Causal Inference Test Research Articles

Identifying Potential Factors Associated With Racial Disparities in COVID-19 Outcomes: Retrospective Cohort Study Using Machine Learning on Real-World Data.

Racial disparities in COVID-19 incidence and outcomes have been widely reported. Non-Hispanic Black patients endured worse outcomes disproportionately compared with non-Hispanic White patients, but the epidemiological basis for these observations was complex and multifaceted. This study aimed to elucidate the potential reasons behind the worse outcomes of COVID-19 experienced by non-Hispanic Black patients compared with non-Hispanic White patients and how these variables interact using an explainable machine learning approach. In this retrospective cohort study, we examined 28,943 laboratory-confirmed COVID-19 cases from the OneFlorida Research Consortium's data trust of health care recipients in Florida through April 28, 2021. We assessed the prevalence of pre-existing comorbid conditions, geo-socioeconomic factors, and health outcomes in the structured electronic health records of COVID-19 cases. The primary outcome was a composite of hospitalization, intensive care unit admission, and mortality at index admission. We developed and validated a machine learning model using Extreme Gradient Boosting to evaluate predictors of worse outcomes of COVID-19 and rank them by importance. Compared to non-Hispanic White patients, non-Hispanic Blacks patients were younger, more likely to be uninsured, had a higher prevalence of emergency department and inpatient visits, and were in regions with higher area deprivation index rankings and pollutant concentrations. Non-Hispanic Black patients had the highest burden of comorbidities and rates of the primary outcome. Age was a key predictor in all models, ranking highest in non-Hispanic White patients. However, for non-Hispanic Black patients, congestive heart failure was a primary predictor. Other variables, such as food environment measures and air pollution indicators, also ranked high. By consolidating comorbidities into the Elixhauser Comorbidity Index, this became the top predictor, providing a comprehensive risk measure. The study reveals that individual and geo-socioeconomic factors significantly influence the outcomes of COVID-19. It also highlights varying risk profiles among different racial groups. While these findings suggest potential disparities, further causal inference and statistical testing are needed to fully substantiate these observations. Recognizing these relationships is vital for creating effective, tailored interventions that reduce disparities and enhance health outcomes across all racial and socioeconomic groups.

Integrative Genetic Variation, DNA Methylation, and Gene Expression Analysis of Escitalopram and Aripiprazole Treatment Outcomes in Depression: A CAN-BIND-1 Study.

Little is known about the interplay between genetics and epigenetics on antidepressant treatment (1) response and remission, (2) side effects, and (3) serum levels. This study explored the relationship among single nucleotide polymorphisms (SNPs), DNA methylation (DNAm), and mRNA levels of four pharmacokinetic genes, CYP2C19, CYP2D6, CYP3A4, and ABCB1, and its effect on these outcomes. The Canadian Biomarker Integration Network for Depression-1 dataset consisted of 177 individuals with major depressive disorder treated for 8 weeks with escitalopram (ESC) followed by 8 weeks with ESC monotherapy or augmentation with aripiprazole. DNAm quantitative trait loci (mQTL), identified by SNP-CpG associations between 20 SNPs and 60 CpG sites in whole blood, were tested for associations with our outcomes, followed by causal inference tests (CITs) to identify methylation-mediated genetic effects. Eleven cis-SNP-CpG pairs (q<0.05) constituting four unique SNPs were identified. Although no significant associations were observed between mQTLs and response/remission, CYP2C19 rs4244285 was associated with treatment-related weight gain (q=0.027) and serum concentrations of ESCadj (q<0.001). Between weeks 2-4, 6.7% and 14.9% of those with *1/*1 (normal metabolizers) and *1/*2 (intermediate metabolizers) genotypes, respectively, reported ≥2 lbs of weight gain. In contrast, the *2/*2 genotype (poor metabolizers) did not report weight gain during this period and demonstrated the highest ESCadj concentrations. CITs did not indicate that these effects were epigenetically mediated. These results elucidate functional mechanisms underlying the established associations between CYP2C19 rs4244285 and ESC pharmacokinetics. This mQTL SNP as a marker for antidepressant-related weight gain needs to be further explored.

