Impact statement

High blood pressure or hypertension is the strongest modifiable risk factor for cardiovascular disease, and it is a complex condition influenced by both genetic and environmental factors. In this review article, we explore the significant milestones to our current understanding of the genetics of hypertension. We highlight key landmarks in blood pressure-related research from the discovery of monogenic forms of hypertension to the era of genome-wide association studies. Alongside the development of polygenic risk scores for cardiovascular risk prediction, the application of multi-omics, single-cell RNA technologies and machine learning are providing new insights into the pathophysiology of hypertension. We also explore the development of pharmacogenomics in hypertension and the role of large-scale biobanks in drug development together with the challenges and future landscape.

The hype in hypertension

Hypertension is the strongest modifiable risk factor for cardiovascular disease, being responsible for the majority of stroke and up to half of cases of coronary heart disease (Perkovic et al., Reference Perkovic, Huxley, Wu, Prabhakaran and MacMahon2007). The global health burden of hypertension is immense, with 1.5 billion people projected to be affected by 2025 (Kearney et al., Reference Kearney, Whelton, Reynolds, Muntner, Whelton and He2005). The societal cost resulting from the morbidity and mortality caused by hypertension has raised an urgent need for innovative approaches, with its prevention being a top priority in governments worldwide and endorsed by the 2023 European Society of Hypertension guidelines (Mancia et al., Reference Mancia, Kreutz, Brunström, Burnier, Grassi, Januszewicz, Muiesan, Tsioufis, Agabiti-Rosei, Algharably, Azizi, Benetos, Borghi, Hitij, Cifkova, Coca, Cornelissen, Cruickshank, Cunha, Danser, Pinho, Delles, Dominiczak, Dorobantu, Doumas, Fernández-Alfonso, Halimi, Járai, Jelaković, Jordan, Kuznetsova, Laurent, Lovic, Lurbe, Mahfoud, Manolis, Miglinas, Narkiewicz, Niiranen, Palatini, Parati, Pathak, Persu, Polonia, Redon, Sarafidis, Schmieder, Spronck, Stabouli, Stergiou, Taddei, Thomopoulos, Tomaszewski, Van de Borne, Wanner, Weber, Williams, Zhang and Kjeldsen2023) and the latest 2024 European Society of Cardiology guidelines (McEvoy et al., Reference McEvoy, McCarthy, Bruno, Brouwers, Canavan, Ceconi, Christodorescu, Daskalopoulou, Ferro, Gerdts, Hanssen, Harris, Lauder, McManus, Molloy, Rahimi, Regitz-Zagrosek, Rossi, Sandset, Scheenaerts, Staessen, Uchmanowicz, Volterrani, Touyz, Abreu, Olsen, Ambrosetti, Androulakis, Bang, Bech, Borger, Boutouyrie, Bronze, Buccheri, Dalmau, De Pablo Zarzosa, Delles, Fiuza, Gabulova, Haugen, Heiss, Ibanez, James, Kapil, Kayikçioglu, Køber, Koskinas, Locati, MacDonald, Mihailidou, Mihaylova, Mindham, Mortensen, Nardai, Neubeck, Nielsen, Nilsson, Pasquet, Pedro, Prescott, Rakisheva, Rietzschel, Rocca, Rossello, Schmid, Shantsila, Sudano, Timóteo, Tsivgoulis, Ungar, Vaartjes, Visseren, Voeller, Vrints, Witkowski, Zennaro, Zeppenfeld, Shuka, Laredj, Pavo, Mirzoyev, van de Borne, Sokolović, Postadzhiyan, Samardzic, Agathangelou, Widimsky, Olsen, El-Kilany, Pauklin, Laukkanen, Boulestreau, Tsinamdzgvrishvili, Kintscher, Marketou, Páll, Hrafnkelsdóttir, Dolan, Wolak, Bilo, Tundybayeva, Mirrakhimov, Trusinskis, Kiwan, Msalem, Badarienė, Banu, Balbi, Caraus, Boskovic, Mouine, Vromen, Bosevski, Midtbø, Doroszko, Dores, Badila, Bini, Simić, Fras, Mazón, Spaak, Burkard, Barakat, Abdessalem, Gunes, Sirenko, Brady and Khamidullaeva2024). The risk for cardiovascular disease attributable to blood pressure (BP) is on a continuous exposure scale (Murray et al., Reference Murray, Aravkin, Zheng, Abbafati, Abbas, Abbasi-Kangevari, Abd-Allah, Abdelalim, Abdollahi, Abdollahpour, Abegaz, Abolhassani, Aboyans, Abreu, Abrigo, Abualhasan, Abu-Raddad, Abushouk, Adabi, Adekanmbi, Adeoye, Adetokunboh, Adham, Advani, Agarwal, Aghamir, Agrawal, Ahmad, Ahmadi, Ahmadi, Ahmadieh, Ahmed, Akalu, Akinyemi, Akinyemiju, Akombi, Akunna, Alahdab, Al-Aly, Alam, Alam, Alam, Alanezi, Alanzi, Alemu, Alhabib, Ali, Ali, Alicandro, Alinia, Alipour, Alizade, Aljunid, Alla, Allebeck, Almasi-Hashiani, Al-Mekhlafi, Alonso, Altirkawi, Amini-Rarani, Amiri, Amugsi, Ancuceanu, Anderlini, Anderson, Andrei, Andrei, Angus, Anjomshoa, Ansari, Ansari-Moghaddam, Antonazzo, Antonio, Antony, Antriyandarti, Anvari, Anwer, Appiah, Arabloo, Arab-Zozani, Ariani, Armoon, Ärnlöv, Arzani, Asadi-Aliabadi, Asadi-Pooya, Ashbaugh, Assmus, Atafar, Atnafu, Atout, Ausloos, Ausloos, Quintanilla, Ayano, Ayanore, Azari, Azarian, Azene, Badawi, Badiye, Bahrami, Bakhshaei, Bakhtiari, Bakkannavar, Baldasseroni, Ball, Ballew, Balzi, Banach, Banerjee, Bante, Baraki, Barker-Collo, Bärnighausen, Barrero, Barthelemy, Barua, Basu, Baune, Bayati, Becker, Bedi, Beghi, Béjot, Bell, Bennitt, Bensenor, Berhe, Berman, Bhagavathula, Bhageerathy, Bhala, Bhandari, Bhattacharyya, Bhutta, Bijani, Bikbov, Sayeed, Biondi, Birihane, Bisignano, Biswas, Bitew, Bohlouli, Bohluli, Boon-Dooley, Borges, Borzì, Borzouei, Bosetti, Boufous, Braithwaite, Breitborde, Breitner, Brenner, Briant, Briko, Briko, Britton, Bryazka, Bumgarner, Burkart, Burnett, Nagaraja, Butt, Santos, Cahill, Cámera, Campos-Nonato, Cárdenas, Carreras, Carrero, Carvalho, Castaldelli-Maia, Castañeda-Orjuela, Castelpietra, Castro, Causey, Cederroth, Cercy, Cerin, Chandan, Chang, Charlson, Chattu, Chaturvedi, Cherbuin, Chimed-Ochir, Cho, Choi, Christensen, Chu, Chung, Chung, Cicuttini, Ciobanu, Cirillo, Classen, Cohen, Compton, Cooper, Costa, Cousin, Cowden, Cross, Cruz, Dahlawi, Damasceno, Damiani, Dandona, Dandona, Dangel, Danielsson, Dargan, Darwesh, Daryani, Das, Gupta, Neves, Dávila-Cervantes, Davitoiu, Leo, Degenhardt, DeLang, Dellavalle, Demeke, Demoz, Demsie, Denova-Gutiérrez, Dervenis, Dhungana, Dianatinasab, Silva, Diaz, Forooshani, Djalalinia, Do, Dokova, Dorostkar, Doshmangir, Driscoll, Duncan, Duraes, Eagan, Edvardsson, Nahas, Sayed, Tantawi, Elbarazi, Elgendy, El-Jaafary, Elyazar, Emmons-Bell, Erskine, Eskandarieh, Esmaeilnejad, Esteghamati, Estep, Etemadi, Etisso, Fanzo, Farahmand, Fareed, Faridnia, Farioli, Faro, Faruque, Farzadfar, Fattahi, Fazlzadeh, Feigin, Feldman, Fereshtehnejad, Fernandes, Ferrara, Ferrari, Ferreira, Filip, Fischer, Fisher, Flor, Foigt, Folayan, Fomenkov, Force, Foroutan, Franklin, Freitas, Fu, Fukumoto, Furtado, Gad, Gakidou, Gallus, Garcia-Basteiro, Gardner, Geberemariyam, Gebreslassie, Geremew, Hayoon, Gething, Ghadimi, Ghadiri, Ghaffarifar, Ghafourifard, Ghamari, Ghashghaee, Ghiasvand, Ghith, Gholamian, Ghosh, Gill, Ginindza, Giussani, Gnedovskaya, Goharinezhad, Gopalani, Gorini, Goudarzi, Goulart, Greaves, Grivna, Grosso, Gubari, Gugnani, Guimarães, Guled, Guo, Guo, Gupta, Gupta, Haddock, Hafezi-Nejad, Hafiz, Haj-Mirzaian, Haj-Mirzaian, Hall, Halvaei, Hamadeh, Hamidi, Hammer, Hankey, Haririan, Haro, Hasaballah, Hasan, Hasanpoor, Hashi, Hassanipour, Hassankhani, Havmoeller, Hay, Hayat, Heidari, Heidari-Soureshjani, Henrikson, Herbert, Herteliu, Heydarpour, Hird, Hoek, Holla, Hoogar, Hosgood, Hossain, Hosseini, Hosseinzadeh, Hostiuc, Hostiuc, Househ, Hsairi, Hsieh, Hu, Hu, Huda, Humayun, Huynh, Hwang, Iannucci, Ibitoye, Ikeda, Ikuta, Ilesanmi, Ilic, Ilic, Inbaraj, Ippolito, Iqbal, SSN, CMS, Islam, SMS, Iso, Ivers, CCD, Iwu, Iyamu, Jaafari, Jacobsen, Jafari, Jafarinia, Jahani, Jakovljevic, Jalilian, James, Janjani, Javaheri, Javidnia, Jeemon, Jenabi, Jha, Jha, Ji, Johansson, John, John-Akinola, Johnson, Jonas, Joukar, Jozwiak, Jürisson, Kabir, Kabir, Kalani, Kalani, Kalankesh, Kalhor, Kanchan, Kapoor, Matin, Karch, Karim, Kassa, Katikireddi, Kayode, Karyani, Keiyoro, Keller, Kemmer, Kendrick, Khalid, Khammarnia, Khan, Khan, Khatab, Khater, Khatib, Khayamzadeh, Khazaei, Kieling, Kim, Kimokoti, Kisa, Kisa, Kivimäki, Knibbs, Knudsen, Kocarnik, Kochhar, Kopec, Korshunov, Koul, Koyanagi, Kraemer, Krishan, Krohn, Kromhout, Defo, Kumar, Kumar, Kurmi, Kusuma, Vecchia, Lacey, Lal, Lalloo, Lallukka, Lami, Landires, Lang, Langan, Larsson, Lasrado, Lauriola, Lazarus, Lee, Lee, LeGrand, Leigh, Leonardi, Lescinsky, Leung, Levi, Li, Lim, Linn, Liu, Liu, Liu, Lo, Lopez, Lopez, Lopukhov, Lorkowski, Lotufo, Lu, Lugo, Maddison, Mahasha, Mahdavi, Mahmoudi, Majeed, Maleki, Maleki, Malekzadeh, Malta, Mamun, Manda, Manguerra, Mansour-Ghanaei, Mansouri, Mansournia, Herrera, Maravilla, Marks, Martin, Martini, Martins-Melo, Masaka, Masoumi, Mathur, Matsushita, Maulik, McAlinden, McGrath, McKee, Mehndiratta, Mehri, Mehta, Memish, Mendoza, Menezes, Mengesha, Mereke, Mereta, Meretoja, Meretoja, Mestrovic, Miazgowski, Miazgowski, Michalek, Miller, Mills, Mini, Miri, Mirica, Mirrakhimov, Mirzaei, Mirzaei, Mirzaei, Mirzaei-Alavijeh, Misganaw, Mithra, Moazen, Mohammad, Mohammad, Mezerji, Mohammadian-Hafshejani, Mohammadifard, Mohammadpourhodki, Mohammed, Mohammed, Mohammed, Mohammed, Mokdad, Molokhia, Monasta, Mooney, Moradi, Moradi, Moradi-Lakeh, Moradzadeh, Moraga, Morawska, Morgado-da-Costa, Morrison, Mosapour, Mosser, Mouodi, Mousavi, Khaneghah, Mueller, Mukhopadhyay, Mullany, Musa, Muthupandian, Nabhan, Naderi, Nagarajan, Nagel, Naghavi, Naghshtabrizi, Naimzada, Najafi, Nangia, Nansseu, Naserbakht, Nayak, Negoi, Ngunjiri, Nguyen, Nguyen, Nguyen, Nigatu, Nikbakhsh, Nixon, Nnaji, Nomura, Norrving, Noubiap, Nowak, Nunez-Samudio, Oţoiu, Oancea, Odell, Ogbo, Oh, Okunga, Oladnabi, Olagunju, Olusanya, Olusanya, Omer, Ong, Onwujekwe, Orpana, Ortiz, Osarenotor, Osei, Ostroff, Otstavnov, Otstavnov, Øverland, Owolabi, MP, Padubidri, Palladino, Panda-Jonas, Pandey, Parry, Pasovic, Pasupula, Patel, Pathak, Patten, Patton, Toroudi, Peden, Pennini, Pepito, Peprah, Pereira, Pesudovs, Pham, Phillips, Piccinelli, Pilz, Piradov, Pirsaheb, Plass, Polinder, Polkinghorne, Pond, Postma, Pourjafar, Pourmalek, Poznańska, Prada, Prakash, Pribadi, Pupillo, Syed, Rabiee, Rabiee, Radfar, Rafiee, Raggi, Rahman, Rajabpour-Sanati, Rajati, Rakovac, Ram, Ramezanzadeh, Ranabhat, Rao, Rao, Rashedi, Rathi, Rawaf, Rawaf, Rawal, Rawassizadeh, Rawat, Razo, Redford, Reiner, Reitsma, Remuzzi, Renjith, Renzaho, Resnikoff, Rezaei, Rezaei, Rezapour, Rhinehart, Riahi, Ribeiro, Ribeiro, Rickard, Rivera, Roberts, Rodríguez-Ramírez, Roever, Ronfani, Room, Roshandel, Roth, Rothenbacher, Rubagotti, Rwegerera, Sabour, Sachdev, Saddik, Sadeghi, Sadeghi, Saeedi, Moghaddam, Safari, Safi, Safiri, Sagar, Sahebkar, Sajadi, Salam, Salamati, Salem, Salem, Salimzadeh, Salman, Salomon, Samad, Kafil, Sambala, Samy, Sanabria, Sánchez-Pimienta, Santomauro, Santos, Santos, Santric-Milicevic, Saraswathy, Sarmiento-Suárez, Sarrafzadegan, Sartorius, Sarveazad, Sathian, Sathish, Sattin, Saxena, Schaeffer, Schiavolin, Schlaich, Schmidt, Schutte, Schwebel, Schwendicke, Senbeta, Senthilkumaran, Sepanlou, Serdar, Serre, Shadid, Shafaat, Shahabi, Shaheen, Shaikh, Shalash, Shams-Beyranvand, Shamsizadeh, Sharafi, Sheikh, Sheikhtaheri, Shibuya, Shield, Shigematsu, Shin, Shin, Shiri, Shirkoohi, Shuval, Siabani, Sierpinski, Sigfusdottir, Sigurvinsdottir, Silva, Simpson, Singh, Singh, Skiadaresi, Skou, Skryabin, Smith, Soheili, Soltani, Soofi, Sorensen, Soriano, Sorrie, Soshnikov, Soyiri, Spencer, Spotin, Sreeramareddy, Srinivasan, Stanaway, Stein, Stein, Steiner, Stockfelt, Stokes, Straif, Stubbs, Sufiyan, Suleria, Abdulkader, Sulo, Sultan, Szumowski, Tabarés-Seisdedos, Tabb, Tabuchi, Taherkhani, Tajdini, Takahashi, Takala, Tamiru, Taveira, Tehrani-Banihashemi, Temsah, Tesema, Tessema, Thurston, Titova, Tohidinik, Tonelli, Topor-Madry, Topouzis, Torre, Touvier, Tovani-Palone, Tran, Travillian, Tsatsakis, Car, Tyrovolas, Uddin, Umeokonkwo, Unnikrishnan, Upadhyay, Vacante, Valdez, Donkelaar, Vasankari, Vasseghian, Veisani, Venketasubramanian, Violante, Vlassov, Vollset, Vos, Vukovic, Waheed, Wallin, Wang, Wang, Watson, Wei, Wei, Weintraub, Weiss, Werdecker, West, Westerman, Whisnant, Whiteford, Wiens, Wolfe, Wozniak, Wu, Wu, Hanson, Xu, Xu, Yadgir, Jabbari, Yamagishi, Yaminfirooz, Yano, Yaya, Yazdi-Feyzabadi, Yeheyis, Yilgwan, Yilma, Yip, Yonemoto, Younis, Younker, Yousefi, Yousefi, Yousefinezhadi, Yousuf, Yu, Yusefzadeh, Moghadam, Zamani, Zamanian, Zandian, Zastrozhin, Zhang, Zhang, Zhao, Zhao, Zhao, Zhou, Ziapour, Zimsen, Brauer, Afshin and Lim2020). Elevated BP adversely affects the heart, kidneys, brain, eyes and vessels, leading to structural and functional changes termed hypertension-mediated organ damage. Clinically, hypertension is diagnosed based on BP measurements, however, BP is a complex trait influenced by a magnitude of physiological and environmental interacting pathways. Approximately 95% of cases of hypertension are referred to as primary or essential hypertension (EH) with genetics contributing approximately 30% of BP variance, and the remainder due to lifestyle factors (Poulter et al., Reference Poulter, Prabhakaran and Caulfield2015). The other 5% of causes is termed secondary hypertension, of which 1% are monogenic disorders (Cowley, Reference Cowley2006). Currently, pharmacological treatments for hypertension are introduced when BP measurements are elevated and there is potential end-organ damage initiation. The major international guidelines recommend a combination of antihypertensive drugs as first-line therapy to improve efficacy and reduce the risk of side effects related to treatment. Challenges such as non-adherence to therapy and resistant hypertension have limited the current ability to ensure adequate BP control in the general population. The ‘precision hypertension’ approach has been proposed to consider an individual’s unique characteristics for better-targeted risk profiling and treatment strategy (Dzau and Hodgkinson, Reference Dzau and Hodgkinson2024). In this review, we highlight some of the key advances (Figure 1) in hypertension genomics together with its challenges and future landscape.

