Evidence map›Paper›PMID 23529720›Full record

ArticleGenetic epidemiology2013

The impact of improved microarray coverage and larger sample sizes on future genome-wide association studies.

Karla J Lindquist, Eric Jorgenson, Thomas J Hoffmann, John S Witte

Abstract read
In one paragraph

Article in Genetic epidemiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

15 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Article
  5. Rare Coding Variants Associated with Breast Cancer.Advances in experimental medicine and biology · 2021
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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

4 authors.

Karla J LindquistDepartment of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158-9001, USA.
Eric Jorgenson
Thomas J Hoffmann
John S Witte

Funding

Training in Molecular& Genetic Epidemiology of CancerR25CA112355 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI WITTE, JOHN S. · 2005 to 2018
$6.5M
Genetic Epidemiology of Prostate Cancer Risk and ProgressionR01CA088164 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI VAN DEN EEDEN, STEPHEN K., WITTE, JOHN S. · 2000 to 2015
$6.1M
Genomewide Association Study of Prostate CancerU01CA127298 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI WITTE, JOHN S. · 2008 to 2012
$5.1M
NCI NIH HHS R01 CA088164NCI NIH HHS R01CA88164NCI NIH HHS R25 CA112355NCI NIH HHS R25CA112355NCI NIH HHS U01 CA127298NCI NIH HHS U01CA127298
6 · The paper itself

Abstract

Genome-wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) associated with complex traits. However, the genetic heritability of most of these traits remains unexplained. To help guide future studies, we address the crucial question of whether future GWAS can detect new SNP associations and explain additional heritability given the new availability of larger GWAS SNP arrays, imputation, and reduced genotyping costs. We first describe the pairwise and imputation coverage of all SNPs in the human genome by commercially available GWAS SNP arrays, using the 1000 Genomes Project as a reference. Next, we describe the findings from 6 years of GWAS of 172 chronic diseases, calculating the power to detect each of them while taking array coverage and sample size into account. We then calculate the power to detect these SNP associations under different conditions using improved coverage and/or sample sizes. Finally, we estimate the percentages of SNP associations and heritability previously detected and detectable by future GWAS under each condition. Overall, we estimated that previous GWAS have detected less than one-fifth of all GWAS-detectable SNPs underlying chronic disease. Furthermore, increasing sample size has a much larger impact than increasing coverage on the potential of future GWAS to detect additional SNP-disease associations and heritability.

Indexed as

Chronic DiseaseGenome, HumanGenome-Wide Association StudyGenotypeHumansModels, GeneticModels, StatisticalOdds RatioOligonucleotide Array Sequence AnalysisPolymorphism, Single NucleotideResearch DesignSample Size

Identifiers

PMID23529720
PMCPMC4291231

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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.