Determining whether two DNA samples originate from the same individual is difficult when the amount of retrievable DNA is limited. This is often the case for ancient, historic, and forensic samples. The most widely used approaches rely on amplification of a defined panel of multi-allelic markers and comparison to similar data from other samples. When the amount retrievable DNA is low these approaches fail.We describe a new method for assessing whether shotgun DNA sequence data from two samples are consistent with originating from the same or different individuals. Our approach makes use of the large catalogs of single nucleotide polymorphism (SNP) markers to maximize the chances of observing potentially discriminating alleles. We further reduce the amount of data required by taking advantage of patterns of linkage disequilibrium modeled by a reference panel of haplotypes to indirectly compare observations at pairs of linked SNPs. Using both coalescent simulations and real sequencing data from modern and ancient sources, we show that this approach is robust with respect to the reference panel and has power to detect positive identity from DNA libraries with less than 1 % random and non-overlapping genome coverage in each sample.We present a powerful new approach that can determine whether DNA from two samples originated from the same individual even when only minute quantities of DNA are recoverable from each.The online version of this article (doi:10.1186/s12864-015-2241-6) contains supplementary material, which is available to authorized users. Recent advances in high-throughput sequencing now allow for small DNA fragments to be extracted and sequenced from ancient or heavily degraded samples . DNA is A common task in forensics and in the analysis of archeological and historical samples is to determine whether two samples originated from the same individual by comparing DNA extracted from each. The most widely adopted approach in forensics is to compare genotype information at a defined set of multi-allelic short tandem repeat (STR) markers , 13. TheThe advent of high-throughput sequencing is enabling new approaches to compare DNA between samples , 17. ForHere we describe a new approach for comparing very sparse DNA sequence data from two samples that differs from existing methods in two important ways. First, we draw on large catalogs of SNP markers with the expectation that only a small fraction of these will be observed in a sample. Second, we completely avoid direct comparison of alleles between samples. In comparisons of poorly preserved samples, direct comparison is difficult since few or zero SNP positions will be observed in both samples and full genotypes are not recoverable. In addition, multiple observations of the same SNP position may be the result of incorrectly mapped reads from similar sequences in the genome. To address this, our method does not require genetic observations at the same loci. Instead, we compare pairs of observations at linked markers to assess whether the observations more likely originated from a single individual or from independent individuals. This is made possible by the large and growing catalog of known segregating genetic variation in humans and the known patterns of linkage present in human populations. Using this approach we show that it is possible to reliably determine if two samples originated from the same individual when the data available are random, non-overlapping shotgun sequence data representing less than 1 % of the genome in both samples.Two loci are in linkage disequilibrium (LD) if their alleles are not randomly associated . As the Consider a pair of SNP loci in physical proximity on the same chromosome in linkage disequilibrium Fig. . AllelesTo estimate these probabilities, we model allele and haplotype frequencies in an approach similar to the one employed by Rodriguez et al. in the identity by descent detection method Parente2 . Allele For every SNP pair within a specified distance, the probabilities under the single individual and two individual models are calculated and compared as a log-likelihood ratio (LLR). A positive value indicates that the probability of observing these bases under the single individual model is higher than that under the two individual model. A negative value indicates that the data are more likely to derive from two different individuals. A single comparison of a pair of SNPs offers little information to distinguish between these two models on its own. However, the two models can become more distinguishable by combining observations from many pairs of SNPs across the genome. To achieve this, we aggregate log-likelihood ratios for pairs of SNPs by sampling pairs from sliding windows across each chromosome. We chose a wide window size (500 kb) and pick a suitable pair of alleles within each window. The human genome, at roughly three gigabases, contains approximately 6000 independent windows for these comparisons. This approach allows us to treat each window as an independent assessment of the two models. Then, we generate an overall LLR by summing the values across all windows where a suitable comparison can be made. Note that in any particular window, it is only necessary that at least one read in each library contains allelic information for at least one SNP position. As described below, this allows analysis of datasets of extremely low overall coverage.We repeat this process in a bootstrapping approach, selecting random pairs of alleles across each window, to construct an empirical distribution of the genome-wide aggregated log-likelihood ratio. From this distribution, we can assess the mean and variance of the LLR from the underlying data.As described here, this method is applied to sets of base observations made from shotgun data from two samples. In addition to this, the method can be applied to a set of base observations made from a single sample. When done in this way, the analysis tests if pairs of observations from a single sample are internally consistent. In this case, positive LLR values indicate that the sample data is consistent with a single, diploid individual. Negative LLR values or values not significantly different from zero indicate that the sample is a mixture of fragments from more than one individual or that the reference panel is too distantly related from the sample individual.e = 10,000). Since this model lacks population bottlenecks that increase genome-wide LD, we consider this to be a conservative model. In each round of simulation, we generated two diploid individuals by drawing two haplotypes each from the simulations results. Alleles from these individuals were sampled at a rate of 0.02 to produce sets of allelic observations similar to what could be achieved in genome sequencing from libraries that represent 0.02 fold genome coverage. At this level of coverage, traditional methods like STR typing could be expected to fail. The remaining haplotypes, i.e., those not drawn for comparison, were used as the reference panel to model allele and haplotype frequencies. From 100 rounds of simulation, we found our method can consistently distinguish between two single allele observation sets that originate from the same or different individuals  and a European female (NA12892) sequenced as part of Illumina’s Platinum Genomes . We samp91 and a We compared each sample to itself and to the other three at the same coverage level using a reference panel of 170 haplotypes from the CEU population . Pairs of SNPs were separated by 1 to 50 Kb and aggregated LLRs were calculated by sampling pairs in 500 Kb windows  and f(B) are the frequencies of alleles A and B in the population.Where In the second model, the observations are made from a diploid individual, where there is an equal chance of the two observations originating from the same chromosome or from different chromosomes.A and B is the frequency that A and B appear on the same chromosome in the population, i.e. the haplotype frequency, f(AB). Otherwise, the probability is the same as independently observing A and B on different chromosomes.In the case where both observations are made from the same chromosome, the probability of observing alleles These two models are compared as a log-likelihood ratio, which is calculated as:γ for pairs of SNPs in a set S of SNP pairs sampled from windows a set size across the genome.Log-likelihood ratios are aggregated across the entire genome through summation of This step can be repeated as a bootstrapping approach to estimate the empirical distribution of the genome-wide aggregated log-likelihood ratio.We performed coalescent simulations to test our method free of base errors and under various demographic scenarios. We used the coalescent simulator ms  to simulThe single simple population model used a constant effective population size of 10,000. The second model, representing the reference population and samples originating in distinct populations, simulated an ancestral population of 10,000 that split 100, 500, 1000, 2000, and 4000 generations ago into two equal sized populations of 10,000 each. The model of recent human history was based off of parameters inferred by Gutenkunst et al.  (see AddAll human sequence and reference panel data used in this study were downloaded from public sources (accession details listed below). Institutional review and ethical approval were not required for this research.We constructed reference panels of single nucleotide polymorphisms (SNPs) using the 1000 Genomes Project Phase one data set . We filtWe obtained Illumina sequencing data from a European male (NA12891) and a European female (NA12892) sequenced as part of Platinum Genomes by Illumina, Inc  from theIllumina sequencing data from DNA extracted from 12 samples from 11 Bronze Age Eurasian humans  were dowhttp://github.com/svohr/tilde.Software written for this manuscript is available at


