Evidence map›Paper›PMID 21778733›Full record

ArticleHuman heredity2011

Copy number variation accuracy in genome-wide association studies.

Peng Lin, Sarah M Hartz, Jen-Chyong Wang, Robert F Krueger, Tatiana M Foroud, Howard J Edenberg, John I Nurnberger, Andrew I Brooks, Jay A Tischfield, Laura Almasy and 9 more

Abstract read
In one paragraph

Article in Human heredity, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 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

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Mapping copy number variable regions correlated with reproduction and production traits in Karan Fries cattle mammalian genomics.Mammalian genome : official journal of the International Mammalian Genome Society · 2025
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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

19 authors.

Peng LinDepartment of Psychiatry, Washington University, St. Louis, MO 63110, USA.
Sarah M Hartz
Jen-Chyong Wang
Robert F Krueger
Tatiana M Foroud
Howard J Edenberg
John I Nurnberger
Andrew I Brooks
Jay A Tischfield
Laura Almasy
Bradley T Webb
Victor M Hesselbrock
Bernice Porjesz
Alison M Goate
Laura J Bierut
John P Rice
COGA Collaborators
COGEND Collaborators
GENEVA Investigators

Funding

Subject CollectionU10AA008401 · NIAAA · SUNY DOWNSTATE MEDICAL CENTER · PI JESSICA E SALVATORE · 1989 to 2026
$162.7M
JH/CIDR Genotyping for Genome-Wide Association StudiesU01HG004438 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI VALLE, DAVID · 2007 to 2011
$24.2M
The Collaborative Genetic Study of Nicotine DependenceP01CA089392 · NCI · WASHINGTON UNIVERSITY · PI BIERUT, LAURA J. · 2001 to 2012
$17.3M
Washington University Institute of Clinical and Translational SciencesKL2TR000450 · NCATS · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2012 to 2016
$8.8M
Genome Wide Association Coordinating CenterU01HG004446 · NHGRI · UNIVERSITY OF WASHINGTON · PI WEIR, BRUCE S. · 2007 to 2011
$6.6M
FAMILY STUDY OF COCAINE DEPENDENCER01DA013423 · NIDA · WASHINGTON UNIVERSITY · PI BIERUT, LAURA JEAN · 2000 to 2004
$2.2M
Case Control Candidate Gene Study of AddictionR01DA019963 · NIDA · WASHINGTON UNIVERSITY · PI BIERUT, LAURA J. · 2007 to 2009
$1.2M
Study of Addiction: Genetics and EnvironmentU01HG004422 · NHGRI · WASHINGTON UNIVERSITY · PI BIERUT, LAURA J. · 2007 to 2010
$1.2M
NCATS NIH HHS KL2 TR000450NCI NIH HHS P01 CA089392NHGRI NIH HHS HHSN268200782096CNHGRI NIH HHS U01 HG004422NHGRI NIH HHS U01 HG004438NHGRI NIH HHS U01HG004438NHGRI NIH HHS U01 HG004446NIAAA NIH HHS U10 AA008401NIDA NIH HHS R01 DA013423NIDA NIH HHS R01 DA019963
6 · The paper itself

Abstract

BACKGROUND/

aimCopy number variations (CNVs) are a major source of alterations among individuals and are a potential risk factor in many diseases. Numerous diseases have been linked to deletions and duplications of these chromosomal segments. Data from genome-wide association studies and other microarrays may be used to identify CNVs by several different computer programs, but the reliability of the results has been questioned.

methodsTo help researchers reduce the number of false-positive CNVs that need to be followed up with laboratory testing, we evaluated the relative performance of CNVPartition, PennCNV and QuantiSNP, and developed a statistical method for estimating sensitivity and positive predictive values of CNV calls and tested it on 96 duplicate samples in our dataset.

resultsWe found that the positive predictive rate increases with the number of probes in the CNV and the size of the CNV, with the highest positive predicted rates in CNVs of at least 500 kb and at least 100 probes. Our analysis also indicates that identifying CNVs reported by multiple programs can greatly improve the reproducibility rate and the positive predicted rate.

conclusionOur methods can be used by investigators to identify CNVs in genome-wide data with greater reliability.

Indexed as

DNA Copy Number VariationsGenome-Wide Association StudyHumansPolymorphism, Single NucleotidePredictive Value of TestsReproducibility of ResultsSoftware

Identifiers

PMID21778733
PMCPMC3153341

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.