The application of genomic selection to sheep breeding could lead to substantial increases in profitability of wool production due to the availability of accurate breeding values from single nucleotide polymorphism (SNP) data. Several key traits determine the value of wool and influence a sheep’s susceptibility to fleece rot and fly strike. Our aim was to predict genomic estimated breeding values (GEBV) and to compare three methods of combining information across traits to map polymorphisms that affect these traits.GEBV for 5726 Merino and Merino crossbred sheep were calculated using BayesR and genomic best linear unbiased prediction (GBLUP) with real and imputed 510,174 SNPs for 22 traits (at yearling and adult ages) including wool production and quality, and breech conformation traits that are associated with susceptibility to fly strike. Accuracies of these GEBV were assessed using fivefold cross-validation. We also devised and compared three approximate multi-trait analyses to map pleiotropic quantitative trait loci (QTL): a multi-trait genome-wide association study and two multi-trait methods that use the output from BayesR analyses. One BayesR method used local GEBV for each trait, while the other used the posterior probabilities that a SNP had an effect on each trait.FGF5, STAT3, KRT86, and ALX4) near SNPs with pleiotropic effects on wool traits.BayesR and GBLUP resulted in similar average GEBV accuracies across traits (~0.22). BayesR accuracies were highest for wool yield and fibre diameter (>0.40) and lowest for skin quality and dag score (<0.10). Generally, accuracy was higher for traits with larger reference populations and higher heritability. In total, the three multi-trait analyses identified 206 putative QTL, of which 20 were common to the three analyses. The two BayesR multi-trait approaches mapped QTL in a more defined manner than the multi-trait GWAS. We identified genes with known effects on hair growth  contains supplementary material, which is available to authorized users. Merino sheep are traditionally bred for wool. The value of a sheep’s fleece depends on many characteristics including fleece weight, fibre diameter, staple strength and length, crimp (or curvature), wool color, and dust penetration . FlystriGenetic variation for these traits is well documented. Estimated heritabilities and correlations for wool traits in Merino sheep are reported in the literature , 6. GeneGenomic prediction is an attractive approach for sheep breeders because estimated breeding values (EBV) can be calculated from DNA marker data (genomic selection ) when anGenome-wide association studies (GWAS), which use similar data to genomic selection, have been widely used to map causal variants in livestock and humans , 13. BotAlthough multi-trait BLUP genomic selection methods are available, they become cumbersome when the number of traits is very large. Here, we present three approximate multi-trait analyses that use the results from single-trait BayesR and GWAS analyses as data to identify the SNPs that are closest to pleiotropic QTL. We applied these methods for 22 traits (each measured at two ages) that describe wool production and quality , and indicator traits associated with susceptibility to breech flystrike on 5726 sheep with genotypes for 510,174 SNPs.The Merino and Merino crossbred animals used in this study were sourced from the Information Nucleus (IN) flock of Cooperative Research Centre for Sheep Industry Innovation (Sheep CRC) , 16. In Prior to shearing at each IN site, the sheep were assessed for a series of visual wool scores, including staple structure (SSTRC), staple weathering (WEATH), wool character (CHAR), fleece rot (FLROT), dust penetration (DUST), and greasy colour (GCOL), and visual breech traits including breech cover (BCOV), crutch cover (CCOV), and dag (DAG) . Each as® HD SNP BeadChip that was developed under the auspices of the International Sheep Genomics Consortium (http://www.farmiq.co.nz/) and includes 606,006 high-density (HD) SNPs, and the Illumina 50 k Ovine SNP chip  that includes 54,241 (50 k) SNPs. All SNPs were mapped to the OAR 3.1 build of the ovine genome sequence using SNPchiMp v.3 [This study used the Ovine InfiniumhiMp v.3 . SporadihiMp v.3 . QualityhiMp v.3 , 22. TheP < 10−5) was extreme. These criteria were applied on each batch of genotypes separately rather than to the whole dataset. Furthermore, if the average call rate per individual was less than 90%, those animals were removed from the SNP data. The final set of genotyped animals used in this study included 5726 animals with phenotypic records for at least one trait: 690 animals were genotyped for 510,174 SNPs, and the remaining 5036 animals were genotyped with the 50 k SNPs, which were imputed to 510,174 SNPs using a multi-breed population of 1735 animals. Cross-validation within these 1735 HD genotypes revealed an average accuracy of imputation  of 0.9871. Most sires of phenotyped animals were genotyped with the HD SNP chip.Stringent quality control procedures were applied to the SNP data, i.e. SNPs were excluded if the call rate per SNP  score above 0.6) was less than 95%, the minor allele frequency was lower than 0.01 or if departure from Hardy–Weinberg equilibrium  was added to the model as a fixed effect, one at a time, and tested for association with the trait:y is the vector of observed phenotypic values of the animals, 1n is an n × 1 vector of 1s , μ is the overall mean, X, Z1, and Z2 are 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 A is the additive relationship matrix constructed from the pedigree of the animals and their ancestors, q, e are the vectors of random effects of breed (including Merino strains), sire-by-flock interaction, and residual error, respectively. Q is a matrix with breed and strain proportions calculated from pedigree , it was retained in the model. All models included contemporary group, flock, drop year, sex, birth type, and rearing type as fixed effects and age of measurement, age of dam and its squared value as covariates. Birth type  and rearing type  were grouped together (BTRT). The significant interactions between fixed effects  were fitted in the model. SNPs were tested for significant association with particular traits at several significance thresholds, and the false discovery rate (FDR) [P < 10−5 and P < 5 × 10−7) to minimise false discoveries . The analysis was performed using the ASReml software . The sam0,Iσq2)) ; si is te (FDR)  was calcGenomic prediction analyses were performed using two methods: GBLUP , 27, witQ) of animals, which were derived from pedigree [MER > 0.90) and 1843 were crossbred MER (0.25 < QMER ≤ 0.90). All crossbred MER animals and the straightbred animals that had sires in common with crossbred MER were in all training sets but not in the validation set. The remaining straightbred MER individuals were split into five sets by allocating all offspring of randomly selected sires to one of the five datasets . In this way, no animal used for validation had paternal half-sibs in the training population. Thus, the analysis was performed five times using each data fold in turn as a validation group and the remaining fourfolds as the training population (i.e. fourfolds plus crossbred MER from above).The same validation populations were used for both methods. All 5726 genotyped and phenotyped animals were assigned to two groups (straightbred MER and crossbred MER) according to their breed using the breed proportions (pedigree . Of the si) was fitted and a was replaced by g, where g is a vector of GEBV ~G is the genomic relationship matrix (GRM). For a SNP to be included in the GRM, its minor allele frequency had to be higher than 0.005, once genotypes  were combined in the whole dataset. Validation animals were included in the GRM but had unknown phenotypes in the calculation of GEBV.GEBV were predicted using Model (1), but no single SNP effect (A). The SNP effects from BayesR were then used to calculate GEBV for animals in the validation sets.The BayesR method  assumes 2) that was estimated by using the 8-generation pedigree of all recorded animals. Thus, we report accuracy as the estimated correlation between GEBV and true breeding values.For each validation population, the accuracy of genomic prediction was calculated as the correlation between GEBV and the adjusted phenotype corrected for fixed effects, which was divided by the square root of the heritability of the trait  multi-trait GWAS (multi-GWAS) following the procedure described by Bolormaa et al. ; (2) appti is a vector of the signed t-values of the effects of the ith SNP for the 44 traits and V−1 is the inverse of the 44 × 44 correlation matrix where the correlation is calculated over the 510,174 estimated SNP effects  between each pair of traits. The power of QTL detection was investigated by comparing the FDR [P values (P < 5 × 10−7) within each 1-Mb window were selected from the multi-trait analysis. These SNPs (P < 5 × 10−7) were assumed to be near QTL that affect wool traits and were also examined as likely candidate gene positions.Multi-trait analyses were performed following the procedure in Bolormaa et al.  based on the FDR  from theThe posterior probability that a SNP had no effect on any trait was calculated as a product of posterior probabilities that a SNP had no effect on any individual trait. We use 1 minus this probability to approximate the probability that a SNP has an effect on at least one trait i.e.: y and trait x and σx are the phenotypic standard deviations of trait y and x. If a window contains a single QTL, we expect this covariance matrix to be dominated by one linear combination of traits representing the QTL effect. Therefore, we carried out a principal component (PC) analysis of the 44 × 44 covariance matrix and examined the highest eigenvalue. This eigenvalue is the variance, in phenotypic standard deviation units, of the linear combination of traits with the largest variance. Across all 9813 windows, the 120 windows with the highest eigenvalues of the first PC (PC1) were arbitrary selected as containing a QTL.GEBV in the 9813 non-overlapping 250-kb windows  for each animal were calculated based on the BayesR effects of all SNPs in the window. A high variance of local GEBV in a window, means that the window includes a QTL for that trait . For eacSLC), which consisted of the linear combination of local standardised GEBV and the PC1 eigenvector across the 44 traits, was calculated for each animal at each of the selected 250-kb windows using the following formula: x is the eigenvector of the PC1 (44 × 1), which was calculated based on the covariance matrix of the standardised local GEBV among 44 traits. Since not all animals were measured for all traits, the missing local GEBV were replaced by the mean of local GEBV across all animals, for which measurements for that particular trait were available in order to calculate a linear combination of the 44 traits. This resulted in a SLC for each animal in each of the 120 chosen windows, which were now used as a pseudo-trait in GWAS within each window to identify the “best” SNP that tagged variation within the segment. The model used in this GWAS was as follows: i was fitted as a fixed effect, one at a time . After performing this new GWAS, the SNP with the highest F value within each of the corresponding 250-kb windows was chosen as the best