Lynx pardinus) has been acknowledged as the most endangered felid species in the world. An intense contraction and fragmentation during the twentieth century left less than 100 individuals split in two isolated and genetically eroded populations by 2002. Genetic monitoring and management so far have been based on 36 STRs, but their limited variability and the more complex situation of current populations demand more efficient molecular markers. The recent characterization of the Iberian lynx genome identified more than 1.6 million SNPs, of which 1536 were selected and genotyped in an extended Iberian lynx sample.The Iberian lynx  contains supplementary material, which is available to authorized users. Genetic markers provide powerful tools for the study and conservation of biodiversity. They provide valuable insights into population sizes, mating systems, relatedness, population structure, and dispersal rates , 2, whicMicrosatellites or short-tandem repeats (STR) have been the markers of choice for the genetic monitoring and management of populations for the last two decades , 9, but All in all, SNPs allow for a notorious increase in the power and accuracy of most genetic analyses when available in sufficient numbers. SNP discovery in non-model species, although still a major hurdle, has been greatly facilitated by recent advancements in Next Generation Sequencing (NGS) . NanoscaLynx pardinus) has been highlighted as the most endangered felid species in the world [The Iberian lynx . In spite of this, the species is still in high risk of extinction, and its survival heavily dependent on continued conservation measures.Early in the twenty-first century the Iberian lynx dramatic decline prompted the implementation of active conservation strategies with the main funding of three successive European LIFE projects . ConservGiven that in recent years many reintroduced populations have been founded by individuals from the admixed CAP population and several translocation events have been carried out between DON and AND, currently all Iberian lynx populations harbour varying degrees of genetic admixture. Such an increasingly complex and interwoven conservation landscape poses new challenges and demands tools as informative and efficient as possible for an integrative management and monitoring of the species.In this study, we analysed data for ca. 1500 SNPs previously selected from whole genome sequences and previously genotyped in 380 Iberian lynx samples from five populations . The resd > 0.6 Mb), and genotyping quality score predicted by the Illumina’s Assay Design Tool algorithm (ADT > 0.9). A total of 380 Iberian lynx blood and muscle samples, including 28 replicates as internal controls, were genotyped for these SNPs using Illumina GoldenGate technology. Genotypes were successfully obtained for 349 samples corresponding to 329 individuals (and 20 replicates).Out of >1.6 million SNPs identified through whole genome sequencing of 11 Iberian lynx males , a totalMarkers and samples with more than 20% of missing genotypes were identified with PLINK v1.90b3.36  and discWith the purpose of refining the pedigree and, ultimately, identifying markers departing from autosomal Mendelian inheritance (including X-linked markers), we assessed Mendelian inconsistencies based on known parent-offspring (PO) relationships using the --mendel function in PLINK. Some of these discrepancies allowed us to identify a few misassignments and amend the current pedigree. Additionally, some previously unknown relationships were uncovered by maximum likelihood analyses using ML-RELATE  and COLODON; N = 91) and all pure AND  individuals, considering as such all individuals born in the respective population before their admixture, as well as any wild- or captive-born descendant from two individuals of the same pure ancestry. For each pool, basic summary statistics per marker such as their MAF, observed and expected heterozygosity , and the FST between the two pools were generated for both the autosomal and the X-linked sets using the snpStats R package [E values between the two pools. The autosomal FIS for each of the two pools was estimated using the R package hierfstat [To characterize the genetic pools of DON and AND, we used all pure DON  procedure [As a first step towards selecting a subset of autosomal SNPs for application to conservation, a two-tailed z-test for HWE was performed on each of the two pools of pure individuals with the R package Weinberg . Resultirocedure .DON, N = 35; for PAND, N = 45), we discarded monomorphic markers within each pool, and we used the R package snpStats to calculate –for both the autosomal and the X-linked panels– the pairwise r2 (the squared correlation coefficient between SNP pairs) between all markers. We applied PLINK’s command --indep-pairwise to both the PDON and the PAND autosomal panels, with the r2 threshold parameter set to a value of 0.5 to prune in one SNP from each pair with r2 above the threshold. By intersecting the two pruned lists with the R package VennDiagram [Next, given that LD between markers in a panel can artificially inflate its power, we defined sample subsets of supposedly unrelated individuals  and an average HO of 0.387 (range: 0.003 – 0.774) [i)Individual identification. We used the advanced frequency-based analysis module in GENALEX 6.5 [ALEX 6.5  to estimALEX 6.5 . Global ii)Parentage assignment. We also used GENALEX to obtain three probability estimates for parentage exclusion per marker: the probability of exclusion for one putative parent when the genotype of the other parent is known (PE1), the probability of exclusion for one putative parent when the other parent’s genotype is missing (PE2) and the probability of exclusion for the putative parent pair (PE3), as described by Jamieson & Taylor [& Taylor . The coriii)Relatedness estimation. We sorted the SNPs by their informativeness for relatedness index (Ir), which was obtained using KININFOR [−5 for SNPs and 0.015 for STRs.KININFOR . Given tKININFOR , 54. Thriv)Ancestry and admixture estimation. We evaluated the power of panels through the rate of correct classification of simulated individuals with varying degrees of admixture. First, HYBRIDLAB [DON, (ii) PAND, (iii) offspring of PDON x PAND cross (F1), (iv) offspring of F1 x F1 (F2), (v) first backcross DON (BcDON), (vi) first backcross AND (BcAND), (vii) second backcross DON (2BcDON), and (viii) second backcross AND (2BcAND), based on the genotypes for all 343 SNPs and 36 STRs. This programme creates random genotypes for a specified hybrid population based on the allelic frequencies from each of the two hybridizing groups. Next, we used a Bayesian-Markov Chain Monte Carlo (MCMC) method implemented in NEWHYBRIDS [r; (iii) same-sized SNP panels but ranked according to their FST between PDON and PAND; (iv) all 36 STRs; (v) 12 top-ranking STRs according to HE; and (vi) 12 STRs of highest FST. Following exploratory runs, a final run of 11,000 iterations (of which the first 1000 were discarded as burn-in), was conducted for every panel using Jeffrey’s priors. Individuals were then assigned to the ancestry category for which the highest posterior probability was obtained, as long as it exceeded 60%. Otherwise they were deemed as “ambiguous” and remained unassigned. Finally, summary matrices with correct, ambiguous, and cross-classification rates were constructed by comparing the true (simulated) ancestry to that inferred using NEWHYBRIDS. Analogously, empirical genotypes in the final dataset were also classified into ancestry categories using the whole and FST-ranked SNP and STR panels.YBRIDLAB  was usedWHYBRIDS , to estiTo optimize marker-assisted monitoring and management, we pursued the identification of the most informative SNPs for individual identification, parentage assignment, relatedness estimation, and ancestry estimation. Based on the allelic frequencies of the 136 individuals from CAP , we estimated statistics relevant to each application (see below). Then, for each statistic, we ranked SNPs and obtained global values for the 343 SNP set as well as for the top 192, 96, 48, 24, and 12 markers. Note that optimal panels should be redesigned for populations with significantly different allelic frequencies. For comparison purposes, the same statistics were also estimated for the panel of 36 STR markers currently used, as well as for a reduced set with the 12 STRs of highest H– 0.774) .i)IndivFelis catus; Additional file In order to identify highly informative SNP panels for genetic monitoring and management, we analysed SNPs which were previously selected from a whole genome variation scan and were genotyped using Illumina GoldenDate technology . Out of −5.None of the 349 successfully genotyped samples were removed on account of high missingness. When comparing replicate samples to estimate an error rate, we could not find a single discrepancy among 29,857 valid pairwise genotype comparisons (after discarding missing data) among 14 duplicates and three triplicates, which translates into an error rate lower than 3.35*10DON and PAND . Nearly all these mismatches occurred between sires and their male offspring, indicating that these markers could be X-linked. None of these markers were in confirmed regions of synteny to domestic cat, but they all were within scaffolds that contain other regions with shared synteny to cat chromosome X. Additionally, three out of the remaining 1471 SNPs were syntenic to cat’s X chromosome. The two more distal markers  behaved as autosomal, suggesting they are located within the pseudo-autosomal region ; however, the third one (2270811), which is found slightly farther from the chromosome end (6.2 Mb), showed significant allelic and genotypic differences between males and females in both PTested PO relationships that were identified as incorrect  had a minimum of eight Mendelian inconsistencies at these 1471 SNPs, and this was a case in which the discarded parent was a close relative of the true parent. After excluding wrong assignments, a total of eight Mendelian errors remained across 108 dyads and 77 triads of high confidence. These discrepancies involved one dyad and four triads, with two individuals probably accruing all eight errors  and high differentiation between the two pools . For the 21 X-linked markers, we observed slightly lower diversity, but again different diversity between pools  and higher differentiation than for autosomal markers   for PDON and 0.020 (σ2 = 0.001) for PAND . Correlations of these with Wang’s Ir were slightly lower (τ > 0.998), but