Genetic variation in both the composition and distribution of fat and muscle in the body is important to human health as well as the healthiness and value of meat from cattle and sheep. Here we use detailed phenotyping and a multi-trait approach to identify genes explaining variation in body composition traits.A multi-trait genome wide association analysis of 56 carcass composition traits measured on 10,613 sheep with imputed and real genotypes on 510,174 SNPs was performed. We clustered 71 significant SNPs into five groups based on their pleiotropic effects across the 56 traits. Among these 71 significant SNPs, one group of 11 SNPs affected the fatty acid profile of the muscle and were close to 8 genes involved in fatty acid or triglyceride synthesis. Another group of 23 SNPs had an effect on mature size, based on their pattern of effects across traits, but the genes near this group of SNPs did not share any obvious function. Many of the likely candidate genes near SNPs with significant pleiotropic effects on the 56 traits are involved in intra-cellular signalling pathways. Among the significant SNPs were some with a convincing candidate gene due to the function of the gene (e.g. glycogen synthase affecting glycogen concentration) or because the same gene was associated with similar traits in other species.Using a multi-trait analysis increased the power to detect associations between SNP and body composition traits compared with the single trait analyses. Detailed phenotypic information helped to identify a convincing candidate in some cases as did information from other species.The online version of this article (doi:10.1186/s12864-016-2538-0) contains supplementary material, which is available to authorized users. Body composition, including the distribution and composition of fat and muscle, is of great interest in a number of species. In humans it is predictive of health outcomes, and in cattle and sheep it affects the efficiency of meat production and the value and healthiness of the meat. Genetic variation in these traits is well documented. In humans the heritability of body mass index has been estimated to be 0.42 . The genLCORL have been implicated as affecting size in several species [Genome wide association studies have been very successful at finding genetic variants, such as single nucleotide polymorphisms (SNPs), associated with body size and composition in humans and livestock , 2, 6, 7 species , but theIn identifying the genes involved and the mechanism by which they act, an advantage of studies in animal species is that more detailed phenotypes relating to body composition can be collected. For example, these can include the amounts of bone, muscle and fat and the chemical composition of the fat and muscle. Since common physiological mechanisms (e.g. growth hormone) affect the growth of muscle, fat and bone and presumably the detailed composition of fat and muscle, detailed phenotypes should help to clarify the mechanism by which genetic variants affect these traits. For instance, selection for leanness and muscling led to reduced muscle oxidative capacity and iron concentration implying a change in the proportions of different muscle fibre types .In this study, we did not record muscle fibre type directly but did record phenotypes associated with fibre type. Muscles vary in muscle fibre type composition . MusclesIt is often suggested that detailed phenotyping will aid the identification of genetic variants affecting complex traits and help to elucidate the pathway by which they act. Here we test this hypothesis by phenotyping sheep for 56 traits associated with muscle and fat growth and composition.Usually GWAS, for instance on human height and BMI, are analysed one trait at a time ignoring patterns of pleiotropy amongst these traits. However, the pattern of pleiotropy may help to identify the gene underlying an association and the mechanism through which it acts. Bolormaa et al.  showed tthat a multi-trait analysis increases power to detect and map QTL,that detailed phenotyping increases power to identify the causal gene underlying the QTL and the mechanism by which it acts,that variants in different genes that act in the same pathway have a similar pattern of effects across traits, andthat variants in the same genes affect growth and body composition in multiple species.In this study we have analysed 56 detailed body composition phenotypes on 10,613 sheep with genotypes for 510,174 SNPs. We have tested four hypotheses:MER; Poll Dorset, PD; Border Leicester, BL; Suffolk, SUF; white Suffolk, WS; Texel, TEX; Corriedale, CORR; Coopworth, COOP; and various Crosses, MIX; Fig. P < 10−5 and P < 5 × 10−7) SNPs. Twenty nine traits had more than 10 significant SNPs (P < 5 × 10−7), resulting in a false discovery rate (FDR) of less than 2.6 %  is evidence of polygenic inheritance [Besides FDR, a Q-Q plot can demonstrate the rate of false positives as shown for 4 traits in Fig. eritance , not infP < 5 × 10−7)  Fig. . This coP < 10−5) within each 1 Mb window (98 SNPs) was selected from the multi-trait analysis in the discovery set  for validation in an independent set of animals . For each of 98 significant SNPs (Table P < 0.05) in the validation sample and all had an effect in the same direction in the validation sample as in the discovery sample , while the 6 separate traits map the QTL between 25,849,323 – 26,445,930 base pairs.The multi-trait analysis showed utility to map QTL more precisely. Figure SNPlead) , SNPs close to the SNPlead remained significant after fitting all 23 SNPlead, which may indicate that the SNPlead is in imperfect LD with the causal mutation or that there are multiple QTL in the region. In fact, there were still many significant SNPs (P < 5 × 10−7) scattered throughout the genome after fitting the 23 SNPlead indicating that there are likely to be many more smaller QTL affecting the 56 traits.The multi-trait analyses identified many narrow regions, containing more than one significant SNPs  and the SNPlead 4 (OAR 11) was more than 0.8, most correlations were moderate to low. A moderate to low correlation suggests QTL with different patterns of effects across traits, however, sampling errors in estimating SNP effects also reduce the absolute value of the correlation. If two QTL affect the same physiological pathway one might expect them to have the same pattern of effects and, hence, a higher correlation. Cluster analysis of the 23 SNPlead  size.Group 1 consisted of 4 SNP 6 Table . This gr 6 Table , indicatlead  and decreased stearic acid (C18:0) and/or some unsaturated FA (Table Group 2 consisted of 4 SNPad Table . These SFA Table . There wFA Table . They colead that influenced meat retail colour (by increasing redness of meat (RCa*) and decreasing lightness of meat (RCl*)) and increased myoglobin and wet iron content in muscle. However, on other traits, they separated into two subgroups (Table lead) that increased meat tenderness , eye muscle area and eye muscle depth, glycogen, isocitrate dehydrogenase (ICDH) activity, and polyunsaturated FA (omega-3 and -6) level and decreased meat pH level (Table lead), and 2 of them (12th and 13th SNPlead) had decreased meat tenderness and increased top side, loin weight and eye muscular area. Additionally, the 13th SNPlead (OAR18_64.5 Mb) decreased intramuscular fatness and increased leanness. They could be described as influencing meat colour and eating quality.Group 3 consisted of 5 SNPlead  increased hot carcass weight and dressing percentage and decreased iron content. Each of the other 5 SNPs (18th-22nd SNPlead) in this group had a significant effect on specific traits including FA composition or fatness or tenderness.Group 5 consisted of 7 SNPs, which were less correlated and do not form a consistent group  that were assigned to the same group as the SNPlead whose linear index was used as the phenotype , we expanded the 60 kb range as the nearest gene (LCORL) was 78 kb away  were annotated to possible candidate genes  genes have been associated with a similar phenotype in other species as discussed below. NCAPG  is closely linked to LCORL, so there may be only one QTL in this region [LCORL and NCAPG are highly correlated. The SNPs tagging LCORL, SLC16A13, GHR, FRYL, and BMPR1B were clustered together as one group within Group 1 .In Group 1, 23 SNPs  , FASN and EVOLV6 code for enzymes used in fatty acid synthesis and the SNPs near them all have an allele that increases the proportion of C10 to C16 saturated FAs  [P = 3.8 × 10−4), 5 proteins , AGPAT9, and ISYNA1) in metabolic pathways (Bonferroni P = 7.6 × 10−3), and 4 proteins  in lipid biosynthesis process (Bonferroni P = 3.8 × 10−2).The 4 SNPoup Fig. . The 11 T9) Fig. . AlthougAs Table . UsuallyAs Table . AGPAT9 program)  confirmslead that were assigned in this group previously. We called Group 3 SNPs “meat colour” SNPs because this is the most consistent feature of the group. The allele that made the meat redder and darker also tended to increase myoglobin and iron content, decrease pH and increase muscling. However, in other respects the SNPs in group 3 differ in their phenotypic effects. The 9th and 10th SNPlead decrease shear force while the 12th and 13th SNPlead increase it. Considering the differences in phenotype and in the function of the candidate genes, there may be no single physiological process that is common to all SNPs in group 3.Group 3 Fig.  consisteMEG3 (or GTL2), MEG8, DLK1, oar-mir-136 (or PEG11), BEGAIN, and WARS  and 13 (tagging Carwell) were highly correlated  was mapped near the genes APOL6 (apolipoprotein L6) and MB (myoglobin) and was strongly associated with meat myoglobin content and also FA composition.One group of SNPs were found within OAR 18 63.3-65.6 Mb near ARS Fig. . This SNn cattle –26. The lead 17 at OAR14_54.5 Mb of Group 5 has a strong association with muscle glycogen content but not with ICDH activity (Table GYS1 (glycogen synthase). The SNPlead 19 (OAR21_39.7 Mb) and 18 (OAR3_21.8 Mb) mapped near genes FADS2  and PNPLA3 (which is involved in both triacylglycerol lipase and acylglycerol O-acyltransferase activities), respectively. In this study, these SNPs were strongly associated with poly-unsaturated FA concentration. Similarly, the linear index of the 21th SNPlead (OAR20_44.1 Mb) was associated with a SNP only 100 kb from the SNPlead and close to the gene ELOVL2 (Table lead had a strong positive effect (t = 6.8) on docosapentaenoic