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A combined linkage, microarray and exome analysis suggests MAP3K11 as a candidate gene for left ventricular hypertrophy

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BMC Medical GenomicsBMC series – open, inclusive and trusted201811:22

https://doi.org/10.1186/s12920-018-0339-9

  • Received: 9 May 2017
  • Accepted: 21 February 2018
  • Published:
Open Peer Review reports

Abstract

Background

Electrocardiographic measures of left ventricular hypertrophy (LVH) are used as predictors of cardiovascular risk. We combined linkage and association analyses to discover novel rare genetic variants involved in three such measures and two principal components derived from them.

Methods

The study was conducted among participants from the Erasmus Rucphen Family Study (ERF), a Dutch family-based sample from the southwestern Netherlands. Variance components linkage analyses were performed using Merlin. Regions of interest (LOD > 1.9) were fine-mapped using microarray and exome sequence data.

Results

We observed one significant LOD score for the second principal component on chromosome 15 (LOD score = 3.01) and 12 suggestive LOD scores. Several loci contained variants identified in GWAS for these traits; however, these did not explain the linkage peaks, nor did other common variants. Exome sequence data identified two associated variants after multiple testing corrections were applied.

Conclusions

We did not find common SNPs explaining these linkage signals. Exome sequencing uncovered a relatively rare variant in MAPK3K11 on chromosome 11 (MAF = 0.01) that helped account for the suggestive linkage peak observed for the first principal component. Conditional analysis revealed a drop in LOD from 2.01 to 0.88 for MAP3K11, suggesting that this variant may partially explain the linkage signal at this chromosomal location. MAP3K11 is related to the JNK pathway and is a pro-apoptotic kinase that plays an important role in the induction of cardiomyocyte apoptosis in various pathologies, including LVH.

Background

Left ventricular hypertrophy (LVH) is a predictor of increased cardiovascular morbidity and mortality [1]. Those with LVH have a 2-fold increased risk of adverse events, particularly ischemic heart disease and chronic heart failure [2, 3]. Increased left ventricular mass maintains cardiac pump performance in response to cardiovascular insults, such as coronary heart disease [3, 4]. Risk factors for LVH are elevated systolic blood pressure, obesity, hypertension, insulin resistance, valvular heart disease and advanced age, among others [2, 5, 6]. LVH proxy measurements can be assessed through noninvasive methods, such as echocardiography and magnetic resonance imaging, however, electrocardiographic measurements are the most used worldwide [7]. LVH proxy measurements include calculations of the Sokolow-Lyon index (SL), the Cornell voltage product (CV) and the 12-lead sum QRS product (12LS). Several studies have demonstrated that genetic factors influence electrocardiographic and echocardiographic measures of LVH [2, 4, 5, 8, 9]. We recently demonstrated that these measures contain a substantial heritable component (SL = 0.46, 12LS = 0.49 and CV = 0.34) [10].

Genome-wide linkage analyses, candidate gene association studies, genome-wide association studies (GWAS) and gene mapping have been conducted to identify genes influencing LVH. In the first GWAS of these traits, two loci, PTGES3 and NMB, reached genome-wide significance. IGF1R and SCN5A were identified and replicated without reaching genome-wide significance [5]. Recently, an expanded GWAS detected a number of novel loci influencing CV, SL, and 12LS [11]. Among these were 32 loci containing genes with known cardiac function, coding for cardiac sarcomere components or related to cardiac myocyte function. Evidence for linkage of echocardiographic LV mass to chromosome 5 (LOD score = 1.6) and electrocardiographic LV mass to chromosome 7 (LOD score = 1.67) [8] and chromosome 12 (LOD score = 2.19 and 3.11) [8, 12] were reported in linkage studies, with the strongest evidence for chromosome 12 [3]. As is the case for other complex outcomes, most candidate genes studies have not been replicated and do not reach genome-wide significance [3].

Exome sequencing has been successfully used for Mendelian disorders [13]. More recently, this technology has been extended to the analysis of non-Mendelian diseases and complex traits, as rare variants with large effects can contribute to the heritability of common traits. The aim of this study was to discover rare variants by linkage analysis in a large family-based study, the Erasmus Rucphen Family (ERF) study. Linked regions were fine-mapped in detail using microarray data and exome sequencing.

Methods

Study population

The ERF study is a family-based study including over 3000 participants descendant from 22 couples that lived in the Rucphen region in the southwest Netherlands in the nineteenth century [14]. All descendants of those couples were invited to visit the clinical research center in the region where they were examined in person [15]. Interviews at the time of blood sampling were performed by medical practitioners and included questions on current medication use and medical history [16]. Additionally, participants were asked to bring their current medications with them to the study center; these were cross-referenced with general practitioner and pharmacy records. Height and weight were measured with the participant in light underclothing and body mass index (kg/m2) was computed. Blood pressure was measured twice on the right arm in a sitting position after at least 5 min rest, using an automated device (OMRON 711, Omron Healthcare, Bannockburn, IL, USA). The average of the two measures was used for analysis. Hypertension status was identified through the use of antihypertensive medication and/or through the assessment of blood pressure measurements according to the guidelines of the World Health Organization [17]. The Medical Ethics Committee of the Erasmus University Medical Center approved the ERF study protocol and all participants, or their legal representatives, provided written informed consent.

ECG interpretation and measurements

Examinations included 12-lead ECG measurements. A 10 s 12-lead ECG (on average, 8 to 10 beats) was recorded with an ACTA-ECG electrocardiograph (Esaote, Florence, Italy) with a sampling frequency of 500 Hz. Digital measurements of the ECG parameters were made using the Modular ECG Analysis System (MEANS) [18, 19]. Briefly, MEANS operates on multiple simultaneously recorded leads, which are transformed to a detection function that brings out the QRS complex and the other parts of the signal. MEANS determines common onsets and offsets for all 12 leads together on one representative averaged beat, with the use of template matching techniques. The measurement and diagnostic performance of MEANS has been extensively evaluated, both by the developers and by others [1922]. The MEANS criteria for MI are mainly based on pathological Q waves, QR ratio, and R-wave progression [20]. A cardiologist, specialized in ECG methodology, ascertained the final diagnosis of MI.

