Despite ongoing reductions in the cost of sequencing technologies, whole genome SNP genotype imputation is often used as an alternative for obtaining abundant SNP genotypes for genome wide association studies. Several existing genotype imputation methods can be efficient for this purpose, while achieving various levels of imputation accuracy. Recent empirical results have shown that the two-step imputation may improve accuracy by imputing the low density genotyped study animals to a medium density array first and then to the target density. We are interested in building a series of staircase arrays that lead the low density array to the high density array or even the whole genome, such that genotype imputation along these staircases can achieve the highest accuracy.For genotype imputation from a lower density to a higher density, we first show how to select untyped SNPs to construct a medium density array. Subsequently, we determine for each selected SNP those untyped SNPs to be imputed in the add-one two-step imputation, and lastly how the clusters of imputed genotype are pieced together as the final imputation result. We design extensive empirical experiments using several hundred sequenced and genotyped animals to demonstrate that our novel two-step piecemeal imputation always achieves an improvement compared to the one-step imputation by the state-of-the-art methods Beagle and FImpute. Using the two-step piecemeal imputation, we present some preliminary success on whole genome SNP genotype imputation for genotyped animals via a series of staircase arrays.From a low SNP density to the whole genome, intermediate pseudo-arrays can be computationally constructed by selecting the most informative SNPs for untyped SNP genotype imputation. Such pseudo-array staircases are able to impute more accurately than the classic one-step imputation. Genome-wide association studies (GWAS) are processes of genetic fine-mapping that find whether common genetic variants are associated with a trait of interest [single nucleotide polymorphism (SNP) — a site in the genome at which different individuals display different nucleotides. In this paper we consider biallelic SNPs in the cattle genome. The 1000 Bull Genomes Project (www.1000bullgenomes.com) has identified more than 28 million single nucleotide variations in cattle. The number of biallelic SNPs with significant minor allele frequency is expected to be around 26.7 million (we use this number throughout the paper), and they are almost evenly distributed across the cattle genome. If we were given the genotype for all 26,700,000 SNPs for all studied individuals, a GWAS would simply be a pure association study. In reality, it is still too expensive to sequence the whole genome of every individual in the study; for most cattle GWAS, instead only a small fraction of the 26,700,000 SNPs are genotyped using commercial arrays. Two series of general purpose commercial gene chips for SNP genotyping in cattle are the Illumina (www.illumina.com) series and the Affymetrix (www.affymetrix.com) series. In our cattle genomics projects, besides sequencing 30 or so individuals per breed, we have genotyped thousands of animals using the Illumina BovineHD BeadChip (Illumina 777 K) that contains more than 777,000 SNPs and the Axiom Genome-Wide BOS 1 Array (Affymetrix 660 K) that contains more than 648,000 SNPs spanning the entire bovine genome. In addition, we have certain low-density and medium-density genotype datasets obtained using the Illumina BovineLD BeadChip (Illumina 6 K) and the Illumina BovineSNP50 BeadChip (Illumina 50 K), respectively.interest . These creferences to impute the whole genomes  for the animals genotyped at various lower density levels, a scenario we designate as the whole genome SNP genotype imputation. For example, the Illumina 6 K chip provides genotypes for around 6000 SNPs across the 29 autosomes, and the animals genotyped with the Illumina 6 K chip will have the remaining 26,694,000 SNPs imputed up from the 6000 SNP genotype, based on the sequence-derived SNP genotypes of the reference sequenced animals. The subsequent GWAS may be done on either the 6000 (i.e. 0.2 %) genotyped SNPs, or can also be done on the imputed genotype including the other 26,694,000 (i.e. 100 %) SNPs.The project goal discussed in this paper is to use the sequenced animals as Note that a typical GWAS genotype dataset that contains thousands of SNPs and hundreds to thousands of individuals is apt to contain missing genotype data. Besides discarding the concerned SNPs from GWAS or perhaps repeating genotyping experiments, computationally inferring the missing data has long been proposed as an alternative, at minimal labor and cost –5. In otcoalescent theory, which states that a short  haplotype allele is expected to be shared by many individuals due to identical-by-descent inheritance. Computationally, these methods can be classified into regression and clustering [All existing genotype imputation methods are based upon the ustering , 12–15, ustering . Among oustering , 16–19, ustering , and Impustering , 22 whicustering , Beagle ustering  which isustering , Mendel-ustering  using “lustering  which isustering . All theone-step imputation, which is not the computational problem we try to address in this paper. Recently, several studies showed evidence that within bovine genomics, the two-step imputation is generally more accurate than the one-step imputation, where the lower density genotyped animals are first imputed to a medium density SNP set and then further imputed to the higher density [More specifically in our whole genome SNP genotype imputation projects, the higher density chip is meant to be the whole genome sequence data, consisting of all 26.7 million SNPs; the lower density chip can be either of the Illumina 6 K, 50 K, 777 K, or the Affymetrix 660 K. Running an imputation method one time to directly impute the whole genome sequences is referred to as the  density –31. For  density , the two density .add-one two-step imputation experiments, in which the median density reference panel contains only one extra SNP than the low density SNP panel. While rotating this extra SNP from the pool of markers in the high density panel, we observed that a portion of them can individually boost the imputation accuracy in the add-one two-step experiment compared to the one-step direct imputation. Inspired by this observation, we are asking the natural question whether building up a staircase of pseudo arrays in between the lower density SNP set and the designated higher density SNP set would give the best imputation result. In this paper, we attempt to answer this question by first presenting a novel two-step piecemeal imputation framework, which essentially builds an intermediate pseudo array by mining the hidden relations between the lower and the higher density arrays. We remark that our pseudo array is not an actually manufactured gene chip, but an artificial one that is computationally derived from a learning procedure, which evaluates and selects some SNP markers based upon their add-one two-step imputation performance. Moreover, the pseudo-arrays are model-dependent, that is, different base imputation programs built upon different models could result in different selection of markers for our two-step piecemeal imputation. We show that by wrapping either Beagle or FImpute in our two-step piecemeal imputation framework, we are able to achieve higher genotype imputation accuracies.  Though we believe most effective imputation methods mentioned earlier can be adopted, the main reason we only go with Beagle and FImpute is their fast speed (i.e. efficiency). Based on the two-step piecemeal imputation, we demonstrate how staircase arrays can be built for whole genome SNP genotype imputation.In our preliminary studies, we have conducted the so called study sample, we mean an animal that is genotyped at the lower density. The SNPs on this lower density chip form a set T. A reference panel consists of individuals genotyped at the higher density. All SNPs of T are assumed to be assayed in the higher density chip; the other SNPs on the higher density chip but not in T form the set U — they are the untyped SNPs in the study samples. For each marker mi∈U, an add-one two-step imputation from T to T∪{mi} to T∪U is conducted to evaluate its potential in imputing the other untyped markers. Next, markers that have similar potentials are clustered together, and correspondingly for each marker cluster, we determine which other markers can be imputed well, forming into a target marker cluster. We select one marker from every marker cluster, together with T, to form the pseudo intermediate array. The SNPs in a target marker cluster, called a tract, are imputed via the add-one two-step imputation where the selected marker is from the associated marker cluster. Piecing these tracts together gives the final imputation result.We briefly describe our two-step piecemeal imputation framework here  and FImpute (version 2.2) as the two base imputation programs respectively, which are fast and memory efficient enough to run them thousands of times on multiple large to huge datasets.http://www.genomecanada.ca) has contributed more than 350 animals to the 1000 Bull Genomes Project. From these projects we derived two datasets: a Holstein sequence collection containing 114 animals, and a Simmental sequence collection containing 82 animals. They were used for the piecemeal imputation method training through a cross validation process (i.e. partitioned into a subset of study samples and another subset of reference samples). They also served as reference samples in all the independent testing experiments.The Canadian Cattle Genome Project  density to impute their genotypes at the density 660 K. The 23 genotyped and sequenced animals are used as study samples in the independent testings from a lower density to impute their whole genome.We used single chromosomes of small size (BTA 27) or medium size (BTA 14) in the development of the piecemeal imputation framework. BTA 27 was used for the Holstein data set and BTA 14 for the Simmental data set. The only challenge to deal with all 29 chromosomes is the need of a huge amount of disk storage, see The numbers of SNPs included in the Illumina 6 K, 50 K, 777 K and the Affymetrix 660 K are summarized in Table On BTA 27, the 114 sequenced Holstein animals have genotype values for 529,674 SNPs. The Illumina 777 K chip contains 10,219 of them, among which 664 are included in the 50 K chip, and 119 of these 664 SNPs are included in the 6 K chip, as summarized in Table We use 5-fold cross validation to empirically examine our piecemeal imputation method, also to construct (a.k.a. train) the staircase pseudo arrays to impute the genotyped animals to their whole genome. The cross validation results also suggest the possible levels of improvement compared with the one-step imputation.aac1 and aacπ, respectively), and the improvement of piecemeal over one-step is shown in the fifth column. For the null hypothesis that the usual one-step accuracies and the two-step piecemeal imputation accuracies have equal mean accuracies, we conducted statistical significance testing. Using Beagle as the base program, the p-values for the three 5-fold cross validation experiments are 0.0215, 0.0005 and 0.0004, respectively, indicating that the improvements by the two-step piecemeal imputation are statistically significant; using FImpute, the corresponding p-values are 0.64, 0.49 and 0.61, suggesting statistically insignificant improvements. From 6 to 50 K, 5 to 100 marker clusters, in increments of 5, were examined and the best piecemeal imputation results are included in the table, while in Fig. Table % net accuracy improvement against Beagle, in all three cases  and a 0.5– 1.0 % net accuracy improvement against FImpute .Analogous results on the Holstein datasets of 114 animals are shown in Table Independent testing is to examine the quality of the selected markers and the defined pieces learned from the training step. The study samples used in the testing are not involved in the training step. The piecemeal imputation accuracies are again compared to the corresponding one-step imputation accuracies, respectively.aac1 and aacπ), respectively. The improvement of the piecemeal over the one-step is shown in the tenth column. Note that for each imputation setting, the selected markers and the defined pieces are taken from the respective cross validation experiment. One exception is that there are 8 markers in the Affymetrix 660 K chip for which the two alleles (i.e. nucleotides) do not agree with the alternating alleles identified through genome sequencing; these 8 markers were excluded and one target marker cluster was discarded in the testing.Columns 8–10 of Table % lower than the 5-fold cross validation.Analogous independent results on the 8 genotyped Holstein animals are shown in Table acc2; the four-step piecemeal imputation accuracy is still denoted as accπ. Similar experiments were done on the 8 Holstein animals on BTA 27 genotyped using the 777 K chip.With the selected markers and their associated target marker clusters from the training step, we experimented with the usual two-step imputation from 6 K to 50 K to 660 K on the 367 genotyped Simmental animals, and the four-step piecemeal imputation from 6 to 660 K. The four-step piecemeal imputation is a result of replacing each usual one-step imputation by a potentially promising two-step piecemeal imputation. The usual two-step imputation accuracy is denoted as acc3; the five-step piecemeal imputation accuracy is denoted as accπ. Note that since we do not have a 660 K to Sequence training step to select markers , the last step in the five-step piecemeal imputation is a direct one-step imputation.For the 23 genotyped and sequenced Simmental animals, we experimented with the usual two-step imputation from 50 to 660 K to Sequence and the four-step piecemeal imputation from 50 K to Sequence, and the usual three-step imputation from 6 K to 50 K to 660 K to Sequence and the five-step piecemeal imputation from 6 K to Sequence. Here “Sequence” refers to all the 529,674 SNPs on BTA 14. The usual three-step imputation accuracy is denoted as All these usual two/three-step imputation