EasyStrata: evaluation and visualization of stratified genome-wide association meta-analysis data

Summary: The R package EasyStrata facilitates the evaluation and visualization of stratified genome-wide association meta-analyses (GWAMAs) results. It provides (i) statistical methods to test and account for between-strata difference as a means to tackle gene-strata interaction effects and (ii) extended graphical features tailored for stratified GWAMA results. The software provides further features also suitable for general GWAMAs including functions to annotate, exclude or highlight specific loci in plots or to extract independent subsets of loci from genome-wide datasets. It is freely available and includes a user-friendly scripting interface that simplifies data handling and allows for combining statistical and graphical functions in a flexible fashion. Availability: EasyStrata is available for free (under the GNU General Public License v3) from our Web site www.genepi-regensburg.de/easystrata and from the CRAN R package repository cran.r-project.org/web/packages/EasyStrata/. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Genetic Risk Score Analysis Supports a Joint View of Two Classification Systems for Age-Related Macular Degeneration

Purpose: The purpose of this study was to evaluate the utility of combining the Clinical Classification (CC) and the Three Continent age-related macular degeneration (AMD) Consortium Severity Scale (3CACSS) for classification of AMD. Methods: In two independent cross-sectional datasets of our population-based AugUR study (Altersbezogene Untersuchungen zur Gesundheit der Universität Regensburg), we graded AMD via color fundus images applying two established classification systems (CC and 3CACSS). We calculated the genetic risk score (GRS) across 50 previously identified variants for late AMD, its association via logistic regression, and area under the curve (AUC) for each AMD stage. Results: We analyzed 2188 persons aged 70 to 95 years. When comparing the two classification systems, we found a distinct pattern: CC “age-related changes” and CC “early AMD” distinguished individuals with 3CACSS “no AMD”; 3CACSS “mild/moderate/severe early AMD” stages, and distinguished CC “intermediate AMD”. This suggested a 7-step scale combining the 2 systems: (i) “no AMD”, (ii) “age-related changes”, (iii) “very early AMD”, (i.e. CC “early”), (iv) “mild early AMD”, (v) “moderate early AMD”, (vi) “severe early AMD”, and (vii) “late AMD”. GRS association and diagnostic accuracy increased stepwise by increased AMD severity in the 7-step scale and by increased restriction of controls (e.g. for CC “no AMD without age-related changes”: AUC = 55.1%, 95% confidence interval [CI] = 51.6, 58.6, AUC = 62.3%, 95% CI = 59.1, 65.6, AUC = 63.8%, 95% CI = 59.3, 68.3, AUC = 78.1%, 95% CI = 73.6, 82.5, AUC = 82.2%, 95% CI = 78.4, 86.0, and AUC = 79.2%, 95% CI = 75.4, 83.0). A stepwise increase was also observed by increased drusen size and area. Conclusions: The utility of a 7-step scale is supported by our clinical and GRS data. This harmonization and full data integration provides an immediate simplification over using either CC or 3CACSS and helps to sharpen the control group

KidneyGPS: a user-friendly web application to help prioritize kidney function genes and variants based on evidence from genome-wide association studies

Background Genome-wide association studies (GWAS) have identified hundreds of genetic loci associated with kidney function. By combining these findings with post-GWAS information (e.g., statistical fine-mapping to identify independent association signals and to narrow down signals to causal variants; or different sources of annotation data), new hypotheses regarding physiology and disease aetiology can be obtained. These hypotheses need to be tested in laboratory experiments, for example, to identify new therapeutic targets. For this purpose, the evidence obtained from GWAS and post-GWAS analyses must be processed and presented in a way that they are easily accessible to kidney researchers without specific GWAS expertise. Main Here we present KidneyGPS, a user-friendly web-application that combines genetic variant association for estimated glomerular filtration rate (eGFR) from the Chronic Kidney Disease Genetics consortium with annotation of (i) genetic variants with functional or regulatory effects (“SNP-to-gene” mapping), (ii) genes with kidney phenotypes in mice or human (“gene-to-phenotype”), and (iii) drugability of genes (to support re-purposing). KidneyGPS adopts a comprehensive approach summarizing evidence for all 5906 genes in the 424 GWAS loci for eGFR identified previously and the 35,885 variants in the 99% credible sets of 594 independent signals. KidneyGPS enables user-friendly access to the abundance of information by search functions for genes, variants, and regions. KidneyGPS also provides a function (“GPS tab”) to generate lists of genes with specific characteristics thus enabling customizable Gene Prioritisation (GPS). These specific characteristics can be as broad as any gene in the 424 loci with a known kidney phenotype in mice or human; or they can be highly focussed on genes mapping to genetic variants or signals with particularly with high statistical support. KidneyGPS is implemented with RShiny in a modularized fashion to facilitate update of input data (https://kidneygps.ur.de/gps/). Conclusion With the focus on kidney function related evidence, KidneyGPS fills a gap between large general platforms for accessing GWAS and post-GWAS results and the specific needs of the kidney research community. This makes KidneyGPS an important platform for kidney researchers to help translate in silico research results into in vitro or in vivo research

