Genome wide association analysis of root hair traits in rice reveals novel genomic regions controlling epidermal cell differentiation

Abstract

Background Genome wide association (GWA) studies demonstrate linkages between genetic variants and traits of interest. Here, we tested associations between single nucleotide polymorphisms (SNPs) in rice (Oryza sativa) and two root hair traits, root hair length (RHL) and root hair density (RHD). Root hairs are outgrowths of single cells on the root epidermis that aid in nutrient and water acquisition and have also served as a model system to study cell differentiation and tip growth. Using lines from the Rice Diversity Panel-1, we explored the diversity of root hair length and density across four subpopulations of rice (aus, indica, temperate japonica, and tropical japonica). GWA analysis was completed using the high-density rice array (HDRA) and the rice reference panel (RICE-RP) SNP sets. Results We identified 18 genomic regions related to root hair traits, 14 of which related to RHD and four to RHL. No genomic regions were significantly associated with both traits. Two regions overlapped with previously identified quantitative trait loci (QTL) associated with root hair density in rice. We identified candidate genes in these regions and present those with previously published expression data relevant to root hair development. We re-phenotyped a subset of lines with extreme RHD phenotypes and found that the variation in RHD was due to differences in cell differentiation, not cell size, indicating genes in an associated genomic region may influence root hair cell fate. The candidate genes that we identified showed little overlap with previously characterized genes in rice and Arabidopsis. Conclusions Root hair length and density are quantitative traits with complex and independent genetic control in rice. The genomic regions described here could be used as the basis for QTL development and further analysis of the genetic control of root hair length and density. We present a list of candidate genes involved in root hair formation and growth in rice, many of which have not been previously identified as having a relation to root hair growth. Since little is known about root hair growth in grasses, these provide a guide for further research and crop improvement.

DIRT/μ – Automated extraction of root hair traits using combinatorial optimization

“…Root hair development can be separated into three distinct and complex pathways: the differentiation of epidermal root cells into root hair cells, the initiation of root hair growth, and root hair elongation underlie different and complex pathways 14 . Therefore, it is unsurprising that the control of RHL and RHD (e.g., in rice) underlies separate genomic regions 15 . Due to their role in nutrient and water uptake, root hairs provide an important breeding target for sustainable agriculture under drought and increasingly nutrient-depleted soils 16,17 .…”

Regulation and Expression of Phytohormones for Root Architectural Trait Development in Rice: A Review

“…Roots are critical for plant growth and development because they anchor the plant to its growing substrate, facilitate absorption of nutrients and water from the soil. Roots also play role in sensing and responding to environmental stimuli such as biotic and abiotic stresses [1,2]. Root growth is frequently influenced by macronutrient levels in the soil, which activate regional and systemic signaling pathways and plants regulate their growth in response to the supply of nutrients.…”

Genome- and Transcriptome-wide Association Studies to Discover Candidate Genes for Diverse Root Phenotypes in Cultivated Rice

“…A genome-wide association study (GWAS) can identify genomic regions for the phenotype of interest using a diversity panel sequenced and genotyped for single nucleotide polymorphisms (SNPs) or insertion/deletion variants (indels), which is less laborious and time-consuming than creating a biparental population for QTL mapping. Beginning with pioneering studies using approximately 200 rice accessions with approximately 30,000 markers for root phenotypes measured in hydroponic cultivation systems (Clark et al 2013 ; Courtois et al 2013 ), GWAS have been conducted for various root phenotypes under different conditions and at different growth stages (Biscarini et al 2016 ; Phung et al 2016 ; Bettembourg et al 2017 ; Wang et al 2018a ; Zhao et al 2018 , 2021a , b ; To et al 2019 ; Xu et al 2020 ; Zhang et al 2020 ; Anandan et al 2022 ; Teramoto et al 2022 ; Xiang et al 2022 ; Hanlon et al 2023 ). However, it is difficult to identify small-effect loci for root phenotypes based only on GWAS because the sample size is often limited as the evaluation of root phenotypes requires digging up the root from the soil, which has extremely low throughput.…”