera  are native to North America where large natural populations exist. P. trichocarpa has an extensive north-south range along the west coast, while P. balsamifera has a northerly range across the continent from Alaska to the North American east coast. Both species are well suited for highly productive bioenergy biomass plantations in north-temperate climates. We are investigating the large reservoirs of natural genotypic and phenotypic variation in wild populations of these species, with an initial focus on P. trichocarpa, to identify allelic variation underlying optimal biofuel and biomass traits that could be used for accelerated domestication. We sampled genetic variation in P. trichocarpa by Illumina transcriptome resequencing of 20 individuals from provenances along a latitudinal gradient from 60?N to 44?N. This analysis revealed extensive nucleotide, gene expression and alternative splicing polymorphism in 10,000 xylem-expressed genes. Alignment of transcript sequences to the P. trichocarpa Nisqually-1 reference genome was used to identify over 500,000 SNPs. These SNPs were combined with a larger SNP set generated by whole genome resequencing of multiple P. trichocarpa individuals carried out by the US DOE Bioenergy Sciences Center (BESC) to populate a P. trichocarpa SNP database. Using the Illumina iSelect process, we generated an Illumina Infinium bead array for genotyping of 34,000 SNPs that tag 3,700 candidate genes selected for their potential involvement in biomass, adaptation, and wood traits. Using this bead array in collaboration with BESC, we obtained candidate gene SNP genotypes for over 700 P. trichocarpa individuals grown in common gardens. This analysis of these data revealed extensive genotypic polymorphism in the targeted gene set. In parallel, we carried out phenotyping of the trees from the wild populations grown in common gardens, quantifying variation in over 85 morphological, growth, physiological, wood chemistry, and wood quality traits. Population genetic analysis of the SNP data, its use for genotype-phenotype association studies to identify candidate gene alleles underlying variation in traits of interest, and the prospects of using the results for marker assisted breeding will be discussed.


Longissimus thoraci, using RNA-Seq. To our knowledge, this represents the first study done in bovine muscle.Genetic information based on molecular markers has increasingly being used in cattle breeding improvement programmes, as a mean to improve conventionally phenotypic selection. Advances in molecular genetics have led to the identification of several genetic markers associated with genes affecting economic traits. Until recently, the identification of the causative genetic variants involved in the phenotypes of interest has remained a difficult task. The advent of novel sequencing technologies now offers a new opportunity for the identification of such variants. Despite sequencing costs plummeting, sequencing whole-genomes or large targeted regions is still too expensive for most laboratories. A transcriptomic-based sequencing approach offers a cheaper alternative to identify a large number of polymorphisms and possibly to discover causative variants. In the present study, we performed a gene-based single nucleotide polymorphism (SNP) discovery analysis in bovine Longissimus thoraci from three Limousin bull calves were subjected to high-throughput sequencing. Approximately 36–46 million paired-end reads were obtained per library. A total of 19,752 transcripts were identified and 34,376 different SNPs were detected. Fifty-five percent of the SNPs were found in coding regions and ~22% resulted in an amino acid change. Applying a very stringent SNP quality threshold, we detected 8,407 different high-confidence SNPs, 18% of which are non synonymous coding SNPs. To analyse the accuracy of RNA-Seq technology for SNP detection, 48 SNPs were selected for validation by genotyping. No discrepancies were observed when using the highest SNP probability threshold. To test the usefulness of the identified SNPs, the 48 selected SNPs were assessed by genotyping 93 bovine samples, representing mostly the nine major breeds used in France. Principal component analysis indicates a clear separation between the nine populations.Messenger RNAs from Longissimus thoracis, especially the large number of non synonymous coding SNPs, may prove useful to study the mechanisms underlying