Germline mutations that inactivate BRCA1 are responsible for breast and ovarian cancer susceptibility. One possible outcome of genetic testing for BRCA1 is the finding of a genetic variant of uncertain significance for which there is no information regarding its cancer association. This outcome leads to problems in risk assessment, counseling and preventive care. The purpose of the present study was to functionally evaluate seven unclassified variants of BRCA1 including a genomic deletion that leads to the in-frame loss of exons 16/17 (Delta exons 16/17) in the mRNA, an insertion that leads to a frameshift and an extended carboxy-terminus (5673insC), and five missense variants (K1487R, S1613C, M1652I, Q1826H and V1833M). We analyzed the variants using a functional assay based on the transcription activation property of BRCA1 combined with supervised learning computational models. Functional analysis indicated that variants S1613C, Q1826H, and M1652I are likely to be neutral, whereas variants V1833M, Delta exons 16/17, and 5673insC are likely to represent deleterious variants. In agreement with the functional analysis, the results of the computational analysis also indicated that the latter three variants are likely to be deleterious. Taken together, a combined approach of functional and bioinformatics analysis, plus structural modeling, can be utilized to obtain valuable information pertaining to the effect of a rare variant on the structure and function of BRCA1. Such information can, in turn, aid in the classification of BRCA1 variants for which there is a lack of genetic information needed to provide reliable risk assessment.