Adaptation of global food systems to climate change is essential to feed the world. Tropical cattle production, a mainstay of profitability for farmers in the developing world, is dominated by heat, lack of water, poor quality feedstuffs, parasites, and tropical diseases. In these systems European cattle suffer significant stock loss, and the cross breeding of taurine x indicine cattle is unpredictable due to the dilution of adaptation to heat and tropical diseases. We explored the genetic architecture of ten traits of tropical cattle production using genome wide association studies of 4,662 animals varying from 0% to 100% indicine. We show that nine of the ten have genetic architectures that include genes of major effect, and in one case, a single location that accounted for more than 71% of the genetic variation. One genetic region in particular had effects on parasite resistance, yearling weight, body condition score, coat colour and penile sheath score. This region, extending 20 Mb on BTA5, appeared to be under genetic selection possibly through maintenance of haplotypes by breeders. We found that the amount of genetic variation and the genetic correlations between traits did not depend upon the degree of indicine content in the animals. Climate change is expected to expand some conditions of the tropics to more temperate environments, which may impact negatively on global livestock health and production. Our results point to several important genes that have large effects on adaptation that could be introduced into more temperate cattle without detrimental effects on productivity. Cattle are a key component of tropical rural sustainability, wealth, and food production for small holder farms, which are vital for global food security The barriers to uptake of more productive cattle in tropical environments are lack of quality nutrition during the dry season, high temperature and humidity during the wet season, high water requirements, and devastating tropical parasites and infections, amongst others Indicine and European taurine cattle differ substantially in many respects, not just in their productive potential in tropical environments, but also in traits such as temperament and appearance. First generation taurine x indicine crossbred cattle show heterosis including resistance to parasites and increased productivity, allowing them to outperform either taurine or indicine purebred cattle in tropical production systems There has been anecdotal observation that some F2 crossbred cattle either perform substantially less favourably than F1 cattle, or seem to inherit the weak points of both parental breeds, which might suggest genes of large effect. However, evidence for genes of large effect in genome wide analyses has so far been lacking. Previous studies of crossbred animals have focussed on meat quality and efficiency of production in feedlots, and so far, only meat tenderness and reproductive performance have shown any evidence of genes of moderate effect We have used a large sample of crossbred cattle of two types in genome wide association studies (GWAS) to analyse 10 traits that are important in extensive tropical environments. Such traits include overall productivity and yearling weight, heat and parasite tolerance, and temperament. So far, GWAS of parasite resistance traits have been performed with small sample sizes and have not identified any genes of large effect To determine whether there are genes of large effect segregating in taurine x indicine composite cattle we performed a genetic analysis of ten traits that are relevant for improved performance in tropical production systems . Here weStephanofilaria infection from the fly salivary glands P = 0.874) between them. This suggests that although the animals differed substantially in the amount of taurine or indicine ancestry, the amount of genetic variation and the additive genetic relationships between traits was similar in the two samples.Trait measurements included 1) measures of coat colour, score (length), and rectal temperatures , which aP<0.005) for 7 out of the 10 observed traits in the Tropical Composite sample, and for 4 traits in the Brahman sample  ancestry in each animal. When Hereford, Shorthorn and Gir were used as the reference samples they provided highly correlated estimates (r>0.91), demonstrating that the specific taurine or indicine breed was not important for quantifying indicine% in these samples . The mean sample . In bothn sample  encouragP<5×10E-8)  . Genetic5×10E-8) , with th5×10E-8) . While cASIP and PLAG1, which had important effects on coat colour or body condition score in both samples, accounted for different amounts of genetic variance in each sample  5 was associated to both traits . Even rervations . Althougrvations  in this utosomes . Regions in size . Moreove in size  and femaPLAG1 on BTA14 associated with hip height and female reproductive performance Slick gene on BTA20 associated with coat score, ability to sweat, rectal temperatures MSRB3 gene on BTA5 as having large effects on several traits.To determine whether any of the linked effects may instead be pleiotropic in origin, in particular effects that might be antagonistic, we evaluated the effect of each SNP on all traits using the summation of standardized effects . Genes oLDHA, IGFBP4, ATP7A, STEAP2 and MON2), muscle metabolism , fertility (INHBC) and the immune system . A large number of these genes were associated with the region containing MSRB3 on BTA5 and PLAG1 on BTA14 as noted above. This suggests not only that some of the genetic variation affects the phenotype through differences in gene expression, that clusters of genes affecting these traits are co-ordinately expressed, but also that genetic variation in physiologically relevant gene networks are deployed by the animal to adapt to tropical conditions.To identify the genes that underpin effects on yearling weight and body condition, we combined information on location of significant SNP effects and gene expression profiles of Tropical Composite cattle under tropical dry season conditions, where typical reductions in weight and body condition occur. SNPs that were significantly associated with yearling weight or body condition score in both samples identified a list of genes that had been examined for gene-expression differences using muscle biopsies before and after a period of low food availability for the Belmont Red breed Climate change is expected to expand conditions of the tropics to more temperate environments, which may impact negatively on global cattle production unless steps are taken to improve cattle adaptation and productivity. Not only will temperate regions become warmer but climatic variability will increase and the range of devastating tropical and subtropical parasites will extend into temperate regions Animal Care and Use Committee approval was not obtained for this study because no new animals were handled in this experiment. The experiment was performed on trait records and DNA samples that had been collected previously. The resource population used in this study had been established by the Cooperative Research Centre for Beef Genetic Technologies (Beef CRC) to understand the genetic links between adaptability and components of herd profitability in northern Australia Tropical cattle are exposed to several environmental stressors such as hot and humid conditions, restricted water supply, periodic challenges from exo- and endo-parasites and the diseases they transmit, and seasonal under-nutrition due to low protein availability and feed digestibility. In this study, we examined 10 traits : flight ftp://ftp.tzv.fal.de/pub/vce6/). Genetic parameter estimates were calculated based on the genotyped animals and their pedigree consisting of 5 generations of ancestors. Analytical models included the fixed effects of contemporary group , age of dam and estimated indicine percent of the individual as covariate; as well as animal as random additive effect and the random residual component. Age of dam was found to be statistically significant as a covariate. Animals that are older, in a tropical area, will have had a longer time to adjust to heat, parasites, diseases, poor fodder, than younger animals, who would also produce less milk than older mothers. These differences will include the development of antibodies, some of which will be passed on to their offspring. Therefore, dams that are older will likely be more successful in raising offspring, and indeed, we see strong significant effects for this covariate. For YWT the additional covariate of AGE12 was also fitted into the model. AGE12 is the average age of the animal at days in which body weight was recorded  and so is a proxy for yearling age. The genetic variance and correlation for each trait was also calculated between males and females. Large differences in these statistics might be due to differential responses of the animals to the environment or because the traits may have different genetic architectures in males and females. To compare the correlation coefficients found for these traits between Brahman and Tropical Composite animals, the differences between correlation coefficients for each pair of traits between Brahman and Tropical Composite were compared using a paired t-test. This generated a single test for the 45 comparisons, rather than comparison the differences of the correlation coefficients to the standard errors and then applying a False Discovery Rate model for the number of tests performed.The estimates of genetic and phenotypic variances, heritability of each trait and phenotypic and genetic correlations between traits were derived from bivariate animal model analyses of Brahman and Tropical Composite performed independently using VCE 6.0.2 . Animals genotyped using the lower density array had their genotypes imputed to higher-density based on the genotypes of relatives, consisting of 589 Tropical Composite and 304 Brahman animals including all available sires, that had been genotyped using the BovineHD BeadChip. The imputation was performed within breed using as reference 519 Brahman and 351 Tropical Composites genotypes using the BovineHD (Illumina) and 30 iterations of BEAGLE 2>0.5 was detected between a pair of SNP one of the SNP was removed, and then LD for the window was re-calculated. Once no more pairs of SNP had r2>0.5 the window moved 10 SNP along the chromosome. This procedure yielded a dataset containing 227,085 SNP. For SNP association analyses, the SNP were consistently encoded for all traits as AA, AB and BB using the TOP/BOT encoding of Illumina (http://http://res.illumina.com/documents/products/technotes/technote_topbot.pdf checked 30 July 2014) and then converted into numerical values of 0, 1, and 2 B alleles.All SNP had been mapped to the UMD build assembled by the University of Maryland We used PLINK v1.07 to calculate multi-dimensional scaling (MDS) and genetic relationship matrices based on the genotypes to quantify the sample substructure for both the full dataset as well as the LD filtered dataset. The Angus and Nelore data were used as representatives of pure European taurine and pure indicine animals respectively. We note that indicine percent lines up with the first principal component of a principal component analysis, whereas African ancestry lines up with the second principal component, where the full diversity of cattle is included in the same analysis th century from various Indian cattle including animals from the Melbourne Zoo, upgraded by American Brahman and the Indu-Brazilian breeds so also includes a small proportion of taurine ancestors The Tropical Composite sample consisted of beef industry lineages formed using indicine, Sanga and taurine cattle To estimate the effect of indicine percent on adaptability-related