CNV Association of Diverse Clinical Phenotypes from eMERGE reveals novel disease biology underlying cardiovascular disease

IJCA-27872; No of Pages 7 International Journal of Cardiology xxx (xxxx) xxx

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CNV Association of Diverse Clinical Phenotypes from eMERGE reveals novel disease biology underlying cardiovascular disease Joseph T. Glessner a,1, Jin Li b,a,1, Akshatha Desai a, Melody Palmer c, Dokyoon Kim d, Anastasia Marie Lucas e, Xiao Chang a, John J. Connolly a, Berta Almoguera a, John B. Harley f,g, Gail P. Jarvik c, Marylyn D. Ritchie e, Patrick M.A. Sleiman a,h,i, Dan M. Roden j, David Crosslin k, Hakon Hakonarson a,h,i,⁎ a

The Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China c Genetic Medicine Clinic, University of Washington Medical Center, Seattle, WA 98195, USA d Department of Biostatistics, Epidemiology and Informatics and Institute for Biomedical Informatics (IBI), Perelman School of Medicine, University of Pennsylvania, PA 19104, USA d Department of Genetics and Institute for Biomedical Informatics (IBI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA f U.S. Department of Veterans Affairs Medical Center, Cincinnati, OH, USA g The Center for Autoimmune Genomics and Etiology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center & University of Cincinnati, Cincinnati, OH, USA h Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA i Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA j Clinical Pharmacology, Vanderbilt University Medical Center, Nashville, TN 37232, USA k Biomedical & Health Informatics, School of Medicine, University of Washington, Seattle, WA 98195, USA b

a r t i c l e

i n f o

Article history: Received 10 January 2019 Received in revised form 15 June 2019 Accepted 16 July 2019 Available online xxxx Keywords: BMI Cardiovascular disease Copy number variations Genome-wide association study Glucose levels Lipid profiling

a b s t r a c t Background: Cardiovascular disease is the leading cause of death in the United States. Consequently, individuals who are genetically predisposed for high risk of cardiovascular disease would benefit most from prevention and early intervention approaches. Among common health risk factors affecting adult populations, we evaluated 23 cardiovascular disease-related traits, including BMI, glucose levels and lipid profiling to determine their associations with low-frequency recurrent copy number variations (CNV) (population frequency b 5%). Results: We examined 10,619 unrelated subjects of European ancestry from the Electronic Medical Records and Genomics (eMERGE) Network who were genotyped with 657,366 markers genome-wide on the Illumina Infinium Quad 660 array. We performed CNV calling based on array marker intensity and evaluated data quality, ancestry stratification, and relatedness to ensure unbiased association discovery. Using a segment-based scoring approach, we assessed the association of all CNVs with each trait. In this large genome-wide analysis of lowfrequency CNVs, we observed 11 novel genome-wide significant associations of low-frequency CNVs with major cardiovascular disease traits. Conclusion: In one of the largest genome-wide studies for low-frequency recurrent CNVs, we identified 11 loci associated with cardiovascular disease and related traits at the genome-wide significance level that may serve as biomarkers for prevention and early intervention studies in subjects who are at elevated risk. Our study further supports the role of low-frequency recurrent CNVs in the pathogenesis of common complex disease traits. © 2019 Published by Elsevier B.V.

1. Introduction Cardiovascular disease, including stroke, is the leading cause of death and disability in the United States. There are an estimated 62 million people with cardiovascular disease and 50 million people with hypertension in the United States [1]. In the early 2000, approximately

⁎ Corresponding author at: The Center for Applied Genomics, Abramson Research Center, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA. E-mail address: [email protected] (H. Hakonarson). 1 JTG and JL contributed equally to this work.

946,000 deaths were attributable to cardiovascular disease, accounting for 39% of all deaths in the United States [1]. It is well established that lipid profiles, glucose levels and type 2 diabetes (T2D) are all important intermediate traits related to cardiovascular disease [2]. Often omitted from studies, thyroid dysfunction is also a common medical problem and known to impact lipid levels as well as a number of other cardiovascular risk factors [3]. There is mounting evidence in support of a strong contribution of heritable genetic factors to cardiovascular disease [4]. Epidemiologic studies and randomized clinical trials have also provided compelling evidence that coronary heart disease is largely preventable with proper diet and exercise [5]. Therefore, it is important to identify genetic variants that have a large impact on cardiovascular disease

https://doi.org/10.1016/j.ijcard.2019.07.058 0167-5273/© 2019 Published by Elsevier B.V.