the selected panels differed in no more than two SNPs between applications – and often in none at all.To obtain informative panels for these applications, we ranked the 343 minimally linked SNPs according to six statistics , estimated based on the allelic frequencies of CAP. We found a high Kendall rank correlation  among alE. The 24 SNP panel already surpassed in individual discrimination power  the 12 STR panel currently used for individual identification , and as few as 12 SNPs could still be informative enough to reliably distinguish non extremely inbred or related individuals . Finally, the 48 SNP panel’s power to discriminate among alternative relationships was slightly lower than that of 36 STRs, but the 96 SNP set well outperforms it, achieving a power higher than 80% even for the most demanding of the three comparisons  and starkly outperformed it for the remaining categories . Notwithstanding, the performance of reduced SNP panels of highest Ir was not satisfactory  classification of pure and F2 individuals, whereas correct assignments were slightly lower for first backcross  and second backcross categories . Only 33 out of the 800 simulated individuals couldn’t be unambiguously assigned to any ancestry level. For smaller panels the classification accuracy fell as expected with size, and particularly so for the less distinguishable backcross and second backcross categories. Overall, the performance of the panel of 48 SNPs was similar to that of the 36 STRs  panels showed higher rates of misclassification of pure individuals as second backcrosses than expected from simulations, whereas a better correlation was observed for smaller panels. The sets of 48 SNPs and 36 STRs behaved similarly to each other and to expectations from simulations, with a slightly lower classification of pure DON individuals with SNPs . In combination with flexible genotyping technologies they do provide novel, efficient and cost-effective tools for the demographic and genetic monitoring and management of this highly endangered and actively managed species. Furthermore, we identified 21 X-linked markers that may prove useful for complementing parentage analyses, determining sex, or genetic studies focused on this chromosome.Genetic monitoring and most forensic applications involve a first step in which genotypes are obtained from new samples and, by checking them against the reference database, are either assigned to already recorded individuals, or identified as previously unsampled individuals. The proposed panel of 24 SNPs has sufficient power to discriminate even between closely related individuals. In a subsequent step in genetic monitoring, novel individuals may be assigned to parents so that a population genealogy is progressively built , other populations lack reliable and reasonably complete genealogies.r share a nearly identical ranking of markers means that panels designed for a less demanding purpose  are nested within those for more demanding applications (e.g. parentage assignment). This enables a module workflow that grants great flexibility to genetic monitoring and management of Iberian lynx populations. For example, non-invasively collected samples would be assigned to specific individuals based on the 12–24 SNP panels. The novel unique genotypes could then be assigned to parents and incorporated to the pedigree after genotyping one of its replicate samples for the next most informative 24–72 SNPs. Finally, individual lynx that are candidates for being translocated or incorporated into captivity could be additionally typed for the remaining SNPs for relatedness estimation.The fact that PID, PIDs, PnE, and IE are poorly differentiated between the two pure ancestries, admixture tests would require their own particular sets of ancestry informative markers, with little overlap with those selected for other applications. For this purpose, using loci with different alleles fixed in each population would be optimal, but these were excluded by the MAF requirement in the initial selection. Fortunately, a sufficient number of highly differentiated SNPs were available to provide enough power for ancestry classification. Simulated individuals of PDON, PAND, F1, and F2 categories are reasonably well classified with the 48 highest FST SNP set and the 36 STR set, whereas less differentiated categories require at least 96 SNPs.We also considered admixture estimation as a potential application of the novel SNP markers, since the two highly differentiated genetic pools have recently been admixed in captivity and in the wild through translocations. Differentiating individuals by ancestry might be most relevant for the assessment of the fitness consequences of admixture, i.e. for the evaluation of genetic rescue or outbreeding depression (e.g. ). Since The observed accuracy of ancestry classification of empirical genotypes with SNP panels became lower than that inferred from simulated genotypes as panel size increased. This is probably due to the additional LD derived from the relatively high relatedness structure and, especially, the recent admixture in current Iberian lynx populations (admixture linkage disequilibrium) . This efLynx rufus), and four out of six from Canadian lynx (Lynx canadensis) [E than any of the selected SNPs.Our observed SNPs to STRs ratio  is at the lower end of or even below the range of 1.7 to 5.56 SNPs per STR reported in humans (see  and the adensis) . This moThese results highlight the importance of the variant discovery and screening phases. Once considered a major limitation of SNP adoption, NGS approaches have greatly facilitated these tasks in non-model organisms, especially when applied to reduced genome subsets or transcriptomes . Still, The decision on how many markers to use for the most demanding applications (e.g. relatedness estimation) will mostly rely on cost-budget considerations, and these heavily depend on the genotyping technology used. The excessive cost of chip-based technologies –but also their limited flexibility and high input sample requirement– have deterred the application of non-invasive SNP-based approaches in wildlife. On the other hand, technologies based on fluorescent detection provide the advantage of a single PCR reaction, thus avoiding multiple manipulation steps , and wheThe Iberian lynx example highlights one valuable practical outcome of whole-genome studies in heavily eroded species, i.e. the identification of scarce highly informative markers that will most likely be missed in smaller scale screenings. Indeed, highly efficient and cost-effective marker panels for genetic monitoring and management are likely the most immediate and feasible contribution of genomics to endangered species conservation.The Iberian lynx is a quite extreme example of genetically eroded and intensively managed species, with ongoing conservation actions including captive breeding, translocations and reintroductions. The long-term success of such actions, and ultimately the viability of the species, will largely depend on the implementation of sound, efficient, and science-driven monitoring and management programmes. As the novel SNP markers presented here provide higher power, efficiency, and flexibility than currently used STRs, they can make a substantial contribution toward this goal by guiding the comprehensive genetic management of captive, remnant and reintroduced populations and, in combination with non-invasive sampling, by complementing –and eventually substituting– monitoring programmes currently based almost exclusively on radiotracking and camera-trapping.Additional file 1: Table S1.Table S2. Results of the Fisher’s exact test on differences in allele and genotype frequencies between sexes for the PDON pool. Table S3. Results of the Fisher’s exact test on differences in allele and genotype frequencies between sexes for the PAND pool. Table S4. Basic population genetics statistics per autosomal marker for both genetic pools. Table S5. Basic population genetics statistics per X-linked marker for both genetic pools. Table S6. Results for HWE analysis per autosomal marker for the PDON pool. Table S7. Results for HWE analysis per autosomal marker for the PAND pool. Table S8. Results for HWE analysis per X-linked marker for the PDON pool. Table S9. Results for HWE analysis per X-linked marker for the PAND pool. Table S10. Hemimatrix of pairwise r2 values between autosomal markers for the PDON pool. Table S11. Hemimatrix of pairwise r2 values between autosomal markers for the PAND pool. Table S12. Hemimatrix of pairwise r2 values between X-linked markers for the PDON pool. Table S13. Hemimatrix of pairwise r2 values between X-linked markers for the PAND pool. Table S14. Hemimatrix of Kendall rank correlation τ values between HE and statistics selected to rank markers. Table S15. Informativeness and rankings for individual identification, parentage assignment, and relatedness estimation of SNPs in the 343 SNP panel. Table S16. Informativeness and rankings for individual identification, parentage assignment, and relatedness estimation of STRs in the 36 STR panel. Table S17. Summary matrices with NEWHYBRIDS’ correct, ambiguous, and cross-classification rates of simulated individuals in eight ancestry levels, for differently-sized and -ranked SNP and STR panels. Table S18. Summary matrices with NEWHYBRIDS’ correct, ambiguous, and cross-classification values (and percentages) of empirical individuals in eight ancestry levels, for different-sized SNP and STR panels. (XLSX 33994 kb)Genotypes of replicate samples. Additional file 2: Figure S1.Figure S2. Family trees accruing all eight Mendelian errors concerning SNP 1317372 (A); SNP 334649 (B); SNP 619378 (C); SNP 2119932 (D); and SNP 2057563 (E). Dark background: allele A; light background: allele B; white background: unknown allele. Figure S3. Ancestry of Iberian lynx individuals in each of the two differentiated genetic pools, as inferred from the analysis of 1,471 autosomal SNPs in STRUCTURE under the assumption of two genetic clusters (K = 2). Each individual is represented by a vertical line with the height of each colour representing the estimated fraction of their genome belonging to each cluster. The black vertical lines delimit groups of individuals of different admixture categories, know a priori from their provenance or recent genealogy . Ten runs of 100,000 MCMC iterations  were performed in STRUCTURE v2.3.4 with highly concordant results. A representative run is depicted here. (PDF 380 kb)Location of syntenic SNPs in the domestic cat’s chromosomes.