traits a linear model was fitted using SAS . The covariates used for the estimation of genetic parameters were used as fixed effects. Given the number of comparisons, the type I significance threshold was Bonferroni adjusted to α = 0.005.Genome-wide association studies (GWAS) were performed separately within each breed and for each of the ten traits using the final dataset with 729,068 SNP. The GWAS were performed one SNP at a time using the same univariate linear mixed models stated above, which included the fixed effects of contemporary group (combination of year and location), age of dam and estimated indicine percent of the individual as covariate; as well as animal as random additive effect and the random residual component and the SNP genotype  as an additional linear covariate. Solutions for the SNP effects and associated P-values were obtained using Qxpak5 Following Bolormaa et al. P is the P-value tested , S is the number of SNP significant at the P-value tested and T is the total number of SNP tested .where i-th SNP was estimated for each sample separately according to the following formula:The percentage of the genetic variance explained by the ip and iq are the allele frequencies for the i-th SNP calculated for each breed, i-th SNP on the trait under analysis, i-th SNP in the absence of dominance where it. Then, the quadratic form it'-1tiV, where V is the correlation matrix among the SNP effects, is distributed approximately as a chi-squared with 10 df under the null hypothesis that the SNP does not affect any of the traits. The correlation matrix V was calculated using the estimated SNP effects across the 729,068 SNPs. Custom code in FORTRAN 95 was developed to perform these operations. SNP were taken as having a significant pleiotropic effect when the quadratic form -1 tt' V exceeded 29.588 . Here we are looking for pleiotropic effects for SNP that had already been demonstrated to be significantly associated to the traits of interest after correction for multiple testing, so no further correction for multiple testing was required.In both the Brahman and the Tropical Composite sample, the effects of 729,068 SNPs estimated from the GWAS were divided by their associated standard errors to obtain a t-value corresponding to the studentized SNP effects Positional candidate genes were defined as those genes within 3,000 bp of a SNP significantly associated (P<0.01) to either body condition score or yearling weight, and in which the same allele was favourably associated with the trait in both samples. To further explore those associated genes and link their responses to a potential environmental stressor, we evaluated their expression using data from a previously described cattle undernutrition experiment There are many SNP across the cattle genome that have one allele fixed or near fixation in indicine cattle while the alternative allele is fixed or near fixation in taurine animals. If a SNP had an allele with frequency>0.95 in Angus (taurine) and <0.05 in Nelore (indicine) animals, that allele was called the “taurine” allele and the alternative allele the “indicine” allele and labelled as a highly divergent SNP.per se were associated to trait values or were contributing to the apparent size of the QTL effect, we collected genotypes for 50 highly divergent SNP distributed across 5 regions of BTA5. As a control, we collected 10 random selections each of genotypes for 50 highly divergent SNP for all sectors of the genome except BTA5 and BTAX. Then, the “indicine” and “taurine” alleles were identified and the number of “taurine” alleles was summed for each animal. To estimate the effect of the sum of “taurine” alleles on adaptability-related traits a linear model was fitted using SAS . The model contained the same fixed effects as the model for estimated indicine percent.We noted a QTL of large effect on BTA5. To determine whether highly divergent SNP Figure S1Correlation of breed composition (indicine%) estimated using different sets of animals as reference populations.(DOCX)Click here for additional data file.Figure S2Manhattan plots of genome wide association for FT, TEMP, EPG, SHEATH, and COLOUR.(DOCX)Click here for additional data file.Figure S3Manhattan plots of genome wide association for FLY, TICK, COAT, COND, and YWT.(DOCX)Click here for additional data file.Table S1Trait definitions and abbreviations.(DOCX)Click here for additional data file.Table S22) and genetic correlation (rg).Bi-variate analysis for same trait in different sexes: Additive genetic variance (Va), heritability (h(DOCX)Click here for additional data file.Table S3Number of significant SNP from GWAS at different levels of stringency for each trait and false discovery rate between brackets.(DOCX)Click here for additional data file.Table S4Summary table of SNP association: overlap regions between Brahman and Tropical Composite (P<0.0001 both breeds).(DOCX)Click here for additional data file.Table S5Selected Pleiotropy results: Studentized SNP effects across the 10 phenotypes for candidate SNP and its closest gene. Highlighted in yellow are effects> 2.0 in absolute value.(DOCX)Click here for additional data file.Table S62 estimated in the Brahman population with and without fitting the covariate “% indicine” in the model.Correlation between SNP additive effects , and statistical model R(DOCX)Click here for additional data file.Dataset S1Linkage Disequilibrium decay on BTA5 compared to the other autosomes.(XLSX)Click here for additional data file.Dataset S2Haplotype distribution in Tropical Composite animals for BTA5 affecting NAVEL and COLOUR.(XLSX)Click here for additional data file.


A reliable and accurate inspection of the hollow axle inner surface is important for the safe operation of high-speed trains. In order to improve the reliability of the inspection, a flexible arrayed eddy current sensor for non-destructive testing of the hollow axle inner surface was designed, fabricated and characterized. The sensor, consisting of two excitation traces and 28 sensing traces, was developed by using the flexible printed circuit board (FPCB) technique to conform the geometric features of the inner surfaces of the hollow axles. The main innovative aspect of the sensor was the new arrangement of excitation/sensing traces to achieve a differential configuration. Finite element model was established to analyze sensor responses and to determine the optimal excitation frequency. Experimental validations were conducted on a specimen with several artificial defects. Results from experiments and simulations were consistent with each other, with the maximum relative error less than 4%. Both results proved that the sensor was capable of detecting longitudinal and transverse defects with the depth of 0.5 mm under the optimal excitation frequency of 0.9 MHz. High-speed railways integrating a number of advanced rail technologies have been developed in many countries for passenger transportation and freight services, and they have become increasingly popular around the world due to their convenient, efficient, reliable and comfortable features. Axles are key components which are subject to dynamic loads in bogie systems, and may directly affect the safety of train operations. The traditional solid axles have been replaced by hollow axles to decrease the unsprung mass and consequently reduce the forces between rails and wheels. Failures and defects caused by material, processing, assembling, fatigue loading and complicated operating environments, can not only be observed on the outer surfaces of the hollow axles, but also on the inner surfaces. Thus, inspection of the inner surfaces of hollow axles plays a key role in the quality inspection process of hollow axles.So far, several non-destructive testing (NDT) techniques have been designed and developed for the inspection of hollow axles in practice. The ultrasonic testing (UT) technology has been widely used in the in-service NDT equipment to monitor hollow axles. Several ultrasonic transducers with different incident angles were arranged in a probe holder, which could rotate and move within a hollow axle, enabling the detection of transverse  and longitudinal  defects at different locations of the hollow axle covering both inner surfaces and outer surfaces ,2. The uFlexible arrayed eddy current sensors, which are suitable for the inspection of components with complex geometries, are becoming a research hotspot ,11,12. CA new flexible arrayed eddy current sensor for the inspection of the hollow axle inner surface is proposed in this paper. The design and the principle of the sensor are presented in Differential-mode eddy current probes are more sensitive to small discontinuities and have the advantage of suppressing lift-off effect, and thus have been widely used in eddy current testing . TherefoThe four-layered FPCB with excitation traces and sensing traces was rolled and mounted on the sensor holder with 28.6 mm in diameter as shown in t denotes the sensing trace number, N is the number of windings, and When an alternate current 6 S/m. Both excitation traces and sensing traces were defined as copper. The domain of the artificial defect was considered as the air.The proposed sensor was simulated using a finite element modeling (FEM) software, COMSOL Multiphysics, to validate the sensor behaviors and optimize the excitation frequencies. To simplify the simulation model, only excitation traces and a differential sensing pair of the sensor were included in the model with the hollow axle, the artificial defect and the air as shown in At first, the model was simulated at 1 MHz with a defect located under one of the sensing traces. The distribution of the eddy current density in the hollow axle steel is shown in To obtain the sensor response at different excitation frequencies, the defect was positioned at different locations relative to the sensor. An experimental system was set up to validate the simulation results and the feasibility of the sensor as shown in Experiments were conducted by using a hollow axle specimen made of ASTM 1050 steel. Both longitudinal and transverse artificial defects with depths of 0.5 mm, 1.0 mm and 2.0 mm studied in the FEM simulation were reproduced on the specimen by electro-erosion. All defects are 0.35 mm in width and 10 mm in length. A differential sensing pair was activated to measure the sensor responses at different excitation frequencies for verification of simulation results. The inspection of the specimen was carried out by using all the differential sensing pairs with the axial and circumferential sampling intervals of 1 mm and 1.29°, respectively. In order to achieve the resolution, the sensor needed to rotate 20 times with a rotation angle of 1.29° after each axial step with a step distance of 1 mm and then rotated reversely after next axial step with the same motion parameters. The excitation and reception of the sensor were implemented after each rotation. The gain of the RITEC RAM-5000 measurement system was set as 13 dB.The inspection of the specimen was conducted at 0.9 MHz. All of the 6 artificial defects were detected as shown in A flexible arrayed eddy current sensor has been presented. This new design is suitable for the inspection of the hollow axle inner surfaces. The optimal excitation frequency, which is 0.9 MHz, is determined by FEM simulation, whose results are in good agreement with the experimental results. The maximum relative error between simulations and experiments is less than 4%. Results from simulations and experiments show the sensor is capable of detecting both longitudinal and transverse defects with depths as small as 0.5 mm. The sensor is more sensitive to the transverse defects, therefore future work is required to increase the sensibility of the sensor to the longitudinal defects.


