Supplemental Material

SUPPLEMENTAL MATERIAL

Supplemental Methods

UK Biobank genetic data

The UK Biobank AxiomTM Array (825,927 markers) and UK BiLEVE AxiomTM Array share

95% of contents and are designed to give genome-wide coverage of single nucleotide variants

(SNVs) and insertion deletion (indels) variants. The detailed description of the array content is available online (http://www.ukbiobank.ac.uk/wp-content/uploads/2014/04/UK-Biobank-

Axiom-Array-Content-Summary-2014.pdf). In addition, SNVs were imputed centrally using

IMPUTE4 software (https://jmarchini.org/impute-4/) using two reference panels: (i) merged

UK10K and 1000 Genome Phase 3 reference panels, and (ii) Haplotype Reference

Consortium (HRC) reference panel. Imputation produced 92,693,895 autosomal SNVs, short indels and large structural variants. Genomic positions of the variants are reported in

Reference Consortium Human Reference 37 (GRCh37) coordinates.

UK Biobank CMR phenotypes

All UK Biobank cardiovascular magnetic resonance (CMR) studies were acquired with a wide bore 1.5 Tesla scanner (MAGNETOM Aera, Syngo Platform VD13A, Siemens Healthcare,

Erlangen, Germany). LV mass and volumes were measured from balanced steady-state free precession (bSSFP) cine short axis images (Supplemental Figure 1). Derivation of LV phenotypes followed 2 phases: (i) manual annotation of the first ~5000 CMR studies to create a reference segmented image dataset, and (ii) fully convolutional neural network (FCN) trained with the aforementioned reference dataset to automatically analyse the remainder of CMR studies. Analysis of CMR images by FCN produced human expert-level performance in image segmentation at a fraction of time as previously described1. The end-diastolic phase of the cardiac cycle represents the largest LV cavity-volume while the end-systolic phase represents the smallest LV cavity-volume. LVEDV and LVESV were computed by Simpson ‘disks summation’ method where the sum of cross-sectional areas of the slices was multiplied by the slide thickness. LVM was calculated by multiplying the difference between total epicardial volume and endocardial volume with the myocardial density (1.05 g/m2). LVSV, LVEF and

LVMVR were defined as: LVSV = LVEDV – LVESV, LVEF = LVSV/LVEDV and LVMVR

= LVM/LVEDV. Since the LV phenotypes are not normally distributed, we transformed the raw LV phenotypes by: (i) taking the residuals after regressing each LV phenotype on the covariates and (ii) applying rank-based inverse normal transformation (INT) to the residuals, prior to fitting the GWAS models.

Sample quality control

A total of 19,804 individuals with CMR imaging data were initially considered (manual analysis N = 5,065; automated analysis N = 14,739). 395 individuals were removed due to poor CMR image quality caused by incomplete coverage of left ventricle or image artefacts.

For automatically analysed CMR studies, the segmentation quality of images with outlying measurements, defined as values more than three interquartile range (IQR) above the first quartile or below the third quartile, was visually reviewed by a European Association of

Cardiovascular Imaging (EACVI) CMR level 3 certified analyst (N.A.).

We identified ancestry clusters by applying k-means clustering algorithm in R2 (version

3.4.3) with k=4 on genotypic principal component 1 and 2 (PC 1 and PC 2) separately. The number of clusters (k) was chosen as 4 to represent the 4 main ethnic groups within the UK

Biobank: White, African, Asian and Chinese. An overall clustering was carried out by intersection of PC1-4means-clusters and PC2-4means-clusters. The largest overlapping cluster represents the European (White) ancestry while discordant clustering between PC1 and PC2 represents ‘Mixed/Other’ category. European ancestry was ascertained only if the self-reported ethnicity agreed with k-means clustering results. In addition to restricting the sample to European ancestry, we excluded individuals with self-reported or hospital diagnosed heart failure or myocardial infarction, extreme phenotypic outliers, LVEF < 50% and missing covariates. The overview of sample selection process is presented in

Supplemental Figure 2.

UK Biobank genetic association analysis

For the heritability and genetic correlation analyses, a high-quality set of model SNVs were selected using the following criteria: minor allele frequency (MAF) > 1%, a Hardy–Weinberg equilibrium (HWE) p-value = 1 × 10−6, and missingness < 0.0015. We excluded the human leukocyte antigen (HLA) region (Chr 6: 28Mb – 34Mb) from our analyses due to its complex linkage disequilibrium (LD) structure. The same set of model SNVs as heritability analysis was used to account for the random effects in the linear mixed-models for the genetic association analyses.

Definitions of covariates

All covariates recorded at the imaging visit were used in the analysis where possible. Standing height (UK Biobank Data-Field 50) was measured with a Seca 202 device. Any missing value in height and weight data was substituted by the estimated height and weight values prior to imaging stages (UK Biobank Data-Fields 12144 and 12143, respectively). Body mass index

(BMI) was calculated as: weight(kg) / Height(m) 2. Systolic and diastolic blood pressure (SBP and DBP) were measured several times during the imaging visit using Omron 705 IT electronic blood pressure monitor and manual sphygmomanometer. We averaged the automated and manual blood pressure readings (UK Biobank Data-Fields 4079, 4080, 93 and 94). The missing blood pressure values were replaced by the averaged systolic and diastolic brachial blood pressure measurements taken during pulse wave analysis at the imaging visit (UK Biobank

Data-Fields 12674, 12675, 12697 and 12698). Participants taking blood pressure medications were identified from the union of those reported to be taking ‘Blood pressure medication’ in the questionnaire (UK Biobank Data-Fields 6153 and 6177) and those taking medications with known blood pressure lowering effect from the verbal interview (UK Biobank Data-Field

20004). SBP and DBP were adjusted for anti-hypertensive medication use by addition of

15mmHg and 10mmHg, respectively. Smoking status was categorised into ‘Current’, ‘Previous’ or ‘Never’ smokers obtained from UK Biobank Data-Field 20116. Regular alcohol use

(Yes/No) was defined as consumption of alcohol at least three times per week (UK Biobank

Data-Field 1558). Presence of dyslipidaemia and diabetes were ascertained from standardised questionnaires and verbal interview (UK Biobank Data-Fields 20002 and 2443) together with the data on the use of lipid-lowering medication and insulin (UK Biobank Data-Fields 6153 and 6177).

Prevalent heart failure and myocardial infarction (MI) were identified from self-reported questionnaires (UK Biobank Data-Field 20002) and main and secondary ICD10 codes (UK Biobank Data-Fields 41202 and 41204, respectively). For MI diagnoses, the algorithmically- defined myocardial infarction outcomes (UK Biobank Data-Fields 42000 to 42005) were additionally used. Prevalence was ascertained by cross-checking the hospital admission dates against the date of visit to the imaging centre.

Conditional analysis

To determine the presence of independent secondary signals within the genome-wide significant loci, we used a conditional analysis (using --cojo-cond command) implemented in genome-wide complex trait analysis (GCTA) tool3. A secondary signal would be declared if:

(i) the original p value of newly identified variant was lower than 1 × 10−6; (ii) there was less than a 1.5-fold difference between the lead SNV and secondary association p values on a – log10 scale, i.e., if –log10(p lead)/-log10(p sec) < 1.5; and (iii) if there was less than a 1.5- fold difference between the main association and conditional association p values on a –log10 scale, i.e., if –log10(p sec)/-log10(p cond) < 1.5.

Percentage variance

We built two linear regression models by: (i) regressing each LV trait on all covariates in the primary model and the genome-wide significant variants for the trait and (ii) regressing each

LV trait on all covariates in the primary model only. The proportion of variance explained by the genome-wide significant variants was given by the difference between the adjusted R2 of these two models.

Secondary analyses

We evaluated the findings from the secondary analyses by: (i) calculating Spearman’s rank correlation coefficients between the β estimates and the p values of the primary and secondary

GWAS results and (ii) assessing if there was less than a 1.5-fold difference between the main association and secondary association p values on a -log10 scale, i.e., if –log10(p prim)/- log10(p sec) < 1.5.

Multi-Ethnic Study of Atherosclerosis (MESA)

(i) Study Design

MESA is a longitudinal study of subclinical cardiovascular disease and risk factors that predict progression to clinically overt cardiovascular disease or progression of the subclinical disease4. Between 2000 and 2002, MESA recruited 6,814 men and women 45 to 84 years of age from Forsyth County, North Carolina; New York City, New York; Baltimore, Maryland;

St. Paul, Minnesota; Chicago, Illinois; and Los Angeles, California. Exclusion criteria were clinical cardiovascular disease, weight exceeding 136 kg (300 lb.), pregnancy, and impediment to long-term participation.

(ii) Left Ventricular traits in MESA

The following left ventricular traits were derived from fast gradient recovery echo (fGRE)

CMR pulse sequence:

1. LV mass (g)

2. LV end-diastolic volume (ml) 3. LV end-systolic volume (ml)

4. LV stroke volume (ml)

5. LV ejection fraction (%)

6. LV mass to end-diastolic volume ratio (g/ml)

(iii) Additional phenotyping of MESA participants

Height was measured to the nearest 0.1 cm with the subject in stocking feet and weight was measured to the nearest pound with the subject in light clothing using a balanced scale.

Systolic blood pressure was measured from oscillometric cuff and adjusted for anti- hypertensive use by adding 15 mmHg5.

(iv) Genotype Data

Participants in the original MESA cohort, the MESA Family Study and the MESA Air

Pollution Study who consented to genetic analyses were genotyped in 2009 using the

Affymetrix Human SNP array 6.0. Genotype quality control (QC) for these data included filter on SNP level call rate < 95%, individual level call rate < 95%, heterozygosity > 53% as described previously. The cleaned genotypic data was deposited with MESA phenotypic data into dbGaP as the MESA SHARe project (study accession phs000209); 8,224 consenting individuals (2,685 White, 2,588 non-Hispanic African-American, 2,174 Hispanic, 777

Chinese) were included, with 897,981 variants passing study specific QC (MESA SHARe included an ancillary study, MESA Family, with approximately 1600 additional African-

American and Hispanic individuals). For GWAS, the University of Michigan imputation server was used for pre-phasing and imputation of the MESA SHARe participants using the

1,000 Genomes Phase 3 integrated variant set.

