RESULTS: In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer's disease; 2. ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors.
CONCLUSION: By merely leveraging gene expression data, CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions. The constructed networks are helpful in the identification of important regulators for complex diseases.
OBJECTIVES: This study aimed to investigate the genetic architecture of EOPD in a multi-ethnic Malaysian cohort.
METHODS: 161 index patients with PD onset ≤50 years were recruited from multiple centers across Malaysia. A two-step approach to genetic testing was used, combining a next-generation sequencing-based PD gene panel and multiplex ligation-dependent probe amplification (MLPA).
RESULTS: Thirty-five patients (21.7%) carried pathogenic or likely pathogenic variants involving (in decreasing order of frequency): GBA1, PRKN, PINK1, DJ-1, LRRK2, and ATP13A2. Pathogenic/likely pathogenic variants in GBA1 were identified in thirteen patients (8.1%), and were also commonly found in PRKN and PINK1 (11/161 = 6.8% and 6/161 = 3.7%, respectively). The overall detection rate was even higher in those with familial history (48.5%) or age of diagnosis ≤40 years (34.8%). PRKN exon 7 deletion and the PINK1 p.Leu347Pro variant appear to be common among Malay patients. Many novel variants were found across the PD-related genes.
CONCLUSIONS: This study provides novel insights into the genetic architecture of EOPD in Southeast Asians, expands the genetic spectrum in PD-related genes, and highlights the importance of diversifying PD genetic research to include under-represented populations.