TYRP1, an important melanosomal enzyme associated with human pigmentary differences. We identify alternate alleles of TYRP1 that induce or disrupt miR-155 regulation and demonstrate that these alleles are selected with different modes among human populations, causing a strong negative correlation between the frequency of miR-155 regulation of TYRP1 in human populations and their latitude of residence. We propose that local adaptation of microRNA regulation acts as a rheostat to optimize TYRP1 expression in response to differential UV radiation. Our findings illustrate the evolutionary plasticity of the microRNA regulatory network in recent human evolution.MicroRNA (miRNA)–mediated gene regulation is of critical functional importance in animals and is thought to be largely constrained during evolution. However, little is known regarding evolutionary changes of the miRNA network and their role in human evolution. Here we show that a number of miRNA binding sites display high levels of population differentiation in humans and thus are likely targets of local adaptation. In a subset we demonstrate that allelic differences modulate miRNA regulation in mammalian cells, including an interaction between miR-155 and  TYRP1, a melanosomal enzyme associated with human pigmentation. We found that the two alternate alleles on the 3′ UTR of TYRP1, either inducing or disrupting repression by miR-155, are under opposite selections among human populations. This results in a strong negative correlation between the degree of fixation of miR-155–mediated repression of TYRP1 in a population and the population's latitude of residence. These observations collectively suggest miR-155 acts a rheostat to optimize TYRP1 expression for local adaptation to differential UV radiation along the latitudes. Our findings demonstrate the plasticity of miRNA regulation in recent human evolution.MicroRNAs (miRNAs) are endogenous small RNAs that bind to their target mRNAs to post-transcriptionally repress protein production. miRNA–mediated gene regulation is usually considered to be strongly conserved among and within species, and thus alteration of such regulations is usually considered as detrimental. However, it is likely that evolutionary divergence of miRNA regulation may actually be selectively advantageous and could even serve as a genetic reservoir for innovation and adaptation. Towards this goal, we identified a number of polymorphic miRNA binding sites that display extreme population differentiation and show evidence of positive selection. We experimentally validated 3 regulations, including a regulation by miR-155 on  They recognize their target mRNAs primarily through sequence complementarity between the seed region of a miRNA  STF. For example, Coop et al. showed that SNPs with high STF values in humans are enriched for genic SNPs in comparison to non-genic SNPs, and interpreted this as evidence of local selection targeting sites in genic regions STF values into different bins  were significantly enriched for miRNA binding sites . Positive selection by definition acts on functional loci so these findings clearly indicate that miRNA binding sites with extreme population differentiation (STF≥0.5) are likely targets of positive selection.Extreme population differentiation can be attributed to several possible factors, such as outliers from neutral drift, population structure, or positive selection for local adapation. Thus we sought to determine the potential sources accounting for the observed differentiation events. Recent population genetic analyses of human variation have shown that much of the recent local adaptation in humans may be caused by subtle changes in allele frequencies in many genes rather than strong changes in a few genes ent bins , and comSTF≥0.5); however we also attempted to control, at least partially, for hitchhiking effects by excluding target-site SNPs linked to any annotated functional variant that lies 500 Kb upstream or downstream of the SNPs , SLC25A19 , and TYRP1 (tyrosinase-related protein 1) showed significant miRNA-dependent inhibition of luciferase expression in an allele-specific manner , the signal of positive selection can be localized directly to the miRNA binding sites using the CLR method  TYRP1 is important for adjusting skin reflectance to protect against excessive UV exposure TYRP1 harbors 2 common SNP variants, rs683 and rs910 that reside in two putative miR-155 binding sites  is thus a target of miR-155. Analysis of the ancestral alleles revealed substantial suppression when compared to a mutant in which all three miR-155 targets were deleted , we next tracked the local selection within individual populations. As shown in de novo binding sites on TYRP1 in YRI, CHB and JPT, likely reflects a requirement in these populations to induce miR-155 suppression on TYRP1.Previous studies suggested that TYRP1. Taken together, these results suggest that in YRI, and particularly in CHB and JPT, the derived alleles of rs683 and rs910 have been a target of positive selection . We investigated this hypothesis further by extending our analysis from the 4 HapMap populations to 37 representative indigenous populations genotyped in the Human Genome Diversity Project (HGDP), spanning 650,000 common SNPs R = −0.63). As the values for different populations might be correlated due to miration history, we cannot apply standard statistical methods to test whether the correlation is significant. However, we note that the correlation between latitude and allele frequency in rs683 is among the 1% most extreme of such correlations in the genome. This is also true if we restrict ourselves to analyses of SNPs with STF≥0.5  incoherent control or incoherent feed-forward loop in describing generic regulatory networks). Such a network architecture is important in maintaining target protein homeostasis and in fine-tuning and buffering target protein expression. Indeed, the amount of skin pigmentation is thought to be balanced between two conflicting and UV-dependent physiological needs, the production of vitamin D and folate TYRP1 in Africans (in low-latitude regions) might ensure proper expression of this gene by dampening potential fluctuations that may shift its expression away from the optimal level, conferring unfavorable pigmentation phenotypes  in this study since we aimed to find the most extreme cases of population differentiation. Future comparative studies aimed at miRNA regulatory sites may reveal many more examples of rewired miRNA interactions. Second, our target prediction was based on TargetScanS, which uses seed matches as the principal mechanism for target recognition by miRNAs. However, other mechanisms might also exist, such as sites with central pairing There are likely many more changes in miRNA interactions that have contributed to human adaptation than reported here. First, we only report signals relating to increased levels of population differentiation, which most likely only reveal a small fraction of the selection that has acted during human evolution since the divergence with chimpanzees. We also used a very stringent cutoff for Fst values for ∼3 million HapMap Phase II SNPs. When controlling for potential hitchhiking effects, we followed the protocol used in HapMap database and computed the pairwise linkage disequilibrium  between the SNPs in question and its flanking SNPs within 500 Kb downstream and upstream. Any SNPs having R2≥0.5 with annotated functional sites within this distance were excluded from the analysis. The function annotation of SNPs was retrieved from dbSNP 130 queried from UCSC Table Browser. Following previous protocol SNP annotations were based on a previous study Allele frequencies of rs683 in world populations were collected from the Human Genome Diversity Project http://haplotter.uchicago.edu/). CLR test was performed using SweepFinder The iHS values and Fay and Wu's H for HapMap populations were extracted from Haplotter  R2 with rs683 using PGEToolbox TYRP1 and none of the SNPs mediates interaction with known miRNAs. Similarly in CHB and JPT, we did not find any known functional variants on TYRP1 in strong LD with rs683. We also expanded the analysis from HapMap SNPs to dbSNP132 by querying the UCSC Table Browser, which includes SNPs genotyped in the 1000 Genomes Project To explore the possibility that rs683 and rs910 changed frequency due to hitchhiking with some other functional variants on this gene, we analyzed all the known HapMap SNPs on We first extracted all SNPs annotated to be in 3′ UTR of human genes, which were annotated by HapMap , and all the SNPs are polarized to the plus strand. These 3′UTR SNPs were then mapped onto the 3′UTR sequences of RefSeq transcripts , and we retained the longest transcripts when multiple sequences are annotated under the same transcript. Sequences with inconsistent annotations were discarded from our analysis. With this mapping procedure, we then generated a set of polymorphic 3′UTR segments, which is a 15 nucleotide window centered at the SNP position. Therefore for each SNP, sequences within the window will present twice each carrying the alternate alleles. TargetScanS All the constructs for this study were derived from the pMIR-REPORTER (Ambion). Human 3′UTR sequences in this study were amplified from genomic DNA of HEK293T cells using PCR. The PCR products were subcloned into pMIR-REPORTER vector. Overlapping PCR and QUICKCHANGE II XL Site-Directed Mutagenesis Kits (Agilent) were used to generate the mutants of 3′UTRs containing different SNPs and deletions of miRNA target sites. pMIR-REPORTER β-galactosidase vector was used as the transfection control. G3R is a gift from Dr. Burton Yang' lab at the University of Toronto, which has the coding sequence of the chicken versican G3 domain in the pMIR-REPORTER vector, and was used as a negative control. All the synthesized miRNA mimics and the mimic negative control were purchased from Dharmacon. miR-CTL in this study is a negative control with sequence from a C.elegans miRNA cel-miR-67, which has minimal sequence identity with miRNAs in human, mouse and rat (by Dharmacon).HEK293T cells were grown in high glucose DMEM containing 10% FBS (Thermo). They were transiently transfected with DNA constructs and miRNA mimics at 40% confluency in 24-well plates using the calcium-phosphate precipitation method. Cells were lysed 48 hours after transfection, and the activities of firefly luciferase and β-galactosidase of total cell lysates were determined using the Firefly Luciferase Assay System (Promega) and the β-gal assay previously described, respectively. To obtain the relative activity, the activity of the firefly luciferase was first normalized to the activity of β-galactosidase to obtain normalized firefly luciferase activity (nFFLuc), and the data were then determined using the following formula: Relative Activity = log2(nFFLucmiR-155/nFFLucmiR-CTL), where nFFLucmiR-155 or nFFLucmiR-CTL is the nFFLuc in the presence of mir-155 mimics or mimic negative control. The concentration of miR-155 was increased as indicated in the figures , with thSK-MEL-19 cells were grown in RPMI 1640 (GIBCO) containing 10% FBS and antibiotics. For genotyping, the lysates of SK-MEL-19 cells were subjected to PCR. The forward primer starts from 116 bps upstream of rs683 whereas the reverse primer starts from 118 bps downstream of rs683. The PCR products were cleaned and sent to DNA sequencing using the forward primer. The sequencing spectra were processed and analyzed using MacVector. SK-MEL-19 cells were transiently transfected with miRNA mimics following the instructions of the RNAi MAX transfection kit (Invitrogen). For immunoblotting analysis, cells lysates were collected 40 hours after transfection. The antibodies used for immunoblotting analysis are: TYRP1  and GAPDH . The ΔCT values were obtained by comparing the amplification of the target cDNA to that of HPRT. The TapMan SNP qPCR kit for rs683  was used to analyze the expression of the TYRP1 transcripts carrying its alternate alleles. This kit included two probes, one conjugated with VIC fluorescence dye to monitor transcripts carrying the ancestral allele and the other conjugated with FAM fluorescence dye to detect the derived allele. To evaluate the cross-hybridization between the probes, a pilot qPCR analysis was performed for the constructs A and B in Figure S1TYRP1 are indicated in block III.Identifying the predicted miRNA binding sites showing population differentiation. (A) Distribution of the context score among the predicted sites affected by SNPs. Prediction confidence was quantified by context score where more negative score indicates more confident prediction. Based on the distribution, a site considered confident if its context score no more than −0.2. (B) Hierarchical clustering of the ancestral allele frequencies in the 4 populations, YRI, CEU, CHB and JPT. The SNPs were clustered into 4 blocks as indicated from block I to block IV, with highly differentiated patterns among the 4 populations. The two SNPs (rs683 and rs910) on (TIF)Click here for additional data file.Figure S2(A–D) The CLR test localizes selection signal on the polymorphic miRNA binding sites (indicated in blue below the X-axis in each panel). The dotted lines indicate the loci for each SNP analyzed.