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Introduction: Observational studies have implicated atrial fibrillation (AF) as a risk factor for a number of cardiovascular diseases (CVD). Whether AF is causally related to these CVD is unknown. Methods: We included participants with both electronic health record-derived phenotypes and imputed genotype data in this study. A genetic instrument for AF consisting of 406 common variants was derived using a pruning and thresholding method (r2 Results: We included 615,635 participants in the study (440,690 European, 118,531 African, 48,908 Hispanic, 7,506 Asian ancestry). Genetic risk of AF was associated with heart failure in both 1-sample (OR/log odds of genetically predicted AF =1.18, 95% CI 1.15 - 1.20) and 2-sample MR (OR/log odds of genetically predicted AF =1.13, 95% CI 1.11 - 1.15). Additionally, genetic risk of AF was associated with heart failure subtypes and non-ischemic cardiomyopathy. There was no association between genetic risk of AF and pulmonary embolism (PE) or deep venous thrombosis (DVT), however, genetic risk of AF was associated with PE after excluding individuals with concurrent or prior DVT (OR1-sample MR 1.08, 95% CI 1.01 - 1.15; OR2-sample MR 1.06, 95% CI 1.03 - 1.08). Lastly, we confirmed an association between genetic risk of AF and ischemic stroke but not all-cause dementia. Our results were robust to a number of sensitivity analyses. Conclusions: In a large multi-ancestry study, we delineate the potential causal contribution of AF to a number of CVD and highlight support for a causal association between AF and isolated PE. Our findings inform how prevention and treatment of AF may affect CVD incidence and outcomes.

Rationale: GWAS (Genome-Wide Association Studies) have identified hundreds of genetic loci associated with atrial fibrillation (AF). However, these loci explain only a small proportion of AF heritability. Objective: To develop an approach to identify additional AF-related genes by integrating multiple omics data. Methods and Results: Three types of omics data were integrated: (1) summary statistics from the AFGen 2017 GWAS; (2) a whole blood EWAS (Epigenome-Wide Association Study) of AF; and (3) a whole blood TWAS (Transcriptome-Wide Association Study) of AF. The variant-level GWAS results were collapsed into gene-level associations using fast set-based association analysis. The CpG-level EWAS results were also collapsed into gene-level associations by an adapted SNP-set Kernel Association Test approach. Both GWAS and EWAS gene-based associations were then meta-analyzed with TWAS using a fixed-effects model weighted by the sample size of each data set. A tissue-specific network was subsequently constructed using the NetWAS (Network-Wide Association Study). The identified genes were then compared with the AFGen 2018 GWAS that contained more than triple the number of AF cases compared with AFGen 2017 GWAS. We observed that the multiomics approach identified many more relevant AF-related genes than using AFGen 2018 GWAS alone (1931 versus 206 genes). Many of these genes are involved in the development and regulation of heart- and muscle-related biological processes. Moreover, the gene set identified by multiomics approach explained much more AF variance than those identified by GWAS alone (10.4% versus 3.5%). Conclusions: We developed a strategy to integrate multiple omics data to identify AF-related genes. Our integrative approach may be useful to improve the power of traditional GWAS, which might be particularly useful for rare traits and diseases with limited sample size.

