Genome-wide analyses of behavioural traits are subject to bias by misreports and longitudinal changes

Genome-wide association studies (GWAS) have discovered numerous genetic variants associated with human behavioural traits. However, behavioural traits are subject to misreports and longitudinal changes (MLC) which can cause biases in GWAS and follow-up analyses. Here, we demonstrate that individuals with higher disease burden in the UK Biobank (n = 455,607) are more likely to misreport or reduce their alcohol consumption levels, and propose a correction procedure to mitigate the MLC-induced biases. The alcohol consumption GWAS signals removed by the MLC corrections are enriched in metabolic/cardiovascular traits. Almost all the previously reported negative estimates of genetic correlations between alcohol consumption and common diseases become positive/non-significant after the MLC corrections. We also observe MLC biases for smoking and physical activities in the UK Biobank. Our findings provide a plausible explanation of the controversy about the effects of alcohol consumption on health outcomes and a caution for future analyses of self-reported behavioural traits in biobank data.

The Genetic Dissection of Ace2 Expression Variation in the Heart of Murine Genetic Reference Population

Background: A high inflammatory and cytokine burden that induces vascular inflammation, myocarditis, cardiac arrhythmias, and myocardial injury is associated with a lethal outcome in COVID-19. The SARS-CoV-2 virus utilizes the ACE2 receptor for cell entry in a similar way to SARS-CoV. This study investigates the regulation, gene network, and associated pathways of ACE2 that may be involved in inflammatory and cardiovascular complications of COVID-19.Methods: Cardiovascular traits were determined in the one of the largest mouse genetic reference populations: BXD recombinant inbred strains using blood pressure, electrocardiography, and echocardiography measurements. Expression quantitative trait locus (eQTL) mapping, genetic correlation, and functional enrichment analysis were used to identify Ace2 regulation, gene pathway, and co-expression networks.Results: A wide range of variation was found in expression of Ace2 among the BXD strains. Levels of Ace2 expression are negatively correlated with cardiovascular traits, including systolic and diastolic blood pressure and P wave duration and amplitude. Ace2 co-expressed genes are significantly involved in cardiac- and inflammatory-related pathways. The eQTL mapping revealed that Cyld is a candidate upstream regulator for Ace2. Moreover, the protein–protein interaction (PPI) network analysis inferred several potential key regulators (Cul3, Atf2, Vcp, Jun, Ppp1cc, Npm1, Mapk8, Set, Dlg1, Mapk14, and Hspa1b) for Ace2 co-expressed genes in the heart.Conclusions:Ace2 is associated with blood pressure, atrial morphology, and sinoatrial conduction in BXD mice. Ace2 co-varies with Atf2, Cyld, Jun, Mapk8, and Mapk14 and is enriched in the RAS, TGFβ, TNFα, and p38α signaling pathways, involved in inflammation and cardiac damage. We suggest that all these novel Ace2-associated genes and pathways may be targeted for preventive, diagnostic, and therapeutic purposes in cardiovascular damage in patients with systemic inflammation, including COVID-19 patients.

Abstract 15554: Mendelian Randomization of 436 Circulating Proteins Identifies Putatively Causal Proteins for Cardiovascular Disease and Its Risk Factors

