Genomic selection (GS) using estimated breeding values (GS-EBV) based on dense marker data is a promising approach for genetic improvement. A simulation study was undertaken to illustrate the opportunities offered by GS for designing breeding programs. It consisted of a selection program for a sex-limited trait in layer chickens, which was developed by deterministic predictions under different scenarios. Later, one of the possible schemes was implemented in a real population of layer chicken.In the simulation, the aim was to double the response to selection per year by reducing the generation interval by 50 %, while maintaining the same rate of inbreeding per year. We found that GS with retraining could achieve the set objectives while requiring 75 % fewer reared birds and 82 % fewer phenotyped birds per year. A multi-trait GS scenario was subsequently implemented in a real population of brown egg laying hens. The population was split into two sub-lines, one was submitted to conventional phenotypic selection, and one was selected based on genomic prediction. At the end of the 3-year experiment, the two sub-lines were compared for multiple performance traits that are relevant for commercial egg production.Birds that were selected based on genomic prediction outperformed those that were submitted to conventional selection for most of the 16 traits that were included in the index used for selection. However, although the two programs were designed to achieve the same rate of inbreeding per year, the realized inbreeding per year assessed from pedigree was higher in the genomic selected line than in the conventionally selected line.The results demonstrate that GS is a promising alternative to conventional breeding for genetic improvement of layer chickens. Genomic selection (GS) as a means of marker-assisted improvement was introduced by Meuwissen et al.  and has Although empirical accuracy of genomic prediction has been studied in most livestock species , studies on the impact of empirical results of GS on genetic gain are not available. In addition to increasing accuracy of selection at young ages, GS is expected to reduce rates of inbreeding per generation because GS provides additional information on Mendelian sampling terms of selection candidates , 5. GS iThe objectives of this study were to design, simulate, implement and retrospectively evaluate a GS program for layer chickens, which could potentially double the response to selection per year, while maintaining the same annual rate of inbreeding, compared to a typical pedigree and performance-based layer selection program. The selection lines for the GS and conventional breeding programs were derived from the same foundation line, and multiple-trait performance was compared over the same time period in the terminal generations.Design of a genomic selection program for layer chickens using deterministic prediction models based on selection theory that would double the response to selection per year but maintain the same rate of inbreeding per year compared to a conventional selection program based on pedigree and phenotypic information. Our aim was to achieve these goals while minimizing genotyping and phenotyping costs by changing the population structure.Evaluation of the performance of this GS strategy by stochastic simulation.Implementation of the GS and conventional breeding strategies, in which both selection lines were derived from the same foundation generation of a real elite purebred layer chicken population.Retrospective evaluation of the realized responses to selection for the two breeding programs.This research was conducted in four steps as follows:Responses to selection and rates of inbreeding were predicted by deterministic methods for a conventional BLUP (best linear unbiased prediction) selection program and for a range of GS programs in order to identify a strategy that could double the response to selection per year with the same rate of inbreeding per year as the conventional program. To reflect selection for egg laying traits, we considered a trait with a heritability of 0.3 and phenotypes available only on females at 1 year of age. The program SelAction  and procThe conventional BLUP strategy assumed selection and mating of 60 males and 360 females per 56-week long generation cycle. The 60 males were selected from 1080 selection candidates (three sons raised per hen) based on their BLUP estimated breeding values (EBV) from phenotypic data on female ancestors and sibs . The 360 females were selected from 2880 selection candidates (eight daughters per hen) based on BLUP EBV from the phenotypes of female ancestors and sibs, as well as individual phenotypes. Deterministic predictions of this program using SelAction showed an expected response of 0.48 phenotypic standard deviation per generation and a rate of inbreeding of 1.38 % per generation.Using the response to selection and rate of inbreeding of the conventional program as targets, predicted responses and rates of inbreeding per generation were evaluated for a large number of GS strategies; the number of sires selected was varied from 25 to 60, the number of dams selected from 40 to 120, and the number of male and female offspring per dam that were genotyped was varied from 3 to 12. The initial accuracy of genomic EBV that were assumed to be available on all selection candidates was set equal to 0.7, which resulted in an asymptotic accuracy after accounting for the Bulmer effect between 0.58 and 0.60 for all GS strategies. Based on deterministic predictions, a GS breeding program with 50 males and 50 females that were selected at each generation from 300 selection candidates per sex  was predicted to result in a similar rate of response per generation as the conventional BLUP program (0.43 vs. 0.48 phenotypic SD), with less than half the rate of inbreeding per 28-week generation cycle (0.59 versus 1.38 %), while minimizing the number of individuals genotyped per generation. Thus, with the generation interval for the GS program being half that of the conventional 56-week program, this GS program was predicted to nearly double the response to selection per year, with a slightly lower rate of inbreeding per year.−5), and recombination, with effective population sizes of 500 for g1-g900 and 100 for g901-g1000. To create the training data, the population was expanded to 1000 individuals in g1001. Breeding values were generated by designating 200 random segregating  SNPs as quantitative trait loci (QTL). Each QTL was assigned an effect that was drawn from a Gamma distribution with shape parameter 0.4 and inverse scale parameter 1.66, following Hayes and Goddard [The conventional and GS programs described above were compared by stochastic simulation to validate these deterministic predictions. A genome that comprised 20 chromosomes of 37.5 cM each, for a total of 750 cM, was simulated. In the foundation generation of the base population (g0), 6001 equally spaced SNPs per chromosome were simulated for 500 individuals, with allele frequencies of 0.5 and in linkage and Hardy-Weinberg equilibrium. To generate mutation-drift equilibrium, the subsequent 1000 generations (g1-g1000) were simulated with random mating, mutation  and the GS program  were simulated. Response and rate of inbreeding were evaluated over four generations of conventional selection and eight generations of GS because the generation interval was reduced by 50 % in GS.j is over all genotyped SNPs, Xij is the number  of copies of allele 1 that individual i carries at SNP j, gj is the allele substitution effect for SNP j, and ei is a random residual. Allele substitution effects gj were assumed to be normally distributed with mean 0 and variance σgj2 with probability 1-π = 0.05 or to be null with probability π = 0.95. Preliminary analyses found that the choice of π had a limited impact on the results. The prior for the variance of the substitution effects σgj2 was χ−2. Estimates of allele substitution effects for each SNP, ĝj, were obtained as posterior means from 1000  cycles of a Gibbs chain, of which the first 100 cycles were discarded as burn-in, combined with 10 cycles of a Metropolis-Hastings algorithm within each Gibbs cycle to obtain samples of σgj2. Preliminary analyses showed that these numbers of cycles led to converged estimates of EBV. For selection candidates, EBV were computed based on their SNP genotypes as:For conventional selection, EBV were estimated at each generation by fitting the conventional animal model to available phenotypic data , 9, usinGS was evaluated with and without retraining. Without retraining, the SNP effects that were estimated based on g1001 were used for all eight cycles of selection. With retraining, SNP effects were re-estimated at each generation, after adding phenotypes of the 300 female candidates from the previous generation to the training dataset.Responses to selection were evaluated based on the mean true breeding value of selection candidates at each generation based on 48 replicates of the simulation. The level of inbreeding at each generation was based on the average pedigree-based inbreeding coefficients of selection candidates, using g1001 as the founder generation. Standard deviations of response and inbreeding were evaluated across the 48 replicates.Based on the simulation results, a GS program was implemented in an experimental pure-bred commercial brown egg layer chicken line that was previously under conventional BLUP selection. To allow side-by-side comparison of the conventional and GS programs, the line was split into two sub-lines (later denoted as genomic and pedigree lines) by random splitting of full-sib families. The pedigree line underwent two subsequent generations of conventional selection, whereas the genomic line underwent four generations of GS. All birds were produced, reared, and managed in facilities of the Hy-Line breeding program by Hy-Line staff, using standard protocols implemented by Hy-Line International.Different population structures were implemented for the two sub-lines, with numbers of contributing sires and dams and the total number of selection candidates shown on Fig. The GS program that was implemented was a slight modification of the one used in the simulation because it applied two-stage female selection. No changes in selection strategy were implemented on the male side: 50 males were selected at a young age on a multi-trait index of genomic EBV (GEBV). In the first stage of female selection, 150 of the 300 female candidates were selected at a young age based on a multi-trait index of GEBV. All 150 females were mated to produce progeny for