Rationale: Genome-wide association studies have identified over 100 genetic loci for atrial fibrillation (AF); recent work described an association between loss-of-function (LOF) variants in TTN and early-onset AF. Objective: We sought to determine the contribution of rare and common genetic variation to AF risk in the general population. Methods: The UK Biobank is a population-based study of 500 000 individuals including a subset with genome-wide genotyping and exome sequencing. In this case-control study, we included AF cases and controls of genetically determined white-European ancestry; analyses were performed using a logistic mixed-effects model adjusting for age, sex, the first 4 principal components of ancestry, empirical relationships, and case-control imbalance. An exome-wide, gene-based burden analysis was performed to examine the relationship between AF and rare, high-confidence LOF variants in genes with ≥10 LOF carriers. A polygenic risk score for AF was estimated using the LDpred algorithm. We then compared the contribution of AF polygenic risk score and LOF variants to AF risk. Results: The study included 1546 AF cases and 41 593 controls. In an analysis of 9099 genes with sufficient LOF variant carriers, a significant association between AF and rare LOF variants was observed in a single gene, TTN (odds ratio, 2.71, P =2.50×10 −8 ). The association with AF was more significant (odds ratio, 6.15, P =3.26×10 −14 ) when restricting to LOF variants located in exons highly expressed in cardiac tissue ( TTN LOF ). Overall, 0.44% of individuals carried TTN LOF variants, of whom 14% had AF. Among individuals in the highest 0.44% of the AF polygenic risk score only 9.3% had AF. In contrast, the AF polygenic risk score explained 4.7% of the variance in AF susceptibility, while TTN LOF variants only accounted for 0.2%. Conclusions: Both monogenic and polygenic factors contribute to AF risk in the general population. While rare TTN LOF variants confer a substantial AF penetrance, the additive effect of many common variants explains a larger proportion of genetic susceptibility to AF.

Background: Risk prediction models for atrial fibrillation (AF) do not give information about when AF might develop. Restricted mean survival time (RMST) quantifies risk into the time domain. Our objective was to use RMST to re-express individualized AF risk predictions. Methods and Results: We included AF-free participants from the Framingham Heart Study community-based cohorts. We predicted new-onset AF over 10-year follow-up according to baseline covariates: age, height, weight, systolic blood pressure, diastolic blood pressure, current smoking, antihypertensive treatment, diabetes mellitus, prevalent heart failure, and prevalent myocardial infarction. First, we fitted a Cox regression model and estimated the 10-year predicted risk of AF. Second, we fitted an RMST model and estimated the predicted mean time free of AF and alive over a time horizon of 10 years. We included 7586 AF-free participants contributing to 11 088 examinations (mean age 61±11 years, 44% were men). During 10-year follow-up, 822 participants developed AF. The Cox and RMST models were in agreement regarding the direction, strength, and statistical significance of associations for all covariates. Low (15%) 10-year predicted risk of AF corresponded to predicted mean time alive and free of AF of 9.9, 9.6, and 8.8 years, respectively. A 60-year-old woman with a body mass index of 25 kg/m 2 , no use of hypertension treatment and no history of heart failure had a predicted mean time alive and free of AF of 9.9 years, whereas a 70-year-old man with a body mass index of 30 kg/m 2 , use of hypertension treatment, and with prevalent heart failure had a predicted mean time alive and free of AF of 7.9 years. Conclusions: The RMST can be used to develop risk prediction models to express results in a time scale. RMST may offer a complementary risk communication tool for AF in clinical practice.

Background: The long-term probability of developing atrial fibrillation (AF) considering genetic predisposition and clinical risk factor burden is unknown. Methods: We estimated the lifetime risk of AF in individuals from the community-based Framingham Heart Study. Polygenic risk for AF was derived using a score of ≈1000 AF-associated single-nucleotide polymorphisms. Clinical risk factor burden was calculated for each individual using a validated risk score for incident AF comprised of height, weight, systolic and diastolic blood pressure, current smoking status, antihypertensive medication use, diabetes mellitus, history of myocardial infarction, and history of heart failure. We estimated the lifetime risk of AF within tertiles of polygenic and clinical risk. Results: Among 4606 participants without AF at 55 years of age, 580 developed incident AF (median follow-up, 9.4 years; 25th–75th percentile, 4.4–14.3 years). The lifetime risk of AF >55 years of age was 37.1% and was substantially influenced by both polygenic and clinical risk factor burden. Among individuals free of AF at 55 years of age, those in low-polygenic and clinical risk tertiles had a lifetime risk of AF of 22.3% (95% confidence interval, 15.4−9.1), whereas those in high-risk tertiles had a risk of 48.2% (95% confidence interval, 41.3−55.1). A lower clinical risk factor burden was associated with later AF onset after adjusting for genetic predisposition ( P Conclusions: In our community-based cohort, the lifetime risk of AF was 37%. Estimation of polygenic AF risk is feasible and together with clinical risk factor burden explains a substantial gradient in long-term AF risk.