acid (DHA or FA_C22_6n3), but not with eicosapentaenoic acid (EPA or FA_C20_5n3) , which is involved in fatty acid biosynthesis, primarily the synthesis of oleic acid (FA_C18_1n-9) by desaturation of stearic acid (FA_C18_0), was located just in 45 kb away from the 15th SNPlead (OAR22_20.3 Mb) , located 8.5 kb-22.5 kb upstream to the SCD gene and all 3 were in LD (r2 = 0.16-0.67) with this SNPlead , 3  were also highly significant and had similar effects in a multi-trait analysis of beef cattle [P < 10−3) within the region of gene LEPR, 1 SNP (P = 1.5 × 10−3) within HMGA2, 1 SNP (P = 4.1 × 10−5) within PLIN3, and 1 SNP (P = 8.8 × 10−6) within PACRG. Saatchi et al. [PLAG1, LCORL, NCAPG, and HMGA2 in their GWAS in 18,000 animals from 10 US beef cattle breeds.Of the genes in Table f cattle . Of the f cattle , in thisi et al.  also fouP < 10−3) in our meta-analysis and that mapped to 55 of the 184 human genes  and PLCD4 (tagged by the 10th SNPlead), and the two genes identified by the linear index of the 10th SNPlead, CYP27A1 and ERBB4. PLCD4, CYP27A1 and ERBB4 map near each other so it is uncertain how many causal mutations are involved , which is known to be strongly associated with human obesity and carriers of the risk allele reported to have increased appetite [FTL gene in ovine genome was partially overlapped with GYS1 gene.We also investigated the overlap of our QTL with genes associated with height, BMI, waist to hip ratio, and obesity in humans, which are well documented and have been validated in multiple studies. We selected the BMI and BMI-related gene lists reported in 3 recent human meta-analyses , 27–29. appetite . The posP < 10−3) in our meta-analysis mapped to 118 of these 494 human genes. Again in 1,000 permutations we did not observe a case with this much overlap between sheep and human lists. Out of these 287 SNPs, 73 SNPs at P < 10−5 were mapped within or near 11 genes which was presented in Additional file LCORL (tagged by the 4 th SNPlead of group 1), GHR (tagged by the 1st SNPlead of group 1), and MARK3 (tagged by SNP associated linear index of the 13th SNPlead of group 3). Gene TP53 from [SLC16A11 that was near the 3rd SNPlead in group 1, so TP53 might be the plausible candidate gene in this region.We also evaluated the overlap between the sheep GWAS results described above and GWAS for human height. Out of 697 SNPs annotated to 604 genes associated with human height [10  for the multi-trait analysis but, since we fitted a breed and a polygenic effect in the model, this deviation is not likely due to uncontrolled population structure. This type of deviation from expectation is observed if many loci cause genetic variation for a particular trait [We have demonstrated that for a wide range of carcass and fat composition traits, a multi-trait GWAS strategy  detected and validated more QTL than simple single-trait GWAS. The FDR was low for the majority of traits studied Table . The Q-Qar trait . The incLCAT affects mature size in cattle even though a causal relationship is not apparent from a metabolic biochemsitry perspective [CAST, CAPN1, and GHR. However, in some cases we identified more than one closely linked genes as candidates. The causal gene could be any one of these or, in some cases, multiple causal variants may exist in the same region. For instance, the effects of SNPlead 11 involve both myoglobin content and FA composition which suggest that both MB and APOL6 play a role.The gene closest to the most significant SNP is not necessarily the gene responsible for the effect on phenotype. However, in some cases the candidate genes in Tables spective . Both tyGYS1 has a large effect on muscle glycogen and pH and a smaller effect on other traits. These cases might be described as measuring a phenotype which is close to the primary action of the gene. For instance, GYS1 codes for the enzyme that synthesises glycogen. By contrast, traits such as body weight are far removed from the direct action of any one gene. When a gene codes for an enzyme it is easier to specify a phenotype close to the primary action of the gene (e.g. the amount of the product) than in many other cases.In some cases they do. For instance, the group 2 genes have a large effect on FA composition and a small effect on fatness. Without the FA data, their effect on fatness would have been overlooked. Similarly, lead did, in some cases, find other genes in the same pathway. For instance, the linear index that best predicts the CAST genotype shows a significant effect of the genotype at CAPN1 and the index based on GYS1 found PPP1R3A. In other cases the clusters do not seem to belong to a common pathway. It seems likely that “mature size” can be affected by many pathways. Nevertheless, the pattern of effects across traits of SNPlead 3 and 4 are so similar that there must be some biological connection. The SNPlead 4 is close to LCORL/NCAPG  while SNPlead 3 is SLC16A11/ALOX12/TP53. SLC16A11 is a monocarboxylate transporter and it does not seem a good candidate for affecting mature size. ALOX12 is arachidonate 12 lipoxygenase, which oxidizes arachidonic acid to a spectrum of bio-active lipid mediators.We used two methods to identify SNPs with a similar pattern of associations across traits–clustering the lead SNPs and using a linear index designed for one SNP to find others with similar effects. The genes that cluster together in group 2 belong to the fat synthesis pathway but the candidate genes in other groups do not share an obvious pathway or mechanism. Using the linear index derived from one SNPPNPLA3, FADS2 and EVOVL2. When all 71 candidate genes in Table PLCD4  and PLCB1 are phospholipases that cleave PIP2  into the second messengers IP3 and DAG (http://www.sabiosciences.com/iapp/ip3.html). ISYNA1 is IP3 synthase 1. PROCA1 also has phospholipase activity. PI4K2B generates PIP4, a starting point for other PI messengers. The linear index derived from PLCD4 was significant for a SNP close to PRKAA2 that is a part of AMP kinase, which is important for energy homeostasis . AMPK connects to the PI3K pathway via Akt, which integrates many signals and in turn affects cell proliferation or apoptosis, and glucose metabolism. GHR and ErbB have numerous connections to the PI3K pathway. LCAT is a lecithin-cholesterol acyl transferase and APOL6 is an apolipoprotein that transports cholesterol. SGK2 is a kinase activated by signals that activate PI3K.Genes related to fat synthesis occurred in groups other than group 2, possibly because FA traits are over represented in our 56 traits. For instance, in group 5 are FRYL, MYO1H, MYO15. MYO15 was also found to affect growth traits in cattle. FARP2 binds to both phospholipids and cytoskeleton and regulates integrin signalling and cell adhesion. ARAP2 is a PIP3 dependent Arf GAP which regulates focal adhesion. The gene closest to SNPlead 14 is laminin alpha 4 (LAMA4) which codes for a major protein in the basement membrane involved in cell adhesion and signalling and is related to the PI3K pathway. TBCK is thought to play a role in actin cytoskeleton organization, cell growth and proliferation via the mTOR pathway. AMPK, AKT and mTOR are critical controllers of energy use and protein synthesis.There are also a number of candidate genes associated with the cytoskeleton – GHR) mediated by intra-cellular signalling pathways, especially PI3K, that control energy homeostasis, insulin sensitivity [GYS) and cell cytoskeleton changes. The gross effect of these pathways is that substrates get directed to different products (e.g. glycogen or FA) and eventually the balance of cell types (muscle fibre types or muscle vs fat) is affected.Thus a hypothesis can be formed that the collection of 56 traits that we have analysed is controlled, in part, by cytokine signals  has an allele that increases tenderness, improves meat colour (i.e. increased redness of meat), increases myoglobin, glycogen, and unsaturated (omega-3 and -6) fatty acids and decreases saturated fatty acids, which is a highly valuable pattern. Selection for this allele would be beneficial in sheep intended for most markets.Sheep breeders are keen to improve the genetic merit of their sheep for carcass and meat quality traits. The pattern of effects of each QTL studied here indicates that some would be more useful for selection than others. Some QTL have an allele with desirable effects on more than one trait and appear to be good targets for selection. For instance, the QTL on OAR 2 (mapped near P < 10−5) across the genome independently associated with each trait. The FDR was lower in the multi-trait analysis than in single trait analyses, showing that it had increased power to detect significant associations with this group of traits implying that many SNPs are associated with more than one trait. The detailed phenotyping of 56 related traits helped to identify convincing candidate genes in cases where the phenotype was closely related to the primary action of the gene (e.g. FA synthesis genes). Cluster analysis arranged the significant SNPs into 5 groups so that SNPs within a group had a similar pattern of phenotypic effects. The genes near group 2 SNPs, which are associated with fatness and fat composition, are predominantly genes involved in FA and fat synthesis. By contrast, the genes near SNPs in group 1, which affect mature size, do not share a clear mechanism. However, these genes are also found in cattle and humans associated with size and fatness traits so it is unlikely that most of them are false associations. Rather it indicates our lack of understanding of the many processes that control mature size. Across the 5 groups there are many genes involved in lipid metabolism. These may act directly on measures of fatness but it seems likely that some of them are involved in signaling pathways within the cell. There was considerable overlap in the genes identified in our study and those reported to affect height and fatness in humans, and body composition in cattle. The incorporation of the identified causal mutations into genomic selection strategies could improve their accuracy and robustness, while allowing targeted selection to achieve more rapid genetic improvement.All traits appear to be highly polygenic with dozens to hundreds of SNPs  SNPs  and the Illumina 50k Ovine SNP chip , comprising 54,241 (50k) SNPs. All SNP were mapped to the OAR 3.1 build of the ovine genome sequence assembled by the SNPchiMp v.3 . The