Evidence Repository - Clinical Genome Resources

The ClinGen Evidence Repository is an FDA-recognized human genetic variant database containing expert-curated assertions regarding variants' pathogenicity and supporting evidence summaries. [Disclaimer]

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Variant: NM_194248.3(OTOF):c.1580-6C>T

CA177561

164870 (ClinVar)

Gene: OTOF

Condition: nonsyndromic genetic deafness

Inheritance Mode: Autosomal recessive inheritance

Link to MONDO

UUID: 88d051fa-2e2f-4648-8461-90b03b59dca0

Approved on: 2022-05-13

Published on: 2022-05-13

HGVS expressions

NM_194248.3:c.1580-6C>T

NM_194248.3(OTOF):c.1580-6C>T

NC_000002.12:g.26481015G>A

CM000664.2:g.26481015G>A

NC_000002.11:g.26703883G>A

CM000664.1:g.26703883G>A

NC_000002.10:g.26557387G>A

NG_009937.1:g.82684C>T

ENST00000272371.7:c.1580-6C>T

ENST00000272371.6:c.1580-6C>T

ENST00000403946.7:c.1580-6C>T

NM_001287489.1:c.1580-6C>T

NM_194248.2:c.1580-6C>T

NM_001287489.2:c.1580-6C>T

Benign

BA1 BP4

Evidence Links 0

Expert Panel

Hearing Loss VCEP

Criteria Specification Information

Criteria Specifications for this VCEP

Evidence submitted by expert panel

Hearing Loss VCEP

The filtering allele frequency (the lower threshold of the 95% CI of 232/24628) of the c.1580-6C>T variant in the OTOF gene is 0.843% for African/African-American chromosomes by gnomAD, which is a high enough frequency to be classified as benign based on thresholds defined by the ClinGen Hearing Loss Expert Panel for autosomal recessive hearing loss variants (BA1). Additionally, splice prediction analysis using MaxEntScan and SpliceAI does not suggest an impact to splicing (BP4). In summary, this variant meets criteria to be classified as benign. ACMG/AMP criteria applied, as specified by the Hearing Loss Expert Panel: BA1, BP4.

BA1

This variant is present in 0.94% (232/24628) of African/African-American alleles in gnomAD v2.1.1 which is a high enough threshold to meet the BA1 cutoff. The filtering AF (95% CI) is 0.843% which meets the BA1 threshold.

BP4

No impact to splicing as shown by splicing predictors SpliceAI and MaxEntScan.

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