The Plant Genome (Jul 2017)

Haplotypes Phased from Population Transcriptomes Detecting Selection in the Initial Adaptation of Miscanthus lutarioriparius to Stressful Environments

  • Cai-yun Zhu,
  • Wei Liu,
  • Li-Fang Kang,
  • Qin Xu,
  • Shi-Lai Xing,
  • Yang-Yang Fan,
  • Zhi-Hong Song,
  • Juan Yan,
  • Jian-Qiang Li,
  • Tao Sang

DOI
https://doi.org/10.3835/plantgenome2016.11.0119
Journal volume & issue
Vol. 10, no. 2

Abstract

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Adaptation is a characteristic that enhances the survival or reproduction of organisms; selection is the critical process leading to adaptive evolution. Therefore, detecting selection is important in studying evolutionary biology. Changes in allele frequency are fundamental to adaptive evolution. The allele frequency of entire genes at the genomic scale is more intensive and precise for analyzing selection effects, compared with simple sequence repeat and single nucleotide polymorphism (SNP) alleles from nuclear gene fragments. Here, we analyzed 29,094 SNPs derived from 80 individuals of 14 L. Liou ex S.L. Chen & Renvoize populations planted near their native habitat (Jiangxia, Hubei Province, JH) and a stressful environment (Qingyang, Gansu Province, QG) to detect selection during initial adaptation. The nucleotide diversity of over 60% of genes was decreased in QG compared with JH, suggesting that most genes were undergoing selection in the stressful environment. We explored a new approach based on haplotype data inferred from RNA-seq data to analyze the change in frequency between two sites and to detect selection signals. In total, 402 and 51 genes were found to be targets of positive and negative selection, respectively. Among these candidate genes, the enrichment of abiotic stress-response genes and photosynthesis-related genes might have been responsible for establishment in the stressful environment. This is the first study assessing the change in allele frequency at the genomic level during adaptation. The method in which allele frequency detects selection during initial adaptation using population RNA-seq data would be useful for developing evolutionary biology.