Variants in CXADR and F2RL1 are associated with blood pressure and obesity in African-Americans in regions identified through admixture mapping

Abstract

Objective

Genetic variants in 296 genes in regions identified through admixture mapping of hypertension, BMI, and lipids were assessed for association with hypertension, blood pressure, BMI, and HDL-C.

Methods

This study identified coding SNPs identified from HapMap2 data that were located in genes on chromosomes 5, 6, 8, and 21, where ancestry association evidence for hypertension, BMI or HDL-C was identified in previous admixture mapping studies. Genotyping was performed in 1,733 unrelated African-Americans from the National Heart, Lung and Blood Institute’s (NHLBI) Family Blood Pressure Project, and gene-based association analyses were conducted for hypertension, systolic blood pressure (SBP), diastolic blood pressure (DBP), BMI, and HDL-C. A gene score based on the number of minor alleles of each SNP in a gene was created and used for gene-based regression analyses, adjusting for age, age², sex, local marker ancestry, and BMI, as applicable. An individual’s African ancestry estimated from 2,507 ancestry-informative markers was also adjusted for to eliminate any confounding due to population stratification.

Results

CXADR (rs437470) on chromosome 21 was associated with SBP and DBP with or without adjusting for local ancestry (p < 0.0006). F2RL1 (rs631465) on chromosome 5 was associated with BMI (p = 0.0005). Local ancestry in these regions was associated with the respective traits as well.

Conclusions

This study suggests that CXADR and F2RL1 likely play important roles in blood pressure and obesity variation, respectively; and these findings are consistent with other studies, so replication and functional analyses are necessary.

Background

More than 82.6 million adults in the United States have cardiovascular disease, which is associated with hypertension, poorly controlled cholesterol levels, and obesity [1]. Poor cardiovascular health is associated with a number of serious outcomes, including stroke and coronary heart disease, and death. It is evident that this is a substantial public health issue.

Approximately one-fourth of adults worldwide have high blood pressure with about 7.5 million deaths worldwide due to high blood pressure [2]. Some 34–67% of the inter-individual variation in blood pressure is thought to be due to genetic factors [3–7]. Similarly, approximately 2.6 million deaths worldwide are due to high cholesterol and about 2.8 million deaths worldwide are due to overweight or obese status [2]. Estimates of the genetic contribution to the phenotypic variation of these diseases are 40–69% for HDL-C, 40–66% for LDL-C, and 16–85% for obesity determined by BMI [8–14]. Genome-wide association studies (GWAS) and admixture mapping analyses in recently admixed populations are two methods that have been used to identify genetic variants associated with blood pressure, lipids, and BMI.

Common genetic variants for hypertension have been identified through GWAS, but the resulting associations are of modest effect sizes. In the CHARGE Consortium and the Global BPgen Consortium blood pressure GWAS, 13 loci were collectively associated with 1.0 mm Hg increase in SBP and 0.5 mm Hg increase in DBP [15, 16]. Recently, the largest blood pressure GWAS identified 29 independent variants at 28 loci (16 novel loci) significantly associated with blood pressure in approximately 200,000 subjects; however these variants collectively accounted for only 0.9% of the phenotypic variance [17].

In a smaller GWAS conducted in 8,591 African-Americans in the Candidate Gene Association Resource (CARe) study, Fox et al. reported two SNPs that were significantly associated with blood pressure but were not replicated [18]. Another GWAS of 1,017 African-Americans identified several SNPs that were significantly associated with SBP, DBP, and hypertension; however, none of these findings were successfully replicated in the larger CARe study [18, 19]. Difficulty in replicating significant findings illustrates the challenges in identifying genetic variants that affect blood pressure in the African-American population.

In the largest GWAS conducted for blood lipids, significant results for 95 loci (59 novel loci) were reported [20]. For HDL-C, 62 loci were significant and 57 loci were significant for LDL-C. A number of loci overlapped between the two traits. In an analysis that included secondary signals in 26 loci, the mapped variants collectively accounted for 25–30% of the genetic variance of each lipid phenotype.

