Causal Inference Test Research Articles

A genomic approach identifies sRAGE as a putatively causal protein for asthma

Asthma is a complex respiratory condition caused by environmental and genetic factors. Although lower concentrations of the anti-inflammatory protein soluble receptor for advanced glycation end products (sRAGE) have been associated with asthma in humans and mouse models, it is uncertain whether sRAGE plays a causal role in asthma. We designed a 2-stage study of sRAGE in relation to asthma with association analysis in FHS participants as well as causal inference testing using Mendelian randomization (MR). We measured plasma levels of sRAGE and performed cross-sectional analysis to examine the association between plasma sRAGE concentration and asthma status in 6546 FHS participants. We then used sRAGE protein advanced glycation end products (pQTLs) derived from a genome-wide association study of plasma sRAGE levels in ∼7000 FHS participants with UK Biobank asthma genome-wide association study in MR to consider sRAGE as a putatively causal protein for asthma. We also performed replication MR using an externally derived sRAGE pQTL from the INTERVAL study. Last, we conducted colocalization using cis-pQTL variants at the advanced glycosylation end-product specific receptor (AGER) locus with variants from the UK Biobank asthma genome-wide association study. Association analysis revealed that each 1 SD increment in sRAGE concentration was associated with a 14% lower odds of asthma in FHS participants (95% CI 0.76-0.96). MR identified sRAGE as putatively causal for and protective against asthma on the basis of self-reported (odds ratio [per 1 SE increment in inverse-rank-normalized sRAGE]= 0.97, 95% CI 0.95-0.99; P= .005) and doctor-diagnosed asthma (odds ratio= 0.97, 95% CI 0.95-0.99; P= .011). Through this genomic approach, we identified sRAGE as a putatively causal, biologically important, and protective protein in relation to asthma. Functional studies incell/animal models are needed to confirm our findings.

Gray Matter Structures Mediate Associations between Neighborhood Socioeconomic Status and Cognition in Adolescents: Application of a Mediation Analysis Method

BACKGROUND AND AIM: Over the last few years, significant developments have been taking place in high-dimensional neuroimaging data analysis. METHODS: To determine which neuroimaging features may mediate effects of neighborhood socioeconomic status (SES) on cognitive assessments, we analyzed data from the Adolescent Brain Cognitive Development (ABCD) study and conducted mediation analysis with the Causal Inference Test (CIT). To facilitate this analysis, we extended the data reduction method, Partition, for application to high-dimensional neuroimaging data. Unlike the previously proposed version that directs reduction based on attribute correlation only, we adopted a strategy for the extension that combines spatial distance with attribute correlation. RESULTS:We performed computer simulation studies to contrast the new version with the original approach and found that incorporating spatial information substantially increased data reduction for the same level of information capture and improved computational efficiency. Neuroimaging data were reduced via the modified Partition approach, yielding 88% reduction for 80% information capture. A sulcal depth feature in the superior frontal language area was identified as consistent with causal mediation (CIT P = 0.04). For this feature, there was strong evidence of marginal associations for i) neighborhood vs cognitive, ii) neighborhood vs sulcal depth, and iii) sulcal depth vs cognitive (P = 6.91e-11, 6.11e-06, and 9.63e-04, respectively). CONCLUSIONS:These results suggest that higher socioeconomic status of a neighborhood may lead to less sulcal depth in the superior frontal language area, resulting in improved cognitive performance. KEYWORDS: Big data, Causal inference, Socio-economic factors

Does obesity mediate the relationship between diabetes and osteoporosis in Chinese elderly population?

Diabetes mellitus (DM), osteoporosis (OP), and obesity (OB) are three complex diseases. OB is associated with both DM and OP, but it is unclear whether OB mediates association between DM and OP. The study aimed to investigate the potential mediation effects of OB on association between DM and bone mineral density (BMD) by the causal inference tests (CIT). A total of 5682 Chinese aged over 65 years were enrolled in an ongoing cohort: Osteoporosis Preventive Project (OPP). Obesity-related indexes, including body mass index (BMI), waist circumference, and waist circumference-hip circumference-ratio (WHR), and BMD at total hip (TH) and femur neck (FN) were measured. Subjects with DM had significant greater values of age, weight, BMI, waist circumference, WHR, and BMD than non-DM subjects. BMD at TH and FN was significantly associated with DM (p < 0.05) with adjustment of age both in males and females. Further CIT showed that OB-related indexes (BMI, waist circumference, and WHR) are significantly mediators in the associations between DM and BMD in females, but not in males. Furthermore, the mediation effects of waist circumference were detected on DM and TH BMD in the females of normal-weight group. Obesity-related indexes, especially waist circumference, serve as significant mediator(s) between DM and OP in Chinese female elderly. Diabetes increases BMD by increasing obesity-related indexes. The findings established the intermediate role of OB underlying the association between DM and OP in human population.

