Full stack optimization based on the findings from the genetic deep dive reports

[CronosTempi]

I think if sequencing gets to 100-200x, that should be it. Obviously, whole exome/genome sequencing is still evolving, but this will come to some ultimate resolution likely fairly soon, and costs will hopefully drop. I think there will be another evolution and that should be it - so maybe in the next 2-3 years(?) another cost, hopefully in the neighborhood of $1K and below. Data storage - just a thumb drive with copies elsewhere. As long as the format is not proprietary, it’s good to go. My M2 Apple Mac Air laptop has a ITB drive, most of it free, so I can have it there as well as on some external drives.

[Beth]

I have a neighbor who knows a ton about this topic. Because I only know him very casually, I have not had a chance to discuss anything with him… (I think he used this information in his career)

I sent him the list of options RapAdmin posted yesterday and asked if he had opinions… all he did was reply with a link

I’ll share that here. I don’t know of any pluses or minuses yet. I do think a doc has to order it. AI says it can be any old doc, though ?

MyOme - Home

[RapAdmin]

Interesting… They seem focused on specific genetic risks and tests related to them (not Whole Genome scanning) - which is fine if thats what you want:

[Beth]

I thought so too but then I found this on their site

In other news, I just set up and account and became a provider …shhhhhhhh

[CronosTempi]

Not at all what I would be looking for. All I need is whole genome data at the highest resolution. Looking for clinical insights is up to my own analysis (see @cl-user examples). Not useful to me. YMMV.

[Beth]

Yeah, I was not really clear on exactly what they do. I only became a ‘provider’ so I could potentially see more info and pricing. If I learn anything, I’ll share.

[cl-user]

@Beth

Yeah, I was not really clear on exactly what they do.

To be fair it’s not very clear. It seems they do a full sequencing but keep the files and then sell specific reports to you. There is no info at all on the sequencing (1x, 30x, 100x) and if you can download your genomic files (VCF and BAM or CRAM).

[Beth]

Thanks for the reality check… skipping them…

[cl-user]

@RapAdmin

Do you have a strategy for tracking how this new knowledge that is published every month, gets updated in the software we use to evaluate our genome, and mapped directly onto our genomes, so we have a better understanding over time as the science progresses?

I asked Claude which I use for those reports how old is its informations and how often should I refresh the reports. Apparently it knows nothing about 2026.

How recent the underlying knowledge actually is

• Foundational mechanistic knowledge (PTPN22 lowers TCR threshold, IL7R rs6897932 disrupts splice silencer, AIRE PHD-domain dominant-negatives, CMV memory inflation biology): stable for 5–15 years, doesn’t need refreshing
• Effect sizes from major GWAS meta-analyses: typically 2–8 years old; usually still current but periodically superseded by larger meta-analyses
• Clinical interventional evidence (rapamycin/RTB101 vaccine trials, colchicine CV trials, senolytic data, CHIP→CV risk modification): moves fast, this is where my training data is most likely stale
• Pharmacogenomic guidelines (CPIC, PharmGKB level changes): updated frequently; my cutoff is ~16 months ago
• Brand-new findings since my cutoff (anything 2026): I have zero information unless I search

How often you should ask for refreshes

A practical schedule:

Per individual report: ask “search for any major 2025–2026 updates on the dominant findings” before I finalize. For this report the dominant findings were PTPN22 R620W, IL6 axis, 9p21 CHIP, rapamycin/mTOR vaccine evidence, and colchicine CV — about 5 targeted searches would catch most novelty.

Cross-report periodically (every 6–12 months): “Has anything changed in the last year that would alter the interpretation of [list of high-impact variants across all reports]?” This catches paradigm shifts — e.g., if a new colchicine trial comes out negative, or a new CHIP-modifier drug enters guidelines, that affects multiple reports.

Triggered by specific events: when a new medication is added, new lab abnormality emerges, or new family history surfaces — those are natural moments to refresh the relevant pathway.

For pharmacogenomics specifically: CPIC updates monthly. PharmGKB level changes happen continuously. Worth a quarterly check on any drug the user is currently taking that has a CPIC guideline.

