Gene-Focused Networks Underlying Phenotypic Convergence in a Systematically Phenotyped Cohort With Heterogeneous Intellectual Disability

Yan Wang; Li-Na Zhu; Xiu-Wei Ma; Fang Yang; Xi-Lin Xu; Yao Yang; Xiao Yang; Wei Peng; Wan-Qiao Zhang; Jin-Yu Liang; Wei-Dong Zhu; Tai-Jiao Jiang; Xin-Lei Zhang; Zhi-Chun Feng

doi:10.3389/fbioe.2020.00045

Gene-Focused Networks Underlying Phenotypic Convergence in a Systematically Phenotyped Cohort With Heterogeneous Intellectual Disability

Front Bioeng Biotechnol. 2020 Feb 7:8:45. doi: 10.3389/fbioe.2020.00045. eCollection 2020.

Authors

Yan Wang^{1

2

3}, Li-Na Zhu^{1

2

3}, Xiu-Wei Ma^{1

2

3}, Fang Yang⁴, Xi-Lin Xu⁴, Yao Yang^{1

2

3}, Xiao Yang^{1

2

3}, Wei Peng^{1

2

3}, Wan-Qiao Zhang^{1

2

3}, Jin-Yu Liang⁵, Wei-Dong Zhu⁵, Tai-Jiao Jiang^{4

6}, Xin-Lei Zhang⁷, Zhi-Chun Feng^{1

2

3}

Affiliations

¹ BaYi Children's Hospital, The Seventh Medical Center of PLA General Hospital, Beijing, China.
² National Engineering Laboratory for Birth Defects Prevention and Control of Key Technology, Beijing, China.
³ Beijing Key Laboratory of Pediatric Organ Failure, Beijing, China.
⁴ Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences, Suzhou, China.
⁵ The Second People's Hospital of Aohanqi, Inner Mongolia, China.
⁶ Center of Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
⁷ Suzhou Geneworks Technology Co., Ltd., Suzhou, China.

Abstract

The broad spectrum of intellectual disability (ID) patients' clinical manifestations, the heterogeneity of ID genetic variation, and the diversity of the phenotypic variation represent major challenges for ID diagnosis. By exploiting a manually curated systematic phenotyping cohort of 3803 patients harboring ID, we identified 704 pathogenic genes, 3848 pathogenic sites, and 2075 standard phenotypes for underlying molecular perturbations and their phenotypic impact. We found the positive correlation between the number of phenotypes and that of patients that revealed their extreme heterogeneities, and the relative contribution of multiple determinants to the heterogeneity of ID phenotypes. Nevertheless, despite the extreme heterogeneity in phenotypes, the ID genes had a specific bias of mutation types, and the top 44 genes that ranked by the number of patients accounted for 39.9% of total patients. More interesting, enriched co-occurrent phenotypes and co-occurrent phenotype networks for each gene had the potential for prioritizing ID genes, further exhibited the convergences of ID phenotypes. Then we established a predictor called IDpred using machine learning methods for ID pathogenic genes prediction. Using10-fold cross-validation, our evaluation shows remarkable AUC values for IDpred (auc = 0.978), demonstrating the robustness and reliability of our tool. Besides, we built the most comprehensive database of ID phenotyped cohort to date: IDminer http://218.4.234.74:3100/IDminer/, which included the curated ID data and integrated IDpred tool for both clinical and experimental researchers. The IDminer serves as an important resource and user-friendly interface to help researchers investigate ID data, and provide important implications for the diagnosis and pathogenesis of developmental disorders of cognition.

Keywords: co-occurrent phenotype; gene-focused networks; intellectual disability; machine learning; pathogenic genes prediction; phenotypic convergence.