Abstract
BACKGROUND
Maternal hypertension has been associated with congenital heart defect occurrence in several studies. We assessed whether maternal genotypes associated with this condition were also associated with congenital heart defect occurrence.
METHODS
We used data from the National Birth Defects Prevention Study to identify non-Hispanic white (NHW) and Hispanic women with (cases) and without (controls) a pregnancy in which a select simple, isolated heart defect was present between 1999 and 2011. We genotyped 29 hypertension-related single nucleotide polymorphisms (SNPs). We conducted logistic regression analyses separately by race/ethnicity to assess the relationship between the presence of any congenital heart defect and each SNP and an overall blood pressure genetic risk score (GRS). All analyses were then repeated to assess 4 separate congenital heart defect subtypes.
RESULTS
Four hypertension-related variants were associated with congenital heart defects among NHW women (N = 1,568 with affected pregnancies). For example, 1 intronic variant in ARHGAP2, rs633185, was associated with conotruncal defects (odds ratio [OR]: 1.3, 95% confidence interval [CI]: 1.1–1.6). Additionally, 2 variants were associated with congenital heart defects among Hispanic women (N = 489 with affected pregnancies). The GRS had a significant association with septal defects (OR: 2.1, 95% CI: 1.2–3.5) among NHW women.
CONCLUSIONS
We replicated a previously reported association between rs633185 and conotruncal defects. Although additional hypertension-related SNPs were also associated with congenital heart defects, more work is needed to better understand the relationship between genetic risk for maternal hypertension and congenital heart defects occurrence.
Keywords: blood pressure, congenital heart defects, genetic risk score, hypertension, maternal genotype
Congenital heart defects affect 1% of live births worldwide and are the most common type of congenital malformation.1,2 Approximately 4% of all neonatal deaths in the United States are attributable to these defects.3 In the majority of affected individuals, the etiology is unknown and likely complex. While there are some etiologic differences across congenital heart defect subtypes (e.g., conotruncal vs. left ventricular outflow tract defects), some risk factors have been associated with many different subtypes.
Maternal hypertension is present in around 10% of pregnancies and has been associated with several congenital heart defect subtypes.4 A recent review and meta-analysis reported a significant association between hypertension and any congenital heart defect across 15 studies (meta relative risk: 1.8, 95% confidence interval [CI]: 1.5–2.2).5 This association persists even after accounting for antihypertensive medication use,6 but the mechanisms that underlie this association remain unclear. For example, overt maternal hypertension may have a direct effect on in utero cardiac development (e.g., hypertension-induced changes to the placental barrier). Additionally, pathways related to the onset of the maternal phenotype may also have independent effects on cardiogenesis during development in pregnancy (e.g., genes with pleiotropic effects).7
Common genetic variants have been extensively studied with regard to hypertension risk. A hypertension/elevated blood pressure genetic risk score (GRS) based on 29 single nucleotide polymorphisms (SNPs) was established by the International Consortium for Blood Pressure and has been validated in diverse populations by several large-scale studies.8–11 The 29 variants were selected for the current study based on their associations with hypertension and performance at hypertension risk prediction in genome-wide association studies among populations with European ancestry and Hispanic ancestry.8–11 A maternal hypertension GRS has also been associated with increased risk for conotruncal heart defects in 1 study.12 To further investigate the association of maternal hypertension with congenital heart defects, we evaluated maternal genotypes associated with these conditions and risk for congenital heart defects, using data from a nationally representative, population-based study of birth defects.
METHODS
Study subjects
Data collected through the National Birth Defects Prevention Study (NBDPS) were used for this study. Specifically, we used data from pregnant women with (cases) and without (controls) a pregnancy in which isolated, simple heart defects were present (described below) and with an estimated delivery date between 1 January 1999 and 31 December 2011. Pregnancies before this period were excluded (i.e., years before mandatory folic acid fortification of food products). The NBDPS recruited subjects identified through birth defects surveillance programs among 10 states in the United States (Arkansas, California, Georgia, Iowa, Massachusetts, New Jersey, New York, Texas, and Utah). A detailed explanation of NBDPS recruitment and data collection has been previously described.13 Information pertaining to maternal characteristics and exposures during pregnancy was collected through computer-assisted telephone interviews with women with and without a pregnancy in which birth defects were present. Maternal hypertension status was self-reported during this interview. Maternal race/ethnicity was also collected, and infant sex and date of birth were abstracted from medical records.
For these analyses, cases were women with a pregnancy (liveborn, stillborn, or terminated) diagnosed with select heart defects (conotruncal, left ventricular outflow tract, right ventricular outflow tract, and septal defects), based on review and classification by NBDPS pediatric cardiologists, using a standardized protocol.14 These defects were selected based on being common and potentially having serious clinical consequences. Diagnostic tests and medical records were used to confirm the phenotype, and to limit heterogeneity. Analyses were conducted among cases with isolated heart defects (i.e., no additional diagnosed major cardiac or noncardiac birth defects was present, as defined by the NBDPS14). Women with index pregnancies in which a chromosomal abnormality or genetic syndrome was present were excluded from the NBDPS. Controls were women with a pregnancy which resulted in a live birth without any birth defects, identified from birth certificates or medical records within each of the 10 NBDPS sites’ surveillance areas. Given limited genotyping resources, NBDPS controls with DNA samples (see below) were randomly selected with a 1:1 control: case ratio, based on conotruncal defects.
Genotyping
We evaluated the 29 SNPs that comprise a previously established hypertension/blood pressure GRS.15,16 DNA samples used for genetic analysis were previously collected by NBDPS laboratories using maternal buccal brushes. The ligation detection reaction assay was used to genotype the 29 SNPs in 1 ligation reaction.17,18 Two multiplex polymerase chain reaction (PCR) reactions were designed to amplify all 29 SNP loci. Qiagen multiplex PCR kit (Cat#: 206145) was used for the multiplex PCR following the manufacture’s protocol. The PCR products were equally mixed and purified by digestion with 1 U of shrimp alkaline phosphatase at 37 °C for 1 hour, followed by deactivation of the phosphatase at 75 °C for 15 minutes. Ligation chain reaction was performed as following: the 20 µl ligation reaction contained 1× ligation buffer, 0.5 µl HiFi Taq DNA Ligase (NEB Cat#: M0647), 1 µl of labeling oligo mixture, 2 µl of probe mixture, and 5 µl of purified PCR product mixture. The cycling program for the ligation reaction was 95 °C for 2 minutes followed by 38 cycles of 94 °C for 1 minute and 56 °C for 4 minutes and a hold at 4 °C. Ligation product (0.5 µl) was loaded into an ABI 3730 DNA analyzer and the raw data were analyzed by GeneMapper 4.1. All primers, probes, and labeling oligos (Supplementary Tables S3 and S4 online) were ordered from Integrated DNA Technologies. Cases or controls with >10% missing genotype data were excluded. SNPs with <5% minor allele frequencies for variants were excluded. SNPs that were found to be out of Hardy–Weinberg equilibrium (P < 0.002, based on a Bonferroni adjustment for 29 total SNPs) among the non-Hispanic white (NHW) or Hispanic control group were excluded from the NHW or Hispanic SNP-level analyses, respectively. SNPs with <95% genotype call rates among the full analytic group were excluded from all SNP-level analyses. Participants with any missing genotype data were excluded from the GRS analysis (described below).
