Evidence map›Paper›PMID 25653314›Full record

ArticleG3 (Bethesda, Md.)2015

Combined QTL and selective sweep mappings with coding SNP annotation and cis-eQTL analysis revealed PARK2 and JAG2 as new candidate genes for adiposity regulation.

Pierre-François Roux, Simon Boitard, Yuna Blum, Brian Parks, Alexandra Montagner, Etienne Mouisel, Anis Djari, Diane Esquerré, Colette Désert, Morgane Boutin and 11 more

Open access · goldAbstract read
In one paragraph

Article in G3 (Bethesda, Md.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.5field-weighted citation impact, top 16% 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

10 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
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  4. Article
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  8. Long noncoding RNA repertoire in chicken liver and adipose tissue.Genetics, selection, evolution : GSE · 2017
    Article
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  10. 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

21 authors at 11 institutions in 3 countries.

Pierre-François RouxINRA, UMR1348 Pegase, Saint-Gilles, 35590, France Agrocampus Ouest, UMR1348 Pegase, Rennes, 35000, France Université Européenne de Bretagne, France.ORCID http://orcid.org/0000-0002-0695-4206
Simon BoitardUMR 7205 OSEB (MNHN-CNRS-EPHE-UPMC), Paris, 75005, France INRA/AgroParisTech, UMR1313 GABI, Jouy-en-Josas, 78352, France.
Yuna BlumDepartment of Medicine/Division of Cardiology, UCLA, Los Angeles, California.
Brian ParksDepartment of Medicine/Division of Cardiology, UCLA, Los Angeles, California.
Alexandra MontagnerUMR1331, INP, UPS, TOXALIM, INRA, Toulouse, France.
Etienne MouiselINSERM, UMR 1048 I2MC, 31432 Toulouse, France.
Anis DjariINRA, SIGENAE, Castanet-Tolosan, 31326, France.
Diane EsquerréINRA, Plateforme GENOTOUL, Castanet-Tolosan, 31326, France.
Colette DésertINRA, UMR1348 Pegase, Saint-Gilles, 35590, France Agrocampus Ouest, UMR1348 Pegase, Rennes, 35000, France Université Européenne de Bretagne, France.
Morgane BoutinINRA, UMR1348 Pegase, Saint-Gilles, 35590, France Agrocampus Ouest, UMR1348 Pegase, Rennes, 35000, France Université Européenne de Bretagne, France.
Sophie LerouxUMR INRA, INPT ENSAT, INPT ENVT-GENPHYSE, Castanet-Tolosan, 31326, France.
Frédéric LecerfINRA, UMR1348 Pegase, Saint-Gilles, 35590, France.
Elisabeth Le Bihan-DuvalINRA, UR83 Recherches Avicoles, Nouzilly, 37380, France.
Christophe KloppINRA, SIGENAE, Castanet-Tolosan, 31326, France.
Bertrand ServinUMR INRA, INPT ENSAT, INPT ENVT-GENPHYSE, Castanet-Tolosan, 31326, France.
Frédérique PitelUMR INRA, INPT ENSAT, INPT ENVT-GENPHYSE, Castanet-Tolosan, 31326, France.
Michel Jean DuclosINRA, UR83 Recherches Avicoles, Nouzilly, 37380, France.
Hervé GuillouUMR1331, INP, UPS, TOXALIM, INRA, Toulouse, France.
Aldons J LusisDepartment of Medicine/Division of Cardiology, UCLA, Los Angeles, California Department of Human Genetics, UCLA, Los Angeles, California.
Olivier DemeureINRA, UMR1348 Pegase, Saint-Gilles, 35590, France Agrocampus Ouest, UMR1348 Pegase, Rennes, 35000, France Université Européenne de Bretagne, France.
Sandrine LagarrigueINRA, UMR1348 Pegase, Saint-Gilles, 35590, France Agrocampus Ouest, UMR1348 Pegase, Rennes, 35000, France Université Européenne de Bretagne, France sandrine.lagarrigue@agrocampus-ouest.fr.
Physiologie Environnement et Génétique pour l'Animal et les Systèmes d'Elevage · FRUniversity of California, Los Angeles · USBiologie des Oiseaux et Aviculture · FRInstitut National de la Recherche Agronomique · MAInstitut National Polytechnique de Toulouse · FRUniversité Européenne de Bretagne · FRUniversité Toulouse III - Paul Sabatier · FRGénétique Physiologie et Systèmes d'Elevage · FRInserm · FRInstitut Agro Rennes-Angers · FRSorbonne Université · FR

Funding

ULTRASTRUCTURE OF THE INTIMA IN EARLY LESION FORMATIONP01HL030568 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI FOGELMAN, ALAN M · 1985 to 2019
$55.2M
Systems genomics of metabolic syndrome traitsP01HL028481 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI REUE, KAREN · 1985 to 2019
$50.2M
Neuroendocrinology, Sex Differences, and ReproductionT32HD007228 · NICHD · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARNOLD, ARTHUR P · 1985 to 2015
$3.9M
NHLBI NIH HHS HL28481NHLBI NIH HHS HL30568NHLBI NIH HHS P01 HL028481NHLBI NIH HHS P01 HL030568NICHD NIH HHS T32 HD007228
6 · The paper itself

Abstract

Very few causal genes have been identified by quantitative trait loci (QTL) mapping because of the large size of QTL, and most of them were identified thanks to functional links already known with the targeted phenotype. Here, we propose to combine selection signature detection, coding SNP annotation, and cis-expression QTL analyses to identify potential causal genes underlying QTL identified in divergent line designs. As a model, we chose experimental chicken lines divergently selected for only one trait, the abdominal fat weight, in which several QTL were previously mapped. Using new haplotype-based statistics exploiting the very high SNP density generated through whole-genome resequencing, we found 129 significant selective sweeps. Most of the QTL colocalized with at least one sweep, which markedly narrowed candidate region size. Some of those sweeps contained only one gene, therefore making them strong positional causal candidates with no presupposed function. We then focused on two of these QTL/sweeps. The absence of nonsynonymous SNPs in their coding regions strongly suggests the existence of causal mutations acting in cis on their expression, confirmed by cis-eQTL identification using either allele-specific expression or genetic mapping analyses. Additional expression analyses of those two genes in the chicken and mice contrasted for adiposity reinforces their link with this phenotype. This study shows for the first time the interest of combining selective sweeps mapping, coding SNP annotation and cis-eQTL analyses for identifying causative genes for a complex trait, in the context of divergent lines selected for this specific trait. Moreover, it highlights two genes, JAG2 and PARK2, as new potential negative and positive key regulators of adiposity in chicken and mice.

Indexed as

Quantitative Trait LociAdipose Tissue, WhiteAdiposityAllelesAnimalsCell LineChickensChromosome MappingGenomeHigh-Throughput Nucleotide SequencingJagged-2 ProteinMembrane ProteinsMiceMolecular Sequence AnnotationMyosinsPolymorphism, Single NucleotideJag2 protein, mouseJagged-2 ProteinMembrane ProteinsMyosinsparkin proteinUbiquitin-Protein Ligasesadipositycis-eQTLsdivergent linesJAG2PARK2QTLselective sweeps

Identifiers

PMID25653314
PMCPMC4390568
OpenAlexW2143640024

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.