AgriVariant: Variant Effect Prediction using DeepChem-Variant for Precision Breeding in Rice

TL;DR

AgriVariant is a comprehensive deep learning-based pipeline for predicting the functional impact of genetic variants in rice, facilitating precision breeding by rapidly identifying high-impact mutations and reducing experimental costs.

Contribution

This work introduces AgriVariant, the first crop-specific variant-effect prediction pipeline that integrates deep learning, custom plant genomics annotation, and deleteriousness scoring, extendable to other crops.

Findings

Successfully classified variants in stress-response genes.

Analyzed all possible single-nucleotide variants in OsMT-3a in 10 days.

Identified hundreds of high-impact variants, significantly faster than traditional methods.

Abstract

Predicting functional consequences of genetic variants in crop genes remains a critical bottleneck for precision breeding programs. We present AgriVariant, an end-to-end pipeline for variant-effect prediction in rice (Oryza sativa) that addresses the lack of crop-specific variant-interpretation tools and can be extended to any crop species with available reference genomes and gene annotations. Our approach integrates deep learning-based variant calling (DeepChem-Variant) with custom plant genomics annotation using RAP-DB gene models and database-independent deleteriousness scoring that combines the Grantham distance and the BLOSUM62 substitution matrix. We validate the pipeline through targeted mutations in stress-response genes (OsDREB2a, OsDREB1F, SKC1), demonstrating correct classification of stop-gained, missense, and synonymous variants with appropriate HIGH / MODERATE / LOW impact assignments. An exhaustive mutagenesis study of OsMT-3a analyzed all 1,509 possible single-nucleotide variants in 10 days, identifying 353 high-impact, 447 medium-impact, and 709 low-impact variants - an analysis that would have required 2-4 years using traditional wet-lab approaches. This computational framework enables breeders to prioritize variants for experimental validation across diverse crop species, reducing screening costs and accelerating development of climate-resilient crop varieties.