Moran Paul, Bromaghin Jeffrey F, Masuda Michele
Conservation Biology Division, Northwest Fisheries Science Center, Seattle, Washington, United States of America.
United States Geological Survey, Alaska Science Center, Anchorage, Alaska, United States of America.
PLoS One. 2014 Jun 6;9(6):e98470. doi: 10.1371/journal.pone.0098470. eCollection 2014.
Many applications in ecological genetics involve sampling individuals from a mixture of multiple biological populations and subsequently associating those individuals with the populations from which they arose. Analytical methods that assign individuals to their putative population of origin have utility in both basic and applied research, providing information about population-specific life history and habitat use, ecotoxins, pathogen and parasite loads, and many other non-genetic ecological, or phenotypic traits. Although the question is initially directed at the origin of individuals, in most cases the ultimate desire is to investigate the distribution of some trait among populations. Current practice is to assign individuals to a population of origin and study properties of the trait among individuals within population strata as if they constituted independent samples. It seemed that approach might bias population-specific trait inference. In this study we made trait inferences directly through modeling, bypassing individual assignment. We extended a Bayesian model for population mixture analysis to incorporate parameters for the phenotypic trait and compared its performance to that of individual assignment with a minimum probability threshold for assignment. The Bayesian mixture model outperformed individual assignment under some trait inference conditions. However, by discarding individuals whose origins are most uncertain, the individual assignment method provided a less complex analytical technique whose performance may be adequate for some common trait inference problems. Our results provide specific guidance for method selection under various genetic relationships among populations with different trait distributions.
生态遗传学中的许多应用都涉及从多个生物种群的混合体中采样个体，并随后将这些个体与其起源的种群相关联。将个体分配到其假定起源种群的分析方法在基础研究和应用研究中都有用处，能提供有关种群特定生活史、栖息地利用、生态毒素、病原体和寄生虫负荷以及许多其他非遗传生态或表型特征的信息。尽管问题最初针对的是个体的起源，但在大多数情况下，最终目的是研究某些特征在种群中的分布。当前的做法是将个体分配到一个起源种群，并研究种群分层内个体间该特征的属性，就好像它们构成了独立样本一样。看起来这种方法可能会使种群特定特征推断产生偏差。在本研究中，我们通过建模直接进行特征推断，绕过了个体分配。我们扩展了一个用于种群混合分析的贝叶斯模型，以纳入表型特征的参数，并将其性能与具有分配最小概率阈值的个体分配方法进行比较。在某些特征推断条件下，贝叶斯混合模型的表现优于个体分配方法。然而，通过舍弃起源最不确定的个体，个体分配方法提供了一种不太复杂的分析技术，其性能对于一些常见的特征推断问题可能就足够了。我们的结果为在具有不同特征分布的种群间各种遗传关系下的方法选择提供了具体指导。
