A Comparison of Heritability Estimates by Classical Twin Modeling and Based on Genome-Wide Genetic Relatedness for Cardiac Conduction Traits

Abstract

Twin studies have found that ~50% of variance in electrocardiogram (ECG) traits can be explained by genetic factors. However, genetic variants identified through genome-wide association studies explain less than 10% of the total trait variability. Some have argued that the equal environment assumption for the classical twin model might be invalid, resulting in inflated narrow-sense heritability (h2) estimates, thus explaining part of the ‘missing h2’. Genomic relatedness restricted maximum likelihood (GREML) estimation overcomes this issue. This method uses both family data and genome-wide coverage of common SNPs to determine the degree of relatedness between individuals to estimate both h2 explained by common SNPs and total h2. The aim of the current study is to characterize more reliably than previously possible ECG trait h2 using GREML estimation, and to compare these outcomes to those of the classical twin model. We analyzed ECG traits (heart rate, PR interval, QRS duration, RV5+SV1, QTc interval, Sokolow-Lyon product, and Cornell product) in up to 3,133 twins from the TwinsUK cohort and derived h2 estimates by both methods. GREML yielded h2 estimates between 47% and 68%. Classical twin modeling provided similar h2 estimates, except for the Cornell product, for which the best fit included no genetic factors. We found no evidence that the classical twin model leads to inflated h2 estimates. Therefore, our study confirms the validity of the equal environment assumption for monozygotic and dizygotic twins and supports the robust basis for future studies exploring genetic variants responsible for the variance of ECG traits.