To understand the genetic architecture of complex traits and bridge the genotype-phenotype gap, it is useful to study intermediate -omics data, e.g. the transcriptome. The present study introduces a method for simultaneous quantification of the contributions from single nucleotide polymorphisms (SNPs) and transcript abundances in explaining phenotypic variance, using Bayesian whole-omics models. Bayesian mixed models and variable selection models were used and, based on parameter samples from the model posterior distributions, explained variances were further partitioned at the level of chromosomes and genome segments.2 population of 440 mice. The genomic variation was covered by 1806 tag SNPs, and transcript abundances were available from 23,698 probes measured in the liver. Explained variances were computed for models using pedigree, SNPs, transcripts, and combinations of these. Comparison of these models showed that for BW, a large part of the variation explained by SNPs could be covered by the liver transcript abundances; this was less true for FI and FE. For BW, the main quantitative trait loci (QTLs) are found on chromosomes 1, 2, 9, 10, and 11, and the QTLs on 1, 9, and 10 appear to be expression Quantitative Trait Locus (eQTLs) affecting gene expression in the liver. Chromosome 9 is the case of an apparent eQTL, showing that genomic variance disappears, and that a tri-modal distribution of genomic values collapses, when gene expressions are added to the model.We analyzed three growth-related traits: Body Weight (BW), Feed Intake (FI), and Feed Efficiency (FE), in an FWith increased availability of various -omics data, integrative approaches are promising tools for understanding the genetic architecture of complex traits. Partitioning of explained variances at the chromosome and genome-segment level clearly separated regulatory and structural genomic variation as the areas where SNP effects disappeared/remained after adding transcripts to the model. The models that include transcripts explained more phenotypic variance and were better at predicting phenotypes than a model using SNPs alone. The predictions from these Bayesian models are generally unbiased, validating the estimates of explained variances. Large amounts of genomic information generated from Single Nucleotide Polymorphism (SNP) microarrays have become available in recent years for many species-3. This However, in the eQTL approach, associations between SNPs, transcript level, and phenotypes are analyzed individually. This is likely to lead to “missing heritability”, becauseThe choice of Bayesian variable selection (BVS) models was due to its features to separate markers with large/moderate or small effects, and to locate the important regions in the genome or transcriptome which serves a better QTL mapping method because it produces clearer signals for QTL. FurtherThe aim of this study was to explore the contributions of various sources of variation, such as population structure, SNP variants, and gene expression levels, to a set of growth related traits  in mice. These traits are very important, both in terms of agricultural production and for obesity in humans. Bayesian mixed models and Bayesian variable selection models were applied to model pedigree, SNPs and/or gene expressions and to derive explained variances for these components. In addition, they were used to partition of SNPs and gene expression by chromosome and genome sections. To validate the estimates of explained variances, the predictive ability of these models was studied using cross validation.2 population of 440 mice was available with complete records for body weight at 8 weeks (BW) and 337 records for feed intake (FI) and feed efficiency (FE), measured during the period 3 weeks to 8 weeks[0 founder lines. Data was obtained in three batches and the sex of the animals was recorded. At the end of the experiment, the mice were sacrificed and liver tissue was extracted for genome-wide expression profiling. RNA isolation, cDNA synthesis, array hybridization, normalization of probe level intensity, and annotation of data were performed as described in detail by[2 population. Density functions of phenotypes are available in Additional filehttp://gbi.agrsci.dk/~pso/BIG_genome_transcriptome/).An M16 × ICR F 8 weeks. An addietail by. Genotypy  by an intercept μ, environmental effects of batch and sex b, a polygenic effect based on pedigree u, regressions on SNP covariates a, regressions on gene expression covariates g, and a model residual e, as:The most complete model used describes phenotypes X is the design matrix for batch and sex effects, Z is a design matrix that links polygenic effects to the observed records, W is a matrix with 1806 SNP covariates, and Q is a matrix with 23,698 gene expression covariates. The SNP and gene expression covariates were centered and scaled to unit variance.where u, a, g, and e in (1), i.e.,A is the numerator relationship matrix based on pedigree information,b, are assigned flat prior distributions, which is the Bayesian analog of fitting “fixed effects” (unshrunken) estimates. A Markov chain Monte Carlo (MCMC) algorithm was applied in the software bayz[u, a and g, requiring a correlation >0.999 from repeated MCMC runs, and by computing the effective sample sizes for the variance components using the R Coda package[Based on work of-22, the are bayz to obtaiview see. The Mon package.y from (1) is var(Zu) + var(Wa) + var(Qg) + var(e). To obtain posterior means (PMs) and posterior standard deviations (PSDs) on the explained variances for SNPs and gene expressions, var(Wa) and var(Qg) were evaluated based on the posterior samples for a and g from the MCMC, i.e., as the PM and PSD of var(Wat) values over MCMC cycles, where at is the posterior sample for a from MCMC cycle t. This procedure is not required for the polygenic variance, because Z is a design matrix, unlike W and Q, which are covariate matrices.The explained variance in a and g as follows:The second model used was a Bayesian variable selection model, where the approach of George and McCulloch was folla,g, anda and g, respectively, indicating whether the ith element in a or g, respectively, comes from the distribution with large or small variance. The varianceswhereribed in. This moribed in, and thea and g in the variable selection model, explained variances were computed and partitioned by chromosome and by genome section. The variable selection model is more suited to make such a partitioning, because unlike the mixed model version, it allows for different variance contributions per SNP. The explained variances were evaluated in the same way as for the mixed model, by evaluating var(Wat) and var(Qgt) over MCMC cycles t, except that the a and g samples are obtained under the mixture model prior assumptions. The same expressions can be straightforwardly evaluated for parts of the SNPs or gene expressions to obtain explained variances per chromosome and for small windows of SNPs within chromosomes. Variance within a chromosome was computed using a 5-SNP sliding window to obtain a genomic variance profile.From the posterior samples for 2 between adjacent SNPs was 0.55, and average R2 between SNPs two apart was 0.39, which we considered sufficiently high to warrant computation of variances in a 5-SNP window. To study the relative importance of family structure, SNPs, and gene expressions, six sub models and the complete model (1) were used. These were models that use only pedigree information (PED), only SNP data (SNP), only gene expression data (GEX), SNP + GEX, PED + GEX, PED + SNP, and the complete model PED + SNP + GEX. These models always included sex and batch effects.It is difficult to choose an optimal windows size as it depends on extend of LD, marker density and an arbitrary cut-off for what is considered important LD. In the data analyzed here, average Rth fold. All predictions from the 11-fold cross validation were collected to compute correlations between predicted and actual phenotypes, and regressions of predicted phenotypes on actual phenotypes, using the whole data set. The slope of the regression lines of predicted phenotypes on actual phenotypes are expected to be 1 if the model produces unbiased predictions, which would validate the estimates of explained variances. The University of Nebraska Institutional Animal Care and Use Committee approved all procedures and protocols.The predictive ability of the models was evaluated using an 11-fold cross-validation. For body weight, 440 records were divided randomly in 11 groups, each with 40 individuals. Feed intake and feed efficiency, with 337 records in total, were randomly divided in 10 groups of 30 records and one group of 37 records. The complete model, including all variance parameters, was re-estimated on each set of 10 folds and predictions were computed for the phenotypes in the remaining 11Table Overall, explained variances increase by adding gene expression information , i.e., in the most complete model (PED + SNP + GEX) explained variances were 88%, 75%, and 71% for BW, FI, and FE respectively. This confirms the assumption that gene expressions can explain a larger part of phenotypic variance than genetic or genomic information, by capturing environmental, and possibly non-additive, genetic effects through the gene expressions. InformaThis model shows that, for these traits, the liver transcriptome contributes a larger portion of explained variance. This is most pronounced for BW, with 18% of explained variance from the genome and 63% from the liver transcriptome. Thus, in this case, the predominant model is that SNPs regulate gene expressions to exert their effect on the phenotype.FigureThis method/approach is suitable for gene-level resolution. However, gene-level resolution is highly data dependent, i.e. it requires high marker density and a study population with LD blocks that span small genomic regions. In this work we have used F2 crosses from outbred lines, which has large LD blocks and this kind of data has limited resolution for fine-mapping of QTL.One may argue that the most complete model is more interesting to investigate genetic architecture and chromosomal/sub-chromosomal variance but as we have shown SNPs and pedigree are largely confounded and they explain about the same variance. This confounded explained variance is getting worse in the case that both Pedigree and SNPs are in one model (PED + SNP model) which is shown in higher confidence intervals of explained variance by pedigree. The model with only omics information (SNP + GEX) is therefore simpler, more accurate and as effective as the model that also uses pedigree information. This is interesting for future applications of omics technologies, because we expect that pedigree information often will be absent.Figures The prediction of phenotypes from these models, using cross-validation, is shown in Table With increased availability of various -omics data, integrative approaches are promising tools for understanding the genetic architecture of complex traits. We have developed a complementary approach to the univariate “eQTL” mapping, by considering Bayesian models that fit all genome-wide SNPs and transcript abundances in one model, and that estimate and partition explained variances by chromosome and genome segments. Our results show that, using gene expressions, more of the phenotypic variance can be explained and phenotypes can be better predicted. Predictions were also shown to be unbiased, which validates the assessed explained variances. The improvement of phenotype predictions using gene expression data will be useful for several applications in agriculture and medicine, although it should be assessed on a case-by-case basis as to whether a suitable tissue can be sampled for the gene expression measurements. Partitioning of the explained genomic variance at the level of chromosomes and genome segments showed clear examples of eQTL locations as regions where genomic variance disappears when gene expressions are added to the model. Our study used only gene expressions from the liver, and an obvious further extension is to include expressions from other tissues. The QTLs that did not disappear when transcripts are added to the model may be eQTLs that affect gene expression in a tissue other than liver. The Bayesian model is quite efficient for handling large sets of covariates, and extensions to include multiple sets of expressions will be