UK Biobank Individuals Research Articles

A graph-embedded topic model enables characterization of diverse pain phenotypes among UK biobank individuals

SummaryLarge biobank repositories of clinical conditions and medications data open opportunities to investigate the phenotypic disease network. We present a graph embedded topic model (GETM). We integrate existing biomedical knowledge graph information in the form of pre-trained graph embedding into the embedded topic model. Via a variational autoencoder framework, we infer patient phenotypic mixture by modeling multi-modal discrete patient medical records. We applied GETM to UK Biobank (UKB) self-reported clinical phenotype data, which contains 443 self-reported medical conditions and 802 medications for 457,461 individuals. Compared to existing methods, GETM demonstrates good imputation performance. With a more focused application on characterizing pain phenotypes, we observe that GETM-inferred phenotypes not only accurately predict the status of chronic musculoskeletal (CMK) pain but also reveal known pain-related topics. Intriguingly, medications and conditions in the cardiovascular category are enriched among the most predictive topics of chronic pain.

Examining the Association between Mitochondrial Genome Variation and Coronary Artery Disease.

Large-scale genome-wide association studies have identified hundreds of single-nucleotide variants (SNVs) significantly associated with coronary artery disease (CAD). However, collectively, these explain <20% of the heritability. Hypothesis: Here, we hypothesize that mitochondrial (MT)-SNVs might present one potential source of this “missing heritability”. Methods: We analyzed 265 MT-SNVs in ~500,000 UK Biobank individuals, exploring two different CAD definitions: a more stringent (myocardial infarction and/or revascularization; HARD = 20,405), and a more inclusive (angina and chronic ischemic heart disease; SOFT = 34,782). Results: In HARD cases, the most significant (p < 0.05) associations were for m.295C>T (control region) and m.12612A>G (ND5), found more frequently in cases (OR = 1.05), potentially related to reduced cardiorespiratory fitness in response to exercise, as well as for m.12372G>A (ND5) and m.11467A>G (ND4), present more frequently in controls (OR = 0.97), previously associated with lower ROS production rate. In SOFT cases, four MT-SNVs survived multiple testing corrections (at FDR < 5%), all potentially conferring increased CAD risk. Of those, m.11251A>G (ND4) and m.15452C>A (CYB) have previously shown significant associations with body height. In line with this, we observed that CAD cases were slightly less physically active, and their average body height was ~2.00 cm lower compared to controls; both traits are known to be related to increased CAD risk. Gene-based tests identified CO2 associated with HARD/SOFT CAD, whereas ND3 and CYB associated with SOFT cases (p < 0.05), dysfunction of which has been related to MT oxidative stress, obesity/T2D (CO2), BMI (ND3), and angina/exercise intolerance (CYB). Finally, we observed that macro-haplogroup I was significantly (p < 0.05) more frequent in HARD cases vs. controls (3.35% vs. 3.08%), potentially associated with response to exercise. Conclusions: We found only spurious associations between MT genome variation and HARD/SOFT CAD and conclude that more MT-SNV data in even larger study cohorts may be needed to conclusively determine the role of MT DNA in CAD.

An unbiased test reveals no enrichment of sexually antagonistic polymorphisms on the human X chromosome.

Mutations with beneficial effects in one sex can have deleterious effects in the other. Such ‘sexually antagonistic’ (SA) variants contribute to variation in life-history traits and overall fitness, yet their genomic distribution is poorly resolved. Theory predicts that SA variants could be enriched on the X chromosome or autosomes, yet current empirical tests face two formidable challenges: (i) identifying SA selection in genomic data is difficult; and (ii) metrics of SA variation show persistent biases towards the X, even when SA variants are randomly distributed across the genome. Here, we present an unbiased test of the theory that SA variants are enriched on the X. We first develop models for reproductive FST—a metric for quantifying sex-differential (including SA) effects of genetic variants on lifetime reproductive success—that control for X-linked biases. Comparing data from approximately 250 000 UK Biobank individuals to our models, we find FST elevations consistent with both X-linked and autosomal SA polymorphisms affecting reproductive success in humans. However, the extent of FST elevations does not differ from a model in which SA polymorphisms are randomly distributed across the genome. We argue that the polygenic nature of SA variation, along with sex asymmetries in SA effects, might render X-linked enrichment of SA polymorphisms unlikely.

