PRDM9 and CPLX1 to have significant effects on genome-wide recombination rate in both sexes. Two novel loci, NEK9 and REC114, were associated with recombination rate in both sexes, whereas three loci, MSH4, SMC3 and CEP55, affected recombination rate in females only. Among the multiple PRDM9 paralogues on the bovine genome, our GWAS of recombination hotspot usage together with linkage analysis identified the PRDM9 paralogue on chromosome 1 to be associated in the U.S. Holstein data. Given the largest sample size ever reported for such studies, our results reveal new insights into the understanding of cattle and mammalian recombination.Meiotic recombination is an essential biological process that generates genetic diversity and ensures proper segregation of chromosomes during meiosis. From a large USDA dairy cattle pedigree with over half a million genotyped animals, we extracted 186,927 three-generation families, identified over 8.5 million maternal and paternal recombination events, and constructed sex-specific recombination maps for 59,309 autosomal SNPs. The recombination map spans for 25.5 Morgans in males and 23.2 Morgans in females, for a total studied region of 2,516 Mb . The male map is 10% longer than the female map and the sex difference is most pronounced in the subtelomeric regions. We identified 1,792 male and 1,885 female putative recombination hotspots, with 720 hotspots shared between sexes. These hotspots encompass 3% of the genome but account for 25% of the genome-wide recombination events in both sexes. During the past forty years, males showed a decreasing trend in recombination rate that coincided with the artificial selection for milk production. Sex-specific GWAS analyses identified  PRDM9 gene to be associated with recombination hotspot usage. These results provide new insights for understanding cattle and mammalian genome recombination.Previous studies on cattle recombination largely focused on males. Using a large Holstein sample from the USDA national database, we studied both male and female recombination by assembling paternal and maternal recombination events in at least three generations. This unique data set provides unprecedented statistical power to study cattle genome recombination in the two sexes: (1) We report for the first time that bulls have more recombination than cows, contrary to the common perception that females have more recombination than males as observed in many mammalian species including humans and mice, and that the sex difference in recombination primarily occurs near the subtelomeric regions of all bovine autosomes; (2) We identify several genes associated with cattle recombination in both females and males, and genes affecting female recombination only; (3) We define putative recombination hotspots and find the cattle  In eukaryotes, meiotic recombination through reciprocal crossovers is an essential biological process that ensures the proper segregation of homologous chromosomes during meiosis –4. Any mCPLX1, RNF212 and PRDM9 [PRDM9 has been found to be associated with the percentage of crossovers in hotspots that is termed as ‘hotspot usage’ [PRDM9 alleles in humans [PRDM9 homologue such as cattle.Considerable variation in recombination rate among individuals has been documented from pedigree-based studies in humans and mice ,10. In hnd PRDM9 –13. Addind PRDM9 –13. Recond PRDM9 –16, and n humans . While aPRDM9 in bulls, but localized the gene to chromosome X. However, PRDM9 has four paralogues in the bovine genome and previous studies have found signals of positive selection associated with the copy on chromosome 1 [PRDM9 paralogue on chromosome 1 is associated with recombination hotspot usage in the U.S. Holstein population.Recombination rate varies considerably between the two sexes in many species, particularly in mammals ,18,19. Imosome 1 . This stCattle are a uniparous species where the population structure generally lacks female recombination information that requires at least three generations. The United States Department of Agriculture (USDA) has received genotypes for over half a million Holstein cattle spanning several generations for genomic selection. This large multi-generational structure overcomes the problem of lacking female recombination information in cattle and provides a unique opportunity to study genome recombination in both females and males with unprecedented statistical power. Utilizing this large sample, the present study seeks to comprehensively survey the sex-specific patterns of meiotic recombination and to understand the genetic basis of individual differences in recombination in males and females. We also aim to generate the first SNP-based recombination maps in the two sexes and to evaluate the trend of meiotic recombination features that might be associated with the long-term artificial selection for dairy production.We extracted a total of 185,917 three-generation families that included one offspring, both parents, and two grandsires per family genotyped by various SNP chips ranging from 3K, 7K, to 770K SNPs from the large Holstein cattle pedigree with over half million genotyped cattle . In eachR = 0.636), which is similar to the results in humans [The recombination map was calculated as the recombination rates between adjacent SNPs based on the AGIL SNP map. Using an EM algorithm , we consn humans . To evalThe sex-specific recombination maps comprised of 59,309 SNP markers for all 29 bovine autosomes, with an average physical distance of 42.4 kb between adjacent SNPs and an average genetic distance of 0.043 cM in males and 0.039 cM in females, respectively. The 59,309 autosomal SNPs covered a total physical length of 2,516 Mb with 986 kb/cM in males and 1,085 kb/cM in females. The physical and genetic lengths of bovine autosomes had strong positive correlations of 0.960 in males and 0.985 in females across the 29 autosomes . Our estCattle male and female recombination rates are unevenly distributed along the genome , consistAlthough our approach has a measure to minimize the effect of genotyping and genome assembly errors by requiring at least three informative markers for a crossover call, we caution that big chunks of genome assembly errors may still lead to spurious recombination hotspots . To filtWe assessed the relationship between recombination rate and chromosomal locations, as recombination rates are known to differ considerably across chromosomal locations, including telomeres and centromeres. After removing the 2-Mb regions at the two ends of each chromosome where the power for identifying crossovers was reduced due to low SNP coverage , we fittAll cattle autosomes are acrocentric with the centromere located at the beginning and the telomere at the end of each chromosome . Males hTo evaluate whether crossover interference contributed to the bi-modal distribution of recombination events along the chromosome , we sepa−7 from the Bonferroni correction.We conducted GWAS analysis of genome-wide recombination rates for 3,224 sires and 53,125 dams separately. We corrected for the effect of SNP number so that the average number of recombination events was the same regardless of the genotyping assay used  Tables. Pfemale = 2.95×10−51; Pmale = 7.34×10−30), was located in the intron of the CPLX1 gene, which was associated with genome-wide recombination rate in humans [Pfemale = 2.62×10−51; Pmale = 1.07×10−16), which was located downstream of PABPN1. A conditional analysis identified four independently associated SNPs at this locus, spanning a 9-Mb window that consisted of several meiosis-related genes, including REC8, REC114, and FMN1 . This SNP was located 10 kb downstream of NEK9 that was related to spindle organization and cell cycle progression during mouse oocyte formation [A total of thirteen loci were identified to have significant effects on recombination rate, four loci on male recombination rate and nine loci on female recombination rate, among which three loci were shared between the two sexes . The thrn humans . Using tand FMN1 . The RECn cattle . The topormation . A condiP-values in females in general . This SNP was also nominally associated in males , exhibiting the same direction of effect in both sexes. Taking into account the difference in power, this association is more likely to be shared between sexes than a female-specific effect. Moreover, this association was 35 kb downstream of the PRDM9 gene, which has been associated with both recombination rate and hotspot usage in the two sexes [Pfemale = 1.53×10−13; Pmale = 6.68×10−5) in the first locus on chromosome 3, showing the same effect direction in both sexes, are less likely to be female-specific.Although the top three associated loci were shared between sexes, differences between sexes were observed among the less significant associations. We observed a trend of smaller  general , indicat general . The femwo sexes ,12. SimiPmale = 1.27×10−7). Among the four female-specific associations, two were inside or near genes closely related to the meiotic pathway. Rs136642773  was located in the intron of MSH4, which is a meiosis-specific MutS homologue that affects crossing over [Pfemale = 1.25×10−7) was upstream of SMC3 that encodes a protein related to meiotic chromosomes and synaptonemal complexes [CEP55  and rs42382307 on the X chromosome .Potential sex-specific associations with recombination rate were found at five loci on chromosomes 3, 26 and X, with one male-specific and four female-specific . SNP rs1ing over ,42. SNP omplexes . The othP-values become smaller from the joint analysis than in the single-marker analysis, for those independent associations in both males and females because of the reduced residual errors [P-values of the single-marker and joint analyses was 1014 for the association at rs137264867 in females.To comprehensively evaluate the associations and estimate their effects, we conducted a joint analysis by including all the significantly associated SNPs in one model as fixed effects . As expel errors . The larPRDM9 and hotspot usage.Based on the 1,792 male and 1,885 female recombination hotspots, we calculated the proportion of recombination events occurring in the hotspots genome-wide, i.e., hotspot usage, for the sire and dam in a three-generation family. To increase accuracy, we only included the high-quality meioses, where the offspring, the parent and the grandsire were genotyped by the 50K SNP chip. We also used the average of multiple measurements of hotspot usage as the phenotype, resulting in a sample size of 1,772 and 12,756 for males and females respectively. We then tested the association between hotspot usage and each of the 310,442 imputed SNPs. To evaluate the effect of different definitions of hotspot, we tested a range of cutoff values, 2, 2.5, 3, 5, and 10 standard deviations, and found that the cutoff value of 2.5 standard deviations had the clearest signal for the association between PRDM9 . This SNP was also associated with genome-wide recombination rate . In females, this SNP was only nominally associated with hotspot usage from the joint analysis, but with an opposite effect , and some of them were potentially sex-specific . We also confirmed the putative role of PRDM9 in the genome-wide recombination rate in both sexes. From GWAS of hotspot usage, we confirmed the cattle PRDM9 gene in both sexes to be the paralogue on chromosome 1 in our population, although the cattle genome encompasses multiple paralogues of PRDM9 and a previous study localized the associated PRDM9 to chromosome X [Our GWAS analyses identified several loci influencing genome-wide recombination rate. Some of these loci had significant influence in both sexes  than those from the original GWAS  at USDA, with one offspring, both parents and two grandsires with SNP genotypes in each family . In eachThe animals in the selected families were genotyped by various genotyping assays , rangingTo