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.

Molecular diversity and landscape genomics of the crop wild relative Triticum urartu across the Fertile Crescent

“…Denser sampling would allow diversity indexes to be computed with higher confidence. For this reason, when studying outlier loci, we decided to employ the MSOD outlier detection method (Wagner et al, 2017). This approach has the advantage of explicitly dealing with spatial relations among individuals in a graph form, thus relying on individual-based information.…”

“…Putative outlier loci, i.e. genomic loci subjected to directional selection, were discovered by sorting all georeferenced samples on a Gabriel graph and using the MSOD method (Wagner et al, 2017). The MSOD aims to detect SNPs loci responding to directional selection on a geographic base whilst accounting for the spatial structure of allelic distribution reported by the graph.…”

Multi-model seascape genomics identifies distinct environmental drivers of selection among sympatric marine species

“…Moran spectral outlier detection (MSOD) uses Moran's eigenvector maps (MEMs) to create power spectrums for each individual SNP, by taking the squared correlation coe cient of allele frequencies with MEM eigenvectors [118]. Candidate SNPs are then identi ed as having power spectra outside of the average spectrum across all SNPs.…”

Editorial 2018

“…Important Statistical Advances reported on improved resources for outlier loci detection with Morgan eigenvector maps (Wagner, Chávez‐Pesqueira, & Forester, ), a novel sequencing approach to infer cytotypes for species with variable levels of ploidy (Gompert & Mock, ) and an advance to genotype–environmental association testing for landscape genomics studies (Stucki et al., ).…”