Personal exposure to airborne organic pollutants and lung function changes among healthy older adults

Epidemiological evidence on the impact of airborne organic pollutants on lung function among the elderly is limited, and their underlying biological mechanisms remain largely unexplored. Herein, a longitudinal panel study was conducted in Jinan, Shandong Province, China, involving 76 healthy older adults monitored over a span of five months repetitively. We systematically evaluated personal exposure to a diverse range of airborne organic pollutants using a wearable passive sampler and their effects on lung function. Participants’ pulmonary function indicators were assessed, complemented by comprehensive multi-omics analyses of blood and urine samples. Leveraging the power of interaction analysis, causal inference test (CIT), and integrative pathway analysis (IPA), we explored intricate relationships between specific organic pollutants, biomolecules, and lung function deterioration, elucidating the biological mechanisms underpinning the adverse impacts of these pollutants. We observed that bis (2-chloro-1-methylethyl) ether (BCIE) was significantly associated with negative changes in the forced vital capacity (FVC), with glycerolipids mitigating this adverse effect. Additionally, 31 canonical pathways [e.g., high mobility group box 1 (HMGB1) signaling, phosphatidylinositol 3-kinase (PI3K)/AKT pathway, epithelial mesenchymal transition, and heme and nicotinamide adenine dinucleotide (NAD) biosynthesis] were identified as potential mechanisms. These findings may hold significant implications for developing effective strategies to prevent and mitigate respiratory health risks arising from exposure to such airborne pollutants. However, due to certain limitations of the study, our results should be interpreted with caution.

Upstream regulation of neurotransmitter signaling genes in metastatic colorectal cancer (mCRC) patients: Data from CALGB/SWOG 80405 trial (Alliance) and MONSTAR-SCREEN-2.

3523 Background: Evidence suggests neurotransmitter signaling may play a crucial role in tumorigenesis of gastrointestinal (GI) cancers, including migration, proliferation, and apoptosis, as well as angiogenesis and the microenvironment. We developed and applied a strategy to identify upstream regulators of neurotransmitters and their associations with patient outcomes. Methods: Prior literature yielded 85 neurotransmitter signaling genes mapped to RNA-seq gene expression in tumor tissue from 545 patients with mCRC enrolled in the phase III CALGB/SWOG 80405 trial. These samples were also sequenced by FoundationOne CDx for DNA damage. Using Cox proportional hazards regression, we identified a subset of CDx genes associated with overall survival (OS) or progression-free survival (PFS). We extended the Causal Inference Test (CIT) for time-to-event data and identified a subset of the 85 signaling genes consistent with mediation of DNA mutation effects on OS and PFS. Another CIT analysis identified potential upstream neurotransmitter regulators, gene expression mediators of mutation effects on the signaling genes. Top genes were then assessed in whole-transcriptome sequencing (WTS) expression data with Caris MI profile collected from 398 patients with advanced CRC enrolled in the MONSTAR-SCREEN-2 trial. Results: At the 0.05 alpha level, we identified 20 signaling genes with gene expression associated with PFS and/or OS, and 68 genes with evidence of DNA mutations associated with PFS and/or OS. False discovery rate (FDR) &lt; 0.05 was applied to filter the 56,674 expression features on PFS and OS associations, yielding 173 unique genes as possible mediators. The CIT yielded 53 putative mediators at FDR &lt; 0.05, suggesting upstream regulation of neurotransmitter signaling. Five genes were identified as targeting at least 8 signaling genes each, PIGU, ANO9, LY6G6D, and GPR160, and FSCN1, where the first 4 are favorable and only FSCN1 is a poorer prognostic. Following up on these 5 genes in MONSTAR-SCREEN-2 by dichotomizing at the median value of WTS expression, 3 were nominally associated with OS, PIGU ( P = 0.050; HR, 0.59; 95%CI, 0.34-1.00), ANO9( P = 0.029; HR, 0.55; 95%CI, 0.32-0.95), LY6G6D( P = 0.0024; HR, 0.44; 95%CI, 0.25-0.76), and FSCN1 was suggestive ( P = 0.079; HR, 1.62; 95%CI, 0.94-2.77), with direction of effects in agreement with 80405 results for all four. Conclusions: Our novel approach identified previously unknown upstream regulators of neurotransmitter signaling associated with treatment outcomes in mCRC. PIGU, FSCN1, and LY6G6D have been suggested in prior literature as targets for CRC and ANO9 has been suggested as a target for GI cancer. These results bolster the strategy of using mediation analysis with large-scale molecular data to identify new therapeutic targets in CRC. NCT00265850.

DNA Methylation in the Anti-Mullerian Hormone Gene and the Risk of Disease Activity in Multiple Sclerosis.

