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Volume 12 Supplement 1

Selected articles from the International Conference on Intelligent Biology and Medicine (ICIBM) 2018: medical genomics

  • Research
  • Open Access

A PheWAS study of a large observational epidemiological cohort of African Americans from the REGARDS study

  • †1,
  • †2, 3,
  • 1,
  • 4,
  • 1,
  • 5,
  • 4, 5,
  • 6,
  • 7,
  • 7,
  • 8,
  • 9,
  • 10,
  • 11,
  • 12,
  • 13,
  • 14,
  • 4Email author and
  • 3, 15Email author
†Contributed equally
BMC Medical Genomics201912 (Suppl 1) :26

https://doi.org/10.1186/s12920-018-0462-7

©  The Author(s). 2019

  • Published:

Abstract

Background

Cardiovascular disease, diabetes, and kidney disease are among the leading causes of death and disability worldwide. However, knowledge of genetic determinants of those diseases in African Americans remains limited.

Results

In our study, associations between 4956 GWAS catalog reported SNPs and 67 traits were examined among 7726 African Americans from the REasons for Geographic and Racial Differences in Stroke (REGARDS) study, which is focused on identifying factors that increase stroke risk. The prevalent and incident phenotypes studied included inflammation, kidney traits, cardiovascular traits and cognition. Our results validated 29 known associations, of which eight associations were reported for the first time in African Americans.

Conclusion

Our cross-racial validation of GWAS findings provide additional evidence for the important roles of these loci in the disease process and may help identify genes especially important for future functional validation.

Keywords

  • PheWAS
  • African Americans
  • Genetics
  • Cardiovascular disease

Background

Genome Wide Association Studies (GWASs) have provided a powerful approach for identifying association between genetic variants and a single phenotype. An alternative and complementary approach to query genotype-phenotype associations is the Phenome-Wide Association Study (PheWAS) [1]. With PheWAS, associations between a specific genetic variant and a wide range of phenotypes can be explored. They are well suited to facilitate the identification of new associations between SNPs and phenotypes as well as SNPs with pleiotropy [2–4]. The PheWAS approach was mainly pioneered by investigators at Vanderbilt University [1] and flourished in various hospital-based cohorts by scanning phenomic data in electronic medical records for genetic associations [1, 4–6] as well as by meta-analyzing data collected in observational cohort studies like the Population Architecture using Genomics and Epidemiology (PAGE) study [2].

As of January 2017, GWASs have identified ~ 44,000 SNPs important for various human phenotypes as summarized in the GWAS catalog [7], which makes it possible to reveal pleiotropic effects and genetic mechanisms shared by different traits. Conducting PheWASs using SNPs which were reported to be associated with one or more traits is an efficient method for replication of previous results and identification of pleiotropic effects.

In this study, we used the REasons for Geographic And Racial Differences in Stroke (REGARDS) Study to examine 4956 GWAS catalog SNPs (Additional file 1) that are included on the Infinium HumanExome-12v1-2_A (exome chip) array from Illumina with a rich collection of phenotypes. The REGARDS study is a population-based, longitudinal study including 30,000 participants (~ 40% African Americans), sampled from the continental US [8]. Among 12,000 African American participants, 7726 were genotyped with the exome chip. Since most PheWAS studies have considered individuals of European ancestry and cross-sectional phenotypes, REGARDS is an excellent resource for both cross-racial validation and identifying pleiotropic effects.

Results

We tested for association between 4956 GWAS catalog SNPs and 67 phenotypes. Genomic inflation factors (λ) generated from including all SNPs for a given phenotype showed good fitting of all models with λ range from 0.95 to 1.12. Table 1 summarizes 29 significant associations passing the significance threshold with P value less than 1.5E-7. S2 compares results extracted from the GWAS catalog on significant PheWAS SNPs to the REGARDS results. The significant associations are in several major phenotype groups: C reactive protein, lipid profile, diabetes, cystatin C, heart event risk, heart rate, and height. We classified the significant SNPs in two ways: 1. the SNP was associated to a phenotype matching previous publications 2. the SNP was associated to a phenotype related to the previously reported phenotype (Additional file 2).
Table 1

Summary of identified significant associations in REGARDS study

SNP ID

Phenotype

Minor allele (effect allele)

Major Allele

Beta or OR

P-value

MAF

First reported in AAs

Matched phenotype

rs10096633

Triglycerides

T

C

− 0.020

4.88E-10

0.4226

 

rs1173727

Height

T

C

0.297

9.89E-08

0.2032

yes

rs12110693

Heart rate

G

A

−1.302

4.28E-11

0.4984

 

rs12740374

LDL Cholesterol

T

G

−4.314

1.64E-10

0.2615

 

rs173539

HDL Cholesterol

T

C

2.337

1.21E-19

0.3647

yes

rs1800775

HDL Cholesterol

C

A

−2.843

1.53E-29

0.4272

yes

rs247616

HDL Cholesterol

T

C

4.309

4.88E-52

0.2528

yes

rs2794520

C reactive protein

T

C

−0.125

3.92E-34

0.2146

 

rs326

Triglycerides

A

G

0.019

8.20E-09

0.4436

 

rs3764261

HDL Cholesterol

A

C

3.050

1.84E-30

0.3165

 

rs6511720

LDL Cholesterol

T

G

−5.624

1.19E-10

0.1337

 

rs6511720

Total Cholesterol

T

G

−6.143

3.14E-10

0.1337

 

rs7412

LDL Cholesterol

T

C

−15.870

2.17E-65

0.1114

 

rs7499892

HDL Cholesterol

T

C

−2.351

1.38E-19

0.3677

yes

rs7553007

C reactive protein

A

G

−0.122

6.61E-34

0.2258

 

rs876537

C reactive protein

T

C

−0.124

7.99E-33

0.2083

 

rs9398652

Heart rate

C

A

−1.339

1.19E-11

0.4956

 