Age effects on white matter microstructure in individuals of self‐identified Indian ancestry from the UK Biobank

AbstractBackgroundThe incidence of dementia in India is 14%, and its prevalence doubles with every five‐year increase in age. Given the lack of ethnic diversity in most brain research to date, it is valuable to study cohorts with diverse genetic and environmental backgrounds, to identify predictors of health and disease that can be generalized to other ethnic groups than the commonly studied populations of European ancestry. To address this gap in the literature, we modeled factors that affect aging patterns in the brain’s white matter in individuals with Indian ancestry using advanced diffusion‐weighted MRI (dMRI).MethodCross‐sectional dMRI data from 123 individuals (males, n=78; females, n=45) of self‐reported Indian ancestry (born in India: 44%; mean age: 60.1±8.3 SD, range: 45‐80) from the UK Biobank were analyzed using diffusion tensor imaging (DTI), the tensor distribution function (TDF), neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator MRI (MAPMRI). Main effects of age, sex, and age‐by‐sex interaction were investigated to characterize trajectories in whole‐brain white matter averages and the corpus callosum (CC), parcellated using the JHU‐ICBM atlas, covarying for years of education, waist/hip ratio, population structure (Figure 1) and the Townsend index. Normative lifespan charts were created to visualize white matter aging trajectories for the major diffusivity metrics.ResultNormative centile curves (Figures 2‐3) show, with increasing age, declining white matter integrity, increased diffusivity, lower white matter density and diffusion restriction at the whole‐brain level and CC (Table 1; Figure 4). There was no detectable effect of sex, nor age‐by‐sex interaction, across whole‐brain metrics. In the CC, a steeper aging trajectory, evidenced by more pronounced white matter changes, was observed in males relative to females (Table 1; Figure 5).ConclusionUsing advanced dMRI modeling, we report diffuse white matter changes with respect to age in a subsample of UK Biobank individuals of self‐reported Indian ancestry. Future studies should include larger sample sizes, comparisons with Indian ancestry individuals residing in India, consider vascular factors, and examine genotype interactions (e.g., with apolipoprotein E genotype), to further characterize risk factors and better understand brain aging in diverse populations with varied genetic and environmental backgrounds.

Abstract 10452: Exploring the Role of Mitochondrial Genetic Variants in Coronary Artery Disease

Introduction: Large-scale genome-wide association studies have identified &gt;200 single-nucleotide variants (SNVs) significantly associated with coronary artery disease (CAD), collectively explaining &lt;20% of the heritability. Hypothesis: Mitochondrial (MT) genetic variants presents an underexplored one potential source of the “missing heritability”. Methods: We analyzed 265 MT-SNVs in ∼500,000 UK Biobank individuals, exploring two different CAD definitions: including (SOFT=34,782) vs. excluding (HARD=20,405) angina, using the array, sex, birth year and first five principal components as covariates. Results: In SOFT cases, four MT-SNVs survived multiple testing correction (at FDR&lt;5%), all conferring increased CAD risk: m.10400C&gt;T (rs28358278, OR=1.28, MAF=0.02, T114T in MT-ND3), m.15301G&gt;A (rs527236045, OR=1.03, MAF=0.04, L185L in MT-CYB), m.11251A&gt;G (rs869096886, OR=1.03, MAF=0.2, L164L in MT-ND4) and m.15452C&gt;A (rs193302994, OR=1.03, MAF=0.2, L236I in MT-CYB), the latter two previously associated with height in the UK Biobank. In HARD cases, no MT-SNVs survived multiple testing correction, the most significant nominal (P &lt;0.05) findings being for four SNVs, whereof m.295C&gt;T (rs41528348, OR=1.05, MAF=0.1, in the D-LOOP) and m.12612A&gt;G (rs28359172, OR=1.05, MAF=0.04, V92V in MT-ND5) could potentially increase CAD risk, whereas m.12372G&gt;A (rs2853499, OR=0.97, MAF=0.22, L12L in MT-ND5), m.11467A&gt;G (rs2853493, OR=0.97, MAF=0.22, L236L in MT-ND4) and m.15301G&gt;A (rs193302991, OR=0.97, MAF=0.04, L185L in MT-CYB) may confer protective effects. Previously, several of these variants have been associated with a number of blood cell traits in the UK Biobank, including the mean corpuscular hemoglobin levels (m.295C&gt;T, m.12612A&gt;G, m.12372G&gt;A) and volume (m.295C&gt;T and m.12612A&gt;G). In addition, m.12372G&gt;A was significantly association with five other blood cell traits, including the mean platelet volume and white/red blood cell count, as well as with several renal biomarkers, and m.12612A&gt;G was associated with height. Conclusions: Our results indicated only spurious associations of the MT-SNV with the occurrence of CAD. More data in larger study cohorts may be needed to conclusively determine the role of MT-DNA in CAD.