We provide an R implementation of our algorithm.Genome-wide association studies (GWAS) with longitudinal phenotypes provide opportunities to identify genetic variations associated with changes in human traits over time. Mixed models are used to correct for the correlated nature of longitudinal data. GWA studies are notorious for their computational challenges, which are considerable when mixed models for thousands of individuals are fitted to millions of SNPs. We present a new algorithm that speeds up a genome-wide analysis of longitudinal data by several orders of magnitude. It solves the equivalent penalized least squares problem efficiently, computing variances in an initial step. Factorizations and transformations are used to avoid inversion of large matrices. Because the system of equations is bordered, we can re-use components, which can be precomputed for the mixed model without a SNP. Two SNP effects (main and its interaction with time) are obtained. Our method completes the analysis a thousand times faster than the R package  Genome-wide association studies with longitudinal phenotypes create opportunities and challenges. On the one hand we can identify genetic variants that are associated with development of traits over time. On the other hand statistical analysis gets more complicated, because (linear) mixed models have to be used.In this paper we discuss the application of the linear mixed model to repeated measures, collected on unrelated individuals. We assume that the number of measurements per person is just a handful, allowing to model only a linear evolution of the trait over time. In a genome-wide analysis the mixed model has to be fitted for every SNP. It contains fixed effects for time, SNP, and their interaction, and possibly other covariates; it has random intercept and slope for change over time. Of main interest is the time x SNP effect, but multiple observations per individual also increase the power to detect a statistically significant main SNP effect.lme4 in R do this efficiently, using special algorithms. It takes approximately 2.0 seconds to fit a mixed model for several thousand individuals. For a single application this is fast, but for GWAS it is far too slow. Fitting one million mixed models, one for each SNP, would take several weeks of non-stop computation. This assumes that the overhead of accessing the SNP data is negligible, which usually is not the case.A mixed model assumes that some model parameters, in the present case intercept and slope per individual, have been drawn from a  distribution with unknown variance. Also unknown is the variance of the observation error. Once these variances are known, it is straightforward to estimate individual slopes and intercepts. The hard work for mixed models is estimating the variances. Common software, like SAS PROC MIXED and 2 or to correct for hidden correlation due to population stratification4. Extensive work has been done on how to speed up computations in the latter case, see e.g.6. Unfortunately it does not solve our problem; see the Discussion.We emphasize that analysis of longitudinal data is different from analysis of cross-sectional outcomes where mixed models are used either to estimate heritability7, which summarizes the developmental pattern of a trait as an individual slope, reducing the dimensionality of the data to one pseudo-observation per individual. This allows the use of our fast GWAS algorithm8 to obtain an approximate p-value for the interaction between SNP and time.In an earlier effort, we proposed the conditional two-step (CTS) approachlme4. Thanks to the sparseness of the equations, memory use is modest.Here, we present a new algorithm for Genome-wide Analysis of Large-scale Longitudinal Outcomes using Penalization (GALLOP) which swiftly computes coefficients and p-values for cross-sectional and longitudinal SNP effects. To arrive at an almost exact solution we exploit several properties of the model. The effect of a SNP generally is (very) small. We estimate the variances in the mixed model without any SNP and assume that they will not change when a SNP is added. This assumption will lead to conservative p-values in case of non-zero SNP-effects. The magnitude of this imprecision is explored in the Results section. Using the equivalence between a mixed model and penalized least squares, a large system of linear equations can be set up. This system is very sparse (it contains many zeros) and only the last rows and columns change from SNP to SNP. With careful organization of the computations a solution is obtained very quickly. No special programming tricks are needed, our program (about 85 lines) is written in pure R and achieves a speed-up by three orders of magnitude, compared to brute-force application of Quick access to SNP data is crucial and we also discuss it. An R implementation of GALLOP algorithm is provided. Simulated and real data are used to illustrate performance.lmer function in the R package lme4. We assessed them via a simulation study and using real data.Two characteristics of our method are of main interest: high speed and accuracy as compared to n = 2000, k = 4, 3 additional covariatesn × k vector of tij’s) drawn from a uniform distribution between 0 and 10Measurements occasions  < 7). That eliminates the danger of finding too many false positive results. Due to overestimated standard errors, the −log 10(p) for larger betas are too pessimistic. Nevertheless, they increase monotonically with larger effect sizes, just with bias downward with respect to the −log 10 (p) from lmer. This loss of power can be solved by lowering the threshold for “GWAS significance” and repeating the analysis for promising SNPs with the correct model. In our simulation study, to find all SNPs for which −log 10 (plmer) > 7.3 we had to use the threshold −log 10 (pGALLOP) > 7.05. In our simulation study the maximum contribution to R2 of the SNP effects around 6%.The estimates are very accurate throughout the entire range of observed values Fig. . The staues Fig. . These a9. Details on the longitudinal BMD data set are provided in ref.7. For this analysis we used SNP data imputed according to the 1000 Genomes Project, which were stored per (part of) a chromosome as DatABEL files. To test our algorithm we used one of the files, which contained 97384 SNPs. We performed the association analyses with three methods: GALLOP, CTS, and lmer (only for 20 K SNPs). Comparison between p-values is shown in Fig. p-values for longitudinal SNP effect, which coincide with our previous results on the real and simulated data. However, p-values from GALLOP are basically exact for main and longitudinal effect, irrespective of minor allele frequency. The analysis took 3.5 minutes for GALLOP, 40 seconds for CTS and 48 hours (extrapolated time based on the 20 K SNPs) for lmer, respectively.To confirm the accuracy of GALLOP on real data, we used the BMD data from the Rotterdam Studylme4, which is the fastest option in R. This speed-up is achieved by combining an accurate approximation with a careful implementation. We showed that our method provides practically exact results. In case of doubt one can always do a full mixed model analysis for each of the most significant SNPs. Generally this is a small number; in case of BMD data 6 genotypes for any MAF reached threshold of −log 10 (p) > 7; so the extra computation time is negligible.We presented a new algorithm for fast genome-wide analysis with longitudinal data. Our method runs a thousand times faster than Our previous approach, conditional two-step (CTS) method combined with semi-parallel regression, computes p-values for the interaction effect about 15 times faster than the GALLOP. However, for CTS, SNP data access is still a bottleneck, 85% of the analysis time is spent on data access −1. System , and Ci is a ki × q-dimensional matrix with constant or time-varying additional covariates . We call model . The software does not explicitly return the P*, but it does return the variance-covariance matrix of the random effects (matrix D) and the variance of measurement error (σ2). In R matrix P* is obtained by calling solve The A22 in (K−1b) = Kq. Applied to I. Let UiΩISi + P*, where Ui is the matrix of eigenvectors with Ui = I and Ωi is the diagonal matrix of positive eigenvalues. Choose An additional computational simplification can be obtained by ensuring that A22 in  is the iplied to , we get8The linearly transformed system becomesiki × 2 dimensional matrix, with SNP values repeated ki times for all individuals in the first column and the SNP values repeated ki times multiplied time occasions in the second column. Repeating SNP data for each individual ki times seems like a time consuming step. However, SNP is constant over time and thus Gi = SNPi * Zi. In our implementation the vector replicating SNP vector k times is never created explicitly. Regardless the value of ki SNP values have to be replicated twice per individual resulting in additional efficiency. The augmented system of equation has the form:βSNP = ′. Note that system , the variances in D* will be inflated. The cross-sectional effect inflates the variance of the random intercept, while the longitudinal effect affects the variance of the random slope. The magnitude of this inflation depends on the β2 and β3. The covariance in D* is influenced only if both SNP effects are non-zero.When a SNP is not important in the model, i.e We can write the system  as16\docβSNP gives usSolving system  for βSNPn + p + 2) × (2n + p + 2) dimensional matrix. However, the inverse of H22 is not needed explicitly. Note that H21. Note that in this case the RHS of the system is two-dimensional.It may seem that, in , \documef system . It can f system  but for σ2. In our case we are interested only in the inference for SNP effects. They are the upper-left part of the expressionTo compute the variance-covariance matrix of the estimated fixed and random effects in a mixed model we need to invert the LHS matrix of system . StandarβSNP are given byUsing the formula for the matrix inverse in block form, the standard errors of Note that this diagonal has already been computed in  showing k rows and that some of the values are missing (coded as NA). To properly estimate the solution of a mixed model the weighting matrix has to be introducedMixed models handle unbalanced data with ease; all subjects, whatever their number of observations, are taken into the analysis. In this sense the concept of missing data in case of mixed models does not exist. However, our algorithm assumes that the phenotype data for every subject consists of nk × nk matrix with 0 or 1 in the diagonal indicating if the observation is valid or not. Note that in practice matrix W does not have to be build, since applying weights is equivalent to replacing rows with missing data by all zeros.Matrix W is a diagonal k times, to be able to calculate cross products in the border matrices. We achieved this by storing the basis of those matrices, calculated using Kronecker products, in a vector instead of a matrix. This way we can summarize the SNP state directly with two numbers per individual, regardless of k.GALLOP is implemented in one relatively short R program, provided in the Supplementary Materials. An important computing challenge was to avoid repeating each SNP value The BMD data are a part of Rotterdam Study and are confidential. The scripts used to generate the data in the simulation study are available from the corresponding author upon request.Supplementary Material


