Work

GIPA: A High-Throughput Computational Toolkit for Genomic Identity and Parentage Analysis in Modern Crop Breeding

Year: 2025

Type: article

Abstract: Modern crop breeding requires efficient tools for genetic identity and parentage verification to manage large-scale programs. To address this, we present GIPA (Genomic Identity and Parentage Analysis), a high-performance toolkit designed for these tasks. GIPA integrates key innovations: a sliding-window algorithm enhances accuracy by correcting genotyping errors, an intelligent system classifies samples by heterozygosity to streamline parentage analysis, and an integrated engine generates intuitive chromosome-level heatmaps. We demonstrate its utility in a soybean backcrossing scenario, where it identified a donor line with 98.02% genomic identity to the recipient, providing a strategy to significantly shorten the breeding program. In maize, its parentage module accurately identified the known parents of commercial hybrids with match scores exceeding 97%, validating its use for variety authentication and quality control. By transforming complex SNP data into clear, quantitative, and visual insights, GIPA provides a robust solution that accelerates data-driven decision-making in plant breeding.

Source: Agronomy

DOI: 10.3390/agronomy15102441

Authors: Yi-Fan Yu, Xiao-Ya Ma, Yue Wan, Zhi-Cheng Shen, Yu-Xuan Ye

Institutions: State Key Laboratory of Rice Biology, Zhejiang University

Language: English

Topic: Genetic Mapping and Diversity in Plants and Animals

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $3,137