Parental and offspring contribution of genetic markers of adult blood pressure in early life: The FAMILY study.

Previous genome wide association studies (GWAS) identified associations of multiple common variants with diastolic and systolic blood pressure traits in adults. However, the contribution of these loci to variations of blood pressure in early life is unclear. We assessed the child and parental contributions of 33 GWAS single-nucleotide polymorphisms (SNPs) for blood pressure in 1,525 participants (515 children, 406 mothers and 237 fathers) of the Family Atherosclerosis Monitoring In early life (FAMILY) study followed-up for 5 years. Two genotype scores for systolic (29 SNPs) and diastolic (24 SNPs) blood pressure were built. Linear mixed-effect regressions showed significant association between rs1378942 in CSK and systolic blood pressure (β = 0.98±0.46, P = 3.4×10-2). The child genotype scores for diastolic and systolic blood pressure were not associated in children. Nominally significant parental genetic effects were found between the SNPs rs11191548 (CYP17A1) (paternal, β = 2.78±1.49, P = 6.1×10-2 for SBP and β = 3.60±1.24, P = 3.7×10-3 for DBP), rs17367504 (MTHFR) (paternal, β = 2.42±0.93, P = 9.3×10-3 for SBP and β = 1.89±0.80, P = 1.8×10-2 for DBP and maternal, β = -1.32±0.60, P = 2.9×10-2 and β = -1.97±0.77, P = 1.0×10-2, for SBP and DBP respectively) and child blood pressure. Our study supports the view that adult GWAS loci have a limited impact on blood pressure during the five first years of life. The parental genetic effects observed on blood pressure in children may suggest epigenetic mechanisms in the transmission of the risk of hypertension. Further replication is needed to confirm our results.

Introduction

In 2008, 978 million adults, or 28% of the global adult population had hypertension (HTN) and the burden of HTN may reach 1.5 billion by 2025 [1,2]. HTN is associated with an increased risk for cardiovascular disease and contributes as such to 7.6 million (13.5%) deaths each year worldwide [1]. Modifiable risk factors for HTN include excessive dietary sodium, physical inactivity, excessive alcohol intake, psychosocial stress and obesity [3]. Non modifiable risk factors include sex, age, but also ethnicity and family history of HTN, suggesting a contribution of genetic determinants in HTN etiology [4]. Twin and family studies have reported heritability estimates of 30–50% for blood pressure (BP) and hypertension [5]. Twelve genes have been associated with Mendelian syndromes causing HTN [5]. Genome-wide association studies (GWAS) have identified 54 common genetic variants associated with systolic blood pressure (SBP) and diastolic blood pressure (DBP) [5,6]. These GWAS signals point toward the role of vasodilatory hormones, ionic regulation by solute channels and vascular smooth muscle growth and signaling in the pathogenesis of HTN [7]. It is noteworthy that most of the GWAS studies for BP have been performed in adults of European ancestry, and only one GWAS for BP has been reported in children and adolescents [5,8,9]. To date, four studies assessed the contribution of SNPs identified in adult GWAS in children and adolescents of European ancestry [9-12]. Oikonen et al. built two genotype scores by using 5 SBP and 8 DBP-associated SNPs and did not find any evidence of association with SBP and DBP from the age of 3 to 18 years (sample size comprised between 340 and 1100) [10]. More recently, Howe et al. studied a unique genotype score based on 29 adult BP SNPs in 8472 children from Australia and United Kingdom and evidenced a nominal association only with SBP at the ages of 6 and 17 years [11]. Early 2016, an international consortium found two novel loci associated with SBP at pre-puberty (4–7 years) and puberty (8–12 years-) [9]. The authors also highlighted an age specific association of the two SNPs.

Parental history of high BP has been associated with higher SBP and DBP in offspring in the literature; some but not all studies reporting sex-specific parental effects [13,14]. Family heritability studies for SBP and DBP support the view that the phenotypic resemblance observed between parents and offspring may be explained in part by genetic determinants [15,16]. However, the parental contribution of genetic markers of adult BP in offspring has never been investigated.

This prompted us to investigate the parental and child contributions of 33 GWAS associated-SNPs for BP in 1,525 participants of the Family Atherosclerosis Monitoring In early life (FAMILY) study followed-up from birth to the age of 5 years.

