Front. Gene. Frontiers in Genetics Front. Gene. 1664-8021 Frontiers Research Foundation 10.3389/fgene.2012.00092 Genetics Methods Article Whole-exome Sequencing and an iPSC-Derived Cardiomyocyte Model Provides a Powerful Platform for Gene Discovery in Left Ventricular Hypertrophy Zhi D. 1 † Irvin M. R. 2 † Gu C. C. 3 Stoddard A. J. 4 Lorier R. 4 Matter A. 4 Rao D. C. 3 Srinivasasainagendra V. 1 Tiwari H. K. 1 Turner A. 4 Broeckel U. 4 † Arnett D. K. 2 * † 1Department of Biostatistics, University of Alabama at Birmingham Birmingham, AL, USA 2Department of Epidemiology, University of Alabama at Birmingham Birmingham, AL, USA 3Division of Biostatistics, Washington University in St. Louis St. Louis, MO, USA 4Department of Medicine, Medical College of Wisconsin Milwaukee, WI, USA

Edited by: Karen T. Cuenco, University of PIttsburgh, USA

Reviewed by: Kai Wang, University of Southern California, USA; Jesus Sainz, Consejo Superior de Investigaciones Cientificas, Spain

*Correspondence: D. K. Arnett, Department of Epidemiology, University of Alabama at Birmingham, 1530 3rd Avenue South, Birmingham, AL 35294-0022, USA. e-mail: arnett@uab.edu

†D. Zhi and M. R. Irvin contributed equally as co-first authors, U. Broeckel and D. K. Arnett as co-senior authors in the execution of this study.

This article was submitted to Frontiers in Applied Genetic Epidemiology, a specialty of Frontiers in Genetics.

 28 05 2012 2012 3 92 22 03 2012 08 05 2012 Copyright © 2012 Zhi, Irvin, Gu, Stoddard, Lorier, Matter, Rao, Srinivasasainagendra, Tiwari, Turner, Broeckel and Arnett. 2012

This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.

Rationale: Left ventricular hypertrophy (LVH) is a heritable predictor of cardiovascular disease, particularly in blacks. Objective: Determine the feasibility of combining evidence from two distinct but complementary experimental approaches to identify novel genetic predictors of increased LV mass. Methods: Whole-exome sequencing (WES) was conducted in seven African-American sibling trios ascertained on high average familial LV mass indexed to height (LVMHT) using Illumina HiSeq technology. Identified missense or nonsense (MS/NS) mutations were examined for association with LVMHT using linear mixed models adjusted for age, sex, body weight, and familial relationship. To functionally assess WES findings, human induced pluripotent stem cell-derived cardiomyocytes (induced pluripotent stem cell-CM) were stimulated to induce hypertrophy; mRNA sequencing (RNA-seq) was used to determine gene expression differences associated with hypertrophy onset. Statistically significant findings under both experimental approaches identified LVH candidate genes. Candidate genes were further prioritized by seven supportive criteria that included additional association tests (two criteria), regional linkage evidence in the larger HyperGEN cohort (one criterion), and publically available gene and variant based annotations (four criteria). Results: WES reads covered 91% of the target capture region (of size 37.2 MB) with an average coverage of 65×. WES identified 31,426 MS/NS mutations among the 21 individuals. A total of 295 MS/NS variants in 265 genes were associated with LVMHT with q-value <0.25. Of the 265 WES genes, 44 were differentially expressed (P < 0.05) in hypertrophied cells. Among the 44 candidate genes identified, 5, including HLA-B, HTT, MTSS1, SLC5A12, and THBS1, met 3 of 7 supporting criteria. THBS1 encodes an adhesive glycoprotein that promotes matrix preservation in pressure-overload LVH. THBS1 gene expression was 34% higher in hypertrophied cells (P = 0.0003) and a predicted conserved and damaging NS variant in exon 13 (A2099G) was significantly associated with LVHMT (P = 4 × 10−6). Conclusion: Combining evidence from cutting-edge genetic and cellular experiments can enable identification of novel LVH risk loci.

 hypertrophy left ventricular mass cardiomyocyte exome genomics Introduction

Echocardiographic measurement of increased left ventricular (LV) mass predicts cardiovascular morbidity and mortality across demographic groups (Benjamin and Levy, 1999). Beyond the established risk factors for LV hypertrophy (LVH; race, age, hypertension, obesity), research convincingly suggests there is a genetic basis of disease (Harshfield et al., 1990). African-American populations may be enriched for risk variants as LVH burden is about twofold greater in African Americans compared to Caucasians and LV mass is strongly correlated in hypertensive African-American siblings (Arnett et al., 2001). Previous linkage (Arnett et al., 2009a; Tang et al., 2009), candidate gene (Rasmussen-Torvik et al., 2005), and genome-wide association studies (GWAS; Arnett et al., 2009b, 2011; Wineinger et al., 2011) in carefully ascertained and well characterized African-American cohorts have uncovered significant LVH risk loci; however, these loci explain only a fraction of the phenotypic variation attributable to familial inheritance.