Patterns of recent natural selection on genetic loci associated with sexually differentiated human body size and shape phenotypes.

Levels of sex differences for human body size and shape phenotypes are hypothesized to have adaptively reduced following the agricultural transition as part of an evolutionary response to relatively more equal divisions of labor and new technology adoption. In this study, we tested this hypothesis by studying genetic variants associated with five sexually differentiated human phenotypes: height, body mass, hip circumference, body fat percentage, and waist circumference. We first analyzed genome-wide association (GWAS) results for UK Biobank individuals (~194,000 females and ~167,000 males) to identify a total of 114,199 single nucleotide polymorphisms (SNPs) significantly associated with at least one of the studied phenotypes in females, males, or both sexes (P<5x10-8). From these loci we then identified 3,016 SNPs (2.6%) with significant differences in the strength of association between the female- and male-specific GWAS results at a low false-discovery rate (FDR<0.001). Genes with known roles in sexual differentiation are significantly enriched for co-localization with one or more of these SNPs versus SNPs associated with the phenotypes generally but not with sex differences (2.73-fold enrichment; permutation test; P = 0.0041). We also confirmed that the identified variants are disproportionately associated with greater phenotype effect sizes in the sex with the stronger association value. We then used the singleton density score statistic, which quantifies recent (within the last ~3,000 years; post-agriculture adoption in Britain) changes in the frequencies of alleles underlying polygenic traits, to identify a signature of recent positive selection on alleles associated with greater body fat percentage in females (permutation test; P = 0.0038; FDR = 0.0380), directionally opposite to that predicted by the sex differences reduction hypothesis. Otherwise, we found no evidence of positive selection for sex difference-associated alleles for any other trait. Overall, our results challenge the longstanding hypothesis that sex differences adaptively decreased following subsistence transitions from hunting and gathering to agriculture.

190-LB: Using Genetics to Uncouple Higher Adiposity from Its Adverse Metabolic Effects

Type 2 diabetes is linked to higher risk of many non-metabolic conditions (e.g. cancers, neuropsychiatric and musculoskeletal disease) which all share a common risk factor: obesity. The causal pathways from excess weight to disease are unknown. We aimed to use specific genetic variants to separately test the causal roles of higher adiposity with and without its adverse metabolic effects. Using metabolic biomarkers from 442,278 UK Biobank individuals, we identified two clusters of body fat % increasing alleles: a) 36 “favourable adiposity” (FA) alleles associated with a favourable metabolic profile (higher HDL and SHBG and lower triglyceride and liver enzymes); b) 38 “unfavourable adiposity” (UFA) alleles associated with an unfavourable profile (lower HDL and SHBG and higher triglyceride and liver enzymes). We used two-sample Mendelian randomization with 43 diseases from non-UK Biobank GWASs. We identified two sets of diseases. 1) Those where the metabolic effect of higher adiposity is the likely primary cause of the disease. Here the FA and UFA clusters had an opposing effect on the risk of disease: type 2 diabetes (FA: 0.11 OR per 1 standard deviation higher body fat % [95%CI: 0.08, 0.16] vs. UFA: 5.50 [4.29, 7.05]), stroke (0.65 [0.52, 0.83] vs. 1.43 [1.23, 1.67]), gout (0.44 [0.29, 0.68] vs. 2.49 [1.88, 3.29]), breast cancer (1.12 [0.64, 1.94] vs. 0.52 [0.39, 0.68]) and colorectal cancer (0.67 [0.52, 0.85] vs. 1.20 [1, 1.50]). 2) Those where the mechanical excess weight (or other non-metabolic effects) is likely a cause of the disease. Here the FA cluster, despite having lower metabolic risk, and the UFA cluster were both associated with higher disease risk: venous thromboembolism (2.52 [1.82, 3.47] vs. 1.63 [1.25, 2.13]), osteoarthritis (1.45 [1.19, 1.76] vs. 2.20 [1.64, 2.95]), depression (2.85 [1.38, 5.89] vs. 2.59 [1.60, 4.21]). Our results assist in understanding the consequences of higher adiposity uncoupled from its adverse metabolic effects in non-metabolic diseases pathophysiological mechanisms. Disclosure S. Martin: None. J. D. Bell: None. T. M. Frayling: None. H. Yaghootkar: None. J. Tyrrell: None. E. L. Thomas: None. M. Bown: None. J. T. Elder: None. C. Kabrhel: Research Support; Self; Diagnostica Stago, Grifols. N. Papadimitriou: None. M. Gunter: None. E. E. Vincent: None. Funding Diabetes UK (17/0005594)

