Examining Rare and Low-Frequency Genetic Variants ...circgenetics.ahajournals.org/content/circcvg/early/2014/11/19/CIRC... · exome chip are detected at low frequencies in a general

DOI: 10.1161/CIRCGENETICS.114.000718

1

Examining Rare and Low-Frequency Genetic Variants Previously Associated

with Lone or Familial Forms of Atrial Fibrillation in an Electronic Medical

Record System: A Cautionary Note

Running title: Weeke et al.; Rare and Low-Frequency Genetic Variants and AF

Peter Weeke, MD1,2; Joshua C. Denny, MD1,3; Lisa Basterache, MS3; Christian Shaffer, BS1;

Erica Bowton, PhD4; Christie Ingram,BS1; Dawood Darbar, MD1; Dan M. Roden, MD1

1Department of Internal Medicine, 3Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN; 2Department of Cardiology, Copenhagen University

Hospital, Gentofte, Denmark; 4Institute for Clinical & Translational Research, Vanderbilt University School of Medicine, Nashville, TN

Correspondence:

Dan M. Roden, MD

Professor of Medicine and Pharmacology

Assistant Vice-Chancellor for Personalized Medicine

Vanderbilt University School of Medicine

1285 Medical Research Building IV

Nashville, TN 37232

Tel: 615-322-0067

Fax: 615-343-4522

E-mail: [email protected]

Journal Subject Codes: [146] Genomics, [89] Genetics of cardiovascular disease, [8] Epidemiology

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Abstract:

Background - Studies in individuals or small kindreds have implicated rare variants in 25

different genes in lone and familial atrial fibrillation (AF) using linkage and segregation analysis,

functional characterization, and rarity in public databases. Here we used a cohort of 20,204

patients of European or African ancestry with electronic medical records (EMRs) and exome

chip data to compare the frequency of AF among carriers and non-carriers of these rare variants.

Methods and Results - The exome chip included 19/115 rare variants, in 9 genes, previously

associated with lone or familial AF. Using validated algorithms querying a combination of

clinical notes, structured billing codes, ECG reports, and procedure codes, we identified 1,056

AF cases (>18 years) and 19,148 non-AF controls (>50 years) with available genotype data on

the Illumina HumanExome BeadChip v.1.0 in the Vanderbilt electronic medical record-linked

DNA repository, BioVU. Known correlations between AF and common variants at 4q25 were

replicated. None of the 19 variants previously associated with AF were overrepresented among

AF cases (P >0.1 for all), and the frequency of variant carriers among non-AF controls was

>0.1% for 14/19. Repeat analyses using non-AF controls aged >60 (n=14,904) >70 (n=9,670),

and >80 (n=4,729) years old did not influence these findings.

Conclusions - Rare variants previously implicated in lone or familial forms AF present on the

exome chip are detected at low frequencies in a general population but are not associated with

AF. These findings emphasize the need for caution when ascribing variants as pathogenic or

causative.

Key words: Genome Wide Association Study, single nucleotide polymorphism, genetics, association studies, atrial fibrillation, gene mutation, family study, penetrance, exome, genome, GWAS

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Atrial fibrillation (AF) is the most prevalent arrhythmia encountered in clinical practice and is

significantly associated with increased risks of stroke, heart failure, and death.1 While there are a

many clinical correlates of AF (e.g. hypertension and diabetes), genetic variability is being

increasingly recognized as an important determinant for developing AF.2-11 12-16 To date, rare

genetic variants in the exonic regions of 25 genes encoding cardiac ion channels, gap junction

proteins, and signaling molecules have been linked with in lone or familial forms of AF.2-11 In

addition, genome-wide association studies (GWAS) have associated multiple common variants

with AF in a general population.13-16 Overall, the effects of common variants on AF

susceptibility are small, whereas the effects of rare variants on AF susceptibility have not been

determined, although they are presumed to be large.17 Establishing the prevalence of such

variants in large population studies with available phenotypic information can be useful i) to

estimate the effect sizes associated with genetic variants; ii) to avoid falsely predicting

individuals being at risk for disease; and iii) to evaluate penetrance. In the present study, we

studied a large cohort of patients with available genotyping data from the Illumina HumanExome

BeadChip v.1.0 genotyping array (“the exome chip”) that interrogates ~250,000 rare and

common exonic variants and linked to de-identified electronic medical records (EMR).18 We

tested the hypothesis that AF is more prevalent in carriers of rare variants previously designated

as pathogenic for AF.