Methods

Subjects

The Family Atherosclerosis Monitoring In earLY life (FAMILY) study has been described elsewhere [17]. FAMILY is an ongoing birth cohort study that includes mothers, fathers and children with a planned follow-up of 10 years. Briefly, over the last 7 years, 859 families including 901 babies, 259 siblings, 857 mothers and 530 fathers were enrolled into the FAMILY study. In this study, we excluded offspring from multiple births, siblings of “index” children due to familial relatedness and phenotypic issues (i.e. absence of phenotypic data at birth). Following these exclusion criteria, 630 mothers, 351 fathers and 544 unrelated children had DNAs extracted and were selected for genotyping. After assessing the family structure between the children and their parents, we selected 515 children, 406 mothers and 237 fathers with genotypic and phenotypic (sex, age and BMI) data for the analysis (406 child/mother pairs, 237 child/father pairs, and 219 trios). Phenotypic characteristics of these individuals are available in the Table 1. Sample sizes at each time of measurement are available in the S1 Fig. The data coordination site of the FAMILY study is the Population Health Research Institute (Hamilton, ON, Canada). Informed consent was obtained from all the adult participants, and the parents provided consent for their children. All procedures were performed in accordance with relevant guidelines and regulations. The study was approved by the Research Ethics Boards at the participating hospitals (Hamilton Health Sciences, St Joseph’s Hospital—Hamilton, Joseph Brant Memorial Hospital, Burlington, ON, Canada).

Table 1

Phenotypic characteristics of the studied population.

	Visit	Father	Mother	Child
				Sample size
N		351	630		544
% male		100%	0%		47.67%
Age (years)	Birth visit	33.92 ± 5.65	32.31 ± 4.67	515	0.007 ± 0.013
	1	NA	33.79 ± 4.61	487	1.08 ± 0.13
	2	NA	NA	470	2.07 ± 0.12
	3	NA	NA	441	3.08 ± 0.16
	5	40.03 ± 5.80	38.36 ± 4.55	383	5.12 ± 0.19
BMI (kg/m2)	Initial visit	28.39 ± 4.76	26.73 ± 6.27	NA	NA
	Birth visit	NA	NA	515	13.99 ± 1.39
	1	NA	26.99 ± 6.68	487	17.54 ± 1.42
	2	NA	NA	470	16.39 ± 1.26
	3	NA	27.646 ± 6.30	441	16.17 ± 1.20
	5	28.45 ± 4.83	27.40 ± 6.640	383	15.87 ± 1.59
SBP (mmHg)	Initial visit	124.5 ± 10.4	113.6 ± 10.6	NA	NA
	Birth visit	NA	NA	181	68.9 ± 10.7
	1	NA	113.5 ± 10.3	318	96.6 ± 11.5
	2	NA	NA	391	96.6 ± 10.6
	3	NA	NA	421	95.8 ± 8.5
	5	124.9 ± 11.3	113.9 ± 12.8	379	99.0 ± 8.3
DBP (mmHg)	Initial visit	79.0 ± 9.6	70.9 ± 8.4	NA	NA
	Birth visit	NA	NA	454	39.2 ± 8.6
	1	NA	73.5 ± 9.1	316	59.6 ± 7.6
	2	NA	NA	391	60.5 ± 7.0
	3	NA	NA	421	60.2± 5.9
	5	78.0 ± 9.7	73.5 ± 10.3	379	60.2 ± 5.6

BMI, body mass index. DBP, diastolic blood pressure. SBP, systolic blood pressure. NA, data not available.

Phenotyping

Offspring’s phenotypic measurements have been performed at birth, 1 year, 2 years, 3 years and 5 years of age (Table 1). Systolic and diastolic blood pressures were measured with a Dinamap Pro100 V2 (GE Medical Systems, Tampa, Florida, USA), which utilizes an oscillometric method, and repeated 3 times at 2 minutes intervals. At birth the measures were performed while the child was sleeping or lying quietly. For all other time measurement (1, 2, 3 and 5 years), the measures were performed while the child was sitting quietly and after resting for at least 5 minutes. The child’s height was recorded from birth to 2 years using an O'Leary pediatric length board (Ellard Inc) then using Harpenden stadiometer with a precision of 0.1 cm. The weight was measured to the nearest 200g in light clothes using an electronic top. BMI was calculated using the following formula: weight (kg)/ height2 (m2).

