Genomic selection (GS) has played an important role in cattle breeding programs. However, genotyping prices are still a challenge for implementation of GS in beef cattle and there is still a lack of information about the use of low-density Single Nucleotide Polymorphisms (SNP) chip panels for genomic predictions in breeds such as Brazilian Braford and Hereford. Therefore, this study investigated the effect of using imputed genotypes in the accuracy of genomic predictions for twenty economically important traits in Brazilian Braford and Hereford beef cattle. Various scenarios composed by different percentages of animals with imputed genotypes and different sizes of the training population were compared. De-regressed EBVs  were used as pseudo-phenotypes in a Genomic Best Linear Unbiased Prediction (GBLUP) model using two different mimicked panels derived from the 50 K (8 K and 15 K SNP panels), which were subsequently imputed to the 50 K panel. In addition, genomic prediction accuracies generated from a 777 K SNP (imputed from the 50 K SNP) were presented as another alternate scenario.The accuracy of genomic breeding values averaged over the twenty traits ranged from 0.38 to 0.40 across the different scenarios. The average losses in expected genomic estimated breeding values (GEBV) accuracy (accuracy obtained from the inverse of the mixed model equations) relative to the true 50 K genotypes ranged from −0.0007 to −0.0012 and from −0.0002 to −0.0005 when using the 50 K imputed from the 8 K or 15 K, respectively. When using the imputed 777 K panel the average losses in expected GEBV accuracy was −0.0021. The average gain in expected EBVs accuracy by including genomic information when compared to simple BLUP was between 0.02 and 0.03 across scenarios and traits.The percentage of animals with imputed genotypes in the training population did not significantly influence the validation accuracy. However, the size of the training population played a major role in the accuracies of genomic predictions in this population. The losses in the expected accuracies of GEBV due to imputation of genotypes were lower when using the 50 K SNP chip panel imputed from the 15 K compared to the one imputed from the 8 K SNP chip panel.The online version of this article (doi:10.1186/s12863-017-0475-9) contains supplementary material, which is available to authorized users. Brazil is a key player in the global beef market exporting throughout the whole world. Currently, Brazil has a herd of more than 212 million cattle .Genetic progress in Hereford and Braford breeding programs have been achieved through traditional genetic evaluations. However, incorporation of genomic information in livestock breeding programs  can rese), large use of key sires world-wide and collaborations among countries for genotype sharing e), largen are 39  and 90 [n are 39 . For beeectively , 207 andectively , and, 44ectively .There is a need to increase the size of training population for successful genomic predictions in beef cattle. However, genotyping costs in commercial herds is still a major constraint for implementation of GS. An alternative to reduce costs is to genotype individuals from commercial breeding programs with low-density single nucleotide polymorphisms (SNP) chip panels, which are more affordable for the producers. These low-density panels can then be imputed to a medium or high density SNP chip panel , 19 and Genotypic, phenotypic and pedigree datasets were obtained from the Conexão Delta G’s Genetic Improvement Program . The dataset contained approximately 520,000 animals from 97 farms located in the South, Southeast, Midwest and Northeast regions of Brazil. Out of these animals there were 683 Hereford and 2,997 Braford animals genotyped (born between 2008 and 2011) plus 130 sires (born between 1982 and 2010).From the total of genotyped animals, there were 624 Hereford and 2,926 Braford animals genotyped with the Illumina BovineSNP50 panel  and 59 Hereford and 71 Braford sires genotyped with the Illumina BovineHD panel . In addition, there were 88 Nellore bulls (Zebu breed used to develop Braford composite breed) from the Paint Breeding Program  genotyped with the Illumina BovineHD panel.−6) were retained for further analyses. Quality control of individuals was based on GenCall score (≥0.15), call rate (≥0.90), heterozygosity deviation (limit of ± 3 SD), repeated sampling and paternity errors. The quality control for imputation to the 777 K SNP panel was the same as the one described before for the imputation to the 50 K SNP panel. The 8 K and 15 K SNP chip panels were used for imputation to 50 K SNP chip panel, and the 50 K SNP panel was used for imputation to the 777 K SNP panel [Single nucleotide polymorphisms that were not present in both 50 K and 777 K SNP chip panels were removed for imputation to the Illumina BovineSNP50 BeadChip . Missing genotypes (0.46%) in the 50 K SNP chip panel were previously imputed using FImpute software v.2.2 . Only SNNP panel  using FINP panel . A qualiscore ≥0.5, call rConexão Delta G’s Genetic Improvement Program - Hereford and Braford (Nellore x Hereford) started around 1970. During its early stages animals were selected based on a selection index that included weight gain, scrotal circumference and conformation score traits . In 1975The independent culling level was carried out systematically since the beginning of the Conexão Delta G’s Genetic Improvement Program for BW, BA and OP traits, particularly in Hereford, and NW, NY, HW and HY in Braford. The SW and SY traits were part of the selection index between the 1970s and 1990s while the selection of TR has been performed with greater emphasis on young bulls in the last decade.The package used to obtain the estimated breeding values (EBVs) for each trait was written in Fortran language and developed by GenSys . Contemporary group was defined based on farm, year-season, sex, and management group. The traits WGBW, CW, CY, PW, PY, MW, MY and SW were pre-adjusted for dam age, birth date, breed, dominance and epistasis effects and environmental interactions (latitude). WGWY was pre-adjusted for calf age, while CW, CY, PW, PY, MW, MY and SY were pre-adjusted for calf and dam age. SCa was adjusted for age at yearling and SCaw was adjusted for age and weight at yearling. TR was pre-adjusted for additive effects of breed. A connectedness analysis was performed prior to each genetic evaluation. The degree of connectedness among contemporary groups was measured based on genetic connections of animals and its common relatives. Genetic connections were weighted by the degree of additive relationship between animals , 28. To ijkl = μ + cgi + aj + mk + pek + eijkl, where yijkl is the phenotype for the animal l, pre-adjusted for the known environmental effects  and genetic fixed effects ; μ is the general mean for the trait; cgi is the effect of contemporary group i (fixed effect); aj is the genetic direct effect of animal j (random effect); mk is the maternal genetic effect of cow k (random effect); pek is the permanent environment effect due to the cow k (random effect); and eijkl is the residual effect associated to the observation ijkl. The required variance components were estimated using Restricted Maximum Likelihood (REML). A robust estimation procedure regarding to the heterogeneity of residual variance within contemporary groups [The general model used for the genetic evaluations was: yy groups  was usedy groups .Two EBV sets were generated: the first one was estimated using all available information to date while the second set was estimated using information from all animals born before 2010. These two sets of EBVs were then used as pseudo-phenotypes in the genomic prediction models for validation and training populations, respectively.r2 = 0.09) and that satisfied the following condition: abs represents the absolute value, EBV is the estimated breeding value, dEBV is the de-regressed EBV and sdEBV is the standard deviation of the EBVs.The second set of EBVs (for the training population) was de-regressed and used as pseudo-phenotypes to estimate genomic markers effects. The approach described by VanRaden and Wiggans  was usedy = 1nμ + Zg + e, where y is the vector of de-regressed EBV for the trait, μ is the overall mean, 1n is a vector of ones, Z is the design matrix that relates de-regressed EBVs to animals, g is the vector of DGV to be predicted, and e is the vector of residual effects. It was assumed that g ~ N  where σ2g is the additive genetics variance and G* is a combined relationship matrix (80% genomic relationship and 20% pedigree-based relationship), and e ~ N  where σ2e is the residual variance and R is a diagonal matrix whose elements account for the differences in reliabilities of the de-regressed EBVs. The reason for using a combined relationship matrix is due to the fact that previous studies, also performed in Brazil, reported gains in accuracies when adding 20% pedigree-based relationship  were estimated using GBLUP method as described in VanRaden  for all ip (e.g. ).rDGV2 and rEBV2 are the reliability of DGV and EBV, respectively.The genomic estimated breeding values (GEBV) were estimated using the blending procedure outlined by Hayes et al.  and descFor the genomic predictions as described in the previous section, the dataset was split into two groups: training and validation populations. To simulate what would happen in practice , the training population included all animals born before 2011, while the validation group included all the animals born in 2011 . Training and validation groups varied in size for each trait  and 15 K scenario . The 20 K panel was slightly superior to the 8 K panel. However, various markers from the 20 K are not included in the 50 K and 777 K, and therefore, when matching panels for imputation 8 K and 20 K become very similar [Three groups of genomic prediction scenarios were designed to mimic situations where different proportion of animals with imputed genotypes  were included in the training set. The first two groups of scenarios were created based on animals genotyped (mimicked from 50 K SNP chip panel) with 8 K and 15 K SNP chip panels and imputed to the 50 K SNP chip panel . For thi similar .The first group of scenarios (SCE1) was created with different percentages of animals with imputed genotypes and unequal training population sizes . The second group of scenarios SCE2) was also created with different percentages of animals with imputed genotypes, however, with same size training populations. The third group of scenarios (SCE3) was based on animals genotyped using the 50 K SNP chip panel and imputed to the 777 K SNP chip panel. More details for all scenarios are shown in the Table  was alsoThe prediction accuracies of GEBV were used to compare the scenarios evaluated. The prediction accuracies were calculated as Pearson’s correlation between DGVs and EBVs  from the validation population. Accuracy obtained from the mixed model equations (expected accuracy) in the validation population was used to quantify losses in GEBV accuracy due to the use of imputed genotypes compared to the true 50 K SNP chip panel. Expected accuracy was also used to quantify the gain in breeding value accuracies when using molecular marker information in the EBV estimation. The factors affecting validation accuracies and losses in expected GEBV accuracies were tested by carrying out an analysis of variance in the ANOVA