Related phenotype

rs12740374

Dyslipidemia

T

G

0.783

1.08E-10

0.2615

 

rs12740374

Total Cholesterol

T

G

−4.152

3.24E-08

0.2615

 

rs247616

Fram_CHD

T

C

−0.041

3.78E-09

0.2528

yes

rs629301

Dyslipidemia

G

T

0.827

4.32E-08

0.3633

 

rs646776

Dyslipidemia

C

T

0.827

4.41E-08

0.3622

yes

rs6511720

Dyslipidemia

T

G

0.737

4.45E-10

0.1337

 

rs7412

Fram_CHD

T

C

−0.066

3.03E-12

0.1114

 

rs7412

Ideal7

T

C

0.210

3.35E-14

0.1114

 

rs7412

Dyslipidemia

T

C

0.525

6.16E-33

0.1114

 

rs7412

Total Cholesterol

T

C

−13.330

2.90E-37

0.1114

 

rs7903146

Diabetes

T

C

1.306

2.30E-12

0.2919

 

rs911119

Cystatin C

C

T

−0.012

6.17E-08

0.356

yes

Beta coefficients were showed for continuous variables and odd ratios (OR) were showed for binary variables. MAF: minor allele frequency. Matched phenotype means the same phenotype and SNP associations have been showed in previous published studies; if similar or related associations have been published before, they are defined as “related phenotype”. If this is the first time that an association was shown in Africa American population, “Yes” was given in the column” First reported in AAs “

Validation of known genetic associations of phenotypes

Among the 29 significant genotype and phenotype associations, 17 have been previously reported for the same phenotype (Table 1 and Additional file 2). The effect directions of the 17 associations were the same as those in the previous reports. For eight of these phenotype –genotype associations, our study represents the first validation in an African American population (see section below). These replications validated the reliability of our PheWAS analysis approaches. We confirmed that C reactive protein level was related to rs2794520 (P = 3.9E-34), rs7553007 (P = 6.6E-34) and rs876537 (P = 8.0E-33), which are located near the CRP gene (Table 1). Five SNPs located near the CETP gene were associated with HDL cholesterol including rs173539 (P = 1.2E-19), rs1800775 (P = 1.5E-29), rs247616 (P = 4.9E-19), rs3764261 (P = 1.8E-30), and rs7499892 (P = 1.4E-19). Two SNPs were significantly associated with heart rate: rs12110693 near LOC644502 gene (P = 4.3E-11) and rs9398652 near GJA1 gene (P = 1.2E-11). We also reproduced the association between rs1173727 near the NPR3 gene and height with P = 9.9E-8. Three SNPs were significantly associated with LDL cholesterol including rs12740374 in the SORT1/ PSRC1/ CELSR2 cluster (P = 1.6E-10), rs6511720 in LDLR (P = 1.2E-10), and rs7412 in APOE (P = 2.2E-65). Rs10096633 in the LPL gene (P = 4.9E-10) and rs326 in the C8orf35/SLC18A1/LPL cluster (P = 8.2E-9) were associated with total cholesterol. Apart from 17 reported associations, the other 12 SNPs were associated with phenotypes that are closely related to previously published associations indexed in the GWAS catalog (Table 1 and Additional file 2).

Cross-racial validation

Eight of our findings were reported in other races previously but not in African Americans. Observed associations of rs173539, rs1800775, rs247616, and rs7499892 with HDL had not been previously reported in African Americans. The other new cross-ethnic validations from our study included rs1173727 with height, rs911119 with cystatin C, rs247616 with the Framingham risk score, and rs646776 with dyslipidemia (Table 1 and Additional file 2). Interestingly, we saw even more significant results for the association between rs247616 and HDL with P = 4.88E-52 and beta value = 4.3 (mg/dL) in REGARDS, compared to P = 9.7E-24 and beta value = 3.0 (mg/dL) in the GWAS catalog report [9] (Additional file 2).

SNPs associated with multiple traits

The 29 significant genotype and phenotype associations involved 20 SNPs, and 11 of these were associated with multiple traits (P-value < 1.0E-7 for the first trait and P < 3.7E-5 for the second trait) (Additional file 3). We also listed the genome-wide significant SNPs for one trait which were suggestively associated with another trait with nominal P < 0.05 in Additional file 3. Figure 1 listed those 11 SNPs and another 8 SNPs which were significantly associated with the first trait (P-value < 1.0E-7) and nominally associated with another trait (P < 0.05). Generally, the pleotropic effects were caused by one SNP associated with multiple correlated phenotypes. In the conditional analysis, the associations were not significant between the second top traits and the corresponding SNPs after including the top traits as the covariate. For example, rs7412 was associated with LDL (P = 7.64E-62) and Cystatin C (P = 1.80E-04) due to a significant association between these two phenotypes (P = 6.48E-06).
Fig. 1
Fig. 1

Heatmap shows the -log10P for association between SNPs with different traits. Shown in colors are the association P values of SNPs which are associated with first trait with P < 1.00E-7 and second trait with P < 0.05. The stars indicate the primary trait associated with the SNPs

Discussion

Our PheWAS presented association of 4956 SNPs with 67 phenotypes using a subset of African Americans from the REGARDS study. Our study validated 29 previous GWAS associations, of which eight associations were reported for the first time in African Americans (AAs). Among many of our findings, 11 SNPs were associated with multiple traits.

We identified 29 significant genotype and phenotype associations. 17 of these have been reported previously. The phenotypes of the other 12 associations were related with those previously reported but not exactly the same. For instance, rs911119 located in the CST3/CST4/CST9 gene cluster was reported previously associated with chronic kidney disease in a European population [10]. Our current study found that in African Americans allele C of rs911119 was negatively associated with the level of cystatin C, which is a biomarker for kidney function (P = 6.2E-8). Rs7903146 in TCF7L2 gene was reported associated with type 2 diabetes in several different populations [11], which agrees with our current results (P = 2.3E-12). Rs247616 in the CETP gene was significantly associated with the Framingham CHD Hard Event Risk Score (Fram_CHD: Risk of Coronary Death or MI over 10 Years) with P = 3.8E-9. While this SNP has not been previously associated with the Framingham risk score, it has been associated with its components as well as related phenotypes including blood metabolite levels, cardiovascular disease risk factors, and lipoprotein-associated phospholipase A2 mass and activity only in Europeans [9, 12, 13]. Rs7412 in the APOE gene was associated with Fram_CHD (P = 3.0E-12), total cholesterol (P = 2.9E-37), lipidemia (P = 6.2E-33) and Ideal7 (the American Heart Association’s “Life’s Simple Seven” score, i.e., total number of ideal risk behaviors or metrics for each of the seven) (P = 3.3E-14). Our findings were consistent with previous studies, which showed that rs7412 was associated with several lipid related phenotypes including LDL cholesterol, lipid metabolism phenotypes, lipid traits, and response to statin therapy [14–17]. Here, we also found that rs629301 (in CELSR2, PSRC1 and SORT1), rs646776 (in CELSR2, PSRC1 and SORT1) and rs6511720 (in LDLR) are associated with dyslipidemia. This is in alignment with previously findings: associations of rs629301 with total cholesterol and LDL cholesterol [18]; associations of rs646776 with total cholesterol, LDL cholesterol, lipid metabolism phenotypes, coronary artery disease, myocardial infarction (early onset), and response to statin therapy in Europeans [19, 20]; associations of rs6511720 with total cholesterol, LDL cholesterol, lipid metabolism phenotypes, lipoprotein-associated phospholipase A2 activity and mass, and cardiovascular disease risk factors [18]. Rs12740374 in CELSR2/PSRC1/SORT1 cluster was associated with two lipid traits: total cholesterol and dyslipidemia in our study, which is closely related with previously reported associations with LDL cholesterol and lipoprotein-associated phospholipase A2 activity and mass [21, 22].

