Methylation → COMT → recovery
The methionine cycle supplies SAMe. COMT uses SAMe to clear catecholamines. The original model follows methyl-donor supply all the way into stress recovery and the ability to wind down.
← Back to The LabMy DNA,
The lab / genetics
My DNA,
in detail.
I sequenced my whole genome and went deep. APOE, methylation, glucose, stress chemistry, and the gut–brain axis form a connected story. This is my analysis of the hardware and the experiments it inspires.
Selected findings
The findings that shape my priorities.
Lipids & brain health
APOE ε3/ε4
APOE ε4 puts long-term brain health and atherogenic lipids at the center of my plan.
Open the analysis ↘Folate & B12MTHFR GA · MTRR GG
Folate activation and B12 recycling meet at the same upstream junction.
Open the analysis ↘Glucose regulationTCF7L2 TT
TCF7L2 TT makes beta-cell function a serious long-term priority.
Open the analysis ↘Medication responseCYP2C19 GA · SLCO1B1 TC
Activation, liver transport, and target sensitivity can change the same prescription in different ways.
Open the analysis ↘System by system
The biology behind the calls.
Open a topic for the pathway, my interpretation, the cross-system connections, and the exact genotypes behind the analysis.
How to read the table
An rsID identifies a variant site. The two letters show my reported alleles. Calls follow the source notes’ letter orientation. “My model” and “my read” describe the interpretations I use to connect the biology.
01Lipids & brain healthAPOE ε3/ε4
APOE ε4 puts long-term brain health and atherogenic lipids at the center of my plan.
The finding that changed my priorities.
APOE ε3/ε4 is the heavyweight finding in my genome. APOE transports cholesterol and other fats, and ε4 is a major common susceptibility allele for late-onset Alzheimer’s disease. It connects two subjects I already track closely: my lipid profile and the health of my brain.
The scale of the finding
A major APOE meta-analysis reported 3.2-fold Alzheimer’s odds for ε3/ε4 versus ε3/ε3 in its White clinic/autopsy subgroup. That is the research behind the original notes’ roughly threefold headline. It is a substantial association, and it is why this finding gets top billing in my analysis.
The brain–lipid connection
The original analysis follows APOE through lipid transport, amyloid biology, and brain inflammation. That is why this result gets more space than a random wellness SNP. My history of Pattern B dyslipidemia gives the lipid side a real measurement trail: ApoB, LDL particles, and small dense LDL.
Sleep moves to the top
My read of the original notes is direct: protect sleep, build metabolic fitness, and keep lipid management active. The sleep analysis and the APOE analysis belong together. Recovery is part of the long-term brain-health strategy.
The stack has a purpose
Omega-3s, methylation support, training, and gut work meet in this section. Homocysteine links back to folate and B12. Fatty acid status links back to FADS. The point of mapping the genome is to see how these pieces interact.
| Gene / site | rsID | Call | Context |
|---|---|---|---|
| APOE | rs429358 | TC | One ε4-determining allele |
| APOE | rs7412 | CC | Together with rs429358, consistent with ε3/ε4 |
| APOE region | rs769449 | GA | Additional regional call |
Research sources
Source analysis: DNA-APOE-2026-04-02.md · 2026-04-02.
02Folate & B12MTHFR GA · MTRR GG
Folate activation and B12 recycling meet at the same upstream junction.
The two-hit methylation model.
The original analysis calls this a two-hit problem: MTHFR C677T GA at folate activation, plus MTRR A66G GG at the B12-recycling step. MTHFR helps make the folate form used to remethylate homocysteine. MTRR helps reactivate the B12-dependent enzyme methionine synthase.
Follow the methyl groups
Folate and B12 support the methionine cycle. Methionine feeds production of SAMe, the methyl donor used across many reactions. COMT uses SAMe to clear catecholamines. This is the bridge from a nutrient pathway to the stress and neurotransmitter story.
The upstream bottleneck
My working model from the notes: methyl-donor supply is a junction worth protecting. If supply becomes limiting, downstream enzymes can have less substrate even when their own genetic calls look ordinary. That is the logic behind putting methylfolate, B12, and TMG together in the protocol.
