Calibrating a coalescent simulation of human genome sequence variation

TLDR

Population genetic models link sequence variation to hypotheses about human history and biology, but until recently relied on arbitrary assumptions; large empirical datasets now enable calibration of these models to match observed allele frequencies, linkage disequilibrium, and population differentiation. The authors present a calibrated coalescent model that generates simulated data for three populations closely resembling empirical allele frequencies, linkage disequilibrium, and population differentiation. The calibrated model produces realistic simulated data that match empirical observations, fulfills a long‑standing need, and is publicly available for broad use in human genetics research.

Abstract

Population genetic models play an important role in human genetic research, connecting empirical observations about sequence variation with hypotheses about underlying historical and biological causes. More specifically, models are used to compare empirical measures of sequence variation, linkage disequilibrium (LD), and selection to expectations under a “null” distribution. In the absence of detailed information about human demographic history, and about variation in mutation and recombination rates, simulations have of necessity used arbitrary models, usually simple ones. With the advent of large empirical data sets, it is now possible to calibrate population genetic models with genome-wide data, permitting for the first time the generation of data that are consistent with empirical data across a wide range of characteristics. We present here the first such calibrated model and show that, while still arbitrary, it successfully generates simulated data (for three populations) that closely resemble empirical data in allele frequency, linkage disequilibrium, and population differentiation. No assertion is made about the accuracy of the proposed historical and recombination model, but its ability to generate realistic data meets a long-standing need among geneticists. We anticipate that this model, for which software is publicly available, and others like it will have numerous applications in empirical studies of human genetics.