Species showing complex life cycles provide excellent opportunities to study the genetic associations between life cycle stages, as selective pressures may differ before and after metamorphosis. The European eel presents a complex life cycle with two metamorphoses, a first metamorphosis from larvae into glass eels (juvenile stage) and a second metamorphosis into silver eels (adult stage). We tested the hypothesis that different genes and gene pathways will be under selection at different life stages when comparing the genetic associations between glass eels and silver eels.We used two sets of markers to test for selection: first, we genotyped individuals using a panel of 80 coding-gene single nucleotide polymorphisms (SNPs) developed in American eel; second, we investigated selection at the genome level using a total of 153,423 RAD-sequencing generated SNPs widely distributed across the genome. Using the RAD approach, outlier tests identified a total of 2413 (1.57 %) potentially selected SNPs. Functional annotation analysis identified signal transduction pathways as the most over-represented group of genes, including MAPK/Erk signalling, calcium signalling and GnRH (gonadotropin-releasing hormone) signalling. Many of the over-represented pathways were related to growth, while others could result from the different conditions that eels inhabit during their life cycle.The observation of different genes and gene pathways under selection when comparing glass eels vs. silver eels supports the adaptive decoupling hypothesis for the benefits of metamorphosis. Partitioning the life cycle into discrete morphological phases may be overall beneficial since it allows the different life stages to respond independently to their unique selection pressures. This might translate into a more effective use of food and niche resources and/or performance of phase-specific tasks .The online version of this article (doi:10.1186/s12864-015-1754-3) contains supplementary material, which is available to authorized users. Many animals show complex life cycles organized into morphologically distinct phases separated by abrupt metamorphic transitions (metamorphosis), as opposed to single static or continuously changing phases . ComplexAnguilla anguilla), a facultative catadromous fish with a particularly complex life cycle that includes two metamorphoses. After spawning in the remote Sargasso Sea, larvae of European eel are transported to the coasts of Europe and North Africa following the Gulf Stream and North Atlantic current. On reaching the continental shelf, eels undergo a first metamorphosis from larvae into glass eels (juvenile stage), which complete their migration into continental feeding habitats as yellow eels. After an extensive feeding/growing period, eels undergo a second metamorphosis into silver eels (adult stage). The so-called “silvering metamorphosis” encompasses morphological  as well as physiological modifications (e.g. loss of digestive tract), together with the development of gonads. These changes are beforehand preparation for the future spawning migration back to the Sargasso Sea, where eels reproduce and die [Our species of interest is the European eel  and a lack of substructuring among larvae collected in the Sargasso Sea (FST = 0.00076). Moreover, no significant genetic differentiation was observed when comparing the samples obtained in Europe vs. the larvae obtained from the spawning area (FST = -0.00012). Panmixia was also confirmed at the genomic level in a study using a large dataset of > 450,000 SNPs from 259 RAD-sequenced European eels [ST = 0.0007). Previous studies based on cohort analysis showed an unpatterned genetic heterogeneity (genetic patchiness) as samples did not group together according to sampling location or cohort, and consequently no pattern on Isolation-by-Distance or Isolation-by-Time was detected [The European eel is an ideal species in which to study local selection. First, it presents a large effective population size  that reviduals; . Despiteean eels , which sdetected . If Eurodetected . One condetected . Any sigdetected . Howeverdetected , 15.Studies of adaptive evolution in European eel have focused on the detection of signatures of local selection in glass eels. Using a panel of 100 coding-gene single nucleotide polymorphisms (SNPs), Ulrik et al.  found siThe power and efficiency of next generation sequencing (NGS) technologies are enabling research use of genomic data to address ecological and evolutionary questions at a genome-wide scale for model and non-model species , 18. Thee ; AR . Moreover, no differences were found in genetic diversity measures when silver eels were compared to glass eels (p > 0.05). Three loci deviated significantly from HWE at all locations  but were not excluded from the analysis, since departures from HWE might be due to selection.Details of all polymorphic loci, including frequency of the most common allele across all sampling locations in our study in silver eels and glass eels are presented in Table ST = 0.0018; p = 0.330). A similar low genetic differentiation was found when considering all silver eel and glass eel samples  or when comparing pooled silver eels vs. pooled glass eels . Low FST values were also obtained when compared silver eels vs. glass eels in all samples: Spain , Ireland  or Iceland .Genetic differentiation across silver eel samples was low and not significant  were identified as hybrids showing a 50 % admixture proportion, and were consequently removed from the analysis (data not shown).ST values: GAPDH (FST = 0.27) and ALD_R (FST = 0.12) when considering all samples and GAPDH (FST = 0.33) when comparing silver eels and glass eels from Iceland.Results from outlier tests are summarized in Table After sequencing of the RAD libraries, average number of reads (90 bp) per individual was 12.2 million for silver eels and 9.6 million for glass eels. After trimming to 75 bp and quality filtered, the average number of high quality reads retained was 10.7 million (86.8 %) for silver eels and 7.9 million (82.2 %) for glass eels. A similar percentage of reads were uniquely aligned to the European eel draft genome (69.3-70.0 %), while 25.6-26.3 % of sequences did not align and 4.5 % were discarded due to multiple alignments.Aligned reads were then assembled into a total of 348,342 loci using Stacks  and nucleotide diversity (Pi = 0.053) compared to the Valencia glass eel sample  and the Burrishoole glass eel sample . The Burrishoole silver eel sample showed slightly lower diversity , presumably due to lower sample size.Measures of genetic diversity at 77,337 loci at all sampling locations are summarized in Table Prior to the analysis of local selection, the presence of hybrids in the data set was assessed in STRUCTURE using a subset of diagnostic SNPs between North Atlantic eels and previously analyzed RAD-sequenced American eels for comparison. A scenario with K = 2 groups corresponding to the two North Atlantic eel species was suggested. Within European eel, all RAD-sequenced silver eels were identified as pure European eel, with no hybrids in the data set.In the RAD data set, outlier tests were first conducted comparing silver eels (N = 31) and glass eels (N = 31) from Valencia using 153,423 SNPs. A total of 2413 SNPs putatively under selection were identified by LOSITAN and 1472 SNPs by BAYESCAN. Since the LOSITAN outliers encompassed those outliers identified by BAYESCAN, the rest of the analysis was conducted only for the LOSITAN outliers.Out of the 2413 candidate SNPs, a hit with a gene was obtained for 1089 (45.13 %) of the SNPs, while the remaining 1324 SNPs (54.87 %) were located in noncoding regions of the genome. Among hits, 966 were located in introns and 123 in exons, including 48 in complete coding sequences (CDS). Hits represented a total of 1018 unique genes, of which 835 (82.02 %) genes were successfully annotated using BLASTX. Subsequently, the KEGG pathway approach for higher-order functional annotation was implemented in DAVID. Using zebrafish as reference genome, a total of 616 zebrafish genes homologous to European eel were mapped to KEGG pathways. Enriched KEGG pathways using a standard setting of gene count = 2 are summarized in Table We also tested for outliers between silver eels and glass eels from Burrishoole, although adult sample size was low (N = 10), as a way to confirm the results above. A total of 2228 putative SNPs under selection were identified by LOSITAN and 1888 SNPs by BAYESCAN, the latter being all included in the list of LOSITAN outliers. Among all outlier SNPs, a hit with a gene was obtained for 949 of the SNPs, 838 corresponding to introns and 111 to exons (including 42 in CDS). In total, 770 (81.13 %) of the hits were successfully annotated using BLASTX. Subsequently, a total of 557 zebrafish genes homologous to European eel were mapped to KEGG pathways in DAVID. The pathway with the highest number of genes was endocytosis (15 genes), while other highly-represented pathways included regulatory and signalling pathways  sliding windows. Figure ST relative to the background were observed in scaffolds 1, 3 and 21, respectively. The average distance between outlier SNPs was around 300,000 bp in scaffolds 1 and 3 and around 220,000 bp in scaffold 21. The closest distance between outlier SNPs was 9000 bp in scaffold 1, 19,000 bp in scaffold 21 but around 125,000 bp in scaffold 3.Finally, average FST-based outlier tests are based on the detection of loci that show significantly high differentiation with significance determined by simulations assuming specific population structure models [We examined the patterns of genomic diversity across life cycle stages in European eel, using a combined approach of candidate coding-gene SNPs and a large-scale genomic analysis of 153,423 SNPs generated by RAD sequencing. We compared two life cycle stages separated by metamorphosis, glass eels (juvenile stage) and silver eels (adult stage), and identified signatures of directional selection. All available information indicates that eel mortality in nature is very high and only a small fraction of the glass eels entering European coasts reach the silver eel stage and migrate back to the Sargasso Sea . Bonhomme models  and are e models –31, althe models  for a crST when comparing glass eel and silver eel samples. GAPDH  is a gene with a major metabolic function and catalyzes the conversion of glyceraldehyde 3-phosphate to D-glycerate 1,3-biphosphate in the glycolysis pathway. In this sense, GAPDH has been linked to growth differences in gene expression studies in fishes [Using the SNP panel developed by Gagnaire et al.  in Amerin fishes . CST  to a specific cell-surface receptor. The activation of the receptor leads to an altered response inside the cell. Examples of cellular responses to extracellular stimulation that require signal transduction include changes in metabolism and gene expression. Following are some major pathways identified in our study:- MAPK/Erk signalling: a complex key signalling pathway that is involved in the regulation of normal cell proliferation, survival, growth and differentiation ; the pat- GnRH signalling: secretion of gonadotropin-releasing hormone from the hypothalamus acts upon its receptor in the anterior pituitary to regulate the production and release of FSH (follicle-stimulating hormone) and LH (luteinizing hormone); together, FSH and LH regulate many aspects of gonadal function in both males and females, including normal growth, sexual development and reproductive function .- Calcium signalling: calcium ions serve a number of important functions and regulate processes as diverse as fertilization, development, learning and memory, mitochondrial function, muscle contraction and secretion; calcium ions are also recognized as very important in ion exchange and osmoregulation .