Space is the Place: Effects of Continuous Spatial Structure on Analysis of Population Genetic Data

Abstract

Real geography is continuous, but standard models in population genetics are based on discrete, well-mixed populations. As a result, many methods of analyzing genetic data assume that samples are a random draw from a well-mixed population, but are applied to clustered samples from populations that are structured clinally over space. Here, we use simulations of populations living in continuous geography to study the impacts of dispersal and sampling strategy on population genetic summary statistics, demographic inference, and genome-wide association studies (GWAS). We find that most common summary statistics have distributions that differ substantially from those seen in well-mixed populations, especially when Wright’s neighborhood size is < 100 and sampling is spatially clustered. “Stepping-stone” models reproduce some of these effects, but discretizing the landscape introduces artifacts that in some cases are exacerbated at higher resolutions. The combination of low dispersal and clustered sampling causes demographic inference from the site frequency spectrum to infer more turbulent demographic histories, but averaged results across multiple simulations revealed surprisingly little systematic bias. We also show that the combination of spatially autocorrelated environments and limited dispersal causes GWAS to identify spurious signals of genetic association with purely environmentally determined phenotypes, and that this bias is only partially corrected by regressing out principal components of ancestry. Last, we discuss the relevance of our simulation results for inference from genetic variation in real organisms.

Predicting Geographic Location from Genetic Variation with Deep Neural Networks

“…We first evaluated our method on genotypes from populations simulated by SLiM v3 (Haller and Messer, 2019), using the model of continuous space described in Battey et al (2019). We simulated a 50 × 50 unit square landscape with expected density ( d ) of 5 individuals per unit area, resulting in census sizes of around 12,500.…”

Isolation by Distance in Populations with Power-law Dispersal

“…Direct measurement of dispersal in natural populations is often difficult or impossible due to practical difficulties in tracking large numbers of individuals over long periods of time. It is often more feasible to instead infer dispersal from spatial patterns of genetic diversity [Cayuela et al, 2018, Bradburd and Ralph, 2019, Battey et al, 2020. Populations with limited dispersal should exhibit "isolation by distance": the more distant individuals are from each other in space, the less related they tend to be [Wright, 1946, Rohlf and Schnell, 1971, Slatkin, 1991.…”

A geostatistical approach to modelling human Holocene migrations in Europe using ancient DNA

“…These clusters are, however, an approximation of a very complex genealogical process. Indeed, the clusters cannot be seen as discrete, originally isolated populations, as there may be both isolation-by-distance and hierarchical population structure within each of these groups (Frantz et al, 2009;Janes et al, 2017;Battey, Ralph, and Kern, 2019). The clusters themselves are also the result of more complex admixture and migration events that occurred before the Holocene (Fu et al, 2016).…”