Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America.
Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America.
PLoS One. 2019 Apr 11;14(4):e0214061. doi: 10.1371/journal.pone.0214061. eCollection 2019.
Gene expression may be an important biological mediator in associations between social factors and health. However, previous studies were limited by small sample sizes and use of differing cell types with heterogeneous expression patterns. We use a large population-based cohort with gene expression measured solely in monocytes to investigate associations between seven social factors and expression of genes previously found to be sensitive to social factors.
We employ three methodological approaches: 1) omnibus test for the entire gene set (Global ANCOVA), 2) assessment of each association individually (linear regression), and 3) machine learning method that performs variable selection with correlated predictors (elastic net).
In global analyses, significant associations with the a priori defined socially sensitive gene set were detected for major or lifetime discrimination and chronic burden (p = 0.019 and p = 0.047, respectively). Marginally significant associations were detected for loneliness and adult socioeconomic status (p = 0.066, p = 0.093, respectively). No associations were significant in linear regression analyses after accounting for multiple testing. However, a small percentage of gene expressions (up to 11%) were associated with at least one social factor using elastic net.
The Global ANCOVA and elastic net findings suggest that a small percentage of genes may be "socially sensitive," (i.e. demonstrate differential expression by social factor), yet single gene approaches such as linear regression may be ill powered to capture this relationship. Future research should further investigate the biological mechanisms through which social factors act to influence gene expression and how systemic changes in gene expression affect overall health.
基因表达可能是社会因素与健康之间关联的重要生物学介质。然而，先前的研究受到样本量小和使用具有不同表达模式的不同细胞类型的限制。我们使用一个基于人群的大型队列，仅在单核细胞中测量基因表达，以调查七个社会因素与先前发现对社会因素敏感的基因表达之间的关联。
我们采用了三种方法学方法：1）对整个基因集进行总体检验（全局 ANCOVA），2）逐个评估每个关联（线性回归），3）使用具有相关预测因子的变量选择的机器学习方法（弹性网络）。
在全局分析中，与先前定义的社会敏感基因集具有显著关联的是主要或终生歧视和慢性负担（p = 0.019 和 p = 0.047）。孤独和成年社会经济地位也存在边缘显著关联（p = 0.066，p = 0.093）。在考虑多次检验后，线性回归分析中没有关联是显著的。然而，使用弹性网络，多达 11%的基因表达与至少一个社会因素相关。
全局 ANCOVA 和弹性网络的结果表明，一小部分基因可能是“社会敏感的”（即通过社会因素表现出差异表达），但线性回归等单一基因方法可能无法捕捉到这种关系。未来的研究应进一步研究社会因素影响基因表达的生物学机制，以及基因表达的系统变化如何影响整体健康。
