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  • Title: Population transcriptomic characterization of the genetic and expression variation of a candidate progenitor of Miscanthus energy crops.
    Author: Yan J, Song Z, Xu Q, Kang L, Zhu C, Xing S, Liu W, Greimler J, Züst T, Li J, Sang T.
    Journal: Mol Ecol; 2017 Nov; 26(21):5911-5922. PubMed ID: 28833782.
    Abstract:
    The use of transcriptome data in the study of the population genetics of a species can capture faint signals of both genetic variation and expression variation and can provide a broad picture of a species' genomic response to environmental conditions. In this study, we characterized the genetic and expression diversity of Miscanthus lutarioriparius by comparing more than 16,225 transcripts obtained from 78 individuals, belonging to 10 populations distributed across the species' entire geographic range. We only observed a low level of nucleotide diversity (π = 0.000434) among the transcriptome data of these populations, which is consistent with highly conserved sequences of functional elements and protein-coding genes captured with this method. Tests of population divergence using the transcriptome data were consistent with previous microsatellite data but proved to be more sensitive, particularly if gene expression variation was considered as well. For example, the analysis of expression data showed that genes involved in photosynthetic processes and responses to temperature or reactive oxygen species stimuli were significantly enriched in certain populations. This differential gene expression was primarily observed among populations and not within populations. Interestingly, nucleotide diversity was significantly negatively correlated with expression diversity within populations, while this correlation was positive among populations. This suggests that genetic and expression variation play separate roles in adaptation and population persistence. Combining analyses of genetic and gene expression variation represents a promising approach for studying the population genetics of wild species and may uncover both adaptive and nonadaptive processes.
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