ELOVL6 gene as the major causative gene of the QTL on pig chromosome 8 for palmitic and palmitoleic acid contents in muscle and backfat. The single nucleotide polymorphism (SNP) ELOVL6:c.-533C > T located in the promoter region of ELOVL6 was found to be highly associated with ELOVL6 expression and, accordingly, with the percentages of palmitic and palmitoleic acids in longissimus dorsi and adipose tissue. The main goal of the current work was to further study the role of ELOVL6 on these traits by analyzing the regulation of the expression of ELOVL6 and the implication of ELOVL6 polymorphisms on meat quality traits in pigs.In previous studies on an Iberian x Landrace cross, we have provided evidence that supported the porcine ELOVL6 gene coupled to RNAseq data re-analysis showed that two isoforms of this gene are expressed in liver and adipose tissue and that they differ in number of exons and 3’UTR length. Although several SNPs in the 3’UTR of ELOVL6 were associated with palmitic and palmitoleic acid contents, this association was lower than that previously observed with SNP ELOVL6:c.-533C > T. This SNP is in full linkage disequilibrium with SNP ELOVL6:c.-394G > A that was identified in the binding site for estrogen receptor alpha (ERα). Interestingly, the ELOVL6:c.-394G allele is associated with an increase in methylation levels of the ELOVL6 promoter and with a decrease of ELOVL6 expression. Therefore, ERα is clearly a good candidate to explain the regulation of ELOVL6 expression through dynamic epigenetic changes in the binding site of known regulators of ELOVL6 gene, such as SREBF1 and SP1.High-throughput sequencing of BAC clones that contain the porcine ELOVL6:c.-394G > A polymorphism as the causal mutation for the QTL on pig chromosome 8 that affects fatty acid composition in pigs.Our results strongly suggest the The online version of this article (doi:10.1186/s12711-015-0111-y) contains supplementary material, which is available to authorized users. ELOVL6 gene is located on chromosome 8 , in a region where a quantitative trait locus (QTL) that affects palmitic and palmitoleic acid contents was previously detected . All reads were mapped against the Escherichia coli genome using the Burrows-Wheeler Alignment tool (bwa v.0.6.2) [de novo assembly were performed using the de novo assembler tool of CLC Genomics Workbench v.6.0.1 [http://www.clcbio.com]. The 3’UTR sequence of ELOVL6 was identified from de novo assembled reads aligned with the corresponding human (GenBank:NM_001130721) and bovine (GenBank:NM_001102155) genes, using the Basic Local Alignment Search Tool (BLAST v2.2.28) [ELOVL6 3’UTR sequence was used as reference sequence to align reads from liver  and adipose tissue  transcriptomes that were obtained in previous studies [Sscrofa10.2) [http://www.ensembl.org/info/data/ftp/index.html] as a combined reference. Finally, SNPs in the ELOVL6 gene were manually identified by comparing the reference sequence with mapped reads with the Integrative Genomic Viewer (IGV v.2.1) . A total of 179 animals including the 144 BC1_LD backcross animals and their corresponding 35 parents from the IBMAP cross (F0 and F1) were genotyped.SNPs Sscrofa10.2 assembly.For the genome-wide association study (GWAS), the 144 animals of the BC1_LD backcross were genotyped with the Porcine SNP60 Illumina BeadChip using the Infinium HD Assay Ultra protocol (Illumina). Raw data had a high genotyping quality  and were visualized and analyzed with the Illumina GenomeStudio software. For subsequent data analysis, a subset of 54 998 SNPs was selected by removing SNPs with a minor allele frequency less than 5%, SNPs with more than 5% missing genotype data and SNPs that were duplicated in the ELOVL6:c.-533C > T, ELOVL6:c.-480C > T, ELOVL6:c.416C > T [ELOVL6:c.1408A > G and ELOVL6:c.1922C > T SNPs were performed on the following phenotypes: RT-qPCR expression data of ELOVL6 mRNA in backfat and C16:0 and C16:1(n-7) fatty acids composition in backfat and intramuscular fat. The position of the SNPs was based on the Sscrofa10.2 genome assembly [http://www.animalgenome.org/repository/pig/]. GWAS were performed with a mixed model  and insulin sensitivity [ELOVL6:c.-394G > A as the causal mutation of the QTL on SSC8 but additional investigations are necessary to validate its effect on ERα binding. Furthermore, to our knowledge, this is the first study that suggests a mechanism for the regulation of ELOVL6 expression in pigs. Therefore, based on the metabolic similarities between pigs and humans [Taken together, the results suggest a new mechanism for the regulation of the expression of pression . Hence, e tissue . Thus, vsitivity . Our resd humans , the regELOVL6 gene and show that two different isoforms are expressed in both liver and adipose tissue. SNP ELOVL6:c.-533C > T was found to be more strongly associated with the expression of ELOVL6 and with the percentages of palmitic and palmitoleic acids in longissimus dorsi and adipose tissue than the two genotyped SNPs of the 3’UTR region. These results indicate that the promoter region of ELOVL6 may be the main regulatory region involved in the variation of ELOVL6 expression in pigs. Interestingly, SNP ELOVL6:c.-394G > A, which is in linkage disequilibrium with SNP ELOVL6:c.-533C > T and is located in the only ERα binding site predicted in the promoter of ELOVL6, was found to be associated with variations in methylation patterns of the region between −529 bp and −350 bp and with ELOVL6 expression. This region contains binding motifs for several regulators of the ELOVL6 gene, which suggests that epigenetic changes may have a central role in the regulation of ELOVL6 expression. Hence, we suggest that SNP ELOVL6:c.-394G > A is most likely responsible for the differential expression of ELOVL6 and, consequently, for the palmitic and palmitoleic acid contents in muscle and backfat.In this paper, we describe the complete genetic structure of porcine


Epistasis, or gene-gene interactions are a plausible source to explain portions of the missing heritability of BMI.Despite heritability estimates of 40–70 % for obesity, less than 2 % of its variation is explained by Body Mass Index (BMI) associated loci that have been identified so far. Using genotypic data from 18,686 individuals across five study cohorts – ARIC, CARDIA, FHS, CHS, MESA – we filtered SNPs (Single Nucleotide Polymorphisms) using two parallel approaches. SNPs were filtered either on the strength of their main effects of association with BMI, or on the number of knowledge sources supporting a specific SNP-SNP interaction in the context of BMI. Filtered SNPs were specifically analyzed for interactions that are highly associated with BMI using QMDR . QMDR is a nonparametric, genetic model-free method that detects non-linear interactions associated with a quantitative trait.p-value of association < 0.1. Prior experimental evidence helps explain the plausible biological interactions highlighted within our results and their relationship with obesity. We identified interactions between genes involved in mitochondrial dysfunction (POLG2), cholesterol metabolism (SOAT2), lipid metabolism (CYP11B2), cell adhesion (EZR), cell proliferation (MAP2K5), and insulin resistance (IGF1R). Moreover, we found an 8.8 % increase in the variance in BMI explained by these seven SNP-SNP interactions, beyond what is explained by the main effects of an index FTO SNP and the SNPs within these interactions. We also replicated one of these interactions and 58 proxy SNP-SNP models representing it in an independent dataset from the eMERGE study.We identified seven novel, epistatic models with a Bonferroni corrected This study highlights a novel approach for discovering gene-gene interactions by combining methods such as QMDR with traditional statistics.The online version of this article (doi:10.1186/s13040-015-0074-0) contains supplementary material, which is available to authorized users. Obesity is a major risk factor for various diseases such as - heart disease, type 2 diabetes and even certain types of cancer , 2. ApprAlthough the current epidemic proportions of obesity can be largely attributed to our lifestyle and food choices, there is also a strong genetic component of obesity. Twin and adoption studies have provided heritability estimates of 40–70 % for obesity . Such stepistasis [The limited success of GWAS has often been attributed to the linear framework employed by these studies. Although, single locus analysis strategies have had success in certain diseases such as age-related macular degeneration and breast cancer –15, manypistasis , 11, 16.pistasis , 18.In this study we aimed to identify interactions between SNPs that are associated with BMI using data from 18,686 individuals across five highly characterized National Heart, Lung and Blood Institute (NHLBI) study cohorts. Individuals were genotyped using the gene-centric ITMAT-Broad-CARe (IBC) array containing approximately 50,000 SNPs.Figure p-value greater than 1.00E–07 or a minor allele frequency (MAF) < 0.05 were also excluded. SNPs were further tested for linkage disequilibrium (LD) – a SNP was removed from each pair of SNPs that had an LD (r2) ≥ 0.6. This reduced our dataset to 17,268 individuals and 28,453 SNPs. Non-founder individuals were also removed from the study population. To check for relatedness between individuals, markers were used for an Identity-by-descent (IBD) analysis using PLINK [Genotyping was performed using the gene-centric ITMAT-Broad-CARe (IBC) array. This array was designed specifically to test over 2,000 loci implicated in