Genetic architecture of low‐temperature germinability in rice revealed by integrated maps of SNPs and structural variation
- 1College of Agronomy Anhui Agricultural University Hefei China
- 2State Key Laboratory of Crop Gene Resources and Breeding Beijing 100081 China
- 3Chinese Academy of Agricultural Sciences Institute of Crop Sciences Beijing 100081 China
Abstract
SUMMARY: Low‐temperature germinability (LTG) limits rice direct seeding, yet its genetic architecture and regulatory roles of structural variations (SVs) remain largely unclear. We evaluated LTG in 403 rice accessions at four temperatures (13°C, 15°C, 18°C, 21°C). Integrating SNP/SV–GWAS, RNA‐seq, gene editing, population genetics, and machine learning, we dissected the genetic basis of LTG and built a genomic selection (GS) prediction model. The panel showed extensive LTG variation, which was negatively correlated with origin latitude. SV‐ and SNP‐based GWAS for LTG detected 119 and 127 QTLs, respectively, with 25 loci overlapping between the two datasets. Combining GWAS and RNA‐seq, we screened 30 candidate genes and 48 favorable haplotypes, with high‐LTG favorable alleles enriched in low‐latitude Xian (Indica) accessions. We identified a deletion in the qLTG3‐1 coding region that negatively regulated LTG, and gene editing (CRISPR/Cas9) confirmed qLT11d is a key gene for LTG. Population genetic analysis identified 29 inter‐subspecies (Xian–Geng) and 37 intra‐Xian divergent regions for LTG, implying shared selected regions across and within subspecies under low temperature. Using SNP and SV data, we built corresponding genomic selection (GS) models with the machine learning‐based MFGP method, achieving high prediction accuracy across all temperatures. All cross‐temperature prediction correlations exceeded 0.55, with optimal performance at 18°C. Our results clarify rice LTG genetic architecture and suggest potential regulatory roles for SVs that merit further investigation, providing candidate genes, favorable haplotypes, and a GS model for LTG genetic improvement in direct seeding rice.
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BibTeX
@article{shi2026genetic,
title = {{Genetic architecture of low‐temperature germinability in rice revealed by integrated maps of SNPs and structural variation}},
author = {Youshuai Shi and Kai Xu and Yamei Lv and Xiaoyu He and Tingting Yu and Fengcai Wu and Lunping Liang and Ting Feng and Liang Zhang and Erbao Liu and Zhikang Li and Wensheng Wang and Min Li and Chaopu Zhang},
journal = {The Plant Journal},
year = {2026},
month = oct,
doi = {10.1111/tpj.71184},
url = {https://doi.org/10.1111/tpj.71184},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Genetic architecture of low‐temperature germinability in rice revealed by integrated maps of SNPs and structural variation
AU - Youshuai Shi
AU - Kai Xu
AU - Yamei Lv
AU - Xiaoyu He
AU - Tingting Yu
AU - Fengcai Wu
AU - Lunping Liang
AU - Ting Feng
AU - Liang Zhang
AU - Erbao Liu
AU - Zhikang Li
AU - Wensheng Wang
AU - Min Li
AU - Chaopu Zhang
PY - 2026
DA - 2026/10/27
JO - The Plant Journal
DO - 10.1111/tpj.71184
UR - https://doi.org/10.1111/tpj.71184
AB - SUMMARY: Low‐temperature germinability (LTG) limits rice direct seeding, yet its genetic architecture and regulatory roles of structural variations (SVs) remain largely unclear. We evaluated LTG in 403 rice accessions at four temperatures (13°C, 15°C, 18°C, 21°C). Integrating SNP/SV–GWAS, RNA‐seq, gene editing, population genetics, and machine learning, we dissected the genetic basis of LTG and built a genomic selection (GS) prediction model. The panel showed extensive LTG variation, which was negatively correlated with origin latitude. SV‐ and SNP‐based GWAS for LTG detected 119 and 127 QTLs, respectively, with 25 loci overlapping between the two datasets. Combining GWAS and RNA‐seq, we screened 30 candidate genes and 48 favorable haplotypes, with high‐LTG favorable alleles enriched in low‐latitude Xian (Indica) accessions. We identified a deletion in the qLTG3‐1 coding region that negatively regulated LTG, and gene editing (CRISPR/Cas9) confirmed qLT11d is a key gene for LTG. Population genetic analysis identified 29 inter‐subspecies (Xian–Geng) and 37 intra‐Xian divergent regions for LTG, implying shared selected regions across and within subspecies under low temperature. Using SNP and SV data, we built corresponding genomic selection (GS) models with the machine learning‐based MFGP method, achieving high prediction accuracy across all temperatures. All cross‐temperature prediction correlations exceeded 0.55, with optimal performance at 18°C. Our results clarify rice LTG genetic architecture and suggest potential regulatory roles for SVs that merit further investigation, providing candidate genes, favorable haplotypes, and a GS model for LTG genetic improvement in direct seeding rice.
SN - 0960-7412
SN - 1365-313X
ER -