The aim of this study was to evaluate the impact of genotype imputation on the performance of the GBLUP and Bayesian methods for genomic prediction. A total of 10,309 Holstein bulls were genotyped on the BovineSNP50 BeadChip (50 k). Five low density single nucleotide polymorphism (SNP) panels, containing 6,177, 2,480, 1,536, 768 and 384 SNPs, were simulated from the 50 k panel. A fraction of 0%, 33% and 66% of the animals were randomly selected from the training sets to have low density genotypes which were then imputed into 50 k genotypes. A GBLUP and a Bayesian method were used to predict direct genomic values (DGV) for validation animals using imputed or their actual 50 k genotypes. Traits studied included milk yield, fat percentage, protein percentage and somatic cell score (SCS). Results showed that performance of both GBLUP and Bayesian methods was influenced by imputation errors. For traits affected by a few large QTL, the Bayesian method resulted in greater reductions of accuracy due to imputation errors than GBLUP. Including SNPs with largest effects in the low density panel substantially improved the accuracy of genomic prediction for the Bayesian method. Including genotypes imputed from the 6 k panel achieved almost the same accuracy of genomic prediction as that of using the 50 k panel even when 66% of the training population was genotyped on the 6 k panel. These results justified the application of the 6 k panel for genomic prediction. Imputations from lower density panels were more prone to errors and resulted in lower accuracy of genomic prediction. But for animals that have close relationship to the reference set, genotype imputation may still achieve a relatively high accuracy. Genomic selection has become a new tool for genetic improvement in livestock species and plants thanks to the discovery of many thousands of single nucleotide polymorphisms (SNP) and cost-effective high-throughput genotyping technology. Since the first publication of Meuwissen et al. Assumptions of SNP marker effects on the trait vary across different statistical methods. In general, GBLUP methods assume that all markers effects are from a normal distribution, while Bayesian methods assume non-normally distributed marker effects with distributions vary in different methods. Performance of different methods depends on the genetic architecture underlying the studied trait Accuracy in estimated breeding values has been increased significantly for young candidates in dairy cattle from application of genomic prediction using the BovineSNP50 BeadChip  Studies have shown that imputation errors affect the accuracy of genomic prediction A total of 10,309 Holstein dairy bulls born between 1950 and 2007 were genotyped on the Illumina BovineSNP50 BeadChip . SNPs with minor allele frequency (MAF) less than 0.05, missing rate more than 15% or P-value from Hardy-Weinberg disequilibrium test smaller than 0.0001 were filtered. After filtering, 35,790 SNPs with known locations on autosomal chromosomes were kept for analyses.Five low density SNP panels including the Illumina Golden Gate Bovine3K BeadChip (3 k), the BovineLD BeadChip (6 k), and three simulated panels containing 384 (L384), 768 (L768), and 1536 (L1536) SNPs were considered in this study. In fact, 2,480 and 6,177 SNPs, from the 3 k and 6 k panel, respectively, coincided with the 50 k panel. Therefore, genotypes of the 2,480 and 6,177 SNPs extracted from the 50 k panel were used instead of re-genotyping animals on the 3 k and 6 k panels. The size of the simulated panels was chosen according to Illumina Golden Gate technology, which allows customized genotyping with 384 to 3,072 SNPs. SNPs in the three simulated panels were selected from the 50 k panel by compromising between uniform marker density and high MAF Milk yield, fat percentage, protein percentage, and somatic cell score (SCS) traits were considered for this study. Official bull proofs and reliabilities in April 2008 and December 2011 were obtained from Canadian Dairy Network (CDN). De-regressed proofs and corresponding reliabilities were derived by CDN from the 2008 bull proofs. Bulls born before 2004 and had reliabilities of de-regressed proofs greater than 0.80 were used as the training data set . Bulls born in or after 2004 and had reliabilities of proofs greater than 0.80 in 2011 were used for validation . Bulls born before 2004, but had less reliable de-regressed proofs in 2008  were used for genotype imputation only. Numbers of bulls in different groups are listed by birth year in Four scenarios were designed to mimic situations that different proportion of bulls in the training and validation sets were genotyped on low density panels. In scenario 0 (S0), all bulls  had genotypes on the 50 k SNP panel. In scenario 1 (S1), all bulls in the training set had genotypes from the 50 k panel, and all bulls in the validation set  had genotypes on low density panels. In scenario 2 (S2) and scenario 3 (S3), 33% and 66% of bulls were randomly selected from the training set, respectively, to have low density genotypes, and all bulls in the validation set had low density genotypes. Size of the reference population was also reduced correspondingly to have the same proportion of animals that had 50 k genotypes. Animals in the training set that had 50 k genotypes were also combined with the reference population for genotype imputation and thus the actual numbers of bulls in the reference population used for genotype imputation were 7,077 for S1, 4,741 for S2, and 2,406 for S3.Genotype imputation was implemented using software FImpute (version 2) developed by Sargolzaei et al. iy is the phenotypic value (de-regressed EBVs) for animal i; μ is the population mean; n is the total number of animals, and m is the total number of SNPs; ijx is the genotype coded 0, 1 or 2 as number of copies from a randomly chosen allele; jβ is the regression coefficient  for SNP j; and ie is the random residual effect.A GBLUP method and a Bayesian method with a spike and slab mixture prior distribution for marker effects were used to predict direct genomic values (DGV) for bulls in the validation set. The GBLUP method used a genomic relationship matrix proposed by VanRaden π and (1 - π), i.e., τ is an arbitrarily small value. π was assigned a uniform prior distribution with a support on . A scaled inverse Chi-square distribution with degree of freedom vβ and a scale τ shrinks the SNP effects towards zero so that it can effectively, but not completely remove an irrelevant SNP from the model. Advantages of using such a two-group mixture distribution have been discussed previously μ was given a flat prior distribution, and the residual was assigned a normal distribution, i.e., ev and a scale The spike and slab mixture prior distribution for SNP effects is a mixture of two normal distributions with weights vβ and ve were arbitrarily set to 4 and 10, respectively. τ was chosen to be 0.0001. The scale parameter jp is the allele frequency for SNP j. Similarly, A Gibbs sampling algorithm was developed to draw inferences from the joint posterior distribution. Hyper-parameters were determined before running Gibbs sampling. A self-developed computer program was written with ANSI C language to implement the Gibbs sampler. The Gibbs sampling was run for 100,000 iterations with the first 20,000 cycles discarded as burn-in. Burn-in period were determined by visually inspecting the Gibbs sampling chain. All samples were kept after the burn-in and the sample means were used as estimates for SNP effects. Direct genomic breeding values for bulls in the validation set were estimated by summing the SNP effects over all loci.Imputed genotypes were compared to the actual genotypes from the 50 k panel and the percentage of genotypes imputed correctly out of the total imputed genotypes was calculated as a measure of imputation accuracy. For genomic prediction, Pearson's correlation coefficient between DGV and bull proofs from 2011 for bulls in the validation set was estimated as a measure of accuracy.Results also shown in the table were the posterior estimates of π, which were 0.96, 0.99, 0.99, and 0.93, for milk yield, fat percentage, protein percentage, and SCS, respectively. π is an indicator on the proportion of SNPs that were expected to have no effects on the trait. When π is small, the trait is likely affected by many QTL with small effects, and when π is large, a few large QTL are expected to influence the trait To show the difference of estimated SNP effects between the GBLUP and Bayesian methods, The trend that the accuracy changed with the density of SNP panel and with the proportion of training bulls being genotyped on the low density panels agreed with results of Weigel et al. The influence of imputation errors on the genomic prediction accuracy also depends on the traits. For example, when bulls in the validation set were imputed from L384 SNP panel, accuracy of DGV predicted via GBLUP was reduced by a rate of 20, 34, 20 and 15%, for milk yield, fat percentage, protein percentage, and SCS, respectively; and for the Bayesian method, the accuracy was dropped by 22, 44, 25, and 15%, respectively. This was likely due to different genetic architectures underlying different traits. For traits affected by a few large QTL, such as fat percentage, accuracy of genomic prediction seemed much more sensitive to imputation errors than traits controlled by many small QTL, such as SCS. A similar trend was observed in a simulation study To examine the imputation errors on SNPs with large effects, two SNPs within the DGAT1 gene region were chosen. The two SNPs had the largest effects estimated from both the GBLUP and Bayesian methods. Imputation accuracies under scenario S1 are shown in Using either the GBLUP or Bayesian method, the 6 k SNP panel performed better than the 3 k panel. In this study, genotypes on both the 3 k and 6 k panel were simulated from the 50 k genotypes. In reality, there are more genotyping errors in 3 k genotypes than in 6 k or 50 k due to the Golden Gate genotyping technology used for the 3 k panel In the future, more and more animals might be genotyped on low density panels. One might have to decide whether to include these animals in the training population to derive genomic prediction equations. From Performance of both the Bayesian and GBLUP methods was influenced by imputation errors. For the traits considered in this study, the Bayesian method performed similar or better than the GBLUP method for genomic prediction under all scenarios and with different densities of the SNP panel or proportions of the training set being imputed. However, for traits affected by a few large QTL, the Bayesian method resulted in greater reductions of accuracy than GBLUP when a very low density of SNP panel was used. Including SNPs with largest effects in the low density panel substantially improved the accuracy of genomic prediction for the Bayesian method. When different low density panels were compared, with all animals in the training set genotyped on 50 k SNP panel, and animals in the validation set genotyped on the 6 k SNP panel, the accuracy of genomic prediction was the greatest for both GBLUP and the Bayesian methods. Imputation from SNP panels with a density lower than 6 k was more prone to errors and resulted in lower accuracy of genomic prediction. But for animals that have close relationship to the reference set, genotype imputation may still achieve a relatively high accuracy. Including genotypes imputed from the 6 k panel achieved almost the same accuracy of genomic prediction as that of the 50 k panel, even when 66% of the training animals were genotyped on the low density panels. These results justified the application of the 6 k panel for genomic prediction.Table S1Estimated π values from the Bayesian model under scenario S2 and S3.(DOCX)Click here for additional data file.


