A Genome-Scan Method to Identify Selected Loci Appropriate for Both Dominant and Codominant Markers: A Bayesian Perspective

By Matthieu Foll, Oscar E. Gaggiotti. Genetics, 2008

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View Paper: https://www.semanticscholar.org/paper/ceb8b074173fee65fbf4f256b17cfea6b5829e20

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In Genetics

Citations in the corpus

A new FST‐based method to uncover local adaptation using environmental variables (2015, methodology): “, 2002), or to a distribution 29 expected under a neutral model of evolution (Beaumont and Balding, 2004; Foll and Gaggiotti, 2008).”
Applications of landscape genetics in conservation biology: concepts and challenges (2010, methodology): “For instance, Foll and Gaggiotti (2008) applied this method it to the periwinkle Littorina saxatilis and identified 21 molecular markers of adaptive relevance.”
Using Environmental Correlations to Identify Loci Underlying Local Adaptation (2010, methodology): “Some of the earliest tests of selection on genetic markers were based on identifying loci that showed extreme allele frequency differences among populations (Cavalli-Sforza, 1966; Lewontin and Krakauer, 1973), using statistics such as FST , and there are now a range of methods predicated on this ide”
A framework for incorporating evolutionary genomics into biodiversity conservation and management (2015, neutral): “tions about demographic history [164-166], although this”
Genome-wide comparative diversity uncovers multiple targets of selection for improvement in hexaploid wheat landraces and cultivars (2013, methodology): “For FST scan, the estimates of singlelocus FST were calculated for each SNP locus using the BayeScan program (33).”
Landscape Genomics: Understanding Relationships Between Environmental Heterogeneity and Genomic Characteristics of Populations (2017, methodology): “More recently, elaborated statistical analysis of FST has been developed following Bayesian approaches (e.g., BAYESCAN, Foll and Gaggiotti 2008) or analyses based on principal components (e.g., PCadapt, Duforet-Frebourg et al. 2016).”
Stacks: an analysis tool set for population genomics (2013, background): “…to local adaptation or even speciation (Lewontin & Krakauer 1973; Maynard Smith & Haigh 1974; Luikart et al. 2003; Beaumont & Balding 2004; Nielsen 2005; Storz 2005; Nielsen et al. 2007; Foll & Gaggiotti 2008; Gaggiotti et al. 2009; Hohen-lohe et al. 2010b, 2012b; Strasburg et al. 2012).”
Finding the Genomic Basis of Local Adaptation: Pitfalls, Practical Solutions, and Future Directions (2016, positive): “Early methods for differentiation outlier detection assumed that the study system followed the island model (assumed by fdist2 [Beau-mont and Nichols 1996]; table A1) or a multinomial Dirichlet distribution (e.g., BayeScan [Foll and Gaggiotti 2008]; table A1).”
Approximate Bayesian Computation in Evolution and Ecology (2010, methodology): “An approach to detecting evidence of locally adaptive selection is by identifying loci that have an increased level of genetic differentiation (Foll & Gaggiotti 2008).”
Divergent selection and heterogeneous genomic divergence (2009, background): “…& Krakauer 1973; Bowcock et al. 1991; Beaumont & Nichols 1996; Black et al. 2001; Schlötterer 2002; Luikart et al. 2003; Beaumont & Balding 2004; Beaumont 2005; Nielsen 2005; Storz 2005; Vasemagi & Primmer 2005; Nachman 2006; Foll & Gaggiotti 2008; Riebler et al. 2008; Stinchcombe & Hoekstra 2008).”
Pcadapt: An R Package to Perform Genome Scans for Selection Based on Principal Component Analysis (2016, methodology): “To perform comparisons, we included population-based software : BayeScan that implements a Bayesian algorithm to detect outliers (Foll and Gaggiotti, 2008), the FLK statistic as implemented in the hapflk sotware that accounts for hierarchical population structure (Bonhomme et al.”