FC 062OBESITY AS A CAUSE OF KIDNEY DISEASE - INSIGHTS FROM MENDELIAN RANDOMISATION STUDIES

Abstract Background and Aims Obesity and kidney diseases are common complex disorders with an increasing clinical and economic impact on healthcare around the globe. We aim to examine if modifiable anthropometric indices of obesity exert putatively causal effects on different measures of kidney health and disease. Method We performed conventional observational and Mendelian randomisation (MR) study to examine if modifiable anthropometric indices of obesity exert putatively causal effects on different kidney health and disease-related phenotypes. These analyses were conducted using approximately 300,000 participants of white-British ancestry from UK Biobank and up to 480,000 participants of predominantly European ancestry from genome-wide association studies. Results The Mendelian randomisation analysis indicated that increasing values of genetically predicted BMI and waist circumference were causally linked to changes in renal function indices including reduced estimated glomerular filtration (PeGFRcystatineC=5.96 × 10-59 for BMI and PeGFRcystatineC=1.72 × 10-69 for waist circumference) and increased blood urea nitrogen (PBUN=2.01 × 10-10 for BMI and PBUN=4.54 × 10-12 for waist circumference) in UK Biobank individuals. These associations were replicated using data from CKDGen Consortium individuals (PeGFRcystatineC=1.47 × 10-5 for BMI and PeGFRcystatineC=7.63 × 10-5 for waist circumference; PBUN=1.96 × 10-4 for BMI and PBUN=3.10 × 10-3 for waist circumference). One standard deviation increase in genetically-predicted BMI and waist circumference decreased the relative odds of kidney health index by 14% and 18% (OR=0.86; 95%CI: 0.82-0.92; P=9.18 × 10-6 for BMI and OR=0.82; 95%CI: 0.75-0.90; P=2.12 × 10-5 for waist circumference). Approximately 13-16% of the causal effect of obesity indices on kidney health was mediated by blood pressure. Obesity increased the risk of both acute and chronic kidney disease of several aetiologies including hypertensive renal disease (OR=1.79; 95%CI: 1.14-2.82; P=1.15 × 10-2 for BMI and OR=2.41; 95%CI: 1.30-4.45; P=5.03 × 10-3 for waist circumference), renal failure (OR=1.51; 95%CI: 1.25-1.83; P=2.60 × 10-5 for BMI and OR=1.86; 95%CI: 1.43-2.42; P=4.16 × 10-6 for waist circumference) and CKD (OR=1.50; 95%CI: 1.16-1.96; P=2.44 × 10-3 for BMI and OR=1.83; 95%CI: 1.28-2.63; P=9.49 × 10-4 for waist circumference) and diabetic nephropathy (OR=1.92; 95%CI: 1.44-2.54; P=6.86 × 10-6 for BMI). Conclusion These findings indicate that obesity is causally linked to indices of renal health and the risk of different kidney diseases. This evidence substantiates the value of weight loss as a strategy of preventing and/or counteracting a decline in kidney health as well as decreasing the risk of renal disease.

Integrated Polygenic Tool Substantially Enhances Coronary Artery Disease Prediction.

There is considerable interest in whether genetic data can be used to improve standard cardiovascular disease risk calculators, as the latter are routinely used in clinical practice to manage preventative treatment. Using the UK Biobank resource, we developed our own polygenic risk score for coronary artery disease (CAD). We used an additional 60 000 UK Biobank individuals to develop an integrated risk tool (IRT) that combined our polygenic risk score with established risk tools (either the American Heart Association/American College of Cardiology pooled cohort equations [PCE] or UK QRISK3), and we tested our IRT in an additional, independent set of 186 451 UK Biobank individuals. The novel CAD polygenic risk score shows superior predictive power for CAD events, compared with other published polygenic risk scores, and is largely uncorrelated with PCE and QRISK3. When combined with PCE into an IRT, it has superior predictive accuracy. Overall, 10.4% of incident CAD cases were misclassified as low risk by PCE and correctly classified as high risk by the IRT, compared with 4.4% misclassified by the IRT and correctly classified by PCE. The overall net reclassification improvement for the IRT was 5.9% (95% CI, 4.7-7.0). When individuals were stratified into age-by-sex subgroups, the improvement was larger for all subgroups (range, 8.3%-15.4%), with the best performance in 40- to 54-year-old men (15.4% [95% CI, 11.6-19.3]). Comparable results were found using a different risk tool (QRISK3) and also a broader definition of cardiovascular disease. Use of the IRT is estimated to avoid up to 12 000 deaths in the United States over a 5-year period. An IRT that includes polygenic risk outperforms current risk stratification tools and offers greater opportunity for early interventions. Given the plummeting costs of genetic tests, future iterations of CAD risk tools would be enhanced with the addition of a person's polygenic risk.