Bos indicus) samples. We found 3,200 missing genotypes among the whole population. NGS re-sequencing data from 8 sires were used to verify the presence of genomic variations within their flanking regions in 81.56% of these missing genotypes. Furthermore, we discovered 3,300 novel SNPs/Indels, 31% of which are located in genes that may affect traits of importance for the genetic improvement of cattle production.High density genotyping panels have been used in a wide range of applications. From population genetics to genome-wide association studies, this technology still offers the lowest cost and the most consistent solution for generating SNP data. However, in spite of the application, part of the generated data is always discarded from final datasets based on quality control criteria used to remove unreliable markers. Some discarded data consists of markers that failed to generate genotypes, labeled as missing genotypes. A subset of missing genotypes that occur in the whole population under study may be caused by technical issues but can also be explained by the presence of genomic variations that are in the vicinity of the assayed SNP and that prevent genotyping probes from annealing. The latter case may contain relevant information because these missing genotypes might be used to identify population-specific genomic variants. In order to assess which case is more prevalent, we used Illumina HD Bovine chip genotypes from 1,709 Nelore ( Despite the strong lasting trend of decreasing costs associated with DNA sequencing caused by the continuing development of Next Generation Sequencing (NGS) technologies, SNP genotyping with DNA chips still offers the lowest cost and the most consistent solution for generating highly repeatable High-Density (HD) SNP data. HD SNP In spite of the application, a portion of SNP genotyping data is always discarded from final datasets based on quality control criteria used to remove unreliable markers. A myriad of biological and technical issues can result in marker failure and low repeatability. As expected, genotyping probes cannot consistently anneal in the presence of any genomic variations  within target sequences and fail to produce accurate genotypes, or in some cases continually generate no genotypes at all, the so-called missing genotypes. Nevertheless, a recent study  has indiThe extent of the aforementioned issues is highly dependent on the divergence between populations used for probe design and the population under study. When samples are derived from the same populations used for generating sequences for probe design, this may not be an issue at all, since the odds of novel unobserved genomic variants within the same population are small. However, the usefulness of HD SNP panels relies on their ability to work on samples from diverse populations, and in these cases the aforementioned technical limitations may produce corresponding genotypes that are consistently missing in either a proportion of samples or even within the entire dataset. Most data quality control procedures routinely and indistinctly discard markers that never generate genotyping data in a specific population or breed in the same manner as other markers that produce varying low call rates. While the latter ought to be discarded because they do not contain useful or reliable information, the former should be further investigated as they might reveal population-specific genomic variant regions, where genetic divergence between populations is higher as consequence of their evolutionary past.Bos taurus) cattle and zebuine (Bos indicus) cattle, were originally derived from northern Europe and the Indian continent, respectively [http://res.illumina.com/documents/products/brochures/brochure_agriculture.pdf). Furthermore, they report that 29,968 SNPs (3.85%) which appear to be flanked by sequence polymorphisms because of breed-specific lower call rates, were retained in the HD panel because they may provide biologically relevant information (Illumina BovineHD Genotyping BeadChip Data Sheet).Contemporary bovine breeds can be subdivided into two closely related genetic groups or subspecies, which diverged 250,000 years ago . Taurineectively , and shoectively . The IllAn initial analysis of a dataset with genotyping data from 1,709 Nelore (zebuine) animals revealed a number of consistently missing genotypes. Do these failed SNPs observed in the Nelore breed actually reveal genomic variant? Do those hypothetical genomic variants occur within biologically relevant loci? To answer these questions, re-sequencing data from historical bulls from the breed, and automated and manual annotation of identified regions were performed.Bos taurus and Bos indicus subspecies, and that these genomic variants observed in Nelore cattle (GVON)s can be found within genes that may affect production traits of importance for genetic improvement in cattle.Genotyping data from a total of 1,709 Nelore animals and re-sequenced NGS data from 8 historical sires were used to identify a total of 3,200 SNPs that consistently failed to generate genotyping data in the Nelore breed (a specific group of SNPs that will be henceforth termed SFNBs–SNPs Failed in Nelore Breed). Further investigation has shown that, within the flanking regions of these 3,200 SFNBs, there were 3,300 novel SNPs/Indels, from which 31% are located on regions containing genes. In the following sections, we present results confirming that SFNBs actually reveal divergent genomic variants between the Specific approval from an Animal Care and Use Committee was not obtained for this study because samples had been previously collected as part of a commercial testing operation and no new animals had to be handled. The experiment was performed on genotyping data generated from DNA samples that had been previously collected. DNA was extracted from semen samples obtained from commercial companies from bulls that are in the market, and from hair and venous blood samples obtained from animals in commercial farms, as part of routine animal handling and testing procedures. Tissues were processed with standard commercial kits. The report is not intended to be a field study and none of the authors were involved in sample collection.A total of 1,709 Nelore samples were genotyped with the Illumina Bovine HD Genotyping BeadChip in a commercial service lab. Genotyping failure frequency was estimated for all SNP markers. Markers that failed to generate genotyping calls in all tested samples were identified and submitted to further analysis.A set of eight bulls representing historical sires in the Nelore breed were re-sequenced using Illumina HiSeq2000 100-bp paired-end reads, with an average depth coverage of >20X. Paired-end reads were mapped onto the UMD 3.1 reference bovine genome  through Genomic variations observed within 100bp upstream and downstream  from SFNBs were identified and annotated with the Variant Effect Predictor (VEP) from Ensembl . The IntThe complete set of the Illumina BovineHD 50bp probe sequences was downloaded from the manufacturer’s website. Each one of the 50bp probe sequences was blasted against the UMD3.1 reference bovine genome. This procedure was necessary for the acquisition of both the probes’ genomic start and end positions and their strand orientation. A C++ program was developed to integrate all the aforementioned information and to classify observed genomic variations according to their position in relation to each SFNB: 50bp Illumina probe target sequence (P1), 50bp adjacent to P1 on the distal side of the assayed SNP, and the symmetrical regions to P1 (S1) and P2 (S2) see .http://www.blast2go.de/) for automated functional annotation. The dataset was blasted against NCBI nr database with default parameters  using blastx. Mapping of sequences to GO terms and GO term assignments were performed using default parameters . Annotations were further augmented using the Annex function of the GO Annotation Toolbox [http://www.genome.jp/kegg/pathway.html) were downloaded for all enzyme codes. The same procedure was adopted for the automatic functional annotation of genes with identified synonymous substitutions in flanking regions of assayed SNPs.Fasta sequences of genes containing at least one identified SFNB were imported into Blast2GO   from different cattle breeds  and confirmed that 3,019 out of the 3,200 SFNBs worked in most samples tested  class I- (MR1) and class II-related genes , which are central to immunity and are among the most polymorphic genes known , were alA large number of olfactory receptor genes (OR) was found to contain SNPs that result in non-synonymous substitutions as well . VertebrOur study has shown that often-discarded missing genotypes can be effectively used to identify population-specific genomic variants which in turn can be used in a wide range of applications. Although whole-genome shotgun sequences can be used to identify the underlying mutations associated with missing genotypes, more cost-effective approaches based on targeted re-sequencing could be used more efficiently, minimizing demands for complex bioinformatics procedures. Recent studies comparing genotyping data from different tissues from the same individual have shown compelling evidence that it is possible to observe tissue-dependent genotypes –59. In tIn human case-control studies HD genotyping data is usually used to identify genotypes or genomic regions associated with a given disease considering two clear premises: (i) patients (cases) were necessarily born with the affected/susceptible genotype; and (ii) the associated genetic marker(s) must be assayed on the HD genotyping chip, or at least be in linkage disequilibrium (LD) with a SNP that is. Most hereditary diseases satisfy the first premise, and the latter is likely to hold true because the most frequent human polymorphisms have been uncovered by the NGS re-sequencing of thousands of samples from different populations , 61. TheIn addition to heritable disease-causing mutations, random or induced DNA alterations may appear in somatic cells after birth and may result in severe illness, such as some cancer types , 65. In Missing genotypes have been predominantly considered an issue to be addressed through imputation-like methods , 70. OnlS1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.


