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Supplemental Materials – Titin loss of function variants in early-onset AF
Supplementary Online Content
Choi SH, Weng LC, Roselli C, et al. Association between titin loss-of-function variants and early-
onset atrial fibrillation. JAMA. doi: 10.1001/jama.2018.18179
eAppendix 1. Detailed Description of Participating Studies That Provided Atrial Fibrillation Cases
eAppendix 2. Whole-Genome Sequencing and Data Processing Methods
eAppendix 3. TTN LOF Variants Identified in Early-Onset AF Cases and Controls Compared to
Previously Identified TTN Variants in Other Cardiovascular and Medical Conditions
eAppendix 4. Acknowledgments
eAppendix 5. Investigators in the TOPMed Program
Supplementary Figures
eFigure 1. Flow Chart of Sample Selection
eFigure 2. Principal Components Analyses of the TOPMed Study Participants
eFigure 3. Box Plots for Quality Control Metrics
eFigure 4. The Quantile-Quantile Plot for Common Variant Association Testing
eFigure 5. Regional Plots for Common Variant Associations
eFigure 6. Regional Associations Plot for the NAV2 Locus for Atrial Fibrillation
eFigure 7. Loss of Function Variants in All Early-Onset Atrial Fibrillation Cases and Controls at TTN
eFigure 8. Comparison of TTN LOF Variants Identified in Early-Onset AF Cases and Controls With
Previously Identified TTN Variants in Other Cardiovascular and Medical Conditions
Supplementary Tables
eTable 1. Early-Onset Atrial Fibrillation Definitions Across Participating Cohorts
eTable 2. Genome-Wide Significant Loci for Atrial Fibrillation
eTable 3. Common Variant Association Analysis of Atrial Fibrillation Compared With Reported
Variants
eTable 4. Meta-Analysis of Top Variant at NAV2 Locus With UK Biobank Participants
eTable 5. List of Titin Loss of Function Variants in Early-Onset Atrial Fibrillation Cases and Controls
eTable 6. Age at Onset Stratified Associations Between Early-Onset AF Cases and Controls in TTN
eTable 7. Sensitivity Analyses for Heart Failure, Gender, Age and Study Location
eTable 8. TTN LOF Variants Observed in Restricted Early-Onset AF Cases and Previously Reported
in Cases With Dilated Cardiomyopathy
eTable 9. Phenotype Definitions From the MyCode Community Health Initiative at Geisinger
eTable 10. Characteristics of Participants From the MyCode Community Health Initiative at Geisinger
eTable 11. Prevalence of Individuals With TTN Loss of Function Variants in Constitutively Expressed
Exons Stratified Between Early-Onset AF Cases and Controls From the MyCode Community Health
Initiative at Geisinger
This supplementary material has been provided by the authors to give readers additional information
about their work.
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Supplemental Materials – Titin loss of function variants in early-onset AF
Supplemental materials
Association between titin loss-of-function variants and early-onset atrial fibrillation
Items Pages
Supplementary Appendices
eAppendix 1. Detailed description of participating studies that provided atrial fibrillation cases 2
eAppendix 2. Whole genome sequencing and Data processing methods 4
eAppendix 3. TTN LOF variants identified in early-onset AF cases and controls compared to previously identified
7
TTN variants in other cardiovascular and medical conditions.
eAppendix 4. Acknowledgments 8
eAppendix 5. Investigators in the TOPMed Program 11
Supplementary Figures
eFigure 1. Flow chart of sample selection 14
eFigure 2. Principal components analyses of the TOPMed study participants 15
eFigure 3. Box plots for quality control metrics 16
eFigure 4. The quantile-quantile plot for common variant association testing 17
eFigure 5. Regional plots for common variant associations 18
eFigure 6. Regional associations plot for the NAV2 locus for atrial fibrillation 19
eFigure 7. Loss of function variants in all early-onset atrial fibrillation cases and controls at TTN 20
eFigure 8. Comparison of TTN LOF variants identified in early-onset AF cases and controls with previously
21
identified TTN variants in other cardiovascular and medical conditions.
Supplementary Tables
eTable 1. Early-onset atrial fibrillation definitions across participating cohorts 22
eTable 2. Genome-wide significant loci for atrial fibrillation 23
eTable 3. Common variant association analysis of atrial fibrillation compared with reported variants 24
eTable 4. Meta-analysis of top variant at NAV2 locus with UK Biobank participants 25
eTable 5. List of titin loss of function variants in early-onset atrial fibrillation cases and controls 26
eTable 6. Age at onset stratified associations between early-onset AF cases and controls in TTN 29
eTable 7. Sensitivity analyses for heart failure, gender, age and study location 30
eTable 8. TTN LOF Variants observed in restricted early-onset AF cases and previously reported in cases with
31
dilated cardiomyopathy
eTable 9. Phenotype definitions from the MyCode Community Health Initiative at Geisinger 32
eTable 10. Characteristics of participants from the MyCode Community Health Initiative at Geisinger 33
eTable 11. Prevalence of individuals with TTN loss of function variants in constitutively expressed exons stratified
34
between early-onset AF cases and controls from the MyCode Community Health Initiative at Geisinger
Supplementary eReferences 35
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Supplementary Appendices
eAppendix 1. Detailed Description of participating studies that provided atrial fibrillation
cases
The Atherosclerosis Risk in Communities (ARIC) study is a prospective population-based study of 15,792 men
and women 45 to 64 years of age at enrollment (73% of European descent), recruited from four communities in the
United States (suburbs of Minneapolis, Minnesota; Washington County, Maryland; Jackson, Mississippi; and
Forsyth County, North Carolina) between 1987-1989 to investigate the epidemiology of cardiovascular disease.
Participants underwent electrocardiograms at baseline and at each follow-up exam (3 exams; 1 exam every 3 years).
Incident atrial fibrillation was classified as the first occurrence of atrial fibrillation as identified from
electrocardiograms at study visits, hospital discharge codes (ICD-9-CM code 427.31 or 427.32) or death certificates
(ICD-9 code 427.3 or ICD-10 code I48).
Cleveland Clinic Lone Atrial Fibrillation GeneBank Study (CCAF) has enrolled patients with lone atrial
fibrillation, defined as atrial fibrillation in the absence of significant structural heart disease. Participants were at
least 18 years of age with a history of recurring or persistent lone atrial fibrillation, ≤50% coronary artery stenosis in
the coronary arteries (if cardiac catheterization done) or with normal stress test results (documentation of normal
cardiac catheterization or stress test required if age ≥50 years), and had normal left ventricular ejection fraction
(LVEF) 50%. Individuals were excluded if they had heart failure, history of significant valvular disease (>2+
valvular regurgitation, any valvular stenosis), significant coronary artery disease (>50% coronary artery stenosis),
prior myocardial infarction, prior percutaneous coronary intervention, or coronary artery bypass graft, or latest
LVEFSupplemental Materials – Titin loss of function variants in early-onset AF
electronically ascertained AF onsetSupplemental Materials – Titin loss of function variants in early-onset AF
eAppendix 2. Whole genome sequencing and Data processing methods
Full details of the TOPMED WGS and data processing are available online (https://goo.gl/ntuJbR)
At Broad Institute of MIT and Harvard, DNA samples are informatically received into the Genomics Platform's
Laboratory Information Management System via a scan of the tube barcodes using a Biosero flatbed scanner. This
registers the samples and enables the linking of metadata based on well position. All samples are then weighed on a
BioMicro Lab's XL20 to determine the volume of DNA present in sample tubes. Following this the samples are
quantified in a process that uses PICO-green fluorescent dye. Once volumes and concentrations are determined, the
samples are handed off to the Sample Retrieval and Storage Team for storage in a locked and monitored -20 walk-in
freezer.