genP < 10−5) occurred. Furthermore, if the average call rate per individual was less than 90 %, those animals were removed from the SNP data. Further details on quality control can be found in Daetwyler et al. [Stringent quality control procedures were applied to the SNP data. SNP were excluded if the call rate per SNP  score above 0.6) was less than 95 % or minor allele frequency were less than 0.01 or an extreme departure from Hardy-Weinberg equilibrium  and the SheepGENOMICS (SG) project [The 10,613 animals  was added to the model, one at a time, and tested for an association with the trait. The analysis was performed using the ASReml software [y is the vector of observed phenotypic values of the animals, 1n is an nx1 vector of 1’s , μ is the overall mean, X, Z1, Z2, and Z3 are all design matrices relating observations to the corresponding fixed and random effects, b is a vector of fixed effects (described below), a is a vector of polygenic additive genetic effects sampled from the distribution N ~ , where σa2 is additive genetic variance and A is the additive relationship matrix constructed from the pedigree of the animals and their ancestors, q, d, s.f, and e are the vectors of random effects of breed (including Merino strains), dam (permanent environment), sire by flock interaction, and residual error, respectively. Q is a matrix with breed and strain proportions calculated from pedigree  [si is a vector of the genotype of each animal at the ith SNP, si is fitted as covariates. The maternal group and/or sire by flock interaction were significant (P < 0.05) to be included in the model for carcass weights, fatness, and muscling traits, whereas it was not significant for tenderness, IMF, meat colour, pH level, and fatty acid traits.Mixed models fitting fixed and random effects simultaneously were used for estimating heritabilities and associations with SNP. The estimates of heritability were calculated based on pedigrees for all animals that have genotype and phenotype data. The same model was used for GWAS, except that each SNP  ; si is aP < 0.05) only for shear force and FA traits. Carcass traits, excluding HCWT, DMLION and IMF, were corrected for HCWT, and saturated FA traits were corrected for IMF. Age of dam and its square and age at observation were fitted as covariates. Age of dam ranged between 1 and 9 years. The age at observation varied from 134 to 705 days.All models included dataset (or project) origin (Sheep CRC and SG), management group, flock, date of observation, birth year, sex, birth type, and rear type as fixed effects. All fixed effects were fitted as nested within a dataset. Flock, date of observation and birth year were combined in one contemporary group. Also, birth type and rear type were grouped together. Also, the laboratory effect was fitted as a fixed effect, and it was significant , A is the number of SNP that were significant at the P -value tested and T is the total number of SNP tested.SNP were tested for a significant association with particular traits at different probability thresholds Table . FollowiIn order to validate statistically significant SNP effects in an independent population, the animals with phenotype and genotype data for each trait were split into five sets by allocating all of the offspring of randomly selected sires to one of the five datasets. Then one of the 5 divisions was randomly used as a validation population and the other 4 divisions as the reference population. Only one 4:1 division of the data was used per trait. In this way no animal used for validation had paternal half sibs in the reference population. The GWAS for 4 traits, which are amongst the traits with the highest number of significant associations, were performed in the reference population Table . For SNP2 statistic was calculated as: multi-trait χ2=, where ti is a 56 × 1 vector of the signed t-values of SNPi effects for the 56 traits, ti’ is a transpose of vector ti (1 × 56), and V−1 is an inverse of the 56 × 56 correlation matrix where the correlation is calculated over the 510,174 estimated SNP effects  of the two traits. The power of QTL detection was investigated by comparing FDR [Multi-trait, meta-analysis, following the procedure in Bolormaa et al.  were perring FDR  calculatring FDR  calculatA linear index of 56 traits that had the maximum correlation with genotypes for significant SNP was used for multi-trait validation. The linear index on individual animals could only be calculated for animals with all traits measured. Not all animals were measured for all traits, so missing values were filled in by a prediction using a multiple regression approach as described by Bolormaa et al. . Using tP < 10−5) within a 1 Mb window (to avoid testing a large number of closely linked SNPs) from the multi-trait analysis in the discovery set were validated in an independent set of animals. After this, a linear index (yI) of 56 traits that had maximum correlation in the discovery population with each selected (significant) SNP was calculated using the following formula [yI = b ' C− 1y, where b’ is the transpose of a vector of the estimated effects of the SNP  on the 56 traits (1 × 56) that was estimated from only the discovery population, C1− is an inverse of the 56 × 56 (co) variance matrix among the 56 traits calculated from the estimated SNP effects of 510,174 SNPs only in the reference population, and y is a 56 × 1 vector of the phenotype values for 56 traits for each animal in the validation sample. The association between each linear index (yI) and each SNP (only significant SNPs in every 1 Mb window from GWAS in discovery population) was then tested in the validation population. The yI was treated as a new trait (dependent variable). The association was assessed by a regression analysis (GWAS) using the following model: yI ~ mean + SNPi + animal + error, where animal and error were fitted as random effects and SNPi were fitted as a covariate one at a time (other fixed effects were removed from the trait measurements before forming the linear index). In order to see whether the SNPs validated in the validation population have the same direction of effects (positive or negative) as SNPs in the discovery population, we also calculated linear index and performed linear index GWAS by using the phenotypes of the discovery population instead of the phenotypes of the validation population. Then the directions of SNP effects for the linear index in both reference and validation populations were checked and the proportion of SNPs whose effects were in the same direction in the reference population was calculated.As a validation, after filling missing values, all data was split into discovery and validation populations using same approach described in the single-trait GWAS section. Then the individual trait GWAS and the multi-trait significance test on signed t-values described in the previous sections were performed using only the discovery population . Only the most significant SNPs ( formula : yI = b lead were selected as follows: On each chromosome the most significant SNP (P < 10−5), based on the multi-trait analysis, was selected. Up to three SNPs on the same chromosome were selected only if they were at least 4 Mb apart and represented two or three different QTL .The 23 SNPlead were fitted simultaneously in the model. The statistical model used was the same as in single-trait GWAS with an addition of the 23 SNPlead, which were fitted simultaneously as covariate effects. Then a multi-trait chi-squared statistic was calculated for each SNP to test the effects of the SNP across traits after fitting the 23 SNPlead.The regression analyses (GWAS) were repeated and, additionally, the 23 SNPlead, the correlation of their effects across the 56 traits was calculated. Highly positive or negative correlations indicate 2 SNPs with the same pattern of effects across traits. Then this correlation matrix was used to do the hierarchical clustering of the 23 SNPlead leading to 5 groups or clusters as shown in the dendrogram drawn using the heatmap function of the R program [The SNP effects estimated from single-trait GWAS based on all animals were used to investigate the relationships between SNPs. For each pair of SNPs among the 23 SNP program .lead, we searched for additional SNPs in the 5 groups defined by the cluster analysis. To do this we used the linear index that showed the highest association with the corresponding SNPlead genotype, as previously defined for validation of the multi-trait analysis. The linear index of traits that had a maximum correlation with the genotypes for each of 23 SNPlead was calculated based on all data. A new GWAS was performed for each of 23 linear indexes (yI) treating it as a new trait (dependent variable). To avoid identifying QTL already represented by the 23 lead SNPs, the 23 SNPlead were also fitted simultaneously in the model. In this way, we could discover new QTL which are associated with one of the linear indices corresponding to the 23 SNPlead. The following model was used: ~ mean + fixed effects + SNPi + leadSNP1 + leadSNP2 + … + leadSNP23 + animal + error, where animal and error were fitted as random effects and the ith SNP  and 23 SNPlead were fitted simultaneously as covariate effects.For each of the 23 SNPP < 5 × 10−7) with at least one of the indexes based on SNPlead were selected for assigning into 5 groups. These additional significant SNPs were assigned to the same group as the SNPlead with whose linear index they had the most significant association.The SNPs that have significant associations (http://genome.ucsc.edu/) and Ensembl (www.ensembl.org/biomart/). If there were more than one gene within 2 Mb, then only one gene was retained within the 2 Mb regions by selecting the nearest to the SNP or the particular gene has concomitant relationship with SNP effects associated for traits studied. GO and KEGG analysis in STRING  network program [The genes that occur within 30 kb of the SNPs in this expanded list of significant SNPs were identified using UCSC Genome Bioinformatics ( program  was usedP < 10−3) in 55 genes associated with fatness in humans. To test if this degree of overlap could be due to chance we randomly selected 184 genes and performed the same analysis. This was done 1,000 times to establish the distribution of the number of significant SNPs and genes under the null hypothesis that the genes that are significantly associated with fatness in humans are no more likely to contain significant SNPs in sheep than expected by chance. To test the results for genes associated with human height, a similar test was performed using 494 randomly selected genes.30 kb upstream and downstream of 184 genes associated with fatness in humans were examined in the multi-trait sheep GWAS and found to contain 137 significant SNPs (The data sets supporting the results of this article are available in the additional files (Additional files Animal Care and Use Committee approval was not obtained for this study because no new animals were handled in this experiment. The experiment was performed on trait records and DNA samples that had been collected previously.