For obesity, one of the earliest GWAS found a genetic variant in the FTO gene on chromosome 16q that was significantly associated with BMI [21]. A multi-stage obesity GWAS later reported significant results for 11 regions (7 novel regions), and the cumulative effect of the 11 loci accounted for less than 1% of the population variability of obesity [22]. Another large GWAS reported 32 loci (18 novel loci) that were associated with BMI, but cumulatively, the loci explained only 2–4% of the genetic variance of BMI [23]. Further, the authors estimated that only 6–11% of the genetic variation of BMI could be explained by almost 300 genetic variants of similar effect sizes that probably exist in a sample size of over 730,000. These analyses clearly demonstrate some of the difficulties of conducting GWAS for complex diseases.

In addition to GWAS, African-American populations and other admixed populations are well-suited for admixture mapping studies to identify genetic variants associated with blood pressure, lipids and BMI [24, 25]. Admixture mapping capitalizes on differences in disease prevalence between parental populations of an admixed population to detect genetic variants associated with the disease. Previously, results of admixture analyses for hypertension, BMI and lipids in African-Americans were reported [26–30]. Several genomic regions were identified for the phenotypes of interest, including 6q24 and 21q21 for hypertension, 5q14-5q32 for BMI, and 8q11-8q21 for high-density lipoprotein (HDL-C).

The present study follows up SNPs identified from HapMap2 data in genes in four genomic regions, 5q14-32, 6q24, 8q11-8q21, and 21q21, that were reported in previous admixture analyses [26–29]. African-American subjects from the National Heart, Lung and Blood Institute’s (NHLBI) Family-Based Blood Pressure Program (FBPP) were genotyped for SNPs in the four admixture regions and for 2,507 ancestry informative markers [31]. The aim of this study was to find variants in coding regions that are associated with systolic and diastolic blood pressure, hypertension, BMI, and HDL-C in these four genomic regions that were identified in admixture mapping studies. Gene-based analyses, rather than single SNP analyses, were used to assess the variants with an emphasis on biological function. In addition, the study was conducted in the African-American population, so there are advantages in utilizing their admixture to further examine the genetic variation of hypertension and in focusing on a population that has been shown to be more likely to have poorly controlled blood pressure than non-Hispanic white hypertensive adults [32].

Methods

Sample

The NHLBI’s Family-Based Blood Pressure Program is a multi-center study that examines the genetic causes of hypertension and related phenotypes in different racial and ethnic groups, African Americans, Asians and Asian Americans, European Americans, and Mexican Americans [31]. As the FBPP study was designed for linkage analysis, each of the networks in the FBPP ascertained families via probands with elevated blood pressure. This study focused on African Americans, who were recruited for three FBPP Networks. The Hypertension Genetic Epidemiology Network (HyperGEN) recruited African American subjects from Birmingham, Alabama and Forsyth County, North Carolina. The Genetic Epidemiology Network of Atherosclerosis (GENOA) included African Americans from Jackson, Mississippi, and the GenNet study recruited African Americans from Maywood, Illinois. Additional details of the FBPP Networks are described elsewhere [31].

In this study, 1,733 unrelated subjects, 18 to 70 years, were selected for analysis from the FBPP data by first selecting the control from the families, if any were available. If there were multiple controls present, the oldest control was selected. For cases, the youngest case in the family was selected. Systolic and diastolic blood pressure measurements were obtained from Dinamap blood pressure monitors. BMI was calculated from weight (in kilograms) and height (in meters) measurements as weight/height².

