Biology subjects

Justice, A. C.

Publications and source records attributed to Justice, A. C..

5 recordsLinked to original sources

bioRxiv2023

Cell-type specific EWAS identifies genes involved in HIV pathogenesis andoncogenesis among people with chronic HIV infection

Epigenome-wide association studies (EWAS) of heterogenous blood cells have identified CpG sites associated with chronic HIV infection, which offer limited knowledge of cell-type specific methylation patterns associated with HIV infection. Applying a computational deconvolution method validated by capture bisulfite DNA methylation sequencing, we conducted a cell type-based EWAS and identified differentially methylated CpG sites specific for chronic HIV infection among five immune cell types in blood: CD4+ T-cells, CD8+ T-cells, B cells, Natural Killer (NK) cells, and monocytes in two independent cohorts (Ntotal=1,134). Differentially methylated CpG sites for HIV-infection were highly concordant between the two cohorts. Cell-type level meta-EWAS revealed distinct patterns of HIV-associated differential CpG methylation, where 67% of CpG sites were unique to individual cell types (false discovery rate, FDR <0.05). CD4+ T-cells had the largest number of HIV-associated CpG sites (N=1,472) compared to any other cell type. Genes harboring statistically significant CpG sites are involved in immunity and HIV pathogenesis (e.g. CX3CR1 in CD4+ T-cells, CCR7 in B cells, IL12R in NK cells, LCK in monocytes). More importantly, HIV-associated CpG sites were overrepresented for hallmark genes involved in cancer pathology (FDR<0.05) (e.g. BCL family, PRDM16, PDCD1LGD, ESR1, DNMT3A, NOTCH2). HIV-associated CpG sites were enriched among genes involved in HIV pathogenesis and oncogenesis such as Kras-signaling, interferon- and -{gamma}, TNF-, inflammatory, and apoptotic pathways. Our findings are novel, uncovering cell-type specific modifications in the host epigenome for people with HIV that contribute to the growing body of evidence regarding pathogen-induced epigenetic oncogenicity, specifically on HIV and its comorbidity with cancers.

cancer biology↗

bioRxiv2021

A New Monocyte Epigenetic Clock Reveals Effects of Alcohol Consumption on Epigenetic Aging in Three Independent Cohorts

BackgroundExcessive alcohol consumption increases the risk of aging-related comorbidities and mortality. Assessing the impact of alcohol consumption on biological age is important for clinical decision-making and prevention. Evidence shows that alcohol alters monocyte function, and age is associated with DNA methylome and transcriptomic changes among monocytes. However, no monocyte-based epigenetic clock is currently available. In this study, we developed a new monocyte-based DNA methylation clock (MonoDNAmAge) by using elastic net regularization. The MonoDNAmAge was validated by benchmarking using epigenetic age acceleration (EAA) in HIV infection. Using MonoDNAmAge clock as well as four established clocks (i.e., HorvathDNAmAge, HannumDNAmAge, PhenoDNAmAge, GrimDNAmAge), we then evaluated the effect of alcohol consumption on biological aging in three independent cohorts (N=2,242). ResultsMonoDNAmAge, comprised of 186 CpG sites, was highly correlated with chronological age (rtraining=0.96, p<2.20E-16; rtesting=0.86, p=1.55E-141). The MonoDNAmAge clock predicted an approximately 10-year age acceleration from HIV infection in two cohorts. Quadratic regression analysis showed a nonlinear relationship between MonoDNAmAge and alcohol consumption in the Yale Stress Center Community Study (YSCCS, pmodel=4.55E-08, px2 =7.80E-08) and in the Veteran Aging Cohort Study (VACS, pmodel=1.85E-02, px2 =3.46E-02). MonoDNAmAge and light alcohol consumption showed a negative linear relationship in the Womens Interagency HIV Study (WIHS, {beta}=-2.63, px=2.82E-06). Heavy consumption increased EAAMonoDNAmAge up to 1.60 years in the VACS while light consumption decreased EAAMonoDNAmAge to 2.66 years in the WIHS. These results were corroborated by the four established epigenetic clocks. ConclusionsWe observed a nonlinear effect of alcohol consumption on epigenetic age that is estimated by a novel monocyte-based "clock" in three distinct cohorts, highlighting the complex effects of alcohol consumption on biological age.

