Mn1, has an unusually large effect on shape variation in our study. A knockout of this gene was previously shown to affect negatively the development of membranous bones of the cranial skeleton, and evolutionary analysis shows that the gene has arisen at the base of the bony vertebrates (Eutelostomi), where the ossified head first appeared. Therefore, Mn1 emerges as a key gene for both skull formation and within-population shape variation. Our study shows that it is possible to identify important developmental genes through genome-wide mapping of high-dimensional shape features in an outbred population.The vertebrate cranium is a prime example of the high evolvability of complex traits. While evidence of genes and developmental pathways underlying craniofacial shape determination is accumulating, we are still far from understanding how such variation at the genetic level is translated into craniofacial shape variation. Here we used 3D geometric morphometrics to map genes involved in shape determination in a population of outbred mice . We defined shape traits via principal component analysis of 3D skull and mandible measurements. We mapped genetic loci associated with shape traits at ~80,000 candidate single nucleotide polymorphisms in ~700 male mice. We found that craniofacial shape and size are highly heritable, polygenic traits. Despite the polygenic nature of the traits, we identified 17 loci that explain variation in skull shape, and 8 loci associated with variation in mandible shape. Together, the associated variants account for 11.4% of skull and 4.4% of mandible shape variation, however, the total additive genetic variance associated with phenotypic variation was estimated in ~45%. Candidate genes within the associated loci have known roles in craniofacial development; this includes 6 transcription factors and several regulators of bone developmental pathways. One gene,  Mn1, is a gene that appeared at a time when animals started to form bony skulls, suggesting that it may be a key gene in this evolutionary innovation. This further suggests that Mn1 and other genes involved in head formation are also responsible for more fine-grained regulation of its shape. Our results confirm that the outbred mouse population used in this study is suitable to identify single genetic factors even under conditions where many genes cooperate to generate a complex phenotype.Formation of the face, mandible, and skull is determined in part by genetic factors, but the relationship between genetic variation and craniofacial development is not well understood. We demonstrate how recent advances in mouse genomics and statistical methods can be used to identify genes involved in craniofacial development. We use outbred mice together with a dense panel of genetic markers to identify genetic loci affecting craniofacial shape. Some of the loci we identify are also known from past studies to contribute to craniofacial development and bone formation. For example, the top candidate gene identified in this study,  Understanding the evolutionary processes that have generated and maintained morphological diversity in nature is a long-standing goal in biology. The cranium and mandible of vertebrates is a good example of such diversity. The fact that the cranial and mandible bones have to be integrated with the brain and sensory systems, as well as with the respiratory and digestive systems, makes this structure a prime example of both high integration and high evolvability.Although information about genes and developmental pathways involved in shape determination keeps accumulating, we are far away from understanding the genotype-phenotype map translating genetic variation into craniofacial shape variation . To apprPrevious experimental work has explored the genetic basis of craniofacial variation in a range of species, including Darwin’s finches –4, cichlEarly work in mice has focused mainly on the mandible. This is a well-established model for the study of complex traits because mandible shape can be approximated in 2 dimensions ,23. SeveHere, we approach the question from a micro-evolutionary perspective by analyzing within-population shape variation. The utility of studying phenotypic variation at the within-population level is well acknowledged, as it permits one to focus on within-species genetic contributions to phenotypic variation . We use We estimated heritability of craniofacial shape, and assessed support for craniofacial shape and size QTLs at 80,027 autosomal SNPs in 592–720 mice. Skull shape is represented as a 132-dimension vector (coordinates of 44 3D landmarks), and mandible shape is represented as a 39-dimension vector (coordinates of 13 3D landmarks). To make the shape data suitable for QTL mapping, we extracted principal components (PCs) that explain the most variance in skull and mandible shape. Specifically, skull shape was represented by the first 22 PCs that capture 84% of the skull shape variation, and mandible shape was represented by the first 21 PCs accounting for 94% of the variation in mandible shape.2 = 0.14, p-value = 0.034; skull, r2 = 0.16, p-value = 0.034  but associated with different PCs . Four QTTogether the 17 QTLs identified for skull shape explain 11.4% of skull variation. The 8 QTLs for mandible shape together explain 4.4% of mandible variation. The effect size of individual SNPs ranges from 0.02 to 1.13% of the total phenotypic variation  as well as large (e.g. PC1) proportions of the total phenotypic variation were found to associate with genetic variants . This paOnly one genomic region was significantly associated with mandible size, while no significant associations were found for skull size. Previous studies found up to 23 QTLs associated with mandible size variation ,25,89, aThe total heritability estimated in this study, ~43% for craniofacial shape and ~36% for craniofacial size, corresponds to SNP heritability estimates. In humans, SNP heritability is considered an underestimate of the narrow sense heritability because it does not take into consideration rare alleles . HoweverUsing a pedigree of wild caught mice and a 2D approach, the heritability of mandible shape and size was found to be 0.61 and 0.49, respectively . Regardless of the method or the experimental design, the heritability estimates for mandible size and shape in mice are high. It remains to be seen if the same pattern is true for the skull; pedigree-derived data need to be collected.In a recent study of wild soay sheep, the SNP heritability of mandible length was estimated to be 53% . Human s2 crosses. Although the resolution was still not high enough to conclusively pinpoint individual genes, it was nonetheless possible to explore all genes within the QTL regions and often we identified a single candidate gene for which previous relevant phenotypic information existed. Moreover, 77% of the regions overlap with previous studies   , a taxonThere are long standing discussions about how to deal experimentally with polygenic traits and their implications for understanding the evolution of such traits ,96. GenoHere we have shown that we have a similar scenario regarding craniofacial shape, which is a complex phenotype with a highly polygenic architecture. It is encouraging to see that even under such seemingly adverse genetic conditions, we can still identify credible candidate genes previously studied in loss of function experiments. This implies that genes occupying central positions in developmental pathways may also be the ones that carry enough natural variation to allow mapping through GWAS. At the same time we identified regions without any previous information related to craniofacial development ; such discoveries contribute new information to the genetics underlying skull and mandible shape determination, and dedicated efforts should be made to understand the phenotypic effect of the genes and regulatory elements falling in such regions.Human studies required ~25,000 individuals to explain 3–5% of height variation with genome-wide-significant SNPs , and ~25All procedures were approved by the University of Chicago Institutional Animal Care and Use Committee (IACUC) in accordance with National Institute of Health guidelines for the care and use of laboratory animals.Mapping population Male mice from the CFW mouse colony, maintained by Charles River Laboratories, were used for genome-wide association mapping. Upon their arrival at the University of Chicago, the mice were subjected to behavioral and physiological tests over the course of 2011 and 2012 . At the end of these experiments, the mice were sacrificed and their heads were stored in ethanol. The average age at the time of sacrifice was 13 weeks (ranging from 12 to 14 weeks). Skulls and mandibles were measured in a subset of 720 mice between 2013 and 2014 at the Max Planck Institute for Evolutionary Biology in Plön, Germany.Mouse heads were scanned using a computer tomograph  at a resolution of 48 cross-sections per millimeter. Using the TINA landmarking tool , 44 threThe symmetric component of the mandible and skull were obtained following . In shorTo define shape features, we computed in MorphoJ principal components (PCs) from the n x 3k covariance matrix of Procrustes coordinates, where n is the number of samples and k is the number of landmarks; 3k represents the number of Procrustes coordinates . PC loadings computed in this analysis define the phenotypes used in the QTL mapping. Differences in age, spanning 2 weeks, did not correlate significantly with shape variation, so we did not use age as a covariate in subsequent analyses.In a separate project that will be presented in more detail in a later publication, areal BMD (aBMD) of the isolated femur was examined. Unexpectedly, we found that CFW mice appear to be predisposed toward abnormally high aBMD. This is a characteristic of the CFW mice that does not appear to be shared with commonly used inbred lab strains. A qualitative analysis of mice with high BMD showed substantial differences in mandible, and modest differences in the skull compared to mice with normal BMD. We therefore assessed covariation of BMD with shape measurements, separately for the skull and the mandible.2 = 1.4%, p < 0.001). However, no individual PC corresponds to these shape differences due to BMD. Therefore, BMD was not used as covariate for skull trait mapping since it would have little to no effect on our ability to map QTLs for skull shape.For the skull, we found a small correlation between BMD and shape  <0.001). BMD accounts for 29% of the variation in the first PC, 8% of the variation in the third PC, but little to no variation in the remaining PCs (maximum r2 is 1.4% for PC6). Therefore, we computed mandible shape residuals by removing the linear effect of BMD; we used these residuals as input to the PCA, then PC loadings from this PCA analysis were used as phenotypes in the QTL mapping for