Evidence map›Paper›PMID 29608557›Full record

ArticlePLoS genetics2018

A high throughput, functional screen of human Body Mass Index GWAS loci using tissue-specific RNAi Drosophila melanogaster crosses.

Thomas J Baranski, Aldi T Kraja, Jill L Fink, Mary Feitosa, Petra A Lenzini, Ingrid B Borecki, Ching-Ti Liu, L Adrienne Cupples, Kari E North, Michael A Province

Open access · goldAbstract read
In one paragraph

Article in PLoS genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
3.1field-weighted citation impact, top 8% 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

25 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Biomolecules · 2025
    Review
  6. Review
  7. Article
  8. Review
  9. Review
  10. Article
  11. Article
  12. Phenotyping ofBiomolecules · 2022
    Review
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Molecular networks in Network Medicine: Development and applications.Wiley interdisciplinary reviews. Systems biology and medicine · 2020
    Review
  20. 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

10 authors at 4 institutions in 1 country.

Thomas J BaranskiDepartment of Internal Medicine, Division of Endocrinology, Metabolism and Lipid Research, Washington University School of Medicine, St. Louis, Missouri, United States of America.
Aldi T KrajaDepartment of Genetics and Center for Genome Sciences and Systems Biology, Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America.
Jill L FinkDepartment of Internal Medicine, Division of Endocrinology, Metabolism and Lipid Research, Washington University School of Medicine, St. Louis, Missouri, United States of America.ORCID 0000-0003-0835-4498
Mary FeitosaDepartment of Genetics and Center for Genome Sciences and Systems Biology, Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America.
Petra A LenziniDepartment of Genetics and Center for Genome Sciences and Systems Biology, Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America.
Ingrid B BoreckiDepartment of Biostatistics, University of Washington, Seattle, Washington, United States of America.
Ching-Ti LiuDepartment of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America.ORCID 0000-0002-0703-0742
L Adrienne CupplesDepartment of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America.
Kari E NorthDepartment of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America.
Michael A ProvinceDepartment of Genetics and Center for Genome Sciences and Systems Biology, Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America.ORCID 0000-0001-6102-774X
Boston University · USWashington University in St. Louis · USUniversity of North Carolina at Chapel Hill · USUniversity of Washington · US

Funding

UNC-CH CENTER FOR ENVIRONMENTAL HEALTH &SUSCEPTIBILITYP30ES010126 · NIEHS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Melissa A. Troester · 2001 to 2026
$36.3M
Washington University Nutrition Obesity Research CenterP30DK056341 · NIDDK · WASHINGTON UNIVERSITY · PI Brian N Finck · 1999 to 2026
$30.2M
Genetic Architecture of Adiposity in Multiple Large CohortsR01DK089256 · NIDDK · WASHINGTON UNIVERSITY · PI BARANSKI, THOMAS JOHN, LIU, CHING-TI · 2010 to 2016
$4.6M
NIDDK NIH HHS P30 DK056341NIDDK NIH HHS R01 DK089256NIEHS NIH HHS P30 ES010126NIH HHS DK089256
6 · The paper itself

Abstract

Human GWAS of obesity have been successful in identifying loci associated with adiposity, but for the most part, these are non-coding SNPs whose function, or even whose gene of action, is unknown. To help identify the genes on which these human BMI loci may be operating, we conducted a high throughput screen in Drosophila melanogaster. Starting with 78 BMI loci from two recently published GWAS meta-analyses, we identified fly orthologs of all nearby genes (± 250KB). We crossed RNAi knockdown lines of each gene with flies containing tissue-specific drivers to knock down (KD) the expression of the genes only in the brain and the fat body. We then raised the flies on a control diet and compared the amount of fat/triglyceride in the tissue-specific KD group compared to the driver-only control flies. 16 of the 78 BMI GWAS loci could not be screened with this approach, as no gene in the 500-kb region had a fly ortholog. Of the remaining 62 GWAS loci testable in the fly, we found a significant fat phenotype in the KD flies for at least one gene for 26 loci (42%) even after correcting for multiple comparisons. By contrast, the rate of significant fat phenotypes in RNAi KD found in a recent genome-wide Drosophila screen (Pospisilik et al. (2010) is ~5%. More interestingly, for 10 of the 26 positive regions, we found that the nearest gene was not the one that showed a significant phenotype in the fly. Specifically, our screen suggests that for the 10 human BMI SNPs rs11057405, rs205262, rs9925964, rs9914578, rs2287019, rs11688816, rs13107325, rs7164727, rs17724992, and rs299412, the functional genes may NOT be the nearest ones (CLIP1, C6orf106, KAT8, SMG6, QPCTL, EHBP1, SLC39A8, ADPGK /ADPGK-AS1, PGPEP1, KCTD15, respectively), but instead, the specific nearby cis genes are the functional target (namely: ZCCHC8, VPS33A, RSRC2; SPDEF, NUDT3; PAGR1; SETD1, VKORC1; SGSM2, SRR; VASP, SIX5; OTX1; BANK1; ARIH1; ELL; CHST8, respectively). The study also suggests further functional experiments to elucidate mechanism of action for genes evolutionarily conserved for fat storage.

Indexed as

Body Mass IndexCrosses, GeneticGenome-Wide Association StudyRNA InterferenceAdipose TissueAnimalsDrosophila melanogasterHumansMiceObesityPolymorphism, Single NucleotideQuantitative Trait Loci

Identifiers

PMID29608557
PMCPMC5897035
OpenAlexW2795547559

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.