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Add odds ratio classification function and corresponding tests
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‎src/mavedb/lib/oddspaths.py‎

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from typing import Optional
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from mavedb.lib.acmg import StrengthOfEvidenceProvided, ACMGCriterion
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def oddspaths_evidence_strength_equivalent(
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ratio: float,
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) -> tuple[Optional[ACMGCriterion], Optional[StrengthOfEvidenceProvided]]:
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"""
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Based on the guidelines laid out in Table 3 of:
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Brnich, S.E., Abou Tayoun, A.N., Couch, F.J. et al. Recommendations for application
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of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant
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interpretation framework. Genome Med 12, 3 (2020).
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https://doi.org/10.1186/s13073-019-0690-2
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Classify an odds (likelihood) ratio into a ACMGCriterion and StrengthOfEvidenceProvided.
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This function infers the ACMG/AMP-style evidence strength category from a
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precomputed odds (likelihood) ratio by applying a series of descending
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threshold comparisons. The mapping is asymmetric: higher ratios favor
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pathogenic (PS3*) evidence levels; lower ratios favor benign (BS3*) evidence
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levels; an intermediate band is considered indeterminate.
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Threshold logic (first condition matched is returned):
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ratio > 350 -> (PS3, VERY_STRONG)
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ratio > 18.6 -> (PS3, STRONG)
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ratio > 4.3 -> (PS3, MODERATE)
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ratio > 2.1 -> (PS3, SUPPORTING)
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ratio >= 0.48 -> Indeterminate (None, None)
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ratio >= 0.23 -> (BS3, SUPPORTING)
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ratio >= 0.053 -> (BS3, MODERATE)
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ratio < 0.053 -> (BS3, STRONG)
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Interval semantics:
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- Upper (pathogenic) tiers use strictly greater-than (>) comparisons.
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- Lower (benign) tiers and the indeterminate band use inclusive lower
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bounds (>=) to form closed intervals extending downward until a prior
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condition matches.
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- Because of the ordering, each numeric ratio falls into exactly one tier.
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Parameters
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----------
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ratio : float
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The odds or likelihood ratio to classify. Must be a positive value in
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typical use. Values <= 0 are not biologically meaningful in this context
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and will be treated as < 0.053, yielding a benign-leaning classification.
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Returns
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-------
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tuple[Optional[ACMGCriterion], Optional[StrengthOfEvidenceProvided]]
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The enumerated evidence strength and criterion corresponding to the ratio.
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Raises
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------
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TypeError
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If ratio is not a real (float/int) number (depending on external validation;
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this function assumes a float input and does not explicitly check type).
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ValueError
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If the ratio is negative (less than 0).
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Examples
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--------
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>>> inferred_evidence_strength_from_ratio(500.0)
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(ACMGCriterion.PS3, StrengthOfEvidenceProvided.VERY_STRONG)
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>>> inferred_evidence_strength_from_ratio(10.0)
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(ACMGCriterion.PS3, StrengthOfEvidenceProvided.MODERATE)
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>>> inferred_evidence_strength_from_ratio(0.30)
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(ACMGCriterion.BS3, StrengthOfEvidenceProvided.SUPPORTING)
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>>> inferred_evidence_strength_from_ratio(0.06)
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(ACMGCriterion.BS3, StrengthOfEvidenceProvided.MODERATE)
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>>> inferred_evidence_strength_from_ratio(0.5)
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(None, None)
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Notes
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-----
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These thresholds reflect predefined likelihood ratio cut points aligning with
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qualitative evidence strength categories. Adjust carefully if underlying
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classification criteria change, ensuring ordering and exclusivity are preserved.
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"""
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if ratio < 0:
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raise ValueError("OddsPaths ratio must be a non-negative value")
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if ratio > 350:
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return (ACMGCriterion.PS3, StrengthOfEvidenceProvided.VERY_STRONG)
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elif ratio > 18.6:
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return (ACMGCriterion.PS3, StrengthOfEvidenceProvided.STRONG)
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elif ratio > 4.3:
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return (ACMGCriterion.PS3, StrengthOfEvidenceProvided.MODERATE)
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elif ratio > 2.1:
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return (ACMGCriterion.PS3, StrengthOfEvidenceProvided.SUPPORTING)
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elif ratio >= 0.48:
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return (None, None)
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elif ratio >= 0.23:
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return (ACMGCriterion.BS3, StrengthOfEvidenceProvided.SUPPORTING)
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elif ratio >= 0.053:
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return (ACMGCriterion.BS3, StrengthOfEvidenceProvided.MODERATE)
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else: # ratio < 0.053
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return (ACMGCriterion.BS3, StrengthOfEvidenceProvided.STRONG)

