Abstract
The availability of high throughput technology for parallel genotyping has opened the field of genetics to genome-wide association studies (GWAS). These studies generate massive amount of genetic data that challenge investigators with issues related to data management, statistical analysis of large data sets, visualization, and annotation of results. We will review the common approach to analysis of GWAS data and then discuss options to learn more from these data.
Keywords
- Single Nucleotide Polymorphism
- Single Nucleotide Polymorphism Genotype
- Single Nucleotide Polymorphism Array
- Monogenic Disease
- Fetal Hemoglobin
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Sebastiani, P., Solovieff, N. (2010). Genome Wide Association Studies. In: Heath, L., Ramakrishnan, N. (eds) Problem Solving Handbook in Computational Biology and Bioinformatics. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09760-2_8
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DOI: https://doi.org/10.1007/978-0-387-09760-2_8
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