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Record W2792839418 · doi:10.1101/254854

Genome-wide selection scans integrated with association mapping reveal mechanisms of physiological adaptation across a salinity gradient in killifish

2018· preprint· en· W2792839418 on OpenAlexafffund
Reid S. Brennan, Timothy M. Healy, Heather J. Bryant, Man Van La, Patricia M. Schulte, Andrew Whitehead

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2018
Typepreprint
Languageen
FieldEnvironmental Science
TopicFish biology, ecology, and behavior
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsBiologyFundulusKillifishLocal adaptationGenome-wide association studyEvolutionary biologyAdaptation (eye)PopulationAssociation mappingGenomeSelection (genetic algorithm)GeneticsSingle-nucleotide polymorphismGenotypeGene

Abstract

fetched live from OpenAlex

Abstract Adaptive divergence between marine and freshwater environments is important in generating phyletic diversity within fishes, but the genetic basis of adaptation to freshwater habitats remains poorly understood. Available approaches to detect adaptive loci include genome scans for selection, but these can be difficult to interpret because of incomplete knowledge of the connection between genotype and phenotype. In contrast, genome wide association studies (GWAS) are powerful tools for linking genotype to phenotype, but offer limited insight into the evolutionary forces shaping variation. Here, we combine GWAS and selection scans to identify loci important in the adaptation of complex physiological traits to freshwater environments. We focused on freshwater (FW)-native and brackish water (BW)-native populations of the Atlantic killifish ( Fundulus heteroclitus ) as well as a population that is a natural admixture of these two populations. We measured phenotypes for multiple physiological traits that differ between populations and that may contribute to adaptation across osmotic niches (salinity tolerance, hypoxia tolerance, metabolic rate, and body shape) and used a reduced representation approach for genome-wide genotyping. Our results show patterns of population divergence in physiological capabilities that are consistent with local adaptation. Selection scans between BW-native and FW-native populations identified genomic regions that presumably aect fitness between BW and FW environments, while GWAS revealed loci that contribute to variation for each physiological trait. There was substantial overlap in the genomic regions putatively under selection and loci associated with the measured physiological traits, suggesting that these phenotypes are important for adaptive divergence between BW and FW environments. Our analysis also implicates candidate genes likely involved in physiological capabilities, some of which validate a priori hypotheses. Together, these data provide insight into the mechanisms that enable diversification of fishes across osmotic boundaries. Author Summary Identifying the genes that underlie adaptation is important for understanding the evolutionary process, but this is technically challenging. We bring multiple lines of evidence to bear for identifying genes that underlie adaptive divergence. Specifically, we integrate genotype-phenotype association mapping with genome-wide scans for signatures of natural selection to reveal genes that underlie phenotypic variation and that are adaptive in populations of killifish that are diverging between marine and freshwater environments. Because adaptation is likely manifest in multiple physiological traits, we focus on hypoxia tolerance, salinity tolerance, and metabolic rate; traits that are divergent between marine and freshwater populations. We show that each of these phenotypes is evolving by natural selection between environments; genetic variants that contribute to variation in these physiological traits tend to be evolving by natural selection between marine and freshwater populations. Furthermore, one of our top candidate genes provides a mechanistic explanation for previous hypotheses that suggest the adaptive importance of cellular tight junctions. Together, these data demonstrate a powerful approach to identify genes involved in adaptation and help to reveal the mechanisms enabling transitions of fishes across osmotic boundaries.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.226
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2018
Admission routes2
Has abstractyes

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