title: Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling.
abstract: Computational prediction yields efficient and scalable initial assessments of how variants of unknown significance may affect human health. However, when discrepancies between these predictions and direct experimental measurements of functional impact arise, inaccurate computational predictions are frequently assumed as the source. Here, we present a methodological analysis indicating that shortcomings in both computational and biological data can contribute to these disagreements. We demonstrate that incomplete assaying of multifunctional proteins can affect the strength of correlations between prediction and experiments; a variant's full impact on function is better quantified by considering multiple assays that probe an ensemble of protein functions. Additionally, many variants predictions are sensitive to protein alignment construction and can be customized to maximize relevance of predictions to a specific experimental question. We conclude that inconsistencies between computation and experiment can often be attributed to the fact that they do not test identical hypotheses. Aligning the design of the computational input with the design of the experimental output will require cooperation between computational and biological scientists, but will also lead to improved estimations of computational prediction accuracy and a better understanding of the genotype-phenotype relationship.
authors.affiliation: Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, Texas.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Gallion
authors.affiliation: Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, Texas.
authors.forename: Amanda
authors.initials: A
authors.lastname: Koire
authors.affiliation: Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas.
authors.forename: Panagiotis
authors.initials: P
authors.lastname: Katsonis
authors.affiliation: Department of Biochemistry, Institute for Research in Immunology and Cancer, Université de Montreal, Quebec, Canada.
authors.forename: Anne-Marie
authors.initials: AM
authors.lastname: Schoenegge
authors.affiliation: Department of Biochemistry, Institute for Research in Immunology and Cancer, Université de Montreal, Quebec, Canada.
authors.forename: Michel
authors.initials: M
authors.lastname: Bouvier
authors.affiliation: Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, Texas.|Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas.
authors.forename: Olivier
authors.initials: O
authors.lastname: Lichtarge
mesh_terms: D002874:Chromosome Mapping; D019295:Computational Biology / Q000379:methods; D056726:Genetic Association Studies; D005838:Genotype; D006801:Humans; D008958:Models, Molecular; D009154:Mutation; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D011487:Protein Conformation; D018343:Receptors, Adrenergic, beta-2 / Q000737:chemistry / Q000235:genetics; D015203:Reproducibility of Results
chemical_list: C553443:ADRB2 protein, human; D018343:Receptors, Adrenergic, beta-2
keywords: SNV; VUS; functional effect of mutations; genotype-phenotype relationship; in silico prediction; variant impact prediction