SNP to tag the QTL.After selecting the 120 top 250-kb windows, we further investigated which SNP, in each of the selected windows, was most highly associated with the PC1 of the (co)variance matrix (the ‘best’ SNP). To identify the best SNP, a pseudo trait (yn) were then predicted using the following formula:ym is a vector of the traits measured on a particular animal, Unn is the inverse of phenotypic covariance matrix between 44 traits with a missing record, and Unn is the inverse of phenotypic covariance matrix of the traits with and without a missing record.The multi-trait validation of SNP effects (i.e. using the linear index approach ) requireTo enable validation of SNP effects in independent animals, the 5726 animals with full phenotypic data  were divided into training and validation populations. The same cross-validation approach was used as described in the genomic prediction section above, except that crossbred animals were not excluded from validation sets. Then, one of the five divisions was randomly used as a validation population and the other four divisions as the training population. Only one 4:1 division  was tested for each of the traits studied.The GWAS for all traits were performed separately in the training and validation populations. SNPs with a significant effect in the training population were validated in the validation population for the five traits that displayed the largest number of significant associations. We counted the number of SNPs for which the effect was in the same direction in both the validation and training populations.The single-trait BayesR analyses for all traits were also performed using only the training population . Then, the three multi-trait analyses described in the previous section  were repeated in the validation set to validate the top SNPs from each multi-trait analysis in the training population.yI) was calculated for each putative QTL (from each of the three methods) and for each animal. It summarised the information across the 44 traits (22 traits at two ages) and was calculated using the following formula [t values) on the 44 traits (1 × 44), which were estimated from only the training population, C−1 is the inverse of the 44 × 44 (co)variance matrix among the 44 traits calculated from the estimated effects of the 510,174 SNPs, only in the training population, and y is a 44 × 1 vector of the phenotype values  for 44 traits for each animal in the validation sample. This resulted in a linear index (yI) for all putative QTL where, for each QTL, each animal had a linear index summary “phenotype”. Then, GWAS, in which each yI was treated as a new trait, were performed using the following model:i was fitted as a fixed effect, one at a time, for significant SNPs (P < 10−5 for each 1 Mbp) discovered in the training population. After performing the GWAS, the significance (P < 0.05) and the consistency of the direction of effects (positive or negative) for the selected significant SNPs were compared between the training and validation populations.The top SNPs in each of the three multi-trait analyses  in the training population were validated separately in an independent set of validation animals. A linear index ( formula : \documehttp://genome.ucsc.edu/) and Ensembl (http://www.ensembl.org/biomart/). If there was more than one gene, we retained only the gene that was located nearest to the SNP or the particular gene with a known effect on wool or hair.The genes that were located 50 kb upstream and downstream of the best SNP were identified using UCSC Genome Bioinformatics . GBLUP provided very similar mean accuracies . However, BayesR tended to result in higher accuracies than GBLUP for traits that had a large number of significant SNPs in the single-trait GWAS , which was lower than for any individual trait tested in the single-trait GWAS  was largest in the single-trait GWAS , which indicates that they may tag the same QTL. There is another SNP with a multi-trait pp ≈ 0.5 that is located 0.5 Mb upstream of these two SNPs.One hundred and two SNPs had an approximate multi-trait posterior probability (pp) higher than 0.3 , which indicates that we did not detect QTL with very large effects for any of the traits. Figure 10 of the first eigenvalues for 120 segments with the highest first eigenvalue is in Fig. For each of the 9813 windows, a PC analysis yielded 44 eigenvalues and eigenvectors. A high first eigenvalue indicates a window that contains a QTL. We selected the 120 windows with the highest eigenvalues as windows that potentially contain a QTL  of the four neighboring windows at around 59.0 Mb on OAR3 and the F values of the SNP effects (×10−3) for PC1 in the corresponding windows. The second segment had a high eigenvalue that explains 98% of the total variance and three SNPs  were most highly associated with the first eigenvector for this segment. The first segment had a comparably lower first eigenvalue but this eigenvalue explained 96% of the total variance and there is only one SNP (circled in yellow) that was associated with the first eigenvector for that segment. The third segment had an even lower first eigenvalue , which probably indicates that there is no QTL in that segment. However, we did observe one SNP that was highly associated with the first eigenvector , for each of the 120 segments, we performed a new GWAS by using the eigenvector of PC1 as a new trait and fitting each SNP, one at a time, within each segment. Thus, the dependent variable was the linear combination of traits as defined by the first eigenvector. By this process, we selected the top SNPs  in each of the top 120 segments. For example, Fig. tor Fig. d, which P < 5 × 10−7) in each 1-Mb window), 102 SNPs with the highest multi-PP and 120 from the analysis of local GEBV. Among these, 75 SNPs overlapped across two or three analyses. Therefore, the total number of SNPs identified was 206. Of these 206 SNPs, seven were identified by all three methods. In addition to these seven SNPs, 64 were identified by both local GEBV and multi-PP, and two by multi-PP and multi-GWAS  although they were not the most significant SNPs in their 1-Mb window, or failed to reach a P < 5 × 10−7. By adding these 38 SNPs to the 64 SNPs from the multi-GWAS, 20 (=7 + 13) are common to all three multi-trait approaches, nine in the multi-LGEBV and multi-GWAS and 18 (=2 + 16) in the multi-PP and multi-GWAS  in the multi-GWAS. In all three analyses, this SNP was associated with YFD. In Bayes R, which generates the statistics for multi-PP and local GEBV, all SNPs are fitted simultaneously in the model. Thus, it is possible that the QTL tracked by the yellow circled SNP differed from the QTL tracked by the blue, orange and green circled SNPs. This hypothesis is supported by the traits that are affected by the SNP. Using the effect of each SNP on the 44 traits as estimated by the GWAS, it is possible to calculate the correlation between any pair of SNPs. Figure These results are illustrated in Fig. P < 10−5) in each 1-Mb window were tested in the validation population. Table P < 0.05 in the validation population varied from 0.07 (=1/14 for YYLD) to 0.30 (=7/23 for AYLD). The percentage of these SNPs that had an effect in the same direction in both the validation and training populations varied from 57 to 86% (Table The most significant SNPs (0.07 =1/1 for YYLDP < 5 × 10−7 in each 1-Mb window), multi-PP, and multi-LGEBV analyses, respectively, in the training population. These SNPs were tested in a GWAS in the validation population (see Table P < 0.05) in the validation population of which 88% had an effect in the same direction in both the validation and training populations  between three SNPs on OAR3 and OAR19 ranged from 0.50 to 0.95). It is likely that each of these clusters of highly correlated SNPs tag one major QTL. In a few cases, moderate correlations (0.6 to 0.8) existed between SNPs located on different chromosomes. For instance, the SNP at OAR3:59.0 Mb (near the FOXI3 gene) has a similar pattern of effects as the SNP at OAR6:37.5 Mb (near LCORL) and OAR23:44.2 and OAR25:35.3 (near MAT1A). In each case, there is an allele that increases SL, SS and FD, but decreases FDCV. However, the SNPs differ in their effects on other traits, such as GFW, thus it is less likely that they act through the same pathway. Similarly, the SNP at OAR13:62.9 (near RALY) and OAR15:72.6 (near ALX4) both have an allele that increases FD and FDCV.If two QTL affect the same physiological pathway, one might expect that they have the same pattern of effects. We assessed the similarity of SNP effects by the correlation between pairs of SNPs across the 44 traits. Two SNPs might have a similar pattern because they affect the same pathway or because they tag the same QTL. Among the 206 SNPs, strong correlations (>0.8) were mainly found among the SNPs located on the same chromosome. For instance, six SNPs were on OAR3:59.0 Mb, three on OAR5:48.5 Mb, four on OAR6:37.5 Mb, five on OAR8:31.2 Mb, six on OAR13:62.9 Mb, three on OAR19:0.6 Mb, five on OAR23:44.3 Mb, and 11 on OAR25:35.3 Mb. Some of these were in high LD . As expected from theory  in whichons Fig. .On average, BayesR and GBLUP resulted in similar GEBV accuracies but BayesR resulted in higher accuracy for traits  for which there was a large number of significant SNPs in the GWAS. In an earlier study on the genomic prediction of wool and carcass traits using the 50 k SNP chip based on the same population, Daetwyler et al.  found noThere are few reports on the accuracy of genomic predictions in sheep –43. PickOur validation approach, in which the relationship between training and validation populations is minimized, is relevant to the commercial use of genomic selection in which a sheep breeder relies on a training population that is not closely related to his own sheep. In our case, the average of the top 10% genomic relationships that a validation individual has with animals in the training population was equal to 0.02. When validation animals are more related to the training animals, it is likely that the estimated genomic prediction accuracies will be higher.The level of genomic prediction accuracy may also depend on the accuracy of imputation . HoweverOverall, the accuracy of GEBV was low. The similar accuracies obtained with GBLUP and BayesR suggest that there are few QTL of moderate or large effect, which is supported by the single-trait GWAS results for many traits. Therefore, we investigated whether combining information from all traits could help to identify QTL for multiple traits.It is known that genetic correlations exist between many of the traits studied here and thus there must be QTL with effects on multiple traits. We used three multi-trait analyses to map these QTL. All three methods involve a degree of approximation, thus it is difficult to apply precise significance tests. However, the fact that there is agreement among the results of the three multi-trait and with the single-trait results supports the conclusion that they detect pleiotropic QTL. This is also supported by the rates of validated individual SNPs.The multi-trait GWAS method used here was previously shown to increase the power of QTL detection , 30. In In dairy cattle, Kemper et al.  showed tP < 5 × 10−7) in each 1-Mb window, the multi-GWAS detected 64 SNPs, but only seven were found by the other two methods. Fifty-five of the SNPs