Brassica napus L. is a global crop of major economic importance, providing canola oil (seed) and vegetables for human consumption and fodder and meal for livestock feed. Characterizing the genetic diversity present in the extant germplasm pool of B. napus is fundamental to better conserve, manage and utilize the genetic resources of this species. We used sequence-based genotyping to identify and genotype 30,881 SNPs in a diversity panel of 782 B. napus accessions, representing samples of winter and spring growth habits originating from 33 countries across Europe, Asia, and America. We detected strong population structure broadly concordant with growth habit and geography, and identified three major genetic groups: spring (SP), winter Europe (WE), and winter Asia (WA). Subpopulation-specific polymorphism patterns suggest enriched genetic diversity within the WA group and a smaller effective breeding population for the SP group compared to WE. Interestingly, the two subgenomes of B. napus appear to have different geographic origins, with phylogenetic analysis placing WE and WA as basal clades for the other subpopulations in the C and A subgenomes, respectively. Finally, we identified 16 genomic regions where the patterns of diversity differed markedly from the genome-wide average, several of which are suggestive of genomic inversions. The results obtained in this study constitute a valuable resource for worldwide breeding efforts and the genetic dissection and prediction of complex B. napus traits.The allotetraploid species  Brassica napus L. is a global crop of considerable economic importance cultivated in many temperate regions around the world. Canola cultivars (devoid of erucic acid and with low glucosinolate levels) represent a major source of food for human and animal consumption, ranking second after soybean with 71.96 million metric tons produced worldwide in 2014–20151. In addition, B. napus is cultivated for its leaves  and its roots . Complementary to food consumption, high erucic acid oil cultivars are highly suitable for a wide range of industrial applications, including production of lubricants and surfactants. B. napus is also considered a promising crop to produce feedstock for biofuels. When used as a rotation crop in the non-irrigated wheat belt of the US, B. napus could contribute to renewable fuel production and significantly reduce greenhouse gas emissions.B. napus used today, where the species originated, and when it was first cultivated. B. napus is an allotetraploid  and the extant species most closely related to its diploid progenitors are the cultivated B. rapa L.  and B. oleracea L. . The frequency distribution of chloroplast haplotypes among B. napus and sister taxa in its lineage strongly suggest that B. napus has a polyphyletic origin, with the initial hybridization event leading to the synthesis of B. napus occurring multiple times and possibly involving different maternal ancestors (B. napus and their respective diploid progenitors (B. rapa or B. oleracea) is estimated to have occurred between 7,500 and 12,500 years ago, a period of time that coincides with the spread of agricultural practices in the Neolithic period  cultivars with low levels of glucosinolates, compounds unfit for livestock consumption, and no erucic acid, a 22-carbon monounsaturated fatty acid that is undesirable in high amounts in human diets  to produce flowers and seeds. In contrast, spring types are sown in spring and flower earlier, a phenotype that is probably linked to local adaptation in environments with extreme winters  and aligned the sequencing reads of each sample against the reference genome of B. napus . The A subgenome contained 38.8% of the SNPs, and the C subgenome the remaining 61.2% (Supplementary Table S1). These values conform to the expected distribution based on subgenome size, as the A subgenome is substantially smaller (∼315 Mb) than the C subgenome (∼527 Mb), and represents only ∼37.5% of the B. napus reference genome assembly. However, while 23.2% of the current B. napus reference genome (v4.1) is annotated as a “gene,” 44.0%  of the SNPs in our data set were located in these regions, thus representing an almost twofold enrichment of SNPs in genic regions. The SNP call rate  distribution had a mean value of 35.2% (corresponding to 275 samples genotyped) and a median of 23.3% . The mean and median sample call rate  was 35.2%  and 30.3%, respectively .After filters were applied to SNPs and samples, the final Figure 2), with the highest variance axis (PC1) mainly separating spring and winter samples, and the second highest variance axis (PC2) splitting WA samples from other samples. In contrast to the winter types that could be separated into European and Asian clusters, all of the spring types  formed a single cluster independent of their geographic origin . Therefore, we considered all of the spring types (n = 185) as a single subpopulation . The structure observed on the PCA enabled us to define three major subpopulations: WE, WA, and SP. We noticed that the WAm samples did not assemble into a discrete cluster and intermixed with the WE and WA clusters. Therefore, unless specified otherwise, the 38 WAm samples were not included in the analyses involving the three major subpopulations.To examine the presence of population structure among the 782 samples, we performed a principal component analysis (PCA) using the 30,881 SNP markers. The samples were labeled according to their growth habit and geographic origin, and distributed as follows: 347 winter Europe (WE), 212 winter Asia (WA), 38 winter America (WAm), 47 spring Europe (SE), 26 spring Asia (SA), and 112 spring America (SAm). The PCA revealed three major clusters . One ∼8 Mb region was located on chromosome 1 of the C subgenome  and another ∼10 Mb region on chromosome C02 . While PC2 also showed some SNPs with elevated loadings, unlike PC1, these SNPs were not found to be clustered within any particular genomic region. Removing these two regions did not changed the overall distribution of the samples on the PCA (data not shown), and thus did not affect the definition of the three subpopulations.After using the PCA to define the three major subpopulations, we examined the 300 SNPs  with the highest principal component (PC) loadings to establish whether specific genomic regions were disproportionately responsible for splitting samples along the PCs. On PC1, we uncovered two major genomic regions with blocks of consecutive SNPs resulting in elevated PC loadings . Of the 1005 SNPs in regions with extreme Fst values , 96.6% (971 SNPs) were located in the C subgenome. For this reason, instead of calculating a genome-wide average Fst, we estimated the average Fst for each subgenome separately. In both subgenomes, the average Fst was the highest when SP was compared to WE and WA (Table 1). This result is in agreement with the PCA performed for each subgenome separately , where the variance explained by PC1 (splitting SP vs. the winter subpopulations) was higher in the C subgenome. Despite different amounts of differentiation between subgenomes, the overall shape of the PCA plot for each subgenome separately was the same as that of a PCA plot for both subgenomes together . Because the SP differentiation was slightly higher in the C subgenome, we also estimated Fst after excluding SNPs located on chromosome C02 in the ∼10 Mb region with extreme differentiation between SP and winter subpopulations . The resulting Fst estimates were only modestly decreased (Table 1), suggesting that the high differentiation of SP did not seem to be driven by outlier regions of the genome.To quantify the genetic differentiation between the three subpopulations detected in the PCA, we estimated average pairwise Fst between WA, WE, and SP. Plotting Fst estimates for each SNP along the genome, we observed several genomic regions with elevated Fst  and the possibility that these SNPs could dominate the genome-wide signal, we analyzed each subgenome separately.In addition to estimating population differentiation, we estimated how much of the total genetic diversity observed in our data set was shared among the three subpopulations . For each SNP, we determined whether the minor allele was observed exclusively in a single subpopulation, or more than one subpopulation. We focused on the minor allele  because, if favorable, this is the allele most likely to be an underutilized source of genetic variation in Figures 3A,B, central intersection). In contrast, a large proportion of SNPs were polymorphic in only one subpopulation (subpopulation-specific SNPs). Our results also show that SP was the subpopulation with the lowest number of SNPs overall . The two winter subpopulations have similar numbers of polymorphic SNPs. Specifically, of all SNPs present in the A subgenome, 22.0% were found only in WE, 24.5% were WA-specific, and 11.2% were SP-specific, for a total of 57.7% subpopulation-specific SNPs . In the C subgenome, 26.4% were found exclusively in WE, 22.4% were found only in WA, and 14.7% were SP-specific, representing a total of 63.5% subpopulation-specific SNPs . Given that the reference genome is of winter type, we considered the possibility that the high number of SNPs observed in the winter subpopulations could be explained by ascertainment bias. The analysis of sample call rate for each subpopulation shows the lowest average sample call rate in WA compared to SP and WE , ruling out ascertainment bias as the reason for the high number of SNPs observed in winter subpopulations.The results show that less than a third of the SNPs  were polymorphic in all three subpopulations . In the A subgenome, 14.1% of the SNPs were WE-specific, 13.7% were