Estimating scale-specific and localized spatial patterns in allele frequency

Abstract

Characterizing spatial patterns in allele frequencies is fundamental to evolutionary biology because such patterns can inform on underlying processes. However, the spatial scales at which changing selection, gene flow, and drift act are often unknown. Many of these processes can operate inconsistently across space (causing non-stationary patterns). We present a wavelet approach to characterize spatial pattern in genotype that helps solve these problems. We show how our approach can characterize spatial patterns in ancestry at multiple spatial scales, i.e. a multi-locus wavelet genetic dissimilarity. We also develop wavelet tests of spatial differentiation in allele frequency and quantitative trait loci (QTL). With simulation we illustrate these methods under a variety of scenarios. We apply our approach to natural populations of Arabidopsis thaliana and traditional varieties of Sorghum bicolor to characterize population structure and locally-adapted loci across scales. We find, for example, that Arabidopsis flowering time QTL show significantly elevated scaled wavelet variance at ~300-1300 km scales. Wavelet transforms of population genetic data offer a flexible way to reveal geographic patterns and underlying processes.

Integrating very high resolution environmental proxies in genotype–environment association studies

“…Furthermore, investigating the multiscale variable concept into landscape genomic analyses in other environments, such as seascapes and riverscapes, would be interesting as environmental variables are becoming available at ever finer resolutions. A novel complementary approach that could be used to select relevant spatial resolutions and validate multiscale GEA findings involves using wavelets to decompose the spatial patterns of genotypes observed across landscapes (Lasky et al., 2022 ). We emphasise that while GEA models are most useful for generating hypotheses, the function of candidate loci must still be validated with field or laboratory studies.…”

Genotype–environment associations to reveal the molecular basis of environmental adaptation

“…Since many of the climate factors that matter for Arabidopsis local adaptation also vary north-to-south (A ˚gren and Schemske, 2012; Monroe et al, 2016;Gienapp et al, 2017), IBD might create spurious associations between genotype and climate (Hancock et al, 2011). These patterns could also be important for many crop species, which have often recently spread from one or a few domestication locations (Romay et al, 2013;Gutaker et al, 2020) and show substantial patterns of IBD (Gutaker et al, 2020;Lasky et al, 2022).…”