This report analyzes your genotyping data from 23andMe. and presents Polygenic Risk Scores (PRS) for 19 traits across 6 health categories. Each PRS reflects the cumulative effect of hundreds to thousands of common genetic variants associated with a trait in large population studies, primarily the UK Biobank.
What is a Polygenic Risk Score? A PRS is not a diagnosis. It quantifies statistical predisposition. Your genetic profile is one of many factors influencing health, alongside lifestyle, environment, and medical history. Most traits are only modestly influenced by genetics; a high PRS increases the statistical probability of a condition but does not predict with certainty that you will develop it.
How to read this report:
- Percentile — where your genetic score sits in the population. The 50th percentile is average; above 80th is considered elevated.
- Odds ratio (OR) — for disease traits, how your odds compare to the population average. An OR of 1.5 means roughly 50% higher odds than average.
- "About Average" — when the 95% confidence interval includes 1.0, the difference from average is not statistically meaningful and the result is labeled About Average.
- Estimated lifetime risk — for disease traits, an approximate lifetime probability derived from your odds ratio and UK Biobank population prevalence. This is a statistical estimate, not a clinical prediction.
- Top contributing variants — each score card has an expandable section showing the individual genetic variants that contribute most to your score. These are the variants with the largest effect on your personal result, but they are not necessarily causal. Polygenic scores combine hundreds to thousands of variants, each contributing a small amount. The listed variants simply had the largest individual weight in your calculation.
- Effect-size distribution — each score card also includes an expandable chart showing how the trait measurement or odds ratio varies across PRS percentile bins in the reference population. Your bin is highlighted. These charts help you understand the magnitude of the genetic effect: a steep curve means the score strongly differentiates risk across the population, while a flat curve means genetics plays a smaller role for that trait.
These scores reflect your genetic predisposition toward each hair color category independently. A higher percentile indicates a stronger genetic tendency for that color.
Light brown hair effect-size distribution
Red hair effect-size distribution
Blonde hair effect-size distribution
Brown hair effect-size distribution
Black hair effect-size distribution
Dark brown hair effect-size distribution
Adult height is one of the most heritable human traits, with genetics explaining roughly 80% of the variation. Hundreds of genetic variants each make small contributions to your final height, alongside nutrition and health during childhood.
Your score for Height is in the top 20%, indicating an elevated genetic predisposition.
Effect size across PRS percentile bins
Effect size per PRS decile, reference line at population mean (5'7", 169.8 cm). Error bars: 95% CI. Source: Tanigawa et al. (2023).
Top contributing variants (10)
| Variant | rsID | Genotype | Effect Weight | Dosage | Gene | Type | |
|---|---|---|---|---|---|---|---|
| 3:185548683:G:A | rs720390 | 1/1 | 0.3944 | 2.00 | — | Intergenic | ↑ |
| 6:19839415:C:T | rs41271299 | 0/1 | 0.6870 | 1.00 | ID4 | Intronic | ↑ |
| 15:100692953:G:A | rs72755233 | 0|1 | -0.5392 | 1.00 | ADAMTS17 | Missense | ↓ |
| 4:18025484:G:A | rs2011603 | 1|1 | -0.2370 | 2.00 | LCORL | Upstream | ↓ |
| 11:75276178:A:C | rs606452 | 1/1 | -0.2352 | 2.00 | SERPINH1 | Intronic | ↓ |
| 15:67457698:A:G | rs35874463 | 0/1 | 0.4458 | 1.00 | SMAD3 | Missense | ↑ |
| 20:34025756:A:G | rs143384 | 0/1 | 0.4271 | 1.00 | GDF5 | 5-prime UTR | ↑ |
| 12:69827658:G:T | rs10748128 | 1/1 | 0.2053 | 2.00 | — | Intergenic | ↑ |
| 6:130349119:C:T | rs6569648 | 1/1 | -0.1934 | 2.00 | L3MBTL3 | Intronic | ↓ |
| 12:66359752:C:A | rs8756 | 1/1 | -0.1905 | 2.00 | HMGA2 | 3-prime UTR | ↓ |
BMI (body mass index) is a common measure of body size calculated from height and weight. While it does not distinguish between fat and muscle mass, it is a useful screening tool. Both genetics and lifestyle influence BMI.