feasible. We have not provided formal statistical tests in this model, but the Bayesian approach lends itself naturally to obtaining confidence intervals for (differences between) parameter estimates. The estimates of total explained variances from the Bayesian mixed model can also be obtained by a residual maximum likelihood (REML) approach. We verified this, and the Bayesian and REML estimates generally agree. However, using REML it is not feasible to utilize mixture priors to better discriminate between SNPs which contribute more or less variance, and to partition the variances at the sub-chromosome level, which is all straightforward in a Bayesian approach.Our approach can easily allow up scaling to higher-density arrays, even to whole-genome sequence data with the variance components analysis as it was for gene expression probes in this study.BW: Body Weight; FI: Feed Intake; FE: Feed Efficiency; SNPs: Single Nucleotide Polymorphisms; REML: Restricted maximum Likelihood; QTL: Quantitative trait loci; eQTL: Expression Quantitative trait loci.The authors declare that they have no competing interests.AE developed the data analysis pipeline, performed statistical analyses, interpreted the results and wrote the manuscript. PS and LJ were involved in project design, statistical analyses, interpretation of results and manuscript editing. DP and MA prepared the data for the analysis. All authors have read and approved the final manuscript.Figure S3. Distribution of phenotypes of traits Body Weight including 440 animals, Feed Intake and Feed Efficiency including 337 animals each.Click here for fileFigure S1. Decomposition of the proportion of variance explained by SNPs at the level of chromosomes and individual SNPs in two models: the independent model SNP and the conditional model SNP+GEX for Feed Intake. (a) explained variances from SNPs in SNP model (black) and SNP+GEX model (white) in each chromosome. (b) explained variance by individual SNPs in SNP model and (c) SNP+GEX model.Click here for fileFigure S2. Decomposition of the proportion of variance explained by SNPs at the level of chromosomes and individual SNPs in two models: the independent model SNP and the conditional model SNP+GEX for Feed Efficiency. (a) explained variances from SNPs in SNP model (black) and SNP+GEX model (white) in each chromosome. (b) explained variance by individual SNPs in SNP model and (c) SNP+GEX model.Click here for fileFigure S4. Comparison of predicted breeding values versus phenotypes in the models using pedigree information only (PED), SNPs information only (SNP) and gene expression information only (GEX) for three traits Body Weight, Feed Intake and Feed Efficiency according to correlation shown in TableClick here for file


Phaeodactylum tricornutum can accumulate eicosapentaenoic acid (EPA) up to 30% of the total fatty acids. This species has been targeted for isolating gene encoding desaturases and elongases for long-chain polyunsaturated fatty acid (LC-PUFA) metabolic engineering. Here we first report the cloning and characterization of Δ5-elongase gene in P. tricornutum. A full-length cDNA sequence, designated PhtELO5, was shown to contain a 1110 bp open reading frame encoding a 369 amino acid polypeptide. The putative protein contains seven transmembrane regions and two elongase characteristic motifs of FLHXYHH and MYSYY, the latter being typical for microalgal Δ5-elongases. Phylogenetic analysis indicated that PhtELO5 belongs to the ELO5 group, tightly clustered with the counterpart of Thalassiosira pseudonana. Heterologous expression of PhtELO5 in Pichia pastoris confirmed that it encodes a specific Δ5-elongase capable of elongating arachidonic acid and eicosapentaenoic acid. Co-expression of PhtELO5 and IsFAD4 (a ∆4-desaturase from Isochrysis sphaerica) demonstrated that the high-efficiency biosynthetic pathway of docosahexaenoic acid was assembled in the transgenic yeast. Substrate competition revealed that PhtELO5 exhibited higher activity towards n-3 PUFA than n-6 PUFA. It is hypothesized that Phaeodactylum ELO5 may preferentially participate in biosynthesis of transgenic LC-PUFA via a n-3 pathway in the yeast host.The diatom  PUFAs are important structural components that confer membrane fluidity and selective permeability [n-3 PUFAs including DHA and EPA can be used as preventive drugs and supplements [Polyunsaturated fatty acids (PUFAs) are fatty acids of 18 carbons or more in length with two or more double bonds. They can be classified into eability ,3. Deficplements ,5,6. Theplements ,8. One splements .n-3) LC-PUFA biosynthesis can start with ALA as substrate which can be produced by some yeasts and plants. The biosynthesis of DHA from ALA involves a series of desaturation and elongation reactions catalyzed by various desaturases and elongases. Δ5-elongase and Δ4-desaturase are rate-limiting enzymes for DHA biosynthesis [Pavlova salina, Marchantia polymorpha and Thraustochytrium sp. [Phaeodactylum tricornutum has not been characterized. This diatom is rich in n-3 PUFAs with superior genetic resources revealed by genomic research [Phaeodactylum, and the functional stacking of Δ5-elongase and Δ4-desaturase in transgenic yeast realizing efficient formation of DHA.The fatty acid desaturases and elongases play key roles in the processes of the conventional Δ6-pathway, alternative Δ8-pathway and microbial Δ4-pathaway . The ω-3ynthesis ,9. Molecynthesis ,11. Not ynthesis ,16,17,18rium sp. ,21,22,23research ,25. We aP. tricornutum mRNA, XM_002176650.1 that predicted a hypothetical protein XP_002176686.1. NCBI conserved domain database (CDD) analysis indicated that this shortened protein belonged to the ELO-super family, sharing 53% identity with the closest homologue in T. pseudonana. In order to isolate the full length cDNA and the putative gene, primers  search, Clustal W and TMHMM programs. The results indicated that elongase PhtELO5 is an ELO-family protein containing seven transmembrane regions and two characteristic motifs FLHXYHH and MYSYY. The motif MYSYY is common in all Δ5-elongases analyzed indicating that it may be unique for ELO5 elongases  forming a tight clade but quite different from other ELO5s. Protein sequence distances analyzed using DNA-Star (Lasergene) indicated that PhtELO5 shares the highest identity of 51% with ThpELO5 and low identities of 32.6%–38.2% with other members within the ELO5 group. Interestingly, although Thrasutcohytrium elongase TsELO1 shares the lowest identity of 26.1% with PhtELO5, it reportedly possessed the activity of ELO5. This may be explained by the existence of motif MYSYY in the TsELO1 protein sequence. Together, it is likely that PhtELO5 encodes Δ5-elongase and its evolution history is largely different from those of other microalgae and protists [The translated peptide of longases . These p cluster . Among mprotists ,22,23. P. pastoris. ELO5 expression vector was constructed by using plasmid pHBM906 which allows ELO5 ORF to be driven by the inducible AOX1 promoter [20 PUFAs  (93.1%) than that of converting ARA into DTA (79.4%). The difference in efficiency was confirmed by using another expression vector pAO815, which exhibited the rate of 90.8% and 83.2%, respectively  and theectively . It is lN-terminal 4VR domain and partial transmembrane regions had little influence on the activity of Δ5-elongase. This is in support of the prediction that PhtELO5’s core activity regions range from amino acid 90–328. Together, it is concluded that PhtELO5 indeed possesses the activity of Δ5-elongase as expected. Based on domain analysis of PhtELO5, we carried out domain deletion experiments to verify the function of key ELO-conserved regions. Yeast cells expressing the truncated ELO5 deleted for 4VR region  or ELO5 deleted for 4VR region plus part of the first transmembrane helix (ELO5-Δ2) were shown to have no alteration in fatty acids profiling . The resBglII and BamHI. The resultant target construct pAO-D4E5 contained ELO5 and FAD4 as a cascade driven by strong inducible promoter pAOX1  C-pathway . Control20-PUFAs . For ins n-6 DPA A. Fed wiinto DHA B. Consis20-PUFA substrates. As revealed in n-3), DPA (n-3) →DHA, ARA→DTA and DTA→DPA (n-6) were 90.8%, 90.8%, 83.2% and 90.9%, respectively. However in the presence of dual substrates, the rate of conversion (ARA→DTA) dropped drastically from 83.2% to 67.9%, while conversion rates of other substrates showed little change, ranging from 84.4% to 90.9%. The relatively lower conversion rate upon ARA was also observed in ELO5 single gene expression in strain PHD5.01 (79.4% versus 93.1%). Obviously, compared to EPA, fewer molecules of ARA were incorporated with elongase PhtELO5. It is interpreted that PhtELO5 is likely to have preference for EPA among (Δ5-) C20-PUFA substrates. This finding is consistent with the conclusion that Δ5-elongase is one of the rate-limiting enzymes involved in LC-PUFA biosynthesis [To test the effect of substrate competition, cells of double gene co-expressed strain PDE01 were fed with the same molar concentration of ARA and EPA concurrently. FA compositions and converting rates were determined to relatively estimate the enzymatic activity or preference upon type of (Δ5-) Cynthesis . P. tricornutum is one of the most widely utilized model systems for studying the ecology, physiology, and molecular biology of diatoms. Although the genome of this model diatom has been sequenced, the current knowledge on fatty acid metabolic organization and biochemistry remains fragmentary [P. tricornutum’s fatty acid Δ5-elongase has not been investigated. We therefore conducted the identification and function analysis of P. tricornutum Δ5-elongase.Diatoms are successful groups of unicellular eukaryotic algae playing important roles in global carbon and silica pools. gmentary ,25. Sincgmentary ,23,25. HThrasutcohytrium TsELO1. Although the protein sequence of TsELO1 is largely distant from most Δ5-elongases, it possesses quite high activity of C20-Δ5 elongase [20-PUFA substrates, both ELO5 single- and double-expression strains were able to form new fatty acids of C22 PUFAs, strongly suggesting that PhtELO5 was a key player in orienting DHA synthesis viaΔ4-pathway [n-3 C20-PUFA. This observation is quite different from ELO5 of Pavlova and Thalassiosira which appeared to have little differences on (Δ5-) C20-PUFA substrates [20 fatty acid, we did the C20:0 feeding experiment and the results demonstrated that this saturated fatty acid is not the substrate of PhtELO5  Cthan 50% ,20. The rate DPA . Furtheron level ,28. HoweP. postoris cannot synthesize PUFAs longer than LA and ALA [22 PUFAs can be realized via functional assembly of ELO5/FAD4 in the presence of (Δ5-) C20 PUFA substrates. ELO5 is likely to have a higher converting activity upon n-3 C20 PUFA substrate, whereas FAD4 activity remains comparable in both n-3 and n-6 pathways  was obtained from the Institute of Hydrobiology, Chinese Academy of Sciences . P. tricornutum was grown in f/2-enriched artificial sea water  medium at 25 °C with continuous aeration and illumination . P. pastoris strain GS115 was grown and maintained in YPD medium. The diatom DNA or protein sequence alignment and neighbor-joining tree construction were done with Clustal W and MEGA 5.2.2. Sequence distances were analyzed by DNA-star. Structural domain prediction was carried out using the online server program TMHMM and ESPript 3. P. tricornutumt mRNA available (NCBI Reference Sequence: XM_002176650.1). This partial start and partial stop fragment was linked to a sequence in chromosome 1. In order to isolate its full length cDNA and putative gene, three forward primers Phtelo5-U1, -U2 and -U3 and one reverse primer Phtelo5-D1 . In the resultant vector pHBM-ELO5, ELO5 was placed in between the promoter and transcription terminator regions of the AOX1 gene. Linearized by SalI, pHBM-ELO5 was introduced into P. pastoris strain GS115 by electroporation (Bio-Rad). Concurrently, the empty pHBM906 vector was introduced into GS115 as control. For co-expression of ELO5 and FAD4 in P. pastoris, the expressing vector pAO815 was used to stack the two genes in a cascade. First, IsFAD4 and PtELO5 were individually cloned into pAO815 via EcoRI cloning site to form vector pAO-FAD4 and pAO-ELO5, respectively. Then, pAO-FAD4 was digested with BamHI followed by dephosphorylation with calf intestine alkaline phosphatase according to the manufacturer’s instructions; and PhtELO5 expression cassette (5′AOX1-PhtELO5-TT) was