Abstract
The recent years have seen a growing number of studies investigating evolutionary questions using ancient DNA. To address these questions, one of the most frequently-used method is principal component analysis (PCA). When PCA is applied to temporal samples, the sample dates are, however, ignored during analysis, leading to imperfect representations of samples in PC plots. Here, we present a factor analysis (FA) method in which individual scores are corrected for the effect of allele frequency drift over time. We obtained exact solutions for the estimates of corrected factors, and we provided a fast algorithm for their computation. Using computer simulations and ancient European samples, we compared geometric representations obtained from FA with PCA and with ancestry estimation programs. In admixture analyses, FA estimates agreed with tree-based statistics, and they were more accurate than those obtained from PCA projections and from ancestry estimation programs. A great advantage of FA over existing approaches is to improve descriptive analyses of ancient DNA samples without requiring inclusion of outgroup or present-day samples.
Highlights
The recent years have seen a growing number of studies investigating evolutionary questions using ancient DNA
We developed a factor analysis (FA) method for representing ancestral relationships among samples collected at distinct time points in the past
The objective of our approach was to propose a factorial decomposition of a data matrix similar to a principal component analysis (PCA), in which individual scores are corrected for the effect of allele frequency drift over time
Summary
The recent years have seen a growing number of studies investigating evolutionary questions using ancient DNA. To provide examples of distortion arising in PCA due to uncorrected temporal drift, we performed simulations of a coalescent model for forty-one samples with ages ranging from 0 to 4000 generations in a random mating population with effective size Ne = 10,000 (Fig. 1a–b).
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