study recombination, we included up to 59,309 genome-wide SNPs after quality control filtering and used the USDA-AGIL SNP coordinates that showed a higher quality than the UMD3.1 assembly . PreviouTo compare sex differences on an equal footing, we phased the raw genotypes for the paternal and maternal meioses within a three-generation family without using the possible additional information from multiple offspring , becauseAfter phasing the genotypes of the offspring and parents, we inferred recombination events in the paternal and maternal haplotypes of the offspring by comparing the offspring’s paternal haplotype to the two haplotypes of the sire as well as by comparing the offspring’s maternal haplotype to the two haplotypes of the dam. In the offspring haplotype, a recombination event was defined as a transition from the parent's paternal to maternal haplotype or vice versa. Note that the recombination events defined here were observed crossovers so that the number of observed crossovers could differ from the number of true crossovers when multiple crossover events occurred at the same site. This potential inconsistency between observed and true crossovers typically is addressed by a map function that translates a recombination frequency into a map distance in terms of crossovers. However, the physical distances between two adjacent SNPs were small so that the use of a map function virtually would not make a numerical difference. Therefore, our estimates of crossovers based on recombinants should be close to the true number of underlying crossovers. To further reduce false positives, we required a crossover call to be supported by at least three consecutive informative heterozygous SNPs . In totam to each of the m adjacent SNP intervals in a crossover region, the EM algorithm proceeded in the following iteration steps: 1) M-step: considering a total of n meioses, the overall expected count attributed to a SNP interval divided by n were the maximum likelihood estimate of the probability, and 2) E-step: for a crossover region, the expected count assigned to a SNP interval was estimated as proportional to the current estimate of the probability of crossover for that SNP interval. The M and E steps were iterated until convergence.A recombination event was assigned to a region spanned by two informative SNPs that may not be adjacent to each other. To construct a recombination map, we used an EM-algorithm to calculate the probability of crossing over per meiosis or recombination rate between each pair of consecutive SNPs based on the observed crossover regions . After aWe constructed the male and female recombination maps for 59,309 autosomal SNPs based on >1.8 million paternal and >1.4 million maternal recombination crossovers, which were identified from 70,715 male and 61,616 female meioses that are most informative where the offspring, the parent, and the grandsire were genotyped by 50K SNP chips. Note that some granddams were genotyped by a low density chip or even not genotyped. As described earlier, a correction for the number of SNPs of the granddam was employed in the two sexes separately such that the total number of crossovers after correction was the same regardless of the SNP numbers for granddams. We used the expected number of crossovers per meiosis or recombination rate between adjacent SNPs as the genetic distance in our recombination map, since one crossover event on average corresponds to a genetic distance of 1 Morgan and recombination rate is almost the same as genetic distance for small intervals . AlternaPRDM9 and recombination hotspot usage. For validation purposes, we also defined recombination hotspots via using a standardized recombination rate that was calculated by dividing the original recombination rate between two SNPs by the physical length. After this adjustment of physical lengths, the standard recombination rates vary even less so we used a cutoff value of 0.6 standard deviations to define recombination hotspots.We defined recombination hotspots as the SNP intervals with a recombination rate >2.5 standard deviations from the genome-wide average in males and females separately, because 2.5 standard deviations are highly significant departures from the average recombination in cattle given our large sample sizes in both sexes, consistent with the observations from a recent recombination study in cattle . We testTo ensure the quality of the hotspots identified, we calculated pairwise linkage disequilibrium (LD) statistics between each of the SNPs in or near a recombination hotspot and all other SNPs on the same chromosome using Locusmap . The paiAs expected, the power for identifying recombination events was affected by the number of genotyped SNPs. For male meiosis, the average number of crossovers identified varied from 25.9 to 17.7 depending on the number of measured SNPs of the animals in a three-generation family . OverallTo evaluate the power for identifying crossovers using 50K SNP genotypes, we simulated 50K SNP genotypes for all animals in the real pedigree and used the same phasing and crossover identification procedures as described in previous sections. By defining a positive result as an identified crossover interval overlapping with the true location of a crossover, we calculated the power of crossover identification as the proportion of positive results across all simulated crossover events. As a result, our approach had a power of 97.6% for the three-generation families genotyped by 50K SNP chip, which means that on average only 2.4% crossovers were missed in our recombination map.β = −0.28 and P-value = 0.006; Females: β = −0.11 and P-value = 3.2 × 10−12). To investigate the common pattern across the 29 autosomes, we pooled all the autosomes together using the relative physical position and fitted a smooth spline model for all the data combined , or small minor allele frequency (MAF < 0.001), we retained over 310K genome-wide SNPs in the association studies [Imputed genotypes of 777,962 SNPs on the Illumina BovineHD Genotyping BeadChip (HD) were obtained by running Findhap on measured genotypes with 3K to 50K SNP chips using a reference population of 2,433 animals directly measured with HD SNP chips . After fy = a vector of the phenotype, X = a design matrix of the fixed effects g, including a population mean and the additive effect of the candidate SNP, Z = a design matrix for a random animal effect a, and e = a vector of random residuals. We assume that A is the genomic relationship matrix and R is a diagonal matrix with the ith diagonal element equal to 1/w, where w is the number of phenotypic measurements for the ith animal.We tested for association between each SNP and a phenotype using a linear mixed model with variable residual variances that are inversely proportional to the number of repeated measures of the phenotype, i.e., residual variance is smaller for individuals with more measurements of the phenotype. The model equation in matrix notation isP-values for both recombination rate and hotspot usage in this study Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S3 Fig(DOCX)Click here for additional data file.S4 Fig(DOCX)Click here for additional data file.S5 Fig(DOCX)Click here for additional data file.S6 Fig(DOCX)Click here for additional data file.S7 Fig(DOCX)Click here for additional data file.S8 Fig(DOCX)Click here for additional data file.S9 Fig(DOCX)Click here for additional data file.S10 Fig(DOCX)Click here for additional data file.S11 Fig(DOCX)Click here for additional data file.S12 Fig(DOCX)Click here for additional data file.S13 Fig(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.


Parentage control is moving from short tandem repeats- to single nucleotide polymorphism (SNP) systems. For SNP-based parentage control in cattle, the ISAG-ICAR Committee proposes a set of 100/200 SNPs but quality criteria are lacking. Regarding German Holstein-Friesian cattle with only a limited number of evaluated individuals, the exclusion probability is not well-defined. We propose a statistical procedure for excluding single SNPs from parentage control, based on case-by-case evaluation of the GenCall score, to minimize parentage exclusion, based on miscalled genotypes. Exclusion power of the ISAG-ICAR SNPs used for the German Holstein-Friesian population was adjusted based on the results of more than 25 000 individuals.Z-value. Based on minor allelic frequencies in the Holstein-Friesian population, one minus exclusion probability was equal to 1.4×10−10 and 7.2×10−26, with one and two parents, respectively. Two monomorphic SNPs from the 100-SNP ISAG-ICAR core-panel did not contribute. Simulation of 10 000 parentage control combinations, using the GenCall score data from both BeadChips, showed that with a Z-value greater than 3.66 only about 2.5% parentages were excluded, based on the ISAG-ICAR recommendations . When applied to real data from 1750 single parentage assessments, the optimal threshold was determined to be Z = 5.0, with only 34 censored cases and reduction to four (0.2%) doubtful parentages. About 70 parentage exclusions due to weak genotype calls were avoided, whereas true exclusions (n = 34) were unaffected.Experimental data were derived from routine genomic selection analyses of the German Holstein-Friesian population using the Illumina BovineSNP50 v2 BeadChip  or the EuroG10K variant . Averages and standard deviations of GenCall scores for the 200 SNPs of the ISAG-ICAR recommended panel were calculated and used to calculate the downward Using SNPs for parentage evaluation provides a high exclusion power also for parent identification. SNPs with a low GenCall score show a high tendency towards intra-molecular secondary structures and substantially contribute to false exclusion of parentages. We propose a method that controls this error without excluding too many parent combinations from the evaluation.The online version of this article (doi:10.1186/s12711-014-0085-1) contains supplementary material, which is available to authorized users. Currently, parentage control in cattle is mainly based on short tandem repeats (STR), but is moving towards single nucleotide polymorphism (SNP)-based methods . In ordeFor most STR-based methods, quality control (QC) relies on statistical procedures, such as running samples with a known accuracy as controls and assuming that unknown samples will perform the same way. In contrast, for SNP-based genotyping methods, QC measures are usually specific to each array-based technology. For example, the Illumina bead chip system applies a GenCall score, which is a measure of goodness of the call for any individual SNP of a sample on a chip . These mGC is the GenCall score, n is the chip type, i is the SNP locus and s is the sample.The Illumina BovineSNP50 v2 and EuroG10k BeadChips were used to genotype a German Holstein-Friesian bovine population according to the manufacturers’ recommendations. For both BeadChips, the cluster files supplied by Illumina were used for GenCall and re-clustering was done once for such SNPs with an overall call rate lower than 97%. The new cluster file was stored and used without further modification throughout the study. Overall, based on the manufacturer’s recommendations, genotypes with a GenCall score greater than 0.15 were considered called. Genotype data were obtained for 20 000 and 7000 Holstein-Friesian individuals using the BovineSNP50 v2 and the EuroG10k BeadChip, respectively. From the genotyping data available on the respective BeadChip version, the data of the 200-SNP ISAG-ICAR panel, which consists of 100 core SNPs and 100 additional SNPs  were extracted and the average GenCall score  or two (n = 3) parents tested, and the s denotes the SNP tested.The total error in genotype calling for each SNP in any parentage evaluation was estimated using a simple error summation formula:Z-values greater than 0 were set to 0 for total error calculation.Since error contribution is considered as a one-sided phenomenon , Zs,total values for excluding SNPs from data evaluation. For this, Zs,total for each SNP was calculated from Zs,i for each random combination and the number of SNPs above the threshold value for Zs,total was recorded by sliding the threshold from 2.67 to 6 in Ten thousand random combinations from our real data were used to simulate the censoring effect of using the The threshold for censoring a SNP from parentage evaluation was set to 3.66, which is equivalent to an error control rate of 2.5% when corrected for multiple testing using the Bonferroni procedure.Minor allelic frequencies (MAF) of SNPs in the 200-SNP panel in the German Holstein-Friesian population were calculated for SNPs with a GenCall threshold greater than 0.15, with the exception of SNP ARS-USMARC-Parent-DQ916057-rs29009979, which is not present on the BovineSNP50 v2 BeadChip. Therefore, in this case, the MAF was calculated based on the data obtained from the 7000 Holstein-Friesian individuals genotyped using the EuroG10k BeadChip.The guanine-cytosine (GC) content of the 60-bp genomic sequence on either side of each SNP was calculated and compared to the average GenCall score using a linear correlation model.P of parentage exclusion were used [Standard formulas to convert the allelic frequencies into probability ere used .For one parent:For two parents:pi is the frequency of allele i, n the number of alleles . The total exclusion power is calculated by combining all P values of the tested loci as follows: The probabilities were calculated for each locus tested, where where k is the number of loci used.