the genetic variability of meat quality traits.The RNA-Seq data and the collection of newly discovered coding SNPs improve the genomic resources available for cattle, especially for beef breeds. The large amount of variation present in genes expressed in Limousin  Bos taurus) are considered to have been one of the first animals domesticated by man for agricultural purposes. Approximately 10,000 years ago, cattle ancestors (aurochs) were tamed to provide milk, meat and hides and for draft purposes [Bos taurus was also one of the first animal species to enter the genomics era. In the past few years, genetic information based on molecular markers has increasingly been used in cattle breeding improvement programmes, as a mean to improve conventionally phenotypic selection, particularly for traits with low heritability or for which measurement of phenotype is difficult, expensive, only possible late in life, sex-limited or not possible on selection candidates [acylCoA:diacylglycerol acyltransferase (DGAT1) gene has a major effect on milk yield and composition [Cattle  . HoweverThe identification of these causative genetic variants, also known as quantitative trait nucleotides (QTNs) involves the mapping of quantitative trait loci (QTLs), the discovery of novel genetic markers in the QTL regions, the fine-mapping of QTLs and then the sequencing of candidate genes. This iterative process until recently was very time-consuming, but thanks to the availability of a large number of SNPs and to the relatively low-cost of whole-genome genotyping methodologies, the fine-mapping of QTL regions has now been expedited. In addition, the advent of novel sequencing technologies -23 offeret al. (2009) generated the first single cattle genome sequence by a next-generation sequencing method [The genome of animals from many species has now been sequenced, including the genomes of several bulls -30. For g method . By sequA whole-transcriptome RNA sequencing (RNA-Seq) method has recently been developed to identify and quantify genes expressed in different tissues ,32. ThisLongissimus thoraci, using a whole-transcriptome sequencing approach. To our knowledge, this represents the first study done in bovine muscle. For this purpose, muscle samples from three different Limousin bulls were analysed. We have identified more than 34,000 putative SNPs, including more than 60% novel polymorphisms. To evaluate the accuracy of the SNPs detected, 48 putative SNPs were genotyped. One-hundred percent concordance was observed when a stringent SNP quality criterion was chosen. The RNA-Seq data and the collection of newly discovered coding SNPs improve the genomic resources available for cattle, especially for beef breeds. The large amount of variation present in genes expressed in Limousin Longissimus thoracis, especially the large number of non synonymous coding SNPs, may prove useful to study the mechanisms underlying the genetic variability of meat quality traits.In the present study, we performed a gene-based SNP discovery analysis in bovine Longissimus thoracis transcriptome at single-nucleotide resolution, poly(A)-enriched mRNA from three Limousin bull calves were retrotranscribed and subjected to high-throughput sequencing. The three RNA-Seq libraries were barcode-tagged and sequenced on one lane of an Illumina HiSeq2000 sequencer. Sequencing of cDNA libraries generated a total of 125,781,357 raw paired-end reads with a length of 100 bases, resulting in a total of 25 gigabases. The reads were de-multiplexed to assign reads to each sequenced sample according to its barcode index. Approximately 36 to 46 million paired-end reads were obtained for each library. Reads from each sample were then mapped back to the bovine reference transcriptome. We used the set of Bos taurus Ensembl transcripts v61 RefSeq genes as the reference transcriptome. This set contains transcripts for 22,915 known or novel genes but also pseudogenes. Based on mappings done using the Burrows—Wheeler Aligner (BWA) programme, 63% to 67% of the mapped reads were aligned properly paired  found that ~65% of the RNA-Seq reads they generated while sequencing the milk transcriptome mapped uniquely onto the bovine genome. They also found that ~17,000-19,000 genes were expressed in milk [To obtain a global view of the bovine Gene expression was normalised as paired-end reads mapped per million total uniquely mapped paired-end reads (FPKM). Amongst these