(v) Principal component analysis

We performed principal component analysis (PCA) to adjust for population structure among

MESA participants, as described previously6. We constructed subsets of typed SNVs, thinned for LD within each race/ethnic group. We performed PCA as implemented in the program SMARTPCA7,8 for unrelated subsets of individuals, removing inferred first-degree relatives from the analysis. We constructed histograms and QQ-plots to assess symmetry and normality of the distribution of loadings for each of the resulting principal components (PCs) to determine the optimal number of PCs for genetic association analysis.

(vi) Genetic association analysis

Among the MESA participants with LV phenotypes available, we stratified by race/ethnic group and constructed a subset of unrelated individuals by retaining at most one individual from each family. We further excluded those individuals with top PCs of ancestry > 3.5 SD from the mean within any race/ethnic group. Based on our examination of PCs within each race/ethnic group, we used 3 PCs for analysis of Whites, 1 PC for African Americans, 3 PCs for Hispanics, and 1 PC for Chinese. The sample selection procedure in the MESA cohort is outlined in Supplemental Figure 5. The analyses of LV traits were stratified by ethnic groups on the rank-based inverse normal transformed residuals model. We included covariate adjustment for age, sex, height, weight, medication-adjusted SBP, study site and PCs. ProbABELv0.5.09 was used to conduct genetic association analysis using linear models with robust standard errors.

Bioinformatic annotation

We used ANNOVAR10 to characterise all sentinel variants and their proxies in LD r2 ³ 0.8 for variant location, distance to the nearest genes, amino acid substitution and functional impact based on a range of prediction tools including SIFT and PolyPhen-2. The nonsynonymous variants were classified as ‘damaging’ if two or more methods predicted detrimental effects and ‘probably damaging’ if indicated by a single prediction tool. We used

HaploReg11 to annotate the non-coding variants using supporting information from chromatin state, conservation and protein-binding data. RegulomeDB12 was used to further prioritise the regulatory variants.

We obtained the expression quantitative trait loci (eQTL) association data of all variants in

LD r2 ³ 0.8 with our sentinel variants from GTEx13 dataset v7 (https://gtexportal.org/home/).

We reported the variants significantly associated with gene expression (false discovery rate,

FDR < 0.05) in all tissues and further highlighted the eQTL associations in cardiovascular tissues (heart, aorta, coronary and tibial arteries). We identified potential target genes by performing the long-range chromatin interaction (Hi-C) analysis in FUMA14 (Functional

Mapping and Annotation) web-based platform v1.3.3c. The Hi-C data from left and right ventricles and aorta were obtained from the pre-processed chromatin loops computed by Fit-

Hi-C pipeline15. We only considered the genes which showed significant promotor interactions (at FDR < 1x10-6) with the regulatory variants (RegulomeDB score £ 5) in LD r2

³ 0.8 with our sentinel variants. For gene prioritisation and gene-set enrichment analyses, we entered all variants with suggestive significance (GWAS p values < 1x10-5) into DEPICT

(Data-driven Expression-Prioritised Integration for Complex Traits) tool16. We also investigated these variants for significant enrichment within DNase I sensitive sites identified from ENCODE and Roadmap Epigenomics projects using FORGE17 (Functional element

Overlap analysis of the Results of Genome Wide Association Study) v1.1.

At gene-level annotation, we mapped all genes which are located within 10kb from our sentinel variants using BEDtools18. We ranked the candidate genes using multiple lines of evidence including cardiovascular tissue-specific gene expression and Hi-C data, availability of knockout phenotype in animal models (from International Mouse Phenotyping Consortium

[http://www.mousephenotype.org/] and the Mouse Genome Informatics database

[http://www.informatics.jax.org/]). The druggability of potential causal genes were assessed by accessing the drug-gene interaction database (http://www.dgidb.org/). We evaluated our candidate genes with GeneNetwork19 (https://www.genenetwork.nl/) which uses gene co- regulation data, to predict pathway membership and HPO (human phenotype ontology) term associations. We next carried out an extensive literature review of all candidate genes by accessing information from GeneCards20 (https://www.genecards.org/), OMIM (Online

Mendelian Inheritance in Man) (https://www.omim.org/) and PubMed.

Polygenic risk prediction of heart failure events

Heart failure cases were identified from self-reported questionnaires (UK Biobank Data-Field

20002) and main and secondary ICD10 codes – I50.0 (Congestive heart failure), I50.1 (Left ventricular failure, unspecified), I50.9 (Heart failure, unspecified), I11.0 (Hypertensive heart disease with (congestive) heart failure), I13.0 (Hypertensive heart and renal disease with (congestive) heart failure), I13.2 (Hypertensive heart and renal disease with both (congestive) heart failure and renal failure), I42.0 (Dilated cardiomyopathy) – in UK Biobank Data-Fields

41202 and 41204, respectively. Since the germline DNA variation had been determined prior to disease onset, we included both incident and prevalent cases in the genetic risk score (PRS) analysis. In sensitivity analysis, we excluded individuals with prevalent heart failure and calculated the hazard ratios of incident heart failure for PRS quintiles. PRS was calculated by the summation of the alleles associated with each LV trait weighted by their effect sizes

(PRS = ∑( '( × *(, where Si is the effect size of the effect allele and Gi is the number of the observed effective allele). We removed individuals included in the LV GWASs and their relatives (third-degree or closer; Kinship coefficient ³ 0.044) from our target sample to get unbiased estimates of association between PRS and heart failure outcome. The analysis was restricted to ~1.2 million HapMap3 variants as recommended by the LDPred21 authors. LDPred deploys a Bayesian method to infer the posterior mean effect size of each variant based on a genetic architecture prior and LD information. The priors drawn from the point-normal mixture distribution consider the tuning parameter known as the fraction of causal variant (+) which were selected from a range of values using the training dataset. A range of + values (1.0, 0.3,

0.1, 0.03, 0.01, 0.003, and 0.001) were considered. The best + value was selected based on the largest Nagelkerke R2 value in the logistic regression models of PRS predicting heart failure in the training set.

Supplemental Figures

Supplementary Figure 1. Exemplary segmented left ventricular short axis images

A B C

Panels A and B demonstrate the contouring of left and right ventricle from base to apex at end-diastole and end- systole, respectively. Panel C shows the zoomed-in mid-ventricular slice of left and right ventricles. The red and green contours demarcate the left ventricular endocardial and epicardial borders, respectively, while the yellow contour demarcates the right ventricular endocardial border.

Supplemental Figure 2. Sample selection flowchart

Reason for exclusion CMR studies (N = 19,804) (manual studies = 5,065; automated studies = 14,739) Poor CMR image quality (n = 395)

CMR studies post image QC (N = 19,409) • No genotype (n = 457)

• Non-European (n = 601)

• Mismatched gender (n = 13)

• Poor heterozygosity or high N = 18,307 missingness (n = 31)

1. Individuals with self-reported and hospital-diagnosed MI (N = 403) and HF (N = 78)

2. Extreme phenotypic outliers (3xIQR) LVEDV (N = 16,920) • LVEDV (N = 9) LVESV (N = 16,920) • LVESV (N = 40) LVSV (N = 16,917) • LVSV (N = 7) LVEF (N = 16,923) • LVEF (N = 15) LVM (N = 16,920) • LVM (N = 6) LVMVR (N = 16,884) • LVMVR (N = 42)

3. Low LVEF (<50%) (N = 879)

4. Missing covariate (SBP) (N = 48)

CMR, cardiovascular magnetic resonance; LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVSV, left ventricular stroke volume; LVEF, left ventricular ejection fraction; LVM, left ventricular mass; LVMVR, left ventricular mass to end-diastolic volume ratio; MI, myocardial infarction; HF, heart failure

Supplemental Figure 3. Detailed flowchart of analysis strategy Genome-wide association studies of 6 LV phenotypes (LVEDV, LVESV, LVSV, LVM, LVMVR, LVEF)

Conditional analysis

Primary analysis in UK Biobank European cohort (Nmax = 16,923) Evaluation secondary independent signal in 1-Mb region from sentinel variants using GCTA “cojo-cond” command 1. Inverse-normal rank transformation (IVNT) of LV traits

(i) Obtain the residuals after regressing each LV phenotype on the covariates (age, sex, height, weight, SBP adjusted for anti-hypertensive medication use, phenotype-derivation method (automatic/manual), Secondary analyses array type (UK Biobank vs UK BiLEVE array), and imaging centre) (ii) Apply IVNT to the residuals 1. GWASs: Untransformed LV phenotype ~ SNV + covariates in the primary model 2. Heritability and genetic correlation analyses of IVNT-LV traits using BOLT-REML algorithm 2. Primary GWAS models + additional adjustment for DBP- 3. GWASs: IVNT-LV phenotypes ~ SNV with MAF ≥ 5% and INFO > 0.3 adjusted for anti-hypertensive use, BMI, smoking status, regular alcohol use, dyslipidaemia and diabetes 4. Loci assignment: Sentinel genome-wide significant variant (p < 1x10-8) + proxies (LD r2 > 0.1 in 1- Mb region) Lookup in MESA study

Sentinel GWS variants (N = 11) were looked up for support in each ancestry group of MESA study (European=1,742; African- American=1,083; Hispanic=972; Chinese=586)

14 locus-trait pairs (3 loci shared loci across multiple traits → 8 unique loci) Polygenic risk score analyses

Polygenic prediction of heart failure outcome in the remaining (non-CMR) UK Biobank cohort (cases / controls N = 5137 / 358,879) by genetic risk scores constructed from the LV GWAS summary statistics

11 unique sentinel variants + proxies (LD r2 ≥ 0.8)

Association with Functional annotation Gene expression Enrichment analyses other traits analysis • Coding variation • DEPICT • Literature review (Annovar) • eQTL in • FORGE (DNase 1 • Phenoscanner • Regulatory regions cardiovascular hotspots) • Gene Atlas (Haploreg, tissue (GTEx • GeneNetwork RegulomeDB) v7) • Long-range chromatin interaction (Hi-C in FUMA)

Candidate gene identification

• Mouse knockout models (MGI) • Known association with cardiovascular disease (OMIM disease, Human Phenotype Ontology, literature review) • Functional signals from eQTL and Hi-C analyses • Proximity to sentinel variants (10kb region)

28 candidate genes (8 potential causal genes with ≥ 2 lines of evidence)

LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVSV, left ventricular stroke volume; LVM, left ventricular mass; LVMVR, left ventricular mass to end-diastolic volume ratio; LVEF, left ventricular ejection fraction; SNV, single nucleotide variant; REML, restricted maximal likelihood; SBP, systolic blood pressure; MAF, minor allelic frequency; INFO, imputation quality score; LD, linkage disequilibrium; GCTA, genome-wide complex trait analysis; DBP, diastolic blood pressure; BMI, body mass index; CMR, cardiovascular magnetic resonance imaging; MESA, multi-ethnic study of atherosclerosis; Hi-C, long-range chromatin interaction analysis; FUMA, Functional Mapping and Annotation tool; eQTL, expression quantitative trait loci; GTEx, Genotype-Tissue Expression database; DEPICT, Data-driven Expression-Prioritised Integration for Complex Traits; FORGE, Functional element Overlap analysis of the Results of Genome Wide Association Study; MGI, Mouse Genome Informatics database; OMIM, Online Mendelian Inheritance in Man

Supplemental Figure 4. Histogram of left ventricular CMR phenotypes with superimposed density line

A B

800

600 600 750 600

400 400 500 400

200 200 200 250 Number of individuals Number of individuals Number of individuals Number of individuals

0 0 0 0 50 100 150 200 250 300 50 100 −4 −2 0 2 4 −4 −2 0 2 4 LVEDV (ml) LVESV (ml) Inverse−normal rank−transformed LVEDV residuals Inverse−normal rank−transformed LVESV residuals

600 600 600 600

400 400 400 400

200 200 200 200 Number of individuals Number of individuals Number of individuals Number of individuals

0 0 0 0 50 100 150 50 60 70 80 −4 −2 0 2 4 −4 −2 0 2 4 LVSV (ml) LVEF (%) Inverse−normal rank−transformed LVSV residuals Inverse−normal rank−transformed LVEF residuals

600 600 750 900

400 400 500 600

250 300 200 200 Number of individuals Number of individuals Number of individuals Number of individuals

0 0 0 0 50 100 150 200 0.4 0.6 0.8 1.0 −4 −2 0 2 4 −4 −2 0 2 4 LVM (g) LVMVR (g/ml) Inverse−normal rank−transformed LVM residuals Inverse−normal rank−transformed LVMVR residuals

Panel A shows the distribution of untransformed LV phenotypes and Panel B shows the distribution of inverse- normal rank-transformed residuals of phenotypes after regressing on covariates. CMR, cardiovascular magnetic resonance imaging; LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVSV, left ventricular stroke volume; LVEF, left ventricular ejection fraction; LVM, left ventricular mass; LVMVR, left ventricular mass to end-diastolic volume ratio

Supplemental Figure 5. Sample selection flowchart in the MESA cohort

Reason for exclusion MESA study (N = 6361) (European = 2626, Chinese = 775, African American = 1611, Hispanics =1449) Removing the missing covariates (age, gender, study site, SBP, height and weight) MESA study (N = 6351) (European = 2523, Chinese = 773, African American = 1609, Hispanics =1446) Removing the missing phenotyped individuals MESA study (N = 4688) (European = 1884, Chinese = 634, African American = 1101, Hispanics =1069) Eliminating families MESA study (N = 4546) (European = 1863, Chinese = 598 , African American = 1090, Hispanics = 995 ) Removing PCA outliers MESA study (N = 4383) (European = 1742, Chinese = 586 , African American = 1083, Hispanics = 972)

MESA, Multi-Ethnic Study of Atherosclerosis; SBP, systolic blood pressure; PCA, principal component analysis

Supplemental Figure 6. Quantile-quantile plots of GWAS p values

A B C

D E F

Panel A. left ventricular end-diastolic volume (LVEDV); Panel B. left ventricular end-systolic volume (LVESV); Panel C. left ventricular stroke volume (LVSV); Panel D. left ventricular ejection fraction (LVEF); Panel E. left ventricular mass (LVM); Panel F. left ventricular mass to end-diastolic volume ratio (LVMVR) l, genomic inflation factor

Supplemental Figure 7. LocusZoom plots of LV traits

LVEDV

12 2 100 10 2 100 10 2 100 rs2042995 r r r ● rs7310615 ● ● 0.8 0.8 rs7071853 0.8 ● ● 10 ● ● ● ● ● ● ●● ● ● ● ●●●● ● ● Recombination rate (cM/Mb) ● Recombination rate (cM/Mb) ● ● ● ● 0.6 0.6 ● 0.6 ● ● ●●● ●●●●● ●●● ● 80 8 80 ●● ●● ● ● ●● 8 ● 80 Recombination rate (cM/Mb) ●● ●●●●●●● ● ●● ●●● ●● ● ● ●●●● ● ● ● ●●● ● 0.4 0.4 ●● ● 0.4 ●● ●●●● ● ● ● ●● ● ●●●●● ●●● ● ●●● ● ●●● ●●● 8 ●● ● ●● ●●●● ● ● ● ●●● ●●●● 0.2 0.2 ● ●● ●● ●●● 0.2 ● ●●● ●● ● ●●●● ●●●●●● ● ●●●●●●● ●● ● ●● ●●●● ● ● ● ● ● ●●●● ●● ●● ● ● ● ● ● 60 6 ● ●●● ●●●● ●●● 60 ● ● ● ● ● ● ●●●●●●● ● value) ● value) ● ● 6 60 ●●●● ●● ● ●● ●●● ● ●● − ● ● ● ●●● − ● ●

● ● value) 6 ●●● ● ●●● ● ● ● ● ● ● ● (p (p ● ● ●●● ●● ●● ● − ● ●● ●●● ● ● ● ●●●● ●● ● 10 ● ● ●● ●●●● 10 ● ● ●● ● ● ● ● ● ● ● ●● ● (p ● ● ● g ● ● ● ●● ●●●● ●●●● g ●● ● ●● ● ●● ●●● ● ● ●●● ● 10 ●● ● ●● ●● o ● ●● ● ●●● ● ●● 40 o 4 ●● ● 40 ●● ● ●●● ●●●●●● ● l ● ● ● ●●● ●● ●●●●● l ● ●● ●●● ● ●●●●●●●●● ● ●● ● ●● ● g ●●● ●●●●●● ● ● ● ●●●● ●● ●●●●●●●●●● ●●● ●● ●● ● ●●●●●●●●●●●●●●●●●●●●●●● ● ●●● ● ● - ● ●● ● - ●● ● ●● ●●● ● ●● ●● ● ●● o ●●● ●● ● ● ●●● 4 ● ● ● ● ●● ●● l 4 ● ●●●●●● ●●● ●●●●●● ● ●●●●●● 40 ● ● ● ● ● ●●● ● ● ● ●● ● ● ●● ●●●●● ●●●● ●●●●●●●● ●●● ●●●●●●●● ●●●●●● ●●●●●● ● ● ●● ●● ● ●● - ● ● ●●● ●●●●●●● ● ● ● ●●●●●●●●●●● ●● ●●●● ● ●● ● ● ● ● ●●● ● ● ● ● ● ●●● ● ● ● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●● ●●●●● ● ● ●●● ● ● ●● ● ●●●●●● ● ● ● ●●●● ● ● ●● ● ●● ● ● ● ●● ●●●● ● ●● ● ● ● ● ● ●●● ●●●●● ● ● ●●●●●● ●●●● ●●●●●●●●●● ●● ● ● ● ● ● ●●●●●●●●●●●●●● ● ● ● ● ●●● ● ●●●● ● ● ● ●●●● ●●●●●●●● ● ●●●●● ● ● ● ●●●●● ●●●●●●● ● ● ● ●●● ● ● ● ● ● ● ●●● ●● ● ● ●●●●●● ● ● ●● ●●●●●● ● ● ● ● ● ●●●●●● ●● ● ●●● ● ●●●●●●●● ●● ● ● ● ● ● ●●●●●●● ●●● ● ● ●● ●● ●●●●● ● 20 2 ●●● ●●● ●●●● ● ●●●●● ●● ●● ● ●●●● ●●●●●● ● ●●●●●●●●● ● ● ●●●● 20 ● ● ● ●●●●●●● ●●●●● ● ● ● ● ● ● 2 ● ●●●●● ● ● ● ● ●● ●●●● ●● ●● ●●●●●●●●●●●●●●●● ● ● ● ●●●●●●●●●● ●●●●● ● ● ●● ● ●● ● ●●● ●●● ●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●● ●●●●● ● ●●●● ● ● ●● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●●● ●●●● ● ● ● ●●●● ● ●●● ● ● ● ●●●●● ●●● ●●●●● ● ●●●●●●●●● ● ●● ●● ●●● ● ●●●●●● ●●●●●●●●●●●●●●● ●●●●●●●● ●●●● ● ● ● ● ●●● ● ●● ● ● ●● ● ● ●●● ●●●●●●● ●● ● ● ●● ● ●●●●● ● ●●●●●●● ●● ●● ●●●● ● ● ●●●●●●●●●● ●● ●●●● ● ●●●●●● ●● ●● ●● ●● ● ●●●●●●●● ● 2 ●●● ●●●●●●●● ● ●●● ● ●●●●● ● ●●●● ●●●●● 20 ●●●●●● ●●●● ● ● ● ●●●●●● ●● ●● ●●●● ●●● ●●● ●● ●●●●● ● ● ●●●●● ●●●● ●●●● ● ● ●●●●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●● ●●●●●●●●●●● ● ● ●● ●● ● ● ● ●● ● ● ●●●●●●●●●●●●● ●●● ●● ● ●● ● ●● ● ● ●●●●● ● ● ● ●● ●●● ● ● ● ●●●●● ● ●● ●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●●● ●●●● ●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●● ●●●● ●●●●●● ● ● ●● ●●●●●●●●●●●●●●●● ● ●●●●●● ● ●● ● ●● ●● ● ● ● ●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ● ●●● ● ●● ●●● ● ●●●●●● ●●●●●●●●● ● ●●●● ●●●●● ● ● ●● ●●●●●●●●●●●●● ●●●●●● ●●●●●●●●● ●●●●●●●●●●●●● ●●●● ●● ●● ●●●● ●●●● ●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ●● ● ● ●●●●●●●● ● ●● ● ● ●●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ●● ● ● ●● ● ● ● ●● ● ●●●●●●●●●●●●●●●●●●● ● ●●●●● ●●●●●●●●●●●●● ●● ● ●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●● ●●●●●●●●●●●●●●●●● ●●●●●●●●●● ●●●●●●● ● ●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●● ● ●● ●●●●●●●●● ● ● ● ● ● ● ● ● ● ●●● ● ●● ●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●● ● ● ●● ● ●●●●●●●● ●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ●●● ●●●● ●●●●●●●●●●●●●●● ●●●●●●●●●●●●●● ● ● ●●●●●●● ●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ●● ● ● ●●● ● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ●● ● ●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●● ● ●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●● ●●●●●●●●●●●●●●●●●●●●●● ●● ●●●●●●●●●●●●●● ●●●● ● ●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ● ● ● ●● ●●●● ● ● ● ● ● ● 0 0 0 0 ●●●● ● ●●●●●●●● ● ● ● ● ●●●●●●●● ●●● ● ● ● ●● ●●●●● ●●●●●●● ● ● ● ●●●●●●●●● ● ● ●● ● ●●● ●●● ● ● ●● ● ● ● ●●● ●● 0 ●●●●●●●●● ●● ●● ●● ●● ● ●●● ● ●●● ●●● ● ●●●● ● ● ● 0 OSBPL6 PJVK TTN−AS1 LOC101927055 SESTD1 NANOS1 PRDX3 MIR4681 RGS10 BAG3 INPP5F SEC23IP

