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Pathway enrichment analysis is a ubiquitous computational biology method to interpret a list of genes (typically derived from the association of large-scale omics data with phenotypes of interest) in terms of higher-level, predefined gene sets that share biological function, chromosomal location, or other common features. Among many tools developed so far, Gene Set Enrichment Analysis (GSEA) stands out as one of the pioneering and most widely used methods. Although originally developed for microarray data, GSEA is nowadays extensively utilized for RNA-seq data analysis. Here, we quantitatively assessed the performance of a variety of GSEA modalities and provide guidance in the practical use of GSEA in RNA-seq experiments. We leveraged harmonized RNA-seq datasets available from The Cancer Genome Atlas (TCGA) in combination with large, curated pathway collections from the Molecular Signatures Database to obtain cancer-type-specific target pathway lists across multiple cancer types. We carried out a detailed analysis of GSEA performance using both gene-set and phenotype permutations combined with four different choices for the Kolmogorov-Smirnov enrichment statistic. Based on our benchmarks, we conclude that the classic/unweighted gene-set permutation approach offered comparable or better sensitivity-vs-specificity tradeoffs across cancer types compared with other, more complex and computationally intensive permutation methods. Finally, we analyzed other large cohorts for thyroid cancer and hepatocellular carcinoma. We utilized a new consensus metric, the Enrichment Evidence Score (EES), which showed a remarkable agreement between pathways identified in TCGA and those from other sources, despite differences in cancer etiology. This finding suggests an EES-based strategy to identify a core set of pathways that may be complemented by an expanded set of pathways for downstream exploratory analysis. This work fills the existing gap in current guidelines and benchmarks for the use of GSEA with RNA-seq data and provides a framework to enable detailed benchmarking of other RNA-seq-based pathway analysis tools. [ABSTRACT FROM AUTHOR]

As people age, their ability to maintain homeostasis in response to stressors diminishes. Physical frailty, a syndrome characterized by loss of resilience to stressors, is thought to emerge due to dysregulation of and breakdowns in communication among key physiological systems. Dynamical systems modeling of these physiological systems aims to model the underlying processes that govern response to stressors. We hypothesize that dynamical systems model summaries are predictive of age-related declines in health and function. In this study, we analyze data obtained during 75-gram oral-glucose tolerance tests (OGTT) on 1,120 adults older than 50 years of age from the Baltimore Longitudinal Study on Aging. We adopt a two-stage modeling approach. First, we fit OGTT curves with the Ackerman model—a nonlinear, parametric model of the glucose-insulin system—and with functional principal components analysis. We then fit linear and Cox proportional hazards models to evaluate whether usual gait speed and survival are associated with the stage-one model summaries. We also develop recommendations for identifying inadequately-fitting nonlinear model fits in a cohort setting with numerous heterogeneous response curves. These recommendations include: (1) defining a constrained parameter space that ensures biologically plausible model fits, (2) evaluating the relative discrepancy between predicted and observed responses of biological interest, and (3) identifying model fits that have notably poor model fit summary measures, such as Rpseudo2 , relative to other fits in the cohort. The Ackerman model was unable to adequately fit 36% of the OGTT curves. The stage-two regression analyses found no associations between Ackerman model summaries and usual gait speed, nor with survival. The second functional principal component score was associated with faster gait speed (p<0.01) and improved survival (p<0.01). [ABSTRACT FROM AUTHOR]

An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5×10-8 is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5×10-8), the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development.