Samples undergo fragmentation by means of acoustic shearing using Covaris focused-ultrasonicator, targeting
385 bp fragments. Following fragmentation, additional size selection is performed using a SPRI cleanup. Library
preparation is performed using a commercially available kit provided by KAPA Biosystems (product KK8202) with
palindromic forked adapters with unique 8 base index sequences embedded within the adapter (purchased from
IDT). Following sample preparation, libraries are quantified using quantitative PCR (kit purchased from KAPA
biosystems) with probes specific to the ends of the adapters. This assay is automated using Agilent’s Bravo liquid
handling platform. Based on qPCR quantification, libraries are normalized to 1.7 nM. Samples are then pooled into
24-plexes and the pools are once again qPCRed. Samples are then combined with HiSeq X Cluster Amp Mix 1,2
and 3 into single wells on a strip tube using the Hamilton Starlet Liquid Handling system.
As described in the library construction process, 96 samples on a plate are processed together through library
construction. A set of 24 barcodes is used to index the samples. Barcoding allows pooling of samples prior to
loading on sequencers and mitigates lane-lane effects at a single sample level. The plate is broken up into 4 pools
of 24-samples each. The four pools are taken as columns on the plate (e.g., columns 1-3; 4-6; 7-9; 10-12). From
this format (and given the current yields of a HiSeqX) the 4 pools are spread over 3 flowcells (24 lanes). Cluster
amplification of the templates was performed according to the manufacturer’s protocol (Illumina) using the Illumina
cBot. Flowcells were sequenced on Hi Seq X with sequencing software HiSeq Control Software (HCS) version
3.3.76, then analyzed using RTA2 (Real Time Analysis).
For TOPMed phase 1 data the following versions were used for aggregation, and alignment to hg19_decoy
reference: picard (latest version available at the time of the analysis), GATK (3.1-144-g00f68a3) and BwaMem
(0.7.7-r441).
A sample is considered sequence complete when the mean coverage is >= 30x. Two QC metrics that are
reviewed along with the coverage are the sample Fingerprint LOD score (score which estimates the probability that
the data is from a given individual) and % contamination. At aggregation, we do an all-by-all comparison of the
read group data and estimate the likelihood that each pair of read groups is from the same individual. If any pair has
a LOD score < -20.00, the aggregation does not proceed and is investigated. FP LOD >= 3 is considered passing
concordance with the sequence data (ideally we see LOD >10). A sample will have an LOD of 0 when the sample
failed to have a passing fingerprint. Fluidigm fingerprint is repeated once if failed. Read groups with fingerprints <
-3.00 are blacklisted from the aggregation. If the sample does not meet coverage, it will be topped off for additional
coverage. If a large % of read groups are blacklisted, it will be investigated as a potential sample swap. In terms of
contamination, a sample is considered passing if the contamination is less than 5%. In general, the bulk of the
samples have less than 1% contamination.
At New York Genome Center, genomic DNA samples were submitted in NYGC-provided 2D barcoded matrix
rack tubes. Sample randomization was performed at investigator lab prior to sample submission. Upon receipt, the
matrix racks were inspected for damage and scanned using a VolumeCheck instrument (BioMicroLab), and tube
barcode and metadata from the sample manifest uploaded to NYGC LIMS. Genomic DNA was quantified using the
Quant-iT PicoGreen dsDNA assay (Life Technologies) on a Spectramax fluorometer, and the integrity was
ascertained on a Fragment Analyzer (Advanced Analytical). After sample quantification, a separate aliquot (100ng)
was removed for SNP array genotyping with the HumanCoreExome-24 array (Illumina). Array genotypes were used
to estimate sample contamination (using VerifyIDintensity), for sample fingerprinting, and for downstream quality
control of sequencing data. Investigator was notified of samples that failed QC for total mass, degradation or
contamination, and replacement samples were submitted.
Sequencing libraries were prepared using the TruSeq PCR-free DNA HT Library Preparation Kit (Illumina)
with 500 ng DNA input, following manufacturer’s protocol with minor modifications to account for automation.
Briefly, genomic DNA was sheared using the Covaris LE220 sonicator to a target size of 450 bp (t:78; Duty:15;
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PIP:450; 200 cycles), followed by end-repair and bead based size selection of fragmented molecules. The selected
fragments were A-tailed, and sequence adaptors ligated onto the fragments, followed by two bead clean-ups of the
libraries. These steps were carried out on the Caliper SciClone NGSx workstation (Perkin Elmer). Final libraries are
evaluated for size distribution on the Fragment Analyzer and quantified by qPCR with adaptor specific primers
(Kapa Biosystems).
Final libraries were multiplexed for 8 samples per sequencing lane, with each sample pool sequenced across 8
flow cell lanes. 1% PhiX control was spiked into each library pool. The library pools were quantified by qPCR,
loaded on the to HiSeq X patterned flow cells and clustered on an Illumina cBot following manufacturer’s protocol.
Flow cells were sequenced on the Illumina HiSeq X with 2x150bp reads, using V2 sequencing chemistry, and
Illumina HiSeq Control Software v3.1.26.
Demultiplexing of sequencing data was performed with bcl2fastq2 v2.16.0.10, and sequencing data was
aligned to human reference build 37 (hs37d5 with decoy) using BWA-MEM v0.7.8. Data was further processed
using the GATK best-practices v3.2-2 pipeline, with duplicate marking using Picard tools v1.83, realignment around
indels, and base quality recalibration. Individual sample BAM files were squeezed using Bamutil v1.0.9 with default
parameters -- removing OQ’s, retaining duplicate marking and binning quality scores (binMid) -- and transferred to
the IRC using Globus. Individual sample SNV and indel calls were generated using GATK haplotype caller and
joint genotyping was performed across all the NYGC phase 1 samples.
Prior to release of BAM files to IRC, we ensured that mean genome coverage was >=30x, when aligning to the
~2.86Gb sex specific mappable genome, and that uniformity of coverage was acceptable (>90% of genome covered
>20x). Sample identity and sequencing data quality was confirmed by concordance to SNP array genotypes. Sample
contamination was estimated with VerifyBAMId v1.1.0 (thresholdSupplemental Materials – Titin loss of function variants in early-onset AF
(1) “duplicate removal” is performed, (i.e., the removal of reads with duplicate start positions; Picard
MarkDuplicates; v1.111) (2) indel realignment is performed (GATK IndelRealigner; v3.2) resulting in improved
base placement and lower false variant calls and (3) base qualities are recalibrated (GATK BaseRecalibrator; v3.2).
Sample BAM files were “squeezed” using Bamutil with default parameters and checksummed before being
transferred to the IRC.
All sequence data undergo a QC protocol before they are released to the TOPMed IRC for further processing.
For whole genomes, this includes an assessment of: (1) mean coverage; (2) fraction of genome covered greater than
10x; (3) duplicate rate; (4) mean insert size; (5) contamination ratio; (6) mean Q20 base coverage; (7)
Transition/Transversion ratio (Ti/Tv); (8) fingerprint concordance > 99%; and (9) sample homozygosity and
heterozygosity. All QC metrics for both single-lane and merged data are reviewed by a sequence data analyst to
identify data deviations from known or historical norms. Lanes/samples that fail QC are flagged in the system and
can be re-queued for library prep (< 1% failure) or further sequencing (< 2% failure), depending upon the QC issue.