SP-specific, and 26.8% were exclusive to WA. In the C subgenome, only 17.5% of all SNPs were WE-specific, compared to 18.3% for SP-specific and 24.0% for WA-specific SNPs. This indicates that the initial SNP count in WE  was driven by the higher sample size of this subpopulation. The dramatic decrease in genetic variation present in WE when its sample size was reduced by almost half also indicates that minor alleles in WE tend to be present in only a few samples in the subpopulation.To examine the effect of sample size on these results, we repeated the analysis after downsampling the 347-sample WE and 212-sample WA subpopulations to 185 samples each to equal the SP sample size. With this analysis, the number of SNPs polymorphic in WE decreased to about the same level as SP, and much below that of WA (B. napus samples we used a neighbor-joining (NJ) approach . Because this analysis considered all samples independently rather than grouping them by subpopulation, sample size heterogeneity among subpopulations did not matter. Therefore, we used all 782 samples (including samples of WAm) and full geographic information for the spring samples . Using a mid-point rooting (MPR) method , and among WE samples in the C subgenome . Second, despite observing large groups dominated by samples of the same growth habit, none of the major subpopulations was monophyletic. These apparent discrepancies between clustering based on genetic data and subpopulation classification may reflect recent gene flow during breeding history. Alternatively, this may also be the result of uncertainty in the relationship between samples, as illustrated by relatively low bootstrap support . Notably, the low-support nodes were mostly internal nodes near the base of the tree, where bootstrap support dropped to 40.0 and 44.3% for the A subgenome and C subgenome, respectively. This indicates that while the NJ approach clustered individual samples within each subpopulation effectively, it did not confidently resolve the position of the different subpopulations relative to each other.To examine the phylogenetic relationship between the ) method  to placeB. napus subpopulations. Here, we used only the three subpopulations  with large sample sizes. First, we estimated the average nucleotide diversity (π) for each subpopulation and subgenome (Table 2). In the A subgenome, π was higher in WA (πA = 0.00230) than in the WE (πA = 0.00189) and SP (πA = 0.00208) subpopulations. In the C subgenome, SP had a higher estimate of nucleotide diversity (πC = 0.00227) than WA (πC = 0.00196) and WE (πC = 0.00198). To understand these differences, we calculated the average number of nucleotide mismatches for each of the SNPs (Π) and plotted these statistics along the genome . Interestingly, Π along the A subgenome was systematically elevated in WA, explaining the high π value for the A subgenome of WA. In contrast, the C subgenome of SP contained only a few regions, particularly on chromosomes C02 and C07, where SNP-wise Π was elevated. To examine whether the SNPs in these regions may have biased the average subgenome estimate of π, we recalculated π for the C subgenome of SP after filtering out these two regions. The estimate of π thus obtained was lower (π = 0.00218), but still remained higher than in WE and WA.We explored the distribution of allelic diversity within each subpopulation to further investigate the evolutionary history of Figure 5). In WE, the SFS of the A subgenome was shifted toward singletons  and rare variants (MAC < 9), compared to the C subgenome that had more intermediate frequency variants (MAC > 9). This was exactly the opposite in WA, where the C genome was shifted toward low frequency variants and the A genome had more frequent variants. The SFS of SP overall resembled that of WE, with two differences. First, singletons were more frequent in the C than in the A subgenome of SP. Second, the depletion of rare variants and the excess of common variants in the C subgenome were more accentuated in SP than in WE. This was especially visible in variants with MAC > 21. To further investigate the possible cause of this pattern, we filtered out the ∼10 Mb region on chromosome C02 and the 6.5 Mb region on chromosome C07 that had strikingly high nucleotide diversity in SP . The resultant SFS in SP showed a modest but clear decrease in the number of SNPs with MAC > 21 . This suggests that these two regions partially, but not entirely, explain the excess of intermediate frequency variants in the C subgenome of SP.Secondly, we generated a folded site frequency spectrum (SFS) for both subgenomes of each subpopulation by counting the proportion of SNPs with different minor allele count . All subpopulations and subgenomes had negative values, indicating a global deviation from a neutral model of evolution. Both SP and WE had more negative values in the A subgenome than the C subgenome (Table 2), reflecting the stronger skew toward low frequency variants in the A subgenome observed in the SFS. The C subgenome of SP had the least negative value of all subgenomes and all subpopulations (Table 2), in agreement with the excess of common variants observed on the SFS. In contrast, Tajima’s D was more negative for the C subgenome than for the A subgenome in the WA subpopulation, which confirmed that the C subgenome had more rare variants than the A subgenome in WA. To examine whether the shape of the various SFSs was influenced by strong selective pressures or demographic events, we ran the same analysis on intergenic SNPs alone, which are expected to be evolving under lower selective pressure. For all three major subpopulations, the SFSs based on intergenic SNPs were very similar to the SFSs with all SNPs (results not shown), suggesting that allele frequencies in coding regions were not the main contributors to the overall SFS patterns.To further interpret the global shape of the SFS, we calculated Tajima’s D for the three major subpopulations in the A and C subgenomes  in Π for the SNPs located in or immediately flanking the set of 117 genes compared to the genome-wide average. In WA, however, Π was significantly higher in the gene set . Fst between all pairs of subpopulations was significantly higher for the set of 117 genes compared to the genome-wide average . This is despite the fact that only one gene out of 117 was located within the two large regions with extreme Fst on chromosomes C01 and C02 .Because population structure in Figure 1, tracks C,D and Supplementary Figures S6 and S7). Of these regions, we selected a subset of 16 of the most biologically interesting ones for further exploration (see Supplementary Table S2 for the coordinates of these regions). Among these 16 regions of interest (ROI), ROI 1 and ROI 2 correspond to the two genomic regions with the highest loadings on PC1 , which prompted us to re-examine the PCA results for the 16 ROIs.We also used per-SNP estimates of Fst and Π to identify genomic regions that markedly differed from the subgenome average . In this PCA, we observed that samples clustered in a three-band pattern. Such a pattern is consistent with the presence of two extremely long, non-recombining haplotypes in the subpopulations, as typically observed in genomic regions where a chromosomal inversion is polymorphic in the subpopulation (Starting with the region that had the highest loadings (ROI 2), we first performed a PCA with all the samples, but using only the SNPs from ROI 2 . We observed a discrete three-band pattern on the PCA for six additional regions . The interpretation of this pattern is ambiguous for the four smaller regions (ROIs 11–14 of 3.4 Mb or less) because of the small number of SNPs in these regions. The two largest regions however, ROI 1 on C01 (10.4 Mb) and ROI 6 on C06 (8.8 Mb), showed a more reliable signal and as such are stronger candidates for chromosomal inversions. Notably, ROI 1 corresponds to the second genomic region with the highest PC loadings on the genome-wide PCA .Following the same approach, we examined the other 15 ROIs, performing a PCA on the subset of SNPs located within each ROI alone (Table 1), in ROI 4 (C05), ROI 5 (C06), and ROI 10 (C09), WA showed a stronger differentiation from the other two subpopulations (WE and SP). In ROI 7 (C07), SP did not show a strong differentiation from the two winter subpopulations , but instead had a much higher nucleotide diversity than WE and WA . Such regions may harbor loci of adaptive importance that have driven changes in their nucleotide diversity and allele frequency.The nine other ROIs did not show a pattern characteristic of genomic inversions, but the diversity and differentiation of the SNPs in these regions seem to indicate that they have experienced a different evolutionary history compared to the genome average. For example, while SP was the most differentiated subpopulation genome-wide , about 30% of the reads did not uniquely align to the reference genome and were not used for variant and genotype calling. As a result, although the levels of diversity observed in our study were high, the true levels may be underestimated.We used SBG to characterize the genomic diversity in 782 us e.g., . In addig arrays . By usinB. napus breeding, we occasionally expect genotypes originating from one geographic region to be labeled as a different geographic origin. Second, given that the growth habit is likely determined by a limited number of loci, we expect some samples of a given growth habit to have a genetic background similar to those typical of the other growth habit. Since our data surveyed the genome-wide genetic makeup of the samples, clusters on the PCA are expected to primarily reflect genetic similarities and differences among samples. Together, this explains why there is not a complete genetic differentiation observed among the three major subpopulations we describe.A PCA of 782 samples revealed the existence of strong population structure, with the presence of three major genetic groups. Similar observations had been previously reported by several authors . These gIn order to quantify the genetic differentiation among the groups, we estimated pairwise Fst for each pair of subpopulations. Overall, our Fst estimates were comparable to previously published values . In agreB. napus. Despite the clear population structure observed in the PCA, none of the major subpopulations were completely monophyletic in the NJ trees of the A and C