Methods

BioVU and Exome Chip Study cohorts

BioVU links DNA samples extracted from discarded blood samples to de-identified EMRs, as

previously described.18 In 2012-2013, ~40,000 subjects in BioVU were genotyped on the exome

variants on AF

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chip (http://genome.sph.umich.edu/wiki/Exome_Chip_Design). Subjects for exome chip

genotyping were drawn from the following BioVU cohorts:

1. An elderly cohor -up.

2. A rare diseases cohort: subjects with FDA defined rare diseases.

3. A longitudinal cohort:

a. Healthy longitudinal set = subjects with 1 or 2 notes/year for at least 5 years.

b. Sick longitudinal set = subjects with 3, 4, 5, or 6 notes/year for at least 5 years.

4. The Vanderbilt Electronic Systems for Pharmacogenomic Assessment (VESPA)

cohort: subjects with drug response phenotypes.19

5. A cancer cohort: subjects with cancer according to the Vanderbilt Tumor Registry.

6. A Pediatric cohort: subjects <18 years old with a minimum threshold of clinical

.

For the present study, we included all subjects of European American (EA) and African

American (AA) ancestry from groups 1-5 above.

AF phenotype

To identify AF cases, we used a validated algorithm20 that uses a combination of queries of

structured billing codes (i.e. International Classification of Diseases, version 9-CM [ICD-9],

procedure codes from Current Procedural Terminology [CPT]), and unstructured “natural

language” from clinical notes and electrocardiograms. In brief, AF cases were defined as

individuals who were >18 years old, had an ICD-9 diagnosis for AF or flutter (ICD-9: 427.3,

427.31, and 427.32) or a cardiologist diagnosis of AF as identified by a natural language

processing tool from the unstructured free text of the “ECG impression”. In all instances, patients

with a history of a heart transplant were excluded (CPT: 33935, 3394, and 580; ICD-9: V42.1,

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996.83).

For the present study, non-AF controls were defined as individuals who were >50 years

old and who were not identified as a case by the case algorithm. For sensitivity analyses, we

identified three additional control cohorts of individuals who were not identified as cases and

who were >60 years, >70 years, and >80 years old. In addition, we also defined a “definite non-

AF” control cohort as individuals -9 codes

for AF or atrial flutter (ICD-9: 427.3, 427.31, and 427.32), had no free text mentions AF in their

clinical notes, and no free-text references (including synonyms) to direct-current cardioversion,

atrial tachycardia or multifocal atrial tachycardia, atrioventricular nodal ablation, as previously

described.20 AF case and control numbers are derived from algorithm deployment into BioVU

on January 15, 2014. We stratified all analyses by AA and EA ancestry, as described further

below.

Genotyping and Quality Control

We excluded samples with genotype-phenotype sex discrepancies, samples with genotyping

efficiency <98%, related samples as determined by identity-by-descent, and unintended

duplicates. We removed SNPs that were monomorphic, had low call-rates (<98%), or were

common (minor allele frequency (MAF) >5%) and were out of Hardy-Weinberg equilibrium

(HWE) (HWE P<0.0001).

Ancestry

In order to reduce the influence of population stratification on our results, we identified and

analyzed separately individuals of EA and AA ancestry by calculating principal components

using EIGENSOFT21; the first and second principal components were retained for further

analysis.

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SNP identification

We performed a literature review to identify studies implicating rare genetic variants with lone or

familial forms of AF using the following search terms in PubMed: ((atrial fibrillation (mesh))

OR (atrial fibrillation)) AND ((genetics (mesh)) OR (genetic*)) AND ((mutation (mesh)) OR

(mutation*) OR (polymorphism, single nucleotide (mesh)) OR (polymorphism, single

nucleotide) OR (monogenic*) OR (gwas)). In total, we identified 565 articles meeting these

search criteria as of January 12, 2014. These articles implicated 115 rare genetic variants that had

evidence of i) co-segregation with AF; ii) supporting electrophysiological studies or; iii) were

associated with AF based on large studies of lone AF probands and/or SNP rarity. Supplemental

Table 1 lists additional rare variants not included in a recent review by Olesen et al.22 We cross-

referenced the 115 rare SNPs with the exome chip. Overlapping SNPs were retained for further

analysis.