[cl-user]

@RapAdmin

But, how exactly do we know if these new research paper findings are being integrated into the platforms that we’re using to identify these software platforms

I chatted with Claude to refine some (very) detailed prompt instructions to add to be sure it’s up to date with the latest research.
[Edit: added report to add: Documenting the freshness check]

Literature freshness — required step before any analysis

Before producing or finalizing any pathway report (educational reference,
personalized analysis, or summary card), run a literature freshness check
against the dominant findings. Do this BEFORE writing the report, not after,
so up-to-date evidence is integrated into the original document rather than
added as a contradicting addendum.

When to run the check

• Phase 1 (pathway education): run 3–5 broad searches covering the
  pathway’s core mechanistic claims and any recent paradigm-shifting trials
  in the pathway’s therapeutic space. Integrate findings into the educational
  content directly.

• Phase 3 (personalized analysis): after identifying the user’s top
  5–10 findings but BEFORE writing the report, run 5–10 targeted searches —
  one per top finding plus searches for any therapeutic recommendation you’re
  about to make. Integrate findings into the report’s interpretation and
  recommendations directly. Do not write a recommendation based on training
  data alone for any actively-evolving clinical area.

• Cross-report refresh (when user requests): run 15–20 searches across
  the highest-impact variants and therapeutic recommendations from prior
  reports.

What to search for

For each top finding, run searches covering:

1. Most recent meta-analysis or large GWAS for the variant
   (query pattern: <rsID> <gene> meta-analysis 2025 2026).

2. Most recent RCT or guideline change for any therapeutic recommendation
   being made (query pattern: <drug/intervention> trial 2024 2025 outcomes).

3. CPIC / PharmGKB / DPWG / FDA-label changes for any pharmacogenomic
   finding.

4. Pipeline trials (ClinicalTrials.gov) recruiting or recently reported
   for any actively-evolving therapeutic class.

5. Negative trials specifically: actively search for failed or negative
   trials of any intervention being recommended. Query pattern:
   <intervention> negative trial OR failed OR no benefit 2024 2025. This is
   the most important search type because positive results are over-represented
   in training data.

Actively-evolving areas requiring extra vigilance

Some areas of biomedical evidence change fast enough that training data alone
is unreliable. Identify whether your report touches any of these classes, and
if so, run extra targeted searches before making a recommendation that depends
on them.

By type of evidence (apply across all pathways):

1. Drug-class outcomes trials — any therapeutic class with active phase 3
   trials, especially classes where a recent large negative or positive trial
   may have superseded earlier evidence. Always run the negative-trial search.

2. Pharmacogenomic guideline status — CPIC, DPWG, FDA labeling, and
   PharmGKB level changes. Levels shift; new drugs get added; some guidelines
   get retired. Annual sweep at minimum.

3. Biomarker-guided treatment decisions — whenever a recommendation
   depends on a measurable biomarker (LDL-C, ApoB, Lp(a), HbA1c, hsCRP, IL-6,
   GDF15, homocysteine, vitamin D, telomere length, methylation age, etc.),
   check for updated reference ranges, target levels, and any trial showing
   biomarker-targeted therapy didn’t translate to outcomes.

4. Effect-size meta-analyses for high-impact common variants — any
   variant with effect size large enough to drive a recommendation (typically
   per-allele OR ≥1.3 or homozygous OR ≥1.5). Newer meta-analyses often
   shrink effect sizes through better population correction.

5. HLA-disease associations and HLA-drug hypersensitivity — active CPIC
   curation; new associations published frequently.

6. Polygenic risk scores — PRS construction, validation cohorts, and
   clinical utility evidence are all still evolving. Use cautiously.

7. Supplements and over-the-counter compounds with emerging RCT evidence
   — supplements that get a positive observational signal triggering an RCT
   often come back negative. Vitamin D supplementation, omega-3, antioxidants,
   B-vitamin supplementation for homocysteine reduction, and many others have
   followed this pattern.

8. Senescence, longevity, and healthspan interventions — mostly small
   pilot trials; both positive and negative findings are routinely
   over-extrapolated. High vigilance area.

9. Microbiome-mediated effects — mechanisms posited from observational
   data often fail interventional trials. Vigilance especially for
   microbiome–vaccine, microbiome–drug-metabolism, and microbiome–disease
   claims.