Bloodwork closes the loop
Homocysteine is the useful readout here. I track it with B12 and folate so the analysis has a measured feedback loop. The gene table tells me where to look; the lab history tells me how the current protocol is performing.
| Gene / site | rsID | Call | Context |
|---|---|---|---|
| MTHFR C677T | rs1801133 | GA | Heterozygous |
| MTHFR A1298C | rs1801131 | TT | Genotype in the source analysis |
| MTRR A66G | rs1801394 | GG | Homozygous call |
| MTR A2756G | rs1805087 | AA | Recorded reference call |
| MTHFD1 | rs2236225 | GA | Folate-pathway call |
| PEMT | rs7946 | CT | Choline-pathway call |
Research sources
Source analysis: DNA-Analysis-2026-03-15.md · 2026-03-15.
03Glucose regulationTCF7L2 TT
TCF7L2 TT makes beta-cell function a serious long-term priority.
A major glucose flag, even with good labs.
TT at TCF7L2 rs7903146 is one of the strongest common type 2 diabetes susceptibility findings in these notes. The second TT marker sits in the same linked region. The original analysis focuses on insulin secretion and incretin biology, which gives this result much more meaning than a generic “watch your sugar” label.
Insulin secretion is the story
TCF7L2 research connects the risk allele with differences in beta-cell function and insulin secretion. That focuses the analysis on how the pancreas responds to a glucose load, alongside insulin sensitivity in muscle and other tissues.
Sleep and glucose share a receptor
MTNR1B encodes a melatonin receptor and appears in both the sleep and glucose analyses. Its rs10830963 G allele is associated with higher fasting glucose and reduced beta-cell function in human studies. Meal timing, sleep, and glucose regulation meet at the same biological junction.
My current experiment
Resistance training and metabolic tracking stay central. My protocol now also records tirzepatide at 0.5 mg twice per week, started around early August 2026. I follow HbA1c, fasting glucose, insulin, and body composition as this experiment develops.
| Gene / site | rsID | Call | Context |
|---|---|---|---|
| TCF7L2 | rs7903146 | TT | Susceptibility-associated call |
| TCF7L2 | rs12255372 | TT | Linked marker |
| MTNR1B | rs10830963 | CG | Melatonin-receptor / glucose research |
Research sources
Source analysis: DNA-Analysis-2026-03-15.md · 2026-03-15.
04Fatty acid metabolismFADS1 TT
The fatty acid pathway explains why I track the finished product.
Go straight to EPA and DHA.
The original notes put FADS1 in the critical findings. FADS enzymes participate in the desaturation steps that turn shorter-chain fatty acids into longer-chain products. The rs174547 TT call brings this pathway into my omega-3 analysis.
The conversion chain
Plant omega-3 ALA enters a conversion pathway before contributing to EPA and DHA. That pathway includes several enzyme steps. Preformed EPA and DHA from fish or algae enter further downstream. This is the practical distinction that drove the original analysis.
My protocol logic
My approach is to supply EPA and DHA directly and check the fatty acid panel. I want a measured omega-3 index that shows what reached the blood. The original notes connect that decision to APOE and the brain-health plan.
The result is trackable
This section has an unusually clear feedback loop: intake, fatty acid panel, omega-3 index, then the next result. It makes the pathway analysis useful as part of an ongoing experiment.
| Gene / site | rsID | Call | Context |
|---|---|---|---|
| FADS1 | rs174547 | TT | Fatty acid desaturase region |
Research sources
Source analysis: DNA-Analysis-2026-03-15.md · 2026-03-15.
05Vitamin DCYP2R1 AG · GC AC
Input, liver conversion, transport, kidney activation, receptor signaling.
Map the whole vitamin D pipeline.
The vitamin D analysis follows an entire pipeline. Sun exposure or a supplement provides input. CYP2R1 participates in liver conversion to 25-hydroxyvitamin D. GC carries vitamin D metabolites. CYP27B1 handles activation, and VDR receives the signal in cells.
A pathway with several checkpoints
CYP2R1 AG and GC AC are the main calls in the April analysis. Both regions have been linked with circulating vitamin D levels. Looking at conversion and transport explains why the note goes beyond the amount written on a supplement bottle.
The immune connection
Vitamin D signaling participates in immune regulation. With Hashimoto’s already in my health history, the original notes connect vitamin D status to the larger thyroid and immune picture. This gives vitamin D a defined place in the protocol.
