Robust Identification of Local Adaptation from Allele Frequencies

By Torsten Günther, Graham Coop. Genetics, 2012

621 citations recorded by the source corpus.

Comparing allele frequencies among populations that differ in environment has long been a tool for detecting loci involved in local adaptation. However, such analyses are complicated by an imperfect knowledge of population allele frequencies and neutral correlations of allele frequencies among populations due to shared population history and gene flow. Here we develop a set of methods to robustly test for unusual allele frequency patterns and correlations between environmental variables and allele frequencies while accounting for these complications based on a Bayesian model previously implemented in the software Bayenv. Using this model, we calculate a set of “standardized allele frequencies” that allows investigators to apply tests of their choice to multiple populations while accounting for sampling and covariance due to population history. We illustrate this first by showing that these standardized frequencies can be used to detect nonparametric correlations with environmental variables; these correlations are also less prone to spurious results due to outlier populations. We then demonstrate how these standardized allele frequencies can be used to construct a test to detect SNPs that deviate strongly from neutral population structure. This test is conceptually related to FST and is shown to be more powerful, as we account for population history. We also extend the model to next-generation sequencing of population pools—a cost-efficient way to estimate population allele frequencies, but one that introduces an additional level of sampling noise. The utility of these methods is demonstrated in simulations and by reanalyzing human SNP data from the Human Genome Diversity Panel populations and pooled next-generation sequencing data from Atlantic herring. An implementation of our method is available from http://gcbias.org.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/c3d166bb1ab393f6a6705c55b257a962658be6e2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

Citations in the corpus

Demographic History and Genetic Adaptation in the Himalayan Region Inferred from Genome-Wide SNP Genotypes of 49 Populations (2018, methodology): “…validate our selection signals, we calculated genome-wide associations between allele frequency and altitude using BayEnv v2 (Coop et al. 2010; Gunther and Coop 2013), a Bayesian framework specifically designed to detect correlation between allele frequencies and environmental factors taking…”
Landscape Genomics: Understanding Relationships Between Environmental Heterogeneity and Genomic Characteristics of Populations (2017, methodology): “2015), such as BayEnv (Coop et al. 2010; Günther and Coop 2013), LFMM (Frichot et al.”
Isolation by environment (2014, methodology): “…lines, IBE can also contribute to studying the ecology of local adaptation, currently a topic of major interest (Aitken & Whitlock 2013; Blanquart et al. 2013; G€unther & Coop 2013; Butlin et al. 2014), potentially providing an efficient first step in identifying systems with adaptive divergence.”
A new FST‐based method to uncover local adaptation using environmental variables (2015, methodology): “The outlier approach was further extended to statistics akin to FST (Bonhomme et al. 2010; G€unther & Coop 2013) and also to other unrelated statistics (Duforet-Frebourg, Bazin & Blum 2014).”
Finding the Genomic Basis of Local Adaptation: Pitfalls, Practical Solutions, and Future Directions (2016, positive): “There are multiple ways to characterize these covariances, such as co-variance in allele frequencies among populations (Coop et al. 2010; Günther and Coop 2013; Gautier 2015; table A1), a treelike population history (Bonhomme et al. 2010), and re-latedness or other latent factors in a mixed model.”