Your score for BMI falls somewhat below the population average.
Effect size across PRS percentile bins
Effect size per PRS decile, reference line at population mean (28.0 kg/m²). Error bars: 95% CI. Source: Tanigawa et al. (2023).
Top contributing variants (10)
| Variant | rsID | Genotype | Effect Weight | Dosage | Gene | Type | |
|---|---|---|---|---|---|---|---|
| 16:53800954:T:C | rs1421085 | 1/0 | 0.3454 | 1.00 | FTO | Intronic | ↑ |
| 2:637830:A:G | rs13393304 | 1|1 | 0.1410 | 2.00 | — | Intergenic | ↑ |
| 3:49924940:T:C | rs1062633 | 1|1 | 0.1143 | 2.00 | MST1R | Missense | ↑ |
| 19:47569003:G:A | rs3810291 | 1/1 | 0.1136 | 2.00 | ZC3H4 | 3-prime UTR | ↑ |
| 16:4013467:C:T | rs2531995 | 1/1 | 0.1046 | 2.00 | ADCY9 | 3-prime UTR | ↑ |
| 4:100239319:T:C | rs1229984 | 1|1 | 0.0892 | 2.00 | ADH1B | Missense | ↑ |
| 11:27701365:G:A | rs10835211 | 1/1 | 0.0826 | 2.00 | BDNF | Intronic | ↑ |
| 2:622827:T:C | rs2867125 | 1/1 | 0.0765 | 2.00 | — | Intergenic | ↑ |
| 14:25930988:C:A | rs8015400 | 1|1 | 0.0706 | 2.00 | — | Intergenic | ↑ |
| 16:20370810:C:T | rs9652588 | 1|1 | -0.0702 | 2.00 | PDILT | Missense | ↓ |
Skin tanning ability reflects how your skin responds to UV radiation. People who burn easily and tan poorly have less protective melanin and higher risk of skin cancer. Variants in pigmentation genes like MC1R strongly influence this trait.
Your score for Ease of Skin Tanning is in the top 20%, indicating an elevated genetic predisposition.
Odds ratio across PRS percentile bins
Odds ratio per PRS decile vs. population average (OR = 1.0, dashed). Error bars: 95% CI. Source: Tanigawa et al. (2023).
Top contributing variants (10)
| Variant | rsID | Genotype | Effect Weight | Dosage | Gene | Type | |
|---|---|---|---|---|---|---|---|
| 16:89986117:C:T | rs1805007 | 0/1 | 1.0449 | 1.00 | MC1R | Missense | ↑ |
| 15:28365618:A:G | rs12913832 | 1/1 | 0.3890 | 2.00 | HERC2 | Intronic | ↑ |
| 16:89985844:G:T | rs1805005 | 0/1 | 0.4156 | 1.00 | MC1R | Missense | ↑ |
| 11:89017961:G:A | rs1126809 | 1|0 | 0.2996 | 1.00 | TYR | Missense | ↑ |
| 9:16864521:C:T | rs2153271 | 1/1 | 0.1203 | 2.00 | BNC2 | Intronic | ↑ |
| 11:88911696:C:A | rs1042602 | 1/0 | 0.1579 | 1.00 | TYR | Missense | ↑ |
| 11:68855363:G:A | rs3829241 | 1|1 | 0.0604 | 2.00 | TPCN2 | Missense | ↑ |
| 1:66921933:G:A | rs1772131 | 1|1 | 0.0540 | 2.00 | — | Intergenic | ↑ |
| 20:32738612:C:T | rs1015362 | 0/1 | -0.1023 | 1.00 | — | Intergenic | ↓ |
| 8:116586460:C:T | rs3779881 | 1|1 | -0.0501 | 2.00 | TRPS1 | Intronic | ↓ |
A heart attack (myocardial infarction) occurs when blood flow to part of the heart muscle is blocked, usually by a blood clot in a coronary artery. Family history is one of the strongest risk factors, alongside high cholesterol, blood pressure, smoking, and diabetes.
Your score for Heart attack falls somewhat below the population average.
Odds ratio across PRS percentile bins
Odds ratio per PRS decile vs. population average (OR = 1.0, dashed). Error bars: 95% CI. Source: Tanigawa et al. (2023).
