Identification of genomic features in the classification of loss- and gain-of-function mutation

Abstract

BackgroundAlterations of a genome can lead to changes in protein functions. Through these genetic mutations, a protein can lose its native function (loss-of-function, LoF), or it can confer a new function (gain-of-function, GoF). However, when a mutation occurs, it is difficult to determine whether it will result in a LoF or a GoF. Therefore, in this paper, we propose a study that analyzes the genomic features of LoF and GoF instances to find features that can be used to classify LoF and GoF mutations.MethodsIn order to collect experimentally verified LoF and GoF mutational information, we obtained 816 LoF mutations and 474 GoF mutations from a literature text-mining process. Next, with data-preprocessing steps, 258 LoF and 129 GoF mutations remained for a further analysis. We analyzed the properties of these LoF and GoF mutations. Among the properties, we selected features which show different tendencies between the two groups and implemented classifications using support vector machine, random forest, and linear logistic regression methods to confirm whether or not these features can identify LoF and GoF mutations.ResultsWe analyzed the properties of the LoF and GoF mutations and identified six features which have discriminative power between LoF and GoF conditions: the reference allele, the substituted allele, mutation type, mutation impact, subcellular location, and protein domain. When using the six selected features with the random forest, support vector machine, and linear logistic regression classifiers, the result showed accuracy levels of 72.23%, 71.28%, and 70.19%, respectively.ConclusionsWe analyzed LoF and GoF mutations and selected several properties which were different between the two classes. By implementing classifications with the selected features, it is demonstrated that the selected features have good discriminative power.

Identification of discriminative gene-level and protein-level features associated with pathogenic gain-of-function and loss-of-function variants

“…19 In a separate study, 129 GOF variants from 59 genes and 258 LOF variants from 109 genes were analyzed and six features were proposed to be discriminant. 20 Additionally, a computational method, HMMvar-func, that predicts the functional outcome of a variant has been reported. 21 HMMvar-func was however trained only on two genes, TSHR and TP53, and it defines GOF variants as having acquired a new function, without including GOF variants with enhanced activation, which accounts for the majority of GOF variants.…”

Insight Into the Multiple Branches Traits of a Mutant in Larix olgensis by Morphological, Cytological, and Transcriptional Analyses

“…Somatic mutations are mutations that occur in an organism's cell other than in a gamete, germ cell, or gametocyte (Jung et al, 2015). Most somatic mutations have no phenotypic effect, but some somatic mutations in plants can cause certain changes in leaf and branch shapes (Tian et al, 2021a).…”

Novel NALCN variant linked to temporal lobe epilepsy

“…The gene‐phenotype correlation would hence be atypical here if the variant we report is associated with LOF whereas the mutation is of likely dominant inheritance. Beside the mode of inheritance, non‐LOF mutations (i.e., GOF and dominant‐negative mutations) are typically associated with moderate alterations in protein structure (Gerasimavicius et al, 2022) and potentially mild clinical semiology (Gerasimavicius et al, 2022; Jung et al, 2015). However, referring to the existing literature on NALCN gene mutations, both CLIFAHDD and IHPRF are severe syndromes with numerous commonalities although associated with GOF and LOF anomalies, respectively (Bouasse et al, 2019).…”