TTN truncating variants in hiPSI exons show high penetrance for cardiomyopathy in carriers with atrial fibrillation

Abstract

Background: Truncating variants in TTN (TTNtvs) represent the largest known genetic cause of dilated cardiomyopathies (DCM). At the population level, even when limited to TTNtvs in cardiac-specific exons (hiPSI TTNtvs) penetrance estimates for DCM are low. Recent work shows that individuals harboring TTNtvs have a high prevalence of other cardiac conditions aside from heart failure, in particular, atrial fibrillation (Afib). Objectives: Pinpoint the genetic footprint TTN-related diagnoses aside from DCM, such as Afib, and determine if vetting additional significantly-associated phenotypes better stratifies cardiomyopathy risk across TTN carriers. Methods: We leverage longitudinal EHR and exome sequencing data from two cohorts to determine the penetrance of TTNtvs using multiple gene expression models against Afib, CM, and other cardiac diagnoses. Results: Controlling for CM and Afib, related cardio phenotypes retain only nominal association with TTNtvs. An unbiased sliding window analysis of TTNtvs across the locus confirms the association is specific to hiPSI exons for both CM and Afib, with no meaningful associations in lowPSI exons nor improvements from LOFTEE designations. We find 34% of hiPSI TTNtv carriers with early Afib have a CM diagnosis - a 5-fold increase in risk over non-carriers with early Afib and 47-fold increase over population controls. Conclusion: CM and Afib are often coincident in hiPSI TTNtv carriers, which represent varying and progressive manifestations of structurally-based heart failure. We provide statistical support for a hiPSI variant interpretation model for TTNtvs and evidence for the first population-level screening method with clinical utility for cardiomyopathies, especially in relation to an Afib finding.

A power-based sliding window approach to evaluate the clinical impact of rare genetic variants

“…However, when they are significant, PW LoF models perform well: the average fold improvement for significant PW LoF models over the whole gene model is 1.7x (range 1.2-2.9; binary traits) and change in effect size is 77% (range 10-160%; quantitative traits) (Figure 4). For example, the PW LoF model for TTN with cardiomyopathy improves the OR by 2.9x in the test set by mostly restricting to cardiac-expressed exons, and the PW LoF model for APOB with LDL levels produces an effect size improvement of 111% by removing the last 1082 bases of the gene (two-thirds of the final exon) as well as three single variants that did not show associations ( Table S1, S2 ) 19 . We also test using LOFTEE to remove low confidence LoF variants from the whole gene models, which improves the effect size for some genes, but does not consistently result in a positive change ( Figure S5B, S6B ).…”

“…We first examine if there are differences in how a PW analysis of coding variants (PW coding ) or LoF variants (PW LoF ) separates regions within a gene ( Figure 2 ). We anticipate that PW LoF models will usually implicate the entire gene instead of specific regions, as generally a LoF variant anywhere in the gene is likely to have a similar effect (with tissue-specific splicing causing some exceptions, see 19 ). Overall for the 65 genes, we find that applying PW coding retains a mean of 37% of the carriers in each gene (range 2-98%), while PW LoF retains 72% (range 0-100%) of carriers.…”

“…Future studies with even larger sample sizes may reclassify some portions of the genes tested here, identifying some regions we erroneously excluded or identifying new regions to include. Other improvements will involve focusing on certain classes of variants in the gene, such as those computationally predicted to be gain or loss of function, those with functional data from screenings, and those in transcripts expressed in tissues of interest 19,26,27 .…”

A power-based sliding window approach to evaluate the clinical impact of rare genetic variants in the nucleotide sequence or the spatial position of the folded protein

“… 42 , 43 Other improvements will involve focusing on certain classes of variants in the gene, such as those computationally predicted to be gain or loss of function, those with functional data from screenings, those in transcripts expressed in tissues of interest, and different protein conformations. 41 , 44 , 45 …”