Genotyping

Genomic DNAs were extracted from buffy coats for all the participants. Buffy coats for mothers and fathers were extracted from blood samples collected at the initial visit at the 24–37 weeks of gestation. For the child, the buffy coat comes from cord blood at the delivery. The genotyping was performed using the Illumina Cardio-Metabochip (San Diego, CA, USA). This array has been designed by seven consortia on cardiac, metabolic and anthropometric traits. A selection of 196,725 SNPs for 23 different traits was made. The design and SNP selection of the array have been detailed elsewhere [18]. We selected SNPs that reached genome-wide significance level of association (P<5×10−8) for SBP and/or DBP in at least one population of European ancestry and were available in the Cardio-Metabochip array (lead SNP or proxy). All the SBP and DBP-associated SNPs were extracted from two databases (HuGE Navigator and NHGRI GWAS Catalog). For SNPs that were not available in the Cardio-Metabochip, we searched for proxy SNPs using the Broad Institute website tool SNAP (SNP Annotation and Proxy Search). For those highlighted as missing in the Cardio-Metabochip, we checked their availability using their chromosomal position in the Illumina product file. We used the following criteria to select proxy SNPs: 1) SNPs included in the Cardio-Metabochip 2) r2 >0.95 in European population data issued from the 1000 Genomes Project, 3) selection of a coding non-synonymous SNP if available in the list of proxy, otherwise selection of the SNP located closest to the GWAS lead SNP. To avoid any overlap in the final SNP selection, linkage disequilibrium between all the SNPs was double-checked using SNAP in European population data of the 1000 Genomes Project. We discarded 13 SNPs that displayed r2 > 0.2 with another SNP in the list. Thirty-three SBP and DBP-associated polymorphisms remains for further study (S1 Table). Standard procedures have been used to assess the quality of the genotyping: all 33 SNPs displayed call rates > 99% and are consistent with the Hardy Weinberg Equilibrium (S2 and S3 Tables). As an additional quality control procedure we analyzed the Mendelian transmission patterns of the 33 SNPs. We found recurrent Mendelian inconsistencies in five pedigrees. After excluding the five non-biological fathers from the analysis, only one Mendelian distortion was observed in the whole sample for the 33 SNPs, which therefore successfully passed the quality control test. Data from the five non-biological fathers were excluded from further analyses. We then searched for discrepancies between the reported sex and the one determined using the genetic information. We found 9 discrepancies by using the heterozygosity rate calculated by PLINK. The cryptic relatedness between the children was also verified and we removed six individuals due to evidence of relatedness (second degree relatives). We double-checked the self-reported ethnicity of our individuals using EIGENSTRAT. The 1525 participants of the FAMILY study were predominantly white Caucasians (92.8% Mothers; 89.3% Fathers; 91.1% Offspring). Other participants were South Asians, East Asians, Latino Americans, Africans and Native North Americans.

Statistical analyses

S1 and S2 Files provide datasets for SBP and DBP analyses, respectively. We coded genotypes as 0, 1 and 2, depending on the number of copies of the SBP or DBP increasing alleles. Two genotype scores were calculated by summing the alleles of 24 and 29 SNPs for SBP and DBP, respectively. The genotype scores were used as an ordinal value in the models. Considering the possibility that genetic effects for BP GWAS SNPs may diverge in adult and children populations, we used an unweighted genotype score to prevent any analytical bias. Unweighted and weighted genotype scores for complex traits usually have a comparable performance [19,20]. This is especially true if the differences in genetic effects of SNPs are minor and if the sample size is not very large, two conditions that apply to our study [19,20]. Individuals with more than two missing values were discarded from the calculation of the genotype score and the remaining missing values were imputed using the method of the mean. This imputation was performed for each SNP individually using the arithmetic average of the coded genotypes observed for all the successfully genotyped individuals.

