GAO, JIA LIN / M / 31

Today · chronological

Timeline

When it's time · full breakdown

Session

All-day · non-consumption

Abstinences

North star

Vision

Targets · with deadlines

Goals

Genome × measured · current state

Action Loop

Each card pairs a genetic finding with the measurement it predicts and the most recent value from your wearables/labs. Green = on target. Amber = drifting. Gray = awaiting data.

What the data is telling you

Correlations

Patterns across sleep, training, and social signals over the window you've been tracking. Each pattern's interpretation includes the sample size — small n = thin signal.

Day-of-week patterns

Now → 8w → 3mo → ongoing

Roadmap

Consumption · books / podcasts / films

Media

What you've chosen to consume. Edit output/media.yaml to add or update entries — this is the deliberate-attention log.

Last 12 weeks · sleep + workouts

Streak

Last 7 days · vs prior 7

Weekly recap

People · sorted by attention need

Reach out

From your social-media-graph aggregates. Each person's attention score (0-100) factors days_since_last, intimacy signal, and reciprocity. Higher = longer overdue.

Next 60 days

Birthdays

Reference · prescriber alerts

Drugs to avoid / discuss

Static reference: drug classes flagged by your tendon-vulnerability profile. Mention these to any new prescriber. Live alerts (when a current med matches) live in the Interventions tab.

Profile · context

Health profile

Prescriber alerts

Med watch

Any current medication is cross-checked against your tendon-vulnerability avoid list. Flagged = bring it up at your next visit.

Top of report

Actionable findings

The eight findings below have strong evidence, real-world implications, and would change a doctor's prescribing or screening decisions. Click any card to jump to the full detail.

Pharmacogenomics

Drugs to flag

Findings derived using dbSNP forward-strand convention against CPIC Level A guidelines. These do not change with future re-runs. Photograph for your phone's medical info.

Cardiovascular risk

Cardiology

9p21 risk haplotype is the strongest single-locus CAD signal in your data. Combined with a maternal-grandfather history of heart disease and a borderline-pro-inflammatory cytokine profile, this section warrants the most attention.

Recessive disease carrier status

Carrier

Findings here are asymptomatic in you. Relevance is for family planning — partner screening ($300–400) gives a clean answer for ~280 recessive diseases including the BCKDHA finding below.

Nutrition & metabolism

Nutrition

Curated panel of nutrigenomic SNPs verified against dbSNP forward strand. The MTHFR and vitamin D findings are immediately actionable; the others inform supplementation choices.

Behavior, exercise, sleep

Lifestyle

Variants that don't determine disease but inform how you respond to alcohol, caffeine, training type, salt, and sleep cues.

Polygenic risk scores

PRS

Weighted sums across 18.6 million SNPs from 10 PGS Catalog scoring files. Direction is interpretable; precise percentiles require ancestry-matched reference distributions (proper Dockerised pgsc_calc run, future work). Reading: green = low direction, amber = elevated direction, gray = neutral or low coverage.

Longitudinal · HealthKit

Vitals

Live time-series from your Apple Health export. Each chart is annotated with the genotype-driven target where one applies. Hover any chart for the most recent value and trend.

Garmin · last 90 activities

Workouts

Recent training. HR-zone bars show time spent in each zone — wide right bars = aerobic threshold and above. Training load is Garmin's per-activity score; aerobic-TE 3.0+ is a strong session.

Genotype × medical record

Clinical convergences

Where a genetic prediction matches a lab value or documented clinical event, confidence in the finding is reinforced. Below are the strongest convergences from your record.

Lab values (recent encounters)

Next PCP visit agenda

Tap to check off as you discuss / complete each item. Saved locally in your browser.

Common alleles you don't carry

Reassuring

What's not in your data matters too. None of these absences are guarantees, but they're specific high-impact alleles that aren't in play for you.

What this analysis can't tell you

Limits

A 23andMe chip + imputation handles common variants well but is blind to several classes of clinically important variation. If any of the categories below become relevant via family history or symptoms, get a clinical-grade test for that question.