Advances in targeted capture and high throughput sequencing have recently made whole-exome sequencing (WES) affordable for clinical studies and offer advantages over array-based genotyping for the identification of novel disease-associated loci. The approach has proved powerful for the study of Mendelian disease where the identification of rare, mostly coding mutations clustering in patients is achieved through strict, discrete bioinformatics filters (Robinson et al., 2011). Indeed, this approach has been applied to identify genes underlying familial dilated cardiomyopathy (Norton et al., 2011) and others (Ng et al., 2010a,b; Majewski et al., 2011). Recently, this approach has been extended to non-Mendelian disease (O’Roak et al., 2011; Ramagopalan et al., 2011; Kim et al., 2012). It has been suggested that rare variation within pedigrees can contribute substantially to the heritability of common traits, offering advantages to family based designs (Manolio et al., 2009; Shi and Rao, 2011) such as the non-random ascertainment of extreme phenotype families (Shi and Rao, 2011). The application of WES to uncover genes associated with cardiovascular disease-related quantitative traits such as those that measure LVH has not yet been reported.

A major challenge of genomic studies is the functional assessment of a statistically significant finding in the relevant tissue, which, in the case of LVH, is cardiomyocytes. Until the recent development of stem cell technology, human adult cardiomyocytes were generally not available for functional analyses. Although human adult ventricular myocytes can be cultured, they are not optimal for biochemical and molecular biologic investigations (Berry et al., 2007). Recently, the use of human induced pluripotent stem cell (iPSC)-derived cardiomyocytes allows direct interrogation of the molecular mechanisms occurring in these cells under differing experimental conditions. Further, this model system aims to provide the ability to identify disease pathways and therapeutic targets, which could ultimately lead to more specific, tailored LVH treatment. Therefore, an approach using WES in combination with functional studies in iPSC-derived cardiomyocytes can provide a powerful platform for disease gene discovery.

In the current study we conducted WES to isolate novel loci for LVH in seven hypertensive African-American sibling trios from the Hypertension Genetic Epidemiology Network (HyperGEN) that were enriched for increased LV mass. Concurrently, we implemented an experimental protocol using a novel LVH model system based on human iPSC-derived cardiomyocytes. These cardiomyocytes were subjected to conditions to produce hypertrophy, and mRNA expression was measured in comparison to control cardiomyocytes generated from the same iPSC line. We show combining results from WES and differential gene expression in the LVH model system, may identify novel candidate genes supported by statistical and annotation-based criteria.

 Materials and Methods Study population

The HyperGEN study has been previously described (Williams et al., 2000). Briefly, HyperGEN is part of the Family Blood Pressure Program funded by the National Heart Lung and Blood Institute and was designed to study the genetics of hypertension and related conditions. Families were drawn from population-based cohorts or the community-at-large if sibships had ≥2 siblings who had been diagnosed with hypertension before age 60. The study was later extended to include siblings and offspring of the original sibpairs. Hypertension was defined as current antihypertensive medication use or having an average systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure ≥90 mm Hg measured at two clinic visits. Two of four centers (AL, NC) recruited 1,264 African Americans making up 470 families. This study was approved by all local institutional review boards (University of Minnesota’s Human Research Protection Program Institutional Review Board, University of Alabama at Birmingham’s Institutional Review Board for Human Use, University of Utah’s Institutional Review Board, University of North Carolina at Chapel Hill’s Office of Human Research Ethics Biomedical Institutional Review Board, Boston University’s Medical Campus Institutional Review Board, Medical College of Wisconsin’s Institutional Review Board); all subjects gave informed consent. In the current study, seven African-American sibling trios with history of hypertension and average age <55 years ascertained on the highest average familial LVMHT were selected for exome sequencing.

 Blood pressure measurement and antihypertensive medications

Systolic and diastolic blood pressure is reported as the average of the second and third measures of a series of six sitting blood pressure measurements. Antihypertensive medication treatment was defined as use of drug(s) belonging to one of the following six classes at the time of the study including diuretics, ace inhibitors, beta blockers, alpha blockers, calcium channel blockers, and angiotensin 2 receptor antagonists.

 Echocardiography

Doppler, two-dimensional (2D), and M-mode (2D-guided) echocardiograms were performed following a standardized protocol previously described (Devereux and Roman, 1995). Certified sonographers from each center were trained at the echocardiography reading center (New York Hospital-Cornell Medical Center). Measurements were made at the echocardiography reading center using a computerized review station equipped with a digitizing tablet and monitor overlay used for calibration and quantification (Digisonics, Inc., Houston, TX, USA). LVM was calculated using end-diastolic dimensions by an anatomically validated formula and indexed to height (m2.7; Devereux et al., 1984).

 Whole-exome sequencing Exome capture

Using Agilent SureSelect All Exome Capture 21, index-tagged, paired-end libraries were prepared. As recommended, 3 μg of genomic DNA diluted in 1× Low TE was sheared using a Covaris instrument with a subsequent end repair step. Prior to end repair, 1 μL of DNA was analyzed on an Agilent 2100 Bioanalyzer DNA 1000 chip. All samples recovered at least 2 μg of DNA post-shearing and had an electropherogram distribution peak between 150 and 200 nucleotides on the DNA 1000 chip.

