Evidence map›Paper›PMID 38846349›Full record

ArticleNAR genomics and bioinformatics2024

Detecting co-selection through excess linkage disequilibrium in bacterial genomes.

Sudaraka Mallawaarachchi, Gerry Tonkin-Hill, Anna K Pöntinen, Jessica K Calland, Rebecca A Gladstone, Sergio Arredondo-Alonso, Neil MacAlasdair, Harry A Thorpe, Janetta Top, Samuel K Sheppard and 3 more

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Article in NAR genomics and bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

3 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

13 authors.

Sudaraka MallawaarachchiDepartment of Biostatistics, University of Oslo, Oslo, Norway.ORCID https://orcid.org/0000-0001-8899-3323
Gerry Tonkin-HillDepartment of Biostatistics, University of Oslo, Oslo, Norway.ORCID https://orcid.org/0000-0003-4397-2224
Anna K PöntinenDepartment of Biostatistics, University of Oslo, Oslo, Norway.
Jessica K CallandOslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway.
Rebecca A GladstoneDepartment of Biostatistics, University of Oslo, Oslo, Norway.
Sergio Arredondo-AlonsoDepartment of Biostatistics, University of Oslo, Oslo, Norway.ORCID https://orcid.org/0000-0002-4445-2442
Neil MacAlasdairDepartment of Biostatistics, University of Oslo, Oslo, Norway.
Harry A ThorpeDepartment of Biostatistics, University of Oslo, Oslo, Norway.
Janetta TopDepartment of Medical Microbiology, UMC Utrecht, Utrecht, The Netherlands.ORCID https://orcid.org/0000-0002-4620-8128
Samuel K SheppardIneos Oxford Institute of Antimicrobial Research, Department of Biology, University of Oxford, Oxford, United Kingdom.
David BaldingMelbourne Integrative Genomics, School of BioSciences and School of Mathematics & Statistics, University of Melbourne, Parkville, Victoria, Australia.
Nicholas J CroucherDepartment of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom.ORCID https://orcid.org/0000-0001-6303-8768
Jukka CoranderDepartment of Biostatistics, University of Oslo, Oslo, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Population genomics has revolutionized our ability to study bacterial evolution by enabling data-driven discovery of the genetic architecture of trait variation. Genome-wide association studies (GWAS) have more recently become accompanied by genome-wide epistasis and co-selection (GWES) analysis, which offers a phenotype-free approach to generating hypotheses about selective processes that simultaneously impact multiple loci across the genome. However, existing GWES methods only consider associations between distant pairs of loci within the genome due to the strong impact of linkage-disequilibrium (LD) over short distances. Based on the general functional organisation of genomes it is nevertheless expected that majority of co-selection and epistasis will act within relatively short genomic proximity, on co-variation occurring within genes and their promoter regions, and within operons. Here, we introduce LDWeaver, which enables an exhaustive GWES across both short- and long-range LD, to disentangle likely neutral co-variation from selection. We demonstrate the ability of LDWeaver to efficiently generate hypotheses about co-selection using large genomic surveys of multiple major human bacterial pathogen species and validate several findings using functional annotation and phenotypic measurements. Our approach will facilitate the study of bacterial evolution in the light of rapidly expanding population genomic data.

Identifiers

PMID38846349
PMCPMC11155488

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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.