Genotyping Methods

SNPs from HapMap2 data that were located in the exons of 296 genes in regions on chromosomes 5q14-32, 6q24, 8q11-21, and 21q21 were identified. Each of these regions was defined as the one unit drop of the −log(P) value from the peak in the admixture mapping analysis. As a result, 91 genes on chromosome 5, 117 genes on chromosome 6, 37 genes on chromosome 8, and 51 genes on chromosome 21 were examined in this study. These regions were selected for this study because they showed association evidence to hypertension, BMI, or HDL-C in previous admixture studies [26–29]. Genes on chromosome 5 were examined for association with BMI, genes on chromosome 8 were examined for association with HDL-C, and genes on chromosomes 6 and 21 were examined for association with SBP, DBP, and hypertension. In addition, 2,507 ancestry-informative markers (AIMs) across the genome were assessed to differentiate between African and European local and global ancestry and to adjust for population stratification.

Each of the four networks obtained blood samples from the subjects, and DNA was extracted by standard methods. The subjects were genotyped using the Illumina iSelect Custom Bead Chip at the University of California San Francisco. The genetic data were examined for departures from Hardy-Weinberg equilibrium (HWE) in the hypertension controls. Two SNPs were found to have departures from HWE at the threshold corrected for testing 611 coding SNPs (p = 8 × 10⁻⁵), and these two SNPs were removed from the analysis. In addition, the genotype call rates were all ≥ 95%. The AIMs were selected from SNPs available on the Illumina Human 1M array, the Illumina 650K array, and the Affymetrix 6.0 array.

Statistical Methods

Hypertension was defined as having SBP ≥ 140 mm Hg, DBP ≥ 90 mm Hg, and/or taking prescription medication for high blood pressure [33]. Sub-optimal HDL-C levels were < 40 mg/dL in men and < 50 mg/dL in women, and a sub-optimal low density lipoprotein cholesterol (LDL-C) level was ≥ 100 mg/dL [34]. Obesity was defined as having a body-mass index (kg/m²) ≥ 30 [35].

First, SBP and DBP were imputed for subjects being treated for hypertension by adding 10 mm Hg to SBP and 5 mm Hg to DBP for these subjects, consistent with the CHARGE GWAS strategy [15]. Descriptive statistics were presented in Table 1. The number of minor alleles for each SNP was counted, and the SNPs in each gene were collapsed in an additive manner. Specifically, each SNP was coded as 0, 1 or 2, based on the number of minor alleles present in the SNP; then, a gene score was obtained by summing all SNP values in each gene for each person. Association tests were based on these gene scores.

Table 1.

Summary Statistics

Variable	N	Mean	Median	Min, Max
Age (years)	1733	48.2	48.0	(19.0, 70.0)
BMI (kg/m2)	1733	31.0	29.9	(13.8, 70.7)
Mean Proportion African Ancestry	1733	83.6%	85.5%	(33.5%, 98.8%)
Sex	1733
Male	617 (35.6%)	.	.	.
Female	1116 (64.4%)	.	.	.
Taking Any Anti-Hypertension Medication	1733
Yes	770 (44.4%)	.	.	.
No	963 (55.6%)	.	.	.
SBP, Adjusted for Treatment (mm Hg)	1730	132.9	129.0	(74.0, 237.5)
DBP, Adjusted for Treatment (mm Hg)	1730	75.9	74.7	(42.0, 136.0)

For hypertension, multivariable logistic regression analysis was conducted with adjustment for age, age², sex, and BMI. For SBP, DBP and HDL-C, age, age², sex, and BMI were adjusted for in regression models, and the BMI regression models were adjusted for age, age², and sex. Local ancestry and global African ancestry were estimated from the 2,507 AIMs using ADMIXPROGRAM [36]. To eliminate the effect by population stratification, global ancestry was included in the regression models [30]. Analyses were performed with and without local ancestry as a covariate in the regression models [37]. Finally, multiple comparisons were corrected for by adjusting for the number of genes in all four regions using the Bonferroni correction to obtain adjusted critical p-values.

It was expected that variants with association evidence should show a substantial difference in allele frequencies between African and European populations, since this study focused on variants in regions where local marker-specific ancestry was associated with phenotypic variation. Therefore, the minor allele frequencies were compared for each variant for the presumed representative reference populations for African Americans, the Yoruba in Ibadan, Nigeria and the CEPH (Utah residents with ancestry from northern and western Europe) [38].

