Integrating genetic data with protein levels, known as protein quantitative trait loci (pQTLs), can enhance our understanding of disease mechanisms and provide actionable insights for drug discovery, by guiding the direction of therapeutic interventions, clarifying mechanisms of action, and predicting potential side effects. However, most pQTL studies have focused on the plasma proteome, overlooking tissue-specific effects. Here, we investigate the plasma and tissue proteome and derive tissue-specific pQTLs in a unique dataset derived from a cohort of 284 STARNET patients, predominantly male, with a mean age of 65 years and a high prevalence of coronary artery disease (CAD). Importantly, our dataset includes paired tissue samples from aortic wall, mammary artery, liver, and skeletal muscle alongside plasma, allowing for a comprehensive comparative analysis across tissues—all from the same individuals. We employed the Olink Explore 3.2k platform to assess relative protein levels in each tissue. We identify 608 cis- pQTLs, the majority of which are found in plasma, reflecting greater protein variability. Notably, we find 13 proteins with exclusive tissue-specific pQTLs, underscoring distinct as well as shared genetic influences across tissues. Colocalization analyses reveal shared genetic regulation between tissue proteins and cardiometabolic traits, including LDL, HDL, and triglycerides levels, implicating proteins such as PNLIPRP2, SORT1, and PRSS53 as potential mediators of lipid regulation. Furthermore, Mendelian randomization analyses suggest a liver-specific role for SORT1 and PSRC1 in modulating CAD risk and lipid profiles. Our findings highlight the importance of profiling tissue-shared, and tissue-specific, protein expression and pQTLs to elucidate disease mechanisms and accelerate precision drug and biomarker discovery. ### Competing Interest Statement AEH, SH, MT, MGR, KEH, JMHH and YJ were employees and/or shareholders of Novo Nordisk. JLMB and AR are shareholders and board members of Clinical Gene Networks AB.