various cardiovascular, metabolic and inflammatory phenotypes . The arrng PLINK . For paiP-values, a cut-off value of P < 0.016 was chosen, as there was a distinct separation between SNPs exhibiting a stronger main effect and the rest of the SNPs at this cut-off. This resulted in a final list of 498 markers for further analysis [As an additional filtering step, SNPs were tested for their independent association with the continuous BMI outcome using linear regression. Upon visual inspection of the distribution of analysis .implication index. The implication index is the sum of the number of supporting data sources for each of the genes in a given gene-gene relationship. For our analysis, models with an implication index of five or greater were retained, resulting in a list of 1815 markers . The selected implication index cut-off was slightly more stringent than those used in previous studies [As a parallel filtering procedure, SNPs were also analyzed using Biofilter . Biofilt studies .2, sex, the first three principal components of race computed using EIGENSTRAT software [FTO region. SNPs in the FTO locus are some of the strongest genetic associations identified for obesity risk [FTO locus to increase our ability to identify SNP-SNP models that were not primarily driven by the strong main effect of this gene. The residual BMIs from this regression model were then used as the continuous outcome variable in the QMDR analysis.Baseline BMI values were regressed on age, agesoftware  and the ity risk . Hence, SNPs obtained from the two parallel filtering procedures described above, were tested for association with the continuous BMI outcome using Quantitative Multifactor Dimensionality Reduction (QMDR) . QMDR ism SNPs, k SNPs can be selected to examine a k-order interaction. MDR then constructs a contingency table for these k SNPs, and calculates case–control ratios for each of the possible multi-locus genotypes. Next, the case–control ratio for each multi-locus genotype is compared to the global case–control ratio for the whole dataset. Accordingly, a genotype is considered high-risk if its case–control ratio exceeds the global case–control ratio. Alternatively, it is considered to be low-risk.The original MDR algorithm was designed as a data reduction approach to identify multi-locus genotype combinations that are associated with high or low risk of disease . Within k SNPs, it compares the mean value of the phenotype to the overall mean of the phenotypic trait within the dataset. Hence, a genotype combination is considered high-level if its mean value is larger than the overall mean of the phenotypic trait within the dataset. Otherwise, it is considered low-level. Next, QMDR combines the ‘high-level’ and ‘low-level’ genotypes into respective groups, and compares the phenotypic outcomes between these two groups using a T-test.However, when QMDR constructs a similar contingency table for k-way interaction in the training dataset. The k-way model with the best training score is then used to predict the case–control status in the testing dataset. Ultimately, the best k-order interaction model is chosen based upon the training t-statistic and the highest testing t-statistic is used to select the best overall model for the dataset.QMDR also uses a 10-fold cross-validation procedure similar to the original MDR algorithm. The dataset is divided into 10 portions – 9 portions are used as a training dataset, and the remaining portion is used as a testing dataset. Next, the training t-statistic is calculated for each In the current analyses, we utilized QMDR to specifically test filtered SNPs for all possible two-way (SNP-SNP) interaction models that are associated with the continuous BMI outcome based on their training T-statistic scores. Amongst these models, we selected the 100 best overall SNP-SNP models based on their respective testing T-statistic scores.P-values for SNP-SNP models. P-values were also corrected for multiple testing using standard Bonferroni corrections.Permutation procedures were performed to determine a cut-off threshold for an α = 0.05 significance level. A 1,000 permutations were performed, and in each permuted dataset the 100 best two-way SNP models were selected based on their T-statistic training and testing values. The null distribution of the 100 best SNP models and T-statistic values obtained from all permutations was utilized to calculate explicit test of epistasis, by shuffling genotypic data for each SNP [P-value associated with the original pairwise interactions that were identified.A 1,000 permuted datasets were created using the each SNP . HoweverFTO region, and the main effects of all SNPs within our identified interactions. The full regression model included the identified pairwise interactions in addition to the terms from the reduced model. Adjusted R2 values were used to assess the variance explained by both models. Additionally, a likelihood ratio test was used to compare both models.Linear regression models were used to assess the added variance of the quantitative BMI trait explained by the statistically significant SNP-SNP interactions identified in our analyses. The reduced regression model was built by including the main effect of the index SNP rs11642841 in the http://www.scandb.org). We also searched for evidence of functional relationships between interacting genes using the Integrated Multi-Species Prediction (IMP) web server [To identify known biological evidence supporting the statistically significant pairwise interactions, we mapped each SNP to a corresponding gene using information from dbSNP (build 139) and SCANdb  .The reduced regression model including the main effect of the ASTL and rs1799998 in CYP11B2. We also replicated 58 proxy SNP-SNP models representing this interaction in the eMERGE dataset at a permutation P-value threshold of 0.05  with the FTO SNP rs1558902 previously identified by Speliotes et al. to explain the largest proportion of the variance in BMI [FTO SNP and the SNPs within our interactions.We identified seven novel interactions that are highly associated with BMI. These seven interactions were also explicitly tested for the presence of epistasis. All the identified interactions exhibited an epistatic component. Moreover, we evaluated the increase in explained phenotypic variance by the identified SNP-SNP interactions. The index ASTL and rs1799998 in CYP11B2 related to BMI. This SNP-SNP interaction and 58 LD expanded models representing it were replicated in the eMERGE dataset. The variant rs1799998 has been associated with insulin resistance, diabetes, and metabolic syndrome in humans, but it has not been shown to have an independent association with BMI [ASTL in humans, a specific protease that uses metals in catalytic processes [ASTL to a functional partner of CYP11B2  is known to interact with a functional partner of CAV3 in a number of processes such as signal transduction, endocytosis and focal adhesion  of unknown function. It was found to interact with rs1427463 in POLG2, rs12210959 in F13A1, and rs822682 in SOAT2. Incidentally, none of these other SNPs have been implicated in obesity before. The rs1427463 variant has been associated with height previously in an African ancestry population, which obviously factors into BMI calculations [Four of the seven interactions that we identified include SNPs that have previously been identified as independent signals associated with BMI . These S 7 Table . FLJ3083The use of the IBC array in this study highlights the strengths of this custom array in detecting potentially disease-causing loci that are also supported by a substantial amount of biological evidence. However, while the array has dense coverage in gene-centric regions, it only includes 2000 loci. This limitation of the array was highlighted by the inability of BioFilter to identify any statistically significant SNP-SNP models. The use of methods such as BioFilter may be more suited for larger GWAS datasets including more loci.This study identifies gene-gene interactions that are potentially associated with obesity. Prior experimental evidence suggests the plausible biological relevance of several of the identified loci. However, we also identified a few loci of unknown function. Unfortunately, the inferences that can be drawn from our results are limited by a functional annotation bias – well-studied genes are assigned many annotations while understudied genes often lack annotations. One could speculate that the genes involved in these interactions are multi-functional, thereby connecting various biological processes and pathways. Future work focusing on network-based analyses can help elucidate the additional heritability of BMI that is explained at the biological pathway level. Ultimately, further biological validations will be necessary to determine whether the identified interactions influence the development of obesity. Finally, additional studies are required to better understand gene-environment interactions, to get a more complete understanding of the complex genetic architecture of obesity.Main effects analyses have explained little of the genetic heritability of obesity. The use of methods such as QMDR in conjunction with traditional statistical analyses can unravel this complex network by identifying gene-gene interactions that play key roles in the etiology of obesity. Our QMDR analysis of genotypic data from 5 study cohorts identified novel interactions between genetic variants that are highly associated with BMI. Future studies are necessary to verify the observed associations.


