Statistical approaches in landscape genetics: an evaluation of methods for linking landscape and genetic data
Statistical approaches in landscape genetics: an evaluation of methods for linking landscape and genetic data
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DOI：
10.1111/j.1600-0587.2009.05807.x
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发表时间：
2009-10-01
期刊：
影响因子：
5.9
通讯作者：
Dezzani, Raymond J.
中科院分区：
文献类型：
--
作者：
Balkenhol, Niko;Waits, Lisette P.;Dezzani, Raymond J.
景观遗传学的目标是发现和解释景观对遗传多样性和遗传结构的影响。尽管景观遗传方法越来越受欢迎，但将遗传和景观数据联系起来的统计方法在很大程度上仍未经检验。这种方法评价的缺乏使得难以比较利用不同统计数据的研究，并危及该领域的未来发展和应用。为了探讨景观遗传学中各种统计方法的适用性和可比性，我们模拟了5种景观遗传学情景的数据集。然后，我们用11种方法对这些数据进行了分析，并根据它们的统计能力、1型错误率以及它们引导研究人员得出关于景观-遗传关系的准确结论的总体能力对这些方法进行了比较。结果表明，一些最常用的技术（如Mantel和部分Mantel测试）具有较高的1型错误率，多变量、非线性方法更适合于景观遗传数据分析。此外，不同的方法通常只显示中等程度的一致性。因此，仅用一种方法分析数据集可能会产生与方法相关的结果，从而可能导致错误的结论。在此基础上，提出了选择最佳统计方法组合的建议，并确定了未来景观遗传数据分析的研究需求。
The goal of landscape genetics is to detect and explain landscape effects on genetic diversity and structure. Despite the increasing popularity of landscape genetic approaches, the statistical methods for linking genetic and landscape data remain largely untested. This lack of method evaluation makes it difficult to compare studies utilizing different statistics, and compromises the future development and application of the field. To investigate the suitability and comparability of various statistical approaches used in landscape genetics, we simulated data sets corresponding to five landscape-genetic scenarios. We then analyzed these data with eleven methods, and compared the methods based on their statistical power, type-1 error rates, and their overall ability to lead researchers to accurate conclusions about landscape-genetic relationships. Results suggest that some of the most commonly applied techniques (e.g. Mantel and partial Mantel tests) have high type-1 error rates, and that multivariate, non-linear methods are better suited for landscape genetic data analysis. Furthermore, different methods generally show only moderate levels of agreement. Thus, analyzing a data set with only one method could yield method-dependent results, potentially leading to erroneous conclusions. Based on these findings, we give recommendations for choosing optimal combinations of statistical methods, and identify future research needs for landscape genetic data analyses.