Top contributing variants (10)
| Variant | rsID | Genotype | Effect Weight | Dosage | Gene | Type | |
|---|---|---|---|---|---|---|---|
| 12:111884608:T:C | rs3184504 | 1/0 | -0.0686 | 1.00 | SH2B3 | Missense | ↓ |
| 1:222923351:A:G | rs6683071 | 1/1 | 0.0333 | 2.00 | FAM177B | Missense | ↑ |
| 1:109822166:G:A | rs599839 | 1/1 | 0.0317 | 2.00 | CELSR2 | Downstream | ↑ |
| 17:66449122:G:A | rs883541 | 1/1 | 0.0283 | 2.00 | WIPI1 | Missense | ↑ |
| 9:22115959:A:G | rs2383207 | 0/1 | 0.0524 | 1.00 | CDKN2B-AS1 | Intronic | ↑ |
| 7:35291491:G:A | rs73099187 | 1|1 | -0.0244 | 2.00 | TBX20 | Intronic | ↓ |
| 1:222826481:G:A | rs2291832 | 1|1 | 0.0239 | 2.00 | MIA3 | Intronic | ↑ |
| 2:106823652:C:T | rs13006272 | 1|1 | -0.0227 | 2.00 | — | Intergenic | ↓ |
| 7:129663496:C:T | rs11556924 | 0/1 | -0.0401 | 1.00 | ZC3HC1 | Missense | ↓ |
| 2:196851911:G:A | rs10931715 | 1|1 | 0.0190 | 2.00 | DNAH7 | Missense | ↑ |
Type 2 diabetes occurs when the body becomes resistant to insulin or the pancreas cannot produce enough. It is the most common form of diabetes, closely linked to obesity, physical inactivity, and family history. Early detection and lifestyle changes can significantly reduce risk.
Your score for Type 2 diabetes falls somewhat below the population average.
Odds ratio across PRS percentile bins
Odds ratio per PRS decile vs. population average (OR = 1.0, dashed). Error bars: 95% CI. Source: Tanigawa et al. (2023).
Top contributing variants (10)
| Variant | rsID | Genotype | Effect Weight | Dosage | Gene | Type | |
|---|---|---|---|---|---|---|---|
| 5:169310213:A:C | rs17646221 | 1|1 | 0.0474 | 2.00 | FAM196B | Missense | ↑ |
| 22:50752150:C:T | rs68178377 | 1|1 | 0.0376 | 2.00 | DENND6B | Missense | ↑ |
| 11:64124515:T:C | rs612448 | 1|1 | -0.0338 | 2.00 | CCDC88B | Missense | ↓ |
| 9:22137685:T:G | rs7018475 | 1/1 | 0.0297 | 2.00 | — | Intergenic | ↑ |
| 12:6948468:T:C | rs1129649 | 1|1 | 0.0275 | 2.00 | LEPREL2 | Loss of Function | ↑ |
| 4:37910836:G:A | rs2925951 | 1|1 | -0.0274 | 2.00 | TBC1D1 | Intronic | ↓ |
| 3:183800463:G:A | rs7648737 | 1|1 | -0.0242 | 2.00 | RP11-778D9.9 | Loss of Function | ↓ |
| 9:22132076:A:G | rs2383208 | 0/1 | -0.0452 | 1.00 | — | Intergenic | ↓ |
| 11:32293377:C:A | rs7105928 | 1|1 | -0.0197 | 2.00 | RP1-65P5.1 | Intronic | ↓ |
| 11:8264900:A:G | rs437976 | 1/1 | -0.0196 | 2.00 | LMO1 | Intronic | ↓ |
Hand grip strength is a simple but powerful measure of overall muscular strength and health. Lower grip strength has been linked to higher risk of cardiovascular disease, disability, and mortality. Genetics, physical activity, and nutrition all influence it.
Your score for Hand grip strength is in the top 20%, indicating an elevated genetic predisposition.
Effect size across PRS percentile bins
Effect size per PRS decile, reference line at population mean (32.3 kilograms). Error bars: 95% CI. Source: Tanigawa et al. (2023).