10. Variants whose ClinVar classification has changed — variants
    re-annotated as benign/likely benign after originally being VUS or
    likely pathogenic, or vice versa. Especially relevant for rare
    disease-gene panels.

By signal that should trigger an extra search:

• A recommendation that involves adding, removing, or changing a medication
• A recommendation based on a single primary trial (vs. multiple replications)
• A trial citation older than 5 years where the topic is clinically active
• An effect size that “feels too clean” — high ORs from small studies often
  shrink in larger meta-analyses
• Any topic where the user is on, or considering, an active intervention
  rather than just monitoring

The single most important search type (regardless of pathway):

Always run a “negative trial” search for any intervention being recommended.
Training data heavily over-represents positive trials because positive results
get more citations. The colchicine–CV story is one example among many: COLCOT
and LoDoCo2 were positive, CLEAR SYNERGY was negative and larger, and the
consensus view shifted accordingly.

How to integrate findings

When recent evidence CONFIRMS or REFINES the training-data interpretation:

• Cite the new source inline in the report
• Use the more current effect size or confidence interval
• Note the refinement explicitly if it materially narrows or broadens the
  original framing

When recent evidence CONTRADICTS or WEAKENS a training-data interpretation
or recommendation:

• Do NOT include the original recommendation
• Replace with the current evidence-based position
• If the original direction is now actively wrong (e.g., a positive trial
  superseded by a larger negative trial), state explicitly in the report:
  “An earlier rationale based on [trial X] is no longer supported by
  [trial Y, year]; current evidence is mixed/negative.”
• Never write a recommendation that the freshness check would have
  contradicted

When recent evidence is PENDING (recruiting trial, baseline-only paper):

• Mention the trial by name and registration number
• Set expectation that the recommendation may change once results report
• Do not claim future results

Documenting the freshness check (REQUIRED)

Every report (Phase 1, Phase 3, summary card, addendum, and any future
pathway document) must include a clearly visible “Literature freshness
statement” containing the timestamp information below. This is non-negotiable
because it is the only mechanism by which the user can later determine when
the report needs refreshing.

Where to place it:

• Personalized analysis docx: a dedicated section near the end of the
  document, before the bibliography. Use a level-1 heading so it shows up
  in the table of contents and is easy to find.
• Pathway reference docx: a dated note on the title page or in the
  purpose-and-scope section.
• Summary card / one-pager (markdown or short docx): a single line at
  the top or bottom, e.g.,
  _Literature current through: <month> <year>. Next refresh suggested: <month> <year>._
• Any addendum or correction: same structure as the personalized
  analysis.

What to include:

1. Report generation date — the calendar date the report was produced.
2. Literature current through — the most recent month/year that the
   freshness check explicitly searched. This is the practical “knowledge
   horizon” of the report. Be specific (e.g., “current through May 2026”)
   rather than vague (e.g., “recent”).
3. Underlying knowledge baseline — the model’s training-data cutoff
   used as the starting point before searches. State explicitly so the
   user understands the gap that searches were intended to bridge.
4. Number of searches executed during the freshness check.
5. Top findings searched — list the rsIDs, genes, and interventions
   that were specifically queried. This lets a future refresh see what
   was checked vs. what was assumed-stable.
6. Pending trials whose readout would change interpretation — list
   each by name, registration number, and expected readout window. These
   are the explicit triggers for the next refresh.
7. Suggested next refresh date — based on the cadence rules below.
   Give a specific calendar month/year, not a vague interval.

Example formatting (minimum content):
[Start of example]
Literature freshness statement Report generated: 7 May 2026 Literature current through: May 2026 (targeted searches run on this date) Underlying knowledge baseline: training data through end of January 2026 Searches executed during freshness check: 7 Top findings searched: PTPN22 R620W (rs2476601); IL7R rs6897932; IL6 −174 (rs1800795); 9p21 (rs10757278); JAK2 V617F (rs77375493); rapamycin / mTOR vaccine response; colchicine post-MI Pending trials with readouts that would change interpretation:

• ZEUS (NCT05021835) — ziltivekimab CV outcomes; readout ~2027
• RESTOR (NCT06658093) — daily vs. intermittent mTOR dosing; readout ~2027
• RAP PAC (NCT05949658) — weekly rapalog dosing PK/PD; readout ~2026 Suggested next refresh: November 2026 (6-month cadence applies — see cadence rules), or earlier if any pending trial above reads out, or triggered by new medication, lab abnormality, or family history.