Multiple sclerosis (MS) has a complex pathobiology, with genetic and environmental factors being crucial players. Understanding the mechanisms underlying heterogeneity in disease activity is crucial for tailored treatment. We explored the impact of DNA methylation, a key mechanism in the genetics-environment interplay, on disease activity in MS. Peripheral immune methylome profiling using Illumina Infinium MethylationEPIC BeadChips was conducted on 249 untreated relapsing-remitting MS patients, sampled at the start of disease-modifying treatment (DMT). A differential methylation analysis compared patients with evidence of disease activity (EDA) to those with no evidence of disease activity (NEDA) over 2 years from DMT start. Utilizing causal inference testing (CIT) and Mendelian randomization (MR), we sought to elucidate the relationships between DNA methylation, gene expression, genetic variation, and disease activity. Four differentially methylated regions (DMRs) were identified between EDA and NEDA. Examining the influence of single nucleotide polymorphisms (SNPs), 923 variants were found to account for the observed differences in the 4 DMRs. Importantly, 3 out of the 923 SNPs, affecting DNA methylation in a DMR linked to the anti-Mullerian hormone (AMH) gene, were associated with disease activity risk in an independent cohort of 1,408 MS patients. CIT and MR demonstrated that DNA methylation in AMH acts as a mediator for the genetic risk of disease activity. This study uncovered a novel molecular pathway implicating the interaction between DNA methylation and genetic variation in the risk of disease activity in MS, emphasizing the role of sex hormones, particularly the AMH, in MS pathobiology. ANN NEUROL 2024;96:289-301.

DNA Methylation Mediated the Association of Body Mass Index With Blood Pressure in Chinese Monozygotic Twins.

Obesity is an established risk factor for hypertension, but the mechanisms are only partially understood. We examined whether body mass index (BMI)-related DNA methylation (DNAm) variation would mediate the association of BMI with blood pressure (BP). We first conducted a genomewide DNA methylation analysis in monozygotic twin pairs to detect BMI-related DNAm variation and then evaluated the mediating effect of DNAm on the relationship between BMI and BP levels using the causal inference test (CIT) method and mediation analysis. Ontology enrichment analysis was performed for CpGs using the GREAT tool. A total of 60 twin pairs for BMI and systolic blood pressure (SBP) and 58 twin pairs for BMI and diastolic blood pressure (DBP) were included. BMI was positively associated with SBP (β = 1.86, p = .0004). The association between BMI and DNAm of 85 CpGs reached p < 1×10-4 level. Eleven BMI-related differentially methylated regions (DMRs) within LNCPRESS1, OGDHL, RNU1-44P, NPHS1, ECEL1P2, LLGL2, RNY4P15, MOGAT3, PHACTR3, and BAI2 were found. Of the 85 CpGs, 9 mapped to C10orf71-AS1, NDUFB5P1, KRT80, BAI2, ABCA2, PEX11G and FGF4 were significantly associated with SBP levels. Of the 9 CpGs, 2 within ABCA2 negatively mediated the association between BMI and SBP, with a mediating effect of -0.24 (95% CI [-0.65, -0.01]). BMI was also positively associated with DBP (β = 0.60, p = .0495). The association between BMI and DNAm of 193 CpGs reached p < 1×10-4 level. Twenty-five BMI-related DMRs within OGDHL, POU4F2, ECEL1P2, TTC6, SMPD4, EP400, TUBA1C and AGAP2 were found. Of the 193 CpGs, 33 mapped to ABCA2, ADORA2B, CTNNBIP1, KDM4B, NAA60, RSPH6A, SLC25A19 and STIL were significantly associated with DBP levels. Of the 33 CpGs, 12 within ABCA2, SLC25A19, KDM4B, PTPRN2, DNASE1, TFCP2L1, LMNB2 and C10orf71-AS1 negatively mediated the association between BMI and DBP, with a total mediation effect of -0.66 (95% CI [-1.07, -0.30]). Interestingly, BMI might also negatively mediate the association between the DNAm of most CpG mediators mentioned above and BP. The mediating effect of DNAm was also found when stratified by sex. In conclusion, DNAm variation may partially negatively mediate the association of BMI with BP. Our findings may provide new clues to further elucidate the pathogenesis of obesity to hypertension and identify new diagnostic biomarkers and therapeutic targets for hypertension.

High-resolution DNA methylation screening of the major histocompatibility complex in multiple sclerosis.

The HLA-DRB1 gene in the major histocompatibility complex (MHC) region in chromosome 6p21 is the strongest genetic factor identified as influencing multiple sclerosis (MS) susceptibility. DNA methylation changes associated with MS have been consistently detected at the MHC region. However, understanding the full scope of epigenetic regulations of the MHC remains incomplete, due in part to the limited coverage of this region by standard whole genome bisulfite sequencing or array-based methods. We developed and validated an MHC capture protocol coupled with bisulfite sequencing and conducted a comprehensive analysis of the MHC methylation landscape in blood samples from 147 treatment naïve MS study participants and 129 healthy controls. We identified 132 differentially methylated region (DMRs) within MHC region associated with disease status. The DMRs overlapped with established MS risk loci. Integration of the MHC methylome with human leukocyte antigen (HLA) genetic data indicate that the methylation changes are significantly associated with HLA genotypes. Using DNA methylation quantitative trait loci (mQTL) mapping and the causal inference test (CIT), we identified 643 cis-mQTL-DMRs paired associations, including 71 DMRs possibly mediating causal relationships between 55 single nucleotide polymorphisms (SNPs) and MS risk. The results describe MS-associated methylation changes in MHC region and highlight the association between HLA genotypes and methylation changes. Results from the mQTL and CIT analyses provide evidence linking MHC region variations, methylation changes, and disease risk for MS.