BackgroundRegular fish and omega-3 consumption may have several health benefits and are recommended by major dietary guidelines. Yet, their intakes remain remarkably variable both within and across populations, which could partly owe to genetic influences.ObjectiveTo identify common genetic variants that influence fish and dietary eicosapentaenoic acid plus docosahexaenoic acid (EPA+DHA) consumption.DesignWe conducted genome-wide association (GWA) meta-analysis of fish (n = 86,467) and EPA+DHA (n = 62,265) consumption in 17 cohorts of European descent from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium Nutrition Working Group. Results from cohort-specific GWA analyses (additive model) for fish and EPA+DHA consumption were adjusted for age, sex, energy intake, and population stratification, and meta-analyzed separately using fixed-effect meta-analysis with inverse variance weights (METAL software). Additionally, heritability was estimated in 2 cohorts.ResultsHeritability estimates for fish and EPA+DHA consumption ranged from 0.13-0.24 and 0.12-0.22, respectively. A significant GWA for fish intake was observed for rs9502823 on chromosome 6: each copy of the minor allele (FreqA = 0.015) was associated with 0.029 servings/day (~1 serving/month) lower fish consumption (P = 1.96x10-8). No significant association was observed for EPA+DHA, although rs7206790 in the obesity-associated FTO gene was among top hits (P = 8.18x10-7). Post-hoc calculations demonstrated 95% statistical power to detect a genetic variant associated with effect size of 0.05% for fish and 0.08% for EPA+DHA.ConclusionsThese novel findings suggest that non-genetic personal and environmental factors are principal determinants of the remarkable variation in fish consumption, representing modifiable targets for increasing intakes among all individuals. Genes underlying the signal at rs72838923 and mechanisms for the association warrant further investigation.

IntroductionSex hormones have been implicated in the etiology of a number of diseases. To better understand disease etiology and the mechanisms of disease-risk factor associations, this analysis aimed to investigate the associations of anthropometric, sociodemographic and behavioural factors with a range of circulating sex hormones and sex hormone-binding globulin.MethodsStatistical analyses of individual participant data from 12,330 male controls aged 25-85 years from 25 studies involved in the Endogenous Hormones Nutritional Biomarkers and Prostate Cancer Collaborative Group. Analysis of variance was used to estimate geometric means adjusted for study and relevant covariates.ResultsOlder age was associated with higher concentrations of sex hormone-binding globulin and dihydrotestosterone and lower concentrations of dehydroepiandrosterone sulfate, free testosterone, androstenedione, androstanediol glucuronide and free estradiol. Higher body mass index was associated with higher concentrations of free estradiol, androstanediol glucuronide, estradiol and estrone and lower concentrations of dihydrotestosterone, testosterone, sex hormone-binding globulin, free testosterone, androstenedione and dehydroepiandrosterone sulfate. Taller height was associated with lower concentrations of androstenedione, testosterone, free testosterone and sex hormone-binding globulin and higher concentrations of androstanediol glucuronide. Current smoking was associated with higher concentrations of androstenedione, sex hormone-binding globulin and testosterone. Alcohol consumption was associated with higher concentrations of dehydroepiandrosterone sulfate, androstenedione and androstanediol glucuronide. East Asians had lower concentrations of androstanediol glucuronide and African Americans had higher concentrations of estrogens. Education and marital status were modestly associated with a small number of hormones.ConclusionCirculating sex hormones in men are strongly associated with age and body mass index, and to a lesser extent with smoking status and alcohol consumption.

It has been hypothesized that cellular damage caused by oxidative stress is associated with late-life depression but epidemiological evidence is limited. In the present study we evaluated the association between urinary 8-iso-prostaglandin F2α (8-iso-PGF2α), a biomarker of lipid peroxidation, and depressed mood in a large sample of community-dwelling older adults. Participants were selected from the Health, Aging and Body Composition study, a community-based longitudinal study of older persons (aged 70-79 years). The present analyses was based on a subsample of 1027 men and 948 women free of mobility disability. Urinary concentration of 8-iso-PGF2α was measured by radioimmunoassay methods and adjusted for urinary creatinine. Depressed mood was defined as a score greater than 5 on the 15-item Geriatric Depression Scale and/or use of antidepressant medications. Depressed mood was present in 3.0% of men and 5.5% of women. Depressed men presented higher urinary concentrations of 8-iso-PGF2α than non-depressed men even after adjustment for multiple sociodemographic, lifestyle and health factors (p = 0.03, Cohens d = 0.30). This association was not present in women (depressed status-by-sex interaction p = 0.04). Our study showed that oxidative damage may be linked to depression in older men from a large sample of the general population. Further studies are needed to explore whether the modulation of oxidative stress may break down the link between late-life depression and its deleterious health consequences.

