Analyzing sequencing data is difficult because of the low frequency of rare variants, which may result in low power to detect associations. We consider pathway analysis to detect multiple common and rare variants jointly and to investigate whether analysis at the pathway level provides an alternative strategy for identifying susceptibility genes. Available pathway analysis methods for data from genome-wide association studies might not be efficient because these methods are designed to detect common variants. Here, we investigate the performance of several existing pathway analysis methods for sequencing data. In particular, we consider the global test, which does not consider linkage disequilibrium between the variants in a gene. We improve the performance of the global test by assigning larger weights to rare variants, as proposed in the weighted-sum approach. Our conclusion is that straightforward application of pathway analysis is not satisfactory; hence, when common and rare variants are jointly analyzed, larger weights should be assigned to rare variants. The low frequency of rare variants, however, may result in low power to detect associations. Given currently available sample sizes, there is a need for powerful methods to detect association with multiple rare variants. We investigate whether pooling variants by pathway into a composite test provides an alternative strategy for identifying susceptibility genes.Genome-wide association studies (GWAS) have found hundreds of single-nucleotide polymorphisms (SNPs) that are significantly associated with several complex traits. In most cases, however, these SNPs explain only a small proportion of the known genetic variance. A possible explanation is that causal variants have a lower minor allele frequency (MAF) than common SNPs. Moreover, rare variants are weakly correlated with common SNPs, because MAFs must be similar for two variants to be highly correlated . It follP-value [P-value, the competitive test bases the calculation of the P-value for the gene set on a distribution in which the gene is the sampling unit, whereas the self-contained test takes the subject as the sampling unit.Pathway analysis methods have been developed in the context of gene expression data analysis. There are two general approaches: competitive and self-contained. The distinction lies in the definition of the null hypothesis and in the calculation of the P-value ,3. ConceRecently, these methods have been extended to data from GWAS to detect pathway association. Compared to expression data, several distinctive features in the data from GWAS require different methodological considerations. In contrast to gene expression data, in which each gene contains one expression value, in data from GWAS each gene contains many SNPs that are in linkage disequilibrium (LD). Here lies the distinction between one- and two-stage approaches; the first approach ignores these biological features, and hence the second approach might be more powerful . IncorpoIn this paper, using the data provided by Genetic Analysis Workshop 17 (GAW17)  and seveThe methods we use were designed to detect multiple common variants with small effects. To better deal with the low frequency of rare variants, we propose to weight counts of each rare variant (MAF < 1%) on the basis of the estimated variance under the null hypothesis of no association. We propose the weighted global test (WGT), in which the weights are defined as in the weighted-sum method  for rareOnce significant pathways are identified, we might want to pinpoint the associated genes for further analysis. We consider three methods at the gene level: the combined multivariate and collapsing (CMC) method , the weihttp://www.r-project.org). The R package globaltest can be obtained at http://www.bioconductor.org, and the source code for GRASS (SNPath) can be downloaded at http://linchen.fhcrc.org/grass.html.All programming has been conducted in R  in 9 genes from the VEGF pathway, which influences quantitative risk factor Q1. The MAFs range from 0.07% to 16.5%. The dichotomous phenotype Affected serves as the outcome variable (488 control subjects and 209 case subjects). We apply the pathway analysis methods to the 200 simulated GAW17 data sets to study the power.N individuals at J loci, we let ix = ′ be a column vector in which ijx is the genotype at locus j in individual i. We code each genotype as 0, 1, or 2, corresponding to the number of minor alleles present at that locus. We also let iY denote the disease status of individual i, which is coded 0 for control subjects and 1 for case subjects. We then let X = ′ be an N × J matrix of genotype data and let Y = ′ be the N × 1 vector of disease status. In addition, for two-stage approaches, we denote g = 1, …, G for G genes in a pathway.We begin by establishing some notation. For Y on gene expression measurements X of a set of J SNPs on N samples:The GT is a one-stage approach and is based on a regression model that predicts response from the SNPs of a gene set. We use generalized linear models to model the dependency of response h denotes the link function. Under an assumption that all regression coefficients are sampled from a common distribution with mean 0 and variance τ2, instead of testing H0: β =  = 0, one can test a new null hypothesis: H0: τ2 = 0. Using the notation:where the model simplifies to a random effects model:The null hypothesis can then be tested, based on a score test statistic ,12:(4)R = (1/m)XX′, μ = h−1(α), and μ2 is the second central moment of Y under the null hypothesis . It can be shown that Q is asymptotically normally distributed. However, the p-value is calculated based on permutations of samples (case and control labels).where w = ′ are added to the model as follows:For the weighted version of the GT, the WGT, the weights jn be the total number of subjects genotyped for variant j, j. The weight:We assign more weight to rare variants (MAF < 1%), as calculated in the weighted-sum approach, as follows . Let nj jq computed from control subjects only:is the expected variance based on allele frequencies We assign the weight 1 to common SNPs.The GRASS algorithm is a two-stage approach. At the gene level the GRASS algorithm captures LD structure with principal components analysis. For each gene, it selects all nontrivial eigen-SNPs that explain about 95% of the gene variation, and a least absolute shrinkage and selection operator (LASSO) regression is used to summarize the most representative eigen-SNPs:k ≤ J eigen-SNPs. At the pathway level, the group ridge regression is applied, and the statistic can be written:for The significance of the statistic is determined by permutation of case and control labels.igjX be the genotype at locus j in gene g for individual i. Let ib be the random effect for subject i, and let igb be the random effect of gene g of individual i. Under Hardy-Weinberg equilibrium, igjX given ib and igb is assumed to follow a binomial distribution with the number of trials being 2 and a probability igjπ. The probability is modeled as follows:The EB method is a two-stage approach. In the first step we summarize the subject-specific SNP information using the EB estimates of the random effects of a mixed-effects logistic regression. In the second stage, the obtained EB estimates are used as covariates in the phenotypic model to test for association of genes. For details we refer to Houwing-Duistermaat et al. . Let XigFor each gene the EB estimate is given by:Intuitively, the EB estimate can be interpreted as follows: The more minor alleles an individual has over all the SNPs in a gene, the higher the EB estimate will be for this gene.P-values are smaller than the preset threshold of 0.05 divided by 200, the number of simulated phenotypes.Because none of the methods have been applied to pathway analysis with multiple common and rare variants, we investigate the type I error rate and power of each method. To determine the type I error rate, we simulate phenotype under the null hypothesis of no association; we generate 1,000 samples from the normal distribution and dichotomize the phenotypes by taking the top 30% as case subjects and the rest as control subjects. The type I error rates of the methods are then calculated as the proportion of significant replicates at the nominal level of 5% out of 1,000 replicates. For the type I error rate, 95% of the empirical estimates of 1,000 replicates are expected to fall between 0.036 and 0.064. The empirical power is determined by calculating the number of times the P-values were obtained by analyzing functional and nonfunctional SNPs (28 common SNPs and 97 rare SNPs in 9 genes). As shown in Table We first compared four pathway analysis methods: GT, GRASS, the EB method, and WGT. These tests were performed for 697 unrelated individuals, and the VEGFC, containing only one rare SNP, was not analyzed. Except for FLT1, the power of all three methods was low, and the CMC method outperformed the other methods. For FLT1, for which more rare variants were included, the two methods that assign more weight to rare variants performed better.To narrow down the candidate region and possibly pinpoint the associated genes for further analysis, we conducted an association analysis of each gene in the pathway. Because the WGT performed best at the pathway level, we compared the WGT to the CMC and weighted-sum methods. In Table r2 measure. Figure r2 ≈ 0) between the 125 SNPs. Then, we examined the EB estimates, which reflect the summarized information per gene, for all individuals . As depicted in Figure We also studied the type I error rate by using (200) dichotomized Q4 phenotypes, because Q4 phenotypes are assumed not to be associated with any SNP in the VEGF pathway that influences Q1. This resulted in extremely inflated type I error rates. To investigate possible correlation between dichotomized phenotypes Q4 and the Affected status, we constructed a simple 2 × 2 table. The concordance rates for 200 data sets were 68.2–75.9% with an average of 72.5%; that is, 72.5% of subjects had the same case-control status in both phenotypes. Simulating phenotype under the null hypothesis of no association seems a better practice for estimating type I error rate.r2 measure. If both of two loci have rare alleles, it is possible for D′ to be 1 and r2 to be small. Hence, D′ is hard to interpret with rare alleles, and we chose to use r2, which is a better measure for this situation. New methods might be needed to accommodate the specific features of the sequencing data.Concerning the conjecture of whether pathway analysis would be helpful for detecting multiple common and rare variants jointly, both Figures FLT1, the empirical power of all three methods was low. For FLT1 there were eight rare and three common causal variants; the effect sizes of the common variants ranged from 0.61 to 0.74. In contrast, the power to detect KDR, which has eight rare and two common variants and roughly the same genetic profile as FLT1, was very low. A possible explanation could be that the amount of information contained in the two genes is different. Here, the EB estimates at the gene level could be a useful tool; in Figure FLT1 was higher than that of KDR. In addition, when common and rare variants are analyzed together, the common variants might play a significant role. The effect sizes of the common variants in FLT1 (0.61–0.74) were larger that those in KDR (0.14 and 0.30).When pinpointing specific genes, except We investigated the use of pathway analysis methods by jointly considering common and rare variants for sequencing data. We report promising performance of the WGT, which gives larger weights to rare variants compared to other methods intended for detecting common variants. Our conclusion is that straightforward application of pathway analysis is not satisfactory; hence, when common and rare variants are jointly analyzed using sequencing data, larger weights should be assigned to rare variants.The authors declare that there are no competing interests.HWU preprocessed data, performed the analyses, and wrote the manuscript. HWU and JJHD participated in the development of the methods. HWU, RT, and JJHD interpreted the results of the analysis. All authors read and approved the final manuscript.


