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A phenotype-specific framework for identifying the eye abnormalities causative nonsynonymous-variants

By Han-Kui Liu, Xiao Dang, Li-Ping Guan, Chang-Geng Tian, Sheng-Hai Zhang, Chen Ye, Laurent Christian Asker M. Tellier, Fang Chen, Huan-Ming Yang, Hao-Xiang Sun, Ji-Hong Wu, Jian-Guo Zhang

Posted 13 Apr 2020
bioRxiv DOI: 10.1101/2020.04.13.038059

The most important role of variant pathogenicity predictors is to identify the disease-phenotype causative variant in studying monogenic diseases. In the last decade, machine-learning based predictors exhibited a relatively accurate performance for distinguishing the pathogenic variants and contributed a significant role for all disease-spectrums. Yet, few predictors can investigate the phenotypic significance of variants. Here we presented a phenotype-specific framework aimed to directly point out the phenotypic significance of predicted candidates, and showed its advancing performance in eye abnormalities. By training on eye-abnormalities causative variants, our method presented 96.2% accuracy, 96.1% precision, 93.4% recall for pathogenicity identification. Inconsistent with the modeling performance, identifying the single phenotype-causative variant from various sequencing variants is challenging for all predictors. Underlying the phenotype-oriented, our method significantly promoted the precision and reduced the cost for identifying the single causative variant from thousands of candidates. These advances highlight the significance of the phenotype-specific training method for studying disease. ### Competing Interest Statement The authors have declared no competing interest.

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