We validated eight associations in AAs for the first time. Due to the difference of genetic variants between African Americans and the other races [23], it is interesting to check whether the associated variants reported in other races are associated with the same traits in AAs or not. When SNPs replicate across diverse populations, the gene’s importance in the disease process is emphasized, and consistency of findings may indicate genes that are especially important for future functional validation. Importantly, the effects of eight variants in AAs were of the same directions as in the other reported races.

Conclusions

In this study, we leveraged the rich phenotype collection and the exome chip data in 7726 REGARDS AA participants, and examined the associations between 4956 GWAS catalog SNPs and 67 phenotypes. We validated 29 previous GWAS associations, of which eight associations were reported for the first time in AAs.

Methods

Study population and design

The REGARDS Study is a prospective, longitudinal population-based cohort study [8] of European American and African American adults aged 45 and older. Detailed description of the objectives and design of this study has been published [8]. The baseline telephone interview and separate in-home visit were conducted between 2003 to 2007 [24]. Baseline data collection resulted in a broad range of demographic, diet, and clinical information as well as banked biospecimens which were used to extract DNA and assess multiple clinical measurements [8]. Participants continue to be contacted every 6 months by telephone to identify stroke events and other incident outcomes [8]. The REGARDS study protocol was approved by the institutional review boards of each participating institution, and written informed consents were obtained from all participants. This current study examined phenotypes available in REGARDS participants to explore their association with exome-chip SNP genotypes. A total of 7726 self-reported African Americans with exome chip data were included in our study. The average age of participants was 64.6 years old (standard deviation = 9.0), and 4770 (61.7%) were female.

SNP selection and genotyping

Genotyping was conducted using the Infinium HumanExome-12v1-2_A from Illumina (San Diego, CA, USA). The Illumina exome chip provides genotype data on > 240,000 putative functional variants selected based on over 12,000 individual exome and whole-genome sequences derived from individuals of European, African, Chinese, and Hispanic ancestry (http://genome.sph.umich.edu/wiki/Exome_Chip_Design). Raw genotyping data were called by GenomeStudio (version 2.0). The variant quality control included removing SNPs with call rate < 95%, monoallelic SNPs, multiallelic SNPs, and SNPs that had mapping errors. After further removing first and second degree relatives, samples with technical issues, and samples with mismatched sex, 7726 samples were available for analysis. In total, 4956 autosomal SNPs with minor allele frequency > 0.05 aligned to the GRCh37 reference sequence were matched to GWAS published SNPs catalog V1.0.1, which were reported to be associated with at least one trait with P < 1.0E-5 (Additional file 1) [7, 25].

Phenotypes

Lists of phenotypes included in this study are shown in Table 2 and Table 3. The phenotypes included both baseline and incident events among the 7726 African Americans. Baseline information included medical history, personal history, demographic data, socioeconomic status, cognitive screening, laboratory assays, urine, height, weight, waist circumference, blood pressure, pulse, electrocardiography, and medications in the past 2 weeks [8]. Follow-up events included stroke, coronary heart disease (CHD), myocardial infarction, infection, sepsis, end-stage renal disease, and death. All the phenotypes were binary or continuous variables (See Tables 2-3). Totally, 26 binary and 41 continuous phenotypes were included for current study [26–68]. The binary variables follow a binomial distribution and their frequencies for each category were calculated. Most of the continuous variables followed normal distribution. For variables with large skewness or kurtosis, a logarithm or square root transformation was performed. Obvious outliers with values at more than 10 standard deviations away from the mean were redefined as missing.
Table 2

List of binary phenotypes

Short name

Category

Full description

Number of “yes”

Number of samples

Frequency of “yes” (%)

Prevalent Phenotypes

CogStatus [26, 27]

Aging

Cognitive Status: Normal: defined as cogscore> 4, Impaired: defined as cogscore <=4

744

6195

12.01

Falls [28]

Aging

Self-reported fall in the past year

1166

7704

15.13

Afib [29, 30]

CVD related

Atrial Fibrillation (self-report or ECG evidence)

573

7526

7.61

CAD [31]

CVD related

History of Heart Disease (self-reported MI, CABG, bypass, angioplasty, or stenting OR evidence of MI via ECG

1186

7582

15.64

DVT [32]

CVD related

Self-reported deep vein thrombosis

371

7699

4.82

Hypertension [33, 34]

CVD related

Hypertensive if SBP > =140 or DBP > =90 or self-reported current medication use to control blood pressure

5622

7714

72.88

Dyslipidemia [35]

CVD related

Dyslipidemia: if TC > =240 or LDL > =160 or HDL < =40 or on medication

4171

7604

54.85

MI_SR [31]

CVD related

History of Myocardial Infarction (MI) (self-reported MI OR evidence of MI via ECG

891

7588

11.74

PAD_amputation [36]

CVD related

History of leg amputation

40

7725

0.52

PAD_surgery [36]

CVD related

Self-reported procedure to fix the arteries in legs

162

7709

2.1

Stroke_SR [37, 38]

CVD related

Participant reported stroke at baseline

597

7701

7.75

Stroke_Sympt [39, 40]

CVD related

Presence of stroke symptoms at baseline

1632

7134

22.88

TIA [29, 37]

CVD related

Participant reported Transient ischemic attack at baseline

257

7102

3.62

Diabetes [41]

Diabetes related

Diabetic if fasting glucose> = 126/non-fasting glucose> = 200 or pills or insulin

2335

7639

30.57

Cancer [42]

Other

Have you ever been diagnosed with cancer

526

4895

10.75

Orthopnea [29]

Other

Require more than one pillow to sleep at night

1076

7702

13.97

Dialysis [43]

Renal

Self-reported dialysis

45

7670

0.59

KidneyFailure [43]