transcripts, 14,298 (72%) were identified with more than 1 read per million in at least one library. Some transcripts were represented by many reads. Moreover, 50% of the reads mapped to only 77 transcript sequences and 90% mapped to 2,878 transcripts. The top twenty of these transcripts are shown in Table To assess the consistency of gene expression profile measurements, the pairwise individual-to-individual Pearson correlation coefficient of the gene expression levels was calculated. The correlations were very high between individuals (r > 0.92) . SAMtools can identify single base substitutions as well as small insertions and deletions; however, only SNPs were considered in the current analysis. In total 34,376 different SNP positions were detected with the RNA-Seq reads. Amongst these SNPs, 8,974 (26%) were homozygous in all three sequenced samples, corresponding presumably to differences between Limousin and the Hereford bovine whole-genome reference sequence [et al. (2010) using a similar total number of RNA-Seq reads (~118 millions reads). They identified ~100,000 SNPs located in genes expressed in milk samples from Holstein cows. However, only 33,045 SNPs (32%) were polymorphic within their seven Holstein cows [For SNP calling, BWA was used to map the paired-reads from each sample to the bovine reference genome sequence. The SAM tools package was used for SNP discovery using stringent parameters , suggests that a large fraction of the genetic variability present in Limousin cattle still remains to be discovered.The proportion of transition substitutions were A/G, 36%, and C/T, 37%, compared to transversions A/C, 7%, G/T, 7%, A/T, 4% and C/G, 9%. This corresponds to a transition:transversion ratio of 2.65:1. The observed transition:transversion ratio is closed to the expected ratio (2:1) if all substitutions were equally likely.et al. (2011) have reported up to 57.3% of nscSNPs in coding regions in the whole-genome of a single individual of the Japanese Kuchinoshima-Ushi native cattle breed [versus RNA might contribute to the discrepancy in the rate of nscSNPs found between whole-genome and transcriptome-based studies.Amongst these bi-allelic SNPs, 17,011 (55%) were found using Ensembl’s Variant Effect Predictor in a predicted coding region. 3,791 (22.23%) resulted in an amino acid change  found in 2,438 different genes. The percentage of nonsynonymous changes in the coding region found in our study was lower compared to whole-genome -27 studile breed . They fole breed . Huang ale breed . The broThe deleterious effect of non-synonymous SNPs were analysed using the SIFT and PolyPhen algorithms. In order to use these programmes, sequences flanking the bovine nscSNPs were mapped onto the human genome and custom scripts were used to extract the human position orthologous to each bovine SNP position. We selected only bovines nscSNPs for which the two bases before and the two bases after the SNP exactly matched the human sequence. The human chromosomal position and the bovine alleles were combined to produce “pseudo human” variant positions and then used to query SIFT and PolyPhen. Using this conservative approach, we could retrieve the human “orthologous” position for 206 different bovine nscSNPs.Using SIFT, we found that 90 different “pseudo human” coding variants were damaging. The three Limousin animals used were homozygous or heterozygous for 41 and 68 of these damaging SNPs, respectively. The difference between the number of SNPs found homozygous and heretozygous, reflects the fact that deleterious alleles are less likely to be homozygous. All three Limousin animals were homozygous for 17 damaging ncSNPs, including 13 SNPs with a genotype probability score above 20  and 8 SNPs with a genotype probability score of 99 (in at least one sample).Using PolyPhen-2, we found 69 different damaging “pseudo human” coding variants. 