More than two thousand positions have been linked to blood pressure and together they still lose to a cuff on your arm. Two of the three here came from an east Asian study — unusual on a site whose evidence is mostly European — and two of them sit in the same gene, found on different continents by groups asking different questions.
Solid lines are connections this site curates. Dashed lines mean the two ends share a research paper — worth knowing, and not a claim that one explains the other.
Blood pressure is the trait where genetics has to be honest about its place. It is highly heritable, it has been studied in over a million people, and none of that competes with the thing anyone can do in two minutes: put a cuff on an arm and read the number.
A 2018 study of more than a million people added 535 new blood-pressure loci in one paper. The count has since passed 2,000, and a 2025 review of the field says plainly that the causal genes and pathways behind most of them remain largely unknown.
Each of those positions moves blood pressure by a fraction of a millimetre of mercury. A reading tells you what your blood pressure actually is, today, including everything genetics cannot see — salt, weight, sleep, alcohol, stress, kidney function, the medicines you already take. That is why no guideline anywhere asks for a genotype before treating high blood pressure, and why this page cannot be used to decide anything.
Most of the evidence on this site was gathered in people of European descent, and we say so on the pages where it matters. This page is the exception.
Both variants come from a meta-analysis in the AGEN-BP consortium: 19,608 people of east Asian ancestry, with new genotyping in 10,518 more and replication in a further 20,247.
rs2681472 is also ATP2B1, and it comes from a different study with a distinction worth pulling out.
The CHARGE Consortium looked at 29,136 people and asked three questions rather than one: what moves systolic pressure, what moves diastolic pressure, and what predicts a diagnosis of hypertension. Those are not the same question — the first two are about a measurement, the third is about crossing a line.
Joined with a second consortium (34,433 more people), four loci reached genome-wide significance for systolic pressure and six for diastolic. For hypertension as a diagnosis, exactly one did: ATP2B1.
So two of the three positions on this page sit in the same gene, found by two different groups asking two different questions on two continents. That is about as much corroboration as a blood pressure locus gets.
ATP2B1 encodes a calcium pump — the protein that pushes calcium out of a cell, including the smooth muscle cells that line arteries. How much calcium is inside those cells determines how tightly they squeeze, and how tightly they squeeze is blood pressure.
That mechanism will be familiar to anyone taking a calcium channel blocker: one of the oldest and most-used classes of blood pressure medicine works on the other side of the same traffic. The genetics did not lead to those drugs — they came first — but finding the strongest common signal on the same pathway is a decent check that the pathway matters.
It does not follow that carrying the variant predicts who responds to which drug. That has been looked for and is not established.
That same study found something striking: a very strong blood-pressure signal at chromosome 12q24.13, near ALDH2, with p-values of 7.9 x 10-31 and 1.3 x 10-35 — and it was ethnically specific, showing up in east Asians rather than in European-ancestry samples.
ALDH2 is the gene behind the alcohol flush reaction, and the variant that causes it is common in east Asia and almost absent elsewhere. A gene that governs how much alcohol a person can comfortably drink turning up as a blood-pressure locus in exactly the population where that gene varies is not a coincidence waiting to be explained — alcohol raises blood pressure, and this looks like that effect written into the genome.
It is also a good demonstration of why studying only one ancestry misses things. This signal is invisible in European-ancestry data because the variant is not there to be seen.
The International Genomics of Blood Pressure (iGEN-BP) Consortium asked a more pointed version of the same ancestry question: rather than replicating a European finding in an East Asian sample, it searched from the start for signals whose effect differs by ancestry. Across 289,038 people, principally of East Asian descent with a meta-analysis against European-ancestry data, it reported 19 new loci.
Twenty-nine of the positions on this page come from that study — nine for systolic pressure, twenty for diastolic. Three, including rs6590816 and rs6590811, sit near ARHGAP42, a smooth-muscle contraction gene already established in blood pressure; two more, rs9814480 and rs3774447, sit near CACNA1D, another calcium channel alongside ATP2B1; and rs138088171 and rs62525059 sit near CYP11B1 and CYP11B2, the genes for the last enzymatic step in making aldosterone, the hormone the kidney uses to hold onto sodium.