Renal

Self-reported kidney failure

164

7670

2.14

Incident Phenotypes

CHD [44]

CVD related

Incidence of coronary heart disease until 2012

436

7726

5.64

MI [44]

CVD related

Incidence of myocardial infarction until 2012

284

7726

3.68

Stroke [45]

CVD related

Incidence of Stroke until 20,150,401

287

7726

3.71

Death [46]

Other

Incidence of Death until 20,150,401

1494

7726

19.34

Infection [47, 48]

Other

Incidence of infection

548

7726

7.09

Sepsis [47, 48]

Other

Incidence of sepsis

307

7726

3.97

Severe_sepsis [47, 48]

Other

Incidence of severe sepsis

243

7726

3.15

ESRD [49]

Renal

Incidence of end stage renal disease until 2012

238

7726

3.08

Table 3

The list of continuous phenotypes of this study

Short name

Category

Full description

Data transformation

Number of samples

Mean

Standard deviation

CogScore [26, 27]

Aging

Computed cognitive score

 

6195

5.45

0.85

Falls_number [28]

Aging

Number of times fallen in the past year

log10(x + 1)

1182

0.42

0.2

MCS [50]

Aging

The mental component of the short-form 12 health survey: Mental

 

7352

53.46

9.02

BMI [51]

Body size

Body Mass Index - kg/m2

 

7657

30.84

6.73

Height [51]

Body size

Height

 

7702

66.4

3.88

Waist_cm [51]

Body size

Waist circumference (cm)

 

7673

98.43

15.42

Weight_kg [51]

Body size

Weight (kg)

 

7694

87.99

20.54

ARICStroke

CVD related

ARIC Stroke Risk Score: 10 Year Probability of Ischemic Scroke (%)

log10

6791

0.83

0.47

Cholest [52]

CVD related

Total Cholesterol (mg/dL)

 

7676

193.1

40.9

Crp [53]

CVD related

C reactive protein (mg/L)

log10

7597

0.46

0.52

DBP [54, 55]

CVD related

Diastolic blood pressure - average of two measures (mmHg)

 

7703

78.58

10.11

Fram_CHD_score [56]

CVD related

Framingham CHD Hard Event Risk Score: Risk of Coronary Death or MI over 10 Years (among those free of CHD at baseline)

log10

6381

0.86

0.4

Fram_stroke_score [57]

CVD related

Framingham Stroke Risk Score: 10 Year Probability of Stroke (%) (among those who self-reported never having a stroke at baseline)

log10

6694

0.88

0.39

Hdl [52]

CVD related

HDL Cholesterol (mg/dL)

 

7622

53.46

15.9

Heartrate [58]

CVD related

Heart rate (beats per minute)

 

7627

68.48

11.95

Ideal7 [59]

CVD related

American Heart Association Life simple seven, total number of ideal for each of the seven

 

7726

2.12

1.08

Ldl [52]

CVD related

LDL Cholesterol (mg/dL)

 

7566

116.81

36.42

SBP [54, 55]

CVD related

Systolic blood pressure - average of two measures (mmHg)

 

7703

131.41

17.29

SLFS [60]

CVD related

Family risk score for stroke

 

4293

−0.48

0.33

Stroke_Sym_Number [39, 40]

CVD related

Number of stroke symptoms

 

7134

0.39

0.87

Trigly [52]

CVD related

Triglycerides (mg/dL)

log10

7673

2.01

0.2

Glucose [41]

Diatetes related

Glucose (mg/dL from labs formerly from fromVermont)

sqrt

7676

10.38

1.78

Insulin [41]

Diatetes related

Endogenous Insulin uU/mL

log10

5619

1.09

0.35

CESD [61]

Other

Center for Epidemiologic Studies Depression Scale

 

7670

1.39

2.21

DASH_Score [62]

Other

DASH style diet score

 

4592

23.11

4.25

Diet7 [59]

Other

Life simple seven, diet score

 

4592

1.17

0.37

Education [63]

Other

1 = ‘Less than high school’; 2 = ‘High school graduate’; 3 = ‘Some college’; 4 = ‘College graduate and above’; missing = − 9.

 

7718

2.57

1.08

Income [63]

Other

Income

 

6763

5.7

2.13

MedDietScore [64]

Other

Mediterranean diet score

 

4483

4.43

1.64

PA7 [59]

Other

Life simple seven, physical activity

 

7618

1.89

0.79

PCS [50]

Other

PCS-12: SF-12 Physical

square root

7325

4.55

1.1

Smoke7

Other

Life simple seven, smoking

 

7726

2.63

0.76

TV [65]

Other

watching TV time. 0 = ‘None’; 1 = ‘1–6 h/wk’; 2 = ‘1 h/day’; 3 = ‘2 h/day’; 4 = ‘3 h/day’; 5 = ‘4+ hrs/day’; missing = − 9.

 

5408

3.81

1.39

ACR [66]

Rental

Urinary Albumin/Creatinine ratio (mg/g)

log10

7421

1.09

0.62

Albumin_urine [66]

Rental

Urinary albumin (mg/L)

log10

7423

1.2

0.63

BUN [66]

Rental

Blood-urea-nitrogen (mg/dL)

log10

5472

1.18

0.16

Creatinine_serum [67]

Rental

IDMS Calibrated Creatinine (mg/dL)

log10(x + 1)

7674

0.29

0.09

Creatinine_urine [66]

Rental

Urinary creatinine (mg/dL)

 

7437

152.1

84.59

Cysc [67]

Rental

Cystatin C (mg/L)

log10

7597

0

0.14

EGFR_CKDEPI [68]

Rental

estimated GFR from the CKD-Epi equation

 

7674

87.52

23.67

EGFR_MDRD [68]

Rental

Glomerular Filtration Rate (mL/min/1.73 square meters) using IDMS calibrated creatinine and MDRD equation

 

7674

89.36

27.15

Statistical methods

Single SNP linear or logistic regressions were performed by PLINK for continuous or binary phenotypes respectively using an additive genetic model. The top 10 principal components determined by EIGENSTRAT [69], age, and gender were used as covariates for all phenotypes. Additional covariates were used for cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglyceride, glucose, and insulin. Those covariates included whether the participants were fasted under examination, whether they had self-reported diabetes and took insulin/glucose lowering pills, and whether they had self-reported dyslipidemia and took lipid lowering medication.