mandible shape.For the mandible, there was a stronger correlation between shape and BMD (rThe standard measure of size in geometric morphometrics is the centroid size (CS). This is the measure we used for mapping. Centroid size is defined as the square root of the sum of the squared distances of a set of landmarks from the center of gravity or centroid . The CS PstI and annealing barcoded oligonucleotide adapters to the resulting overhangs. Samples were multiplexed 12 per lane, and sequenced on an Illumina HiSeq 2500 using single-end 100-bp reads. By focusing the sequencing effort on the Pstl restriction sites, we obtained high coverage at a subset of genomic loci. The 100-bp single-end reads were aligned to the Mouse Reference Assembly 38 from the NCBI database (mm10) using bwa [The mice were genotyped using a genotyping-by-sequencing (GBS) approach . In sepasing bwa . We usedsing bwa –109 to dsing bwa , and SNPGBS yields highly variable coverage across samples at the same cut site, hence variants with highly variable genotyping call rates. Therefore, to augment the set of SNPs with available genotypes, we used IMPUTE2  to estimFor QTL mapping, we took an additional step to filter out SNPs with low "imputation quality" assessed by inspecting the IMPUTE2 genotype probabilities , and SNPs with minor allele frequencies less than 2%. After completing this filtering step, we ended up with a final panel of 80,027 SNPs used to map QTLs on autosomal chromosomes.720 mice were used for mapping loci associated with skull traits (shape and size). Due to the correlation between BMD and mandible shape, to assess support for mandible QTLs we used only the 592 mice for which BMD measurements were available.We mapped QTLs for all PCs explaining at least 1% of total phenotypic variation in the sample; this includes 22 PCs capturing 84% of skull shape variation, and 21 PCs capturing 94% of mandible shape variation. Each PC was analyzed separately. To map size variation, the centroid size of mandible and skull was used. Note that the use of PCs restricts the findings to SNPs associated with the shape directions represented by such PCs; therefore genetic variants not aligned with the PC directions will not be detected with this approach.We used the linear mixed model (LMM) implemented in GEMMA  to map t“Proximal contamination” refers to the loss in power to detect a QTL when the causal marker is included in the calculations of the kinship matrix ,114. In th percentile for all phenotypes was 8.9 x 10−7 (-log(p) of 6.04, ranging from 5.97 to 6.16). This average threshold is depicted in A genome-wide significance threshold was calculated separately for each of the phenotypes used in the mapping (43 PCs and centroid size). A commonly used approach for assessing significance is to estimate the null distribution of p-values by randomly permuting the phenotype observations while keeping the genotypes the same. Such a procedure is technically not appropriate here because it fails to account for the lack of exchangeability among the samples, sometimes resulting in inflation of false positives ,118. How2 ≥ 0.8 between the “peak” SNP  and the neighboring SNPs was used to select SNPs belonging to the QTL region. Genes falling within the QTL region were investigated using the MGI database [The LD pattern around the significant SNPs was used to define the QTL regions. A correlation value rdatabase  and liteNumerous SNPs were statistically associated with various PCs; however, the effect of the SNP might go beyond the specific PC and affect other aspects of shape. Once we have identified genetic associations with individual PCs, we estimate the effect of the SNP on total shape. This is accomplished by fitting a standard multivariate regression model to the shape vectors (3k Procrustes coordinates) and SNP genotypes. This multivariate regression was implemented in MorphoJ. We report effect size as the proportion of shape variance explained by the SNP.p-value—in each of the 26 QTLs identified in this study. This window size was chosen to correspond to the mean size of the QTL regions, which is 0.89Mb (see above for the way QTL regions were defined). Once the “true” overlap was determined, 26 genomic regions of 500 Kb and 1 Mb were randomly chosen from the genome and the overlap with previous studies was re-calculated. This was repeated 1,000 times to exclude the possibility that the global pattern of overlap was due to chance . The first 10 principal components of the kinship matrix were included as covariates. An individual REML analysis was done for each chromosome (option–reml–grm–qcovar). Due to the small sample size of this study (~700 mice) it is not possible to fit all the chromosomes at the same time, which results in an inflation of the individual chromosomal estimates. We therefore used the relative  and not the absolute contribution of each chromosome to the total phenotypic variation.Because PCs were used as phenotypes, additional calculations were needed to estimate the chromosomal contribution to the global phenotypes–skull and mandible shape. The additive variance per chromosome per PC was multiplied by the proportion of phenotypic variation represented by that PC. Finally, the values for each chromosome were summed across all PCs.http://dx.doi.org/10.5061/dryad.k543p.The full code and data reproducing the steps of our analyses are available for download at S1 Table(PDF)Click here for additional data file.S2 Table%var, portion of the total skull variation explained by each PC. %cum, cumulative variance explained by the PCs. PVE, proportion of variance in the PC explained by the SNPs used in the QTL mapping. se(PVE), standard error of PVE estimate. PCA was run in MorphoJ. PVE estimates were obtained from the LMM implemented in GEMMA.(PDF)Click here for additional data file.S3 Table%var, portion of the total skull variation explained by each PC. %cum, cumulative variance explained by the PCs. PVE, proportion of variance in the PC explained by the SNPs used in the QTL mapping. se(PVE), standard error of PVE estimate. PCA was run in MorphoJ. PVE estimates were obtained from the LMM implemented in GEMMA.(PDF)Click here for additional data file.S4 Tableet al 2014) associated with the closer enhancer to the peak SNP is shown (**).The intervals were defined by LD blocks around the peak SNP using a threshold of r2 > = 0.8 with the peak SNP. The genes within the QTL regions are listed in this table. When it was not possible to define regions due to a sparse LD signal, the gene overlapping the peak SNP is shown (*), or genes close to the peak SNP that could be considered candidate genes. Where no gene is shown, no genes meeting the above criterion were identified at the locus. The gene (genebody in Attanasio (PDF)Click here for additional data file.S5 Tableet al 2014) associated with the closer enhancer to the peak SNP is shown (**).The intervals were defined by LD blocks around the peak SNP using a threshold of r2 > = 0.8 with the peak SNP. The genes within the QTL regions are listed in this table. When it was not possible to define regions due to a sparse LD signal, the gene overlapping the peak SNP is shown (*), or genes close to the peak SNP that could be considered candidate genes. Where no gene is shown, no genes meeting the above criterion were identified at the locus. The gene (genebody in Attanasio (PDF)Click here for additional data file.S1 FigSee description of the points in (PDF)Click here for additional data file.S2 FigThe number of SNPs per autosomal chromosome are shown.(PDF)Click here for additional data file.S3 Figet al 2014. (b) overlap between 500-Kb regions and Attanasio et al 2014. (c) overlap of 1-Mb regions and Maga et al 2015.26 genomic regions were chosen randomly and their overlap with previous studies was calculated, this was repeated 1000 times. (a) overlap between 1-Mb regions and the results from Attanasio (PDF)Click here for additional data file.S4 FigSNPs rs258942042, rs245694506, rs228570244, and rs30491142 are shown. In grey (dotted lines) is the sample mean of mandible shape. In orange (continuous line) is the mean shape represented by the regression vector of skull shape on SNP genotype . Lateral, dorsal, frontal, and ventral views, as well as the relevant landmarks (solid dots) for each view are shown.(PDF)Click here for additional data file.S5 FigSNPs rs27194486, rs13466556, rs254983846, and rs37378594 are shown. In grey (dotted lines) is the population’s mean mandible shape. In orange (continuous line) is the shape represented by the regression vector of skull shape on SNP genotype . Lateral, dorsal, frontal, and ventral views, as well as the relevant landmarks (solid dots) for each view are shown.(PDF)Click here for additional data file.S6 FigSNPs cfw-13-31734894, rs30352013, rs227631022, and rs26862534 are shown. In grey (dotted lines) is the population’s mean mandible shape. In orange (continuous line) is the shape represented by the regression vector of skull shape on SNP genotype . Lateral, dorsal, frontal, and ventral views, as well as the relevant landmarks (solid dots) for each view are shown.(PDF)Click here for additional data file.S7 FigSNPs rs26992385, rs46747509, rs50079241, and rs31584944 are shown. In grey (dotted lines) is the population’s mean mandible shape. In orange (continuous line) is the shape represented by the regression vector of skull shape on SNP genotype . Lateral, dorsal, frontal, and ventral views, as well as the relevant landmarks (solid dots) for each view are shown.(PDF)Click here for additional data file.S8 FigSNPs rs33217671, rs28219152, rs29977169, rs36343125, rs237064333, rs233696367, and rs221759350 are shown. In grey (dotted lines) is the population’s mean mandible shape. In orange (continuous line) is the shape represented by the regression vector of skull shape on SNP genotype . Lateral, dorsal, frontal, and ventral views, as well as the relevant landmarks (solid dots) for each view are shown.(PDF)Click here for additional data file.S9 FigOnly the PCs with significant associations are shown.(PDF)Click here for additional data file.S10 FigOnly the PCs with significant associations are shown.(PDF)Click here for additional data file.S11 FigGenomic regions associated with (a) mandible shape, and (b) skull shape are shown. The SNP with lowest p-value is labeled and shown in purple. Dots represent neighboring SNPs; color indicates LD (r2) with the labeled SNP.(PDF)Click here for additional data file.S12 Fighttp://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web&PAGE_TYPE=BlastHome) with 3 iterations and a threshold of 0.005 (low complexity filter activated), no matches were found beyond bony fish. (b) Using genomic context analysis with Genomicus (http://www.genomicus.biologie.ens.fr/genomicus-81.01/cgi-bin/search.pl) hits were detected only in Euteleostomi.(a) Using PSI-BLAST at NCBI ((PDF)Click here for additional data file.


