Data from: Local adaptation (mostly) remains local - re-assessing environmental associations of climate-related candidate SNPs in (Arabidopsis halleri)

Numerous landscape genomic studies have identified single-nucleotide polymorphisms (SNPs) and genes potentially involved in local adaptation. Rarely, it has been explicitly evaluated whether these environmental associations also hold true beyond the populations studied. We tested whether putatively adaptive SNPs in <i>Arabidopsis halleri</i> (Brassicaceae), characterized in a previous study investigating local adaptation to a highly heterogeneous environment, show the same environmental associations in an independent, geographically enlarged set of 18 populations. We analysed new SNP data of 444 plants with the same methodology (partial Mantel tests, PMTs) as in the original study and additionally with a latent factor mixed model (LFMM) approach. Of the 74 candidate SNPs, 41% (PMTs) and 51% (LFMM) were associated with environmental factors in the independent dataset. However, only 5% (PMTs) and 15% (LFMM) of the associations showed the same environment-allele relationships as in the original study. In total, we found 11 genes (31%) containing the same association in the original and independent dataset. These can be considered prime candidate genes for environmental adaptation at a broader geographical scale. Our results suggest that selection pressures in highly heterogeneous alpine environments vary locally and signatures of selection are likely to be population-specific. Thus, genotype-by-environment interactions underlying adaptation are more heterogeneous and complex than is often assumed, which might represent a problem when testing for adaptation at specific loci.诸多景观基因组学研究已鉴定出可能参与本地适应的单核苷酸多态性（single-nucleotide polymorphisms, SNPs）与功能基因。但极少有研究明确评估过，这些环境关联是否在已研究种群之外的群体中依然成立。本研究针对前期一项针对高度异质环境下本地适应的研究中所鉴定的高山拟南芥（Arabidopsis halleri，十字花科Brassicaceae）的候选适应性SNPs，在一组独立的、地理范围扩大的18个种群中，检验其是否呈现出一致的环境关联。我们采用与原始研究一致的方法（偏Mantel检验，partial Mantel tests, PMTs），对444株植物的全新SNP数据进行分析，并额外使用潜因子混合模型（latent factor mixed model, LFMM）方法开展分析。在74个候选SNPs中，独立数据集里分别有41%（偏Mantel检验）与51%（潜因子混合模型）的位点与环境因子存在关联。然而，仅5%（偏Mantel检验）与15%（潜因子混合模型）的关联，呈现出与原始研究一致的环境-等位基因关联模式。综上，我们共鉴定出11个基因（占比31%），其在原始数据集与独立数据集中均存在一致的环境关联，这些基因可被视为更大地理尺度下环境适应的核心候选基因。研究结果表明，高度异质的高山环境中的选择压力存在局域差异，且选择信号很可能具有种群特异性。因此，支撑适应过程的基因型-环境互作模式，比通常所假设的更加异质且复杂，这在针对特定位点开展适应性检验时可能会构成挑战。