Background Previous studies assessing the association between body mass index ( BMI ) and atrial fibrillation ( AF ) did not account for time‐varying covariates, which may be affected by previous BMI . We illustrate how the g‐formula can account for time‐varying confounding. Methods and Results We included 4392 participants from the Framingham Heart Study who were AF free at ages 45 to 55 years, and followed them for up to 20 years. We estimated hazard ratios ( HR s) comparing time‐varying nonobese versus obese with Cox models. We used the g‐formula to compare nonobese versus obese and 10% annual decrease in BMI (until normal weight is reached) versus natural course. We estimated HR s and differences in restricted mean survival times, the mean difference in time alive and AF free. We adjusted for sex, age, and time‐varying risk factors. Cox models indicated that nonobese participants had a decreased rate of AF versus obese participants ( HR , 0.83; 95% CI , 0.72–0.97). G‐formula analyses comparing everyone had they been nonobese versus obese yielded stronger associations ( HR , 0.73; 95% CI , 0.58–0.91). The restricted mean survival time was 19.22 years had everyone been nonobese and 19.03 years had everyone been obese (difference, 2.25 months; 95% CI, −0.66 to 5.16). When assessing a 10% annual decrease in BMI , the association was weaker ( HR 0.96; 95% CI , 0.86–1.08). Conclusions Decreased BMI was associated with a lower rate of AF after accounting for time‐varying covariates that depend on previous exposure using the g‐formula, which Cox models cannot accommodate. Absolute measures like the restricted mean survival time difference offer context to relative measures of association.

Background: New “e-Cohort” study designs provide resource-effective methods for collecting participant data. It is unclear if implementing an e-cohort without direct, in-person participant contact can achieve successful participation rates. The FHS-HeH randomized pilot study compared two distinct implementation strategies for co-enrolling participants from the Framingham Heart Study (FHS) into the Health eHeart Study, a digital cohort with infrastructure for collecting mHealth data. Methods: FHS participants who had an email address and smartphone were randomized to one of two approaches: remote vs. on-site support. In the remote arm, participants received an email containing an enrollment URL, and, upon enrollment, were sent four Bluetooth sensor devices. Participants in the on-site arm were invited to visit FHS and were provided in-person support for enrollment and connecting the devices. Results: Compared to participants that declined, individuals that accepted an invitation to participate in our pilot study (n=101 remote , n=101 on-site ) were more often women, highly educated, and younger (Figure 1). All on-site participants completed the consent, compared to 93% of the remote arm. Of participants who consented to participate, connection and initial use of devices was also higher in the on-site arm (100% connected the activity monitor, 94% the blood pressure cuff and scale, and 84% the electrocardiogram) compared to the remote arm (74%, 75%, 80%, and 42%). Roughly 75-78% of those that initially connected in both arms were still using the devices by the 3 rd month and 58-60% were still participating by the 6 th month. Conclusions: Our pilot study demonstrated that deployment of mobile devices among middle-aged and older adults in the context of an on-site clinic visit was associated with higher initial rates of device use as compared to offering only remote support. Once connected, drop-off rates were similar in both groups.

Background— Previous reports have implicated multiple genetic loci associated with AF, but the contributions of genome-wide variation to AF susceptibility have not been quantified. Methods and Results— We assessed the contribution of genome-wide single-nucleotide polymorphism variation to AF risk (single-nucleotide polymorphism heritability, h 2 g ) using data from 120 286 unrelated individuals of European ancestry (2987 with AF) in the population-based UK Biobank. We ascertained AF based on self-report, medical record billing codes, procedure codes, and death records. We estimated h 2 g using a variance components method with variants having a minor allele frequency ≥1%. We evaluated h 2 g in age, sex, and genomic strata of interest. The h 2 g for AF was 22.1% (95% confidence interval, 15.6%–28.5%) and was similar for early- versus older-onset AF (≤65 versus >65 years of age), as well as for men and women. The proportion of AF variance explained by genetic variation was mainly accounted for by common (minor allele frequency, ≥5%) variants (20.4%; 95% confidence interval, 15.1%–25.6%). Only 6.4% (95% confidence interval, 5.1%–7.7%) of AF variance was attributed to variation within known AF susceptibility, cardiac arrhythmia, and cardiomyopathy gene regions. Conclusions— Genetic variation contributes substantially to AF risk. The risk for AF conferred by genomic variation is similar to that observed for several other cardiovascular diseases. Established AF loci only explain a moderate proportion of disease risk, suggesting that further genetic discovery, with an emphasis on common variation, is warranted to understand the causal genetic basis of AF.

