Clustering and Assignment Methods in Landscape Genetics

Ochsenbein, F.; Waits, Lisette P.

doi:10.1002/9781118525258.ch07

Cited by 17 publications

(10 citation statements)

References 93 publications

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“…PCA was thus repeated after excluding significant SNPs identified by GEA tests to evaluate sensitivity to loci putatively under selection. sNMF uses a mean‐squares approach to estimate ancestry proportions and has been shown to produce results similar to structure (Pritchard, Stephens, & Donnelly, ), but is computationally much faster (François & Waits, ). sNMF also uses a cross‐validation approach to identify the most likely number of subpopulations.…”

Section: Methodsmentioning

confidence: 99%

Landscape genomics of Colorado potato beetle provides evidence of polygenic adaptation to insecticides

et al. 2017

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The ability of insect pests to rapidly and repeatedly adapt to insecticides has long challenged entomologists and evolutionary biologists. Since Crow's seminal paper on insecticide resistance in 1957, new data and insights continue to emerge that are relevant to the old questions about how insecticide resistance evolves: such as whether it is predominantly mono- or polygenic, and evolving from standing vs. de novo genetic variation. Many studies support the monogenic hypothesis, and current management recommendations assume single- or two-locus models. But inferences could be improved by integrating data from a broader sample of pest populations and genomes. Here, we generate evidence relevant to these questions by applying a landscape genomics framework to the study of insecticide resistance in a major agricultural pest, Colorado potato beetle, Leptinotarsa decemlineata (Say). Genome-environment association tests using genomic variation from 16 populations spanning gradients of landscape variables associated with insecticide exposure over time revealed 42 strong candidate insecticide resistance genes, with potentially overlapping roles in multiple resistance mechanisms. Measurements of resistance to a widely used insecticide, imidacloprid, among 47 L. decemlineata populations revealed heterogeneity at a small (2 km) scale and no spatial signature of origin or spread throughout the landscape. Analysis of nucleotide diversity suggested candidate resistance loci have undergone varying degrees of selective sweeps, often maintaining similar levels of nucleotide diversity to neutral loci. This study suggests that many genes are involved in insecticide resistance in L. decemlineata and that resistance likely evolves from both de novo and standing genetic variation.

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Section: Methodsmentioning

confidence: 99%

Landscape genomics of Colorado potato beetle provides evidence of polygenic adaptation to insecticides

et al. 2017

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“…While traditional methods of testing for spatial genetic patterns, such as model-based clustering (e.g., structure, Pritchard, Stephens, & Donnelly, 2000) or nonparametric exploratory analyses (e.g., discriminant analysis of principal components [DAPC], Jombart, Devillard, & Balloux, 2010) have been used to characterize genetic diversity across a given area (François & Waits, 2015), other methods which are able to separate spatial and nonspatial genetic variation may be better equipped to detect patterns of genetic differentiation in recently fragmented systems or those with high rates of gene flow (Galpern, Peres-Neto, Polfus, & Manseau, 2014;Jombart, Devillard, Dufour, & Pontier, 2008). These methods use spatial autocorrelation to tease apart patterns of inter-vs. intrapopulation genetic variation, improving the identification of population structure at fine geographical scales (Galpern, Manseau, & Wilson, 2012).…”

mentioning

confidence: 99%

A spatial genomic approach identifies time lags and historical barriers to gene flow in a rapidly fragmenting Appalachian landscape