- Insulin-like growth factor signalling: this pathway plays a central role in the neuroendocrine regulation of animal growth and development. In fish, additional functions include osmoregulatory acclimation, reproductive development and tissue regeneration. Insulin-like growth factor signalling is mediated by two ligands, insulin-like growth factor 1 (IGF-1) and factor 2 (IGF-2), both of which were identified as outliers. The production of IGF-1 is stimulated by growth hormone and is positively correlated with growth as shown in coho salmon  or sea b- Focal adhesion: Related to signalling pathways, focal adhesion was also over-represented in our analysis. Focal adhesions are large protein assembles through which both mechanical force and regulatory signals are transmitted and have central roles in cell migration and morphogenesis as well as regulating cell proliferation and differentiation .Other over-represented pathways were related to metabolism  and detoxification of xenobiotics (i.e. glutathione metabolism). The latter included cytochrome CYP2J6, a member of the cytochrome P450 superfamily of enzymes that catalyze many reactions involved in drug metabolism.Overall, when we consider the functions of the genes, it seems biologically plausible that the genes identified as outliers are indeed under selection. Many of the over-represented pathways were related to growth, while others could result from the different conditions and habitats that eels inhabit throughout their life cycle. In this sense, examination of otolith data suggests a high plasticity of habitat use by eels , with onWhen comparing across methods, a much larger number of genes putatively under selection were identified using the RAD genome scan approach. This is expected, since we screened only 80 SNPs with the American eel SNP panel, while we interrogated over 150,000 SNPs with the RAD approach. However, when considering percentage rather than total number of markers under selection, results were similar. For instance, in the case of Valencia we found 1.57 % of SNPs putatively under selection using the RAD approach , with a similar percentage  found using the American eel SNP array. In the case of Burrishoole, candidate SNPs under selection were 1.45 %  using the RAD approach and 2.5 % (2 out of 80) using the American eel SNP array. Finally, it should be noted that outliers were not shared across methods, which is explained by the different nature of the markers. All SNPs included in the array were located in coding-genes, while the SNPs discovered using the RAD approach were mostly located in non-coding genes, since RAD tags are restriction site generated markers randomly distributed across the genome.ST values relative to the background when comparing glass eels and silver eels fits the prediction that in the case of animals with complex life cycles, different genes and gene pathways will be under selection at different life stages. This is in accordance with the adaptive decoupling hypothesis for the benefits of metamorphosis [The observation in our study of a large number of outlier loci showing higher Forphosis , which pMoran  hypothesIf selection were complementary between life cycle stages separated by metamorphosis, the same genes and pathways would be expected to be under selection before and after metamorphosis and no outlier loci showing high differentiation would be expected. By contrast, in our study we found up to 2413 outlier loci, suggestive of opposing selection between life cycle stages, as predicted by the adaptive decoupling hypothesis.Alternatively, results in our study could reflect changes in genetic frequencies over time rather than a selection effect. One possibility we cannot rule out is a temporal effect, since juveniles and adults in our sampling were not from the same cohort. Following the same cohort in time would be ideal to test our hypothesis of opposing selection during different life stages separated by metamorphosis, however this is virtually impossible in eels. Age at maturity (at which the silvering metamorphosis occurs) is highly variable in eels, ranging from 6 to 20 years or more . This mee). Due to the effects of Ne on the level of linkage disequilibrium, a fast decay of linkage disequilibrium should be generally expected (reviewed in [Genomic regions displaying elevated differentiation relative to the rest of the genome (genomic islands of divergence) have been described in many species, as exemplified in the case of sticklebacks , 29. In iewed in ), which That might be the case of eels. In our study comparing glass eels and silver eels, outlier SNPs showing high genetic differentiation consistent with selection did not group into clusters but were generally spread across the genome. Similarly, no apparent genomic islands of divergence were found when investigating the genomic distribution of outlier SNPs between European and American eel .In contrast to the pattern observed in European eel, genomic clustering of some highly divergent SNPs was observed in Atlantic herring , a speciST-outlier approach, the number of loci under selection might be underestimated. While standard tests of selection (i.e. outlier tests) are powerful tools to detect “hard selective sweeps”, in which a new advantageous mutation arises and spreads quickly to fixation due to natural selection [6 individuals) and associated high genetic variability [Finally, it should be noted that by using an Felection , other selection , and polelection , 45. Botelection , 45, 46.iability , soft swOur data supports the adaptive decoupling hypothesis for the benefits of metamorphosis in European eel since genes and gene pathways under selection were different in pre- and post-silvering metamorphosis. The differences found between juveniles and adults suggests that partitioning the life cycle into discrete stages may be more effective than a single stage in the case of eels. This way,each life stage can perform specific tasks more effectively, i.e. feeding in glass eels, reproducing in silver eels.No experiments were conducted on the animals and animal manipulation was limited to sacrificing fish, using the least painful method to obtain tissue samples for DNA extraction. In all cases, in order to minimize the suffering of the animals used in the study, fish were deeply anaesthetized with MS-222 (3-amonobenzoic acid ethyl ester) or 2-phenoxyethanol 1 % and then painlessly sacrificed. All procedures were conducted by technical staff, who had all the necessary fishing and animal ethics permits. In Iceland, sampling was approved by Holar University College Ethical Committee and conducted according to guidelines and laws on animal welfare in Iceland. In Ireland, all sampling was carried out under authorisation (Sec. 4) of the Fisheries Act 1959-2003 by permission of the Department of Agriculture, Food and Marine. In Spain, samples were obtained from professional fishermen with sampling and ethical treatment of animals approved by the Consejeria de Medio Ambiente of the Comunidad Autonoma de Valencia.A total of 113 silver eels (adult stage) were collected at three locations across the geographical distribution of the species: (i) Valencia (Spain) in the Mediterranean Sea; (ii) Burrishoole (Ireland), in the North Atlantic Ocean; and (iii) four separate sampling sites in southwestern Iceland that were pooled to increase sample size Table . All silA panel of 100 coding-gene single nucleotide polymorphisms (SNPs) developed by Gagnaire et al.  in AmeriWithin-sample genetic diversity was assessed by observed and expected heterozygosities, polymorphism and mean and total number of alleles using GENEPOP  and stanPrior to the test for selection, we tested the presence of hybrid individuals in the dataset using STRUCTURE . We inclA subset of 41 silver eels (31 from Spain and 10 from Ireland) were RAD-sequenced , 52 at BThe analysis of the RAD data was conducted simultaneously for the 41 silver eels plus 60 glass eels  that were first analyzed in Pujolar et al.  but wereThe 90 bp-long RAD sequences obtained from the Illumina runs were sorted according to barcode and quality filtered using the FASTX-Toolkit . Criteriwww.eelgenome.com) using the un-gapped aligner BOWTIE [Quality-filtered reads were then aligned to the European eel draft genome  of 1  or 0 , suggestive of the presence of more than one locus.Prior to the SNP analysis, loci in the catalog were further filtered in order to remove paralogs and otherwise spurious loci according to the following three criteria: exclude loci with extremely higher coverage as it might indicate the presence of more than one locus (threshold used was twice the standard deviation from the mean number of reads); exclude tri-allelic loci since the presence of more than two alleles might result from sequencing errors; exclude loci containing SNPs with observed heterozygosity  between European and American eel [We also tested for hybrids using a subset of species-specific diagnostic SNPs  using a custom-made script [Danio rerio) genome was used as reference for annotation. Prior to the analysis in DAVID, a local BLAST was conducted for significant matches directly against zebrafish Ensembl proteins using BLASTX. Zebrafish Ensembl Gene IDs were obtained from the corresponding Ensembl protein entries using the Biomart data mining tool [Genomic position of the candidate SNPs for local selection were established on the basis of the gene predictions for the European eel genome  were also tested. Windows were restricted to the 30 largest scaffolds  from the European eel draft genome.Finally, patterns of differentiation across genome regions were characterized to test whether genes putatively under selection were grouped into clusters (genomic islands of differentiation) or more scattered across the genome. We estimated levels of genetic differentiation between glass eels and silver eels in Valencia by calculating average Fhttp://datadryad.org/resource/doi:10.5061/dryad.kc1q1. All sequencing files (.fastq) can be found on the NCBI´s Sequence Read Archive under accession number SAMN03786011.The data set supporting the results of this article is available from Dryad:


We envision that this strategy will be useful for the identification of genes for crop traits and molecular breeding of rice cultivars.The genetic diversity of plants has traditionally been employed to improve crop plants to suit human needs, and in the future feed the increasing population and protect crops from environmental stresses and climate change. Genome-wide sequencing is a reality and can be used to make association to crop traits to be utilized by high-throughput marker based selection methods. This study describes a strategy of using next generation sequencing (NGS) data from the rice genome to make comparisons to the high-quality reference genome, identify functional polymorphisms within genes that might result in function changes and be used to study correlations to traits and employed in genetic mapping. We analyzed the NGS data of Oryza sativa ssp indica cv. G4 covering 241 Mb with ∼20X coverage and compared to the reference genome of Oryza sativa ssp. japonica to describe the genome-wide distribution of gene-based single nucleotide polymorphisms (SNPs). The analysis shows that the 63% covered genome consists of 1.6 million SNPs with 6.9 SNPs/Kb, and including 80,146 insertions and 92,655 deletions (INDELs) genome-wide. There are a total of 1,139,801 intergenic SNPs, 295,136 SNPs in intronic/non-coding regions, 195,098 in coding regions, 23,242 SNPs at the five-prime (5′) UTR regions and 22,686 SNPs at the three-prime (3′) UTR region. SNP variation was found in 40,761 gene loci, which include 75,262 synonymous and 119,836 non-synonymous changes, and functional reading frame changes through 3,886 inducing STOP-codon (isSNP) and 729 preventing STOP-codon (psSNP) variation. There are quickly evolving 194 high SNP hotspot genes (>100 SNPs/gene), and 1,513 out of 2,458 transcription factors displaying 2,294 non-synonymous SNPs that can be a major source of phenotypic diversity within the species. All data is searchable at  The world's geometrically growing population has passed 7 billion, and it is estimated that rice production must increase 24% by 2025 to meet the global population demand Arabidopsis thalianaOryza sativa ssp. japonica cv. Nipponbare (Os japonica) and Oryza sativa ssp. indica cv. 9311 (Os indica) contains a wealth of