detected by the multi-GWAS were not identified by the other two methods but 50 of them are located in the windows, which had a low eigenvalue of PC1 with PC1 explaining less than 90% of the total variance. Decreasing the threshold for the multi-GWAS to P < 10−5  and higher than that for most single traits. Among the three multi-trait methods, multi-PP had the highest validation rate. This was unexpected since the two other methods used GWAS in both training and validation, whereas multi-PP did not use GWAS methods in the discovery process. However, a multi-PP can miss some QTL since it relies on finding individual SNPs with a high posterior probability. In some cases, the evidence for a QTL is spread across many SNPs each with a low posterior probability, and thus the local GEBV variance is more likely to detect the QTL than the posterior probability.Stearns et al.  pointed FOXI3 gene, are associated with multiple traits including wool quality and breech conformation traits  at both ages. FOXI3 regulates multiple aspects of hair follicle development and homeostasis and loss of FOXI3 impedes hair follicle down-growth and progression of the hair cycle [FOXI3 displays a highly dynamic expression pattern during hair morphogenesis and cycling. In mice, absence of FOXI3 results in a sparse fur phenotype and poor hair regeneration after hair plucking, and these effects are exacerbated with age due to impaired secondary hair germ activation leading to progressive depletion of stem cells. A SNP at 62.6 Mb on OAR4, which is located near (9.8 kb) the BMPER gene, was detected through multi-LGEBV and multi-GWAS (P = 2 × 10−4). Bone morphogenetic protein (BMP) signalling regulates hair follicle cycle and development [By exploiting pleiotropic effects for mapping QTL, we identified 206 putative QTL, which were close to 130 genes (within a distance of 50 kb on either side of each SNP). In some cases, the known function of the candidate gene fits the observed phenotype well. Table ir cycle . Shirokoir cycle  showed telopment –51. BMP ALOX15, ELOVL6, FASN, NCAPG, and GK5) that are associated with variation in size, fatness and body composition in sheep, cattle and humans [ALOX15, FASN, FTL, and NCAPG were associated with GFW (up to |t| = 8.9), while the SNPs in ELOVL6 and GK5 had significant associations with FD (up to |t| = 4.7). Another SNP with an effect of |t| = 4.3 for GFW is located at 37,559,817 bp on OAR6 with the nearest gene being LCORL (at 107 kb). Not surprisingly, the effects of SNPs in NCAPG and near LCORL were highly correlated (r > 0.8) in our data, which indicates that there may be only one QTL in this region [LCORL SNP identified in our study  is located 29 kb from the LCORL SNP that was detected in a multi-trait GWAS across carcass and growth traits  [We also found significant SNPs Table  close tod humans , 32, 33.d humans ). The SNs region –54. Furt,647 bp) . The effP < 10−5) within these genes.In a single-trait GWAS with the 50 K SNP chip in Chinese Merino sheep, Wang et al.  identifiFASN gene) has an allele that increases greasy fleece weight, wool yield, and staple length and decreases fibre diameter. This pattern of quality  is desirable for sheep breeders and the wool industry because these traits affect the price paid for wool to the producer and the processing efficiency and use of the wool in manufacturing. A SNP on OAR23 at about 44.2 Mb had an allele that increases staple strength and decreases fibre diameter coefficient of variance, breech wrinkle, breech cover, crutch cover and dag, which is a valuable pattern for resistance to breech strike and would reduce the need for flystrike prevention strategies such as mulesing and chemical treatment. SNPs on OAR3:137.1 Mb (KRT86), OAR3:133.9 Mb (WNT1), OAR8:89.0 Mb (MPC), and OAR12:49.9 Mb (RSC1A1) had an allele associated with better staple structure (staple with very fine bundles) and wool character , which is a desirable pattern for wool manufacturing. Generally, for a given trait, SNPs showed a similar association at both of the ages at which the trait was measured. However, none of the associations explained a large fraction of the variance for any trait. Therefore, although incorporation of the identified QTL may not increase the accuracy of single-trait EBV, they may be useful to manage unfavourable genetic correlations between traits.The patterns of the effects of the QTL that we studied here indicate that they have various degrees of usefulness for selection. 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 OAR11 , especially for traits with a low heritability, few records and for which few QTL were identified. In an attempt to identify more QTL for these traits, we examined three approximate multi-trait methods. As well as a multi-trait GWAS, we describe two new multi-trait methods based on single-trait BayesR results. Collectively, these three methods mapped 206 putative QTL of which 20 were common to all methods. Sixteen genes that are located near a significant SNP have known effects on hair growth and a further five significant SNPs are near genes that were previously reported for QTL for growth and body composition. Future research should examine whether genomic prediction accuracy can be increased by using the QTL identified in this paper.


Solanum tuberosum Group Phureja, which is mainly diploid and represents an important genetic resource for potato breeding. The objective of this research was to identify novel genetic variants related with frying quality in loci with key functions in carbohydrate metabolism, with the purpose of discovering genetic variability useful in breeding programs. Therefore, an association analysis was implemented with 109 SNP markers identified in ten candidate genes.Potato frying color is an agronomic trait influenced by the sugar content of tubers. The candidate gene approach was employed to elucidate the molecular basis of this trait in InvGE coding for an apoplastic invertase and one association in the locus SssI coding for a soluble starch synthase. The SNPs SssI-C45711901T and InvGE-C2475454T were associated with sucrose content and frying color, respectively, and were not found previously in tetraploid genotypes. The rare haplotype InvGE-A2475187C2475295A2475344 was associated with higher fructose contents. Our study allowed a more detailed analysis of the sequence variation of exon 3 from InvGE, which was not possible in previous studies because of the high frequency of insertion-deletion polymorphisms in tetraploid potatoes.The analyses revealed four associations in the locus InvGE and SssI associated with frying color and the tuber sugar content measured by High Performance Liquid Chromatography (HPLC). These novel associations might be useful in potato breeding programs for improving quality traits and to increase crop genetic variability. The results suggest that some genes involved in the natural variation of tuber sugar content and frying color are conserved in both Phureja and tetraploid germplasm. Nevertheless, the associated variants in both types of germplasm were present in different regions of these genes. This study contributes to the understanding of the genetic architecture of tuber sugar contents and frying color at harvest in Group Phureja.The association mapping strategy using a candidate gene approach in Group Phureja allowed the identification of novel SNP markers in The online version of this article (doi:10.1186/s12863-017-0489-3) contains supplementary material, which is available to authorized users. Potato frying color is influenced by the sugar content of the tubers, because the hydrolysis of sucrose by invertase is the main source of the reducing sugars glucose and fructose, which are precursors of the Maillard reaction. This reaction produces dark pigments and toxic products such as acrylamide during the non-enzymatic reaction between reducing sugars and free amino acids at high temperatures , 2. The Association mapping is a strategy to study the genetic basis of complex traits that has advantages compared to linkage mapping for the discovery of diagnostic molecular markers . This stn = 4 × = 48). To assess the diagnostic power of genes with key functions in carbohydrate metabolism in diverse germplasm, it is necessary to search for genetic variants associated with frying quality in different potato populations, with the purpose of discovering genetic variability useful in potato breeding.There is a large body of knowledge on the metabolic pathways and enzymes involved in starch synthesis, degradation and transport in potato. Sixty nine functional genes in carbohydrate metabolism have been mapped to linkage maps of the 12 potato chromosomes . SeveralSolanum tuberosum Group Phureja consists mainly of diploid genotypes (2n = 2 × = 24) with short day adaptation and tuber sprouting at harvest [tuberosum subsp. tuberosum that are the most extensively grown worldwide [ harvest . In Ande harvest . Genotyp harvest –23. Thisorldwide . The douorldwide . This georldwide , 29. ThiThe phenotypic assessment of potato frying quality has been performed mostly by means of visual scales of frying color in linkage and association mapping studies , 15, 30.Accessions from the Colombian Central Collection of Group Phureja (CCC) that constitute the Working Collection of the Breeding Program at the National University of Colombia have been characterized for their frying quality at harvest. Plants were cultivated in plots in highlands over 2800 meters above the sea level, where low temperatures favored the accumulation of reducing sugars undesirable for frying purposes . PreviouThe purpose of the research presented here was to find allelic variants associated with sugar contents and frying color in a Group Phureja population using a candidate gene approach. Therefore, the phenotypic assessments aforementioned were used to implement an association mapping strategy in order to elucidate the molecular basis of frying quality in Phureja tubers. The association analysis was performed with SNP markers identified in candidate genes with key function in carbohydrate metabolism, which had shown associations with potato frying color and sugar content in tetraploid potatoes –17, 37.A set of 108 diploid landrace accessions from the Working Collection of the Breeding Program at the National University of Colombia and four commercial diploid cultivars  were used in this study. Chip color data collected in nine environments allowed the estimation of a multi-environmental chip darkening index in a previous study using a Bayesian approach . The ind−1 for the Polymerase Chain Reaction (PCR) amplification. A group of ten candidate genes previously reported with association to the frying quality trait in tetraploid populations were selected for this study: InvGE [Pain-1 [Stp23 [StpL [AGPaseS-a [SssI [GWD [UGPase [Sps [G6pdh [http://solanaceae.plantbiology.msu.edu/cgi-bin/gbrowse/potato/). In addition, seven primer pairs were tested in the Group Phureja accessions in candidate gene regions analyzed by Fischer et al. [LapN and KT-InvInh) and Schreiber et al. [BMY-8/2, PWD, AGPaseS, INV-8/2, and PGM-3).Genomic DNA was isolated with DNeasy Plant Mini Kit™ (Qiagen) from young leaf tissue. DNA was quantified with a NanoDrop 2000c spectrophotometer (Thermo Scientific) and adjusted to a concentration of 10 ng μLy: InvGE , 17, 39, [Pain-1 , 39, 40,1 [Stp23 , 16, Stp23 [StpL , 16, AGPGPaseS-a , SssI [7ssI [GWD , UGPase  [UGPase , Sps [16s [G6pdh , 43 in ts [G6pdh   and those with appropriate quality of the amplicon sequence and with at least four SNPs were selected for amplifying and sequencing in the whole population (Table −1). PCR products were cleaned using ExoSAP-IT® (USB) and custom sequenced at the Max-Planck-Genome-Center Cologne using the dideoxy chain-termination sequencing method, an ABI PRISM Dye Terminator Cycle Sequencing Ready Reaction Kit, and an ABI PRISM 3730 automated DNA Sequencer (Applied Biosystems). SNPs were detected by visual examination of the sequence trace files for overlapping base calling peaks using Geneious Software version 8.0 (Biomatters). The SNPs were scored using Data Acquisition and Analysis Software (DAx) (Van Mierlo Software Consultancy); in the case of heterozygous individuals, overlapping base calling peaks were identified.Amplicons were visualized in 1.5% (w/v) agarose gels in 1× TAE (Tris-acetate-EDTA) buffer and stained with ethidium bromide  [F-test probabilities for the SNP markers were assessed to identify the appropriate model for controlling type I errors caused by population structure and familial relatedness [Association analysis was carried out considering that the Group Phureja accessions studied do not have a marked population structure and present a fast LD decay (mosomes) , 45. Marmosomes)  and an emosomes)  were impmosomes)  for the mosomes) , 47. Quaatedness . Accordiatedness . The QQ p for marker-trait associations was expressed on a -log10 scale. SNP markers with a p-value − log10 > 2 were considered significantly associated with the variable studied. To support the reliability of the associations found with the mixed model approach, an analysis of variance (ANOVA) was performed using a linear model implemented in Genstat software version 17 . Differences between phenotypic means of the genotypic classes were detected considering a level of significance of α = 0.05. Therefore, markers with significant p-values showing effect in the phenotypic variables were retained and presented as associated. LD was estimated between pairs of SNP markers that were found with significant associations in the same chromosome, using the r2 statistic [The probability tatistic . Annotattatistic . Amino atatistic .LapN is the exception, it is not shown in the pathway because it corresponds to a leucine aminopeptidase that was identified associated to the frying quality trait through a comparative proteomics approach [InvGE and SssI amplified sequences. The positions of the SNPs scored in the remaining eight genes studied are shown in the Additional file The test of primers in 16 candidate genes allowed the sequence analysis of 12 amplicon sequences from ten genes in 108 Group Phureja accessions. This analysis led to the identification of 109 SNP markers, 44 of which were previously reported for tetraploid genotypes , the fast LD decay and the high heterozygosity found in the Phureja genotypes supports that the population is highly heterogeneous and provides good resolution for association mapping studies because it is representative of the diversity of this cultivated group [The study of Group Phureja genotypes with a subtle population structure was appropriate to avoid spurious associations that are caused by highly stratified populations , 27. Desed group , 27, 45.InvGE and InvGF in chromosome IX are tandem duplicated genes both coding for apoplastic invertases that form a haplotype block associated with chip quality. They co-localize with the cold-sweetening QTL Sug9a, which was highly reproducible among environments [InvGE locus with chip darkening index, fructose, glucose, and total sugar content supports the reproducibility of the association in a diploid potato population. It also supports the correlations among these variables in Group Phureja as discussed previously [ronments , 54, 55.eviously . These Seviously .InvGE in an association mapping population from Group Phureja. The sequence analysis discovered the novel SNP InvGE-C2475454T with a significant effect on the variation of the multi-environmental chip darkening index. The HPLC analysis in tubers harvested in an environment representative of Colombian highlands complemented the previous result by allowing the discovery of an InvGE haplotype that is associated with a negative effect on fructose content. Since a thorough analysis of this exonic region was not possible in a tetraploid association mapping population [InvGE-C2475454T and the adjacent haplotype, indicating that both regions are inherited independently because they do not form a single LD block. Additional research is required to validate the contrasting effects of the low-frequency variants present in these two gene regions given the outstanding positive effect of the rare allele InvGE-T2475454 on light frying color. Consequently, it might be of relevance to generate and select genotypes homozygous for the T allele, in order to confirm the effect of this rare allele on light frying color.The current study allowed the analysis of the sequence of exon 3 from pulation , it is nInvGE-G2475344A and InvGE-C2475295A that were predicted to cause amino acid changes are expected to have an impact on protein function. Expression studies in tetraploids detected InvGE and InvGF transcripts in leaves and flowers but not in mature tubers [InvGE expressed in the tissue surrounding tuber buds. Functional analyses of the corresponding potato alleles were not consistent with their statistical association with chip quality [InvGE DNA variants with chip quality, further experiments are required to reveal the potential functional influence of apoplastic invertases on sugar content in mature tubers from Group Phureja. Nevertheless, experimental evidence suggests that the association of this locus is more likely to be indirect as a result of LD with a functional gene [The SNP markers e tubers . In conte tubers  found In quality . In fact quality , 59. Exp quality . Becausenal gene , which nInvGF while the other is in the gene SbeII that encodes a starch branching enzyme [SbeII is not predominantly expressed in tubers, antisense inhibition experiments have shown that it has a major effect on starch structure [InvGE, InvGF, and SbeII, the latter potentially having a strong functional association with sugar content in mature tubers. Therefore, it is relevant to explore in Group Phureja the presence of a haplotype block larger than the one reported here for InvGE, through the evaluation of markers in InvGF, SbeII, and other close genomic regions. The identification of associated regions in large haplotype blocks is favorable for breeding purposes because it might allow the design of diagnostic markers with high predictive value due to the low recombination rates in these regions [The possibility of a haplotype block in the distal region of chromosome IX is supported by the co-segregation of two microsatellite loci in landraces from Argentina. One locus is located in the gene g enzyme , 61. Alttructure , 63. Rectructure . A linka regions .SssI-C45711901T reflects the effect of a marker with three genotypic classes showing a minor allele with a higher frequency than the minor alleles from markers in InvGE. Starch synthases catalyze the glycosyl transfer from ADP-glucose to glucan. The expression of SssI in tubers is low, suggesting a minor role of this particular enzyme in starch synthesis in storage organs [SssI in the control of sugar content in tubers. The SNP in SssI is novel since sequence analyses in tetraploids have not reported this nucleotide variation [The association of the SNP e organs . As in tariation .The marker-trait associations found explained a limited amount of the overall trait variability in Group Phureja. Therefore, selecting individuals which combine the superior alleles for these markers will not guarantee per se a better chip quality because there might be other regions influencing the trait, and the interaction with other loci might affect their expression . NeverthInvGE and SssI that were associated with frying quality and sugar contents in Group Phureja. These novel genetic associations need to be validated and afterwards might be useful in potato breeding programs. The results show that some genes related to sugar content and frying color variation are conserved in diploid and tetraploid cultivated potato germplasm. There is a lack of conclusive evidence for a functional association of both genes with frying quality. Therefore, further experiments are required to reveal the potential functional influence of the associated SNPs with sugar contents of mature tubers or to demonstrate that associations are indirect as a result of LD with functional genes. The implementation of a genome wide approach is necessary to analyze comprehensively the genomic regions that control starch-sugar equilibrium. The genome wide study is also required to assess the distribution of LD regions in all the chromosomes to establish the mapping resolution. These strategies will contribute to a broad understanding of the genetic architecture of sugar accumulation and frying color of Group Phureja.The candidate gene approach allowed the identification of novel SNP markers in


Williams syndrome  and 7q11.23 duplication syndrome (Dup7) show contrasting syndromic symptoms. However, within each group there is considerable interindividual variability in the degree to which these phenotypes are expressed. Though software exists to identify areas of copy number variation (CNV) from commonly-available SNP-chip data, this software does not provide non-diploid genotypes in CNV regions. Here, we describe a method for identifying haploid and triploid genotypes in CNV regions, and then, as a proof-of-concept for applying this information to explain clinical variability, we test for genotype-phenotype associations.Blood samples for 25 individuals with WS and 13 individuals with Dup7 were genotyped with Illumina-HumanOmni5M SNP-chips. PennCNV and in-house code were used to make genotype calls for each SNP in the 7q11.23 locus. We tested for association between the presence of aortic arteriopathy and genotypes of the remaining (haploid in WS) or duplicated (triploid in Dup7) alleles.ELN gene was associated with aortic stenosis in WS participants (p < 0.0049) while the A allele of the same SNP was associated with aortic dilation in Dup7.Haploid calls in the 7q11.23 region were made for 99.0% of SNPs in the WS group, and triploid calls for 98.8% of SNPs in those with Dup7. The G allele of SNP rs2528795 in the Commonly available SNP-chip information can be used to make haploid and triploid calls in individuals with CNVs and then to relate variability in specific genes to variability in syndromic phenotypes, as demonstrated here using aortic arteriopathy. This work sets the stage for similar genotype-phenotype analyses in CNVs where phenotypes may be more complex and/or where there is less information about genetic mechanisms.The online version of this article (10.1186/s12881-018-0563-3) contains supplementary material, which is available to authorized users. Williams syndrome  and the reciprocal genetic disorder, 7q11.23 duplication , are caused by hemideletion or duplication, respectively, of approximately 1.6 megabases on chromosome 7q11.23 ) and B-allele frequency (BAF) values