Plasma Extracellular MicroRNAs Associated With Cardiovascular Disease Risk Factors in Middle-Aged and Older Adults.

BACKGROUND: Extracellular microRNAs (miRNAs) are a class of noncoding RNAs that remain stable in the extracellular milieu, where they contribute to various physiological and pathological processes by facilitating intercellular signaling. Previous studies have reported associations between miRNAs and cardiovascular diseases (CVDs); however, the plasma miRNA signatures of CVD and its risk factors have not been fully elucidated at the population level. METHODS AND RESULTS: Plasma miRNA levels were measured in 4440 FHS (Framingham Heart Study) participants. Linear regression analyses were conducted to test the cross-sectional associations of each miRNA with 8 CVD risk factors. Prospective analyses of the associations of miRNAs with new-onset obesity, hypertension, type 2 diabetes, CVD, and all-cause mortality were conducted using proportional hazards regression. Replication was carried out in 1999 RS (Rotterdam Study) participants. Pathway enrichment analyses were conducted and target genes were predicted for miRNAs associated with ≥5 risk factors in the FHS. In the FHS, 6 miRNAs (miR-193b-3p, miR-122-5p, miR-365a-3p, miR-194-5p, miR-192-5p, and miR-193a-5p) were associated with ≥5 risk factors. This miRNA signature was enriched for pathways associated with CVD and several genes annotated to these pathways were predicted targets of the identified miRNAs. Furthermore, miR-193b-3p, miR-194-5p, and miR-193a-5p were each associated with ≥2 risk factors in the RS. Prospective analysis revealed 8 miRNAs associated with all-cause mortality in the FHS. CONCLUSIONS: These findings highlight associations between miRNAs and CVD risk factors that may provide valuable insights into the underlying pathogenesis of CVD.

Epigenetic Age Mediates the Association of Life's Essential 8 With Cardiovascular Disease and Mortality.

BACKGROUND: Life's Essential 8 (LE8) is an enhanced metric for cardiovascular health. The interrelations among LE8, biomarkers of aging, and disease risks are unclear. METHODS AND RESULTS: LE8 score was calculated for 5682 Framingham Heart Study participants. We implemented 4 DNA methylation-based epigenetic age biomarkers, with older epigenetic age hypothesized to represent faster biological aging, and examined whether these biomarkers mediated the associations between the LE8 score and cardiovascular disease (CVD), CVD-specific mortality, and all-cause mortality. We found that a 1 SD increase in the LE8 score was associated with a 35% (95% CI, 27-41; P=1.8E-15) lower risk of incident CVD, a 36% (95% CI, 24-47; P=7E-7) lower risk of CVD-specific mortality, and a 29% (95% CI, 22-35; P=7E-15) lower risk of all-cause mortality. These associations were partly mediated by epigenetic age biomarkers, particularly the GrimAge and the DunedinPACE scores. The potential mediation effects by epigenetic age biomarkers tended to be more profound in participants with higher genetic risk for older epigenetic age, compared with those with lower genetic risk. For example, in participants with higher GrimAge polygenic scores (greater than median), the mean proportion of mediation was 39%, 39%, and 78% for the association of the LE8 score with incident CVD, CVD-specific mortality, and all-cause mortality, respectively. No significant mediation was observed in participants with lower GrimAge polygenic score. CONCLUSIONS: DNA methylation-based epigenetic age scores mediate the associations between the LE8 score and incident CVD, CVD-specific mortality, and all-cause mortality, particularly in individuals with higher genetic predisposition for older epigenetic age.

Enhancing selection of alcohol consumption-associated genes by random forest.