Background— Atrial fibrillation (AF) affects over 33 million individuals worldwide. Genome-wide association studies have identified at least 30 AF loci, but the mechanisms through which individual variants lead to altered disease risk have remained unclear for the majority of these loci. At the 1q24 locus, we hypothesized that the transcription factor PRRX1 could be a strong candidate gene as it is expressed in the pulmonary veins, a source of AF in many individuals. We sought to identify the molecular mechanism, whereby variation at 1q24 may lead to AF susceptibility. Methods and Results— We sequenced a ≈158 kb region encompassing PRRX1 in 962 individuals with and without AF. We identified a broad region of association with AF at the 1q24 locus. Using in silico prediction and functional validation, we identified an enhancer that interacts with the promoter of PRRX1 in cells of cardiac lineage. Within this enhancer, we identified a single-nucleotide polymorphism, rs577676, which alters enhancer activity in a mouse atrial cell line and in embryonic zebrafish and differentially regulates PRRX1 expression in human left atria. We found that suppression of PRRX1 in human embryonic stem cell–derived cardiomyocytes and embryonic zebrafish resulted in shortening of the atrial action potential duration, a hallmark of AF. Conclusions— We have identified a functional genetic variant that alters PRRX1 expression, ultimately resulting in electrophysiological alterations in atrial myocytes that may promote AF.

Background: Biological age ( BA ) may reflect an individual’s aging process better than chronological age ( CA ). The objective of our study was to construct BA measures from different types of biomarkers and test their associations with mortality and age-related disease in a community-based sample. Methods: We selected 6 clinical predictors that capture pulmonary, vascular, atherosclerosis, insulin sensitivity, inflammatory, and kidney domains of aging, and 9 inflammatory biomarkers measured in Framingham Heart Study Offspring cohort participants at exams 7 (1998-2001, N=3539, mean age 62±10) and 8 (2005-2008, N=3021, mean age 67±9). We used the Klemera-Doubal method to calculate a clinical variable BA and an inflammatory marker BA. We computed BA using DNA methylation (DNAm) data at exam 8 using Horvath’s method. For each of the measures we computed the difference ( ΔAge ) between BA and CA and modeled the effects of ΔAge after accounting for CA. We followed participants through 2014 for all-cause mortality (N=713), cardiovascular disease (CVD, N=412), coronary heart disease (CHD, N=223), stroke (N=129), and cancer (N=509). Results: Inflammatory and clinical ΔAge were correlated (=0.35, =0.33, for exams 7 and 8 respectively), and also across exams (inflammatory ΔAge exam 7 vs 8: =0.61; clinical ΔAge: =0.76). DNAm ΔAge was not significantly correlated with exam 8 inflammatory or clinical ΔAge. After adjusting for CA and sex, larger inflammatory and clinical ΔAge, corresponding to older BA than CA, was associated with significantly increased hazards of all-cause mortality, CVD, and CHD (Table). The clinical and inflammatory ΔAge were significant (p Conclusions: Our findings suggest the three BA measures may be complementary in predicting risk for age-related disease.

