AUTHOR=Qi Mengling , Fan Shichao , Wang Zhi , Yang Xiaoxing , Xie Zicong , Chen Ken , Zhang Lei , Lin Tao , Liu Wei , Lin Xinlei , Yan Yan , Yang Yuedong , Zhao Huiying 

TITLE=Identifying Common Genes, Cell Types and Brain Regions Between Diseases of the Nervous System

JOURNAL=Frontiers in Genetics

VOLUME=Volume 10 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2019.01202

DOI=10.3389/fgene.2019.01202

ISSN=1664-8021

ABSTRACT=Background: Diseases of the nervous system are widely considered to be caused by genetic mutations. Discovering common genes, cell types, and brain regions of the diseases is useful for designing common treatments.   
Method: By reviewing 518 articles for 20 diseases of the nervous system, we collected 1607 mutations occurring in 365 genes that are 1.9 and 3.2 times larger than the collections in the Clinvar database, respectively. A combination with the Clinvar leads to 2434 and 424 pathogenic mutations and genes. Using the information, we measured genetic similarities of the diseases by the number of genes causing two diseases. Further detection is on the similarity between diseases in cell types. The disease-related cell types were defined as disease-related gene enrichment among marker genes of cells by analyzing single cell sequencing data. The disease similarity in cell types was obtained by calculating the distances between the enrichment profiles of these genes. The same strategy was applied to measure the disease similarity in the brain regions through analyzing the gene expression data from 10 brain regions. 
Results: The result indicated that proportions of overlapped genes between diseases were significantly correlated to the DMN scores (phenotypic similarity) with Pearson Correlation coefficient 0.40 and P-value = 6.0×10-3. The disease similarity analysis in cell types identified that the distances between enrichment profiles of the disease-related genes were negatively correlated to the DMN scores with Spearman correlation coefficient = -0.26 (P-value = 1.5  10-2). However, the enrichment profile distances of the disease-related genes in the brain regions was not significantly correlated to the DMN score. Besides the similarity of diseases, we identified novel relationships between diseases and cell types. 
Conclusion: We manually constructed a comprehensive dataset for genes and mutations related to 20 nervous system diseases. Using this dataset, the similarities of diseases in genes and cell types were found significantly correlated to the phenotypic similarity. Thus, the phenotypic similarity between the diseases is more likely to be caused by dysfunctions of the same genes or the same types of neurons than the same brain regions. The data is collected into the database NeurodisM (http://biomed-ai.org/neurodism).