ABSTRACT
Many of the best performing methods for prioritization combine multiple predictors using statistical or machine-learning techniques. Exome analysis combines a series of variant filtration and assessment steps with prioritization methods that estimate the likelihood that variants in a gene are disease-causative in a particular case or study. Petrovski and colleagues introduced a method to calculate a rare-variant intolerance score (RVIS) for each gene in the human genome. The genic intolerance approach provides a method of flagging genes that simply show a higher degree of variability than one would expect. The interrelationships between functional pathways and disease are poorly understood, but since gene mutations alter the functions of genes which in turn participate in cellular pathways, a number of groups have used pathway information to assess candidate genes. Many resources for investigating tissue-specific expression have become available, such as the dataset generated by the Genotype-Tissue Expression (GTEx), which comprises RNA-seq expression data for more than thirty distinct human tissues.