Background— Dimethylarginines (DMA) interfere with nitric oxide formation by inhibiting nitric oxide synthase (asymmetrical DMA [ADMA]) and l -arginine uptake into the cell (ADMA and symmetrical DMA [SDMA]). In prospective clinical studies, ADMA has been characterized as a cardiovascular risk marker, whereas SDMA is a novel marker for renal function and associated with all-cause mortality after ischemic stroke. The aim of the current study was to characterize the environmental and genetic contributions to interindividual variability of these biomarkers. Methods and Results— This study comprised a genome-wide association analysis of 3 well-characterized population-based cohorts (Framingham Heart Study [FHS; n=2992], Gutenberg Health Study [GHS; n=4354], and Multinational Monitoring of Trends and Determinants in Cardiovascular Disease Study [MONICA]/Cooperative Health Research in the Augsburg Area, Augsburg, Bavaria, Germany [KORA] F3 [n=581]) and identified replicated loci ( DDAH1, MED23, Arg1 , and AGXT2 ) associated with the interindividual variability in ADMA, l -arginine, and SDMA. Experimental in silico and in vitro studies confirmed functional significance of the identified AGXT2 variants. Clinical outcome analysis in 384 patients of the Leeds stroke study demonstrated an association between increased plasma levels of SDMA, AGXT2 variants, and various cardiometabolic risk factors. AGXT2 variants were not associated with poststroke survival in the Leeds study or were they associated with incident stroke in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. Conclusions— These genome-wide association study support the importance of DDAH1 and MED23/Arg1 in regulating ADMA and l -arginine metabolism, respectively, and identify a novel regulatory renal pathway for SDMA by AGXT2. AGXT2 variants might explain part of the pathogenic link between SDMA, renal function, and outcome. An association between AGXT2 variants and stroke is unclear and warrants further investigation.

Objective: To evaluate the relationship of striatal involvement in Huntington disease (HD) to involvement in other brain regions, CAG repeat size, onset age, and other factors. Methods: We examined patterns of neuropathologic involvement in 664 HD brains submitted to the Harvard Brain Tissue Resource Center. Brains with concomitant Alzheimer or Parkinson changes (n = 82), more than 20% missing data (n = 46), incomplete sample submission (n = 12), or CAG repeat less than 36 (n = 1) were excluded, leaving 523 cases. Standardized ratings from 0 (absent) to 4 (severe) of gross and microscopic involvement were performed for 50 regions. Cluster analysis reduced the data to 2 main measures of involvement: striatal and cortical. Results: The clusters were correlated with each other ( r = 0.42) and with disease duration (striatal: r = 0.35; cortical: r = 0.31). The striatal cluster was correlated with HD repeat size ( r = 0.50). The cortical cluster showed a stronger correlation with decreased brain weight ( r = −0.52) than the striatal cluster ( r = −0.33). The striatal cluster was correlated with younger death age ( r = −0.31) and onset age ( r = −0.46) while the cortical cluster was not ( r = 0.09, r = −0.04, respectively). Conclusions: The 2 brain clusters had different relationships to the HD CAG repeat size, onset age, and brain weight, suggesting that neuropathologic involvement does not proceed in a strictly coupled fashion. The pattern and extent of involvement varies substantially from one brain to the next. These results suggest that regional involvement in HD brain is modified by factors which, if identified, may lend insight into novel routes to therapeutics.

Background— Environmental and genetic correlates of inflammatory marker variability are incompletely understood. In the family-based Framingham Heart Study, we investigated heritability and candidate gene associations of systemic inflammatory biomarkers. Methods and Results— In offspring participants (n=3710), we examined 11 inflammatory biomarkers (CD40 ligand, C-reactive protein, intercellular adhesion molecule-1, interleukin-6, urinary isoprostanes, monocyte chemoattractant protein-1, myeloperoxidase, P-selectin, tumor necrosis factor-α, tumor necrosis factor receptor II, fibrinogen). Heritability and bivariate genetic and environmental correlations were assessed by Sequential Oligogenic Linkage Analysis routines in 1012 family members. We examined 1943 tagging single-nucleotide polymorphisms in 233 inflammatory pathway genes with ≥5 minor allele carriers using a general genetic linear model. Clinical correlates explained 2.4% (CD40 ligand) to 28.5% (C-reactive protein) of the variability in inflammatory biomarkers. Estimated heritability ranged from 10.9% (isoprostanes) to 44.8% (P-selectin). Most correlations between biomarkers were weak although statistically significant. A total of 45 single-nucleotide polymorphism-biomarker associations met the q -value threshold of 0.25. Novel top single-nucleotide polymorphisms were observed in ICAM1 gene in relation to intercellular adhesion molecule-1 concentrations (rs1799969, P =1.32�10 −8 ) and MPO in relation to myeloperoxidase (rs28730837, P =1.9�10 −5 ). Lowest P values for trans -acting single-nucleotide polymorphisms were observed for APCS with monocyte chemoattractant protein-1 concentrations (rs1374486, P =1.01�10 −7 ) and confirmed for IL6R with interleukin-6 concentrations (rs8192284, P =3.36�10 −5 ). Novel potential candidates ( APCS, MPO ) need to be replicated. Conclusions— Our community-based data support the relevance of clinical and genetic factors for explaining variation in inflammatory biomarker traits.

