metaGWASmanager: a toolbox for an automated workflow from phenotypes to meta-analysis in GWAS consortia.

SUMMARY: This article introduces the metaGWASmanager, which streamlines genome-wide association studies within large-scale meta-analysis consortia. It is a toolbox for both the central consortium analysis group and participating studies to generate homogeneous phenotypes, minimize unwanted variability from inconsistent methodologies, ensure high-quality association results, and implement time-efficient quality control workflows. The toolbox features a plug-in-based approach for customization of association testing. RESULTS: The metaGWASmanager toolbox has been successfully deployed in both the CKDGen and MetalGWAS Initiative consortia across hundreds of participating studies, demonstrating its effectiveness in GWAS analysis optimization by automating routine tasks and ensuring the value and reliability of association results, thus, ultimately promoting scientific discovery. We provide a simulated data set with examples for script customization so that readers can reproduce the pipeline at their convenience. AVAILABILITY AND IMPLEMENTATION: GitHub: https://github.com/genepi-freiburg/metaGWASmanager.

Omics feature selection with the extended SIS R package: identification of a body mass index epigenetic multi-marker in the Strong Heart Study.

The statistical analysis of omics data poses a great computational challenge given its ultra-high dimensional nature and frequent between-features correlation. In this work, we extended the Iterative Sure Independence Screening (ISIS) algorithm by pairing ISIS with elastic-net (Enet) and two versions of adaptive Enet (AEnet and MSAEnet) to efficiently improve feature selection and effect estimation in omics research. We subsequently used genome-wide human blood DNA methylation data from American Indians of the Strong Heart Study (N=2,235 participants), measured in 1989-1991, to compare the performance (predictive accuracy, coefficient estimation and computational efficiency) of SIS-paired regularization methods to Bayesian shrinkage and traditional linear regression to identify epigenomic multi-marker of body mass index. ISIS-AEnet outperformed the other methods in prediction. In biological pathway enrichment analysis of genes annotated to BMI-related differentially methylated positions, ISIS-AEnet captured most of the enriched pathways in common for at least two of all the evaluated methods. ISIS-AEnet can favor biological discovery because it identifies the most robust biological pathways while achieving an optimal balance between bias and efficient feature selection. In the extended SIS R package, we also implemented ISIS paired with Cox and logistic regression for time-to-event and binary endpoints, respectively, and bootstrap confidence intervals for the estimated regression coefficients.

Smoking, blood DNA methylation sites and lung cancer risk.

Altered DNA methylation (DNAm) might be a biological intermediary in the pathway from smoking to lung cancer. In this study, we investigated the contribution of differential blood DNAm to explain the association between smoking and lung cancer incidence. Blood DNAm was measured in 2321 Strong Heart Study (SHS) participants. Incident lung cancer was assessed as time to event diagnoses. We conducted mediation analysis, including validation with DNAm and paired gene expression data from the Framingham Heart Study (FHS). In the SHS, current versus never smoking and pack-years single-mediator models showed, respectively, 29 and 21 differentially methylated positions (DMPs) for lung cancer with statistically significant mediated effects (14 of 20 available, and five of 14 available, positions, replicated, respectively, in FHS). In FHS, replicated DMPs showed gene expression downregulation largely in trans, and were related to biological pathways in cancer. The multimediator model identified that DMPs annotated to the genes AHRR and IER3 jointly explained a substantial proportion of lung cancer. Thus, the association of smoking with lung cancer was partly explained by differences in baseline blood DNAm at few relevant sites. Experimental studies are needed to confirm the biological role of identified eQTMs and to evaluate potential implications for early detection and control of lung cancer.

Metabolomic patterns, redox-related genes and metals, and bone fragility endpoints in the Hortega Study.

BACKGROUND: The potential joint influence of metabolites on bone fragility has been rarely evaluated. We assessed the association of plasma metabolic patterns with bone fragility endpoints (primarily, incident osteoporosis-related bone fractures, and, secondarily, bone mineral density BMD) in the Hortega Study participants. Redox balance plays a key role in bone metabolism. We also assessed differential associations in participant subgroups by redox-related metal exposure levels and candidate genetic variants. MATERIAL AND METHODS: In 467 participants older than 50 years from the Hortega Study, a representative sample from a region in Spain, we estimated metabolic principal components (mPC) for 54 plasma metabolites from NMR-spectrometry. Metals biomarkers were measured in plasma by AAS and in urine by HPLC-ICPMS. Redox-related SNPs (N = 341) were measured by oligo-ligation assay. RESULTS: The prospective association with incident bone fractures was inverse for mPC1 (non-essential and essential amino acids, including branched-chain, and bacterial co-metabolites, including isobutyrate, trimethylamines and phenylpropionate, versus fatty acids and VLDL) and mPC4 (HDL), but positive for mPC2 (essential amino acids, including aromatic, and bacterial co-metabolites, including isopropanol and methanol). Findings from BMD models were consistent. Participants with decreased selenium and increased antimony, arsenic and, suggestively, cadmium exposures showed higher mPC2-associated bone fractures risk. Genetic variants annotated to 19 genes, with the strongest evidence for NCF4, NOX4 and XDH, showed differential metabolic-related bone fractures risk. CONCLUSIONS: Metabolic patterns reflecting amino acids, microbiota co-metabolism and lipid metabolism were associated with bone fragility endpoints. Carriers of redox-related variants may benefit from metabolic interventions to prevent the consequences of bone fragility depending on their antimony, arsenic, selenium, and, possibly, cadmium, exposure levels.