KidneyGPS: a user-friendly web application to help prioritize kidney function genes and variants based on evidence from genome-wide association studies

Background Genome-wide association studies (GWAS) have identified hundreds of genetic loci associated with kidney function. By combining these findings with post-GWAS information (e.g., statistical fine-mapping to identify independent association signals and to narrow down signals to causal variants; or different sources of annotation data), new hypotheses regarding physiology and disease aetiology can be obtained. These hypotheses need to be tested in laboratory experiments, for example, to identify new therapeutic targets. For this purpose, the evidence obtained from GWAS and post-GWAS analyses must be processed and presented in a way that they are easily accessible to kidney researchers without specific GWAS expertise. Main Here we present KidneyGPS, a user-friendly web-application that combines genetic variant association for estimated glomerular filtration rate (eGFR) from the Chronic Kidney Disease Genetics consortium with annotation of (i) genetic variants with functional or regulatory effects (“SNP-to-gene” mapping), (ii) genes with kidney phenotypes in mice or human (“gene-to-phenotype”), and (iii) drugability of genes (to support re-purposing). KidneyGPS adopts a comprehensive approach summarizing evidence for all 5906 genes in the 424 GWAS loci for eGFR identified previously and the 35,885 variants in the 99% credible sets of 594 independent signals. KidneyGPS enables user-friendly access to the abundance of information by search functions for genes, variants, and regions. KidneyGPS also provides a function (“GPS tab”) to generate lists of genes with specific characteristics thus enabling customizable Gene Prioritisation (GPS). These specific characteristics can be as broad as any gene in the 424 loci with a known kidney phenotype in mice or human; or they can be highly focussed on genes mapping to genetic variants or signals with particularly with high statistical support. KidneyGPS is implemented with RShiny in a modularized fashion to facilitate update of input data (https://kidneygps.ur.de/gps/). Conclusion With the focus on kidney function related evidence, KidneyGPS fills a gap between large general platforms for accessing GWAS and post-GWAS results and the specific needs of the kidney research community. This makes KidneyGPS an important platform for kidney researchers to help translate in silico research results into in vitro or in vivo researc

Aerobic Training in Rats Increases Skeletal Muscle Sphingomyelinase and Serine Palmitoyltransferase Activity, While Decreasing Ceramidase Activity

Sphingolipids are important components of cell membranes that may also serve as cell signaling molecules; ceramide plays a central role in sphingolipid metabolism. The aim of this study was to examine the effect of 5 weeks of aerobic training on key enzymes and intermediates of ceramide metabolism in skeletal muscles. The experiments were carried out on rats divided into two groups: (1) sedentary and (2) trained for 5 weeks (on a treadmill). The activity of serine palmitoyltransferase (SPT), neutral and acid sphingomyelinase (nSMase and aSMase), neutral and alkaline ceramidases (nCDase and alCDase) and the content of sphingolipids was determined in three types of skeletal muscle. We also measured the fasting plasma insulin and glucose concentration for calculating HOMA-IR (homeostasis model assessment) for estimating insulin resistance. We found that the activities of aSMase and SPT increase in muscle in the trained group. These changes were followed by elevation in the content of sphinganine. The activities of both isoforms of ceramidase were reduced in muscle in the trained group. Although the activities of SPT and SMases increased and the activity of CDases decreased, the ceramide content did not change in any of the studied muscle. Although ceramide level did not change, we noticed increased insulin sensitivity in trained animals. It is concluded that training affects the activity of key enzymes of ceramide metabolism but also activates other metabolic pathways which affect ceramide metabolism in skeletal muscles

Investigating the modulation of genetic effects on late AMD by age and sex: Lessons learned and two additional loci

Late-stage age-related macular degeneration (AMD) is the leading cause of visual impairment in the elderly with a complex etiology.The most important non-modifiable risk factors for onset and progression of late AMD are age and genetic risk factors, however, little is known about the interplay between genetics and age or sex. Here, we conducted a large-scale age-and sex-stratified genome-wide association study (GWAS) using 1000 Genomes imputed genome-wide and ExomeChip data (>12 million variants). The data were established by the International Age-related Macular Degeneration Genomics Consortium (IAMDGC) from 16,144 late AMD cases and 17,832 controls. Our systematic search for interaction effects yielded significantly stronger effects among younger individuals at two known AMD loci (near CFH and ARMS2/HTRA1). Accounting for age and gene-age interaction using a joint test identified two additional AMD loci compared to the previous main effect scan. One of these two is a novel AMD GWAS locus, near the retinal clusterin-like protein (CLUL1) gene, and the other, near the retinaldehyde binding protein 1 (RLBP1), was recently identified in a joint analysis of nuclear and mitochondrial variants. Despite considerable power in our data, neither sex-dependent effects nor effects with opposite directions between younger and older individuals were observed. This is the first genome-wide interaction study to incorporate age, sex and their interaction with genetic effects for late AMD. Results diminish the potential for a role of sex in the etiology of late AMD yet highlight the importance and existence of age-dependent genetic effects

Polygenic scores for estimated glomerular filtration rate in a population of general adults and elderly – comparative results from the KORA and AugUR study

Background Polygenic scores (PGSs) combining genetic variants found to be associated with creatinine-based estimated glomerular filtration rate (eGFRcrea) have been applied in various study populations with different age ranges. This has shown that PGS explain less eGFRcrea variance in the elderly. Our aim was to understand how differences in eGFR variance and the percentage explained by PGS varies between population of general adults and elderly. Results We derived a PGS for cystatin-based eGFR (eGFRcys) from published genome-wide association studies. We used the 634 variants known for eGFRcrea and the 204 variants identified for eGFRcys to calculate the PGS in two comparable studies capturing a general adult and an elderly population, KORA S4 (n = 2,900; age 24–69 years) and AugUR (n = 2,272, age ≥ 70 years). To identify potential factors determining age-dependent differences on the PGS-explained variance, we evaluated the PGS variance, the eGFR variance, and the beta estimates of PGS association on eGFR. Specifically, we compared frequencies of eGFR-lowering alleles between general adult and elderly individuals and analyzed the influence of comorbidities and medication intake. The PGS for eGFRcrea explained almost twice as much (R2 = 9.6%) of age-/sex adjusted eGFR variance in the general adults compared to the elderly (4.6%). This difference was less pronounced for the PGS for eGFRcys (4.7% or 3.6%, respectively). The beta-estimate of the PGS on eGFRcrea was higher in the general adults compared to the elderly, but similar for the PGS on eGFRcys. The eGFR variance in the elderly was reduced by accounting for comorbidities and medication intake, but this did not explain the difference in R2-values. Allele frequencies between general adult and elderly individuals showed no significant differences except for one variant near APOE (rs429358). We found no enrichment of eGFR-protective alleles in the elderly compared to general adults. Conclusions We concluded that the difference in explained variance by PGS was due to the higher age- and sex-adjusted eGFR variance in the elderly and, for eGFRcrea, also by a lower PGS association beta-estimate. Our results provide little evidence for survival or selection bias

Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution.

Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF ≥5%) and nine low-frequency or rare (MAF <5%) coding novel variants. Pathway/gene set enrichment analyses identified lipid particle, adiponectin, abnormal white adipose tissue physiology and bone development and morphology as important contributors to fat distribution, while cross-trait associations highlight cardiometabolic traits. In functional follow-up analyses, specifically in Drosophila RNAi-knockdowns, we observed a significant increase in the total body triglyceride levels for two genes (DNAH10 and PLXND1). We implicate novel genes in fat distribution, stressing the importance of interrogating low-frequency and protein-coding variants

Measurement of the Z/gamma* + b-jet cross section in pp collisions at 7 TeV

The production of b jets in association with a Z/gamma* boson is studied using proton-proton collisions delivered by the LHC at a centre-of-mass energy of 7 TeV and recorded by the CMS detector. The inclusive cross section for Z/gamma* + b-jet production is measured in a sample corresponding to an integrated luminosity of 2.2 inverse femtobarns. The Z/gamma* + b-jet cross section with Z/gamma* to ll (where ll = ee or mu mu) for events with the invariant mass 60 < M(ll) < 120 GeV, at least one b jet at the hadron level with pT > 25 GeV and abs(eta) < 2.1, and a separation between the leptons and the jets of Delta R > 0.5 is found to be 5.84 +/- 0.08 (stat.) +/- 0.72 (syst.) +(0.25)/-(0.55) (theory) pb. The kinematic properties of the events are also studied and found to be in agreement with the predictions made by the MadGraph event generator with the parton shower and the hadronisation performed by PYTHIA.Comment: Submitted to the Journal of High Energy Physic