We did not perform family-based association tests in this study for two reasons. First, larger sample sizes are needed for family-based than regression association tests to achieve comparable statistical power [21]. The low participation rate of fathers in the FAMILY study (515 children, 406 mothers and 237 fathers) adds to the loss of statistical power. Unfortunately, this is a common pitfall of family-based designs where mothers often bring children to clinic visits and thus are included more easily than fathers [22]. Second, the software used in family-based association tests only perform cross-sectional analyses. Longitudinal analyses have been shown to achieve more power than cross-sectional association tests [23,24]. Associations between SNP/genotype scores and BP measurements were assessed using linear mixed-effect regression model to account for the longitudinal nature of the data (5 SBP and DBP measurements). We used the intercept and the age at measurement as random effects and sex, BMI and the principal components as fixed effects. To assess paternal and/or maternal effects on offspring’s SBP and DBP, a linear mixed-effect regression was performed using the parental genetic information (SNPs or genotype score) as predictor and sex, age, BMI, principal components and SNPs/genotype score of the offspring as covariates. The 10 first principal components were computed using all the SNPs passing the quality control filter in the Metabochip and they were defined using EIGENSTRAT [25]. Principal components were added as covariates in all regression models to account for population structure. We handled SBP and DBP missing data at different ages through a missing at random approach in the linear mixed-effect regression model and did not to impute SBP and DBP missing data in our study. This decision was based on three arguments: 1) the percentage of SBP and DBP missing data at each measurement is heterogeneous in FAMILY (S1 Fig); 2) SBP and DBP values vary significantly in early life and a large inter-individual variability is observed at each measurement (S2 Fig); 3) linear mixed-effect regression models handle well the presence of missing data [24]. All the regression analyses were performed using the free software R 3.0.1 with the package lme4 [26].

Hardy-Weinberg equilibrium was tested using a Chi-square test in combination with permutations and bootstrapping. Mendelian incompatibilities were checked using PLINK [27]. Two-tailed P-values are presented in this manuscript. Bonferroni corrected P-values are routinely applied to exploratory genetic association studies. However, they are overly conservative given the high prior likelihood of association in post-GWAS experiments. P < 0.05 was therefore considered significant for post-GWAS associations between offspring SNPs/GSs and BP traits in children. We did not apply a Bonferroni correction for the comparison of genetic effects of SNPs at different ages in children, and between children and adults, as they represent post-hoc analyses for BP-associated SNPs. In contrast, we applied Bonferroni corrections for the exploratory associations of 1) paternal SNPs/GSs and 2) maternal SNPs/GSs with BP traits in children, as no evidence of parent-of-origin effects on BP traits has been reported in literature before. P <2.0×10−3 (0.05/25) and P <1.7×10−3 (0.05/30) was considered as significant for SBP and DBP respectively. We previously applied a similar approach for the study of obesity traits in FAMILY [28]. We compared our significant mixed model results with those obtained on an adult cohort from the International Consortium for Blood Pressure using a Z-test [29,30]. We also performed Z-tests on the child beta values across time to assess potential age-dependent genetic effects.

Results

Evolution of the phenotypes across the follow up

SBP and DBP increased during the first year of life to reach a plateau until the age of 5 years (S2 Fig).

Associations of offspring SNPs and genotype scores with blood pressure in children

Linear mixed-effect regressions on the longitudinal series of data were used to assess the effect of children’s SNPs on SBP and DBP from birth to 5 year (S4 and S5 Tables). The rs11191548 SNP near CYP17A1 showed directionally consistent association with DBP (β = 1.71±0.61, P = 4.6×10−3) (Table 2 and S5 Table). The rs1378942 in CSK showed directionally consistent association with SBP (β = 0.98±0.46, P = 3.4×10−2). Directionally inconsistent association was found for the rs12946454 in PLCD3 and SBP (β = -1.07±0.50, P = 3.3×10−2) (Table 2 and S4 Table). To assess the combined effect of the SBP and DBP SNPs, we tested the association of the children’s genotype score using a linear mixed-effect regression model on the longitudinal series of data (S4 and S5 Tables). Neither the SBP nor DBP genotype scores showed associations with SBP or DBP.

Table 2

Summary of the significant results using mixed-effect regressions.

Effect	Trait	GENE	SNP	R.A.	BETA	SE	P
Children	DBP	CYP17A1	rs11191548	A	1.713	0.605	4.61×10−3
	SBP	CSK	rs1378942	C	0.979	0.462	3.42×10−2
	SBP	PLCD3	rs12946454	T	-1.067	0.501	3.34×10−2
Maternal	DBP	MTHFR	rs17367504	A	-1.320	0.600	2.78×10−2
	DBP	ULK4	rs1717017	C	1.384	0.562	1.39×10−2
	DBP	EBF1	rs12187017	G	-1.013	0.466	2.98×10−2
	SBP	MTHFR	rs17367504	A	-1.974	0.770	1.04×10−2
	SBP	PLEKHA7	rs381815	A	1.314	0.637	3.90×10−2
Paternal	DBP	MTHFR	rs17367504	A	1.888	0.796	1.77×10−2
	DBP	CYP17A1	rs11191548	A	3.602	1.241	3.70×10−3
	SBP	MTHFR	rs17367504	A	2.418	0.930	9.28×10−3

SNP, Single Nucleotide Polymorphism. SBP, Systolic Blood Pressure. DBP, Diastolic Blood Pressure. R.A., Risk Allele. In the children section, we tested the association of the children SNPs with children BP. In the others section, we assessed the effect of maternal or paternal SNP on children phenotypes.