The study's main finding was methodological as much as it was genetic: at ten loci, the East Asian and European signals shared the same neighbourhood of the genome but not the same causal variant, and sometimes not even the same direction of effect. Across the whole genome, the underlying effect sizes still correlate strongly between ancestries — 0.898 for systolic pressure, 0.851 for diastolic — so most of blood pressure's genetics is shared. But the exceptions were not noise; the paper reports some carrying a signature of natural selection acting differently between the two populations, the same idea as the ALDH2 signal above, generalised into a method built to find it on purpose.
Pulse pressure — the gap between systolic and diastolic pressure, a rough marker of how stiff the large arteries have become — has its own genetics, and this page's remaining 13 loci come from four separate studies of it. The largest contribution, seven loci, comes from Kraja et al. 2017: 150,134 people in discovery and 228,245 in replication, cross-checked against whole-blood gene expression data from 12,607 people to implicate 48 genes across multiple independent resources, with three signals specific to kidney tissue — the kind of translational evidence aimed squarely at finding new drug targets rather than just new statistical hits.
rs62011052, near ADAMTS7, is one of them — the same gene already on this site's coronary artery disease page. Arterial stiffness and coronary disease share enough underlying vascular biology that this is a genuinely coherent overlap, not a coincidence of two studies happening to name the same gene.
A fourth of these loci come from Warren et al. 2017, an electronic-health-record study of 99,785 people with over 1.3 million individual blood pressure readings, later combined with two further consortia to 321,262 people total. One more comes from Liang et al. 2017, a study specifically of 31,968 people of African ancestry — its LLPH finding (this page's rs113866309) is one of several loci in that study whose associated allele is common only in African-ancestry populations, the same kind of ancestry-specific genetics this page discusses above for East Asian ancestry. The last comes from Surendran et al. 2018, which validated 107 independent UK Biobank loci and proposed using a genetic risk score to target early lifestyle intervention at the people it identifies as highest-risk.
Feitosa et al. 2018 took a different angle entirely: rather than just testing for a direct genetic effect, it tested whether a variant's effect on blood pressure changes depending on how much alcohol someone drinks. A discovery stage in about 131,000 people across several ancestries found 3,514 candidate SNVs, followed by independent replication in about 440,000 more people. The study confirmed 5 novel blood-pressure loci (380 SNVs in 21 genes) plus 49 previously reported loci in European- and multi-ancestry analyses, and a further correlated meta-analysis added 8 more novel loci. This page holds 35 of this study's variants, split across its pulse-pressure and mean-arterial-pressure interaction results, including an NPPB signal that appears in both — a gene already familiar to this page for its role in blood-pressure regulation.
A 2019 study built its discovery cohort trans-ethnically from the start rather than adding other ancestries onto a European-ancestry-first design: 776,078 people from the Million Veteran Program and collaborating studies. It found 208 novel common blood-pressure SNPs and 53 rare variants across systolic, diastolic and pulse pressure. Beyond the GWAS itself, a transcriptome-wide analysis linked 4,043 blood-pressure associations to the genetically predicted expression of 840 genes in 45 tissues, and single-cell RNA sequencing in mouse kidneys specifically flagged upregulated blood-pressure genes in renal tubule cells — evidence for the kidney's role in blood pressure that comes from a completely different method than a population GWAS alone. This page holds 56 of this study's pulse-pressure variants, plus 3 more from two smaller 2016 meta-analyses that tested rare and low-frequency variants alongside common ones, the same angle this larger study later scaled up trans-ethnically.
Everything that works is already known and none of it requires a test: knowing your numbers, salt, weight, alcohol, movement, sleep, and taking prescribed medicines as prescribed. High blood pressure is common, usually silent, and one of the most treatable causes of stroke and heart disease there is.
