title: Evolutionary balancing is critical for correctly forecasting disease-associated amino acid variants.
abstract: Computational predictions have become indispensable for evaluating the disease-related impact of nonsynonymous single-nucleotide variants discovered in exome sequencing. Many such methods have their roots in molecular evolution, as they use information derived from multiple sequence alignments. We show that the performance of current methods (e.g., PolyPhen-2 and SIFT) is improved significantly by optimizing their statistical models on evolutionarily balanced training data, where equal numbers of positive and negative controls within each evolutionary conservation class are used. Evolutionary balancing significantly reduces the false-positive rates for variants observed at highly conserved sites and false-negative rates for variants observed at fast evolving sites. Use of these improved methods enables more accurate forecasting when concordant diagnosis from multiple methods is regarded as a more reliable indicator of the prediction. Applied to a large exome variation data set, we find that the current methods produce concordant predictions for less than half of the population variants. These advances are implemented in a web resource for use in practical applications (www.mypeg.info, last accessed March 13, 2013).
authors: Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University, USA.
authors: Li
authors: L
authors: Liu
authors: Sudhir
authors: S
authors: Kumar
mesh_terms: D019943:Amino Acid Substitution / Q000235:genetics*; D000596:Amino Acids / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D019143:Evolution, Molecular; D020022:Genetic Predisposition to Disease; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide
chemical_list: D000596:Amino Acids
keywords: computational prediction; evolutionary medicine; nonsynonymous single nucleotide variant