Integrating high-throughput phenotyping and genome-wide association studies for enhanced drought resistance and yield prediction in wheat

Integrating high-throughput phenotyping and genome-wide association studies for enhanced drought resistance and yield prediction in wheat

© 2024 The Author(s). New Phytologist © 2024 New Phytologist Foundation.

Bibliographische Detailangaben
Veröffentlicht in:The New phytologist. - 1979. - 243(2024), 5 vom: 09. Aug., Seite 1758-1775
1. Verfasser: Zhang, Zhen (VerfasserIn)
Weitere Verfasser: Qu, Yunfeng, Ma, Feifei, Lv, Qian, Zhu, Xiaojing, Guo, Guanghui, Li, Mengmeng, Yang, Wei, Que, Beibei, Zhang, Yun, He, Tiantian, Qiu, Xiaolong, Deng, Hui, Song, Jingyan, Liu, Qian, Wang, Baoqi, Ke, Youlong, Bai, Shenglong, Li, Jingyao, Lv, Linlin, Li, Ranzhe, Wang, Kai, Li, Hao, Feng, Hui, Huang, Jinling, Yang, Wanneng, Zhou, Yun, Song, Chun-Peng
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2024
Zugriff auf das übergeordnete Werk:The New phytologist
Schlagworte:Journal Article GWAS drought resistance machine learning phenotyping wheat
LEADER 01000caa a22002652 4500
001 NLM374806802
003 DE-627
005 20240801233424.0
007 cr uuu---uuuuu
008 240712s2024 xx |||||o 00| ||eng c
024 7 |a 10.1111/nph.19942  |2 doi 
028 5 2 |a pubmed24n1488.xml 
035 |a (DE-627)NLM374806802 
035 |a (NLM)38992951 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Zhang, Zhen  |e verfasserin  |4 aut 
245 1 0 |a Integrating high-throughput phenotyping and genome-wide association studies for enhanced drought resistance and yield prediction in wheat 
264 1 |c 2024 
336 |a Text  |b txt  |2 rdacontent 
337 |a ƒaComputermedien  |b c  |2 rdamedia 
338 |a ƒa Online-Ressource  |b cr  |2 rdacarrier 
500 |a Date Completed 01.08.2024 
500 |a Date Revised 01.08.2024 
500 |a published: Print-Electronic 
500 |a Citation Status MEDLINE 
520 |a © 2024 The Author(s). New Phytologist © 2024 New Phytologist Foundation. 
520 |a Drought, especially terminal drought, severely limits wheat growth and yield. Understanding the complex mechanisms behind the drought response in wheat is essential for developing drought-resistant varieties. This study aimed to dissect the genetic architecture and high-yielding wheat ideotypes under terminal drought. An automated high-throughput phenotyping platform was used to examine 28 392 image-based digital traits (i-traits) under different drought conditions during the flowering stage of a natural wheat population. Of the i-traits examined, 17 073 were identified as drought-related. A genome-wide association study (GWAS) identified 5320 drought-related significant single-nucleotide polymorphisms (SNPs) and 27 SNP clusters. A notable hotspot region controlling wheat drought tolerance was discovered, in which TaPP2C6 was shown to be an important negative regulator of the drought response. The tapp2c6 knockout lines exhibited enhanced drought resistance without a yield penalty. A haplotype analysis revealed a favored allele of TaPP2C6 that was significantly correlated with drought resistance, affirming its potential value in wheat breeding programs. We developed an advanced prediction model for wheat yield and drought resistance using 24 i-traits analyzed by machine learning. In summary, this study provides comprehensive insights into the high-yielding ideotype and an approach for the rapid breeding of drought-resistant wheat 
650 4 |a Journal Article 
650 4 |a GWAS 
650 4 |a drought resistance 
650 4 |a machine learning 
650 4 |a phenotyping 
650 4 |a wheat 
700 1 |a Qu, Yunfeng  |e verfasserin  |4 aut 
700 1 |a Ma, Feifei  |e verfasserin  |4 aut 
700 1 |a Lv, Qian  |e verfasserin  |4 aut 
700 1 |a Zhu, Xiaojing  |e verfasserin  |4 aut 
700 1 |a Guo, Guanghui  |e verfasserin  |4 aut 
700 1 |a Li, Mengmeng  |e verfasserin  |4 aut 
700 1 |a Yang, Wei  |e verfasserin  |4 aut 
700 1 |a Que, Beibei  |e verfasserin  |4 aut 
700 1 |a Zhang, Yun  |e verfasserin  |4 aut 
700 1 |a He, Tiantian  |e verfasserin  |4 aut 
700 1 |a Qiu, Xiaolong  |e verfasserin  |4 aut 
700 1 |a Deng, Hui  |e verfasserin  |4 aut 
700 1 |a Song, Jingyan  |e verfasserin  |4 aut 
700 1 |a Liu, Qian  |e verfasserin  |4 aut 
700 1 |a Wang, Baoqi  |e verfasserin  |4 aut 
700 1 |a Ke, Youlong  |e verfasserin  |4 aut 
700 1 |a Bai, Shenglong  |e verfasserin  |4 aut 
700 1 |a Li, Jingyao  |e verfasserin  |4 aut 
700 1 |a Lv, Linlin  |e verfasserin  |4 aut 
700 1 |a Li, Ranzhe  |e verfasserin  |4 aut 
700 1 |a Wang, Kai  |e verfasserin  |4 aut 
700 1 |a Li, Hao  |e verfasserin  |4 aut 
700 1 |a Feng, Hui  |e verfasserin  |4 aut 
700 1 |a Huang, Jinling  |e verfasserin  |4 aut 
700 1 |a Yang, Wanneng  |e verfasserin  |4 aut 
700 1 |a Zhou, Yun  |e verfasserin  |4 aut 
700 1 |a Song, Chun-Peng  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t The New phytologist  |d 1979  |g 243(2024), 5 vom: 09. Aug., Seite 1758-1775  |w (DE-627)NLM09818248X  |x 1469-8137  |7 nnns 
773 1 8 |g volume:243  |g year:2024  |g number:5  |g day:09  |g month:08  |g pages:1758-1775 
856 4 0 |u http://dx.doi.org/10.1111/nph.19942  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d 243  |j 2024  |e 5  |b 09  |c 08  |h 1758-1775