Figure 1.

Key advances in hypertension genomics.

The yellow brick road

The concept that hypertension is a multifactorial disease was first proposed by Page’s Mosaic Theory in 1949 (Page, Reference Page1949). Following evidence from familial studies and the discovery of rare monogenic disorders of hypertension, the genetic contribution of hypertension was recognised. The 1950s witnessed the legendary Platt vs. Pickering debate on the genetic nature of EH. The controversy stemmed from the appearance of BP frequency distribution curves, which led to the discussion of its monogenic or polygenic potential. Platt regarded EH as a distinct condition with rare variants of hypertension as evidence for its single-gene inheritance, whilst Pickering postulated that hypertension was seen only in the extreme of a continuous distribution curve of BP values and therefore determined by a collection of genes (Zanchetti, Reference Zanchetti1986; Brown, Reference Brown2012). Later studies supported the polygenic theory of hypertension with a wide range of heritability estimates for systolic and diastolic BP from 6% to 68% (Kolifarhood et al., Reference Kolifarhood, Daneshpour, Hadaegh, Sabour, Mozafar Saadati, Akbar Haghdoust, Akbarzadeh, Sedaghati-Khayat and Khosravi2019). Differences in environmental conditions, type of study design, trait definition, and analytical techniques may explain the wide variation in heritability estimation of BP traits. In light of the new evidence, Page also acknowledged the genetic influence of hypertension in his revision of the Mosaic Theory in 1982 (Page, Reference Page1982). The 1990s saw a big boom in gene mapping with the launch of The Human Genome Project sequencing the complete human genome by 2003 (International Human Genome Sequencing Consortium, 2004). During this period there were a number of genome-wide linkage analyses performed, including the Framingham Heart Study and the British Genetics of Hypertension (BRIGHT) study, identifying regions in the DNA linked to variations in BP (Levy et al., Reference Levy, DeStefano, Larson, O’Donnell, Lifton, Gavras, Cupples and Myers2000; Caulfield et al., Reference Caulfield, Munroe, Pembroke, Samani, Dominiczak, Brown, Webster, Ratcliffe, O’Shea, Papp, Taylor, Dobson, Knight, Newhouse, Hooper, Lee, Brain, Clayton, Lathrop, Farrall, Connell and Benjamin2003). Although an important step, interpreting results from linkage analyses proved challenging, as the regions in the DNA identified were large, hence difficult to identify the responsible gene. The turn of the millennium welcomed a wave of advancing technology and bioinformatics, paving the way for the era of genome-wide association studies (GWAS).

GWAS for blood pressure

The hunt for genes implicated in BP regulation has been challenging. Before the advent of GWAS, genes and mechanisms for BP were mostly discovered using rat and mouse models and candidate gene studies (Lerman et al., Reference Lerman, Kurtz, Touyz, Ellison, Chade, Crowley, Mattson, Mullins, Osborn, Eirin, Reckelhoff, Iadecola and Coffman2019). Since the launch of single-nucleotide polymorphism (SNP) genotyping arrays in 2005, BP-GWAS of increasing scale has been performed. The first GWAS of hypertension was performed in 2007 by The Wellcome Trust Case Control Consortium (WTCCC). This consortium undertook GWAS of 2,000 cases and 3,000 shared controls for seven complex diseases, including hypertension (Wellcome Trust Case Control Consortium, 2007). Although no single SNP achieved genome-wide significance (p < 5 × 10⁻⁷), six variants were found to have suggestive associations with hypertension (p < 5 × 10⁻⁵). The Family Blood Pressure Program (FBPP) subsequently focussed on these six SNPs in a study of 11,433 individuals recruited from hypertensive families. This study did not replicate the results of the WTCCC study, however, one of the six SNPs (rs1937506) was found to be associated with hypertension in Hispanic Americans and European Americans (Ehret et al., Reference Ehret, Morrison, O’Connor, Grove, Baird, Schwander, Weder, Cooper, Rao, Hunt, Boerwinkle and Chakravarti2008). Subsequently, investigators from the Korean Association Resource (KARE) project analysed the association of the six SNPs in 7,551 unrelated individuals in Korea. The authors reported one intronic SNP (rs7961152 at the BACT1 gene locus) to be associated with hypertension risk (odds ratio 1.29, 95% confidence interval 1.01–1.64, p = 0.004) (Hong et al., Reference Hong, Jin, Cho, Lee, Lee, Cho, Shin, Lee, Park and Oh2009). The WTCCC, FBPP and KARE studies demonstrated that due to the complex genetic architecture of hypertension, a larger sample population may be necessary to identify genetic variants implicated in BP. To increase the sample sizes, consortia were established, to combine data together across many different studies. The first exciting results in BP-GWAS were in 2009 from large meta-analyses of GWAS (n = 34,433) from the Global BP Genetics Consortium (GBPGEN) and Cohorts for Heart and Ageing Research in Genomic Epidemiology-BP (CHARGE-BP) consortium, identifying 11 new loci (Levy et al., Reference Levy, Ehret, Rice, Verwoert, Launer, Dehghan, Glazer, Morrison, Johnson, Aspelund, Aulchenko, Lumley, Köttgen, Vasan, Rivadeneira, Eiriksdottir, Guo, Arking, Mitchell, Mattace-Raso, Smith, Taylor, Scharpf, Hwang, Sijbrands, Bis, Harris, Ganesh, O’Donnell, Hofman, Rotter, Coresh, Benjamin, Uitterlinden, Heiss, Fox, Witteman, Boerwinkle, Wang, Gudnason, Larson, Chakravarti, Psaty and Duijn2009; Newton-Cheh et al., Reference Newton-Cheh, Johnson, Gateva, Tobin, Bochud, Coin, Najjar, Zhao, Heath, Eyheramendy, Papadakis, Voight, Scott, Zhang, Farrall, Tanaka, Wallace, Chambers, Khaw, Nilsson, van der Harst, Polidoro, Grobbee, Onland-Moret, Bots, Wain, Elliott, Teumer, Luan, Lucas, Kuusisto, Burton, Hadley, McArdle, Brown, Dominiczak, Newhouse, Samani, Webster, Zeggini, Beckmann, Bergmann, Lim, Song, Vollenweider, Waeber, Waterworth, Yuan, Groop, Orho-Melander, Allione, Di Gregorio, Guarrera, Panico, Ricceri, Romanazzi, Sacerdote, Vineis, Barroso, Sandhu, Luben, Crawford, Jousilahti, Perola, Boehnke, Bonnycastle, Collins, Jackson, Mohlke, Stringham, Valle, Willer, Bergman, Morken, Döring, Gieger, Illig, Meitinger, Org, Pfeufer, Wichmann, Kathiresan, Marrugat, O’Donnell, Schwartz, Siscovick, Subirana, Freimer, Hartikainen, McCarthy, O’Reilly, Peltonen, Pouta, de Jong, Snieder, van Gilst, Clarke, Goel, Hamsten, Peden, Seedorf, Syvänen, Tognoni, Lakatta, Sanna, Scheet, Schlessinger, Scuteri, Dörr, Ernst, Felix, Homuth, Lorbeer, Reffelmann, Rettig, Völker, Galan, Gut, Hercberg, Lathrop, Zelenika, Deloukas, Soranzo, Williams, Zhai, Salomaa, Laakso, Elosua, Forouhi, Völzke, Uiterwaal, van der Schouw, Numans, Matullo, Navis, Berglund, Bingham, Kooner, Connell, Bandinelli, Ferrucci, Watkins, Spector, Tuomilehto, Altshuler, Strachan, Laan, Meneton, Wareham, Uda, Jarvelin, Mooser, Melander, Loos, Elliott, Abecasis, Caulfield and Munroe2009; Psaty et al., Reference Psaty, O’Donnell, Gudnason, Lunetta, Folsom, Rotter, Uitterlinden, Harris, Witteman and Boerwinkle2009). Seven of these loci were also subsequently reported in a Japanese population (Takeuchi et al., Reference Takeuchi, Isono, Katsuya, Yamamoto, Yokota, Sugiyama, Nabika, Fujioka, Ohnaka, Asano, Yamori, Yamaguchi, Kobayashi, Takayanagi, Ogihara and Kato2010). Following this success story, the two consortia GBPGEN and CHARGE-BP merged to form the International Consortia for BP (ICBP) identifying more novel loci in 2011 (Wain et al., Reference Wain, Verwoert, O’Reilly, Shi, Johnson, Johnson, Bochud, Rice, Henneman, Smith, Ehret, Amin, Larson, Mooser, Hadley, Dörr, Bis, Aspelund, Esko, Janssens, Zhao, Heath, Laan, Fu, Pistis, Luan, Arora, Lucas, Pirastu, Pichler, Jackson, Webster, Zhang, Peden, Schmidt, Tanaka, Campbell, Igl, Milaneschi, Hotteng, Vitart, Chasman, Trompet, Bragg-Gresham, Alizadeh, Chambers, Guo, Lehtimäki, Kühnel, Lopez, Polašek, Boban, Nelson, Morrison, Pihur, Ganesh, Hofman, Kundu, Mattace-Raso, Rivadeneira, Sijbrands, Uitterlinden, Hwang, Vasan, Wang, Bergmann, Vollenweider, Waeber, Laitinen, Pouta, Zitting, McArdle, Kroemer, Völker, Völzke, Glazer, Taylor, Harris, Alavere, Haller, Keis, Tammesoo, Aulchenko, Barroso, Khaw, Galan, Hercberg, Lathrop, Eyheramendy, Org, Sõber, Lu, Nolte, Penninx, Corre, Masciullo, Sala, Groop, Voight and Melander2011; Ehret et al., Reference Ehret, Munroe, Rice, Bochud, Johnson, Chasman, Smith, Tobin, Verwoert, Hwang, Pihur, Vollenweider, O’Reilly, Amin, Bragg-Gresham, Teumer, Glazer, Launer, Hua Zhao, Aulchenko, Heath, Sõber, Parsa, Luan, Arora, Dehghan, Zhang, Lucas, Hicks, Jackson, Peden, Tanaka, Wild, Rudan, Igl, Milaneschi, Parker, Fava, Chambers, Fox, Kumari, Jin Go, van der Harst, Hong Linda Kao, Sjögren, Vinay, Alexander, Tabara, Shaw-Hawkins, Whincup, Liu, Shi, Kuusisto, Tayo, Seielstad, Sim, Hoang Nguyen, Lehtimäki, Matullo, Wu, Gaunt, Charlotte Onland-Moret, Cooper, Platou, Org, Hardy, Dahgam, Palmen, Vitart, Braund, Kuznetsova, Uiterwaal, Adeyemo, Palmas, Campbell, Ludwig, Tomaszewski, Tzoulaki, Palmer, Aspelund, Garcia, Chang, O’Connell, Steinle, Grobbee, Arking, Kardia, Morrison, Hernandez, Najjar, McArdle, Hadley, Brown, Connell, Hingorani, INM, Lawlor, Beilby, Lawrence, Clarke, Hopewell, Ongen, Dreisbach, Li, Hunter Young, Bis, Kähönen, Viikari, Adair, Lee, Chen, Olden, Pattaro, Hoffman Bolton, Köttgen, Bergmann, Mooser, Chaturvedi, Frayling, Islam, Jafar, Erdmann, Kulkarni, Bornstein, Grässler, Groop, Voight, Kettunen, Howard, Taylor, Guarrera, Ricceri, Emilsson, Plump, Barroso, Khaw, Weder, Hunt, Sun, Bergman, Collins, Bonnycastle, Scott, Stringham, Peltonen, Perola, Vartiainen, Brand, Staessen, Wang, Burton, Soler Artigas, Dong, Snieder, Wang, Zhu, Lohman, Rudock, Heckbert, Smith, Wiggins, Doumatey, Shriner, Veldre, Viigimaa, Kinra, Prabhakaran, Tripathy, Langefeld, Rosengren, Thelle, Maria Corsi, Singleton, Forrester, Hilton, McKenzie, Salako, Iwai, Kita, Ogihara, Ohkubo, Okamura, Ueshima, Umemura, Eyheramendy, Meitinger, Wichmann, Shin Cho, Kim, Lee, Scott, Sehmi, Zhang, Hedblad, Nilsson, Davey Smith, Wong, Narisu, Stančáková, Raffel, Yao, Kathiresan, O’Donnell, Schwartz, Arfan Ikram, Longstreth, Mosley, Seshadri, Shrine, Wain, Morken, Swift, Laitinen, Prokopenko, Zitting, Cooper, Humphries, Danesh, Rasheed, Goel, Hamsten, Watkins, Bakker, van Gilst, Janipalli, Radha Mani, Yajnik, Hofman, Mattace-Raso, Oostra, Demirkan, Isaacs, Rivadeneira, Lakatta, Orru, Scuteri, Ala-Korpela, Kangas, Lyytikäinen, Soininen, Tukiainen, Würtz, Twee-Hee Ong, Dörr, Kroemer, Völker, Völzke, Galan, Hercberg, Lathrop, Zelenika, Deloukas, Mangino, Spector, Zhai, Meschia, Nalls, Sharma, Terzic, Kranthi Kumar, Denniff, Zukowska-Szczechowska, Wagenknecht, Gerald, Fowkes, Charchar, PEH, Hayward, Guo, Rotimi, Bots, Brand, Samani, Polasek, Talmud, Nyberg, Kuh, Laan, Hveem, Palmer, van der Schouw, Casas, Mohlke, Vineis, Raitakari, Ganesh, Wong, Shyong Tai, Cooper, Laakso, Rao, Harris, Morris, Dominiczak and Kivimaki2011). Cho et al., (Reference Cho, Go, Kim, Heo, Oh, Ban, Yoon, Lee, Kim, Park, Cha, Kim, Han, Min, Ahn, Park, Han, Jang, Cho, Lee, Cho, Shin, Park, Park, Lee, Cardon, Clarke, McCarthy, Lee, Lee, Oh and Kim2009) also reported 5 new BP loci in East Asians (Cho et al., Reference Cho, Go, Kim, Heo, Oh, Ban, Yoon, Lee, Kim, Park, Cha, Kim, Han, Min, Ahn, Park, Han, Jang, Cho, Lee, Cho, Shin, Park, Park, Lee, Cardon, Clarke, McCarthy, Lee, Lee, Oh and Kim2009). The results from these studies provided new insights into the biology of BP with opportunities for developing new therapies.