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eAppendix 3. TTN LOF variants identified in early-onset AF cases and controls compared
to previously identified TTN variants in other cardiovascular and medical conditions
First, we identified all TTN variants reported in ClinVar database (https://www.ncbi.nlm.nih.gov/clinvar/)2. We then
select pathogenetic variants and aggregated specific phenotypic conditions into broader categories:
- Dilated cardiomyopathy: Dilated Cardiomyopathy, Dominant, Dilated cardiomyopathy 1G, Primary dilated
cardiomyopathy, Dilated cardiomyopathy 1S, Familial dilated cardiomyopathy), hypertrophic cardiomyopathy
- Hypertrophic cardiomyopathy: Familial hypertrophic cardiomyopathy 9, Primary familial hypertrophic
cardiomyopathy, Familial hypertrophic cardiomyopathy 1),
- Skeletal muscle myopathies: Hereditary myopathy with early respiratory failure, Myopathy, Autosomal
recessive centronuclear myopathy, Congenital myopathy
- Other cardiomyopathies: Myopathy early-onset with fatal cardiomyopathy, Left ventricular noncompaction
cardiomyopathy, Arrhythmogenic right ventricular cardiomyopathy type 9, Cardiomyopathy, Arrhythmogenic
right ventricular cardiomyopathy, Noncompaction cardiomyopathy, Right ventricular cardiomyopathy.
In sum, there a total of 5,841 TTN variants reported in the ClinVar database, and 449 variants were predicted to be
pathogenic including 418 for dilated cardiomyopathy, 48 for skeletal myopathies, 43 for other cardiomyopathies,
and 38 for hypertrophic cardiomyopathy.
In a second approach, we also compared the 246 variants from the Cardiodb website (www.cardiodb.org), a
repository for TTN variants associated with dilated cardiomyopathy3.
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eAppendix 4. Acknowledgments
ARIC: The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National
Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C,
HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C,
HHSN268201100011C, and HHSN268201100012C), R01HL087641, R01HL59367 and R01HL086694; National
Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract
HHSN268200625226C. The authors thank the staff and participants of the ARIC study for their important
contributions. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National
Institutes of Health and NIH Roadmap for Medical Research. Funding support for “Building on GWAS for NHLBI-
diseases: the U.S. CHARGE consortium” was provided by the NIH through the American Recovery and
Reinvestment Act of 2009 (ARRA) (5RC2HL102419). This work was additionally supported by American Heart
Association award 16EIA26410001 (Alonso).
Broad Institute: Seung Hoan Choi is the recipient of an analysis support grant from the TOPMed program.
CCAF: is funded by National Institutes of Health grants R01 HL090620 and R01 HL111314 to MKC, JB, JS, and
DVW, the NIH National Center for Research Resources for Case Western Reserve University and The Cleveland
Clinic Clinical and Translational Science Award UL1-RR024989, and the Department of Cardiovascular Medicine
philanthropic research fund, Heart and Vascular Institute, Cleveland Clinic, Cleveland Family Study.
COPDGene: This research used data generated by the COPDGene study, which was supported by NIH grants R01
HL089856 and R01 HL089897. The COPDGene project is also supported by the COPD Foundation through
contributions made by an Industry Advisory Board comprised of AstraZeneca, Boehringer Ingelheim,
GlaxoSmithKline, Novartis, Pfizer, Siemens, and Sunovion.
FHS: This research was conducted using data and resources from Framingham Heart Study (FHS) of the National
Heart Lung and Blood Institute of the National Institutes of Health and Boston University School of Medicine based
on analyses by Framingham Heart Study investigators participating in the SNP Health Association Resource
(SHARe) project. This work was supported by the National Heart, Lung and Blood Institute's Framingham Heart
Study (Contract No. N01-HC-25195; HHSN268201500001I) and its contract with Affymetrix, Inc for genotyping
services (Contract No.N02-HL-6-4278). A portion of this research utilized the Linux Cluster for Genetic Analysis
(LinGA-II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University
School of Medicine and Boston Medical Center. Other support came from R01 HL092577, 1RO1 HL128914.
HVH: The research reported in this article was supported by grants HL127659, HL068986, HL085251, HL095080,
and HL073410 from the National Heart, Lung, and Blood Institute.
MGH AF Study: This work was supported by grants from the National Institutes of Health to Dr. Ellinor
(1RO1HL092577, R01HL128914, K24HL105780). Dr. Ellinor is also supported by an Established Investigator
Award from the American Heart Association (13EIA14220013) and by the Fondation Leducq (14CVD01). Dr.
Lubitz is supported by grants from the NIH (K23HL114724) and by a Doris Duke Charitable Foundation Clinical
Scientist Development Award (2014105). Infrastructure support for the CHARGE Consortium is provided by
HL105756.
Partners HealthCare Biobank: We thank the Broad Institute for generating high-quality sequence data supported
by the NHLBI grant 3R01HL092577-06S1 to Dr. Patrick Ellinor.
Vanderbilt Atrial Fibrillation Ablation Registry: The research reported in this article was supported by grants
from the American Heart Association to Dr. Shoemaker (11CRP742009), Dr. Darbar (EIA 0940116N), and grants
from the National Institutes of Health (NIH) to Dr. Darbar (R01 HL092217), and Dr. Roden (U19 HL65962, and
UL1 RR024975). The project was also supported by a CTSA award (UL1 TR00045) from the National Center for
Advancing Translational Sciences. Its contents are solely the responsibility of the authors and do not necessarily
represent official views of the National Center for Advancing Translational Sciences or the NIH.
© 2018 American Medical Association. All rights reserved.
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Vanderbilt Atrial Fibrillation Registry: The research reported in this article was supported by grants from the
American Heart Association to Dr. Darbar (EIA 0940116N), and grants from the National Institutes of Health (NIH)
to Dr. Darbar (HL092217, HL138737), and Dr. Roden (U19 HL65962, and UL1 RR024975). This project was also
supported by CTSA award (UL1TR000445) from the National Center for Advancing Translational Sciences. Its
contents are solely the responsibility of the authors and do not necessarily represent official views of the National
Center for Advancing Translational Sciences of the NIH.
WGHS: The Women’s Genome Health Study (WGHS) is supported by the National Heart, Lung, and Blood
Institute (HL043851, HL080467, HL099355) and the National Cancer Institute (CA047988 and UM1CA182913)
the Donald W. Reynolds Foundation with collaborative scientific support and funding for genotyping provided by
Amgen. AF endpoint confirmation was supported by HL093613 and HL116690 and a grant from the Harris Family
and Watkin’s Foundation.
DiscovEHR study : This research is supported by Regeneron Pharmaceuticals.