29 SNPs were homozygous and 52 SNPs heterozygous in at least one of the three Limousin samples. All Limousin animals were homozygous for 12 damaging nscSNPs, including 10 SNPs with a genotype probability score above 20  and 6 SNPs with a genotype probability score of 99 (in at least one sample).Fifty damaging nscSNPs were found by both SIFT and PolyPhen-2 algorithms, including 5 high-confidence nscSNPs for which all three Limousin animals are homozygous  were assigned to one or more GO annotations. In total 3,589, 2,892 and 8,172 GO terms were obtained for biological processes, cellular components and molecular functions, respectively. GO term analysis showed a significant enrichment of specific GO terms when comparing the annotations of SNP-containing genes against all unique transcripts from the bovine reference transcriptome. A summary of the classification of these genes into major biological process, cell component and molecular function categories is presented in Additional file versus other QTLs) and the number of SNPs found in these two groups were counted. We then performed a Chi-squared test and found a significant difference (P = 0) in the number of SNPs between the two groups  deposited in the public database AnimalQTLdb . 32,631 in silico detected SNP being a truly polymorphic site, we selected nscSNPs already found in dbSNP. Finally, 48 putative nscSNPs detected in 38 genes were selected  with ADT score >0.6 passed the filtering step. In order to increase the probability of an The 48 selected SNPs were genotyped on the three original Limousin bull calves used for the RNA-Seq work, using llumina’s GoldenGate BeadXpress system. From the 48 SNPs that were genotyped, 11 SNP assays failed to work (23%), equivalent to a conversion rate of ~77%. We had 100% call rate for all remaining 37 SNPs with these three DNA samples  could therefore possibly be true differences between RNA and corresponding DNA samples, due to A-to-I (G) RNA editing (e.g. [A comparison between genotypes obtained by direct genotyping and predicted from the RNA-Seq data show 23 discrepancies (20%) Table . A quickng  were homozygous in all three sequenced samples; 8,199 (97%) were bi-allelic SNPs; 3,123 (37%) were previously found in dbSNP; 6,158 (73%) were found in coding regions and 1,242 (18%) resulted in an amino acid change (in 948 different genes). A list of the high-confidence SNPs is available, as an additional file to this manuscript .To test the usefulness of the identified SNPs, the 48 selected nscSNPs were assessed by genotyping a total of 90 bovine samples (including the three Limousin samples used for the RNA-Seq work) representing the 9 major breeds used in France, an African taurine breed (Watusi), and two other As reported above, 8 SNP assays failed to work in all samples. SNP call rate ranged from 55% (rs42555633) to 100%, whereas the call rate for bovine DNA samples ranged from 93% to 98%.The majority 95%) of the selected SNPs with working assays, generated data with the European bison and the Greater Koudou samples  . The mean observed heterozygosity estimated for all autosomal markers, for each population ranged from 0.259 (+/− 0.176) to 0.386 (+/− 0.230). The mean observed heterozygosities in our populations were similar to values estimated in previous studies, including a study that used a whole-genome SNP panel to characterise the genetic diversity of several French cattle breeds [The observed heterozygosities, expected heterozygosities under HWE for the observed population allele frequencies, and significance level for the test for departures from HWE for each autosomal SNP, are shown in Additional file e breeds ,43.FST = 10.9% and GST = 9.86%) but highly significant from zero (unpublished data). This genetic differentiation among breeds implies that approximately 90% of the total genetic variation was explained by individual variability. A similar genetic differentiation was previously reported in a study carried out on French breeds, using microsatellite markers [The overall genetic differentiation among breeds was moderate . Genetic distances between breeds were measured by pair-wise FST as shown in Additional file FST = 0.0011). These results were in agreement with a previous study that analysed the genetic relationships between BLA, HOL, LIM and SAL populations [FST (FST = 0.0353) [The exact test for population differentiation based on allele frequency variations shows that all breeds tested were significantly different from each other (ulations . Gautier 0.0353) . This diFST estimates indicate a relatively low gene flow between these breeds.The degree of genetic differentiation among the breeds studied and the high levels of significance for the between-population Principal component analysis was performed including all animals and all autosomal loci using allele frequencies to summarise breed relationships. The analysis indicates a clear separation between the nine populations Figure , but alsThe discovered coding SNPs, especially