Replication analyses of the study findings were performed in two cohorts obtained from the NHLBI’s Candidate-gene Association Resource (CARe) [39]. CARe is a multi-network study examining associations between genotypes and phenotypes that are of interest to the NHLBI, including blood pressure, lipids and blood biomarkers. There are five African American cohorts in CARe, and they were genotyped on the Affymetrix 6.0 platform. The replication analyses were conducted on 2,916 African Americans in the ARIC (Atherosclerosis Risk in Communities) cohort and 2,144 African Americans in the JHS (Jackson Heart Study) cohort, as they were most comparable to the FBPP dataset in terms of demographics. The SNPs identified in this study were not available in Affymetrix 6.0 platform; therefore, imputed SNPs in ARIC and JHS were used in the replication analysis and these SNPs had quality scores of R² > 0.92. The local ancestry estimates for the genes of interest were also used for the replication analysis.

All statistical analyses were conducted with PLINK [40] and SAS 9.2 (Cary, North Carolina).

Results

The effects of SNPs in genes in coding regions on chromosomes 5, 6, 8, and 21 on SBP, DBP, hypertension and BMI were evaluated in 1,733 unrelated African-American subjects. The descriptive statistics of the FBPP dataset were presented in Table 1.

In the analysis, 117 genes on chromosome 6 (6q24) and 51 genes on chromosome 21 (21q21) were studied. For BMI, 91 genes in the genomic region 5q14-32 were examined. For HDL-C, 37 genes in the genomic region 8q11-21 were studied. Genes were reported in Table 2 if they were statistically significant after adjusting for the total number of genes tested for each trait. Genes were reported with and without local ancestry in the models to determine if additional variants of interest were present in the same local ancestry region. The effect of local ancestry alone was also included.

Table 2.

Results of Gene-Based Multivariable Regression Models

Dataset	Outcome	Gene (Chr)	SNP(s)	Without Local Ancestry			With Local Ancestry			P value of Local Ancestry
				β	SE(β)	P value	β	SE(β)	P value
FBPP	SBP	CXADR (21)	rs437470	3.17	0.830	0.0001	2.94	0.835	0.0004	0.011
FBPP	DBP	CXADR (21)	rs437470	1.70	0.460	0.0002	1.60	0.463	0.0006	0.001
FBPP	BMI	F2RL1 (5)	rs631465	3.41	1.045	0.0011	3.71	1.056	0.0005	0.352
ARIC & JHS	SBP	CXADR (21)	rs437470	0.27	0.499	0.5918	0.19	0.504	0.7110	0.277
ARIC & JHS	DBP	CXADR (21)	rs437470	0.10	0.291	0.7269	0.04	0.294	0.9005	0.134

SE: Standard Error. (Chr): Chromosome number. Statistically-significant after adjusting for the total number of genes tested. 51 genes on chromosome (chr) 21 were tested for DBP and SBP (critical p-value = 0.000980392), and 91 genes on chromosome 5 were tested for BMI (critical p = 0.000549451) for the FBPP dataset analyses. All SBP and DBP multivariable models were adjusted for age, age², sex, BMI, and global ancestry, unless otherwise noted. All BMI multivariable models were adjusted for age, age², sex, and global ancestry, unless otherwise noted.

The gene CXADR on chromosome 21 was significantly associated with increased SBP (β = 3.17, p = 0.0001) and DBP (β = 1.70, p = 0.0002) before adjusting for local ancestry. This gene was still significant after including local ancestry in the regression models (SBP:β = 2.94, p = 0.0004; DBP:β = 1.60, p = 0.0006). All of these analyses adjusted for age, age², sex, BMI, and global African ancestry; these results were still significant after adjusting for testing 51 genes for SBP and for DBP. Since local ancestry was significant (p = 0.011 for SBP and p = 0.001 for DBP), there may be other variants in this region that are independently associated with SBP and DBP. In this study, CXADR only contained the non-synonymous SNP rs437470. The minor allele was far more prevalent in the YRI population than in the CEU population (C: 40.7% vs 9.7%), and it was moderately common (C: 23.5%) in the HapMap ASW (individuals with African ancestry in Southwest USA) population.