Hypomethylation mediates genetic association with the major histocompatibility complex genes in Sjögren's syndrome.

Differential methylation of immune genes has been a consistent theme observed in Sjögren's syndrome (SS) in CD4+ T cells, CD19+ B cells, whole blood, and labial salivary glands (LSGs). Multiple studies have found associations supporting genetic control of DNA methylation in SS, which in the absence of reverse causation, has positive implications for the potential of epigenetic therapy. However, a formal study of the causal relationship between genetic variation, DNA methylation, and disease status is lacking. We performed a causal mediation analysis of DNA methylation as a mediator of nearby genetic association with SS using LSGs and genotype data collected from 131 female members of the Sjögren's International Collaborative Clinical Alliance registry, comprising of 64 SS cases and 67 non-cases. Bumphunter was used to first identify differentially-methylated regions (DMRs), then the causal inference test (CIT) was applied to identify DMRs mediating the association of nearby methylation quantitative trait loci (MeQTL) with SS. Bumphunter discovered 215 DMRs, with the majority located in the major histocompatibility complex (MHC) on chromosome 6p21.3. Consistent with previous findings, regions hypomethylated in SS cases were enriched for gene sets associated with immune processes. Using the CIT, we observed a total of 19 DMR-MeQTL pairs that exhibited strong evidence for a causal mediation relationship. Close to half of these DMRs reside in the MHC and their corresponding meQTLs are in the region spanning the HLA-DQA1, HLA-DQB1, and HLA-DQA2 loci. The risk of SS conferred by these corresponding MeQTLs in the MHC was further substantiated by previous genome-wide association study results, with modest evidence for independent effects. By validating the presence of causal mediation, our findings suggest both genetic and epigenetic factors contribute to disease susceptibility, and inform the development of targeted epigenetic modification as a therapeutic approach for SS.

Integrated metagenome and metabolome analyses of blood pressure studies in early postmenopausal Chinese women.

We carried out sensitivity analyses on gut microbiota metagenomic sequencing, untargeted metabolome, targeted metabolome for short-chain fatty acids (SCFAs) and human whole genome sequencing from 402 early postmenopausal Chinese women to search for early omics-biomarkers and gain novel insights into the potential mechanisms of BP regulation in postmenopausal women. Clusters of co-abundant gut bacterial species and serum untargeted metabolites were identified by weighted gene co-expression network analysis (WGCNA). Partial least square analysis and joint analysis were performed to detect BP-associated omics-variables. Partial Pearson correlation was conducted to identify the interactions of microbe--host for host BP variation. Mendelian randomization analysis and causal inference test were used to examine causal relationships among gut microbiota, metabolites and BP variation. In the present study, 651 bacterial species and 296 metabolites were binned into 53 and 26 co-abundance clusters by WGCNA, respectively. Then, we totally identified four gut bacterial species, one host metabolites and two SCFAs that were significantly associated with both SBP and DBP. Moreover, we found that gut microbiota would play important roles in host metabolic activity. Finally, our results revealed that increased Bacteroides fragilis could elevate BP via decreased caproic acid, and phenylacetylglutamine mediated the causal relationships of both B. fragilis and Clostridium sp.CAG.226 on DBP variation. Multi-omics datasets integration has the potential to capture complementary effect and their interactions for BP variation, revealed the potential pathogenesis of BP variation and may be useful for studying other complex diseases/traits.