After end repair, the samples were purified using Agencourt AMPure XP beads, and the purified DNA then had “A” Bases added to the 3′ end of the DNA fragments. After “A” base addition, the DNA was again purified with AMPure XP beads. Ligation of the indexing-specific, paired-end adapter was done, and the product was AMPure XP bead purified, then PCR amplified using the Illumina InPE1.0 (forward) PCR primer and the SureSelect Indexing Pre-Capture PCR primer. For PCR, five cycles of amplification were used.

After PCR amplification, amplified product was AMPure XP bead purified and 1 μL was analyzed on an Agilent 2100 Bioanalyzer DNA 1000 chip. All samples showed an electropherogram distribution peak between 250 and 275 nucleotides on the DNA 1000 chip and had a concentration of at least 147 ng/μL.

For hybridization, all samples needed to be at a concentration of 147 ng/μL. To achieve this, an Eppendorf Vacufuge Plus concentrator was used to completely lyophilize the samples. Using the concentrations calculated from results obtained with the DNA 1000 chip, an appropriate amount of nuclease-free water was added to each sample post-complete lyophilization to bring the concentration to 147 ng/μL. Hybridization using the Agilent Hybridization protocol and the Agilent SureSelect All Exome Capture Library was performed. Hybridization was stopped at 24 h. All samples contained at least 20 μL, indicating an optimal capture, and samples were then bead selected using the SureSelect selection protocol utilizing Dynal MyOne Streptavidin T1 (Invitrogen) magnetic beads.

Once selected, the captured libraries were then AMPure XP bead purified and Index barcode tags were added. As recommended for pooling two samples of our capture size, Agilent Index’s #6 (GCCAAT) and #12 (CTTGTA) were used. Each sample received one tag, with half of the samples receiving #6 and the other half #12, one bar-coded with #6 and one with #12, and pooled together on one sequencing flow cell lane. During addition of the index tag, the libraries were amplified with SureSelect Indexing Post-Capture PCR (Forward) Primer and Index PCR (Reverse) Primer, with the Index PCR Primer being the sample-specific index barcode tag. For PCR, 16 cycles of amplification were used.

The prepared libraries were AMPure XP bead purified and 1 μL of library was run out on an Agilent 2100 Bioanalyzer DNA High Sensitivity chip. All samples had an electropherogram distribution peak between 300 and 325 nucleotides on the DNA High Sensitivity chip. To more accurately quantify the libraries for pooling, samples were quantified using the Agilent QPCR NGS Library Quantification Kit.

After quantification samples were pooled to a volume of 20 μL with an equimolar amount of 10 nM, following Agilent’s multiplexing pooling protocol. Each pool was spiked with 1% phiX control to improve base calling while sequencing, as was recommended by Illumina for pooling of two libraries.

 Illumina sequencing

Following Illumina HiSeq sequencing and cBot protocols, each of the pooled, multiplexed, index-tagged, paired-end libraries was denatured, underwent cluster generation onto a HiSeq v1.5 flow cell and was sequenced.

Each of the 10 nM libraries was denatured using the 4–8 pM procedure to generate a final concentration of 5 or 7 pM to load per lane for cluster generation. Once cluster generation onto flow cells was complete, samples were sequenced using the Illumina HiSeq Sequencing Kit (200 cycles) and multiplexing sequencing chemistry.

 Exome read mapping and variant calling method

Basecalling, demultiplexing, read mapping, and initial SNP calling were done using Illumina’s CASAVA v1.7 software. Read mapping was to the whole autosomal sequence from UCSC hg19 (also known as GRCh37), using the default parameters to the ELAND2 program.

Autosomal SNPs were called using the CASAVA v1.7 variant caller with default parameters, except that the chromosomal coverage variation filter was turned off to account for exome capture. SNPs were called where the CASAVA v1.7 score was greater than the default threshold of 10. Genotypes calling at the SNP sites (variant calling) were done by the SAMTools program v0.1.7-6 (r530; Li et al., 2009; Li, 2011). Multi-sampling joint variant calling for all SNP sites discovered by CASAVA was carried out by the mpileup command line with the default parameters. Genotype calls in the target capture region with PHRED-like genotype quality score GQ ≥ 30, totaling 102,089 variants sites, were annotated by the ANNOVAR program (version 2010-12-02; Wang et al., 2010) against the UCSC hg19 refGene annotation.

 Statistical and bioinformatics methods for whole-exome sequencing

In order to identify missense or nonsense (MS/NS) single-nucleotide variants (SNVs) associated with LVMHT we conducted a mixed model regression with LVMHT as the dependent variable controlling for kinship structures as well as age, sex, and weight as covariates using the kinship R package program LMEKIN (Lourenco et al., 2011). We used a false discovery rate (FDR) criterion of q-value <0.25 (P-value <0.00258) for significance; this is more flexible than the usual Bonferroni criterion given our small sample size (N = 21).

