Introduction
============

African Americans have a higher prevalence of hypertension, or chronically high blood pressure, compared with other racial/ethnic groups ([@B69]; [@B68]). Despite this higher burden of disease in African Americans, early genome-wide association studies (GWAS) for hypertension and systolic blood pressure (SBP) and diastolic blood pressure (DBP) were limited to populations of European-descent ([@B40]; [@B45]; [@B64]; [@B32]) or east Asian-descent ([@B35]). More recent GWAS have been performed in ancestrally diverse populations, including African Americans or African-descent populations ([@B2]; [@B71], [@B70]; [@B36]; [@B26]; [@B28]; [@B41]). Collectively, these associated common variants explain 3--6% of the variance for SBP and DBP, and in the largest European-descent study to date account for up to 27% of the estimated single nucleotide polymorphism (SNP)-wide heritability for these traits ([@B21]).

Current GWAS findings explain only a proportion of the expected contribution from additive genetic effects. Previous twin and family studies estimate these traits have moderate to high heritability (30--70%) ([@B22]; [@B51]; [@B39]; [@B30]; [@B37]), suggesting that additional genetic associations have yet to be discovered. Given that GWAS identify common single nucleotide variants (SNVs) for association, additional genetic associations may be found among rare SNVs ([@B17]; [@B52]). Importantly, most GWAS consider only main effects and do not consider interactions with relevant environmental exposures. Two recent and large GWAS of blood pressure have considered alcohol consumption ([@B23]) and smoking ([@B57]), both of which identified novel putative associations for these traits.

Here, we examine the modifying effects of education, a measure of socioeconomic status (SES) and recognized social determinant of health, on SBP and DBP traits among African Americans drawn from a clinical setting. Previous epidemiologic studies suggest that in addition to alcohol consumption and smoking, social environment and specifically SES has a strong influence on blood pressure and hypertension ([@B53]; [@B10]; [@B46]). Further, a GWAS in the Framingham Heart Study accounting for educational attainment identified novel associations for blood pressure traits among European Americans ([@B7]). Based on these prior findings, we hypothesized that educational attainment modifies associations between genetic variants and blood pressure among African Americans. To test this hypothesis, we accessed a large biobank linked to electronic health records (EHRs) in a racially diverse clinical population. We identified two associated SNPs, *ARHGAP22* rs4593967 (SBP) and *IQCK* rs950928 (DBP), neither of which has been previously associated with blood pressure. We also identified a novel SNP-education interaction affecting DBP, suggesting social determinants of health may modify genetic effects contributing to complex human traits.

Materials and Methods {#s1}
=====================

Study Population and Data Collection
------------------------------------

The study population is derived from BioVU, a DNA biobank of the Vanderbilt University Medical Center (VUMC) linked to de-identified EHRs. DNA samples are extracted from discarded blood samples drawn for routine clinical care ([@B50]). These samples are linked to the Synthetic Derivative (SD), the de-identified version of the VUMC EHR. Medical records within the SD are scrubbed of all Health Insurance Portability and Accountability Act (HIPAA) identifiers. This study was approved by the Vanderbilt University Institutional Review Board.

The study population consists of African American adults \>18 years old drawn from a larger study of minority patients with DNA samples in BioVU (*n* = 15,863) ([@B14]). We extracted relevant demographic variables, including race/ethnicity, sex, and age at data extraction available in the SD. Smoking status was extracted using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) tobacco use codes as previously described ([@B65]). Education was extracted from the free text of EHRs using a recently validated text-mining algorithm ([@B29]). Education was modeled as a categorical variable: less than high school, high school, and some college or above. All weight and height measures were extracted from the EHR, and after extensive quality control, as described in [@B27], median values were used to represent individual-level body mass index (BMI).

The median value of all blood pressure measurements within an individual's EHR prior to a recording of blood pressure-altering medications in the patient's medication list were used in analyses. Medications included in the keyword list of anti-hypertensives were angiotensin converting enzyme inhibitors, angiotensin II receptor blocker, beta blockers, non-dihydropyridine calcium channel blockers, hydralazine, Minoxidil, central alpha antagonists, direct renin antagonists, aldosterone antagonists, alpha antagonists, and diuretics including thiazides, K-sparing, and loop diuretics. Any blood pressure measurement found after any mention of these types of medications were excluded from analyses.

