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Unsupervized Quantification of Demographic Structure for Single-copy Alignments

By Adam B Rohrlach, Nigel Bean, Gary Glonek, Barbara Holland, Raymond Tobler, Jonathan Tuke, Alan Cooper

Posted 04 Jun 2018
bioRxiv DOI: 10.1101/338442

Principal components analysis (PCA) has been one of the most widely used exploration tools in genomic data analysis since its introduction in 1978 (Menozzi et al. 1978). PCA allows similarities between individuals to be efficiently calculated and visualized, optimally in two dimensions. While PCA is well suited to analyses concerned with autosomal DNA, no analogue for PCA exists for the analysis and visualization of non-autosomal DNA. In this paper we introduce a statistically valid method for the analysis of single-copy sequence data. We then show that tests for relationships between genetic information and qualitative and quantitative characteristics can be implemented in a rigorous statistical framework. We motivate the use of our method with examples from empirical data.

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