Methylation and transcriptomic expression profiles of HUVEC in the oxygen and glucose deprivation model and its clinical implications in AMI patients.

The obstructed coronary artery undergoes a series of pathological changes due to ischemic-hypoxic shocks during acute myocardial infarction (AMI). However, the altered DNA methylation levels in endothelial cells under these conditions and their implication for the etiopathology of AMI have not been investigated in detail. This study aimed to explore the relationship between DNA methylation and pathologically altered gene expression profile in human umbilical vein endothelial cells (HUVECs) subjected to oxygen-glucose deprivation (OGD), and its clinical implications in AMI patients. The Illumina Infinium MethylationEPIC BeadChip assay was used to explore the genome-wide DNA methylation profile using the Novaseq6000 platform for mRNA sequencing in 3 pairs of HUVEC-OGD and control samples. GO and KEGG pathway enrichment analyses, as well as correlation, causal inference test (CIT), and protein-protein interaction (PPI) analyses identified 22 hub genes that were validated by MethylTarget sequencing as well as qRT-PCR. ELISA was used to detect four target molecules associated with the progression of AMI. A total of 2,524 differentially expressed genes (DEGs) and 22,148 differentially methylated positions (DMPs) corresponding to 6,642 differentially methylated genes (DMGs) were screened (|Δβ|>0.1 and detection p < 0.05). After GO, KEGG, correlation, CIT, and PPI analyses, 441 genes were filtered. qRT-PCR confirmed the overexpression of VEGFA, CCL2, TSP-1, SQSTM1, BCL2L11, and TIMP3 genes, and downregulation of MYC, CD44, BDNF, GNAQ, RUNX1, ETS1, NGFR, MME, SEMA6A, GNAI1, IFIT1, and MEIS1. DNA fragments BDNF_1_ (r = 0.931, p < 0.0001) and SQSTM1_2_NEW (r = 0.758, p = 0.0043) were positively correlated with the expressions of corresponding genes, and MYC_1_ (r = -0.8245, p = 0.001) was negatively correlated. Furthermore, ELISA confirmed TNFSF10 and BDNF were elevated in the peripheral blood of AMI patients (p = 0.0284 and p = 0.0142, respectively). Combined sequencing from in vitro cellular assays with clinical samples, aiming to establish the potential causal chain of the causal factor (DNA methylation) - mediator (mRNA)-cell outcome (endothelial cell ischemic-hypoxic injury)-clinical outcome (AMI), our study identified promising OGD-specific genes, which provided a solid basis for screening fundamental diagnostic and prognostic biomarkers of coronary endothelial cell injury of AMI. Moreover, it furnished the first evidence that during ischemia and hypoxia, the expression of BNDF was regulated by DNA methylation in endothelial cells and elevated in peripheral blood.

Maternal obesity and childhood asthma risk: Exploring mediating pathways.

Growing evidence for the effect of maternal obesity on childhood asthma motivates investigation of mediating pathways. To investigate if childhood body mass index (BMI), gestational weight gain (GWG) and preterm birth mediate the association of maternal obesity on childhood asthma risk. We used electronic medical records from mother-child pairs enrolled in Kaiser Permanente Northern California integrated healthcare system. Children were followed from their birth (2005-2014) until at least age 4 (n = 95,723), age 6 (n = 59,230) or age 8 (n = 25,261). Childhood asthma diagnosis at each age was determined using ICD-9/10 codes and medication dispensings. Prepregnancy BMI (underweight [<18.5], normal [18.5-24.9], overweight [25-29.9], obese [≥30] kg/m2) were defined using height and weight measurements close to the last menstrual period date. Child's BMI (Centers for Disease Control and Prevention BMI-for-age percentiles: underweight [<5th], normal [5th-85th], overweight [85th-95th], obese [>95th]) were obtained using anthropometric measurements taken the year preceding each follow-up age. GWG (delivery weight-prepregnancy weight) was categorised based on Institutes of Medicine recommendations (inadequate, adequate, excessive). Implementing first causal inference test (CIT) then causal mediator models (to decompose the natural direct and indirect effects), we examined the potential mediating effect of childhood BMI, GWG, and preterm birth on the association between prepregnancy BMI (continuous and categorical) and childhood asthma. Overall, risk of childhood asthma increased as prepregnancy BMI increased (age 4 risk ratio: 1.07, 95% confidence interval: 1.04, 1.09, per 5 kg/m2 increase in BMI; similar for age 6 and 8). CIT identified childhood BMI and preterm birth, but not GWG as potential mediators. Causal mediation models confirmed childhood BMI, but not preterm birth, as having a partial mediating effect. Results were similar for age 6 and 8, and when continuous mediators (instead of binary) were assessed. Childhood overweight/obesity has a modest mediating effect on the association between prepregnancy BMI and childhood asthma.

