VISUALIZING GENETIC CONSTRAINTS

• 成果类型: Article
• 署名作者: Gaydos, Travis L.; Heckman, Nancy E.; Kirkpatrick, Mark; Stinchcombe, J. R.; Schmitt, Johanna; Kingsolver, Joel; Marron, J. S.
• 署名单位: MITRE Corporation; University of British Columbia; University of Texas System; University of Texas Austin; University of Toronto; University of California System; University of California Davis; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/12-AOAS603
• 发表日期: 2013
• 页码: 860-882
• 关键词: continuous reaction norms; selection gradient; EVOLUTION; GROWTH; dimensionality; shape

摘要

Principal Components Analysis (PCA) is a common way to study the sources of variation in a high-dimensional data set. Typically, the leading principal components are used to understand the variation in the data or to reduce the dimension of the data for subsequent analysis. The remaining principal components are ignored since they explain little of the variation in the data. However, evolutionary biologists gain important insights from these low variation directions. Specifically, they are interested in directions of low genetic variability that are biologically interpretable. These directions are called genetic constraints and indicate directions in which a trait cannot evolve through selection. Here, we propose studying the subspace spanned by low variance principal components by determining vectors in this subspace that are simplest. Our method and accompanying graphical displays enhance the biologist's ability to visualize the subspace and identify interpretable directions of low genetic variability that align with simple directions.