TOPMed Program: Whole genome sequencing (WGS) for the Trans-Omics in Precision Medicine (TOPMed)
program was supported by the National Heart, Lung and Blood Institute (NHLBI). Centralized read mapping and
genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics
Research Center (3R01HL-117626-02S1). Phenotype harmonization, data management, sample-identity QC, and
general study coordination, were provided by the TOPMed Data Coordinating Center (3R01HL-120393-02S1). We
gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. WGS
for TOPMed studies participating in this manuscript was performed at the following centers:
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Accession Sequencing
TOPMed Study Name Sequencing Support
Number Centera
NHLBI TOPMed: Massachusetts General Hospital
phs001062 BROAD 3R01HL092577-06S1
Atrial Fibrillation (MGH AF) Study
NHLBI TOPMed: The Vanderbilt Genetic Basis
phs001032 BROAD 3R01HL092577-06S1
of Atrial Fibrillation
NHLBI TOPMed: The Vanderbilt Atrial
phs000997 BROAD 3R01HL092577-06S1
Fibrillation Ablation Registry
NHLBI TOPMed: Heart and Vascular Health
phs000993 BROAD 3R01HL092577-06S1
Study (HVH)
NHLBI TOPMed: The Cleveland Clinic Atrial
phs001189 BROAD 3R01HL092577-06S1
Fibrillation Study of the CV/Arrhythmia Biobank
NHLBI TOPMed: Atherosclerosis Risk in
phs001211 BROAD 3R01HL092577-06S1
Communities
NHLBI TOPMed: Novel Risk Factors for the
phs001040 BROAD 3R01HL092577-06S1
Development of Atrial Fibrillation in Women
phs001024 NHLBI TOPMed: Partners HealthCare Biobank BROAD 3R01HL092577-06S1
phs000974 NHLBI TOPMed: The Framingham Heart Study BROAD HHSN268201500014C
NHLBI TOPMed: Genetic Epidemiology of UW
phs000951 3R01HL089856-08S1
COPD (COPDGene) NWGC
UW
phs000954 NHLBI TOPMed: The Cleveland Family Study 3R01HL098433-05S1
NWGC
NHLBI TOPMed: Gene-Environment, Admixture
phs000920 NYGC 3R01HL117004-01S3
and Latino Asthmatics (GALA II) Study
NHLBI TOPMed: Study of African Americans,
phs000921 NYGC 3R01HL117004-01S3
Asthma, Genes and Environment (SAGE)
a
NYGC = New York Genome Center, BROAD = Broad Institute of MIT and Harvard, UW NWGC = University
of Washington Northwest Genomics Center
Role of the Sponsor:
None of the funding agencies had any role in the study design, data collection or analysis, interpretation of the data,
writing of the manuscript, or in the decision to submit the manuscript for publication. The content is solely the
responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung, And
Blood Institute or the National Institutes of Health.
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eAppendix 5. Investigators in the TOPMed Program
Namiko Abe1, Goncalo Abecasis2, Christine Albert3, Nicholette (Nichole) Palmer Allred4, Laura Almasy5, Alvaro
Alonso6, Seth Ament7, Peter Anderson8, Pramod Anugu9, Deborah Applebaum-Bowden10, Dan Arking11, Donna K
Arnett12, Allison Ashley-Koch13, Stella Aslibekyan14, Tim Assimes15, Paul Auer16, Dimitrios Avramopoulos11, John
Barnard17, Kathleen Barnes18,Graham R. Barr19, Emily Barron-Casella11, Terri Beaty11, Diane Becker11, Lewis
Becker11, Rebecca Beer10, Ferdouse Begum11, Amber Beitelshees7, Emelia Benjamin20, Marcos Bezerra21, Larry
Bielak2, Joshua Bis8, Thomas Blackwell2, John Blangero22, Eric Boerwinkle23, Ingrid Borecki8, Russell Bowler24,
Jennifer Brody8, Ulrich Broeckel25, Jai Broome8, Karen Bunting1, Esteban Burchard26, Jonathan Cardwell18, Sara
Carlson8, Cara Carty27, Richard Casaburi28, James Casella11, Mark Chaffin29, Christy Chang7, Daniel Chasman30,
Sameer Chavan18, Bo-Juen Chen1, Wei-Min Chen31, Yii-Der Ida Chen32, Michael Cho30, Seung Hoan Choi29, Lee-
Ming Chuang33, Mina Chung17 , Elaine Cornell34, Adolfo Correa9, Carolyn Crandall28, James Crapo24, Adrienne L.
Cupples35, Joanne Curran22, Jeffrey Curtis2, Brian Custer36, Coleen Damcott7, Dawood Darbar37, Sayantan Das2,
Sean David15, Colleen Davis8, Michelle Daya18, Mariza de Andrade38, Michael DeBaun39, Ranjan Deka40, Dawn
DeMeo30, Scott Devine7, Ron Do41, Qing Duan42, Ravi Duggirala22, Peter Durda34, Susan Dutcher43, Charles
Eaton44, Lynette Ekunwe9, Patrick Ellinor3, Leslie Emery8, Charles Farber31, Leanna Farnam30, Tasha Fingerlin24,
Matthew Flickinger2, Myriam Fornage23, Nora Franceschini42, Mao Fu7, Malia Fullerton8, Lucinda Fulton43, Stacey
Gabriel29, Weiniu Gan10, Yan Gao9, Margery Gass45, Xiaoqi (Priscilla) Geng2, Soren Germer1, Chris Gignoux15,
Mark Gladwin46, David Glahn47, Stephanie Gogarten8, Da-Wei Gong7, Harald Goring22, Charles C. Gu43, Yue
Guan7, Xiuqing Guo32, Jeff Haessler48, Michael Hall9, Daniel Harris7, Nicola Hawley47, Jiang He49, Ben Heavner8,
Susan Heckbert8, Ryan Hernandez26, David Herrington4, Craig Hersh30, Bertha Hidalgo14, James Hixson23, John
Hokanson18, Elliott Hong7, Karin Hoth50, Chao (Agnes) Hsiung51, Haley Huston52, Chii Min Hwu53, Marguerite
Ryan Irvin14, Rebecca Jackson54, Deepti Jain8, Cashell Jaquish10, Min A Jhun2, Jill Johnsen55, Andrew Johnson56,
Craig Johnson8, Rich Johnston57, Kimberly Jones11, Hyun Min Kang2, Robert Kaplan58, Sharon Kardia2, Sekar
Kathiresan29, Laura Kaufman30, Shannon Kelly36, Eimear Kenny41, Michael Kessler7, Alyna Khan8, Greg Kinney18,
Barbara Konkle52, Charles Kooperberg45, Holly Kramer59, Stephanie Krauter8, Christoph Lange60, Ethan Lange18,
Leslie Lange18, Cathy Laurie8, Cecelia Laurie8, Meryl LeBoff30, Seunggeun Shawn Lee2, Wen-Jane Lee53, Jonathon
LeFaive2, David Levine8, Dan Levy61, Joshua Lewis7, Yun Li42, Honghuang Lin35, Keng Han Lin2, Simin Liu62,
Yongmei Liu4, Ruth Loos41, Steven Lubitz3, Kathryn Lunetta35, James Luo61, Michael Mahaney22, Barry Make11,
Ani Manichaikul31, JoAnn Manson30, Lauren Margolin29, Lisa Martin63, Susan Mathai18, Rasika Mathias11, Patrick
McArdle7, Merry-Lynn McDonald14, Sean McFarland64, Stephen McGarvey44, Hao Mei9, Deborah A Meyers65, Julie
Mikulla10, Nancy Min9, Mollie Minear10, Ryan L Minster46, Braxton Mitchell7, May E. Montasser7, Solomon
Musani9, Stanford Mwasongwe9, Josyf C Mychaleckyj31, Girish Nadkarni41, Rakhi Naik11, Pradeep Natarajan66,
Sergei Nekhai67, Deborah Nickerson8, Kari North42, Jeff O'Connell7, Tim O'Connor7, Heather Ochs-Balcom68, James
Pankow6, George Papanicolaou10, Margaret Parker30, Afshin Parsa7, Jessica Tangarone Pattison2, Sara Penchev24,
Juan Manuel Peralta22, Marco Perez15, James Perry7, Ulrike Peters69, Patricia Peyser2, Larry Phillips57, Sam Phillips8,
Toni Pollin7, Wendy Post11, Julia Powers Becker18, Meher Preethi Boorgula18, Michael Preuss41, Dmitry
Prokopenko64, Bruce Psaty8, Pankaj Qasba10, Dandi Qiao30, Zhaohui Qin57, Nicholas Rafaels18, Laura Raffield42,
Ramachandran Vasan35, D.C. Rao43, Laura Rasmussen-Torvik70, Aakrosh Ratan31, Susan Redline30, Robert Reed7,
Elizabeth Regan24, Alex Reiner8, Ken Rice8, Stephen Rich31, Dan Roden39, Carolina Roselli29, Jerome Rotter32, Ingo
Ruczinski11, Pamela Russell18, Sarah Ruuska52, Kathleen Ryan7, Phuwanat Sakornsakolpat30, Shabnam Salimi7,
Steven Salzberg11, Kevin Sandow32, Vijay Sankaran64, Christopher Scheller2, Ellen Schmidt2, Karen Schwander43,
David Schwartz18, Frank Sciurba46, Vivien Sheehan71, Amol Shetty7, Aniket Shetty18, Wayne Hui-Heng Sheu53, M.
