How Many Subpopulations Is Too Many? Exponential Lower Bounds for Inferring Population Histories
Reconstruction of population histories is a central problem in population genetics. Existing coalescent-based methods, like the seminal work of Li and Durbin (Nature, 2011), attempt to solve this problem using sequence data but have no rigorous guarantees. Determining the amount of data needed to correctly reconstruct population histories is a major challenge. Using a variety of tools from information theory, the theory of extremal polynomials, and approximation theory, we prove new sharp information-theoretic lower bounds on the problem of reconstructing population structure—the history of multiple subpopulations that merge, split and change sizes over time. Our lower bounds are exponential in the number of subpopulations, even when reconstructing recent histories. We demonstrate the sharpness of our lower bounds by providing algorithms for distinguishing and learning population histories with matching dependence on the number of subpopulations.
KeywordsPopulation size histories Mixtures of exponentials Sample complexity
This work was funded in part by ONR N00014-16-1-2227, NSF CCF1665252, NSF DMS-1737944, NSF Large CCF-1565235, NSF CAREER Award CCF-1453261, as well as Ankur Moitra’s David and Lucile Packard Fellowship, Alfred P. Sloan Fellowship, and ONR Young Investigator Award.
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