---
license: apache-2.0
tags:
  - pharmacogenomics
  - DPYD
  - variant-classification
  - population-genomics
  - South-Asian
---

# DPYD Variant Pathogenicity Classifier

Trained classifier for DPYD variant functional classification (no_function / decreased_function / normal_function) built by [Anukriti AI](https://anukritiai.com).

## Versions
- v1: Baseline — 273 training rows, 4 decreased_function examples, F1=0.0 for decreased_function
- v2: +Offer 2014 + PharmVar data, SMOTE, domain features — 392 rows, 23 decreased_function examples, F1=0.66 (XGB)
- v3: +AlphaMissense + CADD + VEP annotations on training set — AM importance 0→0.01, sentinel gap closed
- v4: protein-only model — drop clnsig_norm/activity_pct/AF, keep AM/CADD/SIFT/PolyPhen/domain/aa_position/vep_consequence. 13/25 Scaria variants changed class (no_function→decreased_function). CV accuracy 0.91→0.73 (expected — label proxy removed). Best model: lgbm (decreased_function F1=0.25).

## Two-tier inference note
The two tier-2 options for novel population-discovery variants (no ClinVar significance, no measured activity — e.g. the Scaria 2025 Indian cohort) are **complementary, not competing**:
- The **protein-only classifier (v4)** is preferred when class discrimination between `decreased_function` and `no_function` is required. Unlike v1–v3, v4 does not default novel variants to `no_function` (it reclassified 13/25 Scaria variants), at the cost of in-distribution accuracy it does not need on this tail.
- The **raw AlphaMissense score** is sufficient when the clinical question is simply pathogenicity vs. benign.

For variants that *do* carry clinical labels or measured activity, the v2/v3 mixed-feature classifier (decreased_function F1=0.66) remains the better tool. CPIC-assigned variants are handled by the deterministic engine, not any classifier. See [validation paper](https://doi.org/10.5281/zenodo.20727790).

## Ground truth sources
PharmVar activity scores, Offer et al. 2014, GeT-RM 2024 (Gaedigk et al.), ClinVar

## Citation
Abhimanyu R B et al. "Deterministic, Population-Aware Pharmacogenomics Screening." Zenodo. https://doi.org/10.5281/zenodo.20727790