The relative power of genome scans to detect local adaptation depends on sampling design and statistical method

Local adaptation can lead to elevated genetic differentiation at the targeted genetic variant and nearby sites. Selective sweeps come in different forms, and depending on the initial and final frequencies of a favored variant, very different patterns of genetic variation may be produced. If local selection favors an existing variant that had already recombined onto multiple genetic backgrounds, then the width of elevated genetic differentiation (high FST) may be too narrow to detect using a typical windowed genome scan, even if the targeted variant becomes highly differentiated. We therefore used a simulation approach to investigate the power of SNP-level FST (specifically, the maximum SNP FST value within a window) to detect diverse scenarios of local adaptation, and compared it against whole-window FST and the Comparative Haplotype Identity statistic. We found that SNP FST had superior power to detect complete or mostly complete soft sweeps, but lesser power than window-wide statistics to detect partial hard sweeps. To investigate the relative enrichment and nature of SNP FST outliers from real data, we applied the two FST statistics to a panel of Drosophila melanogaster populations. We found that SNP FST had a genome-wide enrichment of outliers compared to demographic expectations, and though it yielded a lesser enrichment than window FST, it detected mostly unique outlier genes and functional categories. Our results suggest that SNP FST is highly complementary to typical window-based approaches for detecting local adaptation, and merits inclusion in future genome scans and methodologies.

Global adaptation complicates the interpretation of genome scans for local adaptation

Evolution Letters ◽

10.1002/evl3.208 ◽

2020 ◽

Author(s):

Tom R. Booker ◽

Sam Yeaman ◽

Michael C. Whitlock

Keyword(s):

Local Adaptation ◽

Genome Scans

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

Molecular Biology and Evolution ◽

10.1093/molbev/msu182 ◽

2014 ◽

Vol 31 (9) ◽

pp. 2483-2495 ◽

Cited By ~ 66

Author(s):

Nicolas Duforet-Frebourg ◽

Eric Bazin ◽

Michael G.B. Blum

Keyword(s):

Local Adaptation ◽

Factor Model ◽

Genome Scans ◽

Bayesian Factor

Iris atrofusca genetic and phenotypic variation, the role of habitat-specific selection in this variation structuring, and conservation implications using quasi in situ guidelines

Israel Journal of Plant Sciences ◽

10.1080/07929978.2015.1042308 ◽

2015 ◽

Vol 63 (4) ◽

pp. 347-354 ◽

Cited By ~ 7

Author(s):

Sergei Volis ◽

Yong-Hong Zhang ◽

Michael Dorman ◽

Michael Blecher

Keyword(s):

Endangered Species ◽

Local Adaptation ◽

Phenotypic Variation ◽

Sampling Design ◽

Conservation Practices ◽

Adaptive Potential ◽

Genetic Structuring ◽

Conservation Implications

Knowing the extent and structure of genetic variation in an endangered species is essential for establishing efficient conservation practices. However, the proper use of this information requires understanding the role of habitat-specific selection in genetic structuring. We present a study of population differentiation in an endangered species that utilizes guidelines of recently a proposedquasi in situconservation approach, i.e. taking into account the scale and spatial pattern of local adaptation since if local adaptation is important, the introduced genotypes must be matched to the local biotic/abiotic conditions. Following this approach, we examined the extent and structure of genetic (AFLP) and phenotypic variation and tested for adaptive significance of this variation in critically endangeredIris atrofuscagrowing in Israel and Jordan. From these results we propose a sampling design that would (i) preserve species adaptive potential and (ii) insure environmental match of the plant material for relocation, reintroduction or enhancement.

Enhanced AFLP genome scans detect local adaptation in high-altitude populations of a small rodent (Microtus arvalis)

Molecular Ecology ◽

10.1111/j.1365-294x.2011.05015.x ◽

2011 ◽

Vol 20 (7) ◽

pp. 1450-1462 ◽

Cited By ~ 98

Author(s):

MARTIN C. FISCHER ◽

MATTHIEU FOLL ◽

LAURENT EXCOFFIER ◽

GERALD HECKEL

Keyword(s):

High Altitude ◽

Local Adaptation ◽

Small Rodent ◽

Microtus Arvalis ◽

Genome Scans

The coupling hypothesis: why genome scans may fail to map local adaptation genes

Molecular Ecology ◽

10.1111/j.1365-294x.2011.05080.x ◽

2011 ◽

Vol 20 (10) ◽

pp. 2044-2072 ◽

Cited By ~ 356

Author(s):

NICOLAS BIERNE ◽

JOHN WELCH ◽

ETIENNE LOIRE ◽

FRANÇOIS BONHOMME ◽

PATRICE DAVID

Keyword(s):

Local Adaptation ◽

Genome Scans

Background selection and the statistics of population differentiation: consequences for detecting local adaptation

