Evidence map›Paper›PMID 30971391›Full record

ArticleG3 (Bethesda, Md.)2019

The Effect of Neutral Recombination Variation on Genome Scans for Selection.

Katie E Lotterhos

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in G3 (Bethesda, Md.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
9.9field-weighted citation impact, top 2% of its field
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

35 citing papers in PubMed, 84 citations in OpenAlex.

  1. Article
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  3. Conservation Units for Anadromous Arctic Char (Evolutionary applications · 2026
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  6. Review
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  11. Resource-explicit interactions in spatial population models.Methods in ecology and evolution · 2024
    Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. The paradox of adaptive trait clines with nonclinal patterns in the underlying genes.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Katie E LotterhosDepartment of Marine and Environmental Sciences, Northeastern University, 430 Nahant Road, Nahant, MA 01908 k.lotterhos@northeastern.edu.ORCID 0000-0001-7529-2771
Northeastern University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recently, there has been an increasing interest in identifying the role that regions of low recombination or inversion play in adaptation of species to local environments. Many examples of groups of adapted genes located within inversions are arising in the literature, in part inspired by theory that predicts the evolution of these so-called "supergenes." We still, however, have a poor understanding of how genomic heterogeneity, such as varying rates of recombination, may confound signals of selection. Here, I evaluate the effect of neutral inversions and recombination variation on genome scans for selection, including tests for selective sweeps, differentiation outlier tests, and association tests. There is considerable variation among methods in their performance, with some methods being unaffected and some showing elevated false positive signals within a neutral inversion or region of low recombination. In some cases the false positive signal can be dampened or removed, if it is possible to use a quasi-independent set of SNPs to parameterize the model before performing the test. These results will be helpful to those seeking to understand the importance of regions of low recombination in adaptation.

Indexed as

adaptationlinkage disequilibriumpopulation structureprincipal components

Identifiers

PMID30971391
PMCPMC6553532
OpenAlexW2936070591

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.