Background Proteogenomic analyses in biobanks provide opportunities to improve understanding of aetiology and drug discovery for type 2 diabetes (T2D). Methods We identified proteins (Olink Explore) associated with glycaemic traits and/or T2D with observational designs in UK Biobank (UKB-EUR, n =33,301). The Bayesian non-negative matrix factorisation (bNMF) was applied to cluster T2D associated proteins incorporating their phenotypic associations with 43 metabolic/anthropometric traits. For clusters leading proteins (top 10% by ranking), two steps colocalization and bidirectional Mendelian randomization were used to investigate three way (i.e., protein metabolic/anthropometric traits T2D) relationships. We performed equivalent genetic analyses in China Kadoorie Biobank (CKB-EAS, n=2,029) to investigate shared/distinct findings. Results 1,793 proteins were observationally associated with glycaemic traits and/or T2D in UKB-EUR, which were classified by bNMF into five clusters (Adiposity, Reduced-adiposity, Lipids, Liver, Kidney) where 906 proteins were cluster-leading. We triangulated observational and genetic evidence identifying five (B4GAT1, DNER, ENO3, HOMX2, OMG), one (ENTR1) and three (RTBDN, TSPAN8, NCR3LG1) proteins potentially affecting T2D in UKB-EUR, CKB-EAS, and both, respectively. In UKB-EUR, six (CD34, FGFBP3, GALNT10, KHK, MENT, MXRA8) were affected by T2D and five (GSTA1, GSTA3, MEGF9, NCAN, SHBG) were bidirectionally associated with T2D. The genetic analyses also revealed potential pathways in T2D aetiology (e.g., effects of RTBDN and TSPAN8 on T2D via BMI and SHBG respectively). Conclusion This study identified multiple candidate proteins involved in the development of T2D that may make useful biomarkers for monitoring disease onset and progression in the future. These findings may inform molecular sub-phenotyping of T2D and more personalised T2D management. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement J.L. is supported by a Novo Nordisk Postdoctoral Fellowship run in partnership with the University of Oxford. CKB are supported by the grants from the National Natural Science Foundation of China (82192904, 82192903, 82192900,82388102, 82192901) and the Noncommunicable Chronic Diseases-National Science and Technology Major Project (2023ZD0510100) in China. In the UK, the UK Medical Research Council (MC\_UU\_00017/1,MC\_UU\_12026/2, MC_U137686851), Cancer Research UK (C16077/A29186; C500/A16896) and the British Heart Foundation (CH/1996001/9454), provide core funding to the Clinical Trial Service Unit and Epidemiological Studies Unit at Oxford University for the project. This research was funded in whole, or in part, by the Wellcome Trust (212946/Z/18/Z, 202922/Z/16/Z, 104085/Z/14/Z, and 088158/Z/09/Z). For the purpose of Open Access, the author has applied a CC-BY public copyright licence to any Author Accepted Manuscript version arising from this submission ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All UK Biobank data are available to the research community via an application, with full details of this process provided on the study website (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access). The CKB is a global resource for the investigation of lifestyle, environmental, blood biochemical, and genetic factors as determinants of common diseases. The CKB study group is committed to making the cohort data available to the scientific community in China, the U.K., and worldwide to advance knowledge about the causes, prevention, and treatment of disease. For detailed information on what data are currently available to open access users and how to apply for it, please visit https://www.ckbiobank.org/data-access/data-access-procedures. A research proposal will be requested to ensure that any analysis is performed by bona fide researchers. Researchers who are interested in obtaining additional information or data that underlies this paper should contact ckbaccess{at}ndph.ox.ac.uk. For any data that are not currently available for open access, researchers may need to develop formal collaboration with a study group. Custom code was used for all statistical analyses in this report. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All UK Biobank data are available to the research community via an application, with full details of this process provided on the study website (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access). The CKB is a global resource for the investigation of lifestyle, environmental, blood biochemical, and genetic factors as determinants of common diseases. The CKB study group is committed to making the cohort data available to the scientific community in China, the U.K., and worldwide to advance knowledge about the causes, prevention, and treatment of disease. For detailed information on what data are currently available to open access users and how to apply for it, please visit https://www.ckbiobank.org/data-access/data-access-procedures. A research proposal will be requested to ensure that any analysis is performed by bona fide researchers. Researchers who are interested in obtaining additional information or data that underlies this paper should contact ckbaccess{at}ndph.ox.ac.uk. For any data that are not currently available for open access, researchers may need to develop formal collaboration with a study group. Custom code was used for all statistical analyses in this report. R scripts for the key analyses are available on GitHub at: https://github.com/JamesLiu-OxfordNN/Proteomics\_Diabetes\_Clustering.git. Other statistical codes (e.g., colocalization and Mendelian randomization) were conducted with the pipeline and server in Novo Nordisk Research Centre of Oxford. Please contact the corresponding author via james.liu{at}ndph.ox.ac.uk.

BACKGROUND:Cardiovascular diseases (CVDs) rank amongst the leading causes of long-term disability and mortality. Predicting CVD risk and identifying associated genes are crucial for prevention, early intervention, and drug discovery. The recent availability of UK Biobank Proteomics data enables investigation of blood proteins and their association with a variety of diseases. We sought to predict 10 year CVD risk using this data modality and known CVD risk factors. METHODS:We focused on the UK Biobank participants that were included in the UK Biobank Pharma Proteomics Project. After applying exclusions, 50,057 participants were included, aged 40-69 years at recruitment. We employed the Explainable Boosting Machine (EBM), an interpretable machine learning model, to predict the 10 year risk of primary coronary artery disease, ischemic stroke or myocardial infarction. The model had access to 2978 features (2923 proteins and 55 risk factors). Model performance was evaluated using 10-fold cross-validation. RESULTS:The EBM model using proteomics outperforms equation-based risk scores such as PREVENT, with a receiver operating characteristic curve (AUROC) of 0.767 and an area under the precision-recall curve (AUPRC) of 0.241; adding clinical features improves these figures to 0.785 and 0.284, respectively. Our models demonstrate consistent performance across sexes and ethnicities and provide insights into individualized disease risk predictions and underlying disease biology. CONCLUSIONS:In conclusion, we present a more accurate and explanatory framework for proteomics data analysis, supporting future approaches that prioritize individualized disease risk prediction, and identification of target genes for drug development.