Statistical analysis
To limit the potential for population stratification, all analyses were limited to Hispanic and NHW women, and were conducted separately for each of these groups. Characteristics of cases and controls were tabulated using counts and proportions.
We used unconditional logistic regression to evaluate the association between risk for heart defects and the maternal genotype for each of the 29 hypertension-related SNPs. In addition to evaluating associations with any heart defect, we also separately assessed 4 specific defect subtypes: conotruncal, left ventricular outflow tract obstructions (LVOTO), right ventricular outflow tract obstructions (RVOTO), and septal defects. We computed odds ratios (ORs) for the association with each SNP. We used an additive genetic model and treated the allele that was associated with an increased risk for hypertension in previous studies as the risk allele.8,19 In other words, the hypertension risk allele was hypothesized to be positively associated with heart defects.
A hypertension/blood pressure GRS for each participant was constructed based on the maternal genotype for each of the 29 hypertension-related SNPs. Each genotype contributed a 0, 1, or 2 to the risk score, based on the number of risk alleles present, multiplied by the previously reported beta-coefficient between that SNP and hypertension/blood pressure.8 This was done to weight the contribution of the genotype to the risk score by the expected magnitude of association with hypertension/blood pressure.9 This risk score was assessed as a continuous variable (to evaluate for linear effects) and a dichotomous categorical variable (to evaluate threshold effects for an extreme exposure status). The continuous GRS was used to compare mean scores between cases and controls of both the NHW and Hispanic groups. The dichotomous GRS was defined using a threshold of above or below the 95th percentile. The categorical GRS was modeled after a previously published score examining a similar genetic risk for the outcome of interest.12 The top 5th percentile of risk was selected as a cutoff value to represent the highest genetic risk.12 Categorization was based on the distributions of the GRS in NHW control mothers. This analysis assessed whether control mothers in the highest percentile for case mothers would have an inflated risk compared with control mothers in the bottom 95th percentile of both groups. Subanalyses were also performed using an unweighted GRS.
To evaluate potential effects related to delivery center, we also repeated analyses, further adjusting for this variable. Among SNPs associated with at least 1 heart defect phenotype in the main analysis (P value <0.05) and for all GRS comparisons, we conducted a sensitivity analysis excluding women with hypertension (i.e., report of having hypertension treated with medications during the pregnancy, chronic hypertension diagnosed prior to the pregnancy, or gestational hypertension during the pregnancy). Analyses were completed using SAS version 9.4 (SAS Institute, Cary, NC). Quality control analyses and exclusions were performed using PLINK v1.07.20 Approval for the study was granted by the Institutional Review Board at each participating NBDPS site and the Centers for Disease Control and Prevention.
RESULTS
After our quality control procedures were implemented, genotype data were available for 2,057 cases (76.2% NHW) and 541 controls (78.2% NHW, Table 1). Septal defects were the most commonly reported heart defect subtype (n = 695, 33.8%), and there were differences in the distributions of heart defect subtypes between NHWs and Hispanics. No SNP deviated from Hardy-Weinberg equilibrium in controls (P < 0.002 after a Bonferroni adjustment for multiple comparisons).
Table 1.
Non-Hispanic white (N = 1,996) | Hispanic (N = 602) | |||
---|---|---|---|---|
Casesa (N = 1,568, 78.6%) | Controls (N = 428, 21.4%) | Casesa (N = 489, 81.2%) | Controls (N = 113, 18.8%) | |
Characteristic | N (%) | N (%) | N (%) | N (%) |
Infant defect | ||||
Conotruncal | 416 (26.5) | — | 127 (26.0) | — |
LVOTO | 336 (21.4) | — | 75 (15.3) | — |
RVOTO | 323 (20.6) | — | 85 (17.4) | — |
Septal | 493 (31.5) | — | 202 (41.3) | — |
Infant sexb | ||||
Male | 835 (53.3) | 227 (53.0) | 275 (56.4) | 60 (53.1) |
Female | 732 (46.7) | 201 (47.0) | 213 (43.6) | 53 (46.9) |
Delivery centerb | ||||
Arkansas | 336 (21.6) | 60 (14.2) | 39 (8.0) | 4 (3.6) |
California | 57 (3.7) | 19 (4.5) | 110 (22.6) | 23 (20.5) |
Georgia | 88 (5.6) | 28 (6.6) | 28 (5.7) | 9 (8.0) |
Iowa | 236 (15.2) | 74 (17.5) | 13 (2.7) | 4 (3.6) |
Massachusetts | 194 (12.5) | 56 (13.2) | 24 (4.9) | 6 (5.4) |
New York | 93 (6.0) | 25 (5.9) | 9 (1.8) | 2 (1.8) |
North Carolina | 141 (9.1) | 54 (12.8) | 28 (5.8) | 10 (8.9) |
Texas | 56 (3.6) | 10 (2.4) | 198 (40.7) | 46 (41.1) |
Utah | 352 (22.7) | 97 (22.9) | 38 (7.8) | 8 (7.1) |
Maternal age | ||||
<20 | 78 (5.0) | 25 (5.8) | 66 (13.5) | 14 (12.4) |
20–24 | 346 (22.1) | 77 (18.0) | 138 (28.2) | 40 (35.4) |
25–29 | 488 (31.1) | 121 (28.3) | 147 (30.0) | 27 (23.9) |
30–34 | 423 (27.0) | 136 (31.8) | 89 (18.2) | 19 (16.8) |
35–39 | 192 (12.2) | 62 (14.5) | 34 (7.0) | 9 (8.0) |
>40 | 41 (2.6) | 7 (1.6) | 15 (3.1) | 4 (3.5) |
Parityb | ||||
Nulliparous | 485 (31.0) | 138 (32.2) | 140 (28.6) | 32 (28.3) |
Multiparous | 1,081 (69.0) | 290 (67.8) | 349 (71.4) | 81 (71.7) |
Prior pregnancy with congenital heart defect | ||||
Yes | 18 (1.2) | 0 (0.0) | 1 (0.2) | 0 (0.0) |
No | 1,550 (98.8) | 428 (100.0) | 488 (99.8) | 113 (100.0) |
Abbreviations: LVOTO, left ventricular outflow tract obstruction; NBDPS, National Birth Defects Prevention Study; RVOTO, right ventricular outflow tract obstruction.
aWomen with offspring with isolated, simple heart defects.
bData missing for variable.
Hypertension-related SNPs
Of the 29 hypertension-related SNPs evaluated, there were 5 associated with heart defect phenotypes among NHWs and/or Hispanics. Among NHWs (n = 1,568 cases), there were 2 SNPs with significant, positive associations and 2 with significant, negative associations (Table 2 and Supplementary Table S1 online). One SNP (rs932764) was positively associated with any heart defect (OR: 1.2, 95% CI: 1.1–1.4), and this SNP was also positively associated with conotruncal, LVOTO, and septal heart defects. One additional hypertension-related SNP (rs633185) was positively associated with conotruncal defects (OR: 1.3, 95% CI: 1.1–1.6) but not with other phenotypes. Negative associations were observed for 2 SNPs (rs12940887 with conotruncal defects and rs13107325 with septal defects).