As the use of next-generation sequencing technologies is becoming more widespread, the need for robust software to help with the analysis is growing as well. A key challenge when analyzing sequencing data is the prediction of genotypes from the reads, i.e. correct inference of the underlying DNA sequences that gave rise to the sequenced fragments. For diploid organisms, the genotyper should be able to predict both alleles in the individual. Variations between the individual and the population can then be analyzed by looking for SNPs (single nucleotide polymorphisms) in order to investigate diseases or phenotypic features. To perform robust and high confidence genotyping and SNP calling, methods are needed that take the technology specific limitations into account and can model different sources of error. As an example, ancient DNA poses special challenges as the data is often shallow and subject to errors induced by post mortem damage.We present a novel approach to the genotyping problem where a probabilistic framework describing the process from sampling to sequencing is implemented as a graphical model. This makes it possible to model technology specific errors and other sources of variation that can affect the result. The inferred genotype is given a posterior probability to signify the confidence in the result. SNPest has already been used to genotype large scale projects such as the first ancient human genome published in 2010.We compare the performance of SNPest to a number of other widely used genotypers on both real and simulated data, covering both haploid and diploid genomes. We investigate the effects of read depth, of removing adapters before mapping and genotyping, of using different mapping tools, and of using the correct model in the genotyping process. We show that the performance of SNPest is comparable to existing methods, and we also illustrate cases where SNPest has an advantage over other methods, e.g. when dealing with simulated ancient DNA.The online version of this article (doi:10.1186/1756-0500-7-698) contains supplementary material, which is available to authorized users. We present a novel approach to the genotyping problem where a probabilistic framework describing the process from sampling to sequencing is implemented as a graphical model. This makes it possible to model technology specific errors and other sources of variation that can affect the result. The inferred genotype is given a posterior probability to signify the confidence in the result. SNPest has already been used to genotype large scale projects such as the first ancient human genome published in 2010.There has been a revolutionizing development in sequencing technology from the first genome sequencing projects, which were initiated in the late 1980s, and up until today. Both the time frame and cost of sequencing have decreased significantly since then, and today a single research lab can generate billions of high-quality base pairs from millions of reads in a short time and for a reasonable price.Next-generation sequencing (NGS) techniques cover a wide range of technologies that succeed the Sanger approach . Common NGS methods generate a large amount of data but the various sources of error need to be modeled for genotyping and SNP calling to work optimally. The SNP calling method has to be robust to noise and, preferably, not biased by the systematic errors in the NGS platform. Furthermore, since the read depth along the sequenced DNA template varies, the genotyper should be able to optimally use all information available, and to produce information about how reliable the inferred genotype is.A new but growing field is the sequencing of ancient DNA, such as the woolly mammoth , NeanderWe present SNPest which models the genotyping and SNP calling from the raw read sequences in a fully probabilistic framework. The problem is described using a generative probabilistic graphical model . There aWe have tested SNPest on both simulated and real data, and we compare its performance to a number of other widely used genotypers: GeMS  and FreeFurthermore, whether the genotyper uses the reference allele as a prior when calling SNPs will affect the outcome. SNPest is able to either use or ignore the reference genome in the calculations, and we test both options in this paper. GeMS uses the reference allele to model the distribution over possible genotypes, and there is no parameter to turn this off. FreeBayes by default does not use the reference allele, but this can be changed when calling the program. We only use the default behavior in the paper. The SAMtools/bcftools method uses the reference allele in the process. GATK also uses the reference allele when genotyping the data.Both SNPest and GeMS are able to specify the maximum number of reads used at each position in the genome. Obviously, limiting the number of nucleotides used will speed up the calculations at the risk of making wrong calls, but it also makes it possible to investigate how varying read depths affect genotype calls. We therefore tested both SNPest and GeMS using read depths of max 5, 10, 20, 30, 40, 50 and 60 on haploid data in order to investigate the robustness of both methods. For low depths, a certain variation in the number of SNPs called can be seen between runs due to the random down sampling of reads . FreeBayes does not have this feature and is only run using all the available data. SNPest is run both with and without using the reference genome as a prior. The other methods were run with default settings as described above.The haploid reads were mapped to the reference genome using two different tools, Bowtie2  and BWA-When a genotyper is run on a data set, a large set of SNP candidates will be found. However, this set will normally not be used directly, and only a subset of these positions will be called as SNPs by the genotyper depending on the amount of evidence for the specific change. How to filter the SNP candidates to create a high confidence set of SNPs depends on the program being used. For each test we report the total number of SNP candidates reported by the genotyper (All) and a smaller, high-quality set (QC). For SNPest, this set consists of all SNPs where the posterior probability  is >30.E. coli strain [The real haploid data is generated from the REL606 i strain  using 25i strain .E. coli genome, and no SNPs are simulated in the data. This resulted in 13,889,340 reads. These reads were furthermore mutated to yield an ancient DNA profile using a script provided by Peter Kerpedjiev  based otion and . This reThe diploid data used is from the 1000 Genome Project . InsteadE. coli data using the haploid mode of SNPest, GeMS and FreeBayes. In this case, we would not expect any SNPs since we are mapping the haploid reference genome to itself.In Table SNPest only reports SNP candidates at the lowest read depth of 5X  and all are removed in the high quality set. As mentioned, FreeBayes was only run using all available data and predicts 125 to 170 SNP candidates, all of which are removed in the filtered data set. GeMS consistently reports 5 SNP candidates when using Bowtie2 as the mapper, of which none pass QC irrespective of read depth. When mapping with BWA-PSSM, between 32 and 36 SNP candidates are reported depending on read depth, of which 2-6 make it through QC. The results do not change significantly when analyzing the data where residual adapters have not been removed although more wrong SNPs are called as expected , the reference might not be known but an extant related species is used as a scaffold instead. Whether or not to use the prior information in the reference genome for genotyping depends on the specific sample. As above, we have run SNPest with and without using the reference genome in the calculations, and we also compare results with and without using the specific model for ancient DNA. The results for SNPest (without using the reference) are shown in Table We first compare SNPest with and without explicitly modeling damage. It is seen that the number of SNP candidates does not change dramatically, although this will of course depend on the actual data set and the amount of damage. The total number of SNP candidates decreases somewhat going from the general error model (55-198 for Bowtie2 and 183-763 for BWA-PSSM) to the specific damage model (51-66 for Bowtie2 and 154-236 for BWA-PSSM). Thus, modeling damage does modestly decrease the number of SNP candidates, and the most pronounced difference is at the lowest depth of 5X where the total number of SNP candidates decreases by appr. 66%. In both cases we only predict high confidence SNPs at the the lowest 5X depth, and the number of SNPs in the high quality set is decreased substantially by using an explicit damage model . It is worth noting that most ancient DNA data sets would be in the very low average depth range. This is due to the nature of the data where the amount of endogenous DNA is very low and further limited by damage and fragmentation. This limits the amount of available DNA irrespective of read lengths and other technological advances.Comparing SNPest to GeMS, we first note that the number of SNP candidates does not differ a lot between the two methods: GeMS has between 55 and 298 SNP candidates, depending on the mapping tool used. SNPest using the standard model has the largest number of SNP candidates at the lowest read depth (763 at 5X using BWA-PSSM). Introducing the damage model reduces the number of SNP candidates so SNPest consistently has fewer  than GeMS at all depths. However, the most significant difference is the number of predicted high confidence SNPs where it is clear that GeMS makes many more wrong calls than SNPest. GeMS predicts between 15 and 97 SNPs, and it is worth noting that GeMS predicts high confidence SNPs even at high read depths, indicating that the approach used in SNPest is well suited for biased data where the expected errors can be modeled. FreeBayes using all available data finds the highest number of SNP candidates , but all are removed in thefiltered set.The performance on diploid data was evaluated for SNPest, GeMS, FreeBayes, GATK’s HaplotypeCaller, and SAMtools coupled with bcftools using the low depth and high depth data sets from the 1000 Genomes Project mentioned above. The test is carried out in a similar way to what was done in the GeMS paper : The mapFor many reasons, genotyping a diploid genome from low depth data is much more challenging than genotyping a haploid genome. First, the number of possible genotypes is much larger (10 vs 4 possible combinations if strandedness is not considered). Second, the risk of sampling only one of the alleles at a heterozygous position increases with lower depth. Third, relying on the reference genome can bias the genotypes towards false heterozygote calls (if the true genotype is a homozygous SNP). Fourth, with lower depth and a diploid genome, errors in the data can have a proportionally higher impact. All this needs to be considered in the process.We investigate a number of metrics to evaluate the quality of the predictions: The