Evidence map›Paper›PMID 29084231›Full record

ArticlePLoS genetics2017

Common, low-frequency, and rare genetic variants associated with lipoprotein subclasses and triglyceride measures in Finnish men from the METSIM study.

James P Davis, Jeroen R Huyghe, Adam E Locke, Anne U Jackson, Xueling Sim, Heather M Stringham, Tanya M Teslovich, Ryan P Welch, Christian Fuchsberger, Narisu Narisu and 9 more

Open access · goldAbstract read
In one paragraph

Article in PLoS genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 2 pooled it
6.2field-weighted citation impact, top 4% 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

39 citing papers in PubMed, 2 syntheses or guidelines pooled it, 57 citations in OpenAlex.

  1. Use of Mendelian Randomization to Unveil Metabolic Markers inJournal of Korean medical science · 2026
    Pooled it
  2. Pooled it
  3. Article
  4. Blood biomarkers and breed genetics of aging in pet dogs.bioRxiv : the preprint server for biology · 2026
    Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. ADAMTS7: a Novel Therapeutic Target in Atherosclerosis.Current atherosclerosis reports · 2023
    Review
  13. Article
  14. Article
  15. GWAS of longitudinal trajectories at biobank scale.American journal of human genetics · 2022
    Article
  16. Review
  17. Review
  18. Article
  19. Article
  20. The Genetic Basis of Hypertriglyceridemia.Current atherosclerosis reports · 2021
    Review
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

19 authors at 6 institutions in 4 countries.

James P DavisDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America.ORCID http://orcid.org/0000-0002-8715-7857
Jeroen R HuygheDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, United States of America.ORCID http://orcid.org/0000-0001-6027-9806
Adam E LockeDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, United States of America.ORCID http://orcid.org/0000-0001-6227-198X
Anne U JacksonDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, United States of America.ORCID http://orcid.org/0000-0002-9672-2547
Xueling SimDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, United States of America.
Heather M StringhamDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, United States of America.ORCID http://orcid.org/0000-0002-2991-6392
Tanya M TeslovichDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, United States of America.
Ryan P WelchDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, United States of America.
Christian FuchsbergerDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, United States of America.
Narisu NarisuNational Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States of America.ORCID http://orcid.org/0000-0002-8483-1156
Peter S ChinesNational Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States of America.ORCID http://orcid.org/0000-0003-3030-9175
Antti J KangasComputational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland.
Pasi SoininenComputational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland.
Mika Ala-KorpelaComputational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland.ORCID http://orcid.org/0000-0001-5905-1206
Johanna KuusistoInstitute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland.
Francis S CollinsNational Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States of America.ORCID http://orcid.org/0000-0002-1023-7410
Markku LaaksoInstitute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland.
Michael BoehnkeDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, United States of America.
Karen L MohlkeDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America.
University of Michigan · USNational Institutes of Health · USUniversity of Eastern Finland · FIUniversity of North Carolina at Chapel Hill · USBaker Heart and Diabetes Institute · AUUniversity of Oulu · FI

Funding

Genetic analysis of type II diabetes in Finnish populationZIAHG000024 · NHGRI · NATIONAL HUMAN GENOME RESEARCH INSTITUTE · PI ERDOS, MICHAEL · 2009 to 2025
$38.2M
Regional Pilot And Feasibility Study Grants ProgramP30DK020572 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Mehboob A Hussain · 2013 to 2026
$24.3M
University of Michigan Training Program in Genomic ScienceT32HG000040 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Sebastian Zoellner · 1995 to 2026
$16.4M
Identifying Genes for Type 2 Diabetes: FUSIONU01DK062370 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BOEHNKE, MICHAEL L, SCOTT, LAURA J. · 2009 to 2021
$11.6M
Targeted Genetic Analysis of T2D and Quantitative TraitsR01DK072193 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KAREN L. MOHLKE · 2005 to 2026
$11.3M
Genetic epidemiology of rare and regulatory variants for metabolic traitsR01DK093757 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KAREN L. MOHLKE · 2011 to 2026
$8.2M
GENETIC ANALYSIS OF TYPE II DIABETES IN FINNISH POPULATIONZ01HG000024 · NHGRI · NATIONAL HUMAN GENOME RESEARCH INSTITUTE · PI COLLINS, FRANCIS S. · 1995 to 2008
$6.2M
The Genetic Epidemiology of Heart, Lung, and Blood TraitsTraining Grant (GenHLB)T32HL129982 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Christy Leigh Avery, KAREN L. MOHLKE · 2016 to 2026
$3.8M
Functional genetic variants for type 2 diabetesU01DK105561 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI MOHLKE, KAREN L. · 2015 to 2019
$2.3M
Intramural NIH HHS Z01 HG000024NHGRI NIH HHS T32 HG000040NHLBI NIH HHS T32 HL129982NIDDK NIH HHS P30 DK020572NIDDK NIH HHS R01 DK072193NIDDK NIH HHS R01 DK093757NIDDK NIH HHS U01 DK062370NIDDK NIH HHS U01 DK105561Wellcome Trust
6 · The paper itself

Abstract

Lipid and lipoprotein subclasses are associated with metabolic and cardiovascular diseases, yet the genetic contributions to variability in subclass traits are not fully understood. We conducted single-variant and gene-based association tests between 15.1M variants from genome-wide and exome array and imputed genotypes and 72 lipid and lipoprotein traits in 8,372 Finns. After accounting for 885 variants at 157 previously identified lipid loci, we identified five novel signals near established loci at HIF3A, ADAMTS3, PLTP, LCAT, and LIPG. Four of the signals were identified with a low-frequency (0.005<minor allele frequency [MAF]<0.05) or rare (MAF<0.005) variant, including Arg123His in LCAT. Gene-based associations (P<10-10) support a role for coding variants in LIPC and LIPG with lipoprotein subclass traits. 30 established lipid-associated loci had a stronger association for a subclass trait than any conventional trait. These novel association signals provide further insight into the molecular basis of dyslipidemia and the etiology of metabolic disorders.

Indexed as

Cholesterol, HDLExomeFinlandGene FrequencyGenome-Wide Association StudyGenotypeHumansLipid MetabolismLipidsLipoproteinsMaleMiddle AgedPolymorphism, Single NucleotidePrincipal Component AnalysisTriglyceridesWhite PeopleCholesterol, HDLLipidsLipoproteinsTriglycerides

Identifiers

PMID29084231
PMCPMC5679656
OpenAlexW2765748387

What OpenQuestion holds

Textmetadata
LicenceCC0
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.