the next generation and before being recorded for phenotypes, which resulted in a generation interval of 28 weeks. A partially cross-classified mating design was implemented to reduce rates of inbreeding and capitalize on the ability of the GS procedure to assign parentage based on genotypes. For this mating design, the 50 selected males and 150 females were divided into five mating groups of 10 males and 30 females. Within each mating group, each male was mated to a different set of three females by artificial insemination. Males were rotated between the sets of females within a mating group. Parentage of the chicks was established based on SNP genotypes. In the second stage, at 42 to 46 weeks of age, after most individual phenotypes had been recorded, the 150 females were re-evaluated based on a combination of GEBV and own performance. From the best 50 dams 300 male and 300 female progeny (now 14 weeks of age) were selected for genotyping and phenotyping. The remaining progeny were kept only for phenotyping. This two-stage selection strategy made it possible to reduce the generation interval by 50 %, while increasing the accuracy of female selection, although at the cost of doubling the number of progeny that were produced and reared.Using progeny that could be uniquely assigned to the parents and excluding animals which had no progeny, each sire had on average 12.4 progeny (ranging from 1 to 31) and each dam 10.8 progeny (ranging from 1 to 15), with 1.9 (ranging from 1 to 7) full-sib progeny on average per sire-dam combination. The numbers of selection candidates, and final numbers of contributing sires and dams are in Fig. All phenotypes used in this study were obtained from routine data collection of Hy-Line International. Both sub-lines were selected based on the same multi-trait index combining 16 production and quality traits measured at early (e) or later (l) ages: age of sexual maturity , body weight at late age , shell color  for the first three eggs (eC3), at early (eCO) and late age (lCO), egg weight (g) for the first three eggs (eE3), at early (eEW) and late age (lEW), puncture score at early (ePS) and late age (lPS), albumen height (mm) at early (eAH) and late age (lAH), and yolk weight (g) at early (eYW) and late age (lYW). Egg production was expressed as ePD and lPD, which are the egg production rates . Early measurements for egg quality traits were taken at 26 to 28 weeks of age. Late measurements for egg quality traits were taken at 42 to 46 weeks of age on birds not culled after early measurements. In addition, egg number  laid in weeks 1 to 10 and in weeks 11 to 20 of production, respectively) were monitored for correlated response. Complete records for late egg production were not available on GS females at the final stage of selection. Early and late egg quality measurements were averages of three to five eggs. Observations that deviated from the within-hatch generation mean by more than three standard deviations were excluded from breeding value estimation and treated as outliers, but all biologically feasible values were retained for line comparison in the final generation.Breeding values of selection candidates in the pedigree sub-line were estimated using the pedigree-based multi-trait BLUP that is used for routine evaluation in the Hy-Line International breeding programs. Hatch-by-generation was used as the only fixed effect to account for contemporary group effects.Selection candidates in the genomic sub-line were genotyped using a custom high-density Illumina SNP panel which provided 23 356 segregating SNPs . The same panel was also used to establish the training population for the first generation, which consisted of all selected parents from the previous five generations: 2708 genotyped animals, of which 1563 were females with individual phenotypes and 1145 were males without phenotypes. In addition, phenotypes of 11 486 progeny of the genotyped individuals were included as progeny means in the training data. In subsequent generations, phenotypes and genotypes of the 300 genotyped female selection candidates were added to the training population, i.e., the retraining option for GS was used.In the final generation, the GS line was expanded to produce enough progeny to make comparisons with the pedigree sub-line valid. A total of 2318 progeny were hatched, of which 1977 were assigned to 49 sires and 148 dams based on low-density SNP genotypes (71 SNPs). Animals that were not genotyped or that had one or more parents not matching the mating scheme were excluded.At each generation, the accuracy of several genomic evaluation methods was evaluated by using phenotypes of the last available generation for validation and the method with the highest accuracy was used for training on a dataset that included the phenotypes of the last generation for each trait. The genomic evaluation methods that were used included: univariate and bivariate GBLUP (using the early and late phenotypes for a given trait), GBLUP with a modified genomic relationship matrix , univariBayesian analyses were performed using the GenSel software , while c1) and GEBV (b2) in this index were derived using selection index theory [r) and the associated estimate of pedigree-based heritability (h2):For the second stage selection of females, the rescaled first stage GEBV were combined with each hen’s adjusted own phenotypes, using a simple index of GEBV and own phenotype for each trait. The weights assigned to own phenotype  in allele frequencies were located on chromosomes 8, 9, and Z (six loci) for the pedigree selected generations and on chromosomes 6, 9, and 12 (four loci) for the genomic selection generations. Although the SNPs with the greatest change in frequency differed between the pedigree and genomic selected generations, several regions showed substantial changes in a consistent direction across the pedigree and genomic selected generations.Use of marker information removes many of the limitations of conventional phenotype-based selection programs, as has been argued and demonstrated by many simulation studies , becauseIn this study, we investigated the various opportunities that GS offers to improve breeding programs of layer chicken by changing their structure. Results clearly demonstrate that GS provides interesting opportunities to reduce generation intervals and the size of breeding programs, which impacts the number of animals that need to be raised and phenotyped on a routine basis. Maximizing response per year for a given rate of inbreeding per year was used as the objective for comparing alternative strategies for GS. This has been suggested as a reasonable objective for balancing short- and long-term responses to selection . Even whThe simulated GS breeding program required much smaller numbers of selection candidates  to achieve rates of genetic gain per generation that approached those of the conventional program. This was due to the greater accuracy of EBV with GS, in particular for males, which had EBV based on pedigree and sibs in the conventional program. In addition, the numbers of individuals that were phenotyped were substantially smaller for the GS program, even with retraining . Lower rearing, housing, and phenotyping requirements would substantially reduce the costs of breeding programs. Whether these reduced costs offset the extra costs associated with genotyping depends on specific prices. The parameters that we used for both the conventional and GS programs are given as examples to illustrate the opportunities provided by GS. Further analyses that consider specific prices, requirements and limitations are needed. The optimal strategy for retraining, including how often and on which animals new phenotypic data should be generated, also requires further investigation and will depend on the species used and the goals of the breeding program.The GS program was successfully implemented, but the short generation interval was not very practical in a commercial breeding program setting. Reproducing females at a very young age increases selection pressure on sexual maturity, and requires using suboptimal sized eggs and hatching them over multiple hatches, which complicates optimization of animal management, especially the lighting program. When selection is initiated, the late maturing females have not started laying yet or produce eggs that are too small to hatch good quality chicks. A more feasible approach would be to use young genomic selected males on older females, which would also improve accuracy of selection because the females would already have own records. A novelty in the implemented GS breeding program was the use of cross-classified mating, in which females are given the opportunity to leave progeny from more than one male parent. Parent assignment with a high level of accuracy based on the number of opposing homozygotes in parent-offspring pairs was possible for almost all offspring from the multi-sire matings. The use of cross-classified mating improves population structure and creates more opportunities for varying chromosomal combinations in the progeny and thus, results in higher effective rates of recombination.In the stochastic simulation, the Bayes-B method of  was usedIn the analyses on real data, we found only small differences between genomic evaluation models  and someAnalyses of real data showed that accumulation of information across generations improved the accuracy of genomic predictions on average . In genePrevious analyses with this data showed a substantial decline in accuracy of GEBV over generations without retraining, since the selection candidates were more distant from the animals in the training population . AlthougFigure This study demonstrates the advantages of using genomic selection combined with cross-classified mating in layer chicken breeding programs, in terms of increased accuracy of prediction relative to pedigree-based parent average, providing the potential to shorten generation intervals and thereby increase response to selection per year. The advantages were reflected in the phenotypic superiority of the genomic sub-line for almost all traits included in the selection index. Genotyping also allowed sire assignment and relaxed traditional constraints on hierarchical matings. Using multiple sire matings improves the population structure and is expected to decrease inbreeding rate. Nevertheless, a sustainable breeding program still requires an effective population of sufficient size. In view of the rapid changes in genotyping technologies and costs, breeding programs based on genomic selection need to be re-evaluated and optimized on a regular basis.