MEANS was used to measure QRS complex duration and the three LVH proxies. Sokolow-Lyon was defined as the sum of the S wave in V1 plus the R wave in V5 or V6, Cornell as the sum of R in aVL and S in V3, and 12-lead as the sum of R to S in all 12 leads; these three voltages were then multiplied by QRS duration to obtain voltage-duration products as an approximation of the area under the QRS complex [2123]. Principal component (PC) analysis was applied to the three original measurements (SL, 12LS and CV) to capture the correlation structure between traits. Two PCs, PC1 and PC2, captured more than 94% of the total variance and were also assessed as phenotypes in these analyses. All traits were adjusted for sex, age, BMI and height and the residuals were rank transformed prior to analysis.

Genotyping and statistical analysis of the linkage study

Illumina’s HumanHap6k Genotyping BeadChip (6 K Illumina Linkage IV Panels®) was used for genotyping for the linkage analyses. All genotyping procedures were performed according to the manufacturer’s protocols. Only markers with a minor allele frequency (MAF) > 0.05 were selected for further analysis. Genotyping errors leading to Mendelian inconsistencies were detected using PedCheck [24]. Unlikely double recombination events were detected using MERLIN [25]. All detected errors were eliminated from the data. A total of 5250 autosomal SNPs with a call rate greater than 95% were utilized for the linkage analyses. Among the 2385 individuals who were phenotyped for LVH measures, 1860 people also had genotype data and were included in the linkage study. Variance component multipoint linkage was performed using the --vc option in MERLIN v.1.0.1 [25, 26]. This program calculates exact IBD sharing probabilities using the Lander-Green algorithm, requiring restrictions on pedigree size. Because of this, the single ERF pedigree with multiple loops was split into non-overlapping fragments of no more than 18 bits with the help of the PedSTR program [27].

Regions of interest with LOD > 1.9 were selected for further analysis. Borders of the linkage areas were defined as LOD score minus 2 support intervals (LOD-2 SI) around the linkage peaks. Genes within the LOD-2 SI were annotated using SCAN (SNP and CNV Annotation Database).

Genotyping and statistical analysis of the association study

Of 2385 phenotyped people, dense genotypes were available for 2128 subjects, typed on 3 different genotyping platforms (Illumina 318 K, Illumina 370 K and Affymetrix 250 K), which were merged first (median number of quality controlled SNPs per individual = 325,500) and then ~ 2.54 million SNPs were imputed using MACH (v1.0.16) [28, 29], with the HapMap build 36 (release 22) CEU population as reference. Within each genotyping batch, only SNPs with a call rate > 98%, MAF > 1% and Hardy-Weinberg Equilibrium P-value > 10− 6 were used for imputations. To account for relatedness, a genomic kinship matrix was computed in GenABEL [30]. This matrix was then incorporated into linear mixed-effects regression models, as implemented in ProbABEL [31], which were used to assess the association of variants in the LOD-2 SI with the LVH phenotypes. P-values were adjusted with the FDR-based q-value technique [32].

Exome sequencing

The exomes of 1336 individual from the ERF population were sequenced “in-house” at the Center for Biomics of the Department of Cell Biology of the Erasmus MC, the Netherlands, using the Agilent version V4 capture kit on an Illumina HiSeq 2000 sequencer using the TruSeq Version 3 protocol. Mean depth base was 74.23× (median = 57×) and mean depth region was 65.26× (median = 52.87×). The sequence reads were aligned to the human genome build 19 (hg19) using BWA and the NARWHAL pipeline [33, 34]. The aligned reads were processed further using the IndelRealigner, MarkDuplicates and TableRecalibration tools from the Genome Analysis Toolkit (GATK) and Picard (http://broadinstitute.github.io/picard/) to remove systematic biases and to recalibrate the PHRED quality scores in the alignments. Genetic variants were called using the Unified Genotyper tool of the GATK. About 1.4 million Single Nucleotide Variants (SNVs) were called and, after removing the low quality variants (QUAL < 150), we retrieved 577,703 SNVs in 1309 individuals. ECG and covariate data were available for 1072 of these samples. Further, for comparison and to predict the functionality of the variants, annotations were also performed using the dbNSFP (database of human non-synonymous SNPs and their functional predictions, http://varianttools.sourceforge.net/Annotation/DbNSFP) and Seattle (http://snp.gs.washington.edu/SeattleSeqAnnotation138/) databases. These databases gave functional prediction results from four different programs, PolyPhen-2, SIFT, MutationTaster and LRT, apart from gene and variant annotations.

We employed a Bonferroni correction for the number of deleterious mutations selected for each trait to correct for multiple comparisons in the exome data: 101 for SL (P-value = 4.9 × 10− 4), 98 for CV (P-value = 5.1 × 10− 4) and 60 for 12 LS (P-value = 8.3 × 10− 4). For the PCs, the numbers were 141 for PC1 (P-value = 3.5 × 10− 4) and 71 for PC2 (P-value = 7.0 × 10− 4).

Replication

Four SNPs (rs139580877, rs138968470, rs35996030 and rs142551296) were selected for replication in the Rotterdam Study (RS). The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in the Netherlands [35].

