Dataset Information

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

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.

SUBMITTER: Wang Y

PROVIDER: S-EPMC7019181 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Wang Yan Y Zhu Li-Na LN Ma Xiu-Wei XW Yang Fang F Xu Xi-Lin XL Yang Yao Y Yang Xiao X Peng Wei W Zhang Wan-Qiao WQ Liang Jin-Yu JY Zhu Wei-Dong WD Jiang Tai-Jiao TJ Zhang Xin-Lei XL Feng Zhi-Chun ZC

Frontiers in bioengineering and biotechnology 20200207

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 correlat ...[more]

PMID: 32117926

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Project description:BackgroundDNA copy number variants (CNVs) are found in 15% of subjects with ID but their association with phenotypic abnormalities has been predominantly studied in smaller cohorts of subjects with detailed yet non-systematically categorized phenotypes, or larger cohorts (thousands of cases) with smaller number of generalized phenotypes.MethodsWe evaluated the association of de novo, familial and common CNVs detected in 78 ID subjects with phenotypic abnormalities classified using the Winter-Baraitser Dysmorphology Database (WBDD) (formerly the London Dysmorphology Database). Terminology for 34 primary (coarse) and 169 secondary (fine) phenotype features were used to categorize the abnormal phenotypes and determine the prevalence of each phenotype in patients grouped by the type of CNV they had.ResultsIn our cohort more than 50% of cases had abnormalities in primary categories related to head (cranium, forehead, ears, eye globes, eye associated structures, nose) as well as hands and feet. The median number of primary and secondary abnormalities was 12 and 18 per subject, respectively, indicating that the cohort consisted of subjects with a high number of phenotypic abnormalities (median De Vries score for the cohort was 5). The prevalence of each phenotypic abnormality was comparable in patients with de novo or familial CNVs in comparison to those with only common CNVs, although a trend for increased frequency of cranial and forehead abnormalities was noted in subjects with rare de novo and familial CNVs. Two clusters of subjects were identified based on the prevalence of each fine phenotypic feature, with an average of 28.3 and 13.5 abnormal phenotypes/subject in the two clusters respectively (P?<?0.05).ConclusionsOur study is a rare example of using standardized, deep morphologic phenotype clustering with phenotype/CNV correlation in a cohort of subjects with ID. The composition of the cohort inevitably influences the phenotype/genotype association, and our studies show that the influence of the de novo CNVs on the phenotype is less obvious in cohorts consisting of subjects with a high number of phenotypic abnormalities. The outcome of phenotype/genotype analysis also depends on the choice of phenotypes assessed and standardized phenotyping is required to minimize variability.

Dataset Information

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

Publications

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

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