Integration of Experiments across Diverse Environments Identifies the Genetic Determinants of Variation in Sorghum bicolor Seed Element Composition

Abstract

Seedling establishment and seed nutritional quality require the sequestration of sufficient element nutrients. The identification of genes and alleles that modify element content in the grains of cereals, including sorghum (Sorghum bicolor), is fundamental to developing breeding and selection methods aimed at increasing bioavailable element content and improving crop growth. We have developed a high-throughput work flow for the simultaneous measurement of multiple elements in sorghum seeds. We measured seed element levels in the genotyped Sorghum Association Panel, representing all major cultivated sorghum races from diverse geographic and climatic regions, and mapped alleles contributing to seed element variation across three environments by genome-wide association. We observed significant phenotypic and genetic correlation between several elements across multiple years and diverse environments. The power of combining high-precision measurements with genome-wide association was demonstrated by implementing rank transformation and a multilocus mixed model to map alleles controlling 20 element traits, identifying 255 loci affecting the sorghum seed ionome. Sequence similarity to genes characterized in previous studies identified likely causative genes for the accumulation of zinc, manganese, nickel, calcium, and cadmium in sorghum seeds. In addition to strong candidates for these five elements, we provide a list of candidate loci for several other elements. Our approach enabled the identification of single-nucleotide polymorphisms in strong linkage disequilibrium with causative polymorphisms that can be evaluated in targeted selection strategies for plant breeding and improvement.

The Interaction of Genotype and Environment Determines Variation in the Maize Kernel Ionome

“…Single seeds were profiled for the quantities of 20 elements using ICP-MS. These measurements were normalized to seed weight and technical sources of variation using a linear model, with the resulting values used as the elemental traits for all analyses (Shakoor et al 2016). After outlier removal, seed element phenotypes were derived by averaging line replicates (kernels subsampled out of pooled ears from a row) within an environment.…”

A Guide to Genome‐Wide Association Mapping in Plants

“…The second reason for thinking carefully about phenotype is because the probability of GWAS succeeding is directly related to the number and effect sizes of genes responsible for variation: The fewer the genes and the larger the effects, the more power for identifying the genes. Consistent with the expectation that it is easier to identify loci underlying variation in traits with a simple genetic basis, some of the strongest statistical signals are for metabolic traits for which there is a single enzyme or transporter that is responsible for distinct phenotypes (Lipka et al., ; Chen et al., ; Matsuda et al., ; Strauch et al., ; Shakoor et al., ). By contrast, we should not expect GWAS to provide extremely strong signals for genes that contribute to variation in highly complex multigenic quantitative traits such as height.…”

Genome-wide association analysis of seedling traits in diverse Sorghum germplasm under thermal stress

“…A total of 265,487 SNPs based on genotype-by-sequencing (GBS) analyses that corresponds to variation in 27,412 annotated genes were utilized [33]. Previous studies demonstrated that this panel and genotype data has sufficient power to dissect complex traits using such populations, including grain yield [62], plant architecture [63] and seed micronutrient composition [64] among other traits.…”