Genetics of Atrial Fibrillation in 2020: GWAS, Genome Sequencing, Polygenic Risk, and Beyond

Abstract

Atrial fibrillation is a common heart rhythm disorder that leads to an increased risk for stroke and heart failure. Atrial fibrillation is a complex disease with both environmental and genetic risk factors that contribute to the arrhythmia. Over the last decade, rapid progress has been made in identifying the genetic basis for this common condition. In this review, we provide an overview of the primary types of genetic analyses performed for atrial fibrillation, including linkage studies, genome-wide association studies, and studies of rare coding variation. With these results in mind, we aim to highlighting the existing knowledge gaps and future directions for atrial fibrillation genetics research.

Keywords: atrial fibrillation; exome; genetics; genome-wide association study; mutation.

Figure 1. Three primary types of genetic analyses for AF

Linkage analyses primarily focus on large families with hereditary forms of AF. The disease associated linked regions can include multiple candidate genes one of which will contain a disease causing mutation. GWAS analyses are based on genotype array data that consists largely of non-coding variants that are presumed to regulate genes in the region or locus. Analyses of coding variation are derived from whole-exome or whole-genome sequencing data. Rare coding or loss-of-function variants are grouped and jointly tested in AF cases versus controls to identify specific disease-causing genes. Please note that these approaches are not mutually exclusive and are often combined depending upon the study design. Abbreviations: AF, atrial fibrillation; GWAS, genome-wide association study; LOF, loss-of-function; SNP, single-nucleotide polymorphism; STR, short tandem repeat.

Figure 2. Major AF-associated genes and lines of evidence

The figure illustrates AF-associated genes that were discovered through family based or gene-based studies. For each gene the lines of evidence are listed. The table includes the evidence from familial AF genetic analysis, whether coding variants in the gene are associated with AF, if the gene lies within an AF GWAS locus, whether loss-of-function variation is associated with AF, and functional evidence that has been reported for the gene in the context of AF. Abbreviations: AF, atrial fibrillation; GWAS, genome-wide association study.

Figure 3. Ancestry of the cases in genome-wide association studies for AF

European ancestry sample is plotted towards the left in white and non-European ancestry is plotted towards the right highlighted in different colors. Plotted is the number of cases included in each published AF GWAS study or meta-analysis. 10 out of 12 studies include predominantly European ancestry samples, shown in white. Two studies are Japanese only and Korean only. Within the multi-ancestry meta-analyses Roselli et al.[28] included the largest proportion of non-European cases including Japanese, Brazilian, African American and Hispanic samples. Abbreviations: AF, atrial fibrillation, AFR, African-American, BRA, Brazilian, EUR, European, HISP, Hispanic, JAP, Japanese, KOR, Korean.

Figure 4. Overview of polygenic risk scores (PRS) for AF

A polygenic risk score is calculated for each individual as a sum of the product of genetic dosage and a weight. The weights are derived from the effect estimates of a genome-wide association study. The PRS of individuals in a population follows a Gaussian distribution. Individuals in the highest percentile of the distributions show an increased risk for AF versus the remaining population. Potential applications of an AF PRS can include improving risk prediction, prioritizing high risk individuals for screening, and examining differential outcomes of AF. Abbreviations: AF, atrial fibrillation, PRS, polygenic risk score.

Figure 5:. Future directions in AF genetics

Overview of emerging technologies and analyses that could shape the field of AF genetics for the next decade. Large-scale rare coding sequence data: With dropping sequencing costs large-scale exome sequencing data sets will become available and accelerate the detection of rare and ultra rare coding variation that associate with AF. Structural genetic variation: whole-genome sequencing data allows the detection of structural variation such as inversions, translocations and large insertions and deletions. Methods to detect structural variation are improving and could lead to uncovering novel structural variant contributions to AF. Polygenic risk for diverse ethnicities: Increasing the contribution of non-European samples in AF GWAS will improve the polygenic risk prediction for diverse ethnicities. Functional cellular knockout assays: Gene knockout studies in relevant cell types, such as atrial cardiomyocytes, will enable the evaluation of AF candidate genes from GWAS loci in the context of functionally relevant readouts. Single cell RNA-sequencing: Next generation sequencing technologies such as the transcriptional profiling of individual cells from cardiac tissue will transform AF genetics and increase the resolution of gene expression profiles to a cell type specific level. Cell type specific expression quantitative trait loci could resolve the causal gene at AF GWAS loci. Machine learning on big data: Machine learning can facilitate the integration of big data sources such as gene expression profiles, proteomics data, protein-protein interaction networks, methylation data, regulatory regions and spatial organization of the DNA. Machine learning algorithms will support the goal to identify causal genes for AF, resolve regulatory mechanisms at AF GWAS loci and uncover patterns that imply disease mechanisms of AF. Abbreviations: AF, atrial fibrillation, LOF, loss-of-function, RNA, ribonucleic acid.