Department of Genetics, Kazimierz Wielki University, Bydgoszcz, Poland.
Mol Ecol Resour. 2023 Jul;23(5):1168-1181. doi: 10.1111/1755-0998.13767. Epub 2023 Feb 26.
In plant populations, parentage analysis helps understand factors shaping individual reproductive success. However, estimating reproductive success determinants based on parentage counts requires decoupling the effects of individual fecundity and propagule dispersal. The neighbourhood model implemented in the NMπ software provides a standard solution for this problem based on the fixed-effects regression-like approach. Nonetheless, it has been recently shown that the method is prone to false discoveries when important fecundity determinants are omitted. To account for the unexplained variance in fecundity, the Bayesian approach was developed based on the new model (the hierarchical neighbourhood model; HNM). Here, I present the NMπ software update that allows the HNM approach to be used in the framework of a friendly interface. More importantly, the HNM approach is now made available for both dispersed (seedlings) and nondispersed (seeds with known mothers) progeny data. The Bayesian approach, among others, selects significant fecundity determinants, estimates the proportion of variance in reproductive potential explained by selected determinants (R ), and provides individual female and male fecundity values. Although the software was designed to handle microsatellite marker data, a solution is proposed for large sets of single nucleotide polymorphisms. The program can be run on Windows (using either a terminal or a graphical interface) as well as (using a terminal) on Linux, or macOS platforms. In any case, NMπ can utilize multicore processors to speed up the analysis. The updated package containing the code, the executable file, the user manual, and example data is available at https://www.ukw.edu.pl/pracownicy/plik/igor_chybicki/3694/.
在植物种群中，亲代分析有助于了解影响个体生殖成功的因素。然而，基于亲代计数来估计生殖成功的决定因素，需要分离个体繁殖力和繁殖体扩散的影响。NMπ 软件中实现的邻域模型提供了一种基于固定效应回归样方法的标准解决方案。然而，最近已经表明，当重要的繁殖力决定因素被忽略时，该方法容易出现错误发现。为了解释繁殖力的未解释方差，基于新模型（分层邻域模型；HNM）开发了贝叶斯方法。在这里，我介绍了 NMπ 软件更新，允许在友好的界面框架中使用 HNM 方法。更重要的是，现在 HNM 方法可用于离散（幼苗）和非离散（已知母本的种子）后代数据。贝叶斯方法等方法选择重要的繁殖力决定因素，估计所选决定因素解释生殖潜力方差的比例（R），并提供个体雌性和雄性繁殖力值。尽管该软件是为处理微卫星标记数据而设计的，但也为大量单核苷酸多态性数据集提出了一种解决方案。该程序可以在 Windows 上运行（使用终端或图形界面），也可以在 Linux 或 macOS 平台上运行（使用终端）。在任何情况下，NMπ 都可以利用多核处理器来加速分析。包含代码、可执行文件、用户手册和示例数据的更新软件包可在 https://www.ukw.edu.pl/pracownicy/plik/igor_chybicki/3694/ 获得。