(TIF)Click here for additional data file.Figure S3(A–D) The luciferase reporter assay in HEK293T revealed four genes did not respond to their predicted miRNA regulators. The ancestral alleles are shaded in blue whereas the derived alleles are shaded in red. The reporter constructs were individually transfected into HEK293T cells with miR-CTL or with the predicted miRNAs as indicated. The luciferase relative activities were obtained and analyzed as described in (TIF)Click here for additional data file.Figure S4Lack of selection signal on the derived allele of rs7198 in YRI (A–B) and CEU (C–D) revealed by CLR test  and Fay-Wu's H test . The derived allele in CEU is 0.67, whose H is marginally significant as shown in (D), but is not supported by CLR test (C). The threshold of statistical significance was not shown in (B) as all the values were insignificant, far beyond the threshold.(TIF)Click here for additional data file.Figure S5Statistical tests for positive selection on the derived allele of rs683 in YRI (A–B) and CEU (C–D). CLR and Fay-Wu's H tests consistently localized the selection signal around the rs683 locus in YRI (A–B), but not in CEU (C–D). The dotted line for H statistic indicates the 5% extreme value among the genome-wide SNPs.(TIF)Click here for additional data file.Figure S6TYRP1 and its 5 kb upstream in YRI (the first row) and CEU (the second row). Each column represents one SNP, and different genomic regions are separated by red vertical bars, including a 5 Kb upstream region. The brighter color indicates higher association (2R) of a given SNP with rs683. The 6th SNP rs12001162 is absent in HapMap CEU population. In CEU and YRI, the strongly linked loci with rs683 are intronic and do not overlap with known splice sites. The only missense SNP is not associated with rs683. The two other SNPs in close proximity to rs683 and rs910 on the 3′ UTR do not interact with any known human miRNAs.Linkage disequilibrium between rs683 and all other known variants on (TIF)Click here for additional data file.Figure S7Plots of the extended haplotype homozygosity (EHH) in the 0.5 Mb region centering at rs683 in CEU (A), East Asia (B) and YRI (C). Haplotypes carrying the ancestral or the derived allele of rs683 are in blue or red, respectively.(TIF)Click here for additional data file.Figure S8STF (≥0.5) (B). In either case the correlation derived by rs683 is among the extreme 1% of all the SNPs analyzed (the red bar).Histograms of the Pearson's correlation coefficients between the absoulte latitides of HGDP populations and the derived frequencies of SNPs across the genome (A) or SNPs with high-(TIF)Click here for additional data file.Figure S9Evaluation of the cross-hybridization between probes for the TaqMan SNP qPCR assay for rs683. In this assay one probe conjugated with VIC fluorescence dye monitors the ancestral allele (the blue circles) and the other probe conjugated with FAM fluorescence dye detects the derived allele (the red squares). Constructs A and B used in (TIF)Click here for additional data file.Table S1The 30 SNPs and 26 human genes identified in this study. The confidence of miRNA target prediction and the associated selection statistics are also shown. Alternative transcripts of these genes as annotated by RefSeq are shown individually. Note that more than one miRNAs can share one target site and thus a single SNP can impact the regulation by multiple microRNAs, for example the SNP rs9893667 on gene NM_006380 (APPBP2) can influence the target site of hsa-miR-362-3p, hsa-miR-329, and hsa-miR-603 (highlighted in bold).(XLS)Click here for additional data file.Table S2http://david.abcc.ncifcrf.gov). In this table, each gene is annotated with its OMIM disease association, gene ontology and KEGG pathways. Note C4orf46 is a hypothetical gene with no function annotation.Summary of molecular functions of the identified genes. Annotations were queried from DAVID ((XLS)Click here for additional data file.Table S3Functional annotation of the 7 genes selected for experimental validation.(XLS)Click here for additional data file.Table S4miRNA families considered in this study. miRNAs were grouped into families based on their seed identity. The miRNA family information was deposited in TargetScanS.(XLS)Click here for additional data file.


Possible single nucleotide polymorphism (SNP) interactions in breast cancer are usually not investigated in genome-wide association studies. Previously, we proposed a particle swarm optimization (PSO) method to compute these kinds of SNP interactions. However, this PSO does not guarantee to find the best result in every implement, especially when high-dimensional data is investigated for SNP–SNP interactions.OR) values  for breast cancer. Based on IPSO algorithm, the jointed effect in terms of SNP barcodes with two to seven SNPs show significantly decreasing OR values . Using PSO algorithm, two to four SNPs show significantly decreasing OR values . Based on the results of 20 simulations, medians of the maximum differences for each SNP barcode generated by IPSO are higher than by PSO. The interquartile ranges of the boxplot, as well as the upper and lower hinges for each n-SNP barcode (n = 3∼10) are more narrow in IPSO than in PSO, suggesting that IPSO is highly reliable for SNP barcode identification.In this study, we propose IPSO algorithm to improve the reliability of PSO for the identification of the best protective SNP barcodes (SNP combinations and genotypes with maximum difference between cases and controls) associated with breast cancer. SNP barcodes containing different numbers of SNPs were computed. The top five SNP barcode results are retained for computing the next SNP barcode with a one-SNP-increase for each processing step. Based on the simulated data for 23 SNPs of six steroid hormone metabolisms and signalling-related genes, the performance of our proposed IPSO algorithm is evaluated. Among 23 SNPs, 13 SNPs displayed significant odds ratio (Overall, the proposed IPSO algorithm is robust to provide exact identification of the best protective SNP barcodes for breast cancer. Genome-wide association studies (GWAS) can identify several highly robust and statistically significant single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility A steroid hormone metabolism and signalling-related genes are implicated in the pathogenesis of breast cancer Many studies hypothesize that the cancer or disease risk is associated with the co-occurrence of SNPs displaying a jointed effect M = N!/[M! (N-M)!]*3MC*3, where N is the number of SNPs or factors, and M is the selected prediction number of SNPs. Many artificial intelligence methods have been proposed to compute the association of genotype frequencies of case and control data. They were demonstrated to be effective in reducing the number of search items among a greater number of SNP combinations, such as multifactor dimensionality reduction (MDR) Currently, analysis of SNP-SNP interactions remains challenge because of the complex combination of data with huge SNPs. Many possible combinations of alleles in SNP-SNP interactions are generated when multiple SNPs are evaluated simultaneously. Mathematically, the possible combinations of SNP interactions between cases and controls is estimated to be Here, we develop an improved PSO algorithm called IPSO that improves the reliability of traditional PSO. This improvement is based on the population initialization step during the PSO process, i.e., keeping good solutions and improving always the concept of best solution during the process; this conservation of superior results yields better solutions for high-order SNP-SNP interactions. We systematically evaluated the joint effects of 23 SNP combinations of six steroid hormone metabolisms and signalling genes involved in breast carcinogenesis. The SNP barcodes generated by the IPSO algorithm were statistically evaluated by the odds ratio and risk ratio to predict breast cancer susceptibility. The results demonstrate that the proposed IPSO method can identify more relevant SNP barcodes for high-dimensional data sets and improved the reliability of the results in the 20 test runs we conducted.PSO is an efficient evolutionary computation learning algorithm developed by Kennedy and Eberhart N particles. Each particle can be regarded as a problem solution in this study.Population: A swarm (population) consisting of D-dimensional vector; the ith particle can be described as ith particle with respect to the Dth dimension. Each dimensional vector in particle position is defined by the number of selected SNPs and the corresponding genotypes for the associated SNPs.Particle position, ith particle is represented by ith particle with respect to the Dth dimension. The new locations of particles are chosen by adding Particle velocity, w: The inertia weight is used to control the impact of the previous velocity of a particle on its current velocity. This control parameter affects the trade-off between the exploration and exploitation abilities of the particle.Inertia weight, ipbest: ipbest is the position of the ith particle with the highest fitness value at a given iteration. It can be currently regarded as a best solution of SNP barcodes so far in terms of thi particle.Individual best value, gbest: The best position of all pbest particles is called global best. It can be currently regarded as a best solution of SNP barcodes so in all particles.Global best value, Termination criteria: The process is stopped after the maximum allowed number of iterations is reached.ipbest by comparing its current fitness to the fitness of its previous position. In a third step, the gbest of all the particles in the population is determined. And finally, the PSO algorithm executes a search for optimal solutions by updating the generations. In each generation, the position and velocity of the ith particle are updated with ipbest and gbest of the swarm population. The update equations can be formulated as:w is the inertia weight. This inertia weight is a positive linear function of time that changes with the generations; r1 and r2 are random numbers between , and c1 and c2 are acceleration constants that control how