amplified from vector pAO-ELO5 with primer ELO5BGL-F and ELO5BGL-R using a high fidelity PCR. Finally, after digestion with BglII, the PhtELO5 cassette was ligated to BamHI-digested and dephosphorylated vector pAO-FAD4, resulting in the target vector designated pAO-D4E5. Various PCR and sequencing were carried out to confirm the accuracy of sequence and structure of the stacked cassettes prior to the transformation. Plasmids and primers used in this study are featured in PhtELO5, the recombinant vectors pHBM-ELO5 and/or pAO-D4E5 were introduced in Pichia pastoris. The representative strains of three independent positive transformants were selected and grown on 50 mL MGY liquid medium  on a shaker (220 rpm) at 28 °C. Cultures of the stationary phase were harvested and washed with sterile deionized water twice and inoculated  into the 500 mL glass Erlenmeyer flasks containing 50 mL MM medium . Methanol, 0.5% (v/v), was supplemented every 24 h to keep inducing gene expression. All yeast cultures were grown for 72 or 96 h at 20 °C and used for fatty acid analysis. To verify the function of PhtELO5 or IgFAD4 cDNA was amplified with primer pairs listed in ACT1 was used as an internal standard. The relative mRNA level of the target gene was normalized against that of standard. Heterologous gene expressions were estimated by Quantitative real-time PCR. Total RNA was isolated from cells using the TRIzol reagent . First-strand cDNA was synthesized using a PrimeScript RT reagent kit with gDNA eraser , Dalian, China). qRT-PCR was performed using iCycler iQ5 real-time PCR system  and SYBR Premix Ex Taq II (Tli RNaseH plus kit) , Dalian, China), according to the manufacturer’s protocols. Full-length P. pastoris transgenic strains carrying various expressing vectors were grown in BMGY medium individually. Each overnight culture was diluted to an OD600 of 0.5 and refreshed by growing for further hours till the OD600 value reached approximately 1.0. The cultures were harvested by centrifugation and resuspended in the same volume of fresh BMGY medium containing 1% NP40, 0.5% methanol and exogenously fed with 100 µM each of (Δ5-) C20 FUFAs. The cultures were grown for 72–96 h with addition of 0.5% methanol every 24 h. Cell were harvested, washed once with three volumes of 0.5% Triton X-100, and once with three volumes of distilled water. The pellets were subjected to analysis of fatty acid composition .Microalgae or yeast cells were dried by the Vacuum Freeze-drying System. Fatty acid methyl esters (FAMEs) were extracted with petroleum ether and trans-methylated with 0.4 M NaOH in methanol . All samP. tricornutum Δ5-elongase gene PhtELO5. Heterologous expression in Pichia confirmed that ELO5 possesses strong activity of Δ5-elongase capable of elongating (Δ5-) C20 PUFA substrates. Substrate competition revealed that PhtELO5 had a higher converting activity towards 20:5n-3 than 20:4n-6 fatty acids. Functional stacking of Phaeodactylum ELO5 and Isochrysis FAD4 in Pichia reconstituted a high-efficiency biosynthetic pathway leading to transgenic production of DHA. We herein report the characterization and functional analysis of


Bird songs range in form from the simple notes of a Chipping Sparrow to the rich performance of the nightingale. Non-adjacent correlations can be found in the syntax of some birdsongs, indicating that the choice of what to sing next is determined not only by the current syllable, but also by previous syllables sung. Here we examine the song of the domesticated canary, a complex singer whose song consists of syllables, grouped into phrases that are arranged in flexible sequences. Phrases are defined by a fundamental time-scale that is independent of the underlying syllable duration. We show that the ordering of phrases is governed by long-range rules: the choice of what phrase to sing next in a given context depends on the history of the song, and for some syllables, highly specific rules produce correlations in song over timescales of up to ten seconds. The neural basis of these long-range correlations may provide insight into how complex behaviors are assembled from more elementary, stereotyped modules. Bird songs range in form from the simple notes of a Chipping Sparrow to the complex repertoire of the nightingale. Recent studies suggest that bird songs may contain non-adjacent dependencies where the choice of what to sing next depends on the history of what has already been produced. However, the complexity of these rules has not been examined statistically for the most elaborate avian singers. Here we show that one complex singer—the domesticated canary—produces a song that is strongly influenced by long-range rules. The choice of how long to repeat a given note or which note to choose next depends on the history of the song, and these dependencies span intervals of time much longer than previously assumed for birdsong. Like most forms of human music, the songs of canaries contain patterns expressed over long timescales, governed by rules that apply to multiple levels of a temporal hierarchy. This vocal complexity provides a valuable model to examine how ordered behaviors are assembled from more elementary neural components in a relatively simple neural circuit. Brains build complex behaviors from simple modules In addition to the detailed quantitative studies of syntax in Bengalese finches in laboratory settings, many field studies have described an array of influences on the delivery of song. For example, some species such as the swamp sparrow engage in antiphonal song type-matching—selecting a song that best matches what the neighbor just sang One of the most complex singers that can be easily reared in a laboratory setting is the domesticated canary. Here we investigated the long, complex songs of the Belgian Waterslager strain. We show that their songs are governed by long-range correlations in syntax at multiple hierarchical levels. The time a bird spends repeating a syllable or the choice of what to sing next depends on the history of the song and correlations between the past and present can extend over durations up to 10 seconds, encompassing 4 or more phrases consisting of dozens of syllable repeats. Canary song, like most popular music, contains structure in a range of time-scales through which sequence flexibility is balanced by long-range order The smallest indivisible unit of the domesticated canary's song is the syllable, a stereotyped sound that typically ranges from 20 to 200 ms in duration. The adult repertoire usually contains between 25–35 distinct syllable types whose acoustic forms are learned by an interplay between innate programming and flexible imitation These two fundamental units of the canary song—syllables and phrases—form distinct time-scales. Syllable durations range from 28 to 480 milliseconds, while phrase time-scales are on the order of a second , and the full song is an order of magnitude longer Fig. 2. before or after the phrase  . Corroborating measurements were found using scores computed from acoustic features defined in the Sound Analysis Pro for MATLAB package Text S1).This qualitative observation can be quantified using a similarity score based on the spectral density images , a systematic shift in acoustic form may occur over the course of a phrase. Also, for many phrase types in canaries, the first syllable of a new phrase type shows a structure that matches neither the preceding nor the succeeding phrase. If a switch in syllable forms is made in the central motor control loops, the syringeal or respiratory pattern may require a finite time to reconfigure. Ongoing phonation during this period of reconfiguration may produce syllable forms that differ from the steady state syllable forms Canary song is organized around a mesoscopic structure, the phrase. Is the larger sequence of song explained by a first-order Markov process in phrases, or is phrase sequencing more complex than a first-order Markov process? To examine this possibility, we first observed that the succession of phrases is quite constrained—each phrase is typically followed by just a few downstream possibilities Fig. 4a.Fig. S1). Here too, a high-resolution analysis of selected examples using spectral density images confirmed that the acoustic form of the syllable in position Y can remain relatively constant even when flanked by diverse phrase types in position X or Z . As for the earlier analysis of syllable forms in phrases of different lengths, this observation was quantified both with spectral density similarity scores (d′<0.35) and through the use of SAP scores (d′<0.1), indicating that variations of syllable form within each group are larger than the separation between syllable forms in different syntactic contexts. .To examine further the constraints placed on canary phrase sequences, we first tested all phrase types for statistically significant second-order structure. Specifically, for a sequence of three phrases XYZ, we asked whether the phrase type X impacted the phrase type probabilities for Z, for a given phrase type Y. The test reveals that this mutual dependence exists for 70% of all examined phrase types . It is possible that the minor acoustic changes in syllables can be largely explained by small-scale drift in peripheral control variables.It is not known whether peripheral motor variables such as air pressure or muscular tone change over the course of a long canary song. Time-dependent changes in the periphery could impact the acoustic details of song The previous analysis indicated that second-order correlations introduce a statistically detectable shift in phrase transition probabilities for most phrase types, but these second-order effects could be weak. Still, weak higher-order correlations could in principle explain the gap between a first-order random model and canary song. However, the next stage of analysis revealed that while higher-order correlations are weak for many phrase types, for some phrase types, strong long-range rules apply to the delivery of song.To examine how long-range correlations varied by phrase type, we constructed a prediction suffix tree (PST) Fig. S7). This corresponds to a propagation of song information over a time-scale of approximately 5 to 10 seconds. Many syllables in this analysis showed no significant structure beyond first-order; just a few syllables are governed by long-range rules.  The structure of example songs with long time-scale correlations is illustrated in Fig. S6 contains similar song barcodes showing the full song context for the examples in .) In the examples given in As a control, we used a 10-fold cross-validation procedure. Prediction suffix trees were computed for the training data, and then the average negative log-likelihood of the test data was computed for each tree Fig. 6. Fig. S8 internal nodes are labeled with blue text labels.  