Alternative Splicing, Internal Promoter, Nonsense-Mediated Decay, or All Three: Explaining the Distribution of Truncation Variants in Titin

Abstract

Background: Truncating mutations in the giant sarcomeric gene Titin are the most common type of genetic alteration in dilated cardiomyopathy. Detailed studies have amassed a wealth of information about truncating variant position in cases and controls. Nonetheless, considerable confusion exists as to how to interpret the pathogenicity of these variants, hindering our ability to make useful recommendations to patients.

Methods and results: Building on our recent discovery of a conserved internal promoter within the Titin gene, we sought to develop an integrative statistical model to explain the observed pattern of Titin truncation variants in patients with dilated cardiomyopathy and population controls. We amassed Titin truncation mutation information from 1714 human dilated cardiomyopathy cases and >69 000 controls and found 3 factors explaining the distribution of Titin mutations: (1) alternative splicing, (2) whether the internal promoter Cronos isoform was disrupted, and (3) whether the distal C terminus was targeted (in keeping with the observation that truncation variants in this region escape nonsense-mediated decay and continue to be incorporated in the sarcomere). A model using these 3 factors had strong predictive performance with an area under the receiver operating characteristic curve of 0.81. Accordingly, individuals with either the most severe form of dilated cardiomyopathy or whose mutations demonstrated clear family segregation experienced the highest risk profile across all 3 components.

Conclusions: We conclude that quantitative models derived from large-scale human genetic and phenotypic data can be applied to help overcome the ever-growing challenges of genetic data interpretation. Results of our approach can be found at http://cvri.ucsf.edu/~deo/TTNtruncationvariant.html.

Keywords: alternative splicing; confusion; dilated cardiomyopathy; human; mutation.

Figure 1.

Analysis of Titin truncation variant data from 1714 dilated cardiomyopathy cases and 69 210 controls reveals 3 primary determinants of mutation distribution. A, The odds that mutations are found in cases vs controls increases with percent spliced in (PSI). Odds ratios were computed with logistic regression models, controlling for whether the Cronos isoform is disrupted. B, Within constitutive exons (PSI>0.95), there is a 3.1-fold increased odds of mutations being found in cases vs controls for mutations that disrupt the Cronos isoform (P=8×10⁻⁸). C, Analysis of odds ratios across bins of 2000 amino acids from N terminus to C terminus reveals 2 primary sources of variation: an increase in risk for mutations that disrupt Cronos (shown by position of dotted line) and a sharp drop in risk for those affecting the distal C terminus. For all plots, error bars, when shown, correspond to the SE.

Figure 2.

Distribution of TTN truncation variants in bins of amino acids from N terminus to C terminus in the (A) dilated cardiomyopathy (DCM) and (B) population control (CTL) groups. A smooth increase in number of variants is seen at the distal C terminus in the CTL group, whereas a corresponding drop is seen in the DCM group. Each bin corresponds to 1000 amino acids for the DCM plot and 500 amino acids for the CTL plot.

Figure 3.

Comparison of 3 factors reveals their relative contribution to the observed distribution of TTN truncation variants. By plotting the increase in odds that a truncating variant is found in cases vs controls per 1 standard unit change for different predictors, one can perform an approximate relative comparison of variable importance.

Figure 4.

Receiver operating characteristic curve for ability to discriminate mutations as belonging to cases or controls using 3 predictors is shown in Figure 3. A model was derived from a training set corresponding to 2 of 3 of the data and then evaluated on a test set of the remaining data. A, Representative plot. B, Distribution of area under the receiver operating characteristic curve (AUROC) values in 100 simulations.