Big data, biobanks and beyond

Over the past decade, large-scale datasets have been developed, one example is the UK Biobank, permitting analysis of up to 500,000 richly phenotyped participants (Bycroft et al., Reference Bycroft, Freeman, Petkova, Band, Elliott, Sharp, Motyer, Vukcevic, Delaneau, O’Connell, Cortes, Welsh, Young, Effingham, McVean, Leslie, Allen, Donnelly and Marchini2018). Leveraging these resources, Warren et al. (Reference Warren, Evangelou, Cabrera, Gao, Ren, Mifsud, Ntalla, Surendran, Liu, Cook, Kraja, Drenos, Loh, Verweij, Marten, Karaman, Lepe, O’Reilly, Knight, Snieder, Kato, He, Tai, Said, Porteous, Alver, Poulter, Farrall, Gansevoort, Padmanabhan, Mägi, Stanton, Connell, Bakker, Metspalu, Shields, Thom, Brown, Sever, Esko, Hayward, van der Harst, Saleheen, Chowdhury, Chambers, Chasman, Chakravarti, Newton-Cheh, Lindgren, Levy, Kooner, Keavney, Tomaszewski, Samani, Howson, Tobin, Munroe, Ehret and Wain2017) performed the first UK Biobank BP-GWAS for the first 150,000 genotyped participants (Warren et al., Reference Warren, Evangelou, Cabrera, Gao, Ren, Mifsud, Ntalla, Surendran, Liu, Cook, Kraja, Drenos, Loh, Verweij, Marten, Karaman, Lepe, O’Reilly, Knight, Snieder, Kato, He, Tai, Said, Porteous, Alver, Poulter, Farrall, Gansevoort, Padmanabhan, Mägi, Stanton, Connell, Bakker, Metspalu, Shields, Thom, Brown, Sever, Esko, Hayward, van der Harst, Saleheen, Chowdhury, Chambers, Chasman, Chakravarti, Newton-Cheh, Lindgren, Levy, Kooner, Keavney, Tomaszewski, Samani, Howson, Tobin, Munroe, Ehret and Wain2017). Hoffman et al. (Reference Hoffmann, Ehret, Nandakumar, Ranatunga, Schaefer, Kwok, Iribarren, Chakravarti and Risch2017) performed a GWAS on long-term average BP from the electronic health records of 99,785 individuals identifying 39 new loci (Hoffmann et al., Reference Hoffmann, Ehret, Nandakumar, Ranatunga, Schaefer, Kwok, Iribarren, Chakravarti and Risch2017). Once all the UK Biobank data became available, Evangelou et al., (Reference Evangelou, Warren, Mosen-Ansorena, Mifsud, Pazoki, Gao, Ntritsos, Dimou, Cabrera, Karaman, Ng, Evangelou, Witkowska, Tzanis, Hellwege, Giri, Velez Edwards, Sun, Cho, Gaziano, Wilson, Tsao, Kovesdy, Esko, Mägi, Milani, Almgren, Boutin, Debette, Ding, Giulianini, Holliday, Jackson, Li-Gao, Lin, Luan, Mangino, Oldmeadow, Prins, Qian, Sargurupremraj, Shah, Surendran, Thériault, Verweij, Willems, Zhao, Amouyel, Connell, de Mutsert, Doney, Farrall, Menni, Morris, Noordam, Paré, Poulter, Shields, Stanton, Thom, Abecasis, Amin, Arking, Ayers, Barbieri, Batini, Bis, Blake, Bochud, Boehnke, Boerwinkle, Boomsma, Bottinger, Braund, Brumat, Campbell, Campbell, Chakravarti, Chambers, Chauhan, Ciullo, Cocca, Collins, Cordell, Davies, de Borst, de Geus, Deary, Deelen, Del Greco, Demirkale, Dörr, Ehret, Elosua, Enroth, Erzurumluoglu, Ferreira, Frånberg, Franco, Gandin, Gasparini, Giedraitis, Gieger, Girotto, Goel, Gow, Gudnason, Guo, Gyllensten, Hamsten, Harris, Harris, Hartman, Havulinna, Hicks, Hofer, Hofman, Hottenga, Huffman, Hwang, Ingelsson, James, Jansen, Jarvelin, Joehanes, Johansson, Johnson, Joshi, Jousilahti, Jukema, Jula, Kähönen, Kathiresan, Keavney, Khaw, Knekt, Knight, Kolcic, Kooner, Koskinen, Kristiansson, Kutalik, Laan, Larson, Launer, Lehne, Lehtimäki, Liewald, Lin, Lind, Lindgren, Liu, Loos, Lopez, Lu, Lyytikäinen, Mahajan, Mamasoula, Marrugat, Marten, Milaneschi, Morgan, Morris, Morrison, Munson, Nalls, Nandakumar, Nelson, Niiranen, Nolte, Nutile, Oldehinkel, Oostra, O’Reilly, Org, Padmanabhan, Palmas, Palotie, Pattie, Penninx, Perola, Peters, Polasek, Pramstaller, Nguyen, Raitakari, Ren, Rettig, Rice, Ridker, Ried, Riese, Ripatti, Robino, Rose, Rotter, Rudan, Ruggiero, Saba, Sala, Salomaa, Samani, Sarin, Schmidt, Schmidt, Shrine, Siscovick, Smith, Snieder, Sõber, Sorice, Starr, Stott, Strachan, Strawbridge, Sundström, Swertz, Taylor, Teumer, Tobin, Tomaszewski, Toniolo, Traglia, Trompet, Tuomilehto, Tzourio, Uitterlinden, Vaez, van der Most, van Duijn, Vergnaud, Verwoert, Vitart, Völker, Vollenweider, Vuckovic, Watkins, Wild, Willemsen, Wilson, Wright, Yao, Zemunik, Zhang, Attia, Butterworth, Chasman, Conen, Cucca, Danesh, Hayward, Howson, Laakso, Lakatta, Langenberg, Melander, Mook-Kanamori, Palmer, Risch, Scott, Scott, Sever, Spector, van der Harst, Wareham, Zeggini, Levy, Munroe, Newton-Cheh, Brown, Metspalu, Hung, O’Donnell, Edwards, Psaty, Tzoulaki, Barnes, Wain, Elliott and Caulfield2018) performed GWAS meta-analyses including data from both the UK Biobank and ICBP and identified 535 new genetic loci influencing BP (Evangelou et al., Reference Evangelou, Warren, Mosen-Ansorena, Mifsud, Pazoki, Gao, Ntritsos, Dimou, Cabrera, Karaman, Ng, Evangelou, Witkowska, Tzanis, Hellwege, Giri, Velez Edwards, Sun, Cho, Gaziano, Wilson, Tsao, Kovesdy, Esko, Mägi, Milani, Almgren, Boutin, Debette, Ding, Giulianini, Holliday, Jackson, Li-Gao, Lin, Luan, Mangino, Oldmeadow, Prins, Qian, Sargurupremraj, Shah, Surendran, Thériault, Verweij, Willems, Zhao, Amouyel, Connell, de Mutsert, Doney, Farrall, Menni, Morris, Noordam, Paré, Poulter, Shields, Stanton, Thom, Abecasis, Amin, Arking, Ayers, Barbieri, Batini, Bis, Blake, Bochud, Boehnke, Boerwinkle, Boomsma, Bottinger, Braund, Brumat, Campbell, Campbell, Chakravarti, Chambers, Chauhan, Ciullo, Cocca, Collins, Cordell, Davies, de Borst, de Geus, Deary, Deelen, Del Greco, Demirkale, Dörr, Ehret, Elosua, Enroth, Erzurumluoglu, Ferreira, Frånberg, Franco, Gandin, Gasparini, Giedraitis, Gieger, Girotto, Goel, Gow, Gudnason, Guo, Gyllensten, Hamsten, Harris, Harris, Hartman, Havulinna, Hicks, Hofer, Hofman, Hottenga, Huffman, Hwang, Ingelsson, James, Jansen, Jarvelin, Joehanes, Johansson, Johnson, Joshi, Jousilahti, Jukema, Jula, Kähönen, Kathiresan, Keavney, Khaw, Knekt, Knight, Kolcic, Kooner, Koskinen, Kristiansson, Kutalik, Laan, Larson, Launer, Lehne, Lehtimäki, Liewald, Lin, Lind, Lindgren, Liu, Loos, Lopez, Lu, Lyytikäinen, Mahajan, Mamasoula, Marrugat, Marten, Milaneschi, Morgan, Morris, Morrison, Munson, Nalls, Nandakumar, Nelson, Niiranen, Nolte, Nutile, Oldehinkel, Oostra, O’Reilly, Org, Padmanabhan, Palmas, Palotie, Pattie, Penninx, Perola, Peters, Polasek, Pramstaller, Nguyen, Raitakari, Ren, Rettig, Rice, Ridker, Ried, Riese, Ripatti, Robino, Rose, Rotter, Rudan, Ruggiero, Saba, Sala, Salomaa, Samani, Sarin, Schmidt, Schmidt, Shrine, Siscovick, Smith, Snieder, Sõber, Sorice, Starr, Stott, Strachan, Strawbridge, Sundström, Swertz, Taylor, Teumer, Tobin, Tomaszewski, Toniolo, Traglia, Trompet, Tuomilehto, Tzourio, Uitterlinden, Vaez, van der Most, van Duijn, Vergnaud, Verwoert, Vitart, Völker, Vollenweider, Vuckovic, Watkins, Wild, Willemsen, Wilson, Wright, Yao, Zemunik, Zhang, Attia, Butterworth, Chasman, Conen, Cucca, Danesh, Hayward, Howson, Laakso, Lakatta, Langenberg, Melander, Mook-Kanamori, Palmer, Risch, Scott, Scott, Sever, Spector, van der Harst, Wareham, Zeggini, Levy, Munroe, Newton-Cheh, Brown, Metspalu, Hung, O’Donnell, Edwards, Psaty, Tzoulaki, Barnes, Wain, Elliott and Caulfield2018). Alongside the GWAS for common variants for BP, there have also been large-scale consortium-based studies focused on the discovery of rare variants across many meta-analysed studies, including UK Biobank (minor allele frequencies of <1%) (Surendran et al., Reference Surendran, Feofanova, Lahrouchi, Ntalla, Karthikeyan, Cook, Chen, Mifsud, Yao, Kraja, Cartwright, Hellwege, Giri, Tragante, Thorleifsson, Liu, Prins, Stewart, Cabrera, Eales, Akbarov, Auer, Bielak, Bis, Braithwaite, Brody, Daw, Warren, Drenos, Nielsen, Faul, Fauman, Fava, Ferreira, Foley, Franceschini, Gao, Giannakopoulou, Giulianini, Gudbjartsson, Guo, Harris, Havulinna, Helgadottir, Huffman, Hwang, Kanoni, Kontto, Larson, Li-Gao, Lindström, Lotta, Lu, Luan, Mahajan, Malerba, NGD, Mei, Menni, Mook-Kanamori, Mosen-Ansorena, Müller-Nurasyid, Paré, Paul, Perola, Poveda, Rauramaa, Richard, Richardson, Sepúlveda, Sim, Smith, Smith, Staley, Stanáková, Sulem, Thériault, Thorsteinsdottir, Trompet, Varga, Velez Edwards, Veronesi, Weiss, Willems, Yao, Young, Yu, Zhang, Zhao, Zhao, Evangelou, Aeschbacher, Asllanaj, Blankenberg, Bonnycastle, Bork-Jensen, Brandslund, Braund, Burgess, Cho, Christensen, Connell, Mutsert, Dominiczak, Dörr, Eiriksdottir, Farmaki, Gaziano, Grarup, Grove, Hallmans, Hansen, Have, Heiss, Jørgensen, Jousilahti, Kajantie, Kamat, Käräjämäki, Karpe, Koistinen, Kovesdy, Kuulasmaa, Laatikainen, Lannfelt, Lee, Lee, Linneberg, Martin, Moitry, Nadkarni, Neville, Palmer, Papanicolaou, Pedersen, Peters, Poulter, Rasheed, Rasmussen, Rayner, Mägi, Renström, Rettig, Rossouw, Schreiner, Sever, Sigurdsson, Skaaby, Sun, Sundstrom, Thorgeirsson, Esko, Trabetti, Tsao, Tuomi, Turner, Tzoulaki, Vaartjes, Vergnaud, Willer, Wilson, Witte, Yonova-Doing, Zhang, Aliya, Almgren, Amouyel, Asselbergs, Barnes, Blakemore, Boehnke, Bots, Bottinger, Buring, Chambers, Chen, Chowdhury, Conen, Correa, Davey Smith, Boer, Deary, Dedoussis, Deloukas, Di Angelantonio, Elliott, Felix, Ferrières, Ford, Fornage, Franks, Franks, Frossard, Gambaro, Gaunt, Groop, Gudnason, Harris, Hayward, Hennig, Herzig, Ingelsson, Tuomilehto, Järvelin, Jukema, SLR, Kee, Kooner, Kooperberg, Launer, Lind, Loos, Majumder, Laakso, McCarthy, Melander, Mohlke, Murray, Nordestgaard, Orho-Melander, Packard, Padmanabhan, Palmas, Polasek, Porteous, Prentice, Province, Relton, Rice, Ridker, Rolandsson, Rosendaal, Rotter, Rudan, Salomaa, Samani, Sattar, Sheu, Smith, Soranzo, Spector, Starr, Sebert, Taylor, Lakka, Timpson, Tobin, van der Harst, van der Meer, Ramachandran, Verweij, Virtamo, Völker, Weir, Zeggini, Charchar, Wareham, Langenberg, Tomaszewski, Butterworth, Caulfield, Danesh, Edwards, Holm, Hung, Lindgren, Liu, Manning, Morris, Morrison, O’Donnell, Psaty, Saleheen, Stefansson, Boerwinkle, Chasman, Levy, Newton-Cheh, Munroe and Howson2020). These studies have yielded >80 rare variants, all having larger effects on BP (~1.5 mmHg per allele, compared to ~0.5 mmHg for common variants) (He et al., Reference He, Kelly, Wang, Liang, Zhu, Cade, Assimes, Becker, Beitelshees, Bielak, Bress, Brody, Chang, Chang, de Vries, Duggirala, Fox, Franceschini, Furniss, Gao, Guo, Haessler, Hung, Hwang, Irvin, Kalyani, Liu, Liu, Martin, Montasser, Muntner, Mwasongwe, Naseri, Palmas, Reupena, Rice, Sheu, Shimbo, Smith, Snively, Yanek, Zhao, Blangero, Boerwinkle, Chen, Correa, Cupples, Curran, Fornage, He, Hou, Kaplan, Kardia, Kenny, Kooperberg, Lloyd-Jones, Loos, Mathias, McGarvey, Mitchell, North, Peyser, Psaty, Raffield, Rao, Redline, Reiner, Rich, Rotter, Taylor, Tracy, Vasan, Morrison, Levy, Chakravarti, Arnett and Zhu2022).