accuracies and the corresponding four/five-step piecemeal imputation accuracies are summarized in Table sequential forward selection, which did not result in any significant improvement. We thus proposed an alternative to partition the higher density SNP set into multiple pieces, which are learned through the add-one two-step imputation experiments. Each piece is then imputed by the corresponding add-one two-step imputation experiment. This procedure laid the foundation for our two-step piecemeal imputation strategy. Nevertheless, our partition scheme is not necessarily optimal, as we adopted the k-means only because it outperformed other clustering methods slightly. In addition, we also experimented with the linkage-disequilibrium (LD) blocks produced by Haploview [Several recent studies in cattle have shown that two-step imputation can be more accurate than the classic one-step imputation –31. Alsoaploview  for findFrom our 5-fold cross validation results, it seems as though the number of marker clusters does not affect the final piecemeal imputation accuracy much. For example, for genotype imputation from 6 to 50 K on the Simmental dataset, the piecemeal imputation accuracies of all the 20 different clustering results are plotted in Fig. k=15, the first five of the 15 marker clusters are plotted in Fig. x-axis represents the physical locus. It is interesting to see that the markers of a cluster are not necessarily close to each other, though they have very similar imputation potentials. The LD between pairs of these markers, by Haploview, are insignificant.We also look into the content of a marker cluster. For example, when mi is used in the second step, of the two-step piecemeal imputation, to impute the other untyped markers of U−{mi}. Comparing the add-one two-step imputation result to the usual one-step imputation, we have seen subtle changes at many untyped markers of U for different selected markers. Indeed, some of them exhibit a gain in accuracy whereas some have a loss in accuracy and yet others are unaffected. This has led us to use the overall gain in accuracy to measure the imputation potential of a selected marker.The imputed genotype for the study samples at a selected marker By setting up a feature vector for a candidate marker to keep a record of the accuracy gains and losses at each untyped marker, we observed from our preliminary two-step imputation experiments (results not shown) that the candidate markers fall into three categories when used for creation of the pseudo-array: 1) those that yield an accuracy gain over the usual one-step imputation; 2) those that yield a net zero gain; 3) those that yield an accuracy loss from the usual one-step imputation. Through clustering these feature vectors, the impact of selecting different markers from a cluster is expected to be reduced to the minimum, as evidenced by our preliminary experiments .For all the markers of a marker cluster, those other untyped markers that can be similarly well imputed in the add-one two-step imputation are the target markers associated with the marker cluster. We have looked into the content of such a target marker cluster. Similar to a marker cluster, it is interesting to see that the markers of a target cluster are not necessarily physically close to each other, nor are the LD between pairs of these markers by Haploview significant.It is also interesting to observe that some target marker clusters are overlapping. Note that target clusters are formed after the marker clusters are determined, that is, in terms of the feature vectors, the marker clusters are formed using the whole vectors, but the target marker clusters are formed by using only the vector entries corresponding to the makers in a marker cluster. Therefore, such a phenomenon of an untyped marker being imputed with high accuracies by several selected markers can be explained.k-means, with results not better than k-means. Thus we go with k-means in the final piecemeal imputation framework.The main reason for marker clustering is to avoid selecting redundant markers to form the intermediate pseudo array, here redundant means the similar potential in imputing the genotype for other SNPs. We had experimented with Haploview to construct the LD blocks for this purpose, which did not result in any conclusive accuracy increase (detailed results not shown). Other popular feature selection methods in machine learning, such as SFS and SBS, were also tested. Based on the feature vectors, we tried clustering methods other than bi-clustering task, and it would be worthwhile to try some good bi-clustering algorithms. Coming back to the LD-based marker selection, though multiple experiments with different thresholds in Haploview did not give good results, we realize that such an approach avoids the add-one two-step imputation experiments in the training phase, and it can be substantially faster. This suggests the need for better LD block estimation/prediction by SNP genotype values.As discussed in the last paragraph, forming the marker clusters and the associated target marker clusters is more like a In our current empirical experiments, we used the population-based option in our base programs. The underlying assumption for such an option is that individuals are unrelated. On the other hand, related animals can certainly bias towards the correct genotype. Therefore, if one would be able to define a degree of relatedness between two individuals based on their SNP genotype, then using only closely related sequenced animals to a study animal as references may potentially lead to more accurate genotype imputation.Animals from different breeds are deemed more distantly related than the same breed animals. We therefore separated the datasets by breeds. In fact, earlier research suggests cattle whole genome SNP genotype imputation should be done breed by breed , which iThe running time of the two-step piecemeal imputation depends on the number of study animals, the number of reference animals, and the higher SNP density. Our experiments were mostly done using the high-performance computing facilities, and we were able to run them in parallel. The same as any other machine learning tasks, the most time-consuming stage in the two-step piece imputation is the training phase, when the add-one two-step experiments are conducted to select the good potential markers. The independent testing, and the real imputation tasks, can be finished much quicker. It is important to point out that in all our experiments reported here, running time was never an issue as it was in hours per run, but the major challenge is the need for a huge disk storage (more than 84 TB) when we were performing whole-genome SNP genotype imputation; and again this was happening in the training phase. We used more than 3TB for storing all the intermediate data, such as the feature vectors, for the two datasets used in this paper. The imputed SNP genotype values are expected to be useful in the downstream data analysis, such as GWAS, and thus the increased computation burden in the piecemeal imputation framework becomes worthy.T, and the reference samples are genotyped on the 50 K SNP set T∪U. The goal is to impute the genotype values on U for the study samples. The top two lines in Fig. T∪U and T, respectively, using their physical loci on the first half of chromosome 14 (BTA 14).A flow chart of the two-step imputation process is depicted in Fig. U, by simply running on the dataset acc1. In our simulation experiments, the genotype of the study samples on the SNPs of U are masked; the imputed genotype is then compared against the true genotype for calculating the imputation accuracy.We first present the training process. Let Beagle be our base imputation algorithm. Denote the set of study samples as U, denoted as M, and append them to set T to create an intermediate pseudo array, such that the subsequent two-step imputation from T to T∪M and then to T∪U yields a higher imputation accuracy. In this step, we will evaluate the potential for each SNP of U in the add-one two-step imputation.Our goal of training is to select a relatively small number of SNPs from mi∈U be a candidate marker , and tentatively set the intermediate pseudo array to contain T and mi only, that is T∪{mi}. Applying the base imputation algorithm (Beagle in our case), we do the add-one two-step imputation from T to T∪{mi} and then from T∪{mi} to T∪U. At the end of the two-step process, we calculate the imputation accuracy for each marker mj of U across all study samples, denoted as aij. Let aii denote the one-step imputation accuracy from T to T∪{mi}. The vector vi=〈ai1,ai2,…,ai|U|〉, is the feature vector for marker mi.Let k-means clustering algorithm to cluster the markers of U represented by their feature vectors, where k is the pre-set number of clusters which can be empirically determined, for example, by the Davies-Bouldin index [C1,C2,…,Ck denote the k clusters of SNPs of U by k-means.Intuitively, two markers of similar feature vectors have about the same performance in the two-step imputation, in terms of imputing the other untyped markers; and thus it is sufficient to include only one of them. Therefore, we apply the in index . In our Ci, if marker mj∈U is consistently imputed well with average imputation accuracy higher than or equal to the one-step imputation accuracy acc1, then mj is a target marker for the cluster Ci. The set of all target markers for cluster Ci form the target marker cluster TCi for the cluster Ci.For each cluster TCi is empty, then no marker of Ci would be selected to form the pseudo array; otherwise, define the contribution of a marker mj of Ci as the add-one two-step imputation accuracy from T∪{mj} to T∪U, with the imputation accuracy calculated over only markers of TCi. The top contribution marker of Ci, denoted as M, and it is for imputing the genotype of SNPs of TCi only. We call this target marker cluster TCi one piece of the imputation.If the target marker cluster We have two sets of sequenced animals  to be used as references. There are also much larger numbers of animals that are genotyped by various Illumina and Affymetrix gene chips, which are the study samples. The project goal is to impute the study animals to their whole sequences. When running the base programs in our experiments, we followed the default settings of the population-based genotype imputation for both Beagle and FImpute. For Beagle, we set the -Xmx parameter to 4GB for memory management without the low memory option for better performance.Recall that we have four levels of SNP density, 6 K, 50 K, HD and Sequence, where HD can be either the Illumina 777 K or the Affymetrix 660K. For each genotype imputation experiment from a lower density to a higher density, we want to select some untyped SNPs to form the pseudo array and partition the other untyped markers into pieces accordingly. We do this training via a 5-fold cross validation. The quality of these selected markers and their associated pieces is examined via independent testing.U. The five-fold cross validation scheme rotates each one of the five folds as the study fold.We use an example genotype imputation experiment for the Simmental group from 6 K to 50 K to explain the procedure. First, we derive the 50 K genotype for all sequenced Simmental animals from their sequence data. Next, these animals are randomly partitioned into five folds of approximately equal size, with the consideration of their country of origins. Four folds of the animals are used as the references, acc1; we also do the add-one two-step imputation to obtain the feature vector for each untyped marker of U. The average of the five feature vectors from the five-fold cross validation defines the final feature vector for each untyped marker. With these vectors, the k-means algorithm is run to cluster the untyped markers into C1,C2,…,Ck (which form a partition of U). Subsequently, we determine the target marker cluster TCi for each cluster Ci , and select the markers U: when an untyped marker does not belong to any target marker cluster, it is imputed in the one-step, otherwise, it is imputed in multiple pieces through a majority vote. The imputed genotype values are compared against the ground truth, which were masked before the imputation, to calculate the piecemeal imputation accuracy as the percentage of correctly imputed genotype. The final piecemeal imputation accuracy from the five-fold cross validation is the average over all five folds, and is denoted as accπ.On the dataset In these experiments, the sequenced animals from the Canadian Cattle Genome Project and the 1000 Bull Genomes Project are used as references. The study samples are the animals that are genotyped by various Illumina and Affymetrix gene chips.TC1,TC2,…, TCk}, respectively. Using these selected markers and their defined pieces, our piecemeal imputation imputes the genotype for the study samples for the selected markers independent testing piecemeal imputation accuracyaccπ as the percentage of correctly imputed genotype.We use the example genotype imputation experiment for the Simmentals from 6 to 50 K to explain the procedure. First, we derive the 50 K genotype for all sequenced Simmental animals from their sequence data; we also derive the 6 K genotype for the genotyped Simmental animals from their genotype data (they are genotyped at density 50 K or higher). Second, from the above 5-fold cross validation process, we have identified a set of markers In this study, we presented a novel two-step SNP genotype imputation strategy called piecemeal genotype imputation, which essentially inserts a pseudo intermediate density pseudo array in between the lower density chip and the target higher density chip. Using the two-step piecemeal imputation, we showed how a stair-case of intermediate SNP arrays can be built for the whole genome SNP genotype imputation. We applied this method to chromosomes 14 and 27 of cattle SNPs identified by whole genome sequencing, by carrying out multiple experiments using various density levels of bovine SNP chips, up to the sequence level. The results show preliminary success of our multi-step piecemeal imputation with an accuracy improvement compared to the classic one-step imputation by the state-of-the-art methods Beagle and FImpute.