Associations of parental SNPs and genotype scores with blood pressure in children

Linear mixed-effect regressions on the longitudinal series of data were used to assess the effect of parental SNPs on SBP and DBP in offspring (S6 and S7 Tables). The regressions of the offspring’s phenotypes highlighted a directionally consistent nominal evidence of association of the paternal genotype of rs11191548 (CYP17A1) for DBP (β = 3.60±1.24, P = 3.7×10−3) and a trend of association with SBP after adjusting for offspring’s genotypes (β = 2.78±1.49, P = 6.1×10−2). Further adjustment for the maternal genotype did not significantly modify the nominal association of the paternal genotype of rs11191548 (CYP17A1) with SBP and DBP (β = 2.24±1.18, P = 5.7×10−2 and β = 2.85±1.00, P = 4.2×10−3, respectively). We did not find any association between the maternal genotype of rs11191548 (CYP17A1) and SBP or DBP. The associations of the child genotype rs11191548 (CYP17A1) with SBP and DBP did not resist to an adjustment by the paternal genotype (Table 2, S6 and S7 Tables). Both the maternal and paternal genotypes of rs17367504 (MTHFR) were nominally associated with SBP and DBP. The nominal associations for SBP and DBP adjusted for the offspring’s genotype were directionally inconsistent when the rs17367504 (MTHFR) maternal genotype was assessed (β = -1.32±0.60, P = 2.9×10−2 and β = -1.97±0.77, P = 1.0×10−2, respectively). In contrast, these nominal associations were directionally consistent when the paternal genotype was assessed (β = 2.42±0.93, P = 9.3×10−3 for SBP and β = 1.89±0.80, P = 1.8×10−2 for DBP). The paternal and maternal nominal associations of rs17367504 (MTHFR) with SBP and DBP were removed when the model was adjusted for the reciprocal parental genotype (Table 2, S6 and S7 Tables). The maternal genotype of rs1717017 (ULK4) was directionally consistent and nominally associated with DBP after adjusting for the offspring’s genotype (β = 1.38±0.56, P = 1.4×10−2). The maternal genotype of rs12187017 near EBF1 was found to be nominally associated in an inconsistent direction with DBP after adjusting for the offspring’s genotype (β = -1.01±0.47, P = 3.0×10−2). These associations disappeared after adjusting for the corresponding paternal genotype (rs1717017 and rs12187017). We did not find any association between the paternal genotype of rs1717017 (ULK4) or rs12187017 (EBF1) and DBP (Table 2 and S7 Table). Linear mixed-effect regressions highlighted a nominal and directionally consistent association between the maternal genotype of rs381815 (PLEKHA7) and SBP (β = 1.31±0.64, P = 3.9×10−2) (Table 2 and S6 Table). The maternal and paternal genotype scores were not associated with child SBP or DBP (S6 and S7 Tables). None of the above-mentioned maternal or paternal associations with BP traits in children survived to a Bonferroni correction.

Age-dependent genetic effects in children

We compared the effect size of the SNPs found to be at least nominally associated with child BP using linear mixed-effect model during the follow-up from birth to 5 year in FAMILY using Z-test calculation between the measurements. Rs1378942 (CSK) and rs12946454 (PLCD3) SNPs did not show a differential effect from birth to 5 year (P = 7.2×10−2 and P = 0.32, respectively) whereas the rs11191548 SNP (CYP17A1) showed a significant decrease in its effect during the 5 years follow-up (β0 = 2.09±0.88 P = 2.4×10−3, β5 = 0.32±0.71, P = 0.65, P = 1.8×10−2) (S8 and S9 Tables).