Imputation of low density (<50K) genotypes to 777K was carried out in either one or two steps. Imputation of ungenotyped parents (n = 37 sires) with four or more offspring to the 50K panel was also assessed. There were 2,946 Braford, 664 Hereford and 88 Nellore animals, from which 71, 59 and 88 were genotyped with the 777K panel, while all others had 50K genotypes. The reference population was comprised of 2,735 animals and 175 bulls for 50K and 777K, respectively. The low density panels were simulated by masking genotypes in the 50K or 777K panel for animals born in 2011. Analyses were performed using both Beagle and FImpute software. Genotype imputation accuracy was measured by concordance rate and allelic R2 was 0.912 and 0.866 for imputation to 50K or to 777K using FImpute, respectively, and 0.890 and 0.826 using Beagle. One and two steps imputation to 777K produced averaged concordance rates of 0.806 and 0.892 and allelic R2 of 0.674 and 0.819, respectively. Imputation of low density panels to 50K, with the exception of 3K, had overall concordance rates greater than 0.940 and allelic R2 greater than 0.919. Ungenotyped animals were imputed to 50K panel with an average concordance rate of 0.950 by FImpute.The average concordance rate using FImpute was 0.943 and 0.921 averaged across all simulated low density panels to 50K or to 777K, respectively, in comparison with 0.927 and 0.895 using Beagle. The allelic R2.FImpute accuracy outperformed Beagle on both imputation to 50K and to 777K. Two-step outperformed one-step imputation for imputing to 777K. Ungenotyped animals that have four or more offspring can have their 50K genotypes accurately inferred using FImpute. All low density panels, except the 3K, can be used to impute to the 50K using FImpute or Beagle with high concordance rate and allelic R Traditional animal breeding methods utilized phenotypic data and relationships among individuals to make informed mating decision to improve traits of economic significance. Recent advances in DNA technology, led to the full sequencing of several species, including cattle  and to tProcedures for imputation of genotypes, a technique that refers to prediction of ungenotyped SNP genotypes, have been the subject of recent studies in some species, such as, dairy cattle ,3, beef The Brazilian cattle industry plays a significant role in the national economy. Brazil has a herd of more than 211 million cattle of which 80% is zebu cattle . HereforThe main objective of this research was to assess accuracy of imputation from lower density SNP panels to genotypes from the Illumina BovineSNP50 and the Illumina BovineHD panels  in Brazilian Braford and Hereford cattle.Animal welfare and use committee approval was not necessary for this study because data were obtained from existing databases.Data was from the Conexão Delta G’s genetic improvement program - Hereford and Braford (Zebu x Hereford) cattle , containing approximately 520,000 animals from 97 farms located in the South, Southeast, Midwest and Northeast regions of Brazil. A total of 683 Hereford and 2,997 Braford animals from these farms were genotyped. Of the genotyped animals, there were 624 Hereford and 2,926 Braford animals genotyped with the Illumina BovineSNP50 panel, and 59 Hereford and 71 Braford animals genotyped with the Illumina BovineHD panel from 17 farms located in the South of Brazil. Data also included 88 Nellore bulls from the Paint Program  genotyped with the Illumina BovineHD panel.−6) [For imputation to the 50K SNP panel, animals genotyped with 777K SNP genotypes had SNPs not contained on the 50K SNP panel removed. This resulted in a population of 3,768 animals genotyped for 49,345 SNPs. Sites were filtered for GenCall score (> = 0.15) ,22, Call−6) ,24. Only−6) ,22, Call−6) ,22, hete−6)  . After the quality control, 218 bulls  and 587,620 SNPs remained.Table For imputation to the 50K SNP panel, the dataset was split into two populations. The imputation population was comprised of all animals born in 2011. The remainder of the population was assigned to the reference population for imputation. This division resulted in 2,735 animals in the reference population when Nellore animals were included and 2,647 when Nellore animals were not included. A total of 963 animals were sorted into the imputation population.Hereford and Braford animals in the reference population included 129 sires born before 2008 and 2,518 animals born between 2008 and 2010. From these 2,518 animals, 3.8% had at least one genotyped offspring.For animals in the imputation population, the 3K, 6K, 8K, 15K and 20K low density SNP panels were created by masking the non-overlapping SNP between the 50K SNP panel and each of these SNP panels. The imputation population included 33 animals with two parents genotyped and 308 animals with one parent genotyped. Moreover, 52% of the imputation animals were offspring of multiple sire matings.The data set for imputation to the 777K SNP panel contained 71, 59 and 88 Braford, Hereford and Nellore animals, respectively. The strategy used to test the imputation was to create three different data sets randomly alternating animals in the reference population and in the imputation population, always keeping the Nellore animals in reference population as the objective was to test the imputation accuracy of Braford and Hereford cattle. Each reference population was composed by 175 animals  and each imputation population had 43 Hereford and Braford animals. For animals in the imputation population the 3K, 6K, 8K, 15K, 20K, 50K, 90iK and 90tK SNP panels were created by masking non-overlapping SNP from 777K SNP panel.All panels, but one, were commercial panels: Illumina Bovine3K (3K), Illumina BovineLD (6K), Illumina BovineSNP50 (50K) and Illumina BovineHD (777K) panels , Beef LD GGP (8K), Indicus LD GGP 20K), GGP Taurus HD (90tK) and GGP Indicus HD (90iK) panels   were selected from the 50K SNP panel using high minor allele frequency, low linkage disequilibrium, and location (approximately evenly spaced between two SNPs in the 8K SNP panel) as selection criteria. The best possible threshold values to meet the three criteria were a minor allele frequency greater than 0.23 and a linkage disequilibrium, as measured by rFor imputation to the 50K SNP panel, four different scenarios were explored as follows: including Nellore genotypes in the reference population and either including pedigree information (NE-P) or not including pedigree information (NE-NP); not including Nellore genotypes in the reference population and either including pedigree information (NNE-P) or not including pedigree information (NNE-NP).For imputation to the 777K SNP panel, a third set of Hereford and Braford bulls were imputed in four different scenarios: including Nellore genotypes and pedigree information in the reference population (NE-P) or including Nellore genotypes and not including pedigree information in the reference population (NE-NP). Each of these two scenarios was carried out in one or two steps. Two-step imputation was carried out only for panels with density less than 50K SNP. Two-step imputation involved: 1) in the first step, the animals genotyped with 3K, 6K, 8K, 15K and 20K SNP panels were imputed to the 50K SNP panel using in the reference population all the animals genotyped with the 50K SNP panel; 2) in the second step, all the animals imputed to the 50K SNP panel were then imputed to the 777K SNP panel using as reference two-thirds of the Hereford and Braford and all Nellore bulls genotyped with the 777K SNP panel. One-step imputation was performed by imputing from the simulated low density panels directly to the 777K SNP panel.2, which corresponds to the square of the correlation between the number of minor alleles in the imputed genotype and the number of minor alleles in the original genotype [Imputation accuracy of above scenarios was assessed by concordance rate (CR), which corresponds to the proportion of genotypes correctly imputed, and by allelic Rgenotype .There were thirty imputation scenarios from low density panels to the 50K SNP panel. Twenty-four scenarios were examined for imputation from low and medium density panels to 777K SNP panel and thirty scenarios were used to assess differences in imputation accuracy in one or two steps Table .Table 3Imputation was carried out by FImpute v.2.2  and BeagImputation methods can be based on linkage disequilibrium information between markers in the population, but also can use the inheritance information within family. Beagle software is based on linkage disequilibrium between markers in the population and uses a Hidden Markov model  for infe2 of each scenario. An arcsine square root [2 to normalize the residuals.Analysis of variance was carried out using the GLM procedure in SAS version 9.2  to compare the average CR and allelic Rare root  transforOf the 3,698 animals genotyped with the 50K SNP panel, ~24% had sire and/or dam genotyped and ~65% had at least one parent unknown in the pedigree. With respect to the animals genotyped with the 777K SNP panel, ~15% had sire and/or dam genotyped and ~35% had at least one parent unknown. Table 2 for imputation to 50K and 777K SNP panels.Table 2 between the two algorithms and between pairs of simulated low density panels, as well as a significant algorithm by panel interaction (P < 0.05). However, there were no significant differences (P > 0.05) in CR and allelic R2 between scenarios (Table There were significant differences (P < 0.05) in CR and allelic Ros Table .Table 62 of 0.962, 0.109 and 0.175 points higher than the 3K SNP panel, respectively  when compared to denser panels , with FImpute being consistently more accurate. Imputation accuracy for 8K and 20K SNP panels were not significantly different using Beagle (P > 0.05) with respect to CR and allelic R2 (Table 2 (>0.967) were obtained using the 15K SNP panel and FImpute.The non-commercial 15K SNP panel resulted in the highest imputation accuracy of the low density panels with an overall CR of 0.973 and allelic RAn important measurement of imputation success is the number of animals imputed with modest accuracy (assumed <0.950 CR here). Using the 15K SNP panel resulted in 93% and 83% of the animals being imputed with a CR above 0.950  for FImpute and Beagle, respectively, while using the 3K SNP panel as the low density panel resulted in only 6.3% and 0.8% of animals above this accuracy threshold using FImpute and Beagle, respectively. The results for the other panels ranged between 62% and 70% using FImpute and between 40% and 48% using Beagle  for the 3K SNP panel, from either FImpute or Beagle, were lower than all other panels with CR values over all BTAs at or below 0.900. All other panels produced CR above 0.930 for all chromosomes. Imputation accuracy was found to be relative to chromosome length with the highest CRs obtained for BTA1 while the lowest CRs were obtained for BTA28 in all scenarios and both algorithms, however little difference was seen across the genome  to the 50K SNP panel was calculated for three different classes of minor allele frequency (MAF) . For the MAF class <0.01 the average CR was close to 1.00 for all panel densities. For SNPs with MAF 0.01-0.05 and >0.05 the average CRs ranged similarly from 0.84 to 0.97, depending on the panel density  (Table There were significant differences (P < 0.05) in CR and allelic R2 of 0.866, while Beagle yielded an average CR of 0.895 and allelic R2 of 0.826  and the lowest CR and allelic R2 with the 3K SNP panel . For the other panels, CR was between 0.898 and 0.952 and allelic R2 was between 0.829 and 0.919 (Table 2 for imputation to the 777K SNP panel (P < 0.05)  when compared to denser panels , with FImpute resulting in consistently higher accuracy of imputation.Using FImpute resulted in an overall average CR of 0.921 and allelic R 2 Table . The higThe distributions of animals in high classes of CR varied between FImpute and Beagle. For FImpute, the proportion of animals imputed above a CR of 0.95 ranged from 12.8% for the 3K SNP panel to 73.6% for the 90iK SNP panel. For the other panels, the proportion of animals was between 20% and 48% . The interaction between number of steps and low density panel showed that the difference between CR and allelic R2 from one to two steps imputation was larger for sparse panels  when compared to denser panels  to imputation in a one-step both when measured by CR and allelic Ros Table . The int2 was 69%, 21% 22% 9% and 4% for 3K, 6K, 8K, 15K and 20K SNP panels, respectively.The relative increase in CR for the two-step imputation with respect to the one-step imputation was 27%, 12%, 11%, 5% and 2% for 3K, 6K, 8K, 15K and 20K SNP panels, respectively, and the relative increase in allelic RThe average CR for imputation from the alternative low density panels  to the 777K SNP panel was calculated for three different classes of MAF . For the MAF class <0.01 the average CR was close to 0.99 for all panel densities, for MAF class 0.01-0.05 and >0.05 the average CRs ranged from 0.84 to 0.97 and from 0.65 to 0.96, respectively, depending on the panel density . Preliminary results obtained by Sargolzaei et al.  and BerrThese results were similar to the ones reported by Berry et al.  studyingAll low density panels, except the 3K SNP panel, can be used to impute to the 50K SNP panel with average concordance rates higher than 0.940. The customized 15K SNP panel yielded the highest percentage of animals with concordance rate above 0.950 of all the low density panels studied.The 50K, 90iK and 90tK SNP panels can be used to impute to the 777K SNP panel with average concordance rates higher than 0.940. A two-step imputation is recommended for lower density panels, making use of all available intermediate density panel genotypes.FImpute outperformed Beagle in all scenarios for imputation to both the 50K and to the 777K SNP panels both in terms of accuracy and computing time required.Ungenotyped animals that have four or more offspring and do not have available biological material to carry out genotyping may have their 50K SNP panel genotype inferred with an average concordance rate of 0.950 in the Hereford/Braford population analyzed.


