Myvariant queries

Version Compatibility

Reference examples tested with: myvariant 1.0.0+, requests 2.31+, pandas 2.2+. myvariant.info aggregates >=21 sources; the operative version of each source is queryable via the _meta field and the /v1/metadata endpoint.

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. dbNSFP version drift is the dominant staleness vector: AlphaMissense was added to dbNSFP v4.4 (~2024); querying dbnsfp.alphamissense.score returns whatever version of dbNSFP is currently loaded; check _meta.src.dbnsfp.version.

MyVariant.info Queries; Aggregated Annotation

'Annotate my variants with ClinVar + gnomAD + CADD + AlphaMissense in one batch' -> Query the BioThings myvariant.info aggregator with field selection and version tracking, then parse nested responses.

• Python: myvariant.MyVariantInfo().getvariant(hgvs_or_rsid, fields=['clinvar', 'gnomad_exome', 'dbnsfp'])
• Python (batch): mv.getvariants(ids_list, fields=...); up to 1000 IDs per request
• Python (search): mv.query('clinvar.gene.symbol:BRCA1 AND clinvar.clinical_significance:Pathogenic')
• REST: GET https://myvariant.info/v1/variant/{hgvs_or_id}?fields=...
• Bulk: POST https://myvariant.info/v1/variant with comma-separated IDs

BioThings Architecture (Wu 2022 Bioinformatics)

myvariant.info is one of three flagship BioThings APIs (with MyGene.info and MyChem.info). All three share the BioThings SDK, which auto-deploys an Elasticsearch index from heterogeneous source files via per-source dataloaders. The 2022 paper formalized the SDK; the architecture itself is older (Xin 2016 Genome Biol).

• Elasticsearch-backed: queries use Lucene operators (AND, OR, NOT, range like dbnsfp.cadd.phred:>20)
• Dotted-field-name syntax for nested JSON
• The _id field is canonical HGVS-g per record (e.g., chr7:g.117199644G>A)

Aggregated Sources: ~21 and Counting

Source	What	Notes
ClinVar	Pathogenicity	Weekly refresh
gnomAD v4 exomes + genomes	Population AF	grpmax_faf95 surfaced
dbSNP Build 156	rsID + alleles	RsMergeArch resolved
dbNSFP v4.x	Meta-aggregator of 40+ in silico predictors	Includes AlphaMissense, REVEL, BayesDel
CADD	Deleteriousness	Genome-wide
CIViC	Cancer interpretation	Per-disease
COSMIC	Somatic variants	Catalogue of Somatic Mutations
EVS	Exome Variant Server	Legacy (deprecated by gnomAD)
ExAC	ExAC frequencies	Legacy (superseded by gnomAD)
GRASP	GWAS associations	--
GWAS Catalog	Curated GWAS	--
Wellderly	Disease-resistant elderly cohort	--
EMV	--	--
DOCM	Database of Curated Mutations	--
ICGC	International cancer	--
MutDB	--	--
GO	Gene Ontology	--
Snpeff	snpEff annotations	--
GeneReviews	Disease/gene reviews	--
MutPred	Functional impact	--

dbNSFP is itself an aggregator. Querying dbnsfp.alphamissense.score returns the version that dbNSFP loaded, not AlphaMissense direct. The lag from publication (Cheng 2023 Science) to integration into myvariant.info is typically 6-18 months via dbNSFP.

Scopes and Query Forms

Endpoint	Method	Use
/v1/variant/{id}	GET	Single canonical-ID lookup
/v1/variant	POST (batched IDs)	Batch lookup, up to 1000 IDs
/v1/query?q={lucene}	GET	Flexible Elasticsearch search
/v1/metadata	GET	Per-source versions

Scopes (the scopes parameter on /v1/query POST) specifies which fields to match an input ID against: hgvs, rsid, dbsnp.rsid, dbnsfp.genename, chrom, _id. The _id is canonical HGVS-g.

Reproducibility: The _meta Field

Every record carries _meta.src showing per-source version:

mv = myvariant.MyVariantInfo()
record = mv.getvariant('chr7:g.140453136A>T', fields=['_meta', 'clinvar', 'dbnsfp.alphamissense'])
print(record['_meta']['src']['dbnsfp']['version'])  # e.g., '4.7a'
print(record['_meta']['src']['clinvar']['version'])  # e.g., '20250901'

For reproducibility, record per-source versions in analysis output alongside results.

