Association of leaf spectral variation with functional genetic variants

Abstract

The application of in-field and aerial spectroscopy to assess functional and phylogenetic variation in plants has led to novel ecological insights and promises to support global assessments of plant biodiversity. Understanding the influence of plant genetic variation on reflectance spectra will help to harness this potential for biodiversity monitoring and improve our understanding of why plants differ in their functional responses to environmental change. Here, we use an unusually well-resolved genetic mapping population in a wild plant, the coyote tobaccoNicotiana attenuata, to associate genetic differences with differences in leaf spectra for plants in a field experiment in their natural environment. We analyzed the leaf reflectance spectra using FieldSpec 4 spectroradiometers on plants from 325 fully genotyped recombinant inbred lines (RILs) ofN. attenuatagrown in a blocked and randomized common garden experiment. We then tested three approaches to conducting Genome-Wide Association Studies (GWAS) on spectral variants. We introduce a new Hierarchical Spectral Clustering with Parallel Analysis (HSC-PA) method which efficiently captured the variation in our high-dimensional dataset and allowed us to discover a novel association, between a locus on Chromosome 1 and the 445-499 nm spectral range, which corresponds to the blue light absorption region of chlorophyll, indicating a genetic basis for variation in photosynthetic efficiency. These associations lie in close proximity to candidate genes known to be expressed in leaves and having annotated functions as methyltransferases, indicating possible underlying mechanisms governing these spectral differences. In contrast, an approach using well-established spectral indices related to photosynthesis, reducing complex spectra to a few dimensionless numbers, was not able to identify any robust associations, while an approach treating single wavelengths as phenotypes identified the same associations as HSC-PA but without the statistical power to pinpoint significant associations. The HSC-PA approach we describe here can support a comprehensive understanding of the genetic determinants of leaf spectral variation which is datadriven but human-interpretable, and lays a robust foundation for future research in plant genetics and remote sensing applications.