After collapsing internal nodes in these figures, the maximum depth of the suffix trees reduce from a range of 4–7 to a range of 2–3 in the 6 birds analyzed here, whereas the log-likelihood of the model changed, on average, by less than 1 percent, indicating that sequence chunks could be regarded as monolithic states without impacting the quality of the model.The apparent statistical depth of the phrase structure could be reduced when sequences of phrases occur as a unit Fig. S11). We emphasize that in spite of the complexity of Fig. S11, the PFA is only a statistical model for phrase transitions. The model does not account for phrase durations, or the fact that phrase durations depend on the recent history of the song–a point documented earlier–these features should be addressed in a more complete statistical model.The PST provides a particularly compact representation of long-range dependencies in song. The compactness of the PST representation is emphasized by comparison of the PST graph with its corresponding probabilistic finite automaton (PFA) Fig. S9, no simple rule was observed—the most frequent syllable transition from a given phrase can increase or decrease in likelihood as a function of the number of times the phrase is produced in a given song. Over the transitions that we could analyze for this property, 33% strictly increased while 50% decreased, and 13% both increased and decreased over the course of a song .We next examined whether the long-range correlations followed a simple adaptation rule. Long-range correlations could appear if the probability of a given phrase transition decays with the frequency of its use. That is, as each phrase transition occurs, its subsequent probability of occurrence is decreased. In the cases examined in Text S1). This search for simple rules explaining the apparent depth of canary song was not exhaustive, and a more extensive analysis involving additional samples from other canaries is needed.We then checked to see if the statistical depth of canary song could be explained by limitations on song duration. The concern here is that some branches in the path of song might lead to unusually long songs that could be prohibited for physiological reasons. We analyzed all examples of context-dependencies and found no evidence for this effect . The speed and convergence properties of the PST algorithm make it possible to quickly cross-validate the structure of the PST over large ensembles of trials, defining the point at which over-fitting occurs. Taken together, these properties suggest that the PST analysis will be generally useful for characterizing the structure of birdsong syntax.A suffix tree of depth 7 for 30 phrase types could in principle have 30∧7 nodes, and even a third order process in 30 phrase types could have 30∧3 nodes. In contrast to this large state space, the PSTs included in Statistically complex phenomena that are best described by higher-order Markov processes such as the PST can be generated by simple physical processes. For example, if circulating neuromodulators in song nuclei depend on the syllables that are sung, and if syllable transitions themselves depend on the hypothetical neuromodulator, then stochastic variations in the beginning of a song could impact future transition probabilities, generating apparent “long-range rules”. In this scenario, additional information is needed to explain why some syllables show strong long-range rules, and others behave in a simple first-order manner.On a fine-grained scale, neural dynamics should be captured by a first-order statistical process. In statistical terms, long-range correlations in syntax imply that multiple “hidden states” can give rise to the observable syllable. This duplication of states statistically does not imply that the motor program for a syllable is duplicated–the smallest change in a syllable program satisfies the duplication of “hidden states.” Recent studies in Bengalese finches have observed that the stereotyped neural program for a syllable depends on its context–in particular, changes were observed in Basal Ganglia projecting neurons in nucleus HVC (used as a proper name) Canary phrase structure and canary syllable form appear to be encoded by separable processes. This distinction is supported by the observation that phrase time-scales are not simply predicted by syllable time-scales. Another line of evidence arises in studies of song learning in juvenile canaries. As juveniles, canaries can learn to imitate artificial songs that lack normal phrase structure Many questions remain about the neural basis of phrase structure in canary song, but we may also wonder about the relevance of the long-range rules for the natural behavior of the species. Is the statistical depth of song an epiphenomenon of little ethological relevance, or do canaries show preferences for songs with long-range order? Can a canary fine-tune the long-range rules to match a tutor song, or can a bird be trained to alter rule-sets in different behavioral contexts? These questions are addressable since canaries readily imitate artificial songs designed to pose specific questions about their vocal learning processes Lashley emphasized that the control of serial order in behavior is one of the most important and least understood aspects of neuroscience over 60 years ago The care of animals in this study was carried out in accordance with Boston University IACUC protocol number 09-007.Canaries (Belgian Waterslager strain) used in this study were a gift from Fernando Nottebohm. Birds were isolated at least two weeks before recording in soundproof boxes and kept on a light-dark cycle matched to the external annual light cycle in Boston (Boston University IACUC protocol number 09-007). All birds were at least one year old before isolation. Song was recorded between the months of March and April.Table S1 for the number of phrases analyzed for individual birds). The annotated sonograms were then scanned and converted into strings, and statistical analysis of the strings was performed using custom MATLAB scripts. Zero to two syllables were excluded per bird because the syllable form consisted of subtypes that could not be labeled consistently.Spectrograms of the song were calculated in MATLAB , and the beginning, end, and syllable identity of each phrase was marked on the image by visual inspection. For all data described here, two independent observers annotated the songs. Observer 1 and observer 2 annotated 33,469 and 36,447 phrases, respectively, between 6 birds (see in) and MD, respectively. We discretized phrase durations by terciles and then used a variation of the Fisher test for in,pathout).For each bird , we first examined the mutual dependence between a given phrase's duration and the path into or out of the phrase. That is, in a given phrase sequence XYZ, we examined the mutual dependence between the length of phrase Y and identity of the syllable type in phrase X or the identity of the syllable type in phrase Z, which we call MD. As a final step, candidate sounds were plotted in two dimensions and a decision boundary was drawn by the user.Custom MATLAB  scripts were used for automated syllable clustering. After choosing a template, spectral features from the template and the rest of the data were computed using a sparse time-frequency representation For a collection of syllables, we first generate a sparse time-frequency representation of each syllable using auditory contours Since the starting matrices A and B is just B is We define a simple similarity measure between two syllable contour images in,pathout) test), we constructed prediction suffix trees (PSTs) for the sequence of canary phrase types using a previously published algorithm Text S1.To test for the existence of structure beyond second-order (as in the MD(pathTo compute block entropy we used standard methods based on Shannon entropy Audio S1Sample series of song bouts from a single canary.(MP3)Click here for additional data file.Audio S2Sample series of song bouts from a single canary, separate from the bird used for Audio S1.(MP3)Click here for additional data file.Figure S1Pie charts of p-values for association tests. Charts computed using data from two observers are shown for six birds (each column is a different bird). The graphical conventions of the pie charts follow (TIFF)Click here for additional data file.Figure S2in,pathout) test show high inter-observer agreement.Scatter plots of test statistic values computed for the MD test. Linear regression lines are given in red and the unity line in blue, along with the r and p values under the abscissa. Each point in the graph is one phrase type for a specific bird.(TIFF)Click here for additional data file.Figure S3out) test show high inter-observer agreement.Scatter plots of test statistic values computed for the MD test. Linear regression lines are given in red and the unity line in blue, along with the r and p values under the abscissa. Each point in the graph is one phrase type for a specific bird.(TIFF)Click here for additional data file.Figure S4in,dur) test show high inter-observer agreement.Scatter plots of test statistic values computed for the MD test. Linear regression lines are given in red and the unity line in blue, along with the r and p values under the abscissa. Each point in the graph is one phrase type for a specific bird.(TIFF)Click here for additional data file.Figure S5Spectral density images demonstrate that syllables occurring in different contexts are highly similar.A, Spectral density images of syllables with different surrounding phrases reveal acoustic similarity. As in B, With the spectral density image from the magenta group as a reference, syllables occurring in different sequences have overlapping similarity score distributions. The syllable types match those shown in Fig. S5a. Summary statistics for all syllables types in Fig. S5a are given in Table S3.(TIFF)Click here for additional data file.Figure S6Full song barcodes for the examples given inandallow for direct visualization of long-range rules. These barcodes represent the full song sequence corresponding to the examples given in . A, Barcodes for the example from B, Barcodes for the example in C, Barcodes for the example from the middle of D, Barcodes for the example from the bottom of (TIFF)Click here for additional data file.Figure S7Using cross-validation to verify PST fits. To select the parameters used in the PST algorithm, we used a 10-fold cross-validation procedure repeated 3 times (with different data splits). As a measure of model performance we used average negative log-likelihood. Here we show cross-validation results as a function of the PST parameter th and 75th percentiles). Also shown is the average maximum order of the PSTs for a given value of (TIFF)Click here for additional data file.Figure S8PSTs for the 4 birds not shown in .(TIFF)Click here for additional data file.Figure S9Shown are the transition probabilities from two different phrases sung by the same bird as a function of repetition number.Left: as phrase X is repeated, the most probable phrase transition from X to G decays, while the transition to S increases. Right: the opposite effect is seen in the same bird. The most probable transition, from H to X, increases as H is repeated.(TIFF)Click here for additional data file.Figure S10Some syllables have context-dependent transitional forms.A, shown on the left are two example sonograms of the same phrase with different preceding phrases. In the two contexts, the first syllable of the phrase has a different transitional form (highlighted by the red and blue boxes). The image on the right is a color channel merge of two spectral density images, which were computing using the first syllable of all phrases in the two different contexts (top sonogram context is the red channel and bottom sonogram context the blue channel). B, the same effect is shown for a different phrase from a different bird.(TIFF)Click here for additional data file.Figure S11Probabilistic finite automata (PFA) for all 6 birds analyzed. For visualization, all edges where p<.05 have been removed, and all edges where p<.2 are shown in thin light gray lines. Each PFA is completely determined by its corresponding PST. From top to bottom, the PFAs correspond to the PST shown in: the top left of Fig. S8; top right of Fig. S8; top of Fig. S8; bottom of Fig. S8.(TIFF)Click here for additional data file.Figure S12Phrase durations for all 6 birds analyzed. Each row corresponds to a different bird , and each group of points indicates the duration of a different phrase type. The colors (arbitrarily chosen) indicate different preceding phrase types. Abrupt changes in duration distribution that co-occur with color changes reveal a context-dependent shift in phrase length.(TIFF)Click here for additional data file.Table S1The total number of phrases analyzed by each observer for individual birds. The bottom row contains the repertoire size for each bird.(DOCX)Click here for additional data file.Table S2Summary statistics for similarity scores for duration groups . STD, standard deviation.(DOCX)Click here for additional data file.Table S3Summary statistics for similarity scores for sequence groups . STD, standard deviation.(DOCX)Click here for additional data file.Table S4Transition probabilities and number of transitions to accompany . The 95% confidence intervals are given in brackets below the transition probabilities and were estimated using a bootstrap procedure with a case resampling scheme. That is, the transition probabilities were estimated after randomly resampling the data with replacement. The confidence interval is then defined as the 2.5th and 97.5th percentiles of the resampled distribution.(DOCX)Click here for additional data file.Table S5Transition probabilities demonstrating alternative paths for prediction suffix trees (PSTs) shown in  and  The sequence DABN comes from the top PST in Fig. S8.(DOCX)Click here for additional data file.Text S1Supplementary .(DOCX)Click here for additional data file.


