Height
The textbook polygenic trait, with the number usually left out: 12,111 positions, none of which means anything alone. It is also the clearest demonstration of why a genetic score built in one population predicts badly in another.
What this condition connects to
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.
Height is the trait every explanation of "polygenic" reaches for, and it deserves the job. It is strongly inherited, it is easy to measure, and it is influenced by an enormous number of positions each contributing a fraction of a centimetre. What most explanations leave out is the actual number.
Twelve thousand positions
The largest study of height so far looked at 5.4 million people of diverse ancestries and identified 12,111 independent positions associated with it. Together those account for essentially all of the heritability that common variants can explain. They are not scattered evenly: they cluster into 7,209 stretches of the genome averaging about 90,000 bases each, together covering roughly 21% of it.
Read that again with a genetic report in mind. A report listing one height variant is showing you one twelve-thousandth of the picture, and this site carries two of them. That is not a flaw in the data; it is what the trait is like. Anyone selling a height prediction from a handful of positions is selling the wrong shape of thing.
The two positions here
Both come from a genome-wide study of population-based Korean cohorts — 8,842 people, with the promising signals re-tested in 7,861 independent Korean samples. That design matters: a signal that survives replication in a separate sample is a different class of finding from one that does not.
- rs6918981 — near HMGA1, a gene involved in regulating growth.
- rs13273123 — near PLAG1, one of the height loci found repeatedly across populations.
The same study reported something worth keeping: for height and body mass index, most of the variants it found overlapped those already reported in European samples. The positions themselves largely transfer between populations, which is a real and slightly surprising result.
Why prediction does not transfer, even when the positions do
Here is the part that gets misread constantly. The 12,111 positions explain about 40% of the variation in height in populations of European ancestry — where they were mostly discovered — and only about 10-20% in populations of other ancestries.
That gap is not evidence that different populations have different height genes. The same study found effect sizes, regions and prioritised genes to be similar across ancestries. What differs is how the variants are packaged: which nearby positions travel with them, and how common each version is. A score built on that packaging in one population is reading a slightly wrong map in another.
It is the single most important caveat on any polygenic score, and it applies to every trait on this site, not just this one. Prediction quality is a property of the study population as much as of the biology.
Rare variants, and where they sit
Common variants are not the whole story. Sequencing 447,461 people and replicating in 225,515 more turned up rare variants with much larger individual effects — one aggregate in the untranslated region of FGF18 is associated with height differences of up to 6 cm, which is roughly ten times what a common variant does.
And the tidy finding: 97% of those rare associations sit near regions the common-variant studies had already flagged. The two approaches converge on the same biology from opposite ends of the frequency spectrum.
What genetics does not explain
Populations have grown taller over the last century faster than any gene pool can change — the answer there is nutrition, infection load and childhood health, not DNA. Within a generation, growing up well fed and healthy is the environmental part of the trait, and it is not small.
None of this is a test, and it is not how growth problems are found. A child whose growth has fallen away from their curve is a question for a doctor, who will look at growth velocity, puberty timing, nutrition and hormone levels — and none of that is answered by a genotype.
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 8743 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.