Additional large biobanks include the Million Veteran Program (MVP, n ~ currently recruiting and with 635,969), which has created one of the largest epidemiologic research infrastructures embedded within the national health care system operated by the US Department of Veteran Affairs, these data have also been used for BP-GWAS (Giri et al., Reference Giri, Hellwege, Keaton, Park, Qiu, Warren, Torstenson, Kovesdy, Sun, Wilson, Robinson-Cohen, Roumie, Chung, Birdwell, Damrauer, SL, Klarin, Cho, Wang, Evangelou, Cabrera, Wain, Shrestha, Mautz, Akwo, Sargurupremraj, Debette, Boehnke, Scott, Luan, Jing-Hua, Willems, Thériault, Shah, Oldmeadow, Almgren, Li-Gao, Verweij, Boutin, Mangino, Ntalla, Feofanova, Surendran, Cook, Karthikeyan, Lahrouchi, Liu, Sepúlveda, Richardson, Kraja, Amouyel, Farrall, Poulter, Laakso, Zeggini, Sever, Scott, Langenberg, Wareham, Conen, Palmer, Attia, Chasman, Ridker, Melander, Mook-Kanamori, Harst, Cucca, Schlessinger, Hayward, Spector, Marjo-Riitta, Hennig, Timpson, Wei-Qi, Smith, Xu, Matheny, Siew, Lindgren, Karl-Heinz, Dedoussis, Denny, Psaty, Howson, Munroe, Newton-Cheh, Caulfield, Elliott, Gaziano, Concato, Wilson, Tsao, Edwards, Susztak, Program, O’Donnell, Hung and Edwards2019; Verma et al., Reference Verma, Huffman, Rodriguez, Conery, Liu, Ho, Kim, Heise, Guare, Panickan, Garcon, Linares, Costa, Goethert, Tipton, Honerlaw, Davies, Whitbourne, Cohen, Posner, Sangar, Murray, Wang, Dochtermann, Devineni, Shi, Nandi, Assimes, Brunette, Carroll, Clifford, Duvall, Gelernter, Hung, Iyengar, Joseph, Kember, Kranzler, Kripke, Levey, Luoh, Merritt, Overstreet, Deak, Grant, Polimanti, Roussos, Shakt, Sun, Tsao, Venkatesh, Voloudakis, Justice, Begoli, Ramoni, Tourassi, Pyarajan, Tsao, O’Donnell, Muralidhar, Moser, Casas, Bick, Zhou, Cai, Voight, Cho, Gaziano, Madduri, Damrauer and Liao2024). Other notable cohorts projected to deliver population-level genomic insights include: Genomics England’s first initiative, the 100,000 Genomes Project identifying the genetic causes of many rare diseases; and FinnGen, a Finnish biobank of 500,000 participants (100,000 Genomes Project Pilot Investigators et al., Reference Smedley, Smith, Martin, Thomas, McDonagh, Cipriani, Ellingford, Arno, Tucci, Vandrovcova, Chan, Williams, Ratnaike, Wei, Stirrups, Ibanez, Moutsianas, Wielscher, Need, Barnes, Vestito, Buchanan, Wordsworth, Ashford, Rehmström, Li, Fuller, Twiss, Spasic-Boskovic, Halsall, Floto, Poole, Wagner, Mehta, Gurnell, Burrows, James, Penkett, Dewhurst, Gräf, Mapeta, Kasanicki, Haworth, Savage, Babcock, Reese, Bale, Baple, Boustred, Brittain, de Burca, Bleda, Devereau, Halai, Haraldsdottir, Hyder, Kasperaviciute, Patch, Polychronopoulos, Matchan, Sultana, Ryten, Tavares, Tregidgo, Turnbull, Welland, Wood, Snow, Williams, Leigh, Foulger, Daugherty, Niblock, Leong, Wright, Davies, Crichton, Welch, Woods, Abulhoul, Aurora, Bockenhauer, Broomfield, Cleary, Lam, Dattani, Footitt, Ganesan, Grunewald, Compeyrot-Lacassagne, Muntoni, Pilkington, Quinlivan, Thapar, Wallis, Wedderburn, Worth, Bueser, Compton, Deshpande, Fassihi, Haque, Izatt, Josifova, Mohammed, Robert, Rose, Ruddy, Sarkany, Say, Shaw, Wolejko, Habib, Burns, Hunter, Grocock, Humphray, Robinson, Haendel, Simpson, Banka, Clayton-Smith, Douzgou, Hall, Thomas, O’Keefe, Michaelides, Moore, Malka, Pontikos, Browning, Straub, Gorman, Horvath, Quinton, Schaefer, Yu-Wai-Man, Turnbull, McFarland, Taylor, O’Connor, Yip, Newland, Morris, Polke, Wood, Campbell, Camps, Gibson, Koelling, Lester, Németh, Palles, Patel, Roy, Sen, Taylor, Cacheiro, Jacobsen, Seaby, Davison, Chitty, Douglas, Naresh, McMullan, Ellard, Temple, Mumford, Wilson, Beales, Bitner-Glindzicz, Black, Bradley, Brennan, Burn, Chinnery, Elliott, Flinter, Houlden, Irving, Newman, Rahman, Sayer, Taylor, Webster, Wilkie, Ouwehand, Raymond, Chisholm, Hill, Bentley, Scott, Fowler, Rendon and Caulfield2021; Kurki et al., Reference Kurki, Karjalainen, Palta, Sipilä, Kristiansson, Donner, Reeve, Laivuori, Aavikko, Kaunisto, Loukola, Lahtela, Mattsson, Laiho, Della Briotta Parolo, Lehisto, Kanai, Mars, Rämö, Kiiskinen, Heyne, Veerapen, Rüeger, Lemmelä, Zhou, Ruotsalainen, Pärn, Hiekkalinna, Koskelainen, Paajanen, Llorens, Gracia-Tabuenca, Siirtola, Reis, Elnahas, Sun, Foley, Aalto-Setälä, Alasoo, Arvas, Auro, Biswas, Bizaki-Vallaskangas, Carpen, Chen, Dada, Ding, Ehm, Eklund, Färkkilä, Finucane, Ganna, Ghazal, Graham, Green, Hakanen, Hautalahti, Hedman, Hiltunen, Hinttala, Hovatta, Hu, Huertas-Vazquez, Huilaja, Hunkapiller, Jacob, Jensen, Joensuu, John, Julkunen, Jung, Junttila, Kaarniranta, Kähönen, Kajanne, Kallio, Kälviäinen, Kaprio, Kerimov, Kettunen, Kilpeläinen, Kilpi, Klinger, Kosma, Kuopio, Kurra, Laisk, Laukkanen, Lawless, Liu, Longerich, Mägi, Mäkelä, Mäkitie, Malarstig, Mannermaa, Maranville, Matakidou, Meretoja, Mozaffari, Niemi, Niemi, Niiranen, O’Donnell, Obeidat, Okafo, Ollila, Palomäki, Palotie, Partanen, Paul, Pelkonen, Pendergrass, Petrovski, Pitkäranta, Platt, Pulford, Punkka, Pussinen, Raghavan, Rahimov, Rajpal, Renaud, Riley-Gillis, Rodosthenous, Saarentaus, Salminen, Salminen, Salomaa, Schleutker, Serpi, Shen, Siegel, Silander, Siltanen, Soini, Soininen, Sul, Tachmazidou, Tasanen, Tienari, Toppila-Salmi, Tukiainen, Tuomi, Turunen, Ulirsch, Vaura, Virolainen, Waring, Waterworth, Yang, Nelis, Reigo, Metspalu, Milani, Esko, Fox, Havulinna, Perola, Ripatti, Jalanko, Laitinen, Mäkelä, Plenge, McCarthy, Runz, Daly and Palotie2023).

The majority of GWAS studies for BP did not initially consider the precise role and biological significance of gene–environment interactions (GxE). To address this gap in knowledge, the CHARGE Gene-Lifestyle Interactions Working Group was formed, and this group has conducted a series of genome-wide interaction studies for various traits and exposures. Recently, the group examined interactions between genotype and the Dietary Approaches to Stop Hypertension (DASH) diet score and systolic BP (Guirette et al., Reference Guirette, Lan, McKeown, Brown, Chen, de Vries, Kim, Rebholz, Morrison, Bartz, Fretts, Guo, Lemaitre, Liu, Noordam, de Mutsert, Rosendaal, Wang, Beilin, Mori, Oddy, Pennell, Chai, Whitton, van Dam, Liu, Tai, Sim, Neuhouser, Kooperberg, Tinker, Franceschini, Huan, Winkler, Bentley, Gauderman, Heerkens, Tanaka, Rooij, Munroe, Warren, Voortman, Chen, Rao, Levy and Ma2024). They demonstrated gene-DASH diet score interaction effects on systolic BP in several loci in European population-specific and cross-population meta-analyses. Additional studies have investigated several other important lifestyle factors, including a study investigating BP × Alcohol, which found 54 loci; and BP × Smoking which found 15 loci (Sung et al., Reference Sung, Winkler, de las Fuentes, Bentley, Brown, Kraja, Schwander, Ntalla, Guo, Franceschini, Lu, Cheng, Sim, Vojinovic, Marten, Musani, Li, Feitosa, Kilpeläinen, Richard, Noordam, Aslibekyan, Aschard, Bartz, Dorajoo, Liu, Manning, Rankinen, Smith, Tajuddin, Tayo, Warren, Zhao, Zhou, Matoba, Sofer, Alver, Amini, Boissel, Chai, Chen, Divers, Gandin, Gao, Giulianini, Goel, Harris, Hartwig, Horimoto, Hsu, Jackson, Kähönen, Kasturiratne, Kühnel, Leander, Lee, Lin, Luan, McKenzie, Meian, Nelson, Rauramaa, Schupf, Scott, Sheu, Stančáková, Takeuchi, Most, Varga, Wang, Wang, Ware, Weiss, Wen, Yanek, Zhang, Zhao, Afaq, Alfred, Amin, Arking, Aung, Barr, Bielak, Boerwinkle, Bottinger, Braund, Brody, Broeckel, Cabrera, Cade, Caizheng, Campbell, Canouil, Chakravarti, Chauhan, Christensen, Cocca and Consortium2018; Feitosa et al., Reference Feitosa, Kraja, Chasman, Sung, Winkler, Ntalla, Guo, Franceschini, Cheng, Sim, Vojinovic, Marten, Musani, Li, Bentley, Brown, Schwander, Richard, Noordam, Aschard, Bartz, Bielak, Dorajoo, Fisher, Hartwig, Horimoto, Lohman, Manning, Rankinen, Smith, Tajuddin, Wojczynski, Alver, Boissel, Cai, Campbell, Chai, Chen, Divers, Gao, Goel, Hagemeijer, Harris, He, Hsu, Jackson, Kähönen, Kasturiratne, Komulainen, Kühnel, Laguzzi, Luan, Matoba, Nolte, Padmanabhan, Riaz, Rueedi, Robino, Said, Scott, Sofer, Stančáková, Takeuchi, Tayo, Most, Varga, Vitart, Wang, Ware, Warren, Weiss, Wen, Yanek, Zhang, Zhao, Afaq, Amin, Amini, Arking, Aung, Boerwinkle, Borecki, Broeckel, Brown, Brumat, Burke, Canouil, Chakravarti, Charumathi, Chen, Connell, Correa, Fuentes, Mutsert, Silva, Deng, Ding, Duan, Eaton, Ehret, Eppinga, Evangelou, Faul, Felix, Forouhi, Forrester, Franco, Friedlander, Gandin, Gao, Ghanbari, Gigante, Gu, Gu, Hagenaars, Hallmans, Harris, He, Heikkinen, Heng, Hirata, Howard, Ikram, Consortium, John, Katsuya, Khor, Kilpeläinen, Koh, Krieger, Kritchevsky, Kubo, Kuusisto, Lakka, Langefeld, Langenberg, Launer, Lehne, Lewis, Li, Lin, Liu, Liu, Loh, Louie, Mägi, McKenzie, Meitinger, Metspalu, Milaneschi, Milani, Mohlke, Momozawa, Nalls, Nelson, Sotoodehnia, Norris, O’Connell, Palmer, Perls, Pedersen, Peters, Peyser, Poulter, Raffel, Raitakari, Roll, Rose, Rosendaal, Rotter, Schmidt, Schreiner, Schupf, Scott, Sever, Shi, Sidney, Sims, Sitlani, Smith, Snieder, Starr, Strauch, Stringham, NYQ, Tang, Taylor, Teo, Tham, Turner, Uitterlinden, Vollenweider, Waldenberger, Wang, Wang, Wei, Williams, Yao, Yu, Yuan, Zhao, Zonderman, Becker, Boehnke, Bowden, Chambers, Deary, Esko, Farrall, Franks, Freedman, Froguel, Gasparini, Gieger, Jonas, Kamatani, Kato, Kooner, Kutalik, Laakso, Laurie, Leander, Lehtimäki, Study, Magnusson, Oldehinkel, Penninx, Polasek, Porteous, Rauramaa, Samani, Scott, Shu, Harst, Wagenknecht, Wareham, Watkins, Weir, Wickremasinghe, Wu, Zheng, Bouchard, Christensen, Evans, Gudnason, Horta, SLR, Liu, Pereira, Psaty, Ridker, Dam, Gauderman, Zhu, Mook-Kanamori, Fornage, Rotimi, Cupples, Kelly, Fox, Hayward, Duijn, Tai, Wong, Kooperberg, Palmas, Rice, Morrison, Elliott, Caulfield, Munroe, Rao, Province and Levy2018). These studies included ~130,000 individuals across multi-ancestry data-sets, but there were limited findings, and analyses in larger sample sizes are currently ongoing.

Polygenic risk score and cardiovascular risk prediction

As GWAS results become publicly available, this has enabled risk prediction modelling to include genetic biomarkers for clinical applications. BP is a highly polygenic trait, influenced by thousands of different SNPs each of which has a small effect on BP. Polygenic risk scores (PRS) have been developed by combining the risk associated with many common DNA sequence variants into one single aggregated risk score (Lewis and Vassos, Reference Lewis and Vassos2020). The first genetic risk score for BP was developed by the ICBP in 2011 by combining together 29 different significant genetic variants into one score (Ehret et al., Reference Ehret, Munroe, Rice, Bochud, Johnson, Chasman, Smith, Tobin, Verwoert, Hwang, Pihur, Vollenweider, O’Reilly, Amin, Bragg-Gresham, Teumer, Glazer, Launer, Hua Zhao, Aulchenko, Heath, Sõber, Parsa, Luan, Arora, Dehghan, Zhang, Lucas, Hicks, Jackson, Peden, Tanaka, Wild, Rudan, Igl, Milaneschi, Parker, Fava, Chambers, Fox, Kumari, Jin Go, van der Harst, Hong Linda Kao, Sjögren, Vinay, Alexander, Tabara, Shaw-Hawkins, Whincup, Liu, Shi, Kuusisto, Tayo, Seielstad, Sim, Hoang Nguyen, Lehtimäki, Matullo, Wu, Gaunt, Charlotte Onland-Moret, Cooper, Platou, Org, Hardy, Dahgam, Palmen, Vitart, Braund, Kuznetsova, Uiterwaal, Adeyemo, Palmas, Campbell, Ludwig, Tomaszewski, Tzoulaki, Palmer, Aspelund, Garcia, Chang, O’Connell, Steinle, Grobbee, Arking, Kardia, Morrison, Hernandez, Najjar, McArdle, Hadley, Brown, Connell, Hingorani, INM, Lawlor, Beilby, Lawrence, Clarke, Hopewell, Ongen, Dreisbach, Li, Hunter Young, Bis, Kähönen, Viikari, Adair, Lee, Chen, Olden, Pattaro, Hoffman Bolton, Köttgen, Bergmann, Mooser, Chaturvedi, Frayling, Islam, Jafar, Erdmann, Kulkarni, Bornstein, Grässler, Groop, Voight, Kettunen, Howard, Taylor, Guarrera, Ricceri, Emilsson, Plump, Barroso, Khaw, Weder, Hunt, Sun, Bergman, Collins, Bonnycastle, Scott, Stringham, Peltonen, Perola, Vartiainen, Brand, Staessen, Wang, Burton, Soler Artigas, Dong, Snieder, Wang, Zhu, Lohman, Rudock, Heckbert, Smith, Wiggins, Doumatey, Shriner, Veldre, Viigimaa, Kinra, Prabhakaran, Tripathy, Langefeld, Rosengren, Thelle, Maria Corsi, Singleton, Forrester, Hilton, McKenzie, Salako, Iwai, Kita, Ogihara, Ohkubo, Okamura, Ueshima, Umemura, Eyheramendy, Meitinger, Wichmann, Shin Cho, Kim, Lee, Scott, Sehmi, Zhang, Hedblad, Nilsson, Davey Smith, Wong, Narisu, Stančáková, Raffel, Yao, Kathiresan, O’Donnell, Schwartz, Arfan Ikram, Longstreth, Mosley, Seshadri, Shrine, Wain, Morken, Swift, Laitinen, Prokopenko, Zitting, Cooper, Humphries, Danesh, Rasheed, Goel, Hamsten, Watkins, Bakker, van Gilst, Janipalli, Radha Mani, Yajnik, Hofman, Mattace-Raso, Oostra, Demirkan, Isaacs, Rivadeneira, Lakatta, Orru, Scuteri, Ala-Korpela, Kangas, Lyytikäinen, Soininen, Tukiainen, Würtz, Twee-Hee Ong, Dörr, Kroemer, Völker, Völzke, Galan, Hercberg, Lathrop, Zelenika, Deloukas, Mangino, Spector, Zhai, Meschia, Nalls, Sharma, Terzic, Kranthi Kumar, Denniff, Zukowska-Szczechowska, Wagenknecht, Gerald, Fowkes, Charchar, PEH, Hayward, Guo, Rotimi, Bots, Brand, Samani, Polasek, Talmud, Nyberg, Kuh, Laan, Hveem, Palmer, van der Schouw, Casas, Mohlke, Vineis, Raitakari, Ganesh, Wong, Shyong Tai, Cooper, Laakso, Rao, Harris, Morris, Dominiczak and Kivimaki2011). The identification of further BP loci has led to the development of PRS with increasing performance to estimate an individual’s risk of hypertension. For example, in 2022, Parcha et al., developed and tested a BP-PRS in a multi-ancestry US cohort (n = 21,897) to evaluate the relative contributions of the traditional cardiovascular risk factors to the development of adverse events in the context of varying BP risk profiles in individuals with no previous cardiovascular disease. They demonstrated that the PRS had an incremental value beyond traditional risk factors highlighting the potential of incorporating genetic information into risk estimates (Parcha et al., Reference Parcha, Pampana, Shetty, Irvin, Natarajan, Lin, Guo, Rich, Rotter, Li, Oparil, Arora and Arora2022). Recently, Keaton et al., (Reference Keaton, Kamali, Xie, Vaez, Williams, Goleva, Ani, Evangelou, Hellwege, Yengo, Young, Traylor, Giri, Zheng, Zeng, Chasman, Morris, Caulfield, Hwang, Kooner, Conen, Attia, Morrison, Loos, Kristiansson, Schmidt, Hicks, Pramstaller, Nelson, Samani, Risch, Gyllensten, Melander, Riese, Wilson, Campbell, Rich, Psaty, Lu, Rotter, Guo, Rice, Vollenweider, Sundström, Langenberg, Tobin, Giedraitis, Luan, Tuomilehto, Kutalik, Ripatti, Salomaa, Girotto, Trompet, Jukema, van der Harst, Ridker, Giulianini, Vitart, Goel, Watkins, Harris, Deary, van der Most, Oldehinkel, Keavney, Hayward, Campbell, Boehnke, Scott, Boutin, Mamasoula, Järvelin, Peters, Gieger, Lakatta, Cucca, Hui, Knekt, Enroth, De Borst, Polašek, Concas, Catamo, Cocca, Li-Gao, Hofer, Schmidt, Spedicati, Waldenberger, Strachan, Laan, Teumer, Dörr, Gudnason, Cook, Ruggiero, Kolcic, Boerwinkle, Traglia, Lehtimäki, Raitakari, Johnson, Newton-Cheh, Brown, Dominiczak, Sever, Poulter, Chambers, Elosua, Siscovick, Esko, Metspalu, Strawbridge, Laakso, Hamsten, Hottenga, de Geus, Morris, Palmer, Nolte, Milaneschi, Marten, Wright, Zeggini, Howson, O’Donnell, Spector, Nalls, Simonsick, Liu, van Duijn, Butterworth, Danesh, Menni, Wareham, Khaw, Sun, Wilson, Cho, Visscher, Denny, Levy, Edwards, Munroe, Snieder and Warren2024) performed the largest single-stage BP GWAS to date (n = 1,028,980 European ancestry individuals), reporting a total of 2,103 independent genetic signals for BP. The BP-PRS generated from this study revealed 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 seven-fold higher odds of hypertension risk (OR = 7.33; 95% CI = 5.54–9.70; P = 4.13 × 10⁻⁴⁴), when comparing individuals in the top (highest genetic risk) versus bottom (lowest risk group) deciles of the PRS in an independent European cohort, Lifelines. The authors also showed that the BP-PRS was significantly associated with higher BP in individuals of African-American ancestry from the All-of-Us Research program in the United States (Keaton et al., Reference Keaton, Kamali, Xie, Vaez, Williams, Goleva, Ani, Evangelou, Hellwege, Yengo, Young, Traylor, Giri, Zheng, Zeng, Chasman, Morris, Caulfield, Hwang, Kooner, Conen, Attia, Morrison, Loos, Kristiansson, Schmidt, Hicks, Pramstaller, Nelson, Samani, Risch, Gyllensten, Melander, Riese, Wilson, Campbell, Rich, Psaty, Lu, Rotter, Guo, Rice, Vollenweider, Sundström, Langenberg, Tobin, Giedraitis, Luan, Tuomilehto, Kutalik, Ripatti, Salomaa, Girotto, Trompet, Jukema, van der Harst, Ridker, Giulianini, Vitart, Goel, Watkins, Harris, Deary, van der Most, Oldehinkel, Keavney, Hayward, Campbell, Boehnke, Scott, Boutin, Mamasoula, Järvelin, Peters, Gieger, Lakatta, Cucca, Hui, Knekt, Enroth, De Borst, Polašek, Concas, Catamo, Cocca, Li-Gao, Hofer, Schmidt, Spedicati, Waldenberger, Strachan, Laan, Teumer, Dörr, Gudnason, Cook, Ruggiero, Kolcic, Boerwinkle, Traglia, Lehtimäki, Raitakari, Johnson, Newton-Cheh, Brown, Dominiczak, Sever, Poulter, Chambers, Elosua, Siscovick, Esko, Metspalu, Strawbridge, Laakso, Hamsten, Hottenga, de Geus, Morris, Palmer, Nolte, Milaneschi, Marten, Wright, Zeggini, Howson, O’Donnell, Spector, Nalls, Simonsick, Liu, van Duijn, Butterworth, Danesh, Menni, Wareham, Khaw, Sun, Wilson, Cho, Visscher, Denny, Levy, Edwards, Munroe, Snieder and Warren2024).

