Evaluating methods for combining rare variant data in pathway-based tests of genetic association

Analyzing sets of genes in genome-wide association studies is a relatively new approach that aims to capitalize on biological knowledge about the interactions of genes in biological pathways. This approach, called pathway analysis or gene set analysis, has not yet been applied to the analysis of rare variants. Applying pathway analysis to rare variants offers two competing approaches. In the first approach rare variant statistics are used to generate p-values for each gene (e.g., combined multivariate collapsing [CMC] or weighted-sum [WS]) and the gene-level p-values are combined using standard pathway analysis methods (e.g., gene set enrichment analysis or Fisher’s combined probability method). In the second approach, rare variant methods (e.g., CMC and WS) are applied directly to sets of single-nucleotide polymorphisms (SNPs) representing all SNPs within genes in a pathway. In this paper we use simulated phenotype and real next-generation sequencing data from Genetic Analysis Workshop 17 to analyze sets of rare variants using these two competing approaches. The initial results suggest substantial differences in the methods, with Fisher’s combined probability method and the direct application of the WS method yielding the best power. Evidence suggests that the WS method works well in most situations, although Fisher’s method was more likely to be optimal when the number of causal SNPs in the set was low but the risk of the causal SNPs was high

GAW20: Methods and strategies for the new frontiers of epigenetics and pharmacogenomics

© 2018 The Author(s). GAW20 provided a platform for developing and evaluating statistical methods to analyze human lipid-related phenotypes, DNA methylation, and single-nucleotide markers in a study involving a pharmaceutical intervention. In this article, we present an overview of the data sets and the contributions analyzing these data. The data, donated by the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) investigators, included data from 188 families (N = 1105) which included genome-wide DNA methylation data before and after a 3-week treatment with fenofibrate, single-nucleotide polymorphisms, metabolic syndrome components before and after treatment, and a variety of covariates. The contributions from individual research groups were extensively discussed prior, during, and after the Workshop in groups based on discussion themes, before being submitted for publication

Genetics Analysis Workshop 20: Methods and Strategies for the New Frontiers of Epigenetics and Pharmacogenomics

GAW20 provided a platform for developing and evaluating statistical methods to analyze human lipid-related phenotypes, DNA methylation, and single-nucleotide markers in a study involving a pharmaceutical intervention. In this article, we present an overview of the data sets and the contributions analyzing these data. The data, donated by the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) investigators, included data from 188 families (N = 1105) which included genome-wide DNA methylation data before and after a 3-week treatment with fenofibrate, single-nucleotide polymorphisms, metabolic syndrome components before and after treatment, and a variety of covariates. The contributions from individual research groups were extensively discussed prior, during, and after the Workshop in groups based on discussion themes, before being submitted for publication

Computing and applying atomic regulons to understand gene expression and regulation

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb.2016.01819/full#supplementary-materialUnderstanding gene function and regulation is essential for the interpretation prediction and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets Atomic Regulons ARs represent fundamental units of function within a cell and could be used to associate genes of unknown function with cellular processes and to enable rational genetic engineering of cellular systems. Here we describe an approach for inferring ARs that leverages large-scale expression data sets gene context and functional relationships among genes. We computed ARs for Escherichia coli based on 907 gene expression experiments and compared our results with gene clusters produced by two prevalent data-driven methods: hierarchical clustering and k-means clustering. We compared ARs and purely data-driven gene clusters to the curated set of regulatory interactions for E. coli found in RegulonDB showing that ARs are more consistent with gold standard regulons than are data-driven gene clusters. We further examined the consistency of ARs and data-driven gene clusters in the context of gene interactions predicted by Context Likelihood of Relatedness CLR analysis finding that the ARs show better agreement with CLR predicted interactions. We determined the impact of increasing amounts of expression data on AR construction and find that while more data improve ARs it is not necessary to use the full set of gene expression experiments available for E. coli to produce high quality ARs. In order to explore the conservation of co-regulated gene sets across different organisms we computed ARs for Shewanella oneidensis Pseudomonas aeruginosa Thermus thermophilus and Staphylococcus aureus each of which represents increasing degrees of phylogenetic distance from E. coli. Comparison of the organism-specific ARs showed that the consistency of AR gene membership correlates with phylogenetic distance but there is clear variability in the regulatory networks of closely related organisms. As large scale expression data sets become increasingly common for model and non-model organisms comparative analyses of atomic regulons will provide valuable insights into fundamental regulatory modules used across the bacterial domain.JF acknowledges funding from [SFRH/BD/70824/2010] of the FCT (Portuguese Foundation for Science and Technology) PhD program. CH and PW were supported by the National Science Foundation under grant number EFRI-MIKS-1137089. RT was supported by the Genomic Science Program (GSP), Office of Biological and Environmental Research (OBER), U.S. Department of Energy(DOE),and his work is a contribution of the Pacific North west National Laboratory (PNNL) Foundational Scientific Focus Area. This work was partially supported by an award from the National Science Foundation to MD, AB, NT, and RO (NSFABI-0850546). This work was also supported by the United States National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Service [Contract No. HHSN272201400027C]