 Induced pluripotent stem cell-derived cardiomyocyte experiments Cell culture

For the cardiomyocyte studies, we utilized human iCell™ Cardiomyocytes derived from iPSCs (Cellular Dynamics International, Madison, WI, USA). iCells were recovered from frozen culture as recommended by the manufacturer and allowed to recover at least 12 days in iCell Maintenance Medium (iCMM) before experimentation. For experiments, cells were plated at 1.5 × 105 cells/well in iCMM, as recommended by the manufacturer for cell-based assays, and grown in pre-coated (0.1% gelatin solution), 12-well dishes at 37°C in a 95% air: 7% CO2 humidified atmosphere. After 14 days of recovery, iCells were washed twice with warmed PBS and starved in iCell serum-free Maintenance Medium (iCSM) for 48 h, then stimulated in iCMM, as described below.

 Cellular model of LVH

Human iCell Cardiomyocytes were plated to confluence, allowed to recover before starving for 48 h, and stimulated with an established stimulant for the beta-adrenergic system, isopreterenol (ISO), for up to 72 h. As a correlate of developing LV hypertrophy, cell culture changes in cell surface area were measured. At least 200 randomly selected stimulated cells were measured as well as unstimulated controls. As shown in Figure 1, our data demonstrate that iCell Cardiomyocytes respond to hypertrophic stimuli by a clear and significant increase in relative cell size (ISO treatment versus control P = 0.0043). We also observed an up-regulation of hypertrophy markers such as intermediate-early genes C-FOS and C-JUN measured by immunochemistry (Lijnen and Petrov, 1999). Together, these data demonstrate successful establishment of a myocyte hypertrophy model in iPS cell-derived cardiomyocytes representative of the hypertrophy phenotype.

(A) Relative cell size of iCell cardiomyocytes under isoproterenol (ISO) hypertrophic stimulation versus control cells. (B) Photographs of iCell cardiomyocytes after hypertrophic stimulation versus controls.

 Stimulation, cell harvest, and RNA extraction

iCells were stimulated for 72 h with ISO (10−5 M; Sigma Aldrich), replenishing every 24 h. After stimulation, cells were harvested with Trizol reagent and the Purlink™ RNA Mini Kit (Invitrogen). Total RNA was extracted per manufacturer’s recommendations, resuspended in nuclease-free water, and quantified/checked for integrity by UV spectrophotometry (NanoDrop™ 2000, Thermo Scientific).

 RNA sequencing

Six paired-end cDNA libraries (three biological replicates of control iPSC cardiomyocytes, three biological replicates of isoproterenol stimulated cardiomyocytes) were prepared and sequenced using Illumina TruSeq RNA Sample Preparation Kit.

Total RNA was extracted and quantified. Following the TruSeq RNA sample preparation low-throughput protocol, 500 ng of total RNA was used to generate index-tagged paired-end cDNA libraries. During library preparation samples were each tagged with Agilent Index #2 (CGATGT), #4 (TGACCA), #5 (ACAGTG), or #6 (GCCAAT). After cDNA libraries were generated using the sample preparation kit, quality of the libraries was checked using 1 μL of sample run on an Agilent 2100 Bioanalyzer DNA 1000 chip. All samples showed an electropherogram peak at ∼260 base pair. Samples were then quantified using the Agilent QPCR NGS Library Quantification Kit.

After quantification, samples were pooled for multiplexing to a volume of 20 μL with an equimolar amount of 10 nM, following Agilent’s multiplexing pooling protocol. Each pool was spiked with 1% phiX control to improve base calling while sequencing, as was recommended by Illumina for pooling of two libraries.

 Illumina sequencing

Following Illumina HiSeq sequencing and cBot protocols, each of the pooled, multiplexed, index-tagged, paired-end libraries was denatured, underwent cluster generation onto a HiSeq v1.5 flow cell and was sequenced.

Each of the 10 nM libraries was denatured using the 4–8 pM procedure to generate a final concentration of 6 pM to load per lane for cluster generation. Once cluster generation onto flow cells was complete, samples were sequenced using the Illumina HiSeq Sequencing Kit (200 cycles) and multiplexing sequencing chemistry.

 RNA-seq assembly

Six paired-end cDNA libraries were sequenced (three biological replicates of control iPSC cardiomyocytes, three biological replicates of isoproterenol stimulated cardiomyocytes). Basecalling and demultiplexing were performed using CASAVA v1.8 from Ilumina. Paired-end fastq sequence reads from each sample were assembled against hg19 using the splicing aligner Tophat v1.3.1 (Trapnell et al., 2009) with the Illumina-supplied hg19 gene-model annotation file (gtf annotation).

 RNA-seq differential expression

Splice-aligned reads were assigned to gene models using the software package HTSeq v0.5.3p3 (http://www-huber.embl.de/users/anders/HTSeq) and the hg19 gtf annotation. Fold-change and differential expression significance values were calculated from gene-level read counts using the DESeq Bioconductor R package v1.2.1 (Anders and Huber, 2010).