[End of example]

Cadence for repeat refreshes

Recommend to the user that they ask for a refresh on any given report:

• After 6 months for actively-evolving areas (anti-inflammatory therapeutics,
  senolytics, GLP-1 / GIP class effects, polygenic risk score utility, any
  pathway where the user is on an active novel-class intervention)
• After 12 months for stable areas (foundational pathway biology, rare
  loss-of-function disease genes, established autoimmune-risk loci,
  decades-stable pharmacology)
• Triggered by: new medication added, new lab abnormality, new family
  history, or news of a major trial readout in the relevant area

The “Suggested next refresh” date in the freshness statement should reflect
the shorter of the relevant cadences above for the report in question.

Failure mode to avoid

The single most common error is writing a recommendation based on training-data
trial evidence (typically 2–4 years old) without checking for a more recent,
larger, or negative trial that has superseded it. Anti-inflammatory agents,
senolytics, supplement-based interventions, and any therapeutic class with
active phase 3 development are the most failure-prone areas. Always run the
negative-trial search before recommending any intervention from these classes.

[RapAdmin]

Do we know if Claude has access to many / most of the academic research papers that are published each year? My impression has always been that only OpenEvidence has access to all the full papers (or a large share of them, via its partnerships with the publishers): About | OpenEvidence

I “think” Claude is just crawling the web like other search engines and just has access to the papers that are not behind the paywall. This seems like it might be a concern… but hard to get around.

[cl-user]

@RapAdmin

I “think” Claude is just crawling the web like other search engines and just has access to the papers that are not behind the paywall. This seems like it might be a concern… but hard to get around.

Yes that’s the case:

When I run web_fetch on a paywalled paper:

• I get whatever the publisher serves to anonymous web traffic
• For most paywalled journals, that’s the abstract, the figure captions, sometimes the introduction’s first paragraph, and the references list
• I do not get the methods, full results, discussion, or supplementary materials
• For some journals (Nature, NEJM, JAMA, Cell, Science, Lancet) the abstract alone is fairly substantive; for others (most Springer, Elsevier, Wiley journals) the abstract is thin

What I’m actually doing in practice

For the literature update I produced earlier, here’s the truth:

• Open-access papers (PMC, PLOS, eLife, BMC, MDPI, bioRxiv, medRxiv, F1000Research): I read full text. These are reliable.
• Paywalled papers with strong abstracts (Nature Medicine, JAMA Cardiology, NEJM, Lancet family): I read the abstract plus whatever the search snippet captures. The abstract usually contains the headline result, effect size, and primary conclusion. For RCT outcomes papers this is often enough to know whether the trial was positive/negative and the magnitude — but not enough to assess methodological quality, subgroup analyses, or caveats.
• Paywalled papers with weak abstracts: I’m flying on snippets. This is the riskiest category and where I’m most likely to misrepresent something.
• Conference presentations (AHA, ACC, ESC late-breakers): I’m relying on news coverage and summary articles like ACC.org or PCRonline write-ups, not the original presentation. These secondary sources are usually reliable for top-line results but lose nuance.

Where this matters for your reports

Looking back at the literature update I generated:

Probably accurate (open-access or strong abstracts):

• CLEAR SYNERGY OASIS-9 — multiple ACC.org and conference summary writeups, top-line result is unambiguous
• TIMI pooled CHIP analysis — Nature Medicine, full abstract and PMC version available
• ZEUS baseline characteristics — JAMA Cardiology, abstract sufficient for design/baseline framing
• bioRxiv rapamycin/p53 paper — open access, full text readable
• The 2025 PTPN22 meta-analyses — both Springer and PLOS One; PLOS is OA
• ClinicalTrials.gov entries (RESTOR, RAP PAC) — fully public

Read at risk:

• Some of the Springer/Elsevier 2025 papers cited in the snippets — I read what the search engine returned and structured citations, which is not the same as reading the paper. I cannot guarantee I represented their conclusions accurately beyond the abstract-level statement.
• The JACC 2025 papers on CHIP in older women and exercise — paywalled, I worked from snippets

What this means for trust

A few practical implications:

1. My citations are accurate (I can verify the paper exists, was published in the journal/year I said, and the rsID/intervention is what I said). I do not fabricate citations.
2. My representation of headline conclusions is usually accurate for major journals because their abstracts are designed to convey the headline.
3. My representation of effect sizes, subgroup analyses, methodological caveats, and conflicting results within a paper is less reliable for paywalled sources. I may miss that a positive primary endpoint had a borderline p-value, or that a trial enrolled an unusual population, or that the authors themselves flagged the result as exploratory.
4. For RCT outcomes, the abstract usually contains the primary endpoint result with effect size and CI — usable.
5. For mechanistic / biomarker / cohort studies, the abstract often elides exactly the details I’d want for nuanced interpretation. Higher risk.

What I should do (and will, going forward)

For the literature freshness checks the standalone prompt sets up, I should:

• Flag access status explicitly for each citation: “open access” / “abstract-only” / “snippet-only” / “secondary-source-only”. This goes in the freshness statement so you can see at a glance which findings are based on full-text reading vs. summary.
• Treat snippet-only and abstract-only sources as informative but not definitive for nuanced claims (effect sizes, caveats, subgroups). Use them for headline conclusions only.
• Preferentially search for open-access versions (PMC IDs, preprint servers, author-deposited versions) of paywalled papers. Many 2024–2025 papers have a PMC mirror within 12 months due to NIH public access policy.
• Acknowledge when I can’t fully read a paper rather than glossing over it.

What you can do on your end

• For any RED-classification finding in a future audit, read the paywalled paper yourself before acting on the recommendation. If it’s a major guideline change or trial reversal, abstract-only is not enough basis for changing your medication or supplement stack.
• If you have institutional access (university, hospital affiliation, ResearchGate full-text request capability), you can pull the full PDFs and paste relevant excerpts into a chat. I can then read the actual content.
• Consider asking me to cite preferentially from open-access journals when the open-access source is comparable quality — PMC, PLOS, eLife, BMC, F1000, JAMA Open, BMJ Open, Lancet open-access partner journals, Frontiers, MDPI’s better journals. This biases the report toward sources I can actually fully read.
• For the highest-stakes findings, ask for a “full-text verification” step: tell me which 2–3 papers from the report you want fully validated, and I’ll attempt deeper fetches on each one and report back honestly on what I could and couldn’t access.

[cl-user]

I’m adding that to the prompt.:

• For each citation, indicate access status:
  “open access” / “abstract-only” / “snippet-only” / “secondary-source-only”.
• For RED or ORANGE classifications based on abstract-only or
  snippet-only sources, flag the limitation explicitly. The user
  may need to obtain the full text before acting on the finding.
• Where possible, prefer open-access sources (PMC, PLOS, eLife, BMC,
  bioRxiv/medRxiv, F1000, JAMA Open, BMJ Open) over paywalled equivalents
  when the evidence quality is comparable. Note in the freshness
  statement how many citations were full-text-readable vs.
  abstract-or-snippet-only.

[RapAdmin]

It’s interesting… many people here have access to academic accounts through their university affiliation (I’ve noticed). And even if you’re not at a university, many universities and medical schools have good libraries and online access of the paywalled publications that the public can use (and you can frequently download the PDFs from the university libraries, or save them to your Google Drive account directly from the library computers). So I guess it would be possible to add and upload key groups of papers if you wanted to make sure its got the latest and best information in a limited topic area.

Obviously, this doesn’t scale well, but might be a fix for some people.

[cl-user]

@RapAdmin

So I guess it would be possible to add and upload key groups of papers if you wanted to make sure its got the latest and best information in a limited topic area.

Regarding the ability to upload key groups of papers for specific topic areas, the new prompt will identify which papers are not fully available. We can then decide which ones are worth the effort to get and provide to Claude.

Making those report already take hours of Claude resources and it’s going to be much worse

Testing that on the upcoming Thymus report next.

[LukeMV]

I am curious what they consider is a very low GGT