By focussing on differences in the mural granulosa cell (MGC) and cumulus cell (CC) transcriptomes from follicles resulting in competent (live birth) and non-competent (no pregnancy) oocytes the study aims on defining a competence classifier expression profile in the two cellular compartments. Design: A case-control study. Setting: University based facilities for clinical services and research. Patients: MGC and CC samples from 60 women undergoing IVF treatment following the long GnRH-agonist protocol were collected. Samples from 16 oocytes where live birth was achieved and 16 age- and embryo morphology matched incompetent oocytes were included in the study.MGC and CC were isolated immediately after oocyte retrieval. From the 16 competent and non-competent follicles, mRNA was extracted and expression profile generated on the Human Gene 1.0 ST Affymetrix array. Live birth prediction analysis using machine learning algorithms (support vector machines) with performance estimation by leave-one-out cross validation and independent validation on an external data set.We defined a signature of 30 genes expressed in CC predictive of live birth. This live birth prediction model had an accuracy of 81%, a sensitivity of 0.83, a specificity of 0.80, a positive predictive value of 0.77, and a negative predictive value of 0.86. Receiver operating characteristic analysis found an area under the curve of 0.86, significantly greater than random chance. When applied on 3 external data sets with the end-point outcome measure of blastocyst formation, the signature resulted in 62%, 75% and 88% accuracy, respectively. The genes in the classifier are primarily connected to apoptosis and involvement in formation of extracellular matrix. We were not able to define a robust MGC classifier signature that could classify live birth with accuracy above random chance level.We have developed a cumulus cell classifier, which showed a promising performance on external data. This suggests that the gene signature at least partly include genes that relates to competence in the developing blastocyst. In order to improve the efficacy of in vitro fertilisation treatment, there is ongoing research to define good non-invasive embryo markers to increase the implantation rate from an average of around 25% achieved with the existing morpho-kinetic markers .The developmental competence of the mammalian oocyte is achieved during folliculogenesis, where cross-talk between oocyte and somatic cells ensures oocyte growth and maturation and the subsequent potential to sustain fertilisation and produce viable embryos . Thus, sSpecific functions of the somatic cells from the pre-ovulatory follicles just prior to ovulation (e.g. progesterone and oestrogen production from the cumulus-oocyte-complex (COC)  and exprTaking the complexity of the preovulatory follicle into account, the aim of identifying single marker genes seems to be less robust than a marker based on the scenario of multi interacting genes, which may be identified by the high trough put techniques such as the expression array. Therefore the present study focus on differences in the MGC and CC transcriptomes between oocytes from competent and non-competent follicles and the aim is to develop classifier profiles in the two cellular compartments in order to be able to select the embryos with the best potential to result in life offspring. To approach the scenario for selecting a competent embryo for transfer in a cohort of embryos, a case control design was chosen matching women’s age and embryo morphology.Thus, in the present study, gene expression profiling of corresponding CC and MGC of individual follicles obtained from women undergoing IVF with elective single embryo Day 3 transfer were associated to no pregnancy after IVF or life birth of a healthy baby.The CC and MGC were collected at the Fertility Clinic, Copenhagen University Hospital, Rigshospitalet, Denmark between February 2009 and June 2010 as a part of a prospective, randomised study ) . The Dan2, regular menstrual cycle, early follicular phase serum FSH levels within 1–12 IU/l and early follicular phase total antral follicle (2–10mm) count ≥ 6 donated CC and MGC. The patients underwent controlled ovarian stimulation in a GnRH agonist protocol as described in [A total of 60 women, scheduled for IVF, aged 25–37 years and with BMI >18 and < 30 kg/mribed in . Oocyte ribed in . To reprFor demographic data t-test and chi-squared tests were used and p<0.05 considered significant.Follicular fluids from all follicles were collected individually in tubes . When an oocyte was obtained, the number of the follicle was recorded and the oocytes were numbered consecutively. Thus, each oocyte could be linked to an individual follicle and hence the correspondent MGC. Immediately after isolation of the COC, the MGC were isolated from the follicular fluid by centrifugation at 400 g in 8 min at room temperature. The pellet was re-suspended in 1 ml HEPES-buffered solution  and placed on a density gradient column  and centrifuged at 400 g for 30 minutes. After centrifugation, the inter-phase containing the MGC was aspirated and the MGC washed twice by mixing with1 ml of the buffer solution followed by centrifugation at 400 g for 8 minutes at room temperature. After the second wash, pellet was re-suspended in 500 μl in a cryo-vial  and stored at -80°C until RNA extraction.Within 1 hour after the oocyte retrieval a part of the cumulus mass was mechanically removed (18G needles) from the COC and transferred to a cryo-vial  with as little fluid as possible and stored at -80°C until RNA extraction.IVF insemination was done with standard IVF procedure at 3 h (±1 h) after oocyte retrieval. All oocytes were followed individually and zygote and embryo quality assessed at 20 h (±1 h), 28 h (±1 h), 44 h (±1 h) and 68 h (±1 h) after oocyte retrieval. The embryo quality evaluation consisted of assessment of cell number and three parameters of embryo morphology: degree of fragmentation, cell stage specific blastomere uniformity and multinucleation .RNA from individual samples was purified using Arcturus PicoPure RNA Isolation kit  according to manufactures instructions but with small modifications. In short: 100 μl extraction buffer was used for initial lysis of each sample, except for samples were sample volume excided 70 μl. For these samples 2 x sample volume of extraction buffer was added. Following lysis and centrifugation, the supernatant was transferred to a 2 ml Eppendorf tube from the kit  where it was mixed 1:1 with 70% ethanol. For some MGC samples with large volume, several transfers and spins were needed to collect all the RNA in the spin tubes. In the end, the RNA was extracted in 12 μl nuclease free water, giving a ~10 μl end volume. The integrity of the RNA was measured using bioanalyzer pico chip  and only samples with a RNA integrity number (RIN) above 7 as analyzed by the 2100 bioanalyzer  were included in the analysis.RNA was amplified and labeled using a pico amplification kit according to manufactures instructions. In short, approximately half of the total RNA from each sample (5 μl of 10 μl) was amplified using the Ovation Pico WTA v.2 RNA Amplification System from NuGEN® Inc.  and biotin labeling was performed with the Encore Biotin Module (NuGEN®). The labeled samples were hybridized to the Human Gene 1.0 ST GeneChip array . The arrays were washed and stained with phycoerytrin conjugated streptavidin (SAPE) using the Affymetrix Fluidics Station® 450, and the arrays were scanned in the Affymetrix GeneArray® 3000 7G scanner to generate fluorescent images, as described in the Affymetrix GeneChip® protocol. Cell intensity files (CEL files) were generated in the GeneChip® Command Console® Software (AGCC) .All samples are MIAME compliant and were handled according to SOP in the Microarray Center. A total of 27 CC and 19 MGC samples were amplified and hybridized to arrays. The 46 samples were submitted to ArrayExpress at EMBL using MIAMExpress. The experiment accession number is E-MTAB-4012.Classifiers were constructed based on expression matrices obtained by Plier normalization . FollowiBased on these data sets, three different classification algorithms  ,20, k-neFollowing the cross validation procedure, we calculated the cross-validation support for each gene showing the percentage of the cross-validation training sets in which each particular gene was selected. The cross-validation support shows the percentage of the cross-validation training sets in which each particular gene was selected, giving the relative strength (weight) of each gene in the signature.For the optimal CC classifier, the predictive probability (estimate of the probability of getting a particular class label) of each samples, was derived from the trained SVM model. This was achieved by transforming the SMV classifier into a probabilistic classifier by calculating the predictive probability of a sample being one or the other type using logit estimates as implemented in the e1071 R library . The preA permutation test was performed in order to determine if the cross-validated misclassification rate of the final CC classification model was lower than expected by chance . In 1000 random permutations of the class label, the entire cross-validation was repeated for classifying the random grouping of the samples. The proportion of the 1000 random permutations that resulted in a smaller or similar cross-validation misclassification rate as obtained with the real data determine the permutation p-value. The statistical significance of the error rate was determined both for the linear SVM, 1-NN, 3-NN and dLDA classifier.www.ingenuity.com). The list of the signature probe sets with p value and fold change was imported into Ingenuity. By application of the Ingenuity Pathway Knowledge Base each gene identifier was overlaid onto a global molecular network linking their functionality to the function of other genes and biological enriched functionality and mechanistic networks of these genes were then generated based on their connectivity and enrichment statistics. A network score was calculated based on the hyper-geometric distribution and calculated with the right-tailed Fisher’s exact test. The score is the negative log of this p-value. The score takes into account, the number of network eligible molecules in the network and its size, as well as the total number of network eligible molecules analyzed and the total number of molecules in the knowledge base that could potentially be included in the network. The score represents the chance of getting a network containing at least the same number of network eligible molecules by chance when randomly picking the number of genes that can be in networks from the knowledge base. The top molecular and cellular functions and networks are shown.The cumulus gene signature was subjected to Ingenuity pathway analysis using IPA Ingenuity software Ingenuity®Systems  was validated on an external data set generated on the Agilent platform using the Whole Human Genome Microarray 4x44K microarray . The fulThe microarray expression profiles were generated in a two-color array with dye-swap of replicate samples