Measure the output
The key record is my 25-hydroxyvitamin D trend alongside the supplement dose at the time of each test. The March and April notes contain a conflicting CYP27B1 interpretation, documented in the source corrections below.
| Gene / site | rsID | Call | Context |
|---|---|---|---|
| CYP2R1 | rs10741657 | AG | Liver conversion pathway |
| GC / binding protein | rs7041 | AC | Vitamin D transport |
| VDR TaqI | rs731236 | AA | Receptor-region call |
| CYP27B1 | rs10877012 | GG | Interpretation differs between March and April |
Research sources
Source analysis: DNA-VitaminD-2026-04-02.md · 2026-04-02.
06Sleep & caffeineADORA2A TT
Caffeine, stress, melatonin signaling, and the clock all converge on sleep.
Protect the shutdown sequence.
The strongest sleep story in the original notes is a cascade. Caffeine can increase arousal. Stress can keep the system activated. Circadian signals set the timing for sleep, and inhibitory signaling helps the brain make the transition. The notes call that transition the shutdown sequence.
Caffeine has two separate jobs
ADORA2A is about response to caffeine, including the anxiety association studied in TT carriers. CYP1A2 is part of caffeine metabolism. Sensitivity and clearance are different mechanisms. The notes’ decaf approach follows that distinction.
The stress–sleep cascade
The original model connects stress recovery, COMT catecholamine clearance, serotonin-to-melatonin production, melatonin receptors, and GABA-mediated inhibition. Each step feeds the next. The model follows arousal through the entire wind-down process.
Sleep is a protected block
My priority is to give sleep protected time and a repeatable shutdown sequence. Consistent timing, morning light, and a deliberate wind-down anchor the plan. APOE makes long-term brain health part of the reason; MTNR1B also connects this priority with glucose regulation.
| Gene / site | rsID | Call | Context |
|---|---|---|---|
| ADORA2A | rs5751876 | TT | Caffeine-response research |
| CYP1A2 | rs762551 | CA | Caffeine-metabolism research |
| MTNR1B | rs10830963 | CG | Melatonin-receptor / glucose research |
| PER3 | rs228642 | CT | Sleep-research marker |
The feedback loop
Sleep duration, caffeine timing, and response to caffeine.
View the related record ↗Research sources
Source analysis: DNA-Sleep-2026-04-02.md · 2026-04-02.
07Exercise & muscleACTN3 CC / RR
The original exercise profile has a clear identity: power first, aerobic capacity built through training.
Power-leaning generalist.
“Power-leaning generalist” is the original analysis’s best shorthand. ACTN3 CC corresponds to RR at R577X, retaining alpha-actinin-3, a protein expressed in fast-twitch muscle fibers. The R allele is enriched in elite power athletes in the original ACTN3 study.
Fast-twitch hardware
ACTN3 gives the power story a specific protein and muscle context. Heavy strength work, explosive movement, and short efforts fit the way the original notes frame this profile. Training performance provides the personal test of that interpretation.
Build the engine too
PPARGC1A encodes PGC-1α, a regulator of mitochondrial biogenesis. PPARA participates in fatty acid metabolism. NOS3 participates in endothelial nitric oxide production. The notes use these pathways to connect power training with aerobic work and blood-flow adaptation.
One lever touches several systems
Exercise connects this page’s strongest themes: muscle, glucose handling, cardiovascular fitness, sleep, and brain health. I track strength, recovery, and DEXA lean mass so the training story has an objective record.
| Gene / site | rsID | Call | Context |
|---|---|---|---|
| ACTN3 R577X | rs1815739 | CC | Recorded as RR |
| PPARGC1A | rs8192678 | CT | Mitochondrial-biogenesis research |
| PPARA | rs4253778 | GC | Fat-metabolism research |
| NOS3 | rs2070744 | CT | Nitric-oxide pathway |
Research sources
Source analysis: DNA-Exercise-Response-2026-04-02.md · 2026-04-02.
08Medication responseCYP2C19 GA · SLCO1B1 TC
Activation, liver transport, and target sensitivity can change the same prescription in different ways.
Drug response deserves its own map.
These are among the most practical findings in the original notes. CYP2C19 rs4244285 GA carries a *2 loss-of-function-associated allele. SLCO1B1 rs4149056 TC carries a reduced-function transporter allele. VKORC1 TT adds a warfarin-sensitivity marker.
Clopidogrel needs activation
Clopidogrel is a prodrug. CYP2C19 helps turn it into its active form. Reduced CYP2C19 function can reduce that activation, which is why CPIC has a dedicated guideline for this gene–drug pair.