Integrative lncRNA–mRNA co‐expression network analysis identifies novel lncRNA E2F3‐IT1 for rheumatoid arthritis

Integrative lncRNA–mRNA co‐expression network analysis identifies novel lncRNA E2F3‐IT1 for rheumatoid arthritis

Integrative analysis identifies potential causal methylation-mRNA regulation chains for rheumatoid arthritis

Genome-wide association studies have identified many genetic loci for rheumatoid arthritis (RA). However, causal factors underlying these loci were largely unknown. The aim of this study was to identify potential causal methylation-mRNA regulation chains for RA. We identified differentially expressed mRNAs and methylations and conducted summary statistic data-based Mendelian randomization (SMR) analysis to detect potential causal mRNAs and methylations for RA. Then causal inference test (CIT) was performed to determine if the methylation-mRNA pairs formed causal chains. We identified 11,170 mRNAs and 24,065 methylations that were nominally associated with RA. Among them, 197 mRNAs and 104 methylations passed the SMR test. According to physical positions, we defined 16 cis methylation-mRNA pairs and inferred 5 chains containing 4 methylations and 4 genes (BACH2, MBP, MX1 and SYNGR1) to be methylation→mRNA→RA causal chains. The effect of SYNGR1 expression in peripheral blood mononuclear cells on RA risk was found to be consistent in both the in-house and public data. The identified methylations located in CpG Islands that overlap promoters in the 5′ region of the genes. The promoter regions showed long-range interactions with other enhancers and promoters, suggesting a regulatory potential of these methylations. Therefore, the present study provided a new integrative analysis strategy and highlighted potential causal methylation-mRNA chains for RA. Taking the evidences together, SYNGR1 promoter methylations most probably affect mRNA expressions and then affect RA risk.

Integrative Analysis of Glucometabolic Traits, Adipose Tissue DNA Methylation, and Gene Expression Identifies Epigenetic Regulatory Mechanisms of Insulin Resistance and Obesity in African Americans.

Decline in insulin sensitivity due to dysfunction of adipose tissue (AT) is one of the earliest pathogenic events in type 2 diabetes. We hypothesize that differential DNA methylation (DNAm) controls insulin sensitivity and obesity by modulating transcript expression in AT. Integrating AT DNAm profiles with transcript profile data measured in a cohort of 230 African Americans (AAs) from the African American Genetics of Metabolism and Expression cohort, we performed cis-expression quantitative trait methylation (cis-eQTM) analysis to identify epigenetic regulatory loci for glucometabolic trait-associated transcripts. We identified significantly associated cytosine-guanine dinucleotide regions for 82 transcripts (false discovery rate [FDR]-P < 0.05). The strongest eQTM locus was observed for the proopiomelanocortin (POMC; ρ = -0.632, P = 4.70 × 10-27) gene. Epigenome-wide association studies (EWAS) further identified 155, 46, and 168 cytosine-guanine dinucleotide regions associated (FDR-P < 0.05) with the Matsuda index, SI, and BMI, respectively. Intersection of EWAS, transcript level to trait association, and eQTM results, followed by causal inference test identified significant eQTM loci for 23 genes that were also associated with Matsuda index, SI, and/or BMI in EWAS. These associated genes include FERMT3, ITGAM, ITGAX, and POMC In summary, applying an integrative multiomics approach, our study provides evidence for DNAm-mediated regulation of gene expression at both previously identified and novel loci for many key AT transcripts influencing insulin resistance and obesity.

Does terrorism trigger online hate speech? On the association of events and time series

Hate speech is ubiquitous on the Web. Recently, the offline causes that contribute to online hate speech have received increasing attention. A recurring question is whether the occurrence of extreme events offline systematically triggers bursts of hate speech online, indicated by peaks in the volume of hateful social media posts. Formally, this question translates into measuring the association between a sparse event series and a time series. We propose a novel statistical methodology to measure, test and visualize the systematic association between rare events and peaks in a time series. In contrast to previous methods for causal inference or independence tests on time series, our approach focuses only on the timing of events and peaks and no other distributional characteristics. We follow the framework of event coincidence analysis (ECA) that was originally developed to correlate point processes. We formulate a discrete-time variant of ECA and derive all required distributions to enable analyses of peaks in time series with a special focus on serial dependencies and peaks over multiple thresholds. The analysis gives rise to a novel visualization of the association via quantile-trigger rate plots. We demonstrate the utility of our approach by analyzing whether Islamist terrorist attacks in Western Europe and North America systematically trigger bursts of hate speech and counter-hate speech on Twitter.

Systemic Investigation of Promoter-wide Methylome and Genome Variations in Gout.