Please cite this article as: J.T. Glessner, J. Li, A. Desai, et al., CNV Association of Diverse Clinical Phenotypes from eMERGE reveals novel disease biology underlying ..., International Journal of Cardiology, https://doi.org/10.1016/j.ijcard.2019.07.058

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together with their associated intermediate traits, as individuals carrying such disease risk factors may benefit from proper prevention efforts, if detected through early screening procedures. Genome wide association studies (GWASs) have uncovered a large number of genetic variants associated with various intermediate traits predisposing individuals to cardiovascular disease including lowdensity lipoprotein cholesterol (LDL) and high-density lipoprotein cholesterol (HDL) levels, hypertension, blood triglycerides, glucose levels, thyroxin levels as well as type 2 diabetes [6–9]. However, these studies are largely focused on genotype association of single nucleotide polymorphisms (SNPs). Recent studies have provided strong evidence that copy number variations (CNVs) make important contribution to the etiology of both common complex diseases and low-frequency disorders [10–12], including hypertension [13] and hypertension related phenotypes [14,15]. However, the contribution of CNVs to cardiovascular disease is still not well studied [16], especially their associations with intermediate quantitative traits related to cardiovascular disease which remain largely unexplored. To identify CNVs contributing to the large spectrum of cardiovascular disease related intermediate traits, we sought to leverage genomewide SNP genotyping intensity data with enrichment of probes for cardiovascular disease to call CNVs and statistically associate differences in observed frequency between those with low vs. high clinical values or those with specific diagnosis vs. those without. This is one of the largest genome-wide analyses of low-frequency CNVs in the context of human cardiovascular disease traits. 2. Methods 2.1. Study samples Data of 14,783 individuals were from Phase I of the consortium of Electronic Medical Records and Genomics (eMERGE) Network of National Institutes of Health (NIH) which is comprised of five study sites. The aim of eMERGE is to develop and apply methods for genomic research using electronic medical records to derive phenotypes. The study was approved by the institutional review board committee at each participating site; and written consent was given by all study participants. 2.2. Phenotypes Clinical variable descriptions were matched based on text similarity searching and were carefully evaluated for units and adjustments to ensure comparable values between eMERGE subjects from different subgroups. Best matching clinical variables from different eMERGE subgroups were subsequently combined. If the best match for an eMERGE subcohort had more than a 10% mismatch rate or was incorrect upon manual review, samples from that eMERGE subcohort were not included for the respective clinical variable analysis. The following phenotypes were analyzed in our study. BMI: Subject's Body Mass Index (BMI) over time. Each observation age corresponds to respective height values that are closest in time. If more than one height value is available for the same observation age, the median value was used. We did not include BMIs during pregnancy or 9 months after pregnancy. ECG abnormalities: The eMERGE consortium previously developed algorithms to identify 1) individuals with normal ECGs and without any cardiac disease, abnormal electrolyte values, or QRS-active medications (https://phekb.org/phenotype/8 [17]) atrial fibrillation, which selects cases based on atrial fibrillation but no presence of a heart transplant, and controls with no evidence of atrial flutter, atrial fibrillation, or atrial tachycardia but with at least one ECG (https://phekb.org/phenotype/78). BMI_First_ECG: Patient's BMI at their first normal ECG. QRS_Complex_Dur: QRS Duration from first normal electrocardiogram. PR_interval: the time in between of the start of the P wave to the beginning of the QRS complex. QT_interval: the time in between of the onset of the QRS complex to the end of the T wave. QTcB: QT interval corrected for heart rate via Bazett's formula. HDL: A value which captures the amount of HDL Cholesterol in the blood measured in milligrams per deciliter (mg/dL). LDL: A value which captures the amount of LDL Cholesterol in the blood measured in milligrams per deciliter (mg/dL). Triglycerides: A value which captures the amount of Triglycerides in the blood measured in milligrams per deciliter (mg/dL). HDL_adjusted: Adjusted baseline HDL (age 59, BMI 29, no estrogen) which is modeled based on same baseline period, age 59 and adjusted for age and body size. LDL_adjusted: Adjusted baseline LDL (age 59, BMI 29, no estrogen).