Table 2.
ORd (95% CI) | ||||||||
---|---|---|---|---|---|---|---|---|
SNPa | Gene/flanking genesb | Risk allelec | Risk allele frequency | Any heart defecte | Conotruncal | LVOTO | RVOTO | Septal |
Non-Hispanic whites | ||||||||
Hypertension SNPs | ||||||||
rs633185 | ARHGAP42 | C | .697 | 1.12 (0.95–1.31) | 1.31 (1.06–1.61) | 1.08 (0.87–1.35) | 1.05 (0.85–1.31) | 1.06 (0.87–1.29) |
rs932764 | PLCE1 | G | .423 | 1.22 (1.05–1.43) | 1.29 (1.06–1.56) | 1.30 (1.06–1.59) | 1.04 (0.85–1.28) | 1.24 (1.03–1.50) |
rs12940887 | ZNF652 | T | .367 | 0.87 (0.74–1.02) | 0.78 (0.64–0.96) | 0.86 (0.70–1.06) | 1.02 (0.83–1.26) | 0.86 (0.71–1.04) |
rs13107325 | SLC39A8 | C | .925 | 0.79 (0.59–1.07) | 0.81 (0.56–1.18) | 0.80 (0.55–1.17) | 1.00 (0.66–1.50) | 0.68 (0.48–0.95 |
rs17367504 | MTHFR | A | .855 | 1.15 (0.94–1.42) | 1.12 (0.86–1.45) | 1.11 (0.83–1.47) | 1.19 (0.89–1.60) | 1.20 (0.93–1.56) |
Hispanic | ||||||||
Hypertension SNPs | ||||||||
rs633185 | ARHGAP42 | C | .477 | 1.25 (0.93–1.67) | 1.37 (0.95–1.96) | 0.99 (0.66–1.49) | 1.38 (0.94–2.02) | 1.19 (0.87–1.65) |
rs932764 | PLCE1 | G | .564 | 1.17 (0.88–1.55) | 1.02 (0.71–1.45) | 1.30 (0.87–1.95) | 1.30 (0.85–1.99) | 1.18 (0.87–1.61) |
rs12940887 | ZNF652 | T | .199 | 1.34 (0.91–1.98) | 1.22 (0.78–1.91) | 0.98 (0.57–1.67) | 1.32 (0.79–2.20) | 1.57 (1.02–2.42) |
rs13107325f | SLC39A8 | C | .966 | 1.64 (0.88–3.05) | 1.54 (0.68–3.50) | 2.34 (0.70–7.81) | 1.23 (0.53–2.88) | 1.76 (0.82–3.78) |
rs17367504 | MTHFR | A | .901 | 0.45 (0.24–0.83) | 0.38 (0.19–0.77) | 0.55 (0.24–1.22) | 0.55 (0.24–1.26) | 0.41 (0.21–0.81) |
Significant values are indicated in bold.
Abbreviations: CI, confidence interval; LVOTO, left ventricular outflow tract obstruction; NBDPS, National Birth Defects Prevention Study; OR, odds ratio; RVOTO, right ventricular outflow tract obstruction; SNP, single nucleotide polymorphism.
aData not shown for SNPs that were not associated among non-Hispanic Whites or among Hispanics.
bFor intergenic variants, the nearest up/downstream genes are listed. Additional annotation details are presented in Supplementary Table S2 online.
cAllele positively associated with hypertension.
dCrude odds ratio for carrying 1 copy of the high-risk allele compared with no copies.
eConotruncal, LVOTO, RVOTO, or septal.
fSNP with Hardy–Weinberg equilibrium P value = 0.03 among Hispanic controls.
Among Hispanics (n = 489 cases), there were 2 associated SNPs, 1 with a significant, positive association and 1 with a significant negative association (Table 2 and Supplementary Table S1 online). One hypertension-related SNP, rs17367504, was negatively associated with the presence of any heart defect (OR: 0.5, 95% CI: 0.2–0.8), and additional negative associations were also observed with conotruncal and septal defects. The other significant hypertension-related SNP (rs12940887) was positively associated with septal defects (OR: 1.6, 95% CI: 1.0–2.4), but not other phenotypes. The directionality of hypertension-related SNPs rs633185 and rs932764 was the same as in NHWs, although the associations for these SNPs were not statistically significant.
Hypertension GRS
To assess aggregate effects of multiple SNPs in combination, we evaluated whether a hypertension GRS was associated with heart defect phenotypes. Because genotype information was missing for 1 or more SNPs from 59 NHW women and 16 Hispanic women, data from these women were excluded from these hypertension GRS analyses. The distribution of the GRS was similar between NHW and Hispanic controls (data not shown). One significant association was found between the hypertension GRS and septal heart defects among NHW women (OR: 2.1, 95% CI: 1.2–3.5) (Table 3). The results were similar for the subanalyses of the unweighted GRS (data not shown).
Table 3.
ORb (95% CI) | |||||
---|---|---|---|---|---|
Comparison | Any heart defectc | Conotruncal | LVOTO | RVOTO | Septal |
Non-Hispanic whites | 1.47 (0.92–2.35) | 1.05 (0.57–1.93) | 1.38 (0.75–2.51) | 1.23 (0.66–2.29) | 2.09 (1.24–3.53) |
Hispanic | 0.93 (0.31–2.85) | 0.44 (0.08–2.42) | 1.15 (0.25–5.28) | 2.50 (0.71–8.83) | 0.56 (0.14–2.29) |
Significant values are indicated in bold.
Abbreviations: CI, confidence interval; GRS, genetic risk score; LVOTO, left ventricular outflow tract obstruction; NBDPS, National Birth Defects Prevention Study; OR, odds ratio; RVOTO, right ventricular outflow tract obstruction.
aThe analysis of cases vs. controls with risk scores greater than the 95th percentile, compared with NHW control scores. Non-Hispanic white and Hispanic 95th percentile GRS cutoff: 1.34.
bCrude odds ratio.
cConotruncal, LVOTO, RVOTO, or septal.
Sensitivity analyses
To evaluate potential effects related to delivery center, we repeated analyses, further adjusting for this variable. We also conducted sensitivity analyses, repeating comparisons after excluding 49 controls (10.0%) and 262 cases (14.6%) who reported having hypertension. For both of these sensitivity analyses, results were similar to those from the main results (Supplementary Tables S5 and S6 online).
Subanalyses were also performed to examine the risk of congenital heart defects based on the continuous maternal hypertension GRS. These results were similar to the main results (e.g., OR for septal defects in NHWs: 2.4, 95% CI: 1.1–4.2). The distribution of the unweighted GRS score is reported in Supplementary Table S7 online. In the subanalysis performed for the unweighted GRS, results were similar to the main results for most comparisons (Supplementary Table S6 online). Of note, the association with septal defects among NHWs was attenuated (and no longer significant), whereas associations among Hispanics with any heart defect and with septal defects were strengthened (and significant). However, the 95% CIs for all 3 of these results overlapped with those from the main analysis.