Benjamin Shoemaker39, Brian Silver72, Edwin Silverman30, Jennifer Smith2, Josh Smith8, Nicholas Smith8, Tanja
Smith1, Sylvia Smoller58, Beverly Snively4, Tamar Sofer30, Nona Sotoodehnia8, Adrienne Stilp8, Elizabeth Streeten7,
Yun Ju Sung43, Jody Sylvia30, Adam Szpiro8, Carole Sztalryd7, Daniel Taliun2, Hua Tang15, Margaret Taub11, Kent
Taylor32, Simeon Taylor7, Marilyn Telen13, Timothy A. Thornton8, Lesley Tinker27, David Tirschwell8, Hemant
Tiwari14, Russell Tracy34, Michael Tsai6, Dhananjay Vaidya11, Peter VandeHaar2, Scott Vrieze73, Tarik Walker18,
Robert Wallace50, Avram Walts18, Emily Wan30, Fei Fei Wang8, Karol Watson28, Daniel E. Weeks46, Bruce Weir8,
Scott Weiss30, Lu-Chen Weng3, Cristen Willer2, Kayleen Williams8, Keoki L. Williams74, Carla Wilson30, James
Wilson9, Quenna Wong8, Huichun Xu7, Lisa Yanek11, Ivana Yang18, Rongze Yang7, Norann Zaghloul7, Yingze
Zhang46, Snow Xueyan Zhao24, Wei Zhao2, Xiuwen Zheng8, Degui Zhi23, Xiang Zhou2, Michael Zody1, Sebastian
Zoellner2
© 2018 American Medical Association. All rights reserved.
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Affiliations
1. New York Genome Center.
2. University of Michigan.
3. Massachusetts General Hospital.
4. Wake Forest Baptist Health.
5. Children’s Hospital of Philadelphia, University of Pennsylvania.
6. University of Minnesota.
7. University of Maryland.
8. University of Washington.
9. University of Mississippi.
10. National Institutes of Health.
11. Johns Hopkins University.
12. University of Kentucky.
13. Duke University.
14. University of Alabama.
15. Stanford University.
16. University of Wisconsin Milwaukee.
17. Cleveland Clinic.
18. University of Colorado at Denver.
19. Columbia University.
20. Boston University, Massachusetts General Hospital.
21. Fundação de Hematologia e Hemoterapia de Pernambuco - Hemope.
22. University of Texas Rio Grande Valley School of Medicine.
23. University of Texas Health.
24. National Jewish Health.
25. Medical College of Wisconsin.
26. University of California, San Francisco.
27. Women’s Health Initiative.
28. University of California, Los Angeles.
29. The Broad Institute.
30. Brigham & Women’s Hospital.
31. University of Virginia.
32. Los Angeles Biomedical Research Institute.
33. National Taiwan University.
34. University of Vermont.
35. Boston University.
36. Blood Systems Research Institute UCSF.
37. University of Illinois at Chicago.
38. Mayo Clinic.
39. Vanderbilt University.
40. University of Cincinnati.
41. Icahn School of Medicine at Mount Sinai.
42. University of North Carolina.
43. Washington University in St Louis.
44. Brown University.
45. Fred Hutchinson Cancer Research Center.
46. University of Pittsburgh.
47. Yale University.
48. Fred Hutchinson Cancer Research Center, Women’s Health Initiative.
49. Tulane University.
50. University of Iowa.
51. National Health Research Institute Taiwan.
52. Blood Works Northwest.
53. Taichung Veterans General Hospital Taiwan.
54. Ohio State University Wexner Medical Center.
55. Blood Works Northwest, University of Washington.
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56. NIH National Heart, Lung, and Blood Institute.
57. Emory University.
58. Albert Einstein College of Medicine.
59. Loyola University.
60. Harvard School of Public Health.
61. NIH National Heart, Lung, and Blood Institute, National Institutes of Health.
62. Brown University, Women’s Health Initiative.
63. George Washington University.
64. Harvard University.
65. University of Arizona.
66. The Broad Institute, Harvard University, Massachusetts General Hospital.
67. Howard University.
68. University at Buffalo.
69. Fred Hutchinson Cancer Research Center, University of Washington.
70. Northwestern University.
71. Baylor College of Medicine.
72. University of Massachusetts Memorial Health Center.
73. University of Colorado at Boulder.
74. Henry Ford Health System.
© 2018 American Medical Association. All rights reserved.
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Supplementary Figures
eFigure 1. Flow chart of sample selection
The flow chart shows sample-selecting processes. 19 studies with 18,526 individuals were participated in NHLBI TOPMed phase I.
2,649 participants who lacked suitable consent (disease specific research) and isolated islandic populations (Barbados and
Samoans) were excluded. Non-European participants (N=6,402) who consist of 5,243 African American, 200 East Asian, 738 Ad
Mixed American, and 221 undetermined ethnic group were removed. In addition, because of non-overlapping cluster with early-
onset AF participants, 1,115 Amish individuals were excluded. After performing quality controls for remaining 8,630 participants, 620
individuals were excluded. Abbreviations: PCA, principal component analysis; QC, quality control
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A B
0.06
0.05
TOPMed Controls TOPMed Controls
TOPMed AF Cases TOPMed AF Cases
1000G AFR Amish
0.04
1000G AMR
1000G EAS
0.04
1000G EUR
0.03
PC2
PC2
0.02
0.02
0.01
0.00
-0.01 0.00
-0.005 0.000 0.005 0.010 0.015 0.00 0.02 0.04 0.06 0.08
PC1 PC1
eFigure 2. Principal components analyses of the TOPMed study participants
eFigure 2A illustrates a plot of the principal component 1 versus 2 for the Trans-Omics for Precision Medicine (TOPMed) Program Phase I participants with consent (N = 15,877) and
1000 genome project participants (N = 1,092) using pruned set of 44,018 common variants. The dotted lines indicate 6 standard deviations from the means of principal component 1
and principal component 2 in 1000G European ancestry (EUR) participants. Colors denote atrial fibrillation status in TOPMed participants or ethnic groups in the 1000G dataset.
