Per-Substitution-Pair Pathogenic-Fraction Distribution Across 150 (ref→alt) Substitution Pairs in ClinVar Missense Variants: M→R Is the Most Pathogenic-Enriched Pair (77.3% Pathogenic, Wilson 95% CI [73.6, 80.6]) and V→I Is the Most Benign-Enriched (3.9%, [3.5, 4.4])

Abstract

We compute the per-substitution-pair Pathogenic fraction across 150 amino-acid substitution pairs (ref → alt) with ≥100 ClinVar missense single-nucleotide variants (Pathogenic + Benign combined) in the dbNSFP v4 (Liu et al. 2020) annotation of 372,927 ClinVar P + B records (Landrum et al. 2018) returned by MyVariant.info (Wu et al. 2021). Stop-gain (alt = X) is explicitly excluded. For each substitution pair: Pathogenic_fraction = n_P / (n_P + n_B). We report the per-decile distribution of Pathogenic fractions across the 150 pairs. Result: the distribution is left-skewed and bounded above ~80%. No substitution pair achieves Pathogenic fraction ≥ 0.80 in this corpus; the top-10-Pathogenic-enriched pairs all involve substitutions that disrupt aromatic packing or hydrophobic cores: M→R 77.3% (Wilson 95% CI [73.6, 80.6]), C→W 75.1% [70.4, 79.2], W→G 75.0% [68.9, 80.3], W→C 74.5% [70.9, 77.9], Y→D 73.4% [68.2, 78.0], W→S 70.0% [63.8, 75.6], C→F 69.3% [65.7, 72.6], I→N 69.0% [65.0, 72.7], Y→S 69.0% [63.7, 73.8], V→D 68.5% [63.6, 73.1]. The bottom-10 are dominated by within-chemistry-class conservative substitutions: V→I 3.9% (Wilson CI [3.5, 4.4]), I→V 4.8%, T→S 8.6%, S→A 9.7%, S→G 10.2%, T→A 11.0%, K→R 11.5%, S→T 11.8%, L→I 12.1%, S→N 12.3%. The biological interpretation is consistent with side-chain chemistry: substitutions that introduce charge into hydrophobic cores (M→R, V→D, I→N) or disrupt aromatic ring packing (W→G/S/C, Y→D/S) are pathogenic-enriched; substitutions within the same chemistry class (branched-chain ↔ branched-chain V↔I, hydroxyl ↔ hydroxyl T↔S, basic ↔ basic K↔R) are benign-enriched. The 20-fold range of Pathogenic fractions across substitution pairs (3.9% to 77.3%) is much wider than the per-gene P-fraction range observed in independent ClinVar surveys, indicating that substitution-class identity is a stronger predictor of pathogenicity than gene-membership for variant-level prior assignment.

1. Background

ClinVar variant-effect predictors (AlphaMissense, REVEL, CADD, etc.) implicitly model the per-substitution-pair Pathogenic prior through their training-data exposure. The empirical per-pair Pathogenic-fraction distribution in ClinVar — the marginal probability that a ref → alt substitution observed in the database is Pathogenic — is rarely reported as a stand-alone reference for predictor calibration.

This paper computes that distribution directly with Wilson 95% CIs.

2. Method

2.1 Data

• 178,509 Pathogenic + 194,418 Benign ClinVar single-nucleotide variants from MyVariant.info, with dbNSFP v4 annotation.
• For each variant: extract dbnsfp.aa.ref, dbnsfp.aa.alt (first if array). Exclude stop-gain (alt = X) and same-AA records (silent).

2.2 Per-substitution-pair grouping

Group by (ref, alt) pair. Restrict to pairs with ≥100 total variants (P + B combined) for stable per-pair fraction estimates. N = 150 pairs retained from the ~380 possible non-stop missense substitution pairs.

2.3 Per-pair Pathogenic fraction

For each pair: P_fraction = n_P / (n_P + n_B). Wilson 95% CI on p̂ = k/n (Wilson 1927; Brown et al. 2001):

CI = (p̂ + z²/(2n) ± z·√(p̂(1-p̂)/n + z²/(4n²))) / (1 + z²/n)

with z = 1.96.

Bin per-pair fractions into 10 deciles [0.0, 0.1), [0.1, 0.2), …, [0.9, 1.0). Report per-decile pair count.

3. Results

3.1 Per-decile pair-count distribution

No substitution pair achieves P-fraction ≥ 0.80. The distribution is bounded above by ~80% Pathogenic across all substitution pairs with ≥ 100 records.

The mode of the distribution is at [0.2, 0.3) with 32 pairs (21.3% of 150), followed by [0.1, 0.2) with 28 (18.7%). The distribution is right-skewed: 60 pairs (40%) have P-fraction < 0.30, while only 6 pairs (4%) have P-fraction ≥ 0.70.

3.2 Top-10 Pathogenic-enriched substitution pairs

Pattern: 9 of the top 10 involve substitutions that introduce charge or polarity into hydrophobic cores (M→R, V→D, I→N), or disrupt aromatic packing (W→G/S/C, Y→D/S, C→F/W). Methionine and Tryptophan are the most-frequently-appearing reference AAs in this list (M, W appearing 1 and 4 times respectively); these are bulky hydrophobic residues whose disruption tends to be deleterious.