Machine learning methods have been used in identifying omics markers for a variety of phenotypes. We aimed to examine whether a supervised machine learning algorithm can improve identification of alcohol-associated transcriptomic markers. In this study, we analysed array-based, whole-blood derived expression data for 17 873 gene transcripts in 5508 Framingham Heart Study participants. By using the Boruta algorithm, a supervised random forest (RF)-based feature selection method, we selected twenty-five alcohol-associated transcripts. In a testing set (30 % of entire study participants), AUC (area under the receiver operating characteristics curve) of these twenty-five transcripts were 0·73, 0·69 and 0·66 for non-drinkers v. moderate drinkers, non-drinkers v. heavy drinkers and moderate drinkers v. heavy drinkers, respectively. The AUC of the selected transcripts by the Boruta method were comparable to those identified using conventional linear regression models, for example, AUC of 1958 transcripts identified by conventional linear regression models (false discovery rate < 0·2) were 0·74, 0·66 and 0·65, respectively. With Bonferroni correction for the twenty-five Boruta method-selected transcripts and three CVD risk factors (i.e. at P < 6·7e-4), we observed thirteen transcripts were associated with obesity, three transcripts with type 2 diabetes and one transcript with hypertension. For example, we observed that alcohol consumption was inversely associated with the expression of DOCK4, IL4R, and SORT1, and DOCK4 and SORT1 were positively associated with obesity, and IL4R was inversely associated with hypertension. In conclusion, using a supervised machine learning method, the RF-based Boruta algorithm, we identified novel alcohol-associated gene transcripts.

Genome-Wide Interaction Analysis With DASH Diet Score Identified Novel Loci for Systolic Blood Pressure.

BACKGROUND: The Dietary Approaches to Stop Hypertension (DASH) diet score lowers blood pressure (BP). We examined interactions between genotype and the DASH diet score in relation to systolic BP. METHODS: We analyzed up to 9 420 585 single nucleotide polymorphisms in up to 127 282 individuals of 6 population groups (91% of European population) from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (n=35 660) and UK Biobank (n=91 622) and performed European population-specific and cross-population meta-analyses. RESULTS: We identified 3 loci in European-specific analyses and an additional 4 loci in cross-population analyses at Pinteraction<5e-8. We observed a consistent interaction between rs117878928 at 15q25.1 (minor allele frequency, 0.03) and the DASH diet score (Pinteraction=4e-8; P for heterogeneity, 0.35) in European population, where the interaction effect size was 0.42±0.09 mm Hg (Pinteraction=9.4e-7) and 0.20±0.06 mm Hg (Pinteraction=0.001) in Cohorts for Heart and Aging Research in Genomic Epidemiology and the UK Biobank, respectively. The 1 Mb region surrounding rs117878928 was enriched with cis-expression quantitative trait loci (eQTL) variants (P=4e-273) and cis-DNA methylation quantitative trait loci variants (P=1e-300). Although the closest gene for rs117878928 is MTHFS, the highest narrow sense heritability accounted by single nucleotide polymorphisms potentially interacting with the DASH diet score in this locus was for gene ST20 at 15q25.1. CONCLUSIONS: We demonstrated gene-DASH diet score interaction effects on systolic BP in several loci. Studies with larger diverse populations are needed to validate our findings.

Circulating metabolites may illustrate relationship of alcohol consumption with cardiovascular disease.

BACKGROUND: Metabolite signatures of long-term alcohol consumption are lacking. To better understand the molecular basis linking alcohol drinking and cardiovascular disease (CVD), we investigated circulating metabolites associated with long-term alcohol consumption and examined whether these metabolites were associated with incident CVD. METHODS: Cumulative average alcohol consumption (g/day) was derived from the total consumption of beer, wine, and liquor on average of 19 years in 2428 Framingham Heart Study Offspring participants (mean age 56 years, 52% women). We used linear mixed models to investigate the associations of alcohol consumption with 211 log-transformed plasma metabolites, adjusting for age, sex, batch, smoking, diet, physical activity, BMI, and familial relationship. Cox models were used to test the association of alcohol-related metabolite scores with fatal and nonfatal incident CVD (myocardial infarction, coronary heart disease, stroke, and heart failure). RESULTS: We identified 60 metabolites associated with cumulative average alcohol consumption (p < 0.05/211 ≈ 0.00024). For example, 1 g/day increase of alcohol consumption was associated with higher levels of cholesteryl esters (e.g., CE 16:1, beta = 0.023 ± 0.002, p = 6.3e - 45) and phosphatidylcholine (e.g., PC 32:1, beta = 0.021 ± 0.002, p = 3.1e - 38). Survival analysis identified that 10 alcohol-associated metabolites were also associated with a differential CVD risk after adjusting for age, sex, and batch. Further, we built two alcohol consumption weighted metabolite scores using these 10 metabolites and showed that, with adjustment age, sex, batch, and common CVD risk factors, the two scores had comparable but opposite associations with incident CVD, hazard ratio 1.11 (95% CI = [1.02, 1.21], p = 0.02) vs 0.88 (95% CI = [0.78, 0.98], p = 0.02). CONCLUSIONS: We identified 60 long-term alcohol consumption-associated metabolites. The association analysis with incident CVD suggests a complex metabolic basis between alcohol consumption and CVD.

