BACKGROUND/RATIONALE:Experimental studies support the role of neurotransmitter genes in dementia risk, but human studies utilizing single variants in candidate genes have had limited success. METHODS:We used the gene-based testing program Versatile Gene-based Association Study to assess whether aggregate variation across 6 neurotransmitter pathways influences risk of cognitive decline in 8159 cognitively normal elderly (≥65 years old) adults from 3 community-based cohorts. RESULTS:Common genetic variation in GNG4 and KCNQ2 was associated with cognitive decline. In human brain tissue data sets, both GNG4 and KCNQ2 show higher expression in hippocampus relative to other brain regions; GNG4 expression decreases with advancing age. Both GNG4 and KCNQ2 show highest expression in fetal astrocytes. CONCLUSION:Genetic variation analyses and gene expression data suggest that GNG4 and KCNQ2 may be associated with cognitive decline in normal aging. Gene-based testing of neurotransmitter pathways may confirm and reveal novel risk genes in future studies of healthy cognitive aging.

Background

Sleep disordered breathing (SDB) is a common disorder that results in oxidative stress and inflammation and is associated with multiple age-related health outcomes. Epigenetic age acceleration is a DNA methylation (DNAm)-based marker of fast biological aging. We examined the associations of SDB traits with epigenetic age acceleration.

Methods

A sample of 622 participants from the Multi-Ethnic Study of Atherosclerosis (MESA) had blood DNAm measured and underwent Type 2 in-home polysomnography that assessed apnea-hypopnea index (AHI), percentage of sleep time with oxygen saturation lower than 90% (Per90), and arousal index. DNAm data provided measures of DNAm-Age acceleration and DNAm-PhenoAge acceleration. The association of each SDB trait with age acceleration was estimated using linear regression, controlling for covariates. In secondary analyses, we studied the associations of SDB traits with epigenetic age acceleration 2-10 years after sleep study in 530 individuals from the Framingham Heart Study (FHS).

Findings

In MESA, AHI was associated with greater DNAm-PhenoAge acceleration (β = 0.03; 95% CI [0.001, 0.06]). Arousal index was associated with greater DNAm-Age acceleration (β = 0.04; 95% CI [0.01, 0.07]). Both associations were stronger in women than men. In the secondary FHS analyses, Per90 was associated with greater DNAm-Age acceleration and this association was stronger in men.

Interpretation

More severe SDB was associated with epigenetic age acceleration in both cohorts. Future work should prospectively study short- and long-term effects of SDB, and whether treatment reduces epigenetic age acceleration among those individuals with SBD.

Funding

National Institutes of Health.

Transcriptome prediction methods such as PrediXcan and FUSION have become popular in complex trait mapping. Most transcriptome prediction models have been trained in European populations using methods that make parametric linear assumptions like the elastic net (EN). To potentially further optimize imputation performance of gene expression across global populations, we built transcriptome prediction models using both linear and non-linear machine learning (ML) algorithms and evaluated their performance in comparison to EN. We trained models using genotype and blood monocyte transcriptome data from the Multi-Ethnic Study of Atherosclerosis (MESA) comprising individuals of African, Hispanic, and European ancestries and tested them using genotype and whole-blood transcriptome data from the Modeling the Epidemiology Transition Study (METS) comprising individuals of African ancestries. We show that the prediction performance is highest when the training and the testing population share similar ancestries regardless of the prediction algorithm used. While EN generally outperformed random forest (RF), support vector regression (SVR), and K nearest neighbor (KNN), we found that RF outperformed EN for some genes, particularly between disparate ancestries, suggesting potential robustness and reduced variability of RF imputation performance across global populations. When applied to a high-density lipoprotein (HDL) phenotype, we show including RF prediction models in PrediXcan revealed potential gene associations missed by EN models. Therefore, by integrating other ML modeling into PrediXcan and diversifying our training populations to include more global ancestries, we may uncover new genes associated with complex traits.

