Evidence map›Paper›PMID 38790175›Full record

ReviewGenes2024

Statistical Genetic Approaches to Investigate Genotype-by-Environment Interaction: Review and Novel Extension of Models.

Vincent P Diego, Eron G Manusov, Marcio Almeida, Sandra Laston, David Ortiz, John Blangero, Sarah Williams-Blangero

Abstract readReview
In one paragraph

Review in Genes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

7 authors.

Vincent P DiegoDepartment of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA.ORCID 0009-0008-3735-6912
Eron G ManusovDepartment of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA.ORCID 0000-0002-6052-1823
Marcio AlmeidaDepartment of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA.
Sandra LastonDepartment of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA.ORCID 0000-0001-5928-7702
David OrtizDepartment of Family Medicine, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA.
John BlangeroDepartment of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA.ORCID 0000-0001-6250-5723
Sarah Williams-BlangeroDepartment of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA.

Funding

Neuroimaging CoreU19AG076581 · NIA · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI GABRIEL Alejandro DE ERAUSQUIN, Gabriela Gonzalez_Aleman · 2023 to 2026
$32.9M
South Texas Alzheimer's Disease Research CenterP30AG066546 · NIA · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI Sudha Seshadri · 2021 to 2026
$24.0M
Workforce Development CoreU54HG013247 · NHGRI · UNIVERSITY OF TEXAS RIO GRANDE VALLEY · PI SARAH A. WILLIAMS-BLANGERO · 2023 to 2026
$10.5M
Rio Grande Valley Alzheimer's Resource Center for Minority Aging Research: Partnerships for ProgressP30AG059305 · NIA · UNIVERSITY OF TEXAS RIO GRANDE VALLEY · PI Gladys E. Maestre · 2018 to 2026
$5.8M
Imaging Genomics of the Aging BrainR01AG058464 · NIA · YALE UNIVERSITY · PI BLANGERO, JOHN, GLAHN, DAVID C · 2018 to 2022
$3.9M
Identification of the Exposome in Fatty Liver Disease in Mexican American Families Using Genetic CorrectionR01MD012564 · NIMHD · UNIVERSITY OF TEXAS RIO GRANDE VALLEY · PI BLANGERO, JOHN, CLARKE, GEOFFREY DAVID · 2018 to 2022
$3.7M
Knapp Community Care Foundation N/ANHGRI NIH HHS U54 HG013247NIA NIH HHS P30 AG059305NIA NIH HHS P30 AG066546NIA NIH HHS R01 AG058464NIA NIH HHS U19 AG076581NIH HHS R01 AG058464, R01 AG058464, U54 HG013247, P30 AG066546, P30 AG059305, and U19 AG076581NIMHD NIH HHS R01 MD012564
6 · The paper itself

Abstract

Statistical genetic models of genotype-by-environment (G×E) interaction can be divided into two general classes, one on G×E interaction in response to dichotomous environments (e.g., sex, disease-affection status, or presence/absence of an exposure) and the other in response to continuous environments (e.g., physical activity, nutritional measurements, or continuous socioeconomic measures). Here we develop a novel model to jointly account for dichotomous and continuous environments. We develop the model in terms of a joint genotype-by-sex (for the dichotomous environment) and genotype-by-social determinants of health (SDoH; for the continuous environment). Using this model, we show how a depression variable, as measured by the Beck Depression Inventory-II survey instrument, is not only underlain by genetic effects (as has been reported elsewhere) but is also significantly determined by joint G×Sex and G×SDoH interaction effects. This model has numerous applications leading to potentially transformative research on the genetic and environmental determinants underlying complex diseases.

Indexed as

Gene-Environment InteractionGenotypeModels, GeneticDepressionHumansMaleModels, Statisticaldepressiongenotype-by-environment interactionheritabilitypolygenic modelsocial determinants of health

Identifiers

PMID38790175
PMCPMC11121143

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.