ABSTRACT
Understanding intraspecific relationships between genetic and functional diversity is a major goal in the field of evolutionary biology and is important for conserving biodiversity. Linking intraspecific molecular patterns of plants to ecological pressures and trait variation remains difficult due to environment-driven plasticity. Next-generation sequencing, untargeted liquid chromatography-mass spectrometry (LC-MS) profiling, and interdisciplinary approaches integrating population genomics, metabolomics, and community ecology permit novel strategies to tackle this problem. We analyzed six natural populations of the disease-threatened Cornus florida L. from distinct ecological regions using genotype-by-sequencing markers and LC-MS-based untargeted metabolite profiling. We tested the hypothesis that higher genetic diversity in C. florida yielded higher chemical diversity and less disease susceptibility (screening hypothesis), and we also determined whether genetically similar subpopulations were similar in chemical composition. Most importantly, we identified metabolites that were associated with candidate loci or were predictive biomarkers of healthy or diseased plants after controlling for environment. Subpopulation clustering patterns based on genetic or chemical distances were largely congruent. While differences in genetic diversity were small among subpopulations, we did observe notable similarities in patterns between subpopulation averages of rarefied-allelic and chemical richness. More specifically, we found that the most abundant compound of a correlated group of putative terpenoid glycosides and derivatives was correlated with tree health when considering chemodiversity. Random forest biomarker and genomewide association tests suggested that this putative iridoid glucoside and other closely associated chemical features were correlated to SNPs under selection.
ABSTRACT
Discovering local adaptation, its genetic underpinnings, and environmental drivers is important for conserving forest species. Ecological genomic approaches coupled with next-generation sequencing are useful means to detect local adaptation and uncover its underlying genetic basis in nonmodel species. We report results from a study on flowering dogwood trees (Cornus florida L.) using genotyping by sequencing (GBS). This species is ecologically important to eastern US forests but is severely threatened by fungal diseases. We analyzed subpopulations in divergent ecological habitats within North Carolina to uncover loci under local selection and associated with environmental-functional traits or disease infection. At this scale, we tested the effect of incorporating additional sequencing before scaling for a broader examination of the entire range. To test for biases of GBS, we sequenced two similarly sampled libraries independently from six populations of three ecological habitats. We obtained environmental-functional traits for each subpopulation to identify associations with genotypes via latent factor mixed modeling (LFMM) and gradient forests analysis. To test whether heterogeneity of abiotic pressures resulted in genetic differentiation indicative of local adaptation, we evaluated Fst per locus while accounting for genetic differentiation between coastal subpopulations and Piedmont-Mountain subpopulations. Of the 54 candidate loci with sufficient evidence of being under selection among both libraries, 28-39 were Arlequin-BayeScan Fst outliers. For LFMM, 45 candidates were associated with climate (of 54), 30 were associated with soil properties, and four were associated with plant health. Reanalysis of combined libraries showed that 42 candidate loci still showed evidence of being under selection. We conclude environment-driven selection on specific loci has resulted in local adaptation in response to potassium deficiencies, temperature, precipitation, and (to a marginal extent) disease. High allele turnover along ecological gradients further supports the adaptive significance of loci speculated to be under selection.