™.All calculations were performed using either standard UNIX commands or Microsoft Excel−5 and 1.4×10−10 using the 200 SNPs of the additional panel if one parent is interrogated. For a complete trio, the values are 2×10−13 and 7.2×10−26, respectively. Figure MAF in the German Holstein-Friesian population differed slightly from the MAF provided by Illumina for Holstein Friesian cattle as shown in Figure −9; EuroG10k bead chip: GenCall score = −0.24 × GC-content + 1.02, F-test - p=5.5×10−6). Using a GenCall score of 0.9 and a GC-content of 46% as delimiters, the Fisher’s exact p value for e.g. the BovineSNP50v2 BeadChip was 5.9×10−9. Sequences with a high GC-content are prone to form secondary intra-molecular structures (secondary structures) [Average GenCall scores from both the BovineSNP50v2 and the EuroG10k BeadChips were calculated and compared to a censoring of SNPs, based on the recommended value of 0.7 . For theuctures) . Such seZtotal = 3.66, which is close to the value obtained from the Gaussian probability function [See Additional file In order to evaluate the losses of resolvable parentages due to censoring of SNPs by integrating a concept of individual QC into the parentage control, a simulation with different threshold values, based on the actual dataset was performed. Ten thousand random combinations for one and two parents were calculated and the GenCall score was used to calculate the total error as indicated above. The number of combinations for both one and two parents that had to be excluded because the number of interrogated SNPs was less than recommended (90 for one and 85 for two parents) was ~2.5%, when the threshold for total error was set to Ztotal was finally tested on 1750 known real parentage control combinations in the same dataset. The dataset contained only combinations with one interrogated parent which is the usual case in routine laboratory diagnosis compared to cases where both parents are questionable. Data without considering the GenCall score were used as side-by-side comparison. Overall, 62 (3.5%) combinations did not reach the necessary number of interrogated SNPs based on the recommended minimum GenCall score of 0.15 (n = 90 SNPs), but it should be noted that the missing SNP on the BovineSNP50v2 BeadChip already contributes in many cases. The influence on the parentage evaluation is substantial, since the number of doubtful cases decreased from more than 400 to only less than 10; the number of exclusions was also greatly reduced to 35 . Random censoring of an equivalent number of SNPs did not alter the results compared to unfiltered data.The Ztotal value was chosen by minimizing the number of unsolved and doubtful parentages in parallel. Figure Ztotal threshold value was equal to 5.0. Using this Ztotal value, only 34 combinations were censored, whereas the number of remaining doubtful parentages was minimized (four cases). The number of true (verified) parentage exclusions, which were part of the evaluation was constant with 34 cases over the whole range of threshold values, which indicates that the approach does not result in erroneous parentage acceptances. The effect of this approach is shown in Figure Ztotal of 5.0 as delimiter. Interestingly, a cluster of five SNPs could be defined that had a high rate of being excluded in both BeadChips. This suggests that re-designing the respective probes to optimize their detection in genotyping analyses might be advisable. Those SNPs that performed poorly and therefore, were most often censored from the analyses are listed in Table In a final risk/reward analysis the best The usefulness of SNPs for parentage control is substantiated by the high exclusion power of the ISAG-ICAR panel SNPs ,13. NeveWe have theoretically and practically evaluated a concept of error minimization by integrating the individual GenCall score that is given for each SNP call for any given sample into the SNP-based parentage control workflow. We show that this leads to an almost complete elimination of false exclusions and doubtful results. Especially the latter is meaningful, since according to the ISAG-ICAR guidelines, such doubtful results would require callbacks and case-by-case discussions. It is conceivable that a re-clustering of each run would also lead to better calls (to a certain extent), but such a strategy does not seem to be feasible under accreditation guidelines, since it would require an enormous documentation effort.Z-values performed by the investigator.The method of error control proposed here is suitable to minimize erroneous parentage exclusions and doubts by pre-censoring only weak genotype calls, an approach that can be automated in the software, or can be done by inspection of the A discussion is probably needed on how the measurable underlying analytical error can be mirrored in planned databases for international parentage evaluations. This seems essential to avoid later issues on both parentage control and search that include low-quality SNP data that are likely to be erroneous.Overall, the ISAG recommendations can be debated, however, currently, they are based on (1) number of SNPs, that are used (a minimum of 90 for one parent and 85 for two parents) and 2) number of conflicting genotypes. These considerations are mostly based on simulations without special consideration of the contribution of a locus for a breed and the achievable reliability. There is no reason to use monomorphic SNPs, since it does not matter whether they are genotyped or not. Finally, given the very high PE of SNP-based parentage control  differs from the requirements for parentage control. Therefore, a re-evaluation of the statistical and quality criteria is necessary.Z-transformed downward GenCall score of > 5.0 as threshold calculated for both BeadChips used for parentage control, the number of rejected and doubtful parentages can be substantially reduced without affecting the true exclusions.In the German Holstein-Friesian population, the recommended core and additional ISAG-ICAR panel contains two SNPs that are practically monomorphic and one SNP with a MAF of 5.8%, which renders them uninformative for parentage control in this population. Although the overall PE is high for both panels, inherent low GenCall scores for several SNPs, e.g. due to the formation of secondary intra-molecular structures in GC-rich sequences, will lead to a loss of data based on the recommended threshold of 0.7 for high accuracy data. In contrast, maintaining a GenCall score of 0.15 will result in an increased number of rejected or doubtful parentages due to miscalled genotypes. Censoring only those SNPs with a combined (offspring and parents)


Reference-free SNP detection, that is identifying SNPs between samples directly from comparison of primary sequencing data with other primary sequencing data and not to a pre-assembled reference genome is an emergent and potentially disruptive technology that is beginning to open up new vistas in variant identification that reveals new applications in non-model organisms and metagenomics. The modern, effcient data structures these tools use enables researchers with a reference sequence to sample many more individuals with lower computing storage and processing overhead. In this article we will discuss the technologies and tools implementing reference-free SNP detection and the potential impact on studies of genetic variation in model and non-model organisms, metagenomics and personal genomics and medicine. It is no overstatement to say that the recent technological advances that have made it possible to sample whole genomes many times over has changed forever the way that geneticists and genomicists design and carry out their experiments. Many of these experiments require the detection of genetic variants as a preliminary, most often single nucleotide polymorphisms (SNPs). The basis of all these experiments is the same: one sequence must be compared with another. The predominant model for this is to have a single genome assembly chosen as the baseline against which all others will be compared. Often this reference genome will have been produced in a large-scale 'big-biology' project by a large consortium using long read Sanger-style sequencing or a hybrid approach that mixes long and short reads, but the defining characteristic will be that a great deal of time and expense has gone into preparing the reference. To avoid this expense later, the genome is not assembled for each further individual sample, instead the sequence reads will be aligned directly to the single assembled reference irrespective of any assembly errors or structure specific to the population or individual sequenced. The consensus from the sequence reads will then be taken as the new sample's genome and SNPs are called using sophisticated statistical methods based on sequence and alignment quality metrics.de novo assembly algorithms designed to use short read data have been implemented. Some examples of these tools are Velvet [k-mers from the sub-sequences of sequence reads and links are made between k-mers that overlap by k-1 with a single nucleotide overhang . In a situation with no read errors and with k long enough to include the longest repeat in a single k-mer then it is theoretically possible to reconstruct the genome by following from k-mer to k-mer along the edges, passing through each k-mer only once. Since sequencing data do contain errors and genomic repeats can be very long  then the read errors can cause dead ends in the path that terminate extension of the contig, repeats in the genome can cause cycles in the graph. True genetic variation will also cause these sub-structures. The different ways in which the De Bruijn graph based assembly programs get around these problems, essentially how they throw out variation-like structure in the graph, is what separates them and is the area on which assembler research is currently focussed. By focussing on these structures, rather than throwing them away, we can identify variation directly from the graph of k-mers. The basic distinct structure that indicates the presence of a SNP is described as a 'bubble'  that visit each node and edge just once on their way back to the starting node. This method's very