were extracted for each SNP using Illumina’s GenomeStudio version 2.0. PennCNV version 1.03  was usedFurther analysis of CNVs was restricted to the 7q11.23 WS region. Using R scripts developed in-house  and within the 7q11.23 WS locus for SNPs in other genes , and those who did not have surgery were categorized as having mild or absent (17/25 WS patients), providing a categorical phenotype for association analyses. Chi-squared tests of the association between the degree of SVAS with every SNP in 7q11.23 genes were performed using R . For SNPs found to be significantly related to SVAS in persons with WS, we used logistic regression to predict aortic arteriopathy based on the interaction between diagnosis (WS or Dup7) and SNP genotype. Because the phenotype in Dup7 is opposite to that in WS (dilation vs. stenosis), we expected the risk alleles at identified SNPs to be opposite in the two CNV groups.CNVs were identified by PennCNV in the 7q11.23 locus for all individuals . For individuals with WS, 80% of those with the ELN rs2528795 G allele had severe SVAS, whereas 84% of those with the A allele did not. In contrast, for individuals with Dup7, 40% of those with at least 2 copies of the A allele had aortic dilation, while no participants (0%) with at least two copies of the G allele had aortic dilation  using Illumina BeadArray chips . There aELN support the use of the pipeline developed here, using commonly-available SNP data to test for genotype-phenotype associations within 7q11.23 and other CNVs. We found that in the context of Dup7, the G allele of rs2528795 in ELN is protective against aortic dilation, but in the context of WS, the same allele increases risk of aortic stenosis. The inverse directionality of risk alleles in the WS and Dup7 groups, along with the consistency of these findings with our hypotheses and prior evidence that ELN is implicated in cardiovascular abnormalities, are a positive initial test of this method. While it is known that mutations of ELN can cause non-syndromic SVAS in an autosomal dominant fashion [ELN variation and cardiovascular phenotypes in WS [Our findings regarding  fashion , 10, thees in WS . Our metELN underlies aortic dilation found in some individuals with Dup7 [ELN sequence variation is indeed related to dilation in Dup7. These findings are supported by considerable a priori evidence: the elastin protein is a biopolymer and a critical component of the extracellular matrix, constituting nearly 30% of the aorta [ELN [ELN variation with genetic variation in other 7q11.23 genes, and throughout the genome, undoubtedly impact expression of arteriopathy, our results support the use of the method developed here to uncover genotype-phenotype links in individuals with CNVs.Further, though it has been hypothesized that involvement of ith Dup7 , 17, thehe aorta . It is frta [ELN , and therta [ELN . While iELN with aortic pathology, the SNP identified here, rs2528795, has not been previously linked to SVAS or aortic dilation. However, one prior study found weak associations between this SNP and autism [ELN has minimal expression in the human brain [As considerable variability exists in the expression of phenotypes caused by 7q11.23 CNVs (as well as other CNVs), one potential explanation, which is tested here, is that variability is due to sequence variation within the affected genomic region. However, there may be other causes for this phenotypic variability. For example, genetic variation outside of the CNV regions of interest, including other SNPs or CNVs, may also impact these phenotypes. Additionally, environmental factors may also play important roles. Though there is significant a priori evidence implicating d autism , though an brain . Similaran brain , despiteELN and aortic arteriopathy, we show that variability in remaining or duplicated alleles in the 7q11.23 CNV regions, as identified in this manner, can be associated with the severity of phenotype expression. This work provides support for applying this approach to uncover novel genetic associations with phenotypes where the clinical presentation is more complex, such as cognitive and brain-based features, and/or where there is less information about causative genes.In summary, we present a method to make genotype calls in individuals with syndromic CNVs. Additionally, using the well-established genotype-phenotype link between Additional file 1:R script used for analyses. This file contains R code used to identify CNV genotypes and to perform association analyses with haploid genotypes. (R 4 kb)


AatII, PstI, MspI) and methylation-insensitive  enzymes for their effectiveness in genome complexity reduction and enrichment of genic SNPs. We discovered that the use of two methylation-sensitive enzymes effectively reduced genome complexity and did not require a size selection step. On the contrary, the genome coverage of libraries constructed with methylation-insensitive enzymes was quite high, and the additional size selection step may be required to increase the overall read depth. We also demonstrated the effectiveness of methylation-sensitive enzymes in enriching for SNPs located in genic regions. When two methylation-insensitive enzymes were used, only 16% of SNPs identified were located in genes and 18% in the vicinity (± 5 kb) of the genic regions, while most SNPs resided in the intergenic regions. In contrast, a remarkable degree of enrichment was observed when two methylation-sensitive enzymes were employed. Almost two thirds of the SNPs were located either inside (32–36%) or in the vicinity (28–31%) of the genic regions. These results provide useful information to help researchers choose appropriate GBS enzymes in oil palm and other crop species.Advances in next generation sequencing have facilitated a large-scale single nucleotide polymorphism (SNP) discovery in many crop species. Genotyping-by-sequencing (GBS) approach couples next generation sequencing with genome complexity reduction techniques to simultaneously identify and genotype SNPs. Choice of enzymes used in GBS library preparation depends on several factors including the number of markers required, the desired level of multiplexing, and whether the enrichment of genic SNP is preferred. We evaluated various combinations of methylation-sensitive (The online version of this article (doi:10.1007/s11032-016-0572-x) contains supplementary material, which is available to authorized users. In recent years, single nucleotide polymorphisms (SNPs) have gained a lot of interest in the scientific and breeding communities. The ubiquity of SNPs in eukaryotic genomes and their usefulness as genetic markers have been well established over the last decade Rafalski . The devReduced representation sequencing has emerged as a method of choice for a wide range of genetic studies, not only because of its cost-effectiveness but also because many research questions can be answered with a small set of markers and do not require whole genome sequencing Peterson . A recenFor applications in marker-trait association analyses, SNP markers located in genic regions are generally more informative than those in the intergenic sequences as they are more likely to be in the vicinity of the quantitative loci (QTL) associated with traits of interest. The choice of restriction enzymes influences the number and position of fragments represented in GBS libraries, which in turn affects the number and genomic location of SNPs discovered. The ability to enrich for genic SNPs is often desirable in any dataset and particularly beneficial for trait-association analyses such as QTL mapping, genome-wide association studies (GWAS), and genomic selection. In plants, transposable and repetitive elements are heavily methylated, while the euchromatic regions exhibit lower degrees of cytosine methylation  in our study as it represents an outcrossing, highly heterozygous, non-model species with a relatively large genome (1.8 Gb)  often produce a large number of small fragments, resulting in a library with low read depth per locus. The use of rare-cutting restriction endonucleases (six cutters) with methylation sensitivity can assist in creating a higher level of complexity reduction by targeting fewer sites, which will lead to higher sampling depth of homologous regions in the genome and reduce the amount of missing data. Here, we evaluated the use of multiple enzyme combinations with and without an additional size selection step to illustrate that GBS protocol can be tailored to achieve varying degrees of complexity reduction. We also demonstrated the effectiveness of methylation-sensitive enzymes in the enrichment of scorable SNPs in genic regions. We used oil palm  and methylation-insensitive (SphI and MseI) enzymes (Table MspI-MspI or MseI-MseI) was prevented. We multiplexed 12 samples per run. For size-selected libraries, we selected fragments of ∼270 bp using the E-Gel® SizeSelect™ Agarose Gels . Fragment size distribution of the PCR-amplified libraries prior to and after the size selection step is shown in Supplemental Fig. Genomic DNA from two parental cultivars (clone A43/9 and clone A) was used to prepare reduced representation libraries, following the modified GBS protocol using two enzymes and a Y-adapter  and sequenced in multiplex on Ion Proton PI™ chips. We obtained a total of 132,965,081 reads covering roughly 15.08 Gb of sequence data, with an average of 5,540,212 reads per sample  was coupled with a four-cutter that also exhibited methyl-sensitivity (MspI), the numbers of loci obtained from both NS and SS libraries were essentially the same across all read depths examined. The additional size selection step does not seem to be necessary when a pair of methylation-sensitive enzymes (PstI/MspI or AatII/MspI) is used to generate the sequencing libraries.The original GBS protocol developed by Elshire et al.  did not PstI or AatII) was used in combination with a methylation-insensitive frequent cutter MseI, the additional size selection step significantly reduced the genome coverage by 20–40% . In this case, the additional size selection step significantly improved the level of genome complexity reduction and as a result, SS libraries yielded higher number of sites covered at >50× read depths. Although the SphI/MseI combination provided great genome coverage at lower depth (10–25×), there was a precipitous decline in the number of loci detected as the read depths increased to 50×, indicating uneven distribution of reads across the genome. Previous estimates of sequencing depth required to accurately call SNPs in whole genome sequencing are variable. Bentley et al. , and the average read depth per sample is inevitably diluted. When the GBS data contain a discernable proportion of uncalled genotypes owing to low read coverage, several methods can be employed for the imputation of those missing data  was employed in GBS library construction, only 16% of SNP loci were located in genes and 18% in the vicinity (± 5 kb) of the genic regions, while the remaining SNPs (66%) resided in the intergenic regions. A combination of methylation-sensitive and methylation-insensitive endonucleases (SphI/MspI and AatII/MseI) yielded approximately 20% genic SNPs. Notably, almost two thirds of total SNPs discovered in the reduced representation libraries generated with two methylation-sensitive enzymes (AatII/MspI and PstI/MspI) were located either inside (32–36%) or in the vicinity (28–31%) of the genic regions. Interestingly, the use of PstI revealed a high percentage of SNPs (∼36%) located in gene space regardless of the methyl-sensitivity of its common-cutting enzyme partner, while an apparent 10% difference in the number of genic SNPs was observed between the AatII/MspI and AatII/MseI libraries.For QTL mapping and association studies, SNP markers located in the expressed portion of the genome are often more informative than those in the intergenic regions. Methylation-sensitive restriction endonucleases have been employed in the construction of reduced representation libraries in order to enrich for hypomethylated gene space and avoid repetitive regions of the genome  yielded a relatively uniform SNP distribution across the physical map  tended to cluster in non-repetitive regions of the genome Supplemental Figure 2(PDF 646 kb)Supplemental Table 1(XLSX 183 kb)