Exomes from 1764 individuals from the RS population were sequenced at an average depth of 20× using the Nimblegen SeqCap EZ V2 capture kit on an Illumina HiSeq 2000 sequencer and the TrueSeq Version 3 protocol. The sequence reads were aligned to hg19 using BWA. Subsequently, the aligned reads were processed further using Picard, SAMtools and GATK. Genetic variants were called using the Unified Genotyper Tool from GATK. Samples with low concordance to genotyping array (< 95%), low transition/transversion ratio (< 2.3), high heterozygote to homozygote ratio (> 2.0) and low call rate (< 80%) were removed from the data. SNVs with a low call rate (< 90%) and out of HWE (P-value < 10− 6) were also removed from the data. The final dataset consisted of 635,814 SNVs in 1450 individuals with complete phenotype and covariate data.

One SNP, rs139580877, was not available in the Rotterdam Study exome data. This variant was imputed using the GIANT 1000 Genomes Phase I Version 3 All reference panel, as previously described [36]. In brief, after filtering SNPs genotyped with the Illumina v3 Infinium II HumanHap550 microarray for deviations from Hardy-Weinberg proportions (P < 1 × 10− 6), call rate (< 98%), MAF (< 0.01), and Mendelian errors (> 100), MACH was used to perform the imputations.

Results

Table 1 shows characteristics of the participants in the LVH linkage, microarray, and exome sequence analyses. The proportion of LVH cases for each proxy measure was determined using published cut-off values [37, 38]. There were no significant differences between these overlapping groups. Table 2 shows the correlation between the traits (r = 0.76 in the adjusted model for SL and 12LS, 0.17 between SL and CV, and 0.48 for CV and 12LS). Table 3 shows the loadings of the three LVH proxies (SL, CV, 12LS) to the two PCs that were constructed. PC1 predominantly captured SL and 12LS, while PC2 correlated strongly with CV and moderately with SL. Table 4 shows the linkage results for the LVH proxy measures, which yielded a total of seven regions with suggestive LOD scores (LOD > 1.9). SL was linked to three regions, with the highest LOD score for chromosome 20 (LOD = 2.64) and two additional regions on chromosomes 4 (LOD = 2.14) and 15 (LOD = 1.92). Suggestive LOD scores for CV were seen on chromosomes 1 (LOD = 2.4) and 6 (LOD = 2.17). There was suggestive linkage of 12LS to chromosomes 5 (LOD = 2.18) and 20 (LOD = 2.12). Linkage results for the principal component analysis of the LVH measures showed one significant LOD score for PC2 on 15q11.2 (LOD = 3.01). This region was also linked to SL (LOD = 1.92). Two regions were suggestively linked to PC1: 11q13.4 (LOD = 2.01) and 20p12.1 (LOD = 2.83), which was also linked to SL and 12LS. For PC2, there were three suggestive linkage results, for chromosomes 6 (LOD = 2.09), 9 (LOD = 2.35) and 22 (LOD = 1.99). The chromosome 6 region was also linked to CV. Plots showing the linked regions by chromosome are provided in Fig. 1. Table 5 shows the top common variant microarray-based association signals under the LVH trait linkage peaks, including P-values and MAF for each SNP. None achieved statistical significance after correction for multiple comparisons.
Table 1

Descriptive statistics of the Erasmus Rucphen Family (ERF) study population

 

Microarray

Linkage

Exon sequence

 

n = 2128

n = 1860

n = 1072

 

Mean (S.D.)

Minimum

Maximum

Mean (S.D.)

Minimum

Maximum

Mean (S.D.)

Minimum

Maximum

Males

899 (42%)

  

775 (42%)

  

408 (38%)

  

Age (y)

47.0 (13.82)

16.6

85.3

46.5 (13.79)

16.6

85.3

46.51 (13.7)

18.7

81.0

BMI (kg/m2)

26.7 (4.57)

15.5

61.8

26.7 (4.58)

15.5

61.8

26.4 (4.3)

15.5

61.8

Height (cm)

167.6 (9.31)

139.3

196.5

167.4 (9.19)

143.6

196.5

166.7 (9.0)

143.6

196.5

Weight (kg)

75.1 (15.16)

41.9

161.0

74.9 (15.5)

41.9

161

73.6 (14.3)

42.1

161.0

SBP (mm Hg)

138.4 (19.5)

85.5

222.0

137.7 (19.1)

85.5

217.0

137.0 (18.7)

85.5

216.0

DBP (mm Hg)

79.9 (9.8)

53.5

124.0

79.7 (9.7)

54.5

120.0

79.1 (9.6)

53.5

120.0

Hypertension

913 (43%)

  

766 (42%)

  

549 (51%)

  

SL

2344 (690.6)

884.0

5288.0

2341 (690.6)

884

52.9

2319 (659.0)

967

5288.0

CV

1173.5 (505.1)

93.1

4126.1

1170.0 (497.3)

93.1

3952.8

1151.6 (659.0)

155.8

3853.0

12LS

13,862 (3812.3)

4993

39,250

13,805 (3767.8)

49.9

39.2

13,610.0 (3628.7)

5485.0

36,364

LVH (SL)

138 (6.5%)

  

120 (6.4%)

  

66 (6.2%)

  

LVH (CV)

41 (1.9%)

  

32 (1.7%)

  

20 (1.9%)

  

LVH (12LS)

176 (8.3%)

  

147 (7.9%)

  

76 (7.1%)

  

Values presented are mean (standard deviation) or n (%)

BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, SL Sokolow-Lyon index, CV Cornell product, 12LS 12-lead sum product

Table 2

Pearson’s correlations between LVH proxy measures

 

Unadjusted

Adjusted

SL – 12LS

0.80

0.76

SL – CV

0.29

0.17

CV – 12LS

0.56

0.48

Adjusted model included age, sex, body-mass index, and height

SL Sokolow-Lyon, CV Cornell Voltage product, 12LS twelve-lead sum product, PC1 first principal component, PC2 second principal component

Table 3

PC loadings for LVH proxies

 

Principal Component

 

PC1

PC2

SL

0.84

−0.48

CV

0.61

0.78

12LS

0.95

−0.08

Adjusted model included age, sex, body-mass index, and height

SL Sokolow-Lyon, CV Cornell Voltage product, 12LS twelve-lead sum product, PC1 first principal component, PC2 second principal component

Table 4

Results of the linkage analyses

Trait

N

Chr.

