Talbot Benoit, Chen Ting-Wen, Zimmerman Shawna, Joost Stéphane, Eckert Andrew J, Crow Taylor M, Semizer-Cuming Devrim, Seshadri Chitra, Manel Stéphanie
Department of Biology, University of Western Ontario, London, Ontario, Canada.
J. F. Blumenbach Institute of Zoology and Anthropology, Georg-August-Universität Göttingen, Göttingen, Germany.
J Hered. 2017 Mar 1;108(2):207-216. doi: 10.1093/jhered/esw077.
Population genomic analysis can be an important tool in understanding local adaptation. Identification of potential adaptive loci in such analyses is usually based on the survey of a large genomic dataset in combination with environmental variables. Phenotypic data are less commonly incorporated into such studies, although combining a genome scan analysis with a phenotypic trait analysis can greatly improve the insights obtained from each analysis individually. Here, we aimed to identify loci potentially involved in adaptation to climate in 283 Loblolly pine (Pinus taeda) samples from throughout the species' range in the southeastern United States. We analyzed associations between phenotypic, molecular, and environmental variables from datasets of 3082 single nucleotide polymorphism (SNP) loci and 3 categories of phenotypic traits (gene expression, metabolites, and whole-plant traits). We found only 6 SNP loci that displayed potential signals of local adaptation. Five of the 6 identified SNPs are linked to gene expression traits for lignin development, and 1 is linked with whole-plant traits. We subsequently compared the 6 candidate genes with environmental variables and found a high correlation in only 3 of them (R2 > 0.2). Our study highlights the need for a combination of genotypes, phenotypes, and environmental variables, and for an appropriate sampling scheme and study design, to improve confidence in the identification of potential candidate genes.
群体基因组分析可能是理解局部适应性的一项重要工具。在此类分析中，潜在适应性位点的识别通常基于对大型基因组数据集与环境变量的综合调查。表型数据较少被纳入此类研究，尽管将基因组扫描分析与表型性状分析相结合能够极大地提升从各项分析中单独获得的见解。在此，我们旨在识别来自美国东南部整个物种分布范围内的283个火炬松样本中可能参与气候适应的位点。我们分析了来自3082个单核苷酸多态性（SNP）位点数据集以及3类表型性状（基因表达、代谢物和全株性状）的表型、分子和环境变量之间的关联。我们仅发现6个SNP位点显示出局部适应性的潜在信号。所识别出的6个SNP中有5个与木质素发育的基因表达性状相关，1个与全株性状相关。我们随后将这6个候选基因与环境变量进行比较，发现其中仅有3个存在高度相关性（R2 > 0.2）。我们的研究强调了需要将基因型、表型和环境变量相结合，并采用适当的抽样方案和研究设计，以提高对潜在候选基因识别的可信度。
