How to deal with Haplotype data: An Extension to the Conceptual Schema of the Human Genome

Abstract

The goal of this work is to describe the advantages of the application of Conceptual Modeling (CM) in complex domains, such as genomics. Nowadays, the study and comprehension of the human genome is a major challenge due to its high level of complexity. The constant evolution in the genomic domain contributes to the generation of ever larger amounts of new data, which means that if we do not manage it correctly data quality could be compromised (i.e., problems related with heterogeneity and inconsistent data). In this paper, we propose the use of a Conceptual Schema of the Human Genome (CSHG), designed to understand and improve our ontological commitment to the domain and also extend (enrich) this schema with the integration of a novel concept: Haplotypes. Our focus is on improving the understanding of the relationship between genotype and phenotype, since new findings show that this question is more complex than was originally thought. Here we present the first steps in our data management approach with haplotypes (variations, frequencies and populations) and discuss the database evolution to support this data. Each new version in our conceptual schema (CS) introduces changes to the underlying database structure that has essential and practical implications for better understanding and managing the relevant information. A solution based on conceptual models gives a clear definition of the domain with direct implications in the medical field (Precision Medicine), in which Genomic Information Systems (GeIS) play a very important role.

Keywords

• haplotypes
• conceptual modeling
• GeIS
• statistical models
• genetic diagnosis