precise description of a bubble means that it has the potential to be extremely specific and generate highly accurate lists of SNPs from graph structure alone  (2k + 2), however the computational time required is great and sensitivity must be compromised, the entire graph cannot be searched and random start sites within the graph must be selected repeatedly to prevent ample coverage, furthermore as graph complexity increases the time to complete increases badly too, rendering these approaches likely unsuitable for metagenomics applications or situations were completeness of SNP detection, rather than simpler marker selection is needed.Another graph structure, or topological method that has appeared recently has taken a very strict approach to the definition of the bubble structures and therefore represents a potentially very effective SNP detection tool. In the 2et al. [k-mers for an input read set and then build bubble microassemblies (or 'mouth' structures in their terminology) based on the k-mer set. The algorithm consists of first picking a seed k-mer by choosing the k-mer with the lowest count that occurs more in one set than another. A low count k-mer is chosen to avoid repeat structures, while it his hoped that a differing set count may be indicative of a SNP. Having chosen a seed, the algorithm assumes that it lies on one path through a SNP and then looks for an opposite k-mer, one substitution different, which would lie on another path through the bubble. If this can be found in the k-mer tree, then a recursive algorithm builds paths left and right of each k-mer until they join up or no k-mer can be found in the tree. Once a bubble has been identified, it is checked for read coherence by checking for coverage from at least two reads. The approach has a significant memory saving and speed advantage over techniques that build a complete De Bruijn graph, as the initial tree structure does not include connectivity information. However, the method does not handle heterozygous SNPs and is limited to pairwise comparisons for data sets containing more than two samples. An improved version of the technique is currently being prepared for publication that addresses some of these limitations ([pers. comm.).Peterlongo et al.  adopt a tations  [et al. ML algorithm to exclude repetetive loci from SNP calling.The Stacks tool developed by Catchen et al.  is desiget al. ,17. The e et al. . Relatedhm (iML) . They noet al. have recently published a tool called MARYGOLD which aims to identify complex bubbles in and between metagenomic samples [Approaches specifically targetted at metagenomic data have been rare, due to the complexity of such datasets. However Nijkamp  samples . Their aet al implemented fast and highly memory efficient data structures and algorithms for general variant detection, and is the first implementation that makes use of explicitly coloured De Bruijn graphs and incorporates a de novo assembly algorithm. Given a specific genome of interest, a variant-size and a k-mer size, the power to detect variants was modelled, interestingly the authors noted that without a reference, it was difficult to distinguish read errors and repeats from genuine SNPs. An extension module to Cortex named Bubbleparse handles this problem explicitly by incorporating extra information on genetic background and applying a maximum likelihood heuristic to rank candidate bubbles and thereby increase accuracy of SNP calling so that false-postive calls can be reduced by five-fold relative to the base algorithm, moving from 50 - 60 % false positive unranked to5 - 10 % after ranking. Bubbleparse can also select and prioritise SNPs that satisfy prior information about genetic background, meaning that it is particularly suited to the identification of SNPs de novo from specific genetic crosses.There are already a number of tools already available for reference-free SNP calling (see Table de novo and for tracking of genetic loci from populations sequenced using the RAD tag approach, a reduced representation sequencing process, Stacks [k-mer list, or stack. A similarity graph between stacks is created to reduce their number and to merge stacks to give the potential SNP loci. A maximum likelihood consensus is used to infer the SNPs and genetic cross data is incorporated to construct haplotypes and create comprehensive, dense genetic maps.Pipelines have also been developed for the identification of polymorphisms , Stacks  is a comk stack, tool [Arabidopsis thaliana. Non-candidate SNPs are discarded by the method.The more recent NIKS, needle in a ck, tool  is a relet al resulted in the tool kisSnp, which evolved into kisSnp2 and is now part of discoSnp [The 'mouth' work of Peterlongo discoSnp ,22. discde novo short read assemblers the SNP calls generated can be embedded in a long species-specific contig, an attribute that promises to have great use in the field of plant breeding. Domesticated crop species typically have very large, highly repetitive genomes that have been refractory to attempts to generate completed genome sequences. A key aim in plant breeding is to identify the genetic variation that contributes to agronomically important traits so that it can be selected effectively and introduced into commercially important crop lines. Closely related wild relatives of crop species, a great example is wheat and related wild grasses, are reservoirs of genes that can confer resistance to pathogens. By using reference-free SNP approaches plant breeders can access a much wider pool of resistance related genes since the alleles that associate with resistance in some individuals or populations can be assayed much more quickly. The ability to generate contigs with trait linked SNPs embedded means standard tools like BLAST can be used to identify the candidate function of the gene from which that contig arose, even when no specific genetic resources are available.The impact of reference-free SNP calling promises to be significant. Genetic studies in non-model organisms will be facilitated greatly - with these methods it is now possible to start assaying genetic variability across many samples without the time and expense of building a reference genome. This alone brings the technology to smaller groups with fewer resources but also allows new questions to be asked across wider phylogenetic groupings than is possible. Although the reference free methods are just as sensitive to nucleotide distance between samples and large genetic distances cannot be assayed any more effectively than with alignment based methods, virtually any organism can be compared against close relatives with just the sequencing and much lower computational overhead. Since many of the reference-free tools are based on the same De Bruijn graph structure as de novo like this means that the complex mutations that make up the genomic pathologies of cancers, for example, can be uncovered much more easily for each patient and case and the changes cross-referenced with the patients own individual reference. Furthermore the recent realisations that our genomes vary even within us, perhaps from tissue to tissue is much more tractable in the reference-free framework.Application of reference-free SNP detection methods and data structures could help to resolve the data storage and access issues associated with alignment-based methods meaning that true genome wide SNP and variant searches can be more easily commoditised for the medical and clinical markets and therefore be a crucial element in the deployment of personal genomics based medicine. Plummeting sequence prices are starting to make individual personal genome sequencing a diagnostic reality. The small data overhead incurred for each new sample sequenced means much more information can be stored for similar cost which brings new diagnostic and research possibilities much closer. Being able to work with sequence regions not present in the available reference genomes and the SNPs in those regions means that new associations between disease and genetic variations can be collected, and the reference free framework is fit to make this research possible directly from the clinic. Variant detection absolutely We are learning that individual organisms genomes can be incredibly diverse and capturing that diversity will become vital in future research. The reference-free SNP detection methods are among the first steps in a new approach that will help relieve us from the limiting prior information in the reference genome.The authors declare that they have no competing interests.RML and DM equally contributed to the text.


Many differences between different ethnic groups have been observed, such as skin color, eye color, height, susceptibility to some diseases, and response to certain drugs. However, the genetic bases of such differences have been under-investigated. Since the HapMap project, large-scale genotype data from Caucasian, African and Asian population samples have been available. The project found that these populations were located in different areas of the PCA  plot. However, as an unsupervised method, PCA does not measure the differences in each single nucleotide polymorphism (SNP) among populations.We applied an advanced mutual information-based feature selection method to detect associations between SNP status and ethnic groups using the latest HapMap Phase 3 release version 3, which included more sub-populations. A total of 299 SNPs were identified, and they can accurately predicted the ethnicity of all HapMap populations. The 10-fold cross validation accuracy of the SMO  model on training dataset was 0.901, and the accuracy on independent test dataset was 0.895.In-depth functional analysis of these SNPs and their nearby genes revealed the genetic bases of skin and eye color differences among populations.The online version of this article (doi:10.1186/s12864-015-2328-0) contains supplementary material, which is available to authorized users. To our knowledge, over the millions of years of evolution, mutations have occurred occasionally and are maintained or lost by inheritance and natural selection. The more than 60 million SNPs are scattered throughout the entire genome, including −50 % on the coding region and the rest on the non-coding region [A single nucleotide polymorphism (SNP) is defined as a single base change in a DNA sequence that occurs in a significant proportion (more than 1 %) of a large population. SNPs occur at a frequency that ranges from 1 in 1000 to 1 in 100 bases. Recently, the NCBI  released the SNP-138 database, which contains more than 60 million SNP sites , which includes 1397 samples and 1,457,897 SNPs among 11 ethnic groups. Because the Chinese and Japanese samples were very similar [We downloaded the genomes of different ethnic groups from the HapMap Phase 3  release  similar , 29, thehttp://cran.r-project.org/web/packages/adegenet/). The genotype matrix was a matrix of 0, 1 and 2, which were the numbers of the minor SNP alleles in that sample.The original PED and MAP files (hapmap3_r3_b36_fwd.consensus.qc.poly.ped.gz and hapmap3_r3_b36_fwd.consensus.qc.poly.map.gz) were transformed into a matrix using PLINK  with “--Because there were too many SNPs and because most of them differed among the ethnic groups, we calculated the Cramer’s V coefficient  for eachThe Cramer’s V coefficient measured the association between SNP status and ethnic groups and was defined as follows:N was the total number of genotype samples, 1397 in our study, k was the number of ethnic groups (k = 9) and r was the number referring to the SNP status . 