Identification of Quantitative Trait Loci Hotspots Affecting Agronomic Traits and High-Throughput Vegetation Indices in Rainfed Wheat

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.

Uncovering the genetic basis for quality traits in the Mediterranean old wheat germplasm and phenotypic and genomic prediction assessment by cross-validation test

“…A frequently used threshold was established at -log 10 P > 3, as previously reported in the literature (Condorelli et al, 2018;Mangini et al, 2018;Sukumaran et al, 2018;Rufo et al, 2021;Wang et al, 2021a). Confidence intervals (CI) for marker-trait associations (MTA) were estimated for each chromosome according to the LD decay reported by (Rufo et al, 2019) using the formula reported by (Chardon et al, 2004).…”

Discovering Loci for Breeding Prospective and Phenology in Wheat Mediterranean Landraces by Environmental and eigenGWAS

“…Interestingly, in QTL hotspot 1B.6, a Cytochrome c oxidase gene was upregulated in leaves and spikes (TraesCS1B01G413200). This gene was previously found to be upregulated in spikes by Rufo et al [ 42 ] in a GWAS for yield and vegetation indices-related traits using the same panel of landraces but also including a panel of modern wheat cultivars.…”

“…Field experiments were conducted under rainfed conditions in Gimenells, Lleida, northeast Spain, during 2016, 2017 and 2018 harvesting seasons, as reported in Rufo et al [ 42 ]. The experiments consisted of a non-replicated augmented design with two replicated checks, the cultivars ‘Anza’ and ‘Soissons’, at a ratio of 1:5 between checks and tested genotypes.…”

“…Compression levels range from “no compression” (compression = 1) when each genotype belongs to its own group, to “maximum compression” (compression = n ) when all genotypes belong to the same group. A common threshold was established at –log 10 p > 3, as previously reported in the literature [ 42 , 50 , 51 , 52 , 53 ]. Confidence intervals (CIs) for marker–trait associations (MTA) were estimated for each chromosome based on the LD decay reported by Rufo et al [ 12 ] and standardized using the formula reported by Chardon et al [ 23 ]: where CI is the LD decay for each chromosome.…”

High-Density Linkage Mapping of Agronomic Trait QTLs in Wheat under Water Deficit Condition using Genotyping by Sequencing (GBS)

“…To develop drought tolerant varieties it is essential to understand the genetic basis of these complex traits [ 4 ], thus identification of quantitative trait loci (QTL) and genes for important agronomical traits such as grain yield and its components is a key component of crop improvement programs [ 5 ]. To date, a large number of QTLs associated with yield component traits and grain yield under water deficit condition [ 1 , 2 , 3 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ] and normal condition [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ] have been identified on all 21 wheat chromosomes.…”