This study addresses the issues in translating the laboratory derived data obtained during discovery phase of research to a clinical setting using a breast cancer model. Laboratory-based risk assessment indi-cated that a family history of breast cancer, reduced folate carrier 1 (RFC1) G80A, thymidylate synthase (TYMS) 5’-UTR 28bp tandem repeat, methylene tetrahydrofolate reductase (MTHFR) C677T and catecholamine-O-methyl transferase (COMT) genetic polymorphisms in one-carbon metabolic pathway increase the risk for breast cancer. Glutamate carboxypeptidase II (GCPII) C1561T and cytosolic serine hydroxymethyl transferase (cSHMT) C1420T polymorphisms were found to decrease breast cancer risk. In order to test the clinical validity of this information in the risk prediction of breast cancer, data was stratified based on number of protective alleles into four categories and in each category sensitivity and 1-specificity values were obtained based on the distribution of number of risk alleles in cases and controls. Receiver operating characteristic (ROC) curves were plotted and the area under ROC curve (C) was used as a measure of discriminatory ability between cases and controls. In subjects without any protective allele, aberrations in one-carbon metabolism showed perfect prediction (C=0.93) while the predictability was lost in subjects with one protective allele (C=0.60). However, predictability increased steadily with increasing number of protective alleles (C=0.63 for 2 protective alleles and C=0.71 for 3 protective alleles). The cut-off point for discrimination was >4 alleles in all predictable combinations. Models of this kind can serve as valuable tools in translational re-search, especially in identifying high-risk individuals and reducing the disease risk either by life style modification or by medical intervention.