High-throughput genotype (HTG) data has been used primarily in genome-wide association (GWA) studies; however, GWA results explain only a limited part of the complete genetic variation of traits. In systems genetics, network approaches have been shown to be able to identify pathways and their underlying causal genes to unravel the biological and genetic background of complex diseases and traits, e.g., the Weighted Gene Co-expression Network Analysis (WGCNA) method based on microarray gene expression data. The main objective of this study was to develop a scale-free weighted genetic interaction network method using whole genome HTG data in order to detect biologically relevant pathways and potential genetic biomarkers for complex diseases and traits.We developed the Weighted Interaction SNP Hub (WISH) network method that uses HTG data to detect genome-wide interactions between single nucleotide polymorphism (SNPs) and its relationship with complex traits. Data dimensionality reduction was achieved by selecting SNPs based on its: 1) degree of genome-wide significance and 2) degree of genetic variation in a population. Network construction was based on pairwise Pearson's correlation between SNP genotypes or the epistatic interaction effect between SNP pairs. To identify modules the Topological Overlap Measure (TOM) was calculated, reflecting the degree of overlap in shared neighbours between SNP pairs. Modules, clusters of highly interconnected SNPs, were defined using a tree-cutting algorithm on the SNP dendrogram created from the dissimilarity TOM (1-TOM). Modules were selected for functional annotation based on their association with the trait of interest, defined by the Genome-wide Module Association Test (GMAT). We successfully tested the established WISH network method using simulated and real SNP interaction data and GWA study results for carcass weight in a pig resource population; this resulted in detecting modules and key functional and biological pathways related to carcass weight.We developed the WISH network method which is a novel 'systems genetics' approach to study genetic networks underlying complex trait variation. The WISH network method reduces data dimensionality and statistical complexity in associating genotypes with phenotypes in GWA studies and enables researchers to identify biologically relevant pathways and potential genetic biomarkers for any complex trait of interest. As proposed by the founders of WGCNA, a differential network can be created by comparing, for example, case and controls, using the same input data (same SNP data matrix), but creating two networks using two groups of individuals . Several measurements, such as differential connectivity, can be used to identify modules with a different SNP effect profile between the two groups. The WISH network method can also be used as a multi-trait network approach. After network creation and module detection, the GMAT of detected modules with other (correlated) phenotypes can be calculated. By comparing the relatedness of modules with different phenotypes, the WISH network method could possibly be used to identify endophenotypes and subsequently pathways of endophenotypes of complex diseases.The WISH network method based on epistatic interactions is theoretically a promising approach to cluster SNPs based on their interconnectedness. It has been shown in the simulation data that the epistatic data follows scale-free topology and clear clusters of highly interconnected SNPs are detected. However, the use of real data did not represent a clear scale-free network and consequently less clear modules are detected with lower correlations with the trait of interest. This could be due to several reasons. First, the number of SNPs in the network limits the size of the network. In the WISH based on genomic correlations the connectivity is measured over > 5000 SNPs and the most highly connected SNPs are concordantly selected for network construction. Because of computational and time limitations, we used only the top 1000 SNPs for the WISH network based on epistatic interactions. Secondly, we used all individuals present in the dataset to estimate the epistatic interaction effect to increase the power. However, the WISH network based on genomic correlations is created based on extreme phenotypes (EBVs). This will result in more extreme interaction effects; however, due to a limited number of individuals in our dataset, this was not possible in this study.The WISH network method was tested on real data, using an F2 pig resource population, and was able to detect biologically relevant pathways using HTG data. We propose the WISH network method as a new systems genetics tool to investigate HTG data in order to improve understanding of the biological and genetic background of complex diseases and traits.We report here the development and application of a new systems genetics tool - the WISH network method - for building networks using HTG data obtained from genetic study populations. The basic principle of WISH network method is based on the existing WGCNA method for building co-expression networks using transcriptomic data. This study addresses the long gap or a need in studying genome-wide genetic interactions in the context of genetic (SNP) networks underlying complex trait variation. The proposed WISH network method has been tested using simulated datasets as well as applied to real experimental data obtained from a pig resource population, genotyped using 60K Porcine SNP Chip and extensively phenotyped for obesity and obesity-related traits. Results have shown that WISH detects biologically relevant modules for carcass weight (results shown) and other obesity-related traits (results not shown). The WISH network method can be used in human genetics to detect pathways of complex diseases as well as in animal and plant genetics to unravel the genetic background and detect genetic biomarkers of economically important production traits. By analysing various traits within the same network, the WISH network method is able to detect endo-phenotypes which could be valuable for treatment of individuals with complex diseases. Moreover, a differential WISH network method can be used in a case-control design to identify conserved pathways in the different groups or to identify pathways that behave differentially between the cases and controls.The WISH network method is an extension of the R-package WGCNA  using HTFor computational reasons not all genome-wide markers on a SNP Chip can be used and therefore a data reduction step is necessary. Also, only those SNPs that have an acceptable (but not very stringent) genome-wide statistical significance would make any sense for further biological interpretation, and SNPs without a reasonable amount of genetic variation provide little information in a network setting. Hence, we select a number of top SNPs fulfilling both criteria for WISH network construction; 1) reaching a genome-wide significant p-value and 2) possessing sufficient amount of variation among the individuals in the population, based on their genotype. Depending on the size of the dataset, individuals can be optionally selected based on extreme phenotypic values.n x n dimensional adjacency matrix A = Aij, where Aij states the connection strength between SNPi and SNPj. The connection strength between the SNPs is defined by the absolute Pearson's correlation between the number of allele copies of pairs of SNPs for all SNP pairs in the data, creating a weighted network with values for Aij between 0 and 1. This adjacency matrix is raised to the power γ (soft thresholding). The connectivity of a SNP (c) can concordantly be calculated by taking the sum of connection strengths between a SNP and all other SNPs. SNPs with a high c can be called hub SNPs, which may be of biological importance to the trait of interest [c (p(c)) follows a power law. The power γ should be chosen in such a way that R2  approaches one, which means the network is approximately scale-free [p(c) and by plotting log(p(c)) versus log(c). Optionally, data can be further reduced by using a threshold value for selection of SNPs based on c, to eliminate the SNPs with an extremely low c.The WISH network describes the relationships between SNPs by specifying an interest ,37. A WIale-free . The assThe adjacency matrix is used to detect the relatedness between pairs of SNPs based on their shared neighbours by calculating the Topological Overlap Measure (TOM) ,40.ij is the number of SNPs to which both SNPi and SNPj are connected, Aij is the adjacency between SNPi and SNPj, and c is the connectivity. The TOM of two SNPs represents the number of shared neighbour-SNPs as a value between 0 and 1; here a TOM of 1 means that a pair of SNPs shares the same neighbour-SNPs and a TOM of 0 means that a pair of SNPs shares no neighbour SNPs at all. The TOM is resulting in a more robust network in the case of many zeroes among the elements in the adjacency matrix or when elements are prone to noise [TOM (1-TOM), a SNP dendrogram was created whereby branches of the dendrogram were cut off by the Dynamic Tree Cut algorithm [TOM as a heat plot, modules can be visualized, and