Magnetic resonance imaging (MRI) traits can serve as more specific measures of degenerative or cerebrovascular brain injury than can be ascertained through personal history, risk factors, clinical signs, or symptoms. They are potentially useful intermediate phenotypes for genetic studies of Alzheimer disease (AD). Recent studies have estimated heritability of white matter hyperintensity (WMH) among cognitively normal family members to be between 0.55 and 0.73. Persons discordant for AD are expected to have substantially different MRI phenotype distributions; our goal was to determine whether MRI traits in siblings discordant for AD are heritable. We measured cerebral atrophy, medial temporal atrophy (MTA), WMH, and a rating of cerebrovascular disease (CVR) via MRI in 815 participants from 424 families of the Multi-Institutional Research in Alzheimer's Genetic Epidemiology Study. Residual heritability after adjustment for covariates ranged from 0.17 (P = 0.009) for MTA to 0.57 (P = 10- 7 ) for CVR. The number of APOE-s4 alleles was significantly associated with WMH (P = 0.01) and CVR (P = 0.005) but not cerebral atrophy (P = 0.25) or MTA (P = 0.83). Heritability remained significant and high after adjusting for APOE genotype, suggesting that a substantial proportion of the additive genetic variation in these MRI traits is explained by other genes. In the Multi-Institutional Research in Alzheimer's Genetic Epidemiology Study of AD-discordant siblings, MRI traits are heritable and are potential endophenotypes for genetic association studies.

Introduction: Genome-wide association studies have identified 14 independent loci for atrial fibrillation (AF). The 4q25 locus upstream of the left-right asymmetry gene PITX2 is, by far, the strongest association signal for AF. However, as with most GWAS loci, the functional variants are noncoding, presumed to be regulatory, and remain unknown. We therefore sought to rapidly identify the functional variants at an AF locus by combining high throughput sequencing and massively parallel reporter assays. Methods and Results: We sequenced a ~750kb region encompassing the PITX2 locus in 462 individuals with early-onset AF from the MGH AF Study and 464 referents from the Framingham Heart Study. The SNP most significantly associated with AF in our sequenced sample was rs2129983, which is 140kb from PITX2 (OR=2.43, P =8.9X10 -16 ). rs2129983 is approximately 1.7kb from the most significantly associated SNP in a prior AF GWAS, rs6817105 (r 2 =0.52). From the targeted sequencing analysis, we identified 262 SNVs with a MAF >0.5% within a genomic region bounded by SNPs with an r2 greater than 0.4 with the top variant. To identify functional variants, we then utilized a massively parallel reporter assay (MPRA) in order to measure enhancer activity at each SNP across the entire AF locus. In both HL-1 and C2C12 myoblasts, MPRA identified many distinct SNP regions with differential enhancer activity. Using AF-association status as a standard, we were able to identify a series of variants that have both differential activity in either cell line tested and also a high level of association (rs17042076, rs4469143). Mechanistically, these functional SNPs are predicted to alter transcription factor binding. Conclusions: We have comprehensively identified the AF-associated variation at 4q25 and determined which of these variants are functional through differential enhancer activity. Here, in addition to identifying the causative variation for AF at 4q25, we provide a generalizable pathway for translating this work to other loci, a method that could expedite the identification of causative genetic variants at other disease loci.

