Evidence map›Paper›PMID 21401569›Full record

ArticleBiometrics2011

Combining disease models to test for gene-environment interaction in nuclear families.

Thomas J Hoffmann, Stijn Vansteelandt, Christoph Lange, Edwin K Silverman, Dawn L DeMeo, Nan M Laird

Open access · bronzeAbstract read
In one paragraph

Article in Biometrics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact, top 88% 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

5 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Detecting gene-environment interactions in human birth defects: Study designs and statistical methods.Birth defects research. Part A, Clinical and molecular teratology · 2015
    Article
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

6 authors at 3 institutions in 2 countries.

Thomas J HoffmannDepartment of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA. tjh@post.harvard.edu
Stijn Vansteelandt
Christoph Lange
Edwin K Silverman
Dawn L DeMeo
Nan M Laird
Harvard University · USCancer Research And Biostatistics · USGhent University · BE

Funding

GRADUATE TRAINING IN BIOSTATISTICST32ES007142 · NIEHS · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI Brent Andrew Coull · 1985 to 2026
$11.9M
Training Program in Psychiatric Genetics and Translational ResearchT32MH017119 · NIMH · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI KARESTAN C KOENEN, Jukka-Pekka Onnela · 1985 to 2026
$8.9M
Training in Molecular& Genetic Epidemiology of CancerR25CA112355 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI WITTE, JOHN S. · 2005 to 2018
$6.5M
FAMILY BASED TESTS OF ASSOCIATION FOR COMPLEX DISEASESR01MH059532 · NIMH · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI LAIRD, NAN MCKENZIE · 1998 to 2010
$2.9M
NCI NIH HHS R25 CA112355NCI NIH HHS R25CA112355NIEHS NIH HHS ES007142NIEHS NIH HHS T32 ES007142NIMH NIH HHS MH17119NIMH NIH HHS R01 MH059532NIMH NIH HHS R01-MH059532NIMH NIH HHS T32 MH017119
6 · The paper itself

Abstract

It is useful to have robust gene-environment interaction tests that can utilize a variety of family structures in an efficient way. This article focuses on tests for gene-environment interaction in the presence of main genetic and environmental effects. The objective is to develop powerful tests that can combine trio data with parental genotypes and discordant sibships when parents' genotypes are missing. We first make a modest improvement on a method for discordant sibs (discordant on phenotype), but the approach does not allow one to use families when all offspring are affected, e.g., trios. We then make a modest improvement on a Mendelian transmission-based approach that is inefficient when discordant sibs are available, but can be applied to any nuclear family. Finally, we propose a hybrid approach that utilizes the most efficient method for a specific family type, then combines over families. We utilize this hybrid approach to analyze a chronic obstructive pulmonary disorder dataset to test for gene-environment interaction in the Serpine2 gene with smoking. The methods are freely available in the R package fbati.

Indexed as

Gene-Environment InteractionModels, GeneticNuclear FamilyComputer SimulationGenetic LinkageGenetic Predisposition to DiseaseHumansMetagenomicsSerpin E2SmokingSerpin E2SERPINE2 protein, human

Identifiers

PMID21401569
PMCPMC3120904
OpenAlexW2105611196

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.