SNP

LOD-2 SI Lower

LOD-2 SI Upper

Position (cM)

LODMAX

SL

1860

4

rs1032328

90,166,159

157,272,456

144.46

2.14

SL

1860

15

rs290370

88,026,435

102,212,431

112.3

1.92

SL

1860

20

rs204115

11,094,951

23,352,685

38.11

2.64

CV

1860

1

rs6619

12,296,232

53,396,842

59.63

2.40

CV

1860

6

rs2040431

72,253,060

117,799,468

108.31

2.17

12LS

1860

5

rs1442470

7,205,420

25,399,905

42.3

2.18

12LS

1860

20

rs466243

11,017,796

23,352,685

40.7

2.12

PC1

1860

11

rs1530354

33,896,047

78,743,080

65.21

2.01

PC1

1860

20

rs2077147

11,990,037

38,247,165

45.09

2.83

PC2

1860

6

rs1391503

87,511,828

105,402,837

99.69

2.09

PC2

1860

9

rs748530

8,378,662

25,788,723

40.22

2.35

PC2

1860

15

rs1562203

23,707,591

0

3.01

PC2

1860

22

rs138383

35,687,558

44,707,606

46.89

1.99

Model adjusted for age, sex, body-mass index, and height

SL Sokolow-Lyon, CV Cornell Voltage product, 12LS twelve-lead sum product, PC1 first principal component, PC2 second principal component, N sample size, Chr. chromosome, LOD-2 SI Lower position of lower boundary of support interval in base pairs, LOD-2 SI Upper position of upper boundary of support interval in base pairs, LOD MAX LOD score at SNP

Fig. 1
Fig. 1

Linkage peaks for the LVH proxy measures. Plots depicting the linked regions by trait and chromosome. The grey dashed horizontal line indicates the threshold for suggestive linkage. The red dashed vertical lines show the borders of the LOD score minus 2 support intervals (LOD-2 SI). The blue circles contain SNPs identified in previous GWAS for these traits in the LOD-2 SI

Table 5

Top association signals from the microarray data under the LVH trait linkage peaks

Outcome

Region

SNP

MAF

Gene

P-value

Q-value

SL

4q26

rs6839953

0.27

TRAM1L1

1.34 × 10−4

0.47

SL

15q26.2

rs11074275

0.48

MCTP2

4.27 × 10−4

0.79

SL

20p12.1

rs721243

0.19

ISM1

7.37 × 10−5

0.15

CV

1p35.1

rs16835131

0.06

SYNC

1.35 × 10−5

0.35

CV

6q15

rs10944412

0.27

RNGTT

4.60 × 10−5

0.93

12LS

5p15.2

rs2589661

0.10

ROPN1L

1.26 × 10− 4

0.46

12LS

20p11.23

rs6106235

0.18

C20orf26

1.69 × 10−5

0.09

PC1

11q12.2

rs1790325

0.04

FADS1

2.85 × 10−5

0.08

PC1

20p12.1

rs13036282

0.005

SPTLC3

2.30 × 10−4

0.63

PC2

6q16.3

rs1475922

0.06

GRIK2

1.64 × 10−4

0.94

PC2

9p24.1

rs10975939

0.003

KDM4C

4.67 × 10−4

1.00

PC2

15q11.2

rs8043191

0.03

CYFIP1

5.95 × 10−3

0.52

PC2

22q13.33

rs2688089

0.45

C22orf34

7.02 × 10−5

0.56

SL Sokolow-Lyon, CV Cornell Voltage product, 12LS twelve-lead sum product, PC1 first principal component, PC2 second principal component, MAF minor allele frequency

Variants in the coding sequence

The results of the search for less frequent exonic variants are summarized in Additional file 1: Table S1. We focused on relatively rare (frequency < 5%) missense variants predicted to be deleterious by at least two of the prediction algorithms used and non-sense variants. This selection yielded 471 variants in 356 genes in the 13 linkage intervals (LOD-2 SI), which we analysed with respect to the LVH proxy measures and PCs. Additional file 1: Table S2 shows the results with a nominal P-value ≤0.05 after regressing out the effects of age, BMI, height and sex. This effort uncovered an A > G variation (rs139580877) in the SPEF2 gene on 5p13.2, which was significantly associated with 12LS when adjusted for multiple testing (P-value = 4.2 × 10− 4). This variant, with 108 carriers in ERF, is predicted to be probably damaging by PolyPhen-2 with a score of 0.972 and as deleterious by SIFT with a score ranging between 0.02 and 0.03. It is a missense variant, among more than 2000 described for this gene. In the principal components analysis, rs138968470, on 11q13.1 in the MAP3K11 gene, was associated with PC1 adjusted for multiple testing (P-value = 3.5 × 10− 4). SKAT-O and burden tests provided some supporting evidence for the association of this gene with LVH proxy measures (Additional file 1: Table S3). Additionally, at the SL chromosome 4 locus, we identified a C > G variation (rs142551296) in PRSS12 that approached significance (P-value = 8.4 × 10− 4). A second, more common intragenic variant inside PRSS12 was nominally associated (rs35996030; P-value = 0.04). We re-ran the linkage analyses conditioning on these variants to see if they explained the observed linkage signals. For PC1, the LOD score in the 11q13.4 linkage region dropped in the conditional analysis (from 2.01 to 0.88), suggesting that the associated variant (rs138968470), or neighbouring variants in linkage disequilibrium (LD), explained the linkage signal. This variant also showed evidence of association with the two traits (12LS and SL) underlying PC1 (P-value = 3.0 × 10− 4 and P-value = 1.2 × 10− 3, respectively). Using Gene Network (http://genenetwork.nl/gene/ENSG00000173327), to perform in-depth analyses of the expression of MAP3K11, demonstrated that its expression is strongly linked to rho signalling (ARGHGEF15, ARHGDIA) (Fig. 2).
Fig. 2
Fig. 2

