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Improving genomic selection accuracy using a dual-path convolutional neural network framework: a terpenoid case

Fuchuan Han^1,2, Ming Gao^1,2, Yunxiao Zhao^1,2

¹State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing, 100091, China.

The New Phytologist

|November 13, 2025

Summary

This summary is machine-generated.

Genomic selection accelerates forest tree breeding. A new deep learning model, PKDP, improved prediction accuracy for key terpenoids in Litsea cubeba by integrating genome-wide and GWAS data.

Keywords:

Litsea cubeba PKDP deep learning genomic selection terpenoids

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Area of Science:

Plant genetics and breeding
Genomics
Metabolomics

Background:

Genomic selection (GS) is crucial for accelerating early breeding in forest trees.
Understanding the genetic basis of terpenoid biosynthesis is key for improving valuable traits in species like Litsea cubeba.
Traditional breeding methods can be slow, necessitating advanced genomic tools for efficient genetic improvement.

Purpose of the Study:

To investigate the genetic architecture of terpenoid biosynthesis in Litsea cubeba.
To develop and evaluate a novel deep learning-based genomic selection (GS) model (PKDP) for enhanced prediction accuracy.
To identify key genes and pathways involved in terpenoid synthesis for molecular breeding.

Main Methods:

Whole-genome resequencing of 945 Litsea cubeba germplasms.
Gas Chromatography-Mass Spectrometry (GC-MS) for terpenoid quantification in 310 samples.
Genome-wide association studies (GWAS) and development of the PKDP deep learning model integrating GWAS loci and genome-wide markers.

Main Results:

GWAS identified 125 candidate genes for terpenoid biosynthesis, including terpene synthase (TPS) gene clusters and MVA/MEP pathway enzymes.
The PKDP deep learning model demonstrated improved predictive ability for key terpenoids (citral, geranial, neral) by 2-10% compared to traditional rrBLUP.
Key genetic factors influencing Litsea cubeba terpenoid synthesis were elucidated.

Conclusions:

The study identified critical genetic factors for Litsea cubeba terpenoid synthesis, advancing molecular mechanism research.
The PKDP model effectively integrates multi-scale genomic information, significantly enhancing GS prediction accuracy.
This research provides an efficient tool for Litsea cubeba genetic improvement and offers innovative strategies for breeding complex traits in forest trees.