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.用于识别参与本地适应（local adaptation）的基因位点（locus，复数形式loci）的基因组扫描（genomic scan）正愈发普遍。此类研究中采用的多数方法均假定，本地适应由存在拮抗多效性（antagonistic pleiotropy）的基因位点介导，且检测的正是受选择的靶位点（selected locus）本身，但极少有方法会考量选择信号（selection signal）如何随时间推移发生变化。然而，多数实证数据集（empirical dataset）的标记密度（marker density）过低，无法确保检测到的即为受选择的靶位点；研究者也鲜有机会知晓选择首次施加的时间；且诸多介导本地适应的基因位点，其作用模式更可能为条件中性（conditional neutrality）而非拮抗多效性。本研究通过模拟数据，评估上述因素对基因型-环境关联（genotype-environment association）检验效能的影响。研究结果显示，当受选择靶位点存在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 model）、混合线性模型（mixed linear model）以及潜因子混合模型（latent factor mixed model）——以及BayEnv工具的表现均较为优异，真阳性（true positive）率高且假阳性（false positive）率低。但所有检验方法对存在条件中性作用的基因位点的检测效能均有所下降；若采样的是与因果位点（causative locus）物理连锁的位点而非因果位点本身，关联检测的成功率会大幅降低。此外，拮抗多效性可使可检测的基因型-环境关联维持的时长远长于条件中性作用。本研究的分析结果表明，若本地适应通常由存在条件中性作用的基因位点介导，那么基因组扫描方法在识别介导本地适应的基因位点方面，其能力将十分有限。