Condorelli Giuseppe E, Maccaferri Marco, Newcomb Maria, Andrade-Sanchez Pedro, White Jeffrey W, French Andrew N, Sciara Giuseppe, Ward Rick, Tuberosa Roberto
Department of Agricultural Sciences, University of Bologna, Bologna, Italy.
Maricopa Agricultural Center, University of Arizona, Tucson, AZ, United States.
Front Plant Sci. 2018 Jun 26;9:893. doi: 10.3389/fpls.2018.00893. eCollection 2018.
High-throughput phenotyping platforms (HTPPs) provide novel opportunities to more effectively dissect the genetic basis of drought-adaptive traits. This genome-wide association study (GWAS) compares the results obtained with two Unmanned Aerial Vehicles (UAVs) and a ground-based platform used to measure Normalized Difference Vegetation Index (NDVI) in a panel of 248 elite durum wheat ( L. ssp Desf.) accessions at different growth stages and water regimes. Our results suggest increased ability of aerial over ground-based platforms to detect quantitative trait loci (QTL) for NDVI, particularly under terminal drought stress, with 22 and 16 single QTLs detected, respectively, and accounting for 89.6 vs. 64.7% phenotypic variance based on multiple QTL models. Additionally, the durum panel was investigated for leaf chlorophyll content (SPAD), leaf rolling and dry biomass under terminal drought stress. In total, 46 significant QTLs affected NDVI across platforms, 22 of which showed concomitant effects on leaf greenness, 2 on leaf rolling and 10 on biomass. Among 9 QTL hotspots on chromosomes 1A, 1B, 2B, 4B, 5B, 6B, and 7B that influenced NDVI and other drought-adaptive traits, 8 showed effects unrelated to phenology.
高通量表型分析平台(HTPPs)为更有效地剖析干旱适应性状的遗传基础提供了新机会。这项全基因组关联研究(GWAS)比较了使用两架无人机(UAV)和一个地面平台在248份硬粒小麦(L. ssp Desf.)优良种质资源的不同生长阶段和水分条件下测量归一化植被指数(NDVI)所获得的结果。我们的结果表明,与地面平台相比,空中平台检测NDVI数量性状位点(QTL)的能力更强,尤其是在终末期干旱胁迫下,分别检测到22个和16个单QTL,基于多QTL模型分别解释了89.6%和64.7%的表型变异。此外,还研究了硬粒小麦种质资源在终末期干旱胁迫下的叶片叶绿素含量(SPAD)、叶片卷曲和干生物量。总共46个显著的QTL影响了跨平台的NDVI,其中22个对叶片绿色度有伴随效应,2个对叶片卷曲有影响,10个对生物量有影响。在影响NDVI和其他干旱适应性状的1A、1B、2B、4B、5B、6B和7B染色体上的9个QTL热点中,8个显示出与物候无关的效应。