Comparison to Alternatives

Tool	Approach	When to use
myvariant.info	Cloud aggregator, ES-backed	Quick batch annotation, no local setup
OpenCRAVAT (Pagel 2020 Cancer Res)	Local install, modular annotators	Offline / PHI-sensitive
VarSome (Kopanos 2019 Bioinformatics 35:1978; commercial)	Hosted, 22 sources	82% ACMG criteria auto-application (highest); clinical labs
Franklin / Genoox	Commercial hosted	59 data sources; family/cohort analysis
GeneBe.net (Stawinski 2024 Clin Genet)	Open-source web + API	Free Tavtigian-point-system-based ACMG; comparable to VarSome
ANNOVAR / VEP / snpEff	Local annotation tools	Pipeline integration, batch annotation, no ACMG

myvariant.info does NOT produce ACMG calls; it is purely an annotation aggregator. Pair with InterVar, GeneBe, or the acmg-classification skill for classification.

Decision Tree by Query Scenario

Scenario	Recommended path	Why
Single variant batch annotation	getvariant(hgvs, fields=...)	One call, all aggregated sources
10-1000 variants	getvariants(list, fields=...)	Batch endpoint, up to 1000
> 1000 variants	Chunk to 1000 + sleep	Rate limit + JSON size
Search by gene + pathogenicity	mv.query('clinvar.gene.symbol:BRCA1 AND clinvar.clinical_significance:Pathogenic', size=200)	Elasticsearch Lucene
ACMG-grade pipeline	myvariant for annotation -> InterVar / GeneBe for classification	myvariant does not produce ACMG calls
Offline / PHI-sensitive	OpenCRAVAT or VEP locally	myvariant requires HTTP
Reproducibility	Always record _meta.src.<source>.version	dbNSFP version is the dominant staleness vector
Source-specific deep dive	Use the source-specific skill (clinvar-lookup, gnomad-frequencies)	myvariant is aggregator-grade, not source-deep

Standard Annotation Workflow

Goal: Annotate a list of variants with the canonical clinical fields for downstream prioritization.

Approach: Batch getvariants with explicit field list; record _meta versions; convert to DataFrame.

import myvariant
import pandas as pd

mv = myvariant.MyVariantInfo()

CLINICAL_FIELDS = [
    'clinvar.clinical_significance',
    'clinvar.review_status',
    'clinvar.variant_id',
    'gnomad_exome.faf95',
    'gnomad_exome.af.af',
    'gnomad_exome.an.an',
    'gnomad_genome.faf95',
    'gnomad_genome.af.af',
    'dbsnp.rsid',
    'dbnsfp.alphamissense.score',
    'dbnsfp.alphamissense.pred',
    'dbnsfp.revel.score',
    'dbnsfp.cadd.phred',
    'dbnsfp.spliceai.master_pred',
    'dbnsfp.spliceai.ds_max',
    'cosmic.cosmic_id',
    'civic.openCravatUrl',
    '_meta'
]

def annotate_variant_list(hgvs_list):
    '''Batch-annotate variants with ClinVar / gnomAD / dbNSFP / COSMIC / CIViC fields.'''
    chunked = [hgvs_list[i:i+1000] for i in range(0, len(hgvs_list), 1000)]
    rows = []
    versions = None
    for chunk in chunked:
        results = mv.getvariants(chunk, fields=CLINICAL_FIELDS)
        for r in results:
            if versions is None and r.get('_meta'):
                versions = {src: meta.get('version') for src, meta in r['_meta'].get('src', {}).items()}
            clinvar = r.get('clinvar', {}) or {}
            gnomad_e = r.get('gnomad_exome', {}) or {}
            gnomad_g = r.get('gnomad_genome', {}) or {}
            dbnsfp = r.get('dbnsfp', {}) or {}
            faf95 = (gnomad_e.get('faf95', {}) or gnomad_g.get('faf95', {})) or {}
            rows.append({
                'variant': r.get('query'),
                'clinvar_sig': clinvar.get('clinical_significance'),
                'clinvar_review': clinvar.get('review_status'),
                'gnomad_grpmax_faf95': faf95.get('popmax'),
                'grpmax_ancestry': faf95.get('popmax_population'),
                'gnomad_af': gnomad_e.get('af', {}).get('af') or gnomad_g.get('af', {}).get('af'),
                'rsid': r.get('dbsnp', {}).get('rsid'),
                'alphamissense': dbnsfp.get('alphamissense', {}).get('score'),
                'revel': dbnsfp.get('revel', {}).get('score'),
                'cadd_phred': dbnsfp.get('cadd', {}).get('phred'),
                'spliceai_ds_max': dbnsfp.get('spliceai', {}).get('ds_max')
            })
    return pd.DataFrame(rows), versions

Elasticsearch Query Patterns

Goal: Search beyond canonical IDs; e.g., all pathogenic variants in a gene, all variants in a genomic region with CADD > 20.

