Eleven biosynthetic genes explain the majority of natural variation for carotenoid levels in maize grain

Abstract

Vitamin A deficiency remains prevalent in parts of Asia, Latin America and sub-Saharan Africa where maize is a food staple. Extensive natural variation exists for carotenoids in maize grain; to understand its genetic basis, we conducted a joint linkage and genome-wide association study in the U.S. maize nested association mapping panel. Eleven of the 44 detected quantitative trait loci (QTL) were resolved to individual genes. Six of these were expression QTL (eQTL), showing strong correlations between RNA-seq expression abundances and QTL allelic effect estimates across six stages of grain development. These six eQTL also had the largest percent phenotypic variance explained, and in major part comprised the three to five loci capturing the bulk of genetic variation for each trait. Most of these eQTL had highly correlated QTL allelic effect estimates across multiple traits, suggesting that pleiotropy within this pathway is largely regulated at the expression level. Significant pairwise epistatic interactions were also detected. These findings provide the most comprehensive genome-level understanding of the genetic and molecular control of carotenoids in any plant system, and a roadmap to accelerate breeding for provitamin A and other priority carotenoid traits in maize grain that should be readily extensible to other cereals.

Simultaneous Dissection of Grain Carotenoid Levels and Kernel Color in Biparental Maize Populations with Yellow-to-Orange Grain

“…lut1 was identified in this study for zeinoxanthin and lutein, with PVEs of 4.5% and 2.7%, respectively (Figure 1, Table 3). Within the β -branch of the carotenoid pathway, β -carotene hydroxylase (CRTRB) preferentially converts β -carotene to β -cryptoxanthin and then zeaxanthin (Diepenbrock et al 2021). crtRB5 (also known as hyd5) was identified with PVEs of 1.6% for β -cryptoxanthin and 1.8% for zeaxanthin.…”

“…Within the precursor pathway, DXS has been found to be a rate-limiting activity through genetic engineering and overexpression studies in Arabidopsis and E. coli (Harker and Bramley 1999;Estevez et al 2001). While dxs2 has appeared to be the major controller in maize-with PVEs of 3.5 to 11.3% for dxs2 and 1.2 to 4.1% for dxs3 in the 25 NAM families (Diepenbrock et al 2021)-these ranges were more similar in the present study: 1.0 to 5.2% for dxs2 and 2.2 to 3.3% for dxs3. This could be related to dxs2 having been found to be nearly fixed among yellow inbreds as examined in Fang et al (2020).…”

“…Using the phenotypic data from the 10 families, best linear unbiased estimators (BLUEs) were generated from the Chandler et al (2013) kernel color phenotypes and Diepenbrock et al (2021) carotenoid phenotypes using a process similar to that taken in Diepenbrock et al (2021). Briefly, family, RIL within family, year, and field within year were fitted as random effects as a baseline model.…”

“…Linkage disequilibrium (LD) was examined within the same HapMap v1 and v2 input data set used for GWAS. Specifically, LD was examined for markers exhibiting one or more significant associations in GWAS (hereafter, GWAS variants) as in Diepenbrock et al (2021), by calculating pairwise correlations between GWAS variants and markers within 250 kb of each variant. A null distribution was generated by calculating the same pairwise correlations for 50,000 randomly selected variants with markers within 250 kb of the respective randomly selected variant.…”

The utility of metabolomics as a tool to inform maize biology

“…(2020a) Carotenoids in kernels LC multiple candidate genes GWAS Baseggio et al. (2020) Carotenoids in kernels HPLC multiple candidate genes linkage mapping and GWAS Diepenbrock et al. (2020) Anthocyanin in kernels HPLC and spectrophotometry multiple candidate genes GWAS Chatham and Juvik, (2021) Metabolite biomarkers for salt tolerance LC-MS cts3 , cyp709b2 , ugt , and multiple candidate genes GWAS Liang et al.…”