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dc.contributor.author Wagner, Helene H.
dc.contributor.author Chávez-Pesqueira, Mariana
dc.contributor.author Forester, Brenna R.
dc.date.accessioned 2017-01-09T21:01:41Z
dc.date.available 2017-01-09T21:01:41Z
dc.date.issued 2017-01-09
dc.identifier doi:10.5061/dryad.b12kk
dc.identifier.citation Wagner HH, Chávez-Pesqueira M, Forester BR (2017) Spatial detection of outlier loci with Moran eigenvector maps. Molecular Ecology Resources, online in advance of print.
dc.identifier.issn 1755-098X
dc.identifier.uri http://hdl.handle.net/10255/dryad.133483
dc.description 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.
dc.relation.haspart doi:10.5061/dryad.b12kk/1
dc.relation.isreferencedby doi:10.1111/1755-0998.12653
dc.subject Moran spectral outlier detection
dc.subject Moran eigenvector maps
dc.subject adaptive loci
dc.subject spatial signature
dc.subject Moran spectral randomization
dc.subject loci under selection
dc.subject demographic history
dc.subject sampling design
dc.title Data from: Spatial detection of outlier loci with Moran eigenvector maps (MEM)
dc.type Article
dc.contributor.correspondingAuthor Chávez-Pesqueira, Mariana
prism.publicationName Molecular Ecology Resources

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