Mathivanan, R. K. | Search bioRxiv

bioRxiv2024

Quantitative genetics of photosynthetic trait variation in maize

Natural genetic variation in photosynthesis-related traits can aid both in identifying genes involved in regulating photosynthetic processes and developing crops with improved productivity and photosynthetic efficiency. However, rapidly fluctuating environmental parameters create challenges for measuring photosynthetic parameters in large populations under field conditions. We measured chlorophyll fluorescence and absorbance-based photosynthetic traits in a maize diversity panel in the field using an experimental design that allowed us to estimate and control multiple confounding factors. Controlling the impact of day of measurement and light intensity as well as patterns of two-dimensional spatial variation in the field substantially increased heritability with the heritability of 7 out of 14 traits measured exceeding 0.4. We were able to identify high confidence GWAS signals associated with variation in four spatially corrected traits (the quantum yield of photosystem II, non-photochemical quenching, redox state of QA, and relative chlorophyll content). Insertion alleles for Arabidopsis orthologs of three candidate genes exhibited phenotypes consistent with our GWAS results. Collectively these results illustrate the potential of applying best practices from quantitative genetics research to address outstanding questions in plant physiology and understand the mechanisms underlying natural variation in photosynthesis. Highlights[bullet] Controlling spatial and environmental confounding factors increased heritability of photosynthetic traits. [bullet]GWAS identified high confidence signals associated with variation in relative chlorophyll, {Phi}PSII, {Phi}NPQ, and qL. [bullet]Insertion alleles of the Arabidopsis orthologs of maize candidate genes exhibited photosynthesis related phenotypes consistent with the GWAS results.

plant biology↗

bioRxiv2024

Transcripts and genomic intervals associated with variation in metabolite abundance in maize leaves under field conditions

Plants exhibit extensive environment-dependent intraspecific metabolic variation, which likely plays a role in determining variation in whole plant phenotypes. However, much of the work seeking to use natural variation to link genes and transcripts impacts on plant metabolism has employed data from controlled environments. Here we generate and employ data on variation in the abundance of twenty-six metabolites across 660 maize inbred lines under field conditions. We employ these data and previously published transcript and whole plant phenotype data reported for the same field experiment to identify both genomic intervals (through genome-wide association studies) and transcripts (through both transcriptome-wide association studies and an explainable AI approach based on the random forest) associated with variation in metabolite abundance. Both genome-wide association and random forest-based methods identified substantial numbers of significant associations including genes with plausible links to the metabolites they are associated with. In contrast, the transcriptome-wide association identified only six significant associations. In three cases, genetic markers associated with metabolic variation in our study colocalized with markers linked to variation in non-metabolic traits scored in the same experiment. We speculate that the poor performance of transcriptome-wide association studies in identifying transcript-metabolite associations may reflect a high prevalence of non-linear interactions between transcripts and metabolites and/or a bias towards rare transcripts playing a large role in determining intraspecific metabolic variation.

genomics↗