 Bioinformatic and statistical methods for prioritization of candidate genes

Variants with an association with LVMHT of q-value <0.25 are designated as candidate variants. For each candidate variant, its minor allele frequency (MAF) among African Americans in the NHLBI Exome Sequencing Project (ESP) was obtained from the ESP exome variant server v.0.0.6 http://snp.gs.washington.edu/EVS/ (released September 9, 2011); its GERP score and PolyPhen score were obtained from the SeattleSeqAnnotation131.

Candidate gene prioritization strategy

Information from different statistical analyses and bioinformatic functional annotations were used to generate a composite measure similar to that described by Gu et al. (2007) about the relevance of candidate genes for their potential roles in LVH. We prioritized candidate genes (identified through the intersection of WES findings and differential RNA expression in the cellular model of LVH) by the following seven criteria.

Variant association after exclusion of outlying case (P20)

Many candidate variants significantly associated with LVMHT after correction for multiple testing described above are influenced by an extreme case with LVMHT of 114 g/m2.7 whom we considered may harbor important LVH variants. However, this individual will also harbor false positive findings so we additionally considered the association of candidate variants after exclusion of the extreme case using the regression models described above (N = 20). Add 1 to the composite prioritization score if any candidate variant P < 0.05, otherwise 0. Rationale: More weight is given to variants that are associated with LVMHT both with and without the most extreme case considered.

 Gene-based association (GB)

We calculated gene-based P-values for the association of a genetic score with LVMHT for 21 individuals. Gene-based genetic score was calculated as the weighted sum of variants, similar to Madsen and Browning (2009). Genetic score for gene j was defined as gj=∑i-1KIijnipj(1-pj), where K is the number MS/NS variants in the gene, Iij is the minor allele counts at variant i, ni is the number of individuals among 21 having a high quality genotype calling, and pi is the MAF of African Americans in ESP. For alleles with frequency 0 in ESP, we used an allele frequency of 0.0002, corresponding to the lowest allele frequency in the ESP exome collection, 1 heterozygous among ∼2,500 individuals. If only one missense or non-synonymous variant was present in a gene, the gene-based P-value was set to 1. Association of the gene-based score with LVMHT was tested using mixed linear regression similarly to the single-variant analysis described. Add 1 to the composite prioritization score if P < 0.05, otherwise 0. Rationale: Allelic heterogeneity may be a probable scenario for common disease where multiple rare variation considered together may explain a larger portion of the genetic basis of disease (Madsen and Browning, 2009).

 Conservation (GERP)

We considered the highest Genomic Evolutionary Rate Profiling (GERP) score (Davydov et al., 2010) among all candidate variants. We added 1 to the composite prioritization score if GERP score ≥5, otherwise 0. Rationale: GERP can be used to characterize genomic regions that have been subjected to purifying selection and are enriched for functional elements that may be predisposing to human disease (Cooper et al., 2005).

 Functional annotation (PH)

We used the Polymorphism Phenotyping (PolyPhen) annotation of candidate variants. We added 1 to the composite prioritization score if any of candidate gene variant was annotated as “probably damaging,” otherwise 0. Rationale: PolyPhen is predictive of the possible impact of an amino acid substitution on the structure and function of a human protein using physical and comparative considerations (Ramensky et al., 2002).

 Minor allele frequency

We considered the minimal MAF of all candidate variants among African Americans from the NHLBI exome sequencing project (ESP). Add 1 to the composite prioritization score if MAF <0.01, otherwise 0. Rationale: Rare frequency of an SNV in a general reference population suggests enrichment of the SNV in an extreme phenotype population may be related to disease (Madsen and Browning, 2009).

 Gene expression in cardiomyocyte (GNF)

GNF GeneAtlas2 (Su et al., 2004) gene expression was obtained from the UCSC genome browser database. Add 1 to the composite prioritization score if expression score >1, otherwise 0. Rationale: This confirms expression in the relevant disease tissue.

 Information from existing linkage studies (Linkage)

Existing linkage analysis results for LV mass of the HyperGEN cohort was obtained from the results of a previous work in HyperGEN (Arnett et al., 2009a). Linkage peaks of LOD >1.75 were considered a hit (Rao and Province, 2000). We added 1 to the composite prioritization score if gene was within 50 kb region centered at a linkage hit, otherwise 0. Rationale: Linkage constitutes an independent statistical genetic approach for identifying rare and functional variants within multiply affected families.

 Results

Table 1 presents demographic and phenotypic data measured for each of the seven sibling trios. Hypertension was well controlled in this population by medication. The average number of antihypertensive medications reported at the time of blood pressure measurement was 1.7 ± 1. In comparison to the entire HyperGEN African-American stratum (N = 1264, average LVMHT 42 ± 12 g/m2.7 and 25% LVH) this subset is enriched for LVH (LVMHT 59 ± 16 g/m2.7 and 66% LVH; Arnett et al., 2011). Additionally, intra-family LVH case counts ranging from 1 to 3 provided phenotypic variability necessary for gene-to-trait association analyses.

Phenotypic values for seven hypertensive African-American sibling trios.