SEAS: A System for SEED-Based Pathway Enrichment Analysis

Pathway enrichment analysis represents a key technique for analyzing high-throughput omic data, and it can help to link individual genes or proteins found to be differentially expressed under specific conditions to well-understood biological pathways. We present here a computational tool, SEAS, for pathway enrichment analysis over a given set of genes in a specified organism against the pathways (or subsystems) in the SEED database, a popular pathway database for bacteria. SEAS maps a given set of genes of a bacterium to pathway genes covered by SEED through gene ID and/or orthology mapping, and then calculates the statistical significance of the enrichment of each relevant SEED pathway by the mapped genes. Our evaluation of SEAS indicates that the program provides highly reliable pathway mapping results and identifies more organism-specific pathways than similar existing programs. SEAS is publicly released under the GPL license agreement and freely available at http://csbl.bmb.uga.edu/~xizeng/research/seas/

PUFA omega-3 and omega-6 biomarkers and sleep : a pooled analysis of cohort studies on behalf of the Fatty Acids and Outcomes Research Consortium (FORCE)

Background: n-3 and n-6 PUFAs have physiologic roles in sleep processes. but little is known regarding circulating n-3 and n-6 PUFA and sleep parameters. Objectives: We sought to assess associations between biomarkers of n-3 and n-6 PUFA intake with self-reported sleep duration and difficulty falling sleeping in the Fatty Acids and Outcome Research Consortium. Methods: Harmonized, de novo. individual-level analyses were performed and pooled across 12 cohorts. Participants were 35-96 y old and from 5 nations. Circulating measures included alpha-linolenic acid (ALA), EPA, docosapentaenoic acid (DPA), DHA, EPA + DPA DHA, linoleic acid, and arachidonic acid. Sleep duration (10 cohorts. n = 18.791) was categorized as short (= 9 h). Difficulty falling asleep (8 cohorts, n = 12,500) was categorized as yes or no. Associations between PUFAs, sleep duration, and difficulty falling sleeping were assessed by cross-sectional multinomial logistic regression using standardized protocols and covariates. Cohort-specific multivariable-adjusted ORs per quintile of PUFAs were pooled with inverse-variance weighted meta-analysis. Results: In pooled analysis adjusted for sociodemographic characteristics and health status, participants with higher very long-chain n-3 PUFAs were less likely to have long sleep duration. In the top compared with the bottom quintiles. the multivariable-adjusted ORs (95% CIs) for long sleep were 0.78 (95% CI: 0.65, 0.95) for DHA and 0.76 (95% CI: 0.63, 0.93) for EPA + DPA + DHA. Significant associations for ALA and n-6 PUFA with short sleep duration or difficulty falling sleeping were not identified. Conclusions: Participants with higher concentrations of very long-chain n-3 PUFAs were less likely to have long sleep duration. While objective biomarkers reduce recall bias and misclassification, the cross-sectional design limits assessment of the temporal nature of this relation. These novel findings across 12 cohorts highlight the need for experimental and biological assessments of very long-chain n-3 PUFAs and sleep duration.Peer reviewe

Trans fatty acid biomarkers and incident Type 2 diabetes: pooled analysis from 10 prospective cohort studies in the fatty acids and outcome research consortium (FORCE) (OR33-02-19)

