Units
1. Introduction to Artificial Intelligence in Molecular Biology and Genetics: Roles, Boundaries, Validation, Ethics and Privacy 2. DNA/RNA Sequence Analysis: Alignment, Motif, Open Reading Frame and Basic Bioinformatics 3. Variant Interpretation: Pathogenicity by VCF, HGVS Designation, and ACMG Criteria 4. Protein Structure and AlphaFold: Reading Structure Prediction, Confidence Scores, and Validation 5. CRISPR Design Support: gRNA Selection, Off-Target Effect, and Experimental Validation 6. Omics Data Analysis: RNA-seq, Expression, Statistics and Pathway Enrichment 7. Literature Review and Scientific Writing: Source Verification and the Risk of False Citations 8. Clinical Interpretation: Reporting, Expert Approval, Validation, and Limits 9. The Discipline of Hallucination and Authentication: Made-up Genes, Sequences, Variants, and Attributions 10. Ethics, Privacy and Data Protection: Special Responsibility of Genetic Data 11. End-to-End Case: A Variant's Journey from Discovery to Clinical Report
Unit 3 / 11

Variant Interpretation: Pathogenicity by VCF, HGVS Designation, and ACMG Criteria

Gains:

  • Ability to understand the VCF format, HGVS notation (c./p./g.) and ACMG pathogenicity criteria and have artificial intelligence produce an evidence draft
  • Ability to personally verify each ACMG evidence (population frequency, in silico, segregation, literature) in sources such as ClinVar and gnomAD
  • Ability to apply the principles of catching genome version and coordinate system errors and not leaving the final pathogenicity classification to artificial intelligence

An individual's genome differs from the reference at millions of points; Most of these differences are harmless (“benign”), a few cause disease (“pathogenic”). Variant interpretation is the task of deciding which class a difference falls into and is the heart of clinical genetics. In this unit, you will learn how to use artificial intelligence (AI) as an evidence gathering, summarizing and drafting assistant in variant interpretation; but you will learn why the final pathogenicity decision should always be subject to expert approval.

Critical warning first: Ask the AI ​​"is this variant pathogenic?" Never trust the one-word answer you ask. AI can confidently say a class without evidence; can fit the population frequency of a variant, previous clinical records, or functional studies. The correct approach is to use AI to build the evidence framework and personally verify each piece of evidence at the primary source.

Basic concepts

  • VCF (Variant Call Format): Standard file format that lists variants in a sample; Each line carries chromosome, position, reference base, alternative base and quality information.
  • HGVS notation: Standard writing format with variants. c. its position in the coding sequence, p. protein change, g. Indicates genomic location. For example NM_000546.6:c.743G>A and p.Arg248Gln.
  • gnomAD: Database collecting variant frequencies of hundreds of thousands of people; It shows how common a variant is in society. Common variants are generally not pathogenic.
  • ClinVar: Public database collecting clinical interpretations of variants.
  • ACMG/AMP criteria: The American College of Medical Genetics' system of evidence that divides a variant into five classes: Pathogenic, Likely Pathogenic, Variant of Uncertain Significance (VUS), Possibly Benign, Benign.

ACMG evidence codes: a brief map

The ACMG system defines evidence codes in the pathogenic direction (PVS, PS, PM, PP) and in the benign direction (BA, BS, BP); These come together to determine the class. AI is good at explaining what these codes mean, but you must verify the data on which code is actually implemented.

Code

Meaning (summary)

Where to verify the source

PVS1

Very strong evidence leading to loss of function (e.g. early discontinuation)

Transcript, domain, gene-disease relationship

PS1/PS3

Same amino acid/functional study as known pathogenic

ClinVar, peer-reviewed functional article

PM2

Very rare/not present in populations

gnomAD frequency

PP3/BP4

Computational prediction in pathogenic/benign direction

Estimation tools (consensus)

BA1/BS1

Frequency too high to be pathogenic

gnomAD frequency

BP6/PP5

(No longer recommended) relying on someone else's interpretation

—

Tip: PM2 (rarity) and BA1/BS1 (commonness) decisions are based directly on gnomAD frequency and are the most easily verified evidence. See a variant first on gnomAD; A variant prevalent in more than 1% of the population is most likely not pathogenic.

Step by step: AI-assisted variant interpretation

1. Fix variant to standard notation. Clarify genome version, transcript, and HGVS representation. Variant from AI both c. both p. both g. , then confirm it with a validator (like VariantValidator).

2. Have the AI ​​build the evidence framework. For each ACMG code, “what data should I look at?” Have the AI ​​answer the question — but don't specify the class yet.

3. Verify any evidence at the primary source. gnomAD frequency, ClinVar record, functional study — open and read them all yourself. Check AI's "not available in gnomAD" claim by searching for it in gnomAD.

4. Combine the codes and extract the class. Draft ACMG combination rules with AI; But let the expert approve the final class.

5. Report VUS honestly. If evidence is insufficient, the class is "Uncertain Meaning"; It is a mistake to push it as pathogenic or benign.

three mini cases

Case 1 — Made-up frequency. An expert asked AI about the frequency of a variant gnomAD; “0.002%,” the AI ​​said. When the expert looked it up on gnomAD himself, he found that the frequency of the variant was 1.3% — meaning the evidence for BS1 (strong in the benign direction) was valid and the variant was most likely harmless. AI's fabricated low frequency made the variant falsely appear rare.

Case 2—Sham functional study. A researcher wanted to implement PS3 (functional proof) for a variant; AI gave a persuasive article byline. When the researcher searched on PubMed, he found that there was no such article. False attribution would collapse the chain of evidence.