Significantly larger genetic effects were observed in children from FAMILY in comparison with an adult cohort from the International Consortium for Blood Pressure for the following SNPs: CYP17A1 rs11191548 (DBP) (βchild = 1.71±0.61, βadults = 0.52±0.11, P = 2.6×10−2) and PLCD3 rs12946454 (SBP) (βchild = -1.07±0.50, βadults = 0.60±0.10, P = 5.9×10−4). In contrast, the CSK rs1378942 SNP (SBP) (βchild = 0.98±0.46, βadults = 0.63±0.10, P = 0.23) did not show age-dependent genetic effects.

We assessed potential changes of the parental effects of rs11191548 (CYP17A1) and rs17367504 (MTHFR) SNPs from birth to 5 year. The parental genetic effect of these two SNPs on child’s SBP and DBP did not vary during the follow-up.

Discussion

In this study, we assessed the associations of 33 GWAS SBP/DBP SNPs in the FAMILY birth cohort. The SNP rs1378942 (CSK) showed a significant association with SBP from birth to 5 year in line with previous reports on adults [7,29-31]. CSK (c-Src tyrosine kinase) is a tyrosine kinase with roles in the mediation of the G protein signals to actin cytoskeletal reorganization [32]. Actin remodeling has a direct impact on the constriction of the arterial endothelium in rats and human newborns, supporting genetic effects in early life [33,34]. In line with our data, a nominal association between rs1378942 (CSK) and SBP was recently reported in 8,472 children from Australia and United Kingdom at the age of 6 years [11]. An association of rs1378942 (CSK) with SBP was also reported in 1,027 Chinese obese children [35]. The fact that CSK rs1378942 SNP shows comparable genetic effects on SBP in both FAMILY and adult populations from the International Consortium for Blood Pressure suggests that this SNP contributes equally to SBP variations over the life course. In contrast, the genotype scores based on 24 and 29 SNPs did not show any association with child SBP and DBP in early life in our study. Similarly, Oikonen et al. did not evidence any association between two genotype scores based on 5 SBP and 8 DBP-associated SNPs and SBP or DBP from the age of 3 to 18 years [10]. In contrast, Howe et al. found a nominal association between a genotype score based on 29 adult SBP/DBP SNPs and SBP at the ages of 6 and 17 years [11]. The inconsistency of this finding with our data may relate to the unique nature of the composite genotype score developed by Howe and colleagues, thus making direct comparison difficult [11]. The lack of association between the SBP and DBP genotype scores observed by us and others [10] during childhood and adolescence is consistent with the fact that heritability estimates for both traits increase progressively during this age window to reach a plateau at young adulthood [36]. Similarly, the association of SNPs such as CSK rs1378942 or the genotype score by us and others with child SBP (but not DBP) is in line with the systematically lower heritability estimates found for DBP in comparison with SBP in adolescents and adults [37,38]. As no longitudinal study to date reported heritability estimates for SBP and DBP in the first years of life, we calculated these values in FAMILY and also found a progressive increase of heritability estimates from birth to 5 years and an overall lower heritability for DBP than SBP (DBP: from 1.1 to 26.4%; SBP: from 0.0% to 31.5%; Robiou-du-Pont et al., manuscript in preparation). These results deserve further investigations but show that beyond lifestyle a subset of genetic factors already plays a role in early life.

We are aware of the modest power of our study, as showed by our power calculation simulations (S3 Fig). A suboptimal statistical power inflates both the risk of false negative and false positive associations [21]. This means that, in addition to the association observed between CSK rs1378942 and SBP, other SNPs contributing to BP in early life may have been missed in the present study. We also speculate that some of the associations observed in this study with BP (e.g. rs12946454 in PLCD3) but displaying an inconsistent direction of effect in contrast with previous literature in adults may represent false positive results. Alternatively, we cannot exclude the possibility of age-dependent genetic effects on BP, as recently reported by Simino et al. for SBP and DBP in young versus old adults [39]. An inversion of genetic effect in infancy versus childhood has also been reported for the FTO intron 1 variant in relation with body mass index in eight longitudinal cohorts of European ancestry [40].