Triglycerides_adjusted: Adjusted baseline triglycerides (age 59, BMI 29, no estrogen). HDL_median: Median value of multiple HDL measures. LDL_median: Median value of multiple LDL measures. Triglycerides_median: Median value of multiple Triglycerides measures. Cataract_age_Dx: Age at first diagnosis of cataract. Age in years (50–90 years). Those N90 years recorded as 90 years. PAD: the diagnosis of peripheral artery disease. Hypertension: documented elevated systolic pressure above 140 or diastolic pressure above 90 with at least two readings on separate office visits. 2.3. Genotyping All participating sites followed the same established protocol of eMERGE network. Briefly genomic DNA was extracted from peripheral blood and hybridized to Illumina Infinium Quad 660 array with 657,366 markers. Genotype data was processed as previously described [18]. 2.4. CNV calling We performed genome-wide low-frequency CNV (population frequency b 5%) [19,20] association analysis. The overall bioinformatics workflow is summarized in Supplementary Fig. 1. CNVs were called using optimized PennCNV [21] settings to maximize the putative CNV calls taken forward in evaluation for association to disease traits. Data of the log R ratio (LRR) and the B allele frequency (BAF) of each SNP were collected from all participating sites and analyzed together at the Center for Applied Genomics (CAG) at CHOP. CNV calls were made with the PennCNV algorithm, which is based on a Hidden Markov Model (HMM) as previously described [21]. Only CNVs with N3 probes were generated. Because large CNVs were often split into small fragments during the process of CNV calling, we merged adjacent CNV calls by using the clean_cnv.pl program implemented in PennCNV [21]. 2.5. Principal component analysis Population structure was inferred using the principal component analysis (PCA) Eigenstrat package [22] based SNP genotyping data. Briefly, our dataset was combined with Hapmap dataset and common SNPs in both datasets were LD pruned that there is no pair of SNPs with r2 N 0.2. The first two principal components were plotted and those of non-European ancestry were removed from further analysis (Supplementary Fig. 2). Then PCA was conducted again on samples with European ancestry to derive the genetic relationship. 2.6. Quality control We performed quality control (QC) filtering based on sample and CNV metrics to fully characterize the data quality for genotype and intensity data content. Non-European ancestry individuals were removed based on PCA. Related individuals were first identified and excluded based on pedigree information from clinical records. We further performed identity-by-state (IBS) calculations using PLINK [23] to identify pairs of duplicated or cryptically related samples (PI_HAT ≥ 0.1875). For each pair of duplicated/related samples, the one with a smaller standard deviation of the LogR-ratio (LRR) was kept in the dataset. Gender information was inferred based on the heterozygosity rate of the X-chromosome SNPs and the call rate of the Y-chromosome SNPs. Samples with ambiguous gender information or gender information discrepant from self-reported were excluded from subsequent analysis. CNV QC include the metrics of genotyping rate, the SD of log R ratio (LRR SD), GC base pair wave factor (|GCWF|) and the CNV count of each sample (Supplementary Fig. 3). LRR_SD and |GCWF| are measurement of sample intensity noise and intensity waviness respectively, and CNV count per sample indicates the DNA quality. Each CNV metric was plotted, which usually resulted in a curve composed of a linear phase and an exponential phase. Samples in the exponential phase of the curve indicative of outlier values were removed from association analysis. The actual exclusion threshold for each CNV metric is LRR_SD ≥ 0.3, |GCWF| ≥ 2e-04, CNV count ≥ 600 (Supplementary Fig. 3). 2.7. Association analysis We took a segment-based scoring approach [24] that scans the genome for CNVregion containing consecutive probes with more frequent copy number changes in cases compared with controls, or significantly associated with each continuous trait. Deletions and duplications were analyzed separately genome-wide. Two ped files were generated using ParseCNV [24] with the “–includeped” option, one showing deletion status at each SNP for each sample and the other for duplications. In the deletion ped file, 1 1 represents CN = 0, 1 2 indicates CN = 1 and 2 2 represents all other CN status at this site; in the duplication ped file, 1 1 represents CN = 4, 1 2 indicates CN = 3 and 2 2 represents all other CN status at this site. Linear regression was carried out for quantitative traits and logistic regression was conducted for binary traits on these ped files with PLINK. Age, gender, the first 3 principal components were used as covariates in association analyses. We further focused on SNPs with association P-value b0.05 for better CNV reproducibility in a given region. Afterwards, the resulting association P-value of these SNPs was input back to ParseCNV package. Then neighboring SNPs in proximity (b1 MB) and with similar association significance (±1 log p-value) were collapsed into CNV-regions. The lowest SNP P-

Please cite this article as: J.T. Glessner, J. Li, A. Desai, et al., CNV Association of Diverse Clinical Phenotypes from eMERGE reveals novel disease biology underlying ..., International Journal of Cardiology, https://doi.org/10.1016/j.ijcard.2019.07.058

J.T. Glessner et al. / International Journal of Cardiology xxx (xxxx) xxx value within each CNV-region was used to represent the association of CNV-region with each trait. For multiple testing correction, we considered the number of CNV-regions called for each trait and set the experimental wide threshold of P-valueb1 × 10−4. 2.8. CNV-region quality control For all the CNV-region with nominally significant P-value b0.05 were further QC filtered based on the following criteria: those CNV-regions with number of SNP probes b10, or length b 10 kb or CNV confidence score b 10 were excluded. We manually evaluated the quality of each significant CNV-region by visually reviewing LRR and BAF plots which indicate the raw intensity and genotype values for probes in each region and flanking regions [21] and confirmed their copy number change status. In our previous studies, we have demonstrated that over 90% of the CNV-regions that passed our manual examination of LRR, BAF plots were validated by qPCR.

3. Results

3

were found to be significantly associated with Triglycerides or Triglyceride-related traits (Tables 2 and 3). 3.2. Homozygous deletions significantly associated with eMERGE phenotypes A class of CNV-regions that is likely to have a remarkable impact on gene expression is homozygous deletion. We found 6 homozygous deletion regions significantly associated with four complex traits (Table 4). Among them, three cover gene exons and the others are located in introns of genes. The chr9:8641684-8641791 CNV-region is significantly associated with Triglycerides, and resides within an intron of PTPRD (Protein Tyrosine Phosphatase, Receptor Type D) which plays a role in regulating insulin receptor signaling [30]. The PTPRD gene has also been reported to regulate plasma homocysteine level which is another risk factor for cardiovascular disease [31].