MAP3K11 gene network interactions. Looking for interactions for MAP3K11, we searched Gene Network (http://genenetwork.nl/gene/ENSG00000173327). One hundred twenty-nine gene-gene interactions are shown

Five of the linkage peaks contained loci recently identified in GWAS studies [5, 11]. To determine if the linkage signals were a result of those common variants, linkage was performed a second time, conditioned on the GWAS index SNPs. These analyses demonstrated that the observed peaks were not explained by the GWAS SNPs, although the estimates fluctuated somewhat, likely as a result of smaller sample sizes (Additional file 1: Table S4).

Replication

Summary statistics for the Rotterdam Study sample are provided in Additional file 1: Table S5. The variant rs139580877 was imputed, using the 1000 Genomes reference panel; the imputation quality score (MACH RSQ) for this variant was 0.65, with a minor allele frequency of 0.008. The effect estimate for 12LS was essentially zero, and therefore, did not replicate the ERF findings (Additional file 1: Table S6). The other variants of interest, rs35996030, rs138968470 and rs142551296, were directly genotyped in a subset of the Rotterdam Study cohort (n = 1450). There was no evidence of association for any of these variants in the Rotterdam Study.

Discussion

We performed a linkage study on LVH proxy measurements, and PCs, and identified one significant locus (15q11.2) and 10 suggestive regions (1p34, 4q31, 5p14, 6q15, 6q21, 9p21, 11q13.4, 15q25, 20p12, 22q13). Exome variant analysis in these regions uncovered a missense coding variation in MAP3K11 on 11q134 for PC1; the MAP3K11 variant substantially decreased the LOD score for this peak. The 24 carriers of this missense mutation clustered into five pedigrees in the ERF population (Additional file 1: Figure S2).

Genetic variants discovered by GWAS, based on individual single-nucleotide polymorphisms (SNPs), explain only a small proportion of the heritability of complex traits [10, 39, 40]; we found variants with larger effect sizes compared to the ones found with GWAS. Our analysis of rare coding variants in these linkage regions revealed a variant, rs138968470 on 11q13.1 in the MAP3K11 gene, associated with PC1. Conditional linkage analysis, including the MAP3K11 variant, reduced the LOD score (from 2.01 to 0.88), suggesting that this variant largely explained the linkage signal at this chromosomal location. The SNP is located in the first exon of a gene encoding a protein that belongs to the serine/threonine kinase family of mitogen-activated protein kinases. MAP3K11 (also known as Mixed Lineage Kinase 3 (MLK3)) [34], works as a positive regulator of the c-Jun N-terminal kinase (JNK) signalling pathway [41]. MAP3K11 has a CDC42 and Rac interacting proteins binding domain (CRIB); autophosphorylation of MAP3K11 and the induction of JNK is mediated through this CRIB domain bound to Cdc42/Rac/GTP [42]. JNK, an important member of the mitogen-activated protein kinase family (MAPK), is a pro-apoptotic kinase that plays an important role in the induction of cardiomyocyte apoptosis in various pathologies [43]. Apoptosis increases with LVH, a critical mechanism that mediates the transition from compensated hypertrophy to heart failure [44]. In this way, a damaging mutation in MAP3K11 may be related to regulation of JNK and the subsequent JNK controlled pathway.

The other significant missense variant was rs139580877, located on 5p14. This variant is in exon 9 of the gene that encodes the sperm flagellar protein (SPEF2), which has been postulated to play an important role in spermatogenesis and flagellar assembly [45]. This SNP was not found to be responsible for the linkage signal in the region, despite its strong association. The association with this relatively common variant (MAF = 0.015) could not be confirmed in the Rotterdam Study. One additional finding was studied further: a C/G variant (rs142551296) in the PRSS12 gene, underlying the SL locus on chromosome 4, which approached significance (P-value = 8.4 × 10− 4), but did not replicate in the Rotterdam Study. Absence of replication could be related to imputation quality for rs139580877 and the low number of carriers for the other SNPs (Additional file 1: Table S4).

A number of the linkage peaks contained SNPs identified in a large GWAS of these traits. Linkage analysis, conditioned on the index SNPs from the GWAS, did not significantly alter the linkage results. This suggests that the linkage peaks were not driven by the common variants identified in the GWAS.

No explanatory variants were found for most of the loci (suggestively) linked to LVH, for which there are a number of potential explanations. Linkage peaks are not precise in highlighting the location of the causal variant; even the region of interest cannot be easily pinpointed. Additionally, we did not take into account alternative mechanisms, such as structural and copy number variations (CNVs) or repeats in the linkage regions. Lastly, causal rare variants may be located outside the coding sequence, which we did not include in our sequencing analyses.

Conclusions

In conclusion, 13 loci were identified for ECG LVH proxy measures and PCs using linkage analysis in a large pedigree; these were subsequently fine-mapped with microarray and exome sequence data. Common variation from the microarrays did not explain these peaks. The exome data, though, suggested the involvement of MAP3K11 (11q13) in LVH through the regulation of JNK. However, we cannot exclude the presence of other variants that are in linkage disequilibrium with the MAP3K11 variant (rs138968470) that might explain the observed association.

