PhenoApt leverages clinical expertise to prioritize candidate genes via machine learning.

Journal: American journal of human genetics
PMID: 35063063

Abstract

In recent years, exome sequencing (ES) has shown great utility in the diagnoses of Mendelian disorders. However, after rigorous filtering, a typical ES analysis still involves the interpretation of hundreds of variants, which greatly hinders the rapid identification of causative genes. Since the interpretations of ES data require comprehensive clinical analyses, taking clinical expertise into consideration can speed the molecular diagnoses of Mendelian disorders. To leverage clinical expertise to prioritize candidate genes, we developed PhenoApt, a phenotype-driven gene prioritization tool that allows users to assign a customized weight to each phenotype, via a machine-learning algorithm. Using the ability to rank causative genes in top-10 lists as an evaluation metric, baseline analysis demonstrated that PhenoApt outperformed previous phenotype-driven gene prioritization tools by a relative increase of 22.7%-140.0% in three independent, real-world, multi-center cohorts (cohort 1, n = 185; cohort 2, n = 784; and cohort 3, n = 208). Additional trials showed that, by adding weights to clinical indications, which should be explained by the causative gene, PhenoApt performance was improved by a relative increase of 37.3% in cohort 2 (n = 471) and 21.4% in cohort 3 (n = 208). Moreover, PhenoApt could assign an intrinsic weight to each phenotype based on the likelihood of its being a Mendelian trait using term frequency-inverse document frequency techniques. When clinical indications were assigned with intrinsic weights, PhenoApt performance was improved by a relative increase of 23.7% in cohort 2 and 15.5% in cohort 3. For the integration of PhenoApt into clinical practice, we developed a user-friendly website and a command-line tool.

Authors

  • Zefu Chen
    Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; State Key Laboratory of Complex Severe and Rare Diseases, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China; Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China; DISCO (Deciphering disorders Involving Scoliosis and COmobidities) study group.
  • Yu Zheng
    Department of Thoracic Surgery, West China Hospital of Sichuan University, Chengdu 610041, China.
  • Yongxin Yang
    Beijing Ekitech Co. Ltd., Beijing 100043, China; University of Edinburgh, 10 Crichton Street, Edinburgh, EH8 9AB, United Kindom.
  • Yingzhao Huang
    Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China; Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China; DISCO (Deciphering disorders Involving Scoliosis and COmobidities) study group.
  • Sen Zhao
    College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China.
  • Hengqiang Zhao
    Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China; Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China; DISCO (Deciphering disorders Involving Scoliosis and COmobidities) study group.
  • Chenxi Yu
    Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China; Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China; DISCO (Deciphering disorders Involving Scoliosis and COmobidities) study group.
  • Xiying Dong
    Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China; Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China; DISCO (Deciphering disorders Involving Scoliosis and COmobidities) study group.
  • Yuanqiang Zhang
    Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China; Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China; DISCO (Deciphering disorders Involving Scoliosis and COmobidities) study group.
  • Lianlei Wang
    Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China; Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China; DISCO (Deciphering disorders Involving Scoliosis and COmobidities) study group.
  • Zhengye Zhao
    Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China; Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China; DISCO (Deciphering disorders Involving Scoliosis and COmobidities) study group.
  • Shengru Wang
    Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China; Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China; DISCO (Deciphering disorders Involving Scoliosis and COmobidities) study group.
  • Yang Yang
    Department of Gastrointestinal Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China.
  • Yue Ming
    Beijing Key Laboratory of Work Safety and Intelligent Monitoring, School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
  • Jianzhong Su
    Department of Mathematics, University of Texas at Arlington, Arlington, TX, 76019-0408, USA.
  • Guixing Qiu
    Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; State Key Laboratory of Complex Severe and Rare Diseases, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China; Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China; DISCO (Deciphering disorders Involving Scoliosis and COmobidities) study group.
  • Zhihong Wu
    College of Computer Science, Sichuan University, Chengdu, China.
  • Terry Jianguo Zhang
    Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; State Key Laboratory of Complex Severe and Rare Diseases, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China; Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China; DISCO (Deciphering disorders Involving Scoliosis and COmobidities) study group.
  • Nan Wu
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, Drug Discovery Institute, Departments of Computational Biology and Structural Biology, School of Medicine , University of Pittsburgh , Pittsburgh , Pennsylvania 15261 , United States.

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