As a positive control for our experiment, we also examined the relationship between

AF/non-AF phenotype status and previously-described common AF variants at the 4q25 locus

among EAs (rs6843082, rs2200733, and rs17042171).13, 16, 23

Statistical Analyses

Dichotomous and continuous variables were compared with Fisher’s exact test and the Kruskal-

Wallis test, respectively. Continuous values are presented as medians and interquartile ranges

(IQR). We determined SNP associations with AF using exact logistic regression. We performed

both unadjusted and adjusted analyses (age, sex, first and second principal components [PC]),

which yielded similar estimates unless otherwise noted.

We also performed a series of interaction analyses using logistic regression analysis with

2 main model effect terms (“presence or absence of rare AF variants” and “presence or absence

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of common AF variants”) and a multiplicative interaction term (“presence or absence of rare AF

variants” X “presence or absence of common AF variants”) to test the hypothesis that the co-

occurrence of both common AF variants and rare AF variants modulates the risk of developing

AF (i.e. SNP X SNP interaction). We tested the co-occurrence of each of common vs. each rare

variant and we also collapsed rare and common variants into an allelic test based on the presence

and absence of the variant. We only evaluated SNP X SNP interactions for SNPs that were

identified from studies based on EA populations.

To reduce the risk of misclassification due to age-dependent development of AF, we

repeated all association analyses using additional non-AF control cohorts, with no AF as

described above at age cut-offs of >60, >70, and >80 years. Conservation for single base variants

and prediction of functional effects were assessed using PhastCons, Genome Evolution Rate

Profiler (GERP), Grantham scores, and PolyPhen2.

All analyses were performed using PLINK/SEQ (http://atgu.mgh.harvard.edu/plinkseq)

and SAS, version 9.2 (SAS institute Inc., Cary, NC, USA). A two-sided p-value <0.05 was

considered to be statistically significant.

Ethics

Approval for the present study was obtained from the Vanderbilt Institutional Review Board

(IRB).

Results

We identified 20,204 individuals (18,424 EAs and 1,780 AAs) with available genotype data and

also passed all QC parameters. Table 1 lists the clinical characteristics for AF cases and the non-

, stratified by EA and AA ancestry. Overall, the AF cases were older

(77.8 [IQR: 69.4-83.8] years) than the non- -79.4]

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years, P<0.001). The median age was 74.2 (IQR: 66.6-81.5) years for non-

years (n=13,715), 79.4 (IQR: 74.8-84.6) years for the non- s (n=8,856),

and 84.6 (IQR: 81.9-88.5) years for the non- (n=4,384). For the

definite non-AF controls (n=4,514), the median age was 78.0 (IQR: 66.6-83.9).

Previously-described associations between common genetic variants at the 4q25 locus

and AF were replicated in the present study in both adjusted (rs6843082, odds ratio [OR]=1.28,

95% confidence interval [CI]: 1.15-1.42, p=5.3x10-6; rs2200733, OR=1.33, CI: 1.16-

1.50,p=3.0x10-5; rs17042171, OR=1.33, CI: 1.17-1.51, p=1.9x10-5) and unadjusted additive

models (data not shown).

Cross referencing the 115 rare SNPs previously implicated with lone or familial forms of

AF with the exome chip yielded a total of 19 overlapping SNPs, in 9 genes: NPPA (n=1),

KCNE4 (n=1), GREM2 (n=1), SCN5A (n=11), GATA4 (n=1), KCNJ8 (n=1), KCND3 (n=1),

KCNA5 (n=1), and KCNJ2 (n=1). In the parent studies suggesting a role for these SNPs in lone

or familial forms of AF, 14 SNPs were identified among individuals of EA ancestry, 4 SNPs in

individuals of AA ancestry, and 1 SNP in Asian subjects.

None of the 19 SNPs were significantly overrepresented among EA/AA AF cases

compared to the respective non-AF controls (P>0.1 for all; Table 2). Of the 19 SNPs, 14/19 (10

in EAs and 4 in AAs) were -AF controls (number of risk

allele carriers range among 17,416 EA non-AF controls, range: 19-179 [0.11-1.03%]; number of

risk allele carriers range among 1,732 AA non-AF controls, range: 24-112 [1.39-6.47%]).

Four of the 10 EA SNPs that were common among the non-AF controls were

very rare or absent in the 1,008 EA AF cases (range:0-1), and an additional 4 EA SNPs were rare

among both EA AF cases (range: 0-1 [0-0.1%]) and EA non-AF controls (range: 2-14 [0.01-

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0.08%]). All four SNPs originally reported to be of AA ancestry were common among both AA

AF cases and AA non-AF controls (Table 2).