Top contributing variants (10)
| Variant | rsID | Genotype | Effect Weight | Dosage | Gene | Type | |
|---|---|---|---|---|---|---|---|
| 1:176792249:A:G | rs1325598 | 1|1 | 0.1395 | 2.00 | PAPPA2 | Intronic | ↑ |
| 15:74336633:T:C | rs5742915 | 1/1 | 0.1124 | 2.00 | PML | Missense | ↑ |
| 1:41502680:C:A | rs1740610 | 1|1 | -0.1035 | 2.00 | SCMH1 | Intronic | ↓ |
| 5:130766662:T:C | rs1291602 | 1|1 | 0.0984 | 2.00 | RAPGEF6 | Missense | ↑ |
| 20:34025756:A:G | rs143384 | 0/1 | 0.1563 | 1.00 | GDF5 | 5-prime UTR | ↑ |
| 10:70332580:A:G | rs10823229 | 1|1 | 0.0745 | 2.00 | TET1 | Missense | ↑ |
| 2:224992906:C:T | rs988321 | 1|1 | -0.0715 | 2.00 | — | Regulatory | ↓ |
| 12:14993439:C:T | rs11276 | 0|1 | -0.1397 | 1.00 | ART4 | Missense | ↓ |
| 6:130374102:C:A | rs9388768 | 1|1 | -0.0697 | 2.00 | L3MBTL3 | Missense | ↓ |
| 17:43216281:C:T | rs4986172 | 1/1 | -0.0683 | 2.00 | ACBD4 | Intronic | ↓ |
Asthma is a chronic condition in which the airways become inflamed and narrowed, causing wheezing, shortness of breath, and coughing. It often begins in childhood and frequently co-occurs with allergies and eczema. Genetics and environmental exposures both contribute.
Your score for Asthma is near the population average.
Odds ratio across PRS percentile bins
Odds ratio per PRS decile vs. population average (OR = 1.0, dashed). Error bars: 95% CI. Source: Tanigawa et al. (2023).
Top contributing variants (10)
| Variant | rsID | Genotype | Effect Weight | Dosage | Gene | Type | |
|---|---|---|---|---|---|---|---|
| 11:76291154:G:A | rs61893460 | 1|1 | 0.0782 | 2.00 | — | Intergenic | ↑ |
| 9:6072597:T:C | rs343476 | 1|1 | 0.0661 | 2.00 | — | Intergenic | ↑ |
| 17:38064469:T:C | rs11078928 | 1|0 | -0.0823 | 1.00 | GSDMB | Loss of Function | ↓ |
| 5:110401872:T:C | rs1837253 | 1/0 | 0.0784 | 1.00 | TSLP | Upstream | ↑ |
| 5:110406742:C:T | rs3806933 | 1/1 | -0.0360 | 2.00 | TSLP | 5-prime UTR | ↓ |
| 16:11541896:G:A | rs4238608 | 1/1 | -0.0350 | 2.00 | CTD-3088G3.8 | Missense | ↓ |
| 10:9049253:C:T | rs12413578 | 0/1 | -0.0691 | 1.00 | — | Intergenic | ↓ |
| 5:131901225:A:G | rs2244012 | 0/1 | 0.0666 | 1.00 | RAD50 | Intronic | ↑ |
| 14:69254191:C:T | rs4902647 | 1|1 | -0.0319 | 2.00 | ZFP36L1 | Downstream | ↓ |
| 4:38799710:T:C | rs4833095 | 1/0 | -0.0629 | 1.00 | TLR1 | Missense | ↓ |
Chronotype describes your body's natural preference for when to sleep and wake. "Morning larks" feel most alert early in the day, while "night owls" peak later. This is strongly influenced by genetics and shifts across the lifespan.
Your score for Chronotype falls somewhat below the population average.
Effect size across PRS percentile bins
Effect size per PRS decile, reference line at population mean (3.19). Error bars: 95% CI. Source: Tanigawa et al. (2023).
