1. Dispersal, the movement of individuals between populations, is crucial in many ecological and genetic processes. However, direct identification of dispersing individuals is difficult or impossible in natural populations. By using genetic assignment methods, individuals with unknown genetic origin can be assigned to source populations. This knowledge is necessary in studying many key questions in ecology, evolution and conservation.
2. We introduce a network‐based tool BONE (Baseline Oriented Network Estimation) for genetic population assignment, which borrows concepts from undirected graph inference. In particular, we use sparse multinomial Least Absolute Shrinkage and Selection Operator (LASSO) regression to estimate probability of the origin of all mixture individuals and their mixture proportions without tedious selection of the LASSO tuning parameter. We compare BONE with three genetic assignment methods implemented in R packages radmixture, assignPOP and RUBIAS.
3. Probability of the origin and mixture proportion estimates of both simulated and real data (an insular house sparrow metapopulation and Chinook salmon populations) given by BONE are competitive or superior compared to other assignment methods. Our examples illustrate how the network estimation method adapts to population assignment, combining the efficiency and attractive properties of sparse network representation and model selection properties of the L₁ regularization. As far as we know, this is the first approach showing how one can use network tools for genetic identification of individuals' source populations.
4. BONE is aimed at any researcher performing genetic assignment and trying to infer the genetic population structure. Compared to other methods, our approach also identifies outlying mixture individuals that could originate outside of the baseline populations. BONE is a freely available R package under the GPL licence and can be downloaded at GitHub. In addition to the R package, a tutorial for BONE is available at https://github.com/markkukuismin/BONE/.

中文翻译：

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使用网络估计工具对个体进行遗传分配以分配来源种群

1. 分散，即个体在种群之间的移动，在许多生态和遗传过程中至关重要。然而，在自然种群中直接或分散地识别个体是困难的或不可能的。通过使用遗传分配方法，可以将遗传起源未知的个体分配给来源群体。这些知识对于研究生态，进化和保护中的许多关键问题是必要的。
2. 我们引入了用于遗传种群​​分配的基于网络的工具BONE（面向基线的网络估计），该工具借鉴了无向图推断的概念。特别地，我们使用稀疏多项式最小绝对收缩和选择算子（LASSO）回归来估计所有混合个体的起源及其混合比例的概率，而无需繁琐地选择LASSO调整参数。我们将BONE与在R包radmixture，AssignPOP和RUBIAS中实现的三种遗传分配方法进行了比较。
3. 与其他分配方法相比，BONE给出的模拟数据和真实数据（岛屿麻雀种群和奇努克鲑鱼种群）的起源和混合比例估计的概率具有竞争性或优越性。我们的示例说明了网络估计方法如何适应人口分配，结合了稀疏网络表示的效率和吸引人的属性以及L ₁正则化的模型选择属性。据我们所知，这是第一个显示如何使用网络工具对个人源种群进行遗传鉴定的方法。
4. BONE的对象是进行遗传分配并试图推断遗传种群结构的任何研究人员。与其他方法相比，我们的方法还确定了可能来自基准群体之外的外围混合个体。BONE是GPL许可下的免费R包，可以从GitHub下载。除了R包外，还可以在https://github.com/markkukuismin/BONE/上找到有关BONE的教程。