Evidence map›Paper›PMID 29491948›Full record

ArticleCurrent zoology2016

Detecting polygenic selection in marine populations by combining population genomics and quantitative genetics approaches.

Pierre-Alexandre Gagnaire, Oscar E Gaggiotti

Abstract read
In one paragraph

Article in Current zoology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Pierre-Alexandre GagnaireUniversité Montpellier 2, Place Eugène Bataillon, 34095 Montpellier Cedex 5, France.
Oscar E GaggiottiScottish Oceans Institute, University of St Andrews, East Sands, St Andrews, KY16 9LB, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Highly fecund marine species with dispersive life-history stages often display large population sizes and wide geographic distribution ranges. Consequently, they are expected to experience reduced genetic drift, efficient selection fueled by frequent adaptive mutations, and high migration loads. This has important consequences for understanding how local adaptation proceeds in the sea. A key issue in this regard, relates to the genetic architecture underlying fitness traits. Theory predicts that adaptation may involve many genes but with a high variance in effect size. Therefore, the effect of selection on allele frequencies may be substantial for the largest effect size loci, but insignificant for small effect genes. In such a context, the performance of population genomic methods to unravel the genetic basis of adaptation depends on the fraction of adaptive genetic variance explained by the cumulative effect of outlier loci. Here, we address some methodological challenges associated with the detection of local adaptation using molecular approaches. We provide an overview of genome scan methods to detect selection, including those assuming complex demographic models that better describe spatial population structure. We then focus on quantitative genetics approaches that search for genotype-phenotype associations at different genomic scales, including genome-wide methods evaluating the cumulative effect of variants. We argue that the limited power of single locus tests can be alleviated by the use of polygenic scores to estimate the joint contribution of candidate variants to phenotypic variation.

Indexed as

local adaptation, genome scans, quantitative genetics, genotype-phenotype association, polygenic scores

Identifiers

PMID29491948
PMCPMC5804256

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.