the right cut-off method can be chosen.where Nto noise . Using tlgorithm  represenA module is defined as a cluster of highly interconnected SNPs, forming a sub-network which may correspond to a pathway or protein complex, resulting in substantial practical importance in biology. A second visualization to control the detection of modules by the chosen cut-off method is plotting the correlations between module eigenSNPs. The module eigenSNP is the first principal component of the module and can be seen as a representative effect of the module on the trait of interest. Optionally, modules with a strong correlation can be merged. Secondly, the module eigenSNP can be used to examine the relationship between the trait of interest and the module, resulting in a Genome-wide Module Association Test (GMAT) to identify which modules are relevant for downstream analyses.http://cran.r-project.org/web/packages/NCBI2R/index.html) with default parameters which uses a list of SNPs as input and gives (if present) the genes and their annotation in the indicated region. The flanking distance could be changed depending on the species used in the study. In the next step, the genes in those SNP lists can be used for functional annotation analyses, for example using the Gene Ontology Enrichment Software Toolkit (GOEAST) with default parameters, which will reveal if identified SNP modules are biologically relevant to the trait of interest. GOEAST detects highly enriched GO-terms in the genes present in the SNP modules and defines highly enriched pathways, which can help to understand the results biologically. Enriched GO-terms with a p-value (estimated by a hypergeometric test) below 0.05 were assigned to be significant.After detection of the potentially interesting SNP modules via GMAT, genes in the SNP modules have to be detected. To detect the positions of the SNPs and the genes present in those regions, we used the R-package NCBI2R (available at i*SNPj) interaction:The second option for creating a network using HTG data is creating an adjacency matrix based on the epistatic interactions. We used a linear model to estimate the regression coefficient for the epistatic , SNPi*SNPj is the interaction between the pair of SNPs as a covariate, and ε is the residual component. This analysis was performed using the R-package ASReml-R [snp: SNP information , 2) gwas: GWAS results, e.g. from GenAbel, 3) phen: data frame with one column for the individual ID, one column for the sex, and afterwards the columns for the phenotypes and/or fixed effects, and 4) gen: a genotype matrix of m-by-n with m the number of animals and n the number of SNPs. R-codes using those four data frames are provided in Figure where ASReml-R . In totai*SNPj component is used as input for the adjacency matrix. The regression coefficients are normalized to create a data matrix with values between 0 and 1. From here on, the methods are comparable to the methods used in the WISH based on genomic correlations. First, we detected which power γ results in the highest scale-free topology index (R2) and we raised the adjacency matrix to this power. Secondly, the TOM was calculated and the dissimilarity-TOM was used to create a SNP dendrogram. Modules were again detected using the Dynamic Tree Cut algorithm. Thirdly, potential biologically relevant modules were detected by calculating the GMAT. For the calculation of the module eigengenes, the genomic matrix of m-by-n was used to calculate the module eigengene per module, per individual. Modules with a significant GMAT above a certain threshold were selected for downstream analysis. At last, genes and KEGG-pathways in the modules were detected using the R-package NCBI2R, where KEGG-pathways with a Bonferroni corrected p-value below 0.05 were assigned to be significant.The regression coefficient resulting from the SNPPhenotype and genotype data (60K Porcine SNP Chip) of an F2 pig resource population were available to test and assess the WISH network on real data , and in HNK conceived the original idea of WISH networks and GMAT. LJAK and HNK developed the WISH methodology and LJAK implemented the methodology on experimental data. LJAK wrote the first draft of the manuscript. Both authors wrote, read, and approved the final version of the manuscript.The authors declare that they have no competing interests.GOEAST results on carcass weight (based on genomic correlations)Click here for file


We sorted SNP pairs by distance and obtained a set of LD means  using bins of 5 Kb. We identified 15,246 segments of at least 1 Kb, among the 19 breeds, consisting of sets of at least 3 adjacent SNPs so that, for each SNP, r2 within its neighbors in a 100 Kb range, to the right side of that SNP, were all bigger than, or all smaller than, the corresponding expected mean, and their P-value were significant after a Benjamini-Hochberg multiple testing correction. In addition, to account just for homogeneously distributed regions we considered only SNPs having at least 15 SNP neighbors within 100 Kb. We defined such segments as structural variations. By grouping all variations across all animals in the sample we defined 9,146 regions, involving a total of 53,137 SNPs; representing the 6.40% (160.98 Mb) from the bovine genome. The identified structural variations covered 3,109 genes. Clustering analysis showed the relatedness of breeds given the geographic region in which they are evolving. In summary, we present an analysis of structural variations based on the deviation of the expected short range LD between SNPs in the bovine genome. With an intuitive and simple definition based only on SNPs data it was possible to discern closeness of breeds due to grouping by geographic region in which they are evolving.Genomic structural variations represent an important source of genetic variation in mammal genomes, thus, they are commonly related to phenotypic expressions. In this work, ∼770,000 single nucleotide polymorphism genotypes from 506 animals from 19 cattle breeds were analyzed. A simple LD-based structural variation was defined, and a genome-wide analysis was performed. After applying some quality control filters, for each breed and each chromosome we calculated the linkage disequilibrium ( It is well known that among human beings, 99.9% of DNA sequence is identical Kadri et al. (2012) Structural variations based on genotyping data have recently been the focus of interest, especially of the Copy Number Variation (CNV) type. The most used approach for detection of CNVs is based on two measures of signal intensity in each SNP: the Log R ratio , and the B Allele Frequency . The combination of Log ratio and B Allele Frequency is used to infer copy number changes in the genome Matukumalli et al (2011)Illumina, Inc, in collaboration with bovine researchers has developed the BovineHD panel of 777,962 SNPs genome-wide, making it the highest density panel in livestock. Taurus and Indicus subspecies of the Bos taurus species. We grouped them by purpose and geographical origin as follows: British meat breeds are the Angus, Hereford and Red Angus. European meat breeds are Charolais, Limousin, Piedmontese and Romagnola. Dairy British breeds are Guernsey and Jersey. European dairy breeds are Brown Swiss, Holstein and Norwegian Red. The Indicus group includes the Brahman, Nelore and Gir breeds. The African breeds are formed by the N'Dama and Sheko and lastly, the hybrid breeds (Taurus x Indicus) are formed by the Santa Gertrudis and BeefMaster.A set of 777,962 SNPs assayed by the BovineHD Genotyping BeadChip was provided by the International Bovine HapMap Consortium Quality control filters were applied in order to eliminate technology errors of the “no call” type, to eliminate all the genotypes that violated the Hardy-Weinberg equilibrium, and monomorphic markers . After these quality control filters we guarantee a global quality over all the samples and we worked, in average between the 19 breeds, with a total of 523,651 markers which represents 67.31% of the initial information , is defined by a Markov chain, and it models how the haplotype pair copied from the reference panel changes during the sequence due to a recombination map (p) defined throughout all the genome.P, generates the observed variation of the genotype vectors regarding to the copied haplotypes of the reference panel through a mutation rate (θ ) 2r) represented as:p1 and p2 are the minor and major allele frequencies in the SNP1 respectively, q1 and q2 are the minor and major allele frequencies in the SNP2 respectively, and p11 corresponds to the frequency observed between both minor alleles in the same individual throughout the whole population. In addition, a sample size correction was applied to all computed 2r values using the following equation:n represents the number of haplotypes in the sample The LD coefficient selected for this study was the squared correlation coefficient between pairs of SNPs (r2) of short range (≤100 Kb), sorting SNP pairs by distance, and obtaining the means using bins of 5 Kb. Then we interrogate SNP by SNP looking for segments of at least 1 Kb, consisting of a set of at least 3 adjacent SNPs so that, for each SNP, r2 within its