Further analysis will need to be performed to demonstrate the involvement of this protein in LVH. A number of other suggestively linked peaks were determined. We could not explain these with microarray or exonic sequence variants at present, asking for more extensive follow-up outside the coding regions.

Abbreviations

12LS: 

12-lead sum QRS product

BMI: 

Body mass index

CRIB: 

CDC42 and Rac interacting proteins binding domain

CV: 

Cornell voltage product

ECG: 

Electrocardiogram

ERF: 

Erasmus Rucphen Family Study

GATK: 

Genome Analysis Toolkit

GWAS: 

Genome Wide Association Studies

HWE: 

Hardy Weinberg Equilibrium

JNK: 

c-Jun N-terminal kinase

LD: 

Linkage disequilibrium

LVH: 

Left ventricular hypertrophy

MAF: 

Minor allele frequency

MEANS: 

Modular ECG Analysis System

MI: 

Myocardial infarction

PC: 

Principal component

SL: 

Sokolow-Lyon index

SNP: 

Single-nucleotide polymporphisms

SNVs: 

Single nucleotide variants

MAPK: 

Mitogen activated protein kinase family

SPEF2: 

Sperm flagellar protein

CNV: 

Copy number variations

SBP: 

Systolic blood pressure

DBP: 

Diastolic blood pressure

dbNSFP: 

Database of human non-synonymous SNPs and their functional predictions

Declarations

Acknowledgements

We are grateful to all study participants and their relatives, general practitioners and neurologists for their contributions and to P. Veraart for her help in genealogy, J. Vergeer for the supervision of the laboratory work and P. Snijders for his help in data collection. Najaf Amin is supported by the Hersenstichting Nederland (the Netherlands Brain Foundation) (Project Number F2013(1)-28).

Rotterdam study: We are grateful to the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists.

We thank Mr. Pascal Arp, Ms. Mila Jhamai and Mr. Marijn Verkerk for their help in creating the RS-Exome-Sequencing database.

Funding

The Erasmus Rucphen Family (ERF) study as a part of EUROSPAN (European Special Populations Research Network) was supported by European Commission FP6 STRP Grant No. 018947 (LSHG-CT-2006- 01947) and also received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013)/Grant Agreement HEALTH-F4–2007-201413 by the European Commission under the programme “Quality of Life and Management of the Living Resources” of 5th Framework Programme (No. QLG2-CT-2002-01254). High-throughput analysis of the ERF data was supported by joint grant from Netherlands Organization for Scientific Research and the Russian Foundation for Basic Research (NWO- RFBR 047.017.043), and Russian Federal Agency of Scientific Organizations projects VI.53.2.2 and 0324–2015-0003. Exome sequencing analysis in ERF was supported by the Netherlands Organization for the Health Research and Development grant for Project No. 91111025. This work was also supported by the Federal Agency of Scientific Organizations #0324–2015-0003 (IZ, AK and TA). Finally, this work was further funded by the European Union's Horizon 2020 research and innovation programme as part of the Common mechanisms and pathways in Stroke and Alzheimer's disease (CoSTREAM) project (www.costream.eu, grant agreement No 667375) and the European Union’s Horizon 2020 research and innovation programme Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE) under the grant agreement No 645740 as part of the Personalized pREvention of Chronic DIseases (PRECeDI) project.

The Rotterdam Study is funded by Erasmus Medical Center (MC) and Erasmus University, Rotterdam; Netherlands Organization for the Health Research and Development; the Research Institute for Diseases in the Elderly; the Ministry of Education, Culture and Science; the Ministry for Health, Welfare and Sports; the European Commission; and the Municipality of Rotterdam. The Exome-Sequencing dataset was funded by the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) sponsored Netherlands Consortium for Healthy Aging (NCHA; Project No. 050–060-810), by the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, and by the and by a Complementation Project of the Biobanking and Biomolecular Research Infrastructure Netherlands (BBMRI-NL; www.bbmri.nl; Project Number CP2010–41).

Availability of data and materials

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to them containing information that could compromise research participant privacy/consent.

Authors’ contributions

Formal Analysis: CTS, IVZ, MN, MEvdB, NA, AD, EvL, AIG, LBP, JAK, AVK, TIA, CMvD, AI. Writing – Original Draft Preparation: CTS, RW, CMvD, AI. Investigation and Resources: CMR, BAO, BHS, AGU, CMvD. Software: AVK. Supervision: CMR, RW, TIA, CMvD, AI. Conceptualization: CMvD, AI. Writing – Review & Editing: ALL AUTHORS. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The Medical Ethics Committee of the Erasmus University Medical Center approved the ERF study protocol and all participants, or their legal representatives, provided written informed consent. Approval MEC 213.57512002.

Consent for publication

Not applicable.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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

Authors’ Affiliations

(1)
Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
(2)
Center for Research in Genetics and Genomics (CIGGUR), Institute of Translational Medicine (IMT), GENIUROS Research group, School of Medicine and Health Science, Universidad del Rosario, Bogotá, Colombia
(3)
Doctoral Program in Biomedical Sciences, Universidad del Rosario, Bogotá, Colombia
(4)
Institute of Cytology and Genetics SD RAS, Novosibirsk, Russia
(5)
Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
(6)
Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands
(7)
Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands
(8)
Center for Medical Systems Biology, Leiden, the Netherlands
(9)
Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
(10)
Inspectorate of Health care, The Hague, the Netherlands
(11)
CARIM School for Cardiovascular Diseases, Maastricht Centre for Systems Biology (MaCSBio), and Department of Biochemistry, Maastricht University, Maastricht, the Netherlands