Summary: Sodium bisulfite conversion followed by sequencing  has become popular for studying human epigenetic profiles. Identifying single nucleotide polymorphisms (SNPs) is important for quantification of methylation levels and for study of allele-specific epigenetic events such as imprinting. However, SNP calling in such data is complex and time consuming. Here, we present an ultrafast and memory-efficient package named BS-SNPer for the exploration of SNP sites from BS-Seq data. Compared with Bis-SNP, a popular BS-Seq specific SNP caller, BS-SNPer is over 100 times faster and uses less memory. BS-SNPer also offers higher sensitivity and specificity compared with existing methods.Availability and implementation: BS-SNPer is written in C++ and Perl, and is freely available at https://github.com/hellbelly/BS-Snper.Contact: bolund@biomed.au.dk, kdso@clin.au.dk or orntoft@ki.au.dkSupplementary information:Supplementary data are available at Bioinformatics online. Sodium bisulfite conversion followed by sequencing (BS-Seq) has become popular for studying human epigenetic profiles . Whole gSNP identification is important for identification of allele-specific epigenetic events such as, gamete specific and genetic imprinting . HoweverSupplementary Fig. S1). Thus, Watson and Crick strands should be independently treated to reduce such errors.Another problem is that true C/T SNPs in samples cannot be distinguished from C/T substitutions caused by bisulfite conversion and, thus, could be misidentified as unmethylated Cs , sequencTo our knowledge, Bis-SNP is a popular BS-Seq data based SNP calling tool. Another BS-Seq specific SNP caller, MethylExtract, is slightly faster than Bis-SNP . The formulae to calculate the frequencies are listed in P(G|D), using Bayes’ formula. The posterior distribution is built upon two components: the prior distribution of each genotype P(G), and the likelihood P(D|G), which is the probability of observing reads D given genotype G. For the prior P(G), we referred to the model of SOAPsnp (P(D|g = G) = iD, where i = 1, 2,n, and n is the number of reads. We used average error rate instead of full evaluation in error rate, which increases the modeling speed. The genotype with largest probability is recognized as the final SNP. See Supplementary Text for more details.In brief, the Bayesian inference of each genotype is based on its posterior distributions, Our previous RRBS data (Supplementary Table S2). For example, in Normal tissue, 2730 SNPs were detected by BS-SNPer, among which 2335 were validated by exome data [false positive rate (FPR) 14.47%]. Bis-SNP detected 3483 SNPs, while 2011 of them were validated . As exome sequencing detected 2873 SNPs for this sample, false negative rate of BS-SNPer was 18.73%, whereas that of Bis-SNP and MethylExtract was 30% and 57.01%, respectively (Supplementary Table S2).All reads of four samples were aligned to the GRCh37 assembly (hg19) of the human genome using the program BSMAP with options ‘-z 64 -p 12 -s 16 -v 10 -q 2’. The alignments were then fed into both BS-SNPer, MethylExtract and Bis-SNP under same conditions. We evaluated the algorithms on a system equipped with a six-core Intel Xeon E5650 2.66 GHz processor and 32 GB memory. The system runs on 64-bit Red Hat Enterprise Linux 4.1. The minimal running time of four samples of MethylExtract and Bis-SNP was 6 and 20 h, respectively, whereas BS-SNPer only used around 10 min for each sample. Compared with Bis-SNP, the increase in speed was more than 100 times in all four cases, including normal tissue , pRCC, local IVC, distant MB, of our published data (In conclusion, based on a dynamic matrix algorithm and Bayesian statistical framework, we present BS-SNPer, a SNP calling tool for BS-Seq data. BS-SNPer provides high performance in terms of speed, memory usage, accuracy and sensitivity.


Bemisia tabaci complex) are an important pest of cotton in Pakistan, its taxonomic diversity is poorly understood. As DNA barcoding is an effective tool for resolving species complexes and analyzing species distributions, we used this approach to analyze genetic diversity in the B. tabaci complex and map the distribution of B. tabaci lineages in cotton growing areas of Pakistan.Although whiteflies  System assigned the 766 sequences to 15 BINs, including nine from Pakistan. Representative specimens of each Pakistan BIN were analyzed for mtCOI-3′ to allow their assignment to one of the putative species in the B. tabaci complex recognized on the basis of sequence variation in this gene region. This analysis revealed the presence of Asia II 1, Middle East-Asia Minor 1, Asia 1, Asia II 5, Asia II 7, and a new lineage “Pakistan”. The first two taxa were found in both Punjab and Sindh, but Asia 1 was only detected in Sindh, while Asia II 5, Asia II 7 and “Pakistan” were only present in Punjab. The haplotype networks showed that most haplotypes of Asia II 1, a species implicated in transmission of the cotton leaf curl virus, occurred in both India and Pakistan.Sequence diversity in the DNA barcode region (mtCOI-5′) was examined in 593 whiteflies from Pakistan to determine the number of whitefly species and their distributions in the cotton-growing areas of Punjab and Sindh provinces. These new records were integrated with another 173 barcode sequences for B. tabaci complex. The dominant haplotypes in the B. tabaci complex were shared by India and Pakistan. Asia II 1 was previously restricted to Punjab, but is now the dominant lineage in southern Sindh; its southward spread may have serious implications for cotton plantations in this region.DNA barcodes successfully discriminated cryptic species in  Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is now recognized as a cryptic species complex The whitefly, B. tabaci complex comes from the examination of sequence diversity in the mitochondrial cytochrome c oxidase I (COI) gene. Frohlich et al. B. tabaci, employing the 3′ end of the gene, a standard adopted by subsequent investigators with the result that 383 different haplotypes have now been identified for this gene region B. tabaci complex have varied global invasion histories A number of DNA-based techniques have been used to identify species of whiteflies B. tabaci complex are due, at least in part, to competition B. tabaci complex and the viruses they transmit.Prior studies have shown that local differences in the abundance of different species within the B. tabaci, there is no ‘translation table’ to connect the lineages of this species which have been recognized based on their COI-3′ sequence with their COI-5′ counterparts The rise of DNA barcoding as a tool for species identification across the animal kingdom B. tabaci found in India and Pakistan, reveal their genetic diversity and subsequently test if their distributions have shifted in the cotton-growing areas of Punjab and Sindh since a study in 2007–2009 B. tabaci complex and begin construction of the ‘translation’ matrix from COI-3′ to COI-5′. Because barcode reference libraries enable species identification, the study provides insights into the diversity, movement, and distributional patterns of species in the B. tabaci complex in the region.In this study, we use DNA barcodes to discriminate the lineages of No specific permissions were required for this study. The study did not involve endangered or protected species.www.simplemappr.net). Samples were collected using an aspirator, then transferred to 95% ethanol and stored at −20°C until analysis. Two to three whiteflies were chosen from each collection site, producing a total of 649 specimens for barcode analysis. Individual whiteflies were labeled, assigned specimen numbers and photographed. Specimen data along with the collection information were added to the project MAWFL (Whitefly Species Complex of Pakistan) in BOLD (www.boldsystems.org), the Barcode of Life Data System B. tabaci available in GenBank (173) were also analyzed to gain a better understanding of the global patterns of barcode diversity in B. tabaci.Adult whiteflies were collected by sampling 255 sites within Punjab and Sindh from 2010 to 2013. Sampling followed protocols outlined by Ahmed et al. 2O with 0.5 µL of 10 mg RNase A/mL.Genomic DNA was extracted from most specimens at the Canadian Centre for DNA Barcoding using the protocol described by Porco et al. TAAACTTCTGGATGTCCAAAAAATCA) following the PCR conditions; 94°C (1 min), 5 cycles of 94°C (40 s), 45°C (40 s), 72°C (1 min); 35 cycles of 94°C (40 s), 51°C (40 s), 72°C (1 min) and final extension of 72°C (5 min). Amplification of COI-3′ was performed with primer pair C1-J-2183 (CAACATTTATTTTGATTTTTTGG)/TL2-N-3014 (TCCAATGCACTAATCTGCCATATTA) Amplification of the barcode region (COI-5′) was performed with primer pair LepF2_t1 (TGTAAAACGACGGCCAGTAATCATAARGATATYGG)/LepR1  in cases where morphological identifications are difficult B. tabaci available on BOLD and GenBank were assembled to assess the growth in coverage since the most recent report B. tabaci on BOLD ). These barcode records were used in a combined analysis with the Pakistan data to determine the number of COI-5′ lineages in the B. tabaci complex and to ascertain genetic distances among these lineages.All barcode data for B. tabaci complex as this topic has seen extensive work B. tabaci complex. Boykin et al. B. tabaci complex is based on sequence diversity in COI-3′, we also sequenced this gene region for representative specimens from each COI-5′ BIN detected in our study. This enabled their assignment to one of the species recognized on the basis of COI-3′ sequence variation by comparing each COI-3′ sequence to the reference sequences for the species in the B. tabaci complex Bemisia dataset This study does not evaluate evolutionary relations in the B. tabaci were represented by variable number of sequences, a consensus sequence for each BIN or species was obtained using the ‘Consensus Barcode Generator’ function of TaxonDNA www.hiv.lanl.gov/cgi-bin/findmodel/findmodel.cgi). Bayesian posterior probabilities were calculated from the sample points once the MCMC algorithm began to converge. Convergence was determined when the standard deviation of split frequencies went below 0.02 and the PSRF  approached 1, and both runs had properly converged to a stationary distribution after the burn-in stage (discarded the first 25% of samples). The trees generated through this process were visualized using FigTree v1.4.0. Two whitefly species, Trialeurodes vaporariorum (AY521265) and Bemisia afer (EU825777) were included in the analysis as outgroups. The parsimony analysis was performed using the same datasets with the TNT  v1.1 ( Willi Hennig Society Edition) ClustalW nucleotide sequence alignments FsDB. tabaci species from Pakistan  were calculated using Arlequin v.3.5 B. tabaci complex were mapped using GPS coordinates and an online tool (www.simplemappr.net).Genetic diversity indices and neutrality tests  among the sequences from Pakistan ranged from 0.0%–19.9% with a mean of 4%. The BIN system assigned these sequences to nine BINs. The pairwise distances in the combined (Pakistan + GenBank) B. tabaci sequences ranged from 0%–20% (mean = 8%). Maximum intraspecific (within BIN) distances ranged from 0.0%–2.3%, while the nearest-neighbor (NN) distances ranged from 1% to 16.7% (B. tabaci barcodes were assigned to 15 unique BINs (B. tabaci complex are shown in Barcode compliant sequences (>500 bp of COI-5′) were recovered from 589 of the 649 specimens (90%) from Pakistan . Another 173 sequences for to 16.7% . Except to 16.7% . The comque BINs . Ten BINque BINs . Two othque BINs , while tB. tabaci BINs from Pakistan showed their correspondence with five of the 34 putative species B. tabaci: Asia 1, Asia II 1, Asia II 5, Asia II 7 and MEAM 1  . Taken as a whole, the COI-3′ based phylogenetic tree of B. tabaci  for the barcode (The phylogenetic tree of ia II 7)  which clia II 7) , Table 1ia II 7) ) and thuia II 7) . DNA bar. tabaci  was simi. tabaci . The rel barcode  and the  barcode  sequenceThe genetic diversity indices are presented in B. tabaci complex identified through COI-3′ analysis showed marked variation in abundance in Pakistan with Asia II 1 comprising 88%, MEAM 1–7%, Asia II 7–2%, Asia 1–2% and Asia II 5–0.3% of the individuals.The five species of the 2 = 203; p = 0.0) and for species composition  which was clearly skewed towards Asia II 1. The host information on whitefly specimens from India and Pakistan showed that the species of B. tabaci complex in Pakistan were recorded on multiple plants  were found on cotton in Pakistan. Asia II 7 was only collected in a Malaise trap in Pakistan, but has been recorded on cotton in India. The levels of sequence divergence at COI-3′ and COI-5′ were generally congruent (data not shown), indicating the interchangeability of the markers. An earlier study of whiteflies from Pakistan Analysis of sequence diversity in COI-5′ revealed that six species of the B. tabaci indicated the presence of 15 deeply divergent lineages, including 12 from the Indo-Pakistan region. Multiple genotypes of B. tabaci have previously been reported from the Indo-Pakistan subcontinent B. tabaci in India, while Rekha et al. The analysis of all currently available COI-5′ data for B. tabaci complex. The NN barcode distances in the B. tabaci complex ranged between 1.0%–16.7% with the new putative species, “Pakistan”, showing a NN distance of 14.3%. Further, both the BI and parsimony analysis showed that the “Pakistan” lineage formed a separate branch on the tree and was phylogenetically closer to species in the African group. Previous researchers have used genetic distances and phylogenetic analysis to determine the relationships and taxonomic status of species of the B. tabaci complex The COI-3′ sequence of one whitefly (BIN: ABX2616) from northern Punjab showed 13.7% K2P divergence from any known lineage, indicating that it represents a new species in the B. tabaci across Punjab and Sindh from 2007 to 2009 It has been established that begomovirus spread and diversification is linked to the genetic and phenotypic variability of whiteflies B. tabaci complex to validate these impacts. Experimental assessment of vector competence for each member of the complex as well as evaluation of their host preference would also improve understanding of the epidemiology of CLCuD in Pakistan.The prevalence of Asia II 1 in Sindh is important because Ahmed et al. B. tabaci complex within Pakistan seem to show the same dynamic distributional shifts detected in other regions Species in the Table S1Bemisia tabaci included in the study.BOLD process IDs, GenBank accessions, collection locations and host plants of (XLS)Click here for additional data file.Table S2Bemisia tabaci complex as determined by Dinsdale et al. COI-3′ and COI-5′ (barcode) K2P distances among respective taxonomic units of (XLS)Click here for additional data file.