Supplemental Table 2 presents the in silico functional predictions of the 19 SNPs and the

derived EA and AA MAFs from the exome sequencing project of 6500 individuals.25 Of the 19

variants, 9 were predicted to disrupt protein function by PolyPhen2 (the PolyPhen2 score ranges

from 0 to 1, with the threshold for probably damaging at 0.85).

Repeat analyses using the definite EA and AA non-AF controls (n=4514 and n= 541,

respectively), the EA and AA non-AF controls aged >60 years (n=13,715 and n=1,189,

respectively), the EA and AA non-AF controls aged >70 years (n=8,856 and n=714,

respectively), and the EA and AA non-AF controls aged >80 years (n=4,388 and n=341,

respectively), did not influence the results.

SNP x SNP interaction

Table 3 presents the number of rare variant carriers as a function of common AF variant carrier

status and AF status. Here there was no significant interaction between co-occurrence of

common AF variants (rs6843082, rs2200733, and rs17042171) and rare genetic variants and AF

susceptibility (P=0.47). We also found no association between overall rare AF variant carrier

status (ignoring common variants) and AF susceptibility (P=0.81).

Discussion

In the present study we found that the rare or low-frequency variants interrogated by the exome

chip and previously implicated in lone or familial forms of AF were not associated with AF in a

general population; a substantial proportion of the non-AF controls carried rare or low frequency

variants previously labeled as being AF causative or pathogenic. Moreover, co-occurrence of

common variants and rare variants on the exome chip did not appear to modulate AF

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susceptibility risk. These data stress the need for caution when assigning pathogenicity of rare

variants.

We evaluated 19 rare variants previously implicated with lone or familial forms of AF by

linking genotypic information with phenotypic data from the Vanderbilt EMR. Notably, we did

not find any of the 19 rare variants previously implicated with lone or familial forms AF to be

enriched among patients with AF in either EA or AA populations. In fact, 14/19 variants were

relatively common among non-AF controls (>0.1%), which suggests that these variants may not

be causative (i.e. the monogenic cause of AF). It is possible that these variants are associated

with very low penetrance or contribute in some as yet undefined fashion to AF susceptibility. For

example, we can postulate that we did not identify a true pathogenic effect of the variants

because of a missing “second hit”; the “two-hit” hypothesis wherein the combination of a rare

genetic variant and another risk factor (e.g. a common AF variant or a clinical risk factor [e.g.

hypertension]) modifies the risk of AF.26 We recently reported an interaction between rare and

common AF risk variants modulating the penetrance of familial AF,27 but this signal was not

present in the general population studied here. While we determined that 14/19 variants are not

strongly correlated with AF, we are unable to determine the correlation with AF for 5/19 as they

were very rare or absent among both cases and controls.

For most genetic studies, replication is vital as it adds confidence and lessens the risk of a

false positive chance finding. However, for many studies implicating rare genetic variants,

replication is not possible given the rarity of the phenotype in question (unable to accrue enough

cases), the rarity of the variant in question (investigators would need to screen thousands of

subjects to gain adequate statistical power), or both. In the absence of readily available

replication cohorts for rare variants, variant function or causality can be inferred by providing

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convincing functional data. For rhythm disorders such as AF, function or causality is commonly

inferred through strong genotype-phenotype co-segregation patterns2, 3, 7, 9, 28, evidence of

electrophysiological effects3, 5-10, 29, and/or rarity of the variant in question in various control

populations under the assumption that truly pathogenic variants are under a strong negative

selection pressure.2, 4, 8, 9, 28 While such methods represent contemporary approaches for inferring

function or causality in studies involving arrhythmia susceptibility, they provide no information

regarding variant effects on disease susceptibility.

The idea that rare variants previously associated with disease may represent false positive

or at best weak associations, as has been suggested by others using public databases or isolated

cases of normal individuals harboring previously-reported pathogenic variants. For example,

Ploski et al. recently reported a patient with a normal phenotype and the Q247X deletion in

muscle ring finger protein 1 (TRIM63), a variant which has previously been reported by family

and functional studies as a cause of hypertrophic cardiomyopathy (HCM). 30, 31 In addition,

several studies have questioned the pathogenicity of genetic variants previously associated with

rare monogenic syndromes such as catecholaminergic polymorphic ventricular tachycardia,

Brugada syndrome, HCM, dilated cardiomyopathy, and arrhythmogenic right ventricular

cardiomyopathy,32-34 based on variant prevalence reported by the exome variant server

(http://evs.gs.washington.edu/EVS/).25 However, this resource does not include phenotypic

information, and did include some subjects with cardiovascular diseases such as DCM or

extreme lipid values.35, 36 This study suggests broadly-phenotyped EMR-linked genotype data

may be a useful resource for evaluating rare variants for pathogenicity.