Objectives To assess prospective association between circulating biomarkers of individual trans fatty acids (TFAs) and incident type 2 diabetes (T2D) in diverse populations. Methods A harmonized analysis of individual level data was conducted for TFA biomarkers and incident T2D by pooling ten prospective cohort or nested-case-control studies from five countries (Australia, Germany, Iceland, UK, and USA). Fatty acids (FAs) were measured in plasma phospholipid, red blood cell membrane phospholipid, or total plasma collected between 1990–2008 from 22,711 participants aged ≥18 years without prevalent diabetes. Evaluated TFAs included trans-16:1n-9, sum of trans-18:1 isomers (trans-18:1n6 to trans-18:1n12), sum of trans-18:2 isomers (cis/trans-18:2, trans/cis-18:2, trans/trans-18:2), and individual trans-18:2 isomers. The multivariable-adjusted relative risk or odds ratio was estimated in each cohort by lipid compartments using a pre-specified protocol for definitions of exposures, covariates, and outcomes for statistical analysis. Association estimates were pooled using fixed-effects inverse-variance weighted meta-analysis. Results During an average maximum of 14 years of follow-up, 2244 cases of incident T2D were identified. Median levels of TFAs across cohorts were 0.05–0.18% total FAs for trans-16:1n-9, 0.09–2.05% for total trans-18:1, 0.10–0.73% for total trans-18:2, and 0.01–0.36% for individual trans-18:2 isomers. In overall pooled analysis, TFAs evaluated per inter-quintile range were not significantly associated with risk of T2D (Figure 1). Findings were consistent when TFAs were assessed categorically in study specific-quintiles, and when associations were pooled within lipid compartment (i.e., phospholipids vs. total plasma). Conclusions Overall, biomarker levels of TFAs were not significantly associated with risk of incident T2D in this international pooling project. Findings may be due to mixed TFA sources (industrial vs. ruminant), a general decline in TFA exposure during this period, or no effect of circulating TFA on diabetes. Associations of TFA biomarkers with T2D at higher exposures should be investigated

Fatty Acid Biomarkers of Dairy Fat Consumption and Incidence of Type 2 Diabetes: A Pooled Analysis of Prospective Cohort Studies

Background We aimed to investigate prospective associations of circulating or adipose tissue odd-chain fatty acids 15:0 and 17:0 and trans-palmitoleic acid, t16:1n-7, as potential biomarkers of dairy fat intake, with incident type 2 diabetes (T2D). Methods and findings Sixteen prospective cohorts from 12 countries (7 from the United States, 7 from Europe, 1 from Australia, 1 from Taiwan) performed new harmonised individual-level analysis for the prospective associations according to a standardised plan. In total, 63,682 participants with a broad range of baseline ages and BMIs and 15,180 incident cases of T2D over the average of 9 years of follow-up were evaluated. Study-specific results were pooled using inverse-variance±weighted meta-analysis. Prespecified interactions by age, sex, BMI, and race/ethnicity were explored in each cohort and were meta-analysed. Potential heterogeneity by cohort-specific characteristics (regions, lipid compartments used for fatty acid assays) was assessed with metaregression. After adjustment for potential confounders, including measures of adiposity (BMI, waist circumference) and lipogenesis (levels of palmitate, triglycerides), higher levels of 15:0, 17:0, and t16:1n-7 were associated with lower incidence of T2D. In the most adjusted model, the hazard ratio (95% CI) for incident T2D per cohortspecific 10th to 90th percentile range of 15:0 was 0.80 (0.73±0.87); of 17:0, 0.65 (0.59± 0.72); of t16:1n7, 0.82 (0.70±0.96); and of their sum, 0.71 (0.63±0.79). In exploratory analyses, similar associations for 15:0, 17:0, and the sum of all three fatty acids were present in both genders but stronger in women than in men (pinteraction \u3c 0.001). Whereas studying associations with biomarkers has several advantages, as limitations, the biomarkers do not distinguish between different food sources of dairy fat (e.g., cheese, yogurt, milk), and residual confounding by unmeasured or imprecisely measured confounders may exist. Conclusions In a large meta-analysis that pooled the findings from 16 prospective cohort studies, higher levels of 15:0, 17:0, and t16:1n-7 were associated with a lower risk of T2D

Fatty acids in the de novo lipogenesis pathway and incidence of type 2 diabetes: A pooled analysis of prospective cohort studies