to avoid systematic noise in the data related to the choice of dye (cye-5 or cye-3). We downloaded the preprocessed data of normalized unlogged expression values representing one-color channel intensities. The data was used for validation of the gene signature after applying a log2 transformation. Gene symbols were used to select the probes on the 4x44K array that overlap with the unique genes in the 30-gene signature. Due to redundant representation of some of the genes on the Agilent array, the gene signature was represented by 68 redundant mRNA transcripts (representing 25 unique genes) on the Agilent 4x44 platform. For assessing the performance of this gene signature, represented by 25 unique genes, the probe with the intermediate expression value for each of the genes with redundant probes was used to define the 25-gene signature in the external data sets.The external data sub sets  were tested one by one. For each sub set, a test set data object was constructed and the overlapping gene signature was tested using SVM classification with linear kernels. The predictive probabilities were derived and visualized along with the predictive probability of the training set samples.ROC curve characteristics analysis was performed on all data set  with the end point LB or NP in the training data, and blastocyst or embryo of poor quality used in the validation data set. A ROC curve was generated for each partition of the validation data set. The ROC curve was created by plotting the true positive rate  versus the false positive rate . The area under the curve (AUC) indicates the degree of predictive ability of the gene expression, where 0.5 is random chance and 1.0 is perfect predictive ability. The ROC and AUC were produced using the functionality of the R library ROCR.A total of 46 expression profiles were generated, encompassing 27 CC samples and 19 MGC samples, of which 12 CC sample and 9 MGC samples, respectively, were obtained from follicles where the oocyte after implantation gave rise to a live birth of a healthy baby (LB) and 15 CC and 10 MGC samples, respectively, which did not lead to pregnancy (NP). The expression profiles of the CC and MGC samples were analyzed separately. The basic demographic parameters for the women which CC and MGC were analyzed are presented in We constructed a classifier to discriminate between patients where IVF treatment lead to live birth compared with no pregnancy, based on expression profiles on CC or MGC from the pre-ovulatory follicles.We examined different methods for the construction of an optimal classifier of oocyte competence and tested several cut-off values of the test statistics with or with-out fold change and expression above back-ground filtering. Three different classifier models , k-nearest neighbors and SVM with linear kernel function were generated and tested by LOOCV. Overall accuracy, as well as PPV and NPV were derived for both the cumulus and granulosa data separately, as shown in The overall best performance of the classifier models applied on the CC was achieved with PLIER normalized unlogged expression values by the linear SVM model and 3-NN and resulted in a LOOCV accuracy of 0.81 and 0.85, respectively . On the The performance range of the classifier models applied on PLIER normalized MGC with a p-value cut-off of 0.01 was 11% - 37% for dLDA, 3-NN (k = 3) linear SVM . EvaluatThe CC data show high levels of variance between the samples within each group and relatively small overall expression differences between the LB and NP groups. To increase the power of the analysis we applied a filter to firstly exclude un-annotated probe sets and further exclude genes with an average expression below back-ground level  . TraininRerunning the training of the of the linear SVM after filtering for genes expressed above background, the overall accuracy of the SVM with radial kernel was unchanged (81%), when using a signature representing 30 unique genes . The fulROC curve analysis to distinguish between true positive rate and false positive rate of the final linear SVM classifier (annotated probes filtered to exclude probes expressed below background level) resulted in an AUC value of 0.86 indicating a good performance of the CC classifier .We furthermore assessed the strength of each of the 30 genes in the signature by the cross-validation support, which shows the percentage of the cross-validation training sets in which each particular gene was selected . AnalysiIn order to assess whether the misclassification rate was lower than what would be expected by chance we performed 1000 random permutation of the samples. For each permutation the entire LOOCV process was repeated and the proportion of the 1000 random permutations resulting in smaller misclassification rates determined the statistical significance of the classifiers error rate. The significance of the misclassification rate after 1000 permutations was 0.284, 0.002, 0.009, for the diagonal linear discriminant analysis classifier, the 3-nearest neighbors’ classifier and the linear SVM classifier, respectively. All but the d-LDA classifier was statistically significant compared to the random setting.We used the trained SVM classifier to derive the predictive probability of a sample belonging to one class or the other by the use of logit estimates . The preWe tested the cumulus classifier on an external data set published by Feuerstein et al, 2012 downloaded from the public expression profile repository, gene expression omnibus (GEO) at NCBI . The datThe SVM predictions were translated into predictive probabilities and visualized alongside the predictive probabilities of the training data for comparison . In agreDespite the fact that gene classification signatures often can be difficult to interpret with respect to the biology of the underlying disease; and missing biologic interpretation does not warrant poor clinical usefulness of well-established biomarkers , the proMechanistic networks were generated based on sets of connected upstream regulators that in conjunction would result in the gene expression changes observed in the dataset. Two mechanistic networks were generated with network scores above 6. The first network was enriched in functions related to Cellular Movement, Nervous System Development and Function, Cellular Growth and Proliferation with a network score of 24 . Eleven The study demonstrated that is possible to develop a classifier based on selection of 30 genes from whole genome microarray data from CC that predict the chance of pregnancy in an IVF setting with 81%. This classifier proved its value by prediction of the ability of embryos to reach the blastocyst stage in vitro from an independent data set. Therefore this classifier has the potential to be developed into a clinical useful tool if larger studies can confirm the results obtained in this study.In contrast, we were not able to define a robust MGC classifier signature that could classify live birth with accuracy above random chance level.The aim of this study was to use machine learning techniques to build CC and MGC transcriptomic classifiers to predict oocyte competence. Several combinations of microarray data pre-processing  and feature selection were tested during cross validation rounds to derive the best performing algorithm and classifier gene signature –30.Aside from the inherent heterogeneity of the data, we were able to correctly classify LB patients by leave one cross validation based on a 30 gene cumulus cell signature, and the cross validation support data showed that more than half of the genes in the signature were included in 89 percent or more of the cross validation runs, indicating that these genes have a stronger impact on the outcome and hence have a high relative weight in the classifier. We furthermore proved the classifier and the signature to be statistic significant compared to random classification of LB versus NP in 1000 random permutations of the samples as described .The signature was furthermore tested for its ability to predict developmental competence as defined by the ability to reach the blastocyst stage in an external validation data set. The data represented 3 sets of transcriptomes from CC corresponding to oocytes that gave rise to 4-cell embryos on day 2 after fertilisation that either failed (incompetent) or succeeded to develop to full blastocysts (competent) after 5 days in vitro culture . Our claWhereas we observed a varying performance of the different CC classifiers depending on the choice of normalization and classification algorithm applied, all tested combinations of MGC classifiers showed poor performance in discriminating oocytes leading to live birth from oocytes which failed to establish a pregnancy. These data indicate that that gene expression in MGC does not reflect competence. However, the high degree of variability across samples, the small fold differences between the two end-points (NP and LB), combined with the lower number of samples in the MGC data set (19 MGC compared to 27 CC), with the resulting reduced power of the analysis may influence the ability to detect a set of discriminating signature genes during cross validation.Further, explanation for the small expression changes observed in both cell types may be that the true signal of competence is blurred by the massive changes in the follicle originating from the ovulation trigger 34–36 hours before oocyte retrieval and isolation of cells –34. FurtSince the overall aim of the project was to define predictors (genes) of LB and not to detect differentially expressed genes, a significance level of p < 0.01 in a univariate t-test was used for feature inclusion. This significance level is not sufficient significant to exclude false discoveries since no genes were significant after multiple testing correction (data not shown). This is in agreement with a recent study, where no genes were significantly expressed with FDR below 0.05 in CC and MGC of oocytes leading to pregnancy or not . In someAlthough many of the signature genes showed modest level of expression changes in the training data set and do not overlap with other studies –13,38,39P53, BAX, BCL-2 were not related to competence in our data.The 30 annotated genes in the CC classifier signature were subjected to Ingenuity Pathway analysis. Downstream effect analysis suggests increased activity of apoptosis in the CC surrounding oocytes with developmental competence to develop, implant and succeed in birth of a healthy baby. None of the classifier signature genes have previously been shown to be regulated in CC of oocytes determined to be of good quality, mature or developmentally competent by different measures and standards –13,38,43NR2F1, COL4A1, GAL, DCLK1, MRC2, FLNA, SOCS6, RAB33A, FN1, BGN) showed an overlap with genes that previously have been shown to be differentially expressed between corresponding CC and MGC in the preovulatory follicle [COL4A1) was 2–10 fold higher expressed in CC as compared to MGC indicating a specific function of the gene products in the CC compartment in the ovulatory follicle [Of the 30 genes in the classifier, 10 .S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.