3.1. CNV-regions significantly associated with eMERGE phenotypes 3.3. Pathway analysis of significant CNV-regions We conducted a genome-wide low-frequency CNV analysis to understand the contribution of this class of structural variants on various complex human traits related to cardiovascular disease. A total of 10,619 unrelated samples of European ancestry with high quality data after QC filtering were included for CNV association study; and 23 clinical variables with the best matching across eMERGE subcohorts were analyzed. The number and the distribution of which are summarized in Table 1. To identify CNV loci potentially contributing to cardiovascular disease residuals, we applied a segment-based scoring approach. The overall association of copy number status with each trait at each SNP is summarized in Supplementary Fig. 4 (deletions) and Supplementary Fig. 5 (duplications), respectively. The number of merged CNV-regions for each trait is summarized in Supplementary Table 1. An average of 119 CNV-regions were called for each trait, thus we define the experimental-wide significance as P b 1 × 10−4. Among the resulting CNV-regions that surpassed this threshold, we further performed CNV-region filtering (see Methods). We found 47 unique CNV-regions associated with 12 traits (P-value b1 × 10−4), including 11 regions at genome-wide significance level (Tables 2 and 3). Among these CNVregions, 27 cover gene exons, which are likely to directly affect the encoded protein products; the rest may still affect gene expression via transcriptional regulation mechanism. For example, 8 out of 11 genome-wide significant CNV-regions (P-valueb5 × 10−8) contains genome-wide significant GTEx eQTL SNPs in cardiovascular disease trait relevant tissue types, which may have outstanding effect on the expression level of the corresponding eQTL genes (Supplementary Data 1). A total of 14 significant CNV-regions were found to be associated with BMI (Tables 2 and 3); two of them span N150 probes, covering N20 genes. Gene BACE2 (Beta-Site APP-Cleaving Enzyme 2) has been previously reported to be correlated with beta-cell mass and insulin area scores [25,26]. A deletion region (chr1:2397844-2428909) overlapping with exons of gene PLCH2 is associated with phenotype hypertension (Table 3). Gene PLCH2 has been considered as a marker gene for blood pressure based on both SNP and gene expression data [27]. To determine if CNVs contribute to abnormality in lipoprotein profile, genome wide CNV analysis was performed in relation with HDL levels. Four CNV-regions were found to be significantly associated with elevated HDL levels, with one deletion region associated with HDL levels in subjects who were on lipid lowering drugs (Table 3). An interesting gene covered by one of the CNV-regions is UGT2A1, an important functional component of the retinol metabolism pathway, which is related to heart development. For the other lipoprotein types, we identified 3 CNV-regions significantly associated with LDL and LDL related phenotypes (Table 3). The CNV-region impacting SLC12A7 is noteworthy because of the known involvement of solute carrier family members in diverse metabolism pathways and association with different human diseases including cardiovascular disease [28,29]. Triglyceride is the major type of body fat in humans. Seventeen CNV-regions

Next, we pursued pathway analysis of the CNV-regions shown in Tables 2 and 3 using different methods. We tested 134 genes covered by 47 CNV-regions observed using STRINGS [32], and we noted extensive interactions between these genes (Supplementary Fig. 6) and the enrichment of 16 immune-related KEGG (Kyoto Encyclopedia of Genes and Genomes) [33] pathways (Supplementary Table 2). However, further analysis revealed that the enrichment of these pathways is driven by the multiple IFNA genes in a large CNV-region (chr22:30282465-30337120). We then applied another method, INRICH (INterval enRICHment analysis) [34], which is a permutation based test for enrichment of LD-independent genomic regions within biologically relevant gene sets. In this analysis, 47 CNV-Regions were merged to 32 non-overlapping LD-independent genomic intervals. We found the pathway “Retinol metabolism” to be significantly enriched in the tested intervals (empirical P-value = 6 × 10−4; corrected Pvalue = 0.0338), (Supplementary Table 3). 4. Discussion In the present study, we identified 11 genome-wide significant CNVregions that are associated with intermediate traits important for cardiovascular disease. High BMI is an indicator of obesity, which is an important risk factor for cardiovascular disease [35]. In our study, we found several CNVregions conferring increased risk for high BMI. At least 2 genes impacted by these CNV-regions have been linked to cardiovascular disease, including CTNNA3 (chr10:67958177-68040325, deletion, β = 18.18, Pval = 1.53E-05) and BACE2 (chr21:42591611-42665680,del, β = 23.69, P-val = 3.60E-05). CTNNA3 is primarily expressed in intercalated discs of the heart, regulating cell-cell adhesion between cardiac muscle cells [36,37]. Missense mutation and small deletion have been reported in patients with right ventricular dysplasia and common variants in CTNNA3 have been linked to metabolite levels and heart failure in African American population [38,39]. Ctnna3 knockout mice present with cardiovascular disease related phenotypes in keeping with progressive cardiomyopathy and ventricular arrhythmias [36]. In addition, CTNNA3 SNPs have also been associated with childhood obesity in the Hispanic population [40]. BACE2 encodes an integral membrane glycoprotein, the deposition of which in the wall of cerebral microvessels leads to the development of cardiovascular disease, including stroke and dementia [41,42]. Interestingly, BACE2 regulates pancreatic β cell function and mass [26]. Inhibition of mouse Bace2 function results in increased level of beta cell mass and insulin secretion [25]. We observed significant association of CNV-region in the EFNA5 (Ephrin A5) gene (chr5:106182573-106233074, del), which is a member of the ephrin gene family, with BMI (β = 34.781, P-val = 2.32E09) and BMI_First_ECG (β = 35.61, P-val = 1.74E-08). EFNA5 is a known candidate gene for intima-media thickness of the common and