far a particle moves in a single generation. Velocities Vmin, Vmax]D, and the particles’ positions are limited within D, thus determining the size of the steps the particle is allowed to take through the solution space.The PSO algorithm can be divided into four steps within a process period. First, particles are respectively initialized in a population of random solutions. Then each particle finds its own pbest and gbest solutions. The procedure is repeated in each successive iteration until the termination conditions are reached.This study proposes a new idea to improve the stability of results obtained with particle swarm optimization. We conserve the best results in the each SNP barcode prediction, which allows us to offer better results for high-order SNP-SNP interactions. The retention of the best results in PSO is very simple and can be done without increasing the computational complexity of the process. The difference between IPSO and PSO is that the proposed new idea is applied in the population initialization step during the PSO process. The IPSO proceeds as follows: The initial population is generated by our strategy and then the fitness values of all individuals in the population are calculated by a fitness function. The particles are repositioned according to their own In IPSO, every particle in a population is associated with a solution group. We define a particle based on the number of selected SNPs, and the genotype associated with the corresponding SNPs; the SNPs cannot be repeatedly selected. The particle encoding can thus be represented by:i,jSNP represents the selected SNP, i,jGenotype represents the three possible genotypes once  i,jSNP is selected, m represents the size of the population, and n represents the number of SNPs selected. Initial particles are randomly generated in this study. For example, let P = 3,4,8, Genotype2,1,3) and 2,1,3Genotype represents the chosen genotypes . In this case, the selected SNPs with their corresponding genotypes are represented as , , and , respectively.where 1,3, Genotype1,2)(SNP is one of the top five 2-SNP barcodes (step 1-1). Subsequently, this 2-SNP barcode is applied to search for the best combination of the 3-SNP barcode with a maximum difference value between the case and control data (step 2-2a); in this example the search result is 1,3,i, Genotype1,2,j),. If the exhaustive search algorithm finds the answer to be i = 6 and j = 1 , the best 2-SNP barcode 1,3, Genotype1,2). are generated differently. For the 2-SNP barcode, we only apply the exhaustive search algorithm to compute and check all possible 2-SNP combinations to give the best five results for all 2-SNP barcodes. To generate the n-SNP barcode (n ≧3), the steps for population initialization are illustrated in Meanwhile, the 3-SNP barcodes are generated in a random way (step 2-2b) and then sorted by the order of the fitness values (step 3-3b). The result from step 3-3a is used to replace the worst of the top five SNP barcodes (step 3-4). Finally, the updated 3-SNP barcode population is ready for the PSO computation, in which the top five in amongst the 3-SNP barcodes (step 3-5) are determined. Now, the top five SNP barcodes can be used to start the generation of the next higher order SNP barcodes. The steps are described in detail by the annotated IPSO pseudo-code in the next two sections.C represents the total number of SNP interactions in the case group, and N represents the total number of SNP interactions in the control group. iP represents the ith particle. The fitness value definition can be divided into three steps. First, the total number of intersections of the case data set and the ith particle is calculated as n(C∩iP). Second, the total number of intersections of the control data set and the ith particle is calculated as n(N∩iP). Finally, Eq (3) is used to calculate the fitness value that is the difference between the intersection of the case and the particle and the intersection of the control and the particle. For example, 1,2, Genotype2,1)P = , the fitness value is determined by subtracting 76 from 141, giving -65. If the fitness is negative, the absolute value is taken to obtain a fitness value of 65.In this study, the fitness value means are used to compute the difference between the case and control data from the selected SNP combinations. The focus lies on specific SNP combinations to obtain the highest fitness value, i.e., the maximum SNP combination difference between cases and controls. The concept uses the intersection of set theory to compute the difference between cases and controls. The intersection of two sets is the set that contains all elements of one of these sets that also belong to the other set, but no other elements. A high fitness value indicates the best combination of an SNP and genotypes. The relevant equation is shown below:pbest) and the global best position (gbest) when moving. If the fitness value of a particle iP in the current iteration is better than the fitness value of pbest in the previous iteration, pbest is updated to that of iP. If the fitness value of particle iP in the current iteration is better than gbest in the previous iteration and is the best one in the current iteration, gbest is updated to that of iP. Each particle then adjusts its direction based on pbest and gbest in the following iteration.Each particle finds its personal best position  and Vmin was equal to – (Xmax – Xmin). These parameters have been optimized by Kennedy and Eberhart The population size parameter was set to 50  and odds ratio (OR) are used to determine the best SNP barcode and quantitatively measure the breast cancer risk. The boxplots were analysed by SigmaPlot 9.0 .http://bioinfo.kmu.edu.tw/brca-steroid-96SNP.xlsx.The data set for the steroid hormones and their signalling and metabolic pathways (96 SNPs for 8 genes) were obtained from the breast cancer association study in 4 (rs3020314) includes 4551 genotypes in the original data, which contain 2132 for CC, 1970 for CT, and 449 for TT, respectively (the step for pseudo-code 04). In each SNP, the percentage of each genotype is calculated, for the above instance, 2132/4551 (46.85%) for CC, 1970/4551 (43.29%) for CT, and 449/4551 (9.86%) for TT. Based on these percentages, the modified data for SNP4 is obtained by multiplication of the percentage with the amount of the entire data set, i.e., 46.85%×5000 = 2343 for CC, 43.28%×5000 = 2164 for CT and 9.86%×5000 = 493 for TT (the step for pseudo-code 05 to 11). The simulated data for SNP4 has thus been normalized to 5000 (2343+2164+493 = 5000). Accordingly, all original data are normalized to the same number in this manner.The normalization procedure is provided in the “pseudo-code for randomly generated data” as shown in OR and 95% CI) for each SNP from 6 steroid hormone metabolisms and signalling-related genes . Some SNPs  for breast cancer; their OR values range from 1.268 to 0.846. The other SNPs show no statistically significant OR for breast cancer.Based on our simulated data, Using the IPSO algorithm, the best SNP-SNP interaction is evaluated by the difference between cases and controls for all the SNP barcodes. After computation, the top five of the 2-SNP barcodes can be listed in order of the difference between cases and controls: SNPs (4-19)-genotype (1-1), SNPs (4-23)-genotype (1-2), SNPs (4-9)-genotype (1-2), SNPs (19-23)-genotype (1-2), and SNPs (9-23)-genotype (2-2). The differences in the number of cases and controls for these SNP barcodes are 174, 168, 158, 150, and 146, respectively (data not shown). In this study, as shown in 23)-genotypes (1-1-2) vs. SNPs (4-19)-genotypes (1-1), where the bold letters indicate the newly selected SNP. The 4-SNP barcode contains the 3-SNP barcode, i.e., SNPs (4-9-19-23)-genotypes (1-2-1-2) vs. SNPs (4-19-23)-genotypes (1-1-2).With the conservation of the top five results, we found that the best for n-SNP barcode contains the corresponding best (n-1)-barcode. For example, the 3-SNP barcode contains the 2-SNP barcode, i.e., SNPs . Since the SNP barcodes listed in The best n-SNP barcodes (n = 3 to 10) calculated by the IPSO algorithm are listed in We compare IPSO with PSO for the reliability and the ability to identify SNP barcodes to support the advantage of the top-five strategy. The performances of the PSO and IPSO algorithms from 20 simulation runs -genotype (1-1)] is higher than that of [SNPs (4-19-23)-genotype (1-1-2)], suggesting that SNP 4 and SNP 19 are more associated with breast cancer protection than SNP 23. Accordingly, an order of impact on breast cancer for the SNPs listed in  to 0.57 . In contto 0.846 .Some SNPs may display different impacts on the protection of breast cancer in terms of the individual SNPs or the combinational SNPs. For example, some individual SNPs such as SNPs 3 and 9 are not significantly associated with breast cancer , but theA key issue of detecting SNP-SNP interactions in genome-wide case-control study is the computational efficiency. The computational complexity of IPSO algorithm is estimated by the objective function computation. If there are M number of iterations and N number of solutions (particles) in the population, then the objective function computation has O(MN) computational complexity. The effective feature of the top 5 strategy computation is only storing the top 5 solutions in each iteration. If there are K solutions in the archive, storing the solutions in the archive has O(M+K) computational complexity. If the archive and the iteration have the same numbers, the overall complexity of IPSO is O(MN+K).c1 and c2 are acceleration constants that control how far a particle moves in a single generation, and they respectively control the exploitation and exploration ability in each search. In order to balance the exploitation and exploration, the c1 and c2 are suggested the same as 2.Although the optimal parameters of PSO were demonstrated by Kennedy and Eberhart Although we explored the benefit of IPSO algorithm for SNP interaction based on the simulated breast cancer study, the IPSO algorithm is not exclusively into breast cancer data and can be applied to other real data sets. After running these algorithms using another disease with the real dataset IPSO can overcome the limitations imposed on computational time for complex SNP interactions for GWAS because IPSO has the following advantages: 1) IPSO allows robust analysis of high-order SNP combinations for GWAS studies and generates the best SNP barcodes; 2) IPSO is an improved evolutionary algorithm without exhaustive search; 3) IPSO only needs two parameters for computation without complex settings; and 4) Its computational complexity is unaffected by the size of data sets.In conclusion, we propose an improved PSO algorithm to perform a powerful breast cancer association analysis in terms of SNP-SNP interactions with 23 SNPs. Our strategy successfully improves on the performance of traditional PSO in terms of the reliability with a combination of more statistically significant SNPs associated with breast cancer protection. With the help of the IPSO algorithm, the best fitness of cases and controls can be identified. The algorithm can potentially be applied to identify complex SNP-SNP (gene-gene) interactions for different diseases, even in cases where a large number of SNPs is involved in genome-wide association studies.Table S1The estimated protective SNP combinations on the occurrence of breast cancer as determined by IPSO.(PDF)Click here for additional data file.Table S2The estimated protective SNP combinations on the occurrence of breast cancer as determined by PSO.(PDF)Click here for additional data file.Table S3Wilcoxon Signed-Rank test for IPSO and PSO.(PDF)Click here for additional data file.