Genotyping and Quality Control
------------------------------

Genotyping of 15,863 DNA samples from non-European descent individuals was performed using the Metabochip, a custom Illumina genotyping array designed to target SNPs and surrounding genomic regions associated with metabolic traits and cardiovascular disease ([@B9]; [@B62]). We restricted the following quality control and statistical analyses to DNA samples from African Americans in BioVU (*n* = 11,301). All genotyping quality control was performed using PLINK 1.9 ([@B11]). After the removal of SNPs with a minor allele frequency of less than 5%, SNPs with a Hardy-Weinberg Equilibrium exact test *p*-value of less than 1 × 10^-7^, and SNPs with a genotyping call rate of less than 95%, a total of 115,834 variants remained ([Supplementary Figure S1](#SM1){ref-type="supplementary-material"}). We further removed 967 samples for either ambiguous sex, missing genotypes (\>5%), or relatedness (twins, full siblings, parent/offspring) ([Supplementary Figure S1](#SM1){ref-type="supplementary-material"}). A total of 10,334 DNA samples passed genotyping quality control. After quality control, global ancestry was estimated using unsupervised ADMIXTURE analysis, assuming *K* = 2 ([@B3]). Linkage disequilibrium (*r*^2^) was calculated using 1000 Genomes Phase 3 data and an expectation-maximization algorithm adapted from Haploview ([@B6]) available through rAggr ([@B20]).

Local ancestry for rs6687976 was assigned as previously described ([@B24]). Briefly, SHAPEITv2 ([@B15]) and the 1000 Genomes Phase 3 reference panel^[1](#fn01){ref-type="fn"}^ were used to phase the genotype data. RFMix ([@B43]) was used to assign local ancestry. Phased chromosomal haplotypes were matched to Yoruba and CEPH/European ancestral population panels from 1000 Genomes.

Statistical Analysis
--------------------

Inclusion criteria included African American adults with available Metabochip genotyping data and complete information on age, sex, BMI, premedication SBP, premedication DBP, smoking status, and education level. A total of 2,577 African Americans met genotyping quality control and had relevant covariates ([Supplementary Figure S2](#SM1){ref-type="supplementary-material"}). All statistical analyses were performed using PLINK 1.9 ([@B11]) or R ([@B48]). Linear regression models were used to identify genetic variants associated with either premedication SBP or premedication DBP. A Bonferroni adjusted *p*-value of 4.32 × 10^-7^ was used to determine significance. A main effects model included covariates for age, age squared, sex, BMI, smoking status, and percent global African ancestry:

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A second main effects model included the same covariates, but also included education. To examine the interaction between genetic variants and education and how it may affect blood pressure, we modeled two-way interactions using a linear regression model and the same covariates as in our main effects model:

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The decision was made to focus on a set of SNPs which had a *p*-value of less than 1.4 × 10^-5^ from the main effects model to reduce issues with multiple testing. This significance threshold was chosen based on a Bonferroni correction for the number of SNPs that would remain if SNPs with an *r*^2^-value of greater than 0.1 were removed from our dataset. For this set of significant SNPs, we used a model which included the main effects of education and the SNP, as well as the interaction term between education and the genetic variants. The significance threshold for the interaction models was based on the number of SNPs tested for association with premedication SBP and DBP (*p* \< 0.01 and *p* \< 0.003, respectively).

Results
=======

Population Characteristics
--------------------------

The final study population for analysis included 2,577 African American adults with Metabochip genotyping data and complete phenotype data ([Supplementary Figures S1, S2](#SM1){ref-type="supplementary-material"}). Among this study population, the majority were female (71%) with a median age of 38 years and median BMI of 26.8 kg/m^2^ ([Table 1](#T1){ref-type="table"}). Compared with a larger African American BioVU population genotyped on the Metabochip ([@B14]), this subset had proportionally more females, was younger, and had a lower median BMI. The median premedication SBP and DBP were within the normal clinical range (122 and 74 mmHg, respectively) and most of the population was never smokers (87%; [Table 1](#T1){ref-type="table"}). The median percent global African ancestry was 81.7%. The majority of participants had at least a high school degree ([Table 1](#T1){ref-type="table"}). The median premedication SBP and DBP in this final study sample were statistically different (*p* \< 0.05) from the larger study sample missing education data in the EHR but varied by only 3 mmHg ([Supplementary Table S1](#SM1){ref-type="supplementary-material"}).