subgenomes. This result is in agreement with previous studies showing at least three distinct groups within Asian oilseed cultivars  and SNPs that are polymorphic within multiple subpopulations . Our results show that only about a third of the SNPs are polymorphic in all three subpopulations. Once corrected for differences in subpopulation size, SP and WE were found to have an equivalent level of diversity. This was true whether we counted the total number of polymorphic SNPs in each subpopulation, or the number of polymorphic SNPs exclusively found in one subpopulation (subpopulation-specific SNPs). Previous work reported a lower genetic diversity in spring types . However, with limited phenotypic and historical information, interpretation of the variation observed in our data would be purely speculative. For this reason, we exclusively focused on growth habit — a trait that was experimentally assessed in this study for all 782 samples — and the major genes involved in flowering time and vernalization pathways, as they are most likely to be associated with natural variation for this particular phenotype. For all three subpopulations, our results show that Fst in the SNPs overlapping or immediately flanking this set of genes was significantly higher than in the rest of the genome. In contrast, the average Π for SNPs in flowering time and vernalization genes was not significantly different from the genome average, except for WA where Π was marginally higher than in the rest of the genome. Significantly higher differences in allele frequencies (measured by Fst) in flowering time and vernalization genes between subpopulations of different growth habits is not surprising. However, because flowering time is fundamental to plant reproduction, we could have expected to find a concomitant signal of purifying selection (low average Π) in these same regions. Similarly, Brassica species  of these ROIs showed a distribution of genetic diversity suggestive of a genomic inversion segregating in the subpopulations. Inter-specific inversions seem to be pervasive in the evolution of  species , and evion event . HoweverOne characteristic of genomic inversions is that they locally suppress recombination. As a consequence, new mutations are not exchanged between the genomic segments of each orientation. The lack of recombination then increases genetic differentiation and leads to characteristic signatures on PCA plots e.g.,  such as B. napus, most recent genetic maps based on high-density SNP markers were constructed using families obtained by crosses of two winter lines  in Ames, Iowa (n = 564), the Centre for Genetic Resources (CGN) in the Netherlands (n = 170), and the University of Idaho (UI) Brassica Breeding Program in Moscow, Idaho . Using the records maintained by the institutions providing seed for the samples, the accessions, hereafter referred to as samples, were selected to represent the global diversity of B. napus. As such, the data set included samples from 33 different countries. Growth habit of record for these samples were validated experimentally (see Supplementary Methods).The diversity panel used in this experiment consisted of 843 For each of the 843 samples, young leaf tissue was harvested from a single plant for DNA extraction. Sequencing libraries were constructed for Illumina single-end sequencing according to the method described in Single-end sequencing (100 nt) was performed using an Illumina HiSeq 2000 , where each library of 96 samples was divided over eight lanes. Clusters for each library were generated on a HiSeq flow cell using the TruSeq Single Read Cluster Kit v3, according to manufacturer’s instructions. Following the completion of the run, image analyses, error estimation and base calling were performed using the Illumina Pipeline (HCS 1.5.15.1/RTA v1.13.48 or HCS 2.0.5/RTA v1.17.20) to generate primary data.The FASTQ formatted  primary B. napus ‘Darmor-bzh’ , SW band width (-w 3), and off-diagonal X-dropoff (-d 5), with all other parameters as default. For each sample, reads with multiple equally probable alignments were eliminated and only reads with a single best match were used for variant detection and genotype calling. A summary of read mapping results are shown in Supplementary Table S4. Sequence data for the 782 samples analyzed in this study have been deposited at NCBI under BioProject PRJNA298631 and SRR accessions are listed in Supplementary Table S3.The demultiplexed reads were mapped to the reference genome of mor-bzh’  using thmor-bzh’  in BWA vmor-bzh’ . Due to B. napus , ortholoVariant detection and genotype calling were performed using the UnifiedGenotyper (UG) tool in the software GATK version 3.1-1 . The UG Supplementary Figure S9A) and the majority of SNPs had a high proportion of missing genotypes . This prompted us to apply a series of stringent quality control filters to the raw SNPs and genotypes to select a subset of high-confidence SNPs suitable for population genetics analysis. Post-processing coverage per sample is given in Supplementary Table S3, while the whole filtering pipeline is summarized in Supplementary Figure S10. In brief, we filtered out indels and SNPs with more than two alleles. We also removed low confidence genotypes (with less than 4× coverage) and set heterozygous calls with strong allelic imbalance to homozygote for the most frequent allele. Furthermore, on a sample by sample basis, we discarded all genotype calls at a locus when the reads of the sample aligned at that locus produce more than three mismatches compared to the reference genome, as a high density of sample-specific polymorphism per locus is likely the result of alignment errors. Finally, we removed SNPs that produced a genotype call in less than 30 samples. The final data set was composed of 30,881 high-quality SNPs. This final SNP marker data set can be visualized and downloaded from the HRJ project website4.In the following, we refer to a genomic position that is variable in at least one sample as a SNP. We use the term genotype to refer to the alleles at a SNP scored in each sample. The raw vcf file produced by GATK contained 1,081,925 SNPs. Of those SNPs, 112,668 were different from the reference genome but fixed for the alternate allele. Thus, this resulted in 969,257 SNPs that were variable among our samples. Among these 969,257 SNPs, a large proportion (34.9%) of the genotypes had less than 4× coverage  involved the elimination of 61 outlier and low quality samples . From the initial 843 samples, 44 with more than 80% missing genotypes across the 42,402 SNPs were removed. An additional 17 samples with a marked discrepancy between their observed location on a PCA plot and their reported growth habit and geographic origin (passport data) were also removed. Further details of the filters applied to samples are provided in Supplementary Methods and Supplementary Table S5. Only the remaining 782 samples were used in the analyses, and their geographic origin and growth habit is provided in Supplementary Table S3.The filtering pipeline  method to compute the distance matrix in SplitsTree4 v4.13.1 . The disFst between pairs of subpopulations was estimated using the Hudson estimator for genome-wide data  as suggeWe generated a SFS for each subpopulation and each subgenome by probabilistically downsampling each data set to a sample size of 100 chromosomes (50 diploid samples) according to the method described in D statistics . Both quantities, Π and π, are related by π = Π/L where L is the length of the sequence queried. For one SNP, (L = 1), Π equals π.We used the downsampled SFS to estimate both the genome-wide average nucleotide diversity, π  and the atistics . CalculaB. napus genome annotation version 5 (Brassica_napus.annotation_v5.onchr.gff) generated by (5.To annotate SNPs as genic (within a gene) or intergenic (not within a gene), we used the rated by  and downFLC copies provided in Arabidopsis thaliana amino acid sequence to find orthologs in the B. napus nucleotide genome sequence with the TBLASTN function of the BLAST+ program  was used as a proxy for the B. napus gene function. The genomic coordinates of all the flowering time and vernalization gene copies used in this analysis are provided in Supplementary Table S6, and further details of the BLAST procedure are provided in Supplementary Methods.To examine whether the evolutionary history of the three major subpopulations carries the hallmark of local adaptation, we studied genes associated with variation in growth habit. We used the genomic coordinates for the nine  program . The funP-value is the number of times out of 1000 the summary statistics (Π or Fst) for flowering time and vernalization genes was greater than the one observed for the subset of randomly selected SNPs. This test was implemented with a custom python script.We tested whether the 117 flowering time and vernalization genes identified above were associated with elevated Fst or low average number of pairwise differences (Π) more often than expected by chance. To estimate the values of Π and Fst for each of the 117 genes, we used 40 SNPs representative of each gene. This allowed estimates to be less sensitive to local sample call rate and SNP density. For each gene, these representative SNPs included all the SNPs within the gene (if any) plus as many closest flanking SNPs on each side of the gene as needed for a total of 40 SNPs. For each gene, we calculated an average of Π and Fst over the 40 representative SNPs, and averaged these values over the 117 genes . We compared these statistics against a subset of 4680 SNPs randomly chosen across the genome. To ensure an unbiased comparison, we selected these random SNPs to have similar characteristics as the set of SNPs representative of flowering time and vernalization genes. The SNP set of the A genome was composed of 821 intergenic and 1499 genic SNPs and the set of the C genome was composed of 1087 intergenic and 1273 genic SNPs. We repeated the random selection of SNPs 1,000 times, and for each iteration, we calculated an average of Π and Fst across sampled SNPs. The EG, ET, DI, and MG prepared the manuscript. EG, ET, and DI processed the sequencing data; performed the bioinformatics and population genetic analyses. MW, JBD, and JB provided germplasm management, collection and analyzed growth habit data. ED performed the preliminary bioinformatic analysis of SBG data. HW managed the experimental design and SBG data production. DG developed a genome browser to visualize the genomic data. DG, JMD, MJ, JB, and MG provided experimental design and coordination.Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer. The SBG technology is protected by patents and patent applications owned by Keygene N.V.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


