Evidence map›Paper›PMID 28090672›Full record

ArticleGenetic epidemiology2017

A powerful statistical framework for generalization testing in GWAS, with application to the HCHS/SOL.

Tamar Sofer, Ruth Heller, Marina Bogomolov, Christy L Avery, Mariaelisa Graff, Kari E North, Alex P Reiner, Timothy A Thornton, Kenneth Rice, Yoav Benjamini and 2 more

Open access · greenAbstract read
In one paragraph

Article in Genetic epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 5 pooled it
5.3field-weighted citation impact, top 5% 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

32 citing papers in PubMed, 5 syntheses or guidelines pooled it, 49 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

12 authors at 5 institutions in 2 countries.

Tamar SoferDepartment of Biostatistics, University of Washington, Seattle, WA, USA.
Ruth HellerDepartment of Statistics and Operations Research, Tel-Aviv University, Tel-Aviv, Israel.
Marina BogomolovFaculty of Industrial Engineering and Management, Technion-Israel Institute of Technology, Haifa, Israel.
Christy L AveryDepartment of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
Mariaelisa GraffDepartment of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
Kari E NorthDepartment of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
Alex P ReinerDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Timothy A ThorntonDepartment of Biostatistics, University of Washington, Seattle, WA, USA.
Kenneth RiceDepartment of Biostatistics, University of Washington, Seattle, WA, USA.
Yoav BenjaminiDepartment of Biostatistics, University of Washington, Seattle, WA, USA.
Cathy C LaurieDepartment of Biostatistics, University of Washington, Seattle, WA, USA.
Kathleen F KerrDepartment of Biostatistics, University of Washington, Seattle, WA, USA.
University of Washington · USUniversity of North Carolina at Chapel Hill · USFred Hutch Cancer Center · USTechnion – Israel Institute of Technology · ILTel Aviv University · IL

Funding

Statistical Methods for X-Chromosome Genetic DataP01GM099568 · NIGMS · UNIVERSITY OF WASHINGTON · PI THORNTON, TIMOTHY ALVIN · 2012 to 2016
$7.3M
Genetic Studies of Blood Cell Traits in Multi-Ethnic CohortsR01HL129132 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LI, YUN, REINER, ALEXANDER P · 2016 to 2019
$2.7M
Thalassemia Clinical Research Network, CanadaU01HL065233 · NHLBI · UNIVERSITY HEALTH NETWORK · PI OLIVIERI, NANCY F · 2000 to 2009
$2.6M
GENETIC ARCHITECTURE OF PLASMA T-PA AND PAI-1R01HL065234 · NHLBI · VANDERBILT UNIVERSITY · PI MOORE, JASON H. · 2000 to 2004
$1.7M
HISPANIC COMMUNITY HEALTH STUDY-268065233N01HC065233 · HC · COORDINATING CENTER · PI CHAMBLESS, LLOYD E · 2006 to 2006
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NHLBI NIH HHS HHSN268201300005CNHLBI NIH HHS N01 HC065233NHLBI NIH HHS N01 HC065234NHLBI NIH HHS N01 HC065235NHLBI NIH HHS N01 HC065236NHLBI NIH HHS N01 HC065237NHLBI NIH HHS R01 HL129132NIGMS NIH HHS P01 GM099568
6 · The paper itself

Abstract

In genome-wide association studies (GWAS), "generalization" is the replication of genotype-phenotype association in a population with different ancestry than the population in which it was first identified. Current practices for declaring generalizations rely on testing associations while controlling the family-wise error rate (FWER) in the discovery study, then separately controlling error measures in the follow-up study. This approach does not guarantee control over the FWER or false discovery rate (FDR) of the generalization null hypotheses. It also fails to leverage the two-stage design to increase power for detecting generalized associations. We provide a formal statistical framework for quantifying the evidence of generalization that accounts for the (in)consistency between the directions of associations in the discovery and follow-up studies. We develop the directional generalization FWER (FWER

Indexed as

Genome, HumanModels, StatisticalAlgorithmsComputer SimulationFollow-Up StudiesGenome-Wide Association StudyGenomicsHispanic or LatinoHumansLinkage DisequilibriumPhenotypePolymorphism, Single Nucleotidemultiple testingone-sided P-valuesshared genetics

Identifiers

PMID28090672
PMCPMC5340573
OpenAlexW2474020767

What OpenQuestion holds

Textmetadata
LicenceTDM
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.