Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels

Abstract

Maize kernel oil is a valuable source of nutrition. Here we extensively examine the genetic architecture of maize oil biosynthesis in a genome-wide association study using 1.03 million SNPs characterized in 368 maize inbred lines, including 'high-oil' lines. We identified 74 loci significantly associated with kernel oil concentration and fatty acid composition (P < 1.8 × 10(-6)), which we subsequently examined using expression quantitative trait loci (QTL) mapping, linkage mapping and coexpression analysis. More than half of the identified loci localized in mapped QTL intervals, and one-third of the candidate genes were annotated as enzymes in the oil metabolic pathway. The 26 loci associated with oil concentration could explain up to 83% of the phenotypic variation using a simple additive model. Our results provide insights into the genetic basis of oil biosynthesis in maize kernels and may facilitate marker-based breeding for oil quantity and quality.

RNA sequencing reveals the complex regulatory network in the maize kernel

“…The data set in this study has been successfully used in exploring the genetic architecture of oil biosynthesis and accumulation in maize kernel, which is a typical quantitative trait controlled by polygenic loci 46 . The results showed that 74 highly significantly associated loci were responsible for oil concentration and fatty acid composition 5 . Twenty-one of the 74 associated polymorphisms were located in known fatty acid biosynthesis genes, including the three previously reported loci DGAT1-2, FATB and FAD2.…”

“…In the last decades, many genes that are essential for maize kernel development and nutrient accumulation have been characterized using genetic mutants or map-based cloning methods 2,3 . Linkage or association analyses have identified more than a hundred of loci or candidate genes underlying kernel-related traits 4,5 . Moreover, the transcriptome profiles of maize kernel have already been analysed in two elite inbred lines [6][7][8] , identifying candidate genes and coexpression networks involved in kernel developmental pathways.…”

High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth

“…To model the biomass and evaluate the measuring accuracy of RAP-Maize, a subset of the maize association mapping panel (Yang et al, 2011;Li et al, 2013; 387 individuals from 100 genotypes with three to four replicates each [see "Materials and Methods"]) was grown separately and measured for three traits automatically and manually in four stages. In the previous study (Yang et al, 2014), the side-projected area (SA) showed good correlation with manually measured fresh weight and dry weight, with r 2 .…”

“…Fertilizing was carried out at sowing, V5, and V9 stage (60 kg of water + 370.68 g of carbamide + 330.76 g of potassium dihydrogen phosphate + 94.24 g of potassium chloride, to be fully dissolved with 150 mL of liquid fertilizer for each plant per time). A subset of the association mapping population (Yang et al, 2011;Li et al, 2013), including 100 diversity inbred lines, was randomly selected and sown on the same day in a phenotypic platform with four replications, screened using the RAP, and measured manually at 36, 48, 60, and 70 d after sowing (Supplemental Table S11). Destructively measured traits were obtained for biomass modeling, and the correlation between manual measurement and automatic measurement was calculated.…”

“…With the rapid development of next-generation sequencing and high-density single-nucleotide polymorphism (SNP) genotyping technologies, linkage mapping and genome-wide association studies have been used widely to dissect the genetic architecture of agriculturally important traits in commercial maize (Buckler et al, 2009;Kump et al, 2011;Tian et al, 2011;Li et al, 2013;Wen et al, 2014Wen et al, , 2015. Many genes and variants underlying agriculturally important traits have been discovered in crops (Kesavan et al, 2013;Zuo and Li, 2013;Martinez et al, 2016).…”