procedure of SAS version 9.2 .As shown in Table In the SCE1, we investigated the prediction accuracies of genomic breeding values when increasing the size of the training population by inclusion of imputed genotypes (imputed from 8 K or 15 K to 50 K). Tables P > 0.05) for the majority of the traits (55%) and in general, when there was a significant difference, 15 K performed better than 8 K (Table P < 0.05) were observed in 45% and 40% of the traits, respectively. In general, the accuracies were higher when using the true 50 K SNP panel. However, for BW higher accuracies were observed when using imputed genotypes from 8 K and 15 K.The differences between the 8 K and 15 K SNP panels were not significant (P < 0.05). In general, larger training populations (i.e. including larger proportion of imputed genotypes) provided greater prediction accuracies. Regarding to the panel used, 60% of the comparisons between 8 K and 15 K SNP chip panels and the true 50 K panel were statistically significant (P < 0.05) and in most cases the true 50 K SNP panel provided greater accuracies  for all traits (Table P < 0.05) in 45% and 60% of the cases, respectively (Table P > 0.05) for the majority of traits (Table Table ts Table . The comly Table . When thts Table .Table 6DP < 0.05) and were higher when using the 8 K SNP panel in relation to the 15 K SNP panel. For the 8 K and 15 K SNP panel the average losses in expected GEBV accuracy were between −0.0002 and −0.0011 across scenarios  50 K SNP panel (P > 0.05) in validation accuracies when including different percentage of imputed animals (Table P > 0.05) between the 8 K and 15 K SNP chip panels and the true 50 K SNP chip panel (Table P < 0.05) for traits not included in the selection index. Using imputed genotypes from 8 K and 15 K to the 50 K SNP chip panel, the average losses in GEBV expected accuracy ranged between −0.0004 and −0.0013 and −0.0003 to −0.0013, respectively.There were no statistically significant differences . The average DGV validation accuracies were 0.31 and 0.50 for traits included or not in the selection index, respectively Table . The aveThe average expected EBV accuracy in the training population was 0.64 and ranged from 0.52 to 0.74. For the validation population, the average expected EBV accuracy was 0.63 and ranged between 0.51 and 0.73. Average expected GEBV accuracy was 0.66 for the scenario with all animals and 60% imputed genotypes (SCE1-60% and SCE2-60%) and 0.65 for the SCE3 scenario. The increase in average expected GEBV accuracy in the validation population by adding the information of the markers was 0.03. The average expected DGV accuracy across traits was 0.40 Table . The incr = 0.64). The greater validation accuracies observed for traits with higher heritability estimates  has also been reported in the literature  cattle, reported an increase of 8%  when using imputed HD genotypes compared to true 50 K genotypes. Brito et al. [In theory, increasing the number of SNPs in a panel will increase the level of linkage disequilibrium (LD) between a SNP and a QTL and consequently there should be an improvement in accuracies of genomic predictions of breeding values. This assumption has been confirmed by other studies in the literature that reported increased prediction accuracies when using imputed HD genotypes compared to medium density genotypes (e.g. 50 K). For instance, Boison et al. , workingo et al. , in a sto et al.  and Vazqo et al.  also repo et al. , 41, 42.o et al. . TherefoDespite the losses in expected GEBV accuracies when using 8 K and 15 K SNP panels were statistically significant when compared to the ones attained using the true 50 K SNP chip panel, they were smaller with the 15 K compared to 8 K SNP chip panel. These results can be explained by the highest concordance rate in the 50 K SNP panel imputed from the 15 K SNP panel , lower gThe traits related to fitness  had higher values of validation accuracy in relation to the other traits, including those from the selection index. These higher values are probably associated with greater genetic variability due to a weaker selection. The results found by Akanno et al. , workingValidation accuracies for BW in the SCE2 scenario were not influenced by either the panel or the percentage of imputed animals in the training population. Different results were observed in SCE1, where both the panel and the number of animals in the training population influenced the validation accuracy of BW. Hayes et al.  showed tThe losses in expected GEBV accuracy in each scenario were always analyzed relatively to the scenario where only true genotypes were used. For traits not included in the selection index over the different scenarios, the losses in expected GEBV accuracy were, on average, higher compared to the group of traits included in the selection index used in the Brazilian Braford and Hereford breeding program. However, the losses in expected GEBV accuracy for the majority of traits were greater when using 50 K genotypes imputed from the 8 K SNP panel compared to 15 K. A greater percentage of animals with imputed genotypes was associated to higher losses in expected GEBV accuracy, regardless of the scenario investigated. In other words, losses in expected GEBV accuracy were higher due to greater error rates in the genotyping imputation process .The percentage of animals with imputed genotypes in the training population did not significantly influence the validation accuracy (Pearson’s correlation), but the size of the training population did influence the validation accuracies. The losses in expected GEBV accuracy due to imputation of genotypes were lower when using the 50 K SNP panel imputed from the 15 K SNP panel instead of imputation from the 8 K SNP panel. Therefore, using the low-density panels may allow Brazilian Braford and Hereford cattle breeders to genotype more animals, preferentially using 15 K or 50 K SNP chip panels, and consequently enlarging the size of the training population, which might in fact increase the accuracy of the DGV.


Gossypium spp.), as the world’s most utilized textile fibre source, is an important, economically valuable crop worldwide. Understanding the genomic variation of tetraploid cotton species is important for exploitation of the excellent characteristics of wild cotton and for improving the diversity of cotton in breeding. However, the discovery of DNA polymorphisms in tetraploid cotton genomes has lagged behind other important crops.Cotton  method, which will be applied in introgression genetics and breeding with G. hirsutum cv. Emian22 as the receptor and the other species as donors.A total of 111,795,823 reads, 467,735 specific length amplified fragment (SLAF) tags and 139,176 high-quality DNA polymorphisms were identified using specific length amplified fragment sequencing (SLAF-seq), including 132,880 SNPs and 6,296 InDels between the reference genome (TM-1) and the five tetraploid cotton species. Intriguingly, gene ontology (GO) enrichment analysis revealed that a number of significant terms were related to reproduction in Using SLAF-seq, a large number of DNA polymorphisms were identified. The comprehensive analysis of DNA polymorphisms provided invaluable insights into the different tetraploid cotton species. More importantly, the identification of numerous interspecific genetic variations provides the basis and is very practical for future introgression breeding. The results presented herein provide a valuable genomic resource for new insights into the genetics and breeding of cotton.The online version of this article (doi:10.1186/s12864-017-3643-4) contains supplementary material, which is available to authorized users. G. herbaceum  and G. arboreum , which are Old World cottons from Asia-Africa, and two New World tetraploid cottons from the Americas, which are G. hirsutum or upland cotton  and G. barbadense or extra-long staple fibre cotton  [G. arboreum (A2) and G. raimondii (D5) [Cotton is the most important natural textile fibre and is a significant oilseed crop, providing approximately 35% of the total fibre used in the world , profounG. barbadense has been used to improve fibre quality in upland cotton, as the second domesticated tetraploid cotton. However, it has some limitations, such as low yield and limited adaption. On the contrary, wild cotton germplasms harbour extensive genetic diversity and potential practicability, and they have rich sources of novel traits and are currently being mined to improve many beneficial agronomic traits. For example, G. tomentosum contains many unique agronomic traits, including insect-pest resistance, salt tolerance, heat tolerance, drought tolerance, nectarilessness and lint colour. G. mustelinum has fine fibre quality and Verticillium wilt resistantce. G. darwinii has many excellent traits, such as drought tolerance, finer fibre fineness, Fusarium wilt and Verticillium wilt resistance [1-genome [2-genome [Upland cotton is the primary source for cotton production, representing over 95% of the cotton fibre produced in the world ; however1-genome , 7 and A2-genome  and the 2-genome , 11 haveSingle nucleotide polymorphisms (SNPs) are known to be of considerable importance because they have a much higher abundance in the genome and are used to determine the population structure and for linkage disequilibrium (LD) analysis . SimilarFortunately, with the rapid developments and applications of NGS technologies, many complexity reduction approaches have been developed based on NGS platforms, such as genotype-by-sequencing (GBS), 2b-RAD and RRLs. Therefore, with the successful application of these methods, a large number of sequence polymorphisms have been detected, including SNPs and InDels . RecentlG. hirsutum cv. Emian22 (AD1), G. barbadense acc. 3–79 (AD2), G. tomentosum (AD3), G. mustelinum (AD4) and G. darwinii (AD5) genomes. Using the SLAF-seq data, an integrated analysis was carried out. Therefore, there were three main objectives in this study. First, interspecific variations were explored and characterized. Second, the phylogenetic trees of tetraploid cotton species were reconstructed. Third, the interspecific SNP and InDel markers were developed for future introgression breeding. In summary, the new DNA polymorphisms presented in this study will dramatically increase the efficiency for future research into high-density interspecific mapping, introgression breeding, genetic dissection and gene utilization in cotton.Here, the SLAF-seq method was used to identify DNA polymorphisms between the reference (TM-1)  and 5 teG. hirsutum cv. Emian22 (Gh_E22), G. barbadense acc. 3–79 (Gb_3-79), G. tomentosum (Gt), G. mustelinum (Gm) and G. darwinii (Gd) were 28.80, 25.14, 21.12, 24.73, and 31.13, respectively.In the present study, a new variation resource was generated that had direct relevance to the current scenario of cotton breeding strategies. Using an Illumina high-throughput sequencing platform, a total of 117,795,823 80-bp long paired-end reads were generated from the five tetraploid cotton species, including two cultivars and three wild species  and transversions  based on nucleotide substitutions Fig. . Among tThe DNA polymorphisms in transposable elements (TEs) were also explored in the five cotton species. The percentages of SNPs/InDels in TEs for Gh_E22, Gb_3-79, Gt, Gm and Gd were 76.89%/73.71%, 77.91%/68.97%, 79.77%/72.69%, 79.63%/72.27%, and 78.57%/72.89%, respectively. The number of SNPs and