Coffee intake measures the amount of coffee a person typically consumes. Genetics influence both the metabolism of caffeine and the perceived taste and reward from drinking coffee. Variants in genes like CYP1A2 and AHR affect how quickly caffeine is broken down, which in turn influences consumption patterns.
Score Details
This score comes from the PGS Catalog, an open repository of published polygenic scoring models used in genomic research.
- PGS ID
- PGS001126
- Score Name
- GBE_INI1498
- Reported Trait
- Coffee intake
- Additional Info
- https://biobankengine.stanford.edu/RIVAS_HG19/snpnet/INI1498
- Development Method
- snpnet
- Number of Variants
- 3,154
- Original Genome Build
- GRCh37
- Weight Type
- NR
- Release Date
- October 21, 2021
Source Publication
Significant sparse polygenic risk scores across 813 traits in UK Biobank.
Development Samples
Polygenic risk scores are developed against large reference datasets. This score was built using the UK Biobank, a biomedical cohort with genetic and health data from roughly 500,000 participants — large enough to identify reliable genetic associations.
| Ancestry | Sample Size | Country | Cohort |
|---|---|---|---|
| European(white British ancestry) | 252,192 | UK | UKB |
Ancestry Distribution
Development (252,192 individuals)
Evaluation (5 sample sets)
Performance Evaluations
Polygenic risk scores are usually developed and validated in populations of European ancestry. Predictive accuracy can vary across ancestries because of differences in genetic architecture, linkage disequilibrium patterns, and allele frequencies. For quantitative traits, R² is the proportion of variance in the trait explained by the score. Values substantially below the best-performing ancestry are highlighted to flag reduced transferability.
| AncestryThe broad ancestral population of the evaluation sample. | NNumber of individuals in the evaluation sample. | R²Proportion of variance explained by the full model (PGS + covariates). Higher is better. | PGS R²Proportion of variance explained by the PGS alone, without covariates. Isolates the predictive power of the genetic score. |
|---|---|---|---|
| African | 5,753 | 0.0203[0.0135, 0.0270] | 0.0024[0.0001, 0.0048] |
| East Asian | 1,455 | 0.0530[0.0323, 0.0736] | 0.0059[-0.0013, 0.0132] |
| European | 23,191 | 0.0228[0.0192, 0.0265] | 0.0062[0.0042, 0.0081] |
| South Asian | 6,844 | 0.0285[0.0212, 0.0357] | 0.0019[-0.0000, 0.0038] |
| European | 62,965 | 0.0157[0.0139, 0.0176] | 0.0083[0.0070, 0.0097] |
Covariates: age, sex, UKB array type, Genotype PCs
Downloads & Links
Frequently asked questions
Can I check my coffee intake polygenic risk score with 23andMe data?
Yes. Upload a raw data file from 23andMe, AncestryDNA, MyHeritage, or tellmeGen, or a single-sample whole-genome VCF, and the Genomisaur Health Report computes PGS001126 along with the other scores in your report. Array files are imputed to roughly 30 million variants first. Results are ready in about 20 minutes, and no new DNA test is needed.
What does a high polygenic risk score for coffee intake mean?
It means your genetic variants, added up, place you higher than most people in the reference population for coffee intake. It is a statement about relative genetic predisposition, not a diagnosis: lifestyle, environment, and family history matter too, and many people with high scores never develop the condition.
Where does the PGS001126 score come from?
PGS001126 is listed in the PGS Catalog and was published by Tanigawa Y et al. in PLoS Genet (2022). It combines 3,154 genetic variants. Performance metrics and the ancestry of the study participants are shown on this page.
PGS obtained from the Catalog should be cited appropriately, and used in accordance with any licensing restrictions set by the authors. See EBI Terms of Use (https://www.ebi.ac.uk/about/terms-of-use/) for additional details.