The threshold of significance level for PheWASs is not straightforward and multiple approaches have been used in other PheWAS studies [2–4]. The PAGE study used five population-based studies representing major racial/ethnic groups, and their threshold is “ P<0.01 observed in two or more PAGE studies for the same SNP, phenotype class, and race/ethnicity, and consistent direction of effect” [2]. The Environmental Architecture for Genes Linked to Environment (EAGLE) study used similar threshold with an additional condition for allele frequency > 0.01 and sample size > 200 [4]. The Norfolk Island study performed a principal component analysis of phenotypes and used principal components as the final phenotypes. A P value of 1.84E-7 was considered the threshold for a significant association between a component and SNP [3]. In our study, the criteria for a significant association between a single SNP and a single phenotype with Bonferroni correction was defined as P value = \( \frac{0.05}{4956\ast 67} \)=1.5E-7. In our study, significant genotype and phenotype associations involved 20 SNPs. Therefore, the significance threshold for a second trait of the pleiotropic effect is P = 0.05/(67*20) = 3.7E-5.

Notes

Abbreviations

GWAS: 

Genome-Wide Association Study

HDL: 

High-Density lipoprotein

LDL: 

Low-Density Lipoprotein

PAGE: 

Population Architecture using Genomics and Epidemiology

PheWAS: 

Phenome-Wide Association Study

REGARDS: 

REasons for Geographic and Racial Differences in Stroke

SNP: 

single nucleotide polymorphism

Declarations

Acknowledgements

The authors thank the other investigators, the staff, and the participants of the REGARDS study for their valuable contributions. A full list of participating REGARDS investigators and institutions can be found at http://www.regardsstudy.org.

Funding

The REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort is supported by a cooperative agreement U01 NS041588 from the National Institute of Neurological Disorders and Stroke, National Institutes of Health, Department of Health and Human Service. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Neurological Disorders and Stroke or the National Institutes of Health. Representatives of the funding agency have been involved in the review of the manuscript but not directly involved in the collection, management, analysis or interpretation of the data.

X.Z is supported by University of Alabama at Birmingham Statistical Genetics Post-Doctoral Training Grant (NIH T32HL072757). X.G. and D.Z. are partially supported by Agriculture and Food Research Initiative Competitive Grant no. 2015–67015-22975 from the USDA National Institute of Food and Agriculture (NIFA), and USDA Aquaculture Research Program Competitive Grant no. 2014–70007-22395. This work was also supported by 1RC4MD005964. Publication charges for this article have been funded by NIH R01HG010086.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

About this supplement

This article has been published as part of BMC Medical Genomics Volume 12 Supplement 1, 2019: Selected articles from the International Conference on Intelligent Biology and Medicine (ICIBM) 2018: medical genomics. The full contents of the supplement are available online at https://bmcmedgenomics.biomedcentral.com/articles/supplements/volume-12-supplement-1.

Authors’ contributions

MI, DA, and DZ designed the study. XZ, XG, VS, and DZ analyzed data. XZ, XG, NC, MI, and DZ wrote the manuscript. All the authors have participated in data interpretation, and read and approved the final manuscript.

Ethics approval and consent to participate

Our study has been approved by the appropriate internal review boards at the University of Texas Health Science Center at Houston and all other participating institutions, and it abides by the Declaration of Helsinki principles.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35233, USA
(2)
BGI-Shenzhen, Shenzhen, 518083, China
(3)
School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
(4)
Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35233, USA
(5)
Department of Medicine, University of Alabama at Birmingham, Birmingham, AL 35233, USA
(6)
Department of Emergency Medicine, University of Alabama at Birmingham, Birmingham, AL 35233, USA
(7)
Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
(8)
Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
(9)
Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN 46202, USA
(10)
Division of General Internal Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
(11)
Department of Medicine and Pathology, Larner College of Medicine at the University of Vermont, Burlington, VT 05405, USA
(12)
Department of Neurology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
(13)
Cardiovascular Research Institute, Department of Community Health and Preventive Medicine, Morehouse School of Medicine, Atlanta, GA 30310, USA
(14)
College of Public Health, University of Kentucky, Lexington, KY 40506, USA
(15)
School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA

References

  1. Denny JC, Ritchie MD, Basford MA, Pulley JM, Bastarache L, Brown-Gentry K, Wang D, Masys DR, Roden DM, Crawford DC. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics. 2010;26(9):1205–10.View ArticleGoogle Scholar
  2. Pendergrass SA, Brown-Gentry K, Dudek S, Frase A, Torstenson ES, Goodloe R, Ambite JL, Avery CL, Buyske S, Buzkova P, et al. Phenome-wide association study (PheWAS) for detection of pleiotropy within the population architecture using genomics and epidemiology (PAGE) network. PLoS Genet. 2013;9(1):e1003087.View ArticleGoogle Scholar
  3. Benton MC, Lea RA, Macartney-Coxson D, Hanna M, Eccles DA, Carless MA, Chambers GK, Bellis C, Goring HH, Curran JE, et al. A Phenomic scan of the Norfolk Island genetic isolate identifies a major pleiotropic effect locus associated with metabolic and renal disorder markers. PLoS Genet. 2015;11(10):e1005593.View ArticleGoogle Scholar
  4. Hall MA, Verma A, Brown-Gentry KD, Goodloe R, Boston J, Wilson S, McClellan B, Sutcliffe C, Dilks HH, Gillani NB, et al. Detection of pleiotropy through a phenome-wide association study (PheWAS) of epidemiologic data as part of the environmental architecture for genes linked to environment (EAGLE) study. PLoS Genet. 2014;10(12):e1004678.View ArticleGoogle Scholar
  5. Pendergrass SA, Brown-Gentry K, Dudek SM, Torstenson ES, Ambite JL, Avery CL, Buyske S, Cai C, Fesinmeyer MD, Haiman C, et al. The use of phenome-wide association studies (PheWAS) for exploration of novel genotype-phenotype relationships and pleiotropy discovery. Genet Epidemiol. 2011;35(5):410–22.View ArticleGoogle Scholar
  6. Namjou B, Marsolo K, Caroll RJ, Denny JC, Ritchie MD, Verma SS, Lingren T, Porollo A, Cobb BL, Perry C, et al. Phenome-wide association study (PheWAS) in EMR-linked pediatric cohorts, genetically links PLCL1 to speech language development and IL5-IL13 to eosinophilic esophagitis. Front Genet. 2014;5:401.View ArticleGoogle Scholar
  7. Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H, Klemm A, Flicek P, Manolio T, Hindorff L, et al. The NHGRI GWAS catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014;42(Database issue):D1001–6.View ArticleGoogle Scholar
  8. Howard VJ, Cushman M, Pulley L, Gomez CR, Go RC, Prineas RJ, Graham A, Moy CS, Howard G. The reasons for geographic and racial differences in stroke study: objectives and design. Neuroepidemiology. 2005;25(3):135–43.View ArticleGoogle Scholar
  9. Smith EN, Chen W, Kähönen M, Kettunen J, Lehtimäki T, Peltonen L, Raitakari OT, Salem RM, Schork NJ, Shaw M. Longitudinal genome-wide association of cardiovascular disease risk factors in the Bogalusa heart study. PLoS Genet. 2010;6(9):e1001094.View ArticleGoogle Scholar
  10. Köttgen A, Pattaro C, Böger CA, Fuchsberger C, Olden M, Glazer NL, Parsa A, Gao X, Yang Q, Smith AV. New loci associated with kidney function and chronic kidney disease. Nat Genet. 2010;42(5):376–84.View ArticleGoogle Scholar
  11. Ng MC, Shriner D, Chen BH, Li J, Chen W-M, Guo X, Liu J, Bielinski SJ, Yanek LR, Nalls MA. Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes. PLoS Genet. 2014;10(8):e1004517.View ArticleGoogle Scholar
  12. Shin S-Y, Fauman EB, Petersen A-K, Krumsiek J, Santos R, Huang J, Arnold M, Erte I, Forgetta V, Yang T-P. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46(6):543–50.View ArticleGoogle Scholar
  13. Grallert H, Dupuis J, Bis JC, Dehghan A, Barbalic M, Baumert J, Lu C, Smith NL, Uitterlinden AG, Roberts R. Eight genetic loci associated with variation in lipoprotein-associated phospholipase A2 mass and activity and coronary heart disease: meta-analysis of genome-wide association studies from five community-based studies. Eur Heart J. 2012;33(2):238–51.View ArticleGoogle Scholar
  14. Kettunen J, Tukiainen T, Sarin A-P, Ortega-Alonso A, Tikkanen E, Lyytikäinen L-P, Kangas AJ, Soininen P, Würtz P, Silander K. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat Genet. 2012;44(3):269–76.View ArticleGoogle Scholar
  15. Rasmussen-Torvik LJ, Pacheco JA, Wilke RA, Thompson WK, Ritchie MD, Kho AN, Muthalagu A, Hayes MG, Armstrong LL, Scheftner DA. High density GWAS for LDL cholesterol in African Americans using electronic medical records reveals a strong protective variant in APOE. Clin Transl Sci. 2012;5(5):394–9.View ArticleGoogle Scholar
  16. Wu Y, Marvelle AF, Li J, Croteau-Chonka DC, Feranil AB, Kuzawa CW, Li Y, Adair LS, Mohlke KL. Genetic association with lipids in CLHNS: waist circumference modifies an APOA5 effect on triglyceride levels. J Lipid Res. 2013; jlr. P042077.Google Scholar
  17. Chasman. Genetic determinants of statin-induced low-density lipoprotein cholesterol reduction: the justification for the use of statins in prevention: an intervention trial evaluating Rosuvastatin (JUPITER) trial (vol 5, pg 257, 2012). Circ Cardiovasc Genet. 2012;5(3):E27–7.Google Scholar
  18. Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, Buchkovich ML, Mora S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45(11):1274–83.View ArticleGoogle Scholar
  19. Surakka I, Horikoshi M, Mägi R, Sarin A-P, Mahajan A, Lagou V, Marullo L, Ferreira T, Miraglio B, Timonen S. The impact of low-frequency and rare variants on lipid levels. Nat Genet. 2015;47(6):589–97.View ArticleGoogle Scholar
  20. Chasman DI, Pare G, Mora S, Hopewell JC, Peloso G, Clarke R, Cupples LA, Hamsten A, Kathiresan S, Mälarstig A. Forty-three loci associated with plasma lipoprotein size, concentration, and cholesterol content in genome-wide analysis. PLoS Genet. 2009;5(11):e1000730.View ArticleGoogle Scholar
  21. Lettre G, Palmer CD, Young T, Ejebe KG, Allayee H, Benjamin EJ, Bennett F, Bowden DW, Chakravarti A, Dreisbach A. Genome-wide association study of coronary heart disease and its risk factors in 8,090 African Americans: the NHLBI CARe project. PLoS Genet. 2011;7(2):e1001300.View ArticleGoogle Scholar
  22. Chu AY, Guilianini F, Grallert H, Dupuis J, Ballantyne CM, Barratt BJ, Nyberg F, Chasman DI, Ridker PM. Genome-Wide Association Study Evaluating Lp-PLA2 Mass and Activity at Baseline and Following Rosuvastatin Therapy. Circ Cardiovasc Genet. 2012; CIRCGENETICS. 112.963314.Google Scholar
  23. Jorde LB, Wooding SP. Genetic variation, classification and'race'. Nat Genet. 2004;36:S28–33.View ArticleGoogle Scholar
