Genetic dissection of root architectural plasticity and identification of candidate loci in response to drought stress in bread wheat

Abstract

Background The frequency of droughts has dramatically increased over the last 50 years, causing yield declines in cereals, including wheat. Crop varieties with efficient root systems show great potential for plant adaptation to drought stress, however; genetic control of root systems in wheat under field conditions is not yet well understood. Results Natural variation in root architecture plasticity (phenotypic alteration due to changing environments) was dissected under field-based control (well-irrigated) and drought (rain-out shelter) conditions by a genome-wide association study using 200 diverse wheat cultivars. Our results revealed root architecture and plasticity traits were differentially responded to drought stress. A total of 25 marker-trait associations (MTAs) underlying natural variations in root architectural plasticity were identified in response to drought stress. They were abundantly distributed on chromosomes 1 A, 1B, 2 A, 2B, 3 A, 3B, 4B, 5 A, 5D, 7 A and 7B of the wheat genome. Gene ontology annotation showed that many candidate genes associated with plasticity were involved in water-transport and water channel activity, cellular response to water deprivation, scavenging reactive oxygen species, root growth and development and hormone-activated signaling pathway-transmembrane transport, indicating their response to drought stress. Further, in silico transcript abundance analysis demonstrated that root plasticity-associated candidate genes were highly expressed in roots across different root growth stages and under drought treatments. Conclusion Our results suggest that root phenotypic plasticity is highly quantitative, and the corresponding loci are associated with drought stress that may provide novel ways to enable root trait breeding.

Genetic control of root architectural traits under drought stress in spring barley (Hordeum vulgare L.)

“…DNA extraction, SNP genotyping methodology, and Hapmap genotypic file are provided by Bouhlal et al (2022). SNP data imputation was carried out in TASSEL 5.0 with LinkImpute (LD-kNNi) to eliminate missing SNPs with minor allele frequency of <5% (Siddiqui, Gabi, et al, 2023). To determine the population structure of the barley genotypes used in this study, a model-based clustering method using STRUCTURE 2.3.4 software (Dido et al, 2022;Pritchard et al, 2002) was run on the genotypic data.…”

“…Therefore, the objective of our study was to identify genomic loci showing associations with simultaneous effects on several RSA trait changes under drought conditions. To achieve this, we used “shovelomics” approaches for digging out the upper part of the root systems (Klein et al., 2020; Siddiqui, Kailash et al., 2023; Siddiqui, Gabi, et al, 2023; York et al., 2018) to phenotype eight root traits using 200 diverse spring barley genotypes at the complete flowering stage under well‐watered and drought stress conditions in two barley growing seasons (2020 and 2021). Phenotypic data were analyzed using mixed linear model (MLM) and GWAS approaches based on 23.8K markers obtained from a 50K iSelect barley single nucleotide polymorphism (SNP) array.…”