Additional file 1: Tables S1–S5 of Prediagnostic plasma concentrations of organochlorines and risk of B-cell non-Hodgkin lymphoma in envirogenomarkers: a nested case-control study

with additional results can be found in the Additional file documents; RKelly_Additional_files.docx. (DOCX 72 kb

Additional file 1 of A systematic comparison of statistical methods to detect interactions in exposome-health associations

Supplementary results (tables and figures). (PDF 1220 kb

DNA Methylome Marks of Exposure to Particulate Matter at Three Time Points in Early Life

Maternal exposure to airborne particulate matter (PM) has been associated with restricted fetal growth and reduced birthweight. Here, we performed methylome-wide analyses of cord and children’s blood DNA in relation to residential exposure to PM smaller than 10 μm (PM₁₀). This study included participants of the Avon Longitudinal Study of Pregnancy and Childhood (ALSPAC, cord blood, <i>n</i> = 780; blood at age 7, <i>n</i> = 757 and age 15–17, <i>n</i> = 850) and the EXPOsOMICS birth cohort consortium including cord blood from ENVIR<i>ON</i>AGE (<i>n</i> = 197), INMA (<i>n</i> = 84), Piccolipiù (<i>n</i> = 99) and Rhea (<i>n</i> = 75). We could not identify significant CpG sites, by meta-analyzing associations between maternal PM₁₀ exposure during pregnancy and DNA methylation in cord blood, nor by studying DNA methylation and concordant annual exposure at 7 and 15–17 years. The CpG cg21785536 was inversely associated with PM₁₀ exposure using a longitudinal model integrating the three studied age groups (−1.2% per 10 μg/m³; raw <i>p</i>-value = 3.82 × 10^–8). Pathway analyses on the corresponding genes of the 100 strongest associated CpG sites of the longitudinal model revealed enriched pathways relating to the GABAergic synapse, p53 signaling and NOTCH1. We provided evidence that residential PM₁₀ exposure in early life affects methylation of the CpG cg21785536 located on the EGF Domain Specific O-Linked <i>N</i>-Acetylglucosamine Transferase gene

Improving Visualization and Interpretation of Metabolome-Wide Association Studies: An Application in a Population-Based Cohort Using Untargeted ¹H NMR Metabolic Profiling

¹H NMR spectroscopy of biofluids generates reproducible data allowing detection and quantification of small molecules in large population cohorts. Statistical models to analyze such data are now well-established, and the use of univariate metabolome wide association studies (MWAS) investigating the spectral features separately has emerged as a computationally efficient and interpretable alternative to multivariate models. The MWAS rely on the accurate estimation of a metabolome wide significance level (MWSL) to be applied to control the family wise error rate. Subsequent interpretation requires efficient visualization and formal feature annotation, which, in-turn, call for efficient prioritization of spectral variables of interest. Using human serum ¹H NMR spectroscopic profiles from 3948 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we have performed a series of MWAS for serum levels of glucose. We first propose an extension of the conventional MWSL that yields stable estimates of the MWSL across the different model parameterizations and distributional features of the outcome. We propose both efficient visualization methods and a strategy based on subsampling and internal validation to prioritize the associations. Our work proposes and illustrates practical and scalable solutions to facilitate the implementation of the MWAS approach and improve interpretation in large cohort studies

Comparison of the marginal phenotype-SNP associations provided by GUESS and SNPTEST in the multiple traits analysis of TG-LDL-APOB.

<p>(To increase readability, the log₁₀(BFs) are truncated at 20). (A) Genome-wide log₁₀(BF) obtained from GUESS. Significant SNPs found associated at 5% FDR are depicted by black dots (with the SNP's name) whereas significant SNPs that are also in the top Best Model Visited are represented by red dots (with the SNP's name). (B) Genome-wide log₁₀(BF) obtained from SNPTEST. The horizontal dashed line indicates the level of log₁₀(BF) that provides strong evidence of a phenotype-SNP association with Marginal Posterior Probability of inclusion close to 1. For comparison purposes, SNPs found by GUESS are highlighted (their name is printed). SNPs with log₁₀(BF)>5 are coloured coded according to the level of pairwise Pearson correlation with the closest significant GUESS SNP (see colour bar for correlation scale). (C) log₁₀(BF) signal obtained from SNPTEST in a region of chromosome 11 spanning nearly 500 Kb (116,519,739–116,845,104 bp). The horizontal dashed line and colour code used to identify relevant SNPs are as defined in (B). Top bars indicate the position of genes in the region retrieved from Ensembl R66. (D) Scatterplot of genome-wide log₁₀(BF) of TG-LDL-APOB obtained from GUESS and SNPTEST. The colour code used to identify relevant SNPs and the horizontal dashed line are as defined in (A) and (B).</p

Comparison of the marginal phenotype-SNP associations provided by GUESS, SNPTEST and piMASS in the single trait analysis of TG.