Indels was calculated in TE and non-TE regions  varied across all 26 chromosomes. Herein, SNPs were observed to be unevenly distributed among the chromosomes and between the At and Dt in each of cotton species, with more SNPs in the At than the Dt Fig. . LikewisIn addition, the total number of DNA polymorphisms and their density (number per 1 Mb) varied across the 26 cotton chromosomes in the five cotton species, and the total number of DNA polymorphisms was found to be positively correlated with the chromosome length , the distributions of SNPs and InDels within various genomic features were revealed. In general, a similar distribution pattern between SNPs and InDels was observed in the five species Fig. . The perTo further investigate the effect of SNPs in the coding sequence (CDS) region Fig. , amino aGene ontology (GO) enrichment analysis revealed putative functions of the genes containing non-synonymous SNP variations among the five cotton species. Based on the reference genome annotation, a number of significantly enriched GO terms in each of the three main categories  were identified. The significantly enriched GO terms, which were genes involved in biological processes, molecular function and cellular component of the GO classification, revealed striking differences between the five cotton species  and Gt (AD3), one consisting of Gm (AD4), and the third containing Gb_3-79 (AD2) and Gd  between Gh_E22 and Gb_3–79, 1 (10%) between Gh_E22 and Gt, 2 (20%) between Gh_E22 and Gm, and 3 (30%) between Gh_E22 and Gd.G. hirsutum and G. tomentosum, the other is G. mustelinum, and the third is G. barbadense and G. darwinii [In the present study, a high quality of 111,735,304 80-bp long paired-end reads and 467,735 SLAFs were generated using high-throughput SLAF-seq. The average sequencing depth of each cotton species was 26.18×. Additionally, a total of 114,833 homozygous SNPs, and 5,859 homozygous InDels were identified. Interspecific DNA polymorphisms for Gb_3–79, Gt, Gm and Gd occurred at a much higher rate compared to intraspecific DNA polymorphisms for Gh_E22 Fig. , especiaG. hirsutum and G. barbadense [G. hirsutum (2.194) and G. barbadense (2.210) of a previous report in cotton [For the SNPs, the non-synonymous/synonymous SNP ratios in different cotton species were 1.42–1.91 Fig ., which wrbadense . Howeverrbadense , 26, whin cotton , IR64/Pon cotton , and JS-n cotton . Furthern cotton .However, as for InDels, the length distribution of InDels was found to be larger than that in rice  and lessG. barbadense and G. hirsutum.Functional annotation of these genes containing variations revealed putative characteristics in different cotton species Fig. . In Gb_3Gossypium has been researched broadly in phylogeny and a consensus phylogenetic tree was generated [In previous studies, the genus enerated , 36. Theenerated , and it G. hirsutum and G. barbadense was investigated for its yield and fibre quality improvement, including heterosis, combining ability and genetic effects [Interspecific introgression breeding is a traditional method and requires labour, time and funds; however, it can be expedited by molecular marker-assisted selection. In a recent study, the cotton breeding potential of introgression lines between  effects . Similar effects . As is kA large number of high-quality DNA polymorphisms through SLAF-seq were identified in five tetraploid cotton species. Further comprehensive analysis of DNA polymorphisms provided valuable insights into the different tetraploid cotton species, especially between cultivated and wild cottons. To better utilize the novel characteristics of drought tolerance, defence responses and salt stress tolerance, etc. in wild cottons, numerous interspecific variations were identified here, which will be very practical in future introgression breeding. Overall, the comprehensive data generated in this study provided insights into cotton evolution and a resource for future researches in high-density interspecific mapping, introgression breeding improvement in cotton.G. hirsutum cv. Emian22 , G. barbadense acc. 3–79 , G. tomentosum , G. mustelinum , and G. darwinii , which were denoted hereafter as Gh_E22, Gb_3-79, Gt, Gm and Gd, respectively. To investigate the evolutionary relationship between diploid and polyploid cottons, three additional diploid genomic sequence reads were also analysed, which were G. herbaceum , G. arboreum , and G. raimondii  [Five tetraploid cotton species were employed in our study:  26, D5) , 40.The elite cultivar Gh_E22 and the genetic and cytogenetic standard line Gb_3–79  were groRsaI + HaeIII  were selected to digest purified genomic DNA. To maintain sequence depth uniformity, the objective fragments of 314 ~ 344 bp in size were selected. Secondly, an adenine nucleotide (A) overhang was added to the digested fragments. The dual-index paired-end adapters’ ligation, the adapter-modified ends obtainment and polymerase chain reaction (PCR) were carried out step by step. Subsequently, PCR products were purified using an E.Z.N.A.® Cycle Pure Kit  and the purified PCR products were incubated at 37 °C with MseI, T4 DNA ligase, ATP, and Solexa adapter. The reaction products were purified with a Gel Extraction Kit  and electrophoresed on a 2% (w/v) agarose gel [The SLAF libraries were constructed based on the result of a pre-designed scheme, which was used to determine the optimized restriction enzymes and sizes of restriction fragments to optimize SLAF yields and obtain the maximize SLAF-seq efficiency. Three criteria were considered in the pre-design experiment: (i) The final number and length of the SLAFs must be suitable for the specific experimental system and must meet the expected one; (ii) The distribution of SLAFs must be even in the genome; and (iii) The repeated SLAFs must be avoided. The procedure followed was as described previously by Sun et al. , with mirose gel . Thirdlyrose gel .http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). All reads were processed for more stringent quality control and filtered. Those reads containing adaptor/primer contamination and low-quality bases were removed. The sequence of the reference genome TM-1 was downloaded from the CottonGen database (https://www.cottongen.org). Subsequently, the high-quality trimmed paired-reads were mapped onto the cotton reference genome TM-1 using Burrows-Wheeler Aligner (BWA) software (v0.7.10) [http://broadinstitute.github.io/picard/). Only the mapped reads with high mapping quality (MQ ≥ 20) and high base quality (Q ≥ 30) were considered for downstream analysis.To assess the initial quality of the raw sequence data, the software of FastQC was used (v0.7.10) . The mapv0.7.10)  and PicaDNA polymorphisms were called with the Genome Analysis Toolkit  software , Samtoolhttp://bioinformatics.psb.ugent.be/webtools/Venn/). The synonymous and non-synonymous SNPs, and large-effect SNPs and InDels were identified with G. hirsutum (TM-1) [−10 and MCscan software [The genomic distribution of DNA polymorphisms was analysed and visualized with Circos software , and them (TM-1)  as a refm (TM-1) . To idensoftware  with defG. herbaceum (A1) and G. arboreum (A2); while the closest extant diploid of the Dt donor was similar to G. raimondii (D5) [G. herbaceum (A1), G. arboreum (A2) and G. raimondii (D5) [It is known that allopolyploid cottons contain two homologous subgenomes, the A-genome and D-genome donors. According to previous reports, the At donor was likely the two extant A genome species, dii (D5) . At and dii (D5) , 40 wereThe software SNPhylo  and MEGAp-value cut-off of ≤0.01.To explore variations, we focused on analysing the putative functions of the genes. Based on the cotton genome annotation, the putative functions of the genes were assigned. The conserved domain (s) was predicted by searching the cotton genome annotation. The Gene Ontology enrichment analysis was performed using Fisher’s exact test in Blast2GO version 2.8  with a pInterspecific markers between Gh_E22 and the other four species were identified. To assess and validate the SNPs for future applications, a total of 40 SNPs on chromosome A01 for Gh_E22 vs. Gb_3-79, Gh_E22 vs. Gt, Gh_E22 vs. Gm and Gh_E22 vs. Gd were randomly selected to design flanking primers. These flanking primers were designed from the 150 bp flanking sequences of the 40 randomly selected SNPs using BatchPrimer3  with an Additional file 1: Table S1.Distribution statistics of SLAFs and polymorphic SLAFs on each chromosome. (XLS 25 kb)Additional file 2: Figure S1.Distribution and total number of SNPs and InDels detected on the cotton chromosomes. (a) The distribution of SNPs and InDels detected on all the 26 chromosomes of cotton (1 Mb window size). I: the chromosomes; II: gene density; III: the total number of homozygous SNPs; IV: the total number of homozygous InDels; V: the homologous genes between At and Dt indicated by different colours including SNPs. SNPs in At is indicated by red line, Dt indicated by yellow line, SNPs in both At and Dt indicated by blue line.  The number of SNPs (b) and InDels (c) detected on each cotton chromosome are illustrated in the bar graphs by different colours. Total number on each cotton chromosome is indicated by red bar graphs. The homozygous number on each cotton chromosome is indicated by green bar graphs. (TIF 5665 kb)Additional file 3: Figure S2.The number of SNPs and InDels in TE and non-TE regions in different cotton species. (a) The number of SNPs in TE and non-TE regions. (b) The number of InDels in TE and non-TE regions. (TIF 551 kb)Additional file 4: Table S2.Statistics of SNPs and InDels detected in transposable elements among the five cotton species. (XLS 24 kb)Additional file 5: Table S3.Frequency of SNPs and InDels detected among the five cotton species on individual cotton chromosome. (XLS 28 kb)Additional file 6: Table S4.Non-synonymous SNPs present in the coding region of genes among the five cotton species. (XLS 220 kb)Additional file 7: Table S5.List of large-effect SNPs and InDels and genes harboring at least one large-effect SNPs and InDels in the five cotton species (provided as separate file). (XLS 40 kb)Additional file 8: Table S6.The significantly enriched GO terms in each cotton species. (XLS 40 kb)Additional file 9: Table S7.List of the DNA polymorphisms in Gh_E22 vs. Gb_3-79, Gh_E22 vs. Gt, Gh_E22 vs. Gm and Gh_E22 vs. Gd. (XLS 11624 kb)