###### 

Study population characteristics representing African American adults from a biobank with electronic health record (EHR)-extracted blood pressure.

  Characteristic                                              Number of individuals *n* = 2,577
  ----------------------------------------------------------- -----------------------------------
  Sex                                                         
      Male                                                    753 (29%)
      Female                                                  1,824 (71%)
  Median age in years (±SD)                                   38.2 (±15.4)
  Median percent global African ancestry (range)              81.7% (1.0--99.9)
  Education level                                             
      Less than high school                                   328 (12.7%)
      High school degree or equivalent                        1518 (58.9%)
  At least some college                                       731 (28.4%)
  Smoking Status                                              
      Never smokers                                           2,242 (87%)
      Ever smokers                                            335 (13%)
  Median body mass index (kg/m^2^; ±SD)                       26.8 (±6.8)
  Median premedication systolic blood pressure (mmHg; ±SD)    122.0 (±13.6)
  Median Premedication diastolic blood pressure (mmHg; ±SD)   74.4 (±9.0)

The population in this study was a subset of African Americans from the Vanderbilt University Medical Center (VUMC) biobank, BioVU. Samples were drawn from BioVU in 2011. All individuals had Metabochip genotype data which passed quality control measures. Individuals also had complete phenotype data which included age, sex, education level, smoking status, median body mass index (BMI), median premedication systolic blood pressure (SBP), and median diastolic blood pressure (DBP). These phenotypes were derived from the electronic health record. African ancestry was determined using ADMIXTURE. SD, standard deviation.

Predictors of Systolic and Diastolic Blood Pressure
---------------------------------------------------

In univariate analyses ([Table 2](#T2){ref-type="table"}), both premedication SBP and DBP were significantly associated with increasing age, male sex, and increasing BMI. SBP increased with age and DBP increased with age until around the age of 60, then began decreasing ([Supplementary Figures S3, S4](#SM1){ref-type="supplementary-material"}).

###### 

Univariate analyses between relevant covariates and median premedication blood pressure values among African American adults.

  Variable                                Effect estimate, β (standard error)   *p*-value     Effect estimate, β (standard error)   *p*-value
  --------------------------------------- ------------------------------------- ------------- ------------------------------------- -------------
  Education level                                                                                                                   
      Less than high school               REF                                                 REF                                   
      High school degree and equivalent   0.16 (0.83)                           0.84          1.95 (0.54)                           0.0003^∗^
      At least some college               1.26 (0.91)                           0.17          2.54 (0.59)                           \<0.0001^∗^
  Median age, years                       0.31 (0.01)                           \<0.0001^∗^   0.19 (0.01)                           \<0.0001^∗^
  Sex                                                                                                                               
      Male                                REF                                                 REF                                   
      Female                              --4.52 (0.52)                         \<0.0001^∗^   --1.48 (0.35)                         0.0001^∗^
  Median BMI (kg/m^2^)                    0.38 (0.04)                           \<0.0001^∗^   0.25 (0.02)                           \<0.0001^∗^
  Smoking status                                                                                                                    
      Never smoker                        REF                                                 REF                                   
      Ever smoker                         0.56 (0.72)                           0.43          0.45 (0.48)                           0.35
  Median global African ancestry          --0.46 (1.89)                         0.809         --1.24 (1.24)                         0.32

Prior to genetic analyses, covariates were examined to determine their association with the outcomes, premedication systolic (SBP) and diastolic blood pressure (DBP) in the study population, a subset of African Americans drawn from the Vanderbilt University Medical Center biobank BioVU (

n

= 2,577). Each linear regression model had either median premedication SBP or median premedication DBP as the outcome. The covariates included in each model were education level, median age, sex, body mass index (BMI), smoking status, and global African ancestry. Both premedication SBP and DBP were significantly associated with age, sex, and BMI. Premedication DBP is also significantly associated with education level. The symbol "

∗

" indicates statistical significance.

Neither premedication SBP nor premedication DBP was associated with smoking status or global African ancestry. Also, education was not significantly associated with either premedication SBP or premedication DBP ([Table 2](#T2){ref-type="table"} and [Supplementary Figures S5, S6](#SM1){ref-type="supplementary-material"}). Of all the variables tested, age and premedication DBP significantly co-varied with education ([Supplementary Table S2](#SM1){ref-type="supplementary-material"}).