Substantial advances have been made in identifying common genetic variants influencing cardiometabolic traits and disease outcomes through genome wide association studies. Nevertheless, gaps in knowledge remain and new questions have arisen regarding the population relevance, mechanisms, and applications for healthcare. Using a new high-resolution custom single nucleotide polymorphism (SNP) array (Metabochip) incorporating dense coverage of genomic regions linked to cardiometabolic disease, the University College-London School-Edinburgh-Bristol (UCLEB) consortium of highly-phenotyped population-based prospective studies, aims to: (1) fine map functionally relevant SNPs; (2) precisely estimate individual absolute and population attributable risks based on individual SNPs and their combination; (3) investigate mechanisms leading to altered risk factor profiles and CVD events; and (4) use Mendelian randomisation to undertake studies of the causal role in CVD of a range of cardiovascular biomarkers to inform public health policy and help develop new preventative therapies. After decades of inconsistent findings, firm genetic associations are finally being reported for risk of coronary heart disease (CHD), arterial aneurysmal disease, peripheral vascular disease and diabetes single trait or disease end-point, e.g. diabetes . Because of LD between variants, multiple associated SNPs are observed at each locus, mostly in non-coding DNA regions http://www.hapmap.org) and the 1000 Genomes Project (http://www.1000genomes.org), allowing inclusion of SNPs across a wide range of the allele frequency spectrum, including those with a minor allele frequency of less than 0.1%.These residual uncertainties can be addressed by higher resolution SNP typing at associated loci using gene-centric arrays, such as the Metabochip UCL-LSHTM-Edinburgh-Bristol (UCLEB) Consortium has been established to allow interrogation of genetic associations using the Metabochip. The consortium consists of 12 well-established prospective observational studies comprising over 30,000 participants: Northwick Park Heart Study II (NPHS II), Whitehall-II Study (WHII), British Regional Heart Study (BRHS), English Longitudinal Study of Ageing (ELSA), MRC National Survey of Health and Development (MRC NSHD), 1958 Birth Cohort (1958BC), Edinburgh Artery Study (EAS), Edinburgh Type 2 Diabetes Study (ET2DS), Edinburgh Heart Disease Prevention Study (EHDPS), Aspirin for Asymptomatic Atherosclerosis Trial (AAAT), Caerphilly Prospective Study (CaPS) and the British Women's Heart and Health Study (BWHHS). The unique properties of the consortium provide an opportunity to investigate genetic determinants of risk factors for CVD through Metabochip-wide association analyses (MWAs), as well as associations with measures of subclinical disease such as carotid intima media thickness (cIMT), and with clinical events. Integration of genotype, biomarker and disease outcome data can also provide insight into disease mechanisms and potential therapeutic targets.The th to 9th decades of life, a time at which the majority of common, non-communicable diseases manifest, rendering the consortium a valuable continuing source for cases of incident diseases. All studies have longitudinal follow up (range 5–62 years) and details of incident disease  has been used to investigate the presence of population structure and exclude outliers. The age at recruitment ranges from birth (MRC NSHD and 1958BC) to >90 years (ELSA), with most cohorts recruiting subjects in mid-life. Despite the range of ages at the time of recruitment, the current age of participants spans the 5ease see Table 1.Each of the studies has a defined sampling frame, inclusion criteria, and procedures for the collection and recording of demographic details, biological samples, and clinical measures. Each of the 12 cohorts has a wide range of clinical and biological measures with overlap across studies, facilitating pooled analyses with substantial power. Many have also used common or comparable measurement methods with many of the blood markers having been measured in the same laboratory. All studies follow participants for incident disease and mortality, and have an ongoing programme of clinical assessments/biological sampling.http://www.1000genomes.org) dataset (1 million typed and imputed SNPs) from 8 studies consisting of around 21,000 samples, more than 100 phenotypes. These include a maximum of 70 blood and other biomarkers (of which 19 are available in more than 15000 participants); carotid ultrasound measures of atherosclerosis from 7200 individuals across 5 studies ; around 2000 cases of prevalent and incident type 2 diabetes; and almost 6000 cardiovascular events , or any condition precluding informed consent. Non-fasting plasma samples were taken at baseline and annually until year 6. A standard ECG was recorded and coded according to Minnesota criteria at baseline and at 6 years. Baseline plus 5 annual repeat measures are available in the entire cohort on BMI, lipids, DBP, SBP, fibrinogen and FVII. Single measures of homocysteine, Lp(a), and CRP are available. Repeat phenotypes were measured at the same centres and are complete for all those mentioned. Smoking status is known. Endpoints for CHD were fatal and non-fatal MI based on WHO criteria, silent MI or coronary revascularization procedures and sudden unexplained death. Information on fatal cancers and diabetes has also been recorded. DNA was extracted from blood samples collected in 2000.From 1978 to 1980, 7735 men aged 40–59 were recruited from general practices across the UK. A wide range of phenotypic measures is available for established risk markers such as lipids, blood pressure and inflammatory and haemostatic markers. Most of these measures were taken both at recruitment and re-examination, which occurred in 1998–2000 when men were aged 60–79. At this re-examination 4252 participants attended and DNA was extracted for 3945. A case-control sample was selected using 1095 cases with prevalent data at re-examination or incident cases of CHD or stroke over the next 8 years and 1358 controls. The controls were frequency matched based on being in the same town and within the same fixed 5-year age band as the cases. Data on important behavioural variables such as cigarette and alcohol consumption, as well as physical activity, have been regularly collected through follow up. Well validated outcome variables such as major coronary heart disease, stroke, diabetes, and revascularisation, as well as cause-specific mortality, continue to be collected from medical records almost 30 years after recruitment.http://www.ucl.ac.uk/pcph/research-groups-themes/brhs-pub). between 1985 and 1988 involved 20 London based Civil service departments. Genetic samples were collected in 2004 from over 6,000 participants. The study is highly phenotyped for cardiovascular and other ageing related health outcomes, with 9 phases of follow up , over 20 years of follow up. A wide variety of health behaviour and environmental data are also collected and the participants are consented for linkage to recorded clinical data such as Hospital Episode Statistics (HES), the Office of National Statistics mortality data and the national registry of acute coronary syndromes in England and Wales .http://www.ucl.ac.uk/whitehallII/). aged over 50 years recruited from the Health Surveys for England in 1998, 1999 and 2001. Genetic data were collected at Wave 2 of the study (2004/5). A wide range of phenotypic measures relevant to ageing are available. These measures were made at Wave 0 of the study  and at follow up (2004/5). Data on health behaviours and a wide range of health outcomes are available. Nearly all participants (97%) are also consented to linkage to routine data such as HES, which allows for the assessment of health outcomes and cause specific mortality. A case-control sample was selected using 412 cases and 1573 controls. Controls and cases were matched by sex and 5-year age bands at Wave 2.http://www.ifs.org.uk/elsa/)... The biomedical survey at age 45 years included collection of blood samples and DNA from about 8000 participants. Immortalised cell lines providing an unlimited resource for future genetic studies have been created for 7500 participants. Follow-up for disease outcomes is by self-reports of doctor diagnosed events and record linkage for fatal events.This study is based on men aged between 45 to 59 years who resided in the small South Wales town of Caerphilly between the examination dates of 1979 &1983. Of the 2818 eligible, 2512, (89%) were recruited. The men were studied at baseline (Phase 1) and each subsequent 5 year period (Phase 2–5) and have therefore been followed up for around 20 years. An additional 447 patients were recruited at phase 2. The cohort has a wide range of cardiovascular phenotypes and at phase 3, cognitive function was also assessed, which has been supplemented with clinical dementia and cognitive impairment at phase 5. DNA was extracted from blood samples collected in 1992–1994. Follow-up for disease outcomes is by self-report from participants, who are also linked to hospital episode discharge summaries for validation checks to comply with WHO criteria, as well as death certificates for fatal events.http://www.bris.ac.uk/social-community-medicine/projects/caerphilly/about/).. Follow-up for disease outcomes is by biennial medical record review  and cancer registrations and death certificates obtained from the National Health Service (NHS) Central Registration. Detailed follow-up is collected on coronary heart disease, stroke, type 2 diabetes, pulmonary embolism, deep vein thrombosis and cancer events. The UCLEB case-control sample was selected using 523 cases with prevalent disease at recruitment or incident cases of CHD or stroke up to 2010. 1501 controls were frequency matched based on being in the same town and within the same fixed 5-year age band as the cases.http://www.lshtm.ac.uk/eph/ncde/research/bwhhs/index.html).(At baseline (August 1987-September 1988), an age-stratified random sample of men and women, aged 55–74 years, was selected from the age-sex registers of ten general practices with catchment populations spread geographically and socioeconomically throughout the city of Edinburgh. Subjects were excluded if they were unfit to participate . These exclusions were replaced by other randomly sampled subjects. The study population is almost exclusively European. DNA was extracted at 5 years follow-up. Physical examinations were performed by specially trained research nurses using standardised operating procedures. The quality of clinical measurements were checked before and during the study by repeat measurements taken intermittently by the study co-ordinator. Individual observer measurements were assessed for drift. Blood assays were performed in accredited laboratories using international standards. Subjects have been followed up for 20 years for cardiovascular events, using repeat self-reporting questionnaires, record linkage for hospitalisations and deaths, and validation of events against pre-specified criteria through searching of hospital and GP notes. Comprehensive clinical examination was repeated at 5 and 12 years after commencement of the study, resulting in repeat measurements of several key variables.At baseline (1985–1987), all men aged 30–59 years and registered with one of two general practices in the city of Edinburgh, were invited to participate in this study. The response rate was 69% and follow-up of non-responders showed that there was no substantial bias. DNA was extracted at baseline. Physical examinations were performed by a specially trained research nurse using standardised operating procedures. Blood assays were performed in accredited laboratories using international standards. Subjects have been followed up after 20 years for cardiovascular events, using repeat self-reporting questionnaires and record linkage for hospitalisations and deaths.At baseline (August 2006 to August 2007), an age-stratified random sample of men and women with type 2 diabetes, aged 60–74 years, was selected from the Lothian Diabetes Register (LDR), a comprehensive database of subjects with known type 2 diabetes living in Lothian. Subjects were excluded if they did not meet WHO criteria for type 2 diabetes, or if they were physically unable to complete the clinical and cognitive examination. The study population is almost exclusively European. DNA was extracted at baseline. Physical examinations were performed by specially trained research nurses using standardised operating procedures. The quality of measurements was checked using observation of research staff by study investigators and inter-observer variability assessments were made for key variables. Blood assays were performed in accredited laboratories using international standards. Retrospective data on cardiovascular disease and selected physical and biochemical variables were retrieved using record linkage for hospitalisations and deaths since 1985 and using data from the LDR. Subjects returned for further clinical examination after one year and were examined again after they had participated for 4 years.At baseline (1998 to 2000), all men and women aged 50–75 and registered with participating general practices spread throughout Edinburgh, Glasgow and Lanarkshire , were invited to participate in this study. Subjects were excluded if they had a history of MI, stroke, angina or peripheral arterial disease (PAD) and included if they had an ankle brachial index of 0.9 or less. They were therefore a healthy, but moderately increased risk population, in terms of cardiovascular disease. Subjects were further excluded if they were currently using aspirin or other antiplatelet or had a contraindication to aspirin therapy. Subjects were followed up for 8 years for cardiovascular events, using annual contacts with subjects, record linkage for hospitalisations and rigorous validation of events against pre-defined criteria using hospital and GP notes. Physical examination was performed at baseline and after 5 years by specially trained research nurses using standard operating procedures, and blood assays were performed in accredited laboratories using international standards. DNA was extracted 3 months after recruitment.Figure S1.The workflow developed to co-ordinate genotyping, merge and collate with harmonised phenotype and disease end-point data is summarised in Informed consent was obtained for all subjects included in UCLEB research. Written approval from individual Research Ethics Committees to use anonymised individual level data has been obtained by each participating study. All data obtained and generated within the UCLEB consortium are fully anonymised, contain no personal identifiers and adhere to the contributing studies restrictions on deductive disclosure. Senior investigators from each participating study were invited to join the UCLEB steering group to discuss key projects to be carried forward and to assess the potential for external collaborations. An analysis group meets weekly focusing on methods development and analyses. There are regular weekly teleconferences organised from the coordinating centre at UCL during which the whole consortium is updated on various aspects of genotype and phenotype data management, and on-going and proposed analyses. An access-restricted Google-hosted website has been set up to facilitate sharing of minutes, analysis plans, project proposals and other useful resources. The cohorts included in the UCLEB consortium have individual policies and mechanisms for data sharing and all have an excellent track record in this regard. Most of the studies have contributed to the highly cited Emerging Risk Factors Collaboration reports on CV risk factors and biomarkers, and to GWAS consortia and other large-scale genetic discovery efforts. Opportunities for data sharing are maximised because requests to individual studies are also possible, e.g. for projects that focus on certain study designs or where outcomes are available only in a subset. The proposals to access UCLEB data are evaluated by members of the steering committee using a standardised data request form. Aggregate SNP data for specifically requested traits are shared with external collaborators according to the external project analysis plan.Around 21,000 individuals across the cohorts have been typed using Metabochip see Table 4,Figure S1).Duplicate samples have been genotyped to compute the error rate. Initial quality control analysis on genotyped data identified any problem samples that have been subsequently excluded in further analysis. These included: checks for discordance between reported and genetically-determined ethnicity, replicate concordance, sample mix-up (unknown duplicates and comparison to previously genotyped data where available), gender ambiguity and cryptic relatedness  plus a 250 SNP overlap with the penultimate chunk.http://www.sph.umich.edu/csg/abecasis/MACH/download/), which implements a Markov Chain Monte Carlo (MCMC) Haplotyping algorithm Each chunk was phased using the program MACH1 , which offers a useful range of analytical functions coupled with the flexibility of the R programming environment for data manipulation and further analysis.The UCLEB data was collated in a dosage format, which is readily utilised by the analytical package PLINK Phenotypic data available for sharing from each study were harmonized to agreed units and categories determined by individual study and phenotype data managers. Data were then collated at the UCLEB coordinating centre Table 3.Analysis plans have been written that can be adapted by each working group for specific analyses, and Stata, PLINK and R syntax are available for these analyses. In order to address the core aims of UCLEB, five broad analytical approaches will be applied.2 value . SNPs most likely to mark or represent causal sites will be identified using variable selection methods, as well as conditional analyses to identify independent SNPs at each locus applying locus-specific Bonferroni correction.Metabochip-wide genetic discovery analysis to be run individually on each study, from which study-specific estimates will be pooled by fixed effects meta-analysis weighting by inverse variance using the METAL program, as well as by random effects meta-analysis, to generate summary estimates for each trait. Medication use for the main drug classes affecting cardiometabolic traits  has been carefully recorded. Previously validated methods Analysis of multiple phenotypes (phenome scans) for improving power and investigating pleiotropy. Considering the extra information provided by the covariance between the traits allows the joint analysis of multiple phenotypes, in order to increase power. One such implementation is MultiPhen Application of genetic associations in risk prediction models. The absolute, relative and attributable genetic risk of CVD events  will be estimated and compared with the associations of non-genetic risk factors using SNPs identified by prior GWAS that are included in Metabochip. Mendelian randomisation (MR) analyses. MR studies typically quantify and compare three associations: (i) biomarker-disease in prospective cohort studies; (ii) genotype-biomarker in cross-sectional or prospective cohort studies; and (iii) genotype-disease in prevalent case-control studies or studies of incident cases and controls nested within a cohort study. Triangulation of the risk estimates provides evidence on causation, with the magnitude of the causal association being estimated by instrumental variables regression. Despite emerging successes of MR, two limitations remain. First, many biomarker-associated SNPs are of weak effect, compromising power and necessitating large sample sizes. Second, although SNPs identified for an index association with a single CVD risk factor/biomarker are not generally associated with exogenous exposures influencing CVD risk , they are frequently associated with a diverse range of endogenous biomarkers. This can compromise interpretation of an MR analysis based on a single locus. The issue of power is addressed in UCLEB both through use of gene scores and through external collaboration with other studies and consortia to ensure case numbers are not limiting. The use of genetic instruments comprising SNPs from different genetic loci, each independently associated with the biomarker of interest, increases power, since each SNP contributes incrementally to the marker variance, and also helps reduce non-specificity, because the relative genetic effect on traits other than biomarker of interest tends to attenuate.Genetic effects on risk factor/biomarker trajectories. Analysis of risk factor trajectories will involve generation of standard deviation scores for each trait, cross-sectional descriptive analyses at different ages, and the development of hierarchical mixed models which account for correlation between repeated measures, as well as age-by-genotype interaction tests. We will use multiple imputation for the management of missing data.Figure S2 gives additional information on the analyses that have been prioritised by UCLEB.−7 (GWAS level) were adopted for the former and 10−4 for the latter. All sample size estimates correspond to the total number of individuals and assume a 10-year CVD event rate of 16%, based on average age, follow-up period and gender of participants in UCLEB cohorts.Power and sample size calculations were conducted to evaluate the ability of the UCLEB datasets to discover novel loci and to test the effect of loci validated for an index trait on other traits and outcomes. Conservative alpha values of 1×10α+zβ)2/q2 where zα and zβ are values of the standard normal distribution for specified alpha and beta error and q2 is the amount of variance explained, which is determined by effect size and minor allele frequency in the case of single SNPs. In addition to alpha and sample size, power depends on the variance explained by a SNP, which is in turn related to minor allele frequency  and effect size (d) and is given by q2 = 2pAu(1-pAu)d2. Tables S1a and S1b present the resulting power estimates in order to be able to discover SNPs that explain as little as 0.5% of the variance with 80% power. The tables provide power for sample sizes in the range 2,500 and 20,000 to detect variance in a quantitative trait of 0.5–5% at a p-value of 10−7 (Table S1a), and a p-value of 10−4 (Table S1b).For quantitative traits, a good approximation for the sample size is given by N = (zTables S2a and S2b) and rare alleles (see Tables S2c and S2d). Higher MAFs are often accompanied by smaller effect sizes while rare alleles have a higher likelihood of larger effects. As illustrated by Tables S2a–d, the combination of very small MAF and very small effect leads to a substantial inflation of the effect of sample size on power. With an overall sample of 30,000 subjects, and a 16% 10-year event rate, power estimates mostly exceed 80% for a range of plausible effect sizes. For example, with a total sample of 27,323 individuals among whom the 10-year event rate is 16%, power to detect a common SNP with MAF of 15% and odds ratio of 1.2 is 80%. With regard to rare alleles that have a frequency of only 1%, achieving a power of 80% for an odds ratio of 2, requires 18,456 subjects.For disease events, power depends additionally on incidence rate. A differentiation is made between common alleles  the range of measures; (2) longitudinal follow-up that also allows exploration of genetic effects on trajectories, and at critical periods; (3) the addition of new phenotypic detail with new waves of resurvey and data collection, which enrich the resource, so that the value of the investment in genotyping extends beyond any single trait or outcome; (4) assessment of context dependent genetic effects because of exhaustive information on diet, records (and sometimes objective measures) of smoking and physical activity, as well as education, employment, medication and many other environmental factors A number of large consortia have already been assembled to discover and subsequently fine map genetic associations of cardiometabolic traits and disease end points In conclusion, UCLEB provides a stable, long-term resource for large-scale, integrated genomics analyses, with the potential to add proteomic and metabolomic technologies as they emerge. The archived biological samples will facilitate more comprehensive phenomic analysis to match the breadth of genomic data. The integration of multiple layers of –omics data within the framework of cohort studies should eventually lead to a more comprehensive understanding of the mechanisms of common disease Figure S1UCLEB workflow.(DOCX)Click here for additional data file.Figure S2Prioritised analyses in UCLEB.(DOCX)Click here for additional data file.Table S1(DOCX)Click here for additional data file.Table S2(DOCX)Click here for additional data file.


For the XIV QTLMAS workshop, a dataset for traits with complex genetic architecture has been simulated and released for analyses by participants. One of the tasks was to estimate direct genomic values for individuals without phenotypes. The aim of this paper was to compare results of different approaches used by the participants to calculate direct genomic values for quantitative trait (QT) and binary trait (BT).Participants applied 26 approaches for QT and 15 approaches for BT. Accuracy for QT was between 0.26 and 0.89 for males and between 0.31 and 0.89 for females, and for BT ranged from 0.27 to 0.85. For QT, percentage of lost response to selection varied from 8% to 83%, whereas for BT the loss was between 15% and 71%.Bayesian model averaging methods predicted breeding values slightly better than GBLUP in a simulated data set. The methods utilizing genomic information performed better than traditional pedigree based BLUP analyses. Bivariate analyses was slightly advantageous over single trait for the same method. None of the methods estimated the non-additivity of QTL affecting the QT, which may be one of the constrains in accuracy observed in real data. An idea of genomic selection (GS) has been presented nearly a decade ago  and sincGenetic basis of a phenotypic trait – its genetic architecture - is often complex. A particular trait may be, for example, controlled by many genes with small effects or by several major genes. Genes that control one trait may also control other trait(s), i.e. they are pleiotropic and the traits are genetically correlated. A gene variant may have an effect when it is inherited from a parent of one sex but not from the other (i.e. imprinting) or its effect will be present only when several alleles are in a particular combination (i.e. epistasis or haplotype effect). Genomic selection opens new opportunities in the analyses of complex traits.A number of approaches have been developed to obtain direct genomic values (DGV) or genomic-enhanced breeding values (GEBV) ,6. BecauFor the XIV QTLMAS workshop, a dataset for traits with complex genetic architecture has been simulated and released for analyses by participants . One of http://jay.up.poznan.pl/qtlmas2010/dataset.html.Simulated, four-generation pedigree consisted of 3,226 individuals, descended from 20 founders, each mating resulted in 30 offspring. The last generation consisted of 900 young individuals with no progeny and no phenotypes. All 3,226 individuals had 100 Mb long genomes consisting of 5 chromosomes. In total, 37 out of 10,1031 single nucleotide polymorphism (SNP) markers were assumed to be QTL of which two had major effects. One of the simulated traits was a quantitative trait (QT) and the other one was a binary trait (BT). Heritability for QT, due to imprinting, was higher for males (0.52) than for females (0.39). True breeding values (TBV) for QT were calculated as a summation of effects of 30 additive QTL, haplotype effects (QTL pairs 31-32 and 33-34) and effects of imprinted QTL . Heritability for BT was 0.48. TBV for BT were calculated as a summation of effects of 22 additive QTL. Simulated pedigree, genome, marker and phenotypic data were made available for analyses. More detailed description of simulation can be found in [Eleven groups submitted their estimates of DGV. Participants applied several methods and often different variants of same method ,10-17. IFive criteria were used to compare the applied genomic selection methods: (1) Accuracy being the Pearson correlation between true breeding values (TBV) and DGV. (2) Bias of estimates calculated as the linear regression coefficient (TBV = M + b*DGV +E) (unbiased estimates are expected to have regression coefficient of 1), (3) mean square difference (MSD) between TBV and DGV, (4) % of shared ID when selecting top 10%  based on DGV vs. TBV, and (5) selection loss from selecting on DGV instead of TBV as a proportion of response using TBV. Due to a presence of imprinting, the average genetic values for males and females were different, and therefore, accuracies and regression coefficients for these groups were calculated separately.For QT, the accuracy was between 0.26 and 0.89 for males and between 0.31 and 0.89 for females  to 77% Table . The besHigh percentage of shared ID, generally, was associated with low level of loss in genetic gain. For QT, percentage of lost response to selection when 10% are selected based on DGV instead of TBV varied from low (8%) to very high (83%). Pedigree BLUP resulted in 62% to 79% of loss, whereas approaches using genomic information, in general, resulted in smaller loss. Most of analyses using BayesB as well as BayesCpi and TA-BLUP appeared to be superior to other methods and caused only 8% to 9% of loss. Percentage of loss for most of the GBLUP approaches was close to 20%. For BT, loss in response to selection was, usually, at higher level. The smallest observed loss was 15% for bivariate BayesB and the biggest was 71% for PLSR. Pedigree BLUP caused from 74% to 79% of loss. Bivariate analyses were superior to univariate for both traits.When phenotypes for young individuals are not available, the approaches that use genomic information had superior performance compared to the methods that were based solely on pedigree information. Use of genomic information, therefore, led to improved breeding value estimation, which was also found by others ,18-24. TWhen the same approach is used by different researchers, similar results are expected, which was not always the case in our comparison. One GBLUP implementation, for example, was about 0.20 less accurate than other GBLUP analyses. This suggests that the methods may be very sensitive to data preparation and that their implementations may vary in performance.Bivariate analyses, in general, performed better than univariate analyses for the same approach. This was expected as the two simulated traits were indeed genetically correlated. Differences between univariate and bivariate analyses were especially apparent for BT, for which phenotypes carry less information. More complex approaches, requiring initial estimation of marker effects or use of machine learning techniques applied to QT provided similar or inferior results to simpler methods.Bayesian model averaging methods predicted breeding values slightly better than GBLUP in a simulated data set, where traits had complex genetic architecture  and were affected by relatively small number of QTL. The methods utilizing genomic information performed better than traditional pedigree based BLUP analyses. Bivariate analyses were slightly advantageous over single trait for the same method. None of the methods estimated the non-additivity of QTL affecting the QT, which may be one of the constrains in accuracy observed in real data.The authors declare no competing interests. MS and MP preformed comparative analyses.MP and TS drafted the manuscript. MS and MP preformed comparative analyses. AW MS TS SM and MP critically revised the manuscript and contributed to discussion of the results.


Pinus taeda L.) individuals. Phenotypes of the 165 individuals were obtained from clonally replicated field trials and were used to estimate breeding values for growth (stem volume). Two alternative methods, based on allele frequencies or regression, were used to generate the genomic relationship matrices. The accuracies of genomic estimated breeding values based on the genomic relationship matrices and breeding values estimated based on the average numerator relationship matrix were compared. On average, the accuracy of predictions based on genomic relationships ranged between 0.37 and 0.74 depending on the validation method. We did not detect differences in the accuracy of predictions based on genomic relationship matrices estimated by two different methods. Using genomic relationship matrices allowed modeling of Mendelian segregation within full-sib families, an important advantage over a traditional genetic evaluation system based on pedigree. We conclude that estimation of genomic relationships could be a powerful tool in forest tree breeding because it accurately accounts both for genetic relationships among individuals and for nuisance effects such as location and replicate effects, and makes more accurate selection possible within full-sib crosses.Replacement of the average numerator relationship matrix derived from the pedigree with the realized genomic relationship matrix based on DNA markers might be an attractive strategy in forest tree breeding for predictions of genetic merit. We used genotypes from 3461 single-nucleotide polymorphism loci to estimate genomic relationships for a population of 165 loblolly pine ( The expected additive genetic relationships (genetic covariances) derived from a pedigree are based on probabilities that gene pairs are identical by descent (IBD) . For exaWith advancement in high-throughput genotyping technologies, realized genomic relationships are being used in cattle-breeding programs to increase selection efficiency . SelectiPinus taeda L.) breeding program in the southern United States has completed its third cycle of breeding in 55 years  to complete a breeding cycle. For example, one loblolly pine  the use of genomic relationships based on single-nucleotide polymorphism (SNP) markers to predict genetic merit is as efficient as traditional pedigree-based and phenotype genetic evaluation; and (2) genomic relationship matrices calculated by two different methods, the allele frequency and regression approaches suggested by Thirteen loblolly pine parents were used as females and males to generate nine full-sib families (hereafter family). The majority of full-sib families were obtained by single-pair mating design. Several full-sib families were genetically related by a common male or female parent  of the genomic estimated breeding value (GEBV) was calculated as:2a is the estimated genetic variance, f is the inbreeding level, and 1+f is derived from the diagonal of numerator relationship matrix. For simplicity, the inbreeding coefficient was assumed to be zero for unrelated individuals.Matrix-based analytical methods require datasets with no missing values, but genotypes were missing for 1.2% of the total data points in a 165 pine trees by 3461 genotypes data matrix. We tested the effect of different imputation approaches by comparing the accuracy of breeding value predictions based on datasets with missing values imputed by different methods. Imputation methods were (1) allele frequencies, (2) replacing missing genotype by the major homozygote of the loci, and (3) using allele content as suggested by −A1) derived from pedigree and phenotype. The model in matrix form is as followsy is the vector of observations representing the trait of interest (dependent variable), X and Z are the design or incidence matrices for the vectors of parameters b and u, respectively, which are the fixed covariates  and random effects (tree) to be estimated, respectively. The term u is the vector of breeding values of trees with Var (u) = Aσ2a, where A is the numerator relationship matrix and σ2a is the additive genetic variance. The variance-covariance matrix of random effects (trees) in the linear mixed model is replaced by the A matrix to predict breeding values. The term e is the residual component or vector of residuals with Var (e) = Iσ2e = R, where I is the identity matrix and σ2e is the error variance  = ZZT σ2m = (ZZT/ [2 Σipi (1 − pi)]) σ2a = Gσ2a, where the expression [2 Σipi (1 − pi)] is twice the sum of heterozygosity of the markers, ip is allele frequency at locus i, ZZT represents the number of shared SNP alleles among two individuals, ZT is the transpose of the Z matrix, σ2m is the variance explained by markers, and σ2a is the additive genetic variance. The division of ZZT by 2Σipi (1 − pi) scales G to be analogous to the A matrix, which enables computation of the genomic relationships  plus a constant matrix (E (G) = A + constant). It is simpler to do an extension of single marker association analysis to multiple marker regression to account for all genes (markers) simultaneously  = Iσ2m, where σ2m is the variance explained by markers, and I is the identity matrix. The important difference from model 1 is that we let the sum Za across all marker loci (m) to be equal to the vector of breeding values. In other words, u = Za. The A matrix was replaced by the genomic relationship matrix G derived from allele frequencies as suggested by u as follows:2e /σ2a, the shrinkage factor in BLUP. The predicted value of u is the sum Za over all alleles that the individual inherited. We denoted the genomic estimated breeding values based on allele frequency method as GEBVa.Genomic estimated breeding values were obtained using the following mixed model and substituting the G matrix derived from markers by the regression method, the estimated genetic merits of trees were obtained as:G could be inverted and R could be processed by iteration.Using the We tested two different cross-validation scenarios, as follows: 165 cloned trees were divided into a training data set and a validation data set. In the first scenario, approximately 90% of the cloned trees (148) were sampled for the training set, either within each of the nine families or at random from the whole population without consideration of family origin. The remaining cloned trees were used for the validation (17 clones). In the second scenario, approximately 50% of cloned trees (84) were sampled either within family or randomly from the whole population for training, and the remaining cloned trees were used for validation (81 clones). For each scenario, six independent samples were analyzed. All the analyses were conducted with ASReml software  from the ABLUP and GBLUP regression methods. To avoid bias in the EBV1 for the cloned trees, the pedigree data were omitted, and EBV1 were calculated based only on the phenotypes of the 16 to 50 ramets  of each cloned tree. We report the accuracy of a prediction as the correlation between the predicted genetic values (GEBV) from the cross-validation and the nonpedigree-based estimated breeding value (EBV1), which will determine the potential gain using markers . We prodWe used different methods to impute missing genotypes for calculation of genomic relationships among trees to use in genomic estimated breeding values. The results suggest that different methods of imputing missing genotypes did not have a noticeable effect on the accuracy of predictions in this study . All fouWe compared predictions (EBV1) based on all the phenotypic data without the pedigree with GEBVs for all 165 cloned trees, using marker data to model realized relationships . The cori.e., 0.52−0.55. The last two rows in Efficiency of markers in general is evaluated by a correlation between true and estimated breeding values . Howevervs. 50%), suggesting that a much larger training population (perhaps 1500−2000 trees) would result in even more accurate predictions of breeding value. Breeding values based on realized genomic relationships obtained from SNP markers were as accurate as predictions based on the traditional pedigree-based genetic evaluation, based on comparison of SE estimates. The average SE for all 165 cloned trees using traditional genetic evaluation based on pedigree was 1.14, whereas the average SE for breeding values estimated using genomic relationships was about 0.71. This finding suggests that the marker genotypes used in construction of genomic relationship matrices, by either the allele-frequency method or the regression method, effectively reconstruct the family relationships in this structured population. The accuracy of prediction is higher for cross-validation scenarios that use larger training populations  to capture Mendelian segregation within families . In the  modeled . CapturiWhen pedigree-based expected additive genetic covariances are used as in traditional genetic evaluation, the model assumes constant and categorical covariances between relatives. For example, full-sibs are expected to share 50% of alleles that are IBD, and thus the additive genetic covariance between full-sibs is assumed to always be 0.5 . On the Our results are in agreement with the suggestions of G matrix, with no consideration of inbreeding or selection. The markers used in this study represent a very small fraction of the genome, and probably explained a very small fraction of phenotypic variation. It would be possible to use the average allele frequencies from the base population, as provided by a larger set of families and parents included in previous mating designs. The genomic relationships should be estimated using the allele frequencies from the unselected base population. The regression based method employs the marker effects as dependent variable, and the mean homozygosity is adjusted by regressing the markers on A (dependent variable). This method was efficient for control of bias in the predictions, because it includes a matrix of residuals of the differences between true and expected fraction of alleles shared in common, as well as a measurement residual to account for the fact that the genotyped markers represent only a subset of the entire individual tree genome. When allele frequencies in the base population are different from 0.5, rare alleles contribute more to the genetic resemblance between individuals than common alleles (G matrices and reported similar high correlations (~0.98) between methods, and small differences in the ranking of livestock individuals with different genomic matrices. They suggested that modifications to the G matrix could be important for different species or different populations. For example, the G matrix can be normalized to have average diagonal coefficients equal to 1 to assure compatibility with an A matrix, when either the average inbreeding or the number of generations are low, as is the case in most forest tree breeding populations.We used two methods for calculating genomic relationships . In general, they were comparable in the accuracy of the predicted genomic breeding values. The main advantage of the method based on allele frequencies is that the observed minor allele frequencies were included to scale the Based on minor allele frequency, a subset of markers can be explored to estimate the realized genomic relationship matrix. For example, marker loci should be chosen so that all parents are heterozygous for at least a few rare minor alleles. Selecting loci with low minor allele frequency can create problems with singularities in the genotype matrix, if there are many homozygous genotypes across the individuals. G matrices, compared with Bayesian methods to estimate marker effects, resulted in a slightly greater accuracy of nonlinear models in some simulations  using genomic relationships derived from SNP marker genotypes could be an efficient approach to increase the reliability of breeding value predictions of forest trees, and (2) two different methods of estimating realized genomic relationships from marker genotypes yield equivalent accuracy of breeding value predictions. We found that the predictions based on genomic relationships derived from markers (GBLUP) were as accurate as traditional genetic evaluation based on an average relationship matrix derived from the pedigree. Second, our results showed that genomic relationships generated either by the allele frequency method or the regression method did not differ in the accuracy of genomic estimated breeding values. Realized genomic relationships captured Mendelian segregation among full-sib individuals, even in the absence of phenotypic data, which was not the case with the traditional genetic evaluation. In practical terms, using a genomic relationship matrix is no more difficult than using the traditional numerator relationship matrix derived from a pedigree. The GBLUP model could use raw measurement data rather than deregressed breeding values as phenotypes; it incorporates fixed effects of common environments and genotype by environment (G×E) interactions; and it allows spatial analysis and other more complex variance−covariance structures. These characteristics will allow easy implementation of future applications of GBLUP using standard software available for linear mixed model analyses.


Inter-individual variability in response to both drug classes is well recognized, yet the identity and relative contribution of the genetic players involved are poorly understood. This work is the first genome-wide association study (GWAS) addressing the values and susceptibility of cardiovascular-related traits to a selective β1-blocker, Atenolol (ate), and a β-agonist, Isoproterenol (iso). The phenotypic dataset consisted of 27 highly heritable traits, each measured across 22 inbred mouse strains and four pharmacological conditions. The genotypic panel comprised 79922 informative SNPs of the mouse HapMap resource. Associations were mapped by Efficient Mixed Model Association (EMMA), a method that corrects for the population structure and genetic relatedness of the various strains. A total of 205 separate genome-wide scans were analyzed. The most significant hits include three candidate loci related to cardiac and body weight, three loci for electrocardiographic (ECG) values, two loci for the susceptibility of atrial weight index to iso, four loci for the susceptibility of systolic blood pressure (SBP) to perturbations of the β-adrenergic system, and one locus for the responsiveness of QTc (p<10−8). An additional 60 loci were suggestive for one or the other of the 27 traits, while 46 others were suggestive for one or the other drug effects (p<10−6). Most hits tagged unexpected regions, yet at least two loci for the susceptibility of SBP to β-adrenergic drugs pointed at members of the hypothalamic-pituitary-thyroid axis. Loci for cardiac-related traits were preferentially enriched in genes expressed in the heart, while 23% of the testable loci were replicated with datasets of the Mouse Phenome Database (MPD). Altogether these data and validation tests indicate that the mapped loci are relevant to the traits and responses studied. Atenolol (ate), a selective β1 receptor antagonist, belongs to the preferred β-blocking therapies for the management of heart failure, myocardial infarction, angina and atrial fibrillation, and to the acceptable first-line therapies for hypertension iso) is a β-agonist that may be administered in cases of bradycardia, heart block and asthma iso to rodents and other mammals is also used to model left ventricular hypertrophy (LVH), independently of blood pressure The Inter-individual variability in response to either drug is well recognized. In humans, associated clinical concerns pertain to variance in drug efficacy and occasional adverse side-effects ate and iso, the focus was put on genes coding for β-adrenergic receptors Until recently, the search for pharmacogenetic determinants primarily addressed the effects of single genes and variants on drug disposition and drug response through candidate gene studies. For ate and iso. By generating candidate loci related to a given phenotype in an unbiased manner, genome-wide association studies (GWAS) offer a powerful tool to address this goal.A more detailed understanding of these determinants and associated pathways may ultimately lead to new strategies for better tailored treatment in patients. Our aim was therefore to provide the scientific community with a set of complementary biological hypotheses regarding the genetic determinants of the cardiovascular response to β-blockers or β-agonists So far, a small number of GWAS have successfully addressed drug responses or (adverse) effects in humans To overcome these limitations, our experiments were performed in a genetic reference population (GRP) of 22 inbred mouse strains. The benefit of using such a GRP is that because it is genetically stable and reproducible, it allows data integration across time and (independent) studies and creates the possibility to uncover relationships among genes, pathways, and diseases in well controlled conditions. The phenotypic dataset consisted of the 27 cardiovascular-related traits measured earlier across the various strains and conditions of treatment In a first series of 105 analyses, we searched for associations between the non-transformed values of the 27 traits and ca 80000 informative SNPs of the mouse HapMap resource ctr, ate, iso1 or iso10p-values ≤10−8 and suggestive for p-values ranging from 10−6 to 10−8 , cardiac weight , cardiac indices (VWI and AWI), systolic blood pressure (SBP), heart rate (HR) and ECG values. Readers less interested in these details are referred to 10p = 15.9; The morphological phenotypes were robust and highly correlated p-values was well above that expected by chance, in particular for values measured in ctr and ate-treated mice . They further overlapped with some of the very few suggestive SNPs retrieved with body weight , Adamts3 , Alb , Afp (á-feto-protein), Afm (afamin), and 1700007G11Rik carry the most significant hits  or absent (Ankrd17 (locus 1) and Prkg2 (which resides ca 0.56 Mb downstream of locus 2). Specifically, Ankrd17-deficiency led to serious hemorrhages and embryonic lethality in the mouse Prkg2, several mutant forms cause mammalian dwarfism Alb, Afp, Fgf5 and Bmp3 from the list of putative candidates, while mutants of the remaining genes have not been described Both intervals include several genes, of which ant hits . Our micr absent . FunctioBw26, a 28 Mb QTL that peaks in the direct vicinity of Alb (ADAMTS3 (locus 1) and PRKG2/BMP3 (i.e. immediately downstream of locus 2) are associated with adult height NPFFR2 (locus 1) confers protection against obesity in Scandinavians Our results seem to intersect with data obtained by others . First, y of Alb  and is aiso, is a major risk factor for ventricular dysfunction and heart failure We mapped two, respectively seven suggestive loci for ventricular (VWI and VW/BWS) and atrial (AWI and AW/BWS) weight indices. These regions did not overlap. As left ventricular hypertrophy, such as that induced by rs31137966; Plrg1 and Sfrp2. Plrg1 is a ubiquitous and evolutionarily conserved component of the spliceosome, while Sfrp2 is an enhancer of procollagen C proteinase. Both genes may be relevant candidates. First, they are both expressed in cardiac tissues (Cm67 (CakmC2QTL), a 25 Mb QTL associated with cardiac index Plrg1 induces massive dilatation and atrophy of both cardiac ventricles characteristic of severe dilated cardiomyopathy, further leading to post-natal lethality The most significant region for ventricular indices was tagged by one intergenic SNP of chr 3 , Xpot , and Rassf3 (a member of Ras effectors and tumor suppressor genes). Microarrays indicated that both Xpot and Rassf3 were expressed in cardiac tissues , Tubd1 (tubulin delta 1), Tmem49 (a trans-membrane protein) and miR21. All of them are expressed in cardiac tissues and thus might be relevant for the physiology of cardiac atria. S6 kinase in particular is a pivotal member of the mTOR/p70(S6K) signaling cascade that is involved in the regulation of cell size and scaling. Homozygous disruption of mouse Rps6kb1 does not affect viability or fertility, but induces a significant decrease of body and organ growth, especially during embryogenesis miR-21 is up-regulated in several models mimicking cardiac hypertrophy, such as neonatal cardiomyocytes treated with isomiR-21 contributes to myocardial disease by activating molecular mechanisms of cardiac fibrosis Locus 63, a 45 kb interval defined by four suggestive SNPs sharing identical SDPs, was associated with the AW/BWS ratio of ted mice . Scores Sec16b was suggestively associated with BWE of iso-treated animals  and was suggestive when considering BWE of all mice, using treatment as a covariate (data not shown). Interestingly, human variants of and around SEC16B have been repeatedly associated with body mass index and obesity Five loci were suggestively associated with body weight, of which at least four tagged seemingly relevant candidate regions and/or overlapped hits mapped for related traits. In addition to the two suggestive regions mentioned above (see chapter on scans for non-adjusted AW), an intergenic SNP of chr 1 located ca 0.6 Mb downstream of Luzp1 and Htr1d on chr 4, was suggestively associated with BWE of iso10-treated mice . Polymorphisms around human HTR1D (serotonin receptor 1D) are associated with adult height Another intergenic marker, located between iso10-treated mice, mapping on a ca 7.5 Mb segment of chr 2  and Golim4 (a membrane protein of the Golgi).Significant associations for body weight gain were specifically identified using trait values of  locus7, . Closer  locus7, , which cThe identification of genetic variants affecting human blood pressure and hypertension has been particularly challenging ctr and iso1) or the EMMA scans were inflated with p-values of low to moderate significance  indicated that either no hit passed the suggestive threshold  replicates per strain but has less power to analyze strain means ate- and iso10-mediated effects. These values were generated by computing, for each trait and treated mouse, the difference or the fold change between the value measured in that individual and the average value measured in the ctr mice of the matching strain. The effects of iso10 vs ate were computed following a similar scheme.With the exception of BW, all phenotypes were measured in separate animals for each drug treatment. Thus the values of the pharmacological effects of Altogether, 100 genome-wide scans were analyzed for the pharmacological effects  and S8: iso10, were identified  receptor, while Trhde codes for the Trh-degrading enzyme. Trh represents the most proximal member of the hypothalamic-pituitary-thyroid axis. It is primarily synthesized in the hypothalamus but is also produced in other areas of the brain and in non-neuronal tissues such as the gastrointestinal tract, the heart and reproductive organs. Trh functions as a tripeptide hormone that maintains thyroid hormone homeostasis via regulation of thyroid-stimulating hormone production and secretion. It further controls the release of other hormones, such as prolactin, growth hormone, vasopressin, and insulin. Trh exerts direct roles in cardiovascular physiology in general, and on blood pressure in particular Trh oligonucleotide in SHRs decreased Trh levels and normalized SBP iso enhances Trh expression Trh is able to respond to β-adrenergic activation.Trhr and Trhde modulates the action of Trh on SBP. In humans, a case-control study of 120 hypertensives and 63 normotensives indeed suggested that polymorphisms of the TRHR promoter were associated with essential hypertension β-adrenergic system with the Trh pathway remain to be established.At this stage, it is well conceivable that genetic variation of To substantiate the relevance of the mapped loci further, we explored two complementary strategies. First, we tested whether loci for cardiac traits were enriched in genes expressed in the heart. Second, we searched for replication of the hits mapped by EMMA, using independent datasets of the MPD.p = 0.004, binomial test), indicating that the candidate loci were enriched for genes expressed in cardiac tissues.Cardiac transcript levels were measured in untreated ventricles of C57BL/6J and KK/HlJ hearts by microarrays. Our working hypothesis was that the loci associated with cardiac traits contained more genes expressed in the heart than the rest of the genome. To test for this, we considered the 587 genes located within 500 kb of the SNPs suggestive for cardiac-related traits , that were probed by the arrays. 354 (60%) of these genes were expressed in the heart. This percentage was significantly higher than the fraction (55%) of all genes expressed in the same samples . Of the 35 testable loci, eight (23%) were replicated at least once (p = 0.0003), as only 5% replication would have been expected by chance.The MPD provides a repository of phenotypic traits measured in numerous mouse cohorts ast once . A binomrs31667929 and rs31572758, that both reached genome-wide significance in the replication analysis but were absent from our set of informative SNPs (data not shown). The other replicated region was locus 16, which was defined by a single marker suggestively associated with the non-corrected QT intervals of ctr mice ; second, a significant fraction  of the testable candidate markers was replicated with independent publicly-available datasets. To discuss these results further, the following points need to be considered.The most significant hits identified in this study consist in three candidate loci related to cardiac and body weight, three loci for electrocardiographic (ECG) parameters, two loci for the susceptibility of atrial weight index to in silico mapping in particular is to discern true from false positives without losing (too many) false negatives. By using trait values of eight to ten biological replicates for each phenotype as well as applying a stringent filter to control for missing alleles, conservative thresholds to control for multiple hypotheses testing, and EMMA to correct for population structure and genetic relatedness of the mouse strains, we greatly reduced the number of spurious positive associations. Quality controls, in particular QQ-plots and careful inspections of the patterns of allelic distributions at the top hits further restricted the number of likely false positives.One of the major challenges of association studies in general and of ate and isoA significant fraction of the loci identified for the 27 phenotypes was not specific of trait values measured under a single drug treatment but often overlapped across several if not all scans produced for the given trait. This was concordant with our previous observation that robust phenotypes (i.e. those relating to body and to a lesser extent cardiac weight) were marginally affected by ate or iso were not available but had to be inferred. This said, it is important to emphasize that measuring pharmacological effects in hundreds of individuals may not be easily implementable for invasive phenotypes such as heart weight. One may argue that the approximations are adding experimental noise to the mapped loci, potentially increasing the number of false positives. However, we note that the vast majority of the hits associated with the effects of iso10 vs ate treatments  were also retrieved when analyzing the reverse ate vs iso10 combinations. Had the computed values been noisy, then these results would have been difficult to replicate. Notwithstanding these limitations, our design produced an attractive list of candidate genes. The identification of at least two members of the hypothalamic-pituitary-thyroid axis as putative modulators of the responsiveness of SBP to perturbations of the β-adrenergic system appears particularly relevant. Indeed it seems rather unlikely that such a result would emerge by pure chance. As mentioned in the corresponding chapter, the underlying markers seem to discriminate between “SBP-responsive” and “SBP non-responsive” strains for the considered ate and iso10 treatments. However, we reported earlier that most of the 22 mouse lines reacted to a ten-fold lower dose of iso (i.e. iso1) by reducing SBP Since the phenotypes were measured in separate cohorts for each drug treatment, individual values of the pharmacological effects of β-blockers, ate undergoes little or no metabolism in the liver and is primarily eliminated unchanged by the kidneys iso is metabolized within minutes into inactive metabolites by the liver catechol-O-methyltransferase (COMT) Comt haplotype, the pharmacokinetics of both ate and iso is expected to be only marginally influenced by genetic variation of metabolizing enzymes. This seems well supported by the absence of genes involved in drug metabolism in our list of candidates. Along similar lines, we note that the 22 strains carry the same Adrb1 haplotype, Adrb1 coding for the main subtype of β-adrenergic receptors in the heart.Unlike other On the principle, it is well acknowledged that computational association mapping in inbred mice provides high-resolution mapping. However, the power of this strategy greatly depends on the size and genetic diversity (recombination) of the chosen strains, the complexity of the trait studied, and the frequency of the underlying variants Mus musculus domesticus origin, with limited contributions from Mus musculus musculus and Mus musculus castaneus subspecies M. m. domesticus origin, while the remaining fraction is composed of an estimated 5.2% M. m. musculus and 0.3% M. m. castaneus haplotypes, respectively. There is also a strong bias toward multiple lines sharing the same M. m. musculus haplotype in some regions High-resolution mapping of the mouse phylogeny recently established that the genomes of classical inbred lines were overwhelmingly of At this stage, it would be unreasonable to expect that the identified loci are the causal variants and thus should be confirmed by genotyping. Indeed, the mouse HapMap (ca 80¢000 SNPs) does not contain all of the genetic variation between the strains, which, according to the latest figures, amounts to ca 4 mio SNPs per classical inbred genome Even in inbred mice, mutations naturally occur at a certain rate. For the reasons mentioned above, we did not perform confirmatory genotyping and thus cannot formally exclude that one or the other marker mapped in the phenotyped animals differs from that reported in the HapMap database. While chances that such events would precisely affect the reported candidates are low, we also reasoned that the amount of genetic differences between a phenotyped animal and the genotyped animal of the same strain is anyway several orders of magnitude smaller than the genetic differences between the strains. Moreover, since many of the reported loci are supported by more than a single SNP, it seems unlikely that multiple mutation events (or genotyping errors) would simultaneously affect multiple consecutive variants in any given strain. For these reasons, we believe that natural mutation events have little impact on our general conclusions.In summary, we have three complementary pieces of evidence in favor of the correctness of our results: first, looking at external data overlapping with our studies, we generally observed consistent behavior. Second, among the candidate genes associated with the cardiac-related traits, there is a significant enrichment for genes expressed in the heart. Third, we have accumulated a number of associations pointing to genes that have previously been implicated in cardiac functioning or whose molecular function makes such a role very plausible. Nevertheless, it is important to emphasize that further independent studies will be needed to replicate and refine the loci we identified, to map the underlying causative genes and variants, and to functionally validate them. Future experiments may include more comprehensive expression profiling across the strains and treatments, as well as properly designed replication studies. To improve statistical power and mapping resolution, one may also consider complementing the panel of inbred strains with well-chosen informative lines such as those of the Hybrid Mouse Diversity Panel β-blockers or β-agonists. A more detailed understanding of these determinants and their relationships will ultimately lead to new or improved strategies for personalized treatment in patients suffering from cardiovascular disorders.Knowing which genes are involved in drug response is a key step to understand the biology of the measured traits and associated diseases. Translating these findings into humans can potentially provide valuable insights into the nature of the genes and pathways that modulate inter-individual response variability to therapies with http://www.vd.ch/fr/autorites/departements/dse/consommation-et-affaires-veterinaires/) and were performed in accordance with the National Institutes of Health (NIH) guidelines for the care and use of laboratory animals.All animal procedures described previously ate and iso, we computed for each trait and treated mouse , the difference  or the fold change  between the value measured in that individual and the average value measured in the ctr mice of the matching strain.The details of the phenotypic dataset were described previously EMMA scans ctr, ate, iso1 and iso10). An exception pertains to the analysis of BWS for which a single scan integrated the values of all mice.For each phenotype, four EMMA scans were generated, each one using trait values measured in a single treatment condition (F-test (REMLt) and conservative thresholds of significance were set using the Bonferroni correction for multiple hypotheses testing. The distributions of the obtained versus expected genome-wide p-values were assessed by QQ-plots. Hits were declared nominally significant for p-values≤10−8 and suggestive for p-values ranging from 10−6 to 10−8. The former corresponds to a strict Bonferroni threshold, as -log10(0.05/(47999*89)) = 7.9, where 47999 is the number of SDPs covered by the 79922 informative SNPs and 89 is the number of GWAS that passed QQ-plot inspection. Since such a stringent correction is likely to miss many true positives, we also reported the results at an intermediate significance range. Its upper bound of 10−6 was motivated by estimating the number of truly independent tests. To this end we used the number of eigenvectors required to explain 99.5% of the variation of the data p-value , we inferred the number of independent tests per phenotype to be its inverse (i.e. 2035). The effective total number of tests is thus equal to 16×2035, while the corresponding threshold is -log10(0.05/(16×2035)) = 5.8.The significance of the associations was evaluated with an Whenever possible, the associations obtained as significant at positions with incomplete SDPs were manually imputed and corrected, using information of a compatible nearby variant with a fully known allele set.s, measured phenotype mp and approximated phenotype ap, The percentage of phenotypic variance explained at each locus was calculated with EMMA. Approximated values of the effects of the treatments on 25 of the 27 traits were analyzed using the same settings and filters, with the added condition that for each strain + RNA purification, cDNA libraries preparation, labeling and hybridization onto Affymetrix Mouse Gene 1.0 ST arrays, as well as washing and scanning of the arrays were performed at the Lausanne Genomics Technologies Facility (LGTF) following standard protocols. Each sample was analyzed on a separate array. All arrays passed standard quality controls. Statistical analyses were performed using the statistical language R (http://www.R-project.org) and the Limma Bioconductor package (http://www.Bioconductor.org). Log2 normalized expression signals were calculated from Affymetrix CEL files using the RMA algorithm.The 30 to 40% most apical fractions of the cardiac ventricles were excised from C57BL/6J and KK/HlJ mouse hearts (n = 4 per strain), snap-frozen in liquid nitrogen, and stored at −70°C. Tissues were homogenized with a Tissue Lyzer system in the presence of metal beads (Qiagen). Total RNA was extracted using the mirVana isolation kit of Ambion. RNA quality was confirmed on a Bioanalyzer system (Agilent Technologies). Poly-ATo compute the set of expressed genes, the mixture of two Gaussian functions was fitted to the log expression values, which was bimodal . The firFigure S1Manhattan and QQ-plots for 27 traits measured in ctr mice. QQ-plot-based quality control is indicated as “passed” or “failed”.(PDF)Click here for additional data file.Figure S2Manhattan and QQ-plots for 26 traits measured in ate-treated mice. QQ-plot-based quality control is indicated as “passed” or “failed”.(PDF)Click here for additional data file.Figure S3Manhattan and QQ-plots for 26 traits measured in iso1-treated mice. QQ-plot-based quality control is indicated as “passed” or “failed”.(PDF)Click here for additional data file.Figure S4Manhattan and QQ-plots for 26 traits measured in iso10-treated mice. QQ-plot-based quality control is indicated as “passed” or “failed”.(PDF)Click here for additional data file.Figure S5Manhattan and QQ-plots for the effects of iso10 treatment on 25 traits. QQ-plot-based quality control is indicated as “passed” or failed”. Phenotypes for which any of the differences between an individual trait value and its matching mean strain value exceeded 3 SD are labelled as “var test failed”.(PDF)Click here for additional data file.Figure S6Manhattan and QQ-plots for the effects of ate treatment on 25 traits. QQ-plot-based quality control is indicated as “passed” or failed”. Phenotypes for which any of the differences between an individual trait value and its matching mean strain value exceeded 3 SD are labelled as “var test failed”.(PDF)Click here for additional data file.Figure S7Manhattan and QQ-plots for the effects of iso10 vs ate treatments on 25 traits. QQ-plot-based quality control is indicated as “passed” or failed”. Phenotypes for which any of the differences between an individual trait value and its matching mean strain value exceeded 3 SD are labelled as “var test failed”.(PDF)Click here for additional data file.Figure S8Manhattan and QQ-plots for the effects of ate vs iso10 treatments on 25 traits. QQ-plot-based quality control is indicated as “passed” or failed”. Phenotypes for which any of the differences between an individual trait value and its matching mean strain value exceeded 3 SD are labelled as “var test failed”.(PDF)Click here for additional data file.Figure S9Distribution of cardiac mRNA expression levels as measured by microarrays. The mixture of two Gaussian functions was fitted to the log expression values. The first component, peaking at around 2, fits the non-expressed genes, whereas the second component, peaking at around 6.5, fits the expressed genes. Non-expressed genes are those which belong to the first component with at least 95% confidence, i.e., those genes that have a log expression value less than 2.33 (red line).(PDF)Click here for additional data file.Figure S10Boxplots showing the SBP values of 22 strains treated with ate (in red) and iso10 (in blue). Strains are segregated according to their genotype at marker rs29354390 in Trhrde (locus AE6).(PDF)Click here for additional data file.Figure S11Analysis flowchart. This flowchart summarizes the analysis of the 27 phenotypes measured across the 22 inbred strains. Details referring to the boxes shaded in grey have been published previously http://phenome.jax.org/). Analyses of the drug responses were performed following a similar scheme, except that the values were not physically measured in individual mice but approximated, as detailed in the main text.(PDF)Click here for additional data file.Table S1Phenotypes and abbreviations used in this study.(XLS)Click here for additional data file.Table S2Heritabilities (2h) for 27 traits and trait responsiveness to ate and iso, as computed by EMMA. The values of the genetic variance components for the various traits and responses were computed by fitting EMMA without any SNP but just using the kinship matrix iso10 or iso10 vs ate were ca 10 to15% lower than those of the measured traits, indicating that a significant fraction of trait responsiveness to these treatments was genetically determined. In contrast, the values for the effects of ate were lower , consistent with our previous observation that the phenotypic changes induced by β-blockade were moderate for most phenotypes (XLS)Click here for additional data file.Table S3Suggestive and significant hits mapped for the 27 traits. For each SNP, the association scores are given as the negative logarithm of the p-values calculated in EMMA, across the 105 genome-wide scans (columns K to DK). The data are ranked by position, from chr 1 to chr 17 (rows 5 to 391).(XLS)Click here for additional data file.Table S4Suggestive and significant hits mapped for the effects of the treatments. For each SNP, the association scores are given as the negative logarithm of the p-values calculated in EMMA across 201 genome-wide scans (columns L to HD). The data are ranked by position, from chr 1 to chr 19 (rows 4 to 217).(XLS)Click here for additional data file.Table S52 normalized cardiac expression values of the main candidate genes.Log Transcripts were measured by microarrays in the cardiac ventricles of untreated C57BL/6J and KK/HlJ mice (n = 4 per strain). Values were calculated from Affymetrix CEL files using the RMA algorithm. Data are listed by probeset and are ranked by locus.(XLS)Click here for additional data file.


GIS) as quantitative trait. We compared QTLs amongst premenopausal, ovariectomized and postmenopausal F2[Dahl S x R]-intercross rats using identical phenotype characterization. Postmenopausal F2[Dahl S x R]-intercross rats exhibited increased hypertensive glomerulosclerosis (P<0.01) and equivalent levels of kidney disease when compared to premenopausal and ovariectomized F2[Dahl S x R]-intercross rats respectively. We detected three significant to highly significant GIS-QTLs  and two suggestive GIS-QTLs , all of which were unique to this postmenopausal population. Detection of increased renal disease phenotype in postmenopausal and ovariectomized subjects suggests a protective role of ovarian hormones. Furthermore, the detection of distinct GIS-QTLs in postmenopausal intercross female rats suggests that distinct genetic mechanisms underlie hypertensive glomerulosclerosis in premenopausal and postmenopausal states.The prevalence of hypertension increases after menopause with 75% of postmenopausal women developing hypertension in the United States, along with hypertensive end organ diseases. While human and animal model studies have indicated a protective role for estrogen against cardiovascular disease and glomerulosclerosis, clinical studies of hormone replacement therapy in postmenopausal women have shown polar results with some improvement in hypertension but worsening of hypertensive kidney disease, or no effect at all. These observations suggest that the pathogenesis of postmenopausal hypertension and its target organ complications is more complex than projected, and that loss of endogenous estrogens induces epigenetic changes that alter genetic susceptibility to end-organ complications per se resulting in pathogenetic mechanisms beyond correction by hormone replacement. We studied postmenopausal-induced changes in renal disease and performed a total genome scan for quantitative trait loci (QTLs) affecting kidney disease in postmenopausal 16m-old F2[Dahl S x R]-intercross female rats. We used glomerular injury score ( In general, hypertensive kidney disease is less common in women compared to men Given that some patients have worsening of kidney function with hormone replacement therapy In order to investigate potential differences in genetic mechanisms underlying premenopausal and postmenopausal susceptibility to hypertensive renal disease, we performed a total genome scan for QTLs affecting glomerular injury score (GIS) as a quantitative measure of hypertensive nephrosclerosis To determine whether worse hypertension observed in postmenopausal rats In order to demonstrate the robust spectrum of the hypertensive kidney disease phenotype measured by GIS, histological photomicrographs images are presented representing the tail-ends of the spectrum of GIS scores . HistoloGIS-pm1  gene is located at Chr4-84.2 Mbp. For GIS-pm2 QTL peak on chromosome 3 Avp (arginine vasopressin) gene localizes to Chr3-118.2 Mbp and for GIS-pm3 the CYP4A gene cluster (Chr5-135.5 Mbp) and Edn2 (endothelin 2) at chr5-140.7 Mbp are possible candidates. Interestingly, Adcyap1r1  and its ligand (PACAP) are present in kidney, mainly on tubular epithelial cells GIS-pm2 is a potential candidate based on its well known relationship to chronic kidney disease GIS-pm3 on hypertensive nephrosclerosis as represented in GIS measures. Candidate gene analysis encompassing ±15 Mbp from the peaks of GIS QTLs detected with significant and highly significant linkage revealed several candidate genes. For In conclusion, our study demonstrates the involvement of distinct genetic mechanisms in premenopausal and postmenopausal hypertensive renal disease, and confirms the paradigm that differential genetic mechanisms underlie hypertension and its different target-organ complications in both premenopausal and postmenopausal states. These observations imply that treating hypertension resulting in the lowering of blood pressure does not necessarily address hypertensive target organ complications since the latter have distinct genetic mechanisms as demonstrated by different QTLs. The elucidation of distinct genetic mechanisms in premenopausal and postmenopausal hypertension as well as in hypertensive end organ diseases establishes a primary etiological framework for novel prevention and intervention strategies that needs to be studied further.Altogether, these data mandate that the study of hypertension genes requires stage specific analyses for female hypertensives. Given that current anti-hypertensive therapies and prevention strategies have not eradicated hypertension nor adequately addressed postmenopausal hypertension, studies of postmenopausal hypertension and end-organ diseases need to be prioritized, if not mandatory, especially since hypertension remains a major risk factor for heart disease, kidney disease and stroke, which combine to be the number-one causes of mortality and morbidity in females.This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The protocol was approved by the Committee on the Ethics of Animal Experiments of Boston University School of Medicine (Permit Number: AN-14966).Inbred Dahl S/jrHsd and Dahl R/jrHsd rats were obtained from Harlan . Parental strains  were crossed to produce F1 progeny. The F2 subjects were derived from brother-to-sister mating of F1 hybrids to produce the F2 female (n = 130) segregating population.In order to ascertain postmenopausal status of the female F2 hybrids daily vaginal smears were performed on females to determine the stage of their estrous cycle  essentially as described Female subjects were maintained on a low salt (0.008 % NaCl) diet until high salt (8 % NaCl) challenge began at 14 months of age to avoid raises in blood pressure prior the high salt challenge. Females were fed a high salt (8 % NaCl) diet for 8 weeks. At 16 months of age animals were sacrificed and kidneys were immediately removed, fixed in 4% paraformaldehyde, processed for histology and renal pathology quantified as described P value <0.01 for the interaction effect.Distributions were analyzed for normality; data transformations were done and datasets that passed Kolmogorov-Smirnov normality testing (SigmaStat) were used for linkage analysis. QTL analysis was performed using glomerular injury score (GIS) defined as [% grade III+% grade IV] as quantitative trait. In our postmenopausal female F2 intercross population, previously characterized for blood pressure SNP genotyping was carried out on an Applied Biosystems 7900 Real-Time PCR System. SNPs (n = 97) and SSLP markers (n = 3) were selected from the RGD SNP database. SNP assays (TaqMan assays) were procured from Applied Biosystems and were validated in our laboratory.Analysis of phenotypes in F2 female cohorts was done by one-way ANOVA followed by all pairwise multiple comparisons using Holm-Sidak test. The statistical analysis was performed using the software SigmaPlot for Windows Version 11.0, Build 11.2.0.5 .