Sarcophilus harrisii) is virtually 100% fatal. Devil facial tumour disease (DFTD) has swept across nearly the entire species' range, resulting in localized declines exceeding 90% and an overall species decline of more than 80% in less than 20 years. Despite epidemiological models that predict extinction, populations in long-diseased sites persist. Here we report rare genomic evidence of a rapid, parallel evolutionary response to strong selection imposed by a wildlife disease. We identify two genomic regions that contain genes related to immune function or cancer risk in humans that exhibit concordant signatures of selection across three populations. DFTD spreads between hosts by suppressing and evading the immune system, and our results suggest that hosts are evolving immune-modulated resistance that could aid in species persistence in the face of this devastating disease.Although cancer rarely acts as an infectious disease, a recently emerged transmissible cancer in Tasmanian devils ( A recently emerged infectious cancer has caused the near extinction of the Tasmanian devil, but some populations persist. Here, Epstein et al. provide evidence for possible resistance via rapid evolution in two genomic regions that contain cancer-related immune response genes. Sarcophilus harrisii) in only 20 years1S. harrisii, leaving no uninfected wild populations. Very recently, a second transmissible cancerous cell line (DFT2) has been described in Tasmanian devilsEmerging infectious diseases are increasingly implicated in population declines and even species' extinctions13As the largest remaining marsupial carnivore, Tasmanian devils are nocturnal, highly social, and extremely aggressive toward one another. The frequency-dependent transmission of DFTD, which is spread by biting during social interactions, has led to predictions of Tasmanian devil extinction on the basis of epidemiological models1The extremely strong selection imposed by DFTD could result in devil extinction, tumour extinction, or some type of stable or unstable equilibrium between the host and pathogen. Here, we present results utilizing a unique opportunity to study devil genomic responses to DFTD across both time and space. We have replicated sampling across multiple populations before DFTD arrived and at several time points following initial epizootics. To test for patterns of selection across the Tasmanian devil genome, we generated >90,000 single-nucleotide polymorphisms (SNPs) by sampling >800,000 loci using RAD-seq . The windows that overlapped the candidate regions were in the top 3% of all windows tested across the entire genome, and had combined P values of 0.015 and 0.004 for the chromosome 2 and 3 regions, respectively.First, we tested for changes in the extent of LD in the two candidate regions in pre-DFTD and post-DFTD samples using the Rsb statisticSecond, we investigated the directionality of allele frequency change in the three populations. Within the chromosome 2 candidate region, allele frequency changes are in the same direction in all three populations at many SNPs . This paTHY1 or CD90) is a membrane protein involved in the regulation of a variety of cell–cell interactions including immune system function, cell adhesion and tumour suppressionCbl-c, or CBL2) are associated with changes in cancer risk in humans and mutations in related genes increase the ability to fight off tumours in miceUSP2) is a target of cancer treatments because it is involved in cell-cycle regulation and particularly p53, which regulates apoptosisMRT2) is a target for drugs used to treat myelomaThird, five of the seven genes in these regions contain protein-coding genes associated with cancer risk or immune system function in other mammals . We testFourth, we used a time series analysis to test for relative fitness advantages of SNPs in the two genomic regions by fitting a model of allele frequency changeWe present strong evidence for an evolutionary response to selection imposed by DFTD in two small candidate genomic regions based on multiple lines of evidence. Allele frequency changes in SNPs that were in the top 2.5% pre- and post-DFTD overlapped in these two regions in all three populations. Given our method of detecting candidate gene regions and the extent of LD , this reCD146 and THY1 because they act as immune system regulators and are involved in cell–cell communication and cell adhesion. Given that DFTD is likely infectious due to immune system evasion via improper expression of its own MHC, as well as downregulation of MHC expression in the devil hostImportantly, five of seven genes in the two candidate regions are related to cancer or immune function in other mammals. One gene, cereblon, a myeloma treatment target, is found in strong linkage with the chromosome 2 region. Although there are four candidate genes in chromosome 3, it is likely that only one gene is the cause of the signatures of selection due to the strength of LD at this scale. The two most probable candidates are Taken together, our results suggest that there has been a substantial genomic response in very few generations , three lanes of paired-end 150 bp reads were generated from individually barcoded libraries using a HiSeq 2500. The remaining 288 samples were individually barcoded, multiplexed in pools of 96 samples, and for each pool, six lanes of single-end 150 bp reads were obtained. For two of the pools, the RAD library prep was performed with both 12-PCR cycles and 14-PCR cycles; for these two pools, three lanes of sequencing were obtained for the 12-cycle library and three lanes for the 14-cycle library. All but six of the samples from the focal populations were sequenced as part of the 288-sample single-end-read sequencing run.et al.From the 72-sample run, we obtained 456,639,846 read pairs; after de-multiplexing, quality filtering, PCR-duplicate removal and removal of low-quality alignments, we had 30,290–3.6 million reads per sample. For the other 288 samples, we obtained a total of 2,649,033,674 reads, or 60,000–14.7 million reads per sample  pipeline. For those samples with data from both 12-PCR-cycle and 14-PCR-cycle library preps, the data from different PCR cycles were processed separately until calling SNPs. We first de-multiplexed the reads and removed poor quality reads with process_radtags from the Stacks pipeline . Then for the samples with paired-end data, we removed PCR duplicates with clone_filter; this is not possible with single-end read data. Reads were aligned to the reference genome using bowtie2 ref.  with theFor all further analyses, we filtered out SNPs that were on scaffolds assigned to the X chromosome, SNPs on RAD loci where any SNP had an observed heterozygosity >0.5 across all the samples , SNPs present in less than one-third of the samples (<120 samples) and SNPs with an MAF<0.01 across all the samples; across the entire set of 360 individuals, this resulted in 111,659 SNPs. The intention of the MAF filter was to remove SNPs that were the result of sequencing errors; thresholds set to less than 0.01 had a very marked excess of rare variants. After applying these filters, we conducted further analyses on just the samples from the focal populations, and for most analyses removed SNPs that were genotyped in less than one-third of the individuals in a particular population. With these filters, median proportions of SNP loci genotyped per individual were 69% in Freycinet, 56% in Narawntapu and 64% in West Pencil Pine. Within each population, we imposed additional filters for certain analyses.et al.For analyses requiring phased data, we used fastphase , 35.5–38.9 (Narawntapu) and 23.7–28.2 (West Pencil Pine).In addition to searching for SNPs with large allele frequency changes after DFTD introduction, we also used the method of Mathieson and McVeanR2 measure of linkage disequilibrium, but does not require phased data. Only SNPs with MAF>0.05 in the target population were used. In every population, mean linkage disequilibrium persisted substantially above the background level to at least 100 kb  and BioSamples SAMN05250006-05250365. The genotype data has been deposited at Dryad under doi:10.5061/dryad.r60sv. Any other relevant data is contained within the Article and its The sequence data has been deposited at NCBI under BioProject PRJNA306495 .Supplementary Figures 1-5, Supplementary Tables 1-2 and Supplementary ReferenceRead counts, barcodes, and accession numbers for all samples


The cause of immune-mediated myositis (IMM), characterized by recurrent, rapid-onset muscle atrophy in Quarter Horses (QH), is unknown. The histopathologic hallmark of IMM is lymphocytic infiltration of myofibers. The purpose of this study was to identify putative functional variants associated with equine IMM.A genome-wide association (GWA) study was performed on 36 IMM QHs and 54 breed matched unaffected QHs from the same environment using the Equine SNP50 and SNP70 genotyping arrays.Punadjusted < 1.4 × 10− 6) associated with the IMM phenotype. Associated haplotypes within this region encompassed 38 annotated genes, including four myosin genes . Whole genome sequencing of four IMM and four unaffected QHs identified a single segregating nonsynonymous E321G mutation in MYH1 encoding myosin heavy chain 2X. Genotyping of additional 35 IMM and 22 unaffected QHs confirmed an association (P = 2.9 × 10− 5), and the putative mutation was absent in 175 horses from 21 non-QH breeds. Lymphocytic infiltrates occurred in type 2X myofibers and the proportion of 2X fibers was decreased in the presence of inflammation. Protein modeling and contact/stability analysis identified 14 residues affected by the mutation which significantly decreased stability.A mixed model analysis identified nine SNPs within a ~ 2.87 Mb region on chr11 that were significantly  contains supplementary material, which is available to authorized users. Immuneg humans , dogs [2g humans , 4. Commmyocytes , 6. Thermyocytes , polymyomyocytes , canine myocytes , and equuine IMM , 4. Equivacuoles , 6. Simivacuoles , 9.treptococci spp., or vaccination with influenza, herpes virus-1, or Streptococcus equi subsp. equi 3 to 4 weeks prior to onset [Causes of autoimmune diseases such as IMM are not well understood, but environmental stimuli, combined with a genetic predilection, appear to be important initiating factors , 11.The Genetic associations with IMM have been found with various major histocompatibility complex loci in humans and dogs , 12. BecAll blood and muscle samples were collected with approval from the Animal Care and Use Committee at the University of Minnesota and University of California, Davis.n = 36; 11 geldings, 10 stallions, and 15 mares) were selected based on a history of muscle atrophy  were selected from two herds that had active IMM cases. Unaffected horses had no history of muscle atrophy or stiffness consistent with IMM. Horses were selected such that they were not related at least within one generation. Of the horses used for the GWA, 1/36 affected and 41/54 unaffected horses were used in a previous genetic study [IMM-affected QHs  and four unaffected QHs (2 geldings and 2 mares) were selected for whole-genome sequencing.n = 25/35) and stiffness (n = 4/35) with mild lymphocytic infiltrates in myocytes or vascular cuffing in muscle samples. Unaffected QHs  in this follow-up cohort were housed on the same property as IMM-affected cases and therefore exposed to the same environmental risk factors to develop IMM. While these horses may have gone on to develop myositis later in life, all had no history of disease at the time of sampling. All horses in this cohort were genotyped for the non-synonymous MYH1 E321G variant.The follow-up cohort included an additional 35 IMM QHs , phenotyped clinically by muscle atrophy  and a related breed, Paint horses (6), that were embryo transfer recipient horses of unknown bloodlines were genotyped for the putative variant to assess the prevalence of the MYH1 E321G variant in distantly related or unrelated cohorts. There was no history of IMM in this herd.A cohort of 28 healthy QHs  were available for 110 horses across 21 breeds.Genotyping of the MYH1 E321G variant, additional pedigrees were created of individual families.From the original GWA cohort, pedigrees were available for 23/36 QH. Pedigrees were analyzed using Pedigraph . FollowiDNA isolations were prepared from whole blood  or muscle  samples according to the provided protocols. Genotyping of a subset of samples was performed on the Equine SNP 