Populus tomentosa cellulose synthase gene, PtoCesA4. PtoCesA4 is strongly expressed in developing xylem and leaves. Nucleotide diversity and LD in PtoCesA4, sampled from the P. tomentosa natural distribution, revealed that PtoCesA4 harbors high single nucleotide polymorphism (SNP) diversity (πT = 0.0080 and θw = 0.0098) and low LD , demonstrating that the potential of a candidate-gene-based LD approach in understanding the molecular basis underlying quantitative variation in this species. By combining single SNP, multi-SNP, and haplotype-based associations in an association population of 460 individuals with single SNP linkage analysis in a family-based linkage populations , we identified three strong associations  in both populations. These include two nonsynonymous markers (SNP49 associated with α-cellulose content and SNP59 associated with fiber width) and a noncoding marker (SNP18 associated with α-cellulose content). Variation in RNA transcript abundance among genotypic classes of SNP49 was confirmed in these two populations. Therefore, combining different methods allowed us to examine functional PtoCesA4 allelic variation underlying natural variation in complex quantitative traits related to growth and lignocellulosic biosynthesis.Lignocellulosic biomass from trees provides a renewable feedstock for biofuels, lumber, pulp, paper, and other uses. Dissecting the mechanism underlying natural variation of the complex traits controlling growth and lignocellulose biosynthesis in trees can enable marker-assisted breeding to improve wood quality and yield. Here, we combined linkage disequilibrium (LD)-based association analysis with traditional linkage analysis to detect the genetic effect of a  Wood formation represents a major carbon sink for the biosphere and provides an important renewable resource for lumber, pulp, and paper, and possibly for biofuels feedstocks . As a biAssociation studies are an effective means to bridge the gap in our understanding between complex quantitative traits and the underlying genetic variation at specific candidate genes or multiple loci dispersed genome-wide .Conventional QTL linkage analysis in controlled crosses and alternative LD-based association mapping using diverse germplasms are two broadly used approaches for the dissection of the genetic architecture of complex traits  are involved in cellulose biosynthesis of the secondary walls (Populus spp.) have a long tradition as a model system for studies of angiosperm tree physiology and genetics. CesA homologs have been identified in Populus, and they also have been used to investigate the mechanisms of cellulose biosynthesis (CesA gene was isolated from aspen (P. tremuloides) by CesA family members have been identified in aspen and its hybrids (P. tremula × tremuloides) (CesAs (encoding 17 proteins) have been identified in P. trichocarpa  are expressed in developing xylem tissue undergoing secondary wall thickening  located in the plasma membrane  with single SNP linkage analysis in a family-based linkage population  to identify several associations underlying natural variation of important wood properties. However, to probe the mechanism of this variation, we also examined PtoCesA4 expression and found that it is expressed in developing xylem and that its expression varies in lines with different genotypes for one of the associated markers.Here, candidate gene approaches were used to examine genetic variation in only one of the P. tomentosa individuals representing all of the original provenances in the entire natural distribution region of P. tomentosa  were used for initial SNP association (PtoCesA4 using a direct sequencing method.An association population of 460 unrelated ociation . Forty i1 progeny of controlled crosses between two elite poplar parents (members of the section Populus), clone “YX01”  as the female and clone “LM50” (P. tomentosa) as the male. These two related species are members of the section Populus in the genus Populus. The progeny were grown in 2008 in the Xiao Tangshan horticultural fields of Beijing Forestry University, Beijing, China  using a randomized complete block design with three clonal replications , diameter at breast height (D), stem volume (V), fiber length (FL), fiber width (FW), microfiber angle (MFA), holocellulose, α-cellulose, and lignin content. The growth traits, including tree height (H), diameter at breast height (D), and stem volume (V), were measured during field surveys in 2009 using the methods described by P. tomentosa clone “LM50.” These tissues were immediately frozen in liquid nitrogen and then stored in the laboratory at −80° for later RNA extraction. The P. tomentosa stem developing xylem cDNA library was constructed using the Superscript k System  as part of our effort to identify genes expressed predominantly in the P. tomentosa stems. The details of constructing the cDNA library were previously described by 6 pfu, with an insert size range of 1.0–4.0 kb. Random end-sequencing of 1000 cDNA clones and comparison with Arabidopsis or P. trichocarpa CesA sequences identified a full-length cDNA with high similarity to AtCesA4 (73.6%) or PtiCesA4 (97.4%). Therefore, we named this cDNA PtoCesA4.Developing xylem tissues were collected by scraping the thin (approximately 1.0 mm) and partially lignified layer on the exposed xylem surface at the bottom stems of 1-year-old PtoCesA4 based on cDNA sequence; 6421 bp of genomic DNA sequences for PtoCesA4, including the promoter (1111 bp), were obtained by direct sequencing in the P. tomentosa LM50 clone, using conserved primers, and the BigDye Terminator Cycle Sequencing kit version 3.1  run on a Li-Cor 4300 genetic analyzer . The PtoCesA4 sequence was deposited in GenBank under the accession number KC762249.Total genomic DNA was extracted from young leaves with the DNeasy Plant Mini kit . Specific primers were designed for sequencing PtoCesA4 to the CesAs from other species, the amino acid sequences of CesA from Arabidopsis thaliana, rice (Oryza sativa), maize (Zea mays), Eucalyptus grandis, and Populus trichocarpa were identified from NCBI (http://www.ncbi.nlm.nih.gov) using BLAST . Additional on-column DNase digestions were performed three times during RNA purification using RNase-Free DNase (Qiagen). RNA was quantified based on absorption at 260 nm. Quantified RNA was reverse-transcribed into cDNA with the SuperScript First-Strand Synthesis system and the supplied polythymidylate primers (Invitrogen). All cDNA samples were used for tissue-specific expression analysis of PtoCesA4 using real-time quantitative PCR (RT–qPCR).Total RNA was extracted from various tissues, including root, stem , mature leaf, and apical shoot meristem of 2-year-old PtoCesA4-specific  and internal control (Actin) primer pairs  were designed using Primer Express 3.0 (Applied Biosystems). All reactions were performed in triplicate technical and triplicate biological repetitions, respectively. The results obtained for the different tissues were standardized to the levels of Actin.The qPCR was performed on a DNA Engine Opticon 2 machine (MJ Research) using the LightCycler-FastStar DNA master SYBR Green I kit (Roche). The real-time qPCR and the generated real-time data analysis were performed following the study of Q ≤ 0.10] were targeted in the association and linkage populations, respectively (see Results). Transcript levels were determined by RT-qPCR with gene-specific primers. For each SNP genotypic class, 10 trees were individually sampled by obtaining secondary xylem at 1.3 m from the ground; tissue handling and RNA extractions were performed as described. The differential expression across three or two genotypic classes was tested by ANOVA.Transcript levels were determined for SNP genotypes significantly associated with phenotypic traits to test whether transcript abundance varied in the different SNP genotypic classes. Only significant SNPs [false discovery rate (FDR) PtoCesA4 gene, including 1.111 kb of the promoter, was sequenced and analyzed in 40 unrelated individuals from the P. tomentosa association population to identify SNPs without considering insertions/deletions (INDELs). Sequencher v.4.0 and BioEdit were used for sequence alignment, and manual editing was used to confirm sequence quality and to remove primer sequences. To identify putative SNP variants, eight clones for each individual were randomly picked for initial allele sequencing on a Li-Cor 4300 genetic analyzer . Alignments and SNP discovery for this gene among 40 unrelated individuals described here are based on the P. trichocarpa genome (http://genome.jgi-psf.org/Poptr1/Poptr1.home.html). All 40 sequences have been deposited in GenBank (accession no. KC762252–KC762291). Subsequently, 92 common SNPs  were genotyped by single nucleotide primer extension using a Beckman Coulter sequencing system for all DNA samples.The Diploid sequences were disambiguated into haplotypes using Phase v.2.1 using 10,000 iterations of the Bayesian Markov chain Monte Carlo chain . We thenr2, which is affected by both recombination and differences in allele frequencies between sites  in the candidate gene was calculated with 105 permutations using TASSEL v.2.0.1 (http://www.maizegenetics.net/). To assess the extent of LD within the sequenced region of PtoCesA4, the decay of LD within a specific physical distance (base pairs) between common SNP sites within this gene was estimated by nonlinear regression  was identified based on the significant subpopulation structure in this association population (K = 11) (4 permutations in QVALUE (d) to additive (a) effects estimated from least-square means for each single SNP association. Details of the algorithm and formulas for calculating gene action were previously described (The unified mixed linear model (MLM) was used for single SNP trait analysis, with 10n QVALUE . The modP < 0.05). Model parameters, including 95% credible intervals for SNP effects, were estimated using Markov chain Monte Carlo with 50,000 steps after an initial burn-in of 10,000 steps. All linear mixed-model analyses were conducted using the Bayesian association with the missing data (BAMD) program in R (http://cran.r-project.org/package=BAMD).Bayesian linear mixed models incorporating effects of population structure were used to construct multi-locus models for each trait (P < 0.05) (see Table S2), the haplotype (a block of linked ordered markers) frequencies of loci genotypes were estimated based on genotypic data of 460 individuals, and haplotype-based association tests with growth and wood quality traits were performed using FAMHAP v.19 (http://famhap.meb.uni-bonn.de/index.html). FAMHAP estimates haplotype frequencies using maximum likelihood. Singleton alleles were ignored when constructing the haplotypes, and haplotypes with a frequency <5% were also discarded. The input consisted of genotype matrices with structure analysis matrices (Q) and phenotypic value matrices, and significances of the haplotype associations were identified based on 104 permutation tests. A correction for multiple testing was performed using the positive FDR.On the basis of the information from the LD blocks surrounding the significant SNPs (PtoCesA4 sequences in parents of this linkage population (accession no. KC762249–KC762252), we identified a panel of SNP markers that was based on the common SNPs detected in the association population (see Table S1). Inheritance tests of all SNPs were first examined in the linkage population with 1200 individuals by performing a chi-square (χ2) test at 0.01 probability, and then SNPs following Mendelian expectations (P ≥ 0.01) were used in single-marker analysis in the linkage mapping population . Significant SNPs were calculated by fitting the data to the model y =µ + im + ije, where y is the trait value, µ is the mean, im is the genotype of the ith marker, and ije is the residual associated with the jth individual in the ith genotypic class. Percent phenotypic variance explained by the most significant marker was calculated, and the FDR method was used to perform a correction for multiple testing.Comparing the PtoCesA4 from a cDNA library prepared from the developing xylem zone of P. tomentosa. The cDNA clone PtoCesA4 (GenBank Accession no. KC762292) is 3757 bp in length, and the open reading frame (3129 bp) encodes a polypeptide of 1042 amino acids with an estimated molecular mass of 118.4 kD and a pI of 7.60, flanked by 297 bp of 5′untranslated leader region (5′UTR) and 331 bp of 3′UTR , Arabidopsis, Eucalyptus grandis, and black cottonwood, an unrooted tree was generated from 37 full CesA protein sequences using 1000 replication bootstrap values . Phylogenetic analysis revealed that the cloned PtoCesA4 is an ortholog of the AtCesA4 and PtiCesA4 .To investigate the evolutionary relationship between the PtoCesA4 exhibited xylem-specific expression. Levels of PtoCesA4 mRNA in various poplar tissues, including root, bark, phloem, cambium, developing xylem, mature xylem, mature leaf, and apical shoot meristem, wer examined by RT-qPCR with gene-specific primers and Actin as an internal control. PtoCesA4 transcripts were present in all plant organs, including root, stem, and leaf, with varying patterns of expression (File S1). In leaf and root, PtoCesA4 is most abundant in mature leaf (0.8513). Low abundance is observed in apical shoot meristem and root (0.1092 and 0.1247). In the stem, PtoCesA4 shows the highest abundance in the developing xylem tissue (0.3490), followed by the mature xylem (0.3023); it has moderate abundance in the primary tissues of the bark (0.0838) and phloem (0.0387), and the lowest abundance is found in the cambium (0.0048). Collectively, PtoCesA4 expression in secondary tissues (xylem) was at least 60-fold higher than in primary tissues (cambium)  , suggesture leaf , suggestPtoCesA4, an approximately 6421-bp genomic region of PtoCesA4, including 1111 bp of promoter region, 297 bp of 5′UTR, 3129 bp of exons, 1553 bp of intron, and 331 bp of 3′UTR, was amplified and sequenced from 40 unrelated individuals in a discovery population that encompassed most of the natural range of P. tomentosa. After definition of phased haplotypes among these 40 unrelated individuals using Phase v.2.1, a more detailed analysis of SNP variation was conducted over different regions of PtoCesA4, and the profile of nucleotide diversity at these loci was calculated  were approximately six-fold lower than for synonymous polymorphisms . The Nd/Sd for the exon regions was significantly less than one, reflecting the action of purifying selection at the nonsynonymous sites in exons. Of the 218 single-base changes, 150 (68.8%) were transitions and 68 (31.2%) were transversions, and the ratio of transitions to transversions for these SNPs was approximately 2.17. Furthermore, for synonymous polymorphisms (47) in exons, 39 (80.1%) were transitions, indicating that translational selection has shaped synonymous codon usage.The = 0.0098 . The ave regions . In codiPtoCesA4  to 1.0 (disequilibrium). Several high-LD distinct haplotype blocks  across the sequenced regions were shown. A higher LD level among physically linked loci (the longer haplotype block) is present in the promoter region compared with the other regions . There are larger numbers of markers that are in linkage equilibrium among these distinct haplotype blocks (2r < 0.3) . The nonlinear regression shows a clear and rapid decline of LD with distance in base pairs within PtoCesA4 (2r ≥ 0.1 within 1400 bp)  and approximately 3200 bp (Northeastern region) (Using genotypic data for 92 common SNPs located in 1400 bp) , indicat region) .File S2 and File S3) were conducted with 104 permutations using MLM. In all, 41 significant associations with all nine phenotypic traits were identified at the threshold of P < 0.05, representing 24 SNPs from different regions within PtoCesA4 (see Table S2). Corrections for multiple testing using the FDR method reduced these 41 associations to 14 (Q < 0.10)  . These 1e traits . These le traits , in accoi.e., α-cellulose (5.3%), lignin (3.0%), and H (2.3%). In this case, the mode of gene action seems additive, with the minor allele (A) conferring a lower lignin content and higher values in α-cellulose and H (2R = 2.6%). The genotypic effects on fiber width were significant , consistent with the additive effect of gene action on fiber width . An additive effect of gene action appeared in the D trait . SNP44 in intron 1 and SNP48 in intron 2 were associated with lignin content and D, respectively. Four of the 10 SNP markers exhibited significant associations with at least one trait, suggesting a pleiotropic effect of these loci . In total, 38 associations were obtained across all traits representing nine unique growth and wood properties and 22 unique SNPs. Of the 22 unique SNPs, three were nonsynonymous, four were synonymous, and 15 were noncoding (see Table S3). Ten of these SNPs were associated with more than one trait , which is likely to be attributable to the strong correlation between some wood and growth property traits. Effect sizes for SNPs identified with the multi-locus models when analyzed using a single locus test were nearly two-fold lower than those detected only in the single-marker models (average 2R = 0.015). All of the SNPs identified as significant in the single locus tests (FDR Q < 0.10) were revealed with significant effects in multi-SNP models  (see Table S2) were analyzed with each of the nine traits, and the number of common haplotypes (frequency > 1%) per set varied from 2 to 10, with an average of 5.0. Seventeen significant regions including 80 common haplotypes were identified at the significance threshold of P < 0.05 (details not shown). Multiple test corrections reduced this number to 11, at a significance threshold of Q < 0.10, and 21 significant haplotypes were associated with the five phenotypic traits, excluding MFA and H phenotypes   . SNP89 t < 0.10) , and sig < 0.10) .PtoCesA4 in the linkage population, including six novel alternative SNPs identified in the parents of this hybrid population and 50 corresponding to the positions of common SNPs detected in the association population (see Table S1). Of these SNPs, 46 markers segregated in the 1200 progeny, with a segregation ratio close to 1:2:1 for 26 SNP loci and 1:1 for 20 loci (see Table S1); the 10 significant SNP markers (Q < 0.10) (File S4 and File S5) were conducted in this linkage mapping population. In all, 18 associations were first observed at the threshold of P < 0.05 (Table S4). However, a multiple test correction reduced this number to seven (Q < 0.10) (Q < 0.10)   identifi < 0.10) . Of thes < 0.10) .SNP18 and SNP49 with α-cellulose and SNP59 with fiber width were identified in both association and linkage populations . BecausePtoCesA4 RNA transcript abundance, transcript levels were compared among the different genotypic classes for the 10 significant SNPs    in the l < 0.10) . The gen < 0.10) .In the association population, for the marker SNP49 (exonic) associated with three traits, the highest transcript abundance was found in the AA group (0.6955), followed by the AC group (0.4307), and the transcript levels of the CC group were lowest (0.3308) . In examPtoCesA4, a highly targeted candidate gene belonging to key pathways of secondary cell wall biosynthesis, was investigated using a more comprehensive approach than previous studies of trees  was significantly less than 1 for PtoCesA4, which was commonly observed in natural populations of forest trees  were transition substitutions, indicating that translational selection has shaped synonymous codon usage. This finding was supported by the most common model of synonymous codon usage indicating that one or a few synonymous codons are preferentially used in genes with high codon bias, and such codons usually end in C or G   . This fiPtoCesA4 was analyzed separately within each of the three climatic regions and for the complete natural population  suggests that genome-wide association studies and genomic selection in natural populations may be more feasible in Populus than previously assumed  and the relative kinship matrix (K) as the covariance. In this study, the observed effect of the population structure (when K = 11 or K = 3)  was specifically associated with secondary cell wall development (P. trichocarpa (PtiCesA4) also is expressed in developing xylem tissue undergoing secondary wall thickening  (Fst = 0.028) of all common SNPs identified in the association population, which is consistent with the report that putative functional SNPs in genes in etiologic pathways for CVD show greater population differentiation than nonfunctional SNPs  . Secondapatterns . Similaromentosa , once a omentosa . Two nonomentosa , confirm= 0.075)  for thesnal SNPs .PtoCesA4 (SNP49) was in strong association with multiple traits , and the modes of gene action appeared to be additive, with the minor allele (A) conferring a lower lignin content and higher values in α-cellulose and H. This marker also was identified with these three traits in the multi-SNP analysis (see Table S3). This is consistent with the significant phenotypic correlation between these three traits (PtoCesA4 on certain traits. Wood is composed of cellulose microfibrils embedded in a lignin–hemicellulose matrix. The observed associations of PtoCesA4 with diverse traits suggest that PtoCesA4 influences two distinct pathways (lignin and cellulose biosynthesis) in secondary cell wall synthesis  causes a severe decrease in cellulose content and the thickness of fibers  is close to the two putative transmembrane domains (TMDs) at the N-terminus (217–238 and 250–267), which are involved in CESA protein–protein interactions (PtoCesA4 (OsCesA4); this mutation affects protein abundance in the plasma membrane and results in abnormal cell wall biosynthesis. Additionally, novel point mutations in the TMD have also been reported to affect cellulose synthesis in Arabidopsis . These results support that SNP18 might have a regulatory effect on PtoCesA4 expression, or it could to be in very strong LD with a nearby regulatory polymorphism; the detailed regulatory mechanisms of this locus will require further investigation. Although the mutation in the 5′ flanking region did not result in an amino acid changes, phenotypic traits can be affected because 5′UTRs play crucial roles in the regulation of gene expression, especially for transcriptional mRNA stability, translational efficiency, or subcellular localization  with α-cellulose in the association populations  had both higher average α-cellulose content and higher transcript levels than the homozygotes (CC and TT), suggesting that SNP41 may be a functional polymorphism affecting the regulation of gene expression. However, our follow-up study of this SNP in the linkage population did not support this observation. Similarly, no replication case has been reported in several previous association studies of wood traits (Many functional analyses of SNPs have examined coding regions and splicing sites in candidate genes related to wood traits that can alter proteins and mRNA splicing. However, SNPs in noncoding regulatory regions can also influence important biological regulation (promoter . Genotyppromoter , indicatlization . In our ulations . The estulations , i.e., hPtoCesA4 is highly expressed during secondary cell wall formation. Therefore, selection of optimal candidate genes through different approaches, such as EST database searches, transcript abundance profiles, QTL mapping, and comparison of orthologs in a model or related species, is very important for identifying useful alleles located within functional genes controlling traits of interest (Populus and combined with a recent genome-wide study of LD in P. trichocarpa (P. tomentosa should focus on a better understanding of the variability of haplotype structure across the entire genome. Comparatively, the preliminary application of multi-SNP analysis in PtoCesA4 suggests that it will be promising to conduct association studies with virtually all the related genes that share biological pathways and to have a more complete understanding of the genetic architecture of quantitative variation (Tissue-specific expression profiles revealed that