Genome-Wide Interaction Analysis with DASH Diet Score Identified Novel Loci for Systolic Blood Pressure.

Objective: We examined interactions between genotype and a Dietary Approaches to Stop Hypertension (DASH) diet score in relation to systolic blood pressure (SBP). Methods: We analyzed up to 9,420,585 biallelic imputed single nucleotide polymorphisms (SNPs) in up to 127,282 individuals of six population groups (91% of European population) from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (CHARGE; n=35,660) and UK Biobank (n=91,622) and performed European population-specific and cross-population meta-analyses. Results: We identified three loci in European-specific analyses and an additional four loci in cross-population analyses at P for interaction < 5e-8. We observed a consistent interaction between rs117878928 at 15q25.1 (minor allele frequency = 0.03) and the DASH diet score (P for interaction = 4e-8; P for heterogeneity = 0.35) in European population, where the interaction effect size was 0.42±0.09 mm Hg (P for interaction = 9.4e-7) and 0.20±0.06 mm Hg (P for interaction = 0.001) in CHARGE and the UK Biobank, respectively. The 1 Mb region surrounding rs117878928 was enriched with cis-expression quantitative trait loci (eQTL) variants (P = 4e-273) and cis-DNA methylation quantitative trait loci (mQTL) variants (P = 1e-300). While the closest gene for rs117878928 is MTHFS, the highest narrow sense heritability accounted by SNPs potentially interacting with the DASH diet score in this locus was for gene ST20 at 15q25.1. Conclusion: We demonstrated gene-DASH diet score interaction effects on SBP in several loci. Studies with larger diverse populations are needed to validate our findings.

Alcohol consumption and epigenetic age acceleration across human adulthood.

The alcohol-associated biological aging remains to be studied across adulthood. We conducted linear regression analyses to investigate the associations between alcohol consumption and two DNA methylation-based biological age acceleration metrics in 3823 Framingham Heart Study participants (24-92 years and 53.8% women) adjusting for covariates. We also investigated whether the two epigenetic aging metrics mediated the association of alcohol consumption with hypertension. We found that higher long-term average alcohol consumption was significantly associated with biological age acceleration assessed by GrimAge acceleration (GAA) and PhenoAge acceleration (PAA) in middle-aged (45-64 years, n = 1866) and older (65-92 years, n = 1267) participants while not in young participants (24-44 years, n = 690). For example, one additional standard drink of alcohol (~14 grams of ethanol per day) was associated with a 0.71 ± 0.15-year (p = 2.1e-6) and 0.60 ± 0.18-year (p = 7.5e-4) increase in PAA in middle-aged and older participants, respectively, but the association was not significant in young participants (p = 0.23). One additional standard serving of liquor (~14 grams of ethanol) was associated with a greater increase in GAA (0.82-year, p = 4.8e-4) and PAA (1.45-year, p = 7.4e-5) than beer (GAA: 0.45-year, p = 5.2e-4; PAA: 0.48-year, p = 0.02) and wine (GAA: 0.51-year, p = 0.02; PAA: 0.91-year, p = 0.008) in middle-aged participant group. We observed that up to 28% of the association between alcohol consumption and hypertension was mediated by GAA or PAA in the pooled sample. Our findings suggest that alcohol consumption is associated with greater biological aging quantified by epigenetic aging metrics, which may mediate the association of alcohol consumption with quantitative traits, such as hypertension.

Multi-tissue epigenetic analysis identifies distinct associations underlying insulin resistance and Alzheimer's disease at CPT1A locus.