As part of the study design for the large meta-analyses for BP-GWAS, the impact of biological sex has been understudied, thus results are limited in assessing differences between sexes. Kauko et al. (Reference Kauko, Aittokallio, Vaura, Ji, Ebinger, Niiranen and Cheng2021) developed a sex-specific PRS in FinnGen (N = 218,792) and found the female PRS was more strongly associated with hypertension in women than the male PRS in men (Kauko et al., Reference Kauko, Aittokallio, Vaura, Ji, Ebinger, Niiranen and Cheng2021). Similarly, Shetty et al. (Reference Shetty, Pampana, Patel, Li, Yerabolu, Gaonkar, Arora and Arora2023) developed sex-specific systolic BP-PRS in UK Biobank and tested for associations of developing hypertension in 212,669 participants in the All of Us study. They found the genetic risk of systolic BP was more strongly associated with female PRS (Shetty et al., Reference Shetty, Pampana, Patel, Li, Yerabolu, Gaonkar, Arora and Arora2023). Recently, Yang et al. (Reference Yang, Xu, Gupte, Hoffmann, Iribarren, Zhou and Ganesh2024) performed sex-stratified GWAS analyses of BP traits in the UK Biobank resource, identifying 1,346 previously reported and 29 new BP trait-associated loci. Despite equal sample sizes, sex-stratified GWAS of systolic BP, diastolic BP and pulse pressure identified 1.8-fold more loci in the female-only analyses (N = 174,664) than in the male-only analyses (N = 174,664). These sex-specific loci were enriched for hormone-related transcription factors, in particular, oestrogen receptor 1, and sex-specific polygenic association of BP traits was associated with multiple cardiovascular traits (Yang et al., Reference Yang, Xu, Gupte, Hoffmann, Iribarren, Zhou and Ganesh2024).

Integration of PRS for early disease risk prediction is an area of active research, and with an increased number of loci being found for complex diseases, the percentage of the heritability explained is increasing, and better PRS are being developed (Ge et al., Reference Ge, Chen, Ni, Feng and Smoller2019). With increasing datasets being recruited of non-European ancestry, new loci discovery and population-specific PRS are being developed (Fujii et al., Reference Fujii, Hishida, Nakatochi, Okumiyama, Takashima, Tsuboi, Suzuki, Ikezaki, Shimanoe, Kato, Tamura, Ito, Michihata, Tanoue, Suzuki, Kuriki, Kadota, Watanabe, Momozawa, Wakai and Matsuo2024). Genomics PLC have sought to integrate PRS to re-engineer prevention strategies in healthcare for commercial exploitation (Genomics PLC). In their trial, Fuat et al., (Reference Fuat, Adlen, Monane, Coll, Groves, Little, Wild, Kamali, Soni, Haining, Riding, Riveros-Mckay, Peneva, Lachapelle, Giner-Delgado, Weale, Plagnol, Harrison and Donnelly2024) enrolled 832 participants across 12 UK primary care practices. They observed that the integration of genetic data to a conventional risk algorithm (QRISK2) for cardiovascular disease was accepted by healthcare professionals and participants in primary care with planned changes in prevention strategies (Fuat et al., Reference Fuat, Adlen, Monane, Coll, Groves, Little, Wild, Kamali, Soni, Haining, Riding, Riveros-Mckay, Peneva, Lachapelle, Giner-Delgado, Weale, Plagnol, Harrison and Donnelly2024). These risk prediction tools are a funnel for personalised medicine with potential for population-level risk stratification. However, currently, it is unclear how this genetic information is best integrated into guideline-recommended risk prediction tools. One of the main weaknesses of PRS is that they report genetic risk relative to a given population, thus their contribution is only meaningful in the context of other risk factors, limiting their clinical applicability (Ding et al., Reference Ding, Hou, Burch, Lapinska, Privé, Vilhjálmsson, Sankararaman and Pasaniuc2021; Abramowitz et al., Reference Abramowitz, Boulier, Keat, Cardone, Shivakumar, DePaolo, Judy, Bermudez, Mimouni, Neylan, Kim, Rader, Ritchie, Voight, Pasaniuc, Levin, Damrauer, BioBank, Rader, Ritchie, Weaver, Naseer, Sirugo, Poindexter, Ko, Nerz, Livingstone, Vadivieso, DerOhannessian, Tran, Stephanowski, Santos, Haubein, Dunn, Verma, Kripke, Risman, Judy, Wollack, Verma, Damrauer, Bradford, Dudek and Drivas2024).

GWAS provides candidate genes, disease mechanisms and PRS for assessing relationships between BP and other traits. The PRS however do not provide information on whether there are causal relationships. Mendelian randomisation (MR) is being widely applied to infer causality using genetic data, with power equivalent to that of a randomized controlled trial, overcoming traditional bias attributed to confounders and reverse causation (Burgess et al., Reference Burgess, Butterworth, Malarstig and Thompson2012). There have been several applications of BP in an MR framework (Nazarzadeh et al., Reference Nazarzadeh, Pinho-Gomes, Byrne, Canoy, Raimondi, Solares, Otto and Rahimi2019; Tang et al., Reference Tang, Ma, Lei, Ding, Yang and He2023). In an MR study by Clarke et al., (Reference Clarke, Wright, Walters, Gan, Guo, Millwood, Yang, Chen, Lewington, Lv, Yu, Avery, Lin, Wang, Peto, Collins, Li, Bennett, Parish and Chen2023), higher levels of genetically predicted systolic BP were associated with higher risks of major cardiovascular disease in the range of 120 to 170 mmHg of participants in the China Kadoorie Biobank (Clarke et al., Reference Clarke, Wright, Walters, Gan, Guo, Millwood, Yang, Chen, Lewington, Lv, Yu, Avery, Lin, Wang, Peto, Collins, Li, Bennett, Parish and Chen2023). The associations of lower genetically-predicted systolic BP with lower risks of cardiovascular outcomes down to 120 mmHg challenge the conventional strategy of restricting the initiation of BP-lowering medication to people with systolic BP ≥140 mmHg. These findings provide support for lowering systolic BP for a wider range of the population down to 120 mmHg.

From omics to AI for gene identification

Advances in multi-omics technologies have provided new insights into the pathophysiology of hypertension. The omics approaches target different molecular levels, including the genome, transcriptome, proteome, metabolome and microbiome, providing a comprehensive assessment of the processes by which DNA is transcribed into RNA that is translated into proteins that regulate downstream metabolism. These novel datasets can provide valuable insights into the mechanisms of hypertension, allowing for a better understanding of its pathogenesis and aiding the clinical needs of early diagnosis and monitoring of the treatment response. Several computational approaches have been used to prioritise candidate genes leveraging multi-omic datasets, with most groups using the GWAS results from the 2018 Evangelou et al., study (Evangelou et al., Reference Evangelou, Warren, Mosen-Ansorena, Mifsud, Pazoki, Gao, Ntritsos, Dimou, Cabrera, Karaman, Ng, Evangelou, Witkowska, Tzanis, Hellwege, Giri, Velez Edwards, Sun, Cho, Gaziano, Wilson, Tsao, Kovesdy, Esko, Mägi, Milani, Almgren, Boutin, Debette, Ding, Giulianini, Holliday, Jackson, Li-Gao, Lin, Luan, Mangino, Oldmeadow, Prins, Qian, Sargurupremraj, Shah, Surendran, Thériault, Verweij, Willems, Zhao, Amouyel, Connell, de Mutsert, Doney, Farrall, Menni, Morris, Noordam, Paré, Poulter, Shields, Stanton, Thom, Abecasis, Amin, Arking, Ayers, Barbieri, Batini, Bis, Blake, Bochud, Boehnke, Boerwinkle, Boomsma, Bottinger, Braund, Brumat, Campbell, Campbell, Chakravarti, Chambers, Chauhan, Ciullo, Cocca, Collins, Cordell, Davies, de Borst, de Geus, Deary, Deelen, Del Greco, Demirkale, Dörr, Ehret, Elosua, Enroth, Erzurumluoglu, Ferreira, Frånberg, Franco, Gandin, Gasparini, Giedraitis, Gieger, Girotto, Goel, Gow, Gudnason, Guo, Gyllensten, Hamsten, Harris, Harris, Hartman, Havulinna, Hicks, Hofer, Hofman, Hottenga, Huffman, Hwang, Ingelsson, James, Jansen, Jarvelin, Joehanes, Johansson, Johnson, Joshi, Jousilahti, Jukema, Jula, Kähönen, Kathiresan, Keavney, Khaw, Knekt, Knight, Kolcic, Kooner, Koskinen, Kristiansson, Kutalik, Laan, Larson, Launer, Lehne, Lehtimäki, Liewald, Lin, Lind, Lindgren, Liu, Loos, Lopez, Lu, Lyytikäinen, Mahajan, Mamasoula, Marrugat, Marten, Milaneschi, Morgan, Morris, Morrison, Munson, Nalls, Nandakumar, Nelson, Niiranen, Nolte, Nutile, Oldehinkel, Oostra, O’Reilly, Org, Padmanabhan, Palmas, Palotie, Pattie, Penninx, Perola, Peters, Polasek, Pramstaller, Nguyen, Raitakari, Ren, Rettig, Rice, Ridker, Ried, Riese, Ripatti, Robino, Rose, Rotter, Rudan, Ruggiero, Saba, Sala, Salomaa, Samani, Sarin, Schmidt, Schmidt, Shrine, Siscovick, Smith, Snieder, Sõber, Sorice, Starr, Stott, Strachan, Strawbridge, Sundström, Swertz, Taylor, Teumer, Tobin, Tomaszewski, Toniolo, Traglia, Trompet, Tuomilehto, Tzourio, Uitterlinden, Vaez, van der Most, van Duijn, Vergnaud, Verwoert, Vitart, Völker, Vollenweider, Vuckovic, Watkins, Wild, Willemsen, Wilson, Wright, Yao, Zemunik, Zhang, Attia, Butterworth, Chasman, Conen, Cucca, Danesh, Hayward, Howson, Laakso, Lakatta, Langenberg, Melander, Mook-Kanamori, Palmer, Risch, Scott, Scott, Sever, Spector, van der Harst, Wareham, Zeggini, Levy, Munroe, Newton-Cheh, Brown, Metspalu, Hung, O’Donnell, Edwards, Psaty, Tzoulaki, Barnes, Wain, Elliott and Caulfield2018). For example, Eales et al. (Reference Eales, Jiang, Xu, Saluja, Akbarov, Cano-Gamez, McNulty, Finan, Guo, Wystrychowski, Szulinska, Thomas, Pramanik, Chopade, Prestes, Wise, Evangelou, Salehi, Shakanti, Ekholm, Denniff, Nazgiewicz, Eichinger, Godfrey, Antczak, Glyda, Król, Eyre, Brown, Berzuini, Bowes, Caulfield, Zukowska-Szczechowska, Zywiec, Bogdanski, Kretzler, Woolf, Talavera, Keavney, Maffia, Guzik, O’Keefe, Trynka, Samani, Hingorani, Sampson, Morris, Charchar and Tomaszewski2021) integrated genotype, gene expression, alternative splicing and DNA methylation profiles of up to 430 human kidneys to characterise the effects of BP SNPs from GWAS on renal transcriptome and epigenome (Eales et al., Reference Eales, Jiang, Xu, Saluja, Akbarov, Cano-Gamez, McNulty, Finan, Guo, Wystrychowski, Szulinska, Thomas, Pramanik, Chopade, Prestes, Wise, Evangelou, Salehi, Shakanti, Ekholm, Denniff, Nazgiewicz, Eichinger, Godfrey, Antczak, Glyda, Król, Eyre, Brown, Berzuini, Bowes, Caulfield, Zukowska-Szczechowska, Zywiec, Bogdanski, Kretzler, Woolf, Talavera, Keavney, Maffia, Guzik, O’Keefe, Trynka, Samani, Hingorani, Sampson, Morris, Charchar and Tomaszewski2021). Sheng et al. created maps of expression quantitative trait loci (eQTLs) for 659 kidney samples and identified cell-type eQTLs, and integrated GWAS results with single-cell RNA sequencing (scRNA-seq) and a single-nucleus assay for transposase-accessible chromatin with high-throughput sequencing, a method for identifying regulatory elements in specific cell types. Their study indicated 200 genes for kidney function and hypertension and highlighted endothelial cells and distal tubules as being important for BP (Sheng et al., Reference Sheng, Guan, Ma, Wu, Liu, Qiu, Vitale, Miao, Seasock, Palmer, Shin, Duffin, Pullen, Edwards, Hellwege, Hung, Li, Voight, Coffman, Brown and Susztak2021). More recently, Ganji-Arjenaki et al. (Reference Ganji-Arjenaki, Kamali, Evangelou, Warren, Gao, Ntritsos, Dimou, Esko, Mägi, Milani, Almgren, Boutin, Debette, Ding, Giulianini, Holliday, Jackson, Li -Gao, Lin, Luan, Mangino, Oldmeadow, Prins, Qian, Sargurupremraj, Shah, Surendran, Thériault, Verweij, Willems, Zhao, Amouyel, Connell, Mutsert, ASF, Farrall, Menni, Morris, Noordam, Paré, Poulter, Shields, Stanton, Thom, Abecasis, Amin, Arking, Ayers, Barbieri, Batini, Bis, Blake, Bochud, Boehnke, Boerwinkle, Boomsma, Bottinger, Braund, Brumat, Campbell, Campbell, Chakravarti, Chambers, Chauhan, Ciullo, Cocca, Collins, Cordell, Davies, de Borst, de Geus, Deary, Deelen, FDG, Demirkale, Dörr, Ehret, Elosua, Enroth, Erzurumluoglu, Ferreira, Frånberg, Franco, Gandin, Gasparini, Giedraitis, Gieger, Girotto, Goel, Gow, Gudnason, Guo, Gyllensten, Hamsten, Harris, Harris, Hartman, Havulinna, Hicks, Hofer, Hofman, Hottenga, Huffman, Hwang, Ingelsson, James, Jansen, Jarvelin, Joehanes, Johansson, Johnson, Joshi, Jousilahti, Jukema, Jula, Kähönen, Kathiresan, Keavney, Khaw, Knekt, Knight, Kolcic, Kooner, Koskinen, Kristiansson, Kutalik, Laan, Larson, Launer, Lehne, Lehtimäki, DCM, Lin, Lind, Lindgren, Liu, RJF, Lopez, Lu, Lyytikäinen, Mahajan, Mamasoula, Marrugat, Marten, Milaneschi, Morgan, Morris, Morrison, Munson, Nalls, Nandakumar, Nelson, Niiranen, Nolte, Nutile, Oldehinkel, Oostra, O’Reilly, Org, Padmanabhan, Palmas, Palotie, Pattie, Penninx, Perola, Peters, Polasek, Pramstaller, Nguyen, Raitakari, Rettig, Rice, Ridker, Ried, Riese, Ripatti, Robino, Rose, Rotter, Rudan, Ruggiero, Saba, Sala, Salomaa, Samani, Sarin, Schmidt, Schmidt, Shrine, Siscovick, Smith, Snieder, Sõber, Sorice, Starr, Stott, Strachan, Strawbridge, Sundström, Swertz, Taylor, Teumer, Tobin, Tomaszewski, Toniolo, Traglia, Trompet, Tuomilehto, Tzourio, Uitterlinden, Vaez, van der Most, van Duijn, Verwoert, Vitart, Völker, Vollenweider, Vuckovic, Watkins, Wild, Willemsen, Wilson, Wright, Yao, Zemunik, Zhang, Attia, Butterworth, Chasman, Conen, Cucca, Danesh, Hayward, Howson, Laakso, Lakatta, Langenberg, Melander, Mook-Kanamori, Palmer, Risch, Scott, Scott, Sever, Spector, van der Harst, Wareham, Zeggini, Levy, Munroe, Newton-Cheh, Brown, Metspalu, Psaty, Wain, Elliott, Caulfield, Sardari, de Borst, Snieder and Vaez2024) leveraged the largest GWAS of BP traits with scRNA-seq from 14 mature human kidneys and prioritised myofibroblasts and endothelial cells among the 33 annotated cell types involved in BP regulation (Ganji-Arjenaki et al., Reference Ganji-Arjenaki, Kamali, Evangelou, Warren, Gao, Ntritsos, Dimou, Esko, Mägi, Milani, Almgren, Boutin, Debette, Ding, Giulianini, Holliday, Jackson, Li -Gao, Lin, Luan, Mangino, Oldmeadow, Prins, Qian, Sargurupremraj, Shah, Surendran, Thériault, Verweij, Willems, Zhao, Amouyel, Connell, Mutsert, ASF, Farrall, Menni, Morris, Noordam, Paré, Poulter, Shields, Stanton, Thom, Abecasis, Amin, Arking, Ayers, Barbieri, Batini, Bis, Blake, Bochud, Boehnke, Boerwinkle, Boomsma, Bottinger, Braund, Brumat, Campbell, Campbell, Chakravarti, Chambers, Chauhan, Ciullo, Cocca, Collins, Cordell, Davies, de Borst, de Geus, Deary, Deelen, FDG, Demirkale, Dörr, Ehret, Elosua, Enroth, Erzurumluoglu, Ferreira, Frånberg, Franco, Gandin, Gasparini, Giedraitis, Gieger, Girotto, Goel, Gow, Gudnason, Guo, Gyllensten, Hamsten, Harris, Harris, Hartman, Havulinna, Hicks, Hofer, Hofman, Hottenga, Huffman, Hwang, Ingelsson, James, Jansen, Jarvelin, Joehanes, Johansson, Johnson, Joshi, Jousilahti, Jukema, Jula, Kähönen, Kathiresan, Keavney, Khaw, Knekt, Knight, Kolcic, Kooner, Koskinen, Kristiansson, Kutalik, Laan, Larson, Launer, Lehne, Lehtimäki, DCM, Lin, Lind, Lindgren, Liu, RJF, Lopez, Lu, Lyytikäinen, Mahajan, Mamasoula, Marrugat, Marten, Milaneschi, Morgan, Morris, Morrison, Munson, Nalls, Nandakumar, Nelson, Niiranen, Nolte, Nutile, Oldehinkel, Oostra, O’Reilly, Org, Padmanabhan, Palmas, Palotie, Pattie, Penninx, Perola, Peters, Polasek, Pramstaller, Nguyen, Raitakari, Rettig, Rice, Ridker, Ried, Riese, Ripatti, Robino, Rose, Rotter, Rudan, Ruggiero, Saba, Sala, Salomaa, Samani, Sarin, Schmidt, Schmidt, Shrine, Siscovick, Smith, Snieder, Sõber, Sorice, Starr, Stott, Strachan, Strawbridge, Sundström, Swertz, Taylor, Teumer, Tobin, Tomaszewski, Toniolo, Traglia, Trompet, Tuomilehto, Tzourio, Uitterlinden, Vaez, van der Most, van Duijn, Verwoert, Vitart, Völker, Vollenweider, Vuckovic, Watkins, Wild, Willemsen, Wilson, Wright, Yao, Zemunik, Zhang, Attia, Butterworth, Chasman, Conen, Cucca, Danesh, Hayward, Howson, Laakso, Lakatta, Langenberg, Melander, Mook-Kanamori, Palmer, Risch, Scott, Scott, Sever, Spector, van der Harst, Wareham, Zeggini, Levy, Munroe, Newton-Cheh, Brown, Metspalu, Psaty, Wain, Elliott, Caulfield, Sardari, de Borst, Snieder and Vaez2024). Other efforts include van Duijvenboden et al. (2022) who conducted annotation-informed fine-mapping incorporating tissue-specific chromatin segmentation and colocalisation using transcriptomics and additional gene prioritisation utilising both scRNA-seq and proteomics datasets to identify causal variants and candidate effector genes for BP traits (Duijvenboden et al., Reference Duijvenboden, Ramírez, Young, Olczak, Ahmed, Alhammadi, Bell, Morris and Munroe2023). Kamali et al. (Reference Kamali, Keaton, Haghjooy Javanmard, Edwards, Snieder and Vaez2022) also developed a pipeline to leverage epigenomic and transcriptomic datasets and identified 1,880 prioritised genes for BP and downstream from this, the genes were assessed for druggability and tested for functional enrichment (Kamali et al., Reference Kamali, Keaton, Haghjooy Javanmard, Edwards, Snieder and Vaez2022). The different approaches have highlighted many BP genes for follow-up studies.