The application of next generation sequencing technologies and bioinformatic scripts to identify high frequency SNPs distributed throughout the peach genome is described. Three peach genomes were sequenced using Roche 454 and Illumina/Solexa technologies to obtain long contigs for alignment to the draft 'Lovell' peach sequence as well as sufficient depth of coverage for 'in silico' SNP discovery.The sequences were aligned to the 'Lovell' peach genome released April 01, 2010 by the International Peach Genome Initiative (IPGI). 'Dr. Davis', 'F8, 1-42' and 'Georgia Belle' were sequenced to add SNPs segregating in two breeding populations, Pop DF  and Pop DG ('Dr. Davis' × 'Georgia Belle'). Roche 454 sequencing produced 980,000 total reads with 236 Mb sequence for 'Dr. Davis' and 735,000 total reads with 172 Mb sequence for 'F8, 1-42'. 84 bp × 84 bp paired end Illumina/Solexa sequences yielded 25.5, 21.4, 25.5 million sequences for 'Dr. Davis', 'F8, 1-42' and 'Georgia Belle', respectively. BWA/SAMtools were used for alignment of raw reads and SNP detection, with custom PERL scripts for SNP filtering. Velvet's Columbus module was used for sequence assembly. Comparison of aligned and overlapping sequences from both Roche 454 and Illumina/Solexa resulted in the selection of 6654 high quality SNPs for 'Dr. Davis' vs. 'F8, 1-42' and 'Georgia Belle', distributed on eight major peach genome scaffolds as defined from the 'Lovell' assembly.The eight scaffolds contained about 215-225 Mb of peach genomic sequences with one SNP/~ 40,000 bases. All sequences from Roche 454 and Illumina/Solexa have been submitted to NCBI for public use in the Short Read Archive database. SNPs have been deposited in the NCBI SNP database. Prunus persica L), is a member of the Rosaceae. Other important Rosaceae crop species are cherry, apricot, plum, almond, strawberry, raspberry, and rose. Peach is a model plant for the family Rosaceae due to its small genome size of ~ 230 Mb http://www.rosaceae.org/peach/genome with eight haploid chromosomes .The SNP data sets described in this submission were designed to be used as a resource for future map development and QTL analysis of quality genes in progeny from crosses developed by the University of California, Davis peach and almond breeding programs. One of our objectives was to locate a SNP approximately every 40,000 bases on the physical peach map to assist with the characterization of functional genes after mapping and QTL analysis. Many of the SNPs may be useful in other peach improvement programs with similar objectives and are provided as a community resource without restriction.80% of the Illumina/Solexa raw reads from the present analysis were useable. Inclusion of the Roche 454 sequence greatly improved our alignment to the 'Lovell' sequence because Roche 454 sequences aligned to genome areas that Illumina/Solexa sequences did not uniquely or usefully align to. 93.4% of the combined pileup of Roche 454 and Illumina/Solexa reads aligned to the reference genome. For comparison, Harismendy et al.  found thThe 6654 SNPs from this study were validated by selecting a subset of 1536 SNPs, spaced at reasonably even intervals along the aligned genomes, and mapping them to Pop DF and Pop DG. 1211 SNPs were mapped in both populations. 304, 385, and 549 unique map positions (either one or more SNPs located at those positions) were obtained for Pop DF, Pop DG, and the consensus map, respectively, and are reported in a separate manuscript.The total base output from each lane of the Illumina/Solexa sequencing protocol depends on the number of cycles used during the run, and whether or not the reads are paired end reads. For example, a 40 cycle single read run with 24 million good reads will give 960 Mb bases/lane, while an 85 cycle run of paired end reads will provide nearly 4 Gb. These results are better than those of Harismendy et al. ,13, wherCombining the Roche 454 long reads of ~ 250 bp and ~ 84 bp read length from the Illumina/Solexa method improved sequence alignments. Early reads from Illumina/Solexa rarely exceeded 36 bp, but at the time that this project was conducted read lengths of greater than 84 bp were being generated. (More than 80% of the reads from Illumina/Solexa in the present study were ~ 84 bp in length.) The consensus view from many of the large sequencing centers , suggests 85 bp runs are the best compromise between longest read length and lowest error. Sequences of this size are suitable for SNP discovery applications, for transcriptome analysis, and for bacterial genome analyses . The aliPerl scripts were developed to analyze the sequence information, with the recently released Velvet software including the Columbus module for sequence assembly, to select an initial set of ~6,000 candidate SNPs from those sequences in a cost effective manner.The pyro-sequencing technologies of Roche 454 and Illumina/Solexa were used to generate deep coverage genome sequence (43.2× combined Roche and Illumina/Solexa) from the three peach genomes at a reasonable cost. Customized Perl scripts were developed to evaluate and refine SNP populations and to identify useable SNPs for subsequent mapping. The 11.3 Gb of submitted peach sequence data sets and the 6654 SNP data set are a resource that can be used by the peach breeding community to mark genes of interest in different breeding programs. However, some of these SNPs may not be present for all combinations of peach parents. Additional SNP discovery using the technologies described in this paper will be needed to develop SNP panels optimized for specific parents and cross combinations. The 6654 SNPs discovered in the present study and their distribution on all scaffolds with ~1 SNP/40,000 nucleotide bases will cover the peach genome sufficiently to permit tight SNP linkage to functional genes for breeding and/or functional gene characterization as well as marking QTLs for breeding.The peach sequence data was used to identify SNPs for subsequent mapping and use in breeding strategies. New Peach parents are denoted as DD: 'Dr. Davis'; F8: 'F8, 1-42'; GB: 'Georgia Belle'. Progeny populations from crosses among the parents are Pop DF: 'Dr. Davis' × 'F8, 1-42'; Pop DG: 'Dr. Davis' × 'Georgia Belle'. NCBI is the National Center for Biotechnology Information, while SNPs are single nucleotide polymorphisms.RA collected samples, organized the plant materials, coordinated data collection with JF and wrote first draft of paper. DEP planned project with EO and CHC, coordinated activities of RA, JF, and DL, revised and edited grant proposal and paper, and served as communicating author. EO planned project with CHC and DEP, wrote draft grant proposal. JF developed Perl scripts, analyzed sequences and selected SNPs. AD provided 454 data, TMG provided plant materials. DL was involved in planning the analysis of sequence data. NAJ conducted data analysis for JF. PMG evaluated SNPs after SNP selection. CHC planned project with DEP and EO, obtained the extramural funding for the project. All authors read and approved the manuscript.Script for SNP selection from polymorphic parents. Custom script for selecting SNPs from polymorphic parents in crosses used to identify SNPs that will produce 1:1 segregations in mapping populations.Click here for file


Most recently, with maturing of bovine genome sequencing and high throughput SNP genotyping technologies, a large number of significant SNPs associated with economic important traits can be identified by genome-wide association studies (GWAS). To further determine true association findings in GWAS, the common strategy is to sift out most promising SNPs for follow-up replication studies. Hence it is crucial to explore the functional significance of the candidate SNPs in order to screen and select the potential functional ones. To systematically prioritize these statistically significant SNPs and facilitate follow-up replication studies, we developed a bovine SNP annotation tool (Snat) based on a web interface.i.e., the Affymetrix Bovine 10K chip array and the Illumina 50K chip array, have been annotated by Snat, and the corresponding outputs can be directly downloaded from Snat website. Furthermore, a real dataset involving 20 identified SNPs associated with milk yield in our recent GWAS was employed to demonstrate the practical significance of Snat.With Snat, various sources of genomic information are integrated and retrieved from several leading online databases, including SNP information from dbSNP, gene information from Entrez Gene, protein features from UniProt, linkage information from AnimalQTLdb, conserved elements from UCSC Genome Browser Database and gene functions from Gene Ontology (GO), KEGG PATHWAY and Online Mendelian Inheritance in Animals (OMIA). Snat provides two different applications, including a CGI-based web utility and a command-line version, to access the integrated database, target any single nucleotide loci of interest and perform multi-level functional annotations. For further validation of the practical significance of our study, SNPs involved in two commercial bovine SNP chips, To our best knowledge, Snat is one of first tools focusing on SNP annotation for livestock. Snat confers researchers with a convenient and powerful platform to aid functional analyses and accurate evaluation on genes/variants related to SNPs, and facilitates follow-up replication studies in the post-GWAS era. However, it is infeasible to mine the relevant research data from these public sources by a single query. Furthermore, it is a daunting task to integrate various sources of functional information of interest among the large assortment of data in a manual fashion.Currently, genome wide association studies (GWAS) have been widely accepted as a primary approach for gene identification concerning complex traits. A subset of SNPs related to the trait of interest can be derived from GWAS at a specified level of statistical significance. To further determine true association findings in GWAS, the common strategy is to sift out the most promising SNPs for follow-up replication studies. Hence it is crucial to explore the functional significance of the candidate SNPs in order to screen and select the potential functional ones. So far, a variety of public bioinformatics databases, rez Gene , UniProtrez Gene , Gene Onrez Gene , KEGG PArez Gene  and Animrez Gene , etc., ce.g., SNPit [e.g. GO:0000122 (GO term), bta00340 (KEGG Pathway entry), A7YWP4 (UniProt accession), and more detailed descriptions for these identifiers should be obtained manually via accessing the relevant public domains. Finally, FunctSNP only recognizes the cluster ID (rs#) and accurate position of a SNP in dbSNP, triggering an obviously application limitation since users are usually interested in those SNPs without specific cluster IDs or not included in dbSNP in most cases, such that FunctSNP can not deal with these SNPs under this situation.To deal with SNP annotation, a number of bioinformatics tools have been created. However, the majority of these tools are for humans, ., SNPit , SNPnexu., SNPit , and few., SNPit  is curreFocusing on tackling