BACKGROUND: Insulin resistance (IR) is a major risk factor for Alzheimer's disease (AD) dementia. The mechanisms by which IR predisposes to AD are not well-understood. Epigenetic studies may help identify molecular signatures of IR associated with AD, thus improving our understanding of the biological and regulatory mechanisms linking IR and AD. METHODS: We conducted an epigenome-wide association study of IR, quantified using the homeostatic model assessment of IR (HOMA-IR) and adjusted for body mass index, in 3,167 participants from the Framingham Heart Study (FHS) without type 2 diabetes at the time of blood draw used for methylation measurement. We identified DNA methylation markers associated with IR at the genome-wide level accounting for multiple testing (P < 1.1 × 10-7) and evaluated their association with neurological traits in participants from the FHS (N = 3040) and the Religious Orders Study/Memory and Aging Project (ROSMAP, N = 707). DNA methylation profiles were measured in blood (FHS) or dorsolateral prefrontal cortex (ROSMAP) using the Illumina HumanMethylation450 BeadChip. Linear regressions (ROSMAP) or mixed-effects models accounting for familial relatedness (FHS) adjusted for age, sex, cohort, self-reported race, batch, and cell type proportions were used to assess associations between DNA methylation and neurological traits accounting for multiple testing. RESULTS: We confirmed the strong association of blood DNA methylation with IR at three loci (cg17901584-DHCR24, cg17058475-CPT1A, cg00574958-CPT1A, and cg06500161-ABCG1). In FHS, higher levels of blood DNA methylation at cg00574958 and cg17058475 were both associated with lower IR (P = 2.4 × 10-11 and P = 9.0 × 10-8), larger total brain volumes (P = 0.03 and P = 9.7 × 10-4), and smaller log lateral ventricular volumes (P = 0.07 and P = 0.03). In ROSMAP, higher levels of brain DNA methylation at the same two CPT1A markers were associated with greater risk of cognitive impairment (P = 0.005 and P = 0.02) and higher AD-related indices (CERAD score: P = 5 × 10-4 and 0.001; Braak stage: P = 0.004 and P = 0.01). CONCLUSIONS: Our results suggest potentially distinct epigenetic regulatory mechanisms between peripheral blood and dorsolateral prefrontal cortex tissues underlying IR and AD at CPT1A locus.

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

BACKGROUND: Identifying novel epigenetic signatures associated with serum immunoglobulin E (IgE) may improve our understanding of molecular mechanisms underlying asthma and IgE-mediated diseases. METHODS: 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. FINDINGS: 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. INTERPRETATION: 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. FUNDING: US NIH/NHLBI grants: P01HL132825, K99HL159234. N01-HC-25195 and HHSN268201500001I.

Expression quantitative trait methylation analysis elucidates gene regulatory effects of DNA methylation: the Framingham Heart Study.

Expression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression. The present study describes an eQTM resource of CpG-transcript pairs derived from whole blood DNA methylation and RNA sequencing gene expression data in 2115 Framingham Heart Study participants. We identified 70,047 significant cis CpG-transcript pairs at p < 1E-7 where the top most significant eGenes (i.e., gene transcripts associated with a CpG) were enriched in biological pathways related to cell signaling, and for 1208 clinical traits (enrichment false discovery rate [FDR] ≤ 0.05). We also identified 246,667 significant trans CpG-transcript pairs at p < 1E-14 where the top most significant eGenes were enriched in biological pathways related to activation of the immune response, and for 1191 clinical traits (enrichment FDR ≤ 0.05). Independent and external replication of the top 1000 significant cis and trans CpG-transcript pairs was completed in the Women's Health Initiative and Jackson Heart Study cohorts. Using significant cis CpG-transcript pairs, we identified significant mediation of the association between CpG sites and cardiometabolic traits through gene expression and identified shared genetic regulation between CpGs and transcripts associated with cardiometabolic traits. In conclusion, we developed a robust and powerful resource of whole blood eQTM CpG-transcript pairs that can help inform future functional studies that seek to understand the molecular basis of disease.

Circulating Metabolites May Illustrate Relationship of Alcohol Consumption with Cardiovascular Disease.

