Larrea-Sebal Asier, Benito-Vicente Asier, Fernandez-Higuero José A, Jebari-Benslaiman Shifa, Galicia-Garcia Unai, Uribe Kepa B, Cenarro Ana, Ostolaza Helena, Civeira Fernando, Arrasate Sonia, González-Díaz Humberto, Martín César
Fundación Biofísica Bizkaia, Leioa, Spain.
Instituto Biofisika (UPV/EHU, CSIC), University of the Basque Country, Leioa, Spain.
JACC Basic Transl Sci. 2021 Nov 22;6(11):815-827. doi: 10.1016/j.jacbts.2021.08.009. eCollection 2021 Nov.
Untreated familial hypercholesterolemia (FH) leads to atherosclerosis and early cardiovascular disease. Mutations in the low-density lipoprotein receptor () gene constitute the major cause of FH, and the high number of mutations already described in the makes necessary cascade screening or in vitro functional characterization to provide a definitive diagnosis. Implementation of high-predicting capacity software constitutes a valuable approach for assessing pathogenicity of variants to help in the early diagnosis and management of FH disease. This work provides a reliable machine learning model to accurately predict the pathogenicity of missense variants with specificity of 92.5% and sensitivity of 91.6%.
未经治疗的家族性高胆固醇血症（FH）会导致动脉粥样硬化和早期心血管疾病。低密度脂蛋白受体（）基因突变是FH的主要病因，该基因中已描述的大量突变使得有必要进行级联筛查或体外功能表征以做出明确诊断。实施具有高预测能力的软件是评估基因变异致病性的一种有价值的方法，有助于FH疾病的早期诊断和管理。这项工作提供了一个可靠的机器学习模型，以92.5%的特异性和91.6%的敏感性准确预测基因错义变异的致病性。