50K BeadChip  (1 IMM and 41 unaffected horses), prior to the creation of the Equine SNP 70K BeadChip . Thirty-five IMM QHs and 13 unaffected QHs were genotyped across 74,500 SNP markers with the Equine SNP 70K BeadChip .p > 0.001) were selected. A genome-wide efficient mixed model association was performed using GEMMA software using the standardized relatedness matrix option (−gk 2) [λ). A Bonferroni correction for 39,589 tests (the number of useable SNPs) from the GEMMA analysis, based on a Pgenome-wide of 0.05, was determined as 1.26 × 10− 6.Datasets were merged and only SNPs that passed quality control settings  . PopulatPpermuted = 0.05.For chr11, which demonstrated the only genome-wide significant associations on the GWA, haplotypes were reconstructed on the individual chromosome using Haploview . SNPs wehttp://sourceforge.net/projects/picard/). The Genome Analysis Tool Kit (GATK version v.2.7.4) was used to perform local realignment [Using Illumina’s TruSeq DNA PCR-free library preparation kit  and following the manufacturer’s instructions, libraries were prepared with median insert size of 300–400 bp from the four IMM QHs and four unaffected QHs. The eight libraries were barcoded and pooled across eight lanes of a 125PE flow cell on an Illumina HiSeq2500, generating an average of 10.2× coverage per horse. Following quality trimming, reads were mapped to the EquCab2.0 reference genome using the Burrows-Wheeler Aligner (BWA) version 0.7.5a  using delignment . Variantlignment , 22.P value (< 0.0003), allowing for one heterozygote in the IMM-affected group. Segregating variants within this region were further evaluated using all publically available mapped whole-genome sequences in the NCBI Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra). Putative segregating variants were excluded if found in > 1 breed other than the QH. In addition to variant calling, visual inspection of the raw reads using the Integrated Genomics Viewer [https://genome.ucsc.edu/), scores were determined for each orthologous human variant using the 100 vertebrate score by phastCons (https://genome.ucsc.edu/). Whole genome sequences were deposited in the NCBI Sequence Read Archive (https://submit.ncbi.nlm.nih.gov/subs/sra/) (SRP119975).SnpEFF  and SnpSs Viewer  within tMYH1 E321G variant in an additional cohort of IMM-affected and unaffected QHs and a cohort of unaffected Arabian horses. Amplification of products was performed using endpoint PCR and visualized with the QIAxcel Advance System  and the QIAxcel DNA Screening Kit . The 20-μL PCR reactions were comprised of 2 U of Hot-start TAQ and 2.0 μL of 10× Buffer , 0.25 mM dNTPs , 0.5 μM of both forward and reverse primers , and 20 ng genomic DNA. Standard PCR conditions were performed as follows: hot-start TAQ activation and initial denaturation at 95 °C for 10 min; 35 cycles of 95 °C denaturation for 30 s, 60 °C annealing for 1 min, and 72 °C extension for 1 min; and final extension at 72 °C for 10 min. PCR products were purified using ExoSAP-IT® PCR Product Cleanup Kit . Sanger sequencing was performed using ABI 2500 automated sequencers. Resulting sequences were aligned to EquCab2.0 (http://www.ncbi.nlm.nih.gov/genome/145) and analyzed with Sequencher® software .Primer pairs were designed using Primer3Plus software   and 11 unaffected horses across various breeds . Primers were designed for MYH1 (F-CACCACCAACCCGTATGACT and R-GAAGCCCAAGATCTCAATGG) and the reference gene ACTB (F-AAGGAGAAGCTCTGCTATGTCG and R-GGGCAGCTCGTAGCTCTTC), RT-qPCR was performed, and data were analyzed as previously reported [Skeletal muscle reported .MYH1 variant were modeled using online G23D tool [Homo sapiens myosin gene chain A (PDB ID: 4pa0). The amino acid in the mutated position was modeled using SCcomp [E317G variant was performed using I-Mutant-2 [Conformational changes caused by the identified E321G 23D tool  and Homog SCcomp . Contactg SCcomp , 32. StaMutant-2 , directlFormalin-fixed or fresh skeletal muscle biopsy specimens were obtained by referring veterinarians and shipped on gel packs to the Neuromuscular Diagnostic Laboratory. Fresh samples were frozen in isopentane suspended in liquid nitrogen within 48 of the initial biopsy, and samples were stored at − 80 °C.MYH1 genotype .In a study of IMM published previously, inflammatory cell types were identified in fresh muscle samples obtained from horses of Quarter Horse-related breeds that presented with a history of gross atrophy of gluteal and epaxial muscles evident on physical examination . MacrophMYH1 E321G variant were used as unaffected controls. Unaffected horses had normal muscle mass as assessed by physical examination and lacked lymphocytes or macrophages in muscle biopsies. Frozen samples from semimembranosus  and middle gluteal  muscles were assessed.Adequate well-preserved frozen muscle tissue remained after examining inflammatory cell types from 10 IMM horses that were then used to evaluate fiber types. In six IMM cases, inflammation was identified in a formalin-fixed gluteal sample and fiber typing was performed in a concomitantly submitted fresh semimembranosus sample lacking substantial inflammation. Semimembranosus or gluteal muscle samples from five horses that were homozygous wild-type for the 1 Fab fragments using Zenon® Mouse IgG labeling kits , Alexa Fluor® 488 (A4.74), Alexa Fluor® 594 (NCL-MHCF), and Pacific Blue™ (MAB1628). The three Zenon® labeled antibodies were mixed together, added to the tissue sections, and incubated at 4 °C overnight. A secondary antibody for Collagen V, FITC-rabbit anti-goat IgG  1:500 was applied to the cryosections and incubated for 1 h at room temperature. Sections were subsequently mounted using VECTASHIELD mounting medium  and examined using a fluorescence microscope  with filters designed for each of the different emitting wavelengths. Images were captured and pseudo-colored composites generated.Serial 10-μm sections stained with hematoxylin and eosin (HE) and labeled by immunofluorescence for fiber type were used to identify inflammatory infiltrates within fiber types. Type 1, 2A, 2AX, and 2X muscle fiber types were identified by multiple fluorescent labeling according to Tulloch et al. . BrieflyThe total number of type 1, 2A, 2AX, and 2X muscle fibers was determined for the entire muscle section, and fiber type compositions were determined by dividing the total number of fibers of each type by the total number of muscle fibers counted (range 447 to 3244 muscle fibers/sample). The fiber type composition of IMM samples with inflammation and wild-type samples were compared by genotype and disease status using a two-way ANOVA.From the original GWA cohort, pedigrees were available for 23/36 QH. All affected horses could be traced back to a common sire within eight generations . Genomic inflation (λ) was estimated at 1.98, indicative of population stratification. Due to the elevated genomic inflation, a mixed model analysis was performed utilizing GEMMA with the same filters [λ = 1.02). Nine SNPs on chromosome 11 reached genome-wide significance , spanning ~ 6.3 Mb from chr11: 49,915,548–56,207,873. Of these, four highly significant haplotype blocks (Ppermuted < 0.001) spanning ~ 3.1 Mb from chr11:52379156-55487290 were identified. This region overlapped the ~ 2.87 Mb region flanked by eight of the nine genome-wide significant SNPs from the GWA. Therefore, while the entire 6.3 Mb region was evaluated, the 3.1 Mb region was prioritized.Haplotype analysis of 1109 SNPs on chr11 that passed quality control  using Haploview  identifiMYH1, MYH2, MYH3, and MYH13) and myocardin (MYOCD) were located within the associated region. MYH1 and MYH2 are expressed in adult equine skeletal muscle while MYH3 is expressed at the embryonic stage and MYH13 is expressed primarily in ocular skeletal muscle [The 6.3 Mb region identified through GWA and subsequent haplotype analysis encompassed 148 Ensembl annotated genes, with 277 annotated transcripts in equine skeletal muscle . Within l muscle .Punadjusted < 0.00003). These 628 variants were examined in all publically available mapped whole-genome sequences (n = 110 across 21 breeds) from the Sequence Read Archive . Only 15 of these variants were unique to the QH. As IMM has not been reported in a breed other than QH-related breeds, the other 613 variants were excluded from further analysis. The remaining 15 breed-specific variants were prioritized for further evaluation (Table n = 12) or a LOW synonymous variant (n = 1) . Within the follow-up cohort of 35 mildly affected IMM horses, a significant allelic association was validated (P = 2.95 × 10− 5) with the MYH1 E321G variant. In the two IMM cohorts combined, 87.7% of horses were homozygous or heterozygous for the variant . The subset of horses genotyped included the n = 9 IMM-affected horses that genotyped A/A at chr11:52,993,878, IMM-affected horses that genotyped G/G (n = 11), and unaffected horses that genotyped A/A (n = 11). The nine IMM-affected horses that had genotyped A/A at chr11:52,993,878 also genotyped for the reference allele at the two other segregating variants, resembling the unaffected horse genotype. These nine horses with an inflammatory myopathy therefore appeared to be phenocopies. In the other IMM-affected horses, the two other variants are genotyped as alternate allele/alternate allele. Because these variants lacked a strong functional effect, were non-coding in adult equine skeletal muscle, and lacked any known connection to immune or muscle function, the MYH1 E321G variant was identified as the putative functional variant for equine IMM .There was no significant difference in MYH1 is located in a highly conserved region of the myosin globular head in subfragment-1 between the helix loop-helix region of Helix J and Helix K  for a non-polar glycine (G) that lacks side chains necessary for hydrogen bond formation. Contact area and solute accessibility analyses showed that 14 residues were directly affected by the MYH1 E321G mutation   Fig. , e Small01) Fig. –h. In co01) Fig. –k. The mMYH1 gene associated with susceptibility to a specific myopathy. A GWA study initially identified a ~ 6.3 Mb region on chr11 associated with IMM, and haplotype analyses narrowed this region to ~ 3.1 Mb that encompassed two myosin genes expressed in adult equine muscle, namely MYH1, encoding type 2X myosin heavy chain (MyHC), and MYH2, encoding MyHC 2A. Type 2X fibers comprise 40–80% of all fibers and type 2A comprise 30-40% of all fibers present in muscles typically affected with IMM [MYH1 that resulted in substitution of a charged glutamic acid for a non-polar amino acid, glycine, at 321 in the MyHC globular head. Credence for the significance of the variant was provided by the fact that glutamic acid is highly conserved , and seven were from the follow-up cohort, where IMM-affected cases had a milder degree of myofiber lymphocytic infiltrates and included those with only perivascular lymphocytes. As additional nearby variants segregated with the IMM phenotype at identical frequencies as MYH1 E321G  when using the Ensembl annotation. We have recently demonstrated that this annotation is not highly accurate in the horse, and we have published a tissue-specific transcriptome for the horse based on RNA-sequencing data and integration with other EquCab2.0 annotations, including NCBI [MYH3 variant was identified as non-coding. Therefore, we propose that these variants are in linkage disequilibrium with the