Machine learning (ML) approaches have also been used to prioritise candidate genes discovered through GWAS. ML algorithms build mathematical models that are learnt from training data to make predictions. ML in GWAS has been used to boost statistical power of GWAS, refine PRS produced from GWAS and prioritise candidate genes in post-GWAS-analysis (Li et al., Reference Li, Zhang and Wang2017; Nicholls et al., Reference Nicholls, John, Watson, Munroe, Barnes and Cabrera2020). Additionally, multi-parallel functional experiments have also been applied to gain insights into causality and related molecular mechanisms of genetic variants derived from GWAS. Oliveros et al. (Reference Oliveros, Delfosse, Lato, Kiriakopulos, Mokhtaridoost, Said, McMurray, Browning, Mattioli, Meng, Ellis, Mital, Melé and Maass2023) functionally characterised 4,608 genetic variants in linkage with SNPs at 135 BP loci in vascular smooth muscle cells and cardiomyocytes using parallel reporter assays. This approach demonstrated the potential to identify functionally relevant variants for a better understanding of BP genetic architecture (Oliveros et al., Reference Oliveros, Delfosse, Lato, Kiriakopulos, Mokhtaridoost, Said, McMurray, Browning, Mattioli, Meng, Ellis, Mital, Melé and Maass2023).

Pharmacogenomics, therapeutics and druggability

Genetics research has promoted the discipline of pharmacogenomics exploring the influence of genomic variation on an individual’s response to BP therapy (Roden et al., Reference Roden, McLeod, Relling, Williams, Mensah, Peterson and Driest2019). This is particularly necessary for hypertension as there is a large proportion of individuals who do not respond to current treatments. The development of new drug treatments is therefore one key driver of BP genomics and exploring potential for drug repurposing. Evangelou et al. (Reference Evangelou, Warren, Mosen-Ansorena, Mifsud, Pazoki, Gao, Ntritsos, Dimou, Cabrera, Karaman, Ng, Evangelou, Witkowska, Tzanis, Hellwege, Giri, Velez Edwards, Sun, Cho, Gaziano, Wilson, Tsao, Kovesdy, Esko, Mägi, Milani, Almgren, Boutin, Debette, Ding, Giulianini, Holliday, Jackson, Li-Gao, Lin, Luan, Mangino, Oldmeadow, Prins, Qian, Sargurupremraj, Shah, Surendran, Thériault, Verweij, Willems, Zhao, Amouyel, Connell, de Mutsert, Doney, Farrall, Menni, Morris, Noordam, Paré, Poulter, Shields, Stanton, Thom, Abecasis, Amin, Arking, Ayers, Barbieri, Batini, Bis, Blake, Bochud, Boehnke, Boerwinkle, Boomsma, Bottinger, Braund, Brumat, Campbell, Campbell, Chakravarti, Chambers, Chauhan, Ciullo, Cocca, Collins, Cordell, Davies, de Borst, de Geus, Deary, Deelen, Del Greco, Demirkale, Dörr, Ehret, Elosua, Enroth, Erzurumluoglu, Ferreira, Frånberg, Franco, Gandin, Gasparini, Giedraitis, Gieger, Girotto, Goel, Gow, Gudnason, Guo, Gyllensten, Hamsten, Harris, Harris, Hartman, Havulinna, Hicks, Hofer, Hofman, Hottenga, Huffman, Hwang, Ingelsson, James, Jansen, Jarvelin, Joehanes, Johansson, Johnson, Joshi, Jousilahti, Jukema, Jula, Kähönen, Kathiresan, Keavney, Khaw, Knekt, Knight, Kolcic, Kooner, Koskinen, Kristiansson, Kutalik, Laan, Larson, Launer, Lehne, Lehtimäki, Liewald, Lin, Lind, Lindgren, Liu, Loos, Lopez, Lu, Lyytikäinen, Mahajan, Mamasoula, Marrugat, Marten, Milaneschi, Morgan, Morris, Morrison, Munson, Nalls, Nandakumar, Nelson, Niiranen, Nolte, Nutile, Oldehinkel, Oostra, O’Reilly, Org, Padmanabhan, Palmas, Palotie, Pattie, Penninx, Perola, Peters, Polasek, Pramstaller, Nguyen, Raitakari, Ren, Rettig, Rice, Ridker, Ried, Riese, Ripatti, Robino, Rose, Rotter, Rudan, Ruggiero, Saba, Sala, Salomaa, Samani, Sarin, Schmidt, Schmidt, Shrine, Siscovick, Smith, Snieder, Sõber, Sorice, Starr, Stott, Strachan, Strawbridge, Sundström, Swertz, Taylor, Teumer, Tobin, Tomaszewski, Toniolo, Traglia, Trompet, Tuomilehto, Tzourio, Uitterlinden, Vaez, van der Most, van Duijn, Vergnaud, Verwoert, Vitart, Völker, Vollenweider, Vuckovic, Watkins, Wild, Willemsen, Wilson, Wright, Yao, Zemunik, Zhang, Attia, Butterworth, Chasman, Conen, Cucca, Danesh, Hayward, Howson, Laakso, Lakatta, Langenberg, Melander, Mook-Kanamori, Palmer, Risch, Scott, Scott, Sever, Spector, van der Harst, Wareham, Zeggini, Levy, Munroe, Newton-Cheh, Brown, Metspalu, Hung, O’Donnell, Edwards, Psaty, Tzoulaki, Barnes, Wain, Elliott and Caulfield2018) discovered five loci containing genes that are drug targets for several known antihypertensive classes and Surendran et al. (Reference Surendran, Feofanova, Lahrouchi, Ntalla, Karthikeyan, Cook, Chen, Mifsud, Yao, Kraja, Cartwright, Hellwege, Giri, Tragante, Thorleifsson, Liu, Prins, Stewart, Cabrera, Eales, Akbarov, Auer, Bielak, Bis, Braithwaite, Brody, Daw, Warren, Drenos, Nielsen, Faul, Fauman, Fava, Ferreira, Foley, Franceschini, Gao, Giannakopoulou, Giulianini, Gudbjartsson, Guo, Harris, Havulinna, Helgadottir, Huffman, Hwang, Kanoni, Kontto, Larson, Li-Gao, Lindström, Lotta, Lu, Luan, Mahajan, Malerba, NGD, Mei, Menni, Mook-Kanamori, Mosen-Ansorena, Müller-Nurasyid, Paré, Paul, Perola, Poveda, Rauramaa, Richard, Richardson, Sepúlveda, Sim, Smith, Smith, Staley, Stanáková, Sulem, Thériault, Thorsteinsdottir, Trompet, Varga, Velez Edwards, Veronesi, Weiss, Willems, Yao, Young, Yu, Zhang, Zhao, Zhao, Evangelou, Aeschbacher, Asllanaj, Blankenberg, Bonnycastle, Bork-Jensen, Brandslund, Braund, Burgess, Cho, Christensen, Connell, Mutsert, Dominiczak, Dörr, Eiriksdottir, Farmaki, Gaziano, Grarup, Grove, Hallmans, Hansen, Have, Heiss, Jørgensen, Jousilahti, Kajantie, Kamat, Käräjämäki, Karpe, Koistinen, Kovesdy, Kuulasmaa, Laatikainen, Lannfelt, Lee, Lee, Linneberg, Martin, Moitry, Nadkarni, Neville, Palmer, Papanicolaou, Pedersen, Peters, Poulter, Rasheed, Rasmussen, Rayner, Mägi, Renström, Rettig, Rossouw, Schreiner, Sever, Sigurdsson, Skaaby, Sun, Sundstrom, Thorgeirsson, Esko, Trabetti, Tsao, Tuomi, Turner, Tzoulaki, Vaartjes, Vergnaud, Willer, Wilson, Witte, Yonova-Doing, Zhang, Aliya, Almgren, Amouyel, Asselbergs, Barnes, Blakemore, Boehnke, Bots, Bottinger, Buring, Chambers, Chen, Chowdhury, Conen, Correa, Davey Smith, Boer, Deary, Dedoussis, Deloukas, Di Angelantonio, Elliott, Felix, Ferrières, Ford, Fornage, Franks, Franks, Frossard, Gambaro, Gaunt, Groop, Gudnason, Harris, Hayward, Hennig, Herzig, Ingelsson, Tuomilehto, Järvelin, Jukema, SLR, Kee, Kooner, Kooperberg, Launer, Lind, Loos, Majumder, Laakso, McCarthy, Melander, Mohlke, Murray, Nordestgaard, Orho-Melander, Packard, Padmanabhan, Palmas, Polasek, Porteous, Prentice, Province, Relton, Rice, Ridker, Rolandsson, Rosendaal, Rotter, Rudan, Salomaa, Samani, Sattar, Sheu, Smith, Soranzo, Spector, Starr, Sebert, Taylor, Lakka, Timpson, Tobin, van der Harst, van der Meer, Ramachandran, Verweij, Virtamo, Völker, Weir, Zeggini, Charchar, Wareham, Langenberg, Tomaszewski, Butterworth, Caulfield, Danesh, Edwards, Holm, Hung, Lindgren, Liu, Manning, Morris, Morrison, O’Donnell, Psaty, Saleheen, Stefansson, Boerwinkle, Chasman, Levy, Newton-Cheh, Munroe and Howson2020) reported 23 genes as potential drug targets (Evangelou et al., Reference Evangelou, Warren, Mosen-Ansorena, Mifsud, Pazoki, Gao, Ntritsos, Dimou, Cabrera, Karaman, Ng, Evangelou, Witkowska, Tzanis, Hellwege, Giri, Velez Edwards, Sun, Cho, Gaziano, Wilson, Tsao, Kovesdy, Esko, Mägi, Milani, Almgren, Boutin, Debette, Ding, Giulianini, Holliday, Jackson, Li-Gao, Lin, Luan, Mangino, Oldmeadow, Prins, Qian, Sargurupremraj, Shah, Surendran, Thériault, Verweij, Willems, Zhao, Amouyel, Connell, de Mutsert, Doney, Farrall, Menni, Morris, Noordam, Paré, Poulter, Shields, Stanton, Thom, Abecasis, Amin, Arking, Ayers, Barbieri, Batini, Bis, Blake, Bochud, Boehnke, Boerwinkle, Boomsma, Bottinger, Braund, Brumat, Campbell, Campbell, Chakravarti, Chambers, Chauhan, Ciullo, Cocca, Collins, Cordell, Davies, de Borst, de Geus, Deary, Deelen, Del Greco, Demirkale, Dörr, Ehret, Elosua, Enroth, Erzurumluoglu, Ferreira, Frånberg, Franco, Gandin, Gasparini, Giedraitis, Gieger, Girotto, Goel, Gow, Gudnason, Guo, Gyllensten, Hamsten, Harris, Harris, Hartman, Havulinna, Hicks, Hofer, Hofman, Hottenga, Huffman, Hwang, Ingelsson, James, Jansen, Jarvelin, Joehanes, Johansson, Johnson, Joshi, Jousilahti, Jukema, Jula, Kähönen, Kathiresan, Keavney, Khaw, Knekt, Knight, Kolcic, Kooner, Koskinen, Kristiansson, Kutalik, Laan, Larson, Launer, Lehne, Lehtimäki, Liewald, Lin, Lind, Lindgren, Liu, Loos, Lopez, Lu, Lyytikäinen, Mahajan, Mamasoula, Marrugat, Marten, Milaneschi, Morgan, Morris, Morrison, Munson, Nalls, Nandakumar, Nelson, Niiranen, Nolte, Nutile, Oldehinkel, Oostra, O’Reilly, Org, Padmanabhan, Palmas, Palotie, Pattie, Penninx, Perola, Peters, Polasek, Pramstaller, Nguyen, Raitakari, Ren, Rettig, Rice, Ridker, Ried, Riese, Ripatti, Robino, Rose, Rotter, Rudan, Ruggiero, Saba, Sala, Salomaa, Samani, Sarin, Schmidt, Schmidt, Shrine, Siscovick, Smith, Snieder, Sõber, Sorice, Starr, Stott, Strachan, Strawbridge, Sundström, Swertz, Taylor, Teumer, Tobin, Tomaszewski, Toniolo, Traglia, Trompet, Tuomilehto, Tzourio, Uitterlinden, Vaez, van der Most, van Duijn, Vergnaud, Verwoert, Vitart, Völker, Vollenweider, Vuckovic, Watkins, Wild, Willemsen, Wilson, Wright, Yao, Zemunik, Zhang, Attia, Butterworth, Chasman, Conen, Cucca, Danesh, Hayward, Howson, Laakso, Lakatta, Langenberg, Melander, Mook-Kanamori, Palmer, Risch, Scott, Scott, Sever, Spector, van der Harst, Wareham, Zeggini, Levy, Munroe, Newton-Cheh, Brown, Metspalu, Hung, O’Donnell, Edwards, Psaty, Tzoulaki, Barnes, Wain, Elliott and Caulfield2018; Surendran et al., Reference Surendran, Feofanova, Lahrouchi, Ntalla, Karthikeyan, Cook, Chen, Mifsud, Yao, Kraja, Cartwright, Hellwege, Giri, Tragante, Thorleifsson, Liu, Prins, Stewart, Cabrera, Eales, Akbarov, Auer, Bielak, Bis, Braithwaite, Brody, Daw, Warren, Drenos, Nielsen, Faul, Fauman, Fava, Ferreira, Foley, Franceschini, Gao, Giannakopoulou, Giulianini, Gudbjartsson, Guo, Harris, Havulinna, Helgadottir, Huffman, Hwang, Kanoni, Kontto, Larson, Li-Gao, Lindström, Lotta, Lu, Luan, Mahajan, Malerba, NGD, Mei, Menni, Mook-Kanamori, Mosen-Ansorena, Müller-Nurasyid, Paré, Paul, Perola, Poveda, Rauramaa, Richard, Richardson, Sepúlveda, Sim, Smith, Smith, Staley, Stanáková, Sulem, Thériault, Thorsteinsdottir, Trompet, Varga, Velez Edwards, Veronesi, Weiss, Willems, Yao, Young, Yu, Zhang, Zhao, Zhao, Evangelou, Aeschbacher, Asllanaj, Blankenberg, Bonnycastle, Bork-Jensen, Brandslund, Braund, Burgess, Cho, Christensen, Connell, Mutsert, Dominiczak, Dörr, Eiriksdottir, Farmaki, Gaziano, Grarup, Grove, Hallmans, Hansen, Have, Heiss, Jørgensen, Jousilahti, Kajantie, Kamat, Käräjämäki, Karpe, Koistinen, Kovesdy, Kuulasmaa, Laatikainen, Lannfelt, Lee, Lee, Linneberg, Martin, Moitry, Nadkarni, Neville, Palmer, Papanicolaou, Pedersen, Peters, Poulter, Rasheed, Rasmussen, Rayner, Mägi, Renström, Rettig, Rossouw, Schreiner, Sever, Sigurdsson, Skaaby, Sun, Sundstrom, Thorgeirsson, Esko, Trabetti, Tsao, Tuomi, Turner, Tzoulaki, Vaartjes, Vergnaud, Willer, Wilson, Witte, Yonova-Doing, Zhang, Aliya, Almgren, Amouyel, Asselbergs, Barnes, Blakemore, Boehnke, Bots, Bottinger, Buring, Chambers, Chen, Chowdhury, Conen, Correa, Davey Smith, Boer, Deary, Dedoussis, Deloukas, Di Angelantonio, Elliott, Felix, Ferrières, Ford, Fornage, Franks, Franks, Frossard, Gambaro, Gaunt, Groop, Gudnason, Harris, Hayward, Hennig, Herzig, Ingelsson, Tuomilehto, Järvelin, Jukema, SLR, Kee, Kooner, Kooperberg, Launer, Lind, Loos, Majumder, Laakso, McCarthy, Melander, Mohlke, Murray, Nordestgaard, Orho-Melander, Packard, Padmanabhan, Palmas, Polasek, Porteous, Prentice, Province, Relton, Rice, Ridker, Rolandsson, Rosendaal, Rotter, Rudan, Salomaa, Samani, Sattar, Sheu, Smith, Soranzo, Spector, Starr, Sebert, Taylor, Lakka, Timpson, Tobin, van der Harst, van der Meer, Ramachandran, Verweij, Virtamo, Völker, Weir, Zeggini, Charchar, Wareham, Langenberg, Tomaszewski, Butterworth, Caulfield, Danesh, Edwards, Holm, Hung, Lindgren, Liu, Manning, Morris, Morrison, O’Donnell, Psaty, Saleheen, Stefansson, Boerwinkle, Chasman, Levy, Newton-Cheh, Munroe and Howson2020). Similarly, Keaton et al. (Reference Keaton, Kamali, Xie, Vaez, Williams, Goleva, Ani, Evangelou, Hellwege, Yengo, Young, Traylor, Giri, Zheng, Zeng, Chasman, Morris, Caulfield, Hwang, Kooner, Conen, Attia, Morrison, Loos, Kristiansson, Schmidt, Hicks, Pramstaller, Nelson, Samani, Risch, Gyllensten, Melander, Riese, Wilson, Campbell, Rich, Psaty, Lu, Rotter, Guo, Rice, Vollenweider, Sundström, Langenberg, Tobin, Giedraitis, Luan, Tuomilehto, Kutalik, Ripatti, Salomaa, Girotto, Trompet, Jukema, van der Harst, Ridker, Giulianini, Vitart, Goel, Watkins, Harris, Deary, van der Most, Oldehinkel, Keavney, Hayward, Campbell, Boehnke, Scott, Boutin, Mamasoula, Järvelin, Peters, Gieger, Lakatta, Cucca, Hui, Knekt, Enroth, De Borst, Polašek, Concas, Catamo, Cocca, Li-Gao, Hofer, Schmidt, Spedicati, Waldenberger, Strachan, Laan, Teumer, Dörr, Gudnason, Cook, Ruggiero, Kolcic, Boerwinkle, Traglia, Lehtimäki, Raitakari, Johnson, Newton-Cheh, Brown, Dominiczak, Sever, Poulter, Chambers, Elosua, Siscovick, Esko, Metspalu, Strawbridge, Laakso, Hamsten, Hottenga, de Geus, Morris, Palmer, Nolte, Milaneschi, Marten, Wright, Zeggini, Howson, O’Donnell, Spector, Nalls, Simonsick, Liu, van Duijn, Butterworth, Danesh, Menni, Wareham, Khaw, Sun, Wilson, Cho, Visscher, Denny, Levy, Edwards, Munroe, Snieder and Warren2024) used transcriptome-wide association studies (TWAS) to identify 38 genes, including an established drug target for BP medications (ADRA1A) and five genes targeted by other approved drugs (Keaton et al., Reference Keaton, Kamali, Xie, Vaez, Williams, Goleva, Ani, Evangelou, Hellwege, Yengo, Young, Traylor, Giri, Zheng, Zeng, Chasman, Morris, Caulfield, Hwang, Kooner, Conen, Attia, Morrison, Loos, Kristiansson, Schmidt, Hicks, Pramstaller, Nelson, Samani, Risch, Gyllensten, Melander, Riese, Wilson, Campbell, Rich, Psaty, Lu, Rotter, Guo, Rice, Vollenweider, Sundström, Langenberg, Tobin, Giedraitis, Luan, Tuomilehto, Kutalik, Ripatti, Salomaa, Girotto, Trompet, Jukema, van der Harst, Ridker, Giulianini, Vitart, Goel, Watkins, Harris, Deary, van der Most, Oldehinkel, Keavney, Hayward, Campbell, Boehnke, Scott, Boutin, Mamasoula, Järvelin, Peters, Gieger, Lakatta, Cucca, Hui, Knekt, Enroth, De Borst, Polašek, Concas, Catamo, Cocca, Li-Gao, Hofer, Schmidt, Spedicati, Waldenberger, Strachan, Laan, Teumer, Dörr, Gudnason, Cook, Ruggiero, Kolcic, Boerwinkle, Traglia, Lehtimäki, Raitakari, Johnson, Newton-Cheh, Brown, Dominiczak, Sever, Poulter, Chambers, Elosua, Siscovick, Esko, Metspalu, Strawbridge, Laakso, Hamsten, Hottenga, de Geus, Morris, Palmer, Nolte, Milaneschi, Marten, Wright, Zeggini, Howson, O’Donnell, Spector, Nalls, Simonsick, Liu, van Duijn, Butterworth, Danesh, Menni, Wareham, Khaw, Sun, Wilson, Cho, Visscher, Denny, Levy, Edwards, Munroe, Snieder and Warren2024). However, as previously described, GWAS and downstream bioinformatics analyses do not pinpoint the causal gene, they only provide candidates for further exploration. Functional cellular studies and the development of animal models remain important tools once a gene is identified as having strong potential as a druggable target.