Arabidopsis thaliana circadian clock can be attributed to adaptation to varying environments. To define a basis for such variation, we examined clock speed in a reporter-modified Bay-0 x Shakdara recombinant inbred line and localized heritable variation. Extensive variation led us to identify EARLY FLOWERING3 (ELF3) as a major quantitative trait locus (QTL). The causal nucleotide polymorphism caused a short-period phenotype under light and severely dampened rhythm generation in darkness, and entrainment alterations resulted. We found that ELF3-Sha protein failed to properly localize to the nucleus, and its ability to accumulate in darkness was compromised. Evidence was provided that the ELF3-Sha allele originated in Central Asia. Collectively, we showed that ELF3 protein plays a vital role in defining its light-repressor action in the circadian clock and that its functional abilities are largely dependent on its cellular localization.Natural selection of variants within the DOI:http://dx.doi.org/10.7554/eLife.02206.001 Life on Earth tends to follow a daily rhythm: some animals are awake during the day and asleep at night, whilst others are more active at night, or during the twilight around dawn and dusk. For many living things, these cycles of activity are driven by an internal body clock that helps the organism to adapt to the daily cycle of light and dark—and similar internal clocks also exist in plants.These internal clocks define daily—or circadian—cycles whereby multiple genes are switched ‘on’ or ‘off’ at different time points in every 24-hr period. And, because light and ambient temperatures also vary with time of the day, many organisms use these external signals as cues to reset their own internal clocks. Moreover, the hours of daylight and temperature vary around the world, and also with the seasons, so plants and animals must be able to change how these external signals influence their internal clocks so that they stay in tune with the day/night cycle. However, it is not clear how they do this.Arabidopsis thaliana from different environments: one from Germany, and the other from Tajikistan in Central Asia. These offspring were also genetically engineered so that an enzyme that could give off light was produced under the control of the internal clock. Anwer et al. found that the plants continued to glow and fade with an almost daily rhythm even after external cues, such as changes in temperature or light, had been removed.To explore this question, Anwer et al. grew plants that were from a cross between two types of the model plant ELF3 from the Tajikistan parent had internal clocks that ran faster when the plant was under the light. These plants also gradually stopped glowing as brightly as the German parent when they were kept in the dark, suggesting that their internal clocks were ‘ticking more softly’. It was already known that the ELF3 gene affected the circadian clock in plants, and Anwer et al. thus concluded that the plants with Tajikistan version of this gene, called ELF3-Sha, were also less able to reset their internal clocks to synchronize in response to external cues.Different offspring plants consistently glowed and faded with different rhythms such that some had, for example, a 21-hr day and others a 28-hr day. These differences were caused by many genes that differed from the original German and Tajikistan parent plants, and Anwer et al. ‘mapped’ one of these genetic differences to a single gene. Offspring that inherited a version of a gene called Anwer et al. also showed that the normal ELF3 protein is more likely to be found in the nucleus of a plant cell than the ELF3-Sha version, which might suggest that this protein is involved in switching genes off. Further research is now needed to uncover exactly how the ELF3 protein does this to keep the plant's internal clock ‘ticking’ correctly.DOI:http://dx.doi.org/10.7554/eLife.02206.002 The rotation of the earth leads to environmental changes in ambient light and temperature that define a 24-hr cycle. During these diurnal cycles, light is correlated with warmth, while darkness is correlated with coolness. Thus, to ensure fitness in response to such regular environmental oscillations, many organisms have evolved an internal timing mechanism to predict these cyclic environmental cues, and this is termed the circadian clock. This internal clock synchronizes major developmental and physiological processes. For example, a considerable proportion of metabolism is under clock control , and seaTOC1 by directly binding to its promoter, resulting in the evening accumulation of TOC1, which, in turn, represses CCA1 and LHY expression  genes coupled to the clock-controlled promoter was successfully used to map QTLs for circadian parameters  that accelerates circadian oscillations in a light-dependent manner and a dampened oscillator in darkness. We determined that the periodicity phenotype of ELF3-Sha results from a single encoded amino-acid change A362V, which is an allelic variant that is largely confined to a latitudinal stripe in Central Asia. Furthermore, we identified that the circadian abnormalities in ELF3-Sha are associated to cellular localization defects of ELF3 protein that results in an inability to properly function under light and in darkness. Thus, by characterizing ELF3-Sha, we clarified the molecular mode-of-action of ELF3 in the circadian clock.In this study, we characterized a natural allele of CCR2 promoter, also termed GRP7 . These lines were synchronized to respective photic and thermal entrainment, and free-running periodicity was assessed under identical conditions of constant light and constant temperature. After applying both entrainment protocols, extensive variation in periodicity was observed , the averaged periodicity differences seen in the RILs were similar in magnitude as the periodicity differences seen in the parental lines.A total of 71 RILs derived from the genetic cross of Bayreuth-0 and Shakdara (BxS) were modified to harbor the med GRP7 , fused tobserved ; however as RILs ; Table 1 as RILs ; Table 1 as RILs . The aveCCR2 free-running periodicity followed a normal distribution regardless of the entrainment zeitgeber, where lower and higher extreme periodicities were measured in thermal and photic entrainment, respectively  and thermal entrainment (0.73), respectively . Our effCCR2::LUC in NIL-S was different from the Bay parental line, so that, in NIL-S, CCR2::LUC rhythmicity lost robustness, whereas CCR2::LUC in Bay-0 displayed rhythms for 7 days , and near isogenic line (NIL). Three HIFs were made , each harboring either the Bay allele (HIF-Bay-0) or Sha allele (HIF-Sha) at the Chr2 locus. An analysis of the free-running period of these lines revealed that HIF-Sha always displayed a shorter period as compared to HIF-Bay-0. This period-shortening effect was independent of the preceding LD or TMP entrainment condition . The comr 7 days . Interesom NIL-S .10.7554/ELF3 (AT2G25930) was the only gene in this region with a previously reported function in the Arabidopsis circadian clock. Therefore, we compared the sequence of ELF3-Bay and ELF3-Sha and identified two nonsynonymous changes in ELF3-Sha: an encoded alanine-to-valine transition at position 362 (A362V), and in the C-terminal encoded glutamine stretch, Sha had 8 more than Bay , and constant blue (BB) light. Similar to the near lack of flowering-time phenotype under long days, we did not observe a substantial difference in the hypocotyl length of HIFs, NIL-S, and Bay-0 under RR or BB. Under short days, however, HIF 89-S displayed a marginally elongated hypocotyl length, compared to HIF 539-B, but this response was four times less than that seen in the null elf3  underlying the Chr2 periodicity QTL.Although fine-scale mapping provided strong evidence favoring CR2::LUC . Based oELF3-Sha, we investigated the role of an encoded A362V residue change compared to the difference in the number of encoded glutamines in ELF3-Bay and ELF3-Sha. In this experiment, we separately cloned the promoter and coding regions of ELF3-Bay (SpBc) and ELF3-Sha (SpSc) and then induced encoded A362V and V362A changes in ELF3-Bay (SpBa2v) and ELF3-Sha (SpSv2a) coding regions, respectively. These constructs were then recombined in all possible promoter::coding combinations , compared to ELF3-Bay , a consistent pattern of phase oscillations was detected, supporting the existence of a functional oscillator in both ELF3-Bay and ELF3-A362V that display differences in their respective resetting behavior  is a measure of such precision . Therefot tested . Interest tested , once threstored . Notablybehavior . This coELF3-Sha is a functional allele could be advantageous in determining if ELF3 is involved in parametric entrainment. To test this, we measured the free-running period of Bay and NIL-S under an array of fluences, including RR, BB, and ‘white’ (RB) light. Under diverse intensities and qualities of light, we found that NIL-S displayed a shorter period than Bay. Moreover, both Bay and NIL-S followed Aschoff's rule, displaying shortening of periodicity with increase in fluence rate  after initially entraining under T24 cycles (12 hr light and 12 hr dark). As previously reported for null elf3  maintain the same rhythms when transferred to shorter cycles (T12). In this way, plants resist the resetting of the oscillator by sporadic environmental changes during a diurnal cycle. However, if the short cycles (T12) persist, a robust clock should reset to the new environment. Conversely, when transferred from T24 to T12, mutants with a dysfunctional clock promptly follow the shorter cycles . To testull elf3 , we deteal clock . As expe2 cycles . Interesl 5 days . Since belf3 loss-of-function alleles display arrhythmia , we found that ELF3-Sha accumulation was considerably lower than ELF3-Bay  of 251 accessions from the ‘1001 Genome-Project’  and performed several population-genetics analyses. The sequence comparison revealed the presence of 59 polymorphic sites segregating at the ELF3 locus, including encoded A362V. Out of these 59 polymorphic sites, 16 were synonymous and 43 were nonsynonymous. The A362V was detected in 15 accessions, and in a phylogenetic analysis, all of these grouped together in a single clade, suggesting a common origin  to a QTN level of understanding. In the quantitative analysis of circadian periodicity of a modified BxS population, we identified a QTL and further validated it in HIFs and a NIL  by genetic mapping is a well-established procedure. However, to understand the molecular basis of a locus by characterizing QTL to a quantitative trait nucleotide (QTN) level is still an arduous task . Here, wnd a NIL . Our percillator . In thost notion . Here, wd length .ELF3-Sha could be associated with reduced ELF3 functionality  in temperature-entrained elf3-1 seedlings, emphasizing that ELF3 is an integral part of the oscillator as well as acting as a zeitnehmer  directly binds to the PRR9 promoter to repress transcription, support the repressive action of ELF3 in the core oscillator  used were derived from the Bayreuth-0 (Bay-0) by Shakdara (Sha) RIL collection (termed here BxS) . Multipl dipping . T2 prog dipping . To confelf3-1 and elf3-4  was induced using the QuikChange method . The multi-gateway technology  was used to generate the YFP-tagged lines with