Background: Metabolite signatures of long-term alcohol consumption are lacking. To better understand the molecular basis linking alcohol drinking and cardiovascular disease (CVD), we investigated circulating metabolites associated with long-term alcohol consumption and examined whether these metabolites were associated with incident CVD. Methods: Cumulative average alcohol consumption (g/day) was derived from the total consumption of beer, wine and liquor on average of 19 years in 2,428 Framingham Heart Study Offspring participants (mean age 56 years, 52% women). We used linear mixed models to investigate the associations of alcohol consumption with 211 log-transformed plasma metabolites, adjusting for age, sex, batch, smoking, diet, physical activity, BMI, and familial relationship. Cox models were used to test the association of alcohol-related metabolite scores with fatal and nonfatal incident CVD (myocardial infarction, coronary heart disease, stroke, and heart failure). Results: We identified 60 metabolites associated with cumulative average alcohol consumption (p<0.05/211≈0.00024). For example, one g/day increase of alcohol consumption was associated with higher levels of cholesteryl esters (e.g., CE 16:1, beta=0.023±0.002, p=6.3e-45) and phosphatidylcholine (e.g., PC 32:1, beta=0.021±0.002, p=3.1e-38). Survival analysis identified that 10 alcohol-associated metabolites were also associated with a differential CVD risk after adjusting for age, sex, and batch. Further, we built two alcohol consumption weighted metabolite scores using these 10 metabolites and showed that, with adjustment age, sex, batch, and common CVD risk factors, the two scores had comparable but opposite associations with incident CVD, hazard ratio 1.11(95% CI=[1.02, 1.21],p=0.02) vs 0.88 (95% CI=[0.78, 0.98], p=0.02). Summary: We identified 60 long-term alcohol consumption-associated metabolites. The association analysis with incident CVD suggests a complex metabolic basis between alcohol consumption and CVD.

Smoking, blood DNA methylation sites and lung cancer risk.

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

Rare Dysfunctional Complement Factor I Genetic Variants and Progression to Advanced Age-Related Macular Degeneration.

Purpose: To evaluate associations between rare dysfunctional complement factor I (CFI) genetic variant status and progression to advanced age-related macular degeneration (AAMD), geographic atrophy (GA), and neovascular disease (NV). Design: Prospective, longitudinal study. Participants: Patients aged 55 to 80 years at baseline identifying as White with non-AAMD in 1 or both eyes at baseline were included. Follow-up grades were assigned as early, intermediate, or AAMD (GA or NV). CFI variants were categorized using genotyping and sequencing platforms. Methods: Analyses were performed using the Seddon Longitudinal Cohort Study (N = 2116 subjects, 3901 eyes, and mean follow-up of 8.3 years) and the Age-Related Eye Disease Study (N = 2837 subjects, 5200 eyes, and mean follow-up of 9.2 years). CFI rare variants associated with low serum factor I (FI) protein levels and decreased FI function (type 1), other AMD genetic variants, and demographic, behavioral, and ocular factors were evaluated. Generalized estimating equations methods were used to assess the association between CFI rare variants and progression, independent of other genetic variants and covariates. Main Outcome Measures: Progression to AAMD, GA, or NV. Results: In the prospective cohort of 4953 subjects (9101 eyes with non-AAMD at baseline), 1% were type 1 rare CFI carriers. Over 12 years, progression to AAMD was 44% for carriers and 20% for noncarriers (P < 0.001), 30% of carriers versus 10% of noncarriers progressed to GA (P < 0.001), and 18% of carriers compared with 11% of noncarriers progressed to NV (P = 0.049). CFI carriers were more likely to have a family history of AMD (P for trend = 0.035) and a higher baseline AMD grade (P < 0.001). After adjusting for all covariates, CFI carrier status was associated with progression to GA (odds ratio [OR] = 1.91; 95% confidence interval [CI] = 1.03, 3.52) but not NV (OR = 0.96). Higher body mass index was associated with progression among CFI carriers (body mass index ≥ 25 vs. < 25; OR = 5.8; 95% CI 1.5, 22.3) but not for noncarriers (OR = 1.1; 95% CI = 0.9, 1.3), with P_interaction = 0.011. Conclusions: Results suggest that carriers of rare dysfunctional type 1 CFI variants are at higher risk for progression to AAMD with GA. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

Transcriptome-wide association study of circulating IgE levels identifies novel targets for asthma and allergic diseases.

