Rare Coding Variants Associated With Electrocardiographic Intervals Identify Monogenic Arrhythmia Susceptibility Genes: A Multi-Ancestry Analysis

Abstract

Background: Alterations in electrocardiographic (ECG) intervals are well-known markers for arrhythmia and sudden cardiac death (SCD) risk. While the genetics of arrhythmia syndromes have been studied, relations between electrocardiographic intervals and rare genetic variation at a population level are poorly understood.

Methods: Using a discovery sample of 29 000 individuals with whole-genome sequencing from Trans-Omics in Precision Medicine and replication in nearly 100 000 with whole-exome sequencing from the UK Biobank and MyCode, we examined associations between low-frequency and rare coding variants with 5 routinely measured electrocardiographic traits (RR, P-wave, PR, and QRS intervals and corrected QT interval).

Results: We found that rare variants associated with population-based electrocardiographic intervals identify established monogenic SCD genes (KCNQ1, KCNH2, and SCN5A), a controversial monogenic SCD gene (KCNE1), and novel genes (PAM and MFGE8) involved in cardiac conduction. Loss-of-function and pathogenic SCN5A variants, carried by 0.1% of individuals, were associated with a nearly 6-fold increased odds of the first-degree atrioventricular block (P=8.4×10^-5). Similar variants in KCNQ1 and KCNH2 (0.2% of individuals) were associated with a 23-fold increased odds of marked corrected QT interval prolongation (P=4×10^-25), a marker of SCD risk. Incomplete penetrance of such deleterious variation was common as over 70% of carriers had normal electrocardiographic intervals.

Conclusions: Our findings indicate that large-scale high-depth sequence data and electrocardiographic analysis identifies monogenic arrhythmia susceptibility genes and rare variants with large effects. Known pathogenic variation in conventional arrhythmia and SCD genes exhibited incomplete penetrance and accounted for only a small fraction of marked electrocardiographic interval prolongation.

Keywords: death, sudden, cardiac; epidemiology; genetics; genome; population.

Figure 1.

Flow chart of study and analyses.Top of the figure illustrates different traits detected by the ECG. Genetic association studies were performed for 5 electrocardiographic traits in 9 studies from Trans-Omics in Precision Medicine (TOPMed) as a discovery cohort and findings from discovery analyses were replicated using UK Biobank and MyCode studies (blue). We analyzed genetic variations using both single variant association and gene-based association approaches (orange). Moreover, we calculated frequency of loss-of-function, pathogenic and likely pathogenic variants in long QT syndrome genes and performed association tests between such variants and QT interval. EUR indicates European ancestry; ME, multi-ethnic; PWD, P-wave duration; QRS, QRS duration; QTc, corrected QT interval; WES, whole-exome sequencing; and WGS, whole-genome sequencing.

Figure 2.

Manhattan plots for 5 electrocardiographic traits.A illustrates circular Manhattan plot illustrating genome-wide association testing results between 5 electrocardiographic traits and common variants with minor allele frequency (MAF) >1%. Loci that reached a conventional genome-wide significant threshold (P=5×10⁻⁸, red dotted lines) are annotated with the nearest genes. B shows associations between low-frequency (0.1% ≤ MAF <1%) variants and PR interval. The gray dotted line is the significant threshold (0.05/83 994 variants =6.0×10⁻⁷).

Figure 3.

Association results between electrocardiographic traits and predicted-deleterious variants in genes from candidate loci. Figure 3 illustrates associations between electrocardiographic traits (RR interval, P-wave duration [PWD], PR interval, QRS duration, and corrected QT interval [QTc]) and genes in candidate loci in Trans-Omics in Precision Medicine (TOPMed) using SMMAT. Genes with P>0.05 for all traits were removed from this figure. As shown in the key legend, the gradient of blue colors represents the strength of associations in this heatmap. Genes with a star (*) were significantly associated with an electrocardiographic trait (P<1.2×10⁻⁴); tests with P>0.05 have been made white. The maximum PWD was significantly associated with HAND1 (P=2.4×10⁻⁵). PR interval was significantly associated with SCN5A (P=7.6×10⁻⁷) and PAM (P=4.5×10⁻⁷). QRS duration was significantly associated with CR1L (P=1.2×10⁻⁴). QTc was significantly associated with KCNQ1 (P=2.3×10⁻¹²) and KCNH2 (P=3.2×10⁻⁸). PR indicates PR interval; QRS, QRS duration; and RR, RR interval.

Figure 4.

Forest plots for loss-of-function (LOF), pathogenic or likely pathogenic variants in KCNQ1 and KCNH2 and their effect on corrected QT interval (QTc). Across Trans-Omics in Precision Medicine (TOPMed), UK Biobank and MyCode datasets, KCNQ1 and KCNH2 LOF and pathogenic or likely pathogenic variants significantly and markedly prolonged the QTc, with inverse-variance weighted fixed-effects meta-analyzed effect estimates of 30 ms (P=1.1×10⁻⁶⁷) and 27 ms (P=1.0×10⁻¹⁶) prolongation, respectively.

Figure 5.

Effect of loss-of-function (LOF), pathogenic or likely pathogenic variants in KCNQ1 and KCNH2 on corrected QT interval (QTc) in the population.A illustrates distributions for carriers (red, N=110) of a LOF or pathogenic or likely pathogenic variant in KCNQ1, KCNH2, and noncarriers (gray, N=54 245) in Trans-Omics in Precision Medicine (TOPMed) and UK Biobank. The dotted lines represent QTc cutoffs of 460, 480, and 500 ms. Of the carriers, 15 (13.6%) individuals had QTc interval ≥480 ms while 662 (1.2%) of noncarriers revealed QT prolongation. B illustrates the odds ratio for QTc prolongation at different cutoffs (460, 480, and 500 ms) conferred by LOF, pathogenic or likely pathogenic variants in KCNQ1 and KCNH2 in TOPMed and UK Biobank.