DISCUSSION
This study evaluated the relationships between maternal genotypes for 29 hypertension-associated SNPs and congenital heart defects. We identified a relatively small number of significant positive associations among our GRS analyses and our SNP-level analyses that seemed consistent with our hypothesis that these genotypes would be positively associated with heart defects. Specifically, 1 strong positive GRS association as well as several positive and negative significant associations with specific SNPs were observed. Further, several of these SNP associations were similar across multiple heart defect phenotypes. There were also similar associations observed between Hispanic and NHW groups for several of the same SNP-defect comparisons, although statistical significance varied (i.e., rs633185 and conotruncal defects, rs932764 and any heart defect, rs932764 and LVOTO, and rs932764 and septal defects).
One study conducted by Kaplinski et al. evaluated the association between a similar hypertension GRS (including 25 of the 29 SNPs we assessed) and conotruncal heart defects in a different NHW study population.12 Four of the 5 variants with significant associations observed in our analyses were assessed by Kaplinski et al., as described below. We are unaware of other studies that have evaluated maternal hypertension-related genes and heart defect risk.
The association between the variant for PLCE1 (rs932764) with any heart and LVOTO defects were nearly identical in magnitude and directionality between Hispanic and NHW groups in our analyses. Among NHW women, this variant was associated with any heart, LVOTO, and septal defects. This variant was not associated with conotruncal defects in the study by Kaplinski et al., though other defects were not assessed in those analyses. PLCE1 is involved in the regulation of glomerular filtration in the kidneys through its role in podocyte development, and PLCE1 mutations have been associated with proteinuria.21 Preeclampsia may also be linked to the dysregulation of podocytes, with regulation controlled by phospholipase enzyme producing genes such as PLCE1.22 Further research may be helpful to understand more about the potential role of this gene in human cardiogenesis.
The maternal genotype for an intronic variant in ARHGAP42, rs633185, was positively associated with conotruncal heart defects in pregnancies among NHW women (OR: 1.3, 95% CI: 1.1–1.6) and Hispanic women (OR: 1.4, 95% CI: 1.0–2.0) in our study. This association was also reported among NHWs in the study (OR: 1.2, 95% CI: 1.0–1.5) by Kaplinski et al.12ARHGAP42 modulates vascular resistance, though its role during pregnancy is not well-studied.23ARHGAP42 (also commonly identified as GRAF3) is a Rho-specific GTPase activating protein (GAP) which, when inhibited in animal models, is associated with a significant increased risk of hypertension,23 and RhoGAP has been reported to be positively correlated with functional cardiac development in mice.24 The exact mechanisms by which ARHGAP42 hypertensive-associated variants might affect RhoGAP function and subsequent cardiac development in humans are unclear. Considering the recent findings surrounding the role of RhoGAP in animal models and replication of this association in 2 independent studies in humans, further investigation of this gene may be warranted.
In the present analyses, the categorical, weighted GRS (based on the 95th percentile of the GRS) was positively associated with septal defects (OR: 2.1, 95% CI: 1.2–3.5) among NHWs. A recent meta-analysis also reported significant associations between overt maternal hypertension and ventricular septal defects specifically (relative risk: 1.3, 95% CI: 1.1–1.6), as well as other heart defect subtypes.5 Kaplinski et al. also reported a significant association between a categorical, weighted hypertension GRS (also based on the 95th percentile of the GRS) and conotruncal defects (OR: 1.7, 95% CI: 1.0–2.8),12 though septal defects were not assessed. Based on these prior results and our findings, further investigation of cumulative effects of hypertension-associated variants may be useful.
Our other variant-level results among NHW women either were not comparable with or differed somewhat from those reported in the assessment of conotruncal heart defects among a NHW population by Kaplinski et al.12 Specifically, Kaplinski et al. reported P values suggestive of associations with conotruncal defects for 2 additional maternal hypertension-related SNPs that were not associated with heart defects in our study (rs13139571 and rs1801253).12 Two SNPs in our analyses, rs932764 (OR: 1.2, 95% CI: 1.1–1.4) and rs12940887 (OR: 1.6, 95% CI: 1.0–2.4) had significant associations with conotruncal defects among NHWs. These SNPs were not associated with conotruncal defects in the analyses by Kaplinski et al. (rs932764, OR: 1.0, 95% CI: 0.9–1.2; rs12840887, OR: 1.0, 5% CI: 0.9–1.2). Some of the differences in results between the 2 studies may reflect type I or type II errors in either study (e.g., related to multiple comparisons or underpowered analyses for some comparisons). Differences may also be related to systematic differences between the studies. For instance, the study by Kaplinski et al. implemented a mother–father case–control design; included only NHW participants; did not evaluate LVOTO, RVOTO, or septal defects; and used a clinically recruited population. Their population may have been subject to some selection bias (e.g., due to potential differences in defect severity in a clinical sample vs. a population-based sample). The discrepancy between the SNPs used by each study (e.g., there were 25 SNPs in both GRS, 4 SNPs only in our GRS and 6 SNPs only in their GRS) to create the weighted GRS may also account for the varying results.
In our study, an intronic variant in MTHFR (rs17367504) was negatively associated with 3 heart phenotypes among pregnancies in Hispanic women but not NHW women. This variant is located within a DNase1 hypersensitivity cluster and a transcription factor binding site.25MTHFR is involved in a number of metabolic processes, including folic acid metabolism, and other maternal variants in MTHFR have been negatively associated with heart defects in offspring in prior studies, including studies among predominately Hispanic populations.26–28 Although our results were not similar among NHW (OR: 1.2, 95% CI: 0.9–1.4) and Hispanic (OR: 0.5, 95% CI: 0.2–0.8) women for this SNP, differences in associations between racial/ethnic groups may suggest effect measure modification,29 and/or differences in linkage patterns between race/ethnicity groups may also play a role.30 In fact, the GRS used here has been more strongly associated with high blood pressure among European populations than among Hispanic populations (e.g., P values: 3.6E−153 and 1.7E−3, respectively12). Additionally, some of the individual SNPs that we observed to be associated with heart defects only among NHW women have been reported to be not associated with high blood pressure (i.e., P value >0.05) among Hispanics (e.g., rs633185, rs12940887, rs13107325, and rs17367504).10 This may explain some of the differences in our results between NHW and Hispanic women. Further, there were differences in the distributions of heart defect subtypes between NHWs and Hispanics. If these observed differences are replicated, they might support the notion that some maternal genetic effects involved in heart defects risk could vary by race/ethnicity, as similar effects have been described in other work.31
We observed similar results between participants in the full group and in sensitivity analyses among women who did not report having hypertension, and this consistency might be related to several factors. The assessed maternal variants may have contributed to an intermediate, subclinical phenotype in the mother before overt hypertension would be recognizable, which might have had a clinical effect on the uterine environment that contributed toward subsequent cardiac maldevelopment. Additionally, there could be some pleiotropic effects at play (e.g., related to effects of variants on pathways that are independent of hypertension).
