Abstract
Evolutionary information is the primary tool for detecting functional conservation in nucleic acid and protein. This information has been extensively used to predict structure, interactions and functions in macromolecules. Pathogenicity prediction models rely on multiple sequence alignment information at different levels. However, most accurate genome-wide variant deleteriousness ranking algorithms consider different features to assess the impact of variants. Here, we analyze three different ways of extracting evolutionary information from sequence alignments in the context of pathogenicity predictions at DNA and protein levels. We showed that protein sequence-based information is slightly more informative in the annotation of Clinvar missense variants than those obtained at the DNA level. Furthermore, to achieve the performance of state-of-the-art methods, such as CADD and REVEL, the conservation of reference and variant, encoded as frequencies of reference/alternate alleles or wild-type/mutant residues, should be included. Our results on a large set of missense variants show that a basic method based on three input features derived from the protein sequence profile performs similarly to the CADD algorithm which uses hundreds of genomic features. As expected, our method results in ~ 3% lower area under the receiver-operating characteristic curve (AUC). When compared with an ensemble-based algorithm (REVEL). Nevertheless, the combination of predictions of multiple methods can help to identify more reliable predictions. These observations indicate that for missense variants, evolutionary information, when properly encoded, plays the primary role in ranking pathogenicity.
Highlights
High-throughput sequencing technologies have changed our daily research by rapidly accumulating genomic data and helping to profile patient genomes (MacArthur et al, 2014; Claussnitzer et al, 2020)
It is widely accepted that evolutionary information encoded in multiple sequence alignments of DNAs and proteins is a major resource for scoring variant pathogenicity
We focused on the six conservation features (PC, PP, fref, falt, fwt, fmut) comparing their distributions for the subsets of Pathogenic and Benign variants
Summary
High-throughput sequencing technologies have changed our daily research by rapidly accumulating genomic data and helping to profile patient genomes (MacArthur et al, 2014; Claussnitzer et al, 2020). These studies make variant interpretation a fundamental challenge in precision medicine (Fernald et al, 2011; Capriotti et al, 2012; McInnes et al, 2021). Missense variants by changing a single amino acid in a protein sequence can be neutral or induce loss of function. It is widely accepted that evolutionary information encoded in multiple sequence alignments of DNAs and proteins is a major resource for scoring variant pathogenicity. This paper evaluates the relevance of this information for missense variant predictions by comparing simple scores and simple predictors with the widely used and well-performing Combined Annotation-Dependent Depletion (CADD) algorithm (Rentzsch et al, 2019)
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