Education as a Modifier of Genetic Associations With Systolic and Diastolic Blood Pressure
------------------------------------------------------------------------------------------

To test for possible interactive effects between education and genetic variants associated with SBP and DBP, we examined three models: (1) initial single SNP tests of association without education as a covariate, (2) single SNP tests of association with education as a covariate, and (3) single SNP tests of association with SNP × education interaction terms. In the first model, single SNP tests of association were performed for SBP and DBP using linear regression adjusting for age, age squared, sex, BMI, smoking status, and percent global African ancestry. For both SBP ([Supplementary Figure S7](#SM1){ref-type="supplementary-material"}) and DBP ([Supplementary Figure S8](#SM1){ref-type="supplementary-material"}), only a single SNP was statistically significant using a Bonferroni correction (*p* \< 4.32 × 10^-7^): *ARHGAP22* rs4593967 and *IQCK* rs950928, respectively.

The second set of models included education in addition to other relevant covariates ([Supplementary Figures S9, S10](#SM1){ref-type="supplementary-material"}). The addition of education to the model did not change the most significantly associated SNPs for either SBP or DBP ([Table 3](#T3){ref-type="table"}). In the regression model for SBP that included education, rs4593967 again passed Bonferroni correction (*p* \< 4.32 × 10^-7^), and two other SNPs (rs10921895 and rs3804485) were associated at a suggestive significance threshold (*p* \< 7.24 × 10^-6^). For DBP, rs950928 and rs8056711 passed Bonferroni correction. However, these SNPs have the same effect size and are in perfect linkage disequilibrium (*r*^2^ = 1.0), so they likely represent the same association.

###### 

Characteristics of single nucleotide polymorphisms (SNPs) associated with premedication systolic and diastolic blood pressure with and without education in the model.

               Without education   With education                                                                       
  ------------ ------------------- ---------------- ------------ ------ -------- ------ --------------- -------- ------ -------------------
  rs4593967    SBP                 Intron           *ARHGAP22*   0.14   --2.51   0.46   1.45 × 10^-7^   --2.53   0.48   **1.16 × 10^-7^**
  rs10921895   SBP                 Intergenic                    0.37   --1.52   0.37   5.31 × 10^-6^   --1.55   0.36   3.92 × 10^-6^
  rs3804485    SBP                 Intron           *LY86*       0.41   1.48     0.32   7.11 × 10^-6^   1.51     0.33   5.20 × 10^-6^
  rs950928     DBP                 Intron           *IQCK*       0.36   --1.04   0.24   3.13 × 10^-6^   --1.10   0.22   **4.53 × 10^-7^**
  rs8056711    DBP                 Intron           *IQCK*       0.36   --1.04   0.24   3.13 × 10^-6^   --1.10   0.22   **4.53 × 10^-7^**

In the both sets of linear regression models, median premedication systolic blood pressure (SBP) and median premedication diastolic blood pressure (DBP) were the outcomes. Additionally, both sets of linear regression models included age, age squared, sex, median body mass index (BMI), smoking status, and median percent global African ancestry as covariates. The first set of models did not include education level. The second set of models included education. The addition of education to the model did not change which SNPs were most associated with SBP or DBP. Bolded

p

-values are considered statistically significant after Bonferroni correction.

In the final set of models, education × SNP interaction terms were examined using SNPs associated with SBP or DBP at *p* \< 1.4 × 10^-5^, as described above. No interaction terms met a strict Bonferroni correction ([Supplementary Figures S11, S12](#SM1){ref-type="supplementary-material"}). However, we identified a potential SNP-education interaction affecting DBP, rs6687976 (*p* = 0.052; [Table 4](#T4){ref-type="table"}). This potential interaction remained with the addition of local ancestry to the model. Individuals homozygous for the minor allele and having less than a high school education had higher DBP compared with (1) individuals homozygous for the minor allele and high school education or greater and (2) individuals not homozygous for the minor allele and less than a high school education ([Supplementary Figure S13](#SM1){ref-type="supplementary-material"}). No statistically significant interactions were identified for SBP ([Table 4](#T4){ref-type="table"}).

###### 

Single nucleotide polymorphisms (SNPs) examined for interactions with education level impacting median premedication systolic and diastolic blood pressure.

