Genome Scans for Detecting Footprints of Local Adaptation Using a Bayesian Factor Model

Duforet-Frebourg, Nicolas; Bazin, Éric; Blum, Michaël G. B.

doi:10.1093/molbev/msu182

Cited by 98 publications

(125 citation statements)

References 69 publications

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“…Additionally, we performed genome-wide scan for selection in the panel of SNP data using 131434. Generally, uses Mahalanobis distances and communality statistics between SNPs and the first k principal components (PCs), with appropriate normalization specific to each measure.…”

Section: Methodsmentioning

confidence: 99%

“…Generally, uses Mahalanobis distances and communality statistics between SNPs and the first k principal components (PCs), with appropriate normalization specific to each measure. Selection is detected when SNPs (or other genetic markers) are substantially explained by the first k PCs1334. To evaluate concordance of results from and LFA, we compute Spearman correlation between Mahalanobis/communality statistics using PCs and McFadden’s pseudo R 2 measures using LFs.…”

Section: Methodsmentioning

confidence: 99%

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Population Structure Analysis of Bull Genomes of European and Western Ancestry

Chung

Szyda

Frąszczak

et al. 2017

Sci Rep

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Since domestication, population bottlenecks, breed formation, and selective breeding have radically shaped the genealogy and genetics of Bos taurus. In turn, characterization of population structure among diverse bull (males of Bos taurus) genomes enables detailed assessment of genetic resources and origins. By analyzing 432 unrelated bull genomes from 13 breeds and 16 countries, we demonstrate genetic diversity and structural complexity among the European/Western cattle population. Importantly, we relaxed a strong assumption of discrete or admixed population, by adapting latent variable models for individual-specific allele frequencies that directly capture a wide range of complex structure from genome-wide genotypes. As measured by magnitude of differentiation, selection pressure on SNPs within genes is substantially greater than that on intergenic regions. Additionally, broad regions of chromosome 6 harboring largest genetic differentiation suggest positive selection underlying population structure. We carried out gene set analysis using SNP annotations to identify enriched functional categories such as energy-related processes and multiple development stages. Our population structure analysis of bull genomes can support genetic management strategies that capture structural complexity and promote sustainable genetic breadth.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Population Structure Analysis of Bull Genomes of European and Western Ancestry

Chung

Szyda

Frąszczak

et al. 2017

Sci Rep

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“…Incorporating population structure with latent factors can improve the accuracy of both genetic-environment association and differentiation outlier tests (Frichot et al 2013; Duforet-Frebourg et al 2014; Lotterhos and Whitlock 2015). …”

Section: Mixed Signals: Confounding Effects Of Demography and Populatmentioning

confidence: 99%

Finding the Genomic Basis of Local Adaptation: Pitfalls, Practical Solutions, and Future Directions

Hoban

Kelley

Lotterhos

et al. 2016

The American Naturalist

709

876

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Uncovering the genetic and evolutionary basis of local adaptation is a major focus of evolutionary biology. The recent development of cost-effective methods for obtaining high-quality genome-scale data makes it possible to identify some of the loci responsible for adaptive differences among populations. Two basic approaches for identifying putatively locally adaptive loci have been developed and are broadly used: one that identifies loci with unusually high genetic differentiation among populations (differentiation outlier methods) and one that searches for correlations between local population allele frequencies and local environments (genetic-environment association methods). Here, we review the promises and challenges of these genome scan methods, including correcting for the confounding influence of a species’ demographic history, biases caused by missing aspects of the genome, matching scales of environmental data with population structure, and other statistical considerations. In each case, we make suggestions for best practices for maximizing the accuracy and efficiency of genome scans to detect the underlying genetic basis of local adaptation. With attention to their current limitations, genome scan methods can be an important tool in finding the genetic basis of adaptive evolutionary change.

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“…The approach that is probably the most typical of the genomic era is to scan genomes for signal of selection (mostly selective sweeps and local adaptation). Many methods have been developed in the past decades to detect local adaptation (Beaumont and Balding, 2004;Foll and Gaggiotti, 2008;Bonhomme et al, 2010;Coop et al, 2010;Frichot et al, 2013;Duforet-Frebourg et al, 2014;Guillot et al, 2014). Despite considerable efforts to account for population structures, these methods have been shown to display high error rates (de Villemereuil et al, 2014;Lotterhos and Whitlock, 2014).…”

Section: What Is the Use Of Common Garden Experiments In The Genomic Era?mentioning

confidence: 99%

Common garden experiments in the genomic era: new perspectives and opportunities

Villemereuil¹,

Gaggiotti²,

Mouterde³

et al. 2015

Heredity

291

242

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The study of local adaptation is rendered difficult by many evolutionary confounding phenomena (for example, genetic drift and demographic history). When complex traits are involved in local adaptation, phenomena such as phenotypic plasticity further hamper evolutionary biologists to study the complex relationships between phenotype, genotype and environment. In this perspective paper, we suggest that the common garden experiment, specifically designed to deal with phenotypic plasticity, has a clear role to play in the study of local adaptation, even (if not specifically) in the genomic era. After a quick review of some high-throughput genotyping protocols relevant in the context of a common garden, we explore how to improve common garden analyses with dense marker panel data and recent statistical methods. We then show how combining approaches from population genomics and genome-wide association studies with the settings of a common garden can yield to a very efficient, thorough and integrative study of local adaptation. Especially, evidence from genomic (for example, genome scan) and phenotypic origins constitute independent insights into the possibility of local adaptation scenarios, and genome-wide association studies in the context of a common garden experiment allow to decipher the genetic bases of adaptive traits.

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Genome Scans for Detecting Footprints of Local Adaptation Using a Bayesian Factor Model

Cited by 98 publications

References 69 publications

Population Structure Analysis of Bull Genomes of European and Western Ancestry

Population Structure Analysis of Bull Genomes of European and Western Ancestry

Finding the Genomic Basis of Local Adaptation: Pitfalls, Practical Solutions, and Future Directions

Common garden experiments in the genomic era: new perspectives and opportunities

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