Please cite this article as: J.T. Glessner, J. Li, A. Desai, et al., CNV Association of Diverse Clinical Phenotypes from eMERGE reveals novel disease biology underlying ..., International Journal of Cardiology, https://doi.org/10.1016/j.ijcard.2019.07.058

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Table 1 Clinical fields examined in our study. 1a. Quantitative trait Trait

N

Median

Mean

STD

BMI HDL LDL Triglycerides HDL_adjusted LDL_adjusted HDL_median LDL_median Triglycerides_median Triglycerides_adjusted Cataract_age_dx BMI_First_ECG QRS_Complex_Dur Heart_rate PR_interval QT_interval QTcB

12,398 3029 2965 3036 2499 2301 3813 3797 3811 2354 5485 4624 5348 5916 5135 5135 5916

27.7 49 110 133 49.1 149.5 51 122 122 129.5 71 27.57 88 69 156 386 415

28.63 52.33 112.96 162.38 50.82 150.18 53.52 122.66 135.53 143.50 70.52 28.73 87.83 70.64 156.57 386.62 415.61

6.06 16.82 36.16 174.53 13.46 26.99 14.53 26.32 66.21 64.52 7.86 6.40 9.50 11.06 22.01 29.27 20.29

1b. Binary trait Trait

Number of Cases

Number of Controls

PAD Dementia Cataract T2D Hypothyroidism Hypertension

1662 1459 5489 2824 1362 264

1630 2042 1883 5667 5380 10,187

N = number of samples; Median = the median value of each trait among all subjects analyzed; Mean = the mean value of each trait among all subjects analyzed; STD = the standard deviation of mean value of each trait; Cataract_age_dx = Age at first diagnosis of cataract; BMI_First_ECG = Patient's BMI at their first normal ECG; QRS_Complex_Dur = QRS Duration from first normal electrocardiogram; QTcB: QT interval corrected for heart rate via Bazett's formula. PAD: Peripheral artery disease; T2D: Type 2 Diabetes.

metabolism and insulin resistance [45]. Retinoids are important for heart development and perturbation of the retinoid metabolism is associated with increased risk of cardiovascular disease [46]. CNV-regions may affect target gene expression in different ways. CNV-regions overlapping with gene exons may directly affect gene

internal carotid arteries and atherosclerosis and is associated with stroke and myocardial infarction [43]. SNPs in this gene are associated with insulin sensitivity and fasting plasma glucose levels [44]. Our pathway analysis unveiled enrichment of retinoid metabolism pathway genes which serves an important function in glucose

Table 2 Associations of copy number variation (CNV) with quantitative trait of clinical phenotype values at genome-wide significance level (P-value b5 × 10−8). The ones that cover gene exons are highlighted. Trait

CNV-Region(hg19)