Limitations

The rarity of the SNPs investigated limits the power of our study. In addition, while stratifying

may represent false e e e ppppo

blic dddd ttatt bbbabases or isisisisoooola

rmal individuals harboring previously-reported pathogenic variants. For examp

n

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,

dies ha e q estioned the pathogenicit of genetic ariants pre io sl associated

rmalalalal iiindndndndivivivididididuaaalslslsls harboring previously-reeepopported pathogeg nic c vvvariants. For examp

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nal studies as a cause offf hyhyh pepp rtrophphhiciii cardididiiomyoyy papp thhhhy yy (H(H(H(HCMCMCMCM))). 30303030, 31 In addition,

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by race is important to reduce the likelihood of ancestral informative markers driving and

association, it does mean an additional loss of power in our analysis of individuals of both EA

and AA ancestry. The rarity of the SNPs investigated limits the power of our study. Hence, we

acknowledge the fact that we may be underpowered to truly detect an association between rare

genetic variants previously associated with monogenic or familial forms of AF. A power

calculation is depicted in the supplemental appendix (Supplemental Figure 1). While we tried to

eliminate the effects associated with population stratification by performing separate analysis for

EAs and AA, our results may have been influenced by differences in population substructure.

Although our results suggest that caution should be exercised before extrapolating study findings

on genetic correlates of lone or familial forms of AF to a general form of AF, it is important to

note that this conclusion is based on the findings examining 19 SNPs (with one originally

reported in Asian subjects and so not meaningfully evaluated here). Also, low numbers for the

AA AF cases may have influenced our results. The HumanExome BeadChip v.1.0 includes rare

variants (nonsynonymous variants 3 times and in 2 call sets; splice and stop variants seen 2

times and in 2 call sets) observed among ~9000 individuals of EA ancestry, ~2000 of AA

ancestry, and ~500 samples of Hispanic and Asian ancestry

(http://genome.sph.umich.edu/wiki/Exome_Chip_Design). Thus, singletons, which may be

associated with AF in families, are not interrogated. In the present study we did not perform

orthogonal validation of genotyped variants on the exome chip; genotype data from this platform

is known to yield reliable results.37, 38

While we in the present manuscript were able to replicate known AF associations which

do add confidence in the data, we acknowledge the fact that our study cohort represents patients

either hospitalized or seen in a hospital setting which may have affected our results. Hence,

population substrucucucuctututtu

xtrapolllal titititing stuttt dydydydy ffffiiiin

correlates of lone or familial forms of AF to a general form of AF, it is importan

Asian subjects and so not meaningfully evaluated here). Also, low numbers for

s

ons non mo s ariants 3 times and in 2 call sets; splice and stop ariants seen

corrererelalalaattetetess ofooo lononone or familial forms of AAAF FF tto a general form m of AF, it is importan

iss cccconclusion isisis baaseeede oooon nn the ee ffif ndnndingggs examaminnninining g gg 19 SSSNNPs (wiwwithththh oneeee oooriiigiiiinalllll yy

Asiaiannn susususubjbjbjecctstst aandndd sso o nottt mmmmeanininini gffgfgfulululu llyly eevavaluuuattatatededed hhhherre))e)e). AlAlAlsoso, lolololowwww numbmbbererss ffofor

es may yy have inffffllull enceddd d our resulltl s. TTThehh HHumanEEExome BBBeadCdCdCdChihihihippp v.1.0 includes

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extrapolation of our results to other study cohorts should be done with caution.

Conclusion

A set of rare variants previously implicated with lone or familial forms AF were detected at low

frequencies in a general population, where they were not associated with AF. Collectively, our

results suggest that evaluating the contribution of rare variants to human phenotypes continues to

require assessment of variant frequencies and associated phenotypes in the general population.

Moreover, our data also suggest that the variants present on the exome chip may be too common

to represent truly causative mutations in the setting of diseases of a presumed monogenic

etiology.

Funding Sources: The datasets used for the analyses described were obtained from Vanderbilt University Medical Center’s BioVU which is supported by institutional funding and by the Vanderbilt CTSA grant UL1 TR000445 from NCATS/NIH. This work was also in part supported by NIH grants U19 HL65962, and R01 HL092217). PW was funded by an unrestricted research grant from the Tryg Foundation (J.nr. 7343-09, TrygFonden, Denmark).