the above limitations, we developed a SNP annotation tool (Snat) to provide a wide array of functional SNP annotations for bovine. In Snat, the most novelty is that recent versions of information from FTPs and webpages of the public domains have been extracted and integrated to construct a composite database. Furthermore, SNPs at arbitrary positions can be well annotated through a single query, regardless of whether they are involved in the dbSNP database or not.Snat aims at the design for a novel query scheme which can provide precise and comprehensive annotation for bovine SNPs. This would be helpful for mining potential clues of functional importance. Snat is written in Perl as well as SQL scripts with modular architecture. The design of the query scheme with common interfaces supports multiple options for each annotation task.Developing of Snat consists of two major steps: The first step is the construction of the local database that contains substantial information for SNP annotation. Specifically, documents are retrieved from various aspects of online databases using an automated procedure via Perl program. These online resources include dbSNP , Entrez Besides web-based application of Snat, we also developed an alternative version of Snat in command-line mode, which can be run locally to query and annotate SNPs. Specifically, via downloading the integrated database from the Snat site as a SQL script, users can construct the local database, which is identical with that of web-based version. Based on the local database, SNP annotation can be conducted to print results into a text file via Perl scripts in a command-line fashion. The raw text report, generated by either the locally running Perl programs or downloaded from Snat website, looks not well-formatted but is created with a specified access rule. Thus it can be readily processed via running a Perl program provided by Snat website to produce annotation data structured in TSV files for further analyses.Snat addresses two functions. One is to query and annotate those SNPs involved in dbSNP, the other is to deal with arbitrary single nucleotide loci no matter whether they are included in dbSNP or not  or accurate coordinate of a SNP in dbSNP while Snat can deal with an ambiguous coordinate via searching for the nearest SNP or SNPs within a specified distance away from it , SNP coordinate  or an index file that contains numbers of SNP coordinates . Output reports are generated corresponding to the annotation options in the command. For examples, the option --all means that all annotation information should be included in the output report (see the first command); "-gene symbol name -uniprot acc function -go" means that the symbol and name of gene, the accession and function of UniProt protein and GO information should be included in the output report (see the second command).The command line options db_name and db_user, db_password denote the name, the user name and the password of the local annotation database respectively. Input data can be rs-identifier  related to the SNPs of interest as well as all other SNPs within 5k bp region away from these 20 SNPs were mined and integrated into a raw text : Any operating system supporting Perl and MySQLProgramming language: Perl, SQLOther requirements: Perl DBI and DBD-mysql moduleLicense: Free for non-commercial usageSNP: Single Nucleotide Polymorphism; CGI: Common Gate Interface; HTML: Hypertext Markup Language; TSV: Tab Separated Values; UTR: Untranslated RegionJJ and JL constructed the software and drafted the manuscript. LJ, BZ and WF participated in database construction. JL and QZ designed and supervised the project. All authors read and approved the final manuscript.411 SNPs involved in Illumina Bovine 50K SNP chip but not included in dbSNP. In Illumina Bovine 50K SNP chip, 52,255 markers are explicitly located in chromosomes based on Btau4.0. However, 411 out of these 52255 SNP markers are not included in dbSNP via position comparison based on Btau4.0. All these 411 SNPs are listed in the table. These SNPs can be annotated through "Any locus" function by Snat.Click here for fileTwo examples given to compare Snat with FunctSNP. The examples show the differences between the two tools on features of input data and annotation process.Click here for fileDetailed descriptions on annotation results by Snat. An example is provided to give a detailed explanation on the annotation reports.Click here for fileInformation of 20 significant SNPs identified associated with milk yield. This table lists positions of 20 significant SNPs based on Btau4.0 identified associated with milk yield from our recent GWAS results .Click here for fileThe raw annotation outputs for the 20 significant SNPs using Snat. SNPs within 5k bp region away from these 20 SNPs are annotated by Snat. All the data are printed into the raw text file.Click here for fileA summary of annotation data for the 20 significant SNPs structured in TSV format. The raw annotation outputs of the 20 significant SNPs are further processed to generate a well-structured summary file in TSV format by the program raw2TSV.pl.Click here for fileAnnotation results of the 20 significant SNPs saved in a worksheet. The summaries of annotation data for the 20 SNPs structured in TSV format are saved in the worksheet for more easily viewing.Click here for fileSNPs involved in dbSNP related to each of the 20 SNPs annotated. The cluster IDs (rs#) of all SNPs harbored within 5k bp regions from each of these 20 SNPs are integrated. This file is extracted by the program raw2TSV.pl from the raw text file.Click here for fileNon-redundant UniProt protein information extracted from the raw annotation outputs for the 20 SNPs. The file is generated by the program raw2TSV.pl and contains non-redundant UniProt protein information extracted from the raw annotation outputs for the 20 SNPs.Click here for fileNon-redundant GO terms extracted from the raw annotation outputs for the 20 SNPs. The file is generated by the program raw2TSV.pl and contains non-redundant data of GO terms extracted from the raw annotation outputs for the 20 SNPs.Click here for fileNon-redundant KEGG Pathway information extracted from the raw annotation outputs for the 20 SNPs. The file is generated by the program raw2TSV.pl and contains non-redundant KEGG Pathway information extracted from the raw annotation outputs for the 20 SNPs.Click here for fileThe numbers of QTLs and five most relevant traits corresponding to each of the 20 SNPs. The file is generated by the program raw2TSV.pl and contains the data of the numbers of QTLs and five most relevant traits corresponding to each of the 20 SNPs extracted from the raw annotation outputs.Click here for fileInformation of 105 significant SNPs identified associated with milk production traits based on Btau4.0. This table lists positions of 105 significant SNPs based on Btau4.0 identified associated with milk production traits from our recent GWAS results .Click here for file


Hordeum vulgare L.) and to determine associations to kernel and malting quality parameters.Flavonoids are an important class of secondary compounds in angiosperms. Next to certain biological functions in plants, they play a role in the brewing process and have an effect on taste, color and aroma of beer. The aim of this study was to reveal the haplotype diversity of candidate genes involved in the phenylpropanoid biosynthesis pathway in cultivated barley varieties (PAL), cinnamate 4-hydroxylase (C4H), chalcone synthase (CHS), flavanone 3-hydroxylase (F3H) and dihydroflavonol reductase (DFR) of the phenylpropanoid biosynthesis pathway were partially resequenced in 16 diverse barley reference genotypes. Their localization in the barley genome, their genetic structure, and their genetic variation e.g. single nucleotide polymorphism (SNP) and Insertion/Deletion (InDel) patterns were revealed. In total, 130 SNPs and seven InDels were detected. Of these, 21 polymorphisms were converted into high-throughput pyrosequencing markers. The resulting SNP and haplotype patterns were used to calculate associations with kernel and malting quality parameters.Five genes encoding phenylalanine ammonia-lyase , viscosity (C4H) or to final attenuation (F3H).SNP patterns were found to be highly variable for the investigated genes. The developed high-throughput markers are applicable for assessing the genetic variability and for the determination of haplotype patterns in a set of barley accessions. The candidate genes  Thus,All genes studied here are part of the phenylpropanoid pathway, coding especially for enzymes of the flavonoid synthesis pathway. Their gene products represent the mostly abundant group of secondary metabolites in angiosperms . A wide The high structural diversity of flavonoids is related to many biological functions: Anthocyanins are plant pigments and serve as attractants for pollinators and seed dispersers. Others flavonoids are known to exhibit repellent functions against herbivorous insects . In formThe relationship between genetic diversity and phenotypic performance is assessed by association studies. Source for these investigations are single nucleotide polymorphisms (SNP) and the combination of various SNPs within one gene to haplotypes. Several association studies for specific candidate genes were performed in barley -21. UntiThe aim of the present study was (i) the assessment of allelic diversity of genes representing the phenylpropanoid pathway in barley and (ii) the determination of significant associations of the detected single nucleotide polymorphisms (SNPs) or their resulting haplotypes with kernel and malting quality parameters.Five genes of the phenylpropanoid metabolic pathway were investigated for their abundance of polymorphisms and for associations to kernel and malting quality parameters, which will be further described.PAL encoding gene. For PAL_2 a frequency of 58.8 SNPs/kb was found. Less SNPs were detected in the gene fragment CHS_GM290 with a density of 1.5 SNPs/kb. This high polymorphic variability was also described in the findings of Bundock et al., Kanazin et al. and Rostoks et al. [A high variability of SNP frequency in the investigated fragments of the candidate genes was observed Table . The higs et al. -24. Addis et al. -21 reveaPAL were amplified resulting in a total of 877 bp of the sequenced exonic region. Both fragments have an overlapping identical region of 322 bp. Within this area a completely different SNP pattern was observed with only two identical SNPs between both sequenced fragments  into isoleucine (ATA) and SNP24 leads to an exchange of leucin (CTC) into phenylalanine (TTC). Five out of 35 detected SNPs were converted into CAPS markers  from Triticum aestivum (≥91%).All exon sequences of F3H_1 and GM022 showed high identity to the cDNA X58138 encoding the F3H amino acid sequence of barley . The entGenotyping was performed with 190 cultivars by applying high-througput marker assays for seven SNPs and two InDels. Both InDels of F3H_GM022 ID1 and ID2 were not included into the haplotype and association analysis due to unclear and rare allele pattern. At least, four haplotypes could be determined. The haplotype F3H_H1 is predominantly found in winter cultivars and the other three haplotypes F3H_H2, F3H_H3 and F3H_H4 are mainly abundant in the spring types Table .The DFR gene was partially resequenced. The obtained fragment of 949 bp consists of 174 bp from the third exon, followed by a 105 bp large intron and 585 bp of the fourth and last exon with two stop codons, followed by the 3’UTR of 85 bp. The sizes of the intron and this exon are in accordance to the results from Kristiansen and Rohde . ResequeMseI. Thus, all varieties with this deletion were cutted into fragments of 250 and 312 bp. This