PstI enzyme, ligated to adapters with barcodes identifying individual animals, and then sequenced on Illumina platform. After filtering parameters were applied, 134,528 SNPs were identified in our experimental population of chickens. Of these SNPs, 67,096 had a minimum taxon call rate of 90% and were considered ‘unique tags’. Interestingly, 20.7% of these unique tags have not been previously reported in the dbSNP. Moreover, 92.6% of these SNPs were concordant with a previous Whole Chicken-genome re-sequencing dataset used for validation purposes. The application of CornellGBS in chickens showed high performance to infer SNPs, particularly in exonic regions and microchromosomes. This approach represents a cost-effective (~US$50/sample) and powerful alternative to current genotyping methods, which has the potential to improve whole-genome selection (WGS), and genome-wide association studies (GWAS) in chicken production.Chicken genotyping is becoming common practice in conventional animal breeding improvement. Despite the power of high-throughput methods for genotyping, their high cost limits large scale use in animal breeding and selection. In the present paper we optimized the CornellGBS, an efficient and cost-effective genotyping by sequence approach developed in plants, for its application in chickens. Here we describe the successful genotyping of a large number of chickens (462) using CornellGBS approach. Genomic DNA was cleaved with the  Next-generation sequencing (NGS) analyses have been increasingly employed in production animals, particularly in chickens. NGS generates large amounts of genomic information that can be used to detect genetic variants related to functional alterationsThe use of NGS has enabled to identify SNPs across genomes and allowed the development of pre-designed SNP chips for widespread testing of SNP associations with specific phenotypes of interest6The use of an economical, efficient, and simple-step method of SNPs discovery, validation and characterization that uses reduced representation sequencing generated by restriction enzyme cleavage of target genomes can provide an unbiased genome-wide set of SNP markers in different genomesCornellGBS is a simple reproducible method based on the Illumina sequencing platformPstI and SbfI. Two different experimental animal populations were used in the present study: 444 chickens from five families of the EMBRAPA F2 Chicken Resource Population , 8 chickens from the F1 generation and 18 chickens from the parental line (F0). In the present article, we have optimized the use of CornellGBS in chickens, which was achieved in part by using the Pst1 restriction enzyme for genomic cleavage. We also provide a new set of chicken SNPs that were detected by using this approach. The application of this methodology will open many possibilities for downstream applications in chickens and facilitate SNP discovery in specific populations of chickens. The relevance of applying a cost-effective genotyping method in chickens is enormous, given their world-wide economic relevance as production animalThe present study aims at constructing reduced genome representation sequencing libraries using the CornellGBS approach in chickens. In order to optimize the use of CornellGBS in chickens, cleavage of the chicken genome was tested with two different restriction enzymes, The selection of the appropriate restriction enzyme was based on relevant literature information and took into consideration the number of expected fragments, the fraction of the diploid genome sampled, and the expected number of reads required to obtain a sequencing coverage of ~7X of sequencing coveragedam, dcm and CpG methylation according to the manufacturer (NEB BioLabs). These enzymes were PstI and SbfI. In silico cleavage of the chicken genome was performed with both PstI and SbfI enzymes. Genome cleavage with PstI generated 811,951 fragments, while SbfI generated 45,116 fragments. Fragment size distribution obtained with PstI showed a low amount of discreet size, which is indicative of low repetitive fragmentsWe initially selected two enzymes that are insensitive to in silico digestion generated 159,673 fragments, which were evenly distributed across all the chromosomes, while SbfI cleavage generated 1,186 fragments  . Pst1 ragments . There ileavage) . Importatags) generated by the in silico cleavage (Predicted PstI-Tags) and the tags generated after the in vitro cleavage (Sequenced Pst1-Tags) of 462 individuals . The number of Predicted PstI-Tags obtained was 811,951, while the Sequenced PstI-Tags obtained were 287,819. Detailed information on the size categories of the Predicted PstI-Tags that were actually sequenced is provided in We also performed a comparison between the mapping of fragments  were generated. After quality trimming by the SeqyClean tool9 reads (52%) were retained. A high number of short fragments (<50 bp) sequenced were eliminated in the fragment size filtering (37%), as well as contaminants (11%). Approximately 1.4 billion reads were retained after application of the Tassel filter (reads >64 bp and properly identified with barcodes). These reads were distributed at an average of 145.6 (±26.5) million reads per lane  was ~5.4 million and 92.8% of them could be mapped. The average sequencing coverage depth was ~264 reads per tag (locus) in these ~5.4 million unique tags.The number of unique sequence tags  that aligned against the chicken reference genome (These ~5.4 million unique sequence tags represent a 4.66% coverage of the whole chicken-genome (~50 million bp). The average coverage for the 464 chickens was 5.6 X for the targeted regions.PstI-derived SNPs was 134,528 after applying a mnScov filter of 70%, and 67,096 SNPs after applying a mnScov filter of 90%. After filtering with a mnScov of 90% the average taxon call rate per individual was 97%  of 1%. Two of the 464 individuals showed a minimum taxon coverage (mnTCov) of less than 20% and were eliminated from the analysis. The minimum site coverage (mnScov) filter was used to evaluate the taxon call rate. The number of remaining  was 97% .Additionally, we also tested a mnMAF of 0.05, which generated 300,777 SNPs, as well as a combination of mnTCov of 20% and mnScov of 90%, which generated 61,618 SNPs.PstI-derived SNPs are shown to be separated by a distance of 15 Kb in average, with a median of 55 bp. This indicates clusters of SNPs in regions represented by the cleaved areas (tags). A comparison among the different genotyping methods is shown on When considering a mnMAF of 1% and mnScov filter of 90% the Differences were found between SNP numbers and density (SNP/Mbp) inferred by the three methods used for SNP detection, namely Affymetrix 600 K, Cornell GBS and Illumina chicken 60K bead chip . In ordeWhole Chicken-genome re-sequencing ~11X (WCGR) dataset  in order to perform a validation of the method since both sets were obtained from the same 10 animals (TT and CC lines). The SNPs with more than one alternative allele (less frequent) were eliminated from this analysis. A concordance of 83.91%  in the chromosomal positions of the SNPs detected was observed between the two methods. We found that 92.64% of these concordant markers had concordant genotypes between CornellGBS and WCGR datasets. Also, the consistency in the calls of heterozygosity was tested between these two approaches. This test was performed due to the general assumption that reduced representation methods, like CornellGBS, have limitations in the calling of heterozygous SNPsThe set of 67,096 SNP chromosomal positions obtained with the CornellGBS (mnMAF 1% and mnScov 90%) was compared to the 12,357,602 filtered SNPs from a Out of 31 million possible genotypes , the proportion of heterozygous SNPs was 31%, with 3.1% being missing data see . The ave2 (0.31 ±  0.05) and the F1 generations (0.32 ± 0.10) (1 generation had the highest CV due to the fact that it represents a heterozygous population. The family F2-7816 had a higher CV (25%) compared with the other F2 families due to the low heterozygous call rate for some individuals (25 from 94) in this family.A lower proportion of heterozygous SNPs was found in both parental lines CC (0.20 ± 0.01) and TT (0.26 ± 0.01), followed by the F ± 0.10) . The F1 PstI-derived SNPs (after filtering) from the 462 chickens were annotated against the known genes from the ENSEMBL database  as a reference. The results are available in the The unique set of 67,096 PstI-derived SNPs, 11,372 SNPs had multiple annotations  as they could be considered into multiple variant classifications  and one combination when both parents were heterozygous (AB × AB). The SNPs following each of these segregation patterns in the parents were retained and markers with significant segregation distortion  were removed from the map construction. A total of 6,037 SNPs were retained for linkage map construction after filtering, with 387 of these SNPs being classified as female heterozygous, 2,143 SNPs classified as male heterozygous, and 3,507 SNPs classified as heterozygous in both genders.PstI-derived SNPs, 5,982 generated 53 linkage groups (LG) that corresponded to the chromosomes GGA1-28 and Z  genomic DNA cleavage, (2) adapter ligation with specific barcodes, (3) sequencing of short reads, and (4) bioinformatics analysis.in silico . Although 48% of the reads were discarded, which can be considered a drawback of CornellGBS approachAfter sequencing of the CornellGBS libraries the next step was the bioinformatics analysis. Using the Tassel pipeline ~5.3 million of unique tags were obtained and aligned against the last chicken reference genome  generated 5,478 less SNPs than using a mnMAF of 1%, we proceeded with a mnMAF of 1%. Moreover, because parental pure lines featuring only 5 individuals per strain were used, the previous mnMAF ≥5% would eliminate many important SNPs that might be present in the parental lines.PstI-derived SNPs, out of which 20.7% have not been previously described in the dbSNP database . A previous study that used RAD-Seq in chickensHindIII-derived SNPs