Conflict of Interest Disclosures: None

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ouuuurcccces: Thhe ee ddatttaseetsss ussededed forr thhhe anaaalyyysees deeesccribebed wwere ooobtaiaiiinnned frfrfrommm VaaandeMeddddicicicalalalal CCCenntetet ’’r’r’s s BiBiBiioVo U UU whwhwhwhich isiisis supupupu pooo trtrt dededd bbbyyyy ininnsttststittutututtiiioionanallll fundndndndinining anandddd bybyb ttthhheCTSA grant ULULULUL11 TRTRTRTR000000004040445454545 fffrorororom m m m NCNCNCN ATATATSSSS/N/N/N/NIHIHIHH.... ThThThT isisisi wwwworororo k k k k wawawaass s s alaaa so in part

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lone atrial fibrillaaaatitititionooo

l Episisisistatatatatitititicccc efefefeffefefefectctctctssss ofooc0

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38. Auer PL, Teumer A, Schick U, O'Shaughnessy A, Lo KS, Chami N, et al. Rare and low-frequency coding variants in CXCR2 and other genes are associated with hematological traits. Nat Genet. 2014;46:629-634.

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Table 1: Characteristics EA/AA AF cases and non-AF controls

EA AF Cases EA non-AF Controls AA AF Cases AA non-AF

Controls

N 1,008 17,416 48 1,732

Age, (IQR) 78.1 (69.7-83.8) 69.9 (60.9-79.6) 71.3 (58.3-81.6) 65.5 (57.6-77.5)

Female gender, (%) 458 (46.0) 9088 (52.2) 26 (54.2) 1063 (61.4)

AF, atrial fibrillation; EA, European American; AA, African American, IQR, interquartile range R, interquartitititilllle rangegeee


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Table 2: Prevalence of 19 Variants Previously Associated with Lone or Familial Forms of AF

Gene AminoAcidChange

GenotypePhenotype

Segregation

EP function

Discovery”cohort”ancestry

EAMAF

Risk allelecarriers amongEA AF Cases,

(%)

Risk allele carriers among EA non-AF

Controls*, (%)

EAP†

AAMAF

Risk allele carriers

among AA AF Cases, (%)

Risk allele carriers among

AA non-AF Controls*, (%)

AAP† Ref

-AF controls:

NPPA Ser64Arg S LoF EA 0.003 5/1008 (0.50) 120/17415 (0.69) 0.69 0.0006 0/48 2/1732 (0.11) 0 7

GREM2 Gln76Glu

Increase-in-

inhibitory-effect

EA 0.004 10/1008 (1.00) 133/17415 (0.76) 0.46 0 0/48 0/1732 - 10

KCNE4 Glu141Ala Possible-change EA 0.0005 0/1008 19/17414 (0.11) 0.62 0 0/48 0/1732 - 9

SCN5A Phe2004Leu GoF EA 0.0052 13/1008 (1.30) 179/17414 (1.03) 0.42 0.0003 0/48 1/1731 (0.06) 0 2, 24

SCN5A Thr1304Met GoF EA 0.0009 0/1008 35/17416 (0.20) 0.26 0.0003 0/48 1/1732 (0.06) 0 8

SCN5A Arg1193Gln GoF EA 0.0010 4/1008 (0.40) 33/17416 (0.19) 0.14 0 0/48 0/1732 - 2, 24

SCN5A Leu618Phe - AA 0 0/1006 0/17381 - 0.0071 1/48 (2.1) 24/1725 (1.39) 0.50 2

SCN5A Ser524Tyr - AA 0.0005 0/1008 18/17416 (0.10) 0.62 0.0328 5/48 (10.4) 112/1732 (6.47) 0.25 2

SCN5A Arg481Trp - AA 8x10-5 0/1008 3/17416 (0.02) 1.00 0.0101 2/48 (4.2) 34/1732 (1.96) 0.25 2

SCN5A Leu461Val S - EA/AA 0.0001 1/1008 (0.10) 3/17382 (0.02) 0.20 0.0127 1/48 (2.01) 44/1727 (2.55) 1 2

SCN5A Thr220Ile LoF EA 0.0010 1/1008 (0.10) 35/17416 (0.20) 0.72 0.0006 0/48 2/1732 (0.12) 1 8, 11, 24

SCN5A Ser216Leu GoF EA 0.0015 3/1008 (0.30) 53/17416 (0.30) 1.00 0 0/48 0/1732 - 2, 24

