Evidence map›Paper›PMID 24633160›Full record

ArticleNature genetics2014

Ancestry estimation and control of population stratification for sequence-based association studies.

Chaolong Wang, Xiaowei Zhan, Jennifer Bragg-Gresham, Hyun Min Kang, Dwight Stambolian, Emily Y Chew, Kari E Branham, John Heckenlively, FUSION Study, Robert Fulton and 6 more

Abstract readValidation Study
In one paragraph

Article in Nature genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
87citing papers in PubMed, 4 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

87 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
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  3. Genetic variants at theRMD open · 2021
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27 more citing papers are in PubMed but not listed here.

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

16 authors.

Chaolong Wang1] Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA. [2] Department of Biostatistics, Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA. [3].
Xiaowei Zhan1] Department of Biostatistics, Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA. [2].
Jennifer Bragg-GreshamDepartment of Biostatistics, Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA.
Hyun Min KangDepartment of Biostatistics, Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA.
Dwight StambolianDepartment of Ophthalmology, University of Pennsylvania Medical School, Philadelphia, Pennsylvania, USA.
Emily Y ChewDivision of Epidemiology and Clinical Research, National Eye Institute, Bethesda, Maryland, USA.
Kari E BranhamDepartment of Ophthalmology, University of Michigan Kellogg Eye Center, Ann Arbor, Michigan, USA.
John HeckenlivelyDepartment of Ophthalmology, University of Michigan Kellogg Eye Center, Ann Arbor, Michigan, USA.
FUSION Study
Robert FultonGenome Institute, Washington University School of Medicine, St. Louis, Missouri, USA.
Richard K WilsonGenome Institute, Washington University School of Medicine, St. Louis, Missouri, USA.
Elaine R MardisGenome Institute, Washington University School of Medicine, St. Louis, Missouri, USA.
Xihong LinDepartment of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA.
Anand SwaroopNeurobiology-Neurodegeneration & Repair Laboratory, National Eye Institute, Bethesda, Maryland, USA.
Sebastian Zöllner1] Department of Biostatistics, Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA. [2] Department of Psychiatry, University of Michigan Medical School, Ann Arbor, Michigan, USA.
Gonçalo R AbecasisDepartment of Biostatistics, Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA.

Funding

Large Scale Genome SequencingU54HG003079 · NHGRI · WASHINGTON UNIVERSITY · PI DUTCHER, SUSAN K · 2004 to 2016
$445.7M
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
Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer ResearchR35CA197449 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI XIHONG LIN · 2015 to 2026
$10.9M
Genetic susceptibility and common multi-factorial retinal/macular diseasesZIAEY000475 · NEI · NATIONAL EYE INSTITUTE · PI SWAROOP, ANAND · 2009 to 2014
$6.9M
Statistical Methods for the Spatio-Temporal Assessment of Social Disparities in CancerP01CA134294 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI HANEUSE, SEBASTIEN, HERNAN, MIGUEL · 2008 to 2017
$6.8M
Identifying Genes for Type 2 Diabetes:FUSIONR01DK062370 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BOEHNKE, MICHAEL L, SCOTT, LAURA J. · 2003 to 2025
$6.7M
Population genetics for large-scale sequencing studies of diverse populationsR01HG005855 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Noah Rosenberg, Paul A Scheet · 2010 to 2026
$5.8M
DESIGN AND ANALYSIS OF HUMAN GENE MAPPING STUDIESR01HG000376 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BOEHNKE, MICHAEL L · 1993 to 2016
$5.5M
More Complete Assessment of DNA Variation in Age-Related Macular DegenerationR01EY022005 · NEI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ABECASIS, GONCALO · 2011 to 2014
$4.7M
Robust Software Tools for Variant Identification and Functional AssessmentU01HG006513 · NHGRI · UNIVERSITY OF UTAH · PI ABECASIS, GONCALO, MARTH, GABOR T · 2012 to 2015
$4.2M
Computational and statistical models for human geneticsR01HG007022 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ABECASIS, GONCALO · 2012 to 2016
$3.0M
Statistical Methods for Correlated and High-Dimensional Biomedical DataR37CA076404 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI LIN, XIHONG · 2007 to 2014
$2.5M
Intramural NIH HHSNCI NIH HHS CA076404NCI NIH HHS CA134294NCI NIH HHS P01 CA134294NCI NIH HHS R01 CA076404NCI NIH HHS R29 CA076404NCI NIH HHS R35 CA197449NCI NIH HHS R37 CA076404NEI NIH HHS EY022005NEI NIH HHS R01 EY022005NHGRI NIH HHS HG000376NHGRI NIH HHS HG003079NHGRI NIH HHS HG005552NHGRI NIH HHS HG005855NHGRI NIH HHS HG006513NHGRI NIH HHS HG007022NHGRI NIH HHS R01 HG000376NHGRI NIH HHS R01 HG005855NHGRI NIH HHS R01 HG007022NHGRI NIH HHS R56 HG000376NHGRI NIH HHS RC2 HG005552NHGRI NIH HHS U01 HG006513NHGRI NIH HHS U54 HG003079NIDDK NIH HHS DK062370NIDDK NIH HHS R01 DK062370NIDDK NIH HHS R56 DK062370NIDDK NIH HHS U01 DK062370
6 · The paper itself

Abstract

Estimating individual ancestry is important in genetic association studies where population structure leads to false positive signals, although assigning ancestry remains challenging with targeted sequence data. We propose a new method for the accurate estimation of individual genetic ancestry, based on direct analysis of off-target sequence reads, and implement our method in the publicly available LASER software. We validate the method using simulated and empirical data and show that the method can accurately infer worldwide continental ancestry when used with sequencing data sets with whole-genome shotgun coverage as low as 0.001×. For estimates of fine-scale ancestry within Europe, the method performs well with coverage of 0.1×. On an even finer scale, the method improves discrimination between exome-sequenced study participants originating from different provinces within Finland. Finally, we show that our method can be used to improve case-control matching in genetic association studies and to reduce the risk of spurious findings due to population structure.

Indexed as

Models, GeneticSoftwareBase SequenceComputer SimulationGenetic Association StudiesGenetics, PopulationMolecular Sequence DataPolymorphism, Single NucleotidePrincipal Component AnalysisSequence Analysis, DNA

Identifiers

PMID24633160
PMCPMC4084909

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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.