information that can explain the large amount of morphological, physiological and ecological variation observed in many varieties cultivated for food, since there is a high level of variability present in diverged rice genotypes SNP discovery is valuable for crop improvement in two fundamental ways. First, it reveals DNA variation among varieties, thus providing the tools for selection in breeding programs Oryza sativa ssp. indica (Os indica) and Oryza sativa ssp. japonica (Os japonica) respectively for identification and functional annotation of SNPs between them. This analysis can lead to the development of functional markers focusing on gene based analysis for trait based SNP analysis and utilization for crop improvement.Rapid process in genomic information at whole genome sequence (WGS) level allow us to evaluate and analyze base by base structural variations for their similarity, and the differences reveal potential functional information. In recent years, natural variation has gained significant interest at the genome-wide level and the correlation of these variations to their agronomic traits has been the prime focus Next generation sequencing technology coupled with the growing number of genome sequences, more particularly from within the species, open the opportunities to redesign genotyping strategies based on focused genic information for more effective genetic mapping and genome analysis. Recently, genome-wide variation patterns in rice have been obtained from millions of high-quality SNPs and identified thousands of genes with significantly lower diversity in cultivated compared to wild rice, and a few of these variants have been found associated with important biological features qSH1 gene was shown to cause loss of seed shattering owing to the absence of an abscission layer formation in rice. Another example is the GIF1 (grain incomplete filling 1) gene that shows a restricted expression pattern during grain-filling compared to the wild rice allele, is probably a result of accumulated mutation in the gene's regulatory sequence through domestication DNA polymorphisms have been identified both in animals and plants. In animals, more than 300 diseases and traits have been studied using GWASs O. sativa ssp. indica cv. Guangluai-4 (Os indica) with 20× genome coverage by next generation sequencing (SRA study: ERP000235) were downloaded from NCBI. The total 54,309,982 read count (∼108 million reads) were mapped to the rice O. sativa japonica cv. Nipponbare (Os japonica) reference genome information (version 7) from http://rice.plantbiology.msu.edu/ using SHORE (http://1001genomes.org/).The 73 bp reads of FASTQ data of short read analysis pipeline involves five different ‘OPTIONS’ for processing/analysis. A) SHORE preprocess: prepares index files, local repeat and GC content files from references. B) SHORE import: prepares short read data for processing. C) SHORE Mapflowcell: Short read mapping. D) SHORE merge: Merge and filter of alignment files and E) SHORE consensus: creates consensus sequence from alignment and computes homozygous SNP file with other results. It is designed for projects whose analysis strategy involves mapping of reads to a reference sequence. We used Burrows-Wheeler Aligner (BWA) to align short reads to the rice reference genome (MSU v7) with option -n4 -g3 -c12 and a default of 5 bases Cutoff for base masking using Sanger calibrated qualities, and Cutoff for base masking using chastity values http://sourceforge.net/apps/mediawiki/shore/index.php?title=Shore_consensus. All position SNP calls follow a concordance of ≥80% and support of at least three non-repetitive reads The sequence SHORE analysis pipeline SHORE enables more than 50% of the overlapping reads with weighted and gapped alignments. We used the results of the alignments for discovery of putative SNPs, where each variation supported be at least 3 reads was regarded positive so as to eliminate false positive calls. SHORE calculates a quality score based on information from several features related to the quality of sequence reads and the alignment. The alignment results were subjected to quality filtering, and a cutoff of more than 25 out of 40 quality score was selected and used for functional SNP analysis. SHORE analysis includes heterozygous SNP calls (decision tree approach) for all positions with at least 25% of the bases different to the majority call. It has been reported that residual heterozygosity is present in the original individual used in the IRGSP sequencing, and a small subset of allelic sites within individuals have been identified http://rice.plantbiology.msu.edu) and extracted with functional SNP annotation.The rice genes with annotated SNPs were grouped in gene families according to their gene locus IDs in the MSU database  that currently contains 2,384 putative transcription factors (TF) gene models in O. sativa spp. japonica, distributed in 63 families We could retrieve 2,726 gene loci from 32 rice gene families as described in the MSU rice genome database Oryza sativa (Os) indica and Os japonica respectively using SHOREmap The visualization of genome-wide SNPs was done with the CVit tool https://plantstress-pereira.uark.edu/oryza2.The results of SHORE genome-wide base-by-base analysis of two reference rice genomes were used to develop the SNP2GENE database. We used MySQL as the underlying database to create the table structure, store the results and to allow for efficient searches. The front-end web interface to the database was written in php. We also developed a custom php module for visualization of a selected individual gene. The web interface to the SNP2GENE database is available at O. sativa ssp. indica cv. Guangluai-4 (G4) (Os indica) was analyzed with the objective to identify synonymous-SNP (sSNP) and non-synonymous-SNP (nsSNP) variation within genes. The complete genome sequence of rice Os japonica 12 pseudomolecules including the Syngenta pseudomolecules were used for SNP survey with Os indica next generation sequencing reads available at NCBI  UTR region and 23,242 five-prime (5′) UTR regions, and functional reading frame changes through 3,886 inducing STOP-codon (isSNP) and 729 preventing STOP-codon (psSNP) variation.The NGS genome sequence of rice genotypes  at NCBI . The ana at NCBI . The low at NCBI . We furtwww.plantbiology.msu.edu/) reveals 40,764 loci contribute to SNPs between these two genomes (http://rice.plantbiology.msu.edu). The locus information for the gene families were extracted and SNPs analyzed in the 2726 genes belonging to 32 different gene families and 104 sub-family groups curated by the rice community with functional and/or structural annotation submitted to the Rice Genome Annotation Project (http://rice.plantbiology.msu.edu/annotation_community_families.shtml). Our analysis shows 924 loci comprising10 different gene families are conserved, whereas other gene families have at least one variation in the family  that functions in flower and fruit development The importance of 1.6 million SNPs depends upon their position and effect on gene functions, although the non-functional variation is also important as markers in the genome. In recent years many of the gene based SNPs have been reported for their role in controlling characters like grain filling, plant height, grain weight, amylose content and structure of grain in rice and maize We found an interesting feature of SNP effect in our analysis, there were 195,446 SNPs on CDS  contributing 11% of the total SNPs effects related to change in proteins in the genomes , which ihttp://phylomics.ucdavis.edu/kinase/. The IDs of these kinases were searched in the whole genome SNP analysis database, and 1313 genes showing SNPs were listed as kinase SNPs. We could find 839 kinases falling in the Ka/Ks  variation range from low (0.06) to high (12.0). There are 538 kinase genes that show more than 1.0 Ka/Ks value, with a potential signature for positive selection in evolution and a functional role in plants. These receptor kinases show more variability than other proteins signifying a selective role for the polymorphisms The analysis was extended to identify the Ka/Ks ratios from the identified synonymous and non-synonymous changes for the kinase gene family, as an example to identify potential signatures of their biological functions, phenotypic effects, or expected evolutionary history. The family has potential roles in many traits such as pathogen defense, response to environment and almost all essential cellular functions. The predicted 1467 rice kinase genes were downloaded from indica and japonica and 10% of TIPs have been found in expressed gene regions, which represent important genetic variation Rice has undergone significant phenotypic changes during domestication in different traits like grain size, color, shattering, seed dormancy and tillering, and geneticists have used quantitative trait locus (QTL) mapping to localize the major causative genes responsible for these traits, yielding many trait-related genes in cultivated rice Our analysis of SNPs based on genic regions revealed that 40,761 genes carry 535,537 SNPs. The variation was as high as 33 fold in LOC_Os04g10740, LOC_Os04g10700, LOC_Os04g03100, LOC_Os12g43630 as compared to the average of 4.5 SNP/kb within the genic region. In fact, the genic region possesses 32% of the total genome SNPs and the top 33%  of SNP carrying genes contain 72%  SNPs from the genic region. We next focused on 194 hotspot SNP genes that carried>100 SNPs/gene each, and which contribute to 5%  of genic SNPs .Os japonica in the DRTF currently comprise 2,384 putative transcription factor (TF) gene models, distributed in 63 families Understanding the molecular basis of natural phenotypic diversity is a major challenge in modern genetics and knowing how individual genetic polymorphisms combine to produce phenotypic change could strengthen evolutionary theory and advance applications in crop improvement Oryza sativa is a cultivated, inbreeding species that feeds over half of the world's population. Genome-wide gene based analysis of sequence polymorphisms between the rice two major cultivated rice subspecies, O. sativa ssp japonica cv. Nipponbare and O. sativa ssp indica cv. Guangluai-4, revealed the distribution of SNPs and Insertion/Deletions at the gene level. The sequence polymorphisms commonly occur in both coding and non-coding regions and have significant differences in distribution between different gene families. These variations often affect differences in gene structure between the indica and japonica subspecies, and may contribute to phenotypic adaptation differences.Asian rice Table S1SNP distributions of all rice genes of 12 chromosomes.(XLSX)Click here for additional data file.Table S2Distributions of 3′ UTR SNPs in rice Chromosome 1-12.(XLSX)Click here for additional data file.Table S3Distributions of 5′ UTR SNPs in rice Chromosome 1-12.(XLSX)Click here for additional data file.Table S4Hotspot genes containing 100 SNP and more in the locus.(XLSX)Click here for additional data file.Table S5Distributions of ‘Induced Stop’ coding SNPs in rice Chromosomes 1-12.(XLSX)Click here for additional data file.Table S6Distribution of Stop codon preventing SNPs in rice Chromosome 1.(XLSX)Click here for additional data file.Table S7Distribution of Rice Kinase genes Ka/Ks ratio distribution.(XLSX)Click here for additional data file.