Current knowledge of gout centers on hyperuricemia. Relatively little is known regarding the pathogenesis of gouty inflammation. To investigate the epigenetic background of gouty inflammation independent of hyperuricemia and its relationship to genetics, 69 gout patients and 1455 non-gout controls were included. Promoter-wide methylation was profiled with EPIC array. Whole-genome sequencing data were included for genetic and methylation quantitative trait loci (meQTL) analyses and causal inference tests. Identified loci were subjected to co-methylation analysis and functional localization with DNase hypersensitivity and histone marks analysis. An expression database was queried to clarify biologic functions of identified loci. A transcription factor dataset was integrated to identify transcription factors coordinating respective expression. In total, seven CpG loci involved in interleukin-1β production survived genetic/meQTL analyses, or causal inference tests. None had a significant relationship with various metabolic traits. Additional analysis suggested gouty inflammation, instead of hyperuricemia, provides the link between these CpG sites and gout. Six (PGGT1B, INSIG1, ANGPTL2, JNK1, UBAP1, and RAPTOR) were novel genes in the field of gout. One (CNTN5) was previously associated with gouty inflammation. Transcription factor mapping identified several potential transcription factors implicated in the link between differential methylation, interleukin-1β production, and gouty inflammation. In conclusion, this study revealed several novel genes specific to gouty inflammation and provided enhanced insight into the biological basis of gouty inflammation.

O11 Lymphocyte DNA methylation mediates genetic risk at RA risk loci that are shared with other immune mediated diseases

Abstract Background The aetiology of rheumatoid arthritis (RA) is complex. In particular, the vast majority of disease-associated variants implicated by genome-wide association studies are non-coding, leaving genetic mechanisms of adaptive immune dysregulation unresolved. The contribution to this process of epigenetic factors, including the addition of methyl groups to DNA, also remains uncertain. To address these issues and prioritise causal genes for downstream study, genome-wide data incorporating DNA methylation and gene expression measurements from lymphocyte subsets in an early arthritis inception cohort, were available. Methods Whole genome methylation and transcription data from isolated CD4+ T cells and B cells of &amp;gt; 100 well-characterised inflammatory arthritis patients, all of whom were naïve to immunomodulatory treatments and of Northern European ancestry, were obtained (Illumina HumanHT-12 v4 Expression BeadChip and Infinium MethylationEPIC BeadChip arrays, respectively). Genotyping was undertaken using the Illumina Human CoreExome-24 version 1-0 array. After independent pre-processing, normalisation and quality control of paired CD4+ and B lymphocyte data, methylation quantitative trait loci (meQTLs) were first modelled using the MatrixEQTL package in each cell type. Next, at RA risk-associated cis-CpGs, correlations between site-specific methylation and the expression of genes within ±500Kb identified quantitative trait methylations (eQTMs). To infer directionality of SNP-CpG-transcript associations a causal inference test (CIT) was applied. Multiple testing was accounted for, and in vitro assays were used to validate meQTLs at loci of interest and confirm regulatory mechanisms. Further analysis integrated GWAS data from other immune mediated diseases (IMDs) and additional publically available resources. Results We found strong evidence that disease-associated DNA variants regulate cis-CpG methylation of DNA in CD4+ T and/or B cells at 37% RA loci. In general we observed these variants to preferentially modify methylation at sites mapping to lymphocyte enhancers and regions flanking transcription start sites, and at positions bound by the NFκB transcription factor. Using paired, cell-specific transcriptomic data and a statistical approach to infer causality, we then identified examples where site-specific DNA methylation in turn mediates gene expression, including ORMDL3/GSDMB, IL6ST/ANKRD55, FCRL3 and JAZF1 in CD4+ lymphocytes. Leveraging GWAS data we noted that a number of genes regulated in this way highlight mechanisms common to RA, multiple sclerosis and asthma, distinguishing these IMDs from osteoarthritis which is considered a primarily degenerative disease. To validate our findings, cis-meQTL effects at sentinel loci were replicated by pyrosequencing in an independent cohort of genotyped early arthritis patients, and methylation-mediated regulation of FCRL3 expression downstream of the regulatory SNP was confirmed experimentally using a luciferase reporter assay in Jurkat T-cells. Conclusion Our observations highlight important mechanisms of genetic risk in RA and the wider context of autoimmunity. They confirm the utility of DNA methylation profiling as a tool for causal gene prioritisation and, potentially, therapeutic targeting in complex IMD. Disclosures A.D. Clark None. N. Nair None. A.E. Anderson None. N. Thalayasingam None. N. Naamane None. A.J. Skelton None. J. Diboll None. A. Barton None. S. Eyre None. J.D. Isaacs None. L.N. Reynard None. A.G. Pratt Grants/research support; I am a recipient of an unrestricted, investigator initiated research grant from Pfizer, paid to Newcastle University.