Peroxisome proliferator-activated receptor gamma (PPARG), CCAAT/enhancer binding protein alpha (CEBPA) and retinoid X receptor alpha (RXRA) are nuclear transcription factors that play important roles in regulation of adipogenesis and fat deposition. The objectives of this study were to characterise the variability of these three candidate genes in a mixed sample panel composed of several cattle breeds with different meat quality, validate single nucleotide polymorphisms (SNPs) in a local crossbred population (Angus - Hereford - Limousin) and evaluate their effects on meat quality traits , supporting the association tests with bioinformatic predictive studies.PPARG and CEBPA genes within our mixed panel, including a novel SNP in the latter. Three of these nine, along with seven other SNPs selected from the Single Nucleotide Polymorphism database (SNPdb), including SNPs in the RXRA gene, were validated in the crossbred population (N = 260). After validation, five of these SNPs were evaluated for genotype effects on fatty acid content and composition. Significant effects were observed on backfat thickness and different fatty acid contents (P < 0.05). Some of these SNPs caused slight differences in mRNA structure stability and/or putative binding sites for proteins.Globally, nine SNPs were detected in the PPARG and CEBPA showed low to moderate variability in our sample panel. Variations in these genes, along with RXRA, may explain part of the genetic variation in fat content and composition. Our results may contribute to knowledge about genetic variation in meat quality traits in cattle and should be evaluated in larger independent populations.The online version of this article (doi:10.1186/s40781-016-0095-3) contains supplementary material, which is available to authorized users. PPARG, CEBPA and RXRA may be important and helpful for animal improvement.Fat content and composition are considered major economically important traits in livestock, since variations in these two factors affect several meat properties . These tPPARG and CEBPA have been associated with a group of meat quality traits in Chinese and Korean cattle, including tenderness, backfat thickness, water holding capacity, fatty acid composition, weight at slaughter and marbling, among others   using the Sequenom platform  ≥ 0.02), and showed no variability in H and ¾A. On the other hand, SNPs rs41610552 and rs42016945 showed moderate allele frequencies among the subpopulations (MAF ≥ 0.12). Surprisingly, SNP rs109613657 showed no variability at all (Table P < 0.05) (Table e) of the two balanced SNPs (rs41610552 and rs42016945) varied between 0.22 (¾ H) and 0.49 (LX). Observed heterozygosity (ho) varied between 0.25 (¾ H) and 0.55 (LX). When linkage disequilibrium was analysed, rs207671117 and rs42016945 showed a small block with three haplotypes, two of them with more than 95 % of the haplotype frequencies  to 0.38 (A), and ho ranged from 0.17 (LX) to 0.50 (A) . It is worth mentioning that rs207774429, as happened with rs210446561 (CEBPA), was genotyped efficiently in only 153 samples. The other two SNPs, rs136289117 and rs209839910, showed very low variation and were not considered for association (Table P < 0.01) and rs133517803 in subpopulation ¾A (P < 0.01). The remaining SNPs showed no significant deviations from the theoretical proportions. Three of the SNPs in this gene were part of a linkage block constituted by three haplotypes, with two of them accounting for over 97 % of the haplotype frequencies , but small genetic variation in general. An additive effect on BT was detected (P < 0.05) for rs42016945, while dominance effects were significant (P < 0.05) for rs41610552 on C18:1 cis-9 and rs42016945 on C18:0. For these SNPs, the percentages of phenotypic variance explained ranged from 0.31 to 2.09 %. SNP rs133517803 (RXRA) showed significant effects on several measures: additive effects were detected on C18:1 cis-9; C18:3 cis-6,9,12; C18:3 cis-9,12,15; MUFA and BT; while a dominance effect was detected on C18:1 cis-9; C18:3 cis-9,12,15 and MUFA. For these traits, the SNP explained from 0.35 to 1.77 % of the phenotypic variance  had been associated with BT and marbling in Chinese breeds [RXRA, so this work may provide evidence that SNPs in this gene may be helpful for animal improvement by means of marker-assisted selection programs.If we consider the first approach, our results were partially consistent with previous reports from other authors. Sevane et al.  reportedn breeds , 7 was de breeds , but theSince SNP rs207671117 was located in the 5’ UTR of PPARG, we analysed the possible effects on mRNA stability and putative RBP binding sites. Two highly similar structures were obtained running the UTR sequences of the alternative variants in The Mfold Web Server, but the structure for allele A  seemed slightly more stable than that for allele G . When RBP binding sites were analysed (threshold = 0.8), we found that this SNP was located one base away from a putative binding site for protein FUS (SCORE = 7.36), which is important in maintaining genomic integrity. These same studies were performed for rs42016945 and we found that the structures provided by Mfold were quite different despite the similarity of energy values. The structure for allele G  seemed more stable than structure for allele A . According to the analysis on RBPDB, no sites for RBP were identified promptly at the mutated site, but the SNP was immediately next to a NONO (non-POU domain-containing octamer-binding protein) binding site (SCORE = 8.95). NONO is a protein involved in numerous nuclear processes like unwinding, recombination, DNA binding and regulation of splicing.RXRA) was located in a possible alternative promoter region. Therefore, we searched for putative transcription factor binding sites through PhysBinder. According to this tool, rs133517803 was located over an ESRRB (estrogen-related receptor beta) binding site (threshold = 308), whose role is still not clear.SNPs rs133517803 (PPARG and CEBPA showed low to moderate variability in our mixed sample panel. Variations in these genes, along with RXRA, may explain part of the phenotypic variation in fat content and composition of meat, especially SNPs in RXRA, which explained an important part of the variation in the highly heritable oleic acid percentage and MUFA. The molecular bases of the phenotypic differences may be partially explained by changes in RNA structures, RBP binding sites, codon usage frequencies and TF binding sites. The SNPs we analysed should be evaluated in independent populations with in-vitro and in-vivo analyses to explain the mechanisms by which these polymorphisms may be involved in the traits.


