Methodology & Scientific Basis

Most health traits do not depend on a single gene, but on hundreds of small variations (SNPs) across the genome. We use the GWAS Catalog (Genome-Wide Association Studies) to find which of those variants are present in your file, and for each one we show the trait the study measured and the effect it reported — an odds ratio or a beta — at genome-wide significance (p < 5×10⁻⁸). We do not add those effects into an overall score. Our own audit found that more than half of a category's combined effect came from studies of something other than the condition it names, and that betas were being summed across units that are not comparable, so the aggregate did not measure what its label said.

We analyse genetic variants known to affect drug metabolism. We base our annotations on PharmGKB, an expert-curated knowledge base that classifies clinical evidence (Levels 1A, 1B, 2A, etc.) on how your genotype may influence the efficacy or toxicity of certain drugs.

ClinVar is a public archive: laboratories and researchers submit their interpretations and ClinVar aggregates them. For this report we use the aggregated GERMLINE classification per variant that ClinVar's tabular source provides — ClinVar can also hold somatic clinical impact and oncogenicity classifications, which we do not import. The conditions listed alongside a variant do not each receive their own classification. Records where submitters disagree are kept and shown separately, not discarded. This is not a diagnosis: confirming one needs a certified genetic test, which SNP-chip data is not.