A H Schiemann1, K M Stowell2. 1. Institute of Fundamental Sciences, Massey University, Private Bag 11222, Palmerston North 4442, New Zealand a.h.schiemann@massey.ac.nz. 2. Institute of Fundamental Sciences, Massey University, Private Bag 11222, Palmerston North 4442, New Zealand.
Abstract
BACKGROUND: Malignant hyperthermia (MH) is a pharmacogenetic disorder that has been linked to the skeletal muscle calcium release channel (RYR1) and the α1S subunit of the voltage-dependent L-type calcium channel (CACNA1S). Genomic DNA capture and next generation sequencing are becoming the preferred method to identify mutations in these genes. Bioinformatic pathogenicity prediction of identified variants may help to determine if these variants are in fact disease causing. METHODS: Eight pathogenicity prediction programmes freely available on the web were used to determine their ability to correctly predict the impact of a missense variant on RyR1 or dihydropyridine receptor (DHPR) protein function. We tested MH-causative variants, variants that had been shown to alter calcium release in cells, and common sequence variants in RYR1 and CACNA1S. RESULTS: None of the prediction programmes was able to identify all of the variants tested correctly as either 'damaging' (MH-causative variants, variants that had been shown to alter calcium release in cells) or as 'benign' (common sequence variants). The overall sensitivity of predictions ranged from 84% to 100% depending on the programme used, with specificity from 25% to 83%. CONCLUSIONS: In this study we determined the sensitivity and specificity of bioinformatic pathogenicity prediction tools for RYR1 and CACNA1S. We suggest that the prediction results should be treated with caution, as none of the programmes tested predicted all the variants correctly and should only be used in combination with other available data (functional assays, segregation analysis).
BACKGROUND:Malignant hyperthermia (MH) is a pharmacogenetic disorder that has been linked to the skeletal muscle calcium release channel (RYR1) and the α1S subunit of the voltage-dependent L-type calcium channel (CACNA1S). Genomic DNA capture and next generation sequencing are becoming the preferred method to identify mutations in these genes. Bioinformatic pathogenicity prediction of identified variants may help to determine if these variants are in fact disease causing. METHODS: Eight pathogenicity prediction programmes freely available on the web were used to determine their ability to correctly predict the impact of a missense variant on RyR1 or dihydropyridine receptor (DHPR) protein function. We tested MH-causative variants, variants that had been shown to alter calcium release in cells, and common sequence variants in RYR1 and CACNA1S. RESULTS: None of the prediction programmes was able to identify all of the variants tested correctly as either 'damaging' (MH-causative variants, variants that had been shown to alter calcium release in cells) or as 'benign' (common sequence variants). The overall sensitivity of predictions ranged from 84% to 100% depending on the programme used, with specificity from 25% to 83%. CONCLUSIONS: In this study we determined the sensitivity and specificity of bioinformatic pathogenicity prediction tools for RYR1 and CACNA1S. We suggest that the prediction results should be treated with caution, as none of the programmes tested predicted all the variants correctly and should only be used in combination with other available data (functional assays, segregation analysis).
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