was also confirmed by the pyrosequencing assay for DFR_ID1 , calculated with a publicly available set of DArT and SNP markers [CHS fragments showed polymorphisms between the mapping parents Steptoe and Morex and were localized on different positions in the barley genome, respectively . The InDels of both fragments from F3H were mapped using the pyrosequencing markers F3H_1_ID4 and GM022_ID2. These were localized on the long arm of chromosome 2H, linked with the RFLP marker ABC252 .C4H  by using a combination of population structure and kinship, general linear model (GLM) by using population structure and mixed linear model (MLM) by using kinship. Information about population structure was based on a Q5-matrix with random 22 SSR markers. As it was shown in a previous study, multi-allelic SSR markers serve as a more accurate tool to reveal the population structure .PAL, C4H, F3H, DFR , kernel yield (KY) and pH , a high variance was observed for viscosity of the malt extract. Low viscosity values were mainly found in accessions having the favorable C-allele of SNP1, which was true for all investigated 2-rowed spring barleys.Only one SNP of the DFR, haplotype 3 was found to be significantly associated with sieve fraction (SF), brabender and viscosity. This haplotype is predominantly present in winter cultivars.In case of 2 values explain the percentage of phenotypic variance explained by the model and by the marker. In some cases, the associations of the haplotypes confirm the SNPs, f.e. PAL_SNP1 is specific for PAL_H1  and row number. Most of the developed pyrosequencing markers are applicable for high-throughput genotyping of barley varieties. Several significant associations of the investigated candidate genes with kernel and malting quality traits were detected. Some of the derived SNP-markers can serve as diagnostic tool for marker-assisted selection in breeding programs to achieve new high quality malting varieties.A set of 16 diverse reference genotypes including the mapping parent Steptoe × Morex was used for resequencing and SNP-detection in five candidate genes from the phenylpropanoid pathway. Additionally, 190 barley varieties of European origin  and are shown in Table TM Vers. 4.06 . From the consensus sequences of each candidate gene, primers were designed with Primer3 Vers. 0.4.0 [2, 0.01% gelatine) and contained 100 ng of genomic DNA, 0.2 mM of dCTP, dGTP, dTTP, dATP, 0.2 μM of each primer and 1 U of Taq polymerase. After 3 min at 94°C, 45 cycles were performed with 1 min at 94°C, 1 min at 55°C, 2 min at 72°C and a final extension step of 10 min at 72°C. Successfully amplified gene fragments obtained for the reference genotypes were resequenced. All sequences for each gene were aligned and compared to the template reference sequence with the software SequencherTM, and SNP and InDel polymorphisms were revealed. The exact positions of 3’- and 5’-UTR, introns and exons were determined by using GeneSeqer developed by Schlueter et al. [Sequence information  for five genes from the phenylpropanoid pathway was obtained from NCBI  provided by Biotage  and are summarized in Additional file CAPS marker development was performed using the program SNP2CAPS by Thiel et al. . The digAll SNP-markers found to be polymorphic either between the mapping parents Steptoe x Morex or Morex x Barke were tested on each double haploid (DH) mapping population. The Steptoe x Morex population consisted of a set of 77 double haploid lines. Linkage maps were constructed using the software MapMaker 2.0 . Genetichttp://webblast.ipk-gatersleben.de/barley/) on the database assembly_WGSMorex and in case of fragment GM287 on assembly_WGSBowman.For anchoring the investigated genes on the physical map, a blastN of the PCR-derived sequences was conducted by using the IPK BLAST server  gene fragment PAL_1.Click here for fileSNPs detected within 16 reference genotypes for the phenylalanine ammonia-lyase (PAL) gene fragment PAL_2.Click here for fileGenetic structure of the resequenced fragments C4H_1 and C4H_4 from the cinnamate 4-hydroxylase (C4H) encoding gene. Double lines indicate UTR regions, single lines indicate no sequenced regions. Violet – CAPS marker and high-throughput SNP marker, green – high-throughput SNP marker.Click here for filePositioning of the chalcone synthase (CHS) resequenced gene fragments GM_293 and GM_290, CHS_1, CHS_2 and CHS_3 in relation to cDNA Y09233. Polymorphisms were only detected in the large fragments GM_290 and GM_293. Green – high-throughput SNP marker.Click here for fileDetected SNPs and their resulting haplotypes within 16 reference genotypes for the chalcone synthase (CHS) gene fragment GM_293.Click here for fileDetected SNPs within 16 reference genotypes for the chalcone synthase (CHS) gene fragment GM_287. Four different haplotypes were identified.Click here for fileGenetic structure of dihydoflavonol reductase (DFR) according to Kristiansen and Rohde [. Primer development for resequencing was performed using sequence information of EST contig Hv.23226. Light grey boxes represent exons and double lines between exons indicate the introns. UTRs are also marked by doubled lines. Green – high-throughput SNP marker, violet – CAPS marker and high-throughput SNP marker.nd Rohde . Primer Click here for fileMarker-trait-associations of SNP polymorphisms and haplotypes found in 190 European barley cultivars and selected kernel and malting quality parameters for cinnamate 4-hydroxylase (C4H), flavanone 3-hydroxylase (F3H), dihydoflavonol reductase (DFR), chalcon synthase (CHS) and phenylalanine ammonia-lyase (PAL). Considered statistical models: 1. Mixed linear model (MLM) with principal component analysis (PCA), 2. MLM with kinship, 3. General linear model (GLM) with population structure. Significant at * p < 0.05, ** p < 0.01,*** p < 0.001, n.s. not significant.Click here for fileList of investigated 190 barley varieties.Click here for fileList of primers used for PCR amplification and sequencing.Click here for fileUsed restriction enzymes for CAPS marker development and reaction conditions.Click here for fileTrait statistics of single phenotypic values across 185 varieties (before elimination of outliers) for 22 traits.Click here for filePhenotypic data used for association analysis .Click here for file


Cyprinus carpio) is one of the most important aquaculture species in the world accounting for nearly 14% of freshwater aquaculture production. There are various strains of common carp with different economic traits, however, the genetic mechanism underlying the different traits have not been elucidated yet. In this project, we identified a large number of gene-associated SNPs from four strains of common carp using next-generation sequencing.Single nucleotide polymorphisms (SNPs) have been used as genetic marker for genome-wide association studies in many species. Gene-associated SNPs could offer sufficient coverage in trait related research and further more could themselves be causative SNPs for traits. Common carp  were tested to be true SNPs.Transcriptome sequencing of four strains of common carp  was performed with Solexa HiSeq2000 platform. Transcriptome analysis of common carp using RNA-Seq is a cost-effective way of generating numerous reads for SNP discovery. After validation of identified SNPs, these data will provide a solid base for SNP array designing and genome-wide association studies. Cyprinus carpio) is a widespread freshwater fish of eutrophic waters in lakes and large rivers in Europe and Asia. The wild populations are considered vulnerable to extinction, but the species has also been domesticated and introduced into various environments worldwide. With cultural history of several thousand years, common carp becomes one of the most important food fish with over hundred strains and varieties in the world. Common carp and its closely related Cyprinidae species provide over 30% aquaculture production in the world Common carp  technology and high throughput transcriptome sequencing could also fulfill the purpose Transcriptome sequencing and assembly of common carp had been completed and reported which could serve as reference for cSNP identification. In this study, RNA-Seq had been conducted in four distinct common carp strains. RNA-Seq data had been mapped onto reference transcriptome of common carp, and cSNP had been identified and characterized from these four strains. These cSNP are invaluable resource for genetic and genome research of carps, especially for the design and construction of high throughput SNP genotyping platform in the future.Illumina sequencing was conducted to generate short sequence reads of expressed sequences. The cDNAs were sequenced on Illumina HiSeq2000 platform that generated 114.9 million paired-end reads for mirror carp, 111.4 million for purse red carp, 112.2 million for Xingguo red carp, 105.4 million for Yellow River carp with read length of 50 bp .The short reads of RNA-Seq data from 4 strains had aligned onto reference transcriptome of common carp As reference transcriptome has been aligned to zebrafish EST database by ESTScan software Minor allele frequency (MAF) is an importance factor for SNP loci evaluation.For each strain, MAF of SNPs were calculated and distribution was plotted .As MAF results were calculated from transcriptome data, they may reflect relative abundance of transcription product of two alleles. However, they are probably not linear with real genome allele frequencies as many factors are involved in transcription progresses.SNPs distribution is important for consideration of coverage using SNP markers. Here we analyzed SNPs distribution among all contigs of reference transcriptome, which was shown in Comparative analysis was applied using zebrafish genome as reference , showinghttp://www.ebi.ac.uk/Tools/pfa/iprscan/) and integrated protein databases http://wego.genomics.org.cn). As shown in As gene ontology (GO) analysis has been conducted on assembled transcriptome sequences by using InterProScan  and kept at −20°C until RNA extraction. Total RNA was isolated from 24 samples using TRIZOL  with DNase I following manufacturer’s protocol. Integrity and size distribution of all samples were checked with Bioanalyzer 2100 .Sequencing of 24 samples was conducted in HudsonAlpha Genomic Services Laboratory . Briefly, 100 ng of total RNA was used for cDNA synthesis using Ovation RNA-Seq . The cDNA was then used for Illumina library construction. Adaptors were ligated to the cDNA which had been end-repaired. Each prepared tissue cDNA was sequenced with 50-bp paired-end reads on HiSeq2000. The following analysis of image data and signaling data were processed using the Illumina Pipeline Software according to the manufacturer’s instructions.The raw reads were exported in FASTQ format, which were used as imported files for SNP calling. BWA and SAMtools software were applied to align reads to transcriptome reference and call SNPs. Filtering threshold was set as bellowing, read depth no less than 10, quality score no less than 20. The default parameter was used for quality control of flanking sequences in the step of “mpileup”.Flanking sequences of selected SNPs were extracted and PCR primers were designed. Four samples were used as templates for PCR validation, and each sample was a DNA mixture of ten fish of a strain. PCR products were then sequenced by Sanger method and sequencing results were analyzed by DNASIS MAX v1.0 .In this study, the transcriptome of four strains of common carp were sequenced with Illumina HiSeq2000 platform, and large numbers of SNPs were discovered with an assembled reference transcriptome. Overall, these SNPs identified in this study provide useful resources for subsequent SNP array designing, genome-wide association studies and relative genetic research.