2 is Pearson’s chi-squared statistic, which can be calculated as follows:where Oi,j is the number of the occurrences of SNP status j among ethnic group i and Ei,j is the expected occurrences of SNP status j among ethnic group i, which can be calculated as follows:where ni is the number of samples in ethnic group i and mj is the number of samples with SNP status j.where V coefficient ranges from 0 to 1, where 0 indicates no association between the SNP status and ethnic group and 1 indicates a complete association between SNP status and ethnic group.The Cramer’s V coefficients of the 1,457,897 SNPs were calculated using the function CramerV from R package DescTools https://cran.r-project.org/web/packages/DescTools/. The 2,448 SNPs with Cramer’s V coefficients greater than 0.6 on the training dataset were considered to be candidate SNPs and were analyzed using more advanced machine learning based feature selections [The Cramer’s lections –36 to obhttp://penglab.janelia.org/proj/mRMR/. Unlike a univariate filter, such as Cramer’s V coefficient, mRMR not only considered the associations between SNPs and ethnic groups but also the redundancies between SNPs.We applied a widely used –39 mutuas and Ωt were used to denote the entire set of 2,448 (N) candidate SNPs, the selected m SNPs, and the to-be-selected n SNPs, respectively. The relevance of the SNP f from Ωt with ethnic group c can be measured with mutual information [I):Ω, Ωormation , 42 (I):R of the SNP f with the selected SNPs can be calculated as follows:In addition, the redundancy fj from Ωt with maximum relevance with ethnic group c and minimum redundancy with the already-selected SNPs, the mRMR function was defined as follows:To obtain the SNP S using the mRMR method is obtained:The mRMR feature evaluation is continued for N rounds, and then a ranked SNP list The SNP with a smaller index h has a better trade-off between relevance and redundancy and is more important for classifying samples from different ethnic groups.Si = {f1, f2, …, fi} included the top i SNPs.Based on the top 2,448 mRMR SNPs, we constructed 2,448 classifiers and applied an Incremental Feature Selection (IFS) method –47 to idBased on the prediction performance of each candidate SNP set, an IFS curve was plotted. The x-axis denoted the number of SNPs, and the y-axis denoted the 10-fold cross validation accuracies using these SNPs.i wasWe used 10-fold cross validation , 49 to ti is the number of samples in ethnic group i and Ti is the number of correctly predicted samples in ethnic group i. The total accuracy [where Naccuracy , 51 was9http://www.cs.waikato.ac.nz/ml/weka/.We constructed the classifiers by using four common predictive methods: SMO , IB1 , Dagging, and RandomForest (random forest) in Weka . Weka isThe SMO method is an algorithm for building support vector machine (SVM) models . The optIB1 was an application of the nearest neighbor method . The samℑ included N samples, they were randomly divided into k subsets that each contained n samples (kn ≤ N). In each subset, a basic model Mi(1 ≤ i ≤ k), was trained on these k subsets. A test sample was predicted to be the ethnic group with most votes.Dagging was used as a meta classifier, and the ethnic group of the test sample was predicted by voting . If the N samples and M SNPs in the training set, each tree was trained using n randomly selected samples. At each node, m features were randomly selected and used to optimize the split. The test sample was predicted to be the ethnic group with the most votes from the decision trees.The random forest algorithm  was an eThe IFS prediction accuracies of these four methods were evaluated by 10-fold cross validation and compared, and the selected model was tested on the independent test dataset.We analyzed the HapMap genotype data, which included 1,457,897 SNPs on 1397 samples from nine ethnic groups. The sample sizes of each ethnic group in the training dataset and independent test dataset are shown in Table We applied the mRMR method to rank the 2,448 SNPs. Then, the top SNPs were optimized using the IFS method. The predictive accuracies of the samples and each ethnic group were elevated using 10-fold cross validation. Four widely used predictive models, i.e., SMO, IB1, Dagging and RandomForest, were compared. Their performances based on using different numbers of top SNPs are shown in Fig. In Table However, the best SMO model requires too many features. To balance the model complexity and predictive performance, we considered the top 299 SNPs used by the SMO to be the optimal SNP set because subsequently, upon adding more SNPs, the performance did not increase greatly. In other words, the IFS curve shown in Fig. We sought to explore how these 299 SNPs differed among ethnic groups and calculated their minor allele frequency in each ethnic group. In Fig. As shown in Fig. Tabassum R and his colleagues reported, DOX5 was a susceptibility gene for type 2 diabetes [In our study, 299 SNPs, which varied significantly among different ethnic groups, were identified. Considering the large number of our SNPs, we selected the 9 SNPs that achieved the highest score in our list. The SNP with the highest score 0.861) was rs6023406, which is located in the intron region of the DOX5 gene. As diabetes , 59. Fur was rs60diabetes . AlthougMikiko S and his group investigated the allele frequency of rs1426654 in Chinese, Sinhalese and Tamils from Sri Lanka, Uygurs, Europeans, and Xhosans (Africans) from South Africa, and Ghanaians using polymerase chain reaction-restriction fragment length polymorphism. They found that the A nucleotide was predominant in the European population but exhibited low levels in the Asian population [rs1426654, which is a coding SNP that scores 0.581 and ranks 2nd in our analysis, was located on chromosome 15, where the G- > A transition changes p.A111T in the SLC24A5 protein. Lamason RL et al. revealed that SLC24A5 affects pigmentation in zebrafish and humans . Recentlrs1325421, the 3rd SNP, which scored 0.515 in our analysis, is located downstream from the PREP gene. PREP could reportedly play an important role in many biological processes, such as the maturation and degradation of peptide hormones and neuropeptides, learning and memory, cell proliferation and differentiation, and glucose metabolism . Considers8049040, which ranked 4th place in our data and is located on chr15:48392415, is nearest to gene ZNF23, which was widely reported among multiple types of cancers, including liver and ovarian cancer –69. Inters13432350, an SNP that ranked 5th in our analysis, is located in EXOC6B. As Evers.C et al. reported, EXOC6B might play an important role in the molecular pathogenesis of intellectual disabilities . Intellers2973133, the 9th-ranked SNP in our data, is located upstream of PRR16 gene. Liu X. et.al reported that dysfunction of PRR16 could lead to Coronary Artery Disease (CAD) . In factIn addition to the top-nine SNPs on our lists, several other SNPs have a potential relationship with the varied characteristics among ethnic groups, such as rs12913832, an SNP ranked in 42nd place, which was scored as 0.386 and is located within an intron of the non-pigment gene HERC. Visser M et al. found that rs12913832 modulates human pigmentation by attenuating chromatin-loop formation between a long-range enhancer and the OCA2 promoter . Mengel In addition to exploring the SNPs directly, we analyzed the functions of 1,397 genes located within a 500 kb range of the 299 SNPs using DAVID. The results are shown in Table Above all, we learned that the various SNPs could contribute to different characteristics, including skin color, eye color and the risk of diseases, especially skin-related disorders, among different populations. Our study revealed a large spectrum of SNPs that could facilitate our understanding of the different characteristics between populations and the underlying mechanisms of molecular evolution.ftp://ftp.hgsc.bcm.tmc.edu/HapMap3-ENCODE/HapMap3/HapMap3v3.All data are public available from HapMap project at


The Smyth line (SL) chicken is the only animal model for autoimmune vitiligo that spontaneously displays all clinical and biological manifestations of the human disorder. To understand the genetic components underlying the susceptibility to develop SL vitiligo (SLV), whole genome resequencing analysis was performed in SLV chickens compared with non-vitiliginous parental Brown line (BL) chickens, which maintain a very low incidence rate of vitiligo.Illumina sequencing technology and reference based assembly on Red Jungle Fowl genome sequences were used. Results of genome resequencing of pooled DNA of each 10 BL and SL chickens reached 5.1x and 7.0x coverage, respectively. The total number of SNPs was 4.8 and 5.5 million in BL and SL genome, respectively. Through a series of filtering processes, a total of ~1 million unique SNPs were found in the SL alone. Eventually of the 156 reliable marker SNPs, which can induce non-synonymous-, frameshift-, nonsense-, and no-start mutations in amino acid sequences in proteins, 139 genes were chosen for further analysis. Of these, 14 randomly chosen SNPs were examined for SNP verification by PCR and Sanger sequencing to detect SNP positions in 20 BL and 70 SL chickens. The results of the analysis of the 14 SNPs clearly showed differential frequencies of nucleotide bases in the SNP positions between BL and SL chickens. Bioinformatic analysis showed that the 156 most reliable marker SNPs included genes involved in dermatological diseases/conditions such as ADAMTS13, ASPM, ATP6V0A2, BRCA2, COL12A1, GRM5, LRP2, OBSCN, PLAU, RNF168, STAB2, and XIRP1. Intermolecular gene network analysis revealed that candidate genes identified in SLV play a role in networks centered on protein kinases , phosphatase (PPP1CA), ubiquitinylation (UBC) and amyloid production (APP).Various potential genetic markers showing amino acid changes and potential roles in vitiligo development were identified in the SLV chicken through genome resequencing. The genetic markers and bioinformatic interpretations of amino acid mutations found in SLV chickens may provide insight into the genetic component responsible for the onset and the progression of autoimmune vitiligo and serve as valuable markers to develop diagnostic tools to detect vitiligo susceptibility.The online version of this article (doi:10.1186/1471-2164-15-707) contains supplementary material, which is available to authorized users. Vitiligo is an acquired hypomelanotic disorder characterized by circumscribed depigmented macules in the skin resulting from the loss of melanocytes. Autoimmunity has been identified as the major etiological factor in vitiligo, although many other factors including infections, stress, neural abnormalities, aberrant melanocyte function, and genetic susceptibility have been implicated . The SmyRecent genome wide association studies (GWAS) in humans to understand the role of genetic components in a variety of autoimmune diseases including vitiligo have identified hundreds of loci harboring risk alleles . SeveralThe SL chicken is an excellent model to conduct a functional verification study of candidate genes that underlie genetic susceptibility for vitiligo due to the tractable, definite phenotype, the high vitiligo incidence in the population (80-90%), the feasibility of in vivo characterization and the relatively short generation time. Recently, microarray analysis examined global gene expression during SLV development and provided comprehensive information at the transcriptome level that supported the multifactorial etiology of vitiligo . In thisGenome sequencing of pooled DNA from 10 non-vitiliginous BL and SL chickens each with confirmed SLV produced ~63 and 89 million sequence reads of 200 bp, respectively  program generated bioinformatics data sets including functional groups  and gene networks for genes containing amino acid changes in SL chicken. The 156 SNPs were found in 139 genes encompassing known- and unknown functions, chromosomal open reading frames, and hypothetical proteins   indicateGene network analysis, which represents the intermolecular connections among interacting genes based on functional knowledge inputs, was performed on genes