PdmIRD: missense variants pathogenicity prediction for inherited retinal diseases in a disease-specific manner.

IF 3.8 2区 生物学 Q2 GENETICS & HEREDITY
Human Genetics Pub Date : 2024-03-01 Epub Date: 2024-03-13 DOI:10.1007/s00439-024-02645-6
Bing Zeng, Dong Cheng Liu, Jian Guo Huang, Xiao Bo Xia, Bo Qin
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Abstract

Accurate discrimination of pathogenic and nonpathogenic variation remains an enormous challenge in clinical genetic testing of inherited retinal diseases (IRDs) patients. Computational methods for predicting variant pathogenicity are the main solutions for this dilemma. The majority of the state-of-the-art variant pathogenicity prediction tools disregard the differences in characteristics among different genes and treat all types of mutations equally. Since missense variants are the most common type of variation in the coding region of the human genome, we developed a novel missense mutation pathogenicity prediction tool, named Prediction of Deleterious Missense Mutation for IRDs (PdmIRD) in this study. PdmIRD was tailored for IRDs-related genes and constructed with the conditional random forest model. Population frequencies and a newly available prediction tool were incorporated into PdmIRD to improve the performance of the model. The evaluation of PdmIRD demonstrated its superior performance over nonspecific tools (areas under the curves, 0.984 and 0.910) and an existing eye abnormalities-specific tool (areas under the curves, 0.975 and 0.891). We also demonstrated the submodel that used a smaller gene panel further slightly improved performance. Our study provides evidence that a disease-specific model can enhance the prediction of missense mutation pathogenicity, especially when new and important features are considered. Additionally, this study provides guidance for exploring the characteristics and functions of the mutated proteins in a greater number of Mendelian disorders.

Abstract Image

PdmIRD:针对特定疾病的遗传性视网膜疾病错义变体致病性预测。
在遗传性视网膜疾病(IRDs)患者的临床基因检测中,准确区分致病变异和非致病变异仍然是一项巨大的挑战。预测变异致病性的计算方法是解决这一难题的主要方法。大多数最先进的变异致病性预测工具都忽略了不同基因之间的特征差异,对所有类型的变异一视同仁。由于错义变异是人类基因组编码区中最常见的变异类型,我们在这项研究中开发了一种新型错义变异致病性预测工具,名为 "IRDs致畸性错义变异预测(PdmIRD)"。PdmIRD 专为 IRDs 相关基因定制,采用条件随机森林模型构建。PdmIRD 中加入了种群频率和一种新的预测工具,以提高模型的性能。对 PdmIRD 的评估表明,它的性能优于非特异性工具(曲线下面积分别为 0.984 和 0.910)和现有的眼部异常特异性工具(曲线下面积分别为 0.975 和 0.891)。我们还证明,使用较小基因面板的子模型性能略有提高。我们的研究证明,疾病特异性模型可以提高错义突变致病性的预测能力,尤其是在考虑到新的重要特征时。此外,这项研究还为探索更多孟德尔疾病中突变蛋白的特征和功能提供了指导。
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来源期刊
Human Genetics
Human Genetics 生物-遗传学
CiteScore
10.80
自引率
3.80%
发文量
94
审稿时长
1 months
期刊介绍: Human Genetics is a monthly journal publishing original and timely articles on all aspects of human genetics. The Journal particularly welcomes articles in the areas of Behavioral genetics, Bioinformatics, Cancer genetics and genomics, Cytogenetics, Developmental genetics, Disease association studies, Dysmorphology, ELSI (ethical, legal and social issues), Evolutionary genetics, Gene expression, Gene structure and organization, Genetics of complex diseases and epistatic interactions, Genetic epidemiology, Genome biology, Genome structure and organization, Genotype-phenotype relationships, Human Genomics, Immunogenetics and genomics, Linkage analysis and genetic mapping, Methods in Statistical Genetics, Molecular diagnostics, Mutation detection and analysis, Neurogenetics, Physical mapping and Population Genetics. Articles reporting animal models relevant to human biology or disease are also welcome. Preference will be given to those articles which address clinically relevant questions or which provide new insights into human biology. Unless reporting entirely novel and unusual aspects of a topic, clinical case reports, cytogenetic case reports, papers on descriptive population genetics, articles dealing with the frequency of polymorphisms or additional mutations within genes in which numerous lesions have already been described, and papers that report meta-analyses of previously published datasets will normally not be accepted. The Journal typically will not consider for publication manuscripts that report merely the isolation, map position, structure, and tissue expression profile of a gene of unknown function unless the gene is of particular interest or is a candidate gene involved in a human trait or disorder.
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