  24. Tamura MK, Wadley V, Yaffe K, McClure LA, Howard G, Go R, Allman RM, Warnock DG, McClellan W. Kidney function and cognitive impairment in US adults: the Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study. Am J Kidney Dis. 2008;52(2):227–234.Google Scholar
  25. The NHGRI-EBI Catalog of published genome-wide association studies. [http://www.ebi.ac.uk/gwas] 2016.
  26. Thacker EL, Gillett SR, Wadley VG, Unverzagt FW, Judd SE, McClure LA, Howard VJ, Cushman M. The American Heart Association Life's simple 7 and incident cognitive impairment: the REasons for geographic and racial differences in stroke (REGARDS) study. J Am Heart Assoc. 2014;3(3):e000635.View ArticleGoogle Scholar
  27. Wadley VG, Unverzagt FW, McGuire LC, Moy CS, Go R, Kissela B, McClure LA, Crowe M, Howard VJ, Howard G. Incident cognitive impairment is elevated in the stroke belt: the REGARDS study. Ann Neurol. 2011;70(2):229–36.View ArticleGoogle Scholar
  28. O'Neal WT, Qureshi WT, Judd SE, Bowling CB, Howard VJ, Howard G, Soliman EZ. Effect of falls on frequency of atrial fibrillation and mortality risk (from the REasons for geographic and racial differences in stroke study). Am J Cardiol. 2015;116(8):1213–8.View ArticleGoogle Scholar
  29. Meschia JF, Merrill P, Soliman EZ, Howard VJ, Barrett KM, Zakai NA, Kleindorfer D, Safford M, Howard G. Racial disparities in awareness and treatment of atrial fibrillation: the REasons for geographic and racial differences in stroke (REGARDS) study. Stroke. 2010;41(4):581.View ArticleGoogle Scholar
  30. Prineas RJ, Soliman EZ, Howard G, Howard VJ, Cushman M, Zhang Z-M, Moy CS. The sensitivity of the method used to detect atrial fibrillation in population studies affects group-specific prevalence estimates: ethnic and regional distribution of atrial fibrillation in the REGARDS study. J Epidemiol. 2009;19(4):177–81.View ArticleGoogle Scholar
  31. Safford MM, Brown TM, Muntner PM, Durant RW, Glasser S, Halanych JH, Shikany JM, Prineas RJ, Samdarshi T, Bittner VA, et al. Association of race and sex with risk of incident acute coronary heart disease events. JAMA. 2012;308(17):1768–74.View ArticleGoogle Scholar
  32. Zakai NA, McClure LA, Judd SE, Safford MM, Folsom AR, Lutsey PL, Cushman M. Racial and regional differences in venous thromboembolism in the United States in 3 cohorts. Circulation. 2014;129(14):1502.View ArticleGoogle Scholar
  33. Howard G, Prineas R, Moy C, Cushman M, Kellum M, Temple E, Graham A, Howard V. Racial and geographic differences in awareness, treatment, and control of hypertension: the REasons for geographic and racial differences in stroke study. Stroke. 2006;37(5):1171–8.View ArticleGoogle Scholar
  34. Safford MM, Halanych JH, Lewis CE, Levine D, Houser S, Howard G. Understanding racial disparities in hypertension control: intensity of hypertension medication treatment in the REGARDS study. Ethn Dis. 2007;17(3):421–6.PubMedGoogle Scholar
  35. Zweifler RM, McClure LA, Howard VJ, Cushman M, Hovater MK, Safford MM, Howard G, Goff DC. Racial and geographic differences in prevalence, awareness, treatment and control of dyslipidemia: the reasons for geographic and racial differences in stroke (REGARDS) study. Neuroepidemiology. 2011;37(1):39–44.View ArticleGoogle Scholar
  36. Wang HE, Shapiro NI, Griffin R, Safford MM, Judd S, Howard G. Chronic medical conditions and risk of sepsis. PLoS One. 2012;7(10):e48307.View ArticleGoogle Scholar
  37. Judd SE, Kleindorfer DO, McClure LA, Rhodes JD, Howard G, Cushman M, Howard VJ. Self-report of stroke, transient ischemic attack, or stroke symptoms and risk of future stroke in the REasons for geographic and racial differences in stroke (REGARDS) study. Stroke. 2013;44(1):55.View ArticleGoogle Scholar
  38. Howard G, McClure LA, Moy CS, Howard VJ, Judd SE, Yuan Y, Long DL, Muntner P, Safford MM, Kleindorfer DO. Self-reported stroke risk stratification: reasons for geographic and racial differences in stroke study. Stroke. 2017;48(7):1737–43.View ArticleGoogle Scholar
  39. Gao L, Meschia JF, Judd SE, Muntner P, McClure LA, Howard VJ, Rhodes JD, Cushman M, Safford MM, Soliman EZ, et al. What stroke symptoms tell us: association of risk factors and individual stroke symptoms in the REasons for geographic and racial differences in stroke (REGARDS) study. J Stroke Cerebrovasc Dis. 2012;21(5):411–6.View ArticleGoogle Scholar
  40. Howard G, Safford MM, Meschia JF, Moy CS, Howard VJ, Pulley L, Gomez CR, Crowther M. Stroke symptoms in individuals reporting no prior stroke or transient ischemic attack are associated with a decrease in indices of mental and physical functioning. Stroke. 2007;38(9):2446–52.View ArticleGoogle Scholar
  41. Carson AP, Muntner P, Kissela BM, Kleindorfer DO, Howard VJ, Meschia JF, Williams LS, Prineas RJ, Howard G, Safford MM. Association of prediabetes and diabetes with stroke symptoms: the REasons for geographic and racial differences in stroke (REGARDS) study. Diabetes Care. 2012;35(9):112140.View ArticleGoogle Scholar
  42. O'Neal WT, Lakoski SG, Qureshi W, Judd SE, Howard G, Howard VJ, Cushman M, Soliman EZ. Relation between cancer and atrial fibrillation (from the REasons for geographic and racial differences in stroke study). Am J Cardiol. 2015;115(8):1090–4.View ArticleGoogle Scholar
  43. McClellan WM, Warnock DG, Judd S, Muntner P, Patzer RE, Bradbury BD, McClure LA, Newsome BB, Howard G. Association of family history of ESRD, prevalent albuminuria, and reduced GFR with incident ESRD. Am J Kidney Dis. 2012;59(1):25–31.View ArticleGoogle Scholar
  44. Moise N, Khodneva Y, Richman J, Shimbo D, Kronish I, Safford MM. Elucidating the association between depressive symptoms, coronary heart disease, and stroke in black and white adults: the REasons for geographic and racial differences in stroke (REGARDS) study. J Am Heart Assoc. 2016;5(8):e003767.View ArticleGoogle Scholar
  45. Howard VJ, McClure LA, Kleindorfer DO, Cunningham SA, Thrift AG, Diez Roux AV, Howard G. Neighborhood socioeconomic index and stroke incidence in a national cohort of blacks and whites. Neurology. 2016;87(22):10.1212.View ArticleGoogle Scholar
  46. Whalen KA, Judd S, McCullough ML, Flanders WD, Hartman TJ, Bostick RM. Paleolithic and Mediterranean diet pattern scores are inversely associated with all-cause and cause-specific mortality in adults. J Nutr. 2017:1217–26.Google Scholar