neighbors in a 100 Kb range, to the right side of that SNP, are all bigger than, or all smaller than, the corresponding expected means, and their P-values from a t-test for equality of means are significant after a Benjamini-Hochberg multiple testing correction. In addition, to account just for homogeneously distributed regions consider only SNPs having at least 15 SNP neighbors within 100 Kb.We define a LD-based Structural Variation as follows: first, for each breed and each chromosome we obtain a set of LD means, called expected means, calculating the linkage disequilibrium p, for each i between 1 and m , then, starting from the largest P-value Pm)( with 0.05 x i/m. Continue as long as Pi)(>0.05 x i/m. Let k be the first time when Pk) and look for differentiation between cattle subgroups. We used R software to perform this analysis. The central idea of PCA is to reduce the dimensionality of a data set which consists of a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. This is achieved by transforming a new set of variables, the principal components (PCs), which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all the original variables Formally, PCA is defined as an orthogonal linear transformation that transforms the data to a new coordinate system such that the greatest variance by any projection of the data comes to lie on the first coordinate , the second greatest variance on the second coordinate, and so on. PCA is theoretically the optimum transform for a given data in least square terms. The procedure for obtaining PCAs can be summarized as follows:TX of n dimensions, T = TX, whose mean vector M and covariance C are described by:Given a vector 1, λ2, …, λ3, and the eigenvectors 1P, 2P, …, dP; arrange them according to their magnitude.Calculate the eigenvalues λd eigenvectors to represent the n variables, d<n. Then the 1P, 2P, …, dP are called the principal components.Select The filtered data set included an average of 523,651 genotypes per breed, among the 29 autosomal chromosomes in 506 animals from 19 breeds . The Brahman may have a higher rate of polymorphism than the remaining indicine breeds because that breed was established by importing primarily bulls and backcrossing to taurine derived females. The Bos taurus taurus breeds were generally moderately variable with an average of 83% of polymorphic SNPs. The Bos taurus indicus breeds show the lowest average polymorphic SNPs with 81%. The lone tropically adapted r2. Values of LD were corrected for sample size using the equation described by Villa-Angulo et al. (2009) Bos taurus indicus breeds: Brahman, Gir, and Nelore, with 0.367, 0.395, and 0.401, respectively, while the breeds with the highest average of LD were Hereford, Jersey, and N'Dama, with 0.639, 0.630, and, 0.616, respectively. On the other hand, in large distances (100 Kb), the breeds with the lowest averages of LD were Piedmontese, Sheko, and Charolais, with 0.085, 0.104, and 0.105, respectively, while the breeds with the highest average of LD were Hereford, Jersey, and Brown Swiss, with 0.222, 0.201, and 0.177, respectively. , using the same samples, the trends obtained are quite similar, even when only high density SNPs regions in chromosomes 6, 14, and 25 were considered r2 values are smaller than the expected means, and as the previous example they were declared significant after correction. At the bottom, an example presumed to contain no structural variation because LD for some of the SNP pairs alternate from being bigger than to smaller than their expected LD mean .For each breed and each chromosome we obtained a set of LD means (expected means) using bins of 5 Kb. Then, for each SNP we calculated LD with each polymorphic SNP within 100 Kb to the right of that SNP. Putative structural variations were defined where all LD measures were bigger than, or all smaller than, their corresponding expected means, for all SNP pairs within 100 Kb region for at least 3 adjacent polymorphic SNPs. Using the above definition we scanned all autosomal chromosomes for each breed. Bos taurus group. While, Nelore, BeefMaster, Sheko, Gir, Brahman and Santa Gertrudis were the breeds with the smallest number of SNPs falling in SVs, with 1560, 1670, 1733, 1756, 1871, and 1992 respectively. All these breeds, are from Bos indicus and composite groups. The average number of SNPs falling in SVs across all breeds was 5531.78. The breeds with the highest average of SNPs per SV were Hereford, Jersey, Red Angus, Guernsey, Brown Swiss, Angus, and Norwegian Red, all from the Bos taurus group, while the breeds with the smallest average of SNPs per SV were Sheko, Brahman, Nelore, BeefMaster, Gir, and Santa Gertrudis, from the Bos indicus and composite groups.From Bos taurus group. Asian breeds resulted with the smallest SV, in SNPs quantity.The 5 breeds with SVs containing the largest number of SNPs : Gir, Brahman and Nelore resulted with positive loads if observed from PC1 and they are displayed really close. In the same way composite breeds: Santa Gertrudis and BeefMaster resulted with positive loads (for PC1), and they are displayed really close too. Some British breeds: Angus, Red Angus, Hereford, Jersey, and Guernsey, and European breeds: Brown Swiss, Norwegian Red and Holstein, all resulted with negative loads, when observed from PC1 axis, and appear relatively separated from the other groups. On the other hand, the two breeds from the African group, N'Dama and Sheko, appear relatively far from each other. It is explained from the fact that Sheko is a Taurus Indicus breed while N'Dama is Taurus Taurus.In order to investigate the closeness in variability among breeds given the amount of identified structural variations; for each breed we constructed a vector of 29 fields, where each field contained the number of SVs in a chromosome. Principal Component Analysis In support of our results that show a clear regional differentiation between breed groups given the quantity of variations, we analyzed the result of a study that was carried out in the “Y” chromosome of European domestic cattle Jiang et al. (2013) Cicconardi et al. (2013) Hou et al.  Liu et al. (2010) Jiang et al (2013) we found 113 overlaps of 358 CNVRs, representing the 31.56%. For Cicconardi et al. (2013), we found 311 overlapping regions, representing the 78.73% of the 395 reported regions. For Hou et al , we found 178, 505 and 267 overlaps of 462, 3438 and 672 variations reported, representing the 38.52%, 14.68% and 39.73% of overlapping regions respectively. Finally for Liu et al. (2010), we considered just the eleven breeds both studies have in common. We found 36 overlapping regions, representing the 25.35% of the 142 reported regions.Given that we found 6.4% of the genome involved with SV, we intuitively suggested overlapping with other types of variations already reported by other groups. Then, we analyzed how much overlap existed between our findings and structural variations from the CNV type. We compared the number, position, and length of structural variations. Next, we searched in NCBI The last analysis was to look within our defined structural variations for regions that were consistent in all 19 breeds. We did not found common regions among all breeds. However, there are 2 regions where 17 breeds coincided, 2 regions where 15 breeds coincided, 2 regions where 14 breeds coincided, 3 regions where 13 breeds coincided and 13 regions where 12 breeds coincided, even when they do not coincide with the total length, at least they share one segment declared as variation.  from the bovine genome. The number of genes covered by these variations was 3,109. From a comparison of overlapping with variations of the CNV type previously reported we found overlapping ranging from 14.68% to 78.73%,An analysis of differentiation based on the number of structural variations genome-wide showed great difference between some breeds and reveled closeness of groups given the geographic region in which they are evolving. Finally, even when there is overlapping of LD-based Structural Variations with CNVs, they capture different genomic variation patterns, and further studies would be necessary in order to elucidate their association to disease and other phenotypic traits.Figure S1Figures S1–S7: MAF Distribution. Average proportions of SNPs of various frequencies per breed and per groups.(DOCX)Click here for additional data file.File S1Table S1. Breeds and number of animals in the sample.(XLSX)Click here for additional data file.File S2Table S2. Final summary of markers for our study.(XLSX)Click here for additional data file.File S3Table S3. Mean and Median MAF average of 19 breeds.(XLSX)Click here for additional data file.File S4Table S4. Average values obtained by 2r for all breeds (LD decay).(XLSX)Click here for additional data file.File S5Table S5. Variants per Chromosome and average per Megabase.(XLSX)Click here for additional data file.File S6Table S6A. Type, name and description of genes found in this study. Table S6B: Number of genes per breeds. Table S6C: Number of genes per chromosome.(XLSX)Click here for additional data file.File S7Table S7. Variation Structural Regions.(XLSX)Click here for additional data file.