Introduction: Genome-wide association studies have identified 9 genomic loci associated with atrial fibrillation (AF). Hypothesis: We sought to identify the functional variant at the 1q24 locus for AF, located upstream of the paired related homeobox 1 gene ( PRRX1 ). Methods: We used morpholino-mediated knockdown in zebrafish to assess the role of PRRX1 in cardiac function and development. To identify potential enhancers at the PRRX1 locus we analyzed DNase hypersensitivity, histone methylation, and mammalian conservation data from ENCODE. Tissue-specific enhancer activity was evaluated by microinjection of eGFP reporter constructs for each putative enhancer into zebrafish and luciferase assays in a mouse atrial myocyte (HL-1) cell line. To determine physical interaction between the AF-associated enhancer and PRRX1 promoter we analyzed available Hi-C data and performed chromatin conformation capture (3C). The functional SNP was localized using luciferase assays in HL-1 cells. The effect of the functional SNP on gene expression in human left atrial tissue was measured by qPCR. Results: Knockdown of the PRRX1 ortholog in zebrafish resulted in atrial dilation and shortening of atrial action potential duration (APD 80 : 114.8±2.2ms vs 126±1.5ms in controls, p=0.0004). Of the 4 regions tested at the 1q24 locus, 2 adjacent regions exhibited enhancer activity in the zebrafish myocardium. 3C demonstrated an increased interaction frequency between the enhancer and PRRX1 promoter regions in cells of cardiac lineage when compared to controls (103±57%, p=0.038). Screening for functional SNPs within these regions revealed that the AF risk allele (C) at SNP rs577676 associated with ~4 fold increased enhancer activity as compared to the non-risk (T) allele in HL-1 cells. Finally, regional eQTL analysis of human atrial tissue showed that rs577676 correlated with PRRX1 expression. Conclusions: We have implicated PRRX1 in cardiac electrophysiology by demonstrating that knockdown of the gene results in atrial dilation and shortening of atrial action potential duration. Further, we have found that SNP rs577676 modifies an enhancer regulating PRRX1 expression.

Background : Chronic subclinical inflammation is a prominent feature of atherosclerotic disease. The genetic background for this pro-inflammatory state is not well-established. Circulating biomarker concentrations have become attractive candidates to measure disease activity and prognosis. Methods : We examined 2356 single-nucleotide polymorphisms (SNPs) in 235 inflammatory pathway genes in association with 11 circulating inflammatory biomarkers in about 1800 Framingham Offspring cohort participants [CD40 ligand, CRP, intercellular adhesion molecule-1, interleukin-6 (IL6), urinary isoprostanes, monocyte chemoattractant protein-1 (MCP1), myeloperoxidase, P-selectin, tumor necrosis factor alpha, tumor necrosis factor receptor-2, fibrinogen]. We created residuals of log transformed biomarker concentrations adjusting for 16 potential confounders. Only SNPs with call rate ≥0.98 and HWE p>0.01, which had at least 5 minor allele carriers entered analyses. False discovery rate (FDR) and q-value methods were applied. Results : We observed similar results with FDR and q-value methods. A total of 45 associations were significant at a cutoff q value of 0.25. The top SNPs were observed in the SELP gene in association with P-selectin concentrations (rs6136 [nonsynonymous], p= 5.17*10 −39 , rs3753305 [intronic], p= 6.17*10 −9 ) and the ICAM1 gene in relation to ICAM-1 concentrations (rs1799969 [coding-nonsynonymous], p= 1.32*10 −8 ). Lowest p-values for trans-acting SNPs were observed for APCS (rs1374486 [function unknown], p= 1.01*10 −7 , and rs6695377 [function unknown], p= 1.85*10 −7 ) with MCP-1 concentrations and for IL6R (rs8192284 [coding-nonsynonimous,intronic], p= 3.36*10 −5 ) with IL6 concentrations. In addition, we could replicate reported findings for rs1799969, and rs5498 in the ICAM-1 gene in relation to ICAM-1 concentrations as well as associations of SNPs rs2857654, rs1024611, and rs2857657 in the CCL-2 gene with MCP-1 concentrations. Conclusions : The results of this candidate gene approach support the relevance of genetic variation for circulating inflammatory biomarker traits. Some former findings were confirmed and novel potential candidates are reported. Our findings merit replication in other cohorts.