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Global optimization algorithm PruneDIRECT as an R package
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.
2016 (English)Report (Other academic)
Place, publisher, year, edition, pages
2016.
National Category
Computational Mathematics Software Engineering
Identifiers
URN: urn:nbn:se:uu:diva-284374OAI: oai:DiVA.org:uu-284374DiVA: diva2:920237
Projects
eSSENCE