Understanding tissue-specific mechanisms of protein regulation gives crucial insights into cardiometabolic disease and informs drug discovery. Most proteomic studies have primarily concentrated on plasma, overlooking tissue-specific effects. Utilizing Olink technology, we assessed relative protein levels across plasma and tissue (aortic wall, mammary artery, liver, and skeletal muscle) from the STARNET cohort: 284 individuals with a high prevalence of coronary artery disease (CAD). We identified 608 cis protein quantitative trait loci (pQTLs), primarily in plasma, reflecting greater protein variability. Of 190 proteins with cis-pQTLs in non-plasma tissues, 50% also had plasma pQTLs, validating Olink technology in these tissues while reinforcing the relevance of plasma data for understanding protein regulation. To identify potential mechanistic pathways linking genetic variants to clinical traits, we performed Bayesian colocalization and Mendelian randomization. These analyses revealed shared genetic regulation between tissues at the gene expression and protein level, and key cardiometabolic traits including low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides. Notably, analyses provide further support to SORT1 and PSRC1 gene and protein expression having liver-specific influences on CAD risk and lipid profiles. We also observed distinct genetic regulation of gene expression and protein within the same tissues, underscoring the value of tissue proteomics for therapeutic insights.

Hypertension affects more than one billion people worldwide. Here we identify 113 novel loci, reporting a total of 2,103 independent genetic signals (P < 5 × 10-8) from the largest single-stage blood pressure (BP) genome-wide association study to date (n = 1,028,980 European individuals). These associations explain more than 60% of single nucleotide polymorphism-based BP heritability. Comparing top versus bottom deciles of polygenic risk scores (PRSs) reveals clinically meaningful differences in BP (16.9 mmHg systolic BP, 95% CI, 15.5-18.2 mmHg, P = 2.22 × 10-126) and more than a sevenfold higher odds of hypertension risk (odds ratio, 7.33; 95% CI, 5.54-9.70; P = 4.13 × 10-44) in an independent dataset. Adding PRS into hypertension-prediction models increased the area under the receiver operating characteristic curve (AUROC) from 0.791 (95% CI, 0.781-0.801) to 0.826 (95% CI, 0.817-0.836, ∆AUROC, 0.035, P = 1.98 × 10-34). We compare the 2,103 loci results in non-European ancestries and show significant PRS associations in a large African-American sample. Secondary analyses implicate 500 genes previously unreported for BP. Our study highlights the role of increasingly large genomic studies for precision health research.

The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand–receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public–private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics 1 .

BACKGROUND & AIMS:Metabolic dysfunction-associated steatotic liver disease (MASLD) affects over 25% of the population and currently has no effective treatments. Plasma proteins with causal evidence may represent promising drug targets. We aimed to identify plasma proteins in the causal pathway of MASLD and explore their interaction with obesity. METHODS:We analysed 2,941 plasma proteins in 43,978 European participants from UK Biobank. We performed genome-wide association study (GWAS) for all MASLD-associated proteins and created the largest MASLD GWAS (109,885 cases/1,014,923 controls). We performed Mendelian Randomization (MR) and integrated proteins and their encoding genes in MASLD ranges to identify candidate causal proteins. We then validated them through independent replication, exome sequencing, liver imaging, bulk and single-cell gene expression, liver biopsies, pathway, and phenome-wide data. We explored the role of obesity by MR and multivariable MR across proteins, body mass index, and MASLD. RESULTS:We found 929 proteins associated with MASLD, reported five novel genetic loci associated with MASLD, and identified 17 candidate MASLD protein targets. We identified four novel targets for MASLD (CD33, GRHPR, HMOX2, and SCG3), provided protein evidence supporting roles of AHCY, FCGR2B, ORM1, and RBKS in MASLD, and validated nine previously known targets. We found that CD33, FCGR2B, ORM1, RBKS, and SCG3 mediated the association of obesity and MASLD, and HMOX2, ORM1, and RBKS had effect on MASLD independent of obesity. CONCLUSIONS:This study identified new protein targets in the causal pathway of MASLD, providing new insights into the multi-omics architecture and pathophysiology of MASLD. These findings advise further therapeutic interventions for MASLD.