Genetic improvement of beef quality will benefit both producers and consumers, and can be achieved by selecting animals that carry desired quantitative trait nucleotides (QTN), which result from intensive searches using genetic markers. This paper presents a genome-wide association approach utilizing single nucleotide polymorphisms (SNP) in the Illumina BovineSNP50 BeadChip to seek genomic regions that potentially harbor genes or QTN underlying variation in carcass quality of beef cattle.Longissimus dorsi muscle area or ribeye area (REA), marbling scores (MRB), lean yield grade by Beef Improvement Federation formulae (BIFYLD), steak tenderness by Warner-Bratzler shear force 7-day post-mortem (LM7D) as well as body composition as determined by partial rib (IMPS 103) dissection presented as a percentage of total rib weight including body cavity fat (BDFR), lean (LNR), bone (BNR), intermuscular fat (INFR), subcutaneous fat (SQFR), and total fat (TLFR).This study used 747 genotyped animals, mainly crossbred, with phenotypes on twelve carcass quality traits, including hot carcass weight (HCW), back fat thickness (BF), Bos taurus autosome (BTA) 6. At a less stringent significance level (P < 0.001), 520 SNP were found significantly associated with mostly individual traits (473 SNP), and multiple traits (47 SNP). Of these significant SNP, 48 were located on BTA6, and 22 of them were in association with hot carcass weight. There were 53 SNP associated with percentage of rib bone, and 12 of them were on BTA20. The rest of the significant SNP were scattered over other chromosomes. They accounted for 1.90 - 5.89% of the phenotypic variance of the traits. A region of approximately 4 Mbp long on BTA6 was found to be a potential area to harbor candidate genes influencing growth. One marker on BTA25 accounting for 2.67% of the variation in LM7D may be worth further investigation for the improvement of beef tenderness.At the genome wide level false discovery rate (FDR < 10%), eight SNP were found significantly associated with HCW. Seven of these SNP were located on This study provides useful information to further assist the identification of chromosome regions and subsequently genes affecting carcass quality traits in beef cattle. It also revealed many SNP that acted pleiotropically to affect carcass quality. This knowledge is important in selecting subsets of SNP to improve the performance of beef cattle. Beef quality and consumer satisfaction are important to the beef industry. Beef quality contributes to consumers’ decision to purchase beef. An improvement of beef quality can increase demand, benefiting both producers and consumers. LeadingLongissimus dorsi muscle area, and backfat, it evaluates marbling through ultrasound intramuscular fat, and cannot measure tenderness. These two traits, tenderness and marbling, can only be obtained after the animal has been slaughtered. Thus there exists a time-delay in identifying elite animals before they can be widely used, and is the major limitation to improving current rates of genetic improvement[Efforts to improve beef quality have been made through conventional beef cattle breed improvement programs using pedigree and performance records to estimate expected progeny differences (EPD). Although Real-time ultrasound imaging has been used on live animals to measure a number of traits, such as rovement. Selectirovement, or doubrovement, or a larovement, have drrovement, Americarovement, and Norrovement.As for multi-breed beef cattle, which constitute a large proportion of the beef population, genomic selection is still in the development stage. However, immediate applications of the 50K panel can include searching the genome for regions that may contain causative mutations underlying genetic variation of the carcass quality traits. The study reported here was designed for this purpose.Seven hundred and forty-seven animals were genotyped, using the Illumina BovineSNP50 BeadChip, including 713 males born between 1998 and 2006, and 34 feedlot heifers born between 1999 and 2005. These 747 animals consisted of 16 purebreds and 731 crossbreds. Number of animals with at least 50% of one of the six major breeds  are presented in Table The test animals were born in one of the three cooperating herds: Elora Beef Research Centre (EBRC), New Liskeard Agriculture Research Station (NLARS), and Agriculture and Agri-food Canada Kapuskasing Experimental Farm (KAP). Cows at these three herds were bred to mostly purebred sires through the extensive use of artificial insemination. Calves were raised with their dams either on pasture or in group pens. NLARS and KAP cattle were transported to EBRC at weaning around 200 days of age to be fed under feedlot conditions typical of the Ontario beef industry.th- and 13th-rib interface, three-fourths of the length of the Longissimus muscle from the backbone. Ribeye area  was the measure of Longissimus muscle area at the 12th- and 13th-rib interface using a tracing of the muscle with the area quantified using an electronic planimeter . Longissimus muscle was assessed for subjective marbling score (MRB) from a scale of 1 (devoid marbling) to 10 (abundant marbling) based on the size and distribution of flecks of intramuscular fat in the Longissimus muscle at the 12th- and 13th-rib interface. Lean yield grade by Beef Improvement Federation formulae (BIFYLD) was calculated using the following formula Yield Grade = 2.50 +  +  +  – . Low BIFYLD values mean higher yields of retail product yield.The animals were slaughtered at an average age of 452 days, at a federally inspected abattoir operated by the University of Guelph. This facility does not use electrical stimulation, which can increase muscle tenderness. Hot carcass weight  is the weight of carcass after harvest and removal of the head, the fore-shank below the knee joint, the hind-shank below the hock joint, gastrointestinal tracts and internal organs. Back fat thickness  was measured perpendicular to the outside surface at the 12Longissimus dorsi that had been aged for 7 days post-mortem (LM7D). Steaks were thawed for 48 hours at 1.5°C, trimmed of external fat and epimysium, and weighed prior to cooking. Steaks were cooked to an internal temperature of 70°C on a Garland Grill . Steak temperature was continually monitored by a Type K flexible high temperature thermocouple  inserted into the geometric centre of each steak. Steaks were turned when they reached an internal temperature of 40°C. Cooked steaks were weighed, placed into individual bags, and immediately chilled in ice water to stop the cooking process. Steaks were then transferred to a chill cooler where they were stored at 1.5°C for 24 hours prior to coring. After equilibrating to room temperature, approximately eight cores of 1.5 cm were removed parallel to the muscle fibres from each steak using a drill press mounted corer. Cores were sheared using a Warner-Bratzler blade on a TA-XT Plus texture analyzer  with crosshead speed set at 3.3 mm s-1. Peak shear force was determined using a custom macro program in Stable Microsystems Exponent software, with the average of the eight peak force values used in data analysis as the shear force value for each animal.Steak tenderness was determined using Warner-Bratzler shear force to measure the amount of force (kg) required to cut through cooked Carcass composition was assessed based on complete separation of a 21 cm rib section into body cavity fat (BDFR), lean (LNR), bone (BNR), intermuscular fat (INFR), subcutaneous fat (SQFR), while total fat (TLFR) was the sum of BDFR, INFR, and SQFR, following a modification of the procedure originally developed by Hankins and Howe. The traBos taurus autosomes (BTA) remained for further analysis. The number of SNP varied among the chromosomes, with BTA1 having the highest number of SNP  and BTA28 having the fewest (608); the longest SNP interval was identified on BTA5 (1.53 Mbp). However average SNP intervals were relatively consistent among the chromosomes, and the overall average distance between two adjacent SNP was 70 kbp.DNA extracted either from blood or meat samples was genotyped using the Illumina BovineSNP50_v1 Beadchip at the Alberta Bovine Genomics laboratory. All 51,620 SNP in this panel went through quality control that excluded SNP with spurious position, low call rates (< 95%), parentage error, out of Hardy Weinberg equilibrium (P < 0.01) or less than 10% minor allele frequency (MAF). A total number of 38,745 SNP on 29 yijkmnpl = + γ1(ageijkmnpl) + γ2(hijkmnpl) + sexi++ tk + gm + hyn + ap + αlxl + eijkmnpl was used, where yijkmnpl was the phenotype, the overall mean, γ1 and γ2 the regression coefficients for fixed effects age (ageijkmnpl) and individual’s heterosis (hijkmnpl), respectively, being fit as covariates, sexi the fixed effect of sex , βj the linear regression coefficients of the jth breed, bj the breed proportion of the jth breed  in the p animal, tk the fixed effect of the kth trial treatment (57 groups), gm the fixed effect of the mth clusters (75 clusters), hyn the random effect of the nth herd of origin by year group (28 groups), ap the random additive genetic effect of individual p, xl the number of copies of the 2nd allele  in the genotype for the lth marker, αl the linear regression coefficient  for the lth marker, s the total number of SNP  included in the analysis, eijkmnpl the random residual effect. Age, heterosis, sex, trial treatment and cluster were assumed to affect all animals equally. Markers were assumed in linkage disequilibrium with quantitative trait loci (QTL) controlling the trait under investigation. Random effects hy, a, and e were assumed uncorrelated with each other. Covariance matrices of the effects were equal toI was an identity matrix, and A the additive numerator relationship matrix among the animals. ASREML[Heritabilities and genetic correlations among the traits were estimated utilizing analytical gradients in the R. ASREML was usedp-values, of true null hypotheses, following a uniform distribution on the interval . However the numbers of true null hypotheses in this study were estimated using the histogram of p values. The interval  was equally partitioned into 20 bins . For each trait, n tests were conducted with observed p values distributed as follows: n1, n2… n19, n20 in bin1, bin2… bin19, bin20, respectively, andnk was the least number of p values among the bins, then 20nk was the estimated number of true null hypotheses, and the FDR threshold wasType I error rate was controlled by the false discovery rate (FDR) proposed by Benjamini and Hochberg. The calet al.[Trait means, heritabilities, genetic and phenotypic correlations, estimated from 747 animals, are presented in Tables et al., who fouet al., who repet al..vice versa. Genetic correlation can suggest the level of similarity in genetic mechanisms between traits, where a higher correlation may be attributed to more positive and less negative contributions from the same loci than a lower correlation[As for rib dissection traits, heritability estimates were moderate (0.30-0.48), genetic correlations among fat related traits varied from low  to moderate , and high . Since the rib dissection traits are intrinsically related, an increase in lean component decreases fat component, and The FDR thresholds for BF, BIF, HCW, REA, MRB, LM7D, BDFR, BNR, INFR, LNR, SQFR, TLFR were 5.12, 6.80, 6.72, 5.40, 4.92, 5.72, 7.19, 7.88, 7.19, 5.13, 5.24, and 5.18%, respectively. Number of significant SNP at different significance levels are presented in Table p the frequency of allele1, q the frequency of allele2, α the allele substitution effect,The allele substitution effect of significant SNP (P < 0.001), and size of the effect in terms of percentage of phenotypic variance are presented in  on chromosome 25 accounted for 2.67% of variation in shear force but has not been documented in the literature for this trait though it is in close proximity to a QTL for beef tenderness described by Gutierrez-Gil et al.