Genome-wide association mapping of leaf metabolic profiles for dissecting complex traits in maize

Abstract

The diversity of metabolites found in plants is by far greater than in most other organisms. Metabolic profiling techniques, which measure many of these compounds simultaneously, enabled investigating the regulation of metabolic networks and proved to be useful for predicting important agronomic traits. However, little is known about the genetic basis of metabolites in crops such as maize. Here, a set of 289 diverse maize inbred lines was genotyped with 56,110 SNPs and assayed for 118 biochemical compounds in the leaves of young plants, as well as for agronomic traits of mature plants in field trials. Metabolite concentrations had on average a repeatability of 0.73 and showed a correlation pattern that largely reflected their functional grouping. Genome-wide association mapping with correction for population structure and cryptic relatedness identified for 26 distinct metabolites strong associations with SNPs, explaining up to 32.0% of the observed genetic variance. On nine chromosomes, we detected 15 distinct SNP-metabolite associations, each of which explained more then 15% of the genetic variance. For lignin precursors, including p-coumaric acid and caffeic acid, we found strong associations (P values 2:7 × 10 −10 to 3:9 × 10 −18 ) with a region on chromosome 9 harboring cinnamoyl-CoA reductase, a key enzyme in monolignol synthesis and a target for improving the quality of lignocellulosic biomass by genetic engineering approaches. Moreover, lignin precursors correlated significantly with lignin content, plant height, and dry matter yield, suggesting that metabolites represent promising connecting links for narrowing the genotypephenotype gap of complex agronomic traits.genetic association | metabolomics | Zea mays

RNA sequencing reveals the complex regulatory network in the maize kernel

“…Through deep RNA-seq, we obtained an average of 70 million reads for each inbred line, which resulted in the recovery of 1.03 million high-quality SNPs in the maize genome. The identified SNPs are of significance to the maize research community, especially in exploring the genetic architecture of quantitative traits in maize using GWAS, as genomic SNPs were often used in previous GWASs in maize, including leaf architecture 33 , leaf metabolites 34 and disease resistance 35,36 . Most of the newly identified SNPs were mapped to gene regions with an average of 40.3 SNPs per gene, which substantially complemented the maize SNP polymorphisms discovered by genome resequencing 13,14 .…”

Genome-Wide Association in Tomato Reveals 44 Candidate Loci for Fruit Metabolic Traits

“…FDR) and the confounding effect of population structure may explain this difference. This has been observed in a study of glucosinolate metabolites in Arabidopsis (Chan et al, 2010) and a study of leaf metabolic profiles in maize (Riedelsheimer et al, 2012). In the latter study, when comparing a linkage mapping experiment and a GWA scan, increased genetic variation was reported, suggesting that the genetic variability is greater in the GWAS, as it relies on a larger genetic pool (from several up to hundreds of individuals), whereas a linkage experiment relies on a much narrower genetic pool (i.e.…”

“…In the latter study, when comparing a linkage mapping experiment and a GWA scan, increased genetic variation was reported, suggesting that the genetic variability is greater in the GWAS, as it relies on a larger genetic pool (from several up to hundreds of individuals), whereas a linkage experiment relies on a much narrower genetic pool (i.e. a couple of parental lines; Riedelsheimer et al, 2012). .…”

“…Following domestication, crops are prone to (1) increased levels of LD, (2) population structure (remote common ancestry of large groups of individuals), and (3) cryptic relatedness (the presence of close relatives in a sample of unrelated individuals; Riedelsheimer et al, 2012). Population structure and cryptic relatedness may lead to false-positive association in GWA studies (Astle and Balding, 2009), but their effect is now relatively well accounted for in mixed linear models (for review, see Sillanpää, 2011;Listgarten et al, 2012).…”

Untargeted metabolic profiling reveals geography as the strongest predictor of metabolic phenotypes of a cosmopolitan weed

“…Specialized metabolites also are the source of many of our plant‐based medicines and therefore have value to human health and well‐being (Briskin, 2000). Much attention has therefore been placed on environmental and geographic factors influencing specialized metabolite production in plants, particularly in crop species and in model plant systems (Agrawal, Conner, Johnson, & Wallsgrove, 2002; Asai, Matsukawa, & Kajiyamal, 2016; Carrari et al., 2006; Dan et al., 2016; Hirai et al., 2004; Lasky et al., 2012; Tarczynski, Jensen, & Bohnert, 1993; Riedelsheimer et al., 2012). However, the importance of natural variation in the environment in explaining metabolite variation within plant species remains little understood (Maldonado et al., 2017; Moore, Andrew, Külheim, & Foley, 2014).…”