Replication Cohorts

DNA from blood samples of surgical patients at the University of Michigan Health System was genotyped on the Illumina HumanCore Exome array. Genotypes of the Haplotype Reference Consortium were imputed into the phased Michigan Genomics Initiative genotypes, resulting in dense mapping of over 7.7 million common variants (MAF > 1%). Individuals were defined as having AF if they had at least two electronic health record (EHR) encounters with ICD-9 code 427.31. AF-free individuals were defined as persons without ICD-9 billing code 427.31 or any other billing codes related to cardiac arrhythmias or conduction disorders. We performed a genome-wide association analysis of AF in 924 AF individuals and 11,037 AF-free individuals, all of European ancestry, by using the Firth-bias-corrected logistic likelihood-ratio test,²⁵ adjusted for age, sex, and PCs 1–4.

The DiscovEHR study comprised a total of 50,726 adult individuals enrolled in the MyCode Community Health Initiative of the Geisinger Health System. Participants were recruited from outpatient primary care and specialty clinics from 2007 to 2016, and available EHR data covered a median of 14 years of clinical care. Samples were processed at Illumina and genotyped on the OmniExpressExome array. GWAS variants with a MAF > 1% were phased with SHAPEIT2, and IMPUTE2 was used for imputing to the 1000 Genomes Project cosmopolitan dataset (June 2014 version). Variants with an info score < 0.7 or a genotyping call rate < 99% were excluded from downstream analysis. AF and flutter case individuals (n = 5,451) were defined as European-ancestry DiscovEHR participants with at least one EHR problem-list entry or at least two diagnosis-code entries for two separate clinical encounters on separate calendar days for ICD-9 code 427.3 (AF and flutter). Control individuals (n = 30,235) were defined as European-ancestry individuals with no EHR diagnosis-code entries (problem list or encounter codes) for ICD-9 code 427 (cardiac dysrhythmias). For each variant, we tested best-guess genotypes for association with AF case-control status by using logistic regression; we included age, age², sex, and ancestry PCs 1–4 as covariates.

From 2006 to 2010, the UK Biobank recruited 40- to 69-year old individuals who were registered with a general medical practitioner within the UK National Health Service.²⁶ We defined individuals as having AF if they had any hospital ICD-9 or ICD-10 code specific to AF or atrial flutter (427.3 or I48, respectively). All other persons were used as control individuals. We restricted analyses to those genotyped persons of European ancestry who passed the UK Biobank’s quality control. Individuals who had withdrawn consent were excluded. After exclusion, the total numbers of case and control individuals were 4,407 and 115,878, respectively. We modeled the association between genotypes of interest and AF by using a logistic regression (SNPTEST v.2.5.2) adjusted for genotype batch and PCs 1–10 under the assumption of an additive genetic model.

We selected 1,158 AF individuals and 5,393 AF-free individuals from the ongoing Tromsø Study (Tromsø 4) for follow-up. Tromsø 4 is a population-based study of more than 27,000 people enrolled from the municipality of Tromsø in Norway between 1994 and 1995.²⁷ Occurrences of AF were identified by the registry of hospital discharge diagnosis at the University Hospital of North Norway (diagnoses from hospitalizations and outpatient clinics) and by the National Causes of Death registry with ICD-9 codes 427.0–427.99 and ICD-10 codes I47–I48. All diagnoses of AF were confirmed by electrocardiography (ECG). DNA samples of the Tromsø Study were extracted from venous blood and genotyped with the Illumina HumanCoreExome 12v.1.1 array, and genotypes of the 1000 Genomes Project (phase 3 release 5; Minimac3) were imputed into their phased haplotypes (SHAPEIT2). Imputed variants with R² ≤ 0.3 were excluded. Association tests of candidate single-nucleotide variants (SNVs) were performed with a logistic Wald test, which added sex and birth year as covariates.

The Mount Sinai BioMe Biobank (BioMe) is an ongoing, prospective hospital- and outpatient-based population research program operated by the Charles Bronfman Institute for Personalized Medicine at Mount Sinai and has enrolled over 33,000 participants since September 2007. BioMe is an EHR-linked biobank that integrates research data and clinical-care information for consenting patients at the Mount Sinai Medical Center, New York, which serves diverse local upper-Manhattan communities with broad health disparities. Information on AF, age, and sex was derived from participants’ EHRs. Age was derived from the day of enrollment. BioMe participants were defined as having AF if they had an ICD-9 code specific to AF (427.31) or atrial flutter (427.32), and AF-free individuals were defined as participants who have had ECG but did not have AF or flutter ICD-9 codes. Study participants were genotyped with the Illumina HumanOmniExpressExome-8 v.1.0 BeadChip array and imputed to the 1000 Genomes Project phase 1 reference panel with IMPUTE2. Association testing was carried out under an additive genetic model with SNPTEST 2.4.1 in 291 AF individuals and 860 AF-free individuals of European ancestry with age, sex, and PCs 1–4 as covariates.

Individuals with AF were recruited from eight major hospitals in Copenhagen, Denmark. Diagnoses of AF were verified by ECG. Control individuals were recruited among healthy blood donors. The final study sample comprised 517 unrelated AF individuals and 350 AF-free individuals, all of European ancestry. SNVs of interest were directly genotyped by Kompetitive Allele-Specific PCR. The association between SNVs and AF was modeled with a logistic regression under the assumption of an additive genetic model.