10.1101/326256 ◽

2018 ◽

Cited By ~ 1

Author(s):

Remi Matthey-Doret ◽

Michael C. Whitlock

Keyword(s):

Local Adaptation ◽

Dna Sequences ◽

Population Differentiation ◽

False Positive Rate ◽

Parameter Estimates ◽

Background Selection ◽

Deleterious Mutations ◽

Effective Population ◽

Genome Scans ◽

Positive Rate

AbstractBackground selection is a process whereby recurrent deleterious mutations cause a decrease in the effective population size and genetic diversity at linked loci. Several authors have suggested that variation in the intensity of background selection could cause variation in FST across the genome, which could confound signals of local adaptation in genome scans. We performed realistic simulations of DNA sequences, using parameter estimates from humans and sticklebacks, to investigate how variation in the intensity of background selection affects different statistics of population differentiation. We show that, in populations connected by gene flow, Weir & Cockerham’s (1984) estimator of FST is largely insensitive to locus-to-locus variation in the intensity of background selection. Unlike FST, however, dXY is negatively correlated with background selection. We also show that background selection does not greatly affect the false positive rate in FST outlier studies. Overall, our study indicates that background selection will not greatly interfere with finding the variants responsible for local adaptation.

Performing Highly Efficient Genome Scans for Local Adaptation with R Package pcadapt Version 4

Molecular Biology and Evolution ◽

10.1093/molbev/msaa053 ◽

2020 ◽

Vol 37 (7) ◽

pp. 2153-2154 ◽

Cited By ~ 2

Author(s):

Florian Privé ◽

Keurcien Luu ◽

Bjarni J Vilhjálmsson ◽

Michael G B Blum

Keyword(s):

Local Adaptation ◽

Principal Components ◽

Computational Efficiency ◽

Computation Time ◽

R Package ◽

Large Reduction ◽

Genome Scans ◽

Highly Efficient ◽

User Friendly

Abstract R package pcadapt is a user-friendly R package for performing genome scans for local adaptation. Here, we present version 4 of pcadapt which substantially improves computational efficiency while providing similar results. This improvement is made possible by using a different format for storing genotypes and a different algorithm for computing principal components of the genotype matrix, which is the most computationally demanding step in method pcadapt. These changes are seamlessly integrated into the existing pcadapt package, and users will experience a large reduction in computation time (by a factor of 20–60 in our analyses) as compared with previous versions.

Unpacking conditional neutrality: genomic signatures of selection on conditionally beneficial and conditionally deleterious mutations

10.1101/549329 ◽

2019 ◽

Author(s):

Jonathan A. Mee ◽

Samuel Yeaman

Keyword(s):

Allele Frequency ◽

Local Adaptation ◽

Home Environment ◽

Cumulative Effect ◽

Antagonistic Pleiotropy ◽

Deleterious Mutations ◽

Genome Scans ◽

Beneficial Mutations ◽

Local Environments ◽

Non Local

AbstractIt is common to look for signatures of local adaptation in genomes by identifying loci with extreme levels of allele frequency divergence among populations. This approach to finding genes associated with local adaptation often assumes antagonistic pleiotropy, wherein alternative alleles are strongly favoured in alternative environments. Conditional neutrality has been proposed as an alternative to antagonistic pleiotropy, but conditionally neutral polymorphisms are transient and it is unclear how much outlier signal would be maintained under different forms of conditional neutrality. Here, we use individual-based simulations and a simple analytical heuristic to show that a pattern that mimics local adaptation at the phenotypic level, where each genotype has the highest fitness in its home environment, can be produced by the accumulation of mutations that are neutral in their home environment and deleterious in non-local environments. Because conditionally deleterious mutations likely arise at a rate many times higher than conditionally beneficial mutations, they can have a significant cumulative effect on fitness even when individual effect sizes are small. We show that conditionally deleterious mutations driving non-local maladaptation may be undetectable by even the most powerful genome scans, as differences in allele frequency between populations are typically small. We also explore the evolutionary effects of conditionally-beneficial mutations and find that they can maintain significant signals of local adaptation, and they would be more readily detectable than conditionally deleterious mutations using conventional genome scan approaches. We discuss implications for interpreting outcomes of transplant experiments and genome scans that are used to study the genetic basis of local adaptation.

Geographic biotype and host-associated local adaptation in a polyphagous species, Lambdina fiscellaria (Lepidoptera: Geometridae) feeding on balsam fir on Anticosti Island, Canada

Bulletin of Entomological Research ◽

10.1017/ber2006464 ◽

2006 ◽

Vol 96 (6) ◽

pp. 619-627 ◽

Cited By ~ 20

Author(s):

C. Hébert ◽

R. Berthiaume ◽

É. Bauce ◽

J. Brodeur

Keyword(s):

Local Adaptation ◽

Balsam Fir ◽

Anticosti Island