with amino acid changes in SLV chickens using the IPA program. The gene network analysis was carried out using the simplest setting of 35 focus molecules to facilitate and summarize the intermolecular connections Table , and 9. Candidate genes in Network #1 are associated with signaling pathways of the mitogen activated protein kinase  and protein kinase C (Pkc) connected to VEGF  and PDGF (platelet derived growth factor) with PLAU in the center Figure . The topThe top functions of Network #2 include Cell Cycle, DNA Replication, Recombination and Repair, and Developmental Disorder Figure  and NetwMolecules in Network #4 are involved in Cell Morphology, Cellular Function and Maintenance, Hair and Skin Development and Function Figure . MoleculMolecules in Network #5 also mainly bind to UBC as discussed in Networks #2 and #3 and the functions include Lipid Metabolism, Small Molecule Biochemistry, Digestive System Development and Function Figure .Network #6 contains molecules involved in Hereditary Disorder, Ophthalmic Disease, Neurological Disease Figure . In thisMolecules in Network #7 mainly function in Cellular Response to Therapeutics, Cellular Assembly and Organization, DNA Replication, Recombination, and Repair. PRKDC  and its interacting protein kinases are mainly involved in this network Figure . In addiVitiligo susceptible loci in human populations identified by several GWAS –10 also In this study, various potential genetic markers showing amino acid changes were identified in the SLV model through genome re-sequencing. When considering functionality based on the interpretation of factors involved, development of vitiligo appeared to be associated with the interactions among cytoskeletal factors , protein kinases , phosphatase (PPP1CA), ubiquitinylation (UBC) and amyloid (APP) production. Further functional validation study, such as allele specific expression of the candidate genes with candidate SNPs at the target tissues involved in SLV development will be carried out using the SL chicken model for spontaneous autoimmune vitiligo.Adult SL chickens with vitiligo and parental non-vitiliginous BL chickens, maintained by G. Erf at the University of Arkansas , were selected from the breeder populations. Blood (3 ml) was collected from 12 birds each following an animal use protocol approved by the University of Arkansas Institutional Animal Care and Use Committee . Genomic DNA was isolated from each whole blood sample using QiaAmp DNA mini kit  following manufacturer’s instructions. DNA quality was determined by agarose gel electrophoresis and 10 samples having the highest quality in each line were pooled to represent each line. Library preparation and Illumina genome sequencing for the pooled DNA samples were performed by the National Center for Genome Resources . Illumina HiSeq system 2x100 bp paired end read technology was used for genome sequencing.Illumina sequencing data received from NCGR was aligned to the chicken reference genome sequence for Red Jungle Fowl (GBK 4.0) that was retrieved from NCBI. For the reference based genome alignment, the NGen genome sequence assembly program of the Lasergene software package  was used. Assembly parameters were as follows: file format, BAM; mer Size, 21; mer skip query, 2; minimum match percentage, 93; maximum gap size, 6; minimum aligned length, 35; match score, 10; mismatch penalty, 20; gap penalty, 30; SNP calculation method, diploid bayesian; minimum SNP percentage, 5; SNP confidence threshold, 10; minimum SNP count, 2; minimum base quality score, 5. After assembly, the SeqMan Pro program of the Lasergene package was used for further analyses including SNP data.JMP genomics  program was used for filtering unique SNPs for vitiligo SL chickens. SNPs occurring in both SL and BL lines were filtered out, leaving behind unique SNPs for each line. To identify highly fixed and homozygous SNPs, the SNPs were filtered based on SNP percentages (SNP%). SNPs with a SNP% of ≥75 (%)  were chosen. The 75% cutoff for SNP selection was set by considering potential sequencing errors that can be generated by the massively parallel sequencing method. Potential causal SL SNPs that induce non-synonymous changes in CDS regions were chosen for further analysis. Since the read depth of many SL SNPs was low, unique SNPs showing ≥10 read depths were considered as reliable SNPs. Reliable and causal SNPs, which were chosen by criteria described above were confirmed by double-checking the raw assembly data with alignment view to reduce false positives.Fourteen randomly chosen SNPs, which induce amino acid changes in the CDS region, were subjected to validation using PCR and Sanger sequencing with larger numbers of SL and BL chickens. Twenty BL and 70 SL chickens that were verified phenotypically to be non-vitiliginous and vitiliginous, respectively, were used for blood sampling. Approximately 100 μL of blood was collected from each bird by wing vein puncture into tubes containing citrate (anticoagulant). Genomic DNA was isolated from whole blood using the Wizard SV 96 Genomic DNA Purification System  following manufacturer’s instructions with modifications. Isolated DNA was quantified using a Nanodrop 1000 spectrophotometer  and a dilution of 1 ng/μL was prepared in 96 well PCR format for all samples. For PCR reaction, forward and reverse primers were designed based on the RJF genome sequence (GenBank assembly ID: GCA_000002315.2) using Primer 3 online software (Table http://www.ingenuity.com). Since IPA is based on human and mouse bioinformatics, functionalities for differentially expressed genes in the chicken were interpreted based primarily on mammalian biological mechanisms. The limit of number of molecules in the network was set to 35, leaving only the most important molecules based on the number of connections for each focus gene (a subset of uploaded significant genes having direct interactions with other genes in the database) to other significant genes [Functional interpretation of 139 genes showing ≥75 SNP%, ≥10 read depths and non-synonymous changes was analyzed in the context of gene ontology and molecular networks using Ingenuity Pathways Analysis .List of 3518 SNPs to induce amino acid changes in SL chicken.(XLSX 432 KB)Additional file 1:Gene name and functions of 139 gene containing amino acid changes showing ≥10 depth counts in SL chicken. Gene symbol, Entrez gene name, cellular location and type were provided. (XLSX 18 KB)Additional file 2:Functional groups of genes containing amino acid changes.(XLSX 12 KB)Additional file 3:List of focus molecules in gene networks. Gene symbols and Entrez gene names were displayed for the illustrations of network analysis. (XLSX 17 KB)Additional file 4:


Sus scrofa chromosome 4 (SSC4).Previously, we identified a major quantitative trait locus (QTL) for host response to Porcine Respiratory and Reproductive Syndrome virus (PRRSV) infection in high linkage disequilibrium (LD) with SNP rs80800372 on GBP5) was differentially expressed (DE) (p < 0.05) in blood from AA versus AB rs80800372 genotyped pigs at 7,11, and 14 days post PRRSV infection. All variants within the GBP5 transcript in LD with rs80800372 exhibited allele specific expression (ASE) in AB individuals (p < 0.0001). A transcript re-assembly revealed three alternatively spliced transcripts for GBP5. An intronic SNP in GBP5, rs340943904, introduces a splice acceptor site that inserts five nucleotides into the transcript. Individuals homozygous for the unfavorable AA genotype predominantly produced this transcript, with a shifted reading frame and early stop codon that truncates the 88 C-terminal amino acids of the protein. RNA-seq analysis confirmed this SNP was associated with differential splicing by QTL genotype (p < 0.0001) and this was validated by quantitative capillary electrophoresis (p < 0.0001). The wild-type transcript was expressed at a higher level in AB versus AA individuals, whereas the five-nucleotide insertion transcript was the dominant form in AA individuals. Splicing and ASE results are consistent with the observed dominant nature of the favorable QTL allele. The rs340943904 SNP was also 100 % concordant with rs80800372 in a validation population that possessed an alternate form of the favorable B QTL haplotype.Within this QTL, guanylate binding protein 5  contains supplementary material, which is available to authorized users. Sus scrofa chromosome 4 (SSC4) at 139136697–140420778 that accounts for 15.7 % and 11.2 % of the genetic variance for host response of young pigs to experimental PRRS virus infection, as measured by serum viremia from 0 to 21 days post infection (dpi) and weight gain to 42 dpi. In addition, a SNP (rs80800372) was identified that appeared to be in complete LD with the QTL. Biological candidate genes in the region that have been previously studied include members of the guanylate binding protein (GBP) family [GBP3 is not assigned to the SSC4 QTL region. The GBP5 protein has previously been shown to play a role in immune response through mediation of inflammasome assembly [Recently, Boddicker et al. , 2 identGBP5 function in a knockout mouse model results in impaired host defense and inflammatory response because GBP5 facilitates nucleotide binding and oligomerization, leucine-rich repeat protein 3 (NLRP3) mediated inflammasome assembly [L. monocytogenes infection [GBP5 expression was upregulated in response to Epstein-Barr viral infection [The GBP5 protein is an important mediator of inflammatory immune response in mammals. Loss of assembly . Specifiassembly . The 62 assembly . Mice lanfection . In addinfection . HoweverGBP5. Furthermore, we show that the mutation is in 100 % linkage disequilibrium (LD) with the previously reported SSC4 QTL in an independent population.The objective of this study was to use functional genomics methods to identify the gene and mutation responsible for the observed QTL on SSC4 for host response to PRRS virus infection. We present evidence for a putative causal mutation based on differential expression, allele specific expression and differential splicing data that created an illegitimate splice site in GBP5 transcript in the Sus scrofa 10.2 genome assembly (Sscrofa build 10.2) was missing the C-terminal end of GBP5 that contains a conserved CAAX box domain required for cellular localization. Using translated BLAST to compare RNA-seq reads to the human protein, exon 11 of GBP5 was identified approximately 200 kb 5′ of the start site of GBP5 in Sscrofa build 10.2. In addition, the predicted Ensembl gene ENSSSCG00000027014 was noted to have nearly equal identity to fragments of GBP2 and GBP4. We also identified several gaps in the reference sequence within the QTL region in Sscrofa build10.2 that were indicative of assembly problems. A transcript reassembly was conducted, after which all transcripts were compared to the pig and human genomes to ensure the presence of the entire protein coding sequence. For reference, the original order of the genes from the Sscrofa build 10.2 is provided in Additional file GBP4, GBP5, GBP6, GTF2B, CCBL2, and PKN2. The GBP1 and GBP2 transcripts were assembled utilizing the existing transcript information and the human transcripts for these genes. The FASTA formatted sequence of all cDNAs corresponding to the eight genes assembled is provided in Additional file ENSSSCG00000027014 transcript from Sscrofa build 10.2 in our de -novo assembly. Thus, this gene may be the product of a genome misassembly, may not be expressed in whole blood, or may be a non-functional gene duplication. The syntenic human chromosomal block to the QTL region includes the GBP3 gene but this gene was not found in the de novo transcriptome assembly or by BLAST search of the human GBP3 protein against either the RNA-seq reads or our de novo transcript assembly of this region. This gene is also missing from Sscrofa build 10.2.Several genomic assembly issues