A genome-wide association study of methamphetamine use among people with HIV.

IF 2.1 4区 医学 Q3 GENETICS & HEREDITY
A Venkataraman, T Jia, S A Ruderman, C B Haas, R M Nance, L S Mixson, K H Mayer, M S Saag, G Chander, R D Moore, J Jacobson, S Napravnik, K Christopolous, W J Lee, B M Whitney, I Peter, H M Crane, J A C Delaney, S Lindström
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引用次数: 0

Abstract

Background: Amphetamine-like stimulants are the most used psychostimulants in the world; methamphetamine use is the most prevalent in people with HIV. Prolonged methamphetamine use can cause lasting damage to the heart, gut, and brain, as well as auditory hallucinations and paranoid thinking. However, relatively little is known about methamphetamine use and its genetic contributors.

Methods: Using genetic information from the Centers for AIDS Research Network of Integrated Clinical Systems (CNICS) cohort, we conducted a multi-ancestry genome-wide association study (GWAS) of methamphetamine use among people with HIV (n = 1,196 reported ever use, n = 4,750 reported never use).

Results: No single nucleotide polymorphism was statistically associated with methamphetamine use at the genome-wide level (p < 5 * 10-8) in our study. Further, we did not replicate previously suggested genetic variants from other studies (all p > 0.05 in our analysis).

Discussion: Our study suggests that there is no single strong genetic contributor to lifetime use of methamphetamine in people with HIV enrolled in CNICS. Larger studies with more refined outcome assessment are warranted to further understand the contribution of genetics to methamphetamine use and use disorder. Investigation into social and environmental contributors to methamphetamine use are similarly necessary.

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来源期刊
BMC Medical Genomics
BMC Medical Genomics 医学-遗传学
CiteScore
3.90
自引率
0.00%
发文量
243
审稿时长
3.5 months
期刊介绍: BMC Medical Genomics is an open access journal publishing original peer-reviewed research articles in all aspects of functional genomics, genome structure, genome-scale population genetics, epigenomics, proteomics, systems analysis, and pharmacogenomics in relation to human health and disease.
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