Air pollution might affect atherosclerosis through DNA methylation changes in cells crucial to atherosclerosis, such as monocytes. We conducted an epigenome-wide study of DNA methylation in CD14+ monocytes and long-term ambient air pollution exposure in adults participating in the Multi-Ethnic Study of Atherosclerosis (MESA). We also assessed the association between differentially methylated signals and cis-gene expression. Using spatiotemporal models, one-year average concentrations of outdoor fine particulate matter (PM_2.5) and oxides of nitrogen (NO_X) were estimated at participants' homes. We assessed DNA methylation and gene expression using Illumina 450k and HumanHT-12 v4 Expression BeadChips, respectively (n = 1,207). We used bump hunting and site-specific approaches to identify differentially methylated signals (false discovery rate of 0.05) and used linear models to assess associations between differentially methylated signals and cis-gene expression. Four differentially methylated regions (DMRs) located on chromosomes 5, 6, 7, and 16 (within or near SDHAP3, ZFP57, HOXA5, and PRM1, respectively) were associated with PM_2.5. The DMRs on chromosomes 5 and 6 also associated with NO_X. The DMR on chromosome 5 had the smallest p-value for both PM_2.5 (p = 1.4×10^-6) and NO_X (p = 7.7×10^-6). Three differentially methylated CpGs were identified for PM_2.5, and cg05926640 (near TOMM20) had the smallest p-value (p = 5.6×10^-8). NO_X significantly associated with cg11756214 within ZNF347 (p = 5.6×10^-8). Several differentially methylated signals were also associated with cis-gene expression. The DMR located on chromosome 7 was associated with the expression of HOXA5, HOXA9, and HOXA10. The DMRs located on chromosomes 5 and 16 were associated with expression of MRPL36 and DEXI, respectively. The CpG cg05926640 was associated with expression of ARID4B, IRF2BP2, and TOMM20. We identified differential DNA methylation in monocytes associated with long-term air pollution exposure. Methylation signals associated with gene expression might help explain how air pollution contributes to cardiovascular disease.

Epigenetic changes, such as DNA methylation, have been hypothesized to provide a link between the social environment and disease development. The purpose of this study was to examine associations between life course measures of socioeconomic status (SES) and DNA methylation (DNAm) in 18 genes related to stress reactivity and inflammation using a multi-level modeling approach that treats DNAm measurements as repeat measures within an individual. DNAm and gene expression were assessed in purified monocytes for a random subsample of 1,264 non-Hispanic white, African-American, and Hispanic participants aged 55-94 from the Multi-Ethnic Study of Atherosclerosis (MESA). After correction for multiple testing, we found that low childhood SES was associated with DNAm in 3 stress-related genes (AVP, FKBP5, OXTR) and 2 inflammation-related genes (CCL1, CD1D), low adult SES was associated with DNAm in one stress-related gene (AVP) and 5 inflammation-related genes (CD1D, F8, KLRG1, NLRP12, TLR3), and social mobility was associated with DNAm in 3 stress-related genes (AVP, FKBP5, OXTR) and 7 inflammation-related genes (CCL1, CD1D, F8, KLRG1, NLRP12, PYDC1, TLR3). In general, low SES was associated with increased DNAm. Expression data was available for 7 genes that showed a significant relationship between SES and DNAm. In 5 of these 7 genes (CD1D, F8, FKBP5, KLRG1, NLRP12), DNAm was associated with gene expression for at least one transcript, providing evidence of the potential functional consequences of alterations in DNAm related to SES. The results of this study reflect the biological complexity of epigenetic data and underscore the need for multi-disciplinary approaches to study how DNAm may contribute to the social patterning of disease.

Background

Despite a lower prevalence of established atrial fibrillation (AF) risk factors, Whites exhibit substantially higher rates of this arrhythmia compared to Blacks. The mechanism underlying this observation is not known. Both inflammation and obesity are risk factors for AF, and adipose tissue is a known contributor to systemic inflammation.

Objectives

We sought to determine the degree to which racial differences in AF risk are explained by differences in inflammation and adiposity.