Case 3 — Confirmation gained. A genetic counselor re-evaluated a “possibly pathogenic” variant with AI. YZ pointed out the newly added conflicting comments on ClinVar; The consultant opened ClinVar, saw that two labs had detected the variant on VUS, and updated the report. Here, AI helped by reminding us of an update that might have gone unnoticed — but again, the human made the decision.

Four copyable templates

1) Establishing an evidence framework (without making the class tell):

Your role: clinical variant interpretation assistant. Set up the ACMG/AMP evidence framework for the following variant: [genome version, transcript, HGVS]. For each possible evidence code (PVS1, PS1-4, PM1-6, PP1-5, BA1, BS1-4, BP1-7) “what data should I look at, in which source, to apply this?” Write. Don't say the class YET; just list what data is required.

2) gnomAD/ClinVar confirmation plan:

Write step by step what exactly I should look for in gnomAD and ClinVar for the following variant: [variant]. Explain which population frequency threshold corresponds to which ACMG code. You don't make up the values; tell me where to find it.

3) Class outline (approved by me):

I gave the following verified evidence: [PM2 is present, PP3 is present, BS1 is absent ...].Draft the possible class and justification according to the ACMG combination rules. Feel free to say "VUS" if the evidence is insufficient. I will approve the final decision.

4) Report draft:

Translate the following classification into a report paragraph in plain language suitable for the patient and the physician: [variant, class, key evidence]. Do not exaggerate claims of certainty; if there is uncertainty, state it clearly. Refer to specialist for clinical decision.

Weak prompt / Strong prompt

Weak: “Is TP53 c.743G>A pathogenic?”

Problem: No genome version/transcript, no evidence required, AI can just tell the class and make up the frequency.

Strong: "Set up the ACMG proof framework for GRCh38, TP53 NM_000546.6:c.743G>A (p.Arg248Gln); tell me what to look for in which source for each code, value fitting, draft the class after I validate it."

Why it is powerful: The context is clear, the evidence path is open, the field of fabrication is narrowed, the decision is in the person's hands.

Common mistakes

  • Relying on the one-word pathogenicity answer. A given class without a chain of evidence is worthless.
  • Failure to verify frequency and attribution. gnomAD frequency and article citations can be adapted; Open it yourself.
  • Avoiding VUS. Saying "pathogenic/benign" when the evidence is insufficient is a clinical error.
  • Genome version/transcript shuffling. Incorrect transcript, incorrect c. It means location.
  • Skipping ClinVar update. Interpretations change over time; Check the most current record.
Caution: Combining ACMG codes is rule-based but requires expert judgment; The same evidence may carry different weight in different gene-disease contexts. The combination of AI is a blueprint, not the final word.

Depth: correctly reading a VCF line and frequency thresholds

Even a single line in a VCF file contains many pitfalls. An example line: 17 43091983 . G A 60 PASS AF=0.0003;DP=45 GT:AD 0/1:22,23. You can have the AI ​​explain the fields here, but confirm three points yourself. First, which genome version does the chromosome-position pair belong to? The same variant appears at a different location in GRCh37 and GRCh38; Without specifying the version, location is meaningless. The second is the GT (genotype) domain: 0/1 heterozygous, 1/1 homozygous; In a recessive disease, a single heterozygous variant alone is not explanatory. Third, DP (read depth) and AD (allele depth): low depth (e.g. DP=8) makes a variant call questionable; The technique may be a false positive. AI explains these fields formally, but “is this call reliable?” The decision is yours.

A concrete example of frequency thresholds: an expert wanted to apply PM2 (rarity) to a variant for a rare disease with recessive inheritance. Artificial intelligence said, "gnomAD doesn't have it, PM2 for sure." When the expert opened gnomAD, he found that the variant had a frequency of 0.8% in a subpopulation — high enough to rule out PM2, given the prevalence of the disease. A common mistake is to look at the overall population frequency and skip subpopulations.

Allele frequency (population)

Typical ACMG comment

note

None / very rare

Can support PM2

Also look at subpopulations

Higher than disease prevalence

BS1 (benign strong)

The recessive/dominant distinction is important

higher than 5%

BA1 (independent benign)

Almost certainly harmless

5) VCF line control template:

Explain the following VCF line field by field, but do not make a reliability judgment: interpret the genome version, GT (zygosity), DP and AD fields separately. List cases where this call may be technically questionable. Line: [paste].

In summary

  • Variant interpretation is evidence-based; AI is powerful at framing and summarizing evidence, but the class decision is up to the expert.
  • Any evidence (gnomAD frequency, ClinVar registration, functional study) must be personally verified at the primary source.
  • Fix the variant by genome version, transcript and HGVS; Confirm with a verifier.
  • If the evidence is insufficient, the honest answer is “Uncertain Significance (VUS).”

Application task

Choose an example variant (e.g. a well-known record from ClinVar). Have the AI ​​build the evidence framework with templates 1 and 2 above. Then open the gnomAD frequency and ClinVar comment yourself and compare them with the AI ​​claims. Note whether there is a difference between the AI ​​and the source in at least one piece of evidence and justify the possible class yourself.

checklist

  • [ ] I fixed the variant with genome version, transcript and HGVS.
  • [ ] I set up the proof framework, I did not make the AI ​​say the class from the beginning.
  • [ ] I personally checked the gnomAD frequency.
  • [ ] I have confirmed the ClinVar registration and up-to-dateness.
  • [ ] I verified functional study citations in PubMed.
  • [ ] If the evidence was insufficient, I did not hesitate to say VUS and approved the decision myself.
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