Evidence map›Paper›PMID 37502843›Full record

ArticlebioRxiv : the preprint server for biology2023

Strong Positive Selection Biases Identity-By-Descent-Based Inferences of Recent Demography and Population Structure in

Bing Guo, Victor Borda, Roland Laboulaye, Michele D Spring, Mariusz Wojnarski, Brian A Vesely, Joana C Silva, Norman C Waters, Timothy D O'Connor, Shannon Takala-Harrison

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Bing GuoInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
Victor BordaInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
Roland LaboulayeInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
Michele D SpringArmed Forces Research Institute of Medical Sciences, Bangkok, Thailand.
Mariusz WojnarskiArmed Forces Research Institute of Medical Sciences, Bangkok, Thailand.
Brian A VeselyArmed Forces Research Institute of Medical Sciences, Bangkok, Thailand.
Joana C SilvaInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
Norman C WatersArmed Forces Research Institute of Medical Sciences, Bangkok, Thailand.
Timothy D O'ConnorInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
Shannon Takala-HarrisonCenter for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD USA.

Funding

Genomic and geospatial analyses of malaria parasite migration to inform eliminationR01AI145852 · NIAID · UNIVERSITY OF MARYLAND BALTIMORE · PI HARRISON, SHANNON TAKALA, O'CONNOR, TIMOTHY DAVID · 2020 to 2024
$3.7M
NIAID NIH HHS R01 AI145852Wellcome Trust
6 · The paper itself

Abstract

Malaria genomic surveillance often estimates parasite genetic relatedness using metrics such as Identity-By-Decent (IBD). Yet, strong positive selection stemming from antimalarial drug resistance or other interventions may bias IBD-based estimates. In this study, we utilized simulations, a true IBD inference algorithm, and empirical datasets from different malaria transmission settings to investigate the extent of such bias and explore potential correction strategies. We analyzed whole genome sequence data generated from 640 new and 4,026 publicly available

Indexed as

Effective population sizeGenetic relatednessIdentity-By-DescentMalariaPopulation structurePositive selection

Identifiers

PMID37502843
PMCPMC10370022

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.