Data from: Signatures of local adaptation in candidate genes of oaks (Quercus spp.) in respect to present and future climatic conditions

Testing how populations are locally adapted and predicting their response to their future environment is of key importance in view of climate change. Landscape genomics is a powerful approach to investigate genes and environmental factors involved in local adaptation. In a pooled amplicon sequencing approach of 94 genes in 71 populations, we tested if >3'500 single nucleotide polymorphisms (SNPs) in the three most common oak species in Switzerland (Quercus petraea, Q. pubescens, Q. robur) show an association with abiotic factors related to local topography, historical climate, and soil characteristics. In the analysis including all species, the most frequently associated environmental factors were those best describing the habitats of the species. In the species-specific analyses, the most important environmental factors and associated SNPs greatly differed among species. However, we identified one SNP and seven genes that were associated to the same environmental factor across all species. We finally used regressions of allele frequencies of the most strongly associated SNPs along environmental gradients to predict the risk of non-adaptedness (RONA), which represents the average change in allele frequency at climate-associated loci theoretically required to match future climatic conditions. RONA is considerable for some populations and species (up to 48% in single populations) and strongly differs among species. Given the long generation time of oaks, some of the required allele frequency changes might not be realistic to achieve based on standing genetic variation. Hence, future adaptedness requires gene flow or planting of individuals carrying beneficial alleles from habitats currently matching future climatic conditions.

鉴于气候变化的大背景，解析种群的本地适应机制并预测其对未来环境的响应，具有至关重要的科学意义。景观基因组学（Landscape Genomics）是探究参与本地适应的基因与环境因子的有力研究手段。我们针对瑞士境内3种最常见栎属树种（Quercus petraea、Q. pubescens、Q. robur）的71个种群的94个基因开展混合扩增子测序，对超过3500个单核苷酸多态性位点（single nucleotide polymorphisms, SNPs）进行分析，以检验这些位点与本地地形、历史气候及土壤特性等非生物因子的关联情况。在涵盖所有物种的整合分析中，关联频率最高的环境因子，恰是最能表征该类群生境特征的因子。在物种特异性分析中，关键环境因子及关联SNPs在不同物种间呈现出显著差异。不过我们仍鉴定出1个SNP位点与7个基因，可在所有研究物种中与同一环境因子产生显著关联。最后，我们借助沿环境梯度分布的强关联SNPs的等位基因频率回归模型，预测了非适应风险（non-adaptedness, RONA）——该指标指为匹配未来气候条件，气候关联位点上的等位基因频率所需发生的理论平均变化量。部分种群与物种的非适应风险数值较高，单一种群最高可达48%，且不同物种间的该指标差异显著。考虑到栎属植物较长的世代周期，基于现存遗传变异，部分所需的等位基因频率变化或难以通过自然种群的遗传变异实现。因此，要实现种群的未来适应，需借助基因流，或引种携带适配当前未来气候生境的有益等位基因的个体。