Identification of novel genetic variants associated with cardiorespiratory fitness.

INTRODUCTION: Low maximal oxygen uptake (VO2max) is a strong and independent risk factor for all-cause and cardiovascular disease (CVD) mortality. For other CVD risk factors, numerous genetic association studies have been performed, revealing promising risk markers and new therapeutic targets. However, large genomic association studies on VO2max are still lacking, despite the fact that VO2max has a large genetic component. METHODS: We performed a genetic association study on 123.545 single-nucleotide polymorphisms (SNPs) and directly measured VO2max in 3470 individuals (exploration cohort). Candidate SNPs from the exploration cohort were analyzed in a validation cohort of 718 individuals, in addition to 7 wild-card SNPs. Sub-analyses were performed for each gender. Validated SNPs were used to create a genetic score for VO2max. In silico analyses and genotype-phenotype databases were used to predict physiological function of the SNPs. RESULTS: In the exploration cohort, 41 SNPs were associated with VO2max (p < 5.0 ∗ 10-4). Six of the candidate SNPs were associated with VO2max also in the validation cohort, in addition to three wild-card SNPs (p < 0.05, in men, women or both). The cumulative number of high-VO2max-SNPs correlated negatively with CVD risk factors, e.g. waist-circumference, visceral fat, fat %, cholesterol levels and BMI. In silico analysis indicated that several of the VO2max-SNPs influence gene expression in adipose tissue, skeletal muscle and heart. CONCLUSION: We discovered and validated new SNPs associated with VO2max and proposed possible links between VO2max and CVD. Studies combining several large cohorts with directly measured VO2max are needed to identify more SNPs associated with this phenotype.

Powerful extreme phenotype sampling designs and score tests for genetic association studies.

We consider cross-sectional genetic association studies (common and rare variants) where non-genetic information is available or feasible to obtain for N individuals, but where it is infeasible to genotype all N individuals. We consider continuously measurable Gaussian traits (phenotypes). Genotyping n < N extreme phenotype individuals can yield better power to detect phenotype-genotype associations, as compared to randomly selecting n individuals. We define a person as having an extreme phenotype if the observed phenotype is above a specified threshold or below a specified threshold. We consider a model where these thresholds can be tailored to each individual. The classical extreme sampling design is to set equal thresholds for all individuals. We introduce a design (z-extreme sampling) where personalized thresholds are defined based on the residuals of a regression model including only non-genetic (fully available) information. We derive score tests for the situation where only n extremes are analyzed (complete case analysis) and for the situation where the non-genetic information on N - n non-extremes is included in the analysis (all case analysis). For the classical design, all case analysis is generally more powerful than complete case analysis. For the z-extreme sample, we show that all case and complete case tests are equally powerful. Simulations and data analysis also show that z-extreme sampling is at least as powerful as the classical extreme sampling design and the classical design is shown to be at times less powerful than random sampling. The method of dichotomizing extreme phenotypes is also discussed.

Gene expression differences between PAXgene and Tempus blood RNA tubes are highly reproducible between independent samples and biobanks.

BACKGROUND: Gene expression profiling from blood is sensitive to technology choices. For example, the main blood RNA collection systems-the PAXgene and Tempus tubes-differently influence RNA expression signatures. The aim of this study was to establish a common RNA isolation protocol for these two systems and investigate if it could reduce the differences in gene expression between them. RESULTS: We collected identical blood samples on the PAXgene and Tempus systems and retrieved blood samples from two independent biobanks-NOWAC and HUNT3-which are based on PAXgene and Tempus, respectively. High-quality RNA was extracted from both sampling systems by using their original protocols and our common modified protocol, and were profiled on Illumina microarrays. Regardless of the protocol used, we found most of the measured transcripts to be differently affected by the two sampling systems. However, our modified protocol reduced the number of transcripts that were significantly differentially expressed between PAXgene and Tempus by approximately 50%. Expression differences between PAXgene and Tempus were highly reproducible both between protocols and between different independent sample sets (Pearson correlation 0.563-0.854 across 47323 probes). Moreover, the modified protocol increased the microRNA output of the system with lowest microRNA yield, the PAXgene system. CONCLUSIONS: Most transcripts are affected by the choice of sampling system, but these effects are highly reproducible between independent samples. We propose that by running a control experiment with samples on both systems in parallel with biologically relevant samples, researchers may adjust for technical differences between the sampling systems.

The PBMC transcriptome profile after intake of oxidized versus high-quality fish oil: an explorative study in healthy subjects.