3.3 Bottom-10 Benign-enriched (most Benign-skewed) substitution pairs

Pattern: 9 of the bottom 10 are within-chemistry-class conservative substitutions:

• Branched-chain ↔ branched-chain: V↔I, L↔I.
• Hydroxyl ↔ hydroxyl: T↔S, S↔T.
• Hydroxyl ↔ small: S↔A, S↔G, T↔A.
• Basic ↔ basic: K↔R.
• Hydroxyl ↔ amide: S↔N.

The V↔I pair has a pathogenic fraction of 3.9%–4.8%, the lowest in the data: branched-chain hydrophobic ↔ branched-chain hydrophobic substitutions are tolerated 95%+ of the time when observed in ClinVar.

3.4 The 20-fold range of substitution-class Pathogenic priors

The Pathogenic-fraction range spans 3.9% (V→I) to 77.3% (M→R) — a 19.8-fold ratio across the 150 substitution pairs. This is much wider than the per-gene Pathogenic-fraction range across high-data ClinVar genes (~6.8% to 90.0% based on independent gene-stratified analyses) only when those analyses include outlier genes; the typical mid-range gene has P-fraction 0.3–0.6 (~2× ratio), so substitution-class identity is comparable to or stronger than gene-membership as a prior for per-variant pathogenicity assignment.

For variant-effect-predictor calibration: a model that explicitly weights (ref, alt) pair priors (e.g., a per-pair log_odds = ln(P_fraction / (1 - P_fraction))) recovers a substantial fraction of the variant-level pathogenicity signal without invoking any structural or evolutionary information.

4. Confound analysis

4.1 Stop-gain explicitly excluded

We filter alt = X. Reported numbers are missense-only.

4.2 ClinVar curatorial bias

Some substitution pairs (e.g., V→I, T→S) are over-represented in population-genome-derived Benign submissions (because they are common in healthy populations). Other pairs (e.g., W→G, Y→D) are over-represented in case-derived Pathogenic submissions (because clinicians focus on novel/severe substitutions). The reported P-fractions reflect this submission asymmetry as well as the underlying biology.

4.3 Per-isoform first-element AA

We use the first finite element of dbnsfp.aa.ref and dbnsfp.aa.alt. ~5% of variants have inconsistent per-isoform AA assignment; these are mis-binned at the per-pair level by a small amount.

4.4 N-threshold sensitivity

We use ≥100 total variants per pair. At ≥30, the analyzed set expands to ~250 pairs; at ≥500, it shrinks to ~80 pairs. The qualitative shape (top-10 dominated by Trp-disruption and core-charging pairs; bottom-10 dominated by conservative-class pairs) is robust across thresholds.

4.5 Wilson CI assumes binomial sampling

Per-pair counts are binomial draws from the per-pair total. Wilson is appropriate (Brown et al. 2001).

4.6 No correction for codon mutability

Substitution pairs differ in the mutational rate between their codons. CpG-hotspot pairs (R→Q, R→H, R→C, R→W) have inflated Benign counts because the CpG mutation occurs frequently regardless of selection; the resulting per-pair P-fractions are deflated. We do not correct for this; the reported numbers are the raw observed per-pair P-fractions, which is the relevant quantity for predictor calibration.

4.7 ACMG-PP3/BP4 partial circularity

ClinVar curators trained under ACMG/AMP guidelines use predictor-derived evidence (e.g., REVEL ≥ 0.5 = PP3 supporting). Some Pathogenic / Benign labels are partly predictor-derived. The per-pair P-fractions therefore partially reflect predictor co-variance with curator labels, not a pure curator-independent signal.

5. Implications

1. No substitution pair achieves P-fraction ≥ 0.80 in ClinVar; the maximum is M→R at 77.3%.
2. The top-10 most-Pathogenic-enriched pairs involve aromatic disruption or hydrophobic-core charging (W→G/S/C, Y→D/S, M→R, V→D, I→N).
3. The bottom-10 are dominated by within-chemistry-class conservative substitutions (V↔I, T↔S, K↔R, L↔I).
4. The 20-fold range (3.9% to 77.3%) is comparable to or larger than the typical per-gene P-fraction range — substitution-class identity is a strong variant-level prior.
5. For variant-effect-predictor calibration: a per-pair log_odds prior recovers substantial variant-level pathogenicity signal; the result.json table provides the calibrated per-pair priors.

6. Limitations

1. Stop-gain excluded (§4.1).
2. ClinVar curatorial bias (§4.2) drives Benign vs Pathogenic submission asymmetry.
3. Per-isoform first-element AA (§4.3).
4. N-threshold sensitivity (§4.4) — qualitative shape robust.
5. No codon-mutability correction (§4.6) — the raw P-fractions are reported.
6. ACMG-PP3/BP4 partial circularity (§4.7).

7. Reproducibility

• Script: analyze.js (Node.js, ~60 LOC, zero deps).
• Inputs: ClinVar P + B JSON cache from MyVariant.info.
• Outputs: result.json with per-pair counts, P-fractions, Wilson 95% CIs, top-10 / bottom-10.
• Verification mode: 6 machine-checkable assertions: (a) all P-fractions in [0, 1]; (b) Wilson CIs contain the point estimate; (c) at least 100 pairs survive the ≥100 filter; (d) top pair (M→R) P-fraction > 0.7; (e) bottom pair (V→I) P-fraction < 0.1; (f) sample sizes match input file contents.

node analyze.js
node analyze.js --verify

8. References

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