Common and Rare Variant Prediction and Penetrance of IBD in a Large, Multi-ethnic, Health System-based Biobank Cohort

Polygenic risk scores (PRS) may soon be used to predict inflammatory bowel disease (IBD) risk in prevention efforts. We leveraged exome-sequence and single nucleotide polymorphism (SNP) array data from 29,358 individuals in the multiethnic, randomly ascertained health system-based BioMe biobank to define effects of common and rare IBD variants on disease prediction and pathophysiology. PRS were calculated from European, African American, and Ashkenazi Jewish (AJ) reference case-control studies, and a meta-GWAS run using all three association datasets. PRS were then combined using regression to assess which combination of scores best predicted IBD status in European, AJ, Hispanic, and African American cohorts in BioMe. Additionally, rare variants were assessed in genes associated with very early-onset IBD (VEO-IBD), by estimating genetic penetrance in each BioMe population. Combining risk scores based on association data from distinct ancestral populations improved IBD prediction for every population in BioMe and significantly improved prediction among European ancestry UK Biobank individuals. Lower predictive power for non-Europeans was observed, reflecting in part substantially lower African IBD case-control reference sizes. We replicated associations for two VEO-IBD genes, ADAM17 and LRBA, with high dominant model penetrance in BioMe. Autosomal recessive LRBA risk alleles are associated with severe, early-onset autoimmunity; we show that heterozygous carriage of an African-predominant LRBA protein-altering allele is associated with significantly decreased LRBA and CTLA-4 expression with T-cell activation. Greater genetic diversity in African populations improves prediction across populations, and generalizes some VEO-IBD genes. Increasing African American IBD case-collections should be prioritized to reduce health disparities and enhance pathophysiological insight.

Associations between gut microbiota and genetic risk for rheumatoid arthritis in the absence of disease: a cross-sectional study

SummaryBackgroundRheumatoid arthritis is a chronic inflammatory autoimmune disease that is associated with reduced life expectancy. The disease is heritable and an extensive repertoire of genetic variants have been identified. The gut microbiota might represent an environmental risk factor for rheumatoid arthritis. We aimed to assess whether known rheumatoid arthritis risk alleles were associated with the gut microbiota in a large population who do not have rheumatoid arthritis.MethodsIn this cross-sectional study done in the UK and Switzerland, we used genotyping and microbiota data from previous studies of the TwinsUK cohort, excluding participants who had ever had a diagnosis of rheumatoid arthritis, as well as their unaffected co-twins. We used blood samples for genotyping and stool samples for the assessment of the gut microbiota. We generated a polygenic risk score (PRS) for rheumatoid arthritis in 1650 TwinsUK participants without the disease, based on 233 GWAS-identified single nucleotide polymorphisms associated with rheumatoid arthritis. We validated the PRS using logistic regression against rheumatoid arthritis diagnosis in 2686 UK Biobank individuals with a confirmed diagnosis of rheumatoid arthritis. Amplicon sequence variants (ASVs) were generated from 16S rRNA gene sequencing of stool samples and assessed for association with the PRS for rheumatoid arthritis. We validated the findings in an independent sample comprised of first-degree relatives of patients with rheumatoid arthritis from the SCREEN-RA cohort. Differential abundance of ASVs present in more than 5% of samples, grouped by ASV taxon annotation, against the rheumatoid arthritis PRS as a continuous variable was assessed using fixed-effects covariates. To account for multiple testing, the false discovery rate calculation was applied to all p values to generate q values, with a significance threshold of 0·05 determined a priori.FindingsWe found that presence of Prevotella spp were positively associated with the rheumatoid arthritis PRS in TwinsUK participants (q<1 × 10−7). This finding was validated in SCREEN-RA participants (n=133) carrying established shared epitope risk alleles (q=0·0011). We also found an association between Prevotella spp and presence of preclinical rheumatoid arthritis phases (q=0·021).InterpretationPrevotella spp in the gut microbiota are associated with the rheumatoid arthritis genotype in the absence of rheumatoid arthritis, including in individuals at high risk of developing rheumatoid arthritis. Our findings suggest that host genotype is associated with microbiota profile before disease onset.FundingVersus Arthritis.

