000			03571nab a22003857a 4500
999			_c60348 _d60340
001			60348
003			MX-TxCIM
005			20240919021226.0
008			190416s2019 sz |||po|p||| 00| 0 eng d
022			_a1664-462X
024	8		_2https://doi.org/10.3389/fpls.2019.00394
040			_aMX-TxCIM
041			_aeng
100	1		_93851 _aSingh, D.
245	1	0	_aHigh-throughput phenotyping enabled genetic dissection of crop lodging in wheat
260			_aSwitzerland : _bFrontiers Media, _c2019.
500			_aPeer review
500			_aOpen Access
520			_aNovel high-throughput phenotyping (HTP) approaches are needed to advance the understanding of genotype-to-phenotype and accelerate plant breeding. The first generation of HTP has examined simple spectral reflectance traits from images and sensors but is limited in advancing our understanding of crop development and architecture. Lodging is a complex trait that significantly impacts yield and quality in many crops including wheat. Conventional visual assessment methods for lodging are time-consuming, relatively low-throughput, and subjective, limiting phenotyping accuracy and population sizes in breeding and genetics studies. Here, we demonstrate the considerable power of unmanned aerial systems (UAS) or drone-based phenotyping as a high-throughput alternative to visual assessments for the complex phenological trait of lodging, which significantly impacts yield and quality in many crops including wheat. We tested and validated quantitative assessment of lodging on 2,640 wheat breeding plots over the course of 2 years using differential digital elevation models from UAS. High correlations of digital measures of lodging to visual estimates and equivalent broad-sense heritability demonstrate this approach is amenable for reproducible assessment of lodging in large breeding nurseries. Using these high-throughput measures to assess the underlying genetic architecture of lodging in wheat, we applied genome-wide association analysis and identified a key genomic region on chromosome 2A, consistent across digital and visual scores of lodging. However, these associations accounted for a very minor portion of the total phenotypic variance. We therefore investigated whole genome prediction models and found high prediction accuracies across populations and environments. This adequately accounted for the highly polygenic genetic architecture of numerous small effect loci, consistent with the previously described complex genetic architecture of lodging in wheat. Our study provides a proof-of-concept application of UAS-based phenomics that is scalable to tens-of-thousands of plots in breeding and genetic studies as will be needed to uncover the genetic factors and increase the rate of gain for complex traits in crop breeding.
526			_aWC _cFP3
546			_aText in English
650		7	_2AGROVOC _93634 _aPhenotypes
650		7	_aWheat _gAGROVOC _2 _91310
650		7	_aPlant breeding _gAGROVOC _2 _91203
700	0		_99093 _aXu Wang
700	1		_aKumar, U. _gFormerly Borlaug Institute for South Asia (BISA) _8INT3331 _9921
700	0		_99094 _aLiangliang Gao
700	0		_99095 _aMuhammad Noor
700	1		_9917 _aImtiaz, M. _gGlobal Wheat Program _8INT3326
700	1		_aSingh, R.P. _gGlobal Wheat Program _8INT0610 _9825
700	1		_92092 _aPoland, J.A.
773	0		_gv. 10, art. 394 _tFrontiers in Plant Science _wu56875 _x1664-462X _dSwitzerland : Frontiers Media, 2019.
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/20116
942			_2ddc _cJA _n0