<p>(To increase readability, the log₁₀(BFs) are truncated at 20). (A) Genome-wide log₁₀(BF) obtained from GUESS. Significant SNPs found associated at an FDR of 5% are depicted by black dots (with the SNP's name) whereas significant SNPs that are also in the top Best Model Visited are represented by red dots (also with the SNP's name). (B) Genome-wide log₁₀(BF) obtained from SNPTEST. The horizontal dashed line indicates the level of log₁₀(BF) that provides strong evidence of a phenotype-SNP association with Marginal Posterior Probability of inclusion close to 1. For comparison purposes, SNPs detected by GUESS are highlighted (their name is printed). SNPs found by SNPTEST with log₁₀(BF)>5 are coloured coded according to the level of pairwise Pearson correlation with the closest significant GUESS SNP (see colour bar for correlation scale). (C) Genome-wide log₁₀(BF) obtained from piMASS. The horizontal dashed line indicates the level of log₁₀(BF) that provides strong evidence for a phenotype-SNP association. (D) log₁₀(BF) signals obtained from SNPTEST in a region of chromosome 11 spanning nearly 500 Kb (116,519,739–116,845,104 bp). The horizontal dashed line and colour code used to identify relevant SNPs are the same as defined in (B). Top bars indicate the position of genes in the region retrieved from Ensembl R66. (E) Scatterplot of genome-wide log₁₀(BF) of TG obtained from GUESS and SNPTEST. Colour code used to identify relevant SNPs and the horizontal dashed line are as defined in (A) and (B). (F) Scatterplot of genome-wide log₁₀(BF) of TG obtained from GUESS and piMASS. The colour code used to identify relevant SNPs and the horizontal dashed line are as defined in (A) and (B).</p

Receiver Operating Characteristic (ROC) curves of SNPTEST (black), SPLS (blue), MLASSO (dark green), (M)ANOVA (purple), piMASS (green) and GUESS (red) for multiple traits and single trait simulated datasets.

<p>For GUESS, ROC curves are obtained using the top Best Model Visited (BMV) (red star) and the Marginal Posterior Probability of Inclusion (MPPI) (solid red line). For SNPTEST, the ROC curve is calculated using log₁₀(BF) while for piMASS ROC curves are obtained using MPPI. (Average) number of SNPs retained by SPLS and MLASSO under different levels of penalization are indicated (A–B). For MANOVA Wilks (A–B) and ANOVA Kruskal (C–D), the ROC curve is derived using the SNPs declared significant over a range of FDR levels. Number of false positives (<i>x</i>-axis) is indicated at the top of the figure while proportion of false positives is presented at the bottom. Given the large number of predictors (273,294), false positives are truncated at 10⁻⁴ at which level a large number already occurs (27.5).</p

Schematic representation of the analysis of single and multiple phenotypes using GUESS.

<p>(A–B) Given a group of single traits (APOA1, APOB, HDL, LDL and TG), we constructed two top-down trees (green and blue colour coded) made by biologically driven combinations of phenotypes and centred on the pathways of LDL (A) and HDL (B). Each branch of the trees was regressed on the whole set of tagged SNPs (∼273K SNPs) using GUESS and adjusting for sex, age and body mass index. (C) Output from GUESS is used to derive the Best Models Visited (BMV), i.e. the most supported multivariate models, and their Model Posterior Probability (MPP), i.e. the fraction of the model space explained by the BMV (MPP of the top BMV and the cumulative MPP of the top five BMV are indicated in the first two columns, respectively). Based on an empirical FDR procedure, we selected a parsimonious set of significant SNPs (indicated on the top of the table with the associated locus) that explains the variation of each branch of the two trees. Merging this information with the list of SNPs in the top BMV allowed us to highlight a robust subset of significant SNPs that repeatedly contribute to the top supported model (significant SNPs are depicted in black whereas significant SNPs that are also in the top BMV are indicated in red). For each SNPs, comparison of the marginal strength of association across different combinations of traits is possible by a new rescaled measure of marginal phenotype-SNP association, Ratio of Bayes Factors (RBF) (phenotype-SNP log₁₀(RBF) is truncated at 20 to increase readability). Based on Ensembl R66 annotation, each locus is classified as: (1) intronic, (2) 3′UTR, (3) downstream, (4) previously associated and (5) a tagSNP of a previously associated SNP. The name of the locus is also reported on the right of each branch of the two trees with the same colour code used in the table: black if the locus is associated with the phenotypes with FDR<5%, red if the locus is also in the top BMV with FDR<5%.</p