MIR548N CCDC141 EIF3A GRK5 TIAL1 MCMBP CCDC63 CUX2 SH2B3 ATXN2−AS ALDH2 ADAM1A

LOC101927027 TTN SNORA19 MIR4682 MYL2 MIR6760 ATXN2 BRAP MIR6761

PRKRA FAM45A LINC01405 PHETA1 ACAD10 MAPKAPK5

FKBP7 FAM45BP LOC105369980 MAPKAPK5−AS1

PLEKHA3 SFXN4 TMEM116

179.2 179.4 179.6 179.8 180 120.8 121 121.2 121.4 121.6 121.8 111.4 111.6 111.8 112 112.2 112.4 Position on chr2 (Mb) Position on chr10 (Mb) Position on chr12 (Mb)

LVESV

12 2 100 2 100 2 r 100 2 100 rs2042995 r 20 r rs200712209rs2042995 r 20 ● rs72840788 ● ● 0.8 0.8 ●●● 0.8 0.8 ● 10 ● ●● ● ● Recombination rate (cM/Mb) ● Recombination rate (cM/Mb) ● Recombination rate (cM/Mb) ●● ● ● 0.6 0.6 ● ●● ●● ● 0.6 80 15 0.6 ●●●● ● ●● ● ● ●● 80 ● ● ● ●● 80 ●● ● ●●● ● ● ● ●● ●● ●●●●● ● ●●● ●● ●●●● ●●● ●●●● ●●● ●●●● ● ● ●●● ●● 0.4 80 ● 0.4 0.4 ●● 0.4 Recombination rate (cM/Mb) 15 15 ● ●● ● ● ● 8 ● ● ● ● ● ● ● ●● ● 0.2 0.2 ● ● ●● 0.2 0.2 ● ●●● ● ●● ● ● ● ● ●● ● ●●● ●●● ● ●●●●●●●● ● ●●● ● ●● ● ●●● ●●●● ●●●●● ●●●●●●● ● ● ● 60 ●●●●● ●●●● ● ●● ●●● 6060 ●● ●● ● ● ● ●● ●●●●● ● ● ● value) value) ●● ● value) ● ● − ● ● − ● 60 ●●● ● − ● ●● ●●●●● ● ● ●●●● 6 ●●●●●● ● ● (p ● (p ●● ●●● ● ●●●● 10 (p ●● ● 10 ● 10 ● ●● ● value) 10 ● ● ● 10 ● ● ● − ● ● 10 ● ● ● g ● ● ●● ●●●●●●●●●● ●●●● g ● ● ● ● ●● ● ●● ● ● g ● o ● ● ● o ●● ● ●● 40 ●● ● ● ●●● ●●●● 40 (p l ●●●● ● ● ● ● ●● l ● ●● ● ●● ●●●●●●● ●●● ●●●● ● ● ● ● ●● o ● ● ●●● ● ●● 40 ● ● l ●●●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● 10 ● - ● ● - ● ● ● ● ● ● ● ● ●● ●●●●● ●● ● ●● ●● ● 4 ●●●● ● ● g ● ● - 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PRKRA TATDN1LOC101927027 TTN SQLE FAM45BP TIAL1 MCMBP

FKBP7 MIR6844 PRKRA WASHC5 SFXN4 GRK5 MIR4682

PLEKHA3 NDUFB9 FKBP7 PRDX3

PLEKHA3 179.2 179.4 179.6 179.8 180 125.4 125.6 125.8 126 126.2 126.4 121 121.2 121.4 121.6 121.8 Position on chr2 (Mb) Position on chr8 (Mb) Position on chr10 (Mb) 179.2 179.4 179.6 179.8 180 Position on chr2 (Mb) LVEF

2 100 2 100 2 100 rs945425 r r rs2042995 r rs34866937 10 ● 12 ●● Recombination rate (cM/Mb) ● ● ●●● 0.8 0.8 10 0.8 ●● ●●●●● ●●● ●●● 80 ●● ●● ●● 0.6 0.6 ● ● ● Recombination rate (cM/Mb) 0.6 ● ● Recombination rate (cM/Mb) ●● 10 ● ● ●● ● 80 ●● 80 8 ● ● ●● ●● ● ● ●●●●●● ●●● ●● ● ●● ●● ●●●●● ●● ● ● ●● ● 0.4 0.4 ● ● ●●● ● 0.4 ● ●● ●●●●●●●●● ●●●● 8 ● ● ●●● ●●● ●●● ●●●●●●●●●● ● ●● ●● ●● ● 0.2 60 0.2 ● ● 0.2 value) ● ● ● ●●●●● ● ●● ● ● ●● − 6 ●●●●●●●●●● ● 8 ●●● ●●●●● ●● ● ●●● 60 60 (p ● ● ●● ●● ● ● value) ●●● ● ● ● value) 10 ● ● ● ●● ● ● ● − ●● ● ● − 6 g ● ● ● ●● ● ● 40 ● ● ●●● ●

o ● ● (p ● ● (p l ● ● ● ● ● ● 4 ●● ●●●● ● ● ● 6 ●●●●●●●● ●● ●● ● 10 ● 10

- ● ● ● ●●●●●●● ●●● ●● ●● ●●● ● ● ● ● g ● ● g ● ● ● ● ●●●●●●●●●●●●● ● ● ●●●●● ● ●● ● o ● ● ●●● 40 o ● 40 ● ● l ● ●● ●●● l ● ●● ● ●●●● ● ● ● ● ●● ●● ● ● ●●●● ●●● ●●● ● ● - ● ● - 4 ● ● ● ●●● ● ● ●●●● ●●●●● 20 ● ●●● ● ● ● 2 ● ● ●●●●● ●●● ●●●●●●●● ●●●●● ● ● ● 4 ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●●●●●● ● ● ● ● ● ● ●●●● ● ● ● ●● ●●●●● ●●●● ●●● ● ●● ●● ●● ● ● ●● ● ● ●●●●●●●●●●●●●● ●● ● ●●●●● ●● ● ●●● ●●● ● ●●●●●●●●● ●●● ●● ●●●●● ●●●●●●●●●●●● ●● ●●●●●●●●●●●●●●●●●●●●●●● ● ●●●●●● ●● ●● ●●●●●●●●●●●●●● ●● ●●●●●●●●●● ● ●● ●● ●● ●●● ●●● ● ● ●● ●●●● ●●●●● ● ● ●●●●●● ●● ●●●●●●● ●● ●●●●● ● ●●●● ●●●●●●●●●●●●●● ●●●●●● ●● ● ●●●●●● ● ●●●●●● ● ● ●● ●●● ●●● ●●●●●●●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●● ● ● ● ● ●● ● ●●●● ●●●● ● ●●● ● ●●●● ●●●●● ● ●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●● ●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●● ● ● ● ●●● ●●● ●●● ● ● ●●● ● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ● ● ●● ● ● ●●●● ●●● ●●●● ● ● ● 20 ●● ● ●●●●●●●● 20 0 ●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●● ●●●●●●●●●●●●●● ●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●● 0 ● ● ● ● ● ● ●●●● ●●●●● ●●●●●●● ●●● ● ●●●●●●●●●●●● ● 2 ● ● ●●●●●●●●●●●●● ● ● 2 ● ● ● ● ● ● ● ● ●●● ●● ●●●●● ●●●●●●●●●●● ●●● ● ●●●●●● ●●●●● ●● ●● ●● ●●●●●●●●● ● ● ●●●● ● ●● ● ●●●●●●●●● ● ●●●●●●●●●●●●● ●● ● ●●● ● ● ● ● ● ●●● ● ●● ● ● ● ●●●●●●●●●●●●●●● ● ●●●●●●● ● ● ●●●●●●●● ●● ● ●●●● ●●●●●● ● ●●● ●●●●● ●●●●● ● ●●● ●● ●●●● ● ●● ●● ●●● ●●●●●●●●●● ● ●● ●●●●●●●●●●● ●●● ● ● ● ●●●●●●●●● ● ●●● ●●●●●●● ● ● ●●●●● ●● ●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●● ●● ●●●●●● ●●●●●●●●●●●●● ●●●●● ●●●●●●●●●●●● ●●●●●●●●●● ●●● ●● ● ●●●● ● ● ●●●● CELA2A DDI2 TMEM82 SPEN SRARP EPHA2 FBXO42 SPATA21 ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ● ● ●●● ● ● ● ●●●●●●●●● ●●● ●●●●● ●●●●●● ●●●●●●●●●●●●●●●●●●●●●●● ●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●● ●●● ● ●● ●● ●●● ●●●●● ●●●●●●●● ●●●●●●● ●●●●● ●●●● ● ●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ● ● ● ● ● ● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●● ●●●●●● ●●● ●● ●● ●●●●●●●●●●●●●●●●●●●●●● ●● ●●●●●●●●●● ●●●●● ●●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●● ●●● ●● ●●● ●● ●●●●●●●● ●● ●●●●● ●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●● ●●●●●●●●●●● ●●●● ● ● ● ●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ●●●●●●●●● ●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●● ●●●●●● ●●●●●●●●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ●● ● ● ● ●●●● ●● ●●●●●●●●●●●●●●●●●●●●● ● ●●●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● CELA2B RSC1A1 UQCRHL ZBTB17 ARHGEF19 SZRD1 LINC01783 0 0 0 0

CASP9 PLEKHM2 FLJ37453 HSPB7 CPLANE2 NECAP2 OSBPL6 PJVK TTN−AS1 LOC101927055 SESTD1 TMEM65 RNF139 MTSS1 MIR4662B ZNF572 NSMCE2

