Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models

Abstract

The rate at which nonsynonymous single nucleotide polymorphisms (nsSNPs) are being identified in the human genome is increasing dramatically owing to advances in whole-genome/whole-exome sequencing technologies. Automated methods capable of accurately and reliably distinguishing between pathogenic and functionally neutral nsSNPs are therefore assuming ever-increasing importance. Here, we describe the Functional Analysis Through Hidden Markov Models (FATHMM) software and server: a species-independent method with optional species-specific weightings for the prediction of the functional effects of protein missense variants. Using a model weighted for human mutations, we obtained performance accuracies that outperformed traditional prediction methods (i.e., SIFT, PolyPhen, and PANTHER) on two separate benchmarks. Furthermore, in one benchmark, we achieve performance accuracies that outperform current state-of-the-art prediction methods (i.e., SNPs&GO and MutPred). We demonstrate that FATHMM can be efficiently applied to high-throughput/large-scale human and nonhuman genome sequencing projects with the added benefit of phenotypic outcome associations. To illustrate this, we evaluated nsSNPs in wheat (Triticum spp.) to identify some of the important genetic variants responsible for the phenotypic differences introduced by intense selection during domestication. A Web-based implementation of FATHMM, including a high-throughput batch facility and a downloadable standalone package, is available at http://fathmm.biocompute.org.uk.

Figure 1

The distribution of the predicted magnitude of effect for disease-associated (shaded region) and functionally neutral (unshaded region) AASs in the SwissVar dataset using our unweighted and weighted methods (A and B, respectively). From this, we calculated prediction thresholds at which both specificity and sensitivity were maximized (−3.0 and −1.5, respectively).

Figure 2

Receiver operating characteristic (ROC) curves for the top-ranking computational prediction algorithms evaluated using the SwissVar dataset. Here, we compare our unweighted method against SIFT and PANTHER (A—full curve; B—10% false positive rate) whereas our weighted method is compared to SNPs&GO and MutPred (C—full curve; D—10% false positive rate). Full ROC curves for all computational prediction algorithms evaluated are made available in Supp. Figure S3.

Figure 3

The intersection of disease-associated amino acid substitutions correctly identified by the top-ranking computational prediction algorithms evaluated using the SwissVar dataset. Here, we compare our unweighted method against SIFT and PANTHER (A) whereas our weighted method is compared to SNPs&GO and MutPred (B).