eFigure 2B illustrates the re-calculated principal component 1 and principal component 2 of TOPMed participants within 6 standard deviations from the mean of principal component 1
and principal component 2 in 1000G EUR participants. Abbreviation: AF, atrial fibrillation; AFR, African; AMR, Ad Mixed American; EAS, East Asian; EUR, European
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A B
1.00
1.8
Heterozygote Homozygote ratio
1.7
0.98
Call rate
1.6
0.96
1.5
95%
0.94
GALAII+SAGE
GALAII+SAGE
COPDGene
COPDGene
Partners
Partners
VAFAR
VAFAR
WGHS
WGHS
CCAF
VAFR
CCAF
VAFR
ARIC
MGH
ARIC
MGH
CFS
FHS
HVH
CFS
FHS
HVH
All
All
C D
20.3
2.160
Transition Transversion ratio
20.2
2.155
SNP INDEL ratio
20.1
2.150
20.0
19.9
2.145
19.8
2.140
GALAII+SAGE
GALAII+SAGE
COPDGene
COPDGene
Partners
Partners
VAFAR
VAFAR
WGHS
WGHS
CCAF
VAFR
CCAF
VAFR
ARIC
MGH
ARIC
MGH
CFS
FHS
HVH
CFS
FHS
HVH
All
All
E F
8000
25000
6000
Allele count = 1
sample size
15000
4000
2000
5000
0
0
GALAII+SAGE
GALAII+SAGE
COPDGene
COPDGene
Partners
Partners
VAFAR
VAFAR
WGHS
WGHS
VAFR
VAFR
CCAF
CCAF
ARIC
MGH
ARIC
MGH
CFS
FHS
HVH
CFS
FHS
HVH
All
All
eFigure 3. Box plots for quality control metrics
eFigure 3 illustrates quality control metrics after we cleaned the data set. Panels A-E show call rate, heterozygote homozygotes ratio, transition
transversion ratio, SNP INDEL ratio, and the number variants with allele count = 1. These panels present a distribution of quality control metrics for all
participants and by each study using a boxplot. Panel F presents the total number of samples and sample sizes by each study after quality control.
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eFigure 4. The quantile-quantile plot for common variant association testing
The quantile-quantile plot displays the observed vs. the expected -log10 (P value) for each variant tested. The genomic inflation factor (lambda) was
1.03.
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eFigure 5. Regional plots for common variant associations
Regional plots for previously described AF loci at the genes KCCN3, PRRX1, PITX2, NEURL1, SOX5, and ZFHX3 (Panels A-F respectively). The most
significant variant at each locus is plotted with diamond shape. Colors of dots represent the degree of linkage disequilibrium (R2) to the top variant. The
lower part of each panel shows the locations of genes at the respective loci. r2, degree of linkage disequilibrium; chr, chromosome; Mb, megabases; cM,
centiMorgan. Regional plots were created using LocusZoom.4
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eFigure 6. Regional associations plot for the NAV2 locus for atrial fibrillation
The most significant variant at each locus is plotted (purple, diamond-shaped) and identified with rsID. Each dot in the plots represent a single variant
and the color of the dot indicates the degree of linkage disequilibrium with the most significant variant, as shown on the top left color chart on each
panel. The lower part of each panel shows the locations of genes at the respective loci. r2, degree of linkage disequilibrium; chr, chromosome; Mb,
megabases; cM, centiMorgan. Regional plots were created using LocusZoom. 4
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Total T T N LOF
participants variants
Early onset
atrial fibrillation 2,752 64
cases
Controls 2,116 22
Titin
Z-disk I-band A-band M-band
Meta
N2BA
N2B
N2A
Nvx1
Nvx2
Nvx3
eFigure 7. Loss of function variants in all early-onset atrial fibrillation cases and controls at TTN
Loss of function variants in early-onset atrial fibrillation cases (first row) and controls (second row) are plotted relative to their genomic position. The sample includes in this figure arose from the rare
variant analyses and included 2,752 early-onset atrial fibrillation cases and 2,166 controls. There were 64 loss of function variants (LOF) in TTN among the early-onset AF cases and 22 LOF variants in
the controls. Variants found in cases with heart failure or left ventricular ejection fractionsSupplemental Materials – Titin loss of function variants in early-onset AF
T T N LOF
variants
Early onset
40
Current AF cases
project TOPMed
22
Controls
DCM 190
Cardiodb
website
Controls 63
DCM 418
SK myopathy 48
ClinVar
database
Other CMP 43
HCM 38
Overview of
titin structure
Z-disk I-band A-band M-band
Transcripts
Meta
N2BA
N2B
N2A
Nvx1
Nvx2
Nvx3
eFigure 8. Comparison of TTN LOF variants identified in early-onset AF cases and controls
with previously identified TTN variants in other cardiovascular and medical conditions
At the top, the TTN loss of function variants in unrelated, early-onset atrial fibrillation cases and controls are plotted relative to their genomic position. In
the second section, the TTN variants reported in dilated cardiomyopathy cases and controls from the Cardiodb website (www.cardiodb.org) are
illustrated3. The third section, includes pathogenic TTN variants from the ClinVar database2. From the ClinVar data, separate variants are indicated for
dilated cardiomyopathy, skeletal myopathies, other cardiomyopathies, and hypertrophic cardiomyopathy. Any variant in common among early onset AF
patients, dilated cardiomyopathy cases and/or controls are colored in red. For consistency with prior reports, the TTN domains (Z-disk, I-band, A-band,
M-band) are illustrated with red, blue, green, and purple colors, respectively3. The region indicated in grey is a large, final exon present in one TTN
transcript (Novex-3). At the bottom is an overview of the TTN transcripts in which each line indicates an exon. The Meta transcript is a curated summary
of the major exons for TTN as described on the Cardiodb website (www.cardiodb.org)3. The N2BA and N2B transcripts are principal cardiac long and
short isoforms of titin, respectively. N2A is expressed in skeletal muscle. Novex-1 (Nvx1) and Novex-2 (Nvx2) are similar to N2B but contains one
additional exon. Novex-3 (Nvx3) is smaller isoform with a large alternative final exon.
Abbreviations: AF, atrial fibrillation; DCM, dilated cardiomyopathy; HCM, hypertrophic cardiomyopathy; CMP, cardiomyopathies.
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Supplementary Tables
eTable 1. Early-onset atrial fibrillation definitions across participating cohorts
Dates of
Enrollment Dates of Phenotype Early-onset
Cohort sequencing Controls Early-onset AF case definitions*
location collection AF
and calling
Minnesota,
Maryland, AF onset prior to 61 years of age in individuals
ARIC 1987-2011 2014-2017 80 -
Mississippi, North without prior history of MI or heart failure.
Carolina, U.S.
AF onset between 18 and 61 years of age and in the
CCAF Ohio, U.S. 2005-2014 2014-2017 355 - absence of preexisting heart failure, MI, or overt
structural heart disease.
AF onset between 30 and 61 years of age in an
individual without prior myocardial infarction, valvular
HVH Washington, U.S. 2005-2007 2014-2017 75 -
heart disease, or heart failure; includes both AF and
atrial flutter.
AF onset prior to 61 years of age and in the absence
FHS Massachusetts, U.S. 1948-2014 2014-2017 108 3477
of preexisting MI or heart failure.
AF onset prior to 66 years of age and in the absence
MGH Massachusetts, U.S. 2001-2014 2014-2017 767 - of preexisting hyperthyroidism, heart failure, MI, or
overt structural heart disease.
AF onset prior to 61 years of age and in the absence
Partners Massachusetts, U.S. 2010-2014 2014-2017 120 - of cardiac surgery, cardiomyopathy, MI, or valvular
heart disease.
AF at less 61 years of age and no prior history of MI,
WGHS Throughout U.S. 1992-2014 2014-2017 115 - CHF, or structural heart disease at the time of AF
onset.