MYH1 E321G variant.In total, 15 variants segregated at identical frequencies with the IMM phenotype Table . While ming NCBI . This cuMYH2 have been reported in a small number of human patients. Recessive truncating deletions in MYH2 result in early-onset weakness confined to extraocular, semitendinosus, gracilis, vastus lateralis, and medial gastrocnemius muscles whereas dominant point mutations in MYH2 result in a later onset of mild progressive weakness [MYH2 mutations result in mild myopathic changes such as variability of fiber size, internalized nuclei, and increased interstitial connective and adipose tissue; however, only recessive deletions produce a total absence of type 2A myofibers [MYH2 mutations, in horses with IMM, the reduction in type 2X myofibers was dependent upon inflammation and, in the absence of inflammation, homozygous IMM horses had a normal proportion of type 2X fibers. In the small number of samples evaluated from homozygous horses with chronic atrophy, we found a decrease in the proportion of type 2X myofibers with a higher proportion of type 2A and 2AX fibers. Our results suggest that lymphocytic destruction of MyHC 2X fibers appears to be a prerequisite for acute inflammation in IMM horses. Previous studies have not identified immunoglobulin bound to myofibers in IMM horses [MYH1 myosin or cytokines have not been studied in equine IMM. The clinical signs and muscle histopathology of IMM appear to be distinct from those previously reported for MYH2 mutations in other species.Mutations in weakness –45. Bothyofibers , 45. UnlM horses ; howeverMYH1 and MYH2, more than 500 disease-causing point mutations have been described in MYH7, with the majority producing hypertrophic or dilated cardiomyopathy [MYH7 mutations are reported to cause skeletal myopathies such as myosin storage myopathies or Laing distal myopathy [MYH7 had similar inflammatory changes to those seen in equine IMM, although the clinical presentation was that of distal limb weakness not the rapid proximal muscle atrophy seen in equine IMM [+, CD4+, and CD8+) were found in this family with Laing distal myopathy, along with rimmed vacuoles, which are not a feature of equine IMM [In contrast to the low frequency of mutations in myopathy , 47. A mmyopathy , 47. Oneuine IMM . Similaruine IMM , 48. Laiuine IMM .Streptococcus sp. and myosin  in DBA/2 mice appears to predispose these mice to immune-mediated myocarditis [MYH7 S545A variant and BALB/c mice without the variant are auto-inoculated with cardiac myosin, chronic myocarditis only occurs when serum from the DBA/2 strain is injected into DBA/2 not BALB/c mice [MYH7 mutation and the equine MYH1 variant are located in the globular head of myosin, it is possible that a mutation in this highly conserved region somehow confers target host susceptibility to myositis.The best characterized link between myosin, inflammation, and muscle disease would be immune-mediated myocarditis . Fragmencarditis , 54. WheB/c mice . The autPerhaps the most intriguing link between myosin and autoimmunity comes from canine masticatory muscle myositis (CMM). CMM presents with painful swelling followed by rapid atrophy of masticatory muscles . SimilarMYH1 mutation is highly associated with susceptibility to IMM in horses. In the absence of inflammation, type 2X muscle fiber type composition is within normal limits in IMM horses; however, within a particular environment, the MYH1 mutation results in invasion and destruction of type 2X myofibers by lymphocytes and rapid onset of gross muscle atrophy.In conclusion, an E321G Additional file 1:Table S1. Across breed genotyping. (XLSX 9 kb)Additional file 2:Figure S1. Within the cohort of genome-wide association horses affected with IMM, 23/36 had available pedigree information. All affected horses (red) could be traced back to a common sire within eight generations. Pedigree analysis supported either an autosomal dominant or autosomal recessive mode of inheritance. Circles = females, squares = males. (TIFF 7954 kb)Additional file 3:Figure S2. Protein alignment of MYH1 gene and M protein of Streptococcus equi (S. equi) (AHI46575.1) showing similarities in regions of the S. equi alignment and the MYH1 gene. Sequences were aligned using CLUSTALX (version 2). (TIFF 15425 kb)


The widespread use of individual sires for artificial insemination promotes the propagation of recessive conditions. Inadvertent matings between unnoticed carriers of deleterious alleles may result in the manifestation of fatal phenotypes in their progeny. Breeding consultants and farmers reported on Vorderwald calves with a congenital skin disease. The clinical findings in affected calves were compatible with epidermolysis bullosa.P = 8.0 × 10−14). The analysis of whole-genome re-sequencing data of one diseased calf, three obligate mutation carriers and 1682 healthy animals from various bovine breeds revealed a nonsense mutation  in the COL7A1 gene that segregates with the disease. The same mutation was previously detected in three calves with dystrophic epidermolysis bullosa from the Rotes Höhenvieh cattle breed. We show that diseased animals from Vorderwald and Rotes Höhenvieh cattle are identical by descent for an 8.72 Mb haplotype encompassing rs876174537 indicating they inherited the deleterious allele from a recent common ancestor.Pedigree analysis indicated autosomal recessive inheritance of epidermolysis bullosa in Vorderwald cattle. We genotyped two diseased and 41 healthy animals at 41,436 single nucleotide polymorphisms and performed whole-genome haplotype-based association testing, which allowed us to map the locus responsible for the skin disease to the distal end of bovine chromosome 22  contains supplementary material, which is available to authorized users. Affecte41–9913) , ITGB4 (48–9913) , 21, KRT40–9913) , LAMA3 (77–9913)  and LAMC78–9913)  genes.COL7A1 gene. We show that a haplotype encompassing the deleterious allele, unexpectedly, also segregates in Rotes Höhenvieh cattle.Herein we present the phenotypic and molecular-genetic characterisation of autosomal recessive epidermolysis bullosa in Vorderwald cattle. We exploit whole-genome genotyping and sequencing data to identify the causal mutation for the skin disease in the Twenty-five Vorderwald calves  with congenital skin lesions were born between the years 2004 and 2015. The calves had extensive hairless, reddened and bloody regions at the fetlocks Fig. -c. AlthoIn order to determine the mode of inheritance of EB, we inspected pedigree records of 25 affected calves. The Vorderwald x Montbéliarde crossbred bull Piras (born in 1996) was in the paternal path of all and in the maternal path of 15 affected calves, suggesting recessive inheritance Fig. . Six disP < 8.6 × 10−7, Fig. P = 8.0 × 10−14) resulted from 19 adjacent haplotypes located between 42,994,215 and 49,273,889 bp. The two diseased calves had a common 21.26 Mb segment of extended homozygosity at the distal end of BTA22   as response variable in a linear regression model. To account for the relationship among the genotyped animals, we considered ten principal components in the regression analysis. A moderate inflation factor of 1.24 indicated that this corrective measure was mostly successful. Fifty-five haplotypes located at the distal end of BTA22 were significantly associated with EB (bp) Fig.  that wasCOL7A1 gene  that was previously detected in three Rotes Höhenvieh calves with dystrophic EB [To identify the causal mutation for EB, one affected calf, its parents and the bull Piras were sequenced to average read depths of 22.78x, 19.02x, 19.57x and 10.24x, respectively. To facilitate the identification of plausible candidate causal variants, we exploited sequence data of another 1682 animals from various bovine breeds that had been generated previously including three key ancestors from the Vorderwald cattle population. None of them was affected by EB. Multi-sample variant calling within the 21.26 Mb segment of extended homozygosity yielded genotypes at 71,169 single nucleotide and short insertion and deletion polymorphisms in four Vorderwald cattle. Of these, 8100 variants were compatible with recessive inheritance that is homozygous for the non-reference allele in the calf with EB and heterozygous in its parents and the bull Piras. However, 7398 of the compatible variants are most likely not causal for EB because they occurred in the homozygous state in at least one of the 1682 healthy animals. In conclusion, 702 variants were homozygous for the non-reference allele in an affected calf, heterozygous in obligate mutation carriers and not homozygous for the non-reference allele in healthy animals. Twenty candidate causal variants were splice site mutations or resided in protein coding regions of the genome  were noticed in the local German Vorderwald cattle population. Genomic regions underlying recessive conditions are usually identified by comparing genome-wide marker data between affected and unaffected individuals . We couCOL7A1 gene segregates with EB. Sequence variants in COL7A1 cause epidermolysis bullosa in various species [COL7A1 occurred several generations ago in an unadmixed Vorderwald animal before the introgression of foreign haplotypes began.The analysis of whole-genome re-sequencing data of 1686 cattle revealed that a nonsense mutation (rs876174537) in the  species , 31–34.  species  detected species , 16. Del species . Since t species , 26, pre species . It is aBetween five and ten Vorderwald bulls are annually selected for artificial insemination. The identification of the mutation causing EB now allows for the reliable detection of animals carrying the defective allele. In 79 bulls that were used for artificial insemination in the past 20 years, among them seven sires of calves with EB, the frequency of the rs876174537 T-allele was 5%. However, the widespread use of an unnoticed mutation carrier for artificial insemination may result in a rapid increase of the frequency of the deleterious allele particularly when considering the small number of Vorderwald cattle . Since tCOL7A1 gene causes recessive epidermolysis bullosa in Vorderwald and Rotes Höhenvieh cattle. Since an admixture of both breeds had not been documented, the defective allele likely occurred several generations ago in an unknown common ancestor of Vorderwald and Rotes Höhenvieh cattle. Our findings demonstrate that deleterious alleles may segregate across cattle breeds without any documented admixture. The identification of the causal mutation now enables the reliable identification of carrier animals. Genome-based mating programs can avoid inadvertent matings of carrier animals thereby preventing the birth of homozygous calves suffering from a painful skin disease.A nonsense mutation in the kinship2 R package [n = 7) and sires (n = 7), respectively, of another 23 diseased calves were collected by breeding consultants and employees of an artificial insemination centre. DNA from ear tissue and semen samples was prepared following standard DNA extraction protocols.Twenty-five Vorderwald calves with EB were born between the years 2004 and 2015. The pedigree of the diseased animals was constructed using the  package . The affplink v1.90 [P > 0.0001), per-SNP and per-individual call-rate higher than 95%), 43 animals  and 41,436 SNPs were retained for association testing. The findhap software (version 3) [Two calves with EB and 14 parents of another 23 affected