Evidence map›Paper›PMID 29187420›Full record

ArticleG3 (Bethesda, Md.)2018

Bayesian Diallel Analysis Reveals

Paul L Maurizio, Martin T Ferris, Gregory R Keele, Darla R Miller, Ginger D Shaw, Alan C Whitmore, Ande West, Clayton R Morrison, Kelsey E Noll, Kenneth S Plante and 6 more

Open access · goldAbstract read
In one paragraph

Article in G3 (Bethesda, Md.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
1.9field-weighted citation impact, top 14% 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

19 citing papers in PubMed, 31 citations in OpenAlex.

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  19. Advancing chemical risk assessment decision-making with population variability data: challenges and opportunities.Mammalian genome : official journal of the International Mammalian Genome Society · 2018
    Article
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

16 authors at 2 institutions in 1 country.

Paul L MaurizioCurriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, North Carolina 27599.ORCID 0000-0002-5859-6260
Martin T FerrisDepartment of Genetics, University of North Carolina at Chapel Hill, North Carolina 27599.ORCID 0000-0003-1241-6268
Gregory R KeeleCurriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, North Carolina 27599.ORCID 0000-0002-1843-7900
Darla R MillerDepartment of Genetics, University of North Carolina at Chapel Hill, North Carolina 27599.ORCID 0000-0002-0781-7254
Ginger D ShawDepartment of Genetics, University of North Carolina at Chapel Hill, North Carolina 27599.ORCID 0000-0003-2590-4973
Alan C WhitmoreDepartment of Genetics, University of North Carolina at Chapel Hill, North Carolina 27599.
Ande WestDepartment of Genetics, University of North Carolina at Chapel Hill, North Carolina 27599.
Clayton R MorrisonDepartment of Genetics, University of North Carolina at Chapel Hill, North Carolina 27599.
Kelsey E NollDepartment of Genetics, University of North Carolina at Chapel Hill, North Carolina 27599.ORCID 0000-0002-3977-0083
Kenneth S PlanteDepartment of Genetics, University of North Carolina at Chapel Hill, North Carolina 27599.
Adam S CockrellDepartment of Epidemiology, University of North Carolina at Chapel Hill, North Carolina 27599.
David W ThreadgillInstitute of Genome Sciences and Society, Texas A&M University, College Station, Texas 77433-2470.ORCID 0000-0003-3538-1635
Fernando Pardo-Manuel de VillenaDepartment of Genetics, University of North Carolina at Chapel Hill, North Carolina 27599.ORCID 0000-0002-5738-5795
Ralph S BaricDepartment of Epidemiology, University of North Carolina at Chapel Hill, North Carolina 27599.ORCID 0000-0001-6827-8701
Mark T HeiseDepartment of Genetics, University of North Carolina at Chapel Hill, North Carolina 27599.
William ValdarDepartment of Genetics, University of North Carolina at Chapel Hill, North Carolina 27599 william.valdar@unc.edu.ORCID 0000-0002-2419-0430
University of North Carolina at Chapel Hill · USTexas A&M University · US

Funding

The Burden of Chikungunya and Dengue Infection in KenyaU54AI081680 · NIAID · OREGON HEALTH & SCIENCE UNIVERSITY · PI BEVAN, MICHAEL J · 2009 to 2013
$40.4M
Unlocking Zika Virus Immune Control and Pathogenesis with the Collaborative CrossU19AI100625 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BARIC, RALPH S, HEISE, MARK T · 2012 to 2021
$36.6M
Molecular Biology of Viral Diseases Predoctoral Training GrantT32AI007419 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Mark T Heise, CARY A MOODY · 1993 to 2026
$4.7M
UNC Predoc Training Progr in Bioinformatics/Comp BiologyT32GM067553 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ELSTON, TIMOTHY C · 2005 to 2019
$2.5M
Statistical Modeling of Complex Traits in Genetic Reference Super-PopulationsR01GM104125 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI VALDAR, WILLIAM · 2012 to 2016
$1.2M
NIAID NIH HHS T32 AI007419NIAID NIH HHS U19 AI100625NIAID NIH HHS U54 AI081680NIGMS NIH HHS R01 GM104125NIGMS NIH HHS T32 GM067553
6 · The paper itself

Abstract

Influenza A virus (IAV) is a respiratory pathogen that causes substantial morbidity and mortality during both seasonal and pandemic outbreaks. Infection outcomes in unexposed populations are affected by host genetics, but the host genetic architecture is not well understood. Here, we obtain a broad view of how heritable factors affect a mouse model of response to IAV infection using an 8 × 8 diallel of the eight inbred founder strains of the Collaborative Cross (CC). Expanding on a prior statistical framework for modeling treatment response in diallels, we explore how a range of heritable effects modify acute host response to IAV through 4 d postinfection. Heritable effects in aggregate explained ∼57% of the variance in IAV-induced weight loss. Much of this was attributable to a pattern of additive effects that became more prominent through day 4 postinfection and was consistent with previous reports of antiinfluenza myxovirus resistance 1 (

Indexed as

Bayes TheoremAnimalsDisease Models, AnimalGenetic Predisposition to DiseaseHaplotypesHumansInfluenza A virusMice, 129 StrainMice, Inbred C57BLMice, Inbred NODMice, Inbred StrainsMyxovirus Resistance ProteinsOrthomyxoviridae InfectionsPhenotypeSpecies SpecificityMx1 protein, mouseMyxovirus Resistance ProteinsBayesian mixed modelcausal effectMPPmultiparental populationsmultiple imputationtreatment response

Identifiers

PMID29187420
PMCPMC5919740
OpenAlexW2767907857

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.