Our results should be interpreted with consideration of the potential limitations and strengths of the study. Several comparisons were made (e.g., multiple heart defect subtypes) and we did not consider conservative corrections for multiple comparisons because these comparisons were not completely independent. Maternal data on race/ethnicity were collected through self-report; however, previous studies have shown high concordance between self-reported race/ethnicity and genetic race/ethnicity based on ancestry informative markers (which were not available for the present analyses) among mothers of infants with birth defects.32 Some of the observed associations may be related to random chance, although this may be less likely for associations that were consistent across multiple phenotypic groups. In the Hispanic study population, 1 SNP, rs13107325, had a minor allele frequency of <5% (3.4%) and Hardy–Weinberg equilibrium P value = 0.03 but was included in analysis. Results for this SNP should therefore be interpreted with caution. We analyzed associations with maternal, not infant, genotypes, and ruling out the possibility of infant genetic effects may be helpful in future work. Due to data availability, there were a limited number of Hispanic controls included in the study population; results in this subpopulation should be interpreted with caution. Data on maternal blood pressure measurements were also not available. Because control selection was based on case subgroup counts, the number of cases in the any heart defect group was higher than the number of controls. Major strengths of the study include the use of NBDPS data collected from multiple states, including a large, population-based sample, well-defined case classification, assessment of heart defect subtypes, and analysis of the 2 largest race/ethnicity groups in the United States.13,33
Our study is among the first reports on the relationship between maternal genotypes for hypertension-related variants and the occurrence of heart defects in pregnancies of Hispanic women. Considering that maternal hypertension is becoming increasingly recognized as a risk factor for heart defects,5 a better understanding of the mechanisms between genetic risk for maternal conditions and congenital heart defects is needed. Future research could involve the assessment of additional maternal genotypes, which continue to be identified in genomic studies; a focus on additional comparisons by race/ethnicity; and further assessment of maternal genetic effect pathways.
Supplementary Material
FUNDING
This work was supported by grants from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (grant no. P01HD070454) and the American Heart Association (16GRNT29660001). This project was supported through Centers for Disease Control and Prevention (CDC) cooperative agreements under PA #96043, PA #02081, FOA #DD09-001, FOA #DD13-003, and NOFO #DD18-001 to the Centers for Birth Defects Research and Prevention participating in the National Birth Defects Prevention Study (NBDPS) and/or the Birth Defects Study To Evaluate Pregnancy exposureS (BD-STEPS). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding sources.
DISCLOSURE
The authors declared no conflict of interest.
REFERENCES
- 1. Zhao QM, Liu F, Wu L, Ma XJ, Niu C, Huang GY. Prevalence of congenital heart disease at live birth in China. J Pediatr 2019; 204:53–58. [DOI] [PubMed] [Google Scholar]
- 2. Reller MD, Strickland MJ, Riehle-Colarusso T, Mahle WT, Correa A. Prevalence of congenital heart defects in metropolitan Atlanta, 1998–2005. J Pediatr 2008; 153:807–813. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Petrini JR, Broussard CS, Gilboa SM, Lee KA, Oster M, Honein MA. Racial differences by gestational age in neonatal deaths attributable to congenital heart defects—United States, 2003–2006. Morbidity and Mortality Weekly Report. Centers for Disease Control and Prevention, U.S. Department of Health and Human Services: Atlanta, GA, 2010. [Google Scholar]
- 4. Hutcheon JA, Lisonkova S, Joseph KS. Epidemiology of pre-eclampsia and the other hypertensive disorders of pregnancy. Best Pract Res Clin Obstet Gynaecol 2011; 25:391–403. [DOI] [PubMed] [Google Scholar]
- 5. Ramakrishnan A, Lee LJ, Mitchell LE, Agopian AJ. Maternal hypertension during pregnancy and the risk of congenital heart defects in offspring: a systematic review and meta-analysis. Pediatr Cardiol 2015; 36:1442–1451. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Fisher SC, Van Zutphen AR, Werler MM, Lin AE, Romitti PA, Druschel CM, Browne ML; National Birth Defects Prevention Study . Maternal antihypertensive medication use and congenital heart defects: updated results from the National Birth Defects Prevention Study. Hypertension 2017; 69:798–805. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Bartels Ä, O’Donoghue K. Cholesterol in pregnancy: a review of knowns and unknowns. Obstet Med 2011; 4:147–151. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Ehret GB, Munroe PB, Rice KM, Bochud M, Johnson AD, Chasman DI, Smith AV, Tobin MD, Verwoert GC, Hwang SJ, Pihur V, Vollenweider P, O’Reilly PF, Amin N, Bragg-Gresham JL, Teumer A, Glazer NL, Launer L, Zhao JH, Aulchenko Y, Heath S, Sober S, Parsa A, Luan J, Arora P, Dehghan A, Zhang F, Lucas G, Hicks AA, Jackson AU, Peden JF, Tanaka T, Wild SH, Rudan I, Igl W, Milaneschi Y, Parker AN, Fava C, Chambers JC, Fox ER, Kumari M, Go MJ, van der Harst P, Kao WH, Sjogren M, Vinay DG, Alexander M, Tabara Y, Shaw-Hawkins S, Whincup PH, Liu Y, Shi G, Kuusisto J, Tayo B, Seielstad M, Sim X, Nguyen KD, Lehtimaki T, Matullo G, Wu Y, Gaunt TR, Onland-Moret NC, Cooper MN, Platou CG, Org E, Hardy R, Dahgam S, Palmen J, Vitart V, Braund PS, Kuznetsova T, Uiterwaal CS, Adeyemo A, Palmas W, Campbell H, Ludwig B, Tomaszewski M, Tzoulaki I, Palmer ND, Aspelund T, Garcia M, Chang YP, O’Connell JR, Steinle NI, Grobbee DE, Arking DE, Kardia SL, Morrison AC, Hernandez D, Najjar S, McArdle WL, Hadley D, Brown MJ, Connell JM, Hingorani AD, Day IN, Lawlor DA, Beilby JP, Lawrence RW, Clarke R, Hopewell JC, Ongen H, Dreisbach AW, Li Y, Young JH, Bis JC, Kahonen M, Viikari J, Adair LS, Lee NR, Chen MH, Olden M, Pattaro C, Bolton JA, Kottgen A, Bergmann S, Mooser V, Chaturvedi N, Frayling TM, Islam M, Jafar TH, Erdmann J, Kulkarni SR, Bornstein SR, Grassler J, Groop L, Voight BF, Kettunen J, Howard P, Taylor A, Guarrera S, Ricceri F, Emilsson V, Plump A, Barroso I, Khaw KT, Weder AB, Hunt SC, Sun YV, Bergman RN, Collins FS, Bonnycastle LL, Scott LJ, Stringham HM, Peltonen L, Perola M, Vartiainen E, Brand SM, Staessen JA, Wang TJ, Burton PR, Soler Artigas M, Dong Y, Snieder H, Wang X, Zhu H, Lohman KK, Rudock ME, Heckbert SR, Smith NL, Wiggins KL, Doumatey A, Shriner D, Veldre G, Viigimaa M, Kinra S, Prabhakaran D, Tripathy V, Langefeld CD, Rosengren A, Thelle DS, Corsi AM, Singleton A, Forrester T, Hilton G, McKenzie CA, Salako T, Iwai N, Kita Y, Ogihara T, Ohkubo T, Okamura T, Ueshima H, Umemura S, Eyheramendy S, Meitinger T, Wichmann HE, Cho YS, Kim HL, Lee JY, Scott J, Sehmi JS, Zhang W, Hedblad B, Nilsson P, Smith GD, Wong A, Narisu N, Stancakova A, Raffel LJ, Yao J, Kathiresan S, O’Donnell CJ, Schwartz SM, Ikram MA, Longstreth WT, Jr., Mosley TH, Seshadri S, Shrine NR, Wain LV, Morken MA, Swift AJ, Laitinen J, Prokopenko I, Zitting P, Cooper JA, Humphries SE, Danesh J, Rasheed A, Goel A, Hamsten A, Watkins H, Bakker SJ, van Gilst WH, Janipalli CS, Mani KR, Yajnik CS, Hofman A, Mattace-Raso FU, Oostra BA, Demirkan A, Isaacs A, Rivadeneira F, Lakatta EG, Orru M, Scuteri A, Ala-Korpela M, Kangas AJ, Lyytikainen LP, Soininen P, Tukiainen T, Wurtz P, Ong RT, Dorr M, Kroemer HK, Volker U, Volzke H, Galan P, Hercberg S, Lathrop M, Zelenika D, Deloukas P, Mangino M, Spector TD, Zhai G, Meschia JF, Nalls MA, Sharma P, Terzic J, Kumar MV, Denniff M, Zukowska-Szczechowska E, Wagenknecht LE, Fowkes FG, Charchar FJ, Schwarz PE, Hayward C, Guo X, Rotimi C, Bots ML, Brand E, Samani NJ, Polasek O, Talmud PJ, Nyberg F, Kuh D, Laan M, Hveem K, Palmer LJ, van der Schouw YT, Casas JP, Mohlke KL, Vineis P, Raitakari O, Ganesh SK, Wong TY, Tai ES, Cooper RS, Laakso M, Rao DC, Harris TB, Morris RW, Dominiczak AF, Kivimaki M, Marmot MG, Miki T, Saleheen D, Chandak GR, Coresh J, Navis G, Salomaa V, Han BG, Zhu X, Kooner JS, Melander O, Ridker PM, Bandinelli S, Gyllensten UB, Wright AF, Wilson JF, Ferrucci L, Farrall M, Tuomilehto J, Pramstaller PP, Elosua R, Soranzo N, Sijbrands EJ, Altshuler D, Loos RJ, Shuldiner AR, Gieger C, Meneton P, Uitterlinden AG, Wareham NJ, Gudnason V, Rotter JI, Rettig R, Uda M, Strachan DP, Witteman JC, Hartikainen AL, Beckmann JS, Boerwinkle E, Vasan RS, Boehnke M, Larson MG, Jarvelin MR, Psaty BM, Abecasis GR, Chakravarti A, Elliott P, van Duijn CM, Newton-Cheh C, Levy D, Caulfield MJ, Johnson T. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 2011; 478:103–109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Fava C, Sjögren M, Montagnana M, Danese E, Almgren P, Engström G, Nilsson P, Hedblad B, Guidi GC, Minuz P, Melander O. Prediction of blood pressure changes over time and incidence of hypertension by a genetic risk score in Swedes. Hypertension 2013; 61:319–326. [DOI] [PubMed] [Google Scholar]
- 10. Beecham AH, Wang L, Vasudeva N, Liu Z, Dong C, Goldschmidt-Clermont PJ, Pericak-Vance MA, Rundek T, Seo D, Blanton SH, Sacco RL, Beecham GW. Utility of blood pressure genetic risk score in admixed Hispanic samples. J Hum Hypertens 2016; 30:772–777. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Fava C, Sjögren M, Olsson S, Lövkvist H, Jood K, Engström G, Hedblad B, Norrving B, Jern C, Lindgren A, Melander O. A genetic risk score for hypertension associates with the risk of ischemic stroke in a Swedish case-control study. Eur J Hum Genet 2015; 23:969–974. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Kaplinski M, Taylor D, Mitchell LE, Hammond DA, Goldmuntz E, Agopian AJ; Pediatric Cardiac Genomics Consortium . The association of elevated maternal genetic risk scores for hypertension, type 2 diabetes and obesity and having a child with a congenital heart defect. PLoS One 2019; 14:e0216477. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Yoon PW, Rasmussen SA, Lynberg MC, Moore CA, Anderka M, Carmichael SL, Costa P, Druschel C, Hobbs CA, Romitti PA, Langlois PH, Edmonds LD. The National Birth Defects Prevention Study. Public Health Rep 2001; 116:32–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Botto LD, Lin AE, Riehle-Colarusso T, Malik S, Correa A; National Birth Defects Prevention Study . Seeking causes: classifying and evaluating congenital heart defects in etiologic studies. Birth Defects Res A Clin Mol Teratol 2007; 79:714–727. [DOI] [PubMed] [Google Scholar]
- 15. Simino J, Rao DC, Freedman BI. Novel findings and future directions on the genetics of hypertension. Curr Opin Nephrol Hypertens 2012; 21:500–507. [DOI] [PubMed] [Google Scholar]