Approach: Lucene syntax in mv.query(); support boolean operators, ranges, wildcards.

def find_pathogenic_in_gene(gene_symbol, max_results=500):
    '''Find ClinVar P/LP variants in a gene.'''
    query = f'clinvar.gene.symbol:{gene_symbol} AND '\
            'clinvar.clinical_significance:(Pathogenic OR "Likely pathogenic")'
    hits = mv.query(query, size=max_results, fields=['_id', 'clinvar.clinical_significance',
                                                       'clinvar.review_status'])
    return hits.get('hits', [])


def find_high_cadd_in_region(chrom, start, end, min_cadd=25):
    '''Find variants in region with CADD phred above threshold.'''
    query = f'chrom:{chrom} AND hg19.start:[{start} TO {end}] AND '\
            f'dbnsfp.cadd.phred:>{min_cadd}'
    return mv.query(query, size=500, fields=['_id', 'dbnsfp.cadd.phred', 'clinvar.clinical_significance'])


def find_alphamissense_pathogenic(gene, min_score=0.564):
    '''Find AlphaMissense pathogenic missense in a gene.

    Note: Cheng 2023 dev cutoff is 0.564 BUT this is NOT the Pejaver-style calibrated
    PP3 threshold. ClinGen has not endorsed AlphaMissense thresholds as of May 2026;
    use AlphaMissense as supporting evidence only.
    '''
    query = f'dbnsfp.genename:{gene} AND dbnsfp.alphamissense.score:>{min_score}'
    return mv.query(query, size=500, fields=['_id', 'dbnsfp.alphamissense', 'clinvar.clinical_significance'])

Per-Operation Failure Modes

1. Stale dbNSFP version

• Trigger: Query dbnsfp.alphamissense.score and trust as current.
• Mechanism: dbNSFP version lags new tools by 6-18 months; AlphaMissense (Cheng 2023) was integrated into dbNSFP 4.4 (~2024).
• Symptom: Predictions appear missing for variants newly scored by AlphaMissense.
• Fix: Check _meta.src.dbnsfp.version; for cutting-edge predictions query AlphaMissense API directly.

2. Treating AlphaMissense dev threshold as PP3-calibrated

• Trigger: Apply AlphaMissense score >0.564 as PP3 evidence.
• Mechanism: Cheng 2023 developer-recommended threshold is NOT the Pejaver 2022 calibration framework; ClinGen has not endorsed strength-graded thresholds for AlphaMissense as of May 2026.
• Symptom: Over-application of PP3 in ACMG classification.
• Fix: Treat AlphaMissense as supporting evidence; defer to clinical-databases/acmg-classification for calibrated thresholds.

3. Stacking REVEL + BayesDel + AlphaMissense as independent evidence

• Trigger: Use multiple in silico predictors as additive PP3 evidence.
• Mechanism: REVEL, BayesDel, VEST4 share ClinVar/HGMD training labels; AlphaMissense is partially independent but correlates strongly with conservation.
• Symptom: Inflated PP3 strength; double-counting.
• Fix: Apply ONE predictor per variant (Pejaver 2022 explicit recommendation).

4. Rate-limit ignorance

• Trigger: Loop sequentially over 10k variants with getvariant().
• Mechanism: ~1000 req/sec aggregate per source IP; loops trigger throttling or 429s.
• Symptom: Increasing latency, eventual failures.
• Fix: Use getvariants(chunk, fields=...) with chunk size 1000; sleep ~0.5s between chunks.

5. Field-path errors silently return None

• Trigger: Query gnomad_exome.faf95.popmax but typo as gnomad_exome.faf or gnomad.exomes.faf95.
• Mechanism: Elasticsearch returns null for non-existent paths; no error raised.
• Symptom: All values None; no error.
• Fix: Use print(mv.getvariant(test_id)) first to inspect actual field structure; check /v1/metadata/fields.

6. Multi-allelic rsID returns one variant only

• Trigger: Query rs12345 and treat returned variant as the variant of interest.
• Mechanism: rsID is a cluster identifier; multi-allelic clusters return multiple records.
• Symptom: Wrong allele returned for ~6-8% of rsIDs.
• Fix: Use HGVS-g instead of rsID for unambiguous lookups; for rsID queries, inspect all returned variants and filter by allele.

7. Sample overlap between sources

• Trigger: Treat ClinVar + gnomAD as independent corroboration.
• Mechanism: ClinVar variants often derive from gnomAD-included individuals; not statistically independent.
• Symptom: False sense of independent evidence in ACMG application.
• Fix: ClinVar P + gnomAD rare is operationally additive evidence per ACMG but understand the populations may overlap.

