Abstract
Understanding the genetic basis of agronomic traits is essential for wheat breeding programs to develop new cultivars with enhanced grain yield under climate change conditions. The use of high-throughput phenotyping (HTP) technologies for the assessment of agronomic performance through drought-adaptive traits opens new possibilities in plant breeding. HTP together with a genome-wide association study (GWAS) mapping approach can be a useful method to dissect the genetic control of complex traits in wheat to enhance grain yield under drought stress. This study aimed to identify molecular markers associated with agronomic and remotely sensed vegetation index (VI)-related traits under rainfed conditions in bread wheat and to use an in silico candidate gene (CG) approach to search for upregulated CGs under abiotic stress. The plant material consisted of 170 landraces and 184 modern cultivars from the Mediterranean basin. The collection was phenotyped for agronomic and VI traits derived from multispectral images over 3 and 2 years, respectively. The GWAS identified 2,579 marker-trait associations (MTAs). The quantitative trait loci (QTL) overview index statistic detected 11 QTL hotspots involving more than one trait in at least 2 years. A CG analysis detected 12 CGs upregulated under abiotic stress in six QTL hotspots and 46 downregulated CGs in 10 QTL hotspots. The current study highlights the utility of VI to identify chromosome regions that contribute to yield and drought tolerance under rainfed Mediterranean conditions.
Highlights
Wheat (Triticum aestivum L.) is the most common cultivated crop worldwide
The aim of the current study is to identify molecular markers linked to important agronomic traits, vegetation indices (VIs), and plant features related to drought resistance assessed by high-throughput phenotyping (HTP), to define the most important quantitative trait loci (QTL) hotspots for such traits and to perform in silico detection of the underlying candidate gene (CG) in those genomic regions
VIs showed higher coefficients of variation (CV) during postanthesis (PA) with values at A ranging from 17.4% for leaf area index (LAI) to 2.1% for normalized difference vegetation index (NDVI), and PA ranging from 54.0% for LAI to 15.3% for green normalized difference vegetation index (GNDVI)
Summary
Wheat (Triticum aestivum L.) is the most common cultivated crop worldwide. To cover the expected food demand of a world population that will increase up to 60% by 2050, wheat production needs to be increased by 1.7% per year (Leegood et al, 2010). Achieving this objective will not be easy, considering the expected negative effects of climate change on wheat yield, in areas, such as the Mediterranean basin, where a rise in temperature by 3–5◦C and a decrease in the annual rainfall by 25–30% have been predicted (Giorgi and Lionello, 2008). The release of improved cultivars with enhanced drought adaptation will be critical for breeding programs focusing on wheat adaptability and stability under rainfed conditions (Graziani et al, 2014; Bhatta et al, 2018)
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