title: Spatial detection of outlier loci with Moran eigenvector maps.
abstract: The spatial signature of microevolutionary processes structuring genetic variation may play an important role in the detection of loci under selection. However, the spatial location of samples has not yet been used to quantify this. Here, we present a new two-step method of spatial outlier detection at the individual and deme levels using the power spectrum of Moran eigenvector maps (MEM). The MEM power spectrum quantifies how the variation in a variable, such as the frequency of an allele at a SNP locus, is distributed across a range of spatial scales defined by MEM spatial eigenvectors. The first step (Moran spectral outlier detection: MSOD) uses genetic and spatial information to identify outlier loci by their unusual power spectrum. The second step uses Moran spectral randomization (MSR) to test the association between outlier loci and environmental predictors, accounting for spatial autocorrelation. Using simulated data from two published papers, we tested this two-step method in different scenarios of landscape configuration, selection strength, dispersal capacity and sampling design. Under scenarios that included spatial structure, MSOD alone was sufficient to detect outlier loci at the individual and deme levels without the need for incorporating environmental predictors. Follow-up with MSR generally reduced (already low) false-positive rates, though in some cases led to a reduction in power. The results were surprisingly robust to differences in sample size and sampling design. Our method represents a new tool for detecting potential loci under selection with individual-based and population-based sampling by leveraging spatial information that has hitherto been neglected.
authors.affiliation: Department of Biology, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, ON, L5L 1C6, Canada.
authors.forename: Helene H
authors.initials: HH
authors.lastname: Wagner
authors.affiliation: Department of Biology, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, ON, L5L 1C6, Canada.|Unidad de Recursos Naturales, Centro de Investigación Científica de Yucatán, Calle 43 No. 130, Col. Chuburná de Hidalgo, CP, 97205, Mérida, Yucatán, México.
authors.forename: Mariana
authors.initials: M
authors.lastname: Chávez-Pesqueira
authors.affiliation: Nicholas School of the Environment, University Program in Ecology, Duke University, Durham, NC, 27708, USA.
authors.forename: Brenna R
authors.initials: BR
authors.lastname: Forester
mesh_terms: D000220:Adaptation, Biological; D056808:Biostatistics / Q000379:methods; D003198:Computer Simulation; D005787:Gene Frequency; D056426:Genetic Loci; D014644:Genetic Variation; D005828:Genetics, Population / Q000379:methods*; D008957:Models, Genetic; D012641:Selection, Genetic
keywords: Moran spectral randomization; demographic history; genotype-environment association; loci under selection; sampling design; spatial signature