Gene-environment interaction analysis of redox-related metals and genetic variants with plasma metabolic patterns in a general population from Spain: The Hortega Study.

BACKGROUND: Limited studies have evaluated the joint influence of redox-related metals and genetic variation on metabolic pathways. We analyzed the association of 11 metals with metabolic patterns, and the interacting role of candidate genetic variants, in 1145 participants from the Hortega Study, a population-based sample from Spain. METHODS: Urine antimony (Sb), arsenic, barium (Ba), cadmium (Cd), chromium (Cr), cobalt (Co), molybdenum (Mo) and vanadium (V), and plasma copper (Cu), selenium (Se) and zinc (Zn) were measured by ICP-MS and AAS, respectively. We summarized 54 plasma metabolites, measured with targeted NMR, by estimating metabolic principal components (mPC). Redox-related SNPs (N = 291) were measured by oligo-ligation assay. RESULTS: In our study, the association with metabolic principal component (mPC) 1 (reflecting non-essential and essential amino acids, including branched chain, and bacterial co-metabolism versus fatty acids and VLDL subclasses) was positive for Se and Zn, but inverse for Cu, arsenobetaine-corrected arsenic (As) and Sb. The association with mPC2 (reflecting essential amino acids, including aromatic, and bacterial co-metabolism) was inverse for Se, Zn and Cd. The association with mPC3 (reflecting LDL subclasses) was positive for Cu, Se and Zn, but inverse for Co. The association for mPC4 (reflecting HDL subclasses) was positive for Sb, but inverse for plasma Zn. These associations were mainly driven by Cu and Sb for mPC1; Se, Zn and Cd for mPC2; Co, Se and Zn for mPC3; and Zn for mPC4. The most SNP-metal interacting genes were NOX1, GSR, GCLC, AGT and REN. Co and Zn showed the highest number of interactions with genetic variants associated to enriched endocrine, cardiovascular and neurological pathways. CONCLUSIONS: Exposures to Co, Cu, Se, Zn, As, Cd and Sb were associated with several metabolic patterns involved in chronic disease. Carriers of redox-related variants may have differential susceptibility to metabolic alterations associated to excessive exposure to metals.

Arsenic exposure and human blood DNA methylation and hydroxymethylation profiles in two diverse populations from Bangladesh and Spain.

BACKGROUND: Associations of arsenic (As) with the sum of 5-mC and 5-hmC levels have been reported; however, As exposure-related differences of the separated 5-mC and 5-hmC markers have rarely been studied. METHODS: In this study, we evaluated the association of arsenic exposure biomarkers and 5-mC and 5-hmC in 30 healthy men (43-55 years) from the Aragon Workers Health Study (AWHS) (Spain) and 31 healthy men (31-50 years) from the Folic Acid and Creatinine Trial (FACT) (Bangladesh). We conducted 5-mC and 5-hmC profiling using Infinium MethylationEPIC arrays, on paired standard and modified (ox-BS in AWHS and TAB in FACT) bisulfite converted blood DNA samples. RESULTS: The median for the sum of urine inorganic and methylated As species (ΣAs) (µg/L) was 12.5 for AWHS and 89.6 for FACT. The median of blood As (µg/L) was 8.8 for AWHS and 10.2 for FACT. At a statistical significance p-value cut-off of 0.01, the differentially methylated (DMP) and hydroxymethylated (DHP) positions were mostly located in different genomic sites. Several DMPs and DHPs were consistently found in AWHS and FACT both for urine ΣAs and blood models, being of special interest those attributed to the DIP2C gene. Three DMPs (annotated to CLEC12A) for AWHS and one DHP (annotated to NPLOC4) for FACT remained statistically significant after false discovery rate (FDR) correction. Pathways related to chronic diseases including cardiovascular, cancer and neurological were enriched. CONCLUSIONS: While we identified common 5-hmC and 5-mC signatures in two populations exposed to varying levels of inorganic As, differences in As-related epigenetic sites across the study populations may additionally reflect low and high As-specific associations. This work contributes a deeper understanding of potential epigenetic dysregulations of As. However, further research is needed to confirm biological consequences associated with DIP2C epigenetic regulation and to investigate the role of 5-hmC and 5-mC separately in As-induced health disorders at different exposure levels.