calves were genotyped with the Illumina BovineSNP50 v2 BeadChip comprising genotypes at 54,609 SNPs. Twenty-seven healthy artificial insemination bulls from the Vorderwald cattle breed were genotyped with the Illumina BovineSNP50 v1 BeadChip comprising genotypes at 54,001 SNPs. The genotype data were combined and 52,339 SNPs that were a common subset of both datasets were considered for further analyses. The per-individual call-rate ranged from 95.10 to 99.87% with an average call-rate of 98.59%. The chromosomal positions of the SNPs corresponded to the UMD3.1 assembly of the bovine genome . We did rsion 3)  was usedN = 8207), all haplotypes  with a frequency above 5% were tested for association with EB using a linear regression model that included a correction term to account for the relationship among the animals: y = µ + ∑j = 110ajPCj + bHT + e, where y was a vector of phenotypes , μ was the intercept, PCj were j = 10 principal components of the genomic relationship matrix that was built based on 40,792 autosomal SNPs using the GCTA software tool [−7  were considered as significantly associated with EB.A sliding window consisting of 20 contiguous SNPs (corresponding to an average haplotype width of 1.23 ± 0.39 Mb) was shifted along the chromosomes in steps of 5 SNPs. Within each sliding window  and sequenced using the HiSeq 2500 instrument (Illumina). The resulting reads were aligned to the University of Maryland reference sequence of the bovine genome (UMD3.1 ) using tlgorithm , 43. IndSAMtools . Duplicard Tools .mpileup of SAMtools along with BCFtools [Beagle phasing and imputation [SAMtools. To identify mutations compatible with recessive inheritance, all polymorphic sites that were detected within the 21.26 Mb segment of extended homozygosity were filtered for variants that were homozygous for the non-reference allele in the affected calf and heterozygous in three haplotype carriers. The genotypes of candidate causal variants were validated in 1682 animals from various bovine breeds that were available from the fifth run of the 1000 bull genomes project [Variant Effect Predictor tool from Ensembl [Single nucleotide and short insertion and deletion polymorphisms were genotyped in four animals simultaneously using the multi-sample approach implemented in BCFtools . Beagle putation  was used project  and our  project , respect Ensembl , 49.We obtained genotypes for the rs876174537 polymorphism in 2 affected calves, 14 parents of affected calves and 72 healthy artificial insemination bulls that were born between 1982 and 2014. DNA of artificial insemination bulls was prepared from semen straws following standard DNA extraction protocols. PCR primers (GGCTGATCGTCTTTGTCACC (forward) and TCAGTCCTGATCCCCAACTC (reverse) as detailed in [GCTA software tool [fastSTRUCTURE software tool [We merged the genotype data of 43 Vorderwald and 671 animals from another six cattle breeds  to perform population genetic analyses. Array-derived genotypes of 250 and 150 randomly selected Fleckvieh and Holstein animals, respectively, were available from previous studies , 50. Theare tool . The estare tool  using be


Salmo salar). To characterize each, whole genome sequencing was performed to 30-fold depth of coverage using 20 fish representing each of three identified sex lineages. SNP polymorphism reveals male fish carry a single copy of the male specific region, consistent with an XX/XY or male heterogametric sex system. Haplotype analysis revealed deep divergence between the putatively ancestral locus on chromosome 2, compared with loci on chromosomes 3 and 6. Haplotypes in fish carrying either the chromosome 3 or 6 loci were nearly indistinguishable, indicating a founding event that occurred following the speciation event that defined Salmo salar from other salmonids. These findings highlight the evolutionarily fluid state of sex determination systems in salmonids, and resolve to the sequence level differences in animals with divergent sex lineages.Teleost fish exhibit a remarkable diversity in the control of sex determination, offering the opportunity to identify novel differentiation mechanisms and their ecological consequences. Here, we perform GWAS using 4715 fish and 46,501 SNP to map sex determination to three separate genomic locations in Atlantic salmon ( The genetic and environment factors that contribute to sexual determination are of fundamental biological interest, both from an evolutionary perspective and within an aquacultural production context. Teleost fish are of particular interest in both domains. They have evolved a spectacularly diverse array of sex determination systems, with examples spanning strict genetic control, total environmental control and complex combinations that interpret both genetic and environmental cues to dictate sexual trajectories. A number of species have been developed into globally important production species, and where there are gender-related differences in commercial value, knowledge concerning sexual determination is required to manipulate sex ratios and enhance production efficiency.SRY1), at least five genes have been identified that control SD in fish including amhy, amhr2, dmY, gsdf and sdY2. Each acts differently, however control over the initiation and rate of cell proliferation appears to be a common feature. The genetic control of sex determination in salmonids involves sdY , and is thought to employ a male heterogametric (XY) system3. The mechanistic action of sdY is less clearly understood than for other fish master SD genes, as it has not previously been implicated with sexual development in other species. The gene is male specific in 13 different salmonid species which strongly suggests it has a controlling function4. Comparison of the genomic location of the SD locus between salmonids indicates it is often non-syntenic. This evoked the concept that sdY is mobile and through speciation has taken unique chromosomal positions6. Interestingly, investigation within Atlantic salmon mapped SEX to multiple chromosomes and the presence of sdY is very strongly (but not perfectly) correlated with maleness8. Multiple locations for sdY within Atlantic salmon suggests that the mechanism driving it’s genomic mobility have continued to occur following the speciation event founding Atlantic salmon. The alternative is that multiple genomic locations predated the radiation of Atlantic salmon and balancing evolutionary pressure has maintained multiple sex lineages to the present.Unlike mammals which share a master sex determining (SD) gene . The effect of subsampling on the proportion of shared SNP between populations  is given in Supplementary Figure −4) and European derived farmed animals (average π = 2.44 × 10−4). All three population estimates are based on small sample sizes and are likely to change as more data become available, however the analysis revealed the TAS breeding population, closed since the importation of animals into Australia in the 1960s, has retained diversity comparable to other aquaculture strains. Relatedness between individual genomes was explored using Principal Component Analysis (PCA) of pairwise genetic distance  due to an increased prevalence of SNP with divergent RAF . Divergence between European fish was very similar when compared with either the TAS (FST = 0.178 ± 1.04 × 10−4) or NA animals (FST = 0.172 ± 1.26 × 10−4). Taken together, the three analytical approaches  report findings entirely consistent with the known population history of the three populations.To measure genome diversity and characterise sex determination loci in Tasmanian Atlantic salmon (TAS), we performed whole genome sequencing (WGS) of 20 fish selected from a range of year classes and families. Read mapping against reference genome ICSASG_v2al Table . Accuracnce Fig. . IndividRAF Fig. . Next, pram Fig. . This resdY gene to assign genotypic sex . Concordance between the two traits was high but not perfect (98%), and discordant individuals were excluded from further analysis. Following the application of genotype quality filters, a total of 46501 SNP were available for analysis. It is important to note array elements used in the assignment of GSEX were excluded. GWAS was performed using linear regression and a case control design before the strength of association to GSEX  and contained the most strongly associated SNP . Male recombination is strongly localized to some telomeric regions in salmonids14, which has likely contributed to our inability to accurately map sex determination. The size, location and peak position for each of the critical intervals is given in Table To begin the characterisation of sex determination, GWAS was performed to i) determine the number of SD regions segregating within the TAS population and ii) map the location of each SD locus onto the reference assembly. To perform GWAS, a total of 4716 animals were genotyped using a custom SNP array. Animals were scored for phenotypic sex by visual assessment . Using the SNP array, animals were also assayed for the presence or absence of the 11. Further, male recombination at telomeres with high sequence homology between homeologs is highly repressed, likely as a result of multivalent pairing at meiosis obstructing recombination14. To assess if the genome duplication and associated complexity might partly explain the identification of multiple SD loci, the homeologous partner region for each of the six GWAS critical intervals was identified  with 165 loci on Ssa05. Even higher non-syntenic pairs were observed for LD with r2 > 0.2 (520 and 482 loci on for Ssa02 and Ssa05 respectively). Similarly, 77 SNP distributed across the acrocentric half of the chromosome (Mb 40–80) had elevated LD with 90 loci on Ssa12 (247 and 315 loci on Ssa2 and Ssa12 at r2 > 0.2). This clearly demonstrated these homeologous regions do not behave independently, but rather have highly correlated allele frequencies despite being on different chromosomes. Importantly, each region has been implicated previously as likely to retain residual tetrasomy, potentially explaining the persistence of high LD11. No elevated LD was detected (r2 > 0.2) for SNP pairs residing on Ssa05 and Ssa12 , which when measured as der Fig. . This resdY gene11. This prompted two groups to build and publish contigs spanning sdY by sequencing three BAC clones isolated from a single male Norwegian fish19. A 20 Kb contig spanning sdY9 was appended to reference ICSASG_v2, before raw read mapping was performed for each fish . Read mapping and depth, when compared between male and female fish, precisely defined the boundary between the MSR and MFCR , SL-03 (n = 2) or SL-06 (n = 4) while the remaining two animals had no assignment  for each animal is recorded in Table We next sought to determine if the mate-pair architecture of the sequenced libraries could be used to assign males into sex lineages. Existing sex lineage assignments were available for most of the 19 animals, based on microsatellite segregation data obtained from the Tasmanian population. This earlier work revealed L-06 n =  while thL-06 n =  while thsdY BAC contig for each animal. In order to visualise homozygosity and heterozygosity across the region, genotypic status at each locus was assessed as B allele frequency (BAF). This reflects the proportion of reads that carry the alternative (non-reference or B) allele. BAF takes values surrounding 0.5 for heterozygous positions and one or zero for homozygous