different promoter-coding combinations. Initially, the promoter and coding regions of ELF3 were separately amplified from Bay-0 and Sha genomic DNA and recombined into pDONR4-P1R or pDONR201 donor vectors, respectively. The respective nucleotide change encoding either the A362V or V362A amino acid was then induced using the QuikChange method (Stratagene). The YFP tag was separately cloned into pDONRp2r-p3 donor vector. These three donor vectors were then recombined in different combinations into pPZP211 destination vector. The final vector was then transformed into Agrobacterium tumerfaciens (strain ABI). For all transformations, the improved floral-dip method was used . For flowering time measurement, plants were grown on soil containing a 3:1 mixture of substrate and vermiculite in a temperature-controlled greenhouse environment with 16L:8D long-day and 8L:16D short-day cycle. The flowering time was scored at the time of bolting (1 cm above rosette leaves) as the total number of days to bolt  with constant temperature of 22°C (LD) or under 12 hr at 16°C: 12 hr of 22°C temperature cycles with constant light (TMP) (∼100 μE light). The bioluminescence measurement and data analysis are as described , 2008. F to bolt .CCR2 rhythmic periodicity after light and temperature entrainment, respectively. Period mean was subsequently used for QTL mapping, which was performed with MapQTL 5.0 . Mapping settings used were as in In total, 60 and 65 BxS lines were assayed for ELF3 Bay-YFP and ELF3 Sha-YFP lines, one slide from each line was prepared, and both slides were put together in the microscope. The plants from each slide were then scanned, one after another, within 30 min and with the same microscope settings. The microscope settings for the For all microscopic work, the Zeiss LSM700 confocal microscope from Carl Zeiss was used, as in ELF3 CDS sequences of accessions were downloaded from the 1001 Genome-Project (http://www.1001genomes.org/). The geographical coordinates for the accessions were obtained from the SALK database . These coordinates were used to map the geographical position of the accessions using Google maps (maps.google.com) (The gle.com) . For seqgle.com) . The phygle.com) . review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent , a Reviewing editor, and 2 reviewers, one of whom, Justin Borevitz, has agreed to reveal his identity.Thank you for sending your work entitled “An The Reviewing editor and the reviewers discussed their comments before we reached this decision, and the Reviewing editor has assembled the following comments to help you prepare a revised submission.This elegant manuscript provides an excellent example of how a natural genetic variant cannot only be reduced to a single nucleotide/amino acid, but that considerable new functional knowledge can emanate from such an approach.You identify a new variant at the ELF3 locus, previously implicated by others in natural genetic variation of shade avoidance responses and circadian clock behavior. However, the precise variant was not identified, and as main contributor a polyglutamine repeat stretch was suggested in one of the studies. The current work shows, however, that the polygulatmine stretch makes only a very minor contribution to ELF3 functional variation.The functional studies in this manuscript are an important contribution to the plant circadian field as null ELF3 alleles are arrhythmic, precluding detailed analysis of their precise role in the circadian network. Using the natural hypomorphic allele, you now show that ELF3 is indeed a core clock gene that is required for both proper circadian rhythms in the light, and also for sustained rhythms in darkness. Furthermore, you show that ELF3 is required for appropriate entrainment to light/dark cycles, indicating a dual role for this protein in the core clock mechanism as well as in the input pathway that connects light signals to the circadian clock. Finally, you provide evidence that this allele affects the subcellular localization of the protein, interfering with the formation of nuclear speckles and resulting in increased cytoplasmic accumulation of this protein, presumably through alterations in the ability of this ELF3 variant to interact with other clock components that localize to the nucleus such as GI.1) The evolution section is the weakest of the paper and should either be largely removed. Alternatively, this should be carried out much more rigorously. For example, one would want to know what the size of the selective sweep is, from natural recombination events flanking the 15 or more Shah class accessions. This would need to be compared to selection signatures at background loci (clock genes for example) in these same geographically isolated strains.2) In the Discussion, there is no need to speculate about a second site modifier as other QTL could buffer the period effect. You could simply conclude that the 362V (ELF3-Sha), so elegantly proven, “has a regional distribution and this warrants further investigation of its potentially local adaptive effects.”Other issues to address:3) The studies by Jimenez-Gomez and Undurraga need to be introduced in the Introduction.A. lyrata and C. rubella sequences of ELF3, and how can this information be used to ascertain selection at the ELF3 locus? Which allele is ancestral and which is derived? It may be that Bay is derived and now at high frequency, or that Shah is part of an Asian selective sweep.4) Strong purifying selection (if this section is maintained): Why was only the coding sequencing retrieved from the 1001 genomes, why not +-15kb around ELF3? How similar are the 15 accessions in a STRUCTURE analysis? What are the 5) 6) What is the Kondara seq? It looks like a near neighbor of the Shah class.7) The average altitude is meaningless. Many alleles from this region would be elevated as it is high country. Also, the histogram is much less informative than a box-and-whiskers plot.8) 9) “Our data reveals that the ELF3 locus has been a target of natural selection, which is maintaining its current allelic state by removing recently arisen mutations.” Displacing signature of selective sweep? How common a signature? How many other SNPs show similar latitudinal band?10) Table: “F shows the variation explained by each factor relative to the error variation.” F is not the variation explained, but the mean SS/error SS; it would be good to show % with total including error adding to 100.11) “TRANS(Genotype)” What is trans? No need to separate this.12) LOD score of 2.4 is low, set by permutations of 71 RILs? Genome wide p < 0.01? The largest QTL is ELF3 and well proven but are other QTL real and truly different between entrainment environments?13) 14)  1) The evolution section is the weakest of the paper and should either be largely removed. Alternatively, this should be carried out much more rigorously. For example, one would want to know what the size of the selective sweep is, from natural recombination events flanking the 15 or more Shah class accessions. This would need to be compared to selection signatures at background loci (clock genes for example) in these same geographically isolated strains.As evolution is complex and is often hard to prove, we understand the reviewers concern about the evolution section of the manuscript and agree that this section had been the weakest in the paper. Therefore, in line with the reviewers’ suggestion, we have removed much of this data. Specifically, the entire analyses of selection [Tajima’s D , Fu and 2) In the Discussion, there is no need to speculate about a second site modifier as other QTL could buffer the period effect. You could simply conclude that the 362V (ELF3-Sha), so elegantly proven, “has a regional distribution and this warrants further investigation of its potentially local adaptive effects.”The text about the second site modifier was deleted and the Discussion was accordingly modified.Other issues to address:3) The studies by Jimenez-Gomez and Undurraga need to be introduced in the Introduction already.Both these studies are now cited in the Introduction section.4) Strong purifying selection (if this section is maintained): Why was only the coding sequencing retrieved from the 1001 genomes, why not +-15kb around ELF3? How similar are the 15 accessions in a STRUCTURE analysis? What are the A. lyrata and C. rubella sequences of ELF3, and how can this information be used to ascertain selection at the ELF3 locus? Which allele is ancestral and which is derived? It may be that Bay is derived and now at high frequency, or that Shah is part of an Asian selective sweep.The selection section was removed, as requested above. Please see our response to comment 1.5)looks like many more the 59 changes; how many are replacement vs synonymous?Our careful re-evaluation of the sequence data confirmed that there are indeed 59 polymorphic sites, out of which 43 are replacements and 16 are synonymous. We have also added this text in the Results: “Out of these 59 polymorphic sites, 16 were synonymous and 43 were nonsynonymous”.The details are as follows: Segregating sites: 59; Total number of Synonymous changes: 16Position in CDS: 249 423 519 522 717 813 873 918 1104 1269 1476 1536 1773 1809 1977 2034Total number of Replacement changes: 431085 (ELF3-Sha) 1204 1288 1512 1551 1558 1628 1691 1700 1732 1772 1874 1895 1907 1982Position in CDS: 21 122 199 202 211 256 302 343 416 461 470 510 515 574 580 664 700 701 715 736 805 849 865 883 922 964 1033 1034 ELF3 as oppose to the amino-acid sequence used in the previous figure (Please see Please note that we have updated the figure without noteworthy changes in the Results interpretation. The updated phylogenetic tree is now based on the nucleotide sequence of 6) What is the Kondara seq? It looks like a near neighbor of the Shah class.ELF3 Sequence is same as that of Sha, except for a single base-pair deletion in Kondara at position 1628. In the updated phylogenetic analysis  The average altitude is meaningless. Many alleles from this region would be elevated as it is high country. Also, the histogram is much less informative than a box-and-whiskers plot.We agree with the reviewers on this point. All data related to average altitude is removed.8)doesn't show anything significant about the site in the protein as the Tajima's D is largely negative genome wide.9) “Our data reveals that the ELF3 locus has been a target of natural selection, which is maintaining its current allelic state by removing recently arisen mutations.” Displacing signature of selective sweep? How common a signature? How many other SNPs show similar latitudinal band?All data related to selection is removed. Please see our response to comment 1.10) Table: “F shows the variation explained by each factor relative to the error variation.” F is not the variation explained, but the mean SS/error SS; it would be good to show % with total including error adding to 100..The definition of “F” was modified as suggested. We could not fully understand what the reviewers meant by “it would be good to show % with total including error adding to 100.” Because of this, we have not yet included this information. If the editor requires this change, we believe we can easily provide this information if the reviewer can more fully explain what is requested11) “TRANS(Genotype)” What is trans? No need to separate this.Trans stands for ‘Transformants’ within each genotype (RIL). This information is also deleted as suggested .12) LOD score of 2.4 is low, set by permutations of 71 RILs? Genome wide p < 0.01? The largest QTL is ELF3 and well proven but are other QTL real and truly different between entrainment environments?The LOD threshold score was calculated by thrice performing the permutation test using 1% genome-wide significance in MapQTL5.0. After each permutation test, different LOD values along with their significance level were obtained by MapQTL5.0. Only the LOD value corresponding to a p<0.01 was selected in each permutation test. The three LOD values obtained by each permutation test were then averaged to gain an overall genome-wide LOD of 2.4. As the LOD scores obtained by permutation test are based on the original phenotypic data (here circadian periodicity), it can vary depending upon the data quality and trait under study. As such, the LOD2.4 obtained in this study is not significantly low.Similar LOD threshold (2.55) has been reported by Darrah et al. Plant Phys., for circadian phase using 65 RILs. In many other studies, similar LOD thresholds have also been reported for other traits such as flowering time  and seed size , as others' reported examples. Further having the LOD threshold at 2.4 does not affect the result of the analysis as all of the QTL detected scored much higher than 2.4. The lowest LOD score of 3.21 was obtained for Chr1 QTL , which iAs the Chr2 QTL displayed the largest effect, we pursued its validation. This was taken to the level of QTL identification. The molecular identity of the other QTLs can be confirmed in subsequent studies.13)window size is too small, should include +-30kb, and genomic frequency should be reported.This figure is now removed. Please see our response to comment 1.14)) color red or blue parental genotype arrows, b) should be a scatter plot TMP vs LD.a) The parental genotypes arrows are now colored in Red, b) we have added a scatter plot for TMP vs LD periodicities . We have


Growth traits represent a main goal in aquaculture breeding programs and may be related to adaptive variation in wild fisheries. Integrating quantitative trait loci (QTL) mapping and next generation sequencing can greatly help to identify variation in candidate genes, which can result in marker-assisted selection and better genetic structure information. Turbot is a commercially important flatfish in Europe and China, with available genomic information on QTLs and genome mapping. Muscle and liver RNA-seq from 18 individuals was carried out to obtain gene sequences and markers functionally related to growth, resulting in a total of 20,447 genes and 85,344 single nucleotide polymorphisms (SNPs). Many growth-related genes and SNPs were identified and placed in the turbot genome and genetic map to explore their co-localization with growth-QTL markers. Forty-five SNPs on growth-related genes were selected based on QTL co-localization and relevant function for growth traits. Forty-three SNPs were technically feasible and validated in a wild Atlantic population, where 91% were polymorphic. The integration of functional and structural genomic resources in turbot provides a practical approach for QTL mining in this species. Validated SNPs represent a useful set of growth-related gene markers for future association, functional and population studies in this flatfish species. Genomic studies offer the possibility of unraveling a huge amount of genetic polymorphisms useful for searching selection signatures in populations and for association studies with productive traits, such as growth or disease resistance, which could eventually be applied in marker-assisted selection (MAS) programs. The application of MAS has enabled increasing productivity in livestock previously subjected to traditional phenotype-based selection . A more ., 2000) . Moreove., 2000) ,6.These strategies require information on the target genes and also the discovery of suitable markers, a difficult task in non-model species with limited genomic resources. Methodologies using genomic information from related species allowed us to identify SNP markers associated with growth and reproduction in fish ,8. HowevGrowth is one of the most important traits in animal breeding and a target in most genetic breeding programs, also in aquaculture species . While mTurbot is a marine flatfish of commercial importance in Europe and China ,25, whosThe main aim of this study was the detection and validation of SNP markers in turbot located in candidate genes involved in growth and associated with growth-related QTLs in this species. We used Illumina RNA-seq to obtain muscle and liver transcriptomes from control and fasted specimens enriched in growth regulation pathways, and used this information to identify growth-related genes and associated SNP markers. Integrating (positioning) our new transcriptome data with previous genetic map markers in the draft turbot genome sequence enabled us to locate the candidate transcripts and compare their positions with growth QTL markers reported in the turbot genetic map. SNPs located in 45 genes were selected based on their co-localization with QTLs and functional relevance for growth. These SNPs were validated in a turbot population and will be useful to perform association analysis with phenotypes for growth traits both in farmed broodstock and natural populations for fisheries management.Takifugu rubripes swim bladder  and gill , which together accounted for 19,388 different genes [de novo assemblers usually produce a higher number of genes with lower mean length and N50 . For example, de novo assembly of the ray-finned fish Coilia nasus liver transcriptome resulted in 65,129 genes with a mean size of 607 bp and N50 of 813 bp [A total of 19,326,140 muscle and 17,466,901 liver reads were obtained from Illumina sequencing, which enriched the transcriptomic background for turbot, useful for functional and comparative genomics in flatfish and teleosts. After trimming, 89.6% of the reads remained and were aligned against the draft turbot genome . Muscle nt genes . Genomesf 813 bp . Cyprinus carpio) (1.31) [Sparus aurata) (1.37) [Ictalurus punctatus) and blue  catfish [Oncorhynchus keta) and sockeye (Oncorhynchus nerka) salmon (around 1) [C. carpio (1.05) [Muscle and liver RNA-seq reads were used for SNP screening. A total of 93,558 polymorphisms were detected and 85,344 of them were SNPs (91.2%), similar to human estimates where SNPs account for 90% of genome variation . The num) (1.31)  and gilt) 1.37) . Values  . Valuesround 1) , close to (1.05) . Still, o (1.05)  or in tho (1.05) . Besideso (1.05) .gh) has been described in liver [npy) both in liver and muscle [prl), consistent with the results in Takifugu rubripes, where prl was only found in the pituitary gland [Both muscle and liver transcriptomes were scanned for a large set of genes (~200) involved in growth regulation previously reported in fish ,22,31,32in liver , and thed muscle , we did ry gland . Hence, All growth-related transcripts found in this study were located in the turbot genome and anchored to the genetic map using common markers ,28. Sixtigf1 and igf2) and leptin receptor (lpr) [myog) associated with weight in pigs and chickens [tgfb) pathway, which regulates cell growth and differentiation, were also included, since they were associated with growth and reproduction in vertebrates [mstn1). Null mutations in this negative regulator gene of muscle development in mammals lead to 30% increased growth in mice or to a “double muscle” phenotype in cattle [Hypophthalmichthys nobilis) [Salmo salar) [pvalb1), where microsatellite variation at the 3′ UTR was associated with weight in Asian seabass  [ghr2), which has been associated with growth in cattle and chicken [Oreochromis niloticus, O. aureus and O. mossambicus) [We selected 45 out of 176 growth-related genes  based on their relevance in growth-related processes in fish and vertebrates, their co-localization with growth QTL markers in turbot and the availability of technically suitable SNPs in the transcriptome . Accordior (lpr) ,49,50, ochickens ,52 and wchickens . Memberstebrates ,55,56,57n cattle ,59. SNP nobilis)  and Atlao salar) , respectcarifer)  and grow chicken  and tilaambicus) .ghrh) was associated with growth differences in Arctic charr  [One SNP was selected for each gene among those available. Sixty percent of the 45 SNPs selected were located within the UTR (1:2 ratio in 5’ and 3’ UTR) and 31% within coding regions. Among the latter, five and nine SNPs were non-synonymous and synonymous mutations, respectively, whereas the others were located within introns of putative alternative transcript variants, as the alignment with orthologous genes from model fish suggested. These data could also be explained by the sequencing of immature transcripts, although intron retention seems to be a general phenomenon playing a role in the regulation of gene expression . Non-synalpinus) .acss3, fbxo32) and four were monomorphic in the tested population . Thus, a total of 43 SNPs were genotyped (95.6%) and 39 (86.7%) were finally polymorphic enough for diversity analysis in the Cantabrian population sample assayed . Minimum allele frequency (MAF) ranged from 0.029  to 0.500 in lepr (mean: 0.221 ± 0.004). These values are in the range of those previously described in turbot [IS values per locus and globally were not significantly different from zero at p < 0.05 and showed a mean value of −0.032 ± 0.0003 (p = 1). Significant linkage disequilibrium (LD) at p < 0.05 was detected between 37 pairs of SNPs out of the total number of pairs of loci tested in the population under study (741 G-tests), which is close to the 5% expected by chance. Seven out of these 37 significant tests involved pairs of closely linked loci (19%), whereas the other cases involved markers in different linkage groups (LG) , corresponding to two pairs of closely linked loci, tnnc2-actc1 and mstn1-igfbp2, as predicted by the mapping of genomic scaffolds  estimates ranged from 0.058 at n turbot ,68 and on turbot ,69. No d± 0.0003 , congruecaffolds . Some clcaffolds  were pri RNA-seq ; 56.9% iAlthough the number of SNPs and the true SNP ratio in RNA-seq may vary due to technical parameters, this technique allows us to obtain a much higher amount of SNPs than previous technologies. SNP calling based on 454 previous transcriptome runs in turbot only detected 866 SNPs, although the efficiency was rather good 79.3%) . In cod,9.3% . InAlthough the aim of this work was to generate an affordable panel of gene-linked SNPs co-localizing with QTL for growth association in a large amount of fish with different genetic backgrounds, the resources presented here can be further exploited at a larger genomic scale. The total number of SNPs generated in this study can be used for designing a more extensive SNP panel as a highly informative tool for studying the architecture of quantitative traits and for increasing the efficiency of selective breeding. Our aim would be the design of a large turbot SNP array based on this information and previous reports ,70, and Turbot juveniles from six unrelated families coming from a breeding strain of Atlantic origin were reared in tanks at water temperature of 18 °C at the facilities of CETGA . Animals were divided in two groups, one fed daily and the other one subjected to nutritional stress by fasting. This study was performed on muscle and liver because these organs play a key role in the regulation of somatic growth in fish, the first one also representing the main edible part of fish ,79. In o® ND-1000 spectrophotometer , respectively. Muscle and liver pooled samples were barcoded and sequenced on an Illumina HiSeq 2000 (100 bp paired-end) using standard protocols. Sequencing output quality was assessed using FastQC v0.11.2 [RNA extraction was performed using the RNeasy mini kit (Qiagen) with DNase treatment following manufacturer’s instructions. RNA quality and quantity were evaluated in a Bioanalyzer 2100  and in a NanoDrop v0.11.2 . Quality v0.11.2 . Specifi v0.11.2  was used v0.11.2 , enablin v0.11.2  that lev v0.11.2  with a m v0.11.2  was used−10) [Growth-related genes were selected based on gene ontology (GO) criteria using Blast2GO , and pre−10) . Once ob−10)  non-redu−10) [Gasterosteus aculeatus and Oryzias latipes) [We checked for co-localization of the turbot growth-related genes in the genetic map with previously described growth QTL markers ,30 also −10) . Genes wlatipes)  was usedvs. coding regions) to select SNPs with higher chances of being biologically relevant.SNP positions within the aligned reads were identified using the pileup function in SAMtools utilities v0.1.19  with a PA total of 45 SNPs located in 45 different genes were selected based on previous specified criteria . DNA was extracted from a piece of caudal fin using standard phenol-chloroform procedures . SNPs weRNA sequencing project: ncbi BioSample accessions SAMN03740585 (muscle) and SAMN03740586 (liver). Sequenom assays for growth-related gene SNPs: RNA-seq is an efficient technique to develop markers for candidate gene association studies related to targeted biological processes. A high number of SNPs were identified in the liver and muscle transcriptomes of turbot, which revealed enrichment in transcripts involved in growth regulation, and allowed for detection of genetic variation in several relevant growth-related genes of fish and vertebrate. Integrating genomic and linkage mapping resources allowed us to place the candidate genes in the turbot genetic map and to check for co-localization with growth QTLs in this species. SNP markers for 43 genes were validated in a turbot population, providing useful tools for fine mapping within QTL regions, population genomics studies, and functional and association analysis with growth phenotypes in turbot.


VRTN. This gene has been suggested to be related to the number of vertebrae, a trait correlated with NT.Dominance has been suggested as one of the genetic mechanisms explaining heterosis. However, using traditional quantitative genetic methods it is difficult to obtain accurate estimates of dominance effects. With the availability of dense SNP (Single Nucleotide Polymorphism) panels, we now have new opportunities for the detection and use of dominance at individual loci. Thus, the aim of this study was to detect additive and dominance effects on number of teats (NT), specifically to investigate the importance of dominance in a Landrace-based population of pigs. In total, 1,550 animals, genotyped for 32,911 SNPs, were used in single SNP analysis. SNPs with a significant genetic effect were tested for their mode of gene action being additive, dominant or a combination. In total, 21 SNPs were associated with NT, located in three regions with additive  and one region with dominant effects (SSC4). Estimates of additive effects ranged from 0.24 to 0.29 teats. The dominance effect of the QTL located on SSC4 was negative (−0.26 teats). The additive variance of the four QTLs together explained 7.37% of the total phenotypic variance. The dominance variance of the four QTLs together explained 1.82% of the total phenotypic variance, which corresponds to one-fourth of the variance explained by additive effects. The results suggest that dominance effects play a relevant role in the genetic architecture of NT. The QTL region on SSC7 contains the most promising candidate gene:  Dominance effects are non-additive effects due to the interaction between alleles at the same locus. In livestock and plant breeding, the main benefits of dominance effects are expected in crossbreeding, since dominance has been suggested as one of the genetic mechanisms explaining heterosis et al. The development of dense SNP (Single Nucleotide Polymorphism) panels offered new opportunities for detection and use of dominance at individual loci. However, genomic selection or genome-wide association studies (GWAS) mainly focused on additive genetic effects and ignored dominance. Recently, a number of studies investigated the importance of non-additive effects in genomic prediction et al.et al.Significant dominance effects on number of piglets born alive and litter size were identified in a GWAS In QTL mapping studies in pigs, number of teats (NT) has been one of the most extensively studied traits. NT is an important trait for breeding programs because the number of piglets in a litter is often larger than the number of functional teats of the sow due to the remarkable improvement in sow prolificacy over the last decades The aim of this study was to detect additive and dominance effects on number of teats, specifically to investigate the importance of dominance using a high-density SNP panel in a Landrace-based population of pigs.The data used for this study were obtained as part of routine data recording in a commercial breeding program. Samples collected for DNA extraction were only used for routine diagnostic purpose of the breeding program. Data recording and sample collection were conducted strictly in line with the Dutch law on the protection of animals (Gezondheids- en welzijnswet voor dieren).2 values>600). Finally, 9,016 SNPs were excluded because a genotype class had a frequency <0.02. This last step was necessary because this study focused on both additive and dominance effects and therefore observations were necessary in all three genotype classes. After these quality checks, 32,911 out of 64,232 SNPs were used for the GWAS.DNA from 1,795 animals was extracted from blood, hair follicles or ear tissue. Genotyping was performed using the Illumina 60K+SNP Porcine Beadchip BB and parent genotype = AA), the genotype of the offspring at that specific locus was set to missing. Further, if the proportion of Mendelian inconsistencies was >0.01, either a pedigree mistake or a mistake during the genotyping process was assumed and the offspring was excluded from the data set. If the proportion of Mendelian inconsistencies was >0.01 for all offspring of a given parent, the parent was excluded as well, however, this was not observed in the current data set. A total of 17 animals (offspring) were excluded based on the described procedure. A further 68 animals were excluded because their NT was not recorded. Finally, 89 animals were excluded because they were the unique observation from their herd-year-season class, leaving 1,550 genotyped and phenotyped animals for this study.In total, 71 individuals with missing genotype frequency >0.05  were excluded. In addition, animals that had at least one of their parents genotyped were checked for pedigree inconsistencies. The parental check consisted of comparing the genotypes of the offspring and their parents (one or both parents) at all loci. If a Mendelian inconsistency was detected  year (y) season (s) j of birth (j = 1 to 291); kSNP was the SNP genotype k  fitted as a fixed effect; lanimal was the random additive genetic effect which was assumed to be distributed as ∼N, which accounted for the (co) variances between animals due to genomic relationships by formation of a G matrix (genomic relationship matrix); and ijkle was the random residual effect which was assumed to be distributed as ∼N. Variance components were re-estimated in each SNP association analysis. The analyses were performed using ASReml v3.0 A single-SNP GWAS for additive and dominance effects on NT was performed using an animal model. To capture both additive and dominance contributions to the variance explained by a SNP in a single model parameter, the genotypes were fitted as a class variable with three levels. The following model was used:G matrix was used to account for genomic relationships and to reduce the risk of false-positive associations due to population stratification and was computed as described by VanRaden Z is a matrix that contains all SNP genotypes of all animals corrected for the allele frequency per SNP; n is the total number of SNPs present in Z and ip is the frequency of the allele B of SNP i. The SNP genotypes were coded as 0, 1 and 2, being 0 = AA, 1 = AB and 2 = BB. Allele frequencies of the current sample were used in the calculations to obtain Z and ip.The P-values from the GWAS was estimated using the estlambda function of the R package GenABEL Residuals were visually inspected for normality based on a QQ-plot of the residuals from model (1) without a SNP effect, using the qqnorm function in R 2) of all SNP-pairs between the two different regions was <0.70, these regions were considered independent. LD estimates were obtained using Haploview v4.2 All significant SNPs located within 5 Mb from another significant SNP were considered to belong to the same QTL region. When more than one QTL region was detected on the same chromosome, linkage disequilibrium (LD) was used to assess the dependence of these effects. If the LD  of the most significant SNP in a QTL region, The total variance explained by each QTL (P<0.01). Dominance effects were declared when the contrast between the average effect of the two homozygous genotypes (AA and BB) and the effect of the heterozygous genotype was significantly different from zero (P<0.01).To determine if the SNP had a significant additive effect, dominance effect or both, contrasts for additive and dominance effects were tested for the most significant SNP in each QTL region. Testing was performed using the option !CONTRAST in ASReml v3.0 29] in model (1). Additive effects were declared when the contrast between the effects of the two homozygous genotypes was significantly different from zero (http://www.ncbi.nlm.nih.gov/projects/mapview/map_search.cgi?).Results from the current study were compared with previously identified QTL using the alignment of genetic and physical maps in PigQTLdb The additive genetic variance for NT estimated using model (1) without a SNP effect was 0.43 and the corresponding heritability was 0.37±0.05. The estimated effects for sex showed that males presented 0.35±0.09 more teats than females. Although NT is a count variable, the residuals follow a normal distribution .An inflation factor of 1.13 was estimated, indicating that any major effects of population stratification were accounted for in the analyses. In total, 21 SNPs were associated with NT . These SOne QTL region was characterized as dominant and three as additive. Estimated effects for QTLs that were characterised as showing additive gene action ranged from 0.24 to 0.29 teats . The QTL that was characterised as showing dominant gene action showed a negative dominance effect (−0.26 teats). The summation of 10  of 6.37. This SNP showed