DNA methylation and cis-regulation of gene expression by prostate cancer risk SNPs.

Genome-wide association studies have identified more than 100 SNPs that increase the risk of prostate cancer (PrCa). We identify and compare expression quantitative trait loci (eQTLs) and CpG methylation quantitative trait loci (meQTLs) among 147 established PrCa risk SNPs in primary prostate tumors (n = 355 from a Seattle-based study and n = 495 from The Cancer Genome Atlas, TCGA) and tumor-adjacent, histologically benign samples (n = 471 from a Mayo Clinic study). The role of DNA methylation in eQTL regulation of gene expression was investigated by data triangulation using several causal inference approaches, including a proposed adaptation of the Causal Inference Test (CIT) for causal direction. Comparing eQTLs between tumors and benign samples, we show that 98 of the 147 risk SNPs were identified as eQTLs in the tumor-adjacent benign samples, and almost all 34 eQTL identified in tumor sets were also eQTLs in the benign samples. Three lines of results support the causal role of DNA methylation. First, nearly 100 of the 147 risk SNPs were identified as meQTLs in one tumor set, and almost all eQTLs in tumors were meQTLs. Second, the loss of eQTLs in tumors relative to benign samples was associated with altered DNA methylation. Third, among risk SNPs identified as both eQTLs and meQTLs, mediation analyses suggest that over two-thirds have evidence of a causal role for DNA methylation, mostly mediating genetic influence on gene expression. In summary, we provide a comprehensive catalog of eQTLs, meQTLs and putative cancer genes for known PrCa risk SNPs. We observe that a substantial portion of germline eQTL regulatory mechanisms are maintained in the tumor development, despite somatic alterations in tumor genome. Finally, our mediation analyses illuminate the likely intermediary role of CpG methylation in eQTL regulation of gene expression.

Lymphocyte DNA methylation mediates genetic risk at shared immune-mediated disease loci

BackgroundDefining regulatory mechanisms through which noncoding risk variants influence the cell-mediated pathogenesis of immune-mediated disease (IMD) has emerged as a priority in the post–genome-wide association study era.ObjectivesWith a focus on rheumatoid arthritis, we sought new insight into genetic mechanisms of adaptive immune dysregulation to help prioritize molecular pathways for targeting in this and related immune pathologies.MethodsWhole-genome methylation and transcriptional data from isolated CD4+ T cells and B cells of more than 100 genotyped and phenotyped patients with inflammatory arthritis, all of whom were naive to immunomodulatory treatments, were obtained. Analysis integrated these comprehensive data with genome-wide association study findings across IMDs and other publicly available resources.ResultsWe provide strong evidence that disease-associated DNA variants regulate cis-CpG methylation in CD4+ T and/or B cells at 37% RA loci. Using paired, cell-specific transcriptomic data and causal inference testing, we identify examples where site-specific DNA methylation in turn mediates gene expression, including FCRL3 in both cell types and ORMDL3/GSDMB, IL6ST/ANKRD55, and JAZF1 in CD4+ T cells. A number of genes regulated in this way highlight mechanisms common to RA and other IMDs including multiple sclerosis and asthma, in turn distinguishing them from osteoarthritis, a primarily degenerative disease. Finally, we corroborate the observed effects experimentally.ConclusionsOur observations highlight important mechanisms of genetic risk in RA and the wider context of immune dysregulation. They confirm the utility of DNA methylation profiling as a tool for causal gene prioritization and, potentially, therapeutic targeting in complex IMD.

High-Dimensional Bayesian Network Inference From Systems Genetics Data Using Genetic Node Ordering.

