Evidence map›Paper›PMID 18262040›Full record

SynthesisLancet (London, England)2008

LDL-cholesterol concentrations: a genome-wide association study.

Manjinder S Sandhu, Dawn M Waterworth, Sally L Debenham, Eleanor Wheeler, Konstantinos Papadakis, Jing Hua Zhao, Kijoung Song, Xin Yuan, Toby Johnson, Sofie Ashford and 21 more

Open access · bronzeAbstract readMeta-Analysis
In one paragraph

Synthesis in Lancet (London, England), 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 192 papers, 14 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
192citing papers in PubMed, 14 pooled it
32.4field-weighted citation impact, top 1% 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

192 citing papers in PubMed, 14 syntheses or guidelines pooled it, 355 citations in OpenAlex.

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132 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

31 authors at 15 institutions in 6 countries.

Manjinder S SandhuDepartment of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, UK. manj.sandhu@srl.cam.ac.uk
Dawn M Waterworth
Sally L Debenham
Eleanor Wheeler
Konstantinos Papadakis
Jing Hua Zhao
Kijoung Song
Xin Yuan
Toby Johnson
Sofie Ashford
Michael Inouye
Robert Luben
Matthew Sims
David Hadley
Wendy McArdle
Philip Barter
Y Antero Kesäniemi
Robert W Mahley
Ruth McPherson
Scott M Grundy
Wellcome Trust Case Control Consortium
Sheila A Bingham
Kay-Tee Khaw
Ruth J F Loos
Gérard Waeber
Inês Barroso
David P Strachan
Panagiotis Deloukas
Peter Vollenweider
Nicholas J Wareham
Vincent Mooser
GlaxoSmithKline (United States) · USUniversity of Cambridge · GBWellcome Sanger Institute · GBMRC Epidemiology Unit · GBSt George's, University of London · GBAddenbrooke's Hospital · GBUniversity Hospital of Lausanne · CHGladstone Institutes · USInstitute of Social and Preventive Medicine · CHMRC Human Nutrition Research · GBThe Heart Research Institute · AUThe University of Texas Southwestern Medical Center · USUniversity of Bristol · GBUniversity of Ottawa · CAUniversity of Oulu · FI

Funding

Medical Research Council G0000934Medical Research Council G0701863Medical Research Council MC_QA137934Medical Research Council MC_U105630924Medical Research Council MC_U106188470Wellcome TrustWellcome Trust 068545/Z/02
6 · The paper itself

Abstract

backgroundLDL cholesterol has a causal role in the development of cardiovascular disease. Improved understanding of the biological mechanisms that underlie the metabolism and regulation of LDL cholesterol might help to identify novel therapeutic targets. We therefore did a genome-wide association study of LDL-cholesterol concentrations.

methodsWe used genome-wide association data from up to 11,685 participants with measures of circulating LDL-cholesterol concentrations across five studies, including data for 293 461 autosomal single nucleotide polymorphisms (SNPs) with a minor allele frequency of 5% or more that passed our quality control criteria. We also used data from a second genome-wide array in up to 4337 participants from three of these five studies, with data for 290,140 SNPs. We did replication studies in two independent populations consisting of up to 4979 participants. Statistical approaches, including meta-analysis and linkage disequilibrium plots, were used to refine association signals; we analysed pooled data from all seven populations to determine the effect of each SNP on variations in circulating LDL-cholesterol concentrations.

findingsIn our initial scan, we found two SNPs (rs599839 [p=1.7x10(-15)] and rs4970834 [p=3.0x10(-11)]) that showed genome-wide statistical association with LDL cholesterol at chromosomal locus 1p13.3. The second genome screen found a third statistically associated SNP at the same locus (rs646776 [p=4.3x10(-9)]). Meta-analysis of data from all studies showed an association of SNPs rs599839 (combined p=1.2x10(-33)) and rs646776 (p=4.8x10(-20)) with LDL-cholesterol concentrations. SNPs rs599839 and rs646776 both explained around 1% of the variation in circulating LDL-cholesterol concentrations and were associated with about 15% of an SD change in LDL cholesterol per allele, assuming an SD of 1 mmol/L.

interpretationWe found evidence for a novel locus for LDL cholesterol on chromosome 1p13.3. These results potentially provide insight into the biological mechanisms that underlie the regulation of LDL cholesterol and might help in the discovery of novel therapeutic targets for cardiovascular disease.

Indexed as

Polymorphism, Single NucleotideAdultAgedCardiovascular DiseasesCholesterol, LDLChromosomes, Human, Pair 1Cohort StudiesEuropeFemaleGenetic VariationGenome, HumanHumansLinear ModelsLinkage DisequilibriumMaleMiddle AgedCholesterol, LDL

Identifiers

PMID18262040
PMCPMC2292820
OpenAlexW2142642125

What OpenQuestion holds

Textmetadata
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.