The success of clinical genomics using next generation sequencing (NGS) requires the accurate and consistent identification of personal genome variants. Assorted variant calling methods have been developed, which show low concordance between their calls. Hence, a systematic comparison of the variant callers could give important guidance to NGS-based clinical genomics. Recently, a set of high-confident variant calls for one individual (NA12878) has been published by the Genome in a Bottle (GIAB) consortium, enabling performance benchmarking of different variant calling pipelines. Based on the gold standard reference variant calls from GIAB, we compared the performance of thirteen variant calling pipelines, testing combinations of three read aligners—BWA-MEM, Bowtie2, and Novoalign—and four variant callers—Genome Analysis Tool Kit HaplotypeCaller (GATK-HC), Samtools mpileup, Freebayes and Ion Proton Variant Caller (TVC), for twelve data sets for the NA12878 genome sequenced by different platforms including Illumina2000, Illumina2500, and Ion Proton, with various exome capture systems and exome coverage. We observed different biases toward specific types of SNP genotyping errors by the different variant callers. The results of our study provide useful guidelines for reliable variant identification from deep sequencing of personal genomes. Recent advances in next generation sequencing (NGS) technology have enabled many researchers access to affordable whole exome or genome sequencing (WES or WGS), leading to the phenomenal achievements in genome sequencing projects such as the personal genome project1456789http://novocraft.com/) are popular, and among many variant callers, the Genome Analysis Tool Kit HaplotypeCaller (GATK-HC)1617Genomic variants, such as single nucleotide polymorphisms (SNPs) and DNA insertions and deletions , are identified by various analysis pipelines with combinations between short read aligners and variant callers. Among many read aligners, BWA-MEMA systematic comparison of variant calling performance requires a gold standard set of reference variant calls. Recently, the Genome in a Bottle (GIAB) consortium published a set of highly confident variant calls for one individual in the 1000 Genome project (sample NA12878)Recently two published studies used the GIAB variant data for comparisons of variant calling pipelines20http://novocraft.com/), and four popular variant callers, GATK-HCIn this study, we compared thirteen pipelines that consist of a combination between three popular read aligners, BWA-MEMhttp://novocraft.com/). We downloaded NA12878 sequence data sets generated by Illumina HiSeq2000, HiSeq2500, Ion Proton from SRAUsing the analysis pipeline summarized in Performance comparison among different variant callers requires a gold standard set of variant calls. We used a set of high-confident SNP and indel calls for NA12878 by GIAB consortium23To compare the overall performance among the thirteen pipelines, we compared the distributions of APR scores of multiple data sets for each pipeline on SNPs and indels . For SNPWe also investigated whether the read aligner or variant caller have more influence on the performance of variant identification. We calculated average standard deviations of APR of data sets with different aligners but the same variant caller, and those with different variant callers but the same aligner. The average standard deviation among data sets with different aligners and those with different variant callers are 3.46e-3 and 4.02e-3 for SNPs, and 0.72e-2 and 7.2e-2 for indels, respectively. These results indicate that variant caller has more influence than read aligner on both SNP and indel identifications, with a much larger effect on indel identification.We assessed the concordance among the four variant callers for each NGS platform. For our concordance analysis, variants identified with different read aligners were merged for each variant caller, and filtered out low confident variants below the quality score (QUAL) threshold of 20. For Illumina data sets, we observed ~92% of concordance among the variant calls by three variant callers  ignoring the reference allele (IR), ii) adding the reference allele (AR), and iii) other SNP calling errors. IR represents a homozygous SNP in the variant call, which is in fact a heterozygous SNP composed of one reference allele and one variant allele in the gold standard. Conversely, AR represents a heterozygous SNP composed of one reference allele and of one variant allele in the variant call, yet a homozygous SNP in the gold standard. The other class of SNP calling errors includes all other types of erroneous SNP calls regardless of the reference allele  and popular read aligners  against twelve sequence data sets for a single genome, NA12878. We employed precision-recall (PR) curves for benchmarking performance, because it is known to be more informative than another popular method, receiver operating characteristic (ROC) curves in benchmarking analysis for imbalanced data set, in which the negatives severely outnumber the positives23Filtration of low quality calls  can affect the overall performance of variant calling pipelines. We observed generally higher performance by pipelines with Freebayes than Samtools in SNP calling with filtered variant calls . One posAnother possible factor that influences variant calling performance is exome coverage. Using the analyzed eleven data sets for Illumina platform, which exhibit a wide range of exome coverage (43.6×–298.5×), we tested for correlation between exome coverage and performance of variant calling. We observed no significant correlation between exome coverage and APR , indicatet al.et al.Comparisons of variant calling pipelines on the GIAB gold standard variant set were conducted by two published studies: Cornish We analyzed concordance of the four callers for each sequencing platform and found high concordance for confident variants , adding the reference allele (AR), and an assortment of other minor cases regardless of the reference allele. We observed that Freebayes has higher preference in generating IR, whereas Samtools and GATK-HC more frequently generate AR. These results suggest that, for accurate variant calls, some caution is merited regarding homozygous SNP calls using Freebayes and heterozygous SNP calls by GATK-HC and Samtools.Taken together, our recommendation is the use of BWA-MEM and Samtools pipeline for SNP calls and BWA-MEM and GATK-HC pipeline for indel calls. For Ion Proton data, Samtools appears the best current approach for both SNPs and indels. However, users of Samtools may need to be more cautious about heterozygous SNPs, because it tends to add reference alleles. We conclude that results from our study will provide practical and comprehensive guidance to more accurate and consistent variant identification, ultimately leading to the clinical-grade personal variant information for genomic medicine.One individual (NA12878) from the 1000 genome project was sequenced in parallel with the HiSeq2000, HiSeq2500 and Ion Proton platforms, and annotated as the gold standard reference benchmarkThe sequence reads downloaded from SRA were converted to bam files of the binary version of sequenced alignment/map files (known as sam files) or fastq files using sratools-2.4.2 depending on their alignment status. For Illumina data sets, we mapped them to the GRCh37 reference genome through BWA-MEM-0.7.10, Bowtie2-2.2.25, and Novoalign-3.02.12. The downloaded data set for Ion Proton was pre-mapped to the GRCh37 by Ion Proton read aligner, Tmap. To remove duplicates in each bam files, we used MarkDuplicate in Picard-bf40841. To estimate exome and genome coverage of each sample, we used the bedtool2 programWe ran four variant calling methods: (1) Genome Analysis Tool Kit-3.3.0 HaplotypeCaller (GATK-HC), (2) Samtools-1.1, (3) Freebayes-0.9.18, and (4) Torrent Variant Caller (TVC)-4.2.3. For calling variants by GATK-HC and Samtools, we ran ReorderSam in Picard, IndelRealigner and BaseRecalibrator in GATK for all samples, by following their own best practice procedures. We also used the default parameters or suggested input files, such as the most recent dbSNP vcf file, a HapMap genotype file and OMNI 2.5 genotype vcf file. For calling variants by Freebayes, we used mark-duplicated bam files, described in the previous section without any additional process. For the genomic sequence data by Ion Proton, we also ran its own variant calling method, TVC-4.2.3 standalone version. As the last step of calling variants, we regularized the representation of the variants by using vcfallelicprimitive module in Vcflib, because the same variant could be represented in different ways by different methods. All methods were tested on Linux CentOS 6.http://www.genomeinabottle.org) on the only exon regions without known structural variants were used as the gold standard variants set. We used NIST_RTG_PlatGen_merged_highconfidence_v0.2_Allannotate.vcf file, which was regularized by vcfallelicprimitive module in Vcflib. Also we used high confident homozygous reference alleles as a negative set which is provided by GIAB as a bed file, union13callableMQonlymerged_addcert_nouncert_excludesimplerep_excludesegdups_excludedecoy_excludeRepSeqSTRs_noCNVs_v2.19_2mindatasets_5minYesNoRatio_AddRTGPlatGenConf_filtNISTclustergt9_RemNISTfilt_RemPartComp_RemRep_RemPartComp_v0.2.bed. Then, the variants within intron and non-protein coding regions were filtered out against exome capture regions of each data set of hg19 protein coding exon regions annotated by UCSC genome browserFor benchmarking variant callers, the highly confident heterozygous and homozygous variant calls provided by NIST GIAB consortium , true negative (TN), false positive (FP), and false negative (FN) variants as follows:TP: variants called by a variant caller as the same genotype as the gold standard dataTN: reference alleles in high confident regions other than gold standard variantsFP: variants called by a variant caller but not in the gold standard variant set.FN: gold standard variants that were not called by a variant callerPrecision: TP/(TP+FP)Recall: TP/(TP+FN)We drew PR curves for SNPs and indels for each of the thirteen variant calling pipelines, separately, and then calculated area under the precision-recall curve (APR) as a summary score. APR ranges from zero to one, where a score of one indicates perfect variant calls.How to cite this article: Hwang, S. et al. Systematic comparison of variant calling pipelines using gold standard personal exome variants. Sci. Rep.5, 17875; doi: 10.1038/srep17875 (2015).


K SNP array, has been successfully developed and used for screens of bi-parental populations. However, the number of robust and well-distributed markers contained on this array was not sufficient to perform genome-wide association analyses in wider germplasm sets, or Pedigree-Based Analysis at high precision, because of rapid decay of linkage disequilibrium. We describe the development of an Illumina Infinium array targeting 20K SNPs. The SNPs were predicted from re-sequencing data derived from the genomes of 13 Malus × domestica apple cultivars and one accession belonging to a crab apple species . A pipeline for SNP selection was devised that avoided the pitfalls associated with the inclusion of paralogous sequence variants, supported the construction of robust multi-allelic SNP haploblocks and selected up to 11 entries within narrow genomic regions of ±5 kb, termed focal points (FPs). Broad genome coverage was attained by placing FPs at 1 cM intervals on a consensus genetic map, complementing them with FPs to enrich the ends of each of the chromosomes, and by bridging physical intervals greater than 400 Kbps. The selection also included ∼3.7K validated SNPs from the IRSC 8K array. The array has already been used in other studies where ∼15.8K SNP markers were mapped with an average of ∼6.8K SNPs per full-sib family. The newly developed array with its high density of polymorphic validated SNPs is expected to be of great utility for Pedigree-Based Analysis and Genomic Selection. It will also be a valuable tool to help dissect the genetic mechanisms controlling important fruit quality traits, and to aid the identification of marker-trait associations suitable for the application of Marker Assisted Selection in apple breeding programs.High-density SNP arrays for genome-wide assessment of allelic variation have made high resolution genetic characterization of crop germplasm feasible. A medium density array for apple, the IRSC 8 Malus × domestica) is the most economically important deciduous fruit tree crop worldwide Cultivated apple  Consortium (IRSC) WGG array contains 7,867 Malus SNP markers as well as 921 SNPs derived from PyrusRecent advances in genomics technologies have enabled the sequencing of the ‘Golden Delicious’ genome In this investigation, we describe the development of such a high density WGG array for apple, using a focal point approach and stringent selection criteria built from experience of the analysis of the apple IRSC array M. micromalus and two M. × domestica double haploid (DH) accessions, X9273 and X9748, which were derived from ‘Golden Delicious’, were included To