Drug-gene interaction databases have enabled a comprehensive catalogue of druggable genes (Gaulton et al., Reference Gaulton, Hersey, Nowotka, Bento, Chambers, Mendez, Mutowo, Atkinson, Bellis, Cibrián-Uhalte, Davies, Dedman, Karlsson, Magariños, Overington, Papadatos, Smit and Leach2017; Cotto et al., Reference Cotto, Wagner, Feng, Kiwala, Coffman, Spies, Wollam, Spies, Griffith and Griffith2018). These open-access online resources have allowed a search by gene of drug-gene interactions or potential for druggability. Canagliflozin, an SGLT2 inhibitor, is an approved and widely used medication in the treatment of type 2 diabetes targeting the gene SLC5A1. However, it was noted that it reduced systolic BP in individuals with type 2 diabetes and chronic kidney disease, providing end-organ protection for this cohort of patients who experience a high burden of hypertension (Ye et al., Reference Ye, Jardine, Oshima, Hockham, Heerspink, Agarwal, Bakris, Schutte, Arnott, Chang, Górriz, Cannon, Charytan, Zeeuw, Levin, Mahaffey, Neal, Pollock, Wheeler, Tanna, Cheng, Perkovic and Neuen2021). Although it is currently not licenced for BP treatment, it highlights the repurposing potential of existing drugs.

The most common distinct cause of hypertension is primary hyperaldosteronism, also known as Conn’s syndrome. It has been shown that some patients with treatment-resistant hypertension, defined as uncontrolled, high BP despite being on three or more different antihypertensive drug classes, have increased aldosterone production. Baxdrostat, an aldosterone synthase inhibitor, targets the gene CYP11B2, which encodes aldosterone synthase in the adrenal gland. The CYP11B2 candidate gene was found to be genome-wide significant in BP-GWAS of Japanese individuals by Kanai et al. (Reference Kanai, Akiyama, Takahashi, Matoba, Momozawa, Ikeda, Iwata, Ikegawa, Hirata, Matsuda, Kubo, Okada and Kamatani2018) and also in subsequent European ancestry BP-GWAS (Keaton et al., Reference Keaton, Kamali, Xie, Vaez, Williams, Goleva, Ani, Evangelou, Hellwege, Yengo, Young, Traylor, Giri, Zheng, Zeng, Chasman, Morris, Caulfield, Hwang, Kooner, Conen, Attia, Morrison, Loos, Kristiansson, Schmidt, Hicks, Pramstaller, Nelson, Samani, Risch, Gyllensten, Melander, Riese, Wilson, Campbell, Rich, Psaty, Lu, Rotter, Guo, Rice, Vollenweider, Sundström, Langenberg, Tobin, Giedraitis, Luan, Tuomilehto, Kutalik, Ripatti, Salomaa, Girotto, Trompet, Jukema, van der Harst, Ridker, Giulianini, Vitart, Goel, Watkins, Harris, Deary, van der Most, Oldehinkel, Keavney, Hayward, Campbell, Boehnke, Scott, Boutin, Mamasoula, Järvelin, Peters, Gieger, Lakatta, Cucca, Hui, Knekt, Enroth, De Borst, Polašek, Concas, Catamo, Cocca, Li-Gao, Hofer, Schmidt, Spedicati, Waldenberger, Strachan, Laan, Teumer, Dörr, Gudnason, Cook, Ruggiero, Kolcic, Boerwinkle, Traglia, Lehtimäki, Raitakari, Johnson, Newton-Cheh, Brown, Dominiczak, Sever, Poulter, Chambers, Elosua, Siscovick, Esko, Metspalu, Strawbridge, Laakso, Hamsten, Hottenga, de Geus, Morris, Palmer, Nolte, Milaneschi, Marten, Wright, Zeggini, Howson, O’Donnell, Spector, Nalls, Simonsick, Liu, van Duijn, Butterworth, Danesh, Menni, Wareham, Khaw, Sun, Wilson, Cho, Visscher, Denny, Levy, Edwards, Munroe, Snieder and Warren2024). It is a once daily oral medication currently under study with promising phase 2 clinical trial results, which may expand the possible choices of therapeutic agents for treatment-resistant hypertension (Freeman et al., Reference Freeman, Halvorsen, Marshall, Pater, Isaacsohn, Pearce, Murphy, Alp, Srivastava, Bhatt and Brown2022).