  SNP in education interaction term   *p*-value for the interaction between high school and SNP   *p*-value for interaction between college and SNP
  ----------------------------------- ----------------------------------------------------------- ---------------------------------------------------
  **Systolic Blood Pressure**                                                                     
  rs4593967_A                         0.886                                                       0.858
  rs10921895_G                        0.896                                                       0.746
  rs3804485_C                         0.260                                                       0.863
  rs11066700_A                        0.178                                                       0.200
  **Diastolic Blood Pressure**                                                                    
  rs950928_G                          0.175                                                       0.298
  rs8056711_C                         0.175                                                       0.297
  rs9931167_A                         0.927                                                       0.503
  rs11648873_T                        0.940                                                       0.478
  rs28681321_A                        0.941                                                       0.461
  rs6687976_A                         **0.052**                                                   0.996
  chr16.19642355_G                    0.094                                                       0.110
  rs3095994_A                         0.738                                                       0.726
  rs1273518_G                         0.218                                                       0.648
  rs1858975_A                         0.076                                                       0.091
  rs4593967_A                         0.512                                                       0.768
  rs8046628_C                         0.181                                                       0.316
  rs6497402_A                         0.062                                                       0.085
  chr16.19641087_A                    0.064                                                       0.076

Median premedication systolic blood pressure (SBP) and diastolic blood pressure (DBP) were outcomes in the linear regression models. Covariates included in the models were age, age squared, sex, body mass index, smoking status, and African ancestry. The main effect of education and the SNP, as well as the SNP × education interaction term were also included in the model. Less than high school was the reference group within the regression models. The

p

-value for the potential SNP-education interaction is bolded.

Discussion
==========

We sought to determine if education, a measure of SES and a recognized social determinant of health, modified genetic associations with SBP and DBP in African Americans. A previous study suggested gene × education interactions occur with blood pressure, but this study was conducted in a European-descent population ([@B7]). Associations between premedication SBP or premedication DBP and genetic variants from the Metabochip were examined, while including known predictors of blood pressure (age, BMI, sex, percent African ancestry, and smoking status) in the model. Results were compared with models which included a main effect for education, and a main effect for education plus a SNP-education interaction term. We observed a suggestive SNP by education interaction affecting DBP, a result not explained by local genetic ancestry. This potential interaction requires statistical replication and further investigation.

Models Without Interaction
--------------------------

In univariate analyses the associations between premedication SBP and DBP and increasing age, male sex as well as increasing BMI were consistent with previous reports ([@B4]; [@B67]; [@B18]). The patterns of associations between SBP and DBP and age across the age continuum are also consistent with previous reports ([@B41]; [@B21]).

Intronic *ARHGAP22* rs4593967 was significantly associated with SBP and has not been previously reported as associated with blood pressure or hypertension. The minor allele frequency for *ARHGAP22* rs4593967 in this African American sample was 0.14, consistent with frequencies reported for African-descent populations included in The Genome Aggregation Database (0.148; [@B38]) and the 1000 Genomes Project (0.176; [@B1]). Conversely, the minor allele is less frequently observed among populations of European (∼0.08) or East Asian-descent (\<0.01). No other common (MAF \> 1%) variants within 500 kb are in strong linkage disequilibrium (*r*^2^ ≥ 0.80) with rs4593967 in African-descent populations from the 1000 Genomes Project. *ARHGAP22* encodes the rho GTPase activating protein 22 and is widely expressed with highest expression levels in the brain. Variants within *ARHGAP22* have been associated with diabetic retinopathy, conduct disorder, daytime sleep, and self-employment ([@B16]; [@B31]; [@B61]; [@B56]), but these associations have not been replicated.