References

  1. Benjamin EJ, Levy D. Why is left ventricular hypertrophy so predictive of morbidity and mortality? Am J Med Sci. 1999;317(3):168–75.View ArticlePubMedGoogle Scholar
  2. Arnett DK, de las Fuentes L, Broeckel U. Genes for left ventricular hypertrophy. Curr Hypertens Rep. 2004;6(1):36–41.View ArticlePubMedGoogle Scholar
  3. Bella JN, Goring HH. Genetic epidemiology of left ventricular hypertrophy. Am J Cardiovasc Dis. 2012;2(4):267–78.PubMedPubMed CentralGoogle Scholar
  4. Arnett DK, Devereux RB, Rao DC, Li N, Tang W, Kraemer R, Claas SA, Leon JM, Broeckel U. Novel genetic variants contributing to left ventricular hypertrophy: the HyperGEN study. J Hypertens. 2009;27(8):1585–93.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Shah S, Nelson CP, Gaunt TR, van der Harst P, Barnes T, Braund PS, Lawlor DA, Casas JP, Padmanabhan S, Drenos F, et al. Four genetic loci influencing electrocardiographic indices of left ventricular hypertrophy. Circ Cardiovasc Genet. 2011;4(6):626–35.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Post WS, Larson MG, Myers RH, Galderisi M, Levy D. Heritability of left ventricular mass: the Framingham heart study. Hypertension. 1997;30(5):1025–8.View ArticlePubMedGoogle Scholar
  7. Foppa M, Duncan BB, Rohde LE. Echocardiography-based left ventricular mass estimation. How should we define hypertrophy? Cardiovasc Ultrasound. 2005;3:17.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Mayosi BM, Avery PJ, Farrall M, Keavney B, Watkins H. Genome-wide linkage analysis of electrocardiographic and echocardiographic left ventricular hypertrophy in families with hypertension. Eur Heart J. 2008;29(4):525–30.View ArticlePubMedGoogle Scholar
  9. Mutikainen S, Ortega-Alonso A, Alen M, Kaprio J, Karjalainen J, Rantanen T, Kujala UM. Genetic influences on resting electrocardiographic variables in older women: a twin study. Ann Noninvasive Electrocardiol. 2009;14(1):57–64.View ArticlePubMedGoogle Scholar
  10. Silva CT, Kors JA, Amin N, Dehghan A, Witteman JC, Willemsen R, Oostra BA, van Duijn CM, Isaacs A. Heritabilities, proportions of heritabilities explained by GWAS findings, and implications of cross-phenotype effects on PR interval. Hum Genet. 2015;134:1211–9.View ArticlePubMedPubMed CentralGoogle Scholar
  11. van der Harst P, van Setten J, Verweij N, Vogler G, Franke L, Maurano MT, Wang X, Mateo Leach I, Eijgelsheim M, Sotoodehnia N, et al. 52 genetic loci influencing myocardial mass. J Am Coll Cardiol. 2016;68(13):1435–48.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Wang L, Beecham A, Di Tullio MR, Slifer S, Blanton SH, Rundek T, Sacco RL. Novel quantitative trait locus is mapped to chromosome 12p11 for left ventricular mass in Dominican families: the family study of stroke risk and carotid atherosclerosis. BMC Med Genet. 2009;10:74.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Zhi D, Irvin MR, Gu CC, Stoddard AJ, Lorier R, Matter A, Rao DC, Srinivasasainagendra V, Tiwari HK, Turner A, et al. Whole-exome sequencing and an iPSC-derived cardiomyocyte model provides a powerful platform for gene discovery in left ventricular hypertrophy. Front Genet. 2012;3:92.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Pardo LM, MacKay I, Oostra B, van Duijn CM, Aulchenko YS. The effect of genetic drift in a young genetically isolated population. Ann Hum Genet. 2005;69(Pt 3):288–95.View ArticlePubMedGoogle Scholar
  15. Aulchenko YS, Heutink P, Mackay I, Bertoli-Avella AM, Pullen J, Vaessen N, Rademaker TA, Sandkuijl LA, Cardon L, Oostra B, et al. Linkage disequilibrium in young genetically isolated Dutch population. Eur J Hum Genet. 2004;12(7):527–34.View ArticlePubMedGoogle Scholar
  16. Sayed-Tabatabaei FA, van Rijn MJ, Schut AF, Aulchenko YS, Croes EA, Zillikens MC, Pols HA, Witteman JC, Oostra BA, van Duijn CM. Heritability of the function and structure of the arterial wall: findings of the Erasmus Rucphen family (ERF) study. Stroke. 2005;36(11):2351–6.View ArticlePubMedGoogle Scholar
  17. 1999 World Health Organization-International Society of Hypertension Guidelines for the Management of Hypertension. Guidelines subcommittee. J Hypertens. 1999;17(2):151–83.Google Scholar
  18. Eijgelsheim M, Aarnoudse AL, Rivadeneira F, Kors JA, Witteman JC, Hofman A, van Duijn CM, Uitterlinden AG, Stricker BH. Identification of a common variant at the NOS1AP locus strongly associated to QT-interval duration. Hum Mol Genet. 2009;18(2):347–57.View ArticlePubMedGoogle Scholar
  19. van Bemmel JH, Kors JA, van Herpen G. Methodology of the modular ECG analysis system MEANS. Methods Inf Med. 1990;29(4):346–53.PubMedGoogle Scholar
  20. Willems JL, Abreu-Lima C, Arnaud P, van Bemmel JH, Brohet C, Degani R, Denis B, Gehring J, Graham I, van Herpen G, et al. The diagnostic performance of computer programs for the interpretation of electrocardiograms. N Engl J Med. 1991;325(25):1767–73.View ArticlePubMedGoogle Scholar
  21. Willems JL, Arnaud P, van Bemmel JH, Bourdillon PJ, Degani R, Denis B, Graham I, Harms FM, Macfarlane PW, Mazzocca G, et al. A reference data base for multilead electrocardiographic computer measurement programs. J Am Coll Cardiol. 1987;10(6):1313–21.View ArticlePubMedGoogle Scholar