Introduction: Despite numerous efforts to understand the molecular mechanisms underlying cardiovascular disease, it remains the leading cause of death worldwide. Circulating proteins are frequently used as clinical biomarkers and may serve as effective drug targets. Hypothesis: Identifying genetic variants associated with circulating protein concentrations (pQTLs) and combining them with genome-wide association study (GWAS) results may bridge a knowledge gap of unexplained heritability, identify potentially causal proteins, and highlight promising therapeutic targets. Methods: We measured concentrations of 436 plasma proteins among 484 Framingham participants using OLINK cardiovascular disease and inflammation panels. For each protein, we conducted a GWAS using 1000G imputed genotypes (imputation quality >20%). Then we performed two sample Mendelian randomization using cis-pQTLs (SNPs within 1Mb of transcriptional start site of the protein coding gene) as instrumental variables and 11 public cardiovascular trait GWAS (coronary artery disease (CAD), HDL cholesterol, LDL cholesterol, triglycerides (TG), body mass index (BMI), obesity, fasting blood glucose, type 2 diabetes, systolic blood pressure, diastolic blood pressure, and Hypertension) as outcomes. Results: At Bonferroni-corrected P<0.05, we identified 5281 cis-pQTLs for 76 proteins. Mendelian randomization identified 13 putatively causal proteins for seven cardiovascular traits: FGF-5, IL-6RA, LPL , and TFPI for CAD; CAPG, IGG_FCrrec, LPL, and TFPI for HDL; KIM1, LPL, and TNFB for TG, ENTPD6 for BMI; FGF-5, TFPI, UMOD, IGFBP3, PXN, and NUCB2 for blood pressure traits. Conclusions: At Bonferroni-corrected P<0.05, we identified 5281 cis-pQTLs for 76 proteins. Mendelian randomization identified 13 putatively causal proteins for seven cardiovascular traits: FGF-5, IL-6RA, LPL , and TFPI for CAD; CAPG, IGG_FCrrec, LPL, and TFPI for HDL; KIM1, LPL, and TNFB for TG, ENTPD6 for BMI; FGF-5, TFPI, UMOD, IGFBP3, PXN, and NUCB2 for blood pressure traits.

The contribution of non-coding regulatory elements to cardiovascular disease

Cardiovascular disease collectively accounts for a quarter of deaths worldwide. Genome-wide association studies across a range of cardiovascular traits and pathologies have highlighted the prevalence of common non-coding genetic variants within candidate loci. Here, we review genetic, epigenomic and molecular approaches to investigate the contribution of non-coding regulatory elements in cardiovascular biology. We then discuss recent insights on the emerging role of non-coding variation in predisposition to cardiovascular disease, with a focus on novel mechanistic examples from functional genomics studies. Lastly, we consider the clinical significance of these findings at present, and some of the current challenges facing the field.

Genome-wide analyses of behavioural traits biased by misreports and longitudinal changes

Genome-wide association studies (GWAS) have discovered numerous genetic variants associated with human behavioural traits. However, behavioural traits are subject to misreports and longitudinal changes (MLC) which can cause biases in GWAS and follow-up analyses. Here, we demonstrate that individuals with higher disease burden in the UK Biobank (n = 455,607) are more likely to misreport or reduce their alcohol consumption (AC) levels, and propose a correction procedure to mitigate the MLC-induced biases. The AC GWAS signals removed by the MLC corrections are enriched in metabolic/cardiovascular traits. Almost all the previously reported negative estimates of genetic correlations between AC and common diseases become positive/non-significant after the MLC corrections. We also observe MLC biases for smoking and physical activities in the UK Biobank. Our findings provide a plausible explanation of the controversy about the effects of AC on health outcomes and a caution for future analyses of self-reported behavioural traits in biobank data.

Whole‐Genome Approach Discovers Novel Genetic and Nongenetic Variance Components Modulated by Lifestyle for Cardiovascular Health

Background Both genetic and nongenetic factors can predispose individuals to cardiovascular risk. Finding ways to alter these predispositions is important for cardiovascular disease prevention. Methods and Results We used a novel whole‐genome approach to estimate the genetic and nongenetic effects on—and hence their predispositions to—cardiovascular risk and determined whether they vary with respect to lifestyle factors such as physical activity, smoking, alcohol consumption, and dietary intake. We performed analyses on the ARIC (Atherosclerosis Risk in Communities) Study (N=6896–7180) and validated findings using the UKBB (UK Biobank, N=14 076–34 538). Lifestyle modulation was evident for many cardiovascular traits such as body mass index and resting heart rate. For example, alcohol consumption modulated both genetic and nongenetic effects on body mass index, whereas smoking modulated nongenetic effects on heart rate, pulse pressure, and white blood cell count. We also stratified individuals according to estimated genetic and nongenetic effects that are modulated by lifestyle factors and showed distinct phenotype–lifestyle relationships across the stratified groups. Finally, we showed that neglecting lifestyle modulations of cardiovascular traits would on average reduce single nucleotide polymorphism heritability estimates of these traits by a small yet significant amount, primarily owing to the overestimation of residual variance. Conclusions Lifestyle changes are relevant to cardiovascular disease prevention. Individual differences in the genetic and nongenetic effects that are modulated by lifestyle factors, as shown by the stratified group analyses, implies a need for personalized lifestyle interventions. In addition, single nucleotide polymorphism–based heritability of cardiovascular traits without accounting for lifestyle modulations could be underestimated.