Therecent development and availability of different genotype by sequencing (GBS) protocols provided a cost-effective approach to perform high-resolution genomic analysis of entire populations in different species. The central component of all these protocols is the digestion of the initial DNA with known restriction enzymes, to generate sequencing fragments at predictable and reproducible sites. This allows to genotype thousands of genetic markers on populations with hundreds of individuals. Because GBS protocols achieve parallel genotyping through high throughput sequencing (HTS), every GBS protocol must include a bioinformatics pipeline for analysis of HTS data. Our bioinformatics group recently developed the Next Generation Sequencing Eclipse Plugin (NGSEP) for accurate, efficient, and user-friendly analysis of HTS data.Here we present the latest functionalities implemented in NGSEP in the context of the analysis of GBS data. We implemented a one step wizard to perform parallel read alignment, variants identification and genotyping from HTS reads sequenced from entire populations. We added different filters for variants, samples and genotype calls as well as calculation of summary statistics overall and per sample, and diversity statistics per site. NGSEP includes a module to translate genotype calls to some of the most widely used input formats for integration with several tools to perform downstream analyses such as population structure analysis, construction of genetic maps, genetic mapping of complex traits and phenotype prediction for genomic selection. We assessed the accuracy of NGSEP on two highly heterozygous F1 cassava populations and on an inbred common bean population, and we showed that NGSEP provides similar or better accuracy compared to other widely used software packages for variants detection such as GATK, Samtools and Tassel.NGSEP is a powerful, accurate and efficient bioinformatics software tool for analysis of HTS data, and also one of the best bioinformatic packages to facilitate the analysis and to maximize the genomic variability information that can be obtained from GBS experiments for population genomics.The online version of this article (doi:10.1186/s12864-016-2827-7) contains supplementary material, which is available to authorized users. Genotype by sequencing (GBS) is a powerful, cost-effective method to obtain genome-wide variability information for populations composed by hundreds of individuals –5. In brA key component of any GBS protocol is the bioinformatics pipeline required to analyze the reads and to obtain polymorphic sites within the sequenced population. Widely used packages such as bwa  or bowtiWe recently developed the software package NGSEP as an accurate, efficient and easy-to-use alternative for analysis of high throughput sequencing (HTS) data and we demonstrated its advantages over other commonly used packages using benchmark whole genome sequencing (WGS) data on humans, yeast and rice . Here weThe deconvolution process allows to distribute the reads obtained from fragments barcoded and sequenced in one Illumina lane, producing one separate file for each sample. It receives a fastq file with the lane information and a text file describing the link between barcodes and samples. Although deconvolution can be generally applied to any type of sequencing in which samples are identified by barcodes, it is particularly important for GBS data because in GBS experiments 96 samples (or even up to 384 samples in some cases) are sequenced per lane to achieve cost efficiency. We also included an option in this module to identify a user-defined tag on each read and then trim the read from the starting point of this tag. This allows to remove contamination of adaptor sequence on the three prime end of the reads, which we have identified as the most important issue affecting the quality of the reads produced by the Elshire protocol for GBS  filtering module to include most of the filtering strategies that can be employed to achieve the balance between variant genome coverage and genotype quality required for different applications. Genotype calls can be filtered based either on genotype quality (GQ field in the VCF genotype format) or total read coverage. Genomic regions can be either selected or filtered out, which is useful to remove markers in undesired regions  or to focus on a particular region of the genome, such as a quantitative trait locus (QTL) or a gene. Samples can also be either selected or filtered out, which allows users to remove samples that were sequenced at lower coverage or to focus on subpopulations within the whole dataset. Single nucleotide polymorfisms (SNPs) can then be selected based on several criteria such as number of individuals genotyped, minor allele frequency (MAF), distance between markers and functional annotations. To select SNPs ameanable for other high throughput SNP genotyping platforms (e.g. PCR based), users can also select limits on the GC-content of the region surrounding each particular SNP. All these filtering approaches can be applied to any VCF file using minimal computational resources, which facilitates users trying several different options for downstream analysis. We also expanded the conversion module to allow users to translate the genotype data to the input formats needed by tools such as DARwin to build distance-based dendograms , JoinMapWe added three modules providing several statistics on the genotypes included in a VCF file. Summary statistics include counts of number of variants discriminated as biallelic SNPs, biallelic indels and other  variants, the MAF distribution and the distribution of SNPs genotyped in different numbers of individuals. It also includes counts of genotype calls per sample, including number of genotyped variants, non-reference genotype calls and heterozygous calls. Counts per sample are also discriminated by gene functional annotations. The diversity statistics module calculates for each variant common statistics for population genomic analysis such as MAF, expected and observed heterozygosity, and deviation from Hardy-Weinberg Equilibrium. Users can provide a text file indicating a clustering of samples in subpopulations, which allows this module to calculate diversity statistics for each subpopulation, facilitating the calculation and genome-wide analysis of F-statistics. Finally, a third function allows to compare the genotype calls on two VCF files (or one VCF file with itself), and calculates the number of differences between every pair of samples. This allows to identify potentially duplicated materials or direct parental relationships.One of the main issues of GBS, compared to high throughput genotyping platforms, is the relatively high percentage of missing data produced by the random distribution of read coverage both across samples and across genomic sites. Although several bioinformatics methods have been developed to perform genotype imputation  and minor allele frequency (MAF) in four different categories , and applying the same filters on number of individuals genotyped, repetitive regions and observed heterozygosity, retaining SNPs consistent with the categories useful to build a genetic map C2 and C3). We found that, among filtered datasets, NGSEP, GATK and Tassel share over 60 % of their predicted SNPs, whereas only up to 46 % of the SNPs reported by Samtools are shared by the other methods  because this score should be related to the confidence assigned by each method to each genotype call. We found that quality scores of GATK and Samtools tend to be smaller than those of NGSEP  are likely to be called due to genotyping errors. In this comparison Tassel showed the worst behavior generating 28,954 spurious SNPs at q≥99, compared to 5,953 spurious SNPs produced by NGSEP at q≥40 and about 1,000 produced by GATK and Samtools at q≥20 and q≥10 respectively. Figure 3Although SNPs in category 4 are in principle not useful for construction of genetic maps from F1 populations, we performed the same analysis for the SNPs in this category to assess the behavior of each method in highly heterozygous sites. In this case Tassel and NGSEP more clearly outperform GATK and Samtools Fig. b and e. As a final assessment of the accuracy of the NGSEP pipeline, we built a draft genetic map for the NxA population using as input one of the most high-quality datasets obtained with NGSEP, which includes 2,422 SNPs. Consistent with previous studies , we obtaAs a benchmark for comparison of methods on populations of inbred lines, we present here preliminary results on GBS of a common bean multiparental advanced generation intercross (MAGIC) population , 25 deve4Ho) larger than 0.05, which in this subset of the population would correspond to SNPs in which at least 5 individuals are heterozygous. Figure 44Ho distribution consistent with the expected percentage of heterozygous calls for this population. In the case of Samtools and GATK, the most likely explanation for this behavior is the effect of the assumption of Hardy-Weinberg equilibrium which in this dataset does not hold due to inbreeding. Tassel on the other hand is probably overcalling heterozygous genotypes because, unlike the other pipelines, it does not take into account base quality scores during genotype calling.We verified if heterozygous genotypes are clustered in a few particular SNPs, which could happen due to repeats not identified in the reference genome or even due to balancing selection, or if they are spread over the whole dataset as expected for errors. We contrasted the total number of SNPs obtained by each method against the number of SNPs with observed heterozygosity  is a powerful and cost-effective protocol to assess the variability of entire populations. However, maximizing the information obtained from GBS experiments requires a bioinformatics pipeline able to understand the particular nature of the reads produced by this protocol and adaptable to optimize its behavior on populations with different characteristics and in particular