were identified in the SSC4 QTL region that made accurate quantification of gene expression problematic. For example, the Seventy sample sequences from 16 pigs and 5 time points following experimental infection were retained after quality control for identification of differentially expressed (DE) genes  . No transcripts were DE at 0 and 4 dpi. Significant transcript log2 fold changes are provided in Fig. pi) Fig. . The log. No tranGBP2 (9 SNPs), GBP4 (9 SNPs), GBP6 (7 SNPs), and GBP5 (6 SNPs). The estimated probability of expression of the alternate allele  averaged across all dpi time points for these SNPs in LD with the QTL are presented in Additional file GBP5 was the only gene with all SNPs in LD that showed consistent main effects for ASE across all SNPs tested, as presented in Fig. A total of 64 SNPs in the eight genes in the SSC4 QTL region were in complete LD with the SSC4 QTL based on concordance with rs80800372 genotype. These 64 SNPs were analyzed for ASE in animals that were heterozygous for rs80800372. All SNPs were filtered by minimum read depth to avoid the analysis of false SNPs due to sequencing errors. Of these 64 SNPs, 43 SNPs exhibited ASE when averaged across dpi while FDR was controlled at 5 %. The greatest number of SNPs identified that exhibited ASE per gene were in GBP2  SNPs, GBGBP5 was DE between QTL genotypes at several dpi, exhibited ASE, and was a strong biological candidate gene, we carefully examined the RNA sequence data of this gene for variants. Interestingly, a T > G SNP (sense strand) was identified in intron 9 of GBP5 that appeared to introduce an additional splice acceptor site five base pairs 5′ to the start of exon 10 in animals that possessed the unfavorable A QTL allele. This splice acceptor SNP was previously assigned the NCBI dbSNP ID rs340943904. The position of rs340943904 relative to SNPs exhibiting ASE and the gene structure of GBP5 is presented in Fig. GBP5 transcript is presented in Additional file GBP5 exon 9, intron 9 and exon 10 were evaluated for splicing consistent with multiple alternatively spliced transcripts . The sequence of the wild-type transcript is provided in Additional file Since GBP5 splicing with the SSC4 QTL genotype, all RNA-seq reads that discriminate the three alternatively spliced transcripts were counted. Altered splicing was observed to segregate consistently with the AA and AB rs80800372 genotypes. A significantly lower number of wild-type splicing read counts was present in AA compared to AB individuals, after standardization of each alternate transcript as the proportion of total reads produced per sample across all three transcripts ), GBP5 expression was measured by RNA-seq and analyzed in AA and AB rs80800372 genotyped pigs at the same five time points  is the most costly infectious disease in the global pig industry  with an GBP5 was identified as DE across multiple time points post infection (FDR < 0.05) and across all time points (FDR = 0.051). This result was validated in an independent RNA-seq experiment from a different pig population obtained through the PRRS host genetics consortium (PHGC). Since this second study was not designed specifically to identify differences in gene expression due to the SSC4 QTL genotype, but also focused on the effects of different host response phenotypes, this result provides strong evidence that GBP5 is DE due to QTL genotype. This result indicates that differential expression of GBP5 is related to or mediates the QTL effect on PRRS host response.Three genes were DE between the AA (unfavorable) and AB (favorable) PRRS host response QTL genotypes (FDR < 0.05). However, only GBP5 was the only gene where all SNPs in LD with QTL exhibited ASE. This result was validated in the independent PHGC5 RNA-seq data set for three of the six SNPs. The three SNPs that were not ASE appeared to be uninformative based on the lack of expression of an alternate allele in half of the AB animals in the PHGC5 dataset, which is supported by the multiple B QTL haplotypes identified in this population [GBP5 exhibited ASE, we hypothesized that ASE in GBP5 may occur due to differences in RNA stability or splicing.To provide additional evidence for differences in expression, an ASE analysis of all SNPs in complete LD with the PRRS host response QTL was conducted to determine if evidence of allelic imbalance exists in correspondence with the QTL genotype. pulation . There aGBP5 exhibited alternative splicing. Concurrently, we identified the rs340943904 SNP in GBP5, for which the allele that was in perfect LD with the unfavorable A allele at the SSC4 QTL inserted five nucleotides into the GBP5 transcript upstream of exon 10. This insertion is expected to result in an early stop codon and thus a non-functional GBP5 protein. Since this SNP was predicted to change the splicing of GBP5, we used RNA-seq reads spanning the specific splice junctions to quantify the three alternatively spliced GBP5 transcripts across all time points. The wild-type splicing transcript was expressed more in individuals with the AB QTL genotype (p < 0.001), while the five nucleotide insertion transcript was more highly expressed in AA individuals (p < 0.001). No difference in expression was observed for the retained intron transcript between genotypes. Splicing consistent with these results was observed in a second population  to facilitate RNA isolation and serum samples were used to quantify viral level using a semi-quantitative TaqMan PCR assay [Sixteen barrows from 8 litters, one AB and one AA individual at the rs80800372 SNP from each litter, were selected from a population of 200 commercial crossbred piglets that had been experimentally infected with PRRS isolate NVSL97-7985 as part of the PRRS Host Genetics Consortium . No BB CR assay .All animal experiments were approved by the Kansas State University Institutional Animal Care and use committee under registration number 3000.TM preserved blood samples using the TempusTM Spin RNA Isolation Kit  according to the manufacturer’s protocol as previously described [HBA and HBB) were reduced using an RNase H based globin reduction method [Total RNA was isolated from 77 available PHGC3 Tempusescribed . A ND-10n method  that useTM library kit  according to the manufacturer’s protocol. Sequencing was done on an Illumina HiSeq machine using 100 cycles and the paired-end read methodology to generate 100 base pair reads, as described by the manufacturer . Samples from each pair of littermates were allocated to one lane, for a total of 8 lanes, such that litter effects were confounded with lane effects and, thus, power to detect genotype effects was maximized. Initial processing of reads from the HiSeq machine used the Illumina CASAVA (v1.8) software.Library construction was conducted at the Iowa State University DNA facility with the TruSeqhttp://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to determine if it had adequate read quality for downstream analyses . Seven of the 77 samples were removed from all further analyses due to poor read mapping (fewer than 1 million reads mapped to the reference genome), or inconsistent sequence-based genotype for the rs80800372 SNP with the SNP chip based genotype for that animal. A total of 70 samples were thus retained for DE analysis . All transcripts were translated and compared against the orthologous human protein to validate the accuracy of the transcripts. When full length transcripts were not identified in the de novo transcript assembly, the complete pig transcript was assembled using the appropriate reads that created the full length protein that exhibited high similarity to the corresponding human orthologue based on the BLAST results.In order to create the transcripts for the SSC4 QTL region, several approaches were utilized. Reads from all time points from one AB genotype animal were combined and trimmed using Sickle  to reducHBB and HBA reference sequences were removed before htseq analysis [To produce a common set of transcript coordinates for all samples, sequence reads for each sample were mapped to the Sscrofa 10.2 reference using Tophat/Bowtie2 , 28. Alianalysis , using tSNPs in the QTL region were used to perform allele-specific expression analysis. Each BAM file produced by Tophat was reformatted using Picard tools  and thenReads from the RNA-seq data were observed to have a 3′ read mapping bias, presumably due to globin depletion and other systematic RNA processing factors . The 3′ Since the objective of this study was to identify potential causal variants for the SSC4 QTL that impact host response to PRRS, we focused on analysis of the differential expression only of genes within the SSC4 QTL region , 2.The trimmed mean of M values (TMM) normalization procedure  from theThe ASE analysis focused on SNP variants within the QTL region. After variant calling, a shell script was developed to extract the genotype and read counts for each sample and each SNP for downstream analysis. Within each individual, reference and alternate allele counts for each SNP were obtained by summing across that individual’s multiple time point samples. Using a count cutoff of at least 2 times the number of time points available for that individual, alleles at a SNP were called as either present or absent in that individual for a given SNP. SNPs were then filtered to compare the behavior of transcripts in LD (i.e. haplotypes) with either the A or B QTL allele. For the initial ASE analyses, SNPs were required to be in perfect LD with the rs80800372 SNP (i.e. the QTL). A total of 64 SNPs in LD with the QTL were analyzed after applying these filters. In addition, a second ASE analysis was conducted that included all heterozygous SNPs regardless of LD status with the QTL. These 67 SNPs were selected using the same count cutoff; however, AA QTL genotyped individuals were included.alt), was obtained and a Wald test was performed to determine whether palt differed from 0.5. Multiple testing correction was conducted using the Benjamini & Hochberg FDR method [Two separate analyses of ASE were conducted. The objective of the first ASE analysis was to determine if the alleles of SNPs in LD with the QTL had coordinated expression patterns during PRRS viral infection. Only SNPs in AB QTL genotyped individuals in LD with the QTL were used in this analysis. A mixed nonlinear logistic regression model was fit to the reference and alternate allele counts for each SNP independently. Reference alleles were defined based on the nucleotide present in the Sscrofa 10.2 genome build whereas differing nucleotides were defined as alternate alleles. This model fit a random effect for each individual, fixed effects for the day post infection and possibly included RIN (both prior to and post globin depletion) numeric values as continuous predictors based on model selection using AIC. For each SNP, an estimate of the probability of observing an alternate allele , +5 bps (inserts five base pairs at the start of exon 10), retained intron . The AATI used internal standards to quantify all alternate transcript peaks at the molarity level. Statistical analysis of the splicing data was conducted using PROC Mixed of SAS. A model was fit to determine if splicing of GBP5, measured as the molarity of each alternate transcript, differed with QTL genotype. The model included the fixed effects for genotype (AA or AB), assay date (the AATI batch effect of samples run together on the same day with 4 levels), alternate transcript abundance , and a random effect of animal to account for common effects of individuals across days and shared effects on alternate transcripts.We used AATI capillary electrophoresis  methodology to validate differential splicing of the GBP5 for all time points in the RNA-seq data. These read counts were then analyzed using the same model as described above for the AATI data to determine if differential splicing of the three alternatively spliced transcripts was observed in the RNA-seq data, with the exception that assay date was not necessary in the model.A script was developed to count the number of reads that uniquely represented the three splice variants of GBP5 T > G SNP in intron 9 at position 139,413,978 bps (rs340943904) as described [GBP5 was genotyped using a common forward primer: 5′-AGCAAGAACAGCAAAGATGT-3′ and specific primers for the G allele : reverse primer: 5′-TGCAAACGTGCCTCTGAAC-3′ or the A allele : reverse primer: 5′-TGCAAACGTGCCTCTGAAA-3′. The PCR annealing temperature was also on 63.5 °C. A total of 58 commercial Yorkshire boars from an independent, validation population were genotyped for these two SNPs to determine the concordance of the GBP5 rs340943904 SNP with the rs80800372 SNP.Allele specific PCR protocols were developed to genotype both the rs80800372 and the escribed . The rs8An additional subset of animals from trial 5 of the PHGC  was obtaGBP5 were deposited at dbSNP under accession numbers: ss1414376806 and ss1414376807. All additional variants identified in this study are under the accession numbers listed in Additional files The data sets supporting the results in this article are available at NCBI. New variants identified for the ASE analysis of