If it has been a while since anyone measured yours, that — not this page — is the useful next step.
Positions joined since this page was written
What this is The text above discusses the variants this page was written around. Since then the catalogue has joined 1248 more positions to it, by shared trait or shared paper. They are listed here by the paper each came from; the text does not describe them, and each variant page carries that study's own record.
Koskeridis F et al. 2024, Nature communications rs4712955 (SCGN), rs11593126 (near PLCE1), rs56014530 (LSP1), rs77098653 (LINC01752), rs2963452 (LINC02227), rs28698483 (NHERF2), rs217230 (near LINC01219), rs1494651 (near LINC02120), rs59980837 (NGF-AS1), rs10980408 (SVEP1), rs6090907 (PREX1), rs55924432 (near STIM2) and 287 more — PMID:39537608
Plotnikov D et al. 2022, Investigative ophthalmology & visual science rs3737136 (WNT2B), rs2493288 (PRDM16), rs17627481 (CUX2), rs169287 (near H3C12), rs4075359 (TNKS), rs10901911 (LINC02750), rs3129171 (LINC03003), rs9221 (HOXB-AS3), rs68085857 (GPATCH2), rs11979110 (near KLF14), rs12414028 (NT5C2), rs11852760 (SEMA7A) and 278 more — PMID:35762941
Keaton JM et al. 2024, Nature genetics rs13210511 (near RPS4XP9), rs1470260 (SBF2), rs78249997 (SYNPO2L-AS1), rs997669 (CCNE1), rs6497759 (TNRC6A), rs7959016 (near TBX3-AS1), rs4924570 (MGA), rs75905900 (near OR4A16), rs12630450 (near TERC), rs223361 (UBE2D3), rs7134440 (TNS2), rs2169137 (MDM4) and 189 more — PMID:38689001
Verma A et al. 2024, Science (New York, N.Y.) rs12222581 (MADD), rs73593734 (CACNB2), rs2059256 (CFDP1), rs11014171 (CACNB2), rs3796592 (GUCY1A1), rs4075082 (near PFKFB4), rs11169294 (near CERS5), rs45603435 (MTHFR), rs13201060 (near RPS4XP9), rs118077107 (NEURL4), rs4470517 (RYK), rs11229543 (ZFP91) and 76 more — PMID:39024449
Jee YH et al. 2025, Nature communications rs1438065 (PDE1A), rs4884958 (DACH1), rs2287783 (TNRC6A), rs78473917 (PREX1), rs57591036 (near RPS29P9), rs13283964 (near PGAM1P2), rs12143164 (near RPL21P24), rs10924415 (GPR137B), rs7556898 (near PRPS1P1), rs13228123 (AUTS2), rs314373 (SLC12A9), rs28857569 (near HNF4G) and 60 more — PMID:40436827
Surendran P et al. 2020, Nature genetics rs4574 (PSMB7), rs10011549 (POLN), rs9328764 (POLN), rs2504779 (CD164L2), rs1434536 (BMPR1B), rs1043809 (B9D1), rs4899260 (near ZFP36L1), rs4075254 (AKAP13), rs2061821 (AKAP13), rs4843074 (AKAP13), rs7648196 (RSRC1), rs8014204 (PROX2) and 48 more — PMID:33230300
Giri A et al. 2019, Nature genetics rs1159201 (MAST4), rs4681794 (ARHGEF3), rs1081707 (near NCAPH), rs11780200 (NOV), rs2849018 (C6orf10), rs10734252 (KCNJ11), rs3130491 (VARS), rs3745348 (MYO9B), rs3744010 (UNC13D), rs11000752 (AGAP5), rs112912733 (ULK4), rs1815614 (SPSB1) and 39 more — PMID:30578418
Sakaue S et al. 2021, Nature genetics rs2254240 (MYBPC3), rs1234988 (ARHGAP42), rs3860770 (near CPEB4), rs79680647 (NT5C2), rs4762911 (near TCP1P3), rs6026729 (ZNF831), rs10958099 (SNTG1), rs3026418 (near IFT81), rs35225531 (near Y_RNA), rs148321568 (SYNPO2L-AS1), rs839758 (SZT2), rs36002174 (TRAPPC9) and 32 more — PMID:34594039
Evangelou E et al. 2018, Nature genetics rs12574332 (UBASH3B), rs72688070 (Y_RNA), rs1215469 (SPRY2), rs6511291 (ZNF100), rs17321041 (DPYSL2), rs10743086 (ST5), rs9456648 (AGPAT4), rs12184466 (RP1-74B13.2), rs702395 (ZMAT2), rs2922895 (MCPH1), rs11112548 (C12orf75), rs55935819 (CACNA1C) and 28 more — PMID:30224653