‎tests/lib/test_odds_paths.py‎

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import pytest
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from mavedb.lib.acmg import ACMGCriterion, StrengthOfEvidenceProvided
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from mavedb.lib.oddspaths import oddspaths_evidence_strength_equivalent
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@pytest.mark.parametrize(
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"ratio,expected_criterion,expected_strength",
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[
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# Upper pathogenic tiers (strict >)
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(351, ACMGCriterion.PS3, StrengthOfEvidenceProvided.VERY_STRONG),
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(350.0001, ACMGCriterion.PS3, StrengthOfEvidenceProvided.VERY_STRONG),
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(350, ACMGCriterion.PS3, StrengthOfEvidenceProvided.STRONG), # boundary
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(19, ACMGCriterion.PS3, StrengthOfEvidenceProvided.STRONG),
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(18.60001, ACMGCriterion.PS3, StrengthOfEvidenceProvided.STRONG),
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(18.6, ACMGCriterion.PS3, StrengthOfEvidenceProvided.MODERATE), # boundary
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(5, ACMGCriterion.PS3, StrengthOfEvidenceProvided.MODERATE),
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(4.30001, ACMGCriterion.PS3, StrengthOfEvidenceProvided.MODERATE),
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(4.3, ACMGCriterion.PS3, StrengthOfEvidenceProvided.SUPPORTING), # boundary
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(2.10001, ACMGCriterion.PS3, StrengthOfEvidenceProvided.SUPPORTING),
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# Indeterminate band
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(2.1, None, None), # boundary just below >2.1
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(0.48, None, None),
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(0.50001, None, None),
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# Benign supporting
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(0.479999, ACMGCriterion.BS3, StrengthOfEvidenceProvided.SUPPORTING),
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(0.23, ACMGCriterion.BS3, StrengthOfEvidenceProvided.SUPPORTING),
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# Benign moderate
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(0.229999, ACMGCriterion.BS3, StrengthOfEvidenceProvided.MODERATE),
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(0.053, ACMGCriterion.BS3, StrengthOfEvidenceProvided.MODERATE),
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# Benign strong
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(0.052999, ACMGCriterion.BS3, StrengthOfEvidenceProvided.STRONG),
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(0.01, ACMGCriterion.BS3, StrengthOfEvidenceProvided.STRONG),
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(0.0, ACMGCriterion.BS3, StrengthOfEvidenceProvided.STRONG),
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# Very high ratio
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(1000, ACMGCriterion.PS3, StrengthOfEvidenceProvided.VERY_STRONG),
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],
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)
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def test_oddspaths_classification(ratio, expected_criterion, expected_strength):
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criterion, strength = oddspaths_evidence_strength_equivalent(ratio)
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assert criterion == expected_criterion
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assert strength == expected_strength
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@pytest.mark.parametrize("neg_ratio", [-1e-9, -0.01, -5])
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def test_negative_ratio_raises_value_error(neg_ratio):
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with pytest.raises(ValueError):
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oddspaths_evidence_strength_equivalent(neg_ratio)
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def test_each_interval_is_exclusive():
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# Sorted representative ratios spanning all tiers
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samples = [
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(0.0, 0.0529999), # BS3 STRONG
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(0.053, 0.229999), # BS3 MODERATE
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(0.23, 0.479999), # BS3 SUPPORTING
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(0.48, 2.1), # Indeterminate
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(2.10001, 4.3), # PS3 SUPPORTING
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(4.30001, 18.6), # PS3 MODERATE
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(18.60001, 350), # PS3 STRONG
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(350.0001, float("inf")), # PS3 VERY_STRONG (no upper bound)
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]
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seen = set()
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for r in samples:
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lower_result = oddspaths_evidence_strength_equivalent(r[0])
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upper_result = oddspaths_evidence_strength_equivalent(r[1])
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assert lower_result == upper_result, f"Mismatch at interval {r}"
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assert all(
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result not in seen for result in [lower_result, upper_result]
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), f"Duplicate classification for ratio {r}"
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seen.add(lower_result)
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@pytest.mark.parametrize(
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"lower,upper",
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[
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(0.053, 0.23), # BS3 MODERATE -> BS3 SUPPORTING transition
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(0.23, 0.48), # BS3 SUPPORTING -> Indeterminate
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(0.48, 2.1), # Indeterminate band
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(2.1, 4.3), # Indeterminate -> PS3 SUPPORTING
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(4.3, 18.6), # PS3 SUPPORTING -> PS3 MODERATE
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(18.6, 350), # PS3 MODERATE -> PS3 STRONG
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(350, 351), # PS3 STRONG -> PS3 VERY_STRONG
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],
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)
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def test_monotonic_direction(lower, upper):
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crit_low, strength_low = oddspaths_evidence_strength_equivalent(lower)
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crit_high, strength_high = oddspaths_evidence_strength_equivalent(upper)
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# If categories differ, ensure ordering progression (not regression to benign when moving upward)
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benign_set = {ACMGCriterion.BS3}
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pathogenic_set = {ACMGCriterion.PS3}
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if crit_low != crit_high:
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# Moving upward should not go from pathogenic to benign
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assert not (crit_low in pathogenic_set and crit_high in benign_set)
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def test_return_types():
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c, s = oddspaths_evidence_strength_equivalent(0.7)
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assert (c is None and s is None) or (isinstance(c, ACMGCriterion) and isinstance(s, StrengthOfEvidenceProvided))

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