Intronic *IQCK* rs950928 was significantly associated with DBP after adjusting for multiple testing. Like *ARHGAP22* rs4593967, the minor allele frequency for *IQCK* rs950928 is higher among populations of African-descent (∼0.40) compared with European-descent populations (∼0.15). *IQCK* rs950928 is in perfect or strong linkage disequilibrium with rs8056711 and rs59009734 in African-descent populations, neither of which has been previously associated with human disease or traits. *IQCK*, which overlaps with several genes including *KNOP1*, encodes for IQ motif containing K and serves as an EF hand protein binding site. Like *ARHGAP22, IQCK* is highly expressed in the brain. A search within the Genotype-Tissue Expression ([@B13]) database suggests that both rs8056711 and rs59009734 may be expression quantitative loci (eQTL), where each addition of the minor allele is associated with higher gene expression for several tissues including the right atrium auricular region of the heart and the aorta. While *IQCK* rs950928 and its associated SNPs rs8056711 and rs59009734 have not been previously associated with any phenotypes, common variants within *IQCK* have previously been associated with blood pressure, BMI, bone density, heart rate, chronic obstructive pulmonary disease, bipolar disorder, and a BMI-education interaction ([@B12]; [@B42]; [@B63]; [@B8]; [@B66]).

Despite the present study's small sample size (*n* = 2,577), there was sufficient power (80%) to detect significant associations with moderate effect size of 1.0 and a minor allele frequency of 0.20. For less common variants (MAF = 0.10), the study was powered to detect alleles with an effect size of 1.5 or greater. For variants with a MAF of 0.05, an effect size of 2.0 was needed in order to detect the variant's effect. This study was not powered to detect any of the variants reported in the recent one million-person GWAS of blood pressure, as the variant with the largest effect size in that study was less than 1.0, with a median effect size of 0.219 mmHg ([@B21]). The limited power due to small sample size and limited directly genotyped variants likely contributed to the lack of replication of SNPs known to be associated with blood pressure in African Americans from previous GWAS.

SNP × Education Interactions
----------------------------

We identified a possible SNP-education interaction affecting DBP for rs6687976 (*p* = 0.052). As the addition of local ancestry to the model did not alter the association, we expect that this observation is a result of true modifying effects of SES rather than ancestry. Individuals with two minor alleles and less than a high school education had higher blood pressure compared to those with two minor alleles and a high school education or those with less than a high school education and fewer minor alleles ([Supplementary Figure S13](#SM1){ref-type="supplementary-material"}). SNP rs6687976 is located within an intergenic region of chromosome 1 (Chr1:105674536 in GRCh37.p13) and has not been previously associated with any human traits within the literature. It is also not identified as an eQTL in GTEx ([@B13]). Despite the limited information known about rs6687976, this result suggests that interactions between markers of social determinants of health and genetic variants affecting blood pressure likely exist, consistent with the findings of other studies that have observed interactions between genetic variants and social factors such as depression ([@B55]), perceived discrimination ([@B60]), and cigarette smoking ([@B59]).

Limitations
-----------

The present study has several limitations. Primarily, the sample size is limited driven by the inclusion criteria of complete phenotype data for a specific racial/ethnic group within the larger clinical dataset. Therefore, we are unable to detect any variants of smaller effect sizes. The requirement for complete data may have also introduced biases that limit the interpretation and generalizability of these data.

In addition to the limited sample size, the study population was also different compared with previously published studies of blood pressure in African American populations. While the proportion of females, median BMI, percent African ancestry, median SBP, and median DBP were comparable with previous studies ([@B47]; [@B19]; [@B5]; [@B25]; [@B33]; [@B49]), the population in this study did have a much lower median age, over 15 years younger. Given that blood pressure increases with age, this younger study population may have reduced variability in blood pressure measurements compared with the older published study populations with right-skewed distributions ([@B67]).

Another limitation was the lack of a replication dataset; therefore, all associations reported here are putative pending statistical replication or corroborative functional data. To date, other studies comparable or larger in sample size have not yet reported associations between these SNPs and blood pressure ([@B28]). Furthermore, the genotyping array used here was also designed to include rare variation collected from the African ancestry samples as part of the 1000 Genomes Project. Therefore, many of the variants on the Metabochip were rare in African ancestry populations ([@B9]) and filtered out during the quality control process as the present study was not powered to detect associations for rare variation.

There were also limitations regarding the phenotype data. All the variables were extracted from EHRs. While these records have extensive amounts of data, the data recorded by healthcare providers are not always accurate and the ability to extract the data can be limited. Furthermore although the positive predictive value of our algorithm was 80% ([@B29]), there may have been inaccurate education information for the individuals within the dataset.