DNAJC16 SLC25A34 CLCNKA LINC01772 MIR548N CCDC141 TRMT12 MIR4662A LOC105375744

SCARNA21B SLC25A34−AS1 CLCNKB CROCCP3 LOC101927027 TTN RNF139−AS1 LINC00964

AGMAT FBLIM1 FAM131C LOC440570 PRKRA TATDN1 SQLE

MIR3675 FKBP7 MIR6844 WASHC5

NBPF1 PLEKHA3 NDUFB9

15.8 16 16.2 16.4 16.6 16.8 179.2 179.4 179.6 179.8 180 125.4 125.6 125.8 126 126.2 126.4 Position on chr1 (Mb) Position on chr2 (Mb) Position on chr8 (Mb)

r2 100 15 rs72840788 ●● 0.8 ●● ●● 0.6 ●● 80

0.4 Recombination rate (cM/Mb) 0.2

10 ● ● 60 value) −

(p ● 10

g ● ● o ●● ● l ● ●●●●●●●●●●●●●●●●●●●●●● 40 - ●● ●●● ● ●●●● ● ● ● ● ●● ● 5 ●● ● ● ●●●●● ● ●●●● ● ● ●●● ●● ●●●●●●●●● ●●● ●●●●●●● ●● ● ● ●● ●●●● ● ● ● ●●●●●●●● ●● ●● ● ● ● ●● ●● ●● ●● ●●●●●●● ●● ● ● ● 20 ● ● ● ●●●●●●●●● ● ● ●● ● ●●● ●● ● ● ●● ● ● ● ● ●● ●●●● ● ●● ●●●●●●●● ● ● ●●●●●●● ●● ● ● ●● ● ●●●●●●●●●●●●● ● ● ● ●●●●● ● ●●●●●●●●●●●● ● ● ●●●●● ●●● ●● ●●● ●●●●●●●● ●●●●●●●●●●● ● ● ● ●●●●●●●●● ● ● ● ●●● ●●●● ●●●●● ●●● ●●● ●●●●●●●●●●●●●●●●●● ●●●●●●●● ● ●● ●● ● ● ●●● ● ● ●●●●● ● ● ●● ●●●●●●●●●● ●●● ●●●●●●●●●●●●●●●● ●●●●●●●●●●●● ● ● ● ●●● ●●●●●●●●●● ●●●●● ● ● ●●●●●● ●●●●●●●●●●●●●●●●●● ●●●●●●●●● ●●●●● ●●●●●●● ●●●●●●●● ●●●●●●● ●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●● ● ●●●●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●● ●●●●●●●●●● ●●●● ● ●●●●●●●●●●●●●●●● ●● ●●● ●●● ●●●●●●●●● ●● ●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● 0 ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● 0

FAM45A MIR4681 RGS10 BAG3 INPP5F SEC23IP

FAM45BP TIAL1 MCMBP

SFXN4 GRK5 MIR4682

PRDX3

121 121.2 121.4 121.6 121.8 Position on chr10 (Mb) LVM

15 2 100 rs2255167 r ● ● ● ● ● 0.8 ● ● ● ● ● ●● ●● ● Recombination rate (cM/Mb) ●●●● ●● ● ● 0.6 80 ● ●●●●●● ●●●●● ● ● ● ● ● ● ● ●●● ●● ●● ● ●● ●●● ● 0.4 ● ● ●●●●●●● ● 10 ●● ● ● ●●●●●●●●●● ● ●● 0.2 ●●● ● ●●●● ●●●● ●●●● 60 ●● ●● ● value) ●● ● ● ● ●● − ● ● ●●● ● ●● (p ●●● ●● ● ● ● ● ●●● 10 ● ● ●● ●●●●●●●●●● ●● ● ● ● ●● g ●● ● ●●●●●●●● o ●● ● ● ●● 40 l ● ● ●● ● ●●●●● ● ●●●● ● ●● - ●● ● ● ● 5 ● ● ●● ●● ● ● ● ●● ● ● ●● ● ●●● ● ● ● ●●● ●●● ●●● ●●●●●● ● ●● ●●●●●● ● ● 20 ●● ● ●● ● ● ● ●● ● ●●● ● ●● ●●●●●●●● ●●● ●● ●●● ● ● ●● ●●● ●● ●●● ● ● ● ●●●●●●●●● ● ● ● ● ● ●● ●●●●● ●●●●●● ●● ●●●●●● ● ● ●● ●●●● ●●●●●●●●●●●●●●● ●●●●●● ●●●●●●●● ● ● ● ● ● ●●●●● ●●● ● ● ● ●●● ●● ●●●●●● ●●●●●●●●●●●●●●● ●●●●●●●●●●●● ●● ●●● ●●● ●●●● ● ● ●● ● ● ●●● ● ●● ● ●●●● ● ● ●●●●●●●●●●● ●●●● ●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●● ●●●●● ●● ●●● ● ●●●●● ● ●●●●●●●● ● ● ●● ● ● ● ● ●●●●●● ●●● ●●● ●●●●● ●● ● ●●●● ● ●● ● ●●●● ●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●● ●● ● ● ● ●●● ●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●● ● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● 0 ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ●●● ● ● ● ●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● 0

OSBPL6 PJVK TTN−AS1 LOC101927055 SESTD1

MIR548N CCDC141

LOC101927027 TTN

PRKRA

FKBP7

PLEKHA3

179.2 179.4 179.6 179.8 180 Position on chr2 (Mb)

LVMVR

12 12 2 100 r2 2 100 100 2 100 r 20 rs149369954rs2042995 r 15 rs6003909 r ● Recombination rate (cM/Mb) rs146170154 ● ● ●● ● ● ●● ● 0.8 0.8 10 ●● ●●●●●● 0.8 ●●●● 0.8 ●●●●●●●●●●●●●●●●●●● Recombination rate (cM/Mb) ●● 80 10 ● ●●●● ●●●●● ● ● Recombination rate (cM/Mb) ● ●●●●●●●●●●●●●●● ●●●● ● 0.6 80 ● 0.6 ● ● ● ●●●●●●●●●●●●●●●●●● ● ● 0.6 80 ●●● 0.6 ● ●●●●●●●●● ●●● ●●● ●●● ●●●●●●● 80 Recombination rate (cM/Mb) ● ● ● ● ●●●● ●● 15 ● ● ● ● 0.4 10 ● ●●●●●● 0.4 ●●●●●●●●● ●●● ● 0.4 ●●●●●●● 0.4 8 ● ●●●● ● ● ● ● ● ●●●● 60 ● ● ●● ● 0.2 value) ●● ● ●●● ● ●●● ● ● ● 8 0.2 ● ●●●●●●●●● ●●● ● ●●●● ●●●● 0.2 − 0.2 ●●●●● ●●●●●●●●● ● ●● ● ● ● 60 60 ●●●●● ●● ● ● ●●● (p ● value) value) ● ● ● ● ● ●●● ● ●● 10 − ● − ● ● ● ● ● ● 6 ●● ● ● g 40 ● 60 ●●●●●●●●● ● ● ● ● ●●● ● ● ● ● o (p ● (p ● ● ●●●●●● 10 ● ●● l ● value) ●●● ● ● ● ● 10 ● 5 ● 10 ● ● ●● ● ● - ● − ● ● ● ● ● g ● ●● g ●● ● 6 ● ● ● ● ● ● ●●●●● ●●●●●●●●●●●● ●●●●●● ●● ● o ● ● ●● ● ● ● ● 40 40 ● o ● ● ●● (p l ● ● ●●●●● ● ● ● ● ● ● l ● ● ●● ● ●● ●●●● ●● ●●●●●●●● ●●●●●●●●●●●●●●● ● ●● ●●● ●●●● ●●● ●●●●●●●●●●●●●●●● - ●●●●●● ●●●●●● ●●●●●●● ● ● ● ● ● ● ●●●●●● 20 10 ● ● ●● ●●●●●●●●●●● ●● ●● ● ● ● ● ●●●●●● - ● ●● ● 4 ● ●●●●●●● ● ●●● ● ● ● ● ● ● ●● ● ●●● ● ●●●●●●●● ● g ● ● ● ● ●● ●●●● ● ● ●● ● ● ● ● ● ●●● ●● ●● ●● ●●●●● ● ● ●●● ● ●●●● ● ● ●● ●● ● ●●● ●●●● ● o ● ● ●●●● ● ● ● ● ● ● ● ● ●● ●● ●●●●● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ●●●● l 40 ● ● ●●● ●●● ●● ● ●●●●●●● ●●● ●●●●●●●●● ●●●●●●● ● ● ● ● ● ●●●● ● ●●●●● ●●●● ●●●● ●●●●●●●●●●●●●●● ●●●●● ●●●●●●●● ●●●● ● ●●●●●● ●● ● ● ● ● ● ● ● ●●●● ●●●●● ●●● ● ● ●●●●●●● ● ●●● ● ●● ●●●●●●●●●●●●●●● ● ●● ●●●●●●●●●●●●●●●●●●●●● ●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● - ● ● ●●●●●● ● ● ●● ●●● ● ●●●●●●● ●● ● ● ● ● ●● ● ● 5 ● ● ● ●●●●●● ● ● ●●● ● ●● ●●●● ●●●●●● ● ●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●● ●●●●● ●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● 4 ● ● ● ● ●●●●● ● ●●● ●●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ● ● ● ● ●● ● ● ●● ●●●●●● ● ● ● ●20 20 0 ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ●●●● ● ● ●● ●●●●●●●● ● ● 0 ●●● 2 ● ● ●●● ●●● ● ●● ● ●● ●● ● ● ●● ● ●●●●●●●●●● ● ● ●●● ● ●●●●●●●●●● ●●●●● ●●●●●●●●●●●●●●● ● ● ●● ● ●● ● ●●●●●●●●●● ●● ● ● ●● ●●●●● ●● ●●●●●●●●●● ●●●●●●●●●●●●●● ●●●●● ●●● ●●● ●●● ● ●●●●● ●●●●●●●●●●●●● ●●●●●●● ●● ● ● ● ● ● ● ●● ●●●●●● ● ● ● ●●●●●● ● ●● ● ●● ●● ●●●●●●●●●●●●●●●●● ● ● ●●●●●●●● ●●●● ●● ● ● ●●●●● ●● ● ● ●●● ●●●●● ●●● ● ●● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●● ●●● ●●●● ● ●●●●●●●●●● ●●●●●● BCR LINC01659 IGLL1 RGL4 SMARCB1 GSTT2B CABIN1 GGT5 ● ● ● ● ● ●● ● ●●●●●●●●●●● ●●●●●● ● ● ●●● ●●●●●●●●●●●●●●●●●●●● ●●●●● ● ● ●●●●●●●●●●● ●●●●●●●●●●●● ●●●● ●●●●●●●●●● ●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ●●● ●● ● ●● ● 20 ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●● ●●● ●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ●● ● ● ●● ●●● ●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● 2 ●● ●●●●●●●●● ● ●●●● ● ● ● ●●● ●●● ●● 0 ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● 0 ●● ●● ●●● ●●●●●●●● ●●●●● ● ● ●● ●●●● ● ● ● ●●● ●●●● ● ●● 0 ●● ● ● ● ● ●● ● ● ●●●●●●●●●●●●●● ● ● ●● 0 LINC02556 DRICH1 ZNF70 SLC2A11 DDT SUSD2 ●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●● ●●●●● ●● ● ● ●●●●●●●●●● ●●●●● ● ● ● ● ●●●●●●●●●●●●●●●●●●●●●● ●●●●●●● ●●●●●●● ● ● ● ●●●●●● ●●●●●● ●●●●●●●● ●●● ● ● ●●● ●●● ●●●●●●●● ● ● ● ●●● ●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●● ●●●●●●●●●●●●●●●●●● ● ●●●●●●●●●● ●●●● ●●●●● ●● ●●●●●●●●●● ● ●●● ● ●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●● ●●●●●●●● ●●●●●●●●●●● UBE2Q2L DNM1P41 SCAND2P ZNF592 SLC28A1 GOLGA6L3 CES5AP1 GUSBP11 DERL3 GSTT2 POM121L9P ●●●●●●●●●●●●● ●●●●●●●●●●●●●●●● ●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●● ● ●●●●●●●● ●●●●●●●●● ●●●●●●●● ●●●●● ●●●●●●●●● OSBPL6 PJVK TTN−AS1 LOC101927055 SESTD1 ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●● ●●●●● ●●●●●● ●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●● ● ●●●●●●●●●●●●●●●●● ●●●●●●●● 0 ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ●●● ●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●● 0 LOC440300 GOLGA6L5PMIR548NWDR73 ALPK3 CCDC141PDE8A ZDHHC8P1 VPREB3 MIF−AS1 GSTT4 SPECC1L

