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Using parallel computing and grid systems for genetic mapping of quantitative traits
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science. (Software Aspects of High-Performance Computing)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science. (ndim)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science. (ndim)
2007 (English)In: Applied Parallel Computing: State of the Art in Scientific Computing, Berlin: Springer-Verlag , 2007, 627-636 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Berlin: Springer-Verlag , 2007. 627-636 p.
Series
Lecture Notes in Computer Science, 4699
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:uu:diva-11546DOI: 10.1007/978-3-540-75755-9_76ISI: 000250904900076ISBN: 978-3-540-75754-2 (print)OAI: oai:DiVA.org:uu-11546DiVA: diva2:39315
Available from: 2007-09-26 Created: 2007-09-26 Last updated: 2013-04-17Bibliographically approved
In thesis
1. Numerical Algorithms for Mapping of Multiple Quantitative Trait Loci in Experimental Populations
Open this publication in new window or tab >>Numerical Algorithms for Mapping of Multiple Quantitative Trait Loci in Experimental Populations
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Most traits of medical or economic importance are quantitative, i.e. they can be measured on a continuous scale. Strong biological evidence indicates that quantitative traits are governed by a complex interplay between the environment and multiple quantitative trait loci, QTL, in the genome. Nonlinear interactions make it necessary to search for several QTL simultaneously. This thesis concerns numerical methods for QTL search in experimental populations. The core computational problem of a statistical analysis of such a population is a multidimensional global optimization problem with many local optima. Simultaneous search for d QTL involves solving a d-dimensional problem, where each evaluation of the objective function involves solving one or several least squares problems with special structure. Using standard software, already a two-dimensional search is costly, and searches in higher dimensions are prohibitively slow.

Three efficient algorithms for evaluation of the most common forms of the objective function are presented. The computing time for the linear regression method is reduced by up to one order of magnitude for real data examples by using a new scheme based on updated QR factorizations. Secondly, the objective function for the interval mapping method is evaluated using an updating technique and an efficient iterative method, which results in a 50 percent reduction in computing time. Finally, a third algorithm, applicable to the imputation and weighted linear mixture model methods, is presented. It reduces the computing time by between one and two orders of magnitude.

The global search problem is also investigated. Standard software techniques for finding the global optimum of the objective function are compared with a new approach based on the DIRECT algorithm. The new method is more accurate than the previously fastest scheme and locates the optimum in 1-2 orders of magnitude less time. The method is further developed by coupling DIRECT to a local optimization algorithm for accelerated convergence, leading to additional time savings of up to eight times. A parallel grid computing implementation of exhaustive search is also presented, and is suitable e.g for verifying global optima when developing efficient optimization algorithms tailored for the QTL mapping problem.

Using the algorithms presented in this thesis, simultaneous search for at least six QTL can be performed routinely. The decrease in overall computing time is several orders of magnitude. The results imply that computations which were earlier considered impossible are no longer difficult, and that genetic researchers thus are free to focus on model selection and other central genetical issues.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2005. 61 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 133
Keyword
Scientific computing
National Category
Bioinformatics (Computational Biology)
Research subject
Scientific Computing
Identifiers
urn:nbn:se:uu:diva-6248 (URN)91-554-6427-0 (ISBN)
Public defence
2006-01-13, Room 2446, Polacksbacken, Lägerhyddsvägen 2D, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2005-12-22 Created: 2005-12-22 Last updated: 2013-02-18Bibliographically approved
2. 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.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2010. 40 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 708
Keyword
QTL Analysis, Grid Computing, Global Optimization, e-Science
National Category
Software Engineering Computational Mathematics
Research subject
Scientific Computing
Identifiers
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)
Opponent
Supervisors
Projects
eSSENCE
Available from: 2010-02-02 Created: 2009-12-17 Last updated: 2011-10-26Bibliographically approved

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Jayawardena, MahenLjungberg, KajsaHolmgren, Sverker

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