Studying the impact of genetic variation on gene regulatory networks is essential to understand the biological mechanisms by which genetic variation causes variation in phenotypes. Bayesian networks provide an elegant statistical approach for multi-trait genetic mapping and modelling causal trait relationships. However, inferring Bayesian gene networks from high-dimensional genetics and genomics data is challenging, because the number of possible networks scales super-exponentially with the number of nodes, and the computational cost of conventional Bayesian network inference methods quickly becomes prohibitive. We propose an alternative method to infer high-quality Bayesian gene networks that easily scales to thousands of genes. Our method first reconstructs a node ordering by conducting pairwise causal inference tests between genes, which then allows to infer a Bayesian network via a series of independent variable selection problems, one for each gene. We demonstrate using simulated and real systems genetics data that this results in a Bayesian network with equal, and sometimes better, likelihood than the conventional methods, while having a significantly higher overlap with groundtruth networks and being orders of magnitude faster. Moreover our method allows for a unified false discovery rate control across genes and individual edges, and thus a rigorous and easily interpretable way for tuning the sparsity level of the inferred network. Bayesian network inference using pairwise node ordering is a highly efficient approach for reconstructing gene regulatory networks when prior information for the inclusion of edges exists or can be inferred from the available data.

Epigenetically regulated co-expression network of genes significant for rheumatoid arthritis.

Aim: To identify epigenetically regulated network of genes in peripheral blood mononuclear cells significant for rheumatoid arthritis (RA). Methods: Differentially expressed genes (DEGs) and their associated differentially expressed miRNAs and differentially methylated positions (DMPs) were identified. Causal inference test (CIT)identified the causal regulation chains. The analyses, for example, weighted gene co-expression network (WGCNA), protein-protein interaction and functional enrichment, evaluated interaction patterns among the DEGs and the associated epigenetic factors. Results: A total of 181 DEGs were identified. The DEGs were significantly regulated by DMPs and/or differentially expressed miRNAs. Causal inference test analyses identified 18 causal chains of DMP-DEG-RA and 16 intermediate DEGs enriched in 'protein kinase inhibitor activity'. BTN2A1 was co-expressed with other 9 intermediate genes and 11 known RA-associated genes and played a pivotal role in the co-expression network. Conclusion: Epigenetically regulated network of genes in peripheral blood mononuclear cells (PBMC)contributed to RA. The causal DMPs and key intermediate genes may serve as potential biomarkers for RA.

Robust policy evaluation from large-scale observational studies.

Under the current policy decision making paradigm we make or evaluate a policy decision by intervening different socio-economic parameters and analyzing the impact of those interventions. This process involves identifying the causal relation between interventions and outcomes. Matching method is one of the popular techniques to identify such causal relations. However, in one-to-one matching, when a treatment or control unit has multiple pair assignment options with similar match quality, different matching algorithms often assign different pairs. Since all the matching algorithms assign pairs without considering the outcomes, it is possible that with the same data and same hypothesis, different experimenters can reach different conclusions creating an uncertainty in policy decision making. This problem becomes more prominent in the case of large-scale observational studies as there are more pair assignment options. Recently, a robust approach has been proposed to tackle the uncertainty that uses an integer programming model to explore all possible assignments. Though the proposed integer programming model is very efficient in making robust causal inference, it is not scalable to big data observational studies. With the current approach, an observational study with 50,000 samples will generate hundreds of thousands binary variables. Solving such integer programming problem is computationally expensive and becomes even worse with the increase of sample size. In this work, we consider causal inference testing with binary outcomes and propose computationally efficient algorithms that are adaptable for large-scale observational studies. By leveraging the structure of the optimization model, we propose a robustness condition that further reduces the computational burden. We validate the efficiency of the proposed algorithms by testing the causal relation between the Medicare Hospital Readmission Reduction Program (HRRP) and non-index readmissions (i.e., readmission to a hospital that is different from the hospital that discharged the patient) from the State of California Patient Discharge Database from 2010 to 2014. Our result shows that HRRP has a causal relation with the increase in non-index readmissions. The proposed algorithms proved to be highly scalable in testing causal relations from large-scale observational studies.