A researcher observed the patients and completed a diary. Data from the patients' diaries were compared with observed and measured data to determine total activity (METs*minutes), activity level and total energy expenditure.s = 0.75, p<0.01) for sedentary  and moderate activities  and a very high correlation  for the total energy expenditure. Comparisons between the patients' diaries and activity monitor data revealed a low correlation (rs 0.29) for total METs*minutes and energy expenditure.Spearman correlations between the patients' and researchers' diaries revealed a high correlation for total METs*minutes (rCoded self-monitoring activity diaries appear feasible as a low-tech alternative to labor-intensive observational diaries for determining sedentary, moderate, and total physical activity and for quantifying energy expenditure in hospitalized stroke patients. Given the poor correlation with objective measurements of physical activity, however, further research is needed to validate its use against a gold-standard measure of physical activity intensity and energy expenditure. The importance of physical activity promoting health has been well documented. Increased daily physical activity reduces cardiovascular risk for people with and without disabilities A variety of objective methods have been used to measure daily physical activity in stroke patients, including activity monitors Pedometer are prescribed as a less expensive and simple alternative for taking objective measurements of physical activity in stroke patients Other methods for determining physical activity in stroke patients include observation For stroke patients, a tool is needed that combines the advantages of the three-day categorized-activity diary with greater detail information about the type and intensity of activities and the position in which the activities are executed, in order to provide accurate information with minimal effort, thus being useful in clinical settings. In stroke patients, activities of short duration occur rarely, if at all. The short time intervals recommended in the Bouchard study are therefore not preferable. Moreover, therapy in rehabilitation centers is often scheduled in 30-minutes blocks. Keeping the diary can help patients to become more aware of their physical activities, possibly strengthening their motivation to adopt a more active lifestyle. To this end, a simplified coded physical-activity diary was developed in which stroke patients choose the dominant activity in performed 30-minute time interval from a pre-defined list of activities, all linked to simple codes. This minimizes writing, making it possible for patients with writing problems to complete the diary. The time was adjusted to the pace of hospitalized stroke patients, who perform fewer activities within 30-minute time interval in a rehabilitation center.To our knowledge, no study in stroke research has investigated the use of a coded self-monitoring activity diary to determine both total energy expenditure and intensity level of various activities, compared against criterion standards of observations and activity monitoring. In the present study the concurrent validity of an activity diary was evaluated in hospitalized stroke patients. We specifically compared self-monitoring diaries to observational diaries and activity-monitor outcomes.The protocol was reviewed and approved by the Medical Ethics Committee of the Antwerp University Hospital, Belgium (no. B30020084906). Patients received oral and written information about the design of the study; they provided written consent and agreed to the publication of the research data.Stroke patients were recruited on a voluntary basis from an inpatient rehabilitation center in Belgium. Inclusion criteria were as follows: (1) a first-ever stroke as defined by the World Health Organization, (2) stroke less than six months ago, (3) ability to move independently with or without a walking aid and (4) understand and carry out simple instructions. Patients were excluded if they were not medically stable, as described by the American College of Cardiology Foundation/American Heart Association On the first day demographic and clinical data were collected, including age, gender, duration and type of stroke, height, weight and the degree of loss of function , light (>1 - <3 METs), moderate (3–6 METs) and vigorous activity (>6 METs) energy expenditure in kcal/30 min, the following formula was used: [/200]×30minutes The coded activity diary was developed based on two existing activity diaries SenseWear Pro2 Armband (SWP2A)  should be worn on the right upper arm. For this study, however, it was worn on the non-hemiplegic upper arm positioned on the triceps muscle halfway between the acromion and the olecranon. The SWP2A was programmed using a computer interface, taking into account the participants' age, gender, height, weight, smoking habits and handedness prior to testing. This SWP2A contains two accelerometers, a galvanic skin response sensor, a heat flux sensor, a skin temperature sensor and a near-body ambient temperature sensor from which the data were stored minute by minute between 8:00 AM and 8:00 PM. Using a proprietary algorithm  the data were converted into Metabolic Equivalents minutes (METs*minutes) and energy expenditure. It has been validated for measuring energy expenditure in 50 healthy and diabetic participants against double-labeled water According to the users manual the All data were analyzed using SPSS . Descriptive statistics were calculated for patient characteristics. Normality was verified with the Kolmogorov-Smirnoff test. Because most of the data were not normally distributed, non-parametric statistics were used.s) was calculated to evaluate the relationship between the patient's diary and the observer's diary and between the patient's diary and the SWP2A. Values less than 0.30 were taken to indicate poor correlations, with values between 0.30 and 0.50 indicating low correlations, between 0.50 and 0.70 moderate correlations, between 0.70 and 0.90 high correlations and greater than 0.90 very high correlations In order to study concurrent validity, a Spearman correlation coefficient  the values of the criterion standard were plotted against the difference between the two methods, thus providing an indication of agreement. The median and percentiles 25 and 75 were calculated.The research sample consisted of 16 patients with a mean age of 68 years (±11) and mean time since stroke of 78 days (±53). Four patients used no walking aids. Almost every activity number was mentioned in the diaries except brushing hair, performing handicraft, and driving a car. A few new codes (N = 4) were listed, such as reading, smoking, resting in a (wheel)chair or in bed. Mostly activities were executed in sitting (84.1%) and standing position (9.9%). It concerned sedentary activities. Patients noted that help was required in 26.3% of all activities.s = 0.74,p<0.01) and moderate  activity levels. A low and non-significant correlation was found for the activity category “light”. When the patients' activity diaries were compared to the SWP2A, the correlation coefficients were not significant.The correlation for METs*minutes in the diaries of the patients and the observers diaries was 0.75 (p<0.001), thus indicating a high correlation . High coGraphic analysis indicated a good level of agreement between both diaries  . Data pos = 0.92, p<0.01) for energy expenditure, as measured between 8:00 AM and 8:00 PM  with regard to energy expenditure.Comparison of the data from the two diaries, revealed a very high correlation  . Most ofThis study assessed the concurrent validity of a coded self-monitoring activity diary for measuring activity level and total energy expenditure in hospitalized stroke patients. The diary generated valid results in comparison to the diary kept simultaneously by an observer, as used to determine sedentary physical activity (<1METs), moderate physical activity (3-6 METs) and total physical activity over 12 daytime hours. A very high correlation between the two diaries was observed for total energy expenditure during daytime hours. Poor correlations were observed, however, when comparing the diary to the SWP2A for measuring activity level and energy expenditure.sedentary and moderate physical activities during daytime hours, while a low correlation was found for light activities. One possible explanation is that activities in the levels of sedentary and moderate activities are more easily recalled than are light activities. Sedentary activities include activities in very low intensity , which are often longer in duration. Moderate activities are more intense , and they are well reported in the daily schedules of rehabilitation centers. The lack of a good correlation between the two diaries with regard to light physical activities could be that these relatively brief activities  of limited duration which are often less planned and remembered than are activities of other levels. Another explanation might have to do with outliers. The scores entered by two patients differed excessively from those entered by the other patients and by the observers. When the Spearman correlation was recalculated excluding the data from these two patients, a moderate correlation for light activities was observed . The correlations for sedentary and moderate levels, however, remained slightly higher. Moderate activity is considered an important activity level in stroke rehabilitation, as it may be sufficient to produce a significant reduction in stroke risk A high correlation was found between the two diaries, when measuring Comparisons of the patients' diaries to the SWP2A data revealed poor correlations. In general, the activity monitor reported lower values for 12-hour energy expenditure than did either diary. One explanation might be that the activity diary over-estimated the energy expenditure and the time in moderate activity as time intervals were long and only one activity was allowed to be reported every 30 minutes. This would suggest a need to shorten the time intervals in which activities are reported. The SWP2A has not been validated to measure energy expenditure against a “gold standard” method in stroke patients, who have inefficient gait patterns causing higher cost of energy for given activity. As such the SWP2A may have underestimated total energy expenditure and time in moderate physical activity. It would be advisable to develop patient-specific algorithms to accurately use the SWP2A in stroke patients.This study is the first study to use a coded self-monitoring activity diary to assess physical activity in stroke patients. Because understanding the instructions of the diary requires a certain level of comprehension, patients with severe cognitive deficits were excluded. No inconvenience was reported. This study showed no missing data points, because all missing activities were collected by an interviewer through recollection, and the previously completed periods facilitated this. We estimate that less than 10% of the activities were missing in the diaries. Missing data often concerned periods during which patients performed sedentary and light physical activities  of long duration (<3 METs). Also evening activities were sometimes forgotten, which could be attributed to the fact that nursing care started at this time moment. In many cases, only one 30-minute period was completed. Patients started filling in the diaries when a new activity started. Considering the fact that the missing activities were filled in at the next day and the type of missing activities were easy to remember and low in percentage, we think that this diary is well applicable in stroke patients.Completing the diary can be a tool for helping patients and family/caregivers reflect on the types of activity level, possibly leading to increases in the activity level. When evaluating the clinical relevance of the results of this study, it is important to note the lack of severe stroke patients in the research sample. Also in this study, no activities were categorized as vigorous by any of the three measurement tools. This is not surprising as aerobic exercises are seldom integrated as part of neuro-motor rehabilitation programs despite evidence supporting the importance Further research is required before the self-monitoring coded-activity diary can be implemented into clinical practice. Studies with objective criterion standards,  and the development and examination of a digitized version are recommended. It might also be advisable to delete the fourth column of the diary, where the perceived intensity of an activity was marked on a rating scale of 6–20. This seemed difficult for patients to fill in. Also this could not be retrospectively added. Also it might be recommendable to shorten the time intervals in which activities are reported, to collect data for periods longer than one day, to ask patients about inconvenience filling in the diary, and provide an additional day to familiarize patients with the process of completing a diary.Coded self-monitoring activity diaries appear feasible as a low-tech alternative to labor-intensive observational diaries for determining sedentary, moderate, and total physical activity and for quantifying energy expenditure in hospitalized stroke patients. Given the poor correlation with the objective measurement of physical activity, however, further research is needed to validate its use against a gold standard measure of physical activity intensity and energy expenditure .Table S1Activity diary.(DOCX)Click here for additional data file.Table S2Scheme of codes that are used to fill in the activity diary.(DOCX)Click here for additional data file.