candidates with 53.3% of them newly reported . Therefore, a reasonable number of novel SNPs were obtained here using the CornellGBS approach .When considering a mnMAF of 1% and mnScov filter of 90%, our study generated a reliable SNP dataset of 67,096 PstI-derived SNPs reported here was higher than HindIII-derived SNPs previously reportedin silico) by HindIII , or by the larger number of genotyped animals in the present study.In spite of the different SNP calling methodologies used (Stacks vs Tassel), the number of ®Axion® genotyping array for chickenThe chromosomal position of the SNPs identified in this study (using CornellGBS and considering a mnMAF of 1% and mnScov filter of 90%) was compared with the Illumina chicken 60K BeadchipDifferences between mean and median were detected only using the CornellGBS approach. This indicates that SNP cluster formation occurs in spite of the markers obtained by the CornellGBS being well spread throughout the genome . With thPstI RE genomic cleavage would be appropriated for DNA methylation profiling, since it apparently enriches for regions of high CpG content.We also investigated SNP density differences related to chromosome size see  using th1919262728et al., unpublished results) obtained from sequencing the same 10 animals. Substantial chromosomal position (~84%) and genotype (~93%) concordances were observed between the two methods. However, the concordance was reduced to ~71% when considering only the heterozygous SNPs. In spite of this, 99.90% of the genotypes were concordant in regions where both methodologies were able to call heterozygous. Therefore, although the CornellGBS had fewer calls of heterozygous in comparison with WCGR, those genotypes that are called are quite reliable.A set of SNPs from the CornellGBS dataset obtained in our study was compared with a WCGR SNPs dataset (Boschiero 0) and the F1 generation, and the same between the F1 and the F2 generations. The exception is, family F2-7816 that presented slightly higher Mendelian errors (11.9%). These error rates are in agreement with the low heterozygous call rate  and high heterozygous CV (>15%) observed in individuals from this family (25 from 94) when compared to the others four families . Therefore, the Mendelian errors observed were minimal and do not compromise the quality of the genotyping performed in the present study. In addition, the linkage map obtained from markers with Mendelian segregation obtained from the five F2 families were grouped in LGs. This grouping generated a fairly dense linkage map. These markers (~99% of them) grouped according to their respective described chromosomes  were classified into functional categories due to their potential to alter the tri-dimensional structure and function of the translated proteinWe also found a small increase in the proportion of SNPs (3.3%) in exonic regions compared to a recent functional classification of 15 million SNPs detected from diverse chicken populations (2.2%)When comparing the CornellGBS and the 60 K Illumina approaches (which have similar SNP density), it was observed that 60 K Illumina detects half (51.6%) of the exonic variants detected by CornellGBS. However, that difference is reduced when only non-synonymous SNPs are considered . When comparing CornellGBS and 600 K Illumina, the proportions of SNPs in exonic regions are similar . This shows that Cornell GBS is as powerful as the 600K panel in detecting SNPs in exonic regions, which is remarkable considering that the 600K panel was designed prioritizing coding regionsOn the downside, CornellGBS seems to be less powerful in detecting SNPs in intergenic regions compared to either the 60 K Illumina or the 600 K Affymetrix approaches . On the other hand, CornellGBS presents a high proportion of SNPs in regions 1kb up- or downstream from UTR compared to either the 60 K Illumina or the 600 K Affymetrix approaches . This is interesting because UTR regions are highly relevant for transcriptional regulationPstI) is responsible for enriching the cleaved genome for microchromosomic or CpG-rich regions.These results indicate that the Cornell GBS approach shows a pattern of SNP profiling that is unique in comparison with other approaches. The unique characteristics of Cornell GBS include better interrogation of specific functional regions, of microchromosomes and of CpG-rich regions compared to other methodologies (60K Illumina or 600K Affymetrix). In particular, we believe that the restriction enzyme used in the present study  genotyping approach in poultry. The method described is capable of performing a reliable SNP profiling in chickens using a large number of animals. In the present study a number of SNPs were discovered, which were well spread throughout all the chromosomes of the chicken genome . This stAll experimental protocols employed in the present study that relate to animal experimentation were performed in accordance with the resolution number 010/2012 approved by the Embrapa Swine and Poultry Ethics Committee on Animal Utilization, in order to ensure compliance with international guidelines for animal welfare.2 families of the EMBRAPA F2 Chicken Resource Population, 10 chickens from their parental lines (5 from each line), and 8 chickens from the F1 generation.This study was conducted using 464 chickens from an experimental population originated and maintained at the dependencies of the Brazilian Agricultural Research Agency, from . The population includes 446 chickens from five F1 generation individuals were originated from a cross between a parental broiler line (TT) and a layer line (CC), both developed at EMBRAPA. To generate the F2 population (TCTC), one F1 male (TC) and three F1 females (TC) were selected from different F1 families and were randomly mated with non-related animals. A more detailed description of the population has been previously provided35The F® Fluorometric Quantitation). Sample quality was assessed using the Nanodrop®2000c spectrophotometer and DNA integrity was checked in 1% agarose gel.Genomic DNA was extracted from blood samples following proteinase K digestion (Promega), DNA precipitation in absolute ethanol, DNA washing in 70% ethanol and resuspension in ultrapure water. DNA samples were quantified in a fluorometer  .om/) see . The in in vitro genomic cleavage of chicken DNA samples with the abovementioned restriction enzymes  taking into consideration the barcode sequence, in order to maximize the balance of the bases at each position in the defined setPstI digestion, adapters were linked to the cohesive ends of the digested DNA with T4 DNA ligase (New England BioLabs®). Approximately 24 samples were polled and purified using QIAquick PCR Purification Kit®(Quiagen). The fragments of each library were amplified by PCR using specific primers for sequencing in the Illumina platform. The purification of PCR reactions was performed using the Agencourt AMPure XP PCR purification kit®(Beckman Coulter) (see KAPA Library Quantification Kit (KAPA Biosystems). Two pools of ~24 samples containing equal concentration of DNA were sequenced per flowcell lane totaling ~48 samples sequenced with different barcodes in each flowcell lane. Sequencing libraries were diluted to 16 pM and clustered using the cBOT (Illumina) equipment. Paired-end sequencing with a read length of 100 bp was performed using the HiSeq2500 instrument from Illumina. For the analysis we used the HiSeq Illumina real-time analysis (RTA) software v1.18.61 update. This software generates a color matrix for the correction of the reads. This is important because HiSeq sequencer uses different lasers to detect G/T and A/C nucleotides. In each cycle, at least one of two nucleotides for each color channel must be read in order to maintain the color balance for each base in the index read sequenced. With this upgrade the color matrix still uses the first four cycles to generate data, like the last version of RTA. However, in the current version the initial matrix is discarded after the template generation is complete. Then, the first 11 cycles of intensity data are used for final estimation of the correction matrix. In order to minimize the issues related to the construction of this matrix, we optimized our protocol using barcodes larger than 4 bp to avoid imbalance between the first bases. The complete laboratory procedures are provided in After ter) see . Each lireads was checked before and after the cleaning by FastQC v.0.11.3Quality trimming was performed in short sequences with SeqyClean tool v. 1.9.10PstI). Fragments are then trimmed to 64 bases and grouped into a single list called “master” by the TASSEL-GBS Discovery Pipeline.The Tassel v.3.0 program was used to process the dataGallus_gallus 4.0, NCBI). The aligned reads were then imputed in the Tassel v.3.0 default pipelineet al. (2014).The alignment of quality-trimmed reads was performed using Bowtie2 tool v.2.2.5The coverage depth of the “unique sequence tags” file was determined using Samtools v.0.1.19®Axion® genotyping array for chickenet al., unpublished results) previously generated with Illumina sequencing with ~11X of sequencing coverage. This dataset contained 12,357,602 filtered SNPs and was generated from the same 10 chickens used in this study . The comparison between these two datasets was performed using CRAN (data.table and reshape2) repository packages for R. More details of the sequencing process of WCGR SNP data can be found in recent publications31We compared the chromosomal positions of the SNPs obtained using the CornellGBS approach with the positions obtained using the following SNP platforms for chickens: Illumina Chicken 60K BeadchipThe set of unique SNPs obtained from 462 chickens using the Tassel v.3.0 tool was annotated using the Variant Effect Predictor (VEP) tool v.71The Mendelian error testing was performed using SNP & Variation Suite v8.41 linkage map. For the linkage analysis, the SNPs were first tested against the expected segregation ratio. The informative genotypes combination were selected for the map construction. Markers with significant segregation distortion  were removed.SNPs present in all families were filtering using Tassel programThe genetic map was constructed using R/OneMap packageThe R/OmicCircus packageHow to cite this article: Pértille, F. et al. High-throughput and Cost-effective Chicken Genotyping Using Next-Generation Sequencing. Sci. Rep.6, 26929; doi: 10.1038/srep26929 (2016).