Measurement of circulating immunoglobulin E (IgE) concentration is helpful for diagnosing and treating asthma and allergic diseases. Identifying gene expression signatures associated with IgE might elucidate novel pathways for IgE regulation. To this end, we performed a discovery transcriptome-wide association study to identify differentially expressed genes associated with circulating IgE levels in whole-blood derived RNA from 5,345 participants in the Framingham Heart Study across 17,873 mRNA gene-level transcripts. We identified 216 significant transcripts at a false discovery rate <0.05. We conducted replication using the meta-analysis of two independent external studies: the Childhood Asthma Management Program (n=610) and the Genetic Epidemiology of Asthma in Costa Rica Study (n=326); we then reversed the discovery and replication cohorts, which revealed 59 significant genes that replicated in both directions. Gene ontology analysis revealed that many of these genes were implicated in immune function pathways, including defense response, inflammatory response, and cytokine production. Mendelian randomization (MR) analysis revealed four genes (CLC, CCDC21, S100A13, and GCNT1) as putatively causal (p<0.05) regulators of IgE levels. GCNT1 (beta=1.5, p=0.01)-which is a top result in the MR analysis of expression in relation to asthma and allergic diseases-plays a role in regulating T helper type 1 cell homing, lymphocyte trafficking, and B cell differentiation. Our findings build upon prior knowledge of IgE regulation and provide a deeper understanding of underlying molecular mechanisms. The IgE-associated genes that we identified-particularly those implicated in MR analysis-can be explored as promising therapeutic targets for asthma and IgE-related diseases.

Identifying Novel Genes and Variants in Immune and Coagulation Pathways Associated with Macular Degeneration.

Purpose: To select individuals and families with a low genetic burden for age-related macular degeneration (AMD), to inform the clinical diagnosis of macular disorders, and to find novel genetic variants associated with maculopathies. Design: Genetic association study based on targeted and whole-exome sequencing. Participants: A total of 758 subjects (481 individuals with maculopathy and 277 controls), including 316 individuals in 72 families. Methods: We focused on 150 genes involved in the complement, coagulation, and inflammatory pathways. Single-variant tests were performed on 7755 variants shared among ≥ 5 subjects using logistic regression. Gene-based tests were used to evaluate aggregate effects from rare and low-frequency variants (at minor allele frequency [MAF] ≤ 5% or ≤ 1%) in a gene using burden tests. For families whose affected members had a low burden of genetic risk based on known common and rare variants related to AMD, we searched for rare variants (MAF < 0.001) whose risk alleles occurred in ≥ 80% of affected individuals but not in controls. Immunohistochemistry was performed to determine the protein expression of a novel gene (coagulation factor II thrombin receptor-like 2 [F2RL2]) in retinal tissues. Main Outcome Measures: Genotypes and phenotypes of macular degeneration. Results: We confirmed the association of a synonymous variant in complement factor H (Ala473, rs2274700, proxy to intronic rs1410996, r 2  = 1) with maculopathy (odds ratio, 0.64; P = 4.5 × 10-4). Higher AMD polygenic risk scores (PRSs) were associated with intermediate and advanced AMD. Among families with low PRSs and no known rare variants for maculopathy, we identified 2 novel, highly penetrant missense rare variants in ADAM15, A disintegrin and metalloprotease, metallopeptidase domain 15 (p.Arg288Cys) and F2RL2 (p.Leu289Arg). Immunohistochemistry analyses revealed F2RL2 protein expression in cone photoreceptor outer segments and Müller glia cells of human and pig retinas. Coagulation factor II thrombin receptor-like 2 expression appeared increased in fibrotic areas in advanced AMD samples with neovascularization, suggesting that F2RL2 may play a role in the progression to advanced macular disease. Conclusions: New missense rare variants in the genes ADAM15 and F2RL2 were associated with maculopathies. Results suggest that novel genes related to the coagulation and immune pathways may be involved in the pathogenesis of macular diseases.

Blood transcriptomic biomarkers of alcohol consumption and cardiovascular disease risk factors: the Framingham Heart Study.

BACKGROUND: The relations of alcohol consumption and gene expression remain to be elucidated. MATERIALS AND METHODS: We examined cross-sectional associations between alcohol consumption and whole blood derived gene expression levels and between alcohol-associated genes and obesity, hypertension, and diabetes in 5531 Framingham Heart Study (FHS) participants. RESULTS: We identified 25 alcohol-associated genes. We further showed cross-sectional associations of 16 alcohol-associated genes with obesity, nine genes with hypertension, and eight genes with diabetes at P < 0.002. For example, we observed decreased expression of PROK2 (ß = -0.0018; 95%CI: -0.0021, -0.0007; P = 6.5e - 5) and PAX5 (ß = -0.0014; 95%CI: -0.0021, -0.0007; P = 6.5e - 5) per 1 g/day increase in alcohol consumption. Consistent with our previous observation on the inverse association of alcohol consumption with obesity and positive association of alcohol consumption with hypertension, we found that PROK2 was positively associated with obesity (OR = 1.42; 95%CI: 1.17, 1.72; P = 4.5e - 4) and PAX5 was negatively associated with hypertension (OR = 0.73; 95%CI: 0.59, 0.89; P = 1.6e - 3). We also observed that alcohol consumption was positively associated with expression of ABCA13 (ß = 0.0012; 95%CI: 0.0007, 0.0017; P = 1.3e - 6) and ABCA13 was positively associated with diabetes (OR = 2.57; 95%CI: 1.73, 3.84; P = 3.5e - 06); this finding, however, was inconsistent with our observation of an inverse association between alcohol consumption and diabetes. CONCLUSIONS: We showed strong cross-sectional associations between alcohol consumption and expression levels of 25 genes in FHS participants. Nonetheless, complex relationships exist between alcohol-associated genes and CVD risk factors.