# SNPs

Tag SNP

CNV type

Freq

Pop Freq

Beta

95% CI

P-value

Closest Gene

BMI

chr12:16262989-16423287

54

rs4598715

del

6.39E-05

36.27

25.07, 47.47

2.67E-10

SLC15A5

BMI

chr22:30282465-30337120

27

rs28698917

del

0.000592

34.93

23.56, 46.3

1.97E-09

MTMR3

BMI

chr5:106229059-106233074

18

cnvi0084391

del

0.00744

34.78

23.4, 46.16

2.32E-09

EFNA5

BMI_1st_ECG

chr12:16262989-16423287

54

rs4598715

del

6.39E-05

37.43

25.3, 49.57

2.25E-09

SLC15A5

BMI_1st_ECG

chr5:106182573-106233074

27

cnvi0084387

del

0.000157

35.61

23.32, 47.9

1.74E-08

EFNA5

Triglycerides

chr11:84339557-84497615

47

rs12278864

del

5.74E-05

3397

2961, 3833

6.43E-48

DLG2

Triglycerides

chr1:195831030-195874856

27

rs10494734

del

0.000351

623.6

449.7, 797.5

5.09E-12

KCNT2

Triglycerides

chr4:725458-735150

11

cnvi0103932

dup

0

1714

1535, 1894

2.78E-69

PCGF3

Triglycerides

chr1:2597551-2629521

19

rs12735969

dup

0.000628

1156

840.4, 1471

1.76E-12

TTC34

Triglycerides_adjusted

chr6:169240106-169247594

26

cnvi0052765

del

0

409.2

295.6, 522.8

2.74E-12

SMOC2

Triglycerides_adjusted

chr3:147115-226067

34

cnvi0100959

dup

0.000118

404.8

286.7, 522.9

3.08E-11

CHL1

Triglycerides_median

chr4:5735303-5779317

35

rs13143108

del

0.000331

403.7

272.5, 534.9

1.98E-09

EVC

Triglycerides_median

chr7:84328077-84644500

34

rs1819081

del

0.00022 1/4616 0.0002 1/5110 0.00016 1/6260 0.0006 1/1664 0.00045 1/2206 0.00045 1/2226 0.0073 10/1368 0.00078 2/2564 0.0022 3/1368 0.00039 1/2584 0.00044 1/2298 0.00026 1/3776 0.00029 1/3480

0.00462

363.4

233.2, 493.6

5.14E-08

SEMA3D

#SNPs = number of SNPs; del = deletion; dup = duplication; Freq = frequency The fraction shows the number of CNV alleles over the number of total alleles tested; Pop Freq = Population Frequency.

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Table 3 Associations of copy number variation (CNV) with quantitative trait of clinical phenotype values at suggestive level of significance (5 × 10−8 b P-value b1 × 10−4). The ones that cover gene exons are highlighted. Trait

CNV-region(hg19)

# Tag SNP SNPs

CNV type

Freq

Pop Freq

BMI

chr11:97255512-97404580

35

rs921561

del

0.000038 28.46

BMI

chr13:20801273-20828837

23

cnvi0049857 del

BMI

chr22:45115579-45133238

10

rs734799

del

BMI

chr10:67958177-68040325

31

rs10762034

del

BMI

chr21:42591611-42665680

45

rs4818225

del

BMI

chr14:48270666-48363587

32

rs1956328

del

BMI

chr9:20754419-21448448

159

rs1539743

dup

0.000098 1/10218 0.00085 3/3518 0.0002 1/5110 0.00033 2/6048 0.00058 1/1726 0.00011 1/8944 0.00023 1/4318

BMI

chr11:95979731-96003324

31

cnvi0076022 dup

BMI

chr11:74555056-76104085

303

rs11236458

dup

BMI

chr7:152567213-152853444

84

rs6464295

dup

BMI_First_ECG

chr13:20801273-20829582

24

cnvi0049865 del

BMI_First_ECG

chr12:21007718-21036533

33

rs4149126

del

BMI_First_ECG

chr10:67958177-68071613

38

rs4745895

del

BMI_First_ECG

chr10:48753013-48916544

27

cnvi0073497 del

BMI_First_ECG

chr17:62856555-62888049

26

cnvi0053699 dup

BMI_First_ECG

chr10:129333397-129437673 46

rs971175

dup

Cataract_age_DX

chr3:24084579-25924698

561

rs9827612

del

Cataract_age_DX

chr2:18265521-18521402

83

rs10495680

del

Cataract_age_DX

chr11:84291710-84343679

20

rs11824888

del

Cataract_age_DX

chr2:168625881-168758019

35

rs836678

dup

Cataract_age_DX

chr7:157880135-157881706

10

cnvi0067636 dup

HDL_adjusted

chr9:30340919-30654630

43

rs10124699

del

HDL_adjusted

chr4:70483042-70516671

29

rs2331685

del

HDL_adjusted

chr19:23633079-24069443

70

rs4993837

del

HDL_adjusted

chr13:111682000-111749116 28

cnvi0057872 dup

LDL_median

chr7:26531720-26645259

71

rs6957717

del

LDL

chr13:23511581-24915555

538

rs1326132

del

LDL

chr10:19275658-19571941

80

rs1999956

del

LDL

chr5:1060326-1072456

33

cnvi0054802 dup

0

Beta

16.53

0.000057 25.47 0.0012

18.18

0

23.69

0.000040 23.16 0

25.11

0.000098 0 1/10218 0.000098 0 1/10218

23.84 23.57

95% CI

P-value

16.99, 39.92 9.734, 23.32 14.07, 36.87 9.954, 26.41 12.51, 34.86 12, 34.31 13.21, 37.01

1.17E-06 BC031306

12.37, 35.3 12.1, 35.04

0.0002 2/10110 0.0015 2/1362 0.0006 1/1664 0.00091 2/2210 0.00045 1/2210 0.00035 1/2828 0.00045 1/2210 0.00022 1/4542

