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

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

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

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

Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV

The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum pT larger than a few GeV is above 95% over the whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4, while the probability to misidentify a hadron as a muon is well below 1%. The efficiency to trigger on single muons with pT above a few GeV is higher than 90% over the full eta range, and typically substantially better. The overall momentum scale is measured to a precision of 0.2% with muons from Z decays. The transverse momentum resolution varies from 1% to 6% depending on pseudorapidity for muons with pT below 100 GeV and, using cosmic rays, it is shown to be better than 10% in the central region up to pT = 1 TeV. Observed distributions of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO

Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV

The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum pT larger than a few GeV is above 95% over the whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4, while the probability to misidentify a hadron as a muon is well below 1%. The efficiency to trigger on single muons with pT above a few GeV is higher than 90% over the full eta range, and typically substantially better. The overall momentum scale is measured to a precision of 0.2% with muons from Z decays. The transverse momentum resolution varies from 1% to 6% depending on pseudorapidity for muons with pT below 100 GeV and, using cosmic rays, it is shown to be better than 10% in the central region up to pT = 1 TeV. Observed distributions of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO

Integration of genome-wide association studies with biological knowledge identifies six novel genes related to kidney function

In conducting genome-wide association studies (GWAS), analytical approaches leveraging biological information may further understanding of the pathophysiology of clinical traits. To discover novel associations with estimated glomerular filtration rate (eGFR), a measure of kidney function, we developed a strategy for integrating prior biological knowledge into the existing GWAS data for eGFR from the CKDGen Consortium. Our strategy focuses on single nucleotide polymorphism (SNPs) in genes that are connected by functional evidence, determined by literature mining and gene ontology (GO) hierarchies, to genes near previously validated eGFR associations. It then requires association thresholds consistent with multiple testing, and finally evaluates novel candidates by independent replication. Among the samples of European ancestry, we identified a genome-wide significant SNP in FBXL20 (P = 5.6 × 10−9) in meta-analysis of all available data, and additional SNPs at the INHBC, LRP2, PLEKHA1, SLC3A2 and SLC7A6 genes meeting multiple-testing corrected significance for replication and overall P-values of 4.5 × 10−4-2.2 × 10−7. Neither the novel PLEKHA1 nor FBXL20 associations, both further supported by association with eGFR among African Americans and with transcript abundance, would have been implicated by eGFR candidate gene approaches. LRP2, encoding the megalin receptor, was identified through connection with the previously known eGFR gene DAB2 and extends understanding of the megalin system in kidney function. These findings highlight integration of existing genome-wide association data with independent biological knowledge to uncover novel candidate eGFR associations, including candidates lacking known connections to kidney-specific pathways. The strategy may also be applicable to other clinical phenotypes, although more testing will be needed to assess its potential for discovery in genera

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