the 8,407 high-confidence SNPs may have a direct functional effet and some of them may be involved in the genetic variability of meat quality traits.th and last exon of CD46, a membrane cofactor protein. This variant leads to a three amino acid shortened protein. None of the sampled animals were homozygous for this mutation. The corresponding bovine gene (ENSBTAG00000005397) has three known different transcripts encoding 343, 361 and 367 amino acid long proteins. The nscSNP modifies the longest bovine protein version; however, as the last three amino acids are not conserved within the bovine proteins or between species, the polymorphism is unlikely to have a functional impact.Among the high-confidence non synonymous coding SNPs, we have identified a single polymorphism resulting in a premature stop codon. SNP rs135279925 (ENSBTAT00000007104:c.1093C>T) is located within the 10growth differentiation factor 8 (GDF8). GDF8 is a known muscle growth factor inhibitor commonly known as myostatin (MSTN). This gene has been identified as the gene responsible for the double-muscling phenotype in cattle [MSTN have been described in many breeds that cause muscle hypertrophy [We also found among the high-confidence nscSNPs, the previously reported F94L mutation (rs110065568: BTA2 g.6213980C>A) in the n cattle -47. Numeertrophy -51, inclertrophy . Limousiertrophy -50,53. Iertrophy -56. The protein kinase adenosine monophosphate-activated α3-subunit (PRKAG3). Studies have shown that mutations in the porcine PRKAG3 affect the glycogen content in muscle, and consequently, ultimate pH, meat colour, water-holding capacity, drip loss, tenderness and cooking loss [PRKAG3 have also been performed and several non synonymous SNPs have been identified, including SNP rs109995479 [PRKAG3 and meat colour traits and cooking loss have been found in cattle [We found among the high-confidence polymorphisms a nscSNP in another bovine gene known to be involved in meat quality traits: the mutation A127S (rs109995479: BTA2 g.107515456C>A) in the ing loss ,58. Beca09995479 -61. Asson cattle . It willmitochondrial propionyl-coA carbolylase beta subunit (PCCB), which is involved in the catabolism of propanoate, an important intermediate in the metabolism of several amino acids. Yang and collaborators [PCCB is associated with fat weight, in pig. Interestingly, the bovine PCCB gene lies within a QTL region for fat thickness at the 12th rib [PCCB could therefore be a good candidate gene for this trait.In addition, we identified several polymorphisms in new candidate genes for several meat quality-related traits. For example, we found a high-confidence non synonymous coding SNP (rs109813896: BTA1 g.134130474G>C) in the gene encoding the borators  have sho12th rib . PCCB coheparin sulfate proteoglycan 2 . This gene encodes a large proteoglycan that is a component of the extracellular matrix. Choi and collaborators [HSPG2 gene is located within a marbling score QTL [We also found seven high-confidence nscSNPs  in the gene encoding the borators  found ancore QTL  and coulLongissimus thoracis, especially the large number of non synonymous coding SNPs, may prove useful to study the mechanisms underlying the genetic variability of meat quality traits. The coding SNPs could also be used to study allele-specific gene expression.Our results represent the first study of gene-based SNPs discovered using RNA-seq in bovine muscle. Our results show that RNA-Seq is a fast and efficient method to identify SNPs in coding regions and we identified more than 34,000 putative SNPs . More than 60% of these SNPs are completely novel. The high percentage of validation confirms the utility of the SNP-mining process and the stringent quality criteria for distinguishing sequence variations from sequencing errors or artifacts introduced during the preparation of the cDNA libraries. The RNA-Seq data and the collection of newly discovered coding SNPs improve the genomic resources available for cattle, especially for beef breeds. The large amount of variation present in genes expressed in Limousin Our approach could be further improved in order to reduce the cost of SNP discovery and validation. Higher multiplexing of cDNA libraries prior to sequencing, would reduce sequencing cost while still allowing SNP discovery and genotype assignment. With continued improvements in next-generation DNA sequencing technologies, throughput will increase while