8.242, 24.43 0 20.59 11.71, 29.46 0.00082 28.14 15.94, 40.35 0.000163 18.67 9.81, 27.52 0.00713 26.01 13.5, 38.53 0 28.88 16.51, 41.24 0 26.2 13.67, 38.72 0 −25.18 −34.45, −15.91

0.00023 1/4328 0.00021 1/4716 0.00023 1/4328 0.00021 1/4784 0.001 1/998 0.00041 1/2458 0.0005 1/2050 0.0013 1/754 0.00058 2/3470 0.0026 2/772

0

0.00066 1/1516 0.0068

0.000031 13.41

6.24E-05 5.74E-05 0 0.00072 0.00027 0 0.0010 0 0

−20.19 −29.77, −10.61 −19.87 −29.32, −10.43 −23.3 −32.86, −13.75 −20.37 −30.03, −10.71 46.11 23.71, 68.51 52.32 26.76, 77.88 60.35 26.24, 76.68 51.62 26.09, 77.16 92.83 56.89, 128.8 84.31 36.94, 131.7

0

151

0

52.04

82.36, 219.7 27.99,

Closest gene

2.01E-06 GJB6 1.24E-05 ARHGAP8,PRR5,PRR5-ARHGAP8 1.53E-05 CTNNA3 3.60E-05 BACE2 4.81E-05 LOC100506433 3.66E-05 DKFZp686L0695,IFNA1,IFNA10, IFNA13,IFNA14,IFNA16,IFNA17, IFNA2,IFNA21,IFNA22P,IFNA4, IFNA5,IFNA6,IFNA7,IFNA8,IFNB1, IFNW1,KIAA1797,KLHL9, PTPLAD2,SNORA30 4.67E-05 MAML2 5.71E-05 AK125821,ARRB1,BC039351,DGAT2, DQ583960,DQ591572,DQ592682, DQ595000,GDPD5,KLHL35, LOC100507050,MAP6,MIR326, MOGAT2,Mir_548,NEU3,OR2AT4, PRKRIR,RPS3,SERPINH1,SLCO2B1, SNORD15A,SNORD15B,SPCS2, TRNA_Pro,UVRAG,WNT11,XRRA1 7.75E-05 ACTR3B,ACTR3C 4.06E-06 GJB6 7.11E-06 SLCO1B3,SLCO1B7 3.89E-05 CTNNA3 4.96E-05 AGAP8,DQ588224,FRMPD2P1 ,PTPN20A,PTPN20B 5.10E-06 DQ578599,LRRC37A3 4.45E-05 NPS 1.12E-07 5S_rRNA,AK131021,LOC100130354, LOC152024,LOC285326,MIR4442, MIR4792,NGLY1,OXSM,RARB,THRB, TOP2B,TRNA 3.75E-05 Mir_652 3.87E-05 DLG2 1.86E-06 B3GALT1 3.72E-05 PTPRN2 6.34E-05 MIR873 6.37E-05 UGT2A1,UGT2A2 6.67E-05 RPSA,ZNF675,ZNF681 8.91E-05 ARHGEF7 4.58E-07 KIAA0087,LOC441204 1.19E-05 AK057396,AK127292,BC038727, BC043582,C1QTNF9,C1QTNF9B, C1QTNF9B-AS1,LINC00327,MIPEP, MIR2276,SACS,SGCG,SPATA13, TNFRSF19 1.85E-05 DQ600701 2.59E-05 MIR4635,SLC12A7 (continued on next page)

Please cite this article as: J.T. Glessner, J. Li, A. Desai, et al., CNV Association of Diverse Clinical Phenotypes from eMERGE reveals novel disease biology underlying ..., International Journal of Cardiology, https://doi.org/10.1016/j.ijcard.2019.07.058

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J.T. Glessner et al. / International Journal of Cardiology xxx (xxxx) xxx

Table 3 (continued) Trait

CNV-region(hg19)

# Tag SNP SNPs

CNV type

Triglycerides

chr8:54511617-54529129

34

rs13275975

del

Triglycerides

chr3:75428675-75589320

24

rs2944654

del

Triglycerides

chr10:34971389-35148739

36

rs10764002

dup

Triglycerides

chr17:34596493-34645966

64

cnvi0081268 dup

Triglycerides_adjusted chr4:6608856-6920466

169

rs9842824

dup

Triglycerides_adjusted chr2:89285770-89290346

11

rs2509779

dup

Triglycerides_median

chr7:84328077-84644500

34

rs1819081

del

Triglycerides_median

chr3:145479777-145752473

75

rs1513410

del

Triglycerides_median

chr7:157891610-157934557

21

cnvi0090407 del

Triglycerides_median

chr1:12502427-12763781

62

rs10271765

dup

Hypertension

chr4:2397844-2428909

26

rs9996449

del

Freq

Pop Freq

8/1172 0.0025 5/2012 0.019 36/1896 0.000643 1/1556 0.024 35/1472 0.00044 1/2060 0.00051 1/1946 0.00029 1/3480 0.0006 2/3344 0.013 45/3480 0.0014 5/3480 0.00069 9/13016