Both clinically informed and brain region agnostic approaches identify neuroimaging derived phenotypes associated with genetically regulated gene expression of late‐onset Alzheimer’s disease genes

AbstractBackgroundGenetically regulated gene expression (GReX) data leverages expression quantitative trait loci to investigate the genetic mechanism of Alzheimer’s disease (AD). This study utilized the GReX‐mediated neuro‐imaging derived phenotypes (NIDPs), summary features derived from brain imaging modalities, as an endophenotype to identify brain regions associated with late‐onset AD (LOAD) via gene expression.MethodThis study contained brain region‐agnostic and region‐informed approaches. Both approaches utilized a set of 25 genes causally associated with LOAD in our previous cross‐tissue transcriptome‐wide association studies (TWAS) (Chen et al., 2021) to identify LOAD‐associated GReX‐mediated NIDPs. We leveraged the UK Biobank (UKBB) neuroimaging TWAS resource to provide GReX‐mediated NIDP information. Region‐agnostic: We assessed associations between GReX of LOAD genes and T1‐derived cortical and subcortical grey matter NIDPs from the UKBB neuroimaging TWAS. Region‐informed: We identified a set of AD‐associated cortical grey matter NIDPs from diagnosed AD patients (Westman et al., 2013). Next, we subset the UKBB neuroimaging TWAS data to those describing associations between GReX of LOAD genes and the selected AD NIDPs.A Benjamini‐Hochberg FDR p‐value threshold of 0.05 was used to establish statistical significance, and causal inference testing on associations between GReX of LOAD genes and NIDPs was conducted by implementing MR‐JTI in both approaches.Result Region‐agnostic: This approach found associations between GReX of 8 LOAD‐associated genes and 14 NIDPs (Figure 1). The most significant association is the GreX of ZNF296 and the right pars opercularis of the inferior frontal cortex, which replicated across 16 different tissues. Region‐informed: A single highly significant causal association between the GReX of PSMC3 and the ventral diencephalon was identified and replicated in gene expression models of 3 different bansal ganglia‐derived tissue models.ConclusionOur study shows the highly significant causal association between the GReX of ZNF296 and the right pars opercularis on LOAD through the region‐agnostic approach and the strong causal association between the GReX of PSMC3 and the ventral diencephalon on LOAD in AD‐associated region‐informed analyses. Our methodologies provide opportunities to explore brain regions associated with LOAD that are mediated by GReX of AD‐associated genes illuminating the impact of LOAD‐associated genes on gene expression and brain physiology.

Integrated genetic and epigenetic analyses uncovered GLP1R association with metabolically healthy obesity.

Both genetic and epigenetic variations of GLP1R influence the development and progression of obesity. However, the underlying mechanism remains elusive. This study aims to explore the mediation roles of obesity-related methylation sites in GLP1R gene variants-obesity association. A total of 300 Chinese adult participants were included in this study and classified into two groups: 180 metabolically healthy obesity (MHO) cases and 120 metabolically healthy normal-weight (MHNW) controls. Questionnaire investigation, physical measurement and laboratory examination were assessed in all participants. 18 single nucleotide polymorphisms (SNPs) and 31 CpG sites were selected for genotype and methylation assays. Causal inference test (CIT) was performed to evaluate the associations between GLP1R genetic variation, DNA methylation and MHO. The study found that rs4714211 polymorphism of GLP1R gene was significantly associated with MHO. Additionally, methylation sites in the intronic region of GLP1R (GLP1R-68-CpG 7.8.9; GLP1R-68-CpG 12.13; GLP1R-68-CpG 17; GLP1R-68-CpG 21) were associated with MHO, and two of these methylation sites (GLP1R-68-CpG 7.8.9; GLP1R-68-CpG 17) partially mediated the association between genotypes and MHO. Not only the gene polymorphism, but also the DNA methylation of GLP1R was associated with MHO. Epigenetic changes in the methylome may in part explain the relationship between genetic variants and MHO.