predicted SNP rate (i.e. the number of predicted SNPs divided by the number of positions); the overlap between predicted SNPs and annotated SNPs in dbSNP, version 139 ; the fraSince we do not know the correct answer for the data, and therefore cannot calculate sensitivity and specificity (or precision) directly, the above metrics are used to elucidate the performance of the different methods despite the shortcomings. The SNP rate can be compared between programs and related back to the expected rate. For the overlap between predicted SNPs and dbSNP it should be noted that dbSNP is not specific for the two chromosomes included in the test but contains common SNPs from the full human genome. Most SNPs from the two chromosomes that are annotated in dbSNP will not be present in the sequence data used here, and some SNPs that are present in the data might not be annotated in dbSNP. However, since dbSNP contains common SNPs it can be assumed that the majority of correct SNP predictions should be in the database (i.e. a large fraction of correctly predicted SNPs should overlap with dbSNP). It can also be assumed that true SNPs might be predicted by multiple programs, which indicates that the fraction of predictions that are exclusively found by one program should be relatively small. The homozygous:heterozygous ratio can show if there is a strong bias in one direction in the predicted SNPs. Previous studies have shown an expected ratio of approximately 0.8 . To partThe low depth results are illustrated in the Venn diagram, Figure Only 741 SNPs are predicted by all methods which is mainly due to the small number of SNPs from FreeBayes - an additional 13,005 SNPs are predicted by the remaining four methods. SNPest has the lowest number of SNPs only found by a single method , with the other methods ranging from 82  to 13,227 . Most of the SNPs predicted by SNPest are also found by the remaining methods . The number of predicted indels varies a lot from 330 for FreeBayes to 7,773 for GATK, with SNPest predicting 454 indels. However, the overlap with dbSNP is around 60% for all methods which indicates very low sensitivity for Freebayes and SNPest. However, as mentioned above, if less than 10 reads support a mutation it is not part of the SNPest high confidence set. If the minimum depth requirement was changed to 5X, the number of predicted indels increases to 3,606.The homozygous:heterozygous ratio for the predicted SNPs varies between methods, with SNPest, SAMtools and GeMS all having a ratio around 0.6, and GATK and FreeBayes having a ratio around 1.1 – i.e. on either side of the expected ratio. In low depth data, using the reference genome in genotyping can strongly affect the predicted SNPs by calling true homozygous SNPs as heterozygous . To test this, SNPest was also run in this mode  to 6,993 , with 29.55% of FreeBayes predictions being found by no other method. SNPest predicts 351 SNPs, or 0.86% of all its predictions, not found by any other method. As before, the number of predicted indels varies a lot from 82 (SNPest) to 5,660 (GATK) but with an overlap with dbSNP around 57% for all methods. Changing the minimum depth to 5X for SNPest increases the number of indels to 2,841 indicating that a more relaxed filtering of indels might be better.The homozygous:heterozygous ratio is similar to what we saw for chromosome 20, with SNPest, SAMtools and GeMS finding around twice as many heterozygous than homozygous SNPs, but this time joined by GATK with a similar ratio. FreeBayes is the outlier with almost five times as many homozygous than heterozygous SNPs. In this case, using the reference genome has a much smaller impact on SNPest predictions because of the amount of observations per site, and the ratio changes from 0.46 to 0.31  are the light intensities Ii and the reference nucleotide H. The unobserved RVs are the nucleotide in the read sequence Ri, the originally sampled nucleotide Si, and the genotype in the diploid genome, G. For a given position in the genome, the combined probability distribution becomes:Let n reads aligned to the genomic position being analyzed, with variables Si, Ri and Ii corresponding to each of the reads. The probability factorizes into five separate probabilities: P(H) is the prior distribution over the reference nucleotide. P(G|H) is the conditional probability distribution over the ten possible genotypes {AA,AC,…,TT} given the reference nucleotide. P(Si|G) is the conditional probability distribution over the actual nucleotide present in the sampled DNA fragment given the genotype. P(Ri|Si) is the conditional probability distribution over the read nucleotides given the actual nucleotide. P(Ii|Ri) is the conditional probability distribution over light intensities given the read nucleotide. The individual probability distributions are described in the following, including how the parameters can be estimated.The product is over the A,C,G,T}, and P(Si|G) is simplified to be the identity matrix.A haploid version of SNPest is obtained by changing the distribution over the genotypes to {S and R parameters. This gives the probability P, and by marginalizing and conditioning on G we can obtain P. The posterior probability of the genotype reflects the confidence that SNPest has in inferring that particular genotype. This information can be used to generate a high confidence subset of genotypes if needed.SNPest calls the genotype with the highest posterior probability at each position. This probability is found by marginalizing the combined probability distribution and summing out the Q are given as a sequence of ASCII characters, where the actual quality scores SC(Q) are offset by some value Δ, SC(Q) = ord(Q)-Δ where ord gives the ASCII value of a character. The higher the quality score SC(Q), the lower the probability of an error:The sequencing machines generally produce Phred-like quality scores together with the reads that provide the probability of error at each position. These qualities QR, and the mapping qualities reported by the mapping tool used, QM. When estimating the genotype, the smallest of the two is used: Q = min. Thus, reads mapped with low mapping quality  will have a smaller weight in the calculations. Similarly, a low quality read, even if mapped with high certainty, will also have low weight in the calculations since it cannot betrusted.SNPest uses both the reported read qualities, P(H), or it can be observed i.e. the nucleotide at the given position in the reference genome can be used. If the reference nucleotide is not used, the default behavior of SNPest is to use a flat prior over the four nucleotides. However, a user-specified distribution can be used instead if required.The model can either use a prior distribution on P(G|H) could be estimated from a known genome , or it By default, SNPest assumes a SNP rate of  in e.g. .P(H=N) be the prior over the four nucleotides =0.25 is used). Further, let % is assumed by default). Then, for nucleotide N, the probability of observing the homozygote genotype, NN, becomes N, there are three genotypes that require just one alternative allele to be chosen, and six genotypes that require two alternative alleles. Assuming Hardy-Weinberg equilibrium, we express the probability distribution as follows: Let the probability of picking one alternative allele be p. Then the probability of the three genotypes requiring just one alternative allele becomes p(1-p) , and the probability of the remaining six genotypes becomes p2. Solving this second degree equation yields the probabilities needed:In the diploid case, the distribution over genotypes can be viewed as a 4×10 matrix with a row for each possible reference nucleotide describing the distribution over the possible genotypes. Let p=0.0333%. As in the haploid case, this does not consider differences in transitions and transversions although that could be incorporated. SNPest comes with scripts to calculate the probability distribution given a specified SNP rate.The probability of picking one alternative allele is P(Si|G). In the haploid case, this collapses to the identity matrix. For a diploid genome there are two possible cases: If the genotype is homozygote in nucleotide a, then P(Si= N|G) = 100% for nucleotide N = a and P(Si= N|G) = 0% for the three other nucleotides, N ≠ a. If it is a heterozygote position, the probability is P(Si= N|G) = 50% for the two alleles and P(Si= N|G) = 0% for the remaining two.Given a genotype, we will be sampling one of the possible alleles with probability P(Ri|Si) models all sources of error from the original DNA to the sequencing step e.g. from damage, PCR amplification errors, etc.In the ideal world, the nucleotide present in the DNA sequence being read will be identical to what was present in the original genome. However, due to various sources of error this will not always be the case. The probability distribution P(Ri|Ii) directly from the quality scores (Eq. 2) assumes the quality measure is correct and perfectly reflects the errors in the data. However, this will not consider e.g. wrong mappings or other sources of error. The expected error distribution is given by P(Ri|Si), and if we expect an error rate of τ, we multiply the vector A, yielding the new probability vector Calculating the length 4 vector A, the highest probability for a read nucleotide is indeed P(R = A|S = A). However, it turns out that the second largest probability is P(R = C|S = A) = P(R = G|S = A), while the smallest probability is P(R = T|S = A). For the other three nucleotides, the smallest probabilities are, respectively, P(R = G|S = C), P(R = C|S = G) and P(R = A|S = T). In all cases, the smallest probability is approximately 1/3 of the second largest probability.The expected errors could be distributed uniformly over all the possible nucleotides. However, from counting actual wrong mappings in a haploid genome a pattern was observed showing that the error distribution depends on the nucleotide in the read: For sample nucleotide τ, the error matrix mentioned above becomes:Using this observation and given an error rate τ = 0.2%.By default, the error rate is estimated to be T is observed in a read, there is a higher probability that it was a C in the sample, i.e. the probability P(R = T|S = C) should increase. Similarly, P(R = A|S = G) should increase. Given an overall expected damage rate, δ, an error matrix Aδ can be calculated by modifying Eq. The DNA damage observed in ancient samples is clustered in the ends of the reads  and dropδ = 3%. For computational reasons, the matrices describing errors, damage etc. are multiplied with the matrix describing the probability distribution P(Ri|Ii) prior to running the program. SNPest comes with scripts to calculate matrices given an expected error rate, 0≤τ < 1, and an expected damage rate, 0 ≤ δ < 1. Setting both τ = 0 and δ = 0 means that the reported quality scores are trusted completely and used directly when estimating genotypes.The damage model used in SNPest uses and expected damage rate of P(Ii|Ri). However, this probability is proportional to P(Ri|Ii) which is given by the quality scores (see Eq. P(Ri) is equal for all Ri, i.e., that all four nucleotides have equal prior probability. Note that SNPest does not use the actual light intensities and does not require anything but the fastq files with quality scores. From this, the posterior probability is calculated as explained above.This distribution models the probability of observing a specific light intensity given the nucleotide in the read. We do not know the form of the conditional probability distribution  see Eq. , when weSNPest takes input generated by the “samtools mpileup -s” command  reporting nucleotides mapped to each position together with read qualities and mapping qualities. The output is in VCF format with a line per position covered in the genome. We provide a Perl script for parsing this output to produce a smaller VCF file with only high quality SNPs and insertions/deletions . The output contains information on the reference nucleotide (if given), the most likely genotype, the posterior probability , read depth used, and average mapping quality. Please refer to the README file provided with the program for more details on how to run SNPest.Project name: SNPestProject home page:https://github.com/slindgreen/SNPestOperating system: Tested on Unix and MacOSProgramming language: C++ and PERLOther requirements:https://github.com/jakob-skou-pedersen/phyLicense: GNU GPL 3Supplementary material for SNPest: A probabilistic graphical model for estimating genotypes.(PDF 534 KB)Additional file 1:


