Data from: The search for loci under selection: trends, biases and progress

Detecting genetic variants under selection using FST outlier analysis (OA) and environmental association analyses (EAA) are popular approaches that provide insight into the genetic basis of local adaptation. Despite the frequent use of OA and EAA approaches and their increasing attractiveness for detecting signatures of selection, their application to field-based empirical data have not been synthesized. Here, we review 66 empirical studies that use Single Nucleotide Polymorphisms (SNPs) in OA and EAA. We report trends and biases across biological systems, sequencing methods, approaches, parameters, environmental variables and their influence on detecting signatures of selection. We found striking variability in both the use and reporting of environmental data and statistical parameters. For example, linkage disequilibrium among SNPs and numbers of unique SNP associations identified with EAA were rarely reported. The proportion of putatively adaptive SNPs detected varied widely among studies, and decreased with the number of SNPs analyzed. We found that genomic sampling effort had a greater impact than biological sampling effort on the proportion of identified SNPs under selection. OA identified a higher proportion of outliers when more individuals were sampled, but this was not the case for EAA. To facilitate repeatability, interpretation and synthesis of studies detecting selection, we recommend that future studies consistently report geographic coordinates, environmental data, model parameters, linkage disequilibrium, and measures of genetic structure. Identifying standards for how OA and EAA studies are designed and reported will aid future transparency and comparability of SNP-based selection studies and help to progress landscape and evolutionary genomics.利用FST离群值分析（FST outlier analysis，OA）与环境关联分析（environmental association analyses，EAA）检测受选择遗传变异，是揭示局部适应遗传机制的常用手段。尽管OA与EAA方法应用频繁，且在检测选择印记方面的吸引力与日俱增，但目前尚无研究对其基于野外实测数据的应用情况进行系统性综述。本文综述了66项在OA与EAA中应用单核苷酸多态性（Single Nucleotide Polymorphisms，SNPs）的实证研究，梳理了不同生物系统、测序方法、分析手段、统计参数及环境变量相关的研究趋势与偏倚，及其对检测选择印记的影响。研究发现，环境数据与统计参数的使用及报告情况存在显著差异，例如单核苷酸多态性间的连锁不平衡（linkage disequilibrium）以及通过EAA鉴定出的独特SNP关联数量极少被提及。各研究中检测到的推定适应性SNP占比差异悬殊，且随分析SNP数量的增加而降低。研究发现，相较于生物采样量，基因组采样量对受选择SNP的鉴定占比影响更大。当采样个体数增多时，OA检测出的离群值占比更高，但EAA未呈现该规律。为提升选择检测研究的可重复性、可解释性与可综述性，本文建议未来研究统一报告地理坐标、环境数据、模型参数、连锁不平衡及遗传结构度量指标。确立OA与EAA研究的设计与报告规范，将有助于提升基于SNP的选择研究的透明度与可比性，进而推动景观基因组学与进化基因组学的发展。