The biological architecture of hypertension is complex, and existing medications target only specific mechanisms in BP regulation, with variable effectiveness across individuals (Thomopoulos et al., Reference Thomopoulos, Parati and Zanchetti2015). The development of gene-editing and RNA-based approaches has inspired new treatment modalities for hypertension. These techniques allow selective and organ-specific modulation of systems involved in BP regulation. Antisense oligonucleotides (ASO) and small interfering RNA (siRNA) have been used to specifically target the hepatic angiotensinogen (AGT) production, with the scope of effectively downregulating the activation of the renin-angiotensin system (Masi et al., Reference Masi, Dalpiaz and Borghi2024). These approaches have the potential to simplify BP treatment regimens with weekly, monthly or even once-only injection of the drugs. Among the various technologies, siRNA and ASO that reduce hepatic AGT production are currently in advanced development, with phase I and II clinical trials showing their safety and effectiveness (Desai et al., Reference Desai, Webb, Taubel, Casey, Cheng, Robbie, Foster, Huang, Rhyee, Sweetser and Bakris2023; Bakris et al., Reference Bakris, Saxena, Gupta, Chalhoub, Lee, Stiglitz, Makarova, Goyal, Guo, Zappe, Desai, Carr, Case, Jaeger, Bruns, Stratman, Kidwell, Cunningham, Piccone, Klein, Acuna, Arora, Clark, Fink, Garcia, George, Gray, Mahaffey, McElheney, Ortiz, Saumya, Wallace, Watts, Zambrana, Denham, Rivera, Avworo, Hedges, Fancher, Abadier, Braud, Cenatiempo, Elwood, Gray, Gray, Griffin, Khan, Kongquee, LaTorraca, Liles, Livingston, Lozano, Lupton, Lyles, Mardini, Moss, Packer, Patel, Russ, Scinicariello, Trull, Warren, Chalhoub, Angel, Dhakhwa, Gabon, Goodwin, Jones, Joyce, Kamble, McFall, Githiiyu, Robinson, Tomlinson, Hadziavdic, Vega, Greenwald, Achan, Ruiz, Buksh, Chauhan, Derisseau, Ferguson, Grabowski, Gumerova, Hernandez, Martin, Patel, Nanda, Perez, Ribeca, Rueda, Guth, Zaki, Green, Mondoc, Buynak, Andree, Brazinsky, Fuller, Green, Ibrahim, Idowu, Lee, Lewis, Luna, McNeal, Owens, Perez, Petty, Smith, Volom, Webb, Williams, Yarosz, Gabra, Andrawis, Aziz, Hanna, Manalo, Helow, Davila, Crisp, Douglas, Everett, Graham, Heard, Mackie, Mayer, Moore, Resurreccion, Sidey, Stephens, Strickland, Camp, Wilder, Witt, Wolfer, Groben, Aeschliman, Audlehelm, Fitz, Greene, Hippen, Johnson, Latcham, Leggett, Link, Lovell, Marlow, Marske, Maxwell, Newsom-Henderson, Oelberg, Ollinger, Risius, Ryther, Waddell, Zellerman, Santofimio, Plantholt, Beatty, Blubaugh, Bootz, Cole, Conners, Coombs, Goddard, Ince, Kiddy, Klimuszka, Kuhn, Lowry, Previll, Rioux, Scott, Shepherd, Urffer, Smiley, Lucksinger, Alvord, Anderson, Block, Chan, Delgado, Frost, Gadbois, Hamlin, Johnson, Juncal, Kelly, Kelly, Kelly, Kerwin, Kuehl, Ostovar, Rackley, Rocha, Sandberg, Sheghewi, Smith, Taucher, Vasquez, Rodriguez, Weisbart, Wescom, Williams, Overcash, Anorve, Asmann, Bovee, Castillo, Chu, Coslet, Davis, Dinan, Dubbula, Esparza, Foster, Garcia, Gonzales, Gonzalez, Kappen, Kornblatt, Lauderdale, Lee, Lindholm, Lindsay, Gomez, Marquez, Meza, Odom, Orel, Paselio, Penziner, Pu, Quillin, Ramirez, Ramirez, Salgado, Shores, Taitingfong, Tande, Tuiletufuga, Tyler, Vega, Zepeda, Anderson, Carhill, Clark, Dean, Devine, DiPeri, Espinal, Hawkins, Irra, Ledezma, Lopez-Wood, Luna, Mansour, Marriott, Perez, Ramsey, Reedy, Spinks, Tanori, Tatelbaum, Vasquez, Vasquez, Vigil, Worthen, Yu, Zuniga, Dennis, Alvarado, Cox, Cullaro, Dunbar, Giorlando, Hannan, Hastings, Hoskins, Krambeck, Laurent, Martinez, Mister, Moss, Muli, Quinn, Reed, Robinson, Saavedra, Simon, Smith, Touchet, Wright, Trevino, DeVries, Allison-Hicks, Light, Rubach, Mai, Osborn, Sheets, Carver, Reichman, Lotfi, Berrios, Vulichi, Naguleswaran, Subhan, Rios, Baron, Khowaja, Nair, Vicente, LLG, USA, Lamichhane, Ekomoda, Nair, Maldonado, Naqvi, Dayani, Patro, Golandaz, Jaka, Rios, Gurung, Gongloor, Coreas, Demir, Celik, McGill, Usdan, Arnold, Heckle, Scatamacchia, Anthony, Marsh, Fair, Stewart, Hamlet, Presley, Granberry, Flowers, Houpt, O’Brien, Evans, Williams, Sinatra, Powell, Flagg, Pipkin, Wheeler, Wells, Gruber, Jones, Toor, Iskiwitz, Boyd, Epps, Cole, Grayson-Mathis, Web, Turner, Reese, Robinson, Goodwin, Parker, Ward, Solomon, Lawson, Pledger, Peterson, Carignan, Oguoma-Richards, Jones, McFarland-Head, Bundeff, Wojtowicz, Holland, Sanders, Simon, Lawrence, Bolton, Lemoine, Harris, Kingsley, Bryant, Clawson, Murphy, Allison, Newman, Preece, Zook, Roznos, Russell, Rodriguez, Roy, Layle, Mehra, Bowen, Johnson, Guillen, Leone, McCormick, Hermann, Sarno, Abellatif, Cleghorn, Elansary, Israel, Leeward, Parra, Harris, Patel, Cawley, Castro, Miroshnikova, Dever, Venereo, Paez, Isla, Martin, Fernandez, Hernandez, Lavoy, Rios, Hernandez, Denenberg, McTier, Reynolds, Thomas, Eubanks, Oliver, Jacobs, Mirkil, Menasche, Yee, Vasquez, Garcia, Elio, Arenas, Buena, Garcia, Cannon, Novotny, Oliver, Cannon, Gatdula, Gillenwater, Rafi, Derian, Ramsey, Culbreth, Fessler, Sorrill, Mauri, Rosen, Acosta, Brauchle, Castaneda, Castells, Castro, MBC, Flerisme, Garcia, Gomez, Hidalgo, Jimenez, Legon, Lella, Lemus, Rico-Aramillo, Rodriguez, Rodriguez, Salazar, Santana, Tercero, Martin, Acevedo, Anderson, Baig, Baker, Basrai, Cabrera, Caroll, Chatmon, Cheema, Chowdhry, Cordero, Deandres, Deshpande, Dileep, Dolfi, Eng, Fountain, Gali, Hernandez, Iskandar, Khan, Knight, Kundapati, Lobo, Lokhandwala, Lokhandwala, Mahmood, Marediya, Martin, Melendez, Moosa, Motiwala, Muhebb, Murray, Rehman, Rivera, Shah, Solorzano, Somani, Soneira, Starr, Thompson, Torres, Tran, Ullah, Valentine, Vaquerano, Yake, Yousif, Zaldivar, Byars, Brown, Gaskin, Clayton, Davis, Dillon, Driggers, Ferrell, Green, Hunt, Jeffers, Kirkland, Lopez, Mayfield, Mercer, Parsons, Patton, Puck, Restrepo, Sheppard, Teachman, Velez, Whitehead, Jain, Asanji, Garrett, Gill, Hester, Jain, Mitchell, Becker, McKeown-Biagas, Fragoso, Holloway, Kowalski, Barr, Colville, Yang, Glasper, Phuah, Miller, Schmidt, Jafri, Sureshbabu, Mir, Sharma, Asaad, Hasnain, Ngo, Hari, Oheri, Patwary, Mohamed, Hernandez, Gioia, Dalal, Ngban, Smith, Leal, Assaf, Martinez, Khan, Castello, Potapenko, Wahaj, Elmachtoub, Akhtar, Siddiqui, Jamal, Rizvi, Sainz, Lucky-Dania, Villarreal, Fernandez, Hampton, Bellamy, Brown, Doran, Manzanares, Palmer, Sanders, Bokhari, Fenuyi, Greer, Jang, Juanillo, Reese, Narla, Okoko, Woodham, Eaton, Khawaja, Mushtag, Bhutto, Solorsano, Ahmed, Shamim, Gonzalez, Ardoin, Moore, Nuncio, Ray, Sikes, Troutman, West, Wilson, Young, Baloch, Karimjee, Carroll, Broachwala, Calais, Laabs, Marler, Rizvi, Syed, Syed, Rizvi, Hariwala, Gloyd, Rizvi, Marcum, Carswell, Fanning, Bull, Corta, James, Martinez, Scurlark, Elliott, Beck, Beck, Glover, Bonson, Patel, Matthew, Davidoff, Vesely, Horton, Barbour, Pereira, Rivero, Alston, Miller, Downing, Crespo, Schwartz, Mederos, Tamayo, Shapiro, Mallet, Plotka, Valladares, Palmer, Deltejo, Garcia, Camacho, Chanza, Morera, Davis, Dawson, Rosales, Torres, Pinero, Cruz, Rivera, Balebona, Basurco, Montoya, Yaniz, Phar, Filgueiras, Fernandez, Skrine, Alcantara, Parodis, Parodis, Jara, Alouption, Yan, Padilla, Cruz, Bran, Flores, Perez, Labovitz, Roth, Javier, Jimenez, Ojeda, Caceres, Misir, Rudd, Ramos, Deeb, Bisharat, Dennis-Saltz, Sutton, Magee, Ashchi, Goldfaden, Sheikh-Ali, Kelly, Saikali, Ramirez, Domingo, Hodge, Greenewalt, Stamschror, Preston, Petschonek, Graf, Sutton, Edson, Charron, Taylor, Knisely, Gore, Jones, Johns, Sheldon, Merritt, Hichkad, Barbee, Riley, Alexander, Dasher, EAT, Bonilla, Loza, Hernandez, Perez, Perez, Perez, Medina, Mantero, Vega, Pedraza, Sosa, Robles, Machado, Sosa, Guzman, Grio, Frias, Kelly, Leibowitz, Nelson, Handy, Greenberg, Vega, Francisco, Soto, Arcadia, Francisco, Escareno, Ayala, Muniz, Espino, Vargas, Jimenez, Lopez, Gallaga, Heredia, Santana, Mendoza-Rodriguez, Santana, Román, Torres, Franceschini, Garcia, Jimenez, Ortiz, Antommattei, Comulada-Rivera, Pintado, Ortiz, Rodriguez, Ramirez, Olofintuyi, Alvarez, Pitts, Samraj, Vasbinder, Averett, Kane, Farley, Kendall, Kemmerlin, Saunders, Livingston, McLean, Mayer, Goodwin, Ngugi, Mishra, Carter, Szymela, Imran, Abdallah, Clarke, Latog, Moore, Lopez, Tagsip, Nepali, Kumaran, Jacinto, Magsino, Batalla, Imran, Montgomery, Riggs, Haamid, Whitt, Hopper, Smith, Kordsmeier, Sherron, Plummer, Jimenez, Newkirk, Murray, Scott, Whitney, Castro, Rhames, Cortes-Maisonet, Martinez, Allende-Vigo, Vila, Rivera, Yunker, Rivera, Rodriguez, Rivera, Blanco, Colon, Cruz, Nweke, Okafor, Nweke, Chaves-Montoya, Patrick, Alvarez, Momeh, Anaele, McArthur, Longonje, Husbands, Williams, Dillingham, Sayyah, Carey, Brock, Hussain, Sultana, Mohammad, Lopez, Bittencourt, Prasad, Akther, Smith, Rocco, Straatmann, Stoneman, Maryas, Gravier, Faggett, Jimenez, Alvarez, Evora, Herrera, Leonardi, Karns, Sisneros, Hong, Rudyk, Medentseva, Alexeeva, Strakhova, Lozyk, Babichev, Yushko, Tseluyko, Zhadan, Butko, Mishchenko, Radchenko, Karpenko, Klimko, Berezhniak, Todoriuk, Bezuglova, Mitskevych, Kizim, Nevolina, Galchenko, Korzh, Pankova, Fylenko, Ziabchenko, Krasnokutskiy, Liashok, Donets, Mishustina, Khrustalyova, Donets, Rybachok, Senchylo, Lyakhova, Takhaieva, Shkroba, Shchypak, Tryshchuk, Pieshkova, Karaia, Yakovleva, Horoshko, Lohdanidi, Belei, Kuzmenko, Demchuk, Cherniuk, Kozliuk, Kovalenko, Adarichev, Vakaliuk, Tymochko, Sovtus, Haliuk, Gaudet, Côté, Milot, Roy, Larouche, Piché, Charest, Dufour, Pageau, Audet-Verreault, Côté, Gagnon, Côté, Morin, Fortin-Mimeault, Brassard, Gagne, Rousseau, Sia, Guzzetti, Gisbert, Dias, Oliveira, Cote, Rodrigues, Blanchard, Labbe, Lemay, Beauchemin, Turcotte, Beaudoin, Saliphod, Lamontagne, Trudel, Beaudoin, Blanchard, Larouche, Robinaud, Paré, Bergeron, Bilodeau, Bellavance, Betit, Fortier, Racine, Perras, Boulanger, Beauchesne, Lehoux, Turgeon, Cote, Tardif, Aggarwal, Aggarwal, Patel, Dhaliwal, Aggarwal, Tellier, Gagne, Laperriere, Shalala, Danchuk, Barrette, Lauzon, Langille, Gagné, Lupien, Brochu, Coallier, Vaillancourt, Lachance, Carbonneau, Thibault, Bouchard, St-Cyr, Rioux, Quenneville, Gagnon, Nony, Laberge, Corbin, Patry, Savage, MacNeil, Murdock, Inglis, Libbus, Smith, Johnson, Walters-Findlay, Harris, Hutchison, Rafuse, Thorsen, MacDonald, Akhras, Assef, Irani, Khayat, Guidolin, Gupta, Price, Gandhi, Chhabra, Gandhi, Acharya, Panchal, Kansal, Bailey, Harvalik, Demchuk, Saxena, Collier, David, Quiros, Patel, Balawon, Galera, Michalska, Piniera, Uddin, Abbott, Faulkner, Yousef, Love, Zagdanski, Slaney, Anderson, Leath, Dunlop, Hardy, Wall, El-Koubani, Kelt, Connolly, Marmion, Alapati, Simmons, Colquhoun, Clyde, Mooty, Mullen, Viljoen, Trevor, Pretswell, Austin, Munthali, Munsoor, Gowda, Ozunlu, Keighley, Jones, James, Pollitt, Littlewood, Al-Sheikhly, Lee, Nistor, Roberts, Mawdsley, Sobolewska, Hannis, Wojtkiewicz, Stopford, Halliwell, Mort, McGrath, Osborne, Merrick, Basikolo, Ghysels, Fenlon, Beech, Munoz, Bilton, Goddard, Gillon, Taylor, Tyson, Connor, Felber, Fallows, Clowes and Cheung2024). The CRISPR (clustered Regularly Interspaced Short Palindromic Repeats) and its associated protein Cas9 is another gene editing tool and the first CRISPR-based human therapy was approved in 2023 for sickle cell disease and β-thalassaemia (Wong, Reference Wong2023). CRISPR-Cas9 gene editing technology has also been utilised in hypertension research in animal models (Cheng et al., Reference Cheng, Waghulde, Mell, Morgan, Pruett-Miller and Joe2017; Sun et al., Reference Sun, Hodgkinson, Pratt and Dzau2021). The application of gene-editing may be an avenue for treating single-gene causes of hypertension. Examples of monogenic hypertension include Liddle syndrome (epithelial sodium channel gain of function), Gordon syndrome (gain of function in 4 genes regulating Na-K-Cl cotransporter activity), mineralocorticoid excess (11-β-hydroxysteroid dehydrogenase type II loss of function), and glucocorticoid-remediable aldosteronism (crossover of adjacent genes, CYP11B1 and CYP11B2 as previously mentioned) (Zappa et al., Reference Zappa, Golino, Verdecchia and Angeli2024). Monogenic forms of hypertension are typically associated with early onset, severe, and resistant hypertension. In cases of monogenic hypertension, where a single gene mutation follows Mendelian inheritance patterns, gene-editing may offer a cure to the disease.

Challenges and future landscape

High-throughput next-generation sequencing technologies continue to evolve, and the cost of whole genome sequencing is continuing to fall. This will increase datasets for dissection of causal genes, BP mechanisms and data for inclusion in risk score algorithms. There are many different ethical issues raised by genomic research. Ancestral bias is an important consideration as most genetic data acquired to date has been predominantly from individuals of European ancestry. Individuals of African ancestry have the highest age-adjusted prevalence of hypertension but are relatively under-represented in BP genetic studies (Franceschini et al., Reference Franceschini, Fox, Zhang, Edwards, Nalls, Sung, Tayo, Sun, Gottesman, Adeyemo, Johnson, Young, Rice, Duan, Chen, Li, Tang, Fornage, Keene, Andrews, Smith, Faul, Guangfa, Guo, Liu, Murray, Musani, Srinivasan, Velez Edwards, Wang, Becker, Bovet, Bochud, Broeckel, Burnier, Carty, Chasman, Ehret, Chen, Chen, Chen, Ding, Dreisbach, Evans, Guo, Garcia, Jensen, Keller, Lettre, Lotay, Martin, Moore, Morrison, Mosley, Ogunniyi, Palmas, Papanicolaou, Penman, Polak, Ridker, Salako, Singleton, Shriner, Taylor, Vasan, Wiggins, Williams, Yanek, Zhao, Zonderman, Becker, Berenson, Boerwinkle, Bottinger, Cushman, Eaton, Nyberg, Heiss, Hirschhron, Howard, Karczewsk, Lanktree, Liu, Liu, Loos, Margolis, Snyder, Go, Kim, Lee, Jeon, Kim, Han, Cho, Sim, Tay, Ong, Seielstad, Liu, Aung, Wong, Teo, Tai, Chen, Chang, Chen, Wu, Kelly, Gu, Hixson, Sung, He, Tabara, Kokubo, Miki, Iwai, Kato, Takeuchi, Katsuya, Nabika, Sugiyama, Zhang, Huang, Zhang, Zhou, Jin, Zhu, Psaty, Schork, Weir, Rotimi, Sale, Harris, Kardia, Hunt, Arnett, Redline, Cooper, Risch, Rao, Rotter, Chakravarti, Reiner, Levy, Keating and Zhu2013). An increase in diverse sampling is being addressed by ongoing efforts of national biobanks which are being used to discover novel and ancestry-specific loci within, for example, Japan, Asia, Africa and Qatar (Genome Research Biobank Project Biobank Japan; GenomeAsia; H3Africa – Human Heredity & Health in Africa; Qatar Biobank). Despite these efforts to increase genetic diversity and representation, there is still more to be done. Bridging this data gap is crucial for equitable genomic testing and ensuring GWAS results are beneficial across populations, and that we avoid reinforcing existing health disparities. Furthermore, genomic data can reveal sensitive information about an individual and their family’s ancestry and health. It is therefore important that biobanks store and provide approved researchers with access to genomic data securely and responsibly. There are also ethical considerations in incorporating genetic risk stratification with implications in the insurance sector. An important step in implementing ethical and governance frameworks that balance these risks will be to ensure that any procedures command public trust.

Demographic changes with the ageing population and increased multimorbidity pose challenges to hypertension management. To date, genomic research in hypertension has been largely focused on aiding diagnosis (especially for monogenic forms of hypertension) and identifying potential target mechanisms for treatment. The next wave of genomics in hypertension has the potential to empower a preventive approach with effective screening at a population scale. Our Future Health, the flagship UK programme with the National Health Service, highlights a strategic partnership between industry, academia, and government together with patients and the public to support preventive approaches to tackling common diseases (Our Future Health). The programme combines clinical and genetic data to calculate disease risk scores with the aim of targeting individuals who are at higher risk of developing certain diseases. This will provide an opportunity to test the potential of new polygenic risk scores in health care and of new diagnostic tests or treatments to see how effective they could be for people at higher risk of certain diseases. These collaborations demonstrate that the long-term applications of genomic technology are likely to proliferate beyond genetic risk tools. New use cases are appearing and will revolutionise healthcare delivery through improved differential diagnosis with genetics and personalised medication selection, optimising safety and efficacy. These hold promise for implementing predictive, personalised and preventive approaches to hypertension management that is enduring. Central to the effective delivery of precision medicine in hypertension is patient and public involvement. A focus on a person-centred approach with emphasis on the patient perspective in research, guidelines and scientific documents ensures that the patient is at the heart of all that we do.