Phenotype-optimized sequence ensembles substantially improve prediction of disease-causing mutation in cystic fibrosis.

Human Mutation
David L MasicaRachel Karchin

Abstract

Cystic fibrosis transmembrane conductance regulator (CFTR) mutation is associated with a phenotypic spectrum that includes cystic fibrosis (CF). The disease liability of some common CFTR mutations is known, but rare mutations are seen in too few patients to categorize unequivocally, making genetic diagnosis difficult. Computational methods can predict the impact of mutation, but prediction specificity is often below that required for clinical utility. Here, we present a novel supervised learning approach for predicting CF from CFTR missense mutation. The algorithm begins by constructing custom multiple sequence alignments called phenotype-optimized sequence ensembles (POSEs). POSEs are constructed iteratively, by selecting sequences that optimize predictive performance on a training set of CFTR mutations of known clinical significance. Next, we predict CF disease liability from a different set of CFTR mutations (test-set mutations). This approach achieves improved prediction performance relative to popular methods recently assessed using the same test-set mutations. Of clinical significance, our method achieves 94% prediction specificity. Because databases such as HGMD and locus-specific mutation databases are growing rapidly, ...Continue Reading

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Citations

Aug 12, 2014·Human Genetics·David L MasicaRachel Karchin
Mar 19, 2016·Human Mutation·Abhishek Niroula, Mauno Vihinen
Feb 23, 2017·Human Mutation·Abhishek Niroula, Mauno Vihinen
Jan 11, 2017·Human Mutation·Abhishek Niroula, Mauno Vihinen
Mar 28, 2018·Expert Review of Respiratory Medicine·Anne BergougnouxCaroline Raynal
Nov 11, 2021·IScience·M Kathryn BrewerMatthew S Gentry

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