different distributions of variability and heterozygosity. Here we report the novel functionalities of NGSEP that were designed and implemented to facilitate the analysis of GBS data, and we demonstrated that NGSEP is a powerful and adaptable framework to genotype populations of an outcrosser highly heterozygous species such as cassava as well as populations of inbred individuals such as the bean MAGIC population. For the complete MAGIC population, we assembled a highly curated dataset of about 20,000 SNPs which is now being used to perform genetic mapping of complex agronomically relevant traits in bean (Manuscript in preparation). Besides the populations described in this study and recent works on analysis of whole genome sequencing data in rice  and peanWe compared the accuracy of NGSEP against that of other widely used software packages for analysis of High throughput Sequencing (HTS) data such as GATK and Samtools, which were originally optimized for low coverage WGS data in humans, and the Tassel GBS pipeline which was specifically built for the GBS protocol developed at Cornell. We believe that this effort itself is a novel contribution to the field because, to the best of our knowledge, comparisons of methods for SNP discovery and genotyping using formal benchmark datasets as a baseline are generally missing from current literature, especially for GBS data and non-model organisms. Our comparisons show that NGSEP provides the best overall accuracy under different scenarios.Comparison against tools commonly used for SNP discovery in human genetics shows that NGSEP outperforms GATK and Samtools mainly because optimization of these tools for high accuracy on low coverage WGS data included the assumption of Hardy-Weinberg Equilibrium (HWE) which does not hold on highly heterozygous sites commonly found on F1 populations, or sites with low heterozygosity such as most of the variable sites in populations of inbred species. Strand bias filters are also not generally adequate for GBS data which probably explains why Samtools reported the lowest sensitivity in these experiments, taking into account that this tool was among the best ranked in our previous benchmarks with WGS data .Compared to Tassel, which is currently the preferred tool for analysis of GBS data, NGSEP also provides better overall accuracy mainly because Tassel seems to overcall heterozygous genotypes. The most likely reason explaining this behavior is that, unlike the other pipelines and for efficiency reasons, Tassel disregards the information included in base quality scores, which restricts to relative allele counts the information to call genotypes. Hence, even sequencing errors tagged during primary analysis with low base quality scores could produce false heterozygous genotypes. We believe that this is the main reason explaining the unexpectedly high number of predicted variants with low MAF in the cassava F1 populations and the large percentage of heterozygous calls in the bean MAGIC population. Other difficulty we found while trying to compare Tassel with other tools was that we could not find a way to run the pipeline on the NxA cassava population because the raw reads were already distributed per sample and were paired-end and not single-end. This issue makes a clear advantage for NGSEP, Samtools, and GATK over Tassel in terms of usability because these pipelines can be directly used on data produced by different GBS protocols.In our experiments Tassel is the most efficient tool for GBS data analysis, followed by Samtools. However in both cases the improved efficiency comes at the cost of lower accuracy. This difference can be critical in applications such as GWAS because several markers with different allele frequencies need to be accurately genotyped within each genomic region to increase the chance of discovering true correlations between genomic and trait variation.We are currently carrying on different efforts to improve the usability of NGSEP, to allow more researchers acquire the capacity to analyze HTS data with lower technical support. Among other improvements, we released for our graphical interface a wizard that greatly simplifies the amount of workload needed to obtain datasets of variants from HTS, and in particular GBS data. Most of the functionalities of NGSEP can also be deployed in a command line environment or integrated in a web portal solution such as Galaxy . We are Bioinformatic analysis of GBS data with NGSEP provides a powerful framework for discovery and genotyping of thousands of genetic markers in entire populations. Analysis of several populations of different crops shows that NGSEP currently provides a great balance between completeness, accuracy, efficiency and usability compared to other software packages. We expect to keep expanding NGSEP with novel functionalities to facilitate genetic mapping of complex traits and to assist the genetic improvement of staple crops through the use of molecular breeding techniques such as genomic selection.NGSEP is written in Java 1.6. It can be deployed as a standalone command-line program, a Graphical User Interface (GUI) implemented as an Eclipse IDE plugin, or using the XML scripts for integration in the Galaxy  environm−4 in the bean population and to 10−3 in both cassava populations. The VCF files produced at this step were used as input for the MergeVariants module, to get the list of variant sites of each population. Afterwards, all samples were genotyped at the variant sites by running the FindVariants module again, keeping all parameters unchanged except for the minimum variant quality score, which we set to zero to retain as many genotype calls as possible. Finally, the MergeVCF module was used to join all the VCFs into a single file for each population.Single-end raw reads from the K family and the MAGIC population were demultiplexed using the Deconvolute module of NGSEP, while paired-end reads from the NxA family were already demultiplexed. Because adaptor contamination was identified in about 35 % of the raw reads sequenced from the K family and the MAGIC population, the first 6-mer of the adaptor sequence (AGATCG) was searched through every read and reads showing this sequenced were trimmed to the initial basepair of this sequence. Trimmed reads were aligned to the Phaseolus vulgaris v1.0  and the −4 in the bean population and to 10−3 in both cassava populations. All other parameters were left with default values.Demultiplexed reads were mapped to their corresponding reference genome using bwa-mem v0.7.12  with the−4 for the bean population, whereas for the cassava populations was left to its default value of 10−3.Alignments from bowtie2  were anaThe raw reads from the sequencer were analyzed using the GBS pipeline from Tassel 3 . The tooWe reanalyzed GBS data taken from two recently sequenced cassava full-sib F1 families. The first family, referred here as the K family, was developed and sequenced as an effort leaded by Universidad Nacional de Colombia to build a highly dense SNP based genetic map useful to identify genes related to resistance to cassava mosaic disease (CMD) and cassava bacterial blight (CBB) . The popThe second family, referred here as the NxA family, is the largest of the nine biparental populations developed by the International Cassava Genetic Map Consortium (ICGMC) to assemble into chromosomes the current reference genome . This faFor both biparental populations, we first checked the integrity of the family to detect and filter unrelated samples using two separate methods: 1) comparing the percentage of homozygous differences between the parental lines and the offspring, and 2) calculating the identity by descent (IBD) coefficient using the software package King . ExcludiHo) and minor allele frequency (MAF) within the population instead of using the parental genotypes. Assuming an infinite population, perfect Mendelian segregation and perfect genotyping, expected values for these parameters on each category are: 1) Ho=0, MAF=0, 2) Ho=0.5, MAF=0.25, 3) Ho=0.5, MAF=0.5 and 4) Ho=1, MAF = 0.5. To account for deviations from these assumptions, we classified the SNPs using these thresholds: 1) Ho<0.2, 2) 0.2≤Ho≤0.8 and MAF ≤ 0.37, 3) 0.2≤Ho≤0.8 and MAF > 0.37, and 4) Ho>0.8.For each family we ran the four SNP calling pipelines described in this study using different values for minimum genotype quality scores. Then we filtered out SNPs that are monomorphic for the alternative allele and SNPs in regions of the reference genome masked as repetitive. Additionally, we filtered out SNPs with less than 100 individuals genotyped in the K family and less than 200 individuals genotyped in the NxA family. We classified the SNPs obtained on each experiment in four possible categories depending on the Mendelian segregation pattern of a typical F1 family based on the four possible genotype combinations of the parents: 1) AAxAA (Monomorphic) 2) AAxAa, 3) AaxAa, and 4) AAxaa. Because the genotypes of the parents could be missing or could contain genotyping errors, we classified each SNP based on its observed heterozygosity  should be an error. On the other hand, SNPs in category 4 should be heterozygous in every sibling. Therefore, heterozygous genotypes in the parents or homozygous genotypes in the siblings should be errors. Categories 2 and 3 include SNPs useful to build a genetic map and for QTL analysis. Hence, our main measure of sensitivity is the number of genotypes obtained in SNPs classified in these two categories. Errors in these categories include deviations from the expected genotypes of the parents and homozygous genotypes for the minor allele in category 2.Map construction was performed using the Maximum Likelihood mapping algorithm implemented in JoinMap 4.1 using the outbreeding full sib family (CP) population type. The main limitation in the number of SNPs included in this analysis was the capacity of the software that did not allow to analyze more than 3,000 SNPs in a 8GB memory machine. We therefore selected the more useful and high-quality SNPs for the construction of the genetic map. First, from the total 228,187 SNPs generated with the best quality in NGSEP (Q60), we filtered out SNPs in repetitive regions and with less than 96 % of missing data, to obtain 26,146 SNPs. Then, we selected SNPs with an observed heterozygosity (Ho) between 0.4 and 0.6, resulting in 4,327 SNPs. Furthermore, we only uploaded to JoinMap the 3,869 SNPs having informative parental genotypes and consistent segregation patterns. After the conventional filters applied in JoinMap (goodness of fit and redundancy of data) we were able to unambiguously map 2,422 SNPs. A LOD threshold of 5.5 was used to construct 18 linkage groups, corresponding to the 18 cassava chromosomes. We used a two point grouping analysis