collected alignment of homologous sequences. Next, we introduce a weighted/species-specific method, which incorporates “pathogenicity weights”. These weights are derived from the relative frequencies of disease-associated and functionally neutral AASs mapping onto conserved protein domains. Using a model weighted for human mutations, we obtained performance accuracies that outperformed traditional prediction methods—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: SNPs&GO and MutPred. We demonstrate that our method, functional analysis through hidden Markov models (FATHMM), can be efficiently applied to all foreseeable high-throughput large-scale genomic datasets, and advances the field with the added benefit of providing phenotypic outcome associations. In addition to demonstrating the predictive capabilities of FATHMM on multiple benchmarks representing human mutations, we have applied it in practice to a large dataset of nsSNPs in wheat (Triticum spp.) to identify some of the key genetic variants responsible for the phenotypic differences introduced by intense selection during domestication and have made this analysis publicly available to the scientific community.