Top contributing variants (10)
| Variant | rsID | Genotype | Effect Weight | Dosage | Gene | Type | |
|---|---|---|---|---|---|---|---|
| 1:182569626:T:C | rs1144566 | 1|1 | 0.0507 | 2.00 | RGS16 | Missense | ↑ |
| 6:55142337:A:G | rs2653349 | 1/1 | 0.0194 | 2.00 | HCRTR2 | Missense | ↑ |
| 2:77213103:C:T | rs7563917 | 1|1 | 0.0156 | 2.00 | AC079117.1 | Non-coding | ↑ |
| 7:96468009:G:A | rs10254255 | 1|1 | -0.0139 | 2.00 | — | Intergenic | ↓ |
| 18:38026570:T:C | rs4799549 | 1|1 | 0.0112 | 2.00 | — | Intergenic | ↑ |
| 17:8108331:A:G | rs1059476 | 1|1 | 0.0108 | 2.00 | AURKB | Missense | ↑ |
| 2:239306268:A:C | rs3739070 | 0/1 | 0.0199 | 1.00 | TRAF3IP1 | Missense | ↑ |
| 7:24081855:A:G | rs10234716 | 1|1 | 0.0099 | 2.00 | — | Intergenic | ↑ |
| 3:185990096:C:T | rs2193587 | 1|1 | 0.0098 | 2.00 | DGKG | Missense | ↑ |
| 22:40554008:T:G | rs6001807 | 1|1 | 0.0095 | 2.00 | TNRC6B | Intronic | ↑ |
How much coffee you drink is partly genetic. Variants in genes that affect caffeine metabolism (like CYP1A2) and caffeine sensitivity influence whether you crave multiple cups a day or find even one too stimulating.
Your score for Coffee intake falls somewhat below the population average.
Effect size across PRS percentile bins
Effect size per PRS decile, reference line at population mean (3.33 cups per day). Error bars: 95% CI. Source: Tanigawa et al. (2023).
Top contributing variants (10)
| Variant | rsID | Genotype | Effect Weight | Dosage | Gene | Type | |
|---|---|---|---|---|---|---|---|
| 7:17284577:T:C | rs4410790 | 1/1 | 0.1128 | 2.00 | — | Intergenic | ↑ |
| 7:17430004:C:A | rs7777586 | 1|1 | -0.0325 | 2.00 | AC019117.1 | Intronic | ↓ |
| 2:634905:T:C | rs6548238 | 1/1 | 0.0268 | 2.00 | — | Regulatory | ↑ |
| 4:89052323:G:T | rs2231142 | 0/1 | -0.0393 | 1.00 | ABCG2 | Missense | ↓ |
| 18:57851763:A:G | rs10871777 | 0/1 | 0.0361 | 1.00 | — | Intergenic | ↑ |
| 1:49910568:G:A | rs1167268 | 1|1 | -0.0167 | 2.00 | AGBL4-IT1 | Intronic | ↓ |
| 17:18775900:A:G | rs4393623 | 1|1 | 0.0149 | 2.00 | PRPSAP2 | Missense | ↑ |
| 2:27730940:T:C | rs1260326 | 1/0 | 0.0295 | 1.00 | GCKR | Missense | ↑ |
| 12:11338781:C:A | rs1669413 | 1/1 | -0.0137 | 2.00 | TAS2R42 | Missense | ↓ |
| 21:30594334:T:C | rs2832268 | 1|1 | 0.0129 | 2.00 | BACH1 | Intronic | ↑ |
Sleep duration is partly heritable and varies naturally between individuals. Genes influencing circadian rhythm and sleep-wake signaling contribute to whether you tend to need more or less sleep each night.
Your score for Sleep duration falls somewhat below the population average.
Effect size across PRS percentile bins
Effect size per PRS decile, reference line at population mean (8.20 hours). Error bars: 95% CI. Source: Tanigawa et al. (2023).
Top contributing variants (10)
| Variant | rsID | Genotype | Effect Weight | Dosage | Gene | Type | |
|---|---|---|---|---|---|---|---|
| 2:114090412:A:G | rs1823125 | 0/1 | 0.0338 | 1.00 | — | Intergenic | ↑ |
| 1:34743076:T:G | rs1342153 | 1|1 | -0.0086 | 2.00 | RP4-657M3.2 | Upstream | ↓ |
| 14:26974414:A:C | rs178189 | 1|1 | 0.0082 | 2.00 | NOVA1 | Intronic | ↑ |
| 5:135620850:T:C | rs1499767 | 1|1 | -0.0079 | 2.00 | TRPC7 | Intronic | ↓ |
| 1:65250982:T:G | rs2483616 | 1|1 | 0.0075 | 2.00 | RAVER2 | Intronic | ↑ |
| 16:78420775:G:A | rs11545029 | 1|1 | -0.0074 | 2.00 | WWOX | Missense | ↓ |
| 10:70332580:A:G | rs10823229 | 1|1 | -0.0070 | 2.00 | TET1 | Missense | ↓ |
| 10:125003630:T:C | rs7089041 | 1|1 | -0.0067 | 2.00 | — | Intergenic | ↓ |
| 17:4764359:C:T | rs8073970 | 1|1 | -0.0064 | 2.00 | MINK1 | Intronic | ↓ |
| 1:31687774:T:C | rs6671739 | 1|1 | 0.0063 | 2.00 | NKAIN1 | Intronic | ↑ |
Genes involved in alcohol metabolism and reward pathways can influence how frequently a person drinks. Variants in enzymes like ADH and ALDH affect how your body processes alcohol and how it makes you feel.