Methods

Baseline serum inflammatory biomarker concentrations and abdominal adiposity (assessed by computed tomography) were quantified in a subset of Black and White participants without prevalent AF in the Health, Aging, and Body Composition (Health ABC) Study. Participants were prospectively followed for the diagnosis of AF using study ECGs and Medicare claims data. Cox proportional hazards models were used to determine the adjusted relative hazard of incident AF between races before and after biomarker adjustment.

Results

Among 2,768 participants (43% Black), 721 developed incident AF over a median follow up of 10.9 years. White race was associated with a heightened adjusted risk of incident AF (HR 1.55, 95% CI 1.30 to 1.84, p < 0.001). Abdominal adiposity was not associated with AF when added to the adjusted model. Among the studied biomarkers, adiponectin, TNF-α, TNF-α SR I, and TNF-α SR II concentrations were each higher among Whites and independently associated with a greater risk of incident AF. Together, these inflammatory cytokines mediated 42% (95% CI 15 to 119%, p = 0.004) of the adjusted race-AF association.

Conclusions

Systemic inflammatory pathways significantly mediate the heightened risk of AF among Whites. The higher level of systemic inflammation and concomitant increased AF risk in Whites is not explained by racial differences in abdominal adiposity or the presence of other pro-inflammatory cardiovascular comorbidities.

Background

Carriers of the T allele of the single-nucleotide polymorphism rs13038305 tend to have lower cystatin C levels and higher cystatin C-based estimated glomerular filtration rate (eGFRcys). Adjusting for this genetic effect on cystatin C concentrations may improve GFR estimation, reclassify cases of chronic kidney disease (CKD), and strengthen risk estimates for cardiovascular disease (CVD) and mortality.

Study design

Observational.

Setting & population

4 population-based cohorts: Atherosclerosis Risk in Communities (ARIC), Cardiovascular Health (CHS), Framingham Heart (FHS), and Health, Aging, and Body Composition (Health ABC) studies.

Predictors

We estimated the association of rs13038305 with eGFRcys and serum creatinine-based eGFR (eGFRcr) and performed longitudinal analyses of the associations of eGFRcys with mortality and cardiovascular events following adjustment for rs13038305.

Outcomes

We assessed reclassification by genotype-adjusted eGFRcys across CKD categories: <45, 45-59, 60-89, and ≥ 90 mL/min/1.73 m(2). We compared mortality and CVD outcomes in those reclassified to a worse eGFRcys category with those unaffected. Results were combined using fixed-effect inverse-variance meta-analysis.

Results

In 14,645 participants, each copy of the T allele of rs13038305 (frequency, 21%) was associated with a 6.4% lower cystatin C concentration, 5.5-mL/min/1.73 m(2) higher eGFRcys, and 36% [95% CI, 29%-41%] lower odds of CKD. Associations with CVD (HR, 1.17; 95% CI, 1.14-1.20) and mortality (HR, 1.22; 95% CI, 1.19-1.24) per 10-mL/min/1.73 m(2) lower eGFRcys were similar with or without rs13038305 adjustment. 1,134 (7.7%) participants were reclassified to a worse CKD category following rs13038305 adjustment, and rates of CVD and mortality were higher in individuals who were reclassified. However, the overall net reclassification index was not significant for either outcome, at 0.009 (95% CI, -0.003 to 0.022) for mortality and 0.014 (95% CI, 0.0 to 0.028) for CVD.

Limitations

rs13038305 explains only a small proportion of cystatin C variation.

Conclusions

Statistical adjustment can correct a genetic bias in GFR estimates based on cystatin C in carriers of the T allele of rs13038305 and result in changes in disease classification. However, on a population level, the effects on overall reclassification of CKD status are modest.

