Data from: Effects of gene action, marker density, and time since selection on the performance of landscape genomic scans of local adaptation

Genomic “scans” to identify loci that contribute to local adaptation are becoming increasingly common. Many methods used for such studies have assumed that local adaptation is created by loci experiencing antagonistic pleiotropy and that the selected locus itself is assayed, and few consider how signals of selection change through time. However, most empirical data sets have marker density too low to assume that a selected locus itself is assayed, researchers seldom know when selection was first imposed, and many locally adapted loci likely experience not antagonistic pleiotropy but conditional neutrality. We simulated data to evaluate how these factors affect the performance of tests for genotype-environment association. We found that three types of regression-based analyses (linear models, mixed linear models, and latent factor mixed models) and an implementation of BayEnv all performed well, with high rates of true positives and low rates of false positives, when the selected locus experienced antagonistic pleiotropy, and when the selected locus was assayed directly. However, all tests had reduced power to detect loci experiencing conditional neutrality, and the probability of detecting associations was sharply reduced when physically linked rather than causative loci were sampled. Antagonistic pleiotropy also maintained detectable genotype-environment associations much longer than conditional neutrality. Our analyses suggest that if local adaptation is often driven by loci experiencing conditional neutrality, genome-scan methods will have limited capacity to find loci responsible for local adaptation.

用于识别介导本地适应的基因位点的基因组扫描（genomic scan）技术正日益普及。此类研究中所采用的多数方法均假设，本地适应由携带拮抗多效性（antagonistic pleiotropy）的基因位点驱动，且检测的正是受选择的靶点位点本身，但极少有研究考量选择信号随时间推移的变化规律。然而，多数实证数据集的标记密度过低，无法确保所检测的即为受选择的靶点位点；研究者也鲜有明确选择首次启动的时间；且诸多介导本地适应的基因位点，其作用模式大概率并非拮抗多效性，而是条件中性性（conditional neutrality）。为评估上述因素对基因型-环境关联（genotype-environment association）检验性能的影响，我们通过模拟数据开展了相关研究。我们发现，当受选择位点呈现拮抗多效性且被直接检测时，三类基于回归的分析方法——线性模型（linear models）、混合线性模型（mixed linear models）以及潜因子混合模型（latent factor mixed models）——以及BayEnv分析方法均表现优异，真阳性率（true positives）较高且假阳性率（false positives）较低。但在检测呈现条件中性性的位点时，所有检验的统计检验效力均出现下降；而当采样的是物理连锁而非因果位点时，检测到关联的概率也会大幅降低。相较于条件中性性，拮抗多效性可使可检测到的基因型-环境关联维持更长时间。我们的分析结果表明，若本地适应常由携带条件中性性的基因位点驱动，则基因组扫描方法在识别介导本地适应的基因位点时，其能力将十分有限。