Daphnia has been studied for more than 150 years in the context of evolution, ecology and ecotoxicology. Although it is rapidly becoming an emergent model for environmental and population genomics, there have been limited genome-wide level studies in natural populations. We report a unique resource of novel Single Nucleotide Polymorphic (SNP) markers for Daphnia pulicaria using the reduction in genomic complexity with the restriction enzymes approach, genotyping-by-sequencing. Using the genome of D. pulex as a reference, SNPs were scored for 53 clones from five natural populations that varied in lake trophic status. Our analyses resulted in 32,313 highly confident and bi-allelic SNP markers. 1,364 outlier SNPs were mapped on the annotated D. pulex genome, which identified 2,335 genes, including 565 within functional genes. Out of 885 EuKaryotic Orthologous Groups that we found from outlier SNPs, 294 were involved in three metabolic and four regulatory pathways. Bayesian-clustering analyses showed two distinct population clusters representing the possible combined effects of geography and lake trophic status. Our results provide an invaluable tool for future population genomics surveys in Daphnia targeting informative regions related to physiological processes that can be linked to the ecology of this emerging eco-responsive taxon.The keystone aquatic herbivore  High-throughput sequencing methodologies have opened up exciting possibilities in the fields of population genetics, conservation and evolutionary biology by enabling the assessment of genetic variation at genome-wide scales. The genotyping-by-sequencing strategy (GBS)Daphnia pulicaria (Forbes 1893), a keystone aquatic herbivore inhabiting many lakes across the Nearctic. Daphnia pulicaria is a member of the D. pulex species complex, being considered as the “lake pulex” because of its closely-related sister taxon D. pulex, which inhabits small ponds. Daphnia pulex was the first crustacean to have its whole genome sequencedDaphnia species complexes have been the subject of numerous genetic studies5671011The purpose of this study was to apply the GBS approach to generate and characterize a genome-wide SNPs map in the water flea  Forbes 193, a keyDaphnia are phosphorus (P) rich crustacean herbivores, considered to be P-limited in natural habitats, and play a major role in whole-lake energy and phosphorus cyclingDaphnia in their habitat (ponds or lakes) seems to have an effect on this organism at several levels such as life-history variation and phenotypic plasticity13D. pulicaria genome from clonal lineages isolated from five natural (lake) populations that varied in trophic  status. By using these populations with different phosphorus load/supply , we characterized outlier SNPs  and mapped them to the D. pulex annotated genome. Our results revealed 294 outlier SNPs that play a role in important metabolic pathways potentially involved in phosphorus use and processing, ranging from cellular to ecosystem levels.Here, we generated a set of SNPs covering the whole The development and characterization of hundreds of SNPs with a regular distribution across the genome will provide an invaluable genetic resource in this emergent model organism, and should give us the option to screen regions under selection. This new methodological approach will allow us to face challenges such as identifying the mechanisms and processes related to the genetic basis of adaptation in natural populationsDaphnia pulex genome after filtering by biallelic and highly confident positions, and without linkage disequilibrium (LD) within a scaffold. Finally, we removed those SNPs with expected heterozygosity (He) greater than 0.5 since they might represent paralogous regions . Confident SNPs were included in a total of 476 scaffolds out of 9,080 described for the D. pulex reference genome , and the number of SNPs per scaffold depends on scaffold length. We found 93.84% of SNPs  in the first 220 scaffolds, whose individual length is greater than 100 Kb.A total of 159 900 538 good barcoded reads (ranging from 571 683 up to 31 600 240) were obtained for the 53 clones/samples analysed, resulting in 57 440 139 tags . Despite mapping short reads against a different reference genome that can limit identification of DNA variants, we detected 32,313 SNPs by mapping our reads onto the nome see . The D. et al.Analysis of highly confident SNPs for 53 samples performed with PCoA showed five clusters corresponding to the five lakes sampled . On the D. pulex reference genome) along 212 different scaffolds , from which 401 genes contained at least one outlier SNP position. The number of SNPs within gene ID ranged from one to six (see Appendix 1 for details).1,499 outlier SNPs were detected by the Lositan programme  spatial scales242627D. pulicaria sampled from one of our five lakes (South Center), using hyper-variable nuclear markers , support clear evolutionary consequences of anthropogenic environmental change  on population structure through timeDaphnia populations show genotype x environment interactions when subjected to different P-conditions. As a consequence, different clones/genotypes are sensitive to P-availability1513Population genetic differentiation among populations of passively dispersed aquatic invertebrates in most cases has been found to be strong, despite the potential dispersal capacity facilitated by water birds and other vectors transporting their diapausing eggs2122Daphnia it has been shown that under low quality food , this organism can maintain P-homeostasis by reducing C-uptake and/or increasing P-retention, which is in agreement with stoichiometric theoryDaphnia at the intra-specific level17Daphnia pulex reference genome, might have an important role in lipid metabolism, mainly when considering exclusive genes involved directly in metabolic pathways  loci found might, amongst others, have important roles in phosphorus processing including assimilation and maintenance see . In factThe new genomic resources reported in our study include parts of the genome that appear to be less affected by selection, and thus, may prove useful for future demographic studies. In addition, we found signatures of selection for candidate genes (see Appendix 1) related to abiotic stressors that will help to unravel adaptive molecular processes related to variation in environmental conditions such as lake eutrophication. Future analyses on these and other gene annotations should shed light on the selective forces structuring, at the genome-wide level, different genotypes and natural populations influenced by environmental pressure. Additionally, our resources and results may improve our understanding of how the genome interacts with the environment to produce the diversity of phenotypes found at the intra-specific level.Daphnia species complexes. In our case, we have identified candidate genes that can be used in the near future to study/manipulate natural populations in assessing physiological responses to varying nutrient environments .Future work that integrates molecular markers, ecological traits and geographic parameters will be necessary to shed light on the effects of both abiotic and biotic environmental factors as selective forces impacting the population genetic structure in Daphnia can develop into males, which are genetic replicates of the mothers, since Daphnia have environmental sex determination. These males can then mate with sexually-receptive females, which leads to the production of sexual  eggs that are produced inside a scleroterized structure termed an ephippium. When the mother molts, the ephippium is shed into the water column, where it either sinks to the bottom sediments or rafts to the shoreline. Ephippia serve as both a diapausing stage as well as a (passive) dispersal stage, which increases the likelihood for population survival during harsh environmental conditions. Ephippial (diapausing) eggs will hatch when favourable conditions appear in natural populations. However, isolated resting eggs  can be hatched under laboratory conditions  allowing one to address studies on population genetic structure, evolutionary biology and adaptive response to environmental changes123233The life cycle of most daphniids is characterized by an asexual (parthenogenetic) mode of reproduction, shifting to a sexual phase at the end of the growing season when resources are scarce or environmental conditions are not favourable. During the first stages of sexual reproduction, some direct-developing  eggs produced by female In this study, we hatched diapausing eggs from the upper sediment layers  of five lakes located in Minnesota (USA). In addition, we established clones from live-caught animals from the water column. The lakes covered a range of trophic states from mesotrophic to hyper-eutrophic conditions. Considering phosphorus concentration ([P]) as one of the major factors involved in lake eutrophication, we covered a range from ~21 to ~220 μg/L [P] . AdditioD. pulicaria clones from the five lakes (ranging from 5 to 15 clones per lake) were raised in separate 900 ml glass jars in the lab. DNA from ~100 pooled individuals per clone was isolated using a commercial kit . DNA concentrations were measured with a Qubit 2.0 Fluorometer (Life Technologies), with values ranging from 8 to 73 ng/μL . Genotyping-by-Sequencing (GBS) libraries were constructed at the Institute for Genomic Diversity  and sequenced on an Illumina HiSeq 2000/2500 , according to Elshire et al.ApeKI (GCWGC) for digestion. A library with unique barcodes for each clone, plus a blank sample, was created.A total of 53 Daphnia pulex reference genome wfleabase.org/genome/Daphnia_pulex/current/fasta/dpulex-all-chromosome-jgi060905.fasta.gz, which contains 9,080 scaffolds. A variant calling format (VCF) file was filtered for variant sites to cover at least 90% of individuals and for biallelic positions.The GBS analysis pipeline (Tassel ver. 4.0), an extension of the Java program TASSEL2D’r2 p-values) were calculated by using Fisher’s exact test for site pairs with two alleles each. P-values ≤ 0.05 were considered significant to remove all but one linked SNPs for further analyses.Linkage disequilibrium (LD) between pairs of variant sites was evaluated by using the software package TASSEL .Population genomic analyses were performed as follows. First, we ran a principal coordinate analysis (PCoA) using the codominant genetic distance matrix generated from highly confident SNPs to elucidate the genetic relationships for the geographic area sampled using GenAlEx ver. 6.50138FDISTFST (force mean FST) and 0.99 confidence interval. In Bayescan, we used 100 000 iterations with a sample size of 10 000 and thinning interval of 10.We performed a genome scan analysis on all individuals to identify the potential adaptive loci, which can be directly under selection or closely-linked  to loci under selection. We used two methods, Lositan programme42Daphnia pulex genome based on the published gene models at http://genome.jgi-psf.org/Dappul/Dappul.download.ftp.html  resulting in the different cellular pathways .Annotated genes included into the KOG_JGI nomenclature  were plotted by iPATH v2 tool46How to cite this article: Muñoz, J. et al. Characterization of genome-wide SNPs for the water flea Daphnia pulicaria generated by genotyping-by-sequencing (GBS). Sci. Rep.6, 28569; doi: 10.1038/srep28569 (2016).