Regulation of transcription and translation are mechanisms through which genetic variants affect complex traits. Expression quantitative trait locus (eQTL) studies have been more successful at identifying cis-eQTL (within 1 Mb of the transcription start site) than trans-eQTL. Here, we tested the cis component of gene expression for association with observed plasma protein levels to identify cis- and trans-acting genes that regulate protein levels. We used transcriptome prediction models from 49 Genotype-Tissue Expression (GTEx) Project tissues to predict the cis component of gene expression and tested the predicted expression of every gene in every tissue for association with the observed abundance of 3,622 plasma proteins measured in 3,301 individuals from the INTERVAL study. We tested significant results for replication in 971 individuals from the Trans-omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA). We found 1,168 and 1,210 cis- and trans-acting associations that replicated in TOPMed (FDR < 0.05) with a median expected true positive rate (π1) across tissues of 0.806 and 0.390, respectively. The target proteins of trans-acting genes were enriched for transcription factor binding sites and autoimmune diseases in the GWAS catalog. Furthermore, we found a higher correlation between predicted expression and protein levels of the same underlying gene (R = 0.17) than observed expression (R = 0.10, p = 7.50 × 10-11). This indicates the cis-acting genetically regulated (heritable) component of gene expression is more consistent across tissues than total observed expression (genetics + environment) and is useful in uncovering the function of SNPs associated with complex traits.

Background

Exposure to adverse social factors has been associated with an altered inflammatory profile, a risk factor for several acute and chronic diseases. Differential gene expression may be a biological mediator in the relationship. In this study, associations between a range of social factors and expression of inflammation-related genes were investigated.

Methods

Social factor and gene expression data were collected from 1,264 individuals in the Multi-Ethnic Study of Atherosclerosis (MESA). Inflammation-related genes were identified from the Gene Ontology database. The associations between social factors and gene expression were first assessed using the Global Analysis of Covariance (Global ANCOVA) gene set enrichment test. When the global test was significant, linear regression and elastic net penalized regression were employed to identify the individual gene transcripts within each gene set associated with the social factor.

Results

Loneliness (p = 0.003), chronic burden (p = 0.002), and major or lifetime discrimination (p = 0.045) were significantly associated with global expression of the chronic inflammatory gene set. Of the 20 transcripts that comprise this gene set, elastic net selected 12 transcripts for loneliness, 8 for chronic burden, and 3 for major or lifetime discrimination. Major or lifetime discrimination was also associated with the inflammatory response (p = 0.029), regulation of the inflammatory response (p = 0.041), and immune response (p = 0.025) gene sets in global analyses, and 53, 136, and 26 transcripts were selected via elastic net for these gene sets respectively. There were no significant associations in linear regression analyses after adjustment for multiple testing.

Conclusions

This study highlights gene expression as a biological mechanism through which social factors may affect inflammation.

For many complex traits, gene regulation is likely to play a crucial mechanistic role. How the genetic architectures of complex traits vary between populations and subsequent effects on genetic prediction are not well understood, in part due to the historical paucity of GWAS in populations of non-European ancestry. We used data from the MESA (Multi-Ethnic Study of Atherosclerosis) cohort to characterize the genetic architecture of gene expression within and between diverse populations. Genotype and monocyte gene expression were available in individuals with African American (AFA, n = 233), Hispanic (HIS, n = 352), and European (CAU, n = 578) ancestry. We performed expression quantitative trait loci (eQTL) mapping in each population and show genetic correlation of gene expression depends on shared ancestry proportions. Using elastic net modeling with cross validation to optimize genotypic predictors of gene expression in each population, we show the genetic architecture of gene expression for most predictable genes is sparse. We found the best predicted gene in each population, TACSTD2 in AFA and CHURC1 in CAU and HIS, had similar prediction performance across populations with R2 > 0.8 in each population. However, we identified a subset of genes that are well-predicted in one population, but poorly predicted in another. We show these differences in predictive performance are due to allele frequency differences between populations. Using genotype weights trained in MESA to predict gene expression in independent populations showed that a training set with ancestry similar to the test set is better at predicting gene expression in test populations, demonstrating an urgent need for diverse population sampling in genomics. Our predictive models and performance statistics in diverse cohorts are made publicly available for use in transcriptome mapping methods at https://github.com/WheelerLab/DivPop.