Family
1 2 3 4 5 6 7
Age, years 46.6 (6) 46.6 (3) 43.6 (2) 43.3 (4) 45.3 (4) 42.6 (7) 52.6 (4)
Sex, number of females 2 3 3 2 1 3 1
Weight, kg 91.3 (12) 75.6 (3) 118.9 (18) 61.8 (14) 121.8 (21) 93.6 (6) 97.0 (9)
Height, m 1.7 (0.2) 1.6 (0.02) 1.7 (0.04) 1.6 (0.08) 1.7 (0.1) 1.6 (0.04) 1.7 (0.1)
Systolic blood pressure, mm Hg 87 (7) 142 (37) 140 (32) 137 (20) 152 (7) 113 (18) 142 (21)
Diastolic blood pressure, mm Hg 53 (8) 82 (20) 83 (22) 85 (9) 89 (9) 70 (2) 80 (9)
LV mass (indexed to height), g/m2.7 48.5 (7) 72.0 (36) 61.3 (16) 49.7 (6) 63.9 (9) 52.9 (7) 64.4 (10)
LV hypertrophy* 1 2 2 1 3 2 3

Data are mean (SD) or counts.

*LV mass (indexed to height), g/m2.7 >51.

Whole-exome sequencing reads covered 91% of the 37.2 MB target capture region with an average coverage of 65× (Table 2). After applying variant calling and quality control filters described we identified 102,089 SNVs among the 21 individuals (Table 3). Of those variants, 31,426 are MS/NS mutations (Table 4) which were examined for association with LVMHT. For tallies of variant type and total per individual, see Table 3.

Basic statistics of exome sequencing per sample.

Sample Percent of captured region with read depth Average coverage of captured region, ×
≥8× ≥10× ≥20×
A2055 93 92 84  90
A2057 88 85 69  39
A2058 92 90 81  73
A2140 93 91 83  85
A2153 88 85 69  38
A2154 89 86 72  43
A2614 89 87 72  43
A2639 89 86 71  42
A2640 90 87 74  48
A2803 91 88 76  51
A2804 88 85 70  41
A2855 91 89 77  54
A3167 88 85 71  43
A3168 93 91 83  81
A3169 92 92 79  60
A3170 94 92 85  94
A3174 92 89 79  57
A3177 93 91 84  88
A3234 93 92 85 109
A3235 93 91 84  98
A3254 92 91 83  90
Average 91 89 78  65

Number of variants within individual by category.

Family Sample # Intronic Intergenic Utr Ncrna Up_down_stream Exonic Synonymous Non-synonymous Splicing Stop All
4698 A2055 8,724 743 1,007 861 124 19,760 10,695 8,976 310 86 31,497
4698 A2057 7,988 700 928 788 106 18,449 9,864 8,490 281 92 29,235
4698 A2058 8,493 748 976 841 110 18,849 10,126 8,647 269 74 30,289
4136 A2140 8,652 778 995 896 115 19,400 10,324 8,982 298 90 31,112
4136 A2153 7,999 667 894 812 102 18,625 10,002 8,536 252 84 29,374
4136 A2154 8,185 713 928 811 119 18,871 10,046 8,735 287 88 29,900
4284 A2614 8,184 687 944 833 109 19,124 10,317 8,712 302 92 30,156
4284 A2639 8,139 719 973 816 107 18,929 10,233 8,603 295 89 29,956
4284 A2640 8,311 720 971 838 115 19,123 10,285 8,744 282 91 30,352
4864 A2803 8,274 689 951 835 109 19,185 10,305 8,791 293 86 30,315
4864 A2804 7,992 687 929 791 117 18,535 9,985 8,461 277 87 29,327
4864 A2855 8,320 682 975 850 106 19,408 10,452 8,853 316 100 30,621
5062 A3167 8,127 660 901 815  98 18,491 9,894 8,500 281 94 29,374
5062 A3168 8,662 765 962 904 127 19,449 10,450 8,906 310 90 31,153
5062 A3174 8,606 735 981 803 125 19,472 10,457 8,927 303 85 31,000
5067 A3169 8,376 661 1,023 854 133 19,140 10,435 8,626 304 76 30,466
5067 A3170 8,636 744 1,018 904 133 19,473 10,540 8,855 319 74 31,191
5067 A3177 8,604 723 1,019 886 132 19,367 10,408 8,867 297 88 31,008
  85 A3234 8,771 743 1,008 917 111 19,423 10,402 8,936 311 84 31,243
  85 A3235 8,709 722 1,017 904 112 19,353 10,357 8,907 291 87 31,085
  85 A3254 8,657 712 990 887 118 19,075 10,224 8,769 303 79 30,708
Total 27,849 2,309 3,307 2,999 389 64,868 33,433 31,057 1,070 369 102,089

Genetic variants found by WES and annotated by ANNOVAR.

Variant type No. variants Genes represented
Intronic 27,849 9,374
Intergenic 2,309 NA
UTR 3,272 2,751
ncRNA 2,999 1,001
Up_down_stream 389 308
Splicing:intronic 151 150
Unknown 252 NA
Exonic 64,868 13,796
  Synonymous 33,433 11,722
  Non-synonymous 31,057 10,268
  Stop 369 339
  Unknown 9 5
All 102,089 18,127

UTR, untranslated region; ncRNA, non-coding RNA. See Table 3 for tally of variants per individual.