genotypes. Figure 4. Specifically, the finding that males lack heterozygosity confirms they carry a single copy of the chromosomal region containing sdY, which is consistent with an XY/XX system for male/female determination.Variants were called within reads mapped to the 20 Kb ). To visualise the relationship between haplogroups  and sex lineages , the microsatellite and WGS derived assignments for each animal are provided graphically in Fig. sdY mariner TC1 elements has been used to suggest transposon mediated gene transfer has occurred19. Alternatively, recent homeologous recombination would also explain the lack of haplotype divergence, and our mapping results revealed the Ssa03 and Ssa06 loci are located in homeologs with elevated sequence similarity.The homozygosity of MSR variants in male fish defined a haplotype for each animal. To explore the divergence between haplotypes, pairwise genetic distance was used to construct a Neighbor-Joining tree Fig. . A clear6BOncorhynchus mykiss) and Chinook salmon (Oncorhynchus tshawytscha). Sequence conservation was generally low, with only a small region spanning sdY retaining inter-species homology in agreement with previous findings19. We successfully assigned alleles for 8 of the 22 Atlantic salmon MSR variants in at least one of the outgroup species. Four of these are phylogenetically informative for the identification of the Salmo salar ancestral state  and European stocks which are estimated to have diverged 500–600 thousand years ago21. This apparent contradiction could be explained by i) gene flow between the two subpopulations; ii) a mutation rate in the MSR so low that the SL-03 and SL-06 haplotypes are indistinguishable after hundreds of thousands of years or iii) that the genomic location of SD loci is not the same in NA and EU populations. Given the failure in this study to accurately position the SD loci in NA derived populations, coupled with the current paucity of mapping data in EU populations, it remains an open question if the loci are identical by state in the two subpopulations.In this study, we used two complimentary approaches to discover and characterise loci responsible for sex determination in Atlantic salmon. GWAS and SNP array data were used to identify three separate SD loci in the TAS breeding population, and locate them to broad chromosomal segments. GWAS identified more than three regions, and evidence was presented to suggest the signals on Ssa05 and Ssa12 are spurious, however it is possible the association detected on Ssa25 may represent an additional SD locus. GWAS was unable to define narrow critical intervals for any of the loci despite the experiment exploiting ample loci and animals . This likely arose due to the non-uniform genomic distribution of male recombinationIn conclusion, we have characterised multiple sex determination loci in Atlantic salmon to further our understanding of a trait which has both ecological interest and important consequences for aquaculture production.22. All animals used in this study were part of the commercial operations of Tassal and Saltas, and their use was in accordance with authorised management practises of both companies and compliant with the Tasmanian Animal Welfare Act (1993). A total of 582 families were generated using 2012, 2013 and 2014 year class broodstock, before the sex of their progeny was assigned using two methods. Phenotypic sex (PSEX) was recorded for 3176 fish by a mix of visual assessment of developing animals in freshwater and dissection at harvest in marine animals. Genotypic sex (GSEX) was assigned in 4715 fish using intensity data from three probe sets designed to detect the presence of exon 3 and exon 4 of sdY (Supplementary Table S1). Animals were assigned as male where each sdY assay returned a positive signal, and female where no intensity data was observed from any sdY assay. DNA samples were extracted from fin clip tissue and genotyped using a custom Tasmanian Salmon 50 K Affymetrix SNP array developed by the Center for Aquaculture Technologies . The SNP content is largely derived from a custom 220,000 SNP Affymetrix array used previously for genotyping the TAS population to identify segregating and polymorphic loci [10]. Raw genotype calls were assessed by cluster analysis to remove failed loci and those with poor cluster separation. Data was then filtered to remove: SNP with call rate < 90%; SNP with minor allele frequency < 1% and individuals with greater than 5% of missing genotypes. A comprehensive pedigree check was performed by comparing the coefficients of the additive relationship matrix and the genomic relationship matrix (G matrix) calculated via the first method described in23. Thirty nine fish were identified with multiple inconsistencies and removed. After all filtering, 46,501 SNP and 4716 animals were used for GWAS. Analysis was carried out using PLINK v1.924. Population substructure for genotyped fish was examined with no significant stratification observed. Binary trait allelic association (case-control) was performed using linear regression before the results were adjusted for multiple testing for each trait (–logistic–adjust). SNP were mapped to ICSASG_v2 to facilitate plotting of SNP significance in genomic order. PLINK v1.9 was also used to assess non-syntenic linkage disequilibrium for SNP pairs drawn from Ssa02, Ssa05 and Ssa12. All pairwise r2 values were reported after setting the LD window to 0.0 (–ld-window-r2) and LD was reported for inter-chromosomal pairs using–inter-chr.The GWAS population is from the SALTAS selective breeding program, described elsewhere10, and were included to facilitate concordance analysis of variant calling. Samples were stored as fin clips under ethanol before DNA extraction was performed immediately prior to the commencement of genome sequencing. A total of 2.5 ug of genomic DNA per fish was used for construction of short insert libraries in preparation for paired-end sequencing to generate 2 × 150 bp reads on Illumina’s HiSeq X Ten system. To compare the TAS animals with other populations, genome sequence from 20 additional farmed Atlantic salmon were downloaded from the SRA (SRP059652). These have been described previously during construction of an Affymetrix Custom SNP array12. Seven are farmed Chilean fish of North American origin (referred to as NA throughout), and include three individuals from the Cascade strain originating from Gaspe Bay, Saint Jean River  and four from Nova Scotia (Canada). It is worthwhile noting the TAS population derives from wild stock sampled from the River Phillip, also located in Nova Scotia. The remaining 13 animals are Chilean of European origin (EU), originating either from Scotland (Lochy and Landcatch strains) or Norway (Fanad and Mowi strains). Data from Illumina whole genome sequencing (WGS) were quality trimmed using quadtrim version 2.0.1 with parameters -q 20 -a 20 -l 75 -p 3 . A 20 Kb contig spanning sdY9 was appended to reference ICSASG_v2 as an unmapped scaffold, to facilitate read alignment to the male specific region (MSR) missing from the female derived reference assembly. It is worthwhile noting this approach offered every read the opportunity to map either to the female reference assembly or the 20 Kb BAC contig. If reads were only mapped against the 20 Kb contig, a higher proportion of reads mapped to the male specific region in female fish (data not shown). Trimmed sequences were mapped to the Salmo salar reference ICSASG_v2 (NCBI accession GCA_000233375.4) using BWA mem with default parameters. Duplicates sequences were removed from the resulting BAM files using samtools rmdup (version 1.3.1). Local realignment around indels was conducted using GATK IndelRealigner (version 3.6.0).A total of 20 animals were sampled from the SALTAS breeding program to present a range of year classes, families, phenotypic sex and sex lineage as summarized in Table Variants were called using GATK Haplotype caller to produce GVCFs subsequently used for joint genotyping from all samples to produce a merged VCF file for all 40 genomes. Variants were filtered using bcftools filter (version 1.3.1) to remove variants: i) with mapping quality < 50; ii) read depth < 5 and iii) variants other than biallelic SNP. Data from six individuals independently genotyped using a custom 220000 SNP Affymetrix array were extracted for use in concordance analysis to assess the quality of WGS variant calling. To define a collection of variants with both WGS derived and array based SNP calls, loci on the Tasmanian Salmon 50 K Illumina SNP array were first mapped to ICSASG_v2 to obtain base pair coordinates. Comparison against WGS SNP identified 16,436 overlap loci suitable for concordance testing. WGS derived SNP were converted to a numerical format  for comparison to array based data, before each genotypic outcome underwent concordance analysis.window-pi 20000–window-pi-step 10000). Reference allele frequencies within population were estimated using Vcftools (–freq). Population divergence, measured as FST, was calculated using–weir-fst-pop. Pairwise values were used as the distance metric in neighbour-joining tree construction in R (v 3.2.5) using the library ape. PCA was performed in PLINK v1.9 following LD based SNP pruning with r2 threshold of 0.3 (–indep-pairwise 500 5 0.3).For comparisons between populations , down sampling was performed to normalise for read depth. TAS genome data was down sampled using GATK version 3.6.0 Printreads (-dfrac = 0.2) before variant calling was performed for each individual using GATK Haplotype caller. Population diversity was assessed as nucleotide diversity (π) using genome sequence, estimated using Vcftools v.01.14 in 20 kb genomic bins with a 10 kb step window  was estimated directly from the resulting VCF by dividing the alternate allele count by the read depth for each SNP in each animal. Filtered SNP were considered to be located in the MSR because they were i) missing in every female and ii) present in 95% of the TAS males sequenced. Haplotypes were used for the construction of a Neighbor-Joining tree using MEGA525. Positions were used for estimation of a distance matrix following removal of positions call rate <75% per SNP and missingness across the collection of male fish >50%. This identified 22 variants in 12 MSR haplotypes used for evolutionary distance estimation using the p-distance method with units of base differences per polymorphic site. Neighbor-Joining tree robustness was assessed using the bootstrap test (1000 replicates).Raw variants called using the 20 Kb Genome sequence of 19 Tasmanian salmon has been deposited to NCBI as BioProject ID PRJNA403334 and individual animal raw sequence datasets are accessioned as SRR6019467 - SRR6019464. Variants called from the 19 animals are available in Dryad Digital Repository with DOI 10.5061/dryad.117hh. The BAC contig used for interpretation of the male specific region is available as Genebank accession KP898412. Genome sequence for the double haploid fish used for variant filtering are accessioned as PRJEB24419 at the European Nucleotide Archive. To compare the TAS animals with other populations, genome sequence from 20 additional farmed Atlantic salmon were downloaded from the SRA (SRP059652). Trimmed sequences were mapped to the Salmo salar reference ICSASG_v2 (NCBI accession GCA_000233375.4). All remaining data is available upon reasonable request for the authors.Supplementary Information