An improved method for whole genome selection of corn hybrids

By using haplotype and RRBLUP models in maize breeding, the problems of incomplete genetic variation capture, detection bias, and high computational resource requirements of the SNP method were solved, achieving more accurate and efficient genome prediction and improving the genetic improvement effect of breeding.

CN122290693APending Publication Date: 2026-06-26CHINA AGRI UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHINA AGRI UNIV
Filing Date
2025-10-14
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing SNP-based genomic selection methods in maize breeding suffer from problems such as incomplete capture of genetic variations, detection bias, generational decay in prediction accuracy, limited causal relationship information, and high computational resource requirements.

Method used

Using haplotypes as predictors, a maize pangenome and haplotype library were constructed, and whole-genome prediction was performed using the Ridge Regression Optimal Linear Unbiased Prediction (RRBLUP) model. The haplotype effect value and parental combining ability were combined for training and prediction.

Benefits of technology

It improves the accuracy and efficiency of genome prediction, captures more genetic variations, reduces the problem of multiple testing, lowers the computational burden, and enhances the utilization of rare variations and genetic diversity.

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Abstract

This application discloses an improved method for whole-genome selection of maize hybrids, belonging to the field of genetic breeding technology. To address the shortcomings of existing technologies, this application provides a computer device to implement the following steps: (A1) Construction of a maize pan-genome: screening core planting and breeding backbone parents, constructing libraries and sequencing, loading reference genomes and genome annotations, and iteratively assembling to construct a pan-genome to obtain a sequence / gene-based pan-genome; (A2) Construction of a maize haplotype library: setting chromosomal reference segments / genes and statistically analyzing the haplotypes of each reference segment to obtain a whole-genome haplotype library; (A3) Haplotype genotyping of the training population: obtaining haplotype data of the training population; (A4) Whole-genome prediction: training the training model data through evaluation dimensions such as haplotype effect value, parental combining ability, and / or prediction accuracy.
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