Numerous risk prediction algorithms based on conventional risk factors for Coronary Heart Disease (CHD) are available but provide only modest discrimination. The inclusion of genetic information may improve clinical utility.We tested the use of two gene scores (GS) in the prospective second Northwick Park Heart Study (NPHSII) of 2775 healthy UK men (284 cases), and Pakistani case-control studies from Islamabad/Rawalpindi (321 cases/228 controls) and Lahore (414 cases/219 controls). The 19-SNP GS included SNPs in loci identified by GWAS and candidate gene studies, while the 13-SNP GS only included SNPs in loci identified by the CARDIoGRAMplusC4D consortium.-3). In combination with the Framingham algorithm the GSs appeared to show improvement in discrimination  and risk classification (net reclassification improvement (NRI), 19-SNP p=0.28, 13-SNP p=0.42) compared to the Framingham algorithm alone, but these were not statistically significant. When considering only individuals who moved up a risk category with inclusion of the GS, the improvement in risk classification was statistically significant . In the Pakistani samples, risk allele frequencies were significantly lower compared to NPHSII for 13/19 SNPs. In the Islamabad study, the mean gene score was higher in cases than controls only for the 13-SNP GS . There was no association with CHD and either score in the Lahore study.In NPHSII, the mean of both gene scores was higher in those who went on to develop CHD over 13.5 years of follow-up (19-SNP p=0.01, 13-SNP p=7x10The performance of both GSs showed potential clinical utility in European men but much less utility in subjects from Pakistan, suggesting that a different set of risk loci or SNPs may be required for risk prediction in the South Asian population. Despite being largely preventable, coronary heart disease (CHD) remains the most common cause of the death worldwide , 2. TherIn order to target lifestyle and therapeutic intervention appropriately, those most at risk of developing CHD must be identified. A number of conventional risk factor (CRF) scores to determine 10-year risk CHD risk have been developed. These include the Framingham risk score  and QRISIt is has been estimated that heritability of CHD is 40–50%  and moreThe objective of this study was to determine whether the inclusion of a CHD risk gene score has clinical utility in the European population. We sought to do this by investigating whether addition of a gene score improves discrimination and prediction over and above the use of the Framingham CRF score alone in a prospective study of middle-aged men from the UK. We then looked to determine if the same genetic variants can be used in individuals from the Indian Subcontinent. Two gene scores, one with 19 SNPs taken from both GWAS and candidate gene studies and one with 13 SNPs where only SNPs in loci identified by the CARDIoGRAMplusC4D consortium were included, were tested.NPHSII is a prospective CHD study of approximately 3000 men as described previously . BrieflyTwo case-control studies from Pakistan were collected. One group was collected from the Rawalpindi Institute of Cardiology, Pakistan. All cases had had an MI as defined by a positive test for troponin T, ST segment changes on electrocardiogram and typical chest pain radiating in the chest that was not relieved at rest. This study will hereafter be referred to as the “Islamabad” study. The study had approval from the Institutional Review Board and Ethics Committee of Shifa College of Medicine, Shifa International Hospital, Islamabad and all subjects gave written informed consent. The second group were collected from hospitals in Lahore, Pakistan. All cases had CHD as defined by echocardiogram or angiography. This study will hereafter be referred to as the “Lahore” study. All subjects gave written informed consent and the study had ethical approval from the institutional ethical committee, University of the Punjab, Lahore. In both studies controls were taken from the general population.The SNPs included in the gene scores are presented in R v3.0.3 [2 tests for categorical variables. Hardy Weinberg equilibrium was assessed using χ2 tests. The Framingham 10-year risk score was calculated using the equations described in [Analysis for NPHSII was conducted using STATA v13.1. AnalysiR v3.0.3 . AnalysiR v3.0.3 . VariablThe baseline characteristics for the participants of NPHSII are presented in APOB, were in Hardy-Weinberg equilibrium. A comparison of risk allele frequency in those who did and those who did not go on to develop CHD is presented in CXCL12), with the risk allele frequency being higher in the CHD group for both SNPs.The genotype frequency of each SNP in NPHSII is shown in -3).The genotype information was combined using a weighted genetic risk score. For the 19 SNP GS, full data for Framingham score plus gene score was available for 1164 individuals, 114 of whom developed CHD. For the 13 SNP GS full data for Framingham score plus gene score was available for 1437 individuals, 143 of whom developed CHD. Both gene scores were higher in those who developed CHD during follow-up  or the 13 SNP GS (p = 0.82) .-3) and the 13 SNP GS (p = 0.01) after adjustment for age.As shown in We then attempted to transpose these scores in two case-control samples from Pakistan. The available conventional risk factor information for both studies is presented in MIA3 rs17465637, CXCL12 rs1746048, MRAS rs9818870, LPL rs1801177 and SMAD3 rs17228212-with an excess of homozygotes. Genotypes were confirmed by sequencing. In the Lahore study, only one SNP–LPA rs10455872- was not in Hardy Weinberg equilibrium. The risk allele frequencies did not differ between the two Pakistani groups as shown in The 19 SNPs were genotyped in both Pakistani sample sets and the results are presented in lower in the controls compared to those in NPHSII who did not go on to develop CHD in both the Islamabad  and the Lahore cohorts .The 19 and 13 SNP GSs were calculated for both Pakistani studies and the results are shown in While the higher quintiles of the 13 SNP GS gene score appear to have a greater odds ratio for outcome compared to the bottom quintile in the Islamabad sample, this was not statistically significant. No trend between quintile of gene score and outcome was observed for the 19 SNP GS in the Islamabad sample or for either GS in the Lahore sample .The aim of including genetic information in CHD risk prediction is to identify those who have a high overall risk but who would be classed as being at low or intermediate risk using conventional risk factor scores. In this study, we found that a 19 SNP GS, which uses CHD risk weighting from published meta-analyses, showed improved risk classification over and above the Framingham score alone in NPHSII when those men who went up a risk category (for example moving into the high risk category) were considered. The exclusion of six SNPs in non-CARDIoGRAMplusC4D loci had a modest detrimental effect on the utility of the gene score in the UK men, reducing the overall NRI non-significantly from 4.5% to 3.0% with a similar non-significant decrease in the number of men correctly reclassified into the high risk group. Thus the 19 SNP GS, appears to have the better utility. By contrast, in two case-control studies from Pakistan the gene scores did not perform as well. While in the subjects from Islamabad the 13 SNP GS showed a significant case-control effect, neither score showed utility in the samples from Lahore.There are a number of potential reasons why the results from the European group were not confirmed in Pakistanis. Firstly both Pakistani studies comprise only a few hundred samples and thus sample size and statistical power is an issue. We estimated that should the same effect found in NPHSII be present in the Pakistani group, to have 80% power to detect it (at the 5% significance threshold), approximately 340 cases and approximately 340 controls would be required for both gene scores. This is well above the number of individuals for whom complete genotyping was available in both Pakistani studies. Secondly, “cases” were defined differently in the two Pakistani studies. There was very little difference between the mean gene score in cases and controls in the Lahore sample which used a much broader definition of CHD, compared to the Islamabad group which used an MI phenotype (more like the “hard” endpoints used in the prospective NPHSII). A further limitation of the study is that using retrospectively collected cases restricts the case group to those who have survived which may introduce a survivor bias to the sample.APOA5 promoter variant has been shown to be directly functional [CELSR2-PSRC1-SORT1 locus, with rs599839 being known to be functional [2 = 0.95 in the Northern European group and r2 = 0.76 in the Japanese group but the two SNPs are not in LD in the Yoruba group . However, a study of approximately 2000 Indians collected from Mumbai and Bangalore found the two SNPs to be in strong LD (r2 = 0.98) in this group. Furthermore, a number of SNPs at the 9p21 locus were genotyped in the PROMIS study of ethnic Pakistanis living in the UK and the LD pattern was found to be similar to Europeans at this locus [LPA gene in individuals of different ethnicities (including South Asians) living in Canada found similar haplotype blocks to be present in the groups studied [CETP polymorphisms in the Punjabi population from Northern India found the LD pattern to differ from that observed in Europeans[A further possibility is SNP selection. While eight of the SNPs are non-synonymous , and the nctional  many arenctional . Data frnctional  shows this locus  and a co studied . TherefoEuropeans but overAPOA5 promoter) in a Punjabi Pakistani case-control study, the risk allele frequency observed for rs599839 (at the SORT1 locus) in that study was much lower than observed here [We can only speculate whether the modest performance of the gene scores observed in Pakistani subjects here will be seen in samples from other parts of the Indian Subcontinent, as there is considerable genetic diversity within the region and since score performance is in part dependent on the frequency of the risk alleles. To date, allele frequency information across the Indian Subcontinent is limited for the SNPs used. A small number of Punjabi subjects (n = 96) from Lahore were genotyped as part of the 1000 genomes project (phase 3) . No diffved here . Anotherved here . ClearlyOverall this work demonstrated that the use of a 19 SNP gene score has the potential to improve risk stratification in European individuals over and above classical CHD risk factors. Reducing the number of SNPs in the score to only the 13 with GWAS-proven effects on CHD risk had a modest detrimental effect on the utility. We do not have definitive evidence of the utility of either score in subjects from Pakistan and larger samples are required to determine this.S1 Fig(TIF)Click here for additional data file.S1 TableHWE = Hardy Weinberg Equilibrium, CI = Confidence Interval.(DOCX)Click here for additional data file.S2 TableComparisons were performed using proportion tests. CI = Confidence Interval.(DOCX)Click here for additional data file.S3 TableComparisons were performed using tests of proportion. CI = Confidence Interval.(DOCX)Click here for additional data file.S4 TableComparisons were performed using proportion tests. CI = Confidence Interval.(DOCX)Click here for additional data file.S5 TableNRI = Net Reclassification Index.(DOCX)Click here for additional data file.S6 TableLogistic regression, age adjusted, was performed for each group. OR = Odds Ratio, CI = Confidence Interval.(DOCX)Click here for additional data file.S7 TableLogistic regression, adjusted for age and sex, was performed for each group. OR = Odds Ratio, CI = Confidence Interval. For the Islamabad study full data was available for 258 participants for the 19 SNP GS (106 controls/152 cases) and 268 participants for the 13 SNP GS (110 controls/158 cases). For the Lahore study full data was available for 438 participants for the 19 SNP GS (130 controls/308 cases) and 490 participants for the 13 SNP GS (145 controls/345 cases).(DOCX)Click here for additional data file.