Hoffmann TJ et al. 2017, Nature genetics rs4475250 (KCNN2), rs7704297 (SH3TC2), rs460105 (DPEP1), rs11631778 (THSD4), rs113161639 (GPR149), rs7546498 (GNB1), rs17286052 (CCNH), rs2246438 (CXCL12), rs4923910 (MAPKBP1), rs6060114 (HM13-AS1), rs6019378 (PREX1), rs7951348 (GUCY1A2) and 10 more — PMID:27841878
Loya H et al. 2025, Nature genetics rs1807579 (TNRC6B), rs7070797 (LINC02625), rs4841093 (ERI1), rs11782995 (near CCN3), rs12755444 (FGGY-DT), rs1813742 (MKLN1), rs9467545 (near SCGN), rs11527181 (near ADO), rs151597 (near NHLRC2), rs343039 (near HERPUD2), rs1687318 (FGD5), rs12677668 (near SNTG1) and 7 more — PMID:39789286
Yang ML et al. 2024, Nature medicine rs4516957 (near RPS4XP9), rs836179 (GPD1), rs6771928 (ULK4), rs17109875 (PLCE1), rs72699046 (WNT2B), rs7102464 (SBF2), rs143175535 (SBF2), rs2071265 (HOTTIP), rs562153361 (near CCN3), rs1887319 (near LINC01752), rs76099932 (near LINC01819), rs7090956 (CACNB2) and 4 more — PMID:38459180
Teixeira SK et al. 2024, Scientific reports rs8114628 (near LINC02871), rs10787515 (near ADRB1), rs11908189 (ZNF831), rs12121543 (MTHFR), rs10858915 (ATP2B1), rs4490571 (LINC02227), rs12664595 (PLEKHG1), rs197913 (GOSR2-DT), rs418085 (near PRPS1P1), rs55815739 (near RPS4XP9), rs10077410 (PRDM6), rs10458914 (NUP160) and 3 more — PMID:39548300
Schoeler T et al. 2023, Nature human behaviour rs7559813 (LINC02576), rs6848130 (near FGF5), rs337125 (PRDM6), rs7603740 (LINC01808), rs7749444 (PRIM2BP), rs11207410 (FGGY-DT), rs4429489 (near PRPS1P1), rs12923957 (near GNPATP), rs6657675 (PRDM16), rs6031400 (JPH2), rs72716117 (LINC02511), rs2129562 (MKLN1) and 1 more — PMID:37106081
Pozarickij A et al. 2024, Nature communications rs10900130 (TMEM72-AS1), rs6590805 (near PPIAP43), rs143448786 (SETD2), rs12041848 (PRDM16), rs11669463 (MYO9B), rs78236399 (near MIR296), rs11191454 (AS3MT), rs138938305 (near SPTBN1-AS2), rs12544694 (EBF2) — PMID:39048560
Gurholt TP et al. 2025, HGG advances rs111423688 (PLEKHM1), rs13140033 (NFKB1-AS1), rs76344126 (ARHGAP27), rs1863244 (IRAG1), rs25771 (near KRT18P16) — PMID:39663699
Shi G 2022, Scientific reports rs115525024 (HOXA13), rs12368847 (DUSP16) — PMID:35879408
Gouveia MH et al. 2021, Scientific reports rs6599176 (ULK4) — PMID:33603002
Hansen MEB et al. 2026, American journal of human genetics rs10512711 (CFAP92) — PMID:42586059
2026-03-22 · A Pooled Blood Genome-Wide Association Study of Hypertension in Sindhi Families: Results from the DISFIN Study. Genes. 2026. DOI:10.3390/genes17030351
A pooled-blood hypertension study in Sindhi families flags five candidate genes not seen before
Hypertension genetics in India's urban Sindhi population is poorly characterized despite the population's high hypertension risk. This study used pooled whole-blood genotyping -- a lower-cost technique that estimates allele frequencies from combined DNA of multiple people rather than genotyping each individual separately -- across 28 pools representing 1,402 participants from families with a type 2 diabetes proband. After adjusting for metabolic comorbidities, batch effects, pooling error, and kinship, 191 of 608,550 tested variants were significantly associated with hypertension, mapping to 180 genes, over half of them (55, 31%) encoding long noncoding RNAs. Most of the implicated genes and lncRNAs had been