[Marker CAPN1_1 in the il et al.. CAPN1_1il et al.,24, and il et al., resultiil et al.,28.et al.[Kuehn et al. estimateet al., thus Anet al.. Its freMarker CAPN1_1 on chromosome 29 was covered by three QTL for beef tenderness-32, and ABCG2 that affects milk production[SPP1 that affects yearling weight, post weaning weight gain, and HCW[et al.[et al.[et al.[There appears to be clusters of significant SNP on chromosome 8 37 Mbp - 40 Mbp), and 20 (4 Mbp - 5Mbp) controlling yield grade and rib eye area, as well as percentage of rib bone, respectively. In addition there was a cluster of 18 SNP on chromosome 6 (36 Mbp - 40 Mbp) significant for carcass weight and rib eye area. Twelve of them were significantly associated with birth weight and weaning weight in a separate study of the same beef cattle population. This reoduction,40, and  Mbp, and and HCW. The chr[et al., McClure[et al., and Nkr[et al., respectSeven SNP  all on chromosome 6 at 37-39 Mbp, significant for HCW in this study were confirmed significant for post-weaning gain and yearling weight  in the USDA crossbred beef cattle population. Six of At less stringent significance levels, significant SNP spread out across the genome and the size of their effect shrank as the significance threshold eased up Table .Investigating the correlations among the traits at SNP level revealed that highly correlated traits as shown in Tables et al.[For pairs of traits where the genetic correlations are close to zero, such as BIFYLD and LM7D, BF and LM7D, BIFYLD and HCW, their scatter plots in Figure et al. suggesteThis study took a genome-wide association approach to seek regions across the bovine genome that could potentially lead to the identification of causative mutations underlying variation in carcass quality traits in beef cattle. Apart from SNP found significantly associated with the twelve traits reported in this study, chromosome 6 appeared to contain regions for future investigations into quantitative trait nucleotides that control growth and weight in beef cattle. Marker BTA-60019 on chromosome 25 needs further research into its contribution to the variation in beef tenderness.CAPN1 gene resulting more tender meat than homozygous TT in a diverse range of breeds.Single nucleotide polymorphisms that were significantly associated with the traits may hold real association with genes controlling those traits. Genomic regions containing such SNP, if densely genotyped or sequenced, could be used to identify causal mutations underlying trait variation, and thus help facilitate the selection process to improve the trait of interest. An example for this is the validation of the effect of the CC at Traits analyzed in the current study had heritability estimates from low to high, thus genetic improvement of the trait using traditional selection would be possible where phenotypes are readily available as part of mainstream recording. For traits that show very low genetic correlations, the current study revealed that such traits may be mainly controlled by two groups of genes, one increasing both traits, the other increasing one trait while decreasing the other trait at the same time. Where this applies, a carefully chosen subset of SNP during selection may lead to improvement in both traits.Bos taurus autosome; EPD: Expected progeny difference; AN: Angus; CH: Charolais; SM: Simmental; PI: Piedmontese; LM: Limousin; GV: Gelbvieh; EBRC: Elora Beef Research Centre; NLARS: New Liskeard Agriculture Research Station; KAP: Kapuskasing experimental farm; MAF: Minor allele frequencyQTN: Quantitative trait nucleotide, referring to a causative mutation that accounts for differences in phenotype; QTL: Quantitative trait loci, referring to regions of chromosome that contains or links to genes underlying a quantitative trait; SNP: Single nucleotide polymorphism; IMPS: Institutional meat purchase specifications; HCW: Hot carcass weight; BF: Back fat thickness; REA: Ribeye area; MRB: Marbling score; BIFYLD: Lean yield grade by Beef Improvement Federation; LM7D: Warner-Bratzler shear force 7-day post mortem; BDFR: Body cavity fat rate; LNR: Lean rate; BNR: Bone rate; INFR: Intermuscular fat rate; SQFR: Subcuraneous fat rate; TLFR: Total fat rate; FDR: False discovery rate; BTA: DL participated in designing the study, carried out the analysis and interpretation of data, initiated and revised the manuscript; SPM sought funding for the project, participated in designing the study, and drafting the manuscript; MS designed the software and participated in drafting the manuscript; MK participated in designing the study and drafting the manuscript; GVV participated in data management and drafting the manuscript; IM helped with trait measurement; ZW, SM, GP sought funding for the project, and helped draft the manuscript. All authors read and approved the final manuscript.Distribution of observed P values of SNP in association with Back fat.Click here for fileDistribution of observed P values of SNP in association with BIF yield grade.Click here for fileDistribution of observed P values of SNP in association with Ribeye area.Click here for fileDistribution of observed P values of SNP in association with Marbling.Click here for fileDistribution of observed P values of SNP in association with Shear force at 7th day post-mortem.Click here for fileDistribution of observed P values of SNP in association with Body cavity fat percentage of rib.Click here for fileDistribution of observed P values of SNP in association with Lean percentage of rib.Click here for fileDistribution of observed P values of SNP in association with Bone percentage of rib.Click here for fileDistribution of observed P values of SNP in association with Intermuscular fat percentage of rib.Click here for fileDistribution of observed P values of SNP in association with Subcutaneous fat percentage of rib.Click here for fileDistribution of observed P values of SNP in association with Total fat percentage of rib.Click here for file Allele substitution effect of significant SNP (at P < 0.001).Click here for file Size of SNP effect expressed as percentage of phenotypic variance.Click here for file


P value 3.87 × 10−12 for SNP rs634990 in Caucasians, and 9.65 × 10−4 for rs8032019 in Asians. The overall meta-analysis provided P value 9.20 × 10−23 for the top SNP rs634990. The risk of myopia versus hyperopia was OR 1.88  for homozygous carriers of the risk allele at the top SNP rs634990, and OR 1.33  for heterozygous carriers. SNPs at locus 15q25 did not replicate significantly . We conclude that common variants at chromosome 15q14 influence susceptibility for myopia in Caucasian and Asian populations world-wide.Myopia is a complex genetic disorder and a common cause of visual impairment among working age adults. Genome-wide association studies have identified susceptibility loci on chromosomes 15q14 and 15q25 in Caucasian populations of European ancestry. Here, we present a confirmation and meta-analysis study in which we assessed whether these two loci are also associated with myopia in other populations. The study population comprised 31 cohorts from the Consortium of Refractive Error and Myopia (CREAM) representing 4 different continents with 55,177 individuals; 42,845 Caucasians and 12,332 Asians. We performed a meta-analysis of 14 single nucleotide polymorphisms (SNPs) on 15q14 and 5 SNPs on 15q25 using linear regression analysis with spherical equivalent as a quantitative outcome, adjusted for age and sex. We calculated the odds ratio (OR) of myopia versus hyperopia for carriers of the top-SNP alleles using a fixed effects meta-analysis. At locus 15q14, all SNPs were significantly replicated, with the lowest The online version of this article (doi:10.1007/s00439-012-1176-0) contains supplementary material, which is available to authorized users. Refractive errors are common optical defects of the visual system. An important refractive error is myopia (nearsightedness), which occurs when the eye elongates beyond the focal plane. The prevalence of myopia is high, affecting about one-third of the world’s population, and reaching over 70 % in certain Asian ethnic groups  reported several susceptibility loci for refractive error and myopia  including 31 cohorts with various ethnicities from 4 different continents.Complete data on refractive error and genome-wide SNPs were available in all 29 population-based studies comprising 49,364 subjects: 42,224 Caucasians and 7,140 Asians Table  and 17,6Table P value 4.53 × 10−14 for top SNP rs634990. Confirmation was achieved in 23 out of 25 Caucasian studies , and in 3 out of 4 Asian studies . Meta-analysis of the discovery and replication cohorts together provided P value 9.20 × 10−23 for SNP rs634990.For locus 15q14, the replication set, consisting of all studies except the ones previously used in the discovery analysis, showed a statistically significant association between SE and all SNPs with a best P 1.22 × 10−4 for SNP rs939661. As a subsequent analysis, we investigated locus 15q25 in more detail, and tested another 26 SNPs in 26 out of 29 cohorts . This set of SNPs was not replicated either, however, meta-analysis including the discovery cohort was still significant .For locus 15q25, neither Caucasian nor Asian validation studies replicated the original association. Meta-analysis of the combined set of the 5 SNPs yielded a lowest 2 21.35 (d.f. 26), P 0.724, I2 0.0 %] or heterozygote carriers . Therefore, only results from fixed effects meta-analysis were used. Figure P < 0.001) for homozygous carriers of the risk allele at the top SNP rs634990, and 1.33  for heterozygous carriers.Genotype distributions for rs634990 at locus 15q14 were available for 28 out of 31 studies . There was no evidence of heterogeneity in the analyses of homozygote carriers [χChromosome 15q was first implicated in refractive error and myopia by genome-wide analysis of two large studies located in Northern Europe  direct sequencing experiments did not reveal a functional variant, but the 15q14 locus appeared to harbor regulatory elements which may influence transcription of these genes  participated in this meta-analysis. 29 population-based as well as 2 case–control studies were included. General methods, descriptives and phenotyping and genotyping methods of the study cohorts can be found in Table 1, the Supplementary Material and Supplementary Table 1, respectively. In short, 22 cohorts consisted of Caucasian, and 5 of Asian study subjects. All studies were performed with the approval of their local Medical Ethics Committee, and written informed consent was obtained from all participants in accordance with the Declaration of Helsinki.All studies used a similar protocol for phenotyping. Exclusion criteria were age ≤10 years, and bilateral cataract surgery, laser refractive procedures or other intra-ocular procedures which might alter refraction. Eligible participants underwent a complete ophthalmologic examination including a non-dilated measurement of refractive error  of 3.57 × 10−3 for 15q14, and 1.0 × 10−2  for 15q25 were considered statistically significant.We selected 19 SNPs within loci 15q14 (14 SNPs) and 15q25 (5 SNPs) with a 2, I2 calculated and corresponding P values) between studies, crude OR with corresponding 95 % CI and P value of moderate and high myopia versus moderate and high hyperopia with a random as well as fixed effects meta-analysis using Stata 11. When these analyses provided similar outcomes, data from fixed effect analysis were used. For studies without subjects with high or moderate hyperopia, emmetropia was used as a reference group. A standard P value of <0.05 was considered statistically significant.From all population-based and case control studies, we obtained genotype distributions of the replicated top SNPs. We calculated heterogeneity Below is the link to the electronic supplementary material.