sequencing costs are expected to decrease. When relevant tissue samples are available, it will soon be reasonable to directly perform association studies using a genotyping RNA-Seq-based approach.All animal experimentation complied with the French Veterinary Authorities’ rules. No ethics approval was required by a specific committee, as the selected animals were not animals bred for experimental reasons.ad libidum with wet corn silage. They were humanely slaughtered in an accredited commercial slaughterhouse when they reached 16 months. Longissimus thoracis (LT) muscle samples were dissected immediately after death and tissue samples were snap frozen in liquid nitrogen and stored at −80°C until analysis.The study was conducted with three Limousin bull calves from a large study on the genetic determinism of beef and meat quality traits . The thrAfter transfer to ice-cold RNeasy RLT lysis buffer , LT tissue samples were homogenised using a Precellys tissue homogeniser . Total RNA was isolated using RNeasy Midi columns (Qiagen) and then treated with RNAse-free DNase I (Qiagen) for 15 min at room temperature according to the manufacturer’s protocols. The concentration of total RNA was measured with a Nanodrop ND-100 instrument  and the quality was assessed with an RNA 6000 Nano Labchip kit using an Agilent 2100 Bioanalyzer . All three samples had an RNA Integrity Number (RIN) value greater than eight.The mRNA-Seq libraries were prepared using the TruSeq RNA Sample Preparation Kit  according to the manufacturer’s instructions. Briefly, Poly-A containing mRNA molecules were purified from 4 μg total RNA of each sample using oligo(dT) magnetic beads and fragmented into 150–400 bp pieces using divalent cations at 94°C for 8 min. The cleaved mRNA fragments were converted to double-stranded cDNA using SuperScript II reverse transcriptase  and primed by random primers. The resulting cDNA was purified using Agencourt AMPure® XP beads . Then, cDNA was subjected to end-repair and phosphorylation and subsequent purification was performed using Agencourt AMPure® XP beads (Beckman Coulter). These repaired cDNA fragments were 3′-adenylated producing cDNA fragments with a single ‘A’ base overhung at their 3′-ends for subsequent adapter-ligation. Illumina adapters containing indexing tags were ligated to the ends of these 3′-adenylated cDNA fragments followed by two purification steps using Agencourt AMPure® XP beads (Beckman Coulter). Ten rounds of PCR amplification were performed to enrich the adapter-modified cDNA library using primers complementary to the ends of the adapters. The PCR products were purified using Agencourt AMPure® XP beads (Beckman Coulter) and size-selected (200 ± 25 bp) on a 2% agarose Invitrogen E-Gel (Thermo Scientific). Libraries were then checked on an Agilent Technologies 2100 Bioanalyzer using the Agilent High Sensitivity DNA Kit and quantified by quantitative PCR with the QPCR NGS Library Quantification kit (Agilent Technologies). After quantification, tagged cDNA libraries were pooled in equal ratios and a final qPCR check was performed post-pooling. The pooled libraries were used for 2×100 bp paired-end sequencing on one lane of the Illumina HiSeq2000 with a TruSeq SBS v3-HS Kit (Illumina). After sequencing, the samples were demultiplexed and the indexed adapter sequences were trimmed using the CASAVA v1.8.2 software (Illumina).Bos taurus reference transcriptome was downloaded from Ensembl . To align the reads back to the assembled reference transcriptome the BWA programme (version 0.5.9-r16) was used [E. coli, phiX and yeast genome sequences. The number of paired-reads uniquely aligning to transcribed regions of each transcript was calculated for all genes in the annotated transcriptome. The transcript paired-read count was calculated as the number of unique paired-reads that aligned within the exons of each transcript, based on the coordinates of mapped reads. The expression level of each gene was calculated in FPKM (fragments per kilobase per million sequenced reads) using a custom script based on Tapnel et al. (2010) [The was used . Reads wwas used  for furt. (2010) .BWA was also used to map reads onto the bovine genome reference sequence  was carried out for each FATIGO analysis, using the Fisher Exact Test with correction for multiple testing.In order to evaluate whether SNP-containing genes were significantly enriched for specific gene ontology (GO) terms and KEGG