2020

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The resolution offered by genomic data sets coupled with recently developed spatially informed analyses are allowing researchers to quantify population structure at increasingly fine temporal and spatial scales. However, both empirical research and conservation measures have been limited by questions regarding the impacts of data set size, data quality thresholds and the timescale at which barriers to gene flow become detectable. Here, we used restriction site associated DNA sequencing to generate a 2,140 single nucleotide polymorphism (SNP) data set for the copperhead snake (Agkistrodon contortrix) and address the population genomic impacts of recent and widespread landscape modification across an ~1,000‐km2 region of eastern Kentucky, USA. Nonspatial population‐based assignment and clustering methods supported little to no population structure. However, using individual‐based spatial autocorrelation approaches we found evidence for genetic structuring which closely follows the path of a historically important highway which experienced high traffic volumes from c. 1920 to 1970 before losing most traffic to a newly constructed alternative route. We found no similar spatial genomic signatures associated with more recently constructed highways or surface mining activity, although a time lag effect may be responsible for the lack of any emergent spatial genetic patterns. Subsampling of our SNP data set suggested that similar results could be obtained with as few as 250 SNPs, and a range of thresholds for missing data exhibited limited impacts on the spatial patterns we detected. While we were not able to estimate relative effects of land uses or precise time lags, our findings highlight the importance of temporal factors in landscape genetics approaches, and suggest the potential advantages of genomic data sets and fine‐scale, spatially informed approaches for quantifying subtle genetic patterns in temporally complex landscapes.

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“…Landscape genetic clustering and assignment methods have largely built upon classical methods from population genetics (e.g., principal components analysis, STRUCTURE, Pritchard, Stephens, & Donnelly, ) by incorporating spatial information (e.g., GENELAND, Guillot, Mortier, & Estoup, ; sPCA, Jombart, Devillard, Dufour, & Pontier, ) and environmental heterogeneity (e.g., constrained ordination, Anderson & Willis, ; POPS, Jay, ) into estimates of population structure and providing quantitative estimates of ancestry for each individual (François & Waits, ). Clustering methods have been relatively popular in studying pathogens and implemented for the inference of landscape barriers affecting both host (Addis, Lowe, Hossack, & Allendorf, ; Cote, Garant, Robert, Mainguy, & Pelletier, ; Cullingham, Kyle, Pond, Rees, & White, ; Frantz, Cellina, Krier, Schley, & Burke, ) and microparasite (Brar et al., ; Rieux et al., ) spatial genetic variation.…”

Section: Common Methodological Approaches In Landscape Genetics and Tmentioning

confidence: 99%

Pathogens in space: Advancing understanding of pathogen dynamics and disease ecology through landscape genetics

Kozakiewicz

Burridge

Funk

et al. 2018

Evolutionary Applications

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Landscape genetics has provided many insights into how heterogeneous landscape features drive processes influencing spatial genetic variation in free‐living organisms. This rapidly developing field has focused heavily on vertebrates, and expansion of this scope to the study of infectious diseases holds great potential for landscape geneticists and disease ecologists alike. The potential application of landscape genetics to infectious agents has garnered attention at formative stages in the development of landscape genetics, but systematic examination is lacking. We comprehensively review how landscape genetics is being used to better understand pathogen dynamics. We characterize the field and evaluate the types of questions addressed, approaches used and systems studied. We also review the now established landscape genetic methods and their realized and potential applications to disease ecology. Lastly, we identify emerging frontiers in the landscape genetic study of infectious agents, including recent phylogeographic approaches and frameworks for studying complex multihost and host‐vector systems. Our review emphasizes the expanding utility of landscape genetic methods available for elucidating key pathogen dynamics (particularly transmission and spread) and also how landscape genetic studies of pathogens can provide insight into host population dynamics. Through this review, we convey how increasing awareness of the complementarity of landscape genetics and disease ecology among practitioners of each field promises to drive important cross‐disciplinary advances.

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Clustering and Assignment Methods in Landscape Genetics

Cited by 17 publications

References 93 publications

Landscape genomics of Colorado potato beetle provides evidence of polygenic adaptation to insecticides

Landscape genomics of Colorado potato beetle provides evidence of polygenic adaptation to insecticides

A spatial genomic approach identifies time lags and historical barriers to gene flow in a rapidly fragmenting Appalachian landscape

Pathogens in space: Advancing understanding of pathogen dynamics and disease ecology through landscape genetics

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