enable the identification of SNPs, a discovery panel comprising the following 13 apple cultivars, including some of the core European apple breeding founder varieties http://www.rosaceae.org/species/malus/malus_x_domestica/genome_v2.0) allowing up to seven mismatches in a four-step exhaustive alignment procedure using GEM http://www.ncbi.nlm.nih.gov/projects/genome/assembly/agp/AGP_Specification_v2.0.shtml).A schematic representation of all the steps in the pipeline for array development is given in Read pairs that always mapped to a single genomic location were kept in the alignment file for that reference at each step, while unaligned read pairs were submitted to the next mapping round. Pairs that did not align uniquely to any of the references were discarded . As a rewww.python.org) were developed to remove variants with low phred-scaled quality scores (i.e. below 20); a high combined read depth ; and a low single-cultivar read depth (i.e. lower than 10) in more than 50% of the cultivars. A minimum phred-scaled quality score of 20 was chosen to ensure that only SNPs with a probability of less than 1% for the alternative allele being called wrongly were selected, and the maximum read depth value was used to ensure the removal of SNPs derived from paralogous regions rather than true heterozygous regions as done previously in the development of the IRSC apple array K peach array M. micromalus, were derived from different genetic backgrounds compared to the other accessions, and absence of sequence coverage in those regions could have been due to genomic variation. Finally, all SNPs with an allele frequency (AF) = 1 were discarded since this equated to all the re-sequenced varieties carrying an allele that was different from the ‘Golden Delicious’ reference genome. Such SNPs were discarded as they were likely to represent potential false SNPs resulting from sequencing errors in the ‘Golden Delicious’ reference sequence, or rare alleles derived from ‘Golden Delicious’. For the same reason, rare alleles derived from other cultivars were discarded during the SNP selection phase described below.A quality filter was then applied to remove potentially unreliable variants. Custom scripts written in Python (http://res.illumina.com/documents/products/technotes/technote_iselect_design.pdf). This third step removed variants that were either indels, A/T or C/G transversions, or tri-allelic SNPs. Additionally, variants located at sites containing additional high quality SNPs in either the up/down-stream 50 bp; or that contained both up-stream and down-stream sequence that appeared multiple times across the genome in the directly flanking 24 bp were removed http://support.illumina.com/array/array_software/assay_design_tool.ilmn) for a preliminary estimation of the conversion rate (the Illumina SNP_Score). Following Illumina guidelines, markers with a SNP_Score below the threshold of 0.7 were discarded, while others were kept as high quality SNPs for the final selection of the 20,000 targets to be included on the 20K Infinium array. In addition to these newly identified and validated SNPs from the discovery panel re-sequencing, 3,670 validated SNPs from the previously developed apple IRSC SNP WGG array were added to this selection. The SNPs selected from the IRSC array were chosen as they were robustly positioned in different genetic linkage maps, including those reported in Quality filtered variants from the pipeline described above were then processed to meet Illumina Infinium II array design requirements , and the number of SNPs within each FP was increased where feasible. Focal points evenly spread across the genome were first identified on the basis of the genetic positions available from previous mapping studies Malus chromosomes, making the number of FPs selected 2,022. Through a SNP short-listing process, up to 11 markers for each FP were selected. Entries featuring the same genotyping profile across the 14 re-sequenced accessions within a given FP were removed to avoid having too many SNPs at a single point potentially derived from the same haplotype. Additionally, SNPs displaying a percentage of heterozygous genotypes in the re-sequencing panel greater than 65% were also excluded as they were most likely to be the result of paralogy instead of true heterozygosity. In cases where 11 or fewer SNPs remained within a given FP, these were all selected for inclusion on the array. Where more than 11 SNPs remained, a step-wise inclusion procedure was followed. Firstly, up to five SNPs were selected that were polymorphic in 35–60% of the panel members, which generally corresponds to 5–8 members. Their heterozygous profiles had to be complementary as far as possible, and panel members had to be more or less equally represented, thus balancing the number of heterozygous SNPs across all (diploid) panel members. Secondly, this first set of highly polymorphic SNPs was complemented with SNPs that were polymorphic in 10–35% of the panel members, usually corresponding to 2–4 members, following the same principles on complementarity and representation. Thirdly, the remaining positions were filled up with SNPs that were polymorphic in just one panel member. This meant that complementarity within any given FP was accounted for as far as possible. This selection process was automated through a customized script that is available upon request from the corresponding author. Finally, the single member heterozygotes were manually scrutinized across all FPs, to ensure that FPs in the same region did not have a single heterozygous SNP for the same panel member. Also, M. micromalus specific SNPs were maintained at reduced frequency because of the currently limited use of this species in breeding  with a GenCall threshold of 0.15 and a SNP filtering pipeline Genotype calls from the 14 accessions of the discovery panel were compared with those obtained through high-coverage Illumina re-sequencing. Genotyping data were exported from GenomeStudio genotyping report with the option “Forward Strand” for consistency with the re-sequencing strand. Regarding re-sequencing data, only calls with a support of at least 10 reads were extracted from the .vcf file and used for the comparison.M. × domestica apple cultivars and one accession of M. micromalus), and an additional 809 million reads from the two DH accessions (http://bioinformatics.tecnoparco.org/fruitbreedomics/node/2). Sequencing resulted in 3,376 million reads from the 14 accessions  newly developed SNPs and 3,160 (success rate 96%) from the IRSC array that were informative in this new array and were mapped in at least one of the full-sib families screened . Additionally, 747 SNPs that featured complex cluster patterns  were found informative by visual inspection but would have required additional elaboration on the calling pipeline or manual annotation as previously reported in Within the framework of the FruitBreedomics project, 21 full sib families were SNP genotyped, using the 20screened . Of the K to ∼8.5K. Examples of a GenomeStudio cluster for a robust mapped SNP and a failed/difficult to score SNP are shown in The number of SNPs successfully mapped for each of the 21 full-sib families varied from ∼5.2K array was assessed for the set of 15,771 validated SNPs. Excluding missing data, on average 12,347 comparisons were made for each of the 14 accessions  and the average concordance was 95%. In the majority of cases, the genotype call discordance identified was for low frequency SNPs observed in just one or two accessions of the discovery panel. This could have been the result of unreliable genotype calls in the re-sequencing analysis, owing to a lower read depth in those specific regions, or it might have been caused by unreliable genotype calls made in the array analysis owing to the presence of additional SNPs in probe binding sequences.The concordance of genotype calls made through re-sequencing with those obtained from genotyping with the 20K WGG array for M. × domestica using Illumina Infinium technology, comprising a total of 18,019 SNPs, has greatly increased the genotyping and analysis opportunities for apple researchers and breeders. High throughput array-based genotyping has revolutionized the study of genome-wide genetic variation, reducing costs and increasing the reliability and efficiency of data produced, as well as significantly reducing the time spent on genotyping itself. Medium or high density arrays are available for a range of other crop plant species, including cherry Malus IRSC array (72%) K SNPs uniformly distributed along the genome of each parent.The outcome of our present work, a 20M. × domestica cultivars M. × domestica. However, the actual SNP density may still be higher, as some of the filtered-out SNPs may actually be true polymorphisms.The final set of 2,578,313 high quality SNPs detected using the 14 re-sequenced accessions of the discovery panel corresponds to an average number of 4.8 SNPs/1,000 bp, in the 530 Mbps assembled and anchored ‘Golden Delicious’ reference genome sequence. This value was similar to the value reported in the apple genome paper Rubus idaeusHordeum vulgareLolium perenneHordeum vulgareLupinus angustifoliusa priori knowledge of the SNPs being interrogated, making them useful for genetic analysis in species where no reference sequence is currently available. In addition, since up to 96 samples can be multiplexed in a single lane of Illumina HiSeq sequencing, the genotyping cost per sample can be as low as USD12.00 at the time of writing (http://www.igd.cornell.edu/index.cfm/page/GBS/GBSpricing.htm). However, despite clear advantages to the use of GBS under certain experimental conditions for under-resourced species, GBS datasets contain a large proportion of missing values and false homozygote calls, due to low, and uneven genome coverage among individuals Click here for additional data file.Table S2List of the 18,019 SNPs included in the 20 K SNP array. For each SNP, the physical location , minor allele frequency (MAF), validation in 21 full-sib families, SNP genotype detected by the Illumina array, Illumina strand, source strand, source sequence, probe sequence, genotype calls (gt) made through re-sequencing and no. of high-quality bases (dp) for each discovery panel member, are reported.(XLSX)Click here for additional data file.Dataset S1VCF file for the all newly discovered variants included in the 20 K SNP array.(ZIP)Click here for additional data file.


However, because linkage disequilibrium is high in such crosses, QTL mapping precision is unsatisfactory and only a few QTL have been found to segregate within outbred strains, which limits their use to improve animal performance. To detect QTL in outbred pig populations of Chinese and Western origins, we performed genome-wide association studies (GWAS) for meat quality traits in Chinese purebred Erhualian pigs and a Western Duroc × (Landrace × Yorkshire) (DLY) commercial population.Understanding the genetic mechanisms that underlie meat quality traits is essential to improve pork quality. To date, most quantitative trait loci (QTL) analyses have been performed on FP-values and effects of 29 516 SNPs that were common to Erhualian, DLY, F2 and Sutai pig populations.Three hundred and thirty six Chinese Erhualian and 610 DLY pigs were genotyped using the Illumina PorcineSNP60K Beadchip and evaluated for 20 meat quality traits. After quality control, 35 985 and 56 216 single nucleotide polymorphisms (SNPs) were available for the Chinese Erhualian and DLY datasets, respectively, and were used to perform two separate GWAS. We also performed a meta-analysis that combined P = 7.97E-09) and was associated with drip loss in Erhualian pigs. Our results suggested that at least two QTL on SSC12 and on SSC15 may have pleiotropic effects on several related traits. All the QTL that were detected by GWAS were population-specific, including 12 novel regions. However, the meta-analysis revealed seven novel QTL for meat characteristics, which suggests the existence of common underlying variants that may differ in frequency across populations. These QTL regions contain several relevant candidate genes.We detected 28 and nine suggestive SNPs that surpassed the significance level for meat quality in Erhualian and DLY pigs, respectively. Among these SNPs, ss131261254 on pig chromosome 4 (SSC4) was the most significant  contains supplementary material, which is available to authorized users. The Erhualian pig breed, like the Meishan breed, is a Chinese fat-type line that is well known for its high prolificacy, superior meat quality and strong resistance to harsh environments; thus, it is often included in Chinese pig breeding programs. Compared to the Erhualian breed, the DLY cross shows greater growth rate and meat productivity and currently has the biggest share of the pork market in China. Our aim was to map population-specific QTL at high resolution for meat quality traits in Chinese purebred Erhualian and western crossbred DLY populations.2 resource population and Chinese Sutai pigs and the identification of a number of QTL that affect meat quality traits [2 and Sutai.Previously, we reported QTL scans using a White Duroc × Erhualian Fy traits -24. The y traits . It has All procedures involving animals followed the guidelines for the care and use of experimental animals approved by the State Council of the People’s Republic of China. The ethics committee of Jiangxi Agricultural University specifically approved this study.2 and Sutai pigs, as described by Ma et al. [2 pigs that originated from nine F1 boars and 59 F1 sows, which were the progeny of two White Duroc sires and 17 Erhualian dams, were slaughtered at 240 ± 5 days of age and were submitted to standardized cutting of the carcass. The Sutai population consisted of 461 offspring from four Sutai boars and 55 Sutai sows that were also slaughtered at 240 ± 5 days of age. The growth rates of these four populations differed in the following order: growth rate of DLY > F2 and Sutai > Erhualian pigs. They were all fed to similar market weight (~90 kg) before slaughter and thus age at slaughter differed for each population.This study involved two populations: Chinese Erhualian pigs and Western DLY pigs. The Erhualian population comprised 168 barrows and 168 gilts, which were born and raised for two to three months on Erhualian cooperatives that were located around the Jiaoxi Changzhou city in Jiangsu province. They were then transferred to a farm in Nanchang city in two batches. The DLY population consisted of 306 males and 304 females, which were raised on a farm in Xiushui city. All experimental animals were fed on a similar diet under a standardized feeding and management regimen, and given free access to water, and then slaughtered at approximately 300 days of age for Erhualian pigs and 180 days for DLY pigs in the same commercial abattoir. The meta-analysis that combined four GWAS included two additional populations: Fa et al. . Brieflylongissimus muscle (LM) between the 10th rib and the first lumbar vertebra and semimembranosus muscle (SM) from the left side of the carcass [Eight meat quality traits, including pH, EZ-drip loss, L* for lightness, a* for redness, b* for yellowness, moisture content, marbling and firmness were measured on the  carcass . Meat ch carcass -29. Meat carcass . Three c’s protocol. Quality control procedures were carried out to remove SNPs with a call rate greater than 90%, a minor allele frequency (MAF) less than 0.01 and a significant deviation from Hardy-Weinberg equilibrium (P ≤ 10−5); moreover, animals with more than 10% missing genotypes or more than 5% Mendelian errors were removed from the dataset. A final set of 35 985 SNPs for 331 Chinese Erhualian and 56 216 SNPs for 610 DLY pigs were used for further statistical analysis. The large difference in number of SNPs that were retained for each population is explained by the fact SNPs on the Illumina PorcineSNP60K Beadchip were primarily ascertained based on a small panel of western pig genomes and that more SNPs had a MAF less than 0.01 for the Chinese Erhualian dataset than for western DLY dataset. SNP chromosomal positions were based on the current pig genome assembly (Sus Scrofa Build 10.2 assembly).Genomic DNA was isolated from ear tissue using a standard phenol/chloroform method and dissolved in Tris-EDTA buffer. DNA quality and concentration were determined using a Nanodrop-1000 spectrophotometer . A total of 336 Chinese Erhualian and 610 DLY pigs were genotyped for 61 565 SNPs using the Illumina PorcineSNP60K Beadchip according to the manufacturerpolygenic function of GenABEL v1.7 [polygenic and mmscore function of GenABEL v1.7 [Heritability of meat quality traits was estimated using the BEL v1.7 . Genome-BEL v1.7 . AssociaBEL v1.7 ,33, whicBEL v1.7 . Sex andBEL v1.7 ,36. AfteSince population stratification is generally considered as a major factor that influences GWAS results , it was P-values and effects of a set of 29 516 SNPs that were common to all four populations i.e. Erhualian, DLY, F2 and Sutai.A meta-analysis was performed using a Z-score approach in the METAL software  that comIn this study, we conducted two separate single-population GWAS i.e. on Erhualian and DLY populations and a meta-analysis of GWAS on four populations. The Q-Q plots of the test statistics for each separate GWAS and the meta-analysis are in Figure S1 [See Additional file P = 7.97E-09). No SNP was associated with marbling at the GWAS significance level. Manhattan plots of GWAS for the 20 meat quality traits that were analyzed in the two populations are in Figure Overall, we identified 37 SNPs (one unmapped SNP) on 14 chromosomes that were significantly associated with pH, drip loss, meat colour, moisture content and firmness in these populations (Table Sus scrofa chromosome), SSC3 and SSC16, respectively. No significant SNP was associated with pH value in the DLY dataset. Five significant SNPs were associated with drip loss in the Erhualian dataset, including four SNPs for drip loss of LM on SSC4 and one SNP for drip loss of SM on SSC1. In the DLY dataset, two SNPs associated with drip loss of LM were identified on SSC13 and SSC9, respectively. No common QTL was found between the two populations.We identified three significant SNPs associated with pH values in the Erhualian dataset, including one for LM_pH24h, one for SM_pH24h and one for LM_pHdrop_45min-24 h, which were localized on SSC9 .Meta-analysis is a powerful method that can reveal otherwise hidden or unclear associations that are detected by independent studies . Our met2 intercrosses between Chinese and Western outbred pig strains, but few QTL have been detected within the founder strains (particularly the Chinese indigenous pig breeds), which restricts the use of maker-assisted selection to improve the performance of the founder animals and that of the crossbred animals. Here, we analyzed replicability of GWAS results between intercrosses and Chinese- and Western-type outbred populations. In the Erhualian dataset, we found a significant SNP ss131225834 for SM_pH24h at 15.27 Mb on SSC3 , SNP ss131352394 for LM_ColorScore_24h at 5.41 Mb and SNP ss478937524 for LM_Firmness at 7.76 Mb , was also identified at 52.72 Mb on SSC12  on SSC7 was shown to contain SNPs that are associated with three related traits, including the most significant SNP ss120019062 for LM_ColorM_L24h at 7.02 Mb (P < 10−4); two adjacent SNPs associated with both traits were within a region of 11.12 Mb (between 84.83 and 95.95 Mb) on SSC9, which indicates that this region may contain a pleiotropic QTL  on SSC7 is detected that contains several SNPs associated with LM_DripEZ_36h (P = 1.01E-04), LM_ColorM_L36h (P = 2.25E-05) and LM_ColorScore_36h (P = 3.44E-05) for the DLY pig data [See Additional file For the DLY pig data, a 6.98 Mb region (between 127.41 and 134.39 Mb) on SSC15 was associated with LM_ColorM_a36h and LM_ColorScore_36h. The correlation coefficient between drip loss and colour score of LM was negative and highly significant  and Sutai (Duroc × Taihu) pigs. Both F2 and Sutai pigs share some genetic background with Duroc and Erhualian pigs, thus these populations may harbour some common QTL alleles. Moreover, meta-analysis is a tool for aggregating information from multiple independent studies so that it can provide more power to identify associated variants with small effect sizes or low frequencies. Therefore, this meta-analysis enabled us to identify several novel loci that were not detected by separate single-population GWAS. In addition to the SNP on SSCX that was detected using the F2 pig data, three additional SNPs for LM_pH24h on SSC3 and SSC7 were only detected by the meta-analysis. SNP ss131380751 on SSC8 for LM_DripEZ, SNP ss131499292 on SSC14 for LM_ColorScore_24h, SNP ss131289803 on SSC5 for LM_MoistureContent and SNP ss131233621 on SSC3 for LM_ColorM_b24h were also only detected by the meta-analysis. The most significant SNP ss131233621 on SSC3 was the first SNP found to be associated with both LM_ColorM_b24h and LM_pH24h. Compared to independent analyses, the joint analysis enabled us to obtain more precise locations and stronger significance for some QTL. For example, the most significant SNP ss131529154 for LM_ColorM_a24h that was revealed by the meta-analysis  identified by the GWAS on the DLY population only. The P-value for the locus for LM_ColorM_a24h on SSC15 was smaller (P = 5.62E-06) in the combined analysis than in the analysis with the DLY (P = 1.78E-05) population only  compared to the GWAS using only the Erhualian population (2.62E-06), which may be caused by opposite allelic directions of this SNP in different populations.PPP2R5A gene located at about 270 kb upstream of the most significant SNP (ss131409440) is the best candidate gene for LM_pH24h. This gene encodes a protein that belongs to the phosphatase 2A regulatory subunit B family and is highly expressed in heart and skeletal muscle [PPP2R5A is involved in the negative control of cell growth and division and plays a role in the Bio Systems Pathways that are directly involved in glycogen metabolism.We searched for promising candidate genes with functional relevance to the studied traits in an interval of 1 Mb centred on the most significant SNP at each significant locus. Based on the Erhualian dataset, the l muscle . PPP2R5AMYH3, a member of the MYH family, which contains the significant SNP ss478937995 on SSC12, is an interesting candidate gene for colour , firmness and marbling. MYH is a large family of motor proteins that share various common features such as ATP hydrolysis, actin binding and potential for kinetic energy transduction. A missense mutation in MYH3 that induces an F4371 amino acid substitution, is known to segregate with distal arthrogryposis in a human family [ELOVL2 at 233 kb downstream of the significant SNP ss478937524 can be regarded as another promising candidate gene for LM_Firmness, because it is involved in the metabolism of lipids and lipoproteins in mammals [KIF18A gene, which is positioned 71 kb away from the significant SNP ss131184920 on SSC2, may contribute to the LM_Firmness trait measured in DLY pigs. KIF18A is a member of the kinesin superfamily of microtubule-associated molecular motors that use the energy derived from ATP hydrolysis to produce force and movement along the microtubule [As suggested by Luo et al. , MYH3, an family . On SSC7 mammals . The KIFrotubule ,47.LDHA gene, which encodes lactate dehydrogenase A that catalyzes the conversion of L-lactate and NAD to pyruvate and NADH in the final step of anaerobic glycolysis. LDHA is found predominantly in muscle tissue and belongs to the lactate dehydrogenase family [MFAP3 (microfibrillar-associated protein 3) and GALNT10 , within a region of 130 kb. GALNT10 encodes a protein that may have increased catalytic activity towards glycosylated peptides compared to that towards non-glycosylated peptides [MFAP3. However, no obvious candidate genes were found for the other two loci on SSC8 and SSC12, where the nearest genes (ENSSSCG00000029791 and NFE2L1) have no clear functional annotations correlated with the phenotype.Based on the Erhualian dataset, four QTL that are involved in LM_MoistureContent were detected on SSC2, SSC8, SSC12 and SSC16. The significant SNP ss131186989 on SSC2 is just 2.5 kb downstream from the site of the e family . On SSC1peptides , and, thASNS gene, which encodes a protein that is involved in the synthesis of asparagine. This gene complements a mutation in the temperature-sensitive hamster mutant ts11, which blocks progression through the G1 phase of the cell cycle at non-permissive temperatures [PRKAG3 gene at 133.80 Mb [PRKAG3 sequence data are required.The most significant SNP ss131398982 for LM_ColorScore_36h in the DLY dataset is located only 47 kb away from the eratures . Within 33.80 Mb ,52. To cBecause precision of QTL location depends on various factors , it is necessary to be cautious when interpreting the results for loci with 95% confidence intervals that are larger than 1 Mb. One cannot exclude that the genes that underlie the QTL are outside the 1 Mb region that was investigated for candidate genes.In conclusion, we performed GWAS analyses for meat quality traits in Chinese Erhualian and western DLY populations. Overall, 28 and nine SNPs surpassed the significance level in the Erhualian and DLY pig data, respectively. The strongest association was obtained between drip loss and a SNP on SSC4 for the Erhualian population. Comparison of the loci that were detected by GWAS of Erhualian and DLY pig data demonstrated that only a few QTL were common to both pig lines, which reflects the large differences in genetic architecture of meat quality traits between the Chinese and western breeds. Our results suggest that several QTL affect multiple traits among which some are reported for the first time. The meta-analysis that combined GWAS of four populations uncovered seven novel loci, which were not detected by single-population GWAS. These novel loci represent variants that may be shared between these populations. For each of the QTL regions detected, we identified several candidate genes based on the possible relationship between gene function and the corresponding trait. This study provides new insights into the genetic basis of meat quality traits in pigs and further investigations are needed to guide the application of these QTL in future swine breeding programs.