Brassica napus has resulted in relatively low diversity, such that B. napus would benefit from germplasm improvement schemes that sustain diversity. As such, samples representative of global germplasm pools need to be assessed for existing population structure, diversity and linkage disequilibrium (LD). Complexity reduction genotyping-by-sequencing (GBS) methods, including GBS-transcriptomics (GBS-t), enable cost-effective screening of a large number of samples, while whole genome re-sequencing (WGR) delivers the ability to generate large numbers of unbiased genomic single nucleotide polymorphisms (SNPs), and identify structural variants (SVs). Furthermore, the development of genomic tools based on whole genomes representative of global oilseed diversity and orientated by the reference genome has substantial industry relevance and will be highly beneficial for canola breeding. As recent studies have focused on European and Chinese varieties, a global diversity panel as well as a substantial number of Australian spring types were included in this study. Focusing on industry relevance, 633 varieties were initially genotyped using GBS-t to examine population structure using 61,037 SNPs. Subsequently, 149 samples representative of global diversity were selected for WGR and both data sets used for a side-by-side evaluation of diversity and LD. The WGR data was further used to develop genomic resources consisting of a list of 4,029,750 high-confidence SNPs annotated using SnpEff, and SVs in the form of 10,976 deletions and 2,556 insertions. These resources form the basis of a reliable and repeatable system allowing greater integration between canola genomics studies, with a strong focus on breeding germplasm and industry applicability.Intensive breeding of  Brassica napus  is a recent allotetraploid originating from natural hybridization and genome duplication events between Brassica rapa and Brassica oleracea, sometime after B. rapa and B. oleracea diverged, between 12,500 and 7,500 years ago (B. napus began relatively recently (400–500 years ago) and no truly wild populations have been recorded  and have remained isolated, with signs of loss of genetic diversity due to genetic drift as of the year 2000  is able to use marker information to maintain diversity, and is a promising approach for the improvement of canola germplasm, while keeping genetic erosion in check . As the B. napus has low levels of overall LD, these same studies reported average LD to extend anywhere between 250 and 1000 kb  in Chinese varieties (r2 = 0.2) across a diverse sample set (r2 = 0.1 (A few studies using a higher density of single nucleotide polymorphism (SNP) markers (c. 10–25K) also report varying levels of LD, with considerable variation between individual chromosomes, and on average less rapid LD decay in the C genome . Notablyarieties  to 490 kmple set . This har2 = 0.1 . As suchBrassica 60K SNP array, which does not allow for novel SNP discovery and any genetic diversity inferences made based on predefined SNPs must be aware of the bias caused by the initial SNP discovery method. The 60K Brassica SNP array also requires the removal of a significant number of markers due to poor alignment, by excluding SNPs whose flanking sequences map to multiple sites in the reference genome  through restriction-site associated DNA (GBS-RAD) method  and varie genome . One stuery bias .B. napus as well as re-synthesized lines. However, only 25 of these varieties were oilseed types, with the rest comprising re-synthesized lines, vegetable, swede, and fodder varieties. The oilseed varieties used by B. napus oilseed breeding germplasm to develop a foundation of high-quality SNPs to base genome studies from, which is also of high industry relevance.Furthermore, many of these systems fail to provide a common standard and platform to integrate studies for maximal benefit, particularly in the case of GBS-RAD and older systems such as AFLPs and SSRs. With the release of a reference genome for canola, the establishment of genomic resources which can be reliably integrated and reproduced between studies and datasets has become possible, and should be pursued. Whole genome re-sequencing (WGR) allows for a greater level of genome interrogation than complexity reduction methods and results in a significant increase in SNP markers. A well-curated list of SNPs with predicted effects, based on the reference genome positions will assist in driving breeding forward through evaluation of germplasm. To date, significant work in this area has been done by Brassiceae-specific whole genome triplication  (>50 bp), which may be the cause of functional genetic differentiation and potential sources of heterosis. SVs are particularly relevant as all plant species have undergone whole genome duplication events followed by diploidization , and thelication , resultie genome , ranginge genome .This study aimed to generate the most comprehensive genome analysis of canola global germplasm to date that is lacking from the literature. As recent studies have focused on European and Chinese varieties, this study selected a significant number of Australian spring types as well as diverse global germplasm, which was initially interrogated for population structure using GBS-transcriptomics (GBS-t), which provides reduced representation of the genome for sequencing by extracting and converting mRNA to RNA-Seq libraries, allowing for cost effective genotyping of a large number of samples . As a reBrassica Germplasm Improvement Program, Grains Innovation Park, Agriculture Victoria Research, Victoria, Australia.Seed was sourced from the Australian Grains Genebank and Denise Barbulescu, National Supplementary Table S1).GBS-t raw sequencing data of 540 samples from Supplementary Table S1 and Supplementary Figure S1). This included 10 samples comprising both Australian spring types and diverse winter types that were used in experimental development of WGR library preparation, 6 commonly studied European winter types, 87 significant Australian spring types, and 46 samples forming a global diversity panel chosen using a custom pipeline, selecting a representative of each branch clade from a neighbor-joining (NJ) tree, to capture overall diversity , according to the manufacturer’s instructions. Two methods of whole genome library preparation were evaluated in 10 samples for sequence coverage and ease of scaling: the Illumina TruSeq PCR-free kit  using physical shearing using the S2 focused-ultrasonicator system  and an enzymatic MspJI  shearing method after the introduction of 5-methyl-dCTP (NEB) described in PRJNA435647 in the NCBI BioProject database2.Quantification of libraries for the purpose of pooling was performed using an Illumina MiSeq Nano 300 cycle run based on the method described in Read mapping and SNP discovery was based on the Supplementary Methods S1 and Supplementary Figure S2).For both the GBS-t and WGR data, the VCF file resulting from initial processing through SAMtools mpileup with the appropriate SNP list was further filtered in R  for a miFor each of the high-confidence SNP sets resulting from the GBS-t and WGR data, Nei’s pair-wise genetic distance was calculated for all samples and subsequently, genetic diversity within canola was calculated using StAMPP . NJ treeTo determine the percentage of variation caused by between- and within-population differences, StAMPP was used to perform an analysis of molecular variance AMOVA; . Due to r2 which was derived from pair-wise comparisons using PLINK  and the whole genome reference sequence . Clustering revealed population structure broadly subdivided based on growth habit with spring, winter, and semi-winter groups, and was confirmed by analysis with STRUCTURE . Clustering is also largely consistent with geographic origin as the majority of Australian varieties in the data sets are spring types, European varieties are winter types and Asian lines have a large proportion of Chinese semi-winter types as well as other Asian winter types. AMOVA revealed that 37.1% (P = 0.000) of total variation could be attributed to differences between populations and 62.9% is due to differences between individuals. From the NJ tree, a representation of global B. napus germplasm diversity was selected for WGR, as well as a significant proportion of Australian spring type lines .The average coverage of the CDS reference genome, excluding two samples with extremely high coverage (>100×), was 8.3× per sample. After genotyping all 633 GBS-t samples using the 226,855 SNP list, these sites were additionally filtered in order to only retain SNPs which were polymorphic and passed quality filtering in this sample set, resulting in 61,037 informative SNPs. To assess population structure, Nei’s genetic distance was calculated and an NJ tree generated , and due to the ease of scaling for high-throughput library preparation, the enzymatic method was used to process all remaining samples.Initial analysis of 10 samples processed using the Illumina TruSeq PCR-free kit using covaris shearing and the MspJI based enzymatic shearing method resulted in similar coverage of the genome . SNP density differed significantly between sub-genomes, with a base change occurring, on average, every 141 bp and 264 bp on the A and C genomes respectively (Table 1).The shotgun sequencing of all 149 samples generated c. 8 billion reads with an average read length of 147 bp, resulting in an average genome coverage of 9.27× per sample. To reduce the computational burden, The whole genomes were used comparatively with the GBS-t global data set to confirm population structure and to evaluate diversity and LD. The WGR data was further used for annotating SNP effects and identifying SVs.Figure 1B) illustrated good consistency and reproducibility with the GBS-t data . In both NJ trees, there is clear differentiation based on growth habit and also geographic origin. Breeding history and geographic isolation has impacted population structure, with Australian spring samples forming a cluster separate from several Canadian and European samples, initially misclassified as winter types but which were subsequently found to be spring types. Similarly, the majority of Chinese varieties group in the same clade, distinct from other Asian varieties.The WGR NJ tree (Table 2). For the c. 4 million WGR high-confidence SNPs, these figures were 593,742 and 203,752, respectively. In both instances, the majority of global