Background and Objectives Angiotensin-converting enzyme (ACE) inhibitors are a commonly prescribed class of medication used to treat heart failure, hypertension, and chronic kidney disease. However, previous observational studies have shown conflicting directions of associations between ACE inhibitors and risk of Alzheimer disease. Genetic evidence has supported a protective effect of cerebral ACE against Alzheimer disease (AD). However, it is unclear whether this effect is mediated through blood pressure and extends to other neurodegenerative diseases. Methods We performed genetic colocalization investigating an effect of cortical ACE expression on AD risk in people of European ancestry. We further investigated whether any effect of ACE expression on AD risk is mediated through changes in blood pressure and whether effects extend to Parkinson disease, small-vessel disease, or cognitive function in a Mendelian randomization paradigm. Results There was genetic evidence supporting a protective effect of cortical ACE expression on AD risk in people of European ancestry. Although higher cortical ACE expression was associated with higher blood pressure, there was no strong evidence to support that its association with AD was mediated through blood pressure nor that ACE expression affected risk of other neurodegenerative traits. Discussion Genetic evidence supports protective effects of cerebral ACE expression on AD, but not other neurodegenerative outcomes in people of European ancestry. Further work is required to investigate whether therapeutic inhibition of ACE increases risk of Alzheimer disease.

Background and Objectives Cardiovascular risk factors have been implicated in the etiology of cerebral small vessel disease (CSVD); however, whether the associations are causal remains unclear in part due to the susceptibility of observational studies to reverse causation and confounding. Here, we use mendelian randomization (MR) to determine which cardiovascular risk factors are likely to be involved in the etiology of CSVD. Methods We used data from large-scale genome-wide association studies of European ancestry to identify genetic proxies for blood pressure, blood lipids, body mass index (BMI), type 2 diabetes, smoking initiation, cigarettes per day, and alcohol consumption. MR was performed to assess their association with 3 neuroimaging features that are altered in CSVD (white matter hyperintensities [WMH], fractional anisotropy [FA], and mean diffusivity [MD]) using genetic summary data from the UK Biobank (N = 31,855). Our primary analysis used inverse-weighted median MR, with validation using weighted median, MR-Egger, and a pleiotropy-minimizing approach. Finally, multivariable MR was performed to study the effects of multiple risk factors jointly. Results MR analysis showed consistent associations across all methods for higher genetically proxied systolic and diastolic blood pressures with WMH, FA, and MD and for higher genetically proxied BMI with WMH. There was weaker evidence for associations between total cholesterol, low-density lipoprotein, smoking initiation, pulse pressure, and type 2 diabetes liability and at least 1 CSVD imaging feature, but these associations were not reproducible across all validation methods used. Multivariable MR analysis for blood pressure traits found that the effect was primarily through genetically proxied diastolic blood pressure across all CSVD traits. Discussion Genetic predisposition to higher blood pressure, primarily diastolic blood pressure, and to higher BMI is associated with a higher burden of CSVD, suggesting a causal role. Improved management and treatment of these risk factors could reduce the burden of CSVD.

Stroke is the second leading cause of death with substantial unmet therapeutic needs. To identify potential stroke therapeutic targets, we estimate the causal effects of 308 plasma proteins on stroke outcomes in a two-sample Mendelian randomization framework and assess mediation effects by stroke risk factors. We find associations between genetically predicted plasma levels of six proteins and stroke ( P ≤ 1.62 × 10 −4 ). The genetic associations with stroke colocalize (Posterior Probability >0.7) with the genetic associations of four proteins (TFPI, TMPRSS5, CD6, CD40). Mendelian randomization supports atrial fibrillation, body mass index, smoking, blood pressure, white matter hyperintensities and type 2 diabetes as stroke risk factors ( P ≤ 0.0071). Body mass index, white matter hyperintensity and atrial fibrillation appear to mediate the TFPI, IL6RA, TMPRSS5 associations with stroke. Furthermore, thirty-six proteins are associated with one or more of these risk factors using Mendelian randomization. Our results highlight causal pathways and potential therapeutic targets for stroke.