In addition to HLA genetic incompatibility, non-HLA difference between donor and recipients of transplantation leading to allograft rejection are now becoming evident. We aimed to create a unique genome-wide platform to facilitate genomic research studies in transplant-related studies. We designed a genome-wide genotyping tool based on the most recent human genomic reference datasets, and included customization for known and potentially relevant metabolic and pharmacological loci relevant to transplantation.HLA, KIR, pharmacogenomic, and metabolic loci important in transplantation. To test concordance and genotyping quality, we genotyped 85 HapMap samples on the array, including eight trios.We describe here the design and implementation of a customized genome-wide genotyping array, the ‘TxArray’, comprising approximately 782,000 markers with tailored content for deeper capture of variants across KIR) region versus comparable platforms. Overall, we show that the genotyping quality and coverage of the TxArray is very high when compared to reference samples and to other genome-wide genotyping platforms.We show low Mendelian error rates and high concordance rates for HapMap samples . We performed genotype imputation across autosomal regions, masking directly genotyped SNPs to assess imputation accuracy and report an accuracy of >0.962 for directly genotyped SNPs. We demonstrate much higher capture of the natural killer cell immunoglobulin-like receptor  contains supplementary material, which is available to authorized users. Since the Organ Procurement and Transplantation Network (OPTN) began its registry in 1987 until mid-2014 over 575,000 solid organ transplantations have been performed in the United States , 4. TherHLA) region, conventionally considered to contain the main genetic factors underpinning allograft rejection. Rejection is observed in following HLA-matched transplantations between full sibling, suggesting that histocompatibility may depend on non-HLA genetic differences. This includes a number of minor histocompatibility antigens, such as the H-Y antigens [HLA genetic disparities exist between D-Rs, and that these differences may manifest as the presentation of polymorphic peptides that the recipient’s immune system recognizes as non-self even in the presence of IST. Indeed, analyses of overall 10-year kidney graft failure rates for cadaver donors showed that 18 % of graft failures were due to HLA factors, as observed through mismatched living donor grafts; and 43 % were attributable to non-immunological factors, and 38 % of the failures were due to immunological reactions against non-HLA factors as seen in HLA-identical sibling grafts [KIR) region comprising a family of 13 genes on chr19q13.46 are known to interact with HLA Class I molecules, and many unique KIR haplotypes identified have been linked to transplantation outcomes [HLA/KIR polymorphisms have also been shown to impact transplantation outcomes since through the generation histo-incompatibilities [HLA genetic determinants of clinical outcomes following organ transplantation have yet to be performed in any systematic well powered fashion to date.Recent advances in genomic technologies and large-scale human reference maps such as the International HapMap Project have led to the development of genome-wide association studies (GWAS) utilizing cost-effective arrays that allow for the rapid interrogation of several hundreds of thousands single nucleotide polymorphisms (SNPs) and copy number variants (CNV) across the human genome –9. Largeantigens , which houtcomes , 13 Addibilities –16. InveP <1 × 10−5), eight of which retained association on replication, of which seven intriguingly are in loci known to have a role in Beta-cell apoptosis [A recent genome-wide study of NODAT was conducted in a prospective cohort of 529 kidney transplant recipients, 57 of whom developed NODAT with 26 SNPs identified in the discovery stage  variants ablating all or parts or a given gene, and cross-cohort meta-analyses in diverse populations. The majority of these samples are contributed as a part of International Genetics & Translational Research in Transplantation Network (iGeneTRAiN), a major international collaboration on the genomics of transplantation [DNA from over 16,000 DNA samples has thus far been genotyped using this array allowing for more robustly powered antation . The objin situ onto a microarray substrate. There are approximately 1.38 million features available for experimental content with each feature approximately 3 μm2 with each SNP feature contains a unique oligonucleotide sequence complementary to the sequence flanking the polymorphic site on either the forward or the reverse strand. Solution probes bearing attachment sites for one of two dyes, depending on the 3' (SNP-site) base  are hybridized to the target complex, followed by ligation for specificity.The Axiom genotyping platform utilizes a two-color, ligation-based assay using 30-mer Oligonucleotide probes synthesized The transplant-specific modules and genome-wide content for the TxArray was designed based on a tiered system built on the main Affymetrix GWAS imputation grids  for the In addition to this core content, additional modules of SNPs were added sequentially so that maximal economy of markers was retained by ensuring no redundant SNPs were added. We describe the tiers sequentially below:Genome-wide imputation grid (approximately 296K markers): The TxArray’s core imputation grid consists of genome-wide approximately 296K SNPs shared in common with the conventional Affymetrix Biobank Array. These include a set of 246K SNPs, also included in the UK Biobank array, that provide high-density coverage (mean r2 >0.81 and 0.90) across European populations (CEU) at minor allele frequencies (MAFs) >1 % and 5 %, respectively.Additional coverage for non-European populations: An additional set of approximately 50K SNPs, covered in the 1KGP Phase I reference panel, were additionally extracted from the Affymetrix-Biobank array to improve the mean coverage achieved in African and other populations. These SNPs were chosen with the goal of achieving comprehensive overlap with already existing UK Biobank Axiom Array and the Axiom Biobank Array, to facilitate additional collaborative efforts where joint or meta-analyses of samples genotyped across these platforms and other conventional GWAS platforms are required [required , 30.Compatibility markers (approximately 18K markers): This module was designed to optimize and standardize genotyping quality control (QC) and sample validation through the use of: Polymorphisms capturing Ancestry informative markers (AIMs); fingerprinting panels; mitochondrial, Y-chromosome; and miRNA binding sites or targets regions were included.HLA and KIR region markers Known phenotype associations curated by the National Human Genome Research Institute (NHGRI) GWAS Catalog , 35, Expression-quantitative trail loci, or eQTLs Lung-tissue specific or pulmonary function-associated markers These constitute markers identified based on reported GWAS signals and candidate gene associations across pharmacogenomic and metabolic phenotypes. Again, to enable cross-platform analysis, where feasible, we also included markers directly overlapping the UK-Biobank array and additional markers for the transplant-specific content. The following UK-Biobank array modules were included; see Fig. HLA MHC region markers, deep coverage of known and predicted LoF variants, and untranslated regions (UTR)-specific module. Note that all positions and variants referenced herein are based on the human genome builds hg19/build37  [HLA-specific coverage, a number of MHC genes and markers mapping variants outside of the HLA-encoding regions are critical players in immune function and some have known roles in histocompatibility . Thus, we included a comprehensive set of MHC markers in addition to the conventional HLA-coding regions  , 37. Whions Fig. .KIR in allo-recognition through its interaction with HLA, we included additional KIR SNPs to enable fine-mapping, imputation, and structural variation association analysis, as well as interaction analyses across KIR and HLA Class I, which has a known role in histocompatibility in HCT, as well as other MHC loci.Additionally, given the important role of HLA markers and 1,546 KIR markers.UK-Biobank array , including 7,348 HLA haplotype tagging SNPs [Multiethnic ing SNPs  (421 SNPHLA imputation panel for SNP2HLA [The Type 1 Diabetes Genetic Consortium (T1DGC) Imputation panel .Non-redundant MHC validated SNPs from existing genotyping platforms used in large-scale studies: (1) Metabochip  and (2) Immunochip  , 27.To build this content and attempt to preserve significant overlap with state-of-the-art, popular genotyping platforms, we curated and included in our design content from the following resources and platforms , European (CEU), and Asian .Drug absorption, metabolism, excretion and toxicity markers, n = approximately 7,500 SNPs including markers derived from PharmGKB . As thesOver 600 transplantation-related genetic association studies were manually curated from PubMed using the following search string: ‘transplant + DNA + donor + recipient AND  AND (SNP OR polymorphism OR variant)’. Key information including PMID number, size and population examined, loci and SNPs studied (including the respective rsID numbers), and number of donors and recipient subjects were collated. An emphasis was placed on sample size, data quality and strength of the described associations to facilitate more powerful meta-analyses with data from existing publications.To maximize the coverage in the CEU, YRI, and ASN populations, we selected an additional non-redundant set of 23.8K variants to boost coverage the total of 91.9K polymorphic sites included in these loci. The SNPs were chosen based on an algorithm that attempts to maximize the expected