Elucidating the genetic architecture of DNA methylation to identify promising molecular mechanisms of disease.

DNA methylation commonly occurs at cytosine-phosphate-guanine sites (CpGs) that can serve as biomarkers for many diseases. We analyzed whole genome sequencing data to identify DNA methylation quantitative trait loci (mQTLs) in 4126 Framingham Heart Study participants. Our mQTL mapping identified 94,362,817 cis-mQTLvariant-CpG pairs (for 210,156 unique autosomal CpGs) at P < 1e-7 and 33,572,145 trans-mQTL variant-CpG pairs (for 213,606 unique autosomal CpGs) at P < 1e-14. Using cis-mQTL variants for 1258 CpGs associated with seven cardiovascular disease (CVD) risk factors, we found 104 unique CpGs that colocalized with at least one CVD trait. For example, cg11554650 (PPP1R18) colocalized with type 2 diabetes, and was driven by a single nucleotide polymorphism (rs2516396). We performed Mendelian randomization (MR) analysis and demonstrated 58 putatively causal relations of CVD risk factor-associated CpGs to one or more risk factors (e.g., cg05337441 [APOB] with LDL; MR P = 1.2e-99, and 17 causal associations with coronary artery disease (e.g. cg08129017 [SREBF1] with coronary artery disease; MR P = 5e-13). We also showed that three CpGs, e.g., cg14893161 (PM20D1), are putatively causally associated with COVID-19 severity. To assist in future analyses of the role of DNA methylation in disease pathogenesis, we have posted a comprehensive summary data set in the National Heart, Lung, and Blood Institute's BioData Catalyst.

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.

Feasibility of the soft attention-based models for automatic segmentation of OCT kidney images.

Clinically, optical coherence tomography (OCT) has been utilized to obtain the images of the kidney's proximal convoluted tubules (PCTs), which can be used to quantify the morphometric parameters such as tubular density and diameter. Such parameters are useful for evaluating the status of the donor kidney for transplant. Quantifying PCTs from OCT images by human readers is a time-consuming and tedious process. Despite the fact that conventional deep learning models such as conventional neural networks (CNNs) have achieved great success in the automatic segmentation of kidney OCT images, gaps remain regarding the segmentation accuracy and reliability. Attention-based deep learning model has benefits over regular CNNs as it is intended to focus on the relevant part of the image and extract features for those regions. This paper aims at developing an Attention-based UNET model for automatic image analysis, pattern recognition, and segmentation of kidney OCT images. We evaluated five methods including the Residual-Attention-UNET, Attention-UNET, standard UNET, Residual UNET, and fully convolutional neural network using 14403 OCT images from 169 transplant kidneys for training and testing. Our results show that Residual-Attention-UNET outperformed the other four methods in segmentation by showing the highest values of all the six metrics including dice score (0.81 ± 0.01), intersection over union (IOU, 0.83 ± 0.02), specificity (0.84 ± 0.02), recall (0.82 ± 0.03), precision (0.81 ± 0.01), and accuracy (0.98 ± 0.08). Our results also show that the performance of the Residual-Attention-UNET is equivalent to the human manual segmentation (dice score = 0.84 ± 0.05). Residual-Attention-UNET and Attention-UNET also demonstrated good performance when trained on a small dataset (3456 images) whereas the performance of the other three methods dropped dramatically. In conclusion, our results suggested that the soft Attention-based models and specifically Residual-Attention-UNET are powerful and reliable methods for tubule lumen identification and segmentation and can help clinical evaluation of transplant kidney viability as fast and accurate as possible.

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 < 5x10 - 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 7,233 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.