The gene F2RL1 on chromosome 5 was associated with obesity as measured by BMI (β = 3.71, p = 0.0005) after adjusting for age, age², sex, and local and global African ancestry. The association with BMI was less significant before adjusting for local ancestry (β = 3.41, p = 0.0011). Local African ancestry was not significant for BMI (p = 0.352). The synonymous SNP rs631465 was the only SNP examined in F2RL1 in our study. The minor allele was similarly infrequent in the YRI population as in the CEU population (A: 0.0% vs 4.1%). Despite testing 37 genes in a region on chromosome 8 that were selected based on admixture mapping results, no significant results were found for HDL-C.

Additional genes that were nominally significant at α = 0.05 but were not significant after correction for multiple comparisons were reported in Table 4. The results in Table 4 were reported from regression models with and without local ancestry. The effect of local ancestry alone was also reported. For models that were more significant with the local ancestry adjustment than without it, the results indicated that there may be additional variants in this region associated with the phenotype.

Table 4.

Results of Gene-Based Multivariable Regression Models that were Nominally Significant at α = 0.05

				Without local ancestry			With local ancestry
Phenotype	Gene	Chr	SNPs	β	SE(β)	P value	β	SE(β)	P value	P Value of Local Ancestry
BMI	ATG10	5	rs3734114, rs1864183	0.50	0.243	0.0380	0.59	0.246	0.0176	0.1929
BMI	IQGAP2	5	rs10036913, rs7722711, rs2431352, rs2910819, rs2455230, rs2431363	0.24	0.109	0.0281	0.25	0.109	0.0238	0.3301
BMI	MRPS27	5	rs17375461, rs10942927	−0.44	0.193	0.0217	−0.43	0.194	0.0279	0.6049
BMI	THBS4	5	rs1866389	0.84	0.391	0.0320	0.92	0.393	0.0195	0.2632
SBP	ENPP1	6	rs9483347, rs1804025	2.10	0.915	0.0220	2.01	0.938	0.0324	0.0310
SBP	GPR1266	6	rs11155242	2.18	0.959	0.0234	2.15	0.959	0.0252	0.0025
SBP	L3MBTL3	6	rs9388768, rs7451021	1.23	0.522	0.0185	1.55	0.563	0.0058	0.0481
SBP	NOX3	6	rs12195525	−2.82	1.242	0.0233	−2.82	1.242	0.0233	0.4296
SBP	RSPO3	6	rs1892172	1.58	0.809	0.0507	1.69	0.813	0.0380	0.0165
DBP	C6orf184	6	rs9400272, rs6927569	−1.35	0.443	0.0023	−1.37	0.451	0.0024	0.0626
DBP	ENPP1	6	rs9483347, rs1804025	1.27	0.507	0.0122	1.28	0.520	0.0135	0.0786
DBP	L3MBTL3	6	rs9388768, rs7451021	0.68	0.290	0.0192	0.82	0.313	0.0091	0.1246
DBP	TXLNB	6	rs17068451, rs9321712	0.75	0.317	0.0186	0.73	0.325	0.0255	0.0112
SBP	DOPEY2	21	rs3827183	2.97	1.248	0.0173	3.10	1.252	0.0134	0.3174
DBP	DOPEY2	21	rs3827183	1.40	0.691	0.0437	1.39	0.694	0.0454	0.2382
HTN	C6orf211	6	rs9397054	−0.20	0.102	0.0484	−0.20	0.103	0.0525	0.2965
HTN	ENPP1	6	rs9483347, rs1804025	0.23	0.091	0.0105	0.22	0.093	0.0162	0.0138
HTN	L3MBTL3	6	rs9388768, rs7451021	0.11	0.051	0.0358	0.14	0.055	0.0126	0.0368
HTN	CXADR	21	rs437470	0.20	0.082	0.0166	0.19	0.083	0.0227	0.1348
HDL-C	LOC137886	8	rs13277646	−1.99	0.796	0.0125	−1.98	0.799	0.0132	0.6924