MAPK13 C6orf222 KCTD20 SRSF3 RAB44 PPIL1 PI16 PIM1 GOLGA2P7 LOC101927027UBE2Q2P1 SEC11A TTN LINC01658 C22orf15 MIF LOC391322 SPECC1L−ADORA2A

BRPF3 ETV7 STK38 PANDAR CPNE5 C6orf89 FGD2 TMEM217 PRKRA LOC642423 CHCHD10 DDTL

PNPLA1 PXT1 MIR3925 MTCH1 GOLGA6L4 LINC00933FKBP7 MMP11 GSTT1−AS1

CDKN1A LOC103171574 PLEKHA3ZSCAN2 GSTT1

DINOL LOC102724034 NMB GSTTP2 179.2 179.4 179.6 179.8 180 Position on chr2 (Mb) 36.2 36.4 36.6 36.8 37 84.8 85 85.2 85.4 85.6 85.8 23.8 24 24.2 24.4 24.6 Position on chr6 (Mb) Position on chr15 (Mb) Position on chr22 (Mb) LVEDV

MIR548N CCDC141 EIF3A GRK5 TIAL1 MCMBP CCDC63 CUX2 SH2B3 ATXN2−AS ALDH2 ADAM1A

LOC101927027 TTN SNORA19 MIR4682 MYL2 MIR6760 ATXN2 BRAP MIR6761

PRKRA FAM45A LINC01405 PHETA1 ACAD10 MAPKAPK5

FKBP7 FAM45BP LOC105369980 MAPKAPK5−AS1

PLEKHA3 SFXN4 TMEM116

179.2 179.4 179.6 179.8 180 120.8 121 121.2 121.4 121.6 121.8 111.4 111.6 111.8 112 112.2 112.4 Position on chr2 (Mb) Position on chr10 (Mb) Position on chr12 (Mb)

LVESV

PRKRA TATDN1LOC101927027 TTN SQLE FAM45BP TIAL1 MCMBP

FKBP7 MIR6844 PRKRA WASHC5 SFXN4 GRK5 MIR4682

PLEKHA3 NDUFB9 FKBP7 PRDX3

o ● ● (p ● ● (p l ● ● ● ● ● ● 4 ●● ●●●● ● ● ● 6 ●●●●●●●● ●● ●● ● 10 ● 10

CASP9 PLEKHM2 FLJ37453 HSPB7 CPLANE2 NECAP2 OSBPL6 PJVK TTN−AS1 LOC101927055 SESTD1 TMEM65 RNF139 MTSS1 MIR4662B ZNF572 NSMCE2

DNAJC16 SLC25A34 CLCNKA LINC01772 MIR548N CCDC141 TRMT12 MIR4662A LOC105375744

SCARNA21B SLC25A34−AS1 CLCNKB CROCCP3 LOC101927027 TTN RNF139−AS1 LINC00964

AGMAT FBLIM1 FAM131C LOC440570 PRKRA TATDN1 SQLE

MIR3675 FKBP7 MIR6844 WASHC5

NBPF1 PLEKHA3 NDUFB9

15.8 16 16.2 16.4 16.6 16.8 179.2 179.4 179.6 179.8 180 125.4 125.6 125.8 126 126.2 126.4 Position on chr1 (Mb) Position on chr2 (Mb) Position on chr8 (Mb)

r2 100 15 rs72840788 ●● 0.8 ●● ●● 0.6 ●● 80

0.4 Recombination rate (cM/Mb) 0.2

10 ● ● 60 value) −

(p ● 10

FAM45A MIR4681 RGS10 BAG3 INPP5F SEC23IP

FAM45BP TIAL1 MCMBP

SFXN4 GRK5 MIR4682

PRDX3

121 121.2 121.4 121.6 121.8 Position on chr10 (Mb) LVM

OSBPL6 PJVK TTN−AS1 LOC101927055 SESTD1

MIR548N CCDC141

LOC101927027 TTN

PRKRA

FKBP7

PLEKHA3

179.2 179.4 179.6 179.8 180 Position on chr2 (Mb)

LVMVR

MAPK13 C6orf222 KCTD20 SRSF3 RAB44 PPIL1 PI16 PIM1 GOLGA2P7 LOC101927027UBE2Q2P1 SEC11A TTN LINC01658 C22orf15 MIF LOC391322 SPECC1L−ADORA2A

BRPF3 ETV7 STK38 PANDAR CPNE5 C6orf89 FGD2 TMEM217 PRKRA LOC642423 CHCHD10 DDTL

PNPLA1 PXT1 MIR3925 MTCH1 GOLGA6L4 LINC00933FKBP7 MMP11 GSTT1−AS1

CDKN1A LOC103171574 PLEKHA3ZSCAN2 GSTT1

Supplemental Figure 8. Location of the sentinel variants and their proxies in LD r2 ³ 0.8

LD, linkage disequilibrium

Supplemental Figure 9. FORGE enrichment analysis

SNPs in DNase1 sites (probably TF sites) in cell lines for all_LV_variants_1E05 8 Skin Lung Blood

Breast ● ES Cell IPS cell Fibroblast Fetal Skin Fetal Fetal Lung Fetal Fetal Brain Fetal Fetal HeartFetal Fetal Testes Fetal Fetal Kidney Fetal Fetal Spleen Fetal Fetal Muscle Fetal Fetal Adrenal Fetal Fetal Thymus Fetal Fetal Placenta Fetal 6 ● Stomach Fetal Fetal Spinal Cord Fetal Fetal Renal Pelvis Fetal Fetal Renal Cortex Fetal Fetal Intestine, Small Intestine, Fetal Fetal Intestine, Large Intestine, Fetal