Your score for Alcohol intake frequency falls somewhat below the population average.
Effect size across PRS percentile bins
Effect size per PRS decile, reference line at population mean (4.84 categories). Error bars: 95% CI. Source: Tanigawa et al. (2023).
Top contributing variants (10)
| Variant | rsID | Genotype | Effect Weight | Dosage | Gene | Type | |
|---|---|---|---|---|---|---|---|
| 4:100239319:T:C | rs1229984 | 1|1 | 0.2145 | 2.00 | ADH1B | Missense | ↑ |
| 2:27730940:T:C | rs1260326 | 1/0 | 0.0336 | 1.00 | GCKR | Missense | ↑ |
| 4:184831715:A:G | rs2278475 | 1|1 | -0.0130 | 2.00 | STOX2 | Intronic | ↓ |
| 4:172552376:G:A | rs1027206 | 1|1 | -0.0120 | 2.00 | RP11-97E7.2 | Intronic | ↓ |
| 11:121801129:T:C | rs1666658 | 1|1 | -0.0117 | 2.00 | — | Regulatory | ↓ |
| 3:49924940:T:C | rs1062633 | 1|1 | -0.0115 | 2.00 | MST1R | Missense | ↓ |
| 10:99713117:T:C | rs533358 | 1|1 | -0.0113 | 2.00 | CRTAC1 | Intronic | ↓ |
| 12:54660427:G:A | rs57281063 | 0|1 | 0.0221 | 1.00 | RP11-968A15.2 | Intronic | ↑ |
| 6:17173376:G:A | rs2181349 | 1|1 | 0.0109 | 2.00 | — | Intergenic | ↑ |
| 10:133978962:T:C | rs2172131 | 1|1 | 0.0098 | 2.00 | JAKMIP3 | Intronic | ↑ |
How old you look relative to your actual age is partly genetic. Genes involved in skin elasticity, pigmentation, and cellular repair processes influence the rate of facial aging. Sun exposure, smoking, and lifestyle also play major roles.
Your score for Facial aging is near the population average.
Odds ratio across PRS percentile bins
Odds ratio per PRS decile vs. population average (OR = 1.0, dashed). Error bars: 95% CI. Source: Tanigawa et al. (2023).
Top contributing variants (10)
| Variant | rsID | Genotype | Effect Weight | Dosage | Gene | Type | |
|---|---|---|---|---|---|---|---|
| 8:130699140:T:C | rs10956486 | 1|0 | 0.0727 | 1.00 | RP11-419K12.1 | Upstream | ↑ |
| 9:16864521:C:T | rs2153271 | 1/1 | -0.0346 | 2.00 | BNC2 | Intronic | ↓ |
| 2:145634153:G:A | rs6738560 | 1|1 | 0.0323 | 2.00 | TEX41 | Intronic | ↑ |
| 9:205964:A:G | rs478882 | 1/1 | -0.0307 | 2.00 | — | Intergenic | ↓ |
| 20:39832628:T:C | rs17265513 | 1/1 | 0.0234 | 2.00 | ZHX3 | Missense | ↑ |
| 2:56040099:T:C | rs10199082 | 1|0 | -0.0463 | 1.00 | — | Intergenic | ↓ |
| 2:219903258:T:G | rs6736922 | 1|1 | 0.0215 | 2.00 | CCDC108 | Loss of Function | ↑ |
| 6:151579432:C:T | rs4869723 | 0|1 | 0.0374 | 1.00 | AKAP12 | Intronic | ↑ |
| 1:201314624:A:G | rs2153715 | 1|1 | -0.0177 | 2.00 | — | Intergenic | ↓ |
| 5:92666066:G:T | rs6863143 | 1|1 | 0.0168 | 2.00 | — | Intergenic | ↑ |
Walking pace is a surprisingly strong predictor of overall health and longevity. It reflects cardiovascular fitness, musculoskeletal health, and neurological function. Genetics influence the traits that contribute to walking speed, including muscle composition and body proportions.