to assign markers to their corresponding linkage group, and within groups they were mapped using the strongest crosslink (SCL). Also, the map was generated using a recombination frequency below 0.45.To validate the accuracy of the genetic map, scaffolds from the version 4.1 of the cassava reference genome were aligned to the recently released version 6.1, available at phytozome  using blA common bean Multi Parental Advanced Generation Inter Cross population (MAGIC) was developed from 8 parental CIAT breeding lines , selected to combine desirable characteristics such as tolerance to heat stress, yield under drought and growth under unfavorable soil conditions such as high Aluminum or low Phosphorous stress. Genotypes were combined by pairwise crosses and subsequently crosses of F1 plants. After the F2 population lines were advanced by single seed descent to F5, when DNA was extracted from young leaves (Additional file BAM, binary sequence alignment/map format; BCF, binary variant call format; CBB, cassava bacterial blight; CIAT, International Center for tropical agriculture; CMD, cassava mosaic disease; CNV, copy number variant; DNA, deoxyribonucleic acid; GATK, The genome analysis toolkit; GBS, genotype by sequencing; GFF, general feature format; GPL, general public license; GQ, genotype quality; GUI, graphical user interface; GWAS, genome-wide association studies; HMM, Hidden Markov model; HTS, high throughput sequencing; HWE, Hardy-Weinberg equilibrium; IBD, identity by descent; ICGMC, International cassava genetic map consortium; IDE, integrated development environment; IITA, International institute for tropical agriculture; LD, linkage disequilibrium; LOD, logarithm of the odds; MAF, minor allele frequency; MAGIC, multiparental advanced generation intercross; NGSEP, next generation sequencing eclipse plugin; PCR, polymerase chain reaction; QTL, quantitative trait locus; RAD, restriction site associated DNA; SAM, sequence alignment/map format; SCL, strongest crosslink; SNP, single nucleotide polymorfism; VCF, variant call format; VCGLS, Vincent J. Coates genomic sequencing laboratory; WGS, whole genome sequencing; XML, extensible markup language


METTL11B (chromosome 4) and 32Kb from MBD5 (chromosome 15), respectively. A list of positional genes, closest to significantly associated SNPs, was used to identify enriched pathways and biological functions related to the QTL for both traits. A number of candidate genes were significantly overrepresented in pathways of immune cell trafficking, lymphoid tissue structure, organ morphology, endocrine system function, lipid metabolism, and energy production. After resequencing the coding region of selected positional and functional candidate genes, six SNPs were genotyped in a subset of boars. SNPs in PRKDC, SELL, NR2E1 and AKRIC3 showed significant associations with EBVs for FCR/D110. The study revealed a number of chromosomal regions and candidate genes affecting FCR/D110 and pointed to corresponding biological pathways related to lipid metabolism, olfactory reception, and also immunological status.Feed conversion efficiency is a measure of how well an animal converts feed into live weight and it is typically expressed as feed conversion ratio (FCR). FCR and related traits like growth rate (e.g. days to 110 kg—D110) are of high interest for animal breeders, farmers and society due to implications on animal performance, feeding costs and environmental sustainability. The objective of this study was to identify genomic regions associated with FCR and D110 in pigs. A total of 952 terminal line boars, showing an individual variation in FCR, were genotyped using 60K SNP-Chips. Markers were tested for associations with estimated breeding values (EBV) for FCR and D110. For FCR, the largest number of associated SNPs was located on chromosomes 4 (30 SNPs), 1 (25 SNPs), X (15 SNPs) and 6 (12 SNPs). The most prominent genomic regions for D110 were identified on chromosomes 15 (10 SNPs), 1 and 4 (both 9 SNPs). The most significantly associated SNPs for FCR and D110 mapped 129.8 Kb from  Improving feed conversion efficiency (FCE) in pigs is a major goal in pig breeding as feed accounts for a high proportion of the total production cost . Selectihttp://www.animalgenome.org/cgi-bin/QTLdb/SS/index). However, most of them were identified using a linkage mapping approach resulting in wide genomic QTL regions. Such linkage data is limited to within-family selection only  ≥ 6) was created allowing a maximum distance of 1Mb between the marker and genes, using the Ensembl database  and was uploaded into Ingenuity Pathways Analysis  in order to investigate relevant pathways and functional categories. Benjamini-Hochberg corrected P values were used to map the genes to the most significant molecular, cellular and physiological systems development functions (P < 0.01). To get insights into the most relevant metabolic and signalling pathways based on the designated list of genes, canonical pathways were displayed , although they did not differ significantly after Benjamini-Hochberg correction. Categories addressing human disease and disorder-associated pathways were excluded from the IPA analysis.A list of genes closest to the significant SNPs (-loghttp://primer3.ut.ee/) (2 (25mM) (Promega), 0.4 μl dNTP mix (10mM each), 0.4 μl of each primer , 0.4 μl Go Taq DNA Polymerase , 30 ng of the DNA pool and filled with dH2O. The cycling conditions were as follows: initial denaturation at 95°C for 135 sec; 35 cycles of 95°C for 45 sec, annealing for 45 sec , and 72°C for 75 sec, subsequently final extension of 72°C for 10 min. PCR products were subjected to electrophoresis on 1.5% agarose gels and visualised. PCR products were purified using the QIAquick PCR Purification Kit (QIAGEN Ltd.) and sequenced commercially . Chromatograms were analysed to identify segregating SNP.Twelve genes with functions relevant to feed efficiency according to IPA were selected from the candidate gene list for validation and further analysis. A set of primers for each gene was designed based on published sequence data (Ensembl database) using Primer3 (.ut.ee/) . GenomicMBD5, OPRD1, AKR1C3, NR2E1, PRKDC and SELL) were selected for genotyping in 436 Maxgo boars as a representative subset of animals. The SNP genotyping was performed using TaqMan® SNP Genotyping Assays . Each 15 μl PCR reaction consisted of 7.5 μl of TaqMan® genotyping master mix , 0.375 μl 40 x genotyping assay mix (Applied Biosystems), 6.125 μl dH2O and 1 μl of genomic DNA (10 ng/μl). Thermal cycling was performed using ABI PRISM® 7500 Real Time PCR System (Applied Biosystems) and the cycling conditions were as follows: initial denaturation at 95°C for 10 min, followed by 40 cycles of 95°C for 15 sec denaturation and 60°C for 1 min annealing/extension. Genotype calling was carried out using proprietary 7500 System SDS software (Applied Biosystems).Based on the PCR product sequencing of the twelve genes, six confirmed SNPs (located in 2) of the regression, the effect size was expressed as the phenotypic variance attributable to the genetic variance at the designated locus.Allele frequencies were computed and deviations from HWE  were tested using Haploview software . Mixed l10[p-value] ≥ 4.7) . Of the 25 SNPs, 5 were located within a 2.37 Mb segment on SSC4 and pointed to Methyltransferase like 11B (METTL11B) and Coagulation Factor V (F5) as positional candidate genes (SELL), Selectin P (SELP) and Protein kinase, DNA-activated, catalytic polypeptide (PRKDC) as putative candidate genes for FCE. On SSC15, Neuronal guanine nucleotide exchange factor (NGEF) and 5-hydroxytryptamine (serotonin) receptor 2B, G protein-coupled (HTR2B) genes were revealed as functional candidate genes, whereas DIS3 and ARL4C were identified as positional candidates. Two significant SNPs on SSC6 were located near Feline Gardner-Rasheed sarcoma viral oncogene homolog (FGR) and Protein tyrosine phosphatase, receptor type, U (PTPRU). A further search for genes with putative relevance for processes related to FCE in this region revealed Tetratricopeptide repeats 1 (WDTC1) and Opioid receptor, delta 1 (OPRD1). Furthermore, a significant SNP mapped to SSC1 was located in an uncharacterised gene and the nearest annotated gene was CD164 molecule, sialomucin (CD164). Nuclear receptor subfamily 2, group E, member 1 (NR2E1) and Sphingomyelin phosphodiesterase 2, neutral membrane (SMPD2) were identified as functional candidate genes in this region.After quality control, 940 individuals and 48,440 SNPs, mapped to the Sscrofa 10.2 pig genome assembly, remained for the further analysis. In total 132 SNPs reached the threshold of suggestive significance for an association with EBV FCR (-log] ≥ 4.7) . The larte genes . A searc10[p-value] ≥ 4.7) . Of the 12 SNPs, 5 were located within a 682 Kb segment (between 2.64 and 3.32 Mb) on SSC15. Three of these markers were located within an intron of Kinesin family member 5C (KIF5C) gene (MBD5) and LY6/PLAUR domain containing 6B (LYPD6B). A further search for genes revealed Methylmalonic aciduria cblD type, with homocystinuria (MMADHC) with functional relations to D110. On SSC10, Kruppel-like factor 6 (KLF6) and Aldo-keto reductase family 1, member C3 (AKR1C3) were identified as positional candidate genes with a putative contribution to D110.In total 71 SNPs reached the threshold of suggestive significance for an association with EBV D110 (-log] ≥ 4.7) . The lar5C) gene . Two remA total of 86 and 16 genes mapped within 1Mb upstream and downstream of significant markers for EBV FCR and D110, respectively, were uploaded into Ingenuity Pathways Analysis. Functional annotation of the positional candidate genes to biological processes and canonical pathways (top 5) is presented in Tables PRKDC, SELL, NR2E1 and OPRD1 respectively, and two SNPs mapped close to the QTL for EBV D110 (rs332368013 and rs81508945) in MBD5 and AKR1C3 respectively were confirmed to be polymorphic in target populations by sequencing. Subsequently, these six SNPs were genotyped in 436 Maxgro boars. Allelic frequencies and HWE are presented in MBD5 significantly departured from HWE  indicating a slight deficiency of homozygotes in the studied population and SNP in OPRD1 displayed minor allele frequency less than 5%. All SNPs, with the exception of the SNP in MBD5 and OPRD1 were tested for association with breeding values for FCR and D110  in and D110 . SNP rs8In this study, a genome-wide association analysis was performed to elucidate the genetic architecture of feed conversion efficiency and growth rate in pigs. A number of candidate genes neighbouring the identified QTL regions were selected for downstream analysis. A further validation confirmed significant associations between these genes and EBV FCR / D110. The most prominent regions