  47. Chaudhary NS, Donnelly JP, Moore JX, Baddley JW, Safford MM, Wang HE. Association of baseline steroid use with long-term rates of infection and sepsis in the REGARDS cohort. Crit Care. 2017;21(1):185.View ArticleGoogle Scholar
  48. Wang HE, Addis DR, Donnelly JP, Shapiro NI, Griffin RL, Safford MM, Baddley JW. Discharge diagnoses versus medical record review in the identification of community-acquired sepsis. Crit Care. 2015;19:42.View ArticleGoogle Scholar
  49. O'Neal WT, Tanner RM, Efird JT, Baber U, Alonso A, Howard VJ, Howard G, Muntner P, Soliman EZ. Atrial fibrillation and incident end-stage renal disease: the REasons for geographic and racial differences in stroke (REGARDS) study. Int J Cardiol. 2015;185:219–23.View ArticleGoogle Scholar
  50. Haley WE, Roth DL, Kissela B, Perkins M, Howard G. Quality of life after stroke: a prospective longitudinal study. Qual Life Res. 2011;20(6):799–806.View ArticleGoogle Scholar
  51. Kramer H, Gutiérrez OM, Judd SE, Muntner P, Warnock DG, Tanner RM, Panwar B, Shoham DA, McClellan W. Waist circumference, body mass index, and ESRD in the REGARDS (reasons for geographic and racial differences in stroke) study. Am J Kidney Dis. 2016;67(1):62–9.View ArticleGoogle Scholar
  52. Booth JN, Colantonio LD, Howard G, Safford MM, Banach M, Reynolds K, Cushman M, Muntner P. Healthy lifestyle factors and incident heart disease and mortality in candidates for primary prevention with statin therapy. Int J Cardiol. 2016;207:196–202.View ArticleGoogle Scholar
  53. Dawood FZ, Judd S, Howard VJ, Limdi NA, Meschia JF, Cushman M, Howard G, Herrington DM, Soliman EZ. High-sensitivity C-reactive protein and risk of stroke in atrial fibrillation (from the reasons for geographic and racial differences in stroke study). Am J Cardiol. 2016;118(12):1826–30.View ArticleGoogle Scholar
  54. Howard G, Lackland DT, Kleindorfer DO, Kissela BM, Moy CS, Judd SE, Safford MM, Cushman M, Glasser SP, Howard VJ. Racial differences in the impact of elevated systolic blood pressure on stroke risk. JAMA Intern Med. 2013;173(1):46–51.View ArticleGoogle Scholar
  55. Glasser SP, Halberg DL, Sands C, Gamboa CM, Muntner P, Safford M. Is pulse pressure an independent risk factor for incident acute coronary heart disease events? The REGARDS study. Am J Hypertens. 2014;27(4):555–63.View ArticleGoogle Scholar
  56. Safford MM, Gamboa CM, Durant RW, Brown TM, Glasser SP, Shikany JM, Zweifler RM, Howard G, Muntner P. Race-sex differences in the management of hyperlipidemia: the REasons for geographic and racial differences in stroke study. Am J Prev Med. 2015;48(5):520–7.View ArticleGoogle Scholar
  57. McClure LA, Kleindorfer DO, Kissela BM, Cushman M, Soliman EZ, Howard G. Assessing the performance of the Framingham stroke risk score in the reasons for geographic and racial differences in stroke cohort. Stroke. 2014;45(6):1716.View ArticleGoogle Scholar
  58. O'Neal WT, Qureshi WT, Judd SE, Meschia JF, Howard VJ, Howard G, Soliman EZ. Heart rate and ischemic stroke: the REasons for geographic and racial differences in stroke (REGARDS) study. Int J Stroke. 2015;10(8):1229–35.View ArticleGoogle Scholar
  59. Kulshreshtha A, Vaccarino V, Judd SE, Howard VJ, McClellan WM, Muntner P, Hong Y, Safford MM, Goyal A, Cushman M: Life's Simple 7 and risk of incident stroke: the reasons for geographic and racial differences in stroke study. Stroke 2013, 44(7): doi: https://doi.org/10.1161/STROKEAHA.111.000352.
  60. Kennedy RE, Howard G, Go RC, Rothwell PM, Tiwari HK, Feng R, McClure LA, Prineas RJ, Banerjee A, Arnett DK. Association between family risk of stroke and myocardial infarction with prevalent risk factors and coexisting diseases. Stroke. 2012;43(4):974.View ArticleGoogle Scholar
  61. Kronish IM, Carson AP, Davidson KW, Muntner P, Safford MM. Depressive symptoms and cardiovascular health by the American Heart Association's definition in the reasons for geographic and racial differences in stroke (REGARDS) study. PLoS One. 2012;7(12):e52771.View ArticleGoogle Scholar
  62. Shimbo D, Levitan EB, Booth JN, Calhoun DA, Judd SE, Lackland DT, Safford MM, Oparil S, Muntner P. The contributions of unhealthy lifestyle factors to apparent resistant hypertension: findings from the Reasons for Geographic And Racial Differences in Stroke (REGARDS) study. J Hypertens. 2013;31(2). https://doi.org/10.1097/HJH.0b013e32835b6be7.
  63. Lewis MW, Khodneva Y, Redmond N, Durant RW, Judd SE, Wilkinson LL, Howard VJ, Safford MM. The impact of the combination of income and education on the incidence of coronary heart disease in the prospective reasons for geographic and racial differences in stroke (REGARDS) cohort study. BMC Public Health. 2015;15:1312.View ArticleGoogle Scholar
  64. Tsivgoulis G, Judd S, Letter AJ, Alexandrov AV, Howard G, Nahab F, Unverzagt FW, Moy C, Howard VJ, Kissela B, et al. Adherence to a Mediterranean diet and risk of incident cognitive impairment. Neurology. 2013;80(18):1684–92.View ArticleGoogle Scholar
  65. McDonnell MN, Hillier SL, Judd SE, Yuan Y, Hooker SP, Howard VJ. Association between television viewing time and risk of incident stroke in a general population: results from the REGARDS study. Prev Med. 2016;87:1–5.View ArticleGoogle Scholar
  66. Warnock DG, Muntner P, McCullough PA, Zhang X, McClure LA, Zakai N, Cushman M, Newsome BB, Kewalramani R, Steffes MW, et al. Kidney function, albuminuria, and all-cause mortality in the REGARDS (reasons for geographic and racial differences in stroke) study. Am J Kidney Dis. 2010;56(5):861–71.View ArticleGoogle Scholar
  67. Colantonio LD, Tanner RM, Warnock DG, Gutiérrez OM, Judd S, Muntner P, Bowling CB. The role of cystatin-C in the confirmation of reduced glomerular filtration rate among the oldest old. Arch Med Sci. 2016;12(1):55.View ArticleGoogle Scholar
  68. Matsushita K, Mahmoodi BK, Woodward M, Emberson JR, Jafar TH, Jee SH, Polkinghorne KR, Shankar A, Smith DH, Tonelli M, et al. Comparison of risk prediction using the CKD-EPI equation and the MDRD study equation for estimated glomerular filtration rate. JAMA. 2012;307(18):1941–51.View ArticleGoogle Scholar
  69. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38(8):904–9.View ArticleGoogle Scholar

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