Determining which blood pressure measurements to use in the study is also a challenge, as measurements can vary widely across the EHR. The median blood pressure measurements were chosen for our study to reduce the influence of this variation. Beyond the inaccuracies and decisions to be made regarding the information within the EHR, blood pressure is difficult to measure within the clinic. Measurements of blood pressure can vary due to the calibration of instruments, the time of day it is measured, and due to illness ([@B34]). Patients also tend to have higher blood pressure within a clinical setting due to stress ([@B34]). To avoid these potential biases as much as possible, we chose median premedication blood pressure values for analysis, thereby avoiding outlier measurements and the changes introduced by blood pressure medications.

Finally, while education is a recognized social determinant of health, it is not a perfect proxy for social experiences. Still, evidence suggests that educational attainment can be a reflection of earning potential and social status ([@B54]; [@B58]). Education has been shown to be associated with life expectancy, numerous biomarkers, and other health outcomes such as obesity and smoking ([@B53]; [@B44]). Low educational attainment itself is not the cause of poor health outcomes, but rather a variable often associated with individual-level behavioral determinants (e.g., smoking) or community-level determinants (e.g., racial segregation) that may influence blood pressure. Neither of these determinants is routinely recorded with the EHR; in contrast, educational attainment is often mentioned in the EHR. The availability of these data coupled with the observation that individual educational attainment is often stable over time make this variable a robust albeit imperfect proxy for social experiences.

Strengths
---------

Despite the limitations within the study, there were also several strengths. Primarily, this is the first study to incorporate EHR-derived education information into a large-scale genetic investigation. This study is a proof of principle that EHR-derived social determinant information can be investigated in a GWAS setting, thus breaking new ground to incorporate social factors in genetic studies among biobank populations. This is also the first analysis to observe an interaction between education and a common genetic variant with blood pressure in an African American population.

Despite the consistent association between social environment and health, social determinants of health are typically not included in genetic studies of health outcomes. For studies that access biobanks, the lack of social determinant data is likely related to the difficulty in accessing these data within the EHR, where they are not usually recorded in structured fields. The algorithms used in our study are the first to extract these important data from EHRs for research purposes ([@B29]).

This study paves the road for the incorporation of education, as well as other social determinants of health, into genetic studies using biobank populations. The SNP-by-education interaction we observed affecting DBP (rs6687976) could suggest an example of a possible biological impact of the adversity experienced due to lower educational achievement. Only individuals homozygous for the minor allele who had less than a high school education experienced an increase in DBP. This association needs to be replicated; however, it suggests a potential pathway for the biological imbedding of stress experiences (represented by lower educational attainment) affecting blood pressure and risk for hypertension. Further studies are needed to support this hypothesis. We anticipate that this research will encourage other investigators to continue to study the genetics of health outcomes associated with racial health disparities and to incorporate social determinants of health within these studies.

Author Contributions
====================

BH conducted the analyses and wrote manuscript. EF-E helped to extract the phenotype data from the electronic health record. MA and DC contributed to guidance on project, and manuscript writing and editing.

Conflict of Interest Statement
==============================

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling Editor declared a past co-authorship with one of the authors DC.

**Funding.** This work was supported by the National Institutes of Health (NIH) U01 HG004798 and its ARRA supplements (DC), as well as National Cancer Institute 1K07CA172294 (MA). This publication was also made possible by the Clinical and Translational Science Collaborative of Cleveland, 4UL1TR0002548, from the National Center for Advancing Translational Sciences (NCATS) component of the National Institutes of Health and NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. The dataset (s) used for the analyses described were obtained from Vanderbilt University Medical Center's BioVU, which is supported by institutional funding and the National Center for Research Resources, grant UL1 RR024975--01 (now at NCATS, grant 2UL1 TR000445--06).

We would like to thank Drs. Alex Fish and William Bush for access to the local genetic ancestry data, and we further thank Dr. Bush for helpful comments during the revision process.

<https://mathgen.stats.ox.ac.uk/impute/impute_v2.html#reference>

Supplementary Material
======================

The Supplementary Material for this article can be found online at: <https://www.frontiersin.org/articles/10.3389/fgene.2019.00428/full#supplementary-material>

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[^1]: Edited by: C. Charles Gu, Washington University in St. Louis, United States

[^2]: Reviewed by: Tesfaye B. Mersha, Cincinnati Children's Hospital Medical Center, United States; Kenneth M. Weiss, The Pennsylvania State University, United States

[^3]: This article was submitted to Applied Genetic Epidemiology, a section of the journal Frontiers in Genetics