  22. de Bruyne MC, Kors JA, Hoes AW, Kruijssen DA, Deckers JW, Grosfeld M, van Herpen G, Grobbee DE, van Bemmel JH. Diagnostic interpretation of electrocardiograms in population-based research: computer program research physicians, or cardiologists? J Clin Epidemiol. 1997;50(8):947–52.View ArticlePubMedGoogle Scholar
  23. Leening MJ, Elias-Smale SE, Felix JF, Kors JA, Deckers JW, Hofman A, Stricker BH, Witteman JC. Unrecognised myocardial infarction and long-term risk of heart failure in the elderly: the Rotterdam study. Heart. 2010;96(18):1458–62.View ArticlePubMedGoogle Scholar
  24. O'Connell JR, Weeks DE. PedCheck: a program for identification of genotype incompatibilities in linkage analysis. Am J Hum Genet. 1998;63(1):259–66.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Abecasis GR, Cherny SS, Cookson WO, Cardon LR. Merlin--rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet. 2002;30(1):97–101.View ArticlePubMedGoogle Scholar
  26. Gudbjartsson DF, Jonasson K, Frigge ML, Kong A. Allegro, a new computer program for multipoint linkage analysis. Nat Genet. 2000;25(1):12–3.View ArticlePubMedGoogle Scholar
  27. Kirichenko AV, Belonogova NM, Aulchenko YS, Axenovich TI. PedStr software for cutting large pedigrees for haplotyping, IBD computation and multipoint linkage analysis. Ann Hum Genet. 2009;73(Pt 5):527–31.View ArticlePubMedGoogle Scholar
  28. Nothnagel M, Ellinghaus D, Schreiber S, Krawczak M, Franke A. A comprehensive evaluation of SNP genotype imputation. Hum Genet. 2009;125(2):163–71.View ArticlePubMedGoogle Scholar
  29. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol. 2010;34(8):816–34.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Aulchenko YS, Ripke S, Isaacs A, van Duijn CM. GenABEL: an R library for genome-wide association analysis. Bioinformatics. 2007;23(10):1294–6.View ArticlePubMedGoogle Scholar
  31. Aulchenko YS, Struchalin MV, van Duijn CM. ProbABEL package for genome-wide association analysis of imputed data. BMC Bioinformatics. 2010;11:134.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci U S A. 2003;100(16):9440–5.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Brouwer RW, van den Hout MC, Grosveld FG, van Ijcken WF. NARWHAL, a primary analysis pipeline for NGS data. Bioinformatics. 2012;28(2):284–5.View ArticlePubMedGoogle Scholar
  34. Li H, Durbin R. Fast and accurate short read alignment with burrows-wheeler transform. Bioinformatics. 2009;25(14):1754–60.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Hofman A, Darwish Murad S, van Duijn CM, Franco OH, Goedegebure A, Ikram MA, Klaver CC, Nijsten TE, Peeters RP, Stricker BH, et al. The Rotterdam study: 2014 objectives and design update. Eur J Epidemiol. 2013;28(11):889–926.View ArticlePubMedGoogle Scholar
  36. Gorski M, van der Most PJ, Teumer A, Chu AY, Li M, Mijatovic V, Nolte IM, Cocca M, Taliun D, Gomez F, et al. 1000 genomes-based meta-analysis identifies 10 novel loci for kidney function. Sci Rep. 2017;7:45040.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Barrios V, Calderon A, Coca A, Gonzalez-Juanatey JR, Sarria A, Rodriguez-Padial L. Computerized interpretation of the electrocardiogram in the diagnosis of left ventricular hypertrophy. The ELECTROPRES project. Rev Clin Esp. 2011;211(8):391–9.PubMedGoogle Scholar
  38. Schillaci G, Battista F, Pucci G. A review of the role of electrocardiography in the diagnosis of left ventricular hypertrophy in hypertension. J Electrocardiol. 2012;45(6):617–23.View ArticlePubMedGoogle Scholar
  39. Fridley BL, Biernacka JM. Gene set analysis of SNP data: benefits, challenges, and future directions. Eur J Hum Genet. 2011;19(8):837–43.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Eichler EE, Flint J, Gibson G, Kong A, Leal SM, Moore JH, Nadeau JH. Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet. 2010;11(6):446–50.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Chadee DN, Kyriakis JM. MLK3 is required for mitogen activation of B-Raf, ERK and cell proliferation. Nat Cell Biol. 2004;6(8):770–6.View ArticlePubMedGoogle Scholar
  42. Du Y, Bock BC, Schachter KA, Chao M, Gallo KA. Cdc42 induces activation loop phosphorylation and membrane targeting of mixed lineage kinase 3. J Biol Chem. 2005;280(52):42984–93.View ArticlePubMedGoogle Scholar
  43. Rui T, Tang Q. IL-33 attenuates anoxia/reoxygenation-induced cardiomyocyte apoptosis by inhibition of PKCbeta/JNK pathway. PLoS One. 2013;8(2):e56089.View ArticlePubMedPubMed CentralGoogle Scholar
  44. Gelpi RJ, Park M, Gao S, Dhar S, Vatner DE, Vatner SF. Apoptosis in severe, compensated pressure overload predominates in nonmyocytes and is related to the hypertrophy but not function. Am J Physiol Heart Circ Physiol. 2011;300(3):H1062–8.View ArticlePubMedGoogle Scholar
  45. Sironen A, Kotaja N, Mulhern H, Wyatt TA, Sisson JH, Pavlik JA, Miiluniemi M, Fleming MD, Lee L. Loss of SPEF2 function in mice results in spermatogenesis defects and primary ciliary dyskinesia. Biol Reprod. 2011;85(4):690–701.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© The Author(s). 2018

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