Penaeus monodon to other species. This is largely due to the susceptibility of P. monodon to white spot syndrome virus disease (Whispovirus sp.) which has impacted production around the world. As female penaeid shrimp grow more rapidly than males, mono-sex production would be advantageous, however little is known about genes controlling or markers associated with sex determination in shrimp. In this study, a mapped set of 3959 transcribed single nucleotide polymorphisms were used to scan the P. monodon genome for loci associated with resistance to white-spot syndrome virus and sex in seven full-sibling tiger shrimp families challenged with white spot syndrome virus.Shrimp culture is a fast growing aquaculture sector, but in recent years there has been a shift away from tiger shrimp hours of survival after white spot syndrome virus infection (P < 0.05 after Bonferroni correction). Nine QTL were significantly associated with hours of survival. Of the SNPs mapping to these and other regions with suggestive associations, many were found to occur in transcripts showing homology to genes with putative immune functions of interest, including genes affecting the action of the ubiquitin-proteasome pathway, lymphocyte-cell function, heat shock proteins, the TOLL pathway, protein kinase signal transduction pathways, mRNA binding proteins, lectins and genes affecting the development and differentiation of the immune system (eg. RUNT protein 1A). Several SNPs significantly associated with sex were mapped to linkage group 30, the strongest associations (P < 0.001 after Bonferroni correction) for 3 SNPs located in a 0.8 cM stretch between positions 43.5 and 44.3 cM where the feminisation gene  mapped.Linkage groups 2, 3, 5, 6, 17, 18, 19, 22, 27 and 43 were found to contain quantitative trait loci significantly associated with The markers for disease resistance and sexual differentiation identified by this study could be useful for marker assisted selection to improve resistance to WSSV and for identifying homogametic female individuals for mono-sex  production. The genes with putative functions affecting immunity and sexual differentiation that were found to closely map to these loci provide leads about the mechanisms affecting these important economic traits in shrimp.The online version of this article (doi:10.1186/1471-2164-15-731) contains supplementary material, which is available to authorized users. Litopenaeus vannamei in China, Thailand, and Indonesia. Production of P. chinensis has been reduced, and no significant change in the production of P. monodon has occurred over the last 13 years, mainly because of difficulties due to disease with white spot syndrome virus in these species and the increased availability of genetically improved specific pathogen free L. vannamei post-larvae. More than 80% of shrimp exports from India are derived from aquaculture production.Crustaceans make up around 10% of the world’s aquaculture production with average growth in production of 15% per year from 1970 reaching 5 million tonnes in 2008 [Whispovirus, WSSV) is a lethal pathogen that can cause up to 100% mortality within 7–10 days on shrimp farms, and has devastated shrimp farming industries across the world . Seleiews by: . Howevervannamei–11, and  monodon, 13, esp monodon. Where gP. monodon to white spot disease has been shown to increase when penaeidin class 5 expression is suppressed by interference mediated gene silencing [Litopenaeus stylirostris) [Shrimp have a very limited adaptive immune response  and lackilencing . Shrimp ilencing . Heat shilencing . Shrimp rostris) .Vibrio harveyi DNA [As yet there are no vaccines or other treatments available with proven efficacy against WSSV, although a number of studies have revealed promising leads. The WSSV binding proteins isolated from viral particles in the haemolymph of shrimp infected with WSSV, have been shown to inhibit the binding of this virus to haemolymph cells and improve survival of shrimp . Injectiveyi DNA . In addiveyi DNA . Interesveyi DNA .Resistance to WSSV is a strong candidate trait for marker-assisted or genomic selection since it appears to have low heritability and has a negative correlation with another selected trait (growth). The lack of reported quantitative trait loci associated with this trait may not be due to the lack of segregating genes for resistance, but could instead be due to the highly virulent nature of WSSV, challenge testing methods that do not deliver accurate resistant phenotypes and because marker resources do not sufficiently cover the genome.Another important factor in shrimp cultivation is sex determination. Female penaeid shrimps grow more rapidly than males and so mono-sex production of females would be advantageous for production . This coP. monodon. A new WSSV challenge testing protocol that aims to deliver more accurate disease resistant phenotypes is devised and utilised. A set of 3959 linkage mapped transcribed gene SNPs are used to genotype 1038 sexed individuals derived from 7 full-sibling families challenged-tested for WSSV.Here we undertake the first comprehensive genome scan for QTL associated with resistance to WSSV and for the sex-determining locus in hours of survival was observed within families  had significant effects on time to death in the model  and 4 individuals with identity by descent ≥0.95. No markers or individuals with a call rate less than 0.1 and minor allele frequency <0.24% were detected. After the quality control, 3824 markers and 1019 individuals were selected for the FASTA and GRAMMAS analysis.hours of survival post-WSSV infection, P < 0.05 after Bonferroni correction) were detected on linkage groups 2, 3, 5, 6, 17, 18, 19, 22, 27 and 43 , occurred in a transcript that shared high homology to a gene encoding runt protein 1a in the signal crayfish Pacifastacus leniusculus. SNP 24034_664 at 47.3 cM on LG 2 in a transcript with homology to the proteasome (macropain) 26 s gene maps in the middle of a broad 41 cM region containing several SNPs in transcripts showing suggestive and significant associations with hours of survival after WSSV infection . Variation at SNP 45605_1545 in a transcript with homology to a gene encoding ubiquitin domain-containing protein ubfd1 on LG 5 was associated with hours of survival (FASTA P < 0.05 after Bonferroni correction). The SNP 40050_2030 occurs in a transcript with homology to a gene encoding 26 s proteasome subunit s9 and maps 4.4 cM from SNP 33044_1018 (suggestive association on LG6) and 0.6 cM from a predicted QTL . The SNP 49912_5110 which occurs in a transcript with homology to the mitogen activated protein kinase gene, mapped 2.3 cM from the QTL position detected by GridQTL analysis on LG17 (P < 0.01 genome-wide significance). The SNP 52376_14757 occurs in a transcript that is homologous to the hect e3 ubiquitin gene and maps 2.4 and 3.9 cM from SNPs 51029_2543 (suggestive association) and 50096_1789 (significant association P < 0.05 after Bonferroni for the QFAM test) at 70.9 and 82.4 cM respectively along LG19. SNPs 48349_91, which occurs in a transcript with homology to proteasome (macropain) 26 s non- 2 gene, and 38683_977, which occurs in a transcript with homology to a gene encoding ubiquitin conjugating enzyme 7 interacting protein, map 12.8 and 0.6 cM respectively from SNP 50096_1789  at 82.4 cM on LG19. Three genes encoding proteins with putative immune function map near to SNP 18472_352  at 27.9 cM on LG22, SNP 52279_11861 which also maps to 27.9 cM on LG 22 and which occurs in a transcript with homology to the serine-threonine protein kinase gene, SNP 42578_2554 which occurs in a transcript with homology to a gene encoding mitogen-activated protein kinase-binding protein 1 which is 1.9 cM distant and SNP 50961_705 which occurs in a transcript showing homology to a gene encoding IGF2 mRNA binding protein and is 2.4 cM distant.Some of the SNPs associated with QTL were found to map within or close to genes with putative immune functions of interest Tables . For exaP < 0.01 and 10 at P < 0.001 significance levels after Bonferroni correction, Additional file P = 1.9E-49) which occurs in a gene encoding G7-c-like protein and von Williebrand factor A domain-containing protein 7  and frequent observation were also employed to give a strong power for detecting QTL.The challenge test experiment used in this study which lodged shrimp in individual baskets was designed so that all shrimp could be collected and sampled within 1 hour of death and to prevent secondary infection . Although the time from infection with WSSV to death is rapid, a controlled route of infection and dosage was chosen to prolong the overall time frame of the experiment as much as possible and to give a spread of hours of survival after WSSV infection. For three of the four QTL detected by linkage analysis, closely mapping SNPs with suggestive associations were detected by GWAS analysis , but was found to be suggestively associated with the trait by the GRAMMAS and FASTA tests. Similarly, SNP 51997_2402 on LG2 was associated with hours survival for the QFAM test (P < 0.05 after Bonferroni) and a closely mapping SNP was suggestively associated using the FASTA test. No agreement for the significant association detected by QFAM at position 82.4 cM was found by GRAMMAS or FASTA tests across LG19. Whereas, significant associations detected on linkage groups 3, 5, 18 and 43 by GRAMMAS or FASTA were supported by corresponding suggestive or significant associations by FASTA or GRAMMAS respectively for the same SNP. FASTA and GRAMMAS, which use genomic control to infer genetic relations