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Efficient optimization algorithms and implementations for genetic analysis of complex traits on a grid system with multicore nodes
2008 (English)In: Conference paper (Refereed)
National Category
Computer Science Computational Mathematics
URN: urn:nbn:se:uu:diva-111590OAI: oai:DiVA.org:uu-111590DiVA: diva2:285721
PARA 2008: State of the Art in Scientific and Parallel Computing
Available from2010-01-12 Created:2009-12-17 Last updated:2012-11-26Bibliographically approved
In thesis
1. An e-Science Approach to Genetic Analysis of Quantitative Traits
Open this publication in new window or tab >>An e-Science Approach to Genetic Analysis of Quantitative Traits
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Many important traits in plants, animals and humans are quantitative, and most such traits are generally believed to be affected by multiple genetic loci. Standard computational tools for mapping of quantitative traits (i.e. for finding Quantitative Trait Loci, QTL, in the genome) use linear regression models for relating the observed phenotypes to the genetic composition of individuals in an experimental population. Using these tools to simultaneously search for multiple QTL is computationally demanding. The main reason for this is the complex optimization landscape for the multidimensional global optimization problems that must be solved. This thesis describes parallel algorithms, implementations and tools for simultaneous mapping of several QTL. These new computational tools enable genetic analysis exploiting new classes of multidimensional statistical models, potentially resulting in interesting results in genetics.

We first describe how the standard, brute-force algorithm for global optimization in QTL analysis is parallelized and implemented on a grid system. Then, we also present a parallelized version of the more elaborate global optimization algorithm DIRECT and show how this can be efficiently deployed and used on grid systems and other loosely-coupled architectures. The parallel DIRECT scheme is further developed to exploit both coarse-grained parallelism in grid systems or clusters as well as fine-grained, tightly-coupled parallelism in multi-core nodes. The results show that excellent speedup and performance can be archived on grid systems and clusters, even when using a tightly-coupled algorithm such as DIRECT. Finally, we provide two distinctly different front-ends for our code. One is a grid portal providing a graphical front-end suitable for novice users and standard forms of QTL analysis. The other is a prototype of an R-based grid-enabled problem solving environment. Both of these front-ends can, after some further refinement, be utilized by geneticists for performing multidimensional genetic analysis of quantitative traits on a regular basis.

Publisher, range
Uppsala: Acta Universitatis Upsaliensis, 2010. 40 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 708
QTL Analysis, Grid Computing, Global Optimization, e-Science
National Category
Software Engineering Computational Mathematics
Research subject
Scientific Computing
urn:nbn:se:uu:diva-111597 (URN)978-91-554-7706-6 (ISBN)
Public defence
2010-02-25, Room 2446, Polacksbacken, Lägerhyddsvägen 2D, Uppsala, 10:15 (English)
Available from2010-02-02 Created:2009-12-17 Last updated:2011-10-26Bibliographically approved

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Jayawardena, MahenHolmgren, Sverker
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