Epigenome-wide DNA methylation association study of circulating IgE levels identifies novel targets for asthma

Identifying novel epigenetic signatures associated with serum immunoglobulin E (IgE) may improve our understanding of molecular mechanisms underlying asthma and IgE-mediated diseases. We performed an epigenome-wide association study using whole blood from Framingham Heart Study (FHS; n=3,471, 46% females) participants and validated results using the Childhood Asthma Management Program (CAMP; n=674, 39% females) and the Genetic Epidemiology of Asthma in Costa Rica Study (CRA; n=787, 41% females). Using the closest gene to each IgE-associated CpG, we highlighted biologically plausible pathways underlying IgE regulation and analyzed the transcription patterns linked to IgE-associated CpGs (expression quantitative trait methylation loci; eQTMs). Using prior UK Biobank summary data from genome-wide association studies of asthma and allergy, we performed Mendelian randomization (MR) for causal inference testing using the IgE-associated CpGs from FHS with methylation quantitative trait loci (mQTLs) as instrumental variables. We identified 490 statistically significant differentially methylated CpGs associated with IgE in FHS, of which 193 (39.3%) replicated in CAMP and CRA (FDR < 0.05). Gene ontology analysis revealed enrichment in pathways related to transcription factor binding, asthma, and other immunological processes. eQTM analysis identified 124 cis-eQTMs for 106 expressed genes (FDR < 0.05). MR in combination with drug-target analysis revealed CTSB and USP20 as putatively causal regulators of IgE levels (Bonferroni adjusted P<7.94E-04) that can be explored as potential therapeutic targets. By integrating eQTM and MR analyses in general and clinical asthma populations, our findings provide a deeper understanding of the multidimensional inter-relations of DNA methylation, gene expression, and IgE levels. US NIH/NHLBI grants: P01HL132825, K99HL159234. N01-HC-25195 and HHSN268201500001I.

DNA methylation mediated genetic risk in severe acne in a young men population.

Acne is a chronic inflammatory skin disease that affects the pilosebaceous follicle and is influenced by heredity, hormones, inflammation, and the environment. At present, the recognized pathogenesis mainly includes four categories: excessive sebum secretion, excessive Cutibacterium acnes proliferation, excessive keratinization of sebaceous glands in hair follicles, and inflammatory mechanisms. Previous studies have found that DNA methylation is closely related to some chronic inflammatory skin diseases, and there is evidence that DNA methylation is controlled by genetic factors, making us want to know the relationship between DNA methylation, genetic variation and acne. In our previous study, we performed genome-wide DNA methylation analysis in peripheral blood samples from 44 patients with severe acne and 44 unaffected normal subjects, and identified 23 differentially methylated probes (DMPs). In this study, we identified single nucleotide polymorphisms (SNPs) associated with severe acne by genome-wide association analysis in these 88 samples. To test the association between SNPs and DMPs, we conducted DNA methylation quantitative trait loci (methQTL) analysis. Next, causal inference testing (CIT) was used to determine whether genetic variation influences DNA methylation, which impacts disease phenotypes. We found 38,269 SNPs associated with severe acne. By methQTL analysis, we obtained 24 SNP-CpG pairs that reached the threshold (FDR < 0.05), which included 7 unique CpGs and 22 unique methQTL SNPs. After CIT analysis, we found that 11 out of 24 pairs of SNP-CpG showed a weakened SNP effect after adjustment for methylation, indicating a methylation-mediated relationship between SNPs and severe acne. These 11 SNP-CpG pairs consist of four unique CpG sites and 11 SNPs, of which three CpG sites, cg03020863, cg20652636, and cg19964325, are located on the gene body of PDGFD, the intron of SH2D6, and the 5'UTR of the IL1R1 gene, respectively. During this study, the DNA methylation of certain genes was found to be influenced by genetic factors and mediated the risk of severe acne in a young Chinese male population, providing a new perspective on the pathogenesis of severe acne.

Causal Inference and Conditional Independence Testing with RCoT

Causal Inference and Conditional Independence Testing with RCoT

Whole genome DNA and RNA sequencing of whole blood elucidates the genetic architecture of gene expression underlying a wide range of diseases

To create a scientific resource of expression quantitative trail loci (eQTL), we conducted a genome-wide association study (GWAS) using genotypes obtained from whole genome sequencing (WGS) of DNA and gene expression levels from RNA sequencing (RNA-seq) of whole blood in 2622 participants in Framingham Heart Study. We identified 6,778,286 cis-eQTL variant-gene transcript (eGene) pairs at p < 5 × 10–8 (2,855,111 unique cis-eQTL variants and 15,982 unique eGenes) and 1,469,754 trans-eQTL variant-eGene pairs at p < 1e−12 (526,056 unique trans-eQTL variants and 7233 unique eGenes). In addition, 442,379 cis-eQTL variants were associated with expression of 1518 long non-protein coding RNAs (lncRNAs). Gene Ontology (GO) analyses revealed that the top GO terms for cis-eGenes are enriched for immune functions (FDR < 0.05). The cis-eQTL variants are enriched for SNPs reported to be associated with 815 traits in prior GWAS, including cardiovascular disease risk factors. As proof of concept, we used this eQTL resource in conjunction with genetic variants from public GWAS databases in causal inference testing (e.g., COVID-19 severity). After Bonferroni correction, Mendelian randomization analyses identified putative causal associations of 60 eGenes with systolic blood pressure, 13 genes with coronary artery disease, and seven genes with COVID-19 severity. This study created a comprehensive eQTL resource via BioData Catalyst that will be made available to the scientific community. This will advance understanding of the genetic architecture of gene expression underlying a wide range of diseases.