AF onset prior to 66 years of age and in the absence
VAFR Tennessee, U.S. 2001-2014 2014-2017 1045 -
of heart failure, MI, or overt structural heart disease.
AF onset prior to 66 years of age and in the absence
VAFAR Tennessee, U.S. 2011-2014 2014-2017 116 -
of heart failure, MI, or overt structural heart disease.
COPDGene Throughout U.S. 2007-2011 2014-2017 - 991 -
CFS Ohio, U.S. 1990-2006 2014-2017 - 484 -
GALA II + Puerto Rico and
2006-2014 2014-2017 - 7 -
SAGE throughout U.S.
ARIC: Atherosclerosis Risk in Communities, CCAF: Cleveland Clinic Lone Atrial Fibrillation GeneBank Study, HVH: Heart and Vascular Health Study, FHS: Framingham Heart Study, MGH:
Massachusetts General Hospital atrial fibrillation Study, Partners: Partners HealthCare Biobank, Women’s WGHS: Genome Health Study, VAFR: Vanderbilt Atrial Fibrillation Registry, VAFAR:
Vanderbilt Atrial Fibrillation Ablation Registry, COPDGene : Genetic Epidemiology of Chronic Obstructive Pulmonary Disease Study, CFS: Cleveland Family Study, GALAII+SAGE :
Pharmacogenomics of Bronchodilator Response in Minority Children with Asthma Study, AF: Atrial fibrillation, MI: Myocardial Infarction, ECG: Electrocardiography, CHF: Congestive heart failure.
*For full case definitions please see eAppendix 2.
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eTable 2. Genome wide significant loci for atrial fibrillation
Location Risk/ref
rsID Chr Gene RAF, % OR 95% CI P value Ref
relative to gene allele
rs12087657 1q21 KCNN3 Upstream T/G 34 1.27 1.18-1.36 2.65x10-8 5
rs651386 1q24 PRRX1 Upstream A/T 56 1.29 1.20-1.38 7.34 x10-10 6
rs78229461 4q25 PITX2 Upstream T/C 16 2.24 2.04-2.45 9.34 x10-51 7
rs10786758 10q24 NEURL1 Intron A/T 51 1.34 1.25-1.44 6.47 x10-13 8
rs2625322 11p15 NAV2 Intron A/G 21 1.32 1.21-1.44 1.46 x10-8 Novel
rs4963776 12p12 SOX5 Upstream G/T 82 1.35 1.23-1.49 2.98 x10-8 9
rs2106261 16q22 ZFHX3 Intron T/C 18 1.42 1.30-1.55 1.17 x10-11 10,11
The most significant variant at each genetic locus is listed. Abbreviations: Chr, chromosome; RAF, risk allele frequency; OR, odds ratio; CI, confidence
interval; Ref, reference.
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eTable 3. Common variant association analysis of atrial fibrillation compared with reported variants
Location Risk/ref RAF, Reported
rsID Chr Gene OR 95% CI P value Distance R2
relative to gene allele % variant
rs12087657 1q21 KCNN3 Upstream T/G 34 1.27 1.18-1.36 2.65x10-8 rs11264280 85375 0.74
rs72700103 1q24 METTL11B Downstream C/A 12 1.33 1.20-1.48 1.70x10-6 rs72700118 14480 0.99
rs651386 1q24 PRRX1 Upstream A/T 56 1.29 1.20-1.38 7.34x10-10 rs520525 47023 0.47
rs74181299 2p14 CEP68 Intron T/C 60 1.11 1.04-1.19 1.11x10-2 rs2540949 259 0.96
rs10205101 2p14 ANXA4 Intron T/C 71 1.12 1.04-1.21 1.17x10-2 rs3771537 8830 0.47
rs16866358 2q31 TTN Downstream G/A 14 1.15 1.04-1.26 1.72x10-2 rs2288327 31569 0.78
rs7650482 3p25 CAND2 Intron G/A 34 1.21 1.12-1.30 8.37x10-6 rs11718898 7018 0.92
rs11129800 3p22 SCN10A Intron C/T 56 1.10 1.03-1.18 1.23x10-2 rs6800541 30462 0.35
rs78229461 4q25 PITX2 Upstream T/C 16 2.24 2.04-2.45 9.34x10-51 rs6843082 20414 0.49
rs13155731 5q22 KCNN2 Intron T/C 20 1.18 1.08-1.28 1.09x10-3 rs337711 3865 0.34
rs529526 5q31 KLHL3 Intergenic C/T 30 1.26 1.17-1.36 1.15x10-7 rs2967791 430380 0.31
rs4294041 6q22 SLC35F1-PLN Intron A/G 58 1.14 1.07-1.23 9.40x10-4 rs4946333 52973 0.72
rs17199931 6q22 GJA1 Downstream T/A 75 1.13 1.04-1.22 9.51x10-3 rs12664873 267767 0.40
rs3815412 7q31 CAV1-CAV2 Intron T/C 77 1.24 1.14-1.34 8.24x10-6 rs1997572 8135 0.51
rs447024 8p22 ASAH1 Downstream C/G 31 1.17 1.08-1.26 3.87x10-4 rs7508 1722 0.89
rs10125609 9q22 C9orf3 Intron A/T 30 1.12 1.04-1.21 1.06x10-2 rs7026071 98111 0.30
rs60820984 10q22 SYNPO2L Upstream C/T 80 1.15 1.05-1.25 7.18x10-3 rs7915134 219398 0.50
rs12411463 10q24 NEURL1 Intron T/C 20 1.43 1.31-1.56 3.59x10-12 rs11598047 9059 0.82
rs35176054 10q24 SH3PXD2A Intron A/T 14 1.20 1.09-1.32 1.70x10-3 rs35176054 0 1
rs75557443 11q24 KCNJ5 Intron T/C 9 1.34 1.19-1.50 2.01x10-5 rs75190942 1795 0.95
rs4963776 12p12 SOX5 Upstream G/T 82 1.35 1.23-1.49 2.98x10-8 rs11047543 8848 0.83
rs7955405 12q24 TBX5 Intron G/A 27 1.26 1.17-1.36 5.16x10-7 rs883079 4066 0.95
rs1152591 14q23 SYNE2 Intron A/G 51 1.12 1.05-1.20 3.72x10-3 rs1152591 0 1
rs3784813 15q24 HCN4 Upstream C/T 76 1.25 1.15-1.35 2.66x10-6 rs74022964 14224 0.57
rs2106261 16q22 ZFHX3 Intron T/C 18 1.42 1.30-1.55 1.17x10-11 rs2106261 0 1
The most significant variant with R2 ≥ 0.3 to the previously reported variant at each genetic locus is listed. Abbreviations: Chr, chromosome; RAF, risk allele frequency; OR, odds ratio; CI, confidence
interval.