Yengo L et al. 2022, Nature rs4899401 (NKX2-1-AS1), rs4596113 (near NPM1P28), rs2451948 (LINC01505), rs4974539 (CPN2), rs6011058 (ZGPAT), rs2168029 (near LYZ), rs4405216 (near MED6P1), rs9348050 (PDE10A), rs9128 (JAK1), rs12305038 (PDE3A), rs17308476 (near LINC01960), rs6874 (SNHG7) and 5522 more — PMID:36224396
Schoeler T et al. 2025, Nature communications rs546681598 (CABP4), rs34074573 (BCL2L2-PABPN1), rs34162548 (PSMA1), rs71564907 (near LINC03006), rs6058378 (SCAND1), rs146370649 (DCAF1), rs141829833 (near GRM2), rs568259398 (UQCC1), rs35848181 (near RPS4XP9), rs79027916 (RALY), rs193054544 (RCBTB1), rs6931945 (LINC03135) and 2124 more — PMID:40374629
Loya H et al. 2025, Nature genetics rs194619 (LINC01526), rs72978905 (near MAP2K2), rs4128460 (near NMBR), rs68007459 (near BAHCC1), rs1548817 (TC2N), rs62466110 (near RN7SL7P), rs12025127 (near LINC02817), rs34849253 (ZNF24TR), rs117327710 (near NIP7P1), rs55726184 (NFATC4), rs58730238 (MSRB3-AS1), rs74684534 (LOXL1) and 267 more — PMID:39789286
Sakaue S et al. 2021, Nature genetics rs12085666 (FAF1), rs2373962 (near KCNH2), rs1322045 (RGP1), rs274668 (near TENT4A), rs1042164 (IER2), rs7992650 (LINC00446), rs2958183 (TCF4), rs74888405 (ZBTB38), rs79732015 (COPB1), rs76450219 (TTC28), rs9790189 (LSAMP), rs1556032 (NFIB-AS1) and 180 more — PMID:34594039
Jee YH et al. 2025, Nature communications rs9716700 (NPR3), rs565692 (near FOXD2), rs10866916 (PLEC), rs9892365 (near TBX2-AS1), rs10514136 (AP3B1), rs2284747 (MFAP2), rs147826719 (CDK6), rs36063247 (near CHCHD7), rs146426492 (PAN2), rs117701289 (KBTBD2), rs145952310 (ZNF70), rs35840638 (ADAP2) and 162 more — PMID:40436827
Schoeler T et al. 2023, Nature human behaviour rs2319817 (RPGRIP1), rs2364232 (near SOCS2), rs7741360 (near BMP6), rs149890 (DLEU1), rs7852498 (CDC14B), rs35281701 (DRAIC), rs73057171 (ZNF628), rs75358242 (TMBIM7P), rs116821267 (KLHL29), rs6579129 (near PIGPP3), rs74946663 (near POLR1F), rs17293411 (near PI15) and 122 more — PMID:37106081
Verma A et al. 2024, Science (New York, N.Y.) rs2727311 (near GH2), rs11155426 (EPM2A), rs17365306 (ITGB8), rs460177 (near RNU2-36P), rs12995572 (MEIS1), rs11628929 (NFATC4), rs878083 (EXOSC5), rs1805737 (PHC1), rs56411722 (AFAP1), rs262654 (PRKCZ), rs6907315 (DSE), rs6129229 (near LINC01734) and 117 more — PMID:39024449
Chiou JS et al. 2022, BMC medicine rs6845999 (HHIP-AS1), rs8007661 (TRIP11), rs8121252 (GNAS), rs28778940 (near PTCH1), rs76157805 (PARPBP), rs2413143 (SYN3), rs645935 (SERPINH1), rs2289635 (DNM3), rs12410416 (DNM3), rs13197753 (LIN28B-AS1), rs140830175 (LINC01562), rs62424499 (near RPSAP72) and 47 more — PMID:35831902
Wood AR et al. 2014, Nature genetics rs9993613 (ADAMTS3), rs3020418 (ESR1), rs3767627 (OTUD7B), rs7692995 (LCORL), rs17556750 (PRKG2), rs9392918 (BMP6), rs6919534 (ZNF76), rs17721822 (BMP2), rs7154721 (TRIP11), rs7568069 (ZNF638), rs8102380 (ILF3), rs2854207 (CSH2) and 23 more — PMID:25282103
He M et al. 2015, Human molecular genetics rs6823268 (ANAPC10), rs7153027 (FBLN5), rs2271266 (NUP37), rs6728302 (DIS3L2), rs2401171 (ADAMTSL3), rs7250071 (ILF3), rs11170624 (ATF7), rs16848425 (ADAMTS3), rs12459943 (DNM2), rs611203 (PLCD4), rs10460436 (NPPC), rs10858250 (QSOX2) and 3 more — PMID:25429064