The UK Biobank Pharma Proteomics Project (UKB-PPP) is a collaboration between the UK Biobank (UKB) and thirteen biopharmaceutical companies characterising the plasma proteomic profiles of 54,306 UKB participants. Here, we describe results from the first phase of UKB-PPP, including protein quantitative trait loci (pQTL) mapping of 1,463 proteins that identifies 10,248 primary genetic associations, of which 85% are newly discovered. We also identify independent secondary associations in 92% of cis and 29% of trans loci, expanding the catalogue of genetic instruments for downstream analyses. The study provides an updated characterisation of the genetic architecture of the plasma proteome, leveraging population-scale proteomics to provide novel, extensive insights into trans pQTLs across multiple biological domains. We highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement proteins, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug target discovery by extending the genetic proxied effect of PCSK9 levels on lipid concentrations, cardio- and cerebro-vascular diseases, and additionally disentangle specific genes and proteins perturbed at COVID-19 susceptibility loci. This public-private partnership provides the scientific community with an open-access proteomics resource of unprecedented breadth and depth to help elucidate biological mechanisms underlying genetic discoveries and accelerate the development of novel biomarkers and therapeutics.

A common variant in the Histone Deacetylase 9 (HDAC9) gene is the strongest genetic risk for large-vessel stroke, and HDAC9 offers a novel target for therapeutic modulation. However, the mechanisms linking the HDAC9 variant with increased stroke risk is still unclear due to the lack of relevant models to study the underlying molecular mechanisms. We generated vascular smooth muscle cells using human induced pluripotent stem cells with the HDAC9 stroke risk variant to assess HDAC9-mediated phenotypic changes in a relevant cells model and test the efficacy of HDAC inhibitors for potential therapeutic strategies. Our human induced pluripotent stem cells derived vascular smooth muscle cells show enhanced HDAC9 expression and allow us to assess HDAC9-mediated effects on promoting smooth muscle cell dysfunction, including proliferation, migration, apoptosis and response to inflammation. These phenotypes could be reverted by treatment with HDAC inhibitors, including sodium valproate and small molecules inhibitors. By demonstrating the relevance of the model and the efficacy of HDAC inhibitors, our model provides a robust phenotypic screening platform, which could be applied to other stroke-associated genetic variants.

Age-related loss of white matter microstructural integrity is a major determinant of cognitive decline, dementia and gait disorders. However, the mechanisms and molecular pathways that contribute to this loss of integrity remain elusive. We performed a genome-wide association study of white matter microstructural integrity as quantified by diffusion MRI metrics (mean diffusivity and fractional anisotropy) in up to 31 128 individuals from UK Biobank (age 45-81 years) based on a two degrees of freedom (2df) test of single nucleotide polymorphism (SNP) and SNP × Age effects. We identified 18 loci that were associated at genome-wide significance with either mean diffusivity (n = 16) or fractional anisotropy (n = 6). Among the top loci was a region on chromosome 6 encoding the human major histocompatibility complex (MHC). Variants in the MHC region were strongly associated with both mean diffusivity [best SNP: 6:28866209_TTTTG_T, beta (standard error, SE) = -0.069 (0.009); 2df P = 6.5 × 10-15] and fractional anisotropy [best SNP: rs3129787, beta (SE) = -0.056 (0.008); 2df P = 3.5 × 10-12]. Of the imputed human leukocyte antigen (HLA) alleles and complement component 4 (C4) structural haplotype variants in the human MHC, the strongest association was with the C4-BS variant [for mean diffusivity: beta (SE) = -0.070 (0.010); P = 2.7 × 10-11; for fractional anisotropy: beta (SE) = -0.054 (0.011); P = 1.6 × 10-7]. After conditioning on C4-BS no associations with HLA alleles remained significant. The protective influence of C4-BS was stronger in older participants [age ≥ 65; interaction P = 0.0019 (mean diffusivity), P = 0.015 (fractional anisotropy)] and in participants without a history of smoking [interaction P = 0.00093 (mean diffusivity), P = 0.021 (fractional anisotropy)]. Taken together, our findings demonstrate a role of the complement system and of gene-environment interactions in age-related loss of white matter microstructural integrity.