Statin transport meets APOE
SLCO1B1 moves statins from blood into the liver. Reduced transporter function affects drug exposure and muscle-symptom risk, with particularly strong evidence for simvastatin. The APOE lipid story and the medication-response story meet here.
A target can matter as much as clearance
VKORC1 encodes the target inhibited by warfarin. Its sensitivity markers address a different mechanism from the enzymes that clear the drug. This section is a useful record to take into a medication review.
| Gene / site | rsID | Call | Context |
|---|---|---|---|
| CYP2C19 *2 marker | rs4244285 | GA | Loss-of-function-associated marker |
| CYP2C19 *3 marker | rs4986893 | GG | Genotype in the source analysis |
| CYP2C19 *17 marker | rs12248560 | CC | Genotype in the source analysis |
| SLCO1B1 | rs4149056 | TC | Statin transport / CPIC guideline |
| VKORC1 | rs9923231 | TT | Warfarin-response marker |
The feedback loop
A clinically confirmed report to discuss with a prescriber.
View the related record ↗Research sources
Source analysis: DNA-Pharmacogenomics-2026-04-02.md · 2026-04-02.
09HistamineAOC1 promoter GG
The original histamine analysis puts clearance capacity and histamine load in the same frame.
The gut environment is the lead.
The strongest idea in the original histamine note is functional capacity. AOC1 encodes diamine oxidase, or DAO, which breaks down extracellular histamine and is expressed in the intestine. HDC makes histamine from histidine. Clearance and production form two sides of the same system.
The tissue matters
The note follows DAO into the gut lining. An enzyme works inside a biological environment: tissue condition, substrate load, and cofactors all matter. That makes the gut reports relevant to the histamine analysis.
Load versus clearance
My working model is to follow food reactions and gut symptoms alongside the balance between histamine production and clearance. The original notes connect this with mast-cell biology and the immune–gut story.
The sleep connection
Histamine also promotes wakefulness in the brain. That gives this analysis another bridge to sleep and arousal. The useful personal record is the timing of symptoms, meals, sleep disruption, and gut results.
10Iron metabolismHFE GG / CC
Hepcidin controls release. Transferrin carries the iron that gets through.
Stored iron and available iron are different.
The original iron analysis follows regulation and delivery. TMPRSS6 encodes matriptase-2, which participates in regulation of hepcidin. Hepcidin controls iron absorption and release from cells. Transferrin carries circulating iron to tissues.
Follow the iron gate
The TMPRSS6 rs855791 AA and TF rs3811647 GA calls are the note’s main iron-status findings. The model asks how regulation and transport affect the iron available for use. That is why the analysis looks beyond one ferritin number.
Read the panel together
Ferritin tracks storage and also responds to inflammation. Transferrin saturation describes how much of the transport protein is carrying iron. Serum iron and blood counts complete the picture. Reading those together gives the pathway a useful measurement trail.
Thyroid, energy, and the brain
Iron participates in thyroid peroxidase function, mitochondrial enzymes, and neurotransmitter synthesis. The original notes connect iron status to fatigue, thyroid function, and the serotonin pathway. This is a system-wide nutrient junction.
11COMT & stress clearanceCOMT Val/Met
The methylation story reaches dopamine and stress chemistry here.
COMT & stress clearance
COMT GA at rs4680 gives the Val/Met result in the original notes. COMT clears catecholamines, including dopamine, by methylation. It consumes SAMe in that reaction. The original analysis follows that requirement upstream into MTHFR and MTRR.
The COMT–SAMe connection
A genetically intermediate enzyme still needs substrate. My working model follows SAMe supply into catecholamine clearance. If methyl-donor supply becomes limiting, COMT has less available to run the reaction. This is the original notes’ central stress–methylation connection.
Track the recovery cost
The original analysis uses this model to think about sustained activation, rumination, and winding down. It connects methylation support with recovery instead of treating them as unrelated protocol items.
The clearance map
The detox note also follows catechol estrogen metabolism and glucuronidation. COMT sits at a shared clearance junction. The detailed value is seeing where several pathways demand resources at the same time.
12The neurocognitive modelDepth, filtering, recovery
High-depth processing, sensitive filtering, and expensive recovery form the original model.
High processing depth. Protect recovery.
My neurocognitive model is “high processing depth + low filtering + thin recovery resources.” The analysis follows neurotransmitter production, transport, reception, and clearance to build that picture. Depth is the asset. Recovery is the resource I protect.