Contemporary dairy breeding goals have broadened to include, along with milk production traits, a number of non-production-related traits in an effort to improve the overall functionality of the dairy cow. Increased indirect selection for resistance to mastitis, one of the most important production-related diseases in the dairy sector, via selection for reduced somatic cell count has been part of these broadened goals. A number of genome-wide association studies have identified genetic variants associated with milk production traits and mastitis resistance, however the majority of these studies have been based on animals which were predominantly kept in confinement and fed a concentrate-based diet (i.e. high-input production systems). This genome-wide association study aims to detect associations using genotypic and phenotypic data from Irish Holstein-Friesian cattle fed predominantly grazed grass in a pasture-based production system (low-input).Significant associations were detected for milk yield, fat yield, protein yield, fat percentage, protein percentage and somatic cell score using separate single-locus, frequentist and multi-locus, Bayesian approaches. These associations were detected using two separate populations of Holstein-Friesian sires and cows. In total, 1,529 and 37 associations were detected in the sires using a single SNP regression and a Bayesian method, respectively. There were 103 associations in common between the sires and cows across all the traits. As well as detecting associations within known QTL regions, a number of novel associations were detected; the most notable of these was a region of chromosome 13 associated with milk yield in the population of Holstein-Friesian sires.A total of 276 of novel SNPs were detected in the sires using a single SNP regression approach. Although obvious candidate genes may not be initially forthcoming, this study provides a preliminary framework upon which to identify the causal mechanisms underlying the various milk production traits and somatic cell score. Consequently this will deepen our understanding of how these traits are expressed. Dairy production is an economically important sector of global agriculture with the top 10 leading dairy companies turning over in excess of $114 billion in 2009 [QTL associated with milk production and SCS have been extensively reviewed by, amongst others, Khatkar et al.  and SmarThe sequencing of the bovine genome and the subsequent HapMap project made large amounts of genetic markers available in the form of single nucleotide polymorphisms (SNPs). This massive increase in marker numbers allied with the emergence of high-throughout genotyping technologies allowed routine genome-wide association studies (GWAS) to be performed in cattle populations. GWAS allow screening of the genome utilising a large number of genetic markers spread across the entire genome to detect genetic variants associated with a particular disease or trait. The majority of recent GWAS employ SNPs as genetic markers. SNPs may not themselves be responsible for the variation observed in a trait, however, due to their close proximity to un-genotyped causal variants they have been co-inherited and so can act as proxies for the unknown causal variants . In thisIn general, GWAS studies to date have identified important genomic regions using single locus association methods whereby each individual SNP is tested for association with the trait of interest. This method has proved useful in its straight-forward implementation and interpretation, however, it has been hampered by the large number of false positive results produced due to the large number of markers and hence individual statistical tests. This problem has been somewhat addressed through multiple correction techniques such as the false discovery rate (FDR) , yet marIrrespective of the statistical approach or study design used, most QTL studies to-date in cattle have largely been undertaken using phenotypic data originating from high-input, concentrate-based dairy production systems. Genotype by environment (GxE) interactions have, however, been reported between high-  and low-input production  systems -14. In gThe objective of this study was to identify regions of the genome associated with milk production traits and SCS in cattle fed a basal diet of grazed grass (low-input system) using single SNP regression in a dataset of 914 Holstein-Friesian sires. A multi-locus, Bayesian approach in the same population and a single SNP regression analysis in a separate population of Holstein-Friesian cows were conducted to provide further support to the associations.Two populations of 914 Holstein-Friesian AI sires and 493 Holstein-Friesian cows were used to quantify associations between genotypic and phenotypic data. A summary of the various phenotypes and the correlations between them in the sire dataset are detailed in Table The use of a single SNP regression approach in a population of 493 Holstein-Friesian cows endeavoured to replicate any previous associations in a semi-independent population. The average additive relationship among the sires was 0.046, was 0.040 among the cows, and was 0.038 between the sires and cows. Significant associations detected for the two statistical approaches in the sires and the single SNP regression in the cows are detailed for each trait in Additional file A number of SNPs were significantly associated with more than one trait. We combined all significant associations  detected for a trait and identified 232, 83, 52 and 3 SNPs which were significantly associated with 2, 3, 4 and 5 traits, respectively  was located 146 base pairs from the DGAT1. A cluster of SNPs on chromosome 20, in a region also known to be associated with milk yield [-6). The genes in this region are listed in Additional file Using the single SNP regression approach, 370 SNPs were identified as being significantly associated (q ≤ 0.05) with milk yield at the 5% FDR in the sire population. The majority of these SNPs (311/370) were located in known QTL regions for milk yield -20. Sevelk yield ,19,24, wThere were two SNPs significantly associated with milk yield using the Bayesian approach in the sires , being the most significant association for fat yield. However, 89 significant SNPs were detected outside known QTL regions from Cattle QTLdb [A total of 370 SNPs were significantly associated (q ≤ 0.05) with fat yield using the single SNP regression in the sires , located on chromosomes 5, 7, and 14, were assigned posterior probabilities of association ≥ 0.8 using all three Bayesian priors and were located within known QTL regions ,29,30. O-4) located at ~50,6 Mb on chromosome 1. Several significantly associated SNPs were clustered on chromosome 1 in a region which overlaps with known QTL regions for protein yield [Using the single SNP regression model, 385 SNPs were significantly associated (q ≤ 0.05) with protein yield in the sires. Most of these SNPs (305/385) were found to lie within known QTL regions for protein yield ,33,34. Uin yield . The genThere were two SNPs significantly associated with protein yield in the sire population using the Bayesian approach. These SNPS, located on chromosomes 11 and 27 were both located in known QTL regions for protein yield ,36. One -92) was detected for a SNP close to the DGAT1 gene, a gene known to heavily affect milk fat percentage [Using the single SNP regression approach in the sires, a total of 216 SNPs were significantly associated (q ≤ 0.05) with fat percentage with 199 of these located within known QTL regions for fat percentage ,35,37,38rcentage . SeveralA total of 12 SNPs were significantly associated with fat percentage using the Bayesian approach in the sire population. Nine of these SNPs were located in known QTL regions ,35,39. T-16) for a SNP close to the DGAT1 gene which has been shown to affect protein percentage [-4) on chromosome 8 was the most strongly associated with protein percentage. In addition a number of novel SNPs located at 50 Mb on chromosome 15 were also moderately  associated. One of these 25 novel SNPs, located on chromosome 5, was also detected as significant when using the Bayesian approach, however, none were significant in the cows.Using the single SNP regression in the sires, there were 229 SNPs significantly associated (q ≤ 0.05) with protein percentage of which 204 SNPs were within known QTL regions for protein percentage ,24,26,30rcentage . In addircentage . Howeverrcentage ,42. An aThere were eight SNPs significantly associated with protein percentage using the Bayesian approach in the sires with five of these SNPs overlapping known QTL regions ,26,43. TOnly nine SNPs were significantly associated (q ≤ 0.05) with SCS using the single SNP regression in the sires, three of these were located within known QTL regions for SCS. These three SNPs were located on chromosomes 6 and 10 ,44,45. TOnly a single SNP, rs41590209 located at ~97 Mb on chromosome 4, was significantly associated with SCS using the Bayesian approach and this fell into a known QTL region for SCS . HoweverThe objective of this study was to identify QTL associated with milk production traits and SCS in Holstein-Friesian cattle from a low-input production system. Both a frequentist and a Bayesian statistical approach were employed to test for association between genotypes and phenotypes. The QTL identified using both the sire and cow populations were spread across all 29 autosomes; the location and frequency of these QTL were in general agreement with those previously reported ,4.A large number  of significant associations were detected across all traits. The majority of these significant associations were located within known QTL for the trait of interest. This shows that our methodology is effective in detecting associated regions of the genome. Also, our findings will help to further refine QTL regions previously detected with microsatellites . The detSeveral significant associations, both within and outside known QTL regions, were detected for SCS. However, associations were considerably less numerous and weaker compared with those for the milk production traits. This may have been due to several inherent problems with the SCS phenotype resulting in reduced power to detect associations. Firstly, the reliability of the SCS proofs, which is an indicator of the amount of information available for an animal, was lower than that of the milk production traits in both the sires and cows. Decreased reliability of SCS means greater uncertainty as to the true breeding value of the animal for that trait. Furthermore, in the sire population, there were 138 fewer animals used to test for associations with SCS which would also decrease the power to detect significant associations. In addition, the lower heritability of SCS when compared to that of the milk production traits may also contribute to the weaker associations identified for SCS . Additionally, the ability to allow variants . The priGenome-wide association studies are susceptible to detection of false positives due to the large number of statistical approaches being performed. One method to confirm or validate a SNP association/QTL is via replication of the association in a separate population as the probability of detecting the same associated variant in two separate populations is small . Our useA number of SNPs in this analysis were significantly associated with more than one trait suggesting that genes with pleiotropic action may have been detected. Typically, in