Replication Analyses

In the replication analyses using local ancestry estimates, association evidence for CXADR and F2RL1 was not seen in the combined ARIC and JHS dataset (N = 3,723). Local ancestry at F2RL1 was marginally associated with BMI in the multivariable model (β = −0.86, p = 0.0549) (Table 3). For SBP and DBP, the association of local ancestry at CXADR was not significant, but the effects were in the same direction as in the FBPP dataset (SBP-β = 1.59, p = 0.2483; DBP-β = 1.23, p = 0.1248).

Table 3.

Results of Gene-Based Multivariable Regression Models Using Local Ancestry for Replication Analysis

Dataset	Outcome	Gene (Chr)	β	SE(β)	P value
ARIC & JHS	SBP	CXADR (21)	1.59	1.377	0.2483
ARIC & JHS	DBP	CXADR (21)	1.23	0.803	0.1248
ARIC & JHS	BMI	F2RL1 (5)	−0.86	0.447	0.0549

SE: Standard Error. (Chr): Chromosome number.

Models were testing local ancestry, instead of the gene of interest in the model. Multivariable SBP and DBP models were adjusted for age, age², sex, BMI, and global ancestry. Multivariable BMI model was adjusted for age, age², sex, and global ancestry.

When only the genes were assessed, the statistically-significant results from the FBPP study did not replicate in the combined ARIC and JHS cohorts (N = 5,044), and the effect sizes were muted compared to the results in the primary dataset. The results for CXADR in the combined ARIC and JHS datasets were in the same direction as in the FBPP analysis after accounting for local ancestry (β-SBP = 0.19, p = 0.71 and β-DBP = 0.04, p = 0.90) (Table 2). The SNP in F2RL1 was not present in the replication sets, so it was not possible to test this gene. It is important to note that the subjects in the ARIC and JHS datasets had SNPs imputed, rather than genotyped directly. Although the imputation quality for each of the SNPs was high, imputed SNPs may have reduced the study’s power to replicate the FBPP findings.

Discussion

An association analysis of genetic variants in genomic coding regions that demonstrated association evidence in previous admixture mapping analyses was conducted [26–29, 31]. Coding SNPs that were identified from the HapMap project, rather than tagging SNPs, were of interest in this study. Thus, the burden due to the multiple comparisons was reduced by increasing the prior probability of the testing SNPs. To further reduce the number of comparisons, the SNPs for each gene were collapsed and a gene based analysis was performed, similar to a rare variant analysis [41]. The advantages of such an approach are a substantial reduction in the number of tests conducted and the incorporation of a biological framework into the analysis.

The gene CXADR on chromosome 21 was identified as being statistically-significantly associated with SBP and DBP after correcting for testing 51 genes. One non-synonymous SNP, rs437470, was identified from the HapMap data in CXADR. Interestingly, there was a substantial difference in the minor allele frequency of rs437470 between the HapMap YRI and CEU samples, suggesting that this result was consistent with the finding that African ancestry is associated with increased blood pressure in admixture mapping studies [28–30]. Adjustment of local African ancestry resulted in models that were still significantly associated with SBP and DBP, suggesting that additional variants in this region may play a role in blood pressure variation. The replication analyses using the local ancestry estimates were consistent with these findings. Furthermore, CXADR plays a role in the electrical conduction of the heart, and it has also been reported to be associated with viral myocarditis and subsequent dilated cardiomyopathy, which is associated with high blood pressure [42, 43]. Recently, a SNP near CXADR was reported to have an association with ventricular fibrillation in acute myocardial infarction [44].

