Close relatives in population samples: Evaluation of the consequences for genetic stock identification

Determining the origin of individuals in mixed population samples is key in many ecological, conservation and management contexts. Genetic data can be analyzed using Genetic Stock Identification (GSI), where the origin of single individuals is determined using Individual Assignment (IA) and population proportions are estimated with Mixed Stock Analysis (MSA). In such analyses, allele frequencies in a reference baseline are required. Unknown individuals or mixture proportions are assigned to source populations based on the likelihood that their multilocus genotypes occur in a particular baseline sample. Representative sampling of populations included in a baseline is important when designing and performing GSI. Here we investigate the effects of family sampling on GSI, using both simulated and empirical genotypes for Atlantic salmon (Salmo salar). We show that non-representative sampling leading to inclusion of close relatives in a reference baseline may introduce bias in estimated proportions of contributing populations in a mixed sample, and increases the amount of incorrectly assigned individual fish. Simulated data further show that the induced bias increases with increasing family structure, but that it can be partly mitigated by increased baseline population sample sizes. Results from standard accuracy tests of GSI (using only a reference baseline and/or self-assignment) gave a false and elevated indication of the baseline power and accuracy to identify stock proportions and individuals. These findings suggest that family structure in baseline population samples should be quantified and its consequences evaluated, before carrying out GSI.

Methods
Tissue samples collected from Atlantic salmon. Tissue samples consisted of fin clips from hatcheries stored individually in labeled tubes with ethanol (95%). DNA was extracted followed by PCR and genotyping of 17 polymorphic microsatellite markers (on average c. 10 alleles/locus).

在诸多生态学、保护生物学与资源管理场景中，确定混合种群样本内个体的起源是核心研究问题。可通过遗传种群识别（Genetic Stock Identification, GSI）技术分析遗传数据：该技术借助个体归属（Individual Assignment, IA）确定单个个体的起源，并通过混合种群分析（Mixed Stock Analysis, MSA）估算各源种群的贡献比例。此类分析需依赖参考基准群体的等位基因频率，进而基于未知个体的多位点基因型在特定基准样本中出现的概率，将未知个体或混合样本的种群比例归属到潜在源种群中。

在设计并开展GSI研究时，对基准群体所包含的种群进行代表性采样至关重要。本研究以大西洋鲑（Salmo salar）的模拟基因型与实测基因型为材料，探究家系采样对GSI的影响。研究结果表明，若采样不具代表性，导致参考基准群体中纳入近亲个体，可能会使混合样本中源种群的贡献比例估算产生偏差，并增加个体错判的数量。模拟数据进一步显示，这种由家系结构引发的偏差会随家系复杂度提升而加剧，但可通过扩大基准群体的种群样本量得到部分缓解。仅使用参考基准群体和/或自我归属开展的GSI标准精度测试，会错误高估基准群体识别种群比例与个体归属的能力与精度。上述研究结果表明，在开展GSI研究前，应当对基准群体样本中的家系结构进行量化，并评估其带来的影响。

## 材料与方法
本研究的组织样本采自大西洋鲑。样本取自养殖厂的鳍剪组织，单独分装于标注好的95%乙醇保存管中。实验先提取DNA，随后通过PCR扩增并对17个多态性微卫星标记进行基因分型（每个位点平均约含10个等位基因）。