A Foundation for Provitamin A Biofortification of Maize: Genome-Wide Association and Genomic Prediction Models of Carotenoid Levels

Abstract

Efforts are underway for development of crops with improved levels of provitamin A carotenoids to help combat dietary vitamin A deficiency. As a global staple crop with considerable variation in kernel carotenoid composition, maize (Zea mays L.) could have a widespread impact. We performed a genome-wide association study (GWAS) of quantified seed carotenoids across a panel of maize inbreds ranging from light yellow to dark orange in grain color to identify some of the key genes controlling maize grain carotenoid composition. Significant associations at the genome-wide level were detected within the coding regions of zep1 and lut1, carotenoid biosynthetic genes not previously shown to impact grain carotenoid composition in association studies, as well as within previously associated lcyE and crtRB1 genes. We leveraged existing biochemical and genomic information to identify 58 a priori candidate genes relevant to the biosynthesis and retention of carotenoids in maize to test in a pathway-level analysis. This revealed dxs2 and lut5, genes not previously associated with kernel carotenoids. In genomic prediction models, use of markers that targeted a small set of quantitative trait loci associated with carotenoid levels in prior linkage studies were as effective as genome-wide markers for predicting carotenoid traits. Based on GWAS, pathway-level analysis, and genomic prediction studies, we outline a flexible strategy involving use of a small number of genes that can be selected for rapid conversion of elite white grain germplasm, with minimal amounts of carotenoids, to orange grain versions containing high levels of provitamin A.

Network-Guided GWAS Improves Identification of Genes Affecting Free Amino Acids

“…Previous metabolic QTL mapping and GWAS on both primary and secondary metabolite profiles have demonstrated that analyses of metabolic ratios based on known biochemical interactions yield stronger associations than GWAS performed on the corresponding metabolites' absolute levels (Wentzell et al, 2007;Vallabhaneni and Wurtzel, 2009;Wurtzel et al, 2012;Angelovici et al, 2013;Gonzalez-Jorge et al, 2013;Lipka et al, 2013;Owens et al, 2014). Our study demonstrates that a network-guided metabolite-based-GWAS performed on unbiased ratios derived from a correlation-based network topology improves the selection of traits for downstream GWAS and can facilitate further identification of significant associations among genomic regions that affect the natural variation of a tight, interconnected FAA metabolic network, such as amino acids (Table II; Supplemental Table S5).…”

“…As such, GWAS has been employed successfully to better resolve the genetic basis of many primary and secondary metabolites (e.g. carotenoids, tocochromanols, glucosinolates, and organic acids) in several model systems, including rice (Oryza sativa), maize (Zea mays), and Arabidopsis (Arabidopsis thaliana; Chan et al, 2011;Riedelsheimer et al, 2012;Gonzalez-Jorge et al, 2013Lipka et al, 2013;Chen et al, 2014;Owens et al, 2014;Verslues et al, 2014). In several cases, GWAS has confirmed genes and their orthologs identified via mutant screens, metabolic QTLs, and other methods (Clarke et al, 1995;Wong et al, 2004;Vallabhaneni and Wurtzel, 2009;Yan et al, 2010;Wurtzel et al, 2012).…”

“…These traits include, for example, the sum of related metabolites or the ratio of two related metabolites, such as precursors or products (Sauer et al, 1999;Weckwerth et al, 2004;Wentzell et al, 2007). With both linkage analysis and GWAS, derived traits that represent ratios based on metabolic pathways or known interactions have generated more significant associations compared with associations from absolute levels of metabolites (Wentzell et al, 2007;Vallabhaneni and Wurtzel, 2009;Wurtzel et al, 2012;Angelovici et al, 2013; GonzalezJorge et al, 2013;Lipka et al, 2013;Owens et al, 2014). One explanation for this phenomenon could be the higher heritability of metabolic ratios compared with content traits (Wentzell et al, 2007).…”

Influence of the moisture at harvest and drying process of the grains on the level of carotenoids in maize (Zea mays)

“…Interest in dietary carotenoids comes from their precursors of vitamin A, antioxidant properties and the association between carotenoid deficiencies and many chronic human diseases (Burt et al, 2011(Burt et al, , 2013. Breeding to increase β-carotene levels in cereal grains, termed provitamin A biofortification, is an economical approach to address dietary vitamin A deficiency in the developing world (Dwens et al, 2014).…”

Obtenção e uso de linhagens quase isogênicas de tomateiro (Solanum lycopersicum L. cv Micro-Tom) afetando a composição de carotenoides: uma ferramenta para o estudo da nutracêutica

“…Porém, como a autora ressalta, são necessários cada vez maiores avanços na pesquisa básica dos fitonutrientes, para que sustentem o design de alimentos saudáveis, biofortificados, tanto para países desenvolvidos quanto os em desenvolvimento. É o que ocorre ao redor do mundo, para diversas culturas, como o arroz (KIEKENS et al, 2015), milho (OWENS et al, 2014), mandioca (BERNI et al, 2014), tomate (DE LA GARZA et al, 2007), entre outros.…”