Evidence map›Paper›PMID 27848076›Full record

ArticleHuman genetics2017

Identifying gene-gene interactions that are highly associated with four quantitative lipid traits across multiple cohorts.

Rishika De, Shefali S Verma, Emily Holzinger, Molly Hall, Amber Burt, David S Carrell, David R Crosslin, Gail P Jarvik, Helena Kuivaniemi, Iftikhar J Kullo and 14 more

Open access · greenAbstract read
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In one paragraph

Article in Human genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.5field-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

6 citing papers in PubMed, 13 citations in OpenAlex.

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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

24 authors at 16 institutions in 5 countries.

Rishika DeDepartment of Genetics, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
Shefali S VermaThe Center for Systems Genomics, The Pennsylvania State University, University Park, PA, USA.
Emily HolzingerThe Center for Systems Genomics, The Pennsylvania State University, University Park, PA, USA.
Molly HallThe Center for Systems Genomics, The Pennsylvania State University, University Park, PA, USA.
Amber BurtDivision of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA.
David S CarrellGroup Health Research Institute, Seattle, WA, USA.
David R CrosslinDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.
Gail P JarvikDivision of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA.
Helena KuivaniemiDivision of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, 7505, South Africa.
Iftikhar J KulloDivision of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA.
Leslie A LangeDepartment of Genetics, University of North Carolina School of Medicine at Chapel Hill, Chapel Hill, NC, USA.
Matthew B LanktreeDepartments of Medicine and Biochemistry, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada.
Eric B LarsonGroup Health Research Institute, Seattle, WA, USA.
Kari E NorthDepartment of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Alex P ReinerDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Vinicius TraganteDepartment of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands.
Gerard TrompDivision of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, 7505, South Africa.
James G WilsonDepartment of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA.
Folkert W AsselbergsDepartment of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands.
Fotios DrenosMRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK.
Jason H MooreInstitute for Biomedical Informatics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Marylyn D RitchieDepartment of Biomedical and Translational Informatics, Geisinger Health System, 205 Hood Center for Health Research, Center Street, Danville, PA, 17821, USA. mdritchie@geisinger.edu.
Brendan KeatingPenn Transplant Institute, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA. bkeating@mail.med.upenn.edu.
Diane Gilbert-DiamondInstitute for Quantitative Biomedical Sciences at Dartmouth, Hanover, NH, USA. Diane.Gilbert-Diamond@dartmouth.edu.
Pennsylvania State University · USKaiser Permanente Washington Health Research Institute · USStellenbosch University · ZAUniversity Medical Center Utrecht · NLUniversity of North Carolina at Chapel Hill · USUniversity of Pennsylvania · USUniversity of Washington Medical Center · USDartmouth College · USFred Hutch Cancer Center · USGeisinger Health System · USMayo Clinic in Arizona · USQuantitative BioSciences · USUniversity College London · GBUniversity of Mississippi Medical Center · USUniversity of Washington · USWestern University · CA

Funding

Genetic Epidemiology of Complex TraitsZIAHG200327 · NHGRI · NATIONAL HUMAN GENOME RESEARCH INSTITUTE · PI BAILEY-WILSON, JOAN ELLEN · 2009 to 2022
$10.5M
Novel methods to identify and replicate complex interactions in common diseaseF31HG008588 · NHGRI · PENNSYLVANIA STATE UNIVERSITY, THE · PI HALL, MOLLY ANN · 2015 to 2015
$9k
NHGRI NIH HHS F31 HG008588
6 · The paper itself

Abstract

Genetic loci explain only 25-30 % of the heritability observed in plasma lipid traits. Epistasis, or gene-gene interactions may contribute to a portion of this missing heritability. Using the genetic data from five NHLBI cohorts of 24,837 individuals, we combined the use of the quantitative multifactor dimensionality reduction (QMDR) algorithm with two SNP-filtering methods to exhaustively search for SNP-SNP interactions that are associated with HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), total cholesterol (TC) and triglycerides (TG). SNPs were filtered either on the strength of their independent effects (main effect filter) or the prior knowledge supporting a given interaction (Biofilter). After the main effect filter, QMDR identified 20 SNP-SNP models associated with HDL-C, 6 associated with LDL-C, 3 associated with TC, and 10 associated with TG (permutation P value <0.05). With the use of Biofilter, we identified 2 SNP-SNP models associated with HDL-C, 3 associated with LDL-C, 1 associated with TC and 8 associated with TG (permutation P value <0.05). In an independent dataset of 7502 individuals from the eMERGE network, we replicated 14 of the interactions identified after main effect filtering: 11 for HDL-C, 1 for LDL-C and 2 for TG. We also replicated 23 of the interactions found to be associated with TG after applying Biofilter. Prior knowledge supports the possible role of these interactions in the genetic etiology of lipid traits. This study also presents a computationally efficient pipeline for analyzing data from large genotyping arrays and detecting SNP-SNP interactions that are not primarily driven by strong main effects.

Indexed as

Epistasis, GeneticPhenotypeBody Mass IndexCardiovascular DiseasesCholesterol, HDLCholesterol, LDLCohort StudiesFemaleGenetic LociGenetic MarkersGenome, HumanGenotyping TechniquesHumansLinear ModelsLinkage DisequilibriumMaleCholesterol, HDLCholesterol, LDLGenetic MarkersTriglycerides

Identifiers

PMID27848076
OpenAlexW2549406157

What OpenQuestion holds

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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.