The gene F2RL1 on chromosome 5 was significantly associated with BMI after correcting for 91 genes. In our analysis, this gene only contains the synonymous SNP rs631465, and the frequency of this SNP’s minor allele was infrequent in both the YRI and CEU populations. Interestingly, the association evidence was improved for this gene when local ancestry was included in the regression model, suggesting that this variant was unlikely to fully explain the association evidence observed in the admixture mapping of BMI [26]. The replication analyses showed consistent results that the local ancestry was significantly associated with BMI, indicating other variants for BMI may exist in this region. The association with obesity as measured by BMI was consistent with the finding that mice lacking this gene were resistant to weight gain [45]. The gene F2RL1 encodes the protein protease-activated receptor 2 (PAR2), and the activation of this receptor via coagulation factor VIIa is a key pathway for obesity. The association of F2RL1 with nadir BMI was also reported recently in a study of expression SNPs (eSNPs) in Roux-en-Y gastric bypass patients [46].

Overall, the results indicated that the local ancestry association regions contain variant(s) of interest for the phenotypes. The results implied that the association between BMI and F2RL1 may be real, but it was not known if rs631465 (F2RL1) is in linkage disequilibrium (LD) with a causal SNP for BMI. Local ancestry of F2RL1 was not significantly associated with BMI in the FBPP analysis, but it was associated with BMI in the multivariable replication analysis in the CARe dataset. This indicated that other local variants of interest for BMI may be present, but rs631465 in F2RL1 likely accounted for some of the admixture evidence in this region. As F2RL1 is a large gene spanning over 16 Kb, different variant(s) in F2RL1 may be associated with BMI [47].

For CXADR, local ancestry was significantly associated with SBP and DBP in the FBPP analysis, indicating that there may be additional variants in this region that are associated with blood pressure in this region. The local ancestry marker for CXADR in the CARe dataset was shifted from the admixture peak that was previously reported in this region, so this may have contributed to the lack of replication with local ancestry [29]. As with F2RL1, the causal SNPs could be elsewhere in the gene as CXADR is over 80 Kb [48]. As the results in CXADR and F2RL1 were significant in this study and consistent with other studies, these genes warrant further study [42–46].

The association results did not replicate in the gene-based analysis using the CARe replication dataset. This difficulty in replicating the gene-level associations is expected a priori as the subjects from the FBPP dataset were ascertained based on a family or personal history of hypertension, but the CARe dataset subjects were recruited into population-based studies. As a result, the difficulty in replicating the gene-level results may also reflect the phenotypic heterogeneity among these cohorts, the possible overestimation of the FBPP effect sizes due to the “winner’s curse,” and the use of imputed variants, which all further reduce statistical power in a replication study.

One of this study’s main strengths was that the results of related admixture mapping analyses were used to narrow down the regions in which the candidate gene analysis was performed. In conducting a gene-based analysis, the interpretations of the study’s results were improved by employing a more biologically relevant framework than was possible from testing all SNPs. As the selection of candidate genes was based on previous findings, the number of tests performed was reduced compared to the number of tests that would be necessary in a genome-wide association study.

Since the gene score method did not account for LD patterns, the effects of the correlation structure between SNPs on the estimates are unclear. This may have resulted in muted effect sizes, as this method only identified genes that contained SNPs in the dataset. Further, this study was limited by the necessity to impute blood pressures for subjects undergoing treatment for hypertension rather than obtaining pre-treatment blood pressures.

This study reported significant associations between hypertension and obesity phenotypes and genes in the African-American community. The genes CXADR and F2RL1 were associated with blood pressure and BMI, and these findings are consistent with replication analyses and published literature. As a result, these genes may be important in predicting the risk of hypertension and obesity, so future replication and functional analyses including resequencing studies are warranted. Furthermore, this study described how local ancestry affected effect estimates of association and demonstrated how local ancestry may be useful in replication analyses. Consideration of such factors may be important in determining the contributions of genetic and genomic effects in disease association studies,