diversity panel exclusive SNPs were located on the A genome while the majority of Australian spring exclusive SNPs were on the C genome (Table 2). Due to limited sampling it is not possible to preclude the presence of these exclusive SNPs in other varieties across sub-populations, but are nonetheless likely indicative of regions associated with selection or drift within sub-populations.Of the 61,037 high-confidence SNPs from the GBS-t data, 3,657 were only found in the global diversity panel and 519 were exclusive to Australian spring types . In the short range, LD decays more rapidly in Asian varieties  compared to European varieties , but long range LD remains higher in Asian than European varieties .Due to the larger sample size, the GBS-t data was used to evaluate LD between geographic populations. From the c. 226K SNP list, informative SNPs which are variant within each sub-population were retained for LD analysis with 12,394 SNPs segregating in Australian varieties, 13,422 in Asian varieties and 8,080 in European varieties. LD decays the least rapidly in Australian varieties, reaching c. 280 kb when Figure 3B). LD extended to c. 700 kb across the whole genome, c. 380 kb in the A genome and c. 1,600 kb in the C genome when r2 = 0.2.The higher SNP density provided by the WGR data was used to examine LD on a chromosome-by-chromosome basis, using 1,256,708 SNPs across the whole genome. LD varied extensively between individual chromosomes, with lowest LD decay observed for chromosome C02 and the fastest decay for A07 (Supplementary Table S2). The majority of SNPs (68.8%) were found to be intergenic . Of the 1,257,527 genic SNPs, 45.7% were intronic, with only a small percentage (0.3%) of these SNPs causing a splice site acceptor or donor region to occur, 11% were located in untranslated regions (UTRs), 18.8% were synonymous, 22.7% non-synonymous, and 1.8% of genic SNPs caused changes to stop codons or lost start codons (Supplementary Table S2). Transitions were more common than transversions with 57% and 43% respectively. Of the 101,040 genes described in the Darmor-bzh reference, 76,419 contained at least one SNP.In order to assess the potential effect of SNPs, the c. 4 million high-confidence SNPs from the whole genomes were annotated using SnpEff . Of the genic deletions, 52.1% were intronic, 23.2% were in CDS regions, 18.6% in UTRs and 6% spanned both CDS regions and UTR. For insertions, the same values were 48.1%, 34.8%, 14.7%, and 2.4%, respectively.The average number of SVs identified by Breakdancer and Pindel, as well as the number of unique SVs remaining after each filtering step is provided in Table 3). The deletions ranged in size from 50 bp to 15,420 bp with a median of 88 bp, and insertions were smaller, ranging from 50 bp to 132 bp with a median of 78 bp (Supplementary Table S4).Deletions and insertions were evenly spread throughout the genome and there was not a higher prevalence in either sub-genome, although broadly within sub-genome, the number of SVs correlated with chromosome length. The smallest chromosome, A10, had the least number of deletions and insertions while A03 had the most deletions and C03, the largest chromosome, had the most insertions , and provisional correction of growth habit. Phenotypic evaluation is necessary for conclusive re-classification; however, a phylogenetic approach is sufficient for growth habit grouping. As gene banks must rely on the information provided upon deposit, issues with misclassification of accessions have been observed (Supplementary Table S1). The re-classified samples consistently cluster as expected based on breeding history and growth habit information found in the canola literature. As such, it is unlikely that the misclassifications found in this study are due to incorrect accession labeling of seed packets or the presence of an erroneous seed in the packet. Subjective attribution of growth habit may have affected the validity of passport data for some accessions.Evaluation of the NJ trees revealed that a phylogeny based method is suitable for the classification of population structure, as was confirmed by the results of STRUCTURE  showed improved prediction accuracy for traits relating to blackleg disease resistance, and n = 94 and n = 55, respectively) and the reference genome is a winter type, such that diversity within Australian springs can still be considered low overall compared to global germplasm.As anticipated, the global diversity panel had more SNPs found during initial SNP discovery based on WGR, more exclusive high-confidence SNPs, deletions and insertions, and greater likelihood of harboring any given SV. While the higher diversity observed in the global diversity panel compared to Australian spring types was expected due to the difference in sample composition, the moderate degree of diversity still present within Australian spring types was surprising. Despite previous findings of low overall diversity due to an isolated breeding history , initialThe other important finding from this evaluation was the degree of diversity present in the Asian population in this study, which may account for a large proportion of diversity observed in the global diversity panel. This analysis was based on the GBS-t data due to a larger sample size, allowing for evaluation of three major growing regions: Australia, Europe, and Asia. Despite the smallest population size, the Asian population retained the most polymorphic SNPs for LD analysis, suggesting a relatively high degree of diversity, and is likely due to the inclusion of non-Chinese Asian varieties. Although Chinese varieties have low levels of diversity due to an isolated breeding history , other gThe effect of population structure on LD has also been observed, with LD signatures between sub-populations of wheat and maize having been found to vary significantly , as was r2 = 0.2) falls between that found by Wang et al.  and Wu et al. , and is consistent with the diversity of the populations used in each study. Due to the strong influence of population structure, diversity, and other factors such as MAF on estimates of LD decay, a conservative interpretation of LD estimates is recommended and needs to be considered within the appropriate context.The evaluation of LD in genomes saturated with markers is particularly valuable for the evaluation of long-range LD, as low SNP density can cause artificial reduction of LD decay, which does not accurately reflect the true extent of LD. For instance, Brassica 60K SNP array (Supplementary Methods S1). Similarly, Due to the highly duplicated nature of the canola genome, quality filtering to remove false positive SNPs is vital and while the most commonly used method involves using a BLAST against the reference genome to remove SNPs whose flanking sequences map to multiple locations, as is common practice when using the NP array , this meNP array . Even usSupplementary Figure S2), as SNPs arising from misalignments will be randomly spread throughout the genome and cause errors which will uniformly reduce LD. However, using stringent filtering parameters is likely to eliminate legitimately heterozygous SNPs, particularly newly arisen mutations which have not yet become fixed. As such, strict heterozygosity filtering will likely reveal more ancient and conserved patterns of LD.Completely filtering out false heterozygosity due to homoeology and other factors such as PCR errors while retaining highly heterozygous variants in a data set of this magnitude would be impractical, and in practice, the method used here is likely to remove the majority of false SNPs caused by homoeologous misalignment. Failing to adequately remove misalignments may affect downstream analyses, particularly LD estimates (bzh reference genome, would greatly ease the abovementioned issues and could be applied in data sets which would struggle to effectively apply the same measures. For example, SNPs identified in highly heterozygous crosses or hybrids could not be reliably filtered on heterozygosity, as a high degree of heterozygosity is expected. Using a set of previously validated SNPs, only loci which are known to be true SNPs could be analyzed. A variant database, such as has been established in Arabidopsis3, which includes not only position but also putative effects, subsequent amino acid changes and protein configuration would be of value to canola research communities.Although the reference genome is the obvious target for improvement of canola genotyping, and ultimately a pan-genome should be considered, the development of a validated set of high-confidence SNPs and a curated list of SVs based on positions in the Darmor-B. napus in this area. Comparing the B. napus species diversity, including a large proportion of re-synthesized lines and vegetable types. As such, a significant proportion of loci from the The SNP list developed by As such, the SNP list developed in this study more fully represents genome-wide polymorphisms present in global breeding germplasm, and although this list needs to be expanded through the addition of validated SNPs from other types of canola, particularly additional Canadian and European spring types, this SNP list provides a common standard for application in other studies, and is a solid foundation for industry-oriented genomics within canola. Furthermore, the annotation of these high-confidence SNPs has allowed for broad characterization of SNP effects, and although these should be interpreted with caution, they provide a basis for further studies on gene effect and regulation, amino acid substitution, and effects on proteins and phenotypes.The annotation of SNP effects broadly followed expectations with the majority of SNPs located in intergenic regions and a transition to transversion ratio of 1.3051, in line with previous findings in canola . SomewhaBrassica napus has undergone extensive duplication throughout its evolutionary history, which has resulted in a high degree of variability and adaptability. Allopolyploids are known to undergo diploidization after whole genome duplication, leading to gene loss and genomic SVs (reviewed by Other chromosomal variants including inversions and duplications, which are difficult to identify using short-read sequencing, should also be investigated. In order to correctly identify repetitive regions including SVs, homoeologous regions, and repetitive elements, long-read sequencing and optical mapping will be required (reviewed by The resources provided by sequencing the genomes of a large number of samples representative of global diversity is the foundation of a global, validated resource such as a variant database. While WGR is necessary to establish such resources, importantly, a low coverage reduced representation transcriptomics-based approach of GBS was shown to be sufficient to correctly identify clades in an NJ tree and was used for reliable side-by-side comparison in this study. As such, canola research should begin to focus on sequencing an increasing number of samples to truly take advantage of the diversity present in canola and increase the power of GS and GWAS.MM prepared the plant materials and performed the sequencing library preparation. MM and FS performed the data analysis. MM, GS, HD, and NC all conceptualized the project and assisted in drafting the manuscript.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.