Reconciliation: When Sources Inside myvariant Disagree

Pattern	Likely cause	Action
dbNSFP REVEL != ClinVar PP3 strength	Different curation cohort	Use Pejaver 2022 calibrated thresholds (see acmg-classification)
ClinVar P + AlphaMissense benign	NMD-escape region, alternative isoform, ClinVar P stale	Cross-check with conservation, splicing predictions
gnomAD AF differs across exome vs genome	Sample sizes differ; exome has 730k, genome 76k	Use exome FAF95 when available; genome as fallback
COSMIC + ClinVar overlap	True dual-classification (germline + somatic)	Report both contexts
Variant missing from one source	Source-specific coverage gaps	Cross-check directly with primary source skill (clinvar-lookup, gnomad-frequencies)

Quantitative Thresholds and Conventions

Threshold	Convention	Source
Batch endpoint cap	1000 IDs per POST	myvariant.info docs
Rate limit	~1000 req/sec aggregate; lower per IP	myvariant.info docs
dbNSFP refresh lag	6-18 months from primary source release	dbNSFP release history
_meta.src field	Per-source version is always available	BioThings SDK convention
Lucene escape	Special chars need \ (e.g., chr7\:140453136)	Elasticsearch convention
Multi-allelic rsID	~6-8% of dbSNP rsIDs are multi-allelic	Phan 2025 NAR
AlphaMissense PP3 calibration	NOT yet ClinGen-endorsed (as of May 2026)	ClinGen SVI
REVEL PP3_Strong calibration	>= 0.932 per Pejaver 2022	Pejaver 2022 AJHG

Common Errors

Symptom	Cause	Solution
KeyError: 'gnomad_exome'	Variant absent from gnomAD exome dataset	Use .get('gnomad_exome', {}) defensively
None for AlphaMissense on rare variants	dbNSFP coverage gap; variant in alt-spliced isoform	Query AlphaMissense API directly, or accept None
Search returns 0 hits despite known matches	Lucene escape on : in chr coords	Quote the chrom-position term or escape :
Batch returns < input IDs	Some IDs not in any source	Check notfound field in response
Different AF in myvariant vs gnomAD browser	dbNSFP version != current gnomAD release	Check _meta.src.gnomad_exome.version
503 on bulk query	Rate limit	Reduce chunk to 500; sleep 1s between
_id doesn't match input	myvariant uses canonical HGVS-g; input was rsID or non-canonical	Re-query by _id after first resolution

Anticipated Reviewer Pushback

Pushback	Standard response
"myvariant.info is just an aggregator; why not query sources directly?"	Aggregator avoids per-source API setup; sufficient for single-source unique queries we defer to source-specific skills.
"Your annotation differs from VarSome"	VarSome uses its own ACMG implementation; myvariant.info does NOT produce ACMG calls; we pair with acmg-classification.
"dbNSFP REVEL differs from REVEL website"	dbNSFP version is on the order of 1 year behind primary; check _meta.src.dbnsfp.version.
"You missed AlphaMissense calibration thresholds"	AlphaMissense is integrated via dbNSFP; PP3 calibration is in clinical-databases/acmg-classification skill.
"Why not OpenCRAVAT?"	OpenCRAVAT requires local install; myvariant is faster for batch annotation. Switch to OpenCRAVAT for PHI-sensitive or offline workflows.

References

• Wu C et al. 2022. BioThings SDK: a toolkit for building high-performance data APIs in biomedical research. Bioinformatics 38:2316.
• Xin J et al. 2016. High-performance web services for querying gene and variant annotation. Genome Biol 17:91.
• Cheng J et al. 2023. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science 381:eadg7492.
• Pejaver V et al. 2022. Calibration of computational tools for missense variant pathogenicity classification. Am J Hum Genet 109:2163.
• Pagel KA et al. 2020. Integrated informatics analysis of cancer-related variants. Cancer Res 80:e29. (OpenCRAVAT)
• Kopanos C et al. 2019. VarSome: the human genomic variant search engine. Bioinformatics 35:1978.
• Stawinski PM et al. 2024. GeneBe; a tool for automated ACMG/AMP variant interpretation. Clin Genet (verify exact volume/pages in the published record before citing).
• myvariant.info docs: https://docs.myvariant.info/en/latest/
• BioThings field metadata: https://myvariant.info/v1/metadata/fields

Related Skills

• clinical-databases/clinvar-lookup - Source-level deep ClinVar queries
• clinical-databases/gnomad-frequencies - Source-level deep gnomAD queries
• clinical-databases/dbsnp-queries - Source-level rsID resolution
• clinical-databases/acmg-classification - ACMG framework with Pejaver calibration
• clinical-databases/variant-prioritization - Pipeline using aggregated annotations