Rheumatoid arthritis (RA) is a highly heritable complex disease with unknown etiology. Multi-ancestry genetic research of RA promises to improve power to detect genetic signals, fine-mapping resolution and performances of polygenic risk scores (PRS). Here, we present a large-scale genome-wide association study (GWAS) of RA, which includes 276,020 samples from five ancestral groups. We conducted a multi-ancestry meta-analysis and identified 124 loci (P < 5 × 10−8), of which 34 are novel. Candidate genes at the novel loci suggest essential roles of the immune system (for example, TNIP2 and TNFRSF11A) and joint tissues (for example, WISP1) in RA etiology. Multi-ancestry fine-mapping identified putatively causal variants with biological insights (for example, LEF1). Moreover, PRS based on multi-ancestry GWAS outperformed PRS based on single-ancestry GWAS and had comparable performance between populations of European and East Asian ancestries. Our study provides several insights into the etiology of RA and improves the genetic predictability of RA. Multi-ancestry genome-wide association analyses identify 124 risk loci for rheumatoid arthritis, of which 34 are novel. A polygenic risk score based on multi-ancestry data showed comparable performance between populations of European and East Asian ancestries.

Essential hypertension is a complex trait where the underlying aetiology is not completely understood. Left untreated it increases the risk of severe health complications including cardiovascular and renal disease. It is almost 15 years since the first genome-wide association study for hypertension, and after a slow start there are now over 1000 blood pressure (BP) loci explaining ∼6% of the single nucleotide polymorphism-based heritability. Success in discovery of hypertension genes has provided new pathological insights and drug discovery opportunities and translated to the development of BP genetic risk scores (GRSs), facilitating population disease risk stratification. Comparing highest and lowest risk groups shows differences of 12.9 mm Hg in systolic-BP with significant differences in risk of hypertension, stroke, cardiovascular disease and myocardial infarction. GRSs are also being trialled in antihypertensive drug responses. Drug targets identified include NPR1 , for which an agonist drug is currently in clinical trials. Identification of variants at the PHACTR1 locus provided insights into regulation of EDN1 in the endothelin pathway, which is aiding the development of endothelin receptor EDNRA antagonists. Drug re-purposing opportunities, including SLC5A1 and canagliflozin (a type-2 diabetes drug), are also being identified. In this review, we present key studies from the past, highlight current avenues of research and look to the future focusing on gene discovery, epigenetics, gene-environment interactions, GRSs and drug discovery. We evaluate limitations affecting BP genetics, including ancestry bias and discuss streamlining of drug target discovery and applications for treating and preventing hypertension, which will contribute to tailored precision medicine for patients.

White matter hyperintensities (WMH) are among the most common radiological abnormalities in the ageing population and an established risk factor for stroke and dementia. While common variant association studies have revealed multiple genetic loci with an influence on their volume, the contribution of rare variants to the WMH burden in the general population remains largely unexplored. We conducted a comprehensive analysis of this burden in the UK Biobank using publicly available whole-exome sequencing data (n up to 17 830) and found a splice-site variant in GBE1, encoding 1,4-alpha-glucan branching enzyme 1, to be associated with lower white matter burden on an exome-wide level [c.691+2T>C, β = -0.74, standard error (SE) = 0.13, P = 9.7 × 10-9]. Applying whole-exome gene-based burden tests, we found damaging missense and loss-of-function variants in HTRA1 (frequency of 1 in 275 in the UK Biobank population) to associate with an increased WMH volume (P = 5.5 × 10-6, false discovery rate = 0.04). HTRA1 encodes a secreted serine protease implicated in familial forms of small vessel disease. Domain-specific burden tests revealed that the association with WMH volume was restricted to rare variants in the protease domain (amino acids 204-364; β = 0.79, SE = 0.14, P = 9.4 × 10-8). The frequency of such variants in the UK Biobank population was 1 in 450. The WMH volume was brought forward by ∼11 years in carriers of a rare protease domain variant. A comparison with the effect size of established risk factors for WMH burden revealed that the presence of a rare variant in the HTRA1 protease domain corresponded to a larger effect than meeting the criteria for hypertension (β = 0.26, SE = 0.02, P = 2.9 × 10-59) or being in the upper 99.8% percentile of the distribution of a polygenic risk score based on common genetic variants (β = 0.44, SE = 0.14, P = 0.002). In biochemical experiments, most (6/9) of the identified protease domain variants resulted in markedly reduced protease activity. We further found EGFL8, which showed suggestive evidence for association with WMH volume (P = 1.5 × 10-4, false discovery rate = 0.22) in gene burden tests, to be a direct substrate of HTRA1 and to be preferentially expressed in cerebral arterioles and arteries. In a phenome-wide association study mapping ICD-10 diagnoses to 741 standardized Phecodes, rare variants in the HTRA1 protease domain were associated with multiple neurological and non-neurological conditions including migraine with aura (odds ratio = 12.24, 95%CI: 2.54-35.25; P = 8.3 × 10-5]. Collectively, these findings highlight an important role of rare genetic variation and the HTRA1 protease in determining WMH burden in the general population.

Objective: Patients with resistant hypertension (RHTN) have increased cardiovascular disease risk. It is believed there may be genetic causes, although robust associations remain to be validated. Over 1,000 genetic loci have been discovered for BP from genome-wide association studies (GWAS), with application of genetic risk scores (GRS) enabling population risk stratification. However it is unknown whether genetic risk influences treatment response. We investigated whether BP associated loci: (i) are associated with RHTN; (ii) mediate the effect of antihypertensives on BP lowering response. Design and method: We analysed 6,266 patients of European ancestry with genetic data from ASCOT. BP-GRSs were constructed for systolic (SBP), diastolic BP (DBP) and pulse pressure (PP) using all available 1,167 pairwise-independent published BP-associated genetic variants, weighted by their effect estimates from our recent BP-GWAS in N∼750k individuals. We tested the BP-GRS for association with: (i) RHTN, adjusted for known non-genetic predictors: sex, age, BMI, diabetes, randomization treatment arm and left ventricular hypertrophy; (ii) BP response to monotherapy treatment, adjusted for sex, age, baseline-BP, dose and baseline antihypertensive use. All analyses adjusted for 10 principal components of ancestry. Results: Each BP-GRS is significantly associated with RHTN (P = 1.13 × 10-13, 1.96 × 10-5, 1.21 × 10-14 for SBP, DBP, PP) with increased genetic risk leading to increased odds of RHTN. Patients in the top 20% of SBP-GRS have 1.74 times greater odds of RHTN than the lowest 20% (P = 6.96 × 10-10). Additional adjustment for baseline-BP does not change conclusions. The association even remains significant (P = 5.11 × 10-4) with stronger effect (OR = 2.52) when restricted only to 1,037 patients untreated at baseline. The BP-GRS is also significantly associated with SBP and DBP response to amlodipine (N = 1,780): patients in the lowest 20% genetic risk group achieve better lowering response by 3.77mmHg mean difference between SBP pre and post treatment (P = 8.27 × 10-4). Conclusions: Our results confirm the hypothesis for a genetic contribution to RHTN, indicating RHTN patients are likely to be those with highest genetic risk of hypertension, which helps to explain the clinical challenge. We show that those with greater BP genetic risk respond less effectively to treatment and provide evidence that BP genes also influence treatment response.