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. Author manuscript; available in PMC: 2019 Aug 1.
Published in final edited form as: Hum Mutat. 2018 Jun 5;39(8):1061–1069. doi: 10.1002/humu.23553

Table 5.

Semi-quantitative approach for TP53 variant bioinformatic prediction using Align-GVGD class (optimized pMSA) and BayesDel score

Category Assumed pathogenic (n) % Assumed benign (n) % Positive LR (95% CI) Proposed ACMG/AMP rule
Optimized Align-GVGD C65 + BayesDel ≥0.16 146 59.11 0 NA 59.11 Moderate evidence of pathogenicity (new rule)
Optimized Align-GVGD C65 + BayesDel <0.16 0 NA 0 NA NA No data
Optimized Align-GVGD C55-C25 + BayesDel ≥0.16 50 20.24 3 4.22 4.79 (1.54, 14.90) Supporting evidence of pathogenicity (PP3)
Optimized Align-GVGD C55-C25 + BayesDel <0.16 7 2.83 5 8.45 0.33 (0.12, 0.97) No evidence
Optimized Align-GVGD C15 + BayesDel ≥0.16 13 5.26 3 4.22 1.25 (0.37, 4.25) No evidence
Optimized Align-GVGD C15 + BayesDel <0.16 4 1.62 5 7.04 0.23 (0.06, 0.83) Supporting evidence of benign impact (BP4)
Optimized Align-GVGD C0 + BayesDel ≥0.16 10 4.05 5 7.04 0.57 (0.20, 1.63) No evidence
Optimized Align-GVGD C0 + BayesDel <0.16 18 7.29 49 69.01 0.11 (0.07, 0.17) Supporting evidence of benign impact (BP4)
Total 247 71