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Murine Drinking Models in the Development of Pharmacotherapies for Alcoholism: Drinking in the Dark and Two-bottle Choice

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

Tongtong Lan¹, Bo Yang¹, Xuefen Zhang¹

¹1Department 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.

Current Genomics

|January 14, 2020

Summary

Genetic research is advancing our understanding of substance use disorders (SUDs). This review covers statistical methods and software crucial for identifying genetic variants linked to SUDs.

Keywords:

Association analysis GCTA Interaction analysis Linkage analysis Meta-analysis Substance dependence

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Area of Science:

Genetics
Neuroscience
Public Health

Background:

Substance Use Disorders (SUDs) represent a significant global health burden.
SUDs result from complex interactions between genetic and environmental factors.
Recent decades have seen progress in identifying SUD-related genetic variants.

Purpose of the Study:

To review statistical methods and software for SUD genetic studies.
To discuss the strengths and limitations of current approaches.

Main Methods:

Review of statistical methodologies used in genetic association studies.
Examination of software tools for analyzing genetic data in SUD research.

Main Results:

Various statistical methods and software facilitate the identification of SUD-related genetic variants.
The article provides an overview of available tools and techniques.

Conclusions:

Effective statistical approaches are essential for advancing SUD genetic research.
Understanding the strengths and limitations of these methods is key to future discoveries.