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Can I read How Computational Experiments Can Improve Our Understanding of the Genetic Architecture of Common Human Diseases on EtoBox?
How Computational Experiments Can Improve Our Understanding of the Genetic Architecture of Common Human Diseases by Moore, Jason H.; Olson, Randal S.; Schmitt, Peter; Chen, Yong; Manduchi, Elisabetta is a Computer Science article available to read on EtoBox.
What is How Computational Experiments Can Improve Our Understanding of the Genetic Architecture of Common Human Diseases about?
Susceptibility to common human diseases such as cancer is influenced by many genetic and environmental factors that work together in a complex manner. The state of the art is to perform a genome-wide association study (GWAS) that measures millions of single-nucleotide polymorphisms (SNPs) throughout the genome followed by a one-SNP-at-a-time statistical analysis to detect univariate associations. This approach has identified thousands of genetic risk factors for hundreds of diseases. However, the genetic risk factors detected have very small effect sizes and collectively explain very little of the overall heritability of the disease. Nonetheless, it is assumed that the genetic component of risk is due to many independent risk factors that contribute additively. The fact that many genetic risk factors with small effects can be detected is taken as evidence to support this notion. It is our working hypothesis that the genetic architecture of common diseases is partly driven by non-additive interactions. To test this hypothesis, we developed a heuristic simulation-based method for conducting experiments about the complexity of genetic architecture. We show that a genetic architecture
Who reads How Computational Experiments Can Improve Our Understanding of the Genetic Architecture of Common Human Diseases?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Moore, Jason H.; Olson, Randal S.; Schmitt, Peter; Chen, Yong; Manduchi, Elisabetta
- Publisher
- MIT Press; MIT Press - Journals (ISSN 1064-5462)
- Published
- 2020
- Language
- EN
- Field
- Computer Science (Physical Sciences)