3vGCIM: a compressed variance component mixed model for detecting QTL-by-environment interactions in RIL population

Mei Li; Yuan-Ming Zhang

doi:10.1016/j.jgg.2025.05.011

3vGCIM: a compressed variance component mixed model for detecting QTL-by-environment interactions in RIL population

doi: 10.1016/j.jgg.2025.05.011

Mei Li^a,b,
Yuan-Ming Zhang^b, ,

a. State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, Hubei 430062, China;
b. College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, China

基金项目:

This work was supported by the National Natural Science Foundation of China (32270673 and 32470657).

详细信息

通讯作者:
Yuan-Ming Zhang,E-mail:soyzhang@mail.hzau.edu.cn

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出版历程
- 收稿日期: 2025-02-28
- 录用日期: 2025-05-30
- 修回日期: 2025-05-29
- 刊出日期: 2026-02-10

3vGCIM: a compressed variance component mixed model for detecting QTL-by-environment interactions in RIL population

Mei Li^a,b,
Yuan-Ming Zhang^{b
, ,}

a. State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, Hubei 430062, China;
b. College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, China

Funds:

This work was supported by the National Natural Science Foundation of China (32270673 and 32470657).

摘要

摘要: Existing quantitative trait locus (QTL) mapping had low efficiency in identifying small-effect and closely linked QTL-by-environment interactions (QEIs) in recombinant inbred lines (RILs), especially in the era of global climate change. To address this challenge, here we integrate the compressed variance component mixed model with our GCIM to propose 3vGCIM for identifying QEIs in RILs, and extend 3vGCIM-random to 3vGCIM-fixed. 3vGCIM integrates genome-wide scanning with machine learning, significantly improving power. In the mixed full model, we consider all possible effects and control for all possible polygenic backgrounds. In simulation studies, 3vGCIM exhibits higher power (∼92.00%), higher accuracy of the estimates for QTL position (∼1.900 cM²) and effect (∼0.050), and lower false positive rate (∼0.48‰) and false negative rate (<8.10%) in three environments of 300 RILs each than ICIM (47.57%; 3.607 cM², 0.583; 2.81‰; 52.43%) and MCIM (60.30%; 5.279 cM², 0.274; 2.17‰; 39.70%). In the real data analysis of rice yield-related traits in 240 RILs, 3vGCIM mines more known genes (57–60) and known gene-by-environment interactions (GEIs) (14–19) and candidate GEIs (21–23) than ICIM (27, 2, and 7), and MCIM (21, 1, and 3), especially in small-effect and linked QTLs and QEIs. This makes 3vGCIM a powerful and sensitive tool for QTL mapping and molecular QTL mapping.
- QTL-by-environment interaction /
- 3vGCIM /
- RIL /
- Rice /
- Machine learning
Abstract: Existing quantitative trait locus (QTL) mapping had low efficiency in identifying small-effect and closely linked QTL-by-environment interactions (QEIs) in recombinant inbred lines (RILs), especially in the era of global climate change. To address this challenge, here we integrate the compressed variance component mixed model with our GCIM to propose 3vGCIM for identifying QEIs in RILs, and extend 3vGCIM-random to 3vGCIM-fixed. 3vGCIM integrates genome-wide scanning with machine learning, significantly improving power. In the mixed full model, we consider all possible effects and control for all possible polygenic backgrounds. In simulation studies, 3vGCIM exhibits higher power (∼92.00%), higher accuracy of the estimates for QTL position (∼1.900 cM²) and effect (∼0.050), and lower false positive rate (∼0.48‰) and false negative rate (<8.10%) in three environments of 300 RILs each than ICIM (47.57%; 3.607 cM², 0.583; 2.81‰; 52.43%) and MCIM (60.30%; 5.279 cM², 0.274; 2.17‰; 39.70%). In the real data analysis of rice yield-related traits in 240 RILs, 3vGCIM mines more known genes (57–60) and known gene-by-environment interactions (GEIs) (14–19) and candidate GEIs (21–23) than ICIM (27, 2, and 7), and MCIM (21, 1, and 3), especially in small-effect and linked QTLs and QEIs. This makes 3vGCIM a powerful and sensitive tool for QTL mapping and molecular QTL mapping.
- QTL-by-environment interaction /
- 3vGCIM /
- RIL /
- Rice /
- Machine learning

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3vGCIM: a compressed variance component mixed model for detecting QTL-by-environment interactions in RIL population

doi: 10.1016/j.jgg.2025.05.011

通讯作者:
Yuan-Ming Zhang,E-mail:soyzhang@mail.hzau.edu.cn

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3vGCIM: a compressed variance component mixed model for detecting QTL-by-environment interactions in RIL population

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3vGCIM: a compressed variance component mixed model for detecting QTL-by-environment interactions in RIL population

doi: 10.1016/j.jgg.2025.05.011

通讯作者: Yuan-Ming Zhang,E-mail:soyzhang@mail.hzau.edu.cn

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3vGCIM: a compressed variance component mixed model for detecting QTL-by-environment interactions in RIL population

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出版历程

目录

通讯作者:
Yuan-Ming Zhang,E-mail:soyzhang@mail.hzau.edu.cn