mean coverage across all three key populations simultaneously instead of one at a time. This was performed by selecting the tagging SNP marker that tags most SNP markers from all three populations first; this strategy enables identification of minimal SNP sets for maximal cross-ethnic coverage  and response to transplant-related medications.We included those previously identified gene candidates and wherever no specific candidate gene has been fine-mapped or independent replicated or validated, we mapped known SNP associations to nearby coding loci and included tagging variants and variants in LD to boost local coverage. We considered a number of recent studies that attempted to replicate GWAS findings , 19, 43,Full details of all the polymorphisms on the array including their chromosome position and additional annotations are outlined in .Affymetrix Biobank array content: We considered a total of approximately 250,000 SNPs from the Axiom Biobank Genotyping Array, including 86,000 putative exonic SNVs and putative LoF variants. As not all of these have been validated and many are not polymorphic in the general populations, we used one of the largest whole-exome sequencing reference datasets available at the time of the design, comprising over 32,000 samples, to annotate and filter these variants based on the observed minor allele counts (MACs). We included only those variants with MACs greater than five observations in this database, which yielded approximately 168K exonic or coding variants and over 16K putative LoF variants. A total of 178,680 unique variants were selected in this module.Human Gene Mutation Database (HGMD): We curated variants of The HGMD LoF database (up until 1 August 2013). Again, as above, we only included MAC observed greater than five times, for a total of 3,571 variants  Coverage: To provide maximal coverage of SNPs that may affect functional gene expression, we additionally focused on the coverage of 5’ (and 3’) UTRs defined as the exonic region between the transcriptional start (stop) and translational start (stop) sites as defined by either the RefSeq or ENSEMBLE human genome (hg19) reference sequences in June 2013. Using a MAF cutoff for inclusion of >1 % or 5 % in CEU and AFR (ASW + YRI) populations, respectively, we included a total of approximately 184,000 SNPs as shown in Additional file genome hg9 referenA priori associations: To focus on known phenotypes, 8,136 SNPs that reached a conventional GWS threshold at P <5 × 10−8 (December 2012) for both quantitative traits and disease-specific reported in NHGRI GWAS Catalog were included.Aside from the modules noted as being shared with the UK-Biobank, the following categories of variants were included in the design for this component:To cover common genomic structural elements by SNP-tagging we included 5,410 markers  selected by identifying the minimal set of markers that will provide the optimal added coverage value . Since the goal is to fill the array and gain additional coverage with the minimal number of markers, we used LD-based pair-wise tagging. Specifically, we focused on improving the coverage of common variants (MAF >2 % in CEU and MAF >5 % in AFR populations) by selectively adding additional variants resulting in selection a total of 135,363 additional markers based on the projected improvements in the overall coverage , Asian , and African (YRI) ancestry, respectively. All analyses were performed with PLINK (1.07/1.9) . First, MHC SNPs is 0.994, and 0.998 for SNPs on the X chromosome . We tested 22,944,075 sample-SNP combinations for concordance and observed a concordance rate of 0.996. Concordance rates for the three populations  were very similar , while 0.341 % of SNPs had a single concordant allele and only 0.002 % of SNPs were fully discordant.We compared the mean coverage (based on maximum achievable r2) across common markers (either MAF >0.05 or 0.01) in the 1KGP achieved by either the marker content designed on the TxArray versus that by a number of conventional genotyping platforms , and Genome-Wide Human SNP Array 6.0 (Affymetrix)).2 cutoff thresholds (from r2 = 0 to 1). The TxArray performed comparably next to these other genotyping SNP chips, which were designed to provide optimal genome-wide coverage even though the TxArray devoted a significant number of markers to transplant-specific, rare loss of function and MHC/KIR specific content.Figure KIR, and MHC regions when compared to the commonly-used Illumina 1M platform  using ShapeIT2/ IMPUTE2 with the 1KGP reference panel (v3), resulting in approximately 38 million variant calls. We masked 0.2 % directly genotyped SNPs, with separate imputation performed with and without these 0.2 % SNPs to assess proxy genome-wide imputation accuracy. We report an accuracy in the range of 96.24 % to 97.71 % for directly genotyped SNPs across the genome for the 12,048 GWAS datasets imputed.We looked at number of SNPs per MAF bins  in all imputed data. We observed that INFO score  for variants below MAF 0.05 declines but as MAF increases, INFO score also increases. In most cases, all variants above MAF 0.05 have INFO scores greater than 0.8 (data not shown). From masked analysis, we looked at concordance among each masked SNP where INFO scores were greater than 0.8, and results indicated very high concordance among all masked SNPs. Comparison of imputation accuracy/metrics from two independent pipelines (both using ShapeIT/IMPUTE2 with 1KGP as the reference population) was performed for The Genomics of Chronic Renal Allograft Rejection (Go-CAR) Study at Penn State and Mount Sinai and >99.998 % of imputed SNPs were concordant.We looked at number of SNPs per MAF bins  in all imputed data as well as after performing masked analysis where 0.1 % of genotyped markers were removed and imputed again to assess the accuracy of our imputation. We observed that INFO score  for variants below MAF 0.05 declines but as MAF increases, info score also increases. In most cases, all variants above MAF 0.05 have info score greater than 0.8.HLA-A, HLA-B, HLA-C, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, and HLA-DRB1 at a four-digit resolution, as well as an additional 3,117 MHC SNPs. We used data collected by the T1DGC as a reference panel, which include 5,225 individuals of European descent. Methods have been described previously in more detail [Using Beagle version 3.0.4, we imputed classical alleles and amino acid polymorphisms in e detail , 50.HLA and KIR, pharmacogenomic, and LoF variants. We demonstrate much deeper coverage in high priority regions such as KIR and tag SNPs in these regions provide much better coverage for populations of African ancestry relative to existing GWAS products.We have designed and implemented a genome-wide SNP array tailored for deeper capture of variation in loci of high priority in transplantation. Our primary goal in the array design was to generate a low-cost genome-wide array while maximizing coverage of known or putative transplant-related content. In attempts to unveil allogenicity between D-R genomes we also augmented the custom content from all available resources for rarer LoF variants that may not be identified using traditional association studies based on imputation. Flexibility in SNP selection afforded the ability to: (1) ensure selective and consistent coverage for a range of prioritized loci across multiple ancestries; (2) provide deeper coverage beyond conventional HapMap populations; (3) to directly assay specific SNPs derived from previously published transplant studies; and (4) updated bona fide associations transplant outcomes and/or pharmacokinetics of immunosuppression medication with genetic polymorphisms in transplant-related genes  [With the recent reports of YP3A4/5) –54, one HLA and KIR content with the most-extensive content known to date. As with other recent custom genotyping arrays in cardiovascular diseases and metabolism as well as in autoimmune diseases, the TxArray facilitates conduct of powerful and cost-effective large-scale genotyping of transplant-related studies. Such platform enable integration with additional ‘omics’ datasets such as transcriptomics, proteomics, and metabolomics to provide richer analyses in a number of the studies using this tool. Additionally, as other transplant related cohorts are utilizing this TxArray for GWAS, validation of results and comprehensive meta-analyses will be much more robust. The TxArray achieves dramatic reduction compared to designing single-trait follow-up reagents, and provides the opportunity for transplant community researchers to perform unbiased genome-wide analysis and cross-consortium independent replications.In designing the transplant SNP v1 array we have also targeted all nsSNPs >MAF 0.01 using information from both HapMap and 1KGP and have tagged to MAFs >0.02 for a large number of key loci related to key transplantation outcomes such as pharmacogenomic and metabolic-related traits. We have also updated the We report the design and implementation of a state-of-the-art, powerful oligonucleotide array that is optimized to interrogate the genome for associations with transplant-related phenotypes and outcomes. This array, the TxArray, includes independent modules that encompass fine-mapping SNPs mapping across the MHC useful for HLA imputation, in drug-response associated loci for the study of pharmacogenomics and adverse drug response, previously-reported genes associated with transplant-related outcomes, among others, that are of high priority among the transplant community. With the advent of this array and the formation of the iGeneTRAIN consortium, it is our aim that the downstream application of such genomics technologies can ultimately generate associations which will be applied as personalized and precision-oriented genomic tools to solve clinical questions and improving patient outcomes in transplantation.