Arsenic Exposure, DNA methylation and Cancer Mortality in the Strong Heart Study

PDS 63: Chemicals and metals: health effects, Exhibition Hall (PDS), Ground floor, August 27, 2019, 10:30 AM - 12:00 PM Background: Inorganic arsenic is an established human carcinogen. Epigenetic dysregulations, in particular DNA methylation (DNAm), are known to be major drivers of tumorigenesis. The objectives of this study were to evaluate the association of DNAm with overall and specific cancer mortality, as well as to assess the potential mediating role of DNAm in the relationship between inorganic arsenic and cancer. Methods: DNAm was measured in blood samples taken at baseline (1989-1991) using the EPIC 850K platform in 2351 individuals of the Strong Heart Study. Data preprocessing included correction for batch effects and cell heterogeneity. Cancer mortality was available through 2008 (244 deaths). Differentially Methylated Positions (DMPs) between cases and non-cases were identified using linear regression in the R package limma as well as Cox regression (hazard ratio of the p90 vs p10 of DNAm). Differential variability was evaluated using the iEVORA algorithm. The potential mediating role of DNAm on the association between inorganic arsenic and cancer was evaluated using the Causal Inference Test (CIT) approach. Results: Several DMPs were found for overall (6509), lung (144), and lymphatic and hematopoietic (29368) cancers. DMPs from linear regression versus survival analysis were not identical, but showed overlap for overall and lymphatic and hematopoietic cancers. They did not overlap for lung cancer. Differentially Variable Positions were found for the three types of cancer. Several CpG sites annotated to the gene NFYA, which has been previously related to leukemia, showed differential variability with lymphatic and hematopoietic cancers. We found no evidence supporting a mediating role of DNAm on the association between inorganic arsenic and cancer. Conclusions: Our results show differentially methylated and differentially variable blood CpGs prospectively associated with later cancers. Further epigenetic studies should examine variability as well as differential means. These results need to be replicated in an external population.

Multi-omics integrative analysis identified SNP-methylation-mRNA: Interaction in peripheral blood mononuclear cells.

Genetic variants have potential influence on DNA methylation and thereby regulate mRNA expression. This study aimed to comprehensively reveal the relationships among SNP, methylation and mRNA, and identify methylation‐mediated regulation patterns in human peripheral blood mononuclear cells (PBMCs). Based on in‐house multi‐omics datasets from 43 Chinese Han female subjects, genome‐wide association trios were constructed by simultaneously testing the following three association pairs: SNP‐methylation, methylation‐mRNA and SNP‐mRNA. Causal inference test (CIT) was used to identify methylation‐mediated genetic effects on mRNA. A total of 64,184 significant cis‐methylation quantitative trait loci (meQTLs) were identified (FDR < 0.05). Among the 745 constructed trios, 464 trios formed SNP‐methylation‐mRNA regulation chains (CIT). Network analysis (Cytoscape 3.3.0) constructed multiple complex regulation networks among SNP, methylation and mRNA (eg a total of 43 SNPs simultaneously connected to cg22517527 and further to PRMT2, DIP2A and YBEY). The regulation chains were supported by the evidence from 4DGenome database, relevant to immune or inflammatory related diseases/traits, and overlapped with previous eQTLs from dbGaP and GTEx. The results provide new insights into the regulation patterns among SNP, DNA methylation and mRNA expression, especially for the methylation‐mediated effects, and also increase our understanding of functional mechanisms underlying the established associations.

Automated versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition

Statisticians have made great progress in creating methods that reduce our reliance on parametric assumptions. However, this explosion in research has resulted in a breadth of inferential strategies that both create opportunities for more reliable inference as well as complicate the choices that an applied researcher has to make and defend. Relatedly, researchers advocating for new methods typically compare their method to at best 2 or 3 other causal inference strategies and test using simulations that may or may not be designed to equally tease out flaws in all the competing methods. The causal inference data analysis challenge, “Is Your SATT Where It’s At?”, launched as part of the 2016 Atlantic Causal Inference Conference, sought to make progress with respect to both of these issues. The researchers creating the data testing grounds were distinct from the researchers submitting methods whose efficacy would be evaluated. Results from 30 competitors across the two versions of the competition (black-box algorithms and do-it-yourself analyses) are presented along with post-hoc analyses that reveal information about the characteristics of causal inference strategies and settings that affect performance. The most consistent conclusion was that methods that flexibly model the response surface perform better overall than methods that fail to do so. Finally new methods are proposed that combine features of several of the top-performing submitted methods.

Rheumatoid arthritis–associated DNA methylation sites in peripheral blood mononuclear cells

ObjectivesTo identify novel DNA methylation sites significant for rheumatoid arthritis (RA) and comprehensively understand their underlying pathological mechanism.MethodsWe performed (1) genome-wide DNA methylation and mRNA expression profiling in peripheral blood...