文献类型： 外文期刊

作者： Luo, Bowen ¹ ; Ma, Peng ² ; Zhang, Chong ² ; Zhang, Xiao ² ; Li, Jing ² ; Ma, Junchi ² ; Han, Zheng ² ; Zhang, Shuhao ² ; Yu, Ting ² ; Zhang, Guidi ² ; Zhang, Hongkai ² ; Zhang, Haiying ² ; Li, Binyang ² ; Guo, Jia ⁶ ; Ge, Ping ⁷ ; Lan, Yuzhou ⁸ ; Liu, Dan ² ; Wu, Ling ² ; Gao, Duojiang ² ; Gao, Shiqiang ² ; Su, Shunzong ⁹ ; Gao, Shibin ¹ ;

作者机构： 1.State Key Lab Crop Gene Explorat & Utilizat Southw, Chengdu 611130, Sichuan, Peoples R China

2.Sichuan Agr Univ, Maize Res Inst, Chengdu 611130, Sichuan, Peoples R China

3.Minist Agr, Key Lab Biol & Genet Improvement Maize Southwest R, Chengdu 611130, Sichuan, Peoples R China

4.Mianyang Acad Agr Sci, Mianyang 621023, Sichuan, Peoples R China

5.Crop Characterist Resources Creat & Utilizat Key L, Chengdu, Peoples R China

6.Sichuan Agr Univ, Rice Res Inst, Chengdu 611130, Sichuan, Peoples R China

7.SaileGene Inc, Beijing 100020, Peoples R China

8.Swedish Univ Agr Sci, Dept Plant Breeding, POB 190, S-23422 Lomma, Sweden

9.Sichuan Agr Univ, Coll Resources, Chengdu 611130, Sichuan, Peoples R China

期刊名称：THEORETICAL AND APPLIED GENETICS （ 影响因子：4.4； 五年影响因子：5.0 ）

ISSN： 0040-5752

年卷期： 2024 年 137 卷 8 期

页码：

收录情况： SCI

摘要： Key messageExtensive and comprehensive phenotypic data from a maize RIL population under both low- and normal-Pi treatments were used to conduct QTL mapping. Additionally, we integrated parental resequencing data from the RIL population, GWAS results, and transcriptome data to identify candidate genes associated with low-Pi stress in maize.AbstractPhosphorus (Pi) is one of the essential nutrients that greatly affect the maize yield. However, the genes underlying the QTL controlling maize low-Pi response remain largely unknown. In this study, a total of 38 traits at both seedling and maturity stages were evaluated under low- and normal-Pi conditions using a RIL population constructed from X178 (tolerant) and 9782 (sensitive), and most traits varied significantly between low- and normal-Pi treatments. Twenty-nine QTLs specific to low-Pi conditions were identified after excluding those with common intervals under both low- and normal-Pi conditions. Furthermore, 45 additional QTLs were identified based on the index value ((Trait_under_LowPi-Trait_under_NormalPi)/Trait_under_NormalPi) of each trait. These 74 QTLs collectively were classified as Pi-dependent QTLs. Additionally, 39 Pi-dependent QTLs were clustered in nine HotspotQTLs. The Pi-dependent QTL interval contained 19,613 unique genes, 6,999 of which exhibited sequence differences with non-synonymous mutation sites between X178 and 9782. Combined with in silico GWAS results, 277 consistent candidate genes were identified, with 124 genes located within the HotspotQTL intervals. The transcriptome analysis revealed that 21 genes, including the Pi transporter ZmPT7 and the strigolactones pathway-related gene ZmPDR1, exhibited consistent low-Pi stress response patterns across various maize inbred lines or tissues. It is noteworthy that ZmPDR1 in maize roots can be sharply up-regulated by low-Pi stress, suggesting its potential importance as a candidate gene for responding to low-Pi stress through the strigolactones pathway.