The serotonin three-layer model
The source follows TPH2 for synthesis, SLC6A4 for transport, and HTR2A for reception. Its “thin serotonin pipeline” is a model of several checkpoints meeting in one system. That gives the sleep and stress discussion a much richer structure than a single neurotransmitter label.
Deep focus has a recovery cost
The original profile puts deep analytical processing, pattern recognition, and hyperfocus at the center of the strengths. Its pressure points are sensory overload, costly context switches, and rumination under stress. My strategy is to protect long focus blocks, batch similar work, control the environment, and build recovery into the schedule.
The whole-system connection
This model brings COMT, methylation, sleep, and the gut–immune axis together. It is the most personal interpretation on the page. The aim is to understand a pattern and shape my experiments around it.
| Gene / site | rsID | Call | Context |
|---|---|---|---|
| TPH2 | rs4570625 | GT | Serotonin-synthesis gene |
| SLC6A4 | rs2020939 | GA | Serotonin-transporter gene |
| HTR2A | rs6311 | CC | Serotonin-receptor gene |
| GABRB3 | rs3751583 | TC | GABA-A receptor beta-3 subunit |
| DBH | rs1611115 | TC | Dopamine-to-norepinephrine enzyme gene |
Research sources
Source analysis: DNA-Autism-Neurocognitive-2026-04-02.md · 2026-04-02.
13Immune regulation & the gutCTLA4 CT60 GG
The immune brake is the central idea in the autoimmune analysis.
Immune regulation & the gut
CTLA4 helps regulate T-cell activation. The original analysis puts immune self-tolerance at the center of my Hashimoto’s story, then connects thyroid autoimmunity with the wider immune and gut picture. CT60 rs3087243 GG is the recorded call used here.
Threat detection needs a brake
The notes frame immune regulation as the ability to control and end a response. CTLA4 is an inhibitory checkpoint. That makes self-tolerance a concrete biological mechanism in the analysis.
Thyroid and gut meet here
My Hashimoto’s history and the ongoing gut reports are the measured context. The original notes connect immune signaling, tissue inflammation, gut repair, and nutrient status into one system.
A shared priority
The immune analysis feeds back into vitamin D, histamine, iron, sleep, and the APOE brain-health story. This is why the protocol has a large gut and recovery component. The genetic analysis gives those choices a connected narrative.
The system map
Where the pathways meet.
The most useful part of the analysis is following one pathway into the next. These connections give the protocol its logic.
Sleep → glucose + brain health
MTNR1B joins melatonin signaling with glucose research. APOE ε4 makes long-term brain health a major priority. Sleep becomes a shared lever across the metabolic and cognitive stories.
Gut → immune load → arousal
The source connects gut condition with immune activity and histamine clearance. Histamine adds a wake-promoting pathway. The gut reports belong beside the sleep and immune analyses.
Training → muscle + metabolic fitness
The power-leaning generalist model gives training a clear direction. DEXA, strength progress, and glucose markers show how the experiment is developing across systems.
Source corrections & open calls
CTLA4 call conflict
The March analysis records rs231775 as AG. The April thyroid note records GG. I need to check the original call before publishing a genotype conclusion for that site.
9p21 risk label
The notes record AA at rs10757278 and call it high risk. The linked study associates G with coronary artery disease. I have removed the high-risk label while the source interpretation is reviewed.
Read the association study ↗Vitamin D interpretation
Both notes record CYP27B1 rs10877012 as GG. March calls activation impaired; April calls it reference. The page keeps the call and flags the conflicting interpretation.
Absent variant entries
Some notes treat “not detected” as a reference call or as proof that a condition is absent. A missing entry requires a coverage and calling check. I have not turned those entries into confirmed genotypes.
Pathway models. The analysis connects established gene functions with my personal interpretations. It does not measure brain neurotransmitter levels or prove a cognitive profile from common SNPs. Counts of variants in a gene do not establish impaired function.
Conversion and screening. FADS1 does not prove zero conversion of plant omega-3s. MTHFR does not measure total methylation capacity. The HFE table covers two common sites, not every inherited iron disorder. The selected medication markers do not establish a complete clinical metabolizer profile.
Call record. Genotypes are transcribed from my March–April notes. The raw sequencing calls have not been independently revalidated for this page.
For entertainment and personal exploration. This is my interpretation of my DNA, not medical advice or a diagnosis. Medication decisions require clinical confirmation and a prescriber’s review.