History and Geography of Human Genes is one of my favorite books; it might rank up there in my “top 10” if I ever wished to enumerate one. But in both Human Evolutionary Genetics, a textbook, and A Genetic and Cultural Odyssey, a biography of L. L. Cavalli-Sforza, it was noted that the PCA maps pioneered in History and Geography of Human Genes have never really caught on. There might be a reason…Interpreting principal component analyses of spatial population genetic variation:
Nearly 30 years ago, Cavalli-Sforza et al. pioneered the use of principal component analysis (PCA) in population genetics and used PCA to produce maps summarizing human genetic variation across continental regions…They interpreted gradient and wave patterns in these maps as signatures of specific migration events…These interpretations have been controversial…but influential…and the use of PCA has become widespread in analysis of population genetics data…However, the behavior of PCA for genetic data showing continuous spatial variation, such as might exist within human continental groups, has been less well characterized. Here, we find that gradients and waves observed in Cavalli-Sforza et al.’s maps resemble sinusoidal mathematical artifacts that arise generally when PCA is applied to spatial data, implying that the patterns do not necessarily reflect specific migration events. Our findings aid interpretation of PCA results and suggest how PCA can help correct for continuous population structure in association studies.
If this critique holds up, it’s a step back for the synthesis of genetics & history. But so it goes. Science is fundamentally about proper method, not congenial outcome. G & p-ter comment futher. G’s point is important to keep in mind:
…These results do not to say that human populations did not expand out of particular regions, just that PCA maps are not the best tool to judge this. The authors also note that this does not invalidate the use of PCA to correct for structure in association studies, and in fact might aid in their interpretation in epidemiological models.
Related: My 10 questions for L. L. Cavalli-Sforza.