reported in other hypertension studies before, but the study flagged five specifically novel candidate genes: SBF2, ARHGAP12, EPAS1, CLEC16A and LRPPRC, plus a lncRNA (FLYWCH-AS1) as the single most significant signal. Because pooled genotyping is a less precise method than individual-level genotyping and this is a single, methodologically nonstandard study in an understudied population, these findings should be read as candidate leads needing replication with individual genotyping, not established hypertension variants. This site's blood pressure page carries 439 variants; none of these five genes are currently among them, and given the pooling methodology, this entry documents them as preliminary candidates rather than validated additions.
Architecture. Blood pressure is a continuous, highly polygenic trait. The 2018 meta-analysis in over one million individuals of European ancestry reported 535 novel loci for systolic, diastolic and pulse pressure, and highlighted shared genetic architecture between blood pressure and lifestyle exposures. Reviews now put the total above 2,000 loci, with causal genes and mechanisms unresolved for most. Per-variant effects are on the order of a fraction of a mmHg, and polygenic scores for blood pressure are not used in clinical decision-making.
The two variants here. Both are recorded from the AGEN-BP meta-analysis of GWAS for systolic and diastolic blood pressure in 19,608 east Asian subjects, with de novo genotyping in 10,518 and further replication in 20,247 east Asian samples. That study identified new loci at ST7L-CAPZA1, FIGN-GRB14, ENPEP and NPR3 plus a newly discovered variant near TBX3 (rs35444 here), replicating all but NPR3 in independent samples, and confirmed seven loci previously identified in European-descent populations — the group ATP2B1 (rs17249754) belongs to. ATP2B1 encodes plasma membrane calcium ATPase 1, expressed in vascular smooth muscle and endothelium, where calcium handling is directly relevant to vascular tone; TBX3 is a T-box transcription factor with established roles in cardiac conduction and limb development, and its blood-pressure mechanism is not established.
The ALDH2 signal. The same study reported strong association at 12q24.13 near ALDH2 (P = 7.9 x 10-31 for SBP, 1.3 x 10-35 for DBP) with ethnic specificity. The functional ALDH2 Glu504Lys variant is common in east Asian populations and effectively absent elsewhere, and alcohol intake is an established determinant of blood pressure; the region is also in extended linkage disequilibrium across 12q24, so the precise causal variant is not settled by association alone. It is a clean example of an ancestry-specific signal that European-ancestry studies cannot detect.
Clinical position. Diagnosis and management of hypertension rest on measurement — office readings confirmed by home or ambulatory monitoring — plus assessment for secondary causes and overall cardiovascular risk. No guideline incorporates common blood-pressure variants into diagnosis, risk stratification or drug choice, and nothing on this page modifies any of that.
What a 23andMe/AncestryDNA export or raw VCF can and can't tell you about Blood Pressure comes down to these specific, well-studied positions — not a diagnosis. 2064 positions are linked to this page; the ones this page's own text discusses are shown first.