It has been hypothesized that cellular damage caused by oxidative stress is associated with late-life depression but epidemiological evidence is limited. In the present study we evaluated the association between urinary 8-iso-prostaglandin F2α (8-iso-PGF2α), a biomarker of lipid peroxidation, and depressed mood in a large sample of community-dwelling older adults. Participants were selected from the Health, Aging and Body Composition study, a community-based longitudinal study of older persons (aged 70-79 years). The present analyses was based on a subsample of 1027 men and 948 women free of mobility disability. Urinary concentration of 8-iso-PGF2α was measured by radioimmunoassay methods and adjusted for urinary creatinine. Depressed mood was defined as a score greater than 5 on the 15-item Geriatric Depression Scale and/or use of antidepressant medications. Depressed mood was present in 3.0% of men and 5.5% of women. Depressed men presented higher urinary concentrations of 8-iso-PGF2α than non-depressed men even after adjustment for multiple sociodemographic, lifestyle and health factors (p = 0.03, Cohen's d = 0.30). This association was not present in women (depressed status-by-sex interaction p = 0.04). Our study showed that oxidative damage may be linked to depression in older men from a large sample of the general population. Further studies are needed to explore whether the modulation of oxidative stress may break down the link between late-life depression and its deleterious health consequences.

Background: Frailty is a dynamic age-related condition of increased vulnerability characterized by declines across multiple physiologic systems and associated with an increased risk of death. We compared the predictive accuracy for one-month and one-year all-cause mortality of four frailty instruments in a large population of hospitalized older patients in a prospective multicentre cohort study. Methods and Findings: On 2033 hospitalized patients aged ≥65 years from twenty Italian geriatric units, we calculated the frailty indexes derived from the Study of Osteoporotic Fractures (FI-SOF), based on the cumulative deficits model (FI-CD), based on a comprehensive geriatric assessment (FI-CGA), and the Multidimensional Prognostic Index (MPI). The overall mortality rates were 8.6% after one-month and 24.9% after one-year follow-up. All frailty instruments were significantly associated with one-month and one-year all-cause mortality. The areas under the receiver operating characteristic (ROC) curves estimated from age- and sex-adjusted logistic regression models, accounting for clustering due to centre effect, showed that the MPI had a significant higher discriminatory accuracy than FI-SOF, FI-CD, and FI-CGA after one month (areas under the ROC curves: FI-SOF = 0.685 vs. FI-CD = 0.738 vs. FI-CGA = 0.724 vs. MPI = 0.765, p<0.0001) and one year of follow-up (areas under the ROC curves: FI-SOF = 0.694 vs. FI-CD = 0.729 vs. FI-CGA = 0.727 vs. MPI = 0.750, p<0.0001). The MPI showed a significant higher discriminatory power for predicting one-year mortality also in hospitalized older patients without functional limitations, without cognitive impairment, malnourished, with increased comorbidity, and with a high number of drugs. Conclusions: All frailty instruments were significantly associated with short- and long-term all-cause mortality, but MPI demonstrated a significant higher predictive power than other frailty instruments in hospitalized older patients. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) are a valuable approach to understanding the genetic basis of complex traits. One of the challenges of GWAS is the translation of genetic association results into biological hypotheses suitable for further investigation in the laboratory. To address this challenge, we introduce Network Interface Miner for Multigenic Interactions (NIMMI), a network-based method that combines GWAS data with human protein-protein interaction data (PPI). NIMMI builds biological networks weighted by connectivity, which is estimated by use of a modification of the Google PageRank algorithm. These weights are then combined with genetic association p-values derived from GWAS, producing what we call 'trait prioritized sub-networks.' As a proof of principle, NIMMI was tested on three GWAS datasets previously analyzed for height, a classical polygenic trait. Despite differences in sample size and ancestry, NIMMI captured 95% of the known height associated genes within the top 20% of ranked sub-networks, far better than what could be achieved by a single-locus approach. The top 2% of NIMMI height-prioritized sub-networks were significantly enriched for genes involved in transcription, signal transduction, transport, and gene expression, as well as nucleic acid, phosphate, protein, and zinc metabolism. All of these sub-networks were ranked near the top across all three height GWAS datasets we tested. We also tested NIMMI on a categorical phenotype, Crohn's disease. NIMMI prioritized sub-networks involved in B- and T-cell receptor, chemokine, interleukin, and other pathways consistent with the known autoimmune nature of Crohn's disease. NIMMI is a simple, user-friendly, open-source software tool that efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses. [ABSTRACT FROM AUTHOR]