BACKGROUND: Marine long-chain polyunsaturated fatty acids are susceptible to oxidation, generating a range of different oxidation products with suggested negative health effects. The aim of the present study was to utilize sensitive high-throughput transcriptome analyses to investigate potential unfavorable effects of oxidized fish oil (PV: 18 meq/kg; AV: 9) compared to high-quality fish oil (PV: 4 meq/kg; AV: 3). METHODS: In a double-blinded randomized controlled study for seven weeks, 35 healthy subjects were assigned to 8 g of either oxidized fish oil or high quality fish oil. The daily dose of EPA+DHA was 1.6 g. Peripheral blood mononuclear cells were isolated at baseline and after 7 weeks and transcriptome analyses were performed with the illuminaHT-12 v4 Expression BeadChip. RESULTS: No gene transcripts, biological processes, pathway or network were significantly changed in the oxidized fish oil group compared to the fish oil group. Furthermore, gene sets related to oxidative stress and cardiovascular disease were not differently regulated between the groups. Within group analyses revealed a more prominent effect after intake of high quality fish oil as 11 gene transcripts were significantly (FDR < 0.1) changed from baseline versus three within the oxidized fish oil group. CONCLUSION: The suggested concern linking lipid oxidation products to short-term unfavorable health effects may therefore not be evident at a molecular level in this explorative study. TRIAL REGISTRATION: ClinicalTrials.gov, NCT01034423.

The eGenVar data management system--cataloguing and sharing sensitive data and metadata for the life sciences.

Systematic data management and controlled data sharing aim at increasing reproducibility, reducing redundancy in work, and providing a way to efficiently locate complementing or contradicting information. One method of achieving this is collecting data in a central repository or in a location that is part of a federated system and providing interfaces to the data. However, certain data, such as data from biobanks or clinical studies, may, for legal and privacy reasons, often not be stored in public repositories. Instead, we describe a metadata cataloguing system and a software suite for reporting the presence of data from the life sciences domain. The system stores three types of metadata: file information, file provenance and data lineage, and content descriptions. Our software suite includes both graphical and command line interfaces that allow users to report and tag files with these different metadata types. Importantly, the files remain in their original locations with their existing access-control mechanisms in place, while our system provides descriptions of their contents and relationships. Our system and software suite thereby provide a common framework for cataloguing and sharing both public and private data. Database URL: http://bigr.medisin.ntnu.no/data/eGenVar/.

High coverage sequencing of DNA from microorganisms living in an oil reservoir 2.5 kilometres subsurface.

Microorganisms colonize a variety of extreme environments, and based on cultivation studies and analyses of PCR-amplified 16S rDNA sequences, microbial life appears to extend deep into the earth crust. However, none of these studies involved comprehensive characterizations of total DNA. Here we report results of a high-coverage DNA pyrosequencing of an apparently representative and uncontaminated sample from a deep sea oil reservoir located 2.5 km subsurface, attributing a pressure and temperature of 250 bars and 85°C respectively. Bioinformatic analyses of the DNA sequences indicate that the reservoir harbours a rich microbial community dominated by a smaller number of taxa. Comparison of the metagenome with sequences in databases indicated that there may have been contact between the oil reservoir and surface communities late in the sequence of geological events leading to oil reservoir formation. One specific gene, encoding a putative enolase, was synthesized and expressed in Escherichia coli. Enolase activity was confirmed and was found to be much more thermotolerant than for a corresponding E. coli enzyme, consistent with the conditions in the oil reservoir.

Representation of molecular structure using quantum topology with inductive logic programming in structure-activity relationships.

The requirement of aligning each individual molecule in a data set severely limits the type of molecules which can be analysed with traditional structure activity relationship (SAR) methods. A method which solves this problem by using relations between objects is inductive logic programming (ILP). Another advantage of this methodology is its ability to include background knowledge as 1st-order logic. However, previous molecular ILP representations have not been effective in describing the electronic structure of molecules. We present a more unified and comprehensive representation based on Richard Bader's quantum topological atoms in molecules (AIM) theory where critical points in the electron density are connected through a network. AIM theory provides a wealth of chemical information about individual atoms and their bond connections enabling a more flexible and chemically relevant representation. To obtain even more relevant rules with higher coverage, we apply manual postprocessing and interpretation of ILP rules. We have tested the usefulness of the new representation in SAR modelling on classifying compounds of low/high mutagenicity and on a set of factor Xa inhibitors of high and low affinity.