Cho HW et al. 2021, Frontiers in genetics rs138997002 (HMGA1), rs6913361 (CYCSP55), rs79658451 (GRM4), rs2780215 (AL354740.1), rs148899910 (SMIM29-AS1), rs76459740 (near RNF41), rs76863928 (HMGA1), rs78420081 (SMIM29), rs6457762 (near KRT18P9), rs10080749 (SMIM29), rs1150779 (SMIM29), rs9368804 (SMIM29-AS1) and 2 more — PMID:34054925
Agrawal S et al. 2022, Nature communications rs13410987 (COBLL1), rs4872393 (near EBF2), rs72810972 (near CPEB4), rs11057402 (CCDC92), rs35641603 (WT1-AS), rs55744247 (FST), rs113602321 (near HLA-DRB5), rs7550430 (LINC01732), rs191578827 (NCOA7), rs10044492 (LINC02227) — PMID:35773277
Graff M et al. 2021, American journal of human genetics rs58680090 (EFEMP1), rs12332985 (DEF6), rs146576224 (ACAN), rs10180829 (UGGT1), rs672769 (CRB1), rs6463331 (IGFBP3) — PMID:33713608
Raghavan S et al. 2022, PLoS genetics rs2197973 (USP44), rs1840086 (SLC4A4), rs3812813 (USP44), rs12143563 (HMCN1), rs4747443 (PIP4K2A), rs113905969 (RPL17P50) — PMID:35653334
Rüeger S et al. 2018, PLoS genetics rs73029259 (near QKI), rs76306191 (DCST1), rs428925 (near LINC02995), rs56006730 (near LINC00485) — PMID:29782485
Fang H et al. 2019, American journal of human genetics rs17097556 (ZRANB2-DT), rs6080859 (near RNU6-192P), rs7102710 (SPON1), rs73790670 (CXXC5) — PMID:31564439
Berndt SI et al. 2013, Nature genetics rs10838708 (PSMC3), rs2744971 (C6orf106), rs10843164 (CCDC91) — PMID:23563607
Liu S et al. 2026, Nature genetics rs73386907 (HELLPAR), rs77060225 (DIS3L2) — PMID:42509370
Fernández-Rhodes L et al. 2022, HGG advances rs6817771 (HHIP), rs6058283 (CPNE1) — PMID:35399580
Gudbjartsson DF et al. 2008, Nature genetics rs6830062 (LCORL) — PMID:18391951
Lango Allen H et al. 2010, Nature rs11830103 (SBNO1) — PMID:20881960
Asgari S et al. 2020, Nature rs200342067 (FBN1) — PMID:32499652
Nagy R et al. 2017, Genome medicine rs144225905 (near LINC02667) — PMID:28270201
Jeon S et al. 2024, GigaScience rs137876247 (ZCCHC10) — PMID:38626723
In the news
2026-01-28 · Namba S, et al., Nature 2026, PMID:41606330
A cross-population compendium of gene-environment interactions
An atlas of gene-environment interactions — where a variant's effect size differs by age, sex or lifestyle — built from 440,210 people of European and Japanese ancestry and replicated in 539,794 more from diverse populations. Decomposing the contributions of age, sex and lifestyle exposed the aetiology of these interactions, including reverse causality from disease-driven changes in diet, and showed that they affect polygenic prediction accuracy and how well scores transfer across populations. Omics-level analyses found sex-discordant genetic effects in lipid metabolism, which the authors connect to failures of genetically supported drug trials. The variant that dominates this catalogue's overlap with the study is rs671 in ALDH2 — the alcohol flush variant — whose effects on liver enzymes (AST, ALT), HDL cholesterol and red-cell indices differ by sex, ever-drinking and smoking; also on this site from the study: rs145954970 (SLC22A11, uric acid, gout), rs11066015 (ACAD10, red-cell count by drinking), rs730154 (CYP19A1, height by sex) and rs35747824 (PDILT, creatinine by age). PMID:41606330.