- 16. Ehret GB, Munroe PB, Rice KM, Bochud M, Johnson AD, Chasman DI, Smith AV, Tobin MD, Verwoert GC, Hwang SJ, Pihur V, Vollenweider P, O’Reilly PF, Amin N, Bragg-Gresham JL, Teumer A, Glazer NL, Launer L, Zhao JH, Aulchenko Y, Heath S, Sõber S, Parsa A, Luan J, Arora P, Dehghan A, Zhang F, Lucas G, Hicks AA, Jackson AU, Peden JF, Tanaka T, Wild SH, Rudan I, Igl W, Milaneschi Y, Parker AN, Fava C, Chambers JC, Fox ER, Kumari M, Go MJ, van der Harst P, Kao WH, Sjögren M, Vinay DG, Alexander M, Tabara Y, Shaw-Hawkins S, Whincup PH, Liu Y, Shi G, Kuusisto J, Tayo B, Seielstad M, Sim X, Nguyen KD, Lehtimäki T, Matullo G, Wu Y, Gaunt TR, Onland-Moret NC, Cooper MN, Platou CG, Org E, Hardy R, Dahgam S, Palmen J, Vitart V, Braund PS, Kuznetsova T, Uiterwaal CS, Adeyemo A, Palmas W, Campbell H, Ludwig B, Tomaszewski M, Tzoulaki I, Palmer ND, Aspelund T, Garcia M, Chang YP, O’Connell JR, Steinle NI, Grobbee DE, Arking DE, Kardia SL, Morrison AC, Hernandez D, Najjar S, McArdle WL, Hadley D, Brown MJ, Connell JM, Hingorani AD, Day IN, Lawlor DA, Beilby JP, Lawrence RW, Clarke R, Hopewell JC, Ongen H, Dreisbach AW, Li Y, Young JH, Bis JC, Kähönen M, Viikari J, Adair LS, Lee NR, Chen MH, Olden M, Pattaro C, Bolton JA, Köttgen A, Bergmann S, Mooser V, Chaturvedi N, Frayling TM, Islam M, Jafar TH, Erdmann J, Kulkarni SR, Bornstein SR, Grässler J, Groop L, Voight BF, Kettunen J, Howard P, Taylor A, Guarrera S, Ricceri F, Emilsson V, Plump A, Barroso I, Khaw KT, Weder AB, Hunt SC, Sun YV, Bergman RN, Collins FS, Bonnycastle LL, Scott LJ, Stringham HM, Peltonen L, Perola M, Vartiainen E, Brand SM, Staessen JA, Wang TJ, Burton PR, Soler Artigas M, Dong Y, Snieder H, Wang X, Zhu H, Lohman KK, Rudock ME, Heckbert SR, Smith NL, Wiggins KL, Doumatey A, Shriner D, Veldre G, Viigimaa M, Kinra S, Prabhakaran D, Tripathy V, Langefeld CD, Rosengren A, Thelle DS, Corsi AM, Singleton A, Forrester T, Hilton G, McKenzie CA, Salako T, Iwai N, Kita Y, Ogihara T, Ohkubo T, Okamura T, Ueshima H, Umemura S, Eyheramendy S, Meitinger T, Wichmann HE, Cho YS, Kim HL, Lee JY, Scott J, Sehmi JS, Zhang W, Hedblad B, Nilsson P, Smith GD, Wong A, Narisu N, Stančáková A, Raffel LJ, Yao J, Kathiresan S, O’Donnell CJ, Schwartz SM, Ikram MA, Longstreth WT Jr, Mosley TH, Seshadri S, Shrine NR, Wain LV, Morken MA, Swift AJ, Laitinen J, Prokopenko I, Zitting P, Cooper JA, Humphries SE, Danesh J, Rasheed A, Goel A, Hamsten A, Watkins H, Bakker SJ, van Gilst WH, Janipalli CS, Mani KR, Yajnik CS, Hofman A, Mattace-Raso FU, Oostra BA, Demirkan A, Isaacs A, Rivadeneira F, Lakatta EG, Orru M, Scuteri A, Ala-Korpela M, Kangas AJ, Lyytikäinen LP, Soininen P, Tukiainen T, Würtz P, Ong RT, Dörr M, Kroemer HK, Völker U, Völzke H, Galan P, Hercberg S, Lathrop M, Zelenika D, Deloukas P, Mangino M, Spector TD, Zhai G, Meschia JF, Nalls MA, Sharma P, Terzic J, Kumar MV, Denniff M, Zukowska-Szczechowska E, Wagenknecht LE, Fowkes FG, Charchar FJ, Schwarz PE, Hayward C, Guo X, Rotimi C, Bots ML, Brand E, Samani NJ, Polasek O, Talmud PJ, Nyberg F, Kuh D, Laan M, Hveem K, Palmer LJ, van der Schouw YT, Casas JP, Mohlke KL, Vineis P, Raitakari O, Ganesh SK, Wong TY, Tai ES, Cooper RS, Laakso M, Rao DC, Harris TB, Morris RW, Dominiczak AF, Kivimaki M, Marmot MG, Miki T, Saleheen D, Chandak GR, Coresh J, Navis G, Salomaa V, Han BG, Zhu X, Kooner JS, Melander O, Ridker PM, Bandinelli S, Gyllensten UB, Wright AF, Wilson JF, Ferrucci L, Farrall M, Tuomilehto J, Pramstaller PP, Elosua R, Soranzo N, Sijbrands EJ, Altshuler D, Loos RJ, Shuldiner AR, Gieger C, Meneton P, Uitterlinden AG, Wareham NJ, Gudnason V, Rotter JI, Rettig R, Uda M, Strachan DP, Witteman JC, Hartikainen AL, Beckmann JS, Boerwinkle E, Vasan RS, Boehnke M, Larson MG, Järvelin MR, Psaty BM, Abecasis GR, Chakravarti A, Elliott P, van Duijn CM, Newton-Cheh C, Levy D, Caulfield MJ, Johnson T; International Consortium for Blood Pressure Genome-Wide Association Studies ; CARDIoGRAM consortium; CKDGen Consortium; KidneyGen Consortium; EchoGen consortium; CHARGE-HF consortium. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 2011; 478:103–109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Liu Y, Hu C, Liu C, Liu D, Mei L, He C, Jiang L, Wu H, Chen H, Feng Y. A rapid improved multiplex ligation detection reaction method for the identification of gene mutations in hereditary hearing loss. PLoS One 2019; 14:e0215212. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Abravaya K, Carrino JJ, Muldoon S, Lee HH. Detection of point mutations with a modified ligase chain reaction (Gap-LCR). Nucleic Acids Res 1995; 23:675–682. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, Buchkovich ML, Mora S, Beckmann JS, Bragg-Gresham JL, Chang HY, Demirkan A, Den Hertog HM, Do R, Donnelly LA, Ehret GB, Esko T, Feitosa MF, Ferreira T, Fischer K, Fontanillas P, Fraser RM, Freitag DF, Gurdasani D, Heikkilä K, Hyppönen E, Isaacs A, Jackson AU, Johansson Å, Johnson T, Kaakinen M, Kettunen J, Kleber ME, Li X, Luan J, Lyytikäinen LP, Magnusson PKE, Mangino M, Mihailov E, Montasser ME, Müller-Nurasyid M, Nolte IM, O’Connell JR, Palmer CD, Perola M, Petersen AK, Sanna S, Saxena R, Service SK, Shah S, Shungin D, Sidore C, Song C, Strawbridge RJ, Surakka I, Tanaka T, Teslovich TM, Thorleifsson G, Van den Herik EG, Voight BF, Volcik KA, Waite LL, Wong A, Wu Y, Zhang W, Absher D, Asiki G, Barroso I, Been LF, Bolton JL, Bonnycastle LL, Brambilla P, Burnett MS, Cesana G, Dimitriou M, Doney ASF, Döring A, Elliott P, Epstein SE, Ingi Eyjolfsson G, Gigante B, Goodarzi MO, Grallert H, Gravito ML, Groves CJ, Hallmans G, Hartikainen AL, Hayward C, Hernandez D, Hicks AA, Holm H, Hung YJ, Illig T, Jones MR, Kaleebu P, Kastelein JJP, Khaw KT, Kim E, Klopp N, Komulainen P, Kumari M, Langenberg C, Lehtimäki T, Lin SY, Lindström J, Loos RJF, Mach F, McArdle WL, Meisinger C, Mitchell BD, Müller G, Nagaraja R, Narisu N, Nieminen TVM, Nsubuga RN, Olafsson I, Ong KK, Palotie A, Papamarkou T, Pomilla C, Pouta A, Rader DJ, Reilly MP, Ridker PM, Rivadeneira F, Rudan I, Ruokonen A, Samani N, Scharnagl H, Seeley J, Silander K, Stančáková A, Stirrups K, Swift AJ, Tiret L, Uitterlinden AG, van Pelt LJ, Vedantam S, Wainwright N, Wijmenga C, Wild SH, Willemsen G, Wilsgaard T, Wilson JF, Young EH, Zhao