Trans-genetic effects of circular RNA expression quantitative trait loci and potential causal mechanisms in autism

Genetic risk variants and transcriptional expression changes in autism spectrum disorder (ASD) were widely investigated, but their causal relationship remains largely unknown. Circular RNAs (circRNAs) are abundant in brain and often serve as upstream regulators of mRNAs. By integrating RNA-sequencing with genotype data from autistic brains, we assessed expression quantitative trait loci of circRNAs (circQTLs) that cis-regulated expression of nearby circRNAs and trans-regulated expression of distant genes (trans-eGenes) simultaneously. We thus identified 3619 circQTLs that were also trans-eQTLs and constructed 19,804 circQTL-circRNA-trans-eGene regulatory axes. We conducted two different types of approaches, mediation and partial correlation tests (MPT), to determine the axes with mediation effects of circQTLs on trans-eGene expression through circRNA expression. We showed that the mediation effects of the circQTLs (trans-eQTLs) on circRNA expression were positively correlated with the magnitude of circRNA-trans-eGene correlation of expression profile. The positive correlation became more significant after adjustment for the circQTLs. Of the 19,804 axes, 8103 passed MPT. Meanwhile, we performed causal inference test (CIT) and identified 2070 circQTL-trans-eGene-ASD diagnosis propagation paths. We showed that the CIT-passing genes were significantly enriched for ASD risk genes, genes encoding postsynaptic density proteins, and other ASD-relevant genes, supporting the relevance of the CIT-passing genes to ASD pathophysiology. Integration of MPT- and CIT-passing axes further constructed 352 circQTL-circRNA-trans-eGene-ASD diagnosis propagation paths, wherein the circRNA-trans-eGene axes may act as causal mediators for the circQTL-ASD diagnosis associations. These analyses were also successfully applied to an independent dataset from schizophrenia brains. Collectively, this study provided the first framework for systematically investigating trans-genetic effects of circQTLs and inferring the corresponding causal relations in diseases. The identified circQTL-circRNA-trans-eGene regulatory interactions, particularly the internal modules that were previously implicated in the examined disorders, also provided a helpful dataset for further investigating causative biology and cryptic regulatory mechanisms underlying the neuropsychiatric diseases.

A Computational Framework for Solving Nonlinear Binary Optimization Problems in Robust Causal Inference

Identifying cause-effect relations among variables is a key step in the decision-making process. Whereas causal inference requires randomized experiments, researchers and policy makers are increasingly using observational studies to test causal hypotheses due to the wide availability of data and the infeasibility of experiments. The matching method is the most used technique to make causal inference from observational data. However, the pair assignment process in one-to-one matching creates uncertainty in the inference because of different choices made by the experimenter. Recently, discrete optimization models have been proposed to tackle such uncertainty; however, they produce 0-1 nonlinear problems and lack scalability. In this work, we investigate this emerging data science problem and develop a unique computational framework to solve the robust causal inference test instances from observational data with continuous outcomes. In the proposed framework, we first reformulate the nonlinear binary optimization problems as feasibility problems. By leveraging the structure of the feasibility formulation, we develop greedy schemes that are efficient in solving robust test problems. In many cases, the proposed algorithms achieve a globally optimal solution. We perform experiments on real-world data sets to demonstrate the effectiveness of the proposed algorithms and compare our results with the state-of-the-art solver. Our experiments show that the proposed algorithms significantly outperform the exact method in terms of computation time while achieving the same conclusion for causal tests. Both numerical experiments and complexity analysis demonstrate that the proposed algorithms ensure the scalability required for harnessing the power of big data in the decision-making process. Finally, the proposed framework not only facilitates robust decision making through big-data causal inference, but it can also be utilized in developing efficient algorithms for other nonlinear optimization problems such as quadratic assignment problems. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was supported by the Division of Civil, Mechanical and Manufacturing Innovation of the National Science Foundation [Grant 2047094]. Supplemental Material: The online supplements are available at https://doi.org/10.1287/ijoc.2022.1226 .

