Genotype-by-socioeconomic status interaction influences heart disease risk scores and carotid artery thickness in Mexican Americans: the predominant role of education in comparison to household income and socioeconomic index

Abstract

Background: Socioeconomic status (SES) is a potent environmental determinant of health. To our knowledge, no assessment of genotype-environment interaction has been conducted to consider the joint effects of socioeconomic status and genetics on risk for cardiovascular disease (CVD). We analyzed Mexican American Family Studies (MAFS) data to evaluate the hypothesis that genotype-by-environment interaction (GxE) is an important determinant of variation in CVD risk factors.Methods: We employed a linear mixed model to investigate GxE in Mexican American extended families. We studied two proxies for CVD [Pooled Cohort Equation Risk Scores/Framingham Risk Scores (FRS/PCRS) and carotid artery intima-media thickness (CA-IMT)] in relation to socioeconomic status as determined by Duncan’s Socioeconomic Index (SEI), years of education, and household income.Results: We calculated heritability for FRS/PCRS and carotid artery intima-media thickness. There was evidence of GxE due to additive genetic variance heterogeneity and genetic correlation for FRS, PCRS, and CA-IMT measures for education (environment) but not for household income or SEI.Conclusion: The genetic effects underlying CVD are dynamically modulated at the lower end of the SES spectrum. There is a significant change in the genetic architecture underlying the major components of CVD in response to changes in education.

Bridging Bone Health: Osteoporosis Disparities in the Rio Grande Valley

“…Osteoporosis poses a significant health concern for Hispanics residing in South Texas, influenced by a multitude of factors such as low education levels, socioeconomic status, genetics, lifestyle choices, and cultural nuances. The RGV symbolizes this scenario, characterized by a generally low socioeconomic status and a substantial Mexican-American population, amplifying the health risks in the region [ 18 ]. Although limited by not having an individual-level data population, investigating whether individuals in lower-income brackets face a higher risk of osteoporosis could provide valuable insights into the complex interplay of socioeconomic factors and bone health in the region.…”

Statistical Genetic Approaches to Investigate Genotype-by-Environment Interaction: Review and Novel Extension of Models

“…We can extend this theory to an environmental spectrum to model G × E for arbitrary continuous environments as opposed to two levels of the environmental variable (for more detail, see the Appendix A). To this end, we employ variance and correlation functions [10,12,13], which we define as follows:…”

“…Our first step is to establish that there is a genetic basis for the trait of interest, which amounts to testing and rejecting h 2 = 0. Moving forward from this point, we advocate a staged hypothesis testing approach that our team has successfully developed and implemented in analyses of the gene-by-environment interaction [10,12,13]. In the first stage, we compared the polygenic model to the G × Sex and to the G × SDHI models.…”

“…There is presently a robust body of approaches to modeling the genotype-by-environment (G×E) interaction in statistical genetics developed from within a linear mixed model framework, which can be divided into genotype-by-dichotomous environment [1][2][3][4][5][6][7][8] and genotype-bycontinuous environment [1,2,7,[9][10][11][12][13] interaction models. The former class has been applied to sex (male/female) [1,[4][5][6][7], disease affection status (affected/unaffected), or presence/absence of environmental exposures (e.g., smoking/non-smoking, basal/high cholesterol-high-fat diet) [2].…”

“…The former class has been applied to sex (male/female) [1,[4][5][6][7], disease affection status (affected/unaffected), or presence/absence of environmental exposures (e.g., smoking/non-smoking, basal/high cholesterol-high-fat diet) [2]. The latter class has been applied to continuous environments such as the age continuum [7,9], physical activity levels and sedentary behavior [10], and socioeconomic status variables [8,13] (e.g., socioeconomic index, household income, or education levels) among others. It would be advantageous to biomedical investigations to combine the two approaches to address interesting questions like a dichotomous sex-specific response to a continuous index of social determinants of health (SDoH), as we do here, or disease affection-status-specific responses to continuous environmental exposures, for example.…”