SNP-skimming: a fast approach to map loci generating quantitative variation in natural populations

Genome-wide association mapping (GWAS) is a method to estimate the contribution of segregating genetic loci to trait variation. A major challenge for applying GWAS to non-model species has been generating dense genome-wide markers that satisfy the key requirement that marker data is error-free. Here we present an approach to map loci within natural populations using inexpensive shallow genome sequencing. This 'SNP skimming' approach involves two steps: an initial genome-wide scan to identify putative targets followed by deep sequencing for confirmation of targeted loci. We apply our method to a test dataset of floral dimension variation in the plant Penstemon virgatus, a member of a genus that has experienced dynamic floral adaptation that reflects repeated transitions in primary pollinator. The ability to detect SNPs that generate phenotypic variation depends on population genetic factors such as population allele frequency, effect size, and epistasis as well as sampling effects contingent on missing data and genotype uncertainty. However, both simulations and the Penstemon data suggest that the most significant tests from the initial SNP skim are likely to be true positives – loci with subtle but significant quantitative effects on phenotype. We discuss the promise and limitations of this method and consider optimal experimental design for a given sequencing effort. Simulations demonstrate that sampling a larger number of individual at the expense of average read depth per individual maximizes the power to detect loci.

全基因组关联作图（Genome-wide association mapping, GWAS）是一种用于估算分离遗传位点对性状变异贡献程度的研究方法。将GWAS应用于非模式物种时，一大核心挑战在于获取满足“标记数据无误差”这一关键要求的高密度全基因组标记。本文提出了一种利用低成本浅层基因组测序技术对自然种群内遗传位点进行作图的方法。该“SNP 扫读（SNP skimming）”策略包含两个步骤：首先开展全基因组初步扫描以鉴定候选靶点，随后对目标位点进行深度测序以完成验证。我们将该方法应用于植物*Penstemon virgatus*的花部尺寸变异测试数据集——该物种所在的钓钟柳属曾经历动态的花部适应性演化，反映出其主要传粉者的多次转变。检测产生表型变异的SNP的能力，取决于多项群体遗传学因素（如群体等位基因频率、效应量以及上位性），同时也受缺失数据与基因型不确定性带来的抽样效应影响。不过，模拟实验与钓钟柳属的实测数据均表明：通过初始SNP扫读得到的最显著关联信号，大概率为真阳性位点——即对表型具有微弱但显著的数量效应的遗传位点。本文还讨论了该方法的应用前景与局限性，并针对特定测序通量下的最优实验设计展开探讨。模拟实验证实：以降低单个体平均测序深度为代价，增加抽样个体的数量，可最大化检测遗传位点的统计效力。