> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getbased.health/llms.txt
> Use this file to discover all available pages before exploring further.
# Import and analyze your raw DNA data with getbased
> Drop a raw DNA file from any major provider and getbased overlays 47 curated SNPs on your lab results — parsed entirely in your browser.
getbased can overlay genetic context on your blood work. Drop a raw DNA file from AncestryDNA, 23andMe, MyHeritage, FTDNA, Living DNA, Illumina GenomeStudio, DNAEra, or another compatible provider and getbased scans it for 47 health-relevant SNPs. Those findings surface in the Genome lens, the Genetic Modifiers dashboard widget, marker details, and AI context. No account, no upload, no cloud processing.
## Supported providers
You need a **raw DNA data file** from your testing service — not a PDF report or health summary, but the underlying data export.
| Provider | File format | How to download |
| ---------------------------------- | ----------------------------------- | ------------------------------------------- |
| **AncestryDNA** | Tab-separated text | Settings → DNA → Download Raw DNA Data |
| **23andMe** | Tab-separated text | Settings → 23andMe Data → Download Raw Data |
| **MyHeritage** | CSV | DNA → Manage DNA kits → Download Raw Data |
| **FTDNA** (Family Tree DNA) | CSV | myFTDNA → Download Raw Data |
| **Living DNA** | Tab-separated text | Dashboard → Download Raw DNA |
| **Illumina GenomeStudio / DNAEra** | CSV with `[Header]`/`[Data]` blocks | Provided by your clinical lab |
getbased identifies files by their content headers, not the filename. If your file isn't recognized, make sure you downloaded the raw data export and not a formatted health report.
## How to import
Common entry points open the same file picker:
* **Genome lens** — open **Genome** and choose **Add your DNA data** when no DNA is loaded.
* **Genetic Modifiers widget** — add the widget to your dashboard and use its empty state.
* **Header import** — use the import button in the header.
* **Drag and drop** — drag your raw DNA file onto the app.
Click **Add your DNA data** in the Genome lens or Genetic Modifiers widget, use the header import button, or drag your raw DNA file onto the page.
getbased groups your findings by impact level before saving anything. Significant results appear first.
Confirm to save. The 47 matched SNP genotypes are stored in your browser. Everything else in the file is discarded.
No AI provider is needed for DNA import. The entire file is parsed locally using a Web Worker — your raw DNA data is never transmitted anywhere.
## What gets extracted
getbased doesn't store your genome. It scans your file for **47 curated SNPs** across 13 categories and discards everything else. Each SNP was selected because it has a known biochemical mechanism, a large effect size, and directly helps interpret a biomarker you're already tracking.
| Category | SNPs | Example genes | Related markers |
| ---------------- | ---- | ----------------------------- | ----------------------------- |
| Methylation | 6 | MTHFR, MTR, MTRR, CBS | Homocysteine, folate, B12 |
| Iron | 5 | HFE, TMPRSS6, TF | Ferritin, transferrin, iron |
| Lipids | 6 | APOE, PCSK9, CETP, LIPC | LDL, HDL, cholesterol, Lp(a) |
| Vitamin D | 4 | VDR, GC, CYP2R1 | Vitamin D |
| Vitamin B12 | 4 | FUT2, TCN2, CUBN | B12, homocysteine |
| Blood sugar | 5 | TCF7L2, PPARG, ADIPOQ, MTNR1B | Glucose, HbA1c, insulin |
| Sex hormones | 5 | SHBG, CYP17A1, CYP19A1 | Testosterone, estradiol, SHBG |
| Thyroid | 3 | DIO1, DIO2, TSHR | TSH, fT3, fT4 |
| Bilirubin | 2 | UGT1A1 | Total bilirubin |
| Fatty acids | 4 | FADS1, FADS2, ELOVL2 | Omega-3, EPA, DHA |
| Alcohol | 1 | ALDH2 | GGT, AST, ALT |
| Caffeine | 1 | CYP1A2 | Cholesterol, hsCRP, glucose |
| Body composition | 1 | FTO | Body fat %, BMI, HbA1c |
### APOE haplotype
The two APOE SNPs (rs429358 + rs7412) are automatically resolved into standard haplotype notation — ε2, ε3, or ε4 — rather than shown as raw genotypes. Your APOE status appears prominently in the Genome lens and in dashboard genetics summaries.
## Effect tiers
Each genotype gets a colored dot indicating how much weight to give the finding when interpreting your labs.
| Indicator | Meaning | Example |
| ---------------------- | ------------------------------------------------------------------ | ----------------------------------------------- |
| Red — significant risk | Meaningful functional impact worth factoring into health decisions | MTHFR C677T AA (\~30% enzyme activity) |
| Yellow — moderate risk | Mild effect, relevant when combined with other factors | HFE C282Y heterozygous |
| Orange — mild risk | Small effect on its own, mainly relevant in combination | MTHFR A1298C heterozygous |
| Green — beneficial | A variant that's actually good news | PCSK9 R46L (\~15% lower lifetime LDL) |
| Gray — informational | A lab-interpretation flag, neither risky nor protective | FUT2 W154X non-secretor (artificially high B12) |
Wild-type genotypes with no meaningful effect get no dot. The import preview groups findings by severity so you see significant results first.