The molecular wiring of this enzymatic pathway defines the ability of a species to biosynthesize LC-PUFA. Exactly when and how in animal evolution a functional LC-PUFA pathway emerged is still elusive. Here we examine key components of the LC-PUFA cascade, the Elovl2/Elovl5 elongases, from amphioxus, an invertebrate chordate, the sea lamprey, a representative of agnathans, and the elephant shark, a basal jawed vertebrate. We show that Elovl2 and Elovl5 emerged from genome duplications in vertebrate ancestry. The single Elovl2/5 from amphioxus efficiently elongates C18 and C20 and, to a marked lesser extent, C22 LC-PUFA. Lamprey is incapable of elongating C22 substrates. The elephant shark Elovl2 showed that the ability to efficiently elongate C22 PUFA and thus to synthesize DHA through the Sprecher pathway, emerged in the jawed vertebrate ancestor. Our findings illustrate how non-integrated “metabolic islands” evolve into fully wired pathways upon duplication and neofunctionalization.The biosynthesis of long-chain polyunsaturated fatty acids (LC-PUFA) provides an intriguing example on how multi-enzymatic cascades evolve. Essential LC-PUFA, such as arachidonic, eicosapentaenoic, and docosahexaenoic acids (DHA), can be acquired from the diet but are also endogenously retailored from C Most species studied so far have a single elovl5 gene with the ability to elongate C18 and C20 PUFA substrates, with marginal activity towards C2221222324elovl5 have been characterized11elovl2 orthologue has been identified only in Atlantic salmonSalmo salar), zebrafishDanio rerio) and rainbow troutOncorhynchus mykiss), and with ray-finned fishes (including most marine species) appearing to lack elovl2 in their genomeselovl2 demonstrated the capacity to elongate DPA and thus contribute to DHA production through the so-called “Sprecher pathway”Elovl5 appears to be unique in its capability to elongate C18 PUFA substrates and, similarly, Elovl2 towards C22 PUFA, while there is an overlap between both enzymes in their capacity to metabolize C20 substrates. However, when exactly Elovl2 and Elovl5 genes diverged and their respective functional fatty acid preferences emerged in metazoan evolution is presently unknown. Interestingly, various mollusk species, including the common octopus (Octopus vulgaris), the noble scallop (Chlamys nobilis) and cuttlefish , have been shown to possess an Elovl gene, phylogenetically basal to the vertebrate Elovl2 and Elovl5303118 PUFA and to lesser extent C2030313334Genes encoding ELOVL proteins have received comparatively less attention, although their action is critical for a complete and functional LC-PUFA pathwayFads and Elovl genes and their biosynthetic abilities in key evolutionary lineages hampers the precise evolutionary profiling of this pathway. Here we investigate the Elovl2/Elovl5 gene repertoire at a key evolutionary moment: the invertebrate/vertebrate transition , the sea lamprey  and the elephant shark , we provide an insightful snapshot into the evolution of critical enzymes dictating the LC-PUFA biosynthetic pathways in chordates.Despite the significant effort made to clarify the LC-PUFA biosynthetic capabilities in some vertebrate lineages, the presently known complement of ansition . By examElovl2 and Elovl5 like genes in a total of 19 species representing all major vertebrate lineages   and the star ascidian (Botryllus schlosseri), despite the former having an Elovl4-like gene with the ability to elongate C18 and C20 PUFAElovl4 can partly contribute to the LC-PUFA biosynthesisWe analyzed the repertoire of nathans) . In addinathans) . The retequences . Within f Elovl2 . TherefoElovl2 and Elovl5 origin to the ancestor of vertebrates. Thus, we hypothesize that genome duplications were involved in the diversification of Elovl2/5 genes. Human Elovl2 and Elovl5 localize to the same human chromosome (Hsa6) though at separate regions  have a vertebrate specific paralog mapping to the Elovl2 locus, GCM2 and MAK, respectively. Also Sycp2l, localizing close to Elovl2, has a paralogue at Hsa20.5 as expected  (Elovl2/5 genomic locus of the pre-duplicated genome of the Florida amphioxus (B. floridae)  and Hsa1, part of linkage group C and D, respectively  . Additioloridae) . Coherenectively . Thus, wElovl2/5 sequences, we examined the syntenic relationships of Elovl2/5 genes in key species. Gnathostome Elovl2 and Elovl5 gene loci were conserved, though with different degrees . In both species, the putative Elovl2 locus includes orthologues of Sycp2l and Gcm2 gene, denoting a strong conservation with the human locus , and lower activity toward the C20 PUFA (20:5n-3 and 20:4n-6). Compared to the sea lamprey Elovl5, the Elovl2 was very efficient in the elongation of C20 to C22, with C18 PUFA being elongated to a lesser extent  residue regarded as critical for elongation of C22 by Elovl222 PUFA as in gnathostome lineages. Coherently, the sea lamprey Elovl2 exhibits a tryptophan (W) typical of Elovl5 sequences  was notably lower when compared to other Elovl2 proteins characterized in the present study and previously reported using similar systems1120 PUFA such as 20:5n-3 and 20:4n-6 to the corresponding C22 PUFA, 22:5n-3 and 22:4n-6, but lost its ability to elongate C18 PUFA (Elovl2 mutant confirms that the cysteine (C) residue indicated above is key for the C22 to C24 elongation ability22 into C24 PUFA.Functional characterization of the sea lamprey equences . Thus, wC18 PUFA . OverallElovl genes, namely Elovl2 and Elovl5, critical in the biosynthetic pathways of LC-PUFAElovl specificities in chordates.Vertebrate radiation encompassed the acquisition of key physiological and anatomical innovations, as a consequence of gene and genome duplications444546Elovl2 and Elovl5 occur only in vertebrate species. Thus, our data support that both Elovl5 and Elovl2 have evolved in agnathans, chondrichthyans, holosteans (spotted gar) and teleosts such as zebrafish and Atlantic salmon. Contrarily to Elovl5, Elovl2 is absent in most of the ray-finned fish branch due to a gene loss event as previously hypothesizedElovl2/5 sequence in invertebrate deuterostomes and protostomes, and its basal position in the tree, defines the transition from invertebrate chordates to vertebrates as the exact timing at which diversification of Elovl2/5 gene family occurred. Typically in mammals, ELOVL2 are enzymes with high elongation efficiency towards C20 and C22 PUFA, and marginal (if any) activity towards C18 substrates818 and C20 PUFA as preferred substrates, but have little or no capability to elongate C22 PUFA8Elovl2/518 and C20 PUFA appearing as preferred substrates for elongation, although some ability to elongate C22 PUFA was also observed  regarded as critical for C22 PUFA elongation by Elovl2Elovl2. Using a site-directed mutagenesis approach, we showed that the mutated lamprey Elovl2 protein lost the ability to elongate C18 PUFA exhibited by the native protein and, more importantly, gained the ability to elongate C22 PUFA. However, the minute capacity to elongate C22 exhibited by the mutated lamprey Elovl2 suggests that other unidentified amino acids are also critical for this function.Phylogenetics and synteny revealed that orthologs of observed . The subFads and Elovl gene repertoire and function in various species allows us to propose that a fragmented LC-PUFA pathway existed early in evolution  and elongation (C18 to C22) enzymatic abilities3031323334Elovl2/5 showing its ability to elongate PUFA ranging from C18 to C22, a full complement of desaturase abilities is likely absent as suggested by in silico searches, with a single Fads-like gene described so far in their genome22 may limit the LC-PUFA biosynthetic pathways regardless of the possible number of genes or desaturase activities existing in lampreys  and Japanese lamprey genome project (http://jlampreygenome.imcb.a-star.edu.sg/), databases through Blastp searches using as reference the annotated human ELOVL2, ELOVL5 and ELOVL4 aa sequences. Accession numbers are available in ELOVL amino acid (aa) sequences were retrieved from Ensembl, GenBank, JGI (Joint Genome Institute), elephant shark genome project . The elephant shark genomic information was collected from Ensembl Pre assembly ESHARK1 (http://ensembl.fugu-sg.org/index.html) and for Japanese lamprey synteny maps were inferred using the draft assembly LetJap1.0 available at GenBank. When possible, we analyzed a 1Mb window centered on the corresponding Elovl gene, using the human locus as reference for comparison. Paralogy studies used the ancestral chordate genome reconstructionElovl genes in addition to phylogenetic analysis reconfirmation using ML methods.Elovl-like gene in amphioxus was achieved by PCR with Phusion® Flash (high-fidelity PCR master mix) using degenerate primers (Elovl2/5-like sequences were identified in the available genome. To obtain the open reading frames (ORF) of the incomplete Elovl2/5-like sequence (ENSPMAG00000005149), we carried out a RACE PCR. The elephant shark Elovl2 sequence was identified in the transcriptome and genome sequenceTotal RNA was isolated from amphioxus  and sea lamprey  using an Illustra RNAspin Mini RNA Isolation Kit . All steps were performed according to the manufacturer’s recommendations, including the on-column treatment of isolated RNA with RNase-free DNaseI. One μg RNA was used for cDNA synthesis using the iScript cDNA Synthesis Kit (Bio-Rad) and following the manufacturer’s specifications. Initial isolation of the  primers . InitialSaccharomyces cerevisiae . Briefly, the ORF of the target genes were cloned into the yeast expression vector pYES2 (Invitrogen) following a two-step routine. First, PCRs with specific primers flanking the full ORF were designed in the 5′ and 3′ UTR of each gene  containing 0.01% BHTFunctional characterization of the ELOVL gene products from amphioxus, sea lamprey and elephant shark were investigated by heterologous expression in yeast ach gene  were perach gene . PCR conFatty acyl methyl esters (FAME), prepared from total lipids extracted from harvested cells, were analyzed using a Thermo Gas Chromatograph  fitted with an on-column injection system and a FID detector. Further confirmation of FAME was performed with an Agilent 6850 Gas Chromatograph system coupled to a 5975 series MSD . The elongation conversion efficiencies from exogenously added PUFA substrates were calculated by the proportion of substrate fatty acid converted to elongated products as ) x 100.How to cite this article: Monroig, Óscar. et al. Evolutionary functional elaboration of the Elovl2/5 gene family in chordates. Sci. Rep. 6, 20510; doi: 10.1038/srep20510 (2016).