pathways compared to all annotated bovine genes, gene enrichment analyses were conducted using the FATIGO tool of the online software suite Babelomics  utilises factors including template GC content, melting temperature, sequence uniqueness, and self-complementarity to filter the candidate SNPs prior to further inspection. The Assay Design Score (assigned between 0 and 1) is indicative of the ability to design suitable oligos within the 60 bp up/down-stream flanking regions, and the expected success of the assay when genotyped with the Illumina GoldenGate chemistry. Following the Illumina guidelines, all SNPs with a score below 0.4 should be discarded; SNPs with a score above 0.4 accepted, with SNPs scoring above 0.6 being used preferentially. SNP flanking sequences were retrieved and only SNP sequences with unambiguous 121 bases (60 bases up/down-stream of each SNP position) were submitted to Illumina to assess the design quality. SNPs with ADS showing a quality score above of 0.6 were retained for analysis.After SNP detection, Bovinae species; the European bison  and a more distantly related species; the Greater Koudou  were also genotyped. Blood samples were collected at the Parc du Rénou Zoo . Genomic DNA was extracted from whole-blood or semen samples using the Qiasymphony SP robotic system and DNA Midi kit (Qiagen). Quality of DNA was checked using a Nanodrop ND-100 spectrophotometer (Thermo Scientific) and quantity was estimated with Quant-iT Picogreen dsDNA kit (Life Technologies) on an ABI 7900HT (Life Technologies). All DNA samples were standardised to 50 ng/μL. All animal manipulations were done according to good animal practice as defined by the French Veterinary Authorities.Ninety bovine DNA samples were genotyped for each selected SNP using Illumina’s GoldenGate assay. These samples include 11 Aubrac (AUB), 11 Blonde d’Aquitaine (BLA), 11 Charolais (CHA), 11 Holstein (HOL), 11 Limousin (LIM), 11 Montbéliard (MON), 11 Salers (SAL), 6 Maine-Anjou (MAN), 6 Normande (NOR) and 1 Watusi (WAT) animals. These animals were not closely related to one another (for at least 4 generations) according to genealogical records from the French Centre de Traitement de l’Information Génétique . To assess the utility of developed markers in related species, two High-throughput genotyping reactions were performed using Illumina’s GoldenGate BeadXpress system, according to the manufacturer’s protocol. Oligonucleotides were designed, synthesised, and assembled into a custom oligo pooled assay (OPA) by Illumina. Automatic allele calling for each SNP was accomplished with the GenomeStudio software (Illumina). All genotypes were manually checked and re-scored if any errors in calling homozygous or heterozygous clusters were evident. Genotype calls were exported in spreadsheets from the GenomeStudio data analysis software for further analysis.F-statistics (FST) according to Weir and Cockerham (1984) [DR) was calculated for each pair of populations based on allele frequencies [Genetic diversity parameters within each population were calculated using the GENETIX 4.05.2 software package . Tests fm (1984) ) using tquencies  using thThe sequencing data have been submitted to the European Nucleotide Archive (accession number ERP002220).The authors declare that they have no competing interests.AD carried out the bioinformatic analysis, under the supervison of CK. DE performed the RNA-Seq experiment. BW and MB contributed to the data analyses. DE and FM performed the SNP genotyping. CM prepared the RNA samples. DR conceived the study, analysed the data and drafted the manuscript. All authors read and approved the final manuscript.Pearson correlation coefficient between individuals.Click here for fileList of damaging SNPs predicted by SIFT and PolypPhen-2.Click here for fileEnrichment of SNP-containing contigs in GO terms.Click here for fileList of putative SNPs located within known QTL regions.Click here for fileChi-squared test details.Click here for fileList of the high-confidence SNPs and annotation.Click here for fileDetails on the observed and expected heterozygosities.Click here for fileFST) between pairs of cattle populations  and Reynold’s genetic distance (DR) between pairs of cattle populations  as observed in this study.Genetic differentiation (Click here for filePrincipal Component Analysis. Per cent value in each axis indicates contribution to the total genetic variation.Click here for file