HOPS: a quantitative score reveals pervasive horizontal pleiotropy in human genetic variation is driven by extreme polygenicity of human traits and diseases

Horizontal pleiotropy, where one variant has independent effects on multiple traits, is important for our understanding of the genetic architecture of human phenotypes. We develop a method to quantify horizontal pleiotropy using genome-wide association summary statistics and apply it to 372 heritable phenotypes measured in 361,194 UK Biobank individuals. Horizontal pleiotropy is pervasive throughout the human genome, prominent among highly polygenic phenotypes, and enriched in active regulatory regions. Our results highlight the central role horizontal pleiotropy plays in the genetic architecture of human phenotypes. The HOrizontal Pleiotropy Score (HOPS) method is available on Github at https://github.com/rondolab/HOPS.

Imputation of behavioral candidate gene repeat variants in 486,551 publicly-available UK Biobank individuals.

Some of the most widely studied variants in psychiatric genetics include variable number tandem repeat variants (VNTRs) in SLC6A3, DRD4, SLC6A4, and MAOA. While initial findings suggested large effects, their importance with respect to psychiatric phenotypes is the subject of much debate with broadly conflicting results. Despite broad interest, these loci remain absent from the largest available samples, such as the UK Biobank, limiting researchers' ability to test these contentious hypotheses rigorously in large samples. Here, using two independent reference datasets, we report out-of-sample imputation accuracy estimates of >0.96 for all four VNTR variants and one modifying SNP, depending on the reference and target dataset. We describe the imputation procedures of these candidate variants in 486,551 UK Biobank individuals, and have made the imputed variant data available to UK Biobank researchers. This resource, provided to the scientific community, will allow the most rigorous tests to-date of the roles of these variants in behavioral and psychiatric phenotypes.

Polymorphisms of genes involved in inflammation and blood vessel development influence the risk of varicose veins

Heredity plays an important role in the etiology of varicose veins (VVs). However, the genetic basis underlying this condition remains poorly understood. Our aim was to replicate top association signals from genome-wide association studies (GWASs) for VVs of lower extremities using 2 independent datasets-our sample of ethnic Russian individuals (709 cases and 278 controls) and a large cohort of British residents from UK Biobank (10 861 cases and 397 594 controls). Associations of polymorphisms rs11121615, rs6712038, rs507666, rs966562, rs7111987, rs6062618, and rs6905288 were validated in the UK Biobank individuals at a Bonferroni-corrected significance level. In Russian cohort, only rs11121615 reached a nominal significance level of P < .05. Results of original GWAS and replication studies were combined by a meta-analysis, and polymorphisms listed above as well as rs111434909 and rs4463578 passed a genome-wide significant threshold. Notably, the majority of these polymorphisms were located within or near genes involved in vascular development and remodeling, and regulation of inflammatory response. Our results confirm the role of these polymorphisms in genetic susceptibility to VVs and indicate the revealed genomic regions as good candidates for further fine-mapping studies and functional analysis. Moreover, our findings implicate inflammation and abnormal vascular architecture in VVs pathogenesis.

Genetic study links components of the autonomous nervous system to heart-rate profile during exercise

Heart rate (HR) responds to exercise by increasing during exercise and recovering after exercise. As such, HR is an important predictor of mortality that researchers believe is modulated by the autonomic nervous system. However, the mechanistic basis underlying inter-individual differences has yet to be explained. Here, we perform a large-scale genome-wide analysis of HR increase and HR recovery in 58,818 UK Biobank individuals. Twenty-five independent SNPs in 23 loci are identified to be associated (p < 8.3 × 10−9) with HR increase or HR recovery. A total of 36 candidate causal genes are prioritized that are enriched for pathways related to neuron biology. No evidence is found of a causal relationship with mortality or cardiovascular diseases. However, a nominal association with parental lifespan requires further study. In conclusion, the findings provide new biological and clinical insight into the mechanistic underpinnings of HR response to exercise. The results also underscore the role of the autonomous nervous system in HR recovery.