Integrative Analysis of DNA Methylation and Gene Expression in the Pathogenesis of Endobronchial Tuberculosis

AbstractThe aim of this study is to identify novel DNA methylation sites significant for endobronchial tuberculosis (EBTB) and comprehensively understand their underlying pathological mechanisms. The following two steps are performed 1) genome‐wide DNA methylation and mRNA expression profiling in endobronchial internal membrane tissue from EBTB patients and healthy controls and 2) a correlation analysis and causal inference tests (CITs) for DNA methylation and mRNA expression data. A total of 1258 DNA methylation regions (DMRs) are found associated with EBTB. The identified DMRs have regulatory effects on mRNA expression. CITs identified four DNA methylation–mRNA–EBTB regulatory chains (ATP6V1B1, KCNAB2, LRP5, and FCER1G). In conclusion, integrated DNA methylation and transcriptome analyses could provide valuable resources for epigenetics studies in EBTB. In addition, the correlation analysis highlighted the importance of a series of regulatory chains in EBTB pathogenesis. The results enhanced our understanding of the important role of DNA methylation in the pathology of EBTB.

Phenome-Wide Association Study of Polygenic Risk Score for Alzheimer's Disease in Electronic Health Records.

Alzheimer’s disease (AD) is the most common form of dementia and a growing public health burden in the United States. Significant progress has been made in identifying genetic risk for AD, but limited studies have investigated how AD genetic risk may be associated with other disease conditions in an unbiased fashion. In this study, we conducted a phenome-wide association study (PheWAS) by genetic ancestry groups within a large academic health system using the polygenic risk score (PRS) for AD. PRS was calculated using LDpred2 with genome-wide association study (GWAS) summary statistics. Phenotypes were extracted from electronic health record (EHR) diagnosis codes and mapped to more clinically meaningful phecodes. Logistic regression with Firth’s bias correction was used for PRS phenotype analyses. Mendelian randomization was used to examine causality in significant PheWAS associations. Our results showed a strong association between AD PRS and AD phenotype in European ancestry (OR = 1.26, 95% CI: 1.13, 1.40). Among a total of 1,515 PheWAS tests within the European sample, we observed strong associations of AD PRS with AD and related phenotypes, which include mild cognitive impairment (MCI), memory loss, and dementias. We observed a phenome-wide significant association between AD PRS and gouty arthropathy (OR = 0.90, adjusted p = 0.05). Further causal inference tests with Mendelian randomization showed that gout was not causally associated with AD. We concluded that genetic predisposition of AD was negatively associated with gout, but gout was not a causal risk factor for AD. Our study evaluated AD PRS in a real-world EHR setting and provided evidence that AD PRS may help to identify individuals who are genetically at risk of AD and other related phenotypes. We identified non-neurodegenerative diseases associated with AD PRS, which is essential to understand the genetic architecture of AD and potential side effects of drugs targeting genetic risk factors of AD. Together, these findings expand our understanding of AD genetic and clinical risk factors, which provide a framework for continued research in aging with the growing number of real-world EHR linked with genetic data.

Integrative blood-derived epigenetic and transcriptomic analysis reveals the potential regulatory role of DNA methylation in ankylosing spondylitis

BackgroundThe currently known risk loci could explain a small proportion of the heritability of ankylosing spondylitis (AS). Epigenetics might account for the missing heritability. We aimed to seek more novel AS-associated DNA methylation alterations and delineate the regulatory effect of DNA methylation and gene expression with integrated analysis of methylome and transcriptome.MethodsEpigenome-wide DNA methylation and mRNA expression were profiled in peripheral blood mononuclear cells (PBMCs) from 45 individuals (AS: health controls (HCs) = 30:15) with high-throughput array. The methylome was validated in an independent cohort (AS: HCs = 12:12). Pearson correlation analysis and causal inference tests (CIT) were conducted to determine potentially causative regulatory effects of methylation on mRNA expression.ResultsA total of 4794 differentially methylated positions (DMPs) were identified associated with AS, 2526 DMPs of which were validated in an independent cohort. Both cohorts highlighted T cell receptor (TCR) signaling and Th17 differentiation pathways. Besides, AS patients manifested increased DNA methylation variability. The methylation levels of 158 DMPs were correlated with the mRNA expression levels of 112 genes, which formed interconnected network concentrated on Th17 cell differentiation and TCR signaling pathway (LCK, FYN, CD3G, TCF7, ZAP70, CXCL12, and PLCG1). We also identified several cis-acting DNA methylation and gene expression changes associated with AS risk, which might regulate the cellular mechanisms underlying AS.ConclusionsOur studies outlined the landscapes of epi-signatures of AS and several methylation-gene expression-AS regulatory axis and highlighted the Th17 cell differentiation and TCR signaling pathway, which might provide innovative molecular targets for therapeutic interventions for AS.