an additive effect of 0.27 teats and a dominance effect of −0.18 teats. However, only the contrast for additive effects was significant for this SNP.The QTL region on SSC6 contained two SNPs with significant associations. This QTL region was located between 101.77 and 104.42 Mb and ALGA0036369 was the most significant SNP with −log10  of 7.59. This SNP showed an additive effect of 0.29 teats, a dominance effect of 0.04 teats and explained 3% of the phenotypic variance.On SSC7, between 103.03 and 103.59 Mb, the highest GWAS peak was found for SNP ASGA0035500 with a −logOn SSC12, between 52.71 and 54.68 Mb, was located the third most significant QTL region which was also the region characterized by the largest number of significant SNPs in this study (15 SNPs). The most significant SNP in this region (ALGA0120076) showed an additive effect of 0.24 teats and a dominance effect of 0.05 teats.P>0.01) and thus, the mode of gene action of this QTL seems purely dominant. This SNP presented a minor allele frequency of 0.31 and a minor genotypic frequency of 0.09  between the 15 SNPs was 0.78  in the region on SSC4 (9 SNPs) was very low (0.15). The low LD between SNPs in this region, and with the single significant SNP in particular, explains why the significant associations could not be confirmed by significant associations of neighbouring SNPs with NT. An alternative explanation for observing only one single significant SNP on SSC4 could be that this SNP was misplaced in the Pig genome build10.2 The QTL region on SSC4 contained only a single significant SNP while the region on SSC12 contained 15 significant SNPs. The QTL region on SSC12 covered 1.97 Mb and the average LD  for the dominant QTL in the region SSC4. As the proportion of SNPs excluded based on their minor genotypic frequency was relatively high, an additional analysis was performed to investigate whether any of these 9,016 excluded SNPs were associated with NT, even though these SNPs do not allow the investigation of the mode of gene action. For this analysis, the least frequent genotype class of these SNPs was set to missing and SNPs with two genotype classes <0.02 were not evaluated (n = 115). None of these SNPs showed a significant association (FDR<0.10).The region detected on SSC7 has been identified as a QTL for NT in other populations (VRTN) gene appeared as the most promising candidate in this region. VRTN encodes a potential DNA binding factor and has been described as an essential factor for development of the embryo in different species et al. VRTN gene. The region was defined by two SNPs: ASGA0035500 and INRA0027623, which were, respectively, the first and the third most significant SNPs for NT in the current study. Later, Fan et al. VRTN gene. Due to the positive relation between NT and number of vertebrae and the similarities between the results on SSC7 of the present study and the results of Fan et al.VRTN gene may also have an effect on NT.The Vertnin VRTN and NT needs to be further investigated in order to validate the effect of this gene on the genetic architecture of NT.In the other QTL regions, no obvious genes that could effectively affect NT were identified. The relationship between The term heterosis was coined by Shull et al. et al.et al.In pigs, negative heterosis  has not often been reported in the literature. Bereskin et al.In the current study, the dominant QTL identified on SSC4 showed negative estimate for dominance effect (−0.26 teats). Based on this locus, negative heterosis would be expected for NT, assuming that dominance effects are the main cause of heterosis. However, it is important to keep in mind that the main cause of heterosis is still under debate. While it has been shown in few studies that dominance is an important factor contributing to heterosis As a further step, the role of dominance effects on the genetic architecture of NT effects should be evaluated in a crossbred population, since non-additive effects are expected to be of importance in crossbreeding The genotype effects of the QTL region on SSC7  showed tA more challenging situation is encountered with the QTL region on SSC4 which may require the adoption of different strategies, such as mate allocation. Applying an additive model for estimating breeding values would not be efficient, since the additive effect of this QTL is close to zero . In caseet al. According to Toro and Varona In this study, four QTLs, three additive and one dominant, were identified by applying a two-step approach; first testing for significant genetic effect and then testing for additive and/or dominant gene action only of the SNPs with significant genetic effects. In total, the Figure S1QQ-plot of the residuals from the linear model without a SNP effect.(TIFF)Click here for additional data file.


The advent of next-generation sequencing has brought about an explosion of single nucleotide polymorphism (SNP) data in non-model organisms; however, profiling these SNPs across multiple natural populations still requires substantial time and resources.Acropora millepora, we show that both qHRM methods can recover the allele proportions from mixtures prepared using two or more individuals of known genotype. We further demonstrate advantages of each method over previously published methods; specifically, the “peaks” method can be rapidly scaled to screen several hundred SNPs at once, whereas the “curves” method is better suited for smaller numbers of SNPs.Here, we introduce two cost-efficient quantitative High Resolution Melting (qHRM) methods for measuring allele frequencies at known SNP loci in pooled DNA samples: the “peaks” method, which can be applied to large numbers of SNPs, and the “curves” method, which is more labor intensive but also slightly more accurate. Using the reef-building coral Compared to genotyping individual samples, these methods can save considerable effort and genotyping costs when relatively few candidate SNPs must be profiled across a large number of populations. One of the main applications of this method could be validation of SNPs of interest identified in population genomic studies.The online version of this article (doi:10.1186/s12863-015-0222-z) contains supplementary material, which is available to authorized users. Population genomics seeks to link genome-wide genetic variation to evolutionary processes. By querying a large number of individuals for multiple single nucleotide polymorphisms (SNPs) distributed across the genome, polymorphisms with unexpected patterns of genetic differentiation can be identified. These SNPs may be linked to loci under divergent selection between disparate environments . Recent i) direct sequencing [ii) primer extension using pyrosequencing [iii) preferential hybridization or amplification using allele-specific primers, such as TaqMan [There are many conceptual approaches to determine allele frequencies from pooled samples, including quencing , 6, SNaPquencing  or MALDIquencing , and  method of SNP genotyping uses a small oligonucleotide probe to increase the differences in melting temperature between single SNP alleles. It makes use of a dye that fluoresces only when intercalated into duplex DNA structures. First, the SNP-bearing locus of interest is amplified with asymmetric PCR such that an excess of one strand is produced. Next, the short probe that overlays the SNP site itself is added to the reaction. The reaction is subsequently heated, cooled and heated again while the loss of fluorescent signal (i.e. duplex DNA disassociation) is monitored. The melting temperature of the probe-SNP duplex region is dependent on the SNP allele state; a perfect match will have a higher melting temperature than will a mismatch . A conveAcropora millepora corals, an emerging ecological genomics model, to demonstrate the new method’s accuracy.In this study, we explore whether the unlabeled probe HRM melting profile can provide quantitative information about frequencies of alternative alleles in a pooled DNA sample. We developed two versions of qHRM that use different dye chemistries, brands of HRM machines and analytical techniques to quantify the relative proportions of each allele. We performed a total of six experiments involving adult Adult corals were used in all experiments. For the allele titration and SNP panel validation experiments (peaks method), corals were collected from Trunk Reef  and Little Pioneer Bay, Orpheus Island , Great Barrier Reef, Queensland, Australia in 2009. Samples used in the Orpheus/Magnetic population comparison experiment (peaks method) were collected from Orpheus Island and Nelly Bay, Magnetic Island . Coral samples used to validate the curves method experiments were collected from 28 coral populations, summarized in Additional file For the coral individuals used in the allele titration and SNP panel validation experiments (peaks method), genomic DNA was extracted using the Qiagen DNeasy Blood and Tissue kit (Qiagen) and was eluted into Buffer EB. Individuals sampled from Orpheus and Magnetic Islands (peaks method) and from the 28 populations across the Great Barrier Reef (curves method) were extracted according to the method described in Wilson et al. . DNA conThe peaks method was developed as a rapid means to quantify SNP allele frequencies across several hundred SNP loci. Briefly, we first established proof of concept using titrated allele frequencies and two SNP markers, then validated a panel of 384 SNPs using six corals, genotyped individually and as a pool. This panel of SNPs was finally applied to samples of two natural coral populations to detect loci with different frequencies between the reefs. The workflow of the peaks method is illustrated in Fig. 2 and 1x High Resolution Master Mix (Roche) in a 15 μl volume. Reactions were heated to 95 °C for 10 minutes, then cycled 55 times as follows: 40 s at 95 °C, 40 s at 60 °C, 40 s at 72 °C, then cooled to 40 °C and held for 20s. Concentrations of target DNA were normalized based on differences in quantification cycle (Cq) by assuming that the fold-difference per each Cq unit difference was equal to 1/E, where E is the amplification factor per PCR cycle for the particular primer set [In order to convert conventional HRM into a quantitative assay for pooled samples, equal amounts of each individual’s DNA must be added to the initial asymmetric PCR reaction. This DNA normalization step is particularly critical for pooling organisms that may have known or unknown assemblages, infections or symbioses as the relative amounts of target and contaminating DNA can vary among samples. To circumvent these problems, we used quantitative PCR (qPCR) to accurately measure the quantity of coral DNA in individual samples. Primers to amplify the SNP locus C23209S177  were useimer set . The accThough within-plate variation among replicates was low for the peaks method, we noticed that between-plate variation could shift the melting profile of all duplex DNA species . Reactions were heated to 95 °C and held for 10 m, then cycled as follows: 40 s at 95 °C, 40 s at 60 °C, 40 s at 72 °C for 55 cycles, which, for most SNPs, was at least 10 cycles past the beginning of the reaction plateau. The reaction was then finally cooled to 40 °C and held for 20 s.In HRM, the asymmetric PCR stage serves to generate an excess of the amplicon strand that is complementary to