●

● ●●

● 4 ● ● P = 3.38

● log10 binomial P P = 2.68

− ● ● ● 2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● 0 ● ● ●●●●●●●●●●●● ● ● ●●● ● ● ●●●●●●●● ●●●● ●● ●●●● ●● ● ● ●●●● H1 H1 H9 H9 CD3 CD3 CD3 CD3 CD4 CD4 CD4 CD4 CD8 CD8 CD8 CD8 CD14 CD14 CD19 CD19 CD19 CD20 CD56 CD56 IMR90 IMR90 IMR90 IMR90 Fetal_Skin Fetal_Lung Fetal_Lung Fetal_Lung Fetal_Lung Fetal_Lung Fetal_Lung Fetal_Lung Fetal_Lung Fetal_Lung Fetal_Lung Fetal_Lung Fetal_Lung Fetal_Brain Fetal_Brain Fetal_Brain Fetal_Brain Fetal_Brain Fetal_Brain Fetal_Brain Fetal_Brain Fetal_Brain Fetal_Brain Fetal_Brain Fetal_Brain Fetal_Heart Fetal_Heart Fetal_Heart Fetal_Heart Fetal_Heart Fetal_Heart Fetal_Heart Fetal_Heart Fetal_Heart Fetal_Heart Fetal_Heart iPS_DF_4.7 iPS_DF_6.9 Fetal_Testes Fetal_Kidney Fetal_Kidney Fetal_Kidney Fetal_Kidney Fetal_Kidney Fetal_Kidney Fetal_Kidney Fetal_Spleen iPS_DF_19.7 Fetal_Thymus Fetal_Thymus Fetal_Thymus Fetal_Thymus Fetal_Thymus Fetal_Thymus Fetal_Thymus Fetal_Thymus Fetal_Thymus Fetal_Thymus iPS_DF_19.11 Fetal_Placenta Fetal_Placenta Fetal_Placenta Fetal_Placenta Fetal_Stomach Fetal_Stomach Fetal_Stomach Fetal_Stomach Fetal_Stomach Fetal_Stomach Fetal_Stomach Fetal_Stomach Fetal_Stomach Fetal_Stomach Fetal_Stomach CD4_Mobilised CD4_Mobilised CD8_Mobilised Breast_vHMEC Breast_vHMEC Fetal_Lung_Left Fetal_Lung_Left Fetal_Lung_Left Fetal_Lung_Left Fetal_Lung_Left Fetal_Lung_Left Fetal_Lung_Left Fetal_Lung_Left Fetal_Lung_Left Fetal_Lung_Left Fetal_Lung_Left Fetal_Lung_Left CD34_Mobilised CD34_Mobilised CD34_Mobilised CD34_Mobilised CD34_Mobilised CD34_Mobilised CD34_Mobilised CD34_Mobilised CD34_Mobilised CD34_Mobilised CD34_Mobilised CD34_Mobilised CD34_Mobilised CD34_Mobilised CD56_Mobilised Fetal_Lung_Right Fetal_Lung_Right Fetal_Lung_Right Fetal_Lung_Right Fetal_Lung_Right Fetal_Lung_Right Fetal_Lung_Right Fetal_Lung_Right Fetal_Lung_Right Fetal_Lung_Right Fetal_Kidney_Left Fetal_Kidney_Left Fetal_Kidney_Left Fetal_Kidney_Left Fetal_Kidney_Left Fetal_Muscle_Leg Fetal_Muscle_Leg Fetal_Muscle_Leg Fetal_Muscle_Leg Fetal_Muscle_Leg Fetal_Muscle_Leg Fetal_Muscle_Leg Fetal_Muscle_Leg Fetal_Muscle_Leg Fetal_Muscle_Leg Fetal_Muscle_Leg Fetal_Muscle_Leg Fetal_Spinal_Cord Fetal_Spinal_Cord Fetal_Spinal_Cord Fetal_Muscle_Arm Fetal_Muscle_Arm Fetal_Muscle_Arm Fetal_Muscle_Arm Fetal_Muscle_Arm Fetal_Muscle_Arm Fetal_Muscle_Arm Fetal_Muscle_Arm Fetal_Muscle_Arm Fetal_Muscle_Arm Fetal_Muscle_Arm Fetal_Muscle_Arm Fetal_Muscle_Arm Fetal_Muscle_Arm Fetal_Muscle_Arm Fetal_Muscle_Arm Fetal_Muscle_Arm Fetal_Renal_Pelvis Fetal_Renal_Pelvis Fetal_Renal_Pelvis Fetal_Renal_Pelvis Fetal_Renal_Pelvis Fetal_Renal_Pelvis Fetal_Renal_Pelvis Fetal_Kidney_Right Fetal_Kidney_Right Fetal_Kidney_Right Fetal_Kidney_Right Fetal_Kidney_Right Fetal_Muscle_Back Fetal_Muscle_Back Fetal_Muscle_Back Fetal_Muscle_Back Fetal_Muscle_Back Fetal_Muscle_Back Fetal_Muscle_Back Fetal_Muscle_Back Fetal_Muscle_Back Fetal_Muscle_Back Fetal_Muscle_Back Fetal_Muscle_Back Fetal_Muscle_Back Fetal_Renal_Cortex Fetal_Renal_Cortex Fetal_Renal_Cortex Fetal_Renal_Cortex Fetal_Renal_Cortex Fetal_Renal_Cortex Fetal_Renal_Cortex Fetal_Renal_Cortex Fetal_Muscle_Trunk Fetal_Muscle_Trunk Fetal_Muscle_Trunk Fetal_Adrenal_Gland Fetal_Adrenal_Gland Fetal_Adrenal_Gland Fetal_Intestine_Small Fetal_Intestine_Small Fetal_Intestine_Small Fetal_Intestine_Small Fetal_Intestine_Small Fetal_Intestine_Small Fetal_Intestine_Small Fetal_Intestine_Small Fetal_Intestine_Small Fetal_Intestine_Small Fetal_Intestine_Small Fetal_Intestine_Small Fetal_Intestine_Small Fetal_Intestine_Large Fetal_Intestine_Large Fetal_Intestine_Large Fetal_Intestine_Large Fetal_Intestine_Large Fetal_Intestine_Large Fetal_Intestine_Large Fetal_Intestine_Large Fetal_Intestine_Large Fetal_Intestine_Large Fetal_Intestine_Large Fetal_Intestine_Large Fetal_Intestine_Large Fetal_Intestine_Large Fetal_Intestine_Large Fetal_Renal_Pelvis_Left Fetal_Renal_Pelvis_Left Fetal_Renal_Pelvis_Left Fetal_Renal_Pelvis_Left Fetal_Renal_Cortex_Left Fetal_Renal_Cortex_Left Fetal_Renal_Cortex_Left Fetal_Renal_Cortex_Left Fetal_Renal_Pelvis_Right Fetal_Renal_Pelvis_Right Fetal_Renal_Pelvis_Right Fetal_Renal_Pelvis_Right Penis_Foreskin_Fibroblast Penis_Foreskin_Fibroblast Penis_Foreskin_Fibroblast Penis_Foreskin_Fibroblast Fetal_Renal_Cortex_Right Fetal_Renal_Cortex_Right Fetal_Renal_Cortex_Right Mesenchymal_Stem_Cells Mesenchymal_Stem_Cells Fetal_Muscle_Upper_Trunk Trophoblast_Cultured_Cells Trophoblast_Cultured_Cells

Cell Penis_Foreskin_Melanocyte Penis_Foreskin_Melanocyte Penis_Foreskin_Keratinocyte Penis_Foreskin_Keratinocyte Penis_Foreskin_Keratinocyte Penis_Foreskin_Keratinocyte Fibroblasts_Fetal_Skin_Back Fibroblasts_Fetal_Skin_Scalp Fibroblasts_Fetal_Skin_Scalp Mesendoderm_Cultured_Cells Mesendoderm_Cultured_Cells

Fibroblasts_Fetal_Skin_Abdomen Fibroblasts_Fetal_Skin_Abdomen Fibroblasts_Fetal_Skin_Biceps_Left Fibroblasts_Fetal_Skin_Biceps_Left Fetal_Muscle_Lower_Limb_Skeletal Fetal_Muscle_Upper_Limb_Skeletal Fibroblasts_Fetal_Skin_Upper_Back Fibroblasts_Fetal_Skin_Upper_Back Neuronal_Progenitor_Cultured_Cells Neuronal_Progenitor_Cultured_Cells Fibroblasts_Fetal_Skin_Biceps_Right Fibroblasts_Fetal_Skin_Biceps_Right Fibroblasts_Fetal_Skin_Quadriceps_Left Fibroblasts_Fetal_Skin_Quadriceps_Left Fibroblasts_Fetal_Skin_Quadriceps_Right Fibroblasts_Fetal_Skin_Quadriceps_Right Enrichment of variants associated with LV phenotypes (P < 1x10-5) was observed in the Roadmap DNase 1 regulatory regions of fetal heart. The x-axis represents the cell types within different tissues of the Roadmap epigenomics project. The dashed and solid pink lines represent the Bonferroni-corrected p values of 0.05 and 0.01 respectively.

Supplemental Figure 10. Cumulative incidence of heart failure stratified by LV-PRS quintiles

+ LVEDV GRS (Lowest 20%) + LVEDV GRS (Highest 20%) + LVESV GRS (Lowest 20%) + LVESV GRS (Highest 20%) 1.25% 1.25% HR = 1.18 (1.03 – 1.36), HR = 1.32 (1.15 – 1.51), +++++++++++++++++++++++++++++++++ +++++++++++++ +++++++++++++++++++++++++++++++ -5 ++ P = 0.02) +++++++++ P = 6.6 x 10 ) +++++++++ +++++++++ ++++++++ 1.00% +++++++++++ 1.00% ++++++ ++++++ ++++++ ++++++++ +++++++++++++++++++++++++++++++++++++++++++++ ++++++ ++++++++++++++++++++++++++++++++ ++++++++++ ++++++++++++++ ++++++++ ++++++++++++++++++++ ++++++ ++++++ ++++ ++++++++ Log−rank ++++ ++++++++ Log−rank ++++ ++++++++++ 0.75% ++++ ++++ 0.75% ++++++ ++++++++ p = 0.015 +++++ ++++++++ p < 0.0001 +++++ ++++++ ++++ ++++++++ ++++ ++++++++ ++++ ++++++ ++++ +++++ +++++++ ++++++ ++++++ ++++ ++++++ ++++++ ++++ ++++++ 0.50% +++++++++++++++ 0.50% ++++++++ ++++ ++++++++++++++ +++++ ++++++++++ +++++++++++++ ++++++++ ++++++ ++++++++++++ +++++ ++++++++ +++++++++++ ++++ +++++ ++++++++++++ +++++ +++++++++ 0.25% ++++++++++++++ 0.25% +++++++++++++++++ +++++++++ +++++++++++++++ +++++++++++++ +++++++++++++ +++++++++++++ ++++++++++++++++++++ Cumulative risk of HF (%) Cumulative ++++++++++++ risk of HF (%) Cumulative ++++++++++++ ++++++++++ ++++++++++ 0.00% +++++++++ 0.00% +++++++ 0 2 4 6 8 10 0 2 4 6 8 10 Follow−up duration (years) Follow−up duration (years) Number at risk Number at risk LVEDV GRS (Lowest 20%) 45277 45080 44655 42922 15139 30 LVESV GRS (Lowest 20%) 45292 45102 44698 42990 15226 41 LVEDV GRS (Highest 20%) 45226 45004 44598 42909 15316 39 LVESV GRS (Highest 20%) 45200 44968 44529 42832 15394 38 0 2 4 6 8 10 0 2 4 6 8 10 Follow−up duration (years) Follow−up duration (years)

+ LVEF GRS (Lowest 20%) + LVEF GRS (Highest 20%) + LVMVR GRS (Lowest 20%) + LVMVR GRS (Highest 20%) 1.25% HR = 0.79 (0.69 – 0.90), 1.25% HR = 0.76 (0.66 – 0.88), +++++++++++++++++++++++++++++++++++ -4 ++++++++ -4 +++++++++++++++++++++++++++++++++++ P = 5.4 x 10 ) ++++++ P = 1.6 x 10 ) +++++++++ ++++++++ ++++++++++ 1.00% +++++++ 1.00% +++++++ ++++++++ ++++++ ++++ ++++++++++++++++++++++++++++++++++++++++ ++++++++++ ++++++ ++++++++++++++++ +++++++ ++++++++ ++++++++++ ++++++ +++++++++++++++++++++++++++++++++++++++ Log−rank ++++ ++++++ Log−rank ++++++ +++++++++++++++++ 0.75% ++++++ ++++++++ 0.75% ++++++ +++++++++++++ p = 0.0021 +++++ ++++++ p < 0.0001 ++++++ ++++++ ++++++ ++++++ ++++ +++++++++ ++++++ ++++++ ++++ ++++++++ ++++++++ ++++++++++ +++++++ ++++++ ++++++++ ++++ ++++ ++++++ 0.50% ++++++ ++++++ 0.50% ++++++++ +++++++ ++++++ ++++++++++ +++++++ +++++++++ +++++ ++++++++ +++++ ++++++++ ++++ +++++++ +++++ ++++++++ ++++++ ++++++ +++++ +++++++++ +++++++ +++++++ ++++++ ++++++ 0.25% ++++++ +++++++ 0.25% +++++++++++++++ +++++++++++++++ ++++++++++++++ ++++++++++++++++ +++++++++++++ ++++++++++++++++++ ++++++++++++ Cumulative risk of HF (%) Cumulative +++++++ risk of HF (%) Cumulative +++++++++ +++++++ +++++++++++++++ 0.00% +++++ 0.00% ++++++++ 0 2 4 6 8 10 0 2 4 6 8 10 Follow−up duration (years) Follow−up duration (years) Number at risk Number at risk LVEF GRS (Lowest 20%) 45220 44989 44540 42838 15331 32 LVMVR GRS (Lowest 20%) 45246 45026 44628 42921 15319 37 LVEF GRS (Highest 20%) 45270 45083 44684 43012 15146 42 LVMVR GRS (Highest 20%) 45264 45060 44638 42937 15202 33 0 2 4 6 8 10 0 2 4 6 8 10 Follow−up duration (years) Follow−up duration (years)