from genomic data, and thereby account for the true genealogy , are thought to be superior to methods such as QFAM, which makes use of the observed genealogy (observed parent-offspring relationships in our study) [For the GWAS analyses, the GRAMMAS and FASTA results were often in agreement, while the results of QFAM analysis were less often in agreement with GRAMMAS or FASTA. For instance, SNP 18472_352 on LG22 was found to be associated with r study) .Several of the SNPs directly associated, or closely linked to WSSV resistance QTL, were found to occur in transcripts that share homology to genes with putative immune functions. Some of the genes, such as heat shock protein 21, c-type lectin and serine-threonine specific protein kinase, have been implicated in affecting the WSSV resistance of crustaceans in other studies , 43–45. P < 0.05 after Bonferroni correction, LG2), SNP 23272_344 in a transcript with homology to the 26 s protease regulatory subunit gene (suggestive association), SNP 40050_2030 in a transcript with homology to the 26 s proteasome subunit s9 gene which maps 0.6 cM from a QTL position predicted by linkage analysis (P < 0.05 chromosome-wide significance on LG6), SNP 17687_140 in a transcript with homology to the proteasome subunit alpha type-7 gene which was 0.5 cM from SNP 32667_1134 , SNP 48349_91 in a transcript with homology to the proteasome (macropain) 26 s non-2 gene which maps 12.7 cM from SNP 50096_1789  and SNP 49666_3836 in a transcript with homology to the 26 s proteasome non-atpase regulatory subunit 11-like gene which maps to the same position as SNP 43412_2186 (suggestive association at 44 cM on LG29).The ubiquitin proteasome pathway has been shown to play an important role in immune defence and more specifically proteasome I is presumed to be involved in intracellular antibody-mediated proteolysis of antibody-bound viruses . Six SNPP. monodon ubiquitination pathway (eg. with conjugating enzyme (E2) in shrimp) and act as viral E3 ubiquitin protein ligases to inhibit apoptosis and affect viral pathogenesis [Fenneropenaeus chinensis ubiquitin-conjugating enzyme E2 has been shown to reduce the mortality of shrimp challenged with WSSV, inhibit replication of WSSV and bind to (and ubiquitinate) WSSV RING domain-containing proteins [F. chinensis are challenged by WSSV [P. monodon to WSSV. Variation in a SNP in a transcript with homology to the ubiquitin domain-containing protein ubfd1 gene (45605_1545) mapping to 21.2 cM along linkage group 5 was found to be associated with WSSV resistance in this study (P < 0.05 after Bonferroni correction for the FASTA test). The SNPs in nine other transcripts with homology to genes involved in the ubiquitin proteasome pathway   which inogenesis , 47. Injproteins , and ubi by WSSV . It follhours of survival on LG17 (Table Interleukin enhancer-binding factor 2 is a transcription factor required for expression of the interleukin 2 gene which regulates the activity of lymphocytes responsible for immunity . A SNP i17 Table .Marsupenaeus japonicus and Procambarus clarkii) are challenged with WSSV [P. monodon tissues, but is down-regulated following infection with WSSV [Heat shock proteins act as intercellular signalling molecules for the regulation of the immune response of many organisms, particularly with regard to lymphocyte mediated responses . The Hspith WSSV , 44. HSPith WSSV .Nuclear factor kappa-light-chain-enhancer of activated B cells  associations with days survival on linkage groups 11, 16, 17, 22 and 28  that are immunologically suppressed by high levels of manganese [P < 0.05 after Bonferroni correction for the QFAM test, Table Pacifastacus leniusculus.The runt protein is up-regulated prior to haemocyte release and is known to be involved in haematopoiesis . The RUNanganese . A SNP aCaenorhabditis elegans[P < 0.001 after Bonferroni correction, Additional file P. monodon.Although sex determination is a simply inherited binary trait in most organisms, the precise genetic processes affecting sex determination have been found to be complex and diverse. SNP 43522_2279 occurs in a transcript for a gene that shares homology with Feminization-1 (Fem-1), a known signal transducing regulator affecting sex determination in the nematode  elegans, 72. ThiFor SNP marker 49245_2916, which showed the strongest association with sex, most males were AA genotypes while most females were AG genotypes. However, for family 4 there was a high proportion of AG male offspring (30/74) and high proportion of GG female offspring (26/74). In this instance the male parent had marker genotype AG (but sex locus genotype ZZ) such that ZZ males were either genotypes AA or AG, and WZ females were either genotypes AG or GG, at the marker locus. Possible explanations for other discrepancies  are that recombination between the marker and sex determining locus occurred, that more than one gene in this linkage group effects sex determination, that environmental conditions during development are also influencing sex determination and/or that some homogametic females naturally occur. These discrepancies highlight that use of a single SNP marker locus for identifying sex will not be possible until the causative mutations for sex determination are identified.P. monodon. In other invertebrates such as C. elegans there are a diversity of molecules and control networks involved in sex determination [C. elegans and other invertebrates such as Drosophila melanogaster will be informative.In summary, indications are that the markers identified by this study, could be useful for the purpose of identifying homogametic females. Detailed studies of mutations and phenotypes in candidate genes mapping in this region of linkage group 30, could lead us to a better understanding of the genetic mechanisms affecting sexual dimorphism in mination . The modet al.[Further research is needed to predict the most effective means of using the markers identified here to assist the genetic improvement of WSSV resistance. Consideration needs to be given to the overall goals of the breeding programs to which marker information is applied. In 2001, Meuwissen et al. devised effect column of Table Validation using a population of tested and genotyped training individuals is necessary to estimate the effects at each genomic interval for GS. Effects estimated at numerous evenly spaced loci across the genome, including the QTL marker loci identified in this study, could be used to calculate genomic estimated breeding values for genomic selection. The weighting placed on each marker in the overall breeding value would depend on the relative allele substitution effect, and standard error, for each QTL  was positioned (44.3 cM). Interval mapping predicted that the QTL was positioned at 45 cM. The feminisation gene is known to be an important component of the CUL-2-based ubiquitin ligase complex and this complex is known to be involved in the control of sex determination in nematodes by promoting proteolysis of the male-repressing transcription factor TRA-1. Future efforts to identify the causative genes affecting these traits should focus on the fine mapping of genes in these regions and mutation experiments to elucidate function. This has been an effective strategy for livestock such as dairy cattle where genes affecting musculature [P. monodon (eg. run by Moana in Hawaii) and the efficacy of marker assisted selection for improving resistance to WSSV should be further evaluated in this and closely related species such as L. vannamei.From evidence in the available literature, genes affecting the action of the ubiquitin-proteasome pathway, lymphocyte-cell function, heat shock protein function, the TOLL pathway, protein kinase signal transduction pathways, mRNA-binding proteins, lectins and the development and differentiation of the immune system (eg. RUNT protein 1A), which were found in this study to closely map to SNPs on linkage groups 1, 2, 5, 6, 9, 11, 15, 17, 19, 21, 22, 24, 25, 28, 29, 32, and 43 suggestively or significantly associated with QTL affecting WSSV resistance in culature  and milkculature , 81 haveAdult males and non-gravid female tiger shrimp from the wild were procured from the East coast of India and kept in the quarantine facility of the Muttukadu Experimental Station (MES) of Central Institute of Brackishwater Aquaculture, 35 km south of Chennai. These shrimp were checked for the presence of WSSV using a simple method to isolate the virus  and a WSA custom-made experimental facility, for preventing cannibalism, was fabricated for challenge studies to achieve recovery of all challenged shrimp. This facility consisted of multiple plastic baskets that were anchored to a support and lodged side-by-side at the same depth (just below the water surface) in a cement tank. Only one shrimp was housed in each basket during the experiment. Each basket had a lid for ease of placing or removing shrimp. The base of each basket had plastic wire mesh stitched to the sides such that feed pellets could be retained and faecal matter could easily pass through.6 copies per μl in a real-time standard curve experiment. Trials were undertaken to compare intramuscular and oral routes of challenge and it was observed that intramuscular injection gave consistent results compared to the venocatch method. Consequent to this finding, all the experimental shrimp were challenged with the WSSV virus following the intramuscular method. The shrimp were injected intramuscularly with 100 μl of 10-5 dilution of virus stock using 1 mL tuberculin syringe. The virus was injected into the muscle tissue between the third and fourth abdominal segments on the lateral side. Extra care was exercised to avoid physical injury to the intestine and aorta running along the dorsal side and nerve cord running along the ventral side of the abdomen. After injection, the shrimp were retained in a 4 tonne cement tank for 6 hours to de-stress and to observe any mortality due to physical injury. De-stressed shrimp were then placed in individual baskets and monitored at hourly intervals for mortality. Simultaneously, twenty juvenile shrimp were injected with 100 μl of TNE (Tris–HCl-NaCl-EDTA) buffer solution and kept in a 100 L FRP tank. Care was taken to inject these shrimp first before challenging the test animals to avoid contamination. These shrimp served as a control and were kept under constant observation until the actual challenge experiment was completed. Each family was challenged on separate occasions. Care was taken to maintain uniform conditions for all individuals and families that were challenged. The salinity of the water, the weight of shrimp, the viral dose and the distribution of shrimp in baskets were similar for all the families.The muscle tissue from juvenile shrimp that were fed with WSSV-infected shrimp meat were used for extraction of WSSV virus following the protocol of . The virContinuous aeration was provided for the experimental and control tanks. The animals were checked for mortality on an hourly basis. Water temperature was recorded on an hourly basis and pH and salinity was recorded