One aspect in which RNA sequencing is more valuable than microarray-based methods is the ability to examine the allelic imbalance of the expression of a gene. This process is often a complex task that entails quality control, alignment, and the counting of reads over heterozygous single-nucleotide polymorphisms. Allelic imbalance analysis is subject to technical biases, due to differences in the sequences of the measured alleles. Flexible bioinformatics tools are needed to ease the workflow while retaining as much RNA sequencing information as possible throughout the analysis to detect and address the possible biases.We present AllelicImblance, a software program that is designed to detect, manage, and visualize allelic imbalances comprehensively. The purpose of this software is to allow users to pose genetic questions in any RNA sequencing experiment quickly, enhancing the general utility of RNA sequencing. The visualization features can reveal notable, non-trivial allelic imbalance behavior over specific regions, such as exons.The software provides a complete framework to perform allelic imbalance analyses of aligned RNA sequencing data, from detection to visualization, within the robust and versatile management class, ASEset.The online version of this article (doi:10.1186/s12859-015-0620-2) contains supplementary material, which is available to authorized users. Regulatory variants that alter gene expression can be examined, based on allelic imbalance (AI), i.e., alleles can be differently expressed in an individual if the regulatory region around a gene differs. In RNA sequencing data, it is possible to determine the allele from which a specific read originates when there is at least one heterozygous SNP in the sequence read . An AI eThe detection of an AI event is not trivial, comprising several steps, including library preparation , sequencThe AllelicImbalance package was developed to address these issues, allowing the user to test AI at a single gene or SNP quickly. Nevertheless, the package is suitable for performing any custom global AI analysis, because there is always a counting step and the need to store counts in a smart container, which facilitates access to custom requests from the user. For genes that have more than one heterozygous SNP and at least one sample, there is a function to visualize AI consistency easily over the gene as an internal validation to select SNPs that are suitable for further AI QTL study Fig. . The pacbarplot, can be used directly on that ASEset class object; experienced users can customize nearly any part of the workflow. The design is based on RNA-sequencing, but AllelicImbalance can be used with any allele that is focused on a count-based technique, such as digital qPCR [AllelicImbalance was developed to provide usability for inexperienced as experienced R-users. For inexperienced users, there is a standard protocol to create an ASEset from bam files, and functions, such as tal qPCR .ASEset is a new object class that summarizes sequencing data see Fig. . The supEqual amounts of reads are expected from two alleles, but one allele might be read more than the other by chance. A greater number of reads improves the estimate of the total distribution. Statistical tests, such as the chi-square and binomial tests, generate the probability that an observed difference is due to this sampling bias. These relatively simple and general tests can be applied directly to ASEset objects and return a matrix with p-values for each SNP and sample. The user can easily apply other custom tests by taking advantage of the ASEset accessor methods to retrieve allele fractions or counts, for example.barplot function . To exemplify how AI can be used in a simple QTL analysis, four genes with high coverage—Using established methods, such as eQTL, it would not have been possible to detect this effect in a limited sample size. In all of the exemplified genes, most reads came from one strand, suggesting that the interference of lncRNAs, for example, is low. But, at least 40 % of human genes are transcribed in both directions , potentiTo compare loci or individuals in which the read depth differs, it can be convenient to plot alleles as a fraction and inspect a wider region of all heterozygous SNPs, for example, of the same gene. Without interference from allele-specific splicing, we expect all SNPs over a gene to show the same pattern of fractions. Figure To illustrate the reduction in mapping bias, we replaced the SNPs in the reference genome with the generic nucleotide indicator N. All SNPs in dbSNP build 138 were masked in this manner, and we then reperformed the alignment with STAR. Figure The AllelicImbalance package will be valuable in examining the genetics of RNA sequencing experiments. This software is a novel tool in the Bioconductor environment, in which no infrastructure that can perform AI analyses exists. The import functions are essential when retrieving allele counts for specific nucleotide positions from all RNA-seq reads. Similarly, the statistical analysis and plotting functions are necessary to identify any allele-specific expression patterns in one’s data. With merely a limited amount of samples, strong genetic effects on gene expression can be discovered.Project name: AllelicImbalance softwarehttp://www.bioconductor.org/packages/release/bioc/html/AllelicImbalance.htmlProject home page: Operating system(s): Linux, Mac OSX, WindowsProgramming language: ROther requirements: NoneLicense: GPL3Any restrictions to use by nonacademics: GPL3GPL3-licensed and available in the Bioconductor framework.Additional file 1: Figure A1-A4. The corresponding barplots to figure 2 for a STAR alignment. Barplots for a TopHat2 alignment. Comparison between STAR, STAR dbSNP-masked reference and TopHat2 for AI fraction consistency in the APOB gene. A glocationplot for the FN1 gene with transcript annotation.Additional file 2:Includes counts, fractions and binomial test p-values for all individuals, rsids and alignment methods.Additional file 3:Includes total counts over all samples for each SNP for the alignment methods for APOB.Additional file 4:Includes total counts over all samples for each SNP for the alignment methods for FN1.