Mylk3 null C57BL/6N mice develop cardiomyopathy, whereas Nnt null C57BL/6J mice do not

The C57BL/6J and C57BL/6N mice have well-documented phenotypic and genotypic differences, including the infamous nicotinamide nucleotide transhydrogenase (Nnt) null mutation in the C57BL/6J substrain, which has been linked to cardiovascular traits in mice and cardiomyopathy in humans. To assess whether Nnt loss alone causes a cardiovascular phenotype, we investigated the C57BL/6N, C57BL/6J mice and a C57BL/6J-BAC transgenic rescuing NNT expression, at 3, 12, and 18 mo. We identified a modest dilated cardiomyopathy in the C57BL/6N mice, absent in the two B6J substrains. Immunofluorescent staining of cardiomyocytes revealed eccentric hypertrophy in these mice, with defects in sarcomere organisation. RNAseq analysis identified differential expression of a number of cardiac remodelling genes commonly associated with cardiac disease segregating with the phenotype. Variant calling from RNAseq data identified a myosin light chain kinase 3 (Mylk3) mutation in C57BL/6N mice, which abolishes MYLK3 protein expression. These results indicate the C57BL/6J Nnt-null mice do not develop cardiomyopathy; however, we identified a null mutation in Mylk3 as a credible cause of the cardiomyopathy phenotype in the C57BL/6N.

Abstract P147: Genome Wide Assessment of Shared Genetic Architecture Between Rheumatoid Arthritis and Cardiovascular Diseases Using the UK Biobank Data

Background: Patients with RA have a 2-10 folds increased risk of cardiovascular diseases (CVD) and CVD accounts for almost 50% of the excess mortality in patients with RA when compared with general population, but the mechanisms underlying such associations are largely unknown. Methods: We examined the genetic correlation, causality, and shared genetic variants between RA (Ncase=6,756, Ncontrol=452,476) and CVD (Ncase=44,246, Ncontrol=414,986) using LD Score regression (LDSC), generalized summary-data-based Mendelian Randomization (GSMR), and cross-trait meta-analysis in the UK Biobank Data. Results: In the present study, RA was significantly genetically correlated with MI, angina, CHD, and CVD after correcting for multiple testing (Rg ranges from 0.40 to 0.43, P<0.05/5). Interestingly, when stratified by frequent usage of aspirin and paracetamol, we observed increased genetic correlation between RA and CVD for participants without aspirin usage ( Rg increased to 0.54 [95%CI: 0.54, 0.78] for angina; P value=6.69х10 -6 ), and for participants with usage of paracetamol ( Rg increased to 0.75 [95%CI: 0.20, 1.29] for MI; P value=8.90х10 -3 ). Cross-trait meta-analysis identified 9 independent loci that were shared between RA and at least one of the genetically correlated CVD traits including PTPN22 at chr1p13.2 , BCL2L11 at chr2q13 , and CCR3 at chr3p21.31 ( P single trait <1х10 -3 and P meta <5х10 -8 ) highlighting potential shared etiology between them which include accelerating atherosclerosis and upregulating oxidative stress and vascular permeability. Finally, Mendelian randomization analyses observed inconsistent instrumental effects and were unable to conclude the causality and directionality between RA and CVD. Conclusion: Our results supported positive genetic correlation between RA and multiple cardiovascular traits, and frequent usage of aspirin and paracetamol may modify their associations, but instrumental analyses were unable to conclude the causality and directionality between them.