once every morning. The water in the experimental and control tanks were exchanged daily (at 50%) when faecal matter and unused feed at the bottom of the tank was siphoned out in the process. Fresh seawater was provided after removing the debris at the bottom. The cleaning process was carried out daily until the last shrimp died.When the challenged shrimp started dying, survival data (time to death) along with sex and wet weight of each shrimp were recorded. The dead shrimp were removed and stored at -80°C for DNA extraction.hours of survival post-WSSV infection), were selected from each family for genotyping to find QTL. In all, 1024 offspring belonging to 7 full-sibling families that were challenge tested as described above, were successfully genotyped. Genomic DNA was extracted from the challenged shrimps using the Phenol Chloroform method as described by [Parents, along with the most susceptible and resistant 40 percentiles of progeny (based on ribed by  with sliP. monodon transcribed genes [P. monodon[Genotyping was performed with 6 K custom developed Illumina Infinium iSelect Beadchips containing 6 K SNPs from ed genes . The SNP monodon. The sex monodon.http://www.cran.r-project.org). The ID was the same as the animal factor, but was used by MCMCglmm to dissociate individual records from the pedigree and give an indication of between individual variance [An animal model was applied to estimate genetic parameters (without accounting for SNP genotype). The animal model decomposed the phenotypic variance into additive genetic and environmental components. Our main interest was whether sex and/or time of challenge (family) should be included as fixed effects in the QTL analysis and whether weight should be included as a covariate. A Markov chain Monte Carlo (MCMC) method using a multi-trait generalised linear mixed effect model (glmm) in a Bayesian estimation framework, with animal breeding value and ID fitted as a random effects, was used for the analysis  was used to obtain covariance components, and the genetic correlation between weight and time to death was estimated as,where σA1A2 is the estimated additive genetic covariance component between the two traits.where The model was run using 300,000 iterations as burn-in, 1 million iterations for sampling and a thinning interval of 500. A “plausible” prior assuming weak genetic control  was used with the smallest possible degree of belief parameter (n = 1).2 was calculated for all adjacent SNP pairs with the PLINK software package .Linkage disequilibrium measured by rP-values were calculated for all trait-by-LG combinations with the significance of the peak F-statistic (putative QTL) estimated after 10,000 chromosome-wide permutation tests. A QTL was found to be genome-wide significant if the chromosome-wide significance level was smaller than 0.0011 (0.05/44), a Bonferroni correction based on the number of linkage groups in P. monodon. This correction was equivalent to a Benjamini Hochberg [q-value of 0.98), such that it was expected that more than 95% of the significant results actually were false positives. QTL were denoted as “suggestive” when P < 0.01 (before Bonferroni correction).Data were analysed using a regression-interval mapping method available through the web-based software GridQTL . The sibHochberg  false dihttp://www.genabel.org). This function was used to exclude individuals or markers with call rate <95%, markers with minor allele frequency <0.24%, individuals with high autosomal heterozygosity (FDR <1%) and individuals with identity by state ≥0.95. Genomic kingship was computed between all pairs of individuals. We performed a pedigree based association analysis where the pedigree is a confounder (where the heritable trait is more similar between close relatives and therefore some degree of association is expected between any genetic marker and any heritable trait). The effect of the confounding pedigree is expected to inflate the resulting null distribution of the chi square test statistic by a certain constant, lambda. Lambda is a function of the traits heritability and pedigree structure (expressed as a kinship matrix). Two fast tests for genome wide association were applied, Family-based Score Test for Association  was modelled asQTL GWAS analyses were performed in several ways. First we determined which markers and individuals should be excluded from the GWAS analysis using the check.marker function in GenABEL  and Gen  using tP-value for the 1 degrees of freedom test was corrected for the inflation factor. Genomic control was applied by dividing the observed test statistic  by the genomic inflation factor λ (where λ is the regression coefficient of the observed χ2 test statistic onto the expected χ2 test statistic). Genomic control is believed by some authors to circumvent the need for Bonferroni correction for multiple testing [where μ was the intercept, G describes the polygenetic effect  and e describes the random residual effects. The joint distribution of residuals in the pedigree was modelled using a multivariate normal distribution with variance-covariance matrix proportional to the identity matrix. A genomic kingship matrix, generated by calculating the average identity-by-state between individuals in the pedigree (ibs in GenABEL), was used as the relationship matrix for FASTA and GRAMMAS. Both FASTA and GRAMMAS exploit maximum likelihood estimates of the intercept from the polygenic model. One thousand permutations were used to estimate genome wide significance for both the FASTA and GRAMMAS tests. The  testing .http://pngu.mgh.harvard.edu/purcell/plink/[P < 0.001, P < 0.01 and P < 0.05 levels after Bonferroni correction based on the number of linkage groups (which was 44 for P. monodon) were noted for all tests. GWAS associations were denoted as “suggestive” when P < 0.01 (before Bonferroni correction). As explained for the linkage analysis, the Bonferroni correction was equivalent to a Benjamini Hochberg [q-value of 0.98).The QFAM analysis module in PLINK (l/plink/) was useHochberg  false diχ2 test of observed and expected allele frequencies in male and female offspring across families under the null hypothesis that the segregation of alleles would be independent of sex. Associations were treated as significant when P < 0.01 after Bonferroni correction based on the number of linkage groups. Regression interval mapping using the sib-pair module was also carried out in GridQTL as described for the WSSV analysis using sex as a phenotype.SNPs significantly associated with sex were detected using a simple P. monodon linkage map and SNP characterisations can be found in [http://www.ncbi.nlm.nih.gov/Genbank). Other supporting data  are included in the additional files section.The supporting high density found in . AnnotatMap position and annotation for 3961 transcripts linkage mapped by[. LG, linkage group. cM, position of SNP on linkage group in centimorgans . GeneID, closest homology to contig from BLAST. Length, length of contig in number of bases. NumHits, number of BLAST matches above threshold . MinEValue, Karlin-Altshul E-score. (XLSX 283 KB)Additional file 1:Map position and tests for association with sex for transcribed SNPs on LG30. LG, linkage group. cM, position of SNP on linkage group in centimorgans. GeneID, closest homology to contig from BLAST. df, degrees of freedom. **, P < 0.01 after Bonferroni correction. ***, P < 0.001 after Bonferroni correction. (XLSX 17 KB)Additional file 2:


Pantherophis guttatus) is a new model species particularly appropriate for investigating the processes generating colours in reptiles because numerous colour and pattern mutants have been isolated in the last five decades. Using our captive-bred colony of corn snakes, transcriptomic and genomic next-generation sequencing, exome assembly, and genotyping of SNPs in multiple families, we delimit the genomic interval bearing the causal mutation of amelanism, the oldest colour variant observed in that species. Proceeding with sequencing the candidate gene OCA2 in the uncovered genomic interval, we identify that the insertion of an LTR-retrotransposon in its 11th intron results in a considerable truncation of the p protein and likely constitutes the causal mutation of amelanism in corn snakes. As amelanistic snakes exhibit white, instead of black, borders around an otherwise normal pattern of dorsal orange saddles and lateral blotches, our results indicate that melanocytes lacking melanin are able to participate to the normal patterning of other colours in the skin. In combination with research in the zebrafish, this work opens the perspective of using corn snake colour and pattern variants to investigate the generative processes of skin colour patterning shared among major vertebrate lineages.The corn snake ( Vertebrate skin coloration provides an effective model system for exploring the genetic determinism and developmental plasticity associated with phenotypic variation. Squamates (lizards and snakes) exhibit a remarkable set of pigmentary and structural elements generating a broad variety of colours and colour patterns e.g.234567. CPantherophis guttatus), a Colubridae species from the southestern United States of America, as an ideal snake model for evolutionary developmental studies121314We are promoting the corn snake  of neural-crest derived cells called melanophores (or melanocytes) that interact with the endocrine, immune, and nervous systems3133343536373839404344e.g., microsatellites or Single Nucleotide Polymorphisms, SNPs) that co-segregate with the phenotype of interest, hence allowing the identification of the genomic interval harbouring the causal mutation. The so-called ‘next generation sequencing’ (NGS) methods have opened the possibility to perform mapping-by-sequencing , in species with excellent genomic resourcesPositional cloning of mutations determining traits of interest is a more effective method which has been successfully performed for many years in model and domestic speciesOCA2 gene as the causal mutation determining the amelanistic phenotype in corn snakes.Here, using our captive-bred colony of corn snakes, we combine an unbiased NGS and exome assembly approachamel allele (see Methods). Second, we de-novo sequenced the corn snake transcriptome using normalised cDNA prepared from multiple organs and developmental stages, generating 2.7–3.2 Gbp of high-quality (Q ≥ 30) data. The corresponding 275 million paired-end reads were assembled (see Methods) into a non-redundant transcriptome containing 92,308 contigs with a cumulative length of 35.7 Mbp (N50 = 423 bp). Third, we selected one of the seven families and sequenced (9.5–19.4x coverage) four genomic DNA libraries generated with samples from (i) the heterozygous mother (amel/+ genotype), (ii) the amelanistic father (amel/amel genotype), (iii) 20 WT heterozygous offspring individuals and (iv) 20 amelanistic offspring individuals.First, we generated a mapping panel consisting of seven families, with a total of 140 wild-type (WT) and 116 amelanistic progeny, by crossing homozygous amelanistic snakes with WT individuals