The hazard ratios compared PRS quintile 5 (Highest 20%) vs quintile 1 (Lowest 20%) and were obtained from the multivariate Cox regression models adjusted for age, sex, BMI, SBP and DBP corrected for anti-hypertensive medication use, smoking status, regular alcohol use, dyslipidaemia, diabetes and 15 PCs. There was no significance difference in the incidence of heart failure between the top and bottom quintiles of LVM-PRS (HR = 0.99 [0.87 – 1.13], P = 0.87). LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVEF, left ventricular ejection fraction; LVM, left ventricular mass; LVMVR, left ventricular mass to end-diastolic volume ratio; PRS, polygenic risk score; HR, hazard ratio

Supplemental table titles and legends

Supplementary Table 1. Cohort characteristics of the UK Biobank

Numbers are mean (standard deviation) or total number (%). *Systolic and diastolic blood pressure are adjusted for anti-hypertensive medication use by adding 15mmHg and 10mmHg, respectively. CMR, cardiovascular magnetic resonance; LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVSV, left ventricular stroke volume; LVEF, left ventricular ejection fraction; LVM, left ventricular mass; LVMVR, left ventricular mass to end-diastolic volume ratio

Supplementary Table 2. Conditional analysis in GCTA

Pre-specified criteria for secondary signal: (i) the original p value of newly identified variant was lower than 1 × 10−6; (ii) there was less than a 1.5-fold difference between the lead SNP and secondary association p values on a –log10 scale, i.e., if –log10(p lead)/-log10(p sec) < 1.5; and (iii) if there was less than a 1.5-fold difference between the main association and conditional association p values on a –log10 scale, i.e., if –log10(p sec)/-log10(p cond) < 1.5. GCTA, genome-wide complex trait analysis tool; UKBB, UK Biobank; LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVEF, left ventricular ejection fraction; LVM, left ventricular mass; LVMVR, left ventricular mass to end-diastolic volume ratio; CHR, chromosome; EA, effect allele; EAF, effect allele frequency; SE, standard error

Supplementary Table 3. Comparison between primary analysis and secondary analysis with additional adjustment for other cardiovascular risk factors

Secondary analyses were additionally controlled for diastolic blood pressure adjusted for anti- hypertensive medication use, body mass index (instead of weight in the primary model), smoking status, regular alcohol use, dyslipidaemia and diabetes. LVEDV, left ventricular end- diastolic volume; LVESV, left ventricular end-systolic volume; LVEF, left ventricular ejection fraction; LVM, left ventricular mass; LVMVR, left ventricular mass to end-diastolic volume ratio; CHR, chromosome; EA, effect allele; NEA, non-effect allele; EAF, effect allele frequency; SE, standard error

Supplementary Table 4. Comparison between primary analysis and analysis with untransformed LV phenotypes

LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVEF, left ventricular ejection fraction; LVM, left ventricular mass; LVMVR, left ventricular mass to end-diastolic volume ratio; CHR, chromosome; EA, effect allele; NEA, non-effect allele; EAF, effect allele frequency; SE, standard error

Supplementary Table 5. Interaction analysis of lead variants and systolic blood pressure

Systolic blood pressure was centered and scaled to standard deviation. LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVEF, left ventricular ejection fraction; LVM, left ventricular mass; LVMVR, left ventricular mass to end-diastolic volume ratio; CHR, chromosome; EA, effect allele; NEA, non-effect allele; EAF, effect allelic frequency; SE, standard error; SNP, single nucleotide polymorphism; SBP, systolic blood pressure

Supplementary Table 6. Interaction analysis of lead variants and sex

The reference sex was female. The bonferroni significant interaction effects are highlighted in green. LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVEF, left ventricular ejection fraction; LVM, left ventricular mass; LVMVR, left ventricular mass to end-diastolic volume ratio; CHR, chromosome; EA, effect allele; NEA, non-effect allele; EAF, effect allele frequency; SE, standard error; SNP, single nucleotide polymorphism

Supplementary Table 7. Cohort characteristics of Multi-ethnic Study of Atherosclerosis (MESA)

Numbers are mean (standard deviation) or total number (%). SBP, systolic blood pressure; LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVSV, left ventricular stroke volume; LVEF, left ventricular ejection fraction; LVM, left ventricular mass; LVMVR, left ventricular mass to end-diastolic volume ratio

Supplementary Table 8. Lookup of the genome-wide significant UK Biobank variants in Multi-ethnic Study of Atherosclerosis (MESA)

Bonferoni-signfiicant variants (P < 0.0036 [0.05/14 for multiple testing of 14 variant-trait pairs]) are highlighted in green and nominally-significant variants (P < 0.05 with concordant direction of effect) are highlighted in yellow. UKBB, UK Biobank; LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVEF, left ventricular ejection fraction; LVM, left ventricular mass; LVMVR, left ventricular mass to end-diastolic volume ratio; CHR, chromosome; EA, effect allele; NEA, non-effect allele; EAF, effect allele frequency; SE standard error

Supplementary Table 9. Lookup of previously reported variants for ECHO traits in the UK Biobank

Variants reaching the conventional genome-wide significance level (P < 5E-08) are highlighted in green and nominally-significant variants (P < 0.05 with concordant direction of effect) are highlighted in yellow. *SH2B3 locus; †MTSS1 locus; UKBB, UK Biobank; LVEDV, left ventricular end-diastolic diameter, LVEDD; left ventricular end-systolic diameter, LVESD; left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVEF, left ventricular ejection fraction; LVM, left ventricular mass; PMID, PubMed ID; CHR, chromosome; EA, effect allele; NEA, non-effect allele; EAF, effect allele frequency; SE, standard error

Supplementary Table 10. Lookup of previously reported variants for LVH identified by ECG in the UK Biobank LVM GWAS

Nominally-significant variants (P < 0.05 with concordant direction of effect) are highlighted in yellow. LVM, left ventricular mass; ECG-LVH, left ventricular hypertrophy identified by electrocardiogram; CHR, chromosome; EA, effect allele; NEA, non-effect allele; EAF, effect allele frequency; SE, standard error Supplementary Table 11. Lookup of the UK Biobank sentinel variants and their proxies (LD r2 >= 0.8) in Phenoscanner v2

Only associations at p < 5x10-8 are shown in the table. Variants associated with relevant cardiovascular traits/diseases are highlighted in green. LD, linkage disequilibrium; UKBB, UK Biobank; CMR, cardiovascular magnetic resonance; LVEDV, left ventricular end- diastolic volume; LVESV, left ventricular end-systolic volume; LVEF, left ventricular ejection fraction; LVMVR, left ventricular mass to end-diastolic volume ratio; EA, effect allele; NEA, non-effect allele; SE, standard error; OR, odds ratio; IVNT, rank-based inverse normal transformation

Supplementary Table 12. Lookup of the UK Biobank sentinel variants and their proxies (LD r2 >= 0.8) in Gene Atlas dataset

Only associations at p < 5x10-8 are shown in the table. Variants associated with relevant cardiovascular traits/diseases are highlighted in green. UKBB, UK Biobank; LD, linkage disequilibrium; CMR, cardiovascular magnetic resonance; LVEDV, left ventricular end- diastolic volume; LVESV, left ventricular end-systolic volume; LVEF, left ventricular ejection fraction; SNP, single nucleotide polymorphism; EA, effect allele; OR, odds ratio; MAF, minor allele frequency

Supplementary Table 13. Lookup of the UK Biobank sentinel variants and their proxies (LD r2 >= 0.8) in GTEx dataset

Variants significantly associated with gene expression in cardiovascular tissue (at FDR < 0.05) are highlighted in green. FDR, false discovery rate; UKBB, UK Biobank; LD, linkage disequilibrium; CMR, cardiovascular magnetic resonance; LVEDV, left ventricular end- diastolic volume; LVESV, left ventricular end-systolic volume; LVEF, left ventricular ejection fraction; LVM, left ventricular mass; LVMVR, left ventricular mass to end-diastolic volume ratio

Supplementary Table 14. Long-range chromatin interaction analysis

*Lead variant for LVM is rs2255167. UKBB, UK biobank; CMR, cardiovascular magnetic resonance; LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end- systolic volume; LVEF, left ventricular ejection fraction; LVM, left ventricular mass; LVMVR, left ventricular mass to end-diastolic volume ratio; CHR, chromosome

Supplementary Table 15. Summary of variant-level annotations

*Lead variant in TTN locus for LVM; †Lead variant in TTN locus for LVEDV, LVESV, LVEF; ‡Lead variant in MTSS1 locus for LVEF; §Lead variant in MTSS1 locus for LVESV. CMR, cardiovascular magnetic resonance; LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVEF, left ventricular ejection fraction; LVM, left ventricular mass; LVMVR, left ventricular mass to end-diastolic volume ratio; CHR, chromosome; EA, effect allele; NEA, non-effect allele; EAF, effect allele frequency; AA, amino acid

Supplementary Table 16. Summary of gene-level annotations

*Lead variant for LVEDV is rs7071853. †Lead variant for LVEF is rs34866937. ‡Lead variant for LVM is rs2255167. UKBB, UK Biobank; CMR, cardiovascular magnetic resonance; LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVEF, left ventricular ejection fraction; LVM, left ventricular mass; LVMVR, left ventricular mass to end-diastolic volume ratio; CHR, chromosome; CV, cardiovascular

Supplementary Table 17. GeneNetwork pathway analyses

GO, gene ontology

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