for EBV FCR were identified on SSC 1, 4, 6 and 15. For EBV D110, the most promising QTLs were detected on porcine chromosome 10 and 15. None of the identified QTL regions overlap for both traits. Alignment of the genetic and physical maps on the Sscrofa 10.2 genome assembly (PigQTLdb) enabled the identified QTLs from the present study to be compared with previously described QTL regions. A QTL from this study located at 78.3 to 80.5 Mb on SSC 6 coincided with a QTL for FCR in a European Wild Boar x Meishan cross mapped in the region of 127.3 cM  . AdditioSELP, SELL, FGR, SELE, F5, FOXO3 and OPRD1) were identified to be involved in immune cell trafficking. Similarly, THEMIS2, SELP, SELL, SELE, F5 and PRKDC were clustered in lymphoid tissue structure and development category. It is well documented that the activity of the immune system is linked to feed intake and therefore provide a relevant aspect for feed efficiency [CEBPD, SELL, ECEL1, NR2E1, SELE, CHRNG, WASF1, GPR3, EIF4E2, HTR2B, CHRND, SELP, KIFAP3, SEC63, SYTL1, PRKDC and FOXO3). A study conducted by Njoku et al. [AKR1C3, MBD5, ACVR2A, AKR1C1/AKR1C2 and AKR1C4) located within 1Mb of the significant markers for EBV D110 were clustered in an endocrine system function and development category. Previous study identified smaller thyroid glands in low residual feed intake pigs [Functional annotation revealed a number of pathways and biological processes significantly overrepresented among the positional candidate genes for EBV FCR and D110. Nearby genes to the significant markers for EBV FCR , which plays a part in DNA double stranded break repair [PRKDC is involved in the signalling pathway responsible for the formation of fat from carbohydrates in the liver [FAS) promoter occurs. However upon feeding, the FAS promoter becomes activated by PRKDC gene. In PRKDC deficient scid (severe combined immunodeficient) mice, feeding-induced transcriptional activation of the FAS gene and lipogenesis were impaired. As a result, reduced triglyceride level and decreased adipose tissue in PRKDC deficient scid mice were observed [SELL) plays a role in lymphocyte trafficking to lymph nodes and Peyer’s patches, as well as targeting lymphocytes and neutrophils to an inflammation source [SELL encoded protein is a member of selectins belonging to a family of transmembrane glycoproteins and its role is to support adhesion of blood leucocytes to the vessel wall upon inflammatory and immunological response [SELL was associated with insulin-dependent diabetes mellitus [Positional and functional genes located within 1 Mb of the GWAS SNPs significantly associated with breeding values for FCR/D110 were selected and examined. On SSC 4, SNP rs80900450 and rs319738340 in k repair . PRKDC in source . The SELresponse . Signifiresponse . Yang etresponse  proposedmellitus .NR2E1 is a member of a ligand dependent transcriptional factors group, which controls a number of biological and disease related processes. NR2E1 is abundantly expressed in the brain where it is involved in neurogenesis [NR1E1 deletion. Moreover, the NR2E1 knockout mice had reduced volume of olfactory bulb [NR1E2 was significantly associated with breeding values for FCR and D110. Olfactory bulb plays an important part in regulating food intake as it is targeted by signals responsible for the regulation of energy balance [ogenesis . Christiogenesis  and Kumaogenesis  reportedory bulb , a firstory bulb . Interes balance , therefoAKR1C3 belongs to a large aldo and keto reductase enzyme family and is expressed in a wide variety of tissues including liver and adipose tissue. The protein encoded by this gene plays a role in conversion of active androgens, oestrogens and prostaglandins to their non-active metabolites [AKR1C3 has been associated with androgen inactivation induced adiposity, where large adipocytes had higher expression level compared to small adipocytes [AKR1C3 expression upon diet induced weight loss [AKR1C3 gene and glucose intolerance. Moreover, White et al. [AKR1C3 was not found significantly associated with the breeding values for FCR or D110.abolites . AKR1C3 ipocytes ,53. Thisght loss . Svenssoght loss  also pose et al.  found anMBD5 is a member of the methyl-CpG-binding domain (MBD) family of proteins. It is highly expressed in neurons [MBD5 knockout mice revealed severe growth retardation and persistent hypoglycemia, hypoinsulinemia, enhanced glucose intolerance and elevated insulin sensitivity. Moreover, mice lacking the MBD5 gene exhibited significantly smaller body size and reduction of subcutaneous and perigonadal fat [MBD5 was out of HWE. The protein encoded by OPRD1, a member of the opioid family of G-protein-coupled receptor, is broadly distributed in a number of brain areas involved in the regulation of energy homeostasis [OPRD1 is highly expressed in olfactory bulb and anterior olfactory nucleus [OPRD1 knockout mice fed with high energy diet were found to be resistant to weight gain and had lower fat mass. They also exhibited higher energy expenditure due to increased thermogenic activity in the brown adipose tissue [OPRD1 gene were significantly associated with anorexia nervosa [OPRD1 for feed efficiency and growth, the minor allele frequency of the identified novel SNP within this gene was lower than 5 percent and thus it was excluded from the further analysis. neurons  and is i neurons . MBD5 al neurons . A studyadal fat . Nevertheostasis . In part nucleus . OPRD1 ke tissue . Additio nervosa ,60. AlthIn summary, the present study demonstrated a number of chromosomal regions significantly associated with feed conversion efficiency and growth rate in the examined terminal pig sire line. Most of the regions were described for the first time, although some of them were located not far from previously reported QTLs. Validation of putative candidate genes from GWAS mapping near the significant SNPs confirmed a number of genes significantly associated with feed conversion efficiency and its related trait, days to 110 kg. Feed efficiency is a highly complex trait affected by a number of factors. This study suggests that the genetic predisposition of analysed traits is driven by lipogenesis, olfactory reception, and also immunological status. In depth characterisation of these genes to determine their molecular architecture and identify the causative mutations would be of benefit. Moreover, it would be useful to validate these SNPs in other commercial pig population regarding their effects on feed conversion efficiency and growth rate.S1 Table(PDF)Click here for additional data file.


M10 is a ten amino acid peptide generated from the intracellular cytoplasmic tail of the hepatocyte growth factor (HGF) receptor c-Met following cleavage by caspase-3. Recently we reported that M10 interacts with Smad2 and demonstrates antifibrotic properties in vitro and in vivo and can be advanced into a novel antifibrotic remedy. The current study was undertaken to develop an immunoassay to measure M10 concentration in biological specimens.An Indirect Enzyme-Linked Immunosorbent Assay (ELISA) for detection of M10 in biological fluids was developed using pharmaceutical grade synthetic M10 as a calibrator and commercially available anti-c-Met C12 antibody.M10 ELISA specifically detected in plasma M10, but not a scrambled peptide, following a single intraperitoneal administration of M10 (1mg/kg) to mice. The detection limit was 9.6 ng/ml, and the measuring limit was between 15 ng/ml and 200 ng/ml. The recovery limits of M10 were between 80% and 120%; intra-assay coefficient of variation was between 5.3% and 6.3%; inter-assay coefficient of variation was between 5.0% and 8.0% over the buffer concentration tested in the range from 15 ng /ml to 250 ng /ml. The peak of M10 concentration following a single intraperitoneal injection (1mg/kg) was achieved within 6 hours and declined to minimal levels by 48 hours. The experimentally obtained half-life for M10 was comparable to the theoretically predicted half-life for M10.We have established a highly sensitive ELISA to detect the antifibrotic peptide M10 in plasma samples, which should prove to be a novel tool to study the pharmacokinetics and efficacy of M10 in the treatment of fibroproliferative disorders. Nearly 45% of all deaths in the developed world are attributed to some type of chronic fibroproliferative disease . Most ofWe recently discovered a 10 amino acid antifibrotic peptide (M10) with antifibrotic properties in vitro and in a murine model of lung fibrosis, which is a natural cleavage product by caspase-3 of the cytoplasmic tail of the c-MET receptor tyrosine kinase , 6. FollDuring the past decade, peptides have gained a wide range of applications in medicine and biotechnology, and therapeutic peptide research is also currently experiencing a renaissance for commercial reasons . PeptideIn this manuscript we present a highly sensitive indirect enzyme-linked immunosorbent assay (ELISA) that was developed using synthetic M10 as an antigen. Next, we successfully used the M10 ELISA to quantify M10 concentrations in plasma samples in order to obtain the pharmacokinetic profile of the peptide.Peptides (M10 and scrambled control peptide) were obtained from GenScript ; anti-Met (C12) antibody was purchased from Santa Cruz Biotechnology  and HRP-conjugated anti-Rabbit IgG antibody was purchased from Rockland . High-affinity polystyrene micro-titer plates were obtained from VWR ; ultra-3, 3’, 5, 5’-tetramethylbenzidine (TMB)-substrate was obtained from Thermoscientific .ad libitum. M10 peptide was solubilized in water and administered intraperitoneally in a dose of 1 mg/kg. Mice were sacrificed by isoflurane overdose, and blood samples were collected by cardiac puncture in the presence of 0.01% of Na-Citrate at time intervals of 15 min, 30 min, 1, 2, 4, 6, 12, 24, and 48 hours after injection. The blood samples (n = 3/group) were centrifuged at 2500 rpm for 10 min, and plasma samples were stored at -80°C. Plasma samples collected from mice injected with water intraperitoneally (n = 3) were used as a negative control.The study was approved by the Institutional Animal Care and Use Committee of the Medical University of South Carolina. Mice (n = 30), C57BL/6 males and females 8–10 weeks old weighing 20–25 g, purchased from Jackson Laboratories  were used in this study. Mice were housed in ventilated racks in specific pathogen free conditions and were provided with food and water available M10 concentration was measured by indirect ELISA developed in our laboratory. Known concentrations of pharmaceutical grade M10 peptide  with purity more than 98% were used to generate the standard curve. Briefly, 100μl of known concentration of M10 (range: 10 ng/ml to 500 ng/ml), blank (PBS without M10), and 100μl of the diluted plasma sample