Identification of QTL-by-environment interaction by controlling polygenic background effect

Fuping Zhao; Lixian Wang; Shizhong Xu

doi:10.1016/j.jgg.2025.01.003

Volume 52 Issue 7

Jul. 2025

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Article Navigation > Journal of Genetics and Genomics > 2025 > 52(7): 915-926

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Identification of QTL-by-environment interaction by controlling polygenic background effect

doi: 10.1016/j.jgg.2025.01.003

a. State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China;
b. Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA

Funds:

This work was supported by the National Key Research and Development Programs of China (2024YFF1000100 and 2021YFD1301102), the National Natural Science Foundation of China (32172702), the State Key Laboratory of Animal Biotech Breeding (XQSWYZQZ-KFYX-4), Zaozhuang Elite Industrial Innovation Program, and Agricultural Science and Technology Innovation Program (ASTIP-IAS-TS-6) to F. Z.. The project was also supported by the United States National Science Foundation (NSF) Collaborative Research Grant (DBI-1458515) to S. X.

Received Date: 2024-07-23
Accepted Date: 2025-01-02
Rev Recd Date: 2024-12-30

Available Online: 2025-07-11

Publish Date: 2025-01-11

Abstract

Abstract

The quantitative trait loci (QTL)-by-environment (Q × E) interaction effect is hard to detect because there are no effective ways to control the genomic background. In this study, we propose a linear mixed model that simultaneously analyzes data from multiple environments to detect Q × E interactions. This model incorporates two different kinship matrices derived from the genome-wide markers to control both main and interaction polygenic background effects. Simulation studies demonstrate that our approach is more powerful than the meta-analysis and inclusive composite interval mapping methods. We further analyze four agronomic traits of rice across four environments. A main effect QTL is identified for 1000-grain weight (KGW), while no QTL are found for tiller number. Additionally, a large QTL with a significant Q × E interaction is detected on chromosome 7 affecting grain number, yield, and KGW. This region harbors two important genes, PROG1 and Ghd7. Furthermore, we apply our mixed model to analyze lodging in barley across six environments. The six regions exhibiting Q × E interaction effects identified by our approach overlap with the SNPs previously identified using EM and MCMC-based Bayesian methods, further validating the robustness of our approach. Both simulation studies and empirical data analyses show that our method outperforms all other methods compared.
- Q × E interaction,
- Main effect,
- Meta-analysis,
- Mixed model,
- Polygenic background

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References(36)

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