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eTable 4. Meta-analysis of top variant at NAV2 locus with UK Biobank participants
TOPMed UK Biobank Meta-analysis
rsID Chr Risk/ref allele
OR 95% CI P value OR 95% CI P value OR 95% CI P value
rs2625322 11p15 A/G 1.32 1.21-1.44 1.46 x10-8 1.11 1.07-1.15 9.70 x 10-10 1.14 1.10-1.28 4.47 x 10-16
The most significant variant at each genetic locus is listed. Abbreviations: Chr, chromosome; OR, odds ratio; CI, confidence interval
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eTable 5. List of titin LOF variants in early-onset atrial fibrillation cases and controls
Position Ref Alt AC1 AN1 AC0 AN0 Annotation HGVS.c HGVS.p
179392275 G A 2 5502 0 4232 Stop gained c.107578C>T p.Gln35860*
179393620 C A 0 5504 1 4232 Stop gained c.106858G>T p.Glu35620*
179400887 C T 1 5502 0 4232 Stop gained c.100587G>A p.Trp33529*
179401900 C T 2 5500 0 4232 Stop gained c.99936G>A p.Trp33312*
179404241 G A 0 5502 1 4230 Stop gained c.98551C>T p.Arg32851*
179404491 CCT C 1 5500 0 4230 Frameshift c.98299_98300delAG p.Arg32767fs
179406294 C A 1 5502 0 4228 Stop gained c.97510G>T p.Glu32504*
179406989 A G 1 5500 0 4230 Splice donor c.97492+2T>C
179408634 AT A 1 5500 0 4230 Frameshift c.96236delA p.Asp32079fs
179410975 C CG 1 5502 0 4228 Frameshift c.95082dupC p.Gly31695fs
179414036 G A 1 5500 0 4232 Stop gained c.92317C>T p.Arg30773*
179414065 CTT C 1 5500 0 4232 Frameshift c.92286_92287delAA p.Ser30763fs
179414529 CCA C 1 5498 0 4232 Frameshift c.91918_91919delTG p.Trp30640fs
179418467 C T 2 5500 0 4230 Stop gained c.89265G>A p.Trp29755*
179418640 C G 1 5504 0 4232 Splice donor c.89197+1G>C
179422238 G A 0 5504 1 4232 Stop gained c.87751C>T p.Arg29251*
179424113 AATAG A 1 5504 0 4232 Frameshift c.86742_86745delCTAT p.Tyr28915fs
179425769 G A 1 5500 0 4232 Stop gained c.85090C>T p.Arg28364*
179432681 C A 1 5498 0 4228 Stop gained c.78178G>T p.Glu26060*
179433313 GA G 1 5490 0 4226 Frameshift c.77545delT p.Ser25849fs
179433713 A AG 1 5492 0 4228 Frameshift c.77145dupC p.Ser25716fs
179440697 G A 1 5500 0 4230 Stop gained c.70162C>T p.Arg23388*
179441549 C CCTTTT 1 5500 0 4224 Frameshift c.69421_69422insAAAAG p.Gly23141fs
179442901 C A 0 5502 1 4226 Stop gained c.68341G>T p.Glu22781*
179449558 G A 1 5498 0 4228 Stop gained c.64810C>T p.Arg21604*
179452764 G A 1 5500 0 4228 Stop gained c.63370C>T p.Gln21124*
179454027 CG C 1 5498 0 4230 Frameshift c.62424delC p.Asp20808fs
179454576 G A 1 5498 0 4228 Stop gained c.61876C>T p.Arg20626*
179457272 C T 1 5502 0 4226 Stop gained c.59460G>A p.Trp19820*
179460478 G T 1 5504 0 4232 Stop gained c.57603C>A p.Cys19201*
179463704 A AT 1 5500 0 4230 Frameshift c.56732dupA p.Asp18911fs
c.54782_54783insCAGAGGTTGCAG
179468631b T TCCTGCAACCTCTG 1 5488 0 4224 Frameshift p.Asp18262fs
G
179468633b A AAT 1 5484 0 4224 Frameshift c.54780_54781insAT p.Ser18261fs
179472209 G A 1 5496 0 4224 Stop gained c.53206C>T p.Arg17736*
179474016 G A 1 5496 0 4224 Stop gained c.52021C>T p.Arg17341*
179474817 G A 0 5494 1 4224 Stop gained c.51436C>T p.Gln17146*
179477885 TA T 2 5498 0 4220 Splice donor c.49648+2delT
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Downloaded From: https://jamanetwork.com/ on 09/07/2021Supplemental Materials – Titin loss of function variants in early-onset AF
Position Ref Alt AC1 AN1 AC0 AN0 Annotation HGVS.c HGVS.p
179480423 A T 0 5496 1 4218 Stop gained c.48405T>A p.Cys16135*
179481350 TG T 1 5496 0 4220 Frameshift c.48167delC p.Pro16056fs
179481455 C G 0 5486 1 4220 Splice donor c.48160+1G>C
179482584 G A 1 5494 0 4220 Stop gained c.47494C>T p.Arg15832*
Frameshift +
179483038 A AACTC 1 5484 0 4220 c.47143_47146dupGAGT p.Phe15716fs
Stop gained
179506963 C T 2 5482 0 4230 Splice donor c.40558+1G>A
179514035 CAA C 0 5504 1 4232 Frameshift c.39995_39996delTT p.Leu13332fs
179516638 CT C 1 5500 0 4226 Frameshift c.39351delA p.Glu13118fs
179519475 CA C 1 5502 0 4230 Frameshift c.38202delT p.Glu12735fs
179519637 A C 1 5502 0 4230 Splice donor c.38122+2T>G
179519637 A G 1 5502 2 4230 Splice donor c.38122+2T>C
179523926 TA T 0 5502 1 4230 Frameshift c.37341delT p.Lys12449fs
179527763 C CGGTGGCA 1 5500 0 4232 Frameshift c.36713_36719dupTGCCACC p.Lys12241fs
179530504 G A 1 5504 0 4232 Stop gained c.35890C>T p.Arg11964*
Splice
179536996 T G 0 5470 1 4176 c.34931-2A>C
acceptor
179537132 A G 1 5488 0 4176 Splice donor c.34930+2T>C
Splice
179549477 C T 0 5490 1 4212 c.32555-1G>A
acceptor
179553855b G GC 0 5500 1 4232 Frameshift c.32019_32020insG p.Leu10674fs
179553856b AGCTG A 0 5500 1 4232 Frameshift c.32015_32018delCAGC p.Pro10672fs
Splice
179554624 C T 3 5504 2 4230 c.31763-1G>A
acceptor
179559325 C G 0 5488 1 4222 Splice donor c.31426+1G>C
179560108 C CT 1 5494 0 4198 Frameshift c.31236dupA p.Val10413fs
Splice
179571683 T G 1 5504 0 4232 c.29042-2A>C
acceptor
179581820 A C 1 5498 0 4228 Splice donor c.25639+2T>G
179586757 G A 1 5498 0 4228 Stop gained c.22633C>T p.Arg7545*
179590643 TCTGA T 1 5504 0 4228 Frameshift c.20402_20405delTCAG p.Leu6801fs
179597615 G A 1 5504 0 4232 Stop gained c.16288C>T p.Arg5430*
179598098 G A 1 5504 0 4232 Stop gained c.15922C>T p.Arg5308*
179598224 G A 0 5504 1 4230 Stop gained c.15796C>T p.Arg5266*
179604012 A ACTTTT 1 5504 0 4232 Frameshift c.13943_13947dupAAAAG p.Phe4650fs
179604100 AC A 1 5504 0 4232 Frameshift c.13859delG p.Gly4620fs
179605373 G T 3 5504 0 4232 Stop gained c.12587C>A p.Ser4196*
179611821 AG A 0 5470 1 4226 Frameshifta c.15305delC p.Thr5102fs
179611877 ATC A 1 5464 0 4222 Frameshifta c.15248_15249delGA p.Arg5083fs
179613187 TC T 0 5492 1 4228 Frameshifta c.13939delG p.Glu4647fs
179613760 ACCTTT A 1 5488 0 4228 Frameshifta c.13362_13366delAAAGG p.Lys4454fs
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