Salisbury, Benjamin A.

Background: Long QT Syndrome (LQTS) genetic testing has been available clinically since August 2004. In cases of bona fide LQTS, the yield of genetic testing is approximately 75%. Recently (HRS 2007), Priori and colleagues have suggested that referral for LQTS genetic testing should be based on the screening QTc citing a yield of < 2% for cases referred to their research program with a QTc < 440 ms. Here, we analyze the effect of QTc on the yield in two of the largest assembled cohorts of unrelated patients (pts) referred for LQTS genetic testing. Methods: From August 1997 to July 2006, 1125 unrelated pts (717 females, average age at diagnosis 23.5 yrs, average QTc 473 ms) were referred to either the Mayo Clinic Windland Smith Rice Sudden Death Genomics Laboratory (N = 541) for research-based LQTS genetic testing or to PGxHealth (N = 574) for the FAMILION ® LQTS genetic test. Comprehensive open reading frame and splice site analysis (60 exons) for LQTS-causing mutations involving KCNQ1 (LQT1), KCNH2 (LQT2), SCN5A (LQT3), KCNE1 (LQT5), and KCNE2 (LQT6) was conducted by either dHPLC/DNA sequencing (Mayo), or by direct DNA sequencing (FAMILION). Results: The probability of a positive genetic test increased dramatically with increasing QTc, ranging from 5% for the 38 pts referred for genetic testing despite a QTc < 400 ms to 60% for the 168 pts with a QTc > 500 ms (p < 0.00001). Akin to the Priori cohort, the yield of the genetic test was similar among the subset of pts with a QTc < 470 ms (64% Priori, 61% Mayo cohort, and 53% FAMILION, p = NS). However, the yield for those pts with a QTc between 440 and 470 ms was much greater in the Mayo (49%) and FAMILION (36%) cohorts compared to the Priori cohort (14%, p < 0.0005). In contrast to the 2% yield observed in Italy, 22% of pts with QTc < 440 ms referred to Mayo Clinic and 14.5% of the FAMILION referrals had a positive genetic test (p < 0.001). Conclusions: Whereas only 2% of patients with a QTc < 440 ms had a positive genetic test in Italy, the yield was ten times greater among those referred to Mayo Clinic despite the same QTc cut-off. The cumulative index of suspicion for LQTS rather than a pre-specified QTc cut-off should guide clinical decision making as to the appropriate utilization of the genetic test.

Background— Hundreds of nonsynonymous single nucleotide variants (nsSNVs) have been identified in the 2 most common long-QT syndrome-susceptibility genes ( KCNQ1 and KCNH2 ). Unfortunately, an ≈3% background rate of rare KCNQ1 and KCNH2 nsSNVs amongst healthy individuals complicates the ability to distinguish rare pathogenic mutations from similarly rare yet presumably innocuous variants. Methods and Results— In this study, 4 tools [(1) conservation across species, (2) Grantham values, (3) sorting intolerant from tolerant, and (4) polymorphism phenotyping] were used to predict pathogenic or benign status for nsSNVs identified across 388 clinically definite long-QT syndrome cases and 1344 ostensibly healthy controls. From these data, estimated predictive values were determined for each tool independently, in concert with previously published protein topology–derived estimated predictive values, and synergistically when ≥3 tools were in agreement. Overall, all 4 tools displayed a statistically significant ability to distinguish between case-derived and control-derived nsSNVs in KCNQ1 , whereas each tool, except Grantham values, displayed a similar ability to differentiate KCNH2 nsSNVs. Collectively, when at least 3 of the 4 tools agreed on the pathogenic status of C-terminal nsSNVs located outside the KCNH2/Kv11.1 cyclic nucleotide−binding domain, the topology-specific estimated predictive value improved from 56% to 91%. Conclusions— Although in silico prediction tools should not be used to predict independently the pathogenicity of a novel, rare nSNV, our results support the potential clinical use of the synergistic utility of these tools to enhance the classification of nsSNVs, particularly for Kv11.1’s difficult to interpret C-terminal region.

Background— Genetic testing for long-QT syndrome (LQTS) has diagnostic, prognostic, and therapeutic implications. Hundreds of causative mutations in 12 known LQTS-susceptibility genes have been identified. Genetic testing that includes the 3 most commonly mutated genes is available clinically. Distinguishing pathogenic mutations from innocuous rare variants is critical to the interpretation of test results. We sought to quantify the value of mutation type and gene/protein region in determining the probability of pathogenicity for mutations. Methods and Results— Type, frequency, and location of mutations across KCNQ1 (LQT1), KCNH2 (LQT2), and SCN5A (LQT3) were compared between 388 unrelated “definite” (clinical diagnostic score ≥4 and/or QTc ≥480 ms) cases of LQTS and >1300 healthy controls for each gene. From these data, estimated predictive values (percent of mutations found in definite cases that would cause LQTS) were determined according to mutation type and location. Mutations were 10 times more common in cases than controls (0.58 per case versus 0.06 per control). Missense mutations were the most common, accounting for 78%, 67%, and 89% of mutations in KCNQ1 , KCNH2 , and SCN5A in cases and >95% in controls. Nonmissense mutations have an estimated predictive value >99% regardless of location. In contrast, location appears to be critical for characterizing missense mutations. Relative frequency of missense mutations between cases and controls ranged from ≈1:1 in the SCN5A interdomain linker to infinity in the pore, transmembrane, and linker in KCNH2. These correspond to estimated predictive values ranging from 0% in the interdomain linker of SCN5A to 100% in the transmembrane/linker/pore regions of KCNH2. The estimated predictive value is also high in the linker, pore, transmembrane, and C terminus of KCNQ1 and the transmembrane/linker of SCN5A. Conclusions— Distinguishing pathogenic mutations from rare variants is of critical importance in the interpretation of genetic testing in LQTS. Mutation type, mutation location, and ethnic-specific background rates are critical factors in predicting the pathogenicity of novel mutations. Novel mutations in low-estimated predictive value regions such as the interdomain linker of SCN5A should be viewed as variants of uncertain significance and prompt further investigation to clarify the likelihood of disease causation. However, mutations in regions such as the transmembrane, linker, and pore of KCNQ1 and KCNH2 may be defined confidently as high-probability LQTS-causing mutations. These findings will have implications for other genetic disorders involving mutational analysis.