Most common and complex diseases, such as diabetes and cancer, are influenced at some level by variation in the genome. To truly address the goal of translational research, genetic variation must be taken into consideration. Research done in public health genetics, specifically in the area of single nucleotide polymorphisms (SNPs), is the first step to understanding human genetic variation. In addition, novel methods are needed to represent and to conduct text mining over textual genotypic data sources. In this paper, we describe the development and evaluation, in the context of a genetic study, of a translational-informatics method that supports both machine-learning text mining (e.g., Conditional random fields) and automated inference for identifying key concepts (e.g., Hypotheses and results). After scaling for inter-annotator agreement, our adjusted overall precision was 64%, with a range of 48% to 80%. While other biological text mining systems have focused on named-entity recognition, the development of tools for genetic studies focusing on hypotheses and results has been relatively rare.