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Plos Genetics : Forward-time Simulations of Human Populations with Complex Diseases, Volume 3

By Allison, David B.

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Book Id: WPLBN0003924534
Format Type: PDF eBook :
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Reproduction Date: 2015

Title: Plos Genetics : Forward-time Simulations of Human Populations with Complex Diseases, Volume 3  
Author: Allison, David B.
Volume: Volume 3
Language: English
Subject: Journals, Science, Genetics
Collections: Periodicals: Journal and Magazine Collection (Contemporary), PLoS Genetics
Historic
Publication Date:
Publisher: Plos

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Allison, D. B. (n.d.). Plos Genetics : Forward-time Simulations of Human Populations with Complex Diseases, Volume 3. Retrieved from http://www.ebooklibrary.org/


Description
Description : Due to the increasing power of personal computers, as well as the availability of flexible forward-time simulation programs like simuPOP, it is now possible to simulate the evolution of complex human diseases using a forward-time approach. This approach is potentially more powerful than the coalescent approach since it allows simulations of more than one disease susceptibility locus using almost arbitrary genetic and demographic models. However, the application of such simulations has been deterred by the lack of a suitable simulation framework. For example, it is not clear when and how to introduce disease mutants—especially those under purifying selection—to an evolving population, and how to control the disease allele frequencies at the last generation. In this paper, we introduce a forward-time simulation framework that allows us to generate large multi-generation populations with complex diseases caused by unlinked disease susceptibility loci, according to specified demographic and evolutionary properties. Unrelated individuals, small or large pedigrees can be drawn from the resulting population and provide samples for a wide range of study designs and ascertainment methods. We demonstrate our simulation framework using three examples that map genes associated with affection status, a quantitative trait, and the age of onset of a hypothetical cancer, respectively. Nonadditive fitness models, population structure, and gene–gene interactions are simulated. Case-control, sibpair, and large pedigree samples are drawn from the simulated populations and are examined by a variety of gene-mapping methods.


 

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