A major concern in conservation genetics is to maintain the genetic diversity of populations. Genetic variation in livestock species is threatened by the progressive marginalisation of local breeds in benefit of high-output pigs worldwide. We used high-density SNP and re-sequencing data to assess genetic diversity of local pig breeds from Europe. In addition, we re-sequenced pigs from commercial breeds to identify potential candidate mutations responsible for phenotypic divergence among these groups of breeds.Our results point out some local breeds with low genetic diversity, whose genome shows a high proportion of regions of homozygosis (>50%) and that harbour a large number of potentially damaging mutations. We also observed a high correlation between genetic diversity estimates using high-density SNP data and Next Generation Sequencing data . The study of non-synonymous SNPs that were fixed in commercial breeds and also in any local breed, but with different allele, revealed 99 non-synonymous SNPs affecting 65 genes. Candidate mutations that may underlie differences in the adaptation to the environment were exemplified by the genes AZGP1 and TAS2R40. We also observed that highly productive breeds may have lost advantageous genotypes within genes involve in immune response – e.g. IL12RB2 and STAB1–, probably as a result of strong artificial in the intensive production systems in pig.The high correlation between genetic diversity computed with the 60K SNP and whole genome re-sequence data indicates that the Porcine 60K SNP Beadchip provides reliable estimates of genomic diversity in European pig populations despite the expected bias. Moreover, this analysis gave insights for strategies to the genetic characterization of local breeds. The comparison between re-sequenced local pigs and re-sequenced commercial pigs made it possible to report candidate mutations to be responsible for phenotypic divergence among those groups of breeds. This study highlights the importance of low input breeds as a valuable genetic reservoir for the pig production industry. However, the high levels of ROHs, inbreeding and potentially damaging mutations emphasize the importance of the genetic characterization of local breeds to preserve their genomic variability.The online version of this article (doi:10.1186/1471-2164-15-601) contains supplementary material, which is available to authorized users. The use of a relatively small number of international high-output or commercial breeds largely explains the increase in livestock productivity over the past decades. In parallel, the number of commercial populations is even decreasing due to consolidation of breeding stock and breeding companies . While hDespite all those inherent properties of local breeds, the long term survival of many of them is not assured . InbreedGenetic characterization of livestock breeds by applying genetic marker technology is needed to enhance breeding and to better direct biodiversity conservation strategies. In pigs, the Porcine SNP60 Bead-array  is a comIn this study, we first assess and compare genetic diversity of low-input breeds from Europe by integrating high-density SNP and re-sequencing data. Secondly, we explore the role of local breeds as reservoirs for genetic variation in a domesticated species. Finally, we assessed differences between local and commercial populations in terms of functional variation and explore evidences for inbreeding in local breeds that could lead to inbreeding depression.Sus scrofa build 10.2 [We genotyped 12 local breeds from United Kingdom, Spain, Italy and Hungary  computed with Genepop . We alsoThe study of parameters at the individual level ─ F_60K and h_NGS ─ allows a direct comparison between genetic diversity using the two marker systems Figure B. In ordThe number of Runs of Homozygosity (ROH) as well as their length varied greatly among populations as estimated from both 60K and NGS. In agreement with the genetic diversity estimates, all the analyses showed that the Mangalica breed had the highest proportion of the genome covered by ROH Figure . The ItaOf all the SNPs discovered by NGS, an average of 0.17% was annotated as non-synonymous variants  were considered as one population and each local breed was used separately to determine Fst. We focussed on those non-synonymous SNPs that were fixed in commercial breeds and also in any local breed but with different allele, i.e. Fst = 1. Moreover, we explored the occurrence of ROH and published QTL overlapping these SNPs.In order to find SNPs that potentially explain phenotypic differences between local populations and high-output pigs, we extracted all possible non-synonymous SNPs and we computed FThis analysis revealed 99 non-synonymous SNPs with different fixed alleles in commercial and at least one of the local breeds, affecting 65 genes  at non-synonymous sites to be overrepresented in genes involved in immune response, anatomical development, behaviour, and sensory perception between commercial and local populations. Local breeds tend to be reared in traditional systems without being subjected to intense artificial selection  as applied to commercial pig populations. As a result of years of different selection pressures and environments, genomic variations underlying phenotypic differences can be expected. We have specifically focussed on non-synonymous variants because they will alter the amino acid sequence of gene products, which may result in different phenotypes . AlthougThe gene AZGP1 stimulates lipid degradation in adipocytes and subsequently is considered a lipid-mobilizing factor . This geIL12RB2 and STAB1, were observed in several local pigs. The IL12RB2 subunit plays an important role in Th1 cell differentiation that is critical for an effective immune response against different types of pathogens [IL12RB2 can lead to a defective IFN-gamma response to microorganisms [It has been observed that selection for economically important traits tends to increase the susceptibility to environmental factors , 45. In athogens . The thrathogens , 48. Therganisms , 50, sugEIF2AK3 gene overlaps with QTLs for osteochondrosis score [EIF2AK3 gene could underlie strong directional selection in commercial breeds. The fact that the same alleles are segregating in both wild boar and low-input breeds supports this hypothesis.The is score  and feetis score . Moreoveis score . Leg weais score . The allAZGP1 and TAS2R40 may underlie a rapid adaptation to different environments, it can also occur due to drift effects in small populations in the absence of selection, or even if the allele is in fact disadvantageous. The fixed alleles in EIF2AK3 and IL12RB2 could potentially result in disadvantageous phenotypes in high-output breeds owing to the strong artificial selection for production traits. We demonstrated that genetic variability found in wild populations is also being preserved in local breeds at genomic sites with potential phenotypic effect. This further highlights the importance of preserving local breeds as a source of genomic diversity that could be used in future selection programs of commercial pigs. However, the results presented also highlight high levels of ROHs, inbreeding and potentially damaging mutations that threat the future of local pig breeds, emphasizing the need of implementing conservation programmes to preserve the genomic variability of low-input breeds.The genes discussed above had different fixed alleles for non-synonymous SNPs between commercial and local pigs. The presence of both alleles, the ancestral and the derived, in wild boars indicates that the variation was present before domestication. While differences in allele frequencies of SNPs in genes such as In this study, we assessed genetic diversity of low-input breeds from different European regions by integrating high-density SNP and re-sequencing data. The comparison of the two marker system estimations provided insights for strategies to the genetic characterization of local breeds. Furthermore, the re-sequenced local pigs were compared with re-sequenced commercial pigs to report candidate mutations responsible for phenotypic divergence among those groups of breeds. We observed that local pig breeds are an important source of genomic variation within-species, and thereby, they represent a genomic stock that could be important for future adaptation to long-term changes in the environment or consumers preferences. However, high levels of inbreeding threaten the long term survival of some of the local breeds studied.Sus scrofa build 10.2 and that had less than 5% missing genotypes. In addition, 1–2 animals of each local breed were selected for re-sequencing with the exception of the Nera Siciliana breed. We also re-sequenced eight individuals that belonged to the commercial, international pig breeds Duroc, Large White, Landrace and Pietrain. The samples used are detailed in Table Blood samples from 315 unrelated domestic pigs were collected and DNA was extracted by using the QIAamp DNA blood spin kit (Qiagen Sciences). The study included domestic pigs that belonged to 12 local breeds from England, Spain, Italy and Hungary. Samples were genotyped using the Illumina Porcine 60K iSelect Beadchip  per manuDNA samples obtained from Chato Murciano pigs were obtained from blood samples collected by veterinarians. This procedure was approved by the Murcia University Ethics Committee and with the consent of the farmers. All the other samples were collected in the framework of the PigBioDiv1 and PigBioDiv2 projects. These DNA samples were obtained from blood samples collected by veterinarians according to national legislation, from tissue samples from animals obtained from the slaughterhouse or, in the case of wild boar, from animals culled within wildlife management programs.Sus scrofa genome, build 10.2 [Library construction and re-sequencing of the samples was performed using 1–3 μg of genomic DNA following the Illumina library prepping protocols (Illumina Inc.). The library insert size ranged for 300–500 bp and fragments were sequenced from both sides yielding two times 100 bp mated sequences. Short read alignment was done against the ild 10.2  using Moild 10.2 .Sus scrofa genome, build 10.2. The mpileup function implemented in SAMtools v1.4-r985 [Archives in BAM format generated with the Mosaik Text function were used for the SNPs calling against the 1.4-r985  was usedhttp://www.r-project.org).We used Genepop 4.2  to compuHeterozygosity was estimated for each individual as the number of heterozygous sites per 50 Kb-bin, corrected for total number of sites per bin . Only biTo estimate the ROH from re-sequencing data, we followed the procedure implemented by Bosse et el. , using ahttp://genome.ucsc.edu). For further analysis, only the non-synonymous sites were considered. The genes that overlap with the non-synonymous mutations were retrieved using Biomart [ANNOVAR  was used Biomart .st value for all non-synonymous mutations was calculated using Genepop 4.2 [st = 1 between the groups (i.e. fixed differences). Moreover, in order to avoid false positives, we exclusively considered those mutations that were homozygous in at least the two animals of the local breed. In the case of the local breeds that had only one animal re-sequenced or when one of the two animals of the breed showed missing data, the SNP was not considered for the functional analysis regardless its Fst value. Those SNPs with missing data in more than three commercial pigs were equally excluded.The Fepop 4.2 . For thiThe sequence of a re-sequenced Warthog was used to ascertain the alleles as ancestral or derived. The genotypes for those SNPs were also obtained from re-sequenced data from two domestic Meishan pigs, one wild boar from South China and two from North China and two European wild boars. The sequencing alignment and SNP discovery of these samples was the same as previously detailed.Finally, we used the Polymorphism Phenotyping (PolyPhen2) algorithm  to prediThe data sets supporting the results of this article are included within the article .Number of genomic variants within exonic, intergenic, and intronic regions.(XLSX 12 KB)Additional file 1:Inbreeding coefficient Vs. Length of ROH using 60K data. Each dot represents an individual and the size of the dots are proportional to number of ROH carried by the pig. The black line highlight the F = 0.00 value. (JPEG 1011 KB)Additional file 2:Example of ROH estimated with 60K and NGS in chromosomes SSC1 and SSC13. The two lines of the same color represent the same animal, with the clearer color representing 60K estimation and the darker NGS results. The lack of detection of short ROH using 60K as well as overestimation of the length of long ROH is observed. From out to inside the circle: MA (orange), CT (green), TA (red), BS (yellow). (PNG 142 KB)Additional file 3:Detail of the mutations classified by Polyphen2. Fixed mutations in local breeds with available Polyphen2 classification. Summary of the total number of potentially damaging mutations, total number of benign mutations and percentage of damaging mutations in each breed. (XLSX 845 KB)Additional file 4:Dendograms of the tested breeds based on pairwise Fstvalues using 60K and NGS data. Dendrogram based on Fst pairwise between local breeds using 60K data; Dendrogram based on Fst pairwise between using 16.409 non-synonymous sites. (PDF 134 KB)Additional file 5:SNPs in coding sequence with extreme differences in allele frequencies (Fst= 1) between commercial and local pig populations.(XLSX 18 KB)Additional file 6:Permutation analysis to test if patterns of overlapping between non-synonymous SNPs and QTLs were significantly different from random.(XLSX 21 KB)Additional file 7: