Explore the vast range of titles available.

Matthew Nelson and colleagues investigate how well genetic evidence for disease susceptibility predicts drug mechanisms. They find a correlation between gene products that are successful drug targets and genetic loci associated with the disease treated by the drug and predict that selecting genetically supported targets could increase the success rate of drugs in clinical development. Over a quarter of drugs that enter clinical development fail because they are ineffective. Growing insight into genes that influence human disease may affect how drug targets and indications are selected. However, there is little guidance about how much weight should be given to genetic evidence in making these key decisions. To answer this question, we investigated how well the current archive of genetic evidence predicts drug mechanisms. We found that, among well-studied indications, the proportion of drug mechanisms with direct genetic support increases significantly across the drug development pipeline, from 2.0% at the preclinical stage to 8.2% among mechanisms for approved drugs, and varies dramatically among disease areas. We estimate that selecting genetically supported targets could double the success rate in clinical development. Therefore, using the growing wealth of human genetic data to select the best targets and indications should have a measurable impact on the successful development of new drugs.

Gene-based tests such as versatile gene-based association study (VEGAS) are commonly used following per-single nucleotide polymorphism (SNP) GWAS (genome-wide association studies) analysis. Two limitations of VEGAS were that the HapMap2 reference set was used to model the correlation between SNPs and only autosomal genes were considered. HapMap2 has now been superseded by the 1,000 Genomes reference set, and whereas early GWASs frequently ignored the X chromosome, it is now commonly included. Here we have developed VEGAS2, an extension that uses 1,000 Genomes data to model SNP correlations across the autosomes and chromosome X. VEGAS2 allows greater flexibility when defining gene boundaries. VEGAS2 offers both a user-friendly, web-based front end and a command line Linux version. The online version of VEGAS2 can be accessed through https://vegas2.qimrberghofer.edu.au/. The command line version can be downloaded from https://vegas2.qimrberghofer.edu.au/zVEGAS2offline.tgz. The command line version is developed in Perl, R and shell scripting languages; source code is available for further development.

We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms ( N eff  = 354,862), neuroticism ( N  = 168,105), and subjective well-being ( N  = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations. MTAG is a new method for joint analysis of summary statistics from genome-wide association studies of different traits. Applying MTAG to summary statistics for depressive symptoms, neuroticism and subjective well-being increased discovery of associated loci as compared to single-trait analyses.

The numerical analysis is accomplished to simulate the heat transfer in double tube heat exchanger with and without porous media by utilizing ANSYS FLUENT 14.1. A geometry system is used to predict the heat transfers and pressure drop characteristics of the flow. Alumina (2.5mm diameter) as a porous media were added in to: inner tube (IP), outer tube (OP) and both tube of heat exchanger (IOP) were tested with the variation of hot and cold fluid inlet temperature, mass flow rate ratio (mr) to evaluate their influence on effectiveness of heat exchanger, Nusselt number, number of heat transfer unit. The evaluating has been performed in the steady-state. Water was used as a working fluid in a double tube heat exchanger. The study was conducted at the hot and cold water mass flow rates between ( 0.0166 - 0.0833kg / s), ( 0.05-0.116 kg/s) respectively. The inlet temperatures of cold and hot water were (20, 25 °C), (47, 55 °C) respectively. Noted from results that adding pad of porous increases effectiveness of the heat exchanger and the increasing rate of transferring heat as mass flow rate ratio increases and the highest value are obtained when alumina is in double pipe. Effectiveness decreases with increase in mass flow rate ratio but increases when using alumina as a porous media and the better value is obtained when alumina is in IOP, IP, OP and NP respectively. Effectiveness increased as the NTU increases.

Estimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from a reference panel of 11 million SNPs, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities (as a fraction of the reference panel) ranging from ≃ 2 × 10-5 to ≃ 4 × 10-3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.

Hugh Watkins and colleagues meta-analyze data from the UK Biobank along with recent genome-wide association studies for coronary artery disease. They identify 13 new loci that were genome-wide significant and 243 loci at a 5% false discovery rate. Genome-wide association studies (GWAS) in coronary artery disease (CAD) had identified 66 loci at 'genome-wide significance' ( P < 5 × 10 −8 ) at the time of this analysis, but a much larger number of putative loci at a false discovery rate (FDR) of 5% (refs. 1 , 2 , 3 , 4 ). Here we leverage an interim release of UK Biobank (UKBB) data to evaluate the validity of the FDR approach. We tested a CAD phenotype inclusive of angina (SOFT; n cases = 10,801) as well as a stricter definition without angina (HARD; n cases = 6,482) and selected cases with the former phenotype to conduct a meta-analysis using the two most recent CAD GWAS 2 , 3 . This approach identified 13 new loci at genome-wide significance, 12 of which were on our previous list of loci meeting the 5% FDR threshold 2 , thus providing strong support that the remaining loci identified by FDR represent genuine signals. The 304 independent variants associated at 5% FDR in this study explain 21.2% of CAD heritability and identify 243 loci that implicate pathways in blood vessel morphogenesis as well as lipid metabolism, nitric oxide signaling and inflammation.

Brendan Bulik-Sullivan, Benjamin Neale, Hilary Finucane, Alkes Price and colleagues introduce a new technique for estimating genetic correlation that requires only genome-wide association summary statistics and that is not biased by sample overlap. Using this method, they find genetic correlations between anorexia nervosa and schizophrenia, and between educational attainment and autism spectrum disorder. Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual-level genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique—cross-trait LD Score regression—for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and educational attainment and several diseases. These results highlight the power of genome-wide analyses, as there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.

Natural convection in cavities is an interesting subject for many researchers. Especially, in recent years, the number of articles written in this regard has grown enormously. This work provides a review of recent natural convection studies. At first, experimental studies were reviewed and, then, numerical studies were examined. Then, the articles were classified based on effective parameters. In each section, numerical studies were examined the parameters added to the cavity such as magnetic forces, fin, porous media and cavity angles. Moreover, studies on non-rectangular cavities were investigated. Free convection in enclosures depends more on the fluid velocity relative to the forced convection, leading to the opposite effect of some parameters that should essentially enhance rate of heat transfer. Nanoparticle addition, magnetic fields, fins, and porous media may increase forced convection. However, they can reduce free convection due to the reduction in fluid velocity. Thus, these parameters need more precision and sometimes need the optimization of effective parameters.

An investigation is conducted to study analytically and numerically the effect of a magnetic field on the species separation induced by the combined effects of convection and Soret phenomenon in an inclined porous cavity saturated by an electrically conductive binary mixture and provided with four impermeable walls. The long sides of the cavity are subject to uniform heat flux while its short ends are adiabatic. Uniform magnetic field is applied perpendicularly to the heated walls. The mixture satisfies the Boussinesq approximation and the porous medium, modeled according to Darcy-Brinkman’s law, is assumed homogeneous and isotropic .The relevant parameters for the problem are the thermal Rayleigh number (RT = 1 to 106), the Lewis number (Le = 10), the inclination angle of the cavity (θ = 0º to 180), the separation parameter (φ = 0.5), the Darcy number (Da = 10-5 to 103), the Hartmann number (Ha = 0 to 100) and the aspect ratio of the cavity (Ar = 12). The limiting cases (Darcy and pure fluid media) are recovered in this study. Optimum conditions leading to maximum separation of species are determined while varying the governing parameters in their respective ranges. Results show that the magnetic field can enhance the species separation in cases where the optimal coupling between thermosolutal diffusion and convection is not achieved in its absence. On the other hand, in cases where this optimal coupling is reached in the absence of the magnetic field, the application of the latter destroys the separation of species.

Expression quantitative trait loci (eQTL) studies are used to understand the regulatory function of non-coding genome-wide association study (GWAS) risk loci, but colocalization alone does not demonstrate a causal relationship of gene expression affecting a trait. Evidence for mediation, that perturbation of gene expression in a given tissue or developmental context will induce a change in the downstream GWAS trait, can be provided by two-sample Mendelian Randomization (MR). Here, we introduce a new statistical method, MRLocus, for Bayesian estimation of the gene-to-trait effect from eQTL and GWAS summary data for loci with evidence of allelic heterogeneity, that is, containing multiple causal variants. MRLocus makes use of a colocalization step applied to each nearly-LD-independent eQTL, followed by an MR analysis step across eQTLs. Additionally, our method involves estimation of the extent of allelic heterogeneity through a dispersion parameter, indicating variable mediation effects from each individual eQTL on the downstream trait. Our method is evaluated against other state-of-the-art methods for estimation of the gene-to-trait mediation effect, using an existing simulation framework. In simulation, MRLocus often has the highest accuracy among competing methods, and in each case provides more accurate estimation of uncertainty as assessed through interval coverage. MRLocus is then applied to five candidate causal genes for mediation of particular GWAS traits, where gene-to-trait effects are concordant with those previously reported. We find that MRLocus’s estimation of the causal effect across eQTLs within a locus provides useful information for determining how perturbation of gene expression or individual regulatory elements will affect downstream traits. The MRLocus method is implemented as an R package available at https://mikelove.github.io/mrlocus .