NEDD8 · rs11158609
See detailed info →See all 2064 linked variants →
The studies behind these variants recruited participants from different ancestries — a result found in one population doesn't always transfer to another. Based on 676 of 2064 linked studies with a resolved discovery ancestry.
Research-derived gene–drug associations only — not a prescription, dosing guide, or medical advice. Always follow your prescriber's guidance.
| Gene | Drug | What the research shows |
|---|---|---|
| NAT2 | Hydralazine | NAT2 splits people into fast and slow acetylators, a difference described in the 1950s and one of the oldest known examples of inherited variation in drug handling. Slow acetylators are exposed to more hydralazine for longer, and are more likely to develop drug-induced lupus — joint pain, rash and antibodies that appear on treatment and usually resolve after it is stopped. The US label carries this warning, and recommends against prolonged high-dose treatment in slow acetylators. There is no CPIC guideline for the pair despite its level A rating, so the guidance that exists is the label and sixty years of clinical description rather than a dosing table. (US prescribing information for hydralazine hydrochloride. CPIC lists the NAT2–hydralazine pair at level A but has not published a guideline for it.) |
| CYP2D6 | Metoprolol | Metoprolol is among the most prescribed beta blockers and CYP2D6 clears it, so a poor metaboliser is exposed to several times more of it than intended. What follows is a lower heart rate and lower blood pressure than the dose was chosen for, and the tiredness and dizziness that come with them — the reasons people stop taking it. CPIC rates the pair level B. The practical point is that blood pressure and pulse are measured at every review anyway, so the genotype explains a response rather than predicting one, and dose follows the measurement. (CPIC lists CYP2D6–metoprolol at level B (see PMID 38951961); no CPIC guideline has been published for the pair.) |
| NELL1 | Hydrochlorothiazide | Hydrochlorothiazide is one of the most widely prescribed antihypertensives, and it is already known to raise blood sugar and triglycerides in some people — the mechanism was not well understood. A genome-wide study across two clinical trials found a variant near NELL1 where each copy of the reported allele corresponded to about a 28 mg/dL rise in triglycerides after starting the drug, genome-wide significant in African American participants. NELL1 represses fat-cell differentiation in other contexts, which the authors suggest as a plausible route to the metabolic effect, though this study establishes the statistical association rather than the mechanism. (Duarte JD et al., The Pharmacogenomics Journal 2014 — genome-wide association analysis of adverse metabolic response to hydrochlorothiazide in African Americans. PMID:23400010) |
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Blood Pressure. MyGeneLog™. https://www.mygenelog.com/conditions/blood-pressure
Not usefully. Thousands of positions each move it by a fraction of a millimetre of mercury, and none of that competes with measuring it. A cuff tells you what your blood pressure is today, including everything genetics cannot see.
Because that is where they were found — and it is unusual for this site, where most evidence comes from European-ancestry cohorts. One of the two, near TBX3, was newly discovered in that study; the other, ATP2B1, was found in European-ancestry work first and confirmed there.
The same east Asian study found a very strong blood-pressure signal beside ALDH2, and only in east Asians — because the variant that causes flushing is common there and almost absent elsewhere. Alcohol raises blood pressure, so a gene governing how much a person drinks comfortably turning up here is the mechanism, not a puzzle.
No. Nothing here changes a diagnosis, a target or a medicine. If your blood pressure has not been measured recently, that is the useful next step, and it is the same advice whatever these positions say.
Closer to two. They were found by different groups, on different continents, asking different questions — one about blood pressure as a measurement in an east Asian cohort, one about hypertension as a diagnosis in a mostly European one. Landing on the same gene from those two directions is corroboration rather than repetition.
Free to reuse. This page's text is original writing from freely-available research, licensed CC BY 4.0 — reuse it, including commercially, with attribution to MyGeneLog™. It's general research-derived information, not medical advice or a diagnosis — see Terms of Use.