0

Beta

95% CI

76.1 156, 337.7 156.8 80.52, 233.1 619.4 390.9, 847.9 177.4 92.92, 261.9 300.2 183.7, 416.8 278.5 161.1, 395.8 363.4 233.2, 493.6 181.2 91.16, 271.3 −39.61 −59.35, −19.86 120.5 61.97, 179 OR = 6.11, 29.06 138.2 246.8

0.003 0.00014 0.0085 0 0 0.0046 0.00096 0 0.00049 5.74E-05

P-value

Closest gene

1.25E-07 AK056897 6.06E-05 DQ584669,FAM86DP 1.41E-07 PARD3 4.30E-05 CCL3L1,CCL3L3,CCL4L1,CCL4L2, D63785,TBC1D3G 5.19E-07 CNO,KIAA0232,LOC93622,MAN2B2, MRFAP1,MRFAP1L1,S100P,TBC1D14 3.75E-06 abParts 5.14E-08 SEMA3D 8.36E-05 PLOD2 8.78E-05 PTPRN2 5.67E-05 AADACL4,AK095438,DHRS3, SNORA59B,VPS13D 2.28E-05 PLCH2

#SNPs = number of SNPs, del = deletion, dup = duplication, Freq = frequenc, The fraction shows the number of CNV alleles over the number of total alleles tested; Pop Freq = Population Frequency.

expression level because of gene dosage effect and potentially even dominant negative effect. As we indicated in Supplementary Data 1, the genome-wide significant regions also contain eQTL SNPs which may have a transcriptional regulatory impact on the expression level of genes that are distant to the CNV-regions. In Tables 2 and 3, we presented the population frequency of each significant CNV-region, which is comparable to their observed frequency in our cohort, demonstrating close to random sampling of the general population without bias in our study. GWAS have identified a number of genetic variants significantly associated with complex human disease traits, however, the heritability attributed to these SNPs is limited. The “missing” part may at least in part lie in the contribution of structural variants. In this regard, lowfrequency CNVs confer risk of larger effect sizes compared to SNPs, and may explain a large fraction of the missing heritability, thus it is important to identify structural variants that impact major disease categories, such as cardiovascular disease and its related risk factors, which could complement GWAS findings. Prevention steps through the adaptation of a healthy lifestyle among individuals who are genetically predisposed to be at high risk based on impactful structural variants as reported in this study may help reduce the incidence of cardiovascular disease. In summary, in one of the largest genome-wide study for lowfrequency recurrent CNVs [47,48], we identified 11 genome-wide significant loci associated with cardiovascular disease and related traits

that may serve as biomarkers for prevention and early intervention studies in subjects who are at elevated risk.

Acknowledgments We gratefully thank all of the patients who participated in this study and all of the control subjects who donated blood samples to various eMERGE subgroups funded by the NIH for genetic research purposes. We thank the technical staff at the Broad Institute for generating the genotypes used in this study and the medical assistants, nurses, and medical staff who recruited the study subjects.

Funding The study was supported by an Institutional Development Award to the Center for Applied Genomics from CHOP (to H.H.), a Research Development Award from the Cotswold Foundation (to H.H.), and NIH grants U01HG008684-01 and U01 HG006830. Conflict of interest statement No conflict of interest.

Table 4 Homozygous deletionss significantly associated with the complex traits examined. Exonic CNVs are highlighted. CNV-Region(hg19)

Phenotype

CNV_type

HomCNVs

P_value

Closest gene

chr1:111930916-111934817 chr1:236702475-236702958 chr9:22481286-22666835 chr9:8641684-8641791 chr12:40878452-40879387 chr16:21897317-21949122

Triglycerides QRS_Complex_Dur BMI Triglycerides T2D QRS_Complex_Dur

del del del del del del

2 1 2 1 13 1

1.51E-06 4.25E-05 4.59E-05 6.02E-18 3.39E-05 1.99E-06

PGCP1 LGALS8 FLJ35282 PTPRD MUC19 DKFZp547E087, DKFZp779K0112, DQ592203,LOC23117

HomCNVs: number of subjects carrying each homozygous deletion.

Please cite this article as: J.T. Glessner, J. Li, A. Desai, et al., CNV Association of Diverse Clinical Phenotypes from eMERGE reveals novel disease biology underlying ..., International Journal of Cardiology, https://doi.org/10.1016/j.ijcard.2019.07.058

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Please cite this article as: J.T. Glessner, J. Li, A. Desai, et al., CNV Association of Diverse Clinical Phenotypes from eMERGE reveals novel disease biology underlying ..., International Journal of Cardiology, https://doi.org/10.1016/j.ijcard.2019.07.058