Strand specific RNA sequencing is rapidly replacing conventional cDNA sequencing as an approach for assessing information about the transcriptome. Alongside improved laboratory protocols the development of bioinformatical tools is steadily progressing. In the current procedure the Illumina TruSeq library preparation kit is used, along with additional reagents, to make stranded libraries in an automated fashion which are then sequenced on Illumina HiSeq 2000. By the use of freely available bioinformatical tools we show, through quality metrics, that the protocol is robust and reproducible. We further highlight the practicality of strand specific libraries by comparing expression of strand specific libraries to non-stranded libraries, by looking at known antisense transcription of pseudogenes and by identifying novel transcription. Furthermore, two ribosomal depletion kits, RiboMinus and RiboZero, are compared and two sequence aligners, Tophat2 and STAR, are also compared.The, non-stranded, Illumina TruSeq kit can be adapted to generate strand specific libraries and can be used to access detailed information on the transcriptome. The RiboZero kit is very effective in removing ribosomal RNA from total RNA and the STAR aligner produces high mapping yield in a short time. Strand specific data gives more detailed and correct results than does non-stranded data as we show when estimating expression values and in assembling transcripts. Even well annotated genomes need improvements and corrections which can be achieved using strand specific data.Researchers in the field should strive to use strand specific data; it allows for more confidence in the data analysis and is less likely to lead to false conclusions. If faced with analysing non-stranded data, researchers should be well aware of the caveats of that approach.The online version of this article (doi:10.1186/1471-2164-15-631) contains supplementary material, which is available to authorized users. The transcriptome has long been studied by reverse transcribing single stranded RNA into double stranded cDNA and assessed with assays such as PCR , 2, micrIncreasing exploration of the transcriptome has led to discoveries of multitude of various RNA species . Of partdUTP second strand marking method, or dUTP method for short, and consists of using dUTPs instead of dTTPs during the synthesis of the second strand in the cDNA synthesis step during sample preparation. Then prior to PCR amplification the uracil in the second strand is degraded using Uracil-N-Glycosylase (UNG). With the second strand partly degraded only the first strand is amplified in the subsequent PCR. This particular strand specific protocol was evaluated as superior in terms of simplicity and data quality in a benchmark study of strand specific protocols [A handful of protocols have been published which retain the strand information of the RNA with varying degree of success and labor intensity. In 2009 Parkhomchuk et al.  publisherotocols .In the current study we modulate specific steps in a scalable transcriptome preparation method  to combiMagnatrix™ 1200 Biomagnetic Workstation (MBS) , which is equipped with a 12 tip head suitable for preparing 12 samples in parallel. The stranded protocol differs from the non-stranded protocol in two ways; First, during cDNA synthesis a CA purification step, carried out on the MBS, is introduced after the first strand synthesis after which the second strand synthesis continues as normal except the nucleotide mix includes dUTPs instead of dTTPs. This CA purification step is necessary to remove all the dTTPs prior to second strand synthesis. Second, after library preparation [second strand digestion step is added. This step ensures that only the first strand survives the subsequent PCR amplification step and hence the strand information of the libraries. Each of these additional steps add 45-60 minutes to the total preparation time, with about 15-20 min of those being hands on. Additional file Apart from the ribosomal depletion step and as described in  .All libraries were sequenced on the Illumina HiSeq 2000 generating 100 bp paired end reads. The 15 libraries were divided into 5 groups depending on how they were prepared as shown in Table Read alignment was performed by Tophat v2.0.4  and StarGraphical representation of these mapping attributes, mapping percentage and mapping speed, for both aligners and a comparison between the handling of raw data and quality data is shown in Figure The robustness of the protocol and the quality aspects of the data were evaluated using the 15 libraries generated from the human cell lines (libraries 1-15) through different metrics; ribosomal RNA in data, strand specificity, duplication rate, gene body coverage and expression correlation. Details from some of these analyses can be found in Additional file To evaluate the efficiency of the ribosomal depletion, the rRNA reads in each library were quantified. On average the libraries treated with RiboMinus contained 65.7% rRNA compared to only 2.24% for the libraries treated with RiboZero. Figure Figure In general, these results show that the protocol works and in particular that the CA purification step is successful in removing dTNPs after the first strand synthesis.Figure Due to the risk of inaccurately identifying reads originating from highly expressed genes as duplicates, the duplicate reads were not removed prior to differential expression analysis.Figure R2 = 0.96. Correlation plots and the Pearson correlation value for each correlation is shown in Additional file To further assess the robustness of the libraries the correlation of expression values between replicates was quantified. The mean Pearson correlation of the 16 possible correlations within replicates was The only difference between libraries in group 3 and group 4 is that the libraries in group 3 are strand specific, generated using the current approach, while the libraries in group 4 are non-stranded, generated using the approach in . In ordeOf the 62893 annotated genes (protein coding and non-coding) 41065 do get assigned low or no expression in all of the six libraries. Of the remaining 21828 genes 245 are found to be significantly differentially expressed genes, hereafter referred to as DEGs. Of these 245 DEGs 69 have higher expression in the non-stranded libraries while 176 DEGs have higher expression in the stranded libraries. Intriguingly the division of DEGs into protein coding genes and non coding genes is different depending on whether the DEGs have a higher expression in the non-stranded data or in the stranded data. So, for the 69 DEGs which show higher expression in the non-stranded data 24 are protein coding and 45 are non-coding while for the 176 DEGs which show higher expression in the stranded data 136 are protein coding and 40 are non coding. This expression profile is shown in Figure To find out why these DEGs arise, coverage plots for a selection of the DEGs with the lowest p-values, were analysed and compared to the annotation used for counting by htseq-count. All DEGs investigated that have a higher expression in the stranded data compared to the non-stranded data have overlapping annotation which results in many reads mapping to those genes being labeled as ambiguous for the non-stranded data and hence resulting in low expression. Explanation for DEGs with higher expression in the non-stranded data compared to the stranded data is not as straightforward but scrutiny revealed three dominant reasons for these DEGs; i) the DEGs have overlapping features that are unnannotated, ii) the DEGs are annotated in the wrong direction or iii) the DEGs have antisense intronic transcripts that get wrongly assigned to them. Additional file This analysis also demonstrates how essential it is to have strand specific libraries for compact genomes with high abundances of overlapping genes since without strand specificity a large proportion of the genes would be labeled as ambiguous.For each library in group 3, 4 and 5 two transcript assemblies were made using Cufflinks . The firFrom these assemblies it was found that strand specific data generates fewer transcripts compared to non-stranded data and the average transcript length is usually shorter for the strand specific data as compared to the non-stranded data. The same holds true when comparing the assemblies between library group 5 treated as strand specific to library group 5 treated as non-stranded. An overview of the assembly results is shown in Additional file The antisense transcription of the pseudogene PTENP1 has previously been reported and suggested to play a role in the regulation of the gene PTEN . From thThe coverage plot in Figure Using an annotation which included the RefSeq isoform, PTENP1-AS, and our two new isoforms, PTENP1-AS2 and PTENP1-AS3, the isoform expression levels were evaluated with Cufflinks. PTENP2-AS is expressed with an average FPKM value of 0.64 but the other isoforms, PTENP1-AS and PTENP3-AS, showed no expression according to Cufflinks.Identification of novel genes was attempted by using the novel assembly from library group 5. By counting the mapped reads towards this novel assembly the most highly expressed genes were investigated. Many of the assembled transcripts were evidently intron transcripts and others matched the current annotation, at least partly. Other transcripts were potentially novel genes.One interesting example are two overlapping novel genes on chromosome 17: 25380000-25500000. Figure Two other interesting findings can be found in the Additional files; a U2OS cell specific transcription on chromosome 6, possibly a pseudogene, is shown in Additional file We have modified an existing non-stranded automated RNA library protocol into a protocol that generates strand specific