Your score for Walking pace is near the population average.
Effect size across PRS percentile bins
Effect size per PRS decile, reference line at population mean (3.32 meters/second). Error bars: 95% CI. Source: Tanigawa et al. (2023).
Top contributing variants (10)
| Variant | rsID | Genotype | Effect Weight | Dosage | Gene | Type | |
|---|---|---|---|---|---|---|---|
| 3:49936102:T:C | rs2230590 | 1/1 | -0.0054 | 2.00 | MST1R | Missense | ↓ |
| 3:184039666:A:G | rs2178403 | 1|1 | 0.0043 | 2.00 | EIF4G1 | Missense | ↑ |
| 7:113605108:T:C | rs12705924 | 1|1 | 0.0042 | 2.00 | PPP1R3A | Intronic | ↑ |
| 18:53210302:G:T | rs613872 | 0/1 | -0.0083 | 1.00 | TCF4 | Intronic | ↓ |
| 4:182733332:T:C | rs4862006 | 1|1 | 0.0041 | 2.00 | — | Intergenic | ↑ |
| 2:41199040:G:A | rs1981804 | 1|1 | 0.0041 | 2.00 | — | Intergenic | ↑ |
| 1:25015638:C:T | rs7511698 | 1|1 | 0.0037 | 2.00 | — | Regulatory | ↑ |
| 6:137760308:C:T | rs9321606 | 1|1 | 0.0036 | 2.00 | — | Intergenic | ↑ |
| 18:37426101:C:A | rs56759062 | 1|1 | -0.0036 | 2.00 | RP11-636O21.1 | Intronic | ↓ |
| 3:88189341:T:C | rs7653652 | 1|1 | -0.0036 | 2.00 | ZNF654 | Missense | ↓ |
The following trait(s) could not be included because the scoring file had insufficient overlap with your genotype data (less than 50% of variants matched).
- Ankylosing spondylitis (PGS001267)
Important Disclaimers
Key Genetic Variants
The Key Genetic Variants section screens for individual high-penetrance variants in genes linked to specific health conditions. Unlike polygenic risk scores, a single variant can significantly increase risk for a condition. Carrier status indicates you carry one or more copies of a risk allele but does not mean you will develop the condition. Penetrance varies by variant. Consult a genetic counselor for clinical interpretation.
What is a Polygenic Risk Score?
A polygenic risk score aggregates the effect of many genetic variants into a single number reflecting genetic predisposition for a trait. Higher scores generally indicate greater genetic predisposition, but genetics is only one factor. Lifestyle, environment, and medical history all play important roles. A high PRS does not mean you will develop a condition.
Percentile & Odds Ratio Estimates
Percentile values are estimated against a reference population of PGP participants matched to your inferred ancestry. Odds ratios reflect relative risk compared to the middle decile of the reference population. These are statistical estimates and exact numbers carry uncertainty. They should not be interpreted as precise personal risk predictions.
Ancestry Limitation
All polygenic scores in this report were trained on European-ancestry data from the UK Biobank (predominantly white British participants). Percentile estimates are calibrated against a European-ancestry PGP reference population (n=119). For individuals of non-European ancestry, scores, percentiles, and risk estimates are likely to be less accurate and may not reflect true genetic predisposition. The odds ratio bins from Tanigawa et al. were also derived from the same European-ancestry cohort. Use this report with extra caution if you are not of European ancestry.
Not a Clinical Test
This report is for research and educational purposes only. It is not a clinical diagnostic test. Consult a healthcare provider or genetic counselor before making any health decisions based on this information.
Methodology
Input processing: Raw consumer genotyping data is normalized into a standardized variant format and aligned to the GRCh37 (hg19) human reference genome. Quality checks are applied to filter low-confidence calls and ensure compatibility with downstream analysis.