This study is the first to assess parental genetic effect of SNPs identified by GWAS on SBP and DBP phenotypes in early life independent of the influence of child genotype. This investigation highlighted a nominal association of the paternal BP increasing CYP17A1 rs11191548 allele with higher child SBP and DBP using a mixed-effect model. Maternal and paternal alleles of rs17367504 in MTHFR display opposite effects on BP in children. While the paternal SBP/DBP increasing allele of rs17367504 (MTHFR) shows a directionally consistent nominal association with offspring’s BP from birth to 5 years, the maternal SBP/DBP increasing allele at the same SNP is nominally associated with a decrease in children’s BP. Beyond the associations described at the MTHFR and CYP17A1 loci, other nominally significant parental effects were observed for rs12187017 (EBF1), rs1717017 (ULK4), rs381815 (PLEKHA7) and offspring BP. Even if the biology of these genes does not enable a trivial explanation for these associations, further replication of these nominally significant results in additional studies is warranted to definitively assess the potential epigenetic transmission of hypertension.

Our study has several strengths. First and most importantly, this report is the first to investigate SNPs that affect BP from birth to five years. This is also the first time that parental effects are studied on BP in young children. Furthermore, the longitudinal FAMILY study provided a unique opportunity to investigate the effects of parental SNPs on offspring BP using mixed-effects models. Of note, several genetic associations have been strengthened by plausible biological arguments. Lastly, the Illumina Cardio-Metabochip allowed us to investigate the most exhaustive list of SNPs so far (N = 33).

One limitation of the study is the modest sample size, which restricted our power to detect associations with small effect sizes and/or low risk allele frequencies. The longitudinal nature of our study and the use of linear mixed-effect regressions compensated to a certain extent the suboptimal power. Another limitation is the low number of fathers recruited. This however constitutes a common feature of birth cohorts focusing principally on mothers and offspring.

In conclusion, we highlighted in this study a significant association of the rs1378942 SNP in CSK with SBP during the first years of life, but no overall association of the GWAS BP SNPs using SBP/DBP genotype scores. Moreover and for the first time, nominally significant parental genetic effects were found between the SNPs rs11191548 (CYP17A1) and rs17367504 (MTHFR) and child BP suggesting possible epigenetic mechanisms in the transmission of susceptibility to hypertension. Our results suggest that the genetic predisposition for hypertension have a limited impact on BP during the first years. Furthermore, the observation of paternal and maternal genetic effects may contribute to explain why maternal risk factors do not account for the global phenotypic variance of child BP [41].

Dataset for systolic blood pressure analyses.

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Dataset for diastolic blood pressure analyses.

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Characteristics of the 33 selected SNPs.

MAF, Minor Allele Frequency in the studied population. GWAS, Genome-Wide Association Study. SNP, Single Nucleotide Polymorphism. CHR, Chromosome. SBP, Systolic Blood Pressure. DBP, Diastolic Blood Pressure.

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Genotype count, call rate and Hardy Weinberg Equilibrium (HEW) test for the FAMILY population.

SNP: Single Nucleotide Polymorphism. CHR: Chromosome. CR: Call Rate. HWE: P-value resulting of Hardy Weinberg Equilibrium test.

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Genotype count and Hardy Weinberg Equilibrium test (HWE) for mothers and fathers.

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Results of regression of offspring genotype for systolic blood pressure.

A linear regression was performed of the offspring genotype at each time of measurement (birth, 1, 2, 3 and 5y) with sex and BMI as adjustment. The linear mixed-effect regression model was performed of the offspring genotype adjusted by sex and BMI as fixed effect and by the intercept and age as random effect.

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Results of regression of offspring genotype for diastolic blood pressure.

A linear regression was performed of the offspring genotype at each time of measurement (birth, 1, 2, 3 and 5y) with sex and BMI as adjustment. The linear mixed-effect regression model was performed of the offspring genotype adjusted by sex and BMI as fixed effect and by the intercept and age as random effect.

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Linear mixed model regression systolic blood pressure.

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Linear mixed model regression on diastolic blood pressure.

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A linear regression was performed of the offspring genotype at each time of measurement (birth, 1, 2, 3 and 5y) with sex and BMI as adjustment. The linear mixed-effect regression model was performed of the offspring genotype adjusted by sex and BMI as fixed effect and by the intercept and age as random effect.

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A linear regression was performed of the offspring genotype at each time of measurement (birth, 1, 2, 3 and 5y) with sex and BMI as adjustment. The linear mixed-effect regression model was performed of the offspring genotype adjusted by sex and BMI as fixed effect and by the intercept and age as random effect.

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Sample size evolution from birth to 5 years for the child only, the duos and trios.

A) For the regression covariates only, B) For the SBP analysis and C) For the DBP analysis.

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Child SBP and DBP evolution across the follow up.

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80% statistical power curves in the FAMILY study.

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