JH, Adair LS, Arveiler D, Assimes TL, Bandinelli S, Bennett F, Bochud M, Boehm BO, Boomsma DI, Borecki IB, Bornstein SR, Bovet P, Burnier M, Campbell H, Chakravarti A, Chambers JC, Chen YI, Collins FS, Cooper RS, Danesh J, Dedoussis G, de Faire U, Feranil AB, Ferrières J, Ferrucci L, Freimer NB, Gieger C, Groop LC, Gudnason V, Gyllensten U, Hamsten A, Harris TB, Hingorani A, Hirschhorn JN, Hofman A, Hovingh GK, Hsiung CA, Humphries SE, Hunt SC, Hveem K, Iribarren C, Järvelin MR, Jula A, Kähönen M, Kaprio J, Kesäniemi A, Kivimaki M, Kooner JS, Koudstaal PJ, Krauss RM, Kuh D, Kuusisto J, Kyvik KO, Laakso M, Lakka TA, Lind L, Lindgren CM, Martin NG, März W, McCarthy MI, McKenzie CA, Meneton P, Metspalu A, Moilanen L, Morris AD, Munroe PB, Njølstad I, Pedersen NL, Power C, Pramstaller PP, Price JF, Psaty BM, Quertermous T, Rauramaa R, Saleheen D, Salomaa V, Sanghera DK, Saramies J, Schwarz PEH, Sheu WH, Shuldiner AR, Siegbahn A, Spector TD, Stefansson K, Strachan DP, Tayo BO, Tremoli E, Tuomilehto J, Uusitupa M, van Duijn CM, Vollenweider P, Wallentin L, Wareham NJ, Whitfield JB, Wolffenbuttel BHR, Ordovas JM, Boerwinkle E, Palmer CNA, Thorsteinsdottir U, Chasman DI, Rotter JI, Franks PW, Ripatti S, Cupples LA, Sandhu MS, Rich SS, Boehnke M, Deloukas P, Kathiresan S, Mohlke KL, Ingelsson E, Abecasis GR; Global Lipids Genetics Consortium . Discovery and refinement of loci associated with lipid levels. Nat Genet 2013; 45:1274–1283. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81:559–575. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Hinkes B, Wiggins RC, Gbadegesin R, Vlangos CN, Seelow D, Nürnberg G, Garg P, Verma R, Chaib H, Hoskins BE, Ashraf S, Becker C, Hennies HC, Goyal M, Wharram BL, Schachter AD, Mudumana S, Drummond I, Kerjaschki D, Waldherr R, Dietrich A, Ozaltin F, Bakkaloglu A, Cleper R, Basel-Vanagaite L, Pohl M, Griebel M, Tsygin AN, Soylu A, Müller D, Sorli CS, Bunney TD, Katan M, Liu J, Attanasio M, O’toole JF, Hasselbacher K, Mucha B, Otto EA, Airik R, Kispert A, Kelley GG, Smrcka AV, Gudermann T, Holzman LB, Nürnberg P, Hildebrandt F. Positional cloning uncovers mutations in PLCE1 responsible for a nephrotic syndrome variant that may be reversible. Nat Genet 2006; 38:1397–1405. [DOI] [PubMed] [Google Scholar]
- 22. Garovic VD. The role of the podocyte in preeclampsia. Clin J Am Soc Nephrol 2014; 9:1337–1340. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Bai X, Lenhart KC, Bird KE, Suen AA, Rojas M, Kakoki M, Li F, Smithies O, Mack CP, Taylor JM. The smooth muscle-selective RhoGAP GRAF3 is a critical regulator of vascular tone and hypertension. Nat Commun 2013; 4:2910. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Gai Z, Zhao J. Genome-wide analysis reveals the functional and expressional correlation between RhoGAP and RhoGEF in mouse. Genomics 2020; 112:1694–1706. [DOI] [PubMed] [Google Scholar]
- 25. Thomsen LC, McCarthy NS, Melton PE, Cadby G, Austgulen R, Nygård OK, Johnson MP, Brennecke S, Moses EK, Bjørge L, Iversen AC. The antihypertensive MTHFR gene polymorphism rs17367504-G is a possible novel protective locus for preeclampsia. J Hypertens 2017; 35:132–139. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Xuan C, Li H, Zhao JX, Wang HW, Wang Y, Ning CP, Liu Z, Zhang BB, He GW, Lun LM. Association between MTHFR polymorphisms and congenital heart disease: a meta-analysis based on 9,329 cases and 15,076 controls. Sci Rep 2014; 4:7311. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. García-Fragoso L, García-García I, Leavitt G, Renta J, Ayala MA, Cadilla CL. MTHFR polymorphisms in Puerto Rican children with isolated congenital heart disease and their mothers. Int J Genet Mol Biol 2010; 2:43–47. [PMC free article] [PubMed] [Google Scholar]
- 28. Balderrábano-Saucedo NA, Sánchez-Urbina R, Sierra-Ramírez JA, García-Hernández N, Sánchez-Boiso A, Klunder-Klunder M, Arenas-Aranda D, Bravo-Hernández G, Noriega-Zapata P, Vizcaíno-Alarcón A. Polymorphism 677C → T MTHFR gene in Mexican mothers of children with complex congenital heart disease. Pediatr Cardiol 2013; 34:46–51. [DOI] [PubMed] [Google Scholar]
- 29. Stevens KN, Hakonarson H, Kim CE, Doevendans PA, Koeleman BP, Mital S, Raue J, Glessner JT, Coles JG, Moreno V, Granger A, Gruber SB, Gruber PJ. Common variation in ISL1 confers genetic susceptibility for human congenital heart disease. PLoS One 2010; 5:e10855. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Franceschini N, Carty CL, Lu Y, Tao R, Sung YJ, Manichaikul A, Haessler J, Fornage M, Schwander K, Zubair N, Bien S, Hindorff LA, Guo X, Bielinski SJ, Ehret G, Kaufman JD, Rich SS, Carlson CS, Bottinger EP, North KE, Rao DC, Chakravarti A, Barrett PQ, Loos RJ, Buyske S, Kooperberg C. Variant discovery and fine mapping of genetic loci associated with blood pressure traits in Hispanics and African Americans. PLoS One 2016; 11:e0164132. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Zhu H, Yang W, Lu W, Etheredge AJ, Lammer EJ, Finnell RH, Carmichael SL, Shaw GM. Gene variants in the folate-mediated one-carbon metabolism (FOCM) pathway as risk factors for conotruncal heart defects. Am J Med Genet A 2012; 158A:1124–1134. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Agopian AJ, Mitchell LE, Glessner J, Bhalla AD, Sewda A, Hakonarson H, Goldmuntz E. Genome-wide association study of maternal and inherited loci for conotruncal heart defects. PLoS One 2014; 9:e96057. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. U.S. Census Bureau PD. Annual Estimates of the Resident Population by Sex, Single Year of Age, Race, and Hispanic Origin for the United States: April 1, 2010 to July 1, 2016. Division USCBP: Washington, DC, 2017. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.