the probe. This excess strand can then form a duplex with the unlabeled probe, whereas if it was not in excess it would preferentially bind to the longer, complementary amplicon strand instead. Though some versions of HRM use symmetric PCR, their applications typically involve whole-amplicon melting analysis. In contrast, unlabeled probe HRM, which yields the increased sensitivity necessary for SNP discrimination, requires asymmetric PCR in order to outcompete the amplicon. The asymmetric PCR reaction prior to each assay’s melt stage was performed as follows on the LightCycler 480 (Roche): 5 ng total pooled DNA, 0.3 μM reverse primer, 0.067 μM forward primer, 0.05 μM each high-melting calibrator forward and reverse oligos, 0.5 μM each low-melting calibrator forward and reverse oligos, 2 mM MgClFollowing the asymmetric target amplification, the reaction plates were unsealed and 0.5 μM of the oligo probe complementary to the excess reverse strand of the amplicon was added. The plates were resealed and then heated to 95 °C for one minute to melt all double-stranded DNA species apart. Subsequently, the plates were rapidly cooled to 45 °C and held for one minute to allow reannealing of all possible probe and amplicon duplexes. Next, the reactions were heated to 95 °C at the maximum rate allowed by the Roche LightCycler 480 (>0.02 ° C/s). During this heating period, the LightCycler 480 monitored the amount of fluorescence over time and collected over 1300 data points over a 50 °C range, producing a high-resolution graph of the melting profile of each species of duplex DNA in the reaction.We calculated the negative first derivative of the melt stage’s decreasing fluorescence curve to produce a profile with discrete peaks centered at the melting temperature of each dissociating duplex Fig. c,d. NextA. millepora transcriptome, C22162S248 and C45133S676 [To demonstrate a linear relationship between known frequencies of a given allele and the qHRM-estimated frequency estimated from a pooled sample, we first identified two individuals homozygous for different alleles of the same SNP. We performed this experiment for two SNP loci mined from the 5133S676 . We normDNA samples from three adult coral individuals sampled from Trunk Reef and three from Orpheus Island were genotyped individually by conventional HRM for 384 loci selected from SNPs previously developed for a previously published coral linkage mapping project  (Addition = 48) and Orpheus Island (n = 50). DNA was normalized, pooled according to reef of origin, and qHRM-genotyped in triplicate using a panel of 384 SNPs. Nineteen of these SNPs were further validated by individual HRM genotyping of each individual contributing to the pools. The set of SNPs to validate was selected to represent loci where the low-melting allele was major (8 SNPs) or minor (11 SNPs), loci with small or large minor allele frequencies , SNPs with more than two alleles , and loci selected to span the range of estimated FST values .We used the peaks method to compare allele frequencies in two populations of corals collected from Magnetic Island , p allele between the two populations, and FST was calculated by averaging all SNP FST values.Global FThe curves method was developed to extend qHRM and demonstrate functionality across another dye chemistry and analytical approach. This method is best suited for highly accurate determination of allele proportions across small numbers of SNPs. It requires the usage of three reference sequences (two homozygous samples and a heterozygous sample) for comparison with an unknown, pooled sample. We demonstrate that reactions are extremely reproducible and robust among different markers and pooled samples. The workflow of the curves method is illustrated in Fig. Each individual sample was amplified individually in an asymmetric qPCR reaction using primers for the locus C70S236  under thAfter normalization of individual samples, each individual was combined into a single pooled sample. The pooled sample was then was amplified asymmetrically with the Rotor-Gene Q machine (Qiagen) with the locus C70S236. Next, 1 μM of probe complementary to the excess strand was added to each reaction. Then, the reactions were heated to 95 °C and held for 60 s, rapidly cooled to 45 °C and held for 150 s, heated again to 95 °C at a rate of 0.1 °C/s with a 2 s hold each step, collecting 500 data points in total.Because many factors such as pipetting errors causing variations in quantity of fluorescent dye and DNA can affect the relative fluorescence levels among normalized DNA samples, it is important to standardize the fluorescence levels to exclude the noise for accurate allele frequency measurements. Fluorescence values of the SNP-specific melt curves were standardized by selecting regions before and after the probe melting phase where nucleotide differences do not lead to variations in fluorescence level, then averaging fluorescence levels of the selected regions among all samples using the Rotor-Gene Q Series Software 2.0.2.4 (Corbett) in order to mitigate the effects of sample- and SNP-specific variation  allows for even small differences in allele frequency to be resolved. These standards set a reference fluorescence level to which unknown samples can be compared for absolute allele frequency quantification Fig. f and alsx) relative to the heterozygote (fhet). After adjusting for empirical differences between the known heterozygote and one of the known homozygotes (fhigh or flow), we add or subtract the value from 0.5 to determine the allele frequency of the unknown sample relative to the low-melting homozygote.The y-axis point at which the greatest difference in fluorescence between the two homozygotes occurs is used to determine the relative proportion of alleles in an unknown sample as it gives the best highest accuracy in estimating allele frequencies , Night (n = 56) and Wilkie (n = 49) Reefs was normalized and individually genotyped using SNP C70S236. Normalized samples were then pooled by population and quantitatively genotyped in duplicate in order to examine the accuracy of the curves method when used with many individuals per pool.DNA sampled from individual corals sourced from Myrmidon , which were all individually normalized as per above.Consistency between the two replicates was measured by estimating the half-confidence interval of the difference as follows:Standard deviations (SD) and mean values were calculated using fluorescence levels in the temperature range where the largest difference in fluorescence levels between the two homozygotes were observed Fig. f. A z vaWe further examined the qHRM reproducibility for the 5 markers and 25 reefs by comparing mean errors. Errors in percentage were calculated as follows:f1 and f2 are two replicates of fluorescence data. Geometric means of errors were used as population- and marker-specific errors.where r = 0.97-0.99). However, the slope of the regression for both SNPs was 0.80  are presented in Fig. .80 Fig. b.Fig. 3DTo further validate the method, DNA samples from six adult coral colonies from two reefs were individually genotyped for 384 SNPs selected from the coral linkage map using conventional HRM. DNA from these six individuals was pooled and the allele frequency at each SNP was estimated using qHRM. Of the 291 SNPs that passed quality filtering, 54 were homozygous, all of which were correctly identified by qHRM. For 237 polymorphic SNPs, the Pearson correlation between the expected and estimated allele frequencies was 0.89 with a regression slope of 0.94  [To demonstrate that qHRM can identify SNPs with different allele frequencies among real populations, we surveyed the same 384 SNPs in pooled samples representing the Magnetic Island and Orpheus Island populations of = 0.041) .r = 0.93, slope = 0.97)  Fig. . Global n = 98) using conventional HRM. The Pearson correlation between expected allele frequencies and the qHRM-estimated frequencies for these 19 SNPs was 0.93 with a regression slope of 0.77  of SNP assays tested for the pool of six individuals collected from Trunk Reef and Orpheus Island. In another assay, we found four distinct melting peaks across six separately HRM-genotyped individuals with a maximum of two alleles for each sample, though a separate SNP assay revealed three alleles within a single sample. This could indicate a high level of polymorphism (multiple SNP sites within the 20-base probe region), a potential genomic duplication event or a chimeric individual   Fig. .Fig. 6DNThe curves qHRM method proved highly accurate on pooled DNA samples. The allele frequencies of Myrmidon, Night and Wilkie populations were estimated by both individual HRM and pooled qHRM genotyping using the marker C70S236. There was very close agreement between the true allele frequency, as calculated by summing the allele frequencies for each individual contributing to each pool, and the qHRM-estimated frequency. The differences between estimated allele frequency and the true values were 0.9 % for Night, 1.1 % for Myrmidon, and 3.6 % for Wilkie Reef. An outlier sample was identified in Wilkie population  for the five markers examined with this method  ranged from 0.77 to 0.98. The peaks qHRM method slightly underestimates the true allele frequency of the low-melting allele . Ostensibly, this could be due to a bias during probe-DNA duplex formation during the initial cooling phase of HRM that favors the perfectly-matching probe. Regardless of its cause, this minor bias is unlikely to affect the conclusions of genetic studies outlined in the beginning of this Discussion since such studies rely upon variation in allele frequency among populations rather than its mean. In this regard, it was particularly encouraging to see very similar allele frequencies as estimated by qHRM between two natural coral populations , indicating that the peaks method is highly reproducible.r = 1 and slope = 0.982) is attributed to the inclusion of heterozygote as reference sample and identification of temperature region that gives the least error in allele frequency estimation. However, since the curves method of qHRM analysis uses reference genotypes to determine the relative contribution of each allele in an unknown sample, an allele that is not represented by the reference genotypes will be missed as a fluorescence produced by a third allele would be merged and difficult to detect by fluorescence of other alleles when pooled.The high reliability of the curves method .The low level of sample-specific errors in the curves method proves that the replicate accuracy was not significantly affected by differences in pipetting errors and DNA quality among samples. The accurate replication among samples of different DNA qualities can be attribute to a short length of PCR product (about 100 base pairs) .Gruber et al.  demonstrThe proposed qHRM methods demonstrated high accuracy and reproducibility in several cross-validation experiments. The key advantages of these methods over other targeted allele frequency profiling techniques include the ease of assay design and low overall cost of the analysis. All of these factors make qHRM a method of choice for cases requiring genotyping a few hundred SNP markers across a large number of populations.The data sets supporting the article are included within the article and its additional files.