In-silico identification of genetic variants associated with chronic lymphocytic leukemia for diagnostic and therapeutic applications

Chronic Lymphocytic Leukemia (CLL) is a genetically complex and clinically heterogeneous hematologic malignancy characterized by clonal proliferation of mature B-cells. Though a number of genome-wide association analyses (GWAS) have identified several susceptibility loci associated with CLL, much of its inherited risk remains unexplored. In this study, we combined summary statistics of multiple GWAS to identify some novel genetic risk factors associated with CLL. Firstly, we identified 2357 significant single nucleotide polymorphisms (SNPs) associated with CLL by using the threshold at p-value < 5 × 10− 8 computed with the METAL web-tool. Then we functionally mapped and annotated these significant SNPs using Linkage Disequilibrium (LD)-based annotation in FUMA, and obtained 204 independent, 59 lead, and 2,075 candidate SNPs across 40 genomic risk loci. The positional and eQTL mapping were performed with candidate SNPs, and we found 60 unique genes. Subsequently, we identified 7 genes (TERT, BCL2L11, FAS, BCL2, CFLAR, CASP8, and LEF1) as the CLL-associated key genes (KGs) by the protein-protein interaction (PPI) network analysis. The KGs-CLL relationship was also supported by DisGNET. KGs-regulatory network analysis identified some key transcription factors and microRNAs as the transcriptional and post-transcriptional regulators. KGs-set enrichment analysis using gene ontology (GO) and KEGG pathways revealed some key biological processes, molecular functions, cellular components, and signaling pathways. All KGs were found to be druggable and showed known drug-gene interactions. Finally, four candidate drug agents guided by KGs, were proposed for CLL. Hence, the results of this study could become valuable assets for the diagnosis and therapies of patients with CLL.