The amount of genetic data available for analysis is rapidly growing, and the importance of analyzing this type of data is paramount to the field of precision medicine, in which diagnostic and prognostic tasks rely on genetic data analysis in many fields, including in oncology. CBRinR is a generic case-based reasoning system (CBR) in R language to analyze genetic data in bioinformatics domains. It is capable of handling both classification tasks and survival analysis tasks from multiomics datasets. CBRinR presents a useful alternative to statistical models such as LASSO regression in genetic domains, and benefits from higher transparency through case-based explanations. This paper describes CBRinR architecture, classification tasks, survival analysis tasks, confounding factors and their effect on fairness, explanations, and comparative analysis with state-of-the-art models in this domain. Experimental evaluations show that CBRinR is as effective in performing diagnostic and survival tasks as the state-of-the-art while affording precise user-level explanations at the case level.