Evidence map›Paper›PMID 31929725›Full record

ReviewCurrent genomics2019

Statistical Methods and Software for Substance Use and Dependence Genetic Research.

Tongtong Lan, Bo Yang, Xuefen Zhang, Tong Wang, Qing Lu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Current genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

5 authors.

Tongtong Lan1Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China; 2Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA.
Bo Yang1Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China; 2Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA.
Xuefen Zhang1Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China; 2Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA.
Tong Wang1Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China; 2Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA.
Qing Lu1Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China; 2Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA.

Funding

Computational Efficient Statistical Tools for Analyzing Substance Dependence Sequencing DataR01DA043501 · NIDA · UNIVERSITY OF FLORIDA · PI LU, QING · 2017 to 2021
$2.1M
Methods and Software for High-dimensional Risk Prediction ResearchR01LM012848 · NLM · UNIVERSITY OF FLORIDA · PI LU, QING · 2018 to 2021
$1.2M
NIDA NIH HHS R01 DA043501NLM NIH HHS R01 LM012848
6 · The paper itself

Abstract

backgroundSubstantial substance use disorders and related health conditions emerged dur-ing the mid-20th century and continue to represent a remarkable 21st century global burden of disease. This burden is largely driven by the substance-dependence process, which is a complex process and is influenced by both genetic and environmental factors. During the past few decades, a great deal of pro-gress has been made in identifying genetic variants associated with Substance Use and Dependence (SUD) through linkage, candidate gene association, genome-wide association and sequencing studies.

methodsVarious statistical methods and software have been employed in different types of SUD ge-netic studies, facilitating the identification of new SUD-related variants.

conclusionIn this article, we review statistical methods and software that are currently available for SUD genetic studies, and discuss their strengths and limitations.

Indexed as

Association analysisGCTAInteraction analysisLinkage analysisMeta-analysisSubstance dependence

Identifiers

PMID31929725
PMCPMC6935956

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.