Abstract
Next generation sequencing and low-cost genotyping technologies are opening up new avenues to translate genomics-based stratification and genetic variant information into improved care and personalized interventions in the clinic. Towards that goal, genome-wide variant information has become an important tool for cohort identification and stratification, phenotype-genotype association studies, discovery of disease markers, prediction of endo-phenotypes, and clinical decision support. This chapter focuses on the use of genetic variant information in the context of pediatric autoimmune diseases and pain management in a pediatric surgery setting. Genome-wide variant detection and discovery, as well as targeted gene sequencing approaches are discussed through the lenses of the resulting informatics challenges, implied tailored research informatics solutions, and integration with clinical informatics systems. These challenges and solutions are illustrated using three specific applications, namely: (i) cohort stratification analysis; (ii) prediction of classical HLA alleles from variant data in the context of pediatric autoimmune diseases; and (iii) predictive decision models for the management of surgical pain and opioid-related adverse outcomes in children.
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Biesiada, J., Sadhasivam, S., Kohram, M., Wagner, M., Meller, J. (2016). Toward Pediatric Precision Medicine: Examples of Genomics-Based Stratification Strategies. In: Hutton, J. (eds) Pediatric Biomedical Informatics. Translational Bioinformatics, vol 10. Springer, Singapore. https://doi.org/10.1007/978-981-10-1104-7_17
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DOI: https://doi.org/10.1007/978-981-10-1104-7_17
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