GATA4 Ala411Val - EA 0.0015 1/1008 (0.10) 56/17410 (0.32) 0.37 0.0011 1/48 (2.1) 3/1731 (0.17) 0.10 4

KCNJ8 Ser422Leu GoF EA 0.0010 2/1008 (0.02) 36/17416 (0.21) 1.00 0 0/48 0/1732 - 5, 6

Variants present in <0.1% of EA/AA non-AF controls:

KCND3 Lys214Arg No Change EA 0.0004 0/1005 14/17380 (0.08) 1.00 0 0/48 0/1727 - 9

SCN5A Arg1897Trp LoF EA 8x10-5 1/1004 (0.10) 2/17353 (0.01) 0.16 0 0/48 0/1724 - 8

SCN5A Glu428Lys S - EA 0.0003 0/1008 12/17415 (0.07) 1.00 0 0/48 0/1732 - 2

KCNA5 Gly568Val S GoF EA 0.0002 0/1001 6/17329 (0.03) 1.00 0 0/48 0/1724 - 9

KCNJ2 Val93Ile S GoF Asian 2x10-5 0/1007 1/17420 (0.006) 1.00 0 0/48 0/1731 - 3

*Free from AF at 50 years old †Unadjusted p-values shown; the adjusted p-values (age, sex, PC1, and PC2) yielded similar estimatesS, Genotype-phenotype segregation; EP, electrophysiological; LoF, loss-of-function; GoF, gain-of-function; EA, European-American; AA, African-American; AF, atrial fibrillation

0.0.0.0.62626262 0 0 0 0

0.424222 0.0.0.0.0000000003030303

E

EAAAA 0....0000000009 0/1008 35353535/17416 (0.20) 0.26 0.0003

EAAAA 0.0.00.0000000010101010 4/4/4/4/1010101008080808 ((((0.0.00 4000)))) 333/117474747416161616 (00.0.0 1999))) 0....14141414 0 0 0

AAAAAAAA 000 0/10000006 0/0/0/0/17171 38381 -- 0.000071

AA 0.0000005000 0/1008 1811 /17416666 (0.10) 0.62 0.0328

AA 8x10101010-5555 0/0/0/0 101010100808080 3/3/3/3/1717174116666 (0(0(0(0.00002)2)2) 1.111 00000000 0.0101

EEEA/AA 0.000 0000000001010101 1/11 1010101008080808 ((((0.000 10101010)))) 3/3/3/3/17171717383838382222 (0(0(0(0.00002)2)2)2) 0.000 20202020 0.000 0127


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Table 3: No interaction between common AF risk variants at the 4q25 locus and 14 rare variants

previously implicated with lone or familial forms of AF among EAs

No AF, (%) AF, (%)

Absence of common 4q25 variants* 459 (60.5) 25 (3.3)

Presence of common 4q25 variants* 259 (34.2) 15 (2.0)

* rs6843082, rs2200733, and rs17042171 AF, atrial fibrillation; SNP, single nucleotide polymorphism, EAs, European Americans Chi-square statistic = 0.85

memememeriririricacacacansnsnsns


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Dawood Darbar and Dan M. RodenPeter Weeke, Joshua C. Denny, Lisa Basterache, Christian Shaffer, Erica Bowton, Christie Ingram,

NoteFamilial Forms of Atrial Fibrillation in an Electronic Medical Record System: A Cautionary Examining Rare and Low-Frequency Genetic Variants Previously Associated with Lone or

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Supplemental Material

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Supplemental Table 1 –Additional Rare and low frequency Variants Associated with Lone or Familial Forms of AF

Gene AminoAcid

Change CHR:POS REF/ALT

Accession Number

Rs# Ref

KCNQ1 p.Gly229Asp 11:2593245 G/A NM_000218.2 rs199472708 1 KCNQ1 p.Val241Phe 11:2593280 G/T NM_000218.2 NA 2 KCNJ2 p.Glu229Val 17:68172076 A/T NM_000891.2 NA 3

JPH2 p.Glu169Lys 20:42788922 C/T NM_020433.4 NA 4

http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=199472708

http://www.ncbi.nlm.nih.gov/nuccore/NM_000891.2

http://www.ncbi.nlm.nih.gov/nuccore/NM_020433.4

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Supplemental Table 2 –In-silico Functional Predictions of the 19 Variants Previously Associated with Lone or Familial Forms of AF

Gene AminoAcid

Change CHR:POS

REF/ALT

Accesion Number

Rs# PP2 Grantham

Score Phast Cons

GERP ESP EA ESP AA Ref.

