Leveraging GWAS data to identify metabolic pathways and networks involved in maize lipid biosynthesis

Abstract

SummaryMaize (Zea mays mays) oil is a rich source of polyunsaturated fatty acids (FAs) and energy, making it a valuable resource for human food, animal feed, and bio‐energy. Although this trait has been studied via conventional genome‐wide association study (GWAS), the single nucleotide polymorphism (SNP)‐trait associations generated by GWAS may miss the underlying associations when traits are based on many genes, each with small effects that can be overshadowed by genetic background and environmental variation. Detecting these SNPs statistically is also limited by the levels set for false discovery rate. A complementary pathways analysis that emphasizes the cumulative aspects of SNP‐trait associations, rather than just the significance of single SNPs, was performed to understand the balance of lipid metabolism, conversion, and catabolism in this study. This pathway analysis indicated that acyl‐lipid pathways, including biosynthesis of wax esters, sphingolipids, phospholipids and flavonoids, along with FA and triacylglycerol (TAG) biosynthesis, were important for increasing oil and FA content. The allelic variation found among the genes involved in many degradation pathways, and many biosynthesis pathways leading from FAs and carbon partitioning pathways, was critical for determining final FA content, changing FA ratios and, ultimately, to final oil content. The pathways and pathway networks identified in this study, and especially the acyl‐lipid associated pathways identified beyond what had been found with GWAS alone, provide a real opportunity to precisely and efficiently manipulate high‐oil maize genetic improvement.

PAST: The Pathway Association Studies Tool to Infer Biological Meaning from GWAS Datasets

“…PAST was based on a method developed by our research group [6]. The original method was subsequently used in two other maize studies [14,15], but required users to customize Perl and R scripts and run Bash scripts. PAST's implementation is completely in R and requires a user to install the package without needing to edit the source code.…”

“…PAST will allow a new interpretation of GWAS results, which should identify associated pathways either when one or a few genes are highly associated with the trait (these would have been identified by the GWAS analysis directly); or when many genes in the pathway are moderately associated with the trait (these would not necessarily have been identified by the GWAS analysis). Such an interpretation will add both additional results, and biological meaning to the association data, as was seen with oil biosynthesis in studies by Li et al [15,20]. While PAST may be useful in bringing biologically useful insights to a GWAS analysis, it will not be able to find order from a chaotic dataset if environmental variation, experimental error, or population structure were not accounted for with the most appropriate analysis model during the association analysis.…”

“…More recently, GWAS-based metabolic pathway analysis has been used as a discovery tool to investigate the genetic basis of complex traits in plants. A pathway-based approach was used to study aflatoxin accumulation [13], corn ear worm resistance [14] and oil biosynthesis [15] in maize. Combining GWAS analysis with metabolic pathway analysis considers all genetic sequences positively associated with the trait of interest, regardless of magnitude, and jointly may highlight which sequences lead to mechanisms for crop improvement and which warrant further study and manipulation, for example, by gene editing.…”

Breeding Crops for Enhanced Food Safety

“…Traits that confer enhanced food safety are likely complex and controlled by multiple genes, presenting challenges to breeding efforts, especially for human pathogen-plant interactions. A starting point could be genome-wide association studies followed by metabolic pathway analysis (Li et al, 2019;Thrash et al, 2020) or functional analysis of mapped intervals (Korte and Farlow, 2013;Bartoli and Roux, 2017). For instance, one could predict various biochemical pathways needed for the synthesis of secondary metabolites with antioxidant and antimicrobial properties that could influence plant-microbe interactions and plant responses to associated microbiota.…”

The Genetic Architecture of Leaf Stable Carbon Isotope Composition inZea maysand the Effect of Transpiration Efficiency on Elemental Accumulation

“…A better understanding of the mechanisms influencing leaf 360 δ 13 C would allow future analyses to move beyond single marker tests and instead look at SNPs 361 in genes representing a particular pathway or process that could be collectively significant. This 362 approach was successfully used to study maize lipid biosynthesis (Li et al 2019). 363…”