Background and Aims: Recent introduction of the Cosmed K4b² portable metabolic analyzer allows measurement of oxygen consumption outside of a laboratory setting in more typical clinical or household environments and thus may be used to obtain information on the metabolic costs of specific daily life activities. The purpose of this study was to assess the accuracy of the Cosmed K4b² portable metabolic analyzer against a traditional, stationary gas exchange system (the Medgraphics D-Series) during steady-state, submaximal walking exercise. Methods: Nineteen men and women (9 women, 10 men) with an average age of 39.8 years (±13.8) completed two 400 meter walk tests using the two systems at a constant, self-selected pace on a treadmill. Average oxygen consumption (VO2) and carbon dioxide production (VCO2) from each walk were compared. Results: Intraclass Correlation Coefficient (ICC) and Pearson correlation coefficients between the two systems for weight indexed VO2 (ml/kg/min), total VO2 (ml/min), and VCO2 (ml/min) ranged from 0.93 to 0.97. Comparison of the average values obtained using the Cosmed K4b² and Medgraphics systems using paired t-tests indicate no significant difference for VO2 (ml/kg/min) overall (p = 0.25), or when stratified by sex (p = 0.21 women, p = 0.69 men). The mean difference between analyzers was - 0.296 ml/kg/min (±0.26). Results were not significantly different for VO2 (ml/min) or VCO2 (ml/min) within the study population (p = 0.16 and p = 0.08, respectively), or when stratified by sex (VO2: p = 0.51 women, p = 0.16 men; VCO2: p = .11 women, p = 0.53 men). Conclusion: The Cosmed K4b² portable metabolic analyzer provides measures of VO2 and VCO2 during steady-state, submaximal exercise similar to a traditional, stationary gas exchange system. [ABSTRACT FROM AUTHOR]

African Americans are disproportionately affected by type 2 diabetes (T2DM) yet few studies have examined T2DM using genome-wide association approaches in this ethnicity. The aim of this study was to identify genes associated with T2DM in the African American population. We performed a Genome Wide Association Study (GWAS) using the Affymetrix 6.0 array in 965 African-American cases with T2DM and end-stage renal disease (T2DM-ESRD) and 1029 population-based controls. The most significant SNPs (n = 550 independent loci) were genotyped in a replication cohort and 122 SNPs (n = 98 independent loci) were further tested through genotyping three additional validation cohorts followed by meta-analysis in all five cohorts totaling 3,132 cases and 3,317 controls. Twelve SNPs had evidence of association in the GWAS (P<0.0071), were directionally consistent in the Replication cohort and were associated with T2DM in subjects without nephropathy (P<0.05). Meta-analysis in all cases and controls revealed a single SNP reaching genome-wide significance (P<2.5×10(-8)). SNP rs7560163 (P = 7.0×10(-9), OR (95% CI) = 0.75 (0.67-0.84)) is located intergenically between RND3 and RBM43. Four additional loci (rs7542900, rs4659485, rs2722769 and rs7107217) were associated with T2DM (P<0.05) and reached more nominal levels of significance (P<2.5×10(-5)) in the overall analysis and may represent novel loci that contribute to T2DM. We have identified novel T2DM-susceptibility variants in the African-American population. Notably, T2DM risk was associated with the major allele and implies an interesting genetic architecture in this population. These results suggest that multiple loci underlie T2DM susceptibility in the African-American population and that these loci are distinct from those identified in other ethnic populations.