bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/453878

Genetic association of photoplethysmography-derived arterial stiffness index with blood pressure and coronary artery disease

Zekavat, S. M.·Haas, M.·Aragam, K.·Emdin, C.·Khera, A. V.·Klarin, D.·Zhao, H.·Natarajan, P.
View original source ↗Export metadata ↓

Abstract

BackgroundArterial stiffness index (ASI) is independently associated with blood pressure and coronary artery disease (CAD) in epidemiologic studies. However, it is unknown whether these associations represent causal relationships.\n\nObjectivesHere, we assess whether genetic predisposition to increased ASI is associated with elevated blood pressure and CAD risk.\n\nMethodsGenome-wide association analysis (GWAS) of finger photoplethysmography-derived ASI was performed in 131,686 participants from the UK Biobank. Across UK Biobank participants not in the ASI GWAS, a 6-variant ASI polygenic risk score was calculated. The ASI polygenic score was associated with systolic and diastolic blood pressures (SBP, DBP, N=208,897), and with incident CAD over 10 years follow-up (N=223,061; 7,534 cases). The lack of CAD association observed was replicated among 184,305 participants (60,810 cases) from the Coronary Artery Disease Genetics Consortium (CARDIOGRAMplusC4D).\n\nResultsWe replicated prior reports of the epidemiologic association of ASI with SBP (Beta 0.55mmHg, [95% CI, 0.45-0.65], P=5.77x10-24), DBP (Beta 1.05mmHg, [95% CI, 0.99-1.11], P=7.27x10-272), and incident CAD (HR 1.08 [95% CI, 1.04-1.11], P=1.5x10-6) in multivariable models. While each SD increase in genetic predisposition to elevated ASI was highly associated with SBP (Beta 4.63 mmHg [95% CI, 2.1-7.2]; P=3.37x10-4), and DBP (Beta 2.61 mmHg [95% CI, 1.2-4.0]; P=2.85x10-4), no association was observed with incident CAD in UK Biobank (HR 1.12 [95% CI, 0.55-2.3]; P=0.75), or with prevalent CAD in CARDIOGRAMplusC4D (OR 0.56 [95% CI, 0.26-1.24]; P=0.15).\n\nConclusionsA genetic predisposition to higher ASI was associated with elevated blood pressure but not with increased risk of developing CAD.\n\nCondensed AbstractArterial stiffness index (ASI) is proposed by some as a surrogate of blood pressure and coronary artery disease (CAD) risk based on epidemiologic analyses. We tested whether genetic predisposition to increased ASI is associated with elevated blood pressure and CAD risk to assess whether these represent causal relationships. We find that a genetic predisposition to higher ASI is associated with elevated systolic (Beta 4.63 mmHg [95% CI, 2.1-7.2]) and diastolic blood pressures (Beta 2.61 mmHg [95% CI, 1.2-4.0]) in the UK Biobank, but not associated with incident CAD in the UK Biobank (P=0.75) or with prevalent CAD in CARDIOGRAMplusC4D (P=0.15). These data support a causal relationship of ASI with blood pressure but do not support the notion that ASI is a suitable surrogate for CAD risk.

Source connections

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zekavat, S. M., Haas, M., Aragam, K., Emdin, C., Khera, A. V., Klarin, D., Zhao, H., Natarajan, P.. 2018-10-26. Genetic association of photoplethysmography-derived arterial stiffness index with blood pressure and coronary artery disease. https://doi.org/10.1101/453878

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Cross-modal transfer enables zero-shot prediction of drug responses across cell types

Genetic perturbation responses capture the functional consequences of gene-level interventions, providing functional information for drug-response prediction beyond baseline gene expression. Within the same cellular context, genetic and chemical perturbations engage shared regulatory networks, providing a biological basis for transferring response information between perturbation modalities. Here we introduce GRAFT, a framework that combines genetic-to-chemical transfer with cross-context prediction to infer drug-induced transcriptional responses without drug measurements in the target context. Across benchmarked LINCS and external contexts, GRAFT improved response correlation, directional agreement and magnitude reconstruction over competing methods, particularly for drugs with context-dependent responses. GRAFT-Adapt further refines predictions of unmeasured responses using a small number of drug measurements from the target context. GRAFT-Atlas spans 13 cellular contexts and 7,856 drug catalog entries, comprising 102,128 predictions for drug-context combinations. The atlas captured recognizable pharmacological neighborhoods, shared functional programs, context-dependent transcriptional changes and structural-functional associations supported by molecular docking. Across ten iPSC lines from distinct donors, GRAFT identified candidate inter-donor differences, with functional support for selected candidates from measured genetic perturbation responses. Together, GRAFT enables perturbation-informed prediction and comparison of drug responses across cellular contexts.

bioinformatics↗
bioRxiv2026

Interpreting graph neural networks reveals microbial association patterns in inflammatory bowel diseases

Traditional machine learning models trained on gut microbiome abundance profiles can distinguish individuals with inflammatory bowel disease (IBD) from non-IBD individuals. However, their interpretation typically considers taxa in isolation, making it difficult to understand how associations between taxa contribute to predictions. Here, we inferred individual-specific microbial association networks from longitudinal metagenomics data of 106 individuals in HMP2/iHMP and used them to train and interpret graph neural network (GNN) models, which we compared with abundance-based models such as Random Forest (RF). Although RF achieved higher predictive performance (AUPRC ~0.59 vs 0.446), interpreting the GNNs revealed that their predictions relied on a densely connected module of co-abundant Bacteroidales species. Unlike the taxa prioritized by RF, these species were not differentially abundant between conditions. In an independent cohort, this module carried most of the predictive signal, and removing its associations reduced performance to near chance. These results show that coordinated association structure, rather than abundance shifts alone, is a feature of the IBD gut microbiome, and that interpreting GNNs can uncover such patterns that complement abundance-based analyses.

bioinformatics↗
bioRxiv2026

FateDriver: Weakly supervised dual-view graph learning for transition-specific prioritization of candidate regulators in cellular reprogramming

Cellular reprogramming supports regenerative medicine, yet the endogenous regulators of a specified source-to-target transition remain incompletely defined. Existing computational methods rank candidate regulators from network topology, trajectory dynamics, or cell-state labels, leaving unused the factors experimentally delivered to induce the transition. We present FateDriver, a weakly supervised dual-view graph neural network that jointly represents cells and genes from single-cell transcriptomes, treats the delivered factors as sparse gene-level labels, and ranks the remaining genes for a specified transition under a positive--unlabeled formulation. Across two OSKM datasets and one GMT dataset, FateDriver ranked first or second among six methods in recovering externally curated reference genes, exceeded a supervision-matched non-graph model despite weaker co-expression with the delivered factors, and achieved the highest recovery of protein targets of pluripotency-inducing chemical cocktails in both OSKM datasets. Holding either the source or the endpoint fixed yielded distinct candidate programs, including a DDX58--interferon axis associated with cells lacking endogenous pluripotency markers. Applying the same framework to candidate source--target pairs prioritized distinct candidate origins for two in vivo partially reprogrammed intestinal populations. FateDriver converts sparse experimental knowledge into transition-specific hypotheses for regulator prioritization from single-cell trajectories.

bioinformatics↗