Evidence map›Paper›PMID 27943075›Full record

ArticlePrevention science : the official journal of the Society for Prevention Research2018

Informing Prevention and Intervention Policy Using Genetic Studies of Resistance.

Brion S Maher, Shawn Latendresse, Michael M Vanyukov

Abstract read
In one paragraph

Article in Prevention science : the official journal of the Society for Prevention Research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Commentary for Special Issue of Prevention Science "Using Genetics in Prevention: Science Fiction or Science Fact?"Prevention science : the official journal of the Society for Prevention Research · 2018
    Article
  2. The Implications of Genetics for Prevention and Intervention Programming.Prevention science : the official journal of the Society for Prevention Research · 2018
    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

3 authors.

Brion S MaherDepartment of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway Ave., Baltimore, MD, 21205, USA. brion@jhu.edu.
Shawn LatendresseDepartment of Psychology and Neuroscience, Baylor University, Waco, TX, USA.
Michael M VanyukovDepartments of Pharmaceutical Sciences, Psychiatry, and Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA.

Funding

Functional Genomics and Epigenomics in HIV of Longitudinal Injection Drug Use TrajectoryR01DA039408 · NIDA · JOHNS HOPKINS UNIVERSITY · PI MAHER, BRION S · 2015 to 2019
$3.1M
Phenotypic refinement of externalizing pathways to alcohol-related behaviorsK01AA020333 · NIAAA · VIRGINIA COMMONWEALTH UNIVERSITY · PI LATENDRESSE, SHAWN J. · 2011 to 2015
$812k
Genes, Environment and Drug Use in Three Baltimore CohortsR01DA036525 · NIDA · JOHNS HOPKINS UNIVERSITY · PI MAHER, BRION S · 2015 to 2017
$727k
NIAAA NIH HHS K01 AA020333NIDA NIH HHS R01 DA036525NIDA NIH HHS R01 DA039408
6 · The paper itself

Abstract

The common paradigm for conceptualizing the influence of genetic and environmental factors on a particular disease relies on the concept of risk. Consequently, the bulk of etiologic, including genetic, work focuses on "risk" factors. These factors are aggregated at the high end of the distribution of liability to disease, the latent variable underlying the distribution of probability and severity of a disorder. However, liability has a symmetric but distinct aspect to risk, resistance to disorder. Resistance factors, aggregated at the low end of the liability distribution and supporting health and recovery, appear to be more promising for effective prevention and intervention. Herein, we discuss existing work on resistance factors, highlighting those with known genetic influences. We examine the utility of incorporating resistance genetics in prevention and intervention trials and compare the statistical power of a series of ascertainment schemes to develop a general framework for examining resistance outcomes in genetically informative designs. We find that an approach that samples individuals discordant on measured liability, a low-risk design, is the most feasible design and yields power equivalent to or higher than commonly used designs for detecting resistance genetic and environmental effects.

Indexed as

Genetic Predisposition to DiseaseGenetic ResearchPolicy MakingHumansRisk FactorsGenetic resistanceHigh-risk designPreventionResilience

Identifiers

PMID27943075
PMCPMC5466512

What OpenQuestion holds

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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.