Adverse Cardiovascular Outcomes and Antihypertensive Treatment: A Genome-Wide Interaction Meta-Analysis in the International Consortium for Antihypertensive Pharmacogenomics Studies

We sought to identify genome-wide variants influencing antihypertensive drug response and adverse cardiovascular outcomes, utilizing data from four randomized controlled trials in the International Consortium for Antihypertensive Pharmacogenomics Studies (ICAPS). Genome-wide antihypertensive drug-single nucleotide polymorphism (SNP) interaction tests for four drug classes (β-blockers, n = 9,195; calcium channel blockers (CCBs), n = 10,511; thiazide/thiazide-like diuretics, n = 3,516; ACE-inhibitors/ARBs, n = 2,559) and cardiovascular outcomes (incident myocardial infarction, stroke, or death) were analyzed among patients with hypertension of European ancestry. Top SNPs from the meta-analyses were tested for replication of cardiovascular outcomes in an independent Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) study (n = 21,267), blood pressure (BP) response in independent ICAPS studies (n = 1,552), and ethnic validation in African Americans from the Genetics of Hypertension Associated Treatment study (GenHAT; n = 5,115). One signal reached genome-wide significance in the β-blocker-SNP interaction analysis (rs139945292, Interaction P = 1.56 × 10−8). rs139945292 was validated through BP response to β-blockers, with the T-allele associated with less BP reduction (systolic BP response P = 6 × 10−4, Beta = 3.09, diastolic BP response P = 5 × 10−3, Beta = 1.53). The T-allele was also associated with increased adverse cardiovascular risk within the β-blocker treated patients’ subgroup (P = 2.35 × 10−4, odds ratio = 1.57, 95% confidence interval = 1.23–1.99). The locus showed nominal replication in CHARGE, and consistent directional trends in β-blocker treated African Americans. rs139945292 is an expression quantitative trait locus for the 50 kb upstream gene NTM (neurotrimin). No SNPs attained genome-wide significance for any other drugs classes. Top SNPs were located near CALB1 (CCB), FLJ367777 (ACE-inhibitor), and CES5AP1 (thiazide). The NTM region is associated with increased risk for adverse cardiovascular outcomes and less BP reduction in β-blocker treated patients. Further investigation into this region is warranted

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

Serum magnesium and calcium levels in relation to ischemic stroke : Mendelian randomization study

ObjectiveTo determine whether serum magnesium and calcium concentrations are causally associated with ischemic stroke or any of its subtypes using the mendelian randomization approach.MethodsAnalyses were conducted using summary statistics data for 13 single-nucleotide polymorphisms robustly associated with serum magnesium (n = 6) or serum calcium (n = 7) concentrations. The corresponding data for ischemic stroke were obtained from the MEGASTROKE consortium (34,217 cases and 404,630 noncases).ResultsIn standard mendelian randomization analysis, the odds ratios for each 0.1 mmol/L (about 1 SD) increase in genetically predicted serum magnesium concentrations were 0.78 (95% confidence interval [CI] 0.69-0.89; p = 1.3 7 10-4) for all ischemic stroke, 0.63 (95% CI 0.50-0.80; p = 1.6 7 10-4) for cardioembolic stroke, and 0.60 (95% CI 0.44-0.82; p = 0.001) for large artery stroke; there was no association with small vessel stroke (odds ratio 0.90, 95% CI 0.67-1.20; p = 0.46). Only the association with cardioembolic stroke was robust in sensitivity analyses. There was no association of genetically predicted serum calcium concentrations with all ischemic stroke (per 0.5 mg/dL [about 1 SD] increase in serum calcium: odds ratio 1.03, 95% CI 0.88-1.21) or with any subtype.ConclusionsThis study found that genetically higher serum magnesium concentrations are associated with a reduced risk of cardioembolic stroke but found no significant association of genetically higher serum calcium concentrations with any ischemic stroke subtype

Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction

Genetic determinants of telomere length from 109,122 ancestrally diverse whole-genome sequences in TOPMed

Genetic studies on telomere length are important for understanding age-related diseases. Prior GWASs for leukocyte TL have been limited to European and Asian populations. Here, we report the first sequencing-based association study for TL across ancestrally diverse individuals (European, African, Asian, and Hispanic/Latino) from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. We used whole-genome sequencing (WGS) of whole blood for variant genotype calling and the bioinformatic estimation of telomere length in n = 109,122 individuals. We identified 59 sentinel variants (p < 5 × 10−9) in 36 loci associated with telomere length, including 20 newly associated loci (13 were replicated in external datasets). There was little evidence of effect size heterogeneity across populations. Fine-mapping at OBFC1 indicated that the independent signals colocalized with cell-type-specific eQTLs for OBFC1 (STN1). Using a multi-variant gene-based approach, we identified two genes newly implicated in telomere length, DCLRE1B (SNM1B) and PARN. In PheWAS, we demonstrated that our TL polygenic trait scores (PTSs) were associated with an increased risk of cancer-related phenotypes

Distance education use in rural schools

A national survey of rural school systems in the United States was conducted to determine the extent to which distance education is being utilized by rural schools, the technologies used, the curriculum areas impacted, the perceived needs for distance education, their satisfaction with distance education, and the barriers to distance education use. Data were collected through telephone surveys with 394 school districts selected at random. Most rural school districts were currently using distance education. The subjects most often offered by distance education were math, foreign language, and English. A large majority of the districts indicated satisfaction with distance education courses; almost half stated they were very satisfied. The majority of students who enrolled in distance education courses completed these courses. Two-thirds of the districts indicated a need for additional distance education courses. Only a small portion of school districts indicated they are able to offer all the advanced and enrichment classes that students need without using distance education. The large majority of the districts did not see connectivity as a barrier to distance education use. Common barriers were funding, scheduling, and difficulty implementing distance education courses. The two most common formats for distance education courses in rural schools were two-way videoconferencing courses and online courses. Implications for future research are discusse

Theileria parva genomics reveals an atypical apicomplexan genome

The discipline of genomics is setting new paradigms in research approaches to resolving problems in human and animal health. We propose to determine the genome sequence of Theileria parva, a pathogen of cattle, using the random shotgun approach pioneered at The Institute for Genomic Research (TIGR). A number of features of the T. parva genome make it particularly suitable for this approach. The G+C content of genomic DNA is about 31 percent, non-coding repetitive DNA constitute less than 1 percent of total DNA and a framework for the 10-12 Mbp genome is available in the form of a physical map for all four chromosomes. Minisatellite sequences are the only dispersed repetitive sequences identified so far, but they are limited in distribution to 13 of 33 SfiI fragments. Telomere and sub-telomeric non-coding sequences occupy less than 10 kbp at each chromosomal end and there are only two units encoding cytoplasmic rRNAs. Three sets of distinct multicopy sequences encoding ORFs have been identified but it is not known if these are associated with expression of parasite antigenic diversity. Protein coding genes exhibit a bias in codon usage and introns when present are unusually short. Like other apicomplexan organisms, T. parva contains two extrachromosomal DNAs, a mitochondrial DNA and a plastid DNA molecule. By annotating the gnome sequence, in combination with the use of microarray technology and comparative genomics, we expect to gain significant insights into unique aspects of the biology of T. parva. We believe that the data will underpin future research to aid in the identification of targets of protective CD8 + cell mediated immune responses, and parasite molecules involved in inducing reversible host leukocyte transformation and tumour-like behaviour of transformed parasitised cells