Imputation: Consumer genotyping arrays typically capture 600,000–700,000 variants. To fill in the millions of variants not directly measured, we perform genome-wide statistical imputation using a large, ethnically diverse reference panel. This process infers likely genotypes at untyped positions based on patterns of linkage disequilibrium observed in reference populations, expanding coverage to approximately 30 million variants across all 22 autosomes. Whole-genome sequencing (WGS) data already covers the full genome and does not require imputation.
Ancestry inference: Your genetic ancestry is estimated by comparing your genotype profile to reference populations spanning five major continental superpopulations (European, African, East Asian, South Asian, and Admixed American). A machine learning classifier trained on thousands of reference samples assigns the most likely ancestry group, which is used to select the appropriate reference distribution for percentile estimation.
Polygenic risk score calculation: Polygenic risk scores are computed by combining the effects of many genetic variants, each weighted according to its published association with a given trait. Scoring weights are sourced from the PGS Catalog, an open database of published polygenic scores. Each variant in your imputed genotype data is matched against the scoring file and the weighted sum is calculated to produce a single composite score per trait.
Percentile and risk estimation: Your raw score for each trait is compared against an empirical reference distribution to determine your percentile ranking. Relative risk (odds ratios) are estimated by mapping your score to population-level decile bins derived from large-scale biobank research. These estimates are stratified by inferred genetic ancestry to improve accuracy.
Frequently Asked Questions
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My report says I have a higher genetic tendency for a trait, but that doesn't match me at all. Why?Polygenic risk scores capture genetic predisposition, not destiny. Many traits are shaped by a combination of genetics, environment, lifestyle, diet, and chance. A high score means your DNA nudges you in that direction compared to average, but plenty of people with high scores never develop a condition and vice versa. Think of it as one piece of a larger puzzle.
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Does a high risk score mean I'm going to get a disease?No. A higher score means you may have a greater genetic predisposition, but it is not a diagnosis or a guarantee. Most common diseases are influenced by many factors beyond genetics, including age, lifestyle, family history, and preventive care. Many people with elevated scores never develop the condition.
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Should I change my medication or start new treatment based on this report?No. This report is for educational and informational purposes only. It is not a clinical test and should never replace advice from a healthcare provider. Do not start, stop, or change any medication based on these results. If something in this report concerns you, bring it to your doctor or a genetic counselor for a proper clinical evaluation.
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Why doesn't my height / BMI / blood pressure match the score?For continuous traits like height or blood pressure, the score reflects your genetic tendency — not a measurement of your actual value. Your real-world measurement is the result of genetics plus everything else: nutrition, exercise, medications, aging, and more. It is completely normal for your actual measurement to differ from what your genetics alone would predict.
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What does "percentile" mean in this report?Your percentile tells you where your score falls compared to a reference population. For example, a 75th percentile means your genetic score is higher than approximately 75% of people in the reference group. It does not mean you have a 75% chance of anything — it is a ranking, not a probability.
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Are these results accurate for all ethnicities?The scores in this report were developed using data primarily from people of European ancestry (UK Biobank). They may be less accurate for individuals of other ancestral backgrounds. Polygenic scores generally transfer less well across populations due to differences in allele frequencies and linkage patterns. We display your inferred ancestry at the top of the report for transparency.
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Can I share this report with my doctor?Yes, you are welcome to share this report with your healthcare provider. It can serve as a conversation starter about your genetic background. However, your doctor will likely want to consider your full medical history, family history, and possibly order clinical-grade genetic testing before making any medical decisions.
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Why are some traits missing from my report?A trait may be excluded if too few of its required genetic variants were found in your data after imputation. Each score requires a minimum overlap with the published variant list to produce reliable results. Excluded traits (if any) are listed near the bottom of the report.
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How is this different from a clinical genetic test?Clinical genetic tests are performed in certified laboratories under strict quality controls (such as CLIA/CAP accreditation) and are interpreted by trained geneticists. This report uses consumer genotyping data and publicly available risk scores for research and educational purposes. It has not been validated for clinical decision-making.
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Will my scores change if I get re-tested?Your DNA does not change, so the raw genetic data would be the same. However, scores could shift slightly if the analysis uses an updated scoring model, different imputation panel, or a larger reference population. The underlying science of polygenic scores is still evolving, and newer models may give somewhat different estimates.