libraries by using the Illumina TruSeq kit in combination with other reagents. This may be useful for other researchers who use the Illumina TruSeq kit and want to make strand specific libraries. However, Illumina has now released a new kit; TruSeq Stranded Total RNA Sample Prep Kit (# RS-122-2201), which combines RiboZero rRNA depletion with a strand specific method similar to the dUTP method. It should be possible to fully automate that protocol on the MBS for generating 12 samples in parallel or on the Agilent Bravo for a higher throughput of 96 samples in parallel.We showed that quality trimming and adapter removal improves the alignment yield and mapping speed, both for Tophat and Star. We set the quality threshold to 20 on the Phred scale and then removed any pairs that contained reads shorter than 20 bp after trimming. By setting the parameters differently this improvement might be different. For example by increasing the quality threshold it is likely that the mapping percentage will increase but that will not neccessarily improve the mapping in absolute terms since many more reads would be discarded prior to mapping. Due to trimming, some of the quality reads are shorter than the raw reads which could explain the improvement in mapping speed of the quality data over the raw data.We also showed that Star outperforms Tophat in alignment yield and mapping speed. This is in accordance with previous reports on alignment yield , 26 and We showed that the RiboZero Gold kit far outperforms the RiboMinus kit in removing ribosomal molecules from RNA samples. It should be noted that the RiboMinus kit is now no longer available for purchase and has been replaced by RiboMinus v2. We show a 2.2% rRNA contamination in the RiboZero treated samples which is better efficiency than previously showed in a comparison study .The markedly higher amount of duplicate reads in the RiboZero libraries compared to the RiboMinus libraries can partly be explained by the much higher sequencing depth of those samples. Still, all the libraries show a considerably high duplication rate. We believe that this can be explained by too many PCR cycles in the libary preparation but in the protocol the number of PCR cycles was fixed at 15. We have now altered the protocol to include a qPCR analysis step to measure the Ct value (concentration threshold in qPCR) which better determines the amount of PCR cycles needed for each library during preparation.We compare our current, strand specific, approach to a previously, non-stranded, approach  and showWe further showed that these genes arise in a systematic manner in such a way that overlapping genes that are annotated get underrepresented in the non-stranded data and genes that have a faulty annotation can get overrepresented in the non-stranded data. Also, genes in the non-stranded data can get overrepresented due to intronic transcripts from the opposing strand.This highlights the importance of having strand specific libraries in order to be better able to make correct assumptions from the data. It also emphasizes the need for accurate and verified annotation which, currently, even for human is erronous and incomplete. By comparing stranded and non-stranded data it is possible to probe the areas of the annotation that need attention. This also shows, in the absence of strand specific data, that researchers need to be extra attentive when interpreting results from overlapping loci in the genome.It should be noted that this method may not be sensitive enough to pick up all antisense behavior, for example if the overlap between the two strands is only a small proportion of the transcribed genes, and evidently more than 1.1% of the genome features antisense transcription.From the transcriptome assembly we show that the strand specific data produces fewer transcripts than the non-stranded data. Also, strand specific data usually produces shorter transcripts. This may indicate that assemblies from non-stranded data generates more false positives than does strand specific data.It must be stressed though that Cufflinks is far from perfect and tends to generate many questionable transcripts as can be seen from the transcripts we removed , A431 (DSMZ) and U2OS (ATCC-LGC). The cell lines were cultured as suggested by the providers and as previously described .For the evaluation of the protocol 15 libraries were used whose attributes are showed in Table From these libraries the data was explored in distinct steps as outlined in Figure μl and 800 ng/ μl. Libraries were constructed as explained above and shown in Table μg of total RNA.The cell lines A431 (skin carcinoma), U251 (brain glioblastoma) and U2OS (bone osteosarcoma) were cultivated, grown and harvested as described earlier . The RNAThe automation was set up on a Magnatrix TM 1200 Biomagnetic Workstation  which is equipped with a 12 tip head and is capable of running custom made scripts. The robot features an in tip magnet processing and a Peltier unit (4-95 C) where the reactions were performed.Our automatic strand specific RNA sequencing library preparation protocol is an adaptation of the dUTP second strand marking protocol utilizing the automation of the Illumina TruSeq protocol along with purification steps using CA beads. The details of the dUTP protocol have been described previously in , 18 and Briefly, the steps of our strand specific protocol are: first strand cDNA synthesis; CA purification; second strand synthesized using dUTPs instead of dTTPs; end reapair, A-tailing and adaptor ligation; second strand digestion with UNG; PCR amplification; and CA purification. A flow diagram, highlighting the main differences between the non-stranded and strand specific protocols, is shown in Additional file The clustering was performed on a cBot cluster generation system using a HiSeq paired-end read cluster generation kit according to the manufacturer’s instructions. All libraries were sequenced on an Illumina HiSeq 2000 as paired-end reads to 100 bp. Base conversion was done using Illumina’s OLB v1.9.The raw sequencing data were processed through a quality trimming process before being mapped to the genome. The reads were mapped to the GRCh37.72 primary assembly of the human genome (ensembl.org) using both Tophat2 v2.0.4 , and STAFor the quality and adapter removal the utility program Trim Galore!  was usedSelected scripts from the quality control package RSeQC  were useTo count read expression the program htseq-count  was usedFor the differential expression (DE) analysis the filtered output from htseq-count was used as an input for the R package DESeq . Prior traw assembly and novel assembly. For the raw assembly all mapped reads were used and no reference annotation was used to guide the assembly. All parameters were kept at default values except for the stranded libraries the parameter ‘–library-type’ was set to ‘fr-firststrand’. After the assembly the assemblies within each group were merged using Cuffmerge.The assembly was carried out using Cufflinks  to generFor the novel assembly the mapped reads were split, using split_bam.py from RSeQC , into twThe IGV genome browser was used to visualize the coverage of the PTENP1 locus along with annotations from RefSeq and Ensembl. From the raw assembly from group 5 new annotation for the PTENP1 asRNA was constucted. This new annotation was then used to evaluate its isoform expression.Htseq-count was used to determine the expression of the novel assembly from library group 5. Then the coverage plots of the highest expressed ‘novel’ genes were investigated using the IGV browser. Many of the alleged ‘novel’ genes turned out to be intronic transcripts wrongly assembled as exons and others overlapped current annotation. Manual searching, however, revealed potentially novel expression with a selected few represented in this study.http://www.ncbi.nlm.nih.gov/Traces/sra/).All the raw sequencing reads have been submitted to the NCBI Sequence Read Archive and are available under accession SRP043027 Additional file 1: Table S1. Overview of the concentration and average fragment length of each library after sample preparation. (XLSX 50 KB)Additional file 2: Table S2. Details of the data processing of each library. (XLSX 51 KB)Additional file 3: Figure S2. Distribution of reads into exonic, intronic and intergenic regions for each library group. (PDF 53 KB)Additional file 4: Figure S3. Expression correlation between all libraries within the same group. (PDF 671 KB)Additional file 5: Figure S4:A-H. Coverage plots along with explanations for some of the differentially expressed genes found when comparing the expression of non-stranded data to strand specific data. (PDF 498 KB)Additional file 6: Table S3. List of all differentially expressed genes found in the DE analaysis between non-stranded and strand specific data. (XLSX 72 KB)Additional file 7: Table S4. Overview of the Cufflinks assembly results. A comparison between non-stranded and strand specific data. (XLSX 51 KB)Additional file 8: Figure S5. Coverage plot showing that the novel transcription shown in Figure Additional file 9: Figure S6. Another novel transcription exclusive to the U2OS cell line. (PDF 80 KB)Additional file 10: Figure S7. Coverage plot indicating that two exons annotated as two genes may actually be two exons from the same gene. (PDF 91 KB)Additional file 11: Figure S8. The raw transcript assembly around the PTENP1 locus highlighting the manual changes made for the proposed assembly. (PDF 133 KB)Additional file 12: Figure S9. The raw transcript assembly around the locus of the novel gene in Figure 5 highlighting the manual changes made for the proposed assembly. (PDF 92 KB)Additional file 13: Supplementary file S1. File listing some of the command lines used for the analysis. (PDF 87 KB)Additional file 14: