Enhancing the detection of complex disease loci by new approaches to imputation

Genotype imputation infers variants, which are not directly assayed in study subjects, by matching inferred study haplotypes with those in external reference panels. Inferred variants are subsequently used to identify genetic loci associated with complex disease. Technological advances in genotyping and sequencing technologies have created a novel generation of high density reference panels, which enable to shed additional light on the genetic architecture of complex traits. But the gain in using these novel high density reference panels for genotype imputation and association analysis is still missing. Thus this work focused on identifying the gain in analyzing variants imputed with high density reference panels compared to analyzing variants imputed with low density reference panels in large scale genome wide data. I showed in my work how high density reference panels increase our knowledge of the genetic maps on kidney function and AMD. I further developed a tool to assist study analysts for imputing genome wide data for meta-analyses in consortia and I optimized the imputation of untyped variants in individual participant data of large scale. First, I compared a meta-analysis of variants imputed with HapMap reference panels with variants imputed with 1000 Genomes reference panels in data from the CKDGen consortium. The comparison of imputation qualities evidenced the overall superiority of the imputation with the 1000 Genomes reference panels and illustrates the increased possibility to detect rare variants associated with complex disease. The meta-analysis on kidney filtration rate of the variants imputed with the 1000 Genomes reference panel confirm the majority of previously reported susceptibly loci for kidney function and furthermore allow the identification of 10 additional loci. Second, I quantified the gain in mega-imputing and mega-analyzing individual participant data compared to meta-imputing the same data per study and meta-analyzing study specific effect estimates. For this analysis I used one of the world’s largest individual participant data set from the IAMDGC. I illustrated that the imputation quality of untyped variants imputed jointly across all studies is superior to the imputation quality of variants imputed separated by study and showed that there is a gain of mega-analyzing imputed variants compared to meta-analyzing the same imputed variants. This gain is even bigger, when mega-analyzing variants imputed jointly is compared to mimicking a realistic scenario in consortia of meta-analyzing variants imputed per study. Third, I facilitate the computational demanding task of genotype imputation with the software PhaseLift, which harmonizes phased study haplotype with any reference panel on any build. This enables study analysts save time in re-imputing study data. Study analysts perform the computational intensive phase estimation once and re-impute the study haplotypes with any novel reference panel on any novel genomic build, without repeating the tedious phase estimation. In optimizing the mega-imputation of large scale genome wide variants across several studies and identifying parameter and constraints for genotype imputation, I assist study analysts to overcome the computational demanding task of imputing large genome wide data. In summary, genome wide association analyses on variants imputed with high density reference panels further chart the genetic map of complex traits, which will ultimately lead to an increased understanding of the biological mechanisms in the health and disease and to improve diagnosis, treatments and prevention of complex disease for patients

Recuperação do turismo pós-pandemia : uma proposta de intervenção para a agência de viagens Mister Gorski Turismo

Orientadora : Luísa BarwinskiMonografia (especialização) – Universidade Federal do Paraná, Setor de Ciências Sociais Aplicadas. Curso de Especialização MBA em MarketingInclui referênciasResumo: A pandemia de Covid-19 no ano de 2020 teve um forte impacto no mercado de turismo. O fechamento de fronteiras de alguns países e medidas de isolamento social acabaram impedindo a realização de atividades turísticas. Com isso, empresas do segmento perderam vendas e tiveram muitos cancelamentos. O presente projeto tem como objetivo desenvolver uma proposta de melhoria para a estratégia de comunicação da Mister Gorski Turismo, uma agência de viagens que teve seus resultados afetados pela epidemia do novo coronavírus. O plano de ação possui uma nova estratégia de marketing digital e propostas de implementação de marketing inbound para a retomada das atividades da empresa no cenário póspandemia

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

1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function

HapMap imputed genome-wide association studies (GWAS) have revealed > 50 loci at which common variants with minor allele frequency > 5% are associated with kidney function. GWAS using more complete reference sets for imputation, such as those from The 1000 Genomes project, promise to identify novel loci that have been missed by previous efforts. To investigate the value of such a more complete variant catalog, we conducted a GWAS meta-analysis of kidney function based on the estimated glomerular filtration rate (eGFR) in 110,517 European ancestry participants using 1000 Genomes imputed data. We identified 10 novel loci with p-value < 5 x 10(-8) previously missed by HapMap-based GWAS. Six of these loci (HOXD8, ARL15, PIK3R1, EYA4, ASTN2, and EPB41L3) are tagged by common SNPs unique to the 1000 Genomes reference panel. Using pathway analysis, we identified 39 significant (FDR < 0.05) genes and 127 significantly (FDR < 0.05) enriched gene sets, which were missed by our previous analyses. Among those, the 10 identified novel genes are part of pathways of kidney development, carbohydrate metabolism, cardiac septum development and glucose metabolism. These results highlight the utility of re-imputing from denser reference panels, until wholegenome sequencing becomes feasible in large samples

Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals

Reduced glomerular filtration rate (GFR) can progress to kidney failure. Risk factors include genetics and diabetes mellitus (DM), but little is known about their interaction. We conducted genome-wide association meta-analyses for estimated GFR based on serum creatinine (eGFR), separately for individuals with or without DM (nDM = 178,691, nnoDM = 1,296,113). Our genome-wide searches identified (i) seven eGFR loci with significant DM/noDM-difference, (ii) four additional novel loci with suggestive difference and (iii) 28 further novel loci (including CUBN) by allowing for potential difference. GWAS on eGFR among DM individuals identified 2 known and 27 potentially responsible loci for diabetic kidney disease. Gene prioritization highlighted 18 genes that may inform reno-protective drug development. We highlight the existence of DM-only and noDM-only effects, which can inform about the target group, if respective genes are advanced as drug targets. Largely shared effects suggest that most drug interventions to alter eGFR should be effective in DM and noDM

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

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

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

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