Ne) revealed a demographic trend of diminishing Ne over time and that recent estimated Ne values for the Kerry breed may be less than the threshold for sustainable genetic conservation. In addition, analysis of genome-wide autozygosity (FROH) showed that genomic inbreeding has increased significantly during the 20 years between 1992 and 2012. Finally, signatures of selection revealed genomic regions subject to natural and artificial selection as Kerry cattle adapted to the climate, physical geography and agro-ecology of southwest Ireland.Kerry cattle are an endangered landrace heritage breed of cultural importance to Ireland. In the present study we have used genome-wide SNP array data to evaluate genomic diversity within the Kerry population and between Kerry cattle and other European breeds. Patterns of genetic differentiation and gene flow among breeds using phylogenetic trees with ancestry graphs highlighted historical gene flow from the British Shorthorn breed into the ancestral population of modern Kerry cattle. Principal component analysis (PCA) and genetic clustering emphasised the genetic distinctiveness of Kerry cattle relative to comparator British and European cattle breeds. Modelling of genetic effective population size ( Bos primigenius)—the progenitor of modern cattle—in the Fertile Crescent region of Southwest Asia , humped zebu (B. indicus) and myriad B. taurus/indicus hybrid populations, have, through genetic drift and natural and artificial selection, diversified into more than 1,100 recognised breeds. However, beginning in the middle of the twentieth century, socioeconomic preferences for large highly productive dairy, beef and dual-purpose breeds have led to extinction and increased vulnerability of more than 200 locally-adapted landrace or native cattle breeds  has provided conservation biologists and animal geneticists with the opportunity to characterise genomic variation and estimate population genetic parameters at very high resolution in threatened or endangered livestock breeds  control region sequence variation for comparative evolutionary studies of genetic diversity in Kerry cattle and other British, European, African and Asian breeds  we have previously described and used for microsatellite-based population genetics analyses  nasal swab DNA collection kits were used for biological sample collection . Nasal swab DNA samples were collected from a total of 75 male and female Kerry cattle and owners were consulted to ensure that a representative sample of animals was obtained. This Kerry population sample group is coded as KY12. Genomic DNA was purified from 0.5 ml of each PG-100 nasal swab sample using the laboratory protocol recommended by the manufacturer (DNA Genotek Inc.).Illumina® Bovine SNP50 BeadChip  was also used as an outgroup for the phylogenetic analyses. Table For comparative population genomics analyses, equivalent SNP data for a range of other breeds were obtained from previously published work —were used to identify animals from the KY92 and KY12 population samples that were parent-offspring pairs. One of the two animals in each pair was then randomly removed to generate the working SNP data set. Following this procedure, quality control and filtering based on recorded SNP genotypes was performed as detailed below for the EU and BI data sets.Prior to quality control and filtering there were 54,057 SNPs in the EU data set  and in the BI data set . SNP quality filtering was performed using PLINK version 1.07  and genomic inbreeding, all SNPs genotyped  were filtered within breeds/populations as detailed above. However, an additional filtering procedure was used to remove SNPs deviating from Hardy-Weinberg equilibrium (HWE) with a P-value threshold of < 0.0001. Also, for the Ne analysis, a more stringent MAF threshold of 0.05 was used.SNP quality control and filtering were performed across breeds/populations (by data set) for construction of phylogenies and ancestor graphs, multivariate analysis, investigation of population structure and detection of signatures of selection. For intrapopulation analyses of effective population size  values for all pairs of Kerry cattle (KY92 and KY12), including the nine blinded sample duplicates for sample verification and tracking purposes.B. taurus NDAM breed sample (n = 22) was used as an outgroup. TreeMix was run without using SNP blocks (as described in the TreeMix software documentation) and ML phylogenetic trees were generated with no migration edges (m = 0) up to ten migration edges (m = 10).Maximum likelihood (ML) phylogenetic trees with ancestry graphs were generated for the EU and BI data sets using the TreeMix (version 1.12) software package  was performed for the composite EU and BI SNP data sets using SMARTPCA from the EIGENSOFT package (version 4.2) with default settings . For the present study, the simple prior approach described by Raj et al.  data for the KY92 and KY12 populations plus a selection of BI and EU breeds using the SNeP software tool as described by Barbato et al.  data using the following equation  to the total length of the autosomal genome covered by SNPs:Individual animal genomic inbreeding was evaluated as genome-wide autozygosity estimated from SNP data using ROH and the n et al. . The FROFROH values for individual animals.PLINK v1.07 was used to define ROH using a sliding window approach and procedures modified from previous recommendations for Illumina® Bovine SNP50 BeadChip and similar SNP data sets , the change in selected allele frequency (ΔSAF) and the cross-population extended haplotype homozygosity (XP-EHH) tests into one composite statistic for each SNP in a population genomics data set. For the present study, we used 36,621 genome-wide SNPs genotyped in 98 individual Kerry cattle samples (from both the KY92 and KY12 populations) and a sample of 102 randomly selected cattle (six random cattle from each breed of the EU data set). To mitigate against false positives, genomic selection signatures were only considered significant if at least one SNP from the set of the top 0.1% genome-wide CSS scores was flanked by at least five SNPs from the set of the top 1% CSS scores.As described in detail by Randhawa et al. , the CSSThe Ensembl BioMart data mining resource . Thereafter, general SNP quality control and filtering led to additional samples being excluded , giving a total filtered KY12 population sample of 62 animals for downstream population genomics analyses.n = 22) was included for the ML phylogenetic tree and ancestry graph analyses, the number of SNPs used was 36,000 from 627 animals for the EU data set and 37,490 from 373 animals for the BI data set. The final numbers of SNPs used for individual breed/population analyses of Ne and genomic inbreeding after all quality control and filtering  are shown in Table After SNP quality control and filtering across the two composite data sets (EU and BI), there were 36,621 autosomal SNPs from 605 individual animals in the EU data set and there were 37,395 autosomal SNPs from 351 animals in the BI data set. When the West African NDAM breed sample  from filtered genome-wide SNP data are reported in Supplementary Table Ho values for each of the breeds/populations used for the present study. The lowest genome-wide Ho value was observed for the West Africa NDAM B. taurus breed, which is likely a consequence of ascertainment bias introduced by a focus on polymorphic SNPs in European B. taurus during design of the Illumina® Bovine SNP50 BeadChip , Holstein (HOLS) or Charolais (CHAR) breeds. In addition, as might be expected, production breeds originally derived from minor island populations (Jersey [JRSY] and Guernsey [GNSY]) also exhibit relatively low Ho values. In the context of genetic conservation it is therefore encouraging that the KY92 and KY12 population samples display intermediate Ho values that are at the upper end of the range observed for the heritage breeds.Generally, as shown in Table m, with the exception of m = 2 migration edges, where the Hereford breed (HRFD) was observed to group with the HOLS breed. The ML tree generated with m = 5 is shown in Figure To examine microevolutionary patterns of genetic differentiation and gene flow among cattle breeds and populations, ML phylogenetic ancestry graphs were generated using TreeMix. For the EU data set, the ML tree topology was consistent for all values of m = 5 for the BI data set. For m = 5, all migration edges stem from the BSHN/Lincoln Red (LNCR) branch, including a medium-weight migration edge connecting to the Kerry cattle branch. These results support the hypothesis that there was significant gene flow during the eighteenth and nineteenth centuries from British Shorthorn cattle into the ancestral populations for a range of modern British and Irish cattle breeds  and second (PC2) principal components are shown in Figures In Figure The PC2 plot axis in Figure Examination of Figures The values for the variation accounted for by PC3, PC4 and PC5 in Figure K = 2 and are differentiated from all other European or British and Irish cattle breeds. The other breed group that is clearly differentiated at K = 2 in Figure chooseK.py script the “true” number of clusters corresponding to the likely number of ancestral populations was estimated to be between 12 and 14 for the EU data set and either 7 or 8 for the BI data set.The results of the fastSTRUCTURE analyses using the EU and BI data sets are shown in Figures For both data sets, animals from the KY12 population sample appear to be more genetically homogenous compared to the KY92 population sample. This observation may be a consequence of increasing use, since the early 1990s, of small numbers of artificial insemination (AI) Kerry sires. It is also noteworthy that the two individual animals detected with a substantial signature of putative historical crossbreeding (KY12_06 and KY12_58) show marked patterns of population admixture in the fastSTRUCTURE results, which are indicated by red arrows in Figures Ne in a selection of production and heritage cattle breeds and populations  are provided in Supplementary Table Ne peaks between 1,500 and 2,000 more than 900 generations ago to Ne values estimated to be less than 200 within the last 20 generations. One the other hand, the DXTR, BSHN, BGAL and LNCR populations display a more severe decline from historical Ne peaks between 2,500 and 4,000 more than 900 generations ago to Ne values estimated to be less than 150 within the last 20 generations.The results from modelling historical Ne trends may be partly a consequence of the relatively small sample sizes for the DXTR, BGAL, and LNCR breeds  that are substantially lower than seen in comparable wild mammal populations  data will tend to underestimate Ne because of physical linkage between many of the SNPs in the data set . This is important from a conservation genetics perspective, indicating that genome-wide autozygosity, which is highly correlated with conventional pedigree-based estimates of inbreeding (FPED) for cattle , which has been associated with neurobiological processes  domestication and behaviour , which encodes a protein that regulates axon guidance, neural circuit formation and vertebrate brain development , which has recently been associated with bovine fertility as an mRNA expression biomarker for oocyte competence in cumulus cells  and BTA16 (DRAXIN) suggests that natural and/or artificial selection related to brain development and behaviour has been important in the microevolution of the Kerry cattle breed. In this regard, it is therefore noteworthy that Kerry cattle, including bulls, are recognised as being comparatively docile and easy to manage  was taken using IPA with the 178 genes located within ± 1.0 Mb of each selection peak  into the ancestors of present-day Kerry cattle  and inbreeding using genome-wide SNP data. As shown in Table Ho) is relatively high in the KY92 and K12 population samples, particularly for endangered heritage cattle breeds. However, it has been long recognised that monitoring Ne is a more important tool for rational breed management and long-term conservation of endangered livestock populations  and visualised in Figure The current genetic status of the Kerry cattle population is underlined by analyses of genetic effective population size . For the 2011-12 Kerry cattle, population owners' consent to sample DNA for research was obtained and individual owners conducted sampling of animals using non-invasive nasal swabs. In this regard, scientific animal protection in Ireland is subject to European Union Directive 2010/63/EU, which states that the Directive does not apply to “practices not likely to cause pain, suffering, distress or lasting harm equivalent to, or higher than, that caused by the introduction of a needle in accordance with good veterinary practice.”With the exception of Kerry cattle sampled during 2011–12, all samples and data was obtained from previously published scientific studies. The re-use of these samples and data is consistent with the 3Rs principles on replacement, refinement and reduction of animals in research (DEM, DAM, AF, and JK conceived and designed the project; DEM, IWR, DAM, AF, and JK organised sample collection and genotyping; SB, GM, IWR, DAM, SP, CC, IASR, and DEM performed the analyses; SB and DEM wrote the manuscript and all authors reviewed and approved the final manuscript.The authors IWR and SP are employed by IdentiGEN, Ltd. The other 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.