From our blog
Clinical detail
Architecture. Height is the canonical highly polygenic quantitative trait. Common SNPs are estimated to account for 40-50% of phenotypic variance, and the 2022 GWAS meta-analysis of 5.4 million individuals resolved 12,111 independent genome-wide significant SNPs that capture nearly all of that common-variant heritability. Those SNPs fall in 7,209 non-overlapping segments with a mean span of about 90 kb, covering approximately 21% of the genome; density is uneven and the denser regions are enriched for biologically relevant genes.
Portability. Out-of-sample estimation gave 40% of phenotypic variance explained in European-ancestry populations using the 12,111 SNPs (45% using all HapMap 3 SNPs), against roughly 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and prioritised genes were similar across ancestries, so the loss is attributed to linkage disequilibrium structure and allele-frequency differences within associated regions rather than to different underlying biology. Any polygenic score for any trait inherits this limitation.
The two rows here. rs6918981 (HMGA1) and rs13273123 (PLAG1) come from a Nature Genetics 2009 genome-wide study of Korean population-based cohorts: 8,842 samples for discovery with replication of promising signals in 7,861 independent Korean samples across eight quantitative traits. For height and BMI specifically, most detected variants overlapped those previously reported in European samples. Per-allele effect estimates for height variants are on the order of a few millimetres and are not clinically interpretable in an individual.
Rare-variant contribution. Whole-genome sequencing in 447,461 UK Biobank participants with replication in 225,515 All of Us participants identified 90 rare and low-frequency single-variant associations across height, BMI and waist-hip ratio, plus 135 coding and 51 non-coding aggregate associations; a 5'UTR aggregate in FGF18 reaches effects of up to 6 cm on height. 97% of rare-variant associations lie near loci already identified by common-variant GWAS, and ultra-rare variants explain a small fraction of heritability relative to common variants.
Clinical boundary. Nothing here concerns monogenic short stature or overgrowth syndromes, skeletal dysplasias, growth hormone deficiency or the endocrine and nutritional causes of growth failure. Those are diagnosed from growth velocity, bone age, puberty staging and targeted testing. A common height variant has no role in that assessment.
Related variants MyGeneLog™ checks for
What a 23andMe/AncestryDNA export or raw VCF can and can't tell you about Height comes down to these specific, well-studied positions — not a diagnosis. 9193 positions are linked to this page; the ones this page's own text discusses are shown first.
See all 9193 linked variants →
Which ancestries this evidence comes from
The studies behind these variants recruited participants from different ancestries — a result found in one population doesn't always transfer to another. Based on 211 of 9193 linked studies with a resolved discovery ancestry.
Sources
Quoting this page
Free to quote and reuse under CC BY 4.0. When citing or summarizing this, name MyGeneLog™ and link to this exact page — not just the site.
Height. MyGeneLog™. https://www.mygenelog.com/conditions/height
Questions about Height
Can a DNA test predict how tall my child will be?
Not usefully. The best available score uses 12,111 positions and explains about 40% of the variation in the populations where it was built — and only about 10-20% elsewhere. A test reading a handful of positions explains far less than that. Mid-parental height and a growth chart do better, and cost nothing.
Do the two variants on this page tell me anything about my height?
Essentially nothing on their own. They are real, replicated associations, and each moves the average by a few millimetres across a population. Between them they are two positions out of more than twelve thousand.
Why do polygenic scores work worse for some populations?
Because the scores were mostly built in European-ancestry samples. The same regions and similar effect sizes show up across ancestries, but which nearby positions travel together, and how common each version is, differs — so a score reads a slightly wrong map. It is a limitation of the studies, not a difference in biology.
If height is so genetic, why is each generation taller?
Because the environmental share is large and it changed. Nutrition, childhood illness and general health moved a long way in a century, and the gene pool did not. Strongly heritable within a population and strongly environmental between generations are not contradictory — they are different questions.
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.