Regression analyses yielded 295 MS/NS candidate variants in 265 genes (“WES genes”) that passed significance criteria for multiple testing (see Supplementary Material). DEseq RNA differential expression results revealed a total of 44 of the 265 WES genes were also differentially expressed with P < 0.05 in the iPSC model of LVH (see Supplementary Material). Those 44 “candidate genes” were further prioritized based on 7 supportive criteria (Table 5). We focus here on five genes that satisfy at least three of the seven criteria in Table 5. Among these five genes are major histocompatibility complex, class I, B (HLA-B), huntingtin (HTT), metastasis suppressor 1 (MTSS1), solute carrier family 5 (sodium/glucose cotransporter), member 12 (SLC5A12), and thrombospondin 1 (THBS1). Adjustment of DEseq P-values (Padj) for the genome-wide list of tested genes (N = 11,746 genes expressed in at least one experimental condition) yielded 11 of 44 of candidate genes significantly differently expressed (Padj < 0.05) including THBS1 (Padj = 0.009), but not any of the remaining prioritized candidates.

Candidate gene (N = 44) prioritization given a composite score of seven supportive functional and/or statistical categories.

Gene Variant count P20 GB GERP PH MAF GNF Linkage Total
HLA-B 34 1 1 0 0 1 0 0 3
HTT 6 0 1 1 0 1 0 0 3
MTSS1 4 0 0 1 0 1 1 0 3
SLC5A12 2 0 1 0 0 1 1 0 3
THBS1 3 0 0 1 1 0 1 0 3
ATP11B 4 0 0 1 0 1 0 0 2
COL6A3 16 0 0 0 1 0 1 0 2
DICER1 2 0 0 0 0 1 0 1 2
GLTPD1 1 0 0 0 0 1 1 0 2
MCM6 1 0 0 1 0 1 0 0 2
MDGA2 2 0 1 1 0 0 0 0 2
MLL3 16 0 0 1 0 1 0 0 2
NIN 6 1 0 0 1 0 0 0 2
PAPPA 4 0 0 0 0 1 1 0 2
RSAD2 3 0 1 0 0 1 0 0 2
ST8SIA5 2 0 1 0 0 1 0 0 2
TXLNB 8 1 0 0 0 1 0 0 2
UGGT1 3 0 1 0 0 0 0 0 2
DCHS2 27 1 0 0 0 0 0 0 1
DDX11 7 0 0 0 0 1 0 0 1
EMCN 1 0 0 0 0 0 1 0 1
IL33 2 0 0 0 0 0 1 0 1
IPO8 2 0 0 0 0 1 0 0 1
IQGAP3 7 1 0 0 0 0 0 0 1
KDM4C 6 0 0 0 1 0 0 0 1
KDM5A 4 0 1 0 0 0 0 0 1
KRCC1 2 0 0 1 0 0 0 0 1
METTL3 1 0 0 0 0 1 0 0 1
POLR1A 8 0 0 1 0 0 0 0 1
SLC24A5 1 0 0 1 0 0 0 0 1
SLC45A2 2 0 1 0 0 0 0 0 1
SMC2 2 0 0 1 0 0 0 0 1
SPPL2A 1 0 0 0 1 0 0 0 1
SYNE1 32 0 0 1 0 0 0 0 1
TTBK2 5 0 0 0 0 1 0 0 1
ASTN2 1 0 0 0 0 0 0 0 0
CHD6 4 0 0 0 0 0 0 0 0
MAP3K6 6 0 0 0 0 0 0 0 0
MAP9 4 0 0 0 0 0 0 0 0
NASP 2 0 0 0 0 0 0 0 0
PCNX 2 0 0 0 0 0 0 0 0
PER3 8 0 0 0 0 0 0 0 0
TMEM52 2 0 0 0 0 0 0 0 0
USP8 2 0 0 0 0 0 0 0 0

P20, variant is associated with the phenotype with P-value <0.05 after exclusion of the phenotypic outlier. GB, gene-based score is associated with the phenotype, P < 0.05; GERP, Genomic Evolutionary Rate Profiling score ≥5; PH, PolyPhen functional prediction is “probably damaging”; MAF, minor allele frequency of candidate variants <1%; GNF, gene expressed in cardiomyocytes according to GNF GeneAtlas2; Linkage, linkage signal with LOD >1.75 within ±50 kb.