Variants present in ≥0.1% of EA/AA non-AF controls: NPPA Ser64Arg 1:11907430 T/G NM_006172.3 61757261 0.024 110 0 3.54 G=24/T=8576 G=2/T=4402

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GREM2 Gln76Glu 1:240656550 G/C NM_022469.3 142343894 0.437 29 0 5.03 C=27/G=8573 C=4/G=4402 6

KCNE4 Glu141Ala 2:223917970 A/C NM_080671.2 149110444 1 107 1 6.17 C=5/A=8595 C=0/A=4406 7

SCN5A Phe2004Leu 3:38591853 A/G NM_198056.2 41311117 0 22 0.992 -1.63 G=26/A=8342 G=2/A=4026 8, 9

SCN5A Thr1304Met 3:38603958 G/A NM_198056.2 199473603 1 81 0.89 4.04 A=4/G=8464 A=1/G=4175 10

SCN5A Arg1193Gln 3:38616876 C/T NM_198056.2 41261344 0.467 43 0.988 1.58 T=11/C=8589 T=0/C=440 8, 9

SCN5A Leu618Phe 3:38645241 G/A NM_198056.2 45488304 0.999 22 0.806 4.18 A=0/G=8416 A=27/G=4095 8

SCN5A Ser524Tyr 3:38645522 G/T NM_198056.2 41313691 0.998 144 1 4.06 T=7/G=8375 T=135/G=3945 8

SCN5A Arg481Trp 3:38646297 G/A NM_198056.2 144511230 0.999 101 0.902 3.29 A=0/G=8270 A=39/G=3897 8

SCN5A Leu461Val 3:38646357 A/C NM_198056.2 41313697 0.285 32 0.998 -0.65 C=2/A=8296 C=34/A=3914 8

SCN5A Thr220Ile 3:38655278 G/A NM_198056.2 45620037 1 89 1 4.3 A=4/G=8404 A=0/G=4032 9-11

SCN5A Ser216Leu 3:38655290 G/A NM_198056.2 41276525 1 145 1 4.3 A=11/G=8421 A=1/G=4089 8, 9

GATA4 Ala411Val 8:11615887 C/T NM_002052.3 55633527 0.012 64 0.001 4.83 T=12/C=8588 T=10/C=4396 12

KCNJ8 Ser422Leu 12:21918667 G/A NM_004982.3 72554071 0.013 145 0.038 4.45 A=19/G=8581 A=1/G=4405 13, 14

Variants present in <0.1% of EA/AA non-AF controls: KCND3 Lys214Arg 1:112524708 T/C NM_172198.2 142744204 0 26 1 5.51 C=4/T=8596 C=0/T=4406

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SCN5A Arg1897Trp 3:38592174 G/A NM_198056.2 45465995 1 101 0.988 3.97 A=3/G=8413 A=0/G=4252 10

SCN5A Glu428Lys 3:38647498 C/T NM_198056.2 199473111 0.993 56 0.899 4.67 T=2/C=8372 T=0/C=4150 8

KCNA5 Gly568Val 12:5155016 G/T NM_002234.3 71581017 0 109 0.001 -1.72 T=3/G=8597 T=0/G=4406 7

KCNJ2 Val93Ile 17:68171457 G/A NM_000891.2 147750704 0.004 29 0.026 3.48 A=2/G=8598 A=1/G=4405 15

PP2, polyphen2; GERP, genomic evolution rate profiler; ESP, exome sequencing project; EA, European-American; AA, African-American; AF, atrial fibrillation PolyPhen-2: scores are evaluated as 0.000 (most probably benign) to 0.999 (most probably damaging).

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Supplemental Figure 1 - Power calculation

Figure Legend: We employed a 1:18 case control ratio based on our study cohort (1,056 AF cases and 19,148 non-AF controls) assuming an additive gene-only model with a MAF=0.01 using a nominal two-sided p-value of <0.05 as our alpha. The effect sizes are reported as beta coefficient. Power calculations were performed using Quanto, (http://hydra.usc.edu/gxe/).

http://hydra.usc.edu/gxe/

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2. Ki CS, Jung CL, Kim HJ, Baek KH, Park SJ, On YK, et al. A KCNQ1 mutation causes age-dependant bradycardia and persistent atrial fibrillation. Pflugers Arch. 2014;466:529-540

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