Curated Multiple Sequence Alignment for the Adenomatous Polyposis Coli (APC) Gene and Accuracy of In Silico Pathogenicity Predictions

Abstract

Computational algorithms are often used to assess pathogenicity of Variants of Uncertain Significance (VUS) that are found in disease-associated genes. Most computational methods include analysis of protein multiple sequence alignments (PMSA), assessing interspecies variation. Careful validation of PMSA-based methods has been done for relatively few genes, partially because creation of curated PMSAs is labor-intensive. We assessed how PMSA-based computational tools predict the effects of the missense changes in the APC gene, in which pathogenic variants cause Familial Adenomatous Polyposis. Most Pathogenic or Likely Pathogenic APC variants are protein-truncating changes. However, public databases now contain thousands of variants reported as missense. We created a curated APC PMSA that contained >3 substitutions/site, which is large enough for statistically robust in silico analysis. The creation of the PMSA was not easily automated, requiring significant querying and computational analysis of protein and genome sequences. Of 1924 missense APC variants in the NCBI ClinVar database, 1800 (93.5%) are reported as VUS. All but two missense variants listed as P/LP occur at canonical splice or Exonic Splice Enhancer sites. Pathogenicity predictions by five computational tools (Align-GVGD, SIFT, PolyPhen2, MAPP, REVEL) differed widely in their predictions of Pathogenic/Likely Pathogenic (range 17.5-75.0%) and Benign/Likely Benign (range 25.0-82.5%) for APC missense variants in ClinVar. When applied to 21 missense variants reported in ClinVar as Benign, the five methods ranged in accuracy from 76.2-100%. Computational PMSA-based methods can be an excellent classifier for variants of some hereditary cancer genes. However, there may be characteristics of the APC gene and protein that confound the results of in silico algorithms. A systematic study of these features could greatly improve the automation of alignment-based techniques and the use of predictive algorithms in hereditary cancer genes.\n\nAuthor SummaryA critical problem in clinical genetics today is interpreting whether a genetic variant is benign or causes disease (pathogenic). Some of the hardest variants to interpret are those that change one amino acid for another in a protein sequence (a \"missense variant\"). Various computer programs are often used to predict whether mutations in disease-associated genes likely cause disease. Most computer programs involve studying how the gene has changed during evolution, comparing the protein sequences of different species by aligning them with each other. Variants in amino acids that have not tolerated mutation during evolution are usually predicted to be pathogenic, and variants in amino acids that have tolerated variation are usually predicted to be benign. High quality alignments are necessary to make accurate predictions. However, creating high quality alignments is difficult, not easily automated, and requires significant manual curation. Results from computer-generated predictions are used in current published guidelines as one tool for evaluating whether variants will disrupt the protein function and cause disease. These guidelines may be applied to genes in which single amino acid substitutions do not commonly cause disease. One such example is the APC gene, which is responsible for Familial Adenomatous Polyposis (FAP). Missense APC changes are not a common cause of FAP. Our analysis of APC demonstrated the difficulty of generating an accurate protein sequence alignment and the tendency of computer tools to overestimate the damaging effects of amino acid substitutions. Our results suggest that the rules for using computer-based tools to predict whether a variant causes disease should be modified when applied to genes in which missense variants rarely cause disease.