this study, a SNP affected multiple production traits with no association with SCS. Examples of this are three SNPs which were significantly associated with all five production traits , and cell pellets were harvested via centrifugation and re-suspended in 450 μL of pre-warmed extraction buffer ; 15 μL of 2-mercaptoethanol was added. Samples were incubated at 55°C for 15 minutes followed by the addition of 10 μL of proteinase K (20 mg/mL). Lysis occurred following overnight incubation at 60°C. DNA was then extracted using the Maxwell instrument  according to the manufacturer's instructions. Details of the DNA extraction method used in the cow dataset are available in Additional file , and celIn total 54,001 biallelic SNPs for 1,957 Holstein-Friesian AI sires with progeny in Ireland were genotyped in this study; the sires were representative of the germplasm used in Irish dairy herds in past years. All animal procedures were carried out according to the provisions of the Irish Cruelty to Animals Act . Sires were genotyped using the Illumina BovineSNP50 Genotyping Beadchip . SNP positions were based on BTAU 4.0. The 2,419 SNPs that were on the X chromosome or whose positions on the genome were unknown were eliminated from the dataset. SNPs on the X chromosome were removed due to large-scale discordance between SNP genotypes and animal sex  indicating poor SNP calling. A further 230 SNPs were discarded that did not conform to Mendelian inheritance patterns between sire and son based on analysis of a larger dataset of animals . The reme SCC) are estimated in Ireland using a repeatability animal model across the first five lactations [Daughter yield deviations (DYD) for milk yield, fat yield, protein yield, fat percentage, protein percentage and SCS along with their respective reliabilities were available from national genetic evaluations undertaken in January 2010 by the Irish Cattle Breeding Federation (ICBF). Summary statistics for the phenotypic data is presented in Table ctations . The parctations . Only siA single SNP regression model and a Bayesian method were both used to detect regions of the genome associated with milk production traits and SCS. In both approaches the dependent variable was either the DYD (sires) or YD (cows). SNPs were included in all models as continuous variables. A pedigree file, containing at least the previous four generations of the genotyped animals ancestors, was generated for each of the sire and cow datasets, respectively. The pedigree consisted of 5,479 animals for the sires and 4,476 individuals in the cows. The relatedness within and between the sires and cows was determined by calculating the average additive relationship between animals using the available pedigree information.The dependent variable (i.e. the respective trait (daughter) yield deviation) was regressed on each SNP individually in a mixed animal model accounting for relationships among animals using the additive genetic relationship matrix in ASReml . The indThe Bayesian method used was BayesB as described by Meuwissen et al.  modifiedGenotypic data from the sires consisting of 40,668 SNPs typed across 914 individuals were used to estimate haplotype blocks. The 'Solid Spine of LD' method (based on the D prime statistic) in Haploview , with deAll significant associations were examined to determine if they overlapped with known QTL regions using an in-house Perl script; if any significant SNP did not overlap with a known QTL region it was considered novel. The location of known QTL regions for the milk production traits and SCS were sourced from Cattle QTLdb . We usedThe authors declare that they have no competing interests.DPB, DJL and AGF designed research; BKM performed the research, analyzed data and wrote the paper with DJL and DPB; JFK, DGB provided genotypic and phenotypic data; EKF carried out the DNA extraction. All authors read and approved the final manuscript.Summary statistics for the cow dataset. This file contains summary statistics for the phenotypic data in the cow validation dataset mentioned in the manuscript. Summary statistics include the total number of phenotypic records (N), mean and standard deviation (σ) for each trait in the cows. Phenotypes in the cows are expressed as yield deviations on a PTA scale.Click here for fileCorrelations between phenotypes for the cow dataset. This file contains Pearson correlations between phenotypes in the cow dataset expressed as yield deviations on a PTA scale.Click here for fileAll Significant Associations. This file contains all significant SNPs detected across all the analyses in the sires and cows. Significant associations are presented separately for each analysis with separate worksheets for each trait.Click here for fileCow Dataset Materials and Methods. This file contains information on the materials and methods used for the cow validation dataset mentioned in the manuscript.Click here for file


For some diseases, e.g. sickle-cell anemia, the causative SNPs are well documented. In most other cases, the detection of disease-causing variants is still a problem. The genome-wide association studies (GWAS) provide insight into SNP-disease relationships. However, GWAS analysis is both experimentally and computationally expensive and fails to properly consider the rare variants, i.e. individual-specific SNPs that have yet to be documented on a population scale. This discrepancy between the deluge of SNP data and the lack of its interpretation spurs the development of the SNP impact annotation/prediction algorithms. In the near future, the study of genetic variation in disease and treatment options will be key for the development of the field of personalized medicine.Advances in high-throughput sequencing, genotyping, and characterization of haplotype diversity are consistently generating vast amounts of genomic data. Single Nucleotide Polymorphisms (SNPs) are the most common type of genetic variation. In the recent years the number of known SNPs has been increasing exponentially; the last release of the NCBI’s dbSNP database contained more than 50 million human SNPs. SNPs are interesting as both markers of evolutionary history and in the context of their phenotypic manifestations  meeting at ISMB/ECCB’2011 in Vienna, Austria (http://snps.uib.es/snp-sig/2011). This meeting attempted to summarize the field’s research advances in the directions of “Annotation and prediction of structural/functional impacts of coding SNPs” and “SNPs and Personal Genomics: GWAS, populations and phylogenetic analysis”. Over 70 scientists actively working in the field and strongly interested in its development have officially registered for the SIG. On the date of the meeting, an even larger number of ISMB participants have gathered to discuss their work, the state of the art, and future perspectives. In all, 17 presentation proposals and 13 posters were submitted to the SIG and eight works where selected for an oral presentation at the meeting.In 2010, the first edition of the Critical Assessment of Genomic Interpretation  was organized to evaluate the ability of available computational methods to predict the phenotypic impacts of genomic variation. Annotation of SNPs was also a hot topic in many other meetings, such as AIMM at ECCB 2010 , the HGVS 2010 meeting  and PSB 2011 . In line with the increasing interest in the genetic variation analysis and annotation, on July 15Distinguished scientists were invited to share their visions of the field past, present, and future: Steven Brenner , Atul Butte (Stanford University), John Moult , Burkhard Rost  and Mauno Vihinen (Lund University). A round table discussion on the most timely and important problems of SNP annotation was held, directed by Christopher Baker (University of New Brunswick), Maricel Kann , Sean Mooney (Buck Institute), Pauline Ng (Genome Institute of Singapore) and Mauno Vihinen (Lund University).method developer tutorial/framework that will be very helpful for all future work in the field [We have invited all SIG presenters to submit full research papers for publication in this special issue of BMC Genomics. We adopted a peer review process to select ten exceptional works. The articles cover different aspects of the field, presenting databases and tools for the annotation of SNPs as well as novel scientific advances achieved based on these resources. The described methods use different types of information derived from sequence, evolution, function and structure to analyze large sets of variations. They address SNP-associated 1) specific protein function  specific,3 changehe field .http://snps.uib.es/snp-sig). Submissions of posters and presentation proposals are welcome.We are now working on the organization of the next edition of the SNP-SIG meeting to be held in the context of the ISMB 2012, Long Beach, California. Further information about the SNP-SIG 2012 is available on our web site (


The bottom-up organization of functional materials with nanoscale precision is a central goal for the development of future sensors, machines, and devices. UnderpinPyrrole-imidazole polyamides (PAs) are a class of small-molecule ligands which provide such control. PAs bind within the minor groove of duplex DNA, enabling one to target 6 to 10 base pair sequences with high binding affinity (nanomolar to subnanomolar) and specificity. Upon Upon9] Uemission , left wa1) from one to three . This is indicative of the extra energy injected into 1@DNA21 (3.0 equiv) is being trapped within the non-sequence selective YO (1) dyes as a consequence of a heterogeneous ensemble of energy transfer events involving both hetero-FRET (i.e. ET from PB to YO and from YO to Cy3) and homo-FRET (i.e. ET between YO dyes) processes. In contrast, 2@DNA21 is a discrete assembly in which the position of a single intercalating YO dye is controlled by PA binding to its target sequence and as a result, only a two-step hetero-FRET process  is observed. We therefore conclude that the PA-programming approach of directing the location of a single intercalating YO dye by PA binding to its orthogonal binding sequence in DNA21 increases the efficiency of energy transport through the DNA duplex.Increasing the number of equivalents of YO (2@DNA21 and 1@DNA21 assemblies (Eq. S1)2@DNA21 a2@DNA21 (1.5 ns) compared to the control DNA21 (2.7 ns) and 1@DNA21 . Consistent with the steady-state data, ET rates of 1@DNA21  approached that of 2@DNA21  and 2@DNA80  assemblies exhibiting shorter lifetimes than their equivalent YO (1) assemblies 1@DNA55  and 1@DNA80 , respectively  in order to estimate the time required for the excitation energy to arrive at Cy3 after excitation of PB (excitation at 380 nm). In all t 2@DNA21 . This trectively .9]2@DNA21 (In summary, we report the first demonstration of a highly efficient DNA-based photonic wire where energy is transported over an unprecedented distance of 80 base pairs or approximately 27 nm. The key aspect in augmenting ET is defining the location of intercalating YO dyes using DNA-binding PAs. Due to the modularity of the approach, the judicious choice of appropriate fluorophores, and optimization of the interfluorophore distance, increasing the lengths of these photonic wires is indeed possible well beyond our exemplar assemblies highlighted here. Since PAs have the ability to target virtually any DNA sequence, we envisage PA programming becoming a valuable tool in the construction of sophisticated multi-dimensional arrays, motors, and circuits where nanometer-level precision is required.