Identification of drought-adaptive QTL underlying variation in root system architecture in Zea mays
Authors: Kirsten Hein; Patrick Woods; Jack Mullen; John McKay
Affiliation: Colorado State University
Keywords: Complex traits
Complex phenotypes are influenced by genetic variation, environmental differences, and genotype-by-environment interactions (G×E). These interacting factors and their relative contributions are therefore of critical importance in understanding complex traits, but at the same time they are challenging to study comprehensively. In part, this challenge stems from the necessary scale: experimental studies of the interaction of genotype and environment must be able to manipulate both factors at a large enough scale to provide the power to detect the relatively small effects of most loci underlying complex traits. Plants, including crops are ideal systems in that thousands of full or half siblings can easily be replicated across multiple environments. In addition, in crop species such as maize, the G×E variance for yield is larger than the corresponding genotypic main effect variance. Because it is predicted that the impact of climate change on drought frequency will impose significant economic cost in the U.S., especially in the southwest and Rocky Mountain states, it is imperative to find solutions to maintain or increase agricultural productivity in ways that ameliorate rather than exacerbate climate change. Enhanced root systems with deeper architecture are predicted to improve seasonal water-use efficiency, predominantly under drought conditions. To understand the genetic basis for root system architectural variation as it relates to crop production and adaptation to target environments, it is imperative to perform phenotyping under agronomically applicable field conditions. The goal of this study is to evaluate how genes interact with droughted and well-watered environments to create complex root phenotypes in the maize (Zea mays L.) system. Quantitative root measurements were collected from small plot field trials of 380 inbred lines of maize across two levels of the environmental factor soil moisture. Genome-wide association (GWAS) identified 85 SNPS significantly associated with root traits, including 12 SNPs that show significant G×E across soil moisture. We then selected biparental recombinant inbred lines that were segregating variation at the genomic regions and gene models identified in the GWAS analysis. Utilizing a computational-based framework proposed by Wen, Pique-Regi, and Luca (2017), we will integrate significant molecular QTLs identified through QTL analysis with the root trait-associated genetic variants to evaluate the enrichment and colocalization of both types of association signals. The result from the integrative analysis has the potential to address more biologically relevant hypotheses by reducing the list of genes within the associated gene set to those genes with the greatest contribution to the overall trait variability using available gene annotation and synteny data.