## Where genetics appears
After import, your genetic context surfaces across the app.
**Genome lens** — the dedicated genetics workspace shows your APOE haplotype, mtDNA data, import details, and top SNP findings grouped by category and severity.
**Dashboard** — the **Genetic Modifiers** widget summarizes actionable SNP context alongside labs and goals. Add or remove it like any other dashboard widget.
**Biomarker detail modal** — when you open any biomarker's detail view, relevant SNPs appear inline with the gene, your genotype, and what it means for that specific marker. SNPs are sorted by severity.
**AI chat** — the AI automatically receives your genetic context when interpreting lab results. Only SNPs relevant to the markers being discussed are included, unless you ask about genetics directly — then your full profile is sent.
**Context cards** — SNPs route to the relevant context cards (diet, supplements, sleep, and so on) so the AI can factor them into lifestyle recommendations.
## mtDNA haplogroup import
In addition to autosomal SNPs, getbased can determine your maternal haplogroup from a mitochondrial DNA mutation file.
### Supported file format
Some labs (including Living DNA) export mtDNA results as a plain CSV with one mutation per line:
```text theme={null}
263G
462T
1438G
10398G
13708A
```
Drop this file onto the app and getbased will:
1. Match your mutations against diagnostic markers from PhyloTree Build 17
2. Resolve your maternal haplogroup (for example, J, H, U, or K)
3. Classify your mitochondrial coupling status using Doug Wallace's framework
4. Compare your haplogroup against your profile's latitude to detect environment-haplotype mismatch
### Manual haplogroup entry
If you already know your maternal haplogroup from 23andMe, FTDNA, or another service but don't have a mutation file, you can enter it directly without uploading anything:
* **Profile editor** — select your haplogroup from the **mtDNA Haplogroup (maternal lineage)** dropdown
Both paths auto-resolve the coupling classification and environment mismatch detection.
### Coupling classification
Based on Wallace (2015, *Cell*), mtDNA haplogroups correlate with mitochondrial electron transport coupling efficiency:
* **Coupled** (L lineages) — efficient ATP production, low heat generation. Evolved in equatorial climates.
* **Uncoupled** (J, T) — more heat per calorie, less ATP. Evolved in cold northern climates.
* **Intermediate** — most other haplogroups fall between these extremes.
The AI uses this to contextualize your lab results, cold tolerance, light exposure, and environmental data.
The coupling classification follows Wallace's mitochondrial paradigm — supported by cybrid studies and population data, but not universally accepted as clinical standard. Individual variation within haplogroups is significant.
## Privacy
Your raw DNA file never leaves your device.
* The file is parsed **entirely in your browser** using a Web Worker — it is never uploaded or transmitted to any server
* Only the 47 matched SNP genotypes are stored, not your full genome
* Stored data includes the rsID, genotype, gene name, variant name, and effect classification
* DNA data is included in your JSON exports and covered by encryption if you have it enabled
* You can delete your genetic data at any time from the Genome lens
## Re-importing
Importing a new file replaces all existing genetic data for the current profile. Use the Genome lens controls or drop a new file onto the page. This is useful if you switch providers or download a newer export from your testing service.
## FAQ
Quality over quantity. Each SNP was selected because it has a well-understood biochemical mechanism, a large effect size, and directly helps interpret a biomarker you're already tracking. Adding hundreds of low-confidence GWAS associations would create noise rather than signal. Every SNP links to its primary research paper so you can verify the evidence yourself.
getbased identifies files by their content headers, not the filename. Make sure you downloaded the raw data export — a text or CSV file — and not a PDF report or formatted health summary. If you're using Illumina GenomeStudio output from a clinical lab, confirm the file includes the `[Header]` and `[Data]` block structure.
Not directly through the app. The SNP list is curated and versioned — every entry must pass strict criteria: a known mechanism, large effect size, well-replicated findings, and clear actionability tied to a tracked biomarker. Community contributions are welcome via pull request on GitHub. Open an issue first with the rsID, which biomarker it maps to, the mechanism, effect size, replication evidence, and what concrete recommendation follows.
APOE status is determined by two SNPs — rs429358 and rs7412 — that must be read together. getbased combines them automatically into the standard notation (ε2/ε3/ε4) rather than showing raw genotypes. The most common result is ε3/ε3, which is considered the neutral baseline.
No. Genetic information provides context for interpreting your lab results — it does not diagnose conditions. Always discuss significant findings with your healthcare provider before making changes to supplements, medications, or treatment plans.
Only the 47 matched SNP genotypes: the rsID, genotype, gene name, variant name, and effect classification. Nothing else from your raw DNA file is retained. The full genome data is processed in memory and immediately discarded after the scan completes.