heterozygous for the i.e., father, mother, and two pools of offspring). After filtering the 510,724 reported variants (see Methods), further analyses were performed only with the 3,278 SNPs  that met the quality and coverage criteria as well as perfectly matched the expected allelic distribution at the amelanistic locus . Contigs with two or more SNPs that did not perfectly co-segregate were then identified as false positives and the 1,025 remaining contigs were aligned (tBLASTx) against the genome assembly of the green Carolina anole lizard (Anolis carolinensis) AnoCar2.0i.e., with an ortholog in the A. carolinensis genome), 146 match the anole lizard chromosome 3, of which a vast majority (103 contigs) define an interval between coordinates 82.7 and 119.5 Mbp. Given the relatively high conservation of synteny between lizard and snake genomes  in all 256 offspring of the seven families segregating the trait . We identified seven recombinants between the amel locus and SNPs in the RAP2A gene and two recombinants between amel and SNPs in the ARHGAP6 gene. We then used these nine individuals to reduce the genomic interval of interest by genotyping SNPs in five additional genes. We identified four and one recombination events between the amel locus and UXS1 (Chr3:108.455–108.515 Mbp) and AKAP17A (Chr3:112.976–113.359 Mbp), respectively, whereas no recombination was observed for SNPs in UNC50, HERC2, and NIPA2  SNPs in two genes flanking the identified interval  is an excellent candidate for the amel locus. Indeed, structural and regulatory variations of the OCA2 gene seem to be major determinants of human skin, hair and eye colour52Astyanax cavefish54555657Even though this 4.9 Mbp interval is relatively large and contains 46 protein coding genes, one of these, the amel/amel) corn snake individuals, we amplified and sequenced 2,776-bp containing the full-length OCA2 transcript. The sequence included a full open reading frame of 2,532-bp corresponding to a 843-aa protein (one aa shorter than in the anole lizard) and a stop codon. The amplification of the orthologous cDNA fragment from four amelanistic corn snake individuals generated the same sequence but with an additional 397-bp fragment inserted between exons 11 and 12 (Using cDNA prepared from skin samples of four homozygous WT (+/+) and four amelanistic (1 and 12 . This inamel/amel) and heterozygous (amel/+) corn snakes. Using sequencing by primer walking, we found that the intron is 2,011 bp in the wild type allele but contains a 5,832-bp insertion  in the amel allele , amelanistic . Moreover, we searched for conserved domains on the NCBI database (http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi) and identified the RNase H domain of the Ty1/Copia family , a reverse transcriptase , an integrase core domain , and a gag-polypeptide of the LTR-copia type , all indicative of the presence of a retrotransposon.We aligned the 5,832-bp genomic fragment against a reference collection of repeats from the Repbase database(i) the nature of the pigments in pigmentary cells such as xanthophores and erythrophores, (ii) the pH or redox state of these pigments, and (iii) the geometric parameters of the structural elements. The latter includes the size, shape and orientation of the iridophore nanocrystals as well as the geometry and level of order of the lattice of these elements. Depending on the species, position on the body, as well as the physiological and behavioural state of the animal, these interactions can produce various colours in the visible range. Chameleons have even the remarkable ability to exhibit complex and rapid structural colour changes through active tuning of a lattice of guanine nanocrystals within a superficial thick layer of dermal iridophoresNon-mammalian vertebrates exhibit a broad range of colours generated through interactions between pigmentary chromatophores and structural nanoscopic elements 1234564Phelsuma exhibit a precise colocalisation of red chromatophores with underlying structural disorganised broadband reflectors , whereas the rest of the dorsolateral skin combines yellow chromatophores with highly ordered narrowband  reflectors. The availability of many mutants, as well as recent transgenic and imaging tools, is making the zebrafish (Danio rerio) an ideal model to study this kind of cellular interactions during developmentIt remains a major challenge to investigate in non-mammalian vertebrates the behaviour of various cell types , and their interactions during development and homeostasis, so as to understand the final skin colour and colour patterns60616263Pantherophis guttatus as particularly appropriate for evolutionary developmental studies1213www.reptilian-transcriptomes.org)65We have recently promoted the corn snake i.e., lizards and snakes) is essentially due to the presence of multiple pigments and structural elements, whereas mammals exhibit only black/brown melanins and essentially no structural colour. However, the importance of melanin in squamates should not be underestimated. For example, many squamates can rapidly disperse/aggregate melanin-containing melanosomes within dermal melanophores to modify skin brightness (without changing much the skin hue) for camouflage, communication and thermoregulation  and a wild-type (WT) individual with heterozygous (amel/+) genotype. These seven crosses generated a total of 140 WT and 116 amelanistic F1 offspring.Maintenance of, and experiments on animals were approved by the Geneva Canton ethical regulation authority (authorisation GE/82/14) and performed according to Swiss law. Corn snakes were bred at the LANE, Geneva. For gene mapping analyses, we established seven families, each of which consisted of a cross between an amelanistic (http://trinityrnaseq.sourceforge.net), with default parameters, to assemble the corn snake transcriptome. Trinity yielded 146,030 contigs with a cumulative length of 71.6 Mbp (N50 = 615 bp). To remove redundancy, we then used BLASTn  to align all contigs against each other; when a contig had multiple hits, only the largest hit of each group was included in the non-redundant set. The final non-redundant transcriptome contains 92,308 contigs with a combined length of 35.7 Mbp (N50 = 423 bp).RNA was extracted from multiple WT embryos , as well as from brain and testes of an adult WT male, using the RNeasy Plus Mini kit (Qiagen) with polyA selection. RNA quality and concentration were analysed with a NanoDrop 2000 spectrophotometer (ThermoScientific) and Qubit 2.0 Fluorometer (Life Technologies). Double-stranded cDNA was prepared with the Mint-2 cDNA synthesis kit (Evrogen) separately for the embryonic and adult tissues. Embryonic and adult cDNA were each normalised with the Trimmer-2 cDNA normalisation kit (Evrogen) and used for the preparation of two libraries with insert size of 200 bp. These were then sequenced on two lanes of an Illumina HiSeq2000 sequencer using 101 cycles per run, yielding 2.7–3.2 Gbp of high-quality (Q ≥ 30) data. The corresponding ~275 million paired-end reads were fed into the Trinity software (amel/+ genotype) individuals from a single family were combined in equimolar concentrations. Similarly, a second pool was made by mixing the DNA samples from 20 amelanistic (amel/amel genotype) individuals from the same single family. These two pools, as well as the individual DNA of the two parents were used to generate (using the TruSeq PE Cluster Kit v3-cBot-HS) the corresponding four genomic DNA libraries (300–400 bp insert size) that were sequenced using paired-ends 100-bp reads technology on an Illumina HiSeq2000. We sequenced the samples on three lanes, one lane for each of the two offspring pools and one lane for the indexed parental DNA samples, and obtained 20.8–34.9 Gbp of high-quality  data per lane, corresponding to an average sequencing depth of 9.5–19.4x for a haploid genome size of 1.8–2.2 Gbp .Genomic DNA was extracted from blood samples of the parents and scale clips from all 256 hatchlings using QIAGEN DNeasy Blood and Tissue kits following the manufacturer’s instructions. RNA and DNA qualities and concentrations were analysed on a NanoDrop 2000 spectrophotometer (ThermoScientific) and a Qubit 2.0 Fluorometer (Life Technologies). The DNA samples from 20 WT (−5), resulting in a final number of 138,849 exome contigs.To maximise the number of detected SNPs, we used the genomic reads of one parent to extend exons of the cDNA contigs with intronic and intergenic sequences in a process called exome assemblyhttp://bio-bwa.sourceforge.net/bwa.shtml) with default parameters. Then, we used FreeBayes v0.9.9.2 (ref. https://github.com/ekg/freebayes) with default settings to call variants. We then filtered the 510,724 reported variants by excluding (in each of the four samples) indels, triallelic SNPs, SNPs with mapping quality <100, and positions with read coverage <18 or >50. Of the resulting 197,907 SNPs, we additionally excluded those that were either fixed  in the WT parent or heterozygous in the amelanistic parent. Finally, from the resulting set of 51,982 SNPs, we removed those that were either fixed  in the pool of WT offspring or heterozygous in the pool of amelanistic offspring. This analysis yielded 3,278 SNPs  perfectly segregating with the expected genotype at the amelanistic locus: heterozygous in the WT parent and offspring, and fixed in the amelanistic parent and offspring. We then checked all contigs for presence of multiple SNPs and selected the 1,025 contigs in which all SNPs co-segregate. Using tBLASTx , we aligned these contigs against the genome assembly of A. carolinensis  with a hit, 146 matched to the chromosome 3 of A. carolinensis, of which 103 cluster in a region between 82.7 and 119.5 Mbp.We then used this reference exome assembly for SNP calling. First, we mapped the genomic reads from each of the four samples  to the exome using BWA v0.7.1 (ref. RAP2A and ARHGAP6) flanking that interval. Then, five additional markers were genotyped in the individuals exhibiting recombination between the amel locus and either RAP2A or ARHGAP6. Primers used for genotyping are given in To confirm the genomic interval identified by the procedure described above, we genotyped SNPs in selected genes of additional offspring individuals. First, all 256 offspring were genotyped for SNPs in two genes  and four amelanistic (amel/amel) snakes using TriReagent Solution (Sigma Aldrich) and we synthesised cDNA with SuperScript III First-Strand System and oligo(dT)20 primers (Life Technologies). Overlapping fragments of OCA2, spanning the complete coding sequence of the OCA2 gene, were PCR amplified from these cDNA samples and sequenced. We amplified  the intron in between exons 11 and 12 of the OCA2 gene (primers shown in amel/amel) and heterozygous (amel/+) corn snakes. PCR products were visualised on 2% agarose gels and sequenced by primer walking.To amplify the coding region of How to cite this article: Saenko, S. V. et al. Amelanism in the corn snake is associated with the insertion of an LTR-retrotransposon in the OCA2 gene. Sci. Rep.5, 17118; doi: 10.1038/srep17118 (2015).