were plated in triplicate in high-affinity polystyrene micro-titer plates. Samples were incubated with gentle rocking overnight at 4°C and blocked with 5% bovine serum albumin in PBS. After washing, samples were incubated for 2 hours at room temperature with anti-cMET rabbit antibody , followed by 1hour incubation with HRP-conjugated anti-Rabbit IgG . M10 was detected with ultra-TMB -substrate, and the reaction was terminated by using 2M sulfuric acid. Absorbance was measured at 450 nm by Spectra Max Plus spectrophotometer. Concentrations of unknown samples were then calculated from their corresponding optical density (OD) values by Softmax Pro 4.8 software  using the standard curve generated from the known concentrations of M10. Control plasma samples collected from mice with intraperitoneal water injection were run in triplicate with each experiment, mean was calculated, and samples were normalized by subtracting the negative control mean value. Final concentrations were calculated by adjusting the sample dilution factor.2O, PBS (pH 7.0) or sodium bicarbonate buffer (pH 9.6) were used as a coating buffer to prepare the calibrators, and then the most favorable condition for hydrophobic interaction was evaluated. To eliminate the batch effects of the capture and detection antibodies, all experiments were conducted by using the same lot of antibodies. A series of calibrators were prepared by dissolving a known concentration of M10 in PBS followed by serial dilutions. PBS without M10 was used as a blank, considered as 0.0 ng/ml concentration to generate the standard curve. Plasma, collected from mice that received intraperitoneal H2O was used as a negative control. Negative control plasma samples spiked with known concentrations of M10 were used as a positive control. Mouse plasma samples collected at distinct time intervals following intraperitoneal M10 injection were aliquoted, stored at –80°C and used as samples. On the day of analysis, a standard curve was generated by plotting the concentration and corresponding absorbance measured at 450 nm. All measurement of controls, samples and calibrators were tested in triplicate. Any samples with high percentage of intra-assay absorbance variability were eliminated from analysis.Samples, controls and standards were allowed to bind to high affinity polystyrene plates in a temperature-controlled environment. A variety of polystyrene plates including Immulon 2HB, Immulon 4 HBX, 100–200 ng of Ig/cm2 capacity medium binding plate, and 400–500 ng of Ig/cm2 capacity high binding plate with or without pretreatment of 3-aminopropyl-thiethoxysilane were tested. Different solvents such as HAntibody working concentrations were determined by titrating both primary and secondary antibodies. A series of diluted primary antibody were tested against different conditions of detection antibody and inherent reactivity was evaluated. The best combination of primary and secondary antibodies was determined by estimating the coefficient of determination (R2) in four-parametric-logistic regression model by using SoftMaxPro (V4.8). ELISA conditions that achieved R2>0.95 were considered for further optimization . The corin-vitro experiments. A scrambled peptide with no biological effects was used in in- vivo experiments. Plasma collection and storage procedures were followed as described above. Samples were diluted in PBS with different dilution factors, such as 20, 40 or 60 to make it fit within the range of the standard curves. The cross-reactivity was also tested by using control mouse plasma spiked with known concentrations of scrambled peptide. In each of these cases, the scrambled peptide generated signals below the detectable range. In M10 ELISA, the limit of detection (LOD) / minimum detectable concentration (MDC) was calculated by analyzing the mean of blank sample values from fifteen wells, in addition with 3 times of Standard Deviation (mean Blank +3*SD) [To test the specificity of this assay, mouse plasma samples were collected following intraperitoneal injection of scrambled peptide. To verify the biological activity of the scrambled peptide, different forms of scrambled peptides were designed and the antifibrotic properties were tested in k +3*SD) . PBS wasTo estimate the lowest detectable M10 concentration, a series of low concentrations of M10 from 1ng/ml to 15 ng/ml in plasma were tested. M10 concentration that generated the highest absorbance above the background signal with a sufficient signal to noise ratio was used as the lowest point to develop the standard curve. Reproducibility between the wells was tested by measuring the intra-assay variance. Three different known concentrations of M10 samples  were run in 24 replicates, and the intra-assay coefficient of variation was calculated. The inter-assay precision was measured by testing known M10 samples  in triplicate on four subsequent independent assays. To measure the analytical recovery, buffer spiked with known concentration of M10 was four times serially diluted. Each of these concentrations was considered as an individual sample and was run in triplicate. The analytical recoveries were calculated by comparing the estimated concentrations with the expected concentrations and are presented in percentage.Statistical analysis of data was performed using Softmax Pro 4.8 software , KaleidaGraph 4.0 , and GraphPad Prism 7 statistical software .To facilitate the direct adsorption of M10 to the assay plate a number of conditions including a variety of polystyrene plates, coating buffers, and incubation temperatures were tested. We observed that M10 dissolved in PBS provides the best hydrophobic interaction with 400–500 ng of Ig/cm2 capacity high binding polystyrene plates. Next, we optimized antibody pair concentrations while keeping the above-described conditions constant. Primary antibody was serially diluted from 200μg/ml to 200ng/ml, whereas the concentration of HRP-conjugated secondary anti-rabbit antibody remained constant 200ng/ml . SubsequBlocking buffer conditions, incubation times and incubation temperatures were determined based on the experimental data characterized by the minimal background signals. Known concentrations of pharmaceutical grade M10 were serially diluted from 250ng/ml to 15.6ng/ml with PBS and measured in triplicate to generate standard curves. A plot of serial dilutions of M10 standard revealed a sigmoidal-type curve typical for most antibody-antigen interactions . The staThe specificity of the M10 ELISA was tested by using scrambled M10 peptide as an antigen in the test sample. The scrambled peptide was diluted in three different concentrations  and tested in the M10 ELISA. These test samples either had no signal or generated signals below background level.Samples representing low and high concentrations of M10 were used to calculate coefficients of variation within and between assays. The minimum detection limit of the assay was estimated by measuring a series of low concentrations of M10 between 0ng/ml and 15ng/ml. The lowest concentration to generate signal above background was 15ng/ml. Each of the concentrations was run in triplicate and three independent experiments were performed. Method validation and quality test of the M10 ELISA were performed by intra-assay precision, inter-assay precision and by recovery estimation. Three different known concentrations of M10 calibrators  were measured in twenty-four replicates in one assay. The co-efficient of variance (CV) of low-, medium- and high-concentration samples was 6.3%, 4.0%, and 5.2%, respectively, indicating excellent intra-assay repeatability .To estimate the inter-assays CV, three known calibrators  of M10 were tested in triplicate in four independent experiments. The CV for inter-assays at three different levels of M10 was 5.0%, 8.01%, and 6.5% . To elimTo determine the antigen recovery, 250ng/ml of M10 was serially diluted at a ratio of 1:1; 1:2; 1:4; 1:8 and 1: 16. Each of these dilutions was considered as an individual sample and run in triplicate over five independent experiments. The analytical recoveries, presented as the estimated concentrations compared with the expected concentrations, ranged between 89.9% and 112.9% .The calculated LOD of M10 Elisa 9.6 ng/ml was estimated according to the previous stated method. The estimated assay range was between15.6 ng/ml and 200.0 ng/ml; the evaluation was based on certain criteria such as, recovery rate, reproducibility and percent of variation .2O. We observed that a peak M10 concentration was achieved within 6 h and then declined to minimal detectable levels by 48 h (½), peak plasma concentration (Cmax) and time to reach the peak plasma concentration (Tmax) of M10 are shown as absolute values in t = N0(1/2)t/t ½; where N0 is the initial quantity of M10, Nt is the quantity of M10 that still remains after 48 h following of M10 injection, and t is a time point of 48 h [To obtain the pharmacokinetic profile of M10 in mouse plasma, C57Bl6 mice were injected intraperitoneally with M10 at a dose of 1mg/kg of body weight. Plasma samples were collected by cardiac puncture in the presence of 0.01% of Na-Citrate at time intervals of 15 min, 30 min, 1h, 2h, 4h, 6h, 12 h, 24 h, and 48 h following injection. Samples were diluted in PBS in order to fit within the range of the standard curves and then tested using the M10 ELISA. Concentration of M10 in plasma samples was calculated by Softmax Pro 4.8 software using the standard curve generated from the known concentration of M10. The samples were normalized by subtracting the negative controls represented by plasma samples obtained from mice injected with H by 48 h . The are of 48 h –19.½ for M10 (6.8±0.7 h) was comparable to the theoretically predicted t ½ for M10 of 7.2h by ProtLifePred software [Using the M10 ELISA, the experimentally obtained tsoftware , 21 , but not in any other mammals , 12. AltTo demonstrate the capacities of M10 ELISA in the current study, we chose to measure some of the PK characteristics of M10 in mice. Importantly, using our ELISA we found that the experimentally determined half-life for M10 is comparable to the theoretically predicted half-life of M10 peptide. The favorable half-life of M10 can be explained by its resistance to many mammalian proteases such as caspases, thrombin, factor Xa, trypsin and granzyme B , 21. ThuIn conclusion, an indirect ELISA method for detection of M10 reported herein is sensitive, specific, reproducible, simple, and adaptable for the analyses of plasma samples. This ELISA will serve as a valuable tool in pharmacokinetic, biodistribution, and toxicity studies of the novel antifibrotic peptide M10.S1 Raw DataThe Pharmacokinetic parameters of M10.(PDF)Click here for additional data file.S2 Raw Data(PDF)Click here for additional data file.