genomics↗

bioRxiv2019

DNA Methylation Signature on Phosphatidylethanol, not Self-Reported Alcohol Consumption, Predicts Hazardous Alcohol Consumption in Two Distinct Populations

The process of diagnosing hazardous alcohol drinking (HAD) is based on self-reported data and is thereby vulnerable to bias. There has been an interest in developing epigenetic biomarkers for HAD that might complement clinical assessment. Because alcohol consumption has been previously linked to DNA methylation (DNAm), here, we aimed to select DNAm signatures in blood to predict HAD from two demographically and clinically distinct populations (Ntotal=1,549). We first separately conducted an epigenome-wide association study (EWAS) for phosphatidylethanol (PEth), an objective measure of alcohol consumption, and for self-reported alcohol consumption in Cohort 1. We identified 102 PEth-associated CpGs, including 32 CpGs previously associated with alcohol consumption or alcohol use disorders. In contrast, no CpG reached epigenome-wide significance on self-reported alcohol consumption. Using a machine learning approach, two subsets of CpGs from EWAS on PEth and on self-reported alcohol consumption from Cohort 1 were separately tested for the prediction of HAD in Cohort 2. We found that a subset of 130 CpGs selected from the EWAS on PEth showed an excellent prediction of HAD with area under the ROC curve (AUC) of 91.31% in training set and 70.65% in validation set of Cohort 2. However, CpGs preselected from the EWAS on self-reported alcohol consumption showed a poor prediction of HAD with AUC 75.18% in the training set and 57.60% in the validation set. Our results demonstrate that an objective measure for alcohol consumption is a more informative phenotype than self-reported data for revealing epigenetic mechanism. The PEth-associated DNAm signature in blood is a robust biomarker for alcohol consumption.

bioinformatics↗

bioRxiv2019

Meta-analysis of problematic alcohol use in 435,563 individuals identifies 29 risk variants and yields insights into biology, pleiotropy and causality

Problematic alcohol use (PAU) is a leading cause of death and disability worldwide. Although genome-wide association studies (GWASs) have identified PAU risk genes, the genetic architecture of this trait is not fully understood. We conducted a proxy-phenotype meta-analysis of PAU combining alcohol use disorder and problematic drinking in 435,563 European-ancestry individuals. We identified 29 independent risk variants, 19 of them novel. PAU was genetically correlated with 138 phenotypes, including substance use and psychiatric traits. Phenome-wide polygenic risk score analysis in an independent biobank sample (BioVU, n=67,589) confirmed the genetic correlations between PAU and substance use and psychiatric disorders. Genetic heritability of PAU was enriched in brain and in genomic conserved and regulatory regions. Mendelian randomization suggested causal effects on liability to PAU of substance use, psychiatric status, risk-taking behavior, and cognitive performance. In summary, this large PAU meta-analysis identified novel risk loci and revealed genetic relationships with numerous other outcomes.

genetics↗

bioRxiv2019

Retrospective Association Analysis of Longitudinal Binary Traits Identifies Important Loci and Pathways in Cocaine Use

Longitudinal phenotypes have been increasingly available in genome-wide association studies (GWAS) and electronic health record-based studies for identification of genetic variants that influence complex traits over time. For longitudinal binary data, there remain significant challenges in gene mapping, including misspecification of the model for the phenotype distribution due to ascertainment. Here, we propose L-BRAT, a retrospective, generalized estimating equations-based method for genetic association analysis of longitudinal binary outcomes. We also develop RGMMAT, a retrospective, generalized linear mixed model-based association test. Both tests are retrospective score approaches in which genotypes are treated as random conditional on phenotype and covariates. They allow both static and time-varying covariates to be included in the analysis. Through simulations, we illustrated that retrospective association tests are robust to ascertainment and other types of phenotype model misspecification, and gain power over previous association methods. We applied L-BRAT and RGMMAT to a genome-wide association analysis of repeated measures of cocaine use in a longitudinal cohort. Pathway analysis implicated association with opioid signaling and axonal guidance signaling pathways. Lastly, we replicated important pathways in an independent cocaine dependence case-control GWAS. Our results illustrate that L-BRAT is able to detect important loci and pathways in a genome scan and to provide insights into genetic architecture of cocaine use.

genetics↗