Funder: Dutch Scientific OrganizationFunder: Foundation Plan AlzheimerFunder: Icelandic Heart AssociationFunder: Academy of FinlandFunder: VicHealth and Cancer Council VictoriaFunder: Juselius FoundationFunder: Uppsala University Hospital and the Swedish Research Council for Health, Working Life and WelfareFunder: the Institut National de la Sante et de la Recherche MedicaleFunder: , the University Bordeaux 2 Victor SegalenFunder: Sanofi; funder-id: http://dx.doi.org/10.13039/100004339Funder: Fondation pour la Recherche Medicale, the Caisse Nationale Maladie des Travailleurs Salaries, Direction Generale de la Sante, MGEN, Institut de la Longevite, Conseils Regionaux d’Aquitaine et Bourgogne, Fondation de France, Ministry of Research–Institut National de la Sante and de la Recherche Medicale Programme CohortesFunder: Caisse Nationale pour la Solidarite et l’AutonomieFunder: Swedish Research Council for Health, Working Life and Welfare, Uppsala City Council, Swedish Research Council, and Swedish Diabetes FoundationBackground: De novo lipogenesis (DNL) is the primary metabolic pathway synthesizing fatty acids from carbohydrates, protein, or alcohol. Our aim was to examine associations of in vivo levels of selected fatty acids (16:0, 16:1n7, 18:0, 18:1n9) in DNL with incidence of type 2 diabetes (T2D). Methods and findings: Seventeen cohorts from 12 countries (7 from Europe, 7 from the United States, 1 from Australia, 1 from Taiwan; baseline years = 1970–1973 to 2006–2010) conducted harmonized individual-level analyses of associations of DNL-related fatty acids with incident T2D. In total, we evaluated 65,225 participants (mean ages = 52.3–75.5 years; % women = 20.4%–62.3% in 12 cohorts recruiting both sexes) and 15,383 incident cases of T2D over the 9-year follow-up on average. Cohort-specific association of each of 16:0, 16:1n7, 18:0, and 18:1n9 with incident T2D was estimated, adjusted for demographic factors, socioeconomic characteristics, alcohol, smoking, physical activity, dyslipidemia, hypertension, menopausal status, and adiposity. Cohort-specific associations were meta-analyzed with an inverse-variance-weighted approach. Each of the 4 fatty acids positively related to incident T2D. Relative risks (RRs) per cohort-specific range between midpoints of the top and bottom quintiles of fatty acid concentrations were 1.53 (1.41–1.66; p < 0.001) for 16:0, 1.40 (1.33–1.48; p < 0.001) for 16:1n-7, 1.14 (1.05–1.22; p = 0.001) for 18:0, and 1.16 (1.07–1.25; p < 0.001) for 18:1n9. Heterogeneity was seen across cohorts (I2 = 51.1%–73.1% for each fatty acid) but not explained by lipid fractions and global geographical regions. Further adjusted for triglycerides (and 16:0 when appropriate) to evaluate associations independent of overall DNL, the associations remained significant for 16:0, 16:1n7, and 18:0 but were attenuated for 18:1n9 (RR = 1.03, 95% confidence interval (CI) = 0.94–1.13). These findings had limitations in potential reverse causation and residual confounding by imprecisely measured or unmeasured factors. Conclusions: Concentrations of fatty acids in the DNL were positively associated with T2D incidence. Our findings support further work to investigate a possible role of DNL and individual fatty acids in the development of T2D

Blood n-3 fatty acid levels and total and cause-specific mortality from 17 prospective studies.

The health effects of omega-3 fatty acids have been controversial. Here we report the results of a de novo pooled analysis conducted with data from 17 prospective cohort studies examining the associations between blood omega-3 fatty acid levels and risk for all-cause mortality. Over a median of 16 years of follow-up, 15,720 deaths occurred among 42,466 individuals. We found that, after multivariable adjustment for relevant risk factors, risk for death from all causes was significantly lower (by 15-18%, at least p < 0.003) in the highest vs the lowest quintile for circulating long chain (20-22 carbon) omega-3 fatty acids (eicosapentaenoic, docosapentaenoic, and docosahexaenoic acids). Similar relationships were seen for death from cardiovascular disease, cancer and other causes. No associations were seen with the 18-carbon omega-3, alpha-linolenic acid. These findings suggest that higher circulating levels of marine n-3 PUFA are associated with a lower risk of premature death.The EPIC Norfolk study (DOI 10.22025/2019.10.105.00004) has received funding from the Medical Research Council (MR/N003284/1 and MC-UU_12015/1) and Cancer Research UK (C864/A14136). NJW, NGF, and FI were supported by the Medical Research Council Epidemiology Unit core funding [MC_UU_12015/1 and MC_UU_12015/5]. NJW and NGF acknowledge support from the National Institute for Health Research Cambridge Biomedical Research Centre [IS-BRC-1215-20014] and NJW is an NIHR Senior Investigator