 Discussion

Whole-exome sequencing provides new genetic information by identifying rare and potentially novel protein-coding variants not available on existing genotyping microarrays. Like previous genomic studies, the functional assessment of novel gene variants associated with LVH pathology identified through WES poses significant challenges. Here we present the first WES analysis of any common, quantitative trait in an African-American sample. We identified 295 variants in 265 genes associated with LV mass indexed to height through WES in 7 hypertensive sibling trios. To functionally assess our findings, we combined evidence obtained using RNA sequencing in a molecular model of LVH using human iPSC-derived cardiomyocytes. Using this approach we discovered 44 genes with evidence of a role in disease pathology and statistical association with LVMHT. We refined the list to five genes applying a prioritization strategy incorporating statistical and annotation-based bioinformatic filters. Among the five genes, THBS1 has previously been shown to promote matrix preservation and prevent chamber dilation in an animal model of LVH (Vanhoutte and Heymans, 2011; Xia et al., 2011) while the other genes are novel LVH candidates. Due to several limitations the findings presented in this manuscript are suggestive. Still we provide proof of concept that a novel cellular model of LVH is a promising platform for the functional assessment of genes highlighted via genomic discovery efforts.

THBS1 is an adhesive glycoprotein that mediates cell-to-cell and cell-to-matrix interactions. It inhibits angiogenesis and activates latent transforming growth factor beta, a protein related to cellular differentiation in many tissues. In a recent report, THBS1-null mice had accentuated cardiac hypertrophy (Xia et al., 2011). In the RNA-seq experiment we observed a 1.34-fold increase in THBS1 expression (P = 0.003) after ISO stimulation (see Supplementary Material) consistent with the up-regulation of the protein in disease. These points make genetic disruption of the protein in humans an interesting topic for follow-up research.

Among the remaining genes we report on, MTSS1 functions in cell proliferation. It is a suspected scaffold protein that interacts with multiple partners to regulate actin dynamics; its down-regulation has been observed in multiple cancer types (Xie et al., 2011). HLA-B is part of a family of genes making up the immune system’s HLA complex which aids in the body’s reaction to a wide range of pathogens. A SNP near HLA-B (rs2523586) was recently shown to be associated (P = 1 × 10−6) with diastolic blood pressure (DBP) in African Americans as part of the Candidate Gene Association Resource (CARe) consortium (N = 8,592) although this effect did not replicate in an independent African-American population (Fox et al., 2011). Trinucleotide repeats in HTT are known to cause Huntington’s disease, although a biological link to LVH is unlikely. Finally, SLC5A12 is a sodium-coupled monocarboxylate transporter indicated in the renal handling of lactate and urate (Thangaraju et al., 2006; Ganapathy et al., 2008). Uric acid has a strong link to CVD and hyperuricemia has been linked to ventricular remodeling in an animal model (Chen et al., 2011; Isik et al., 2012).

We note several limitations to our study. Specifically, our sample size was small and not sufficiently powered to identify single genes and variants associated with LV mass solely through statistical modeling approaches. Additionally, we did not directly test the functional effect of the identified variants using the human iPSC-derived cardiomyocyte model of LVH, rather we relied on differential RNA expression of the corresponding gene to suggest variant functional association to the observed pathology. Many questions remain whether the expression pattern in a cell culture model fully resembles the molecular changes of cardiomyocytes in a complex organ such as the heart (Kong et al., 2010). However, we and others (Carvajal-Vergara et al., 2010) have observed well established changes previously described as characteristic for LVH. Plus, iPSC cardiomyocytes have been used extensively for the study of other cardiovascular disease phenotypes, for example human cardiac cellular electrophysiology (Yokoo et al., 2009; Moretti et al., 2010; Germanguz et al., 2011; Itzhaki et al., 2011). Finally, we employed several cutoff criteria throughout our procedures which, if altered, could influence our findings. This includes FDR criteria for variant association with LVMHT and an un-weighted candidate gene prioritization strategy. Therefore, some false positive and alternatively, false negative findings, may have resulted and further replication is required. Still, we present a procedure designed to limit such false findings by combining evidence from genetic and cellular experiments and further prioritizing our results based on rich evidence from existing studies and publically available databases.

In conclusion, we employed an innovative, iterative approach to identify protein-coding variants associated with LVH in African-American hypertensives. The identified genes with significant variants are linked to cell proliferation, cell adhesion, solute handing, and injury repair. One candidate, THBS1, is involved in injury response in multiple tissues, has been linked to cardiac hypertrophy in an animal model, and is upregulated in our novel cellular model of disease. Results necessitate replication and questions remain about the mechanistic relevance of the specific variants in the detected genes, however the results presented support the expansion of this research. Ultimately, we describe how progress in the discovery of genetic risk factors for LVH may benefit from a tiered approach that integrates evidence from new and existing data including a novel cellular model of LVH.

 Conflict of Interest Statement

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.

 Supplementary Material

The Supplementary Material for this article can be found online at http://www.frontiersin.org/Applied_Genetic_Epidemiology/10.3389/fgene.2012.00092/abstract

HyperGEN: Genetics of Left Ventricular Hypertrophy, ancillary to the Family Blood Pressure Program, http://clinicaltrials.gov/ct/show/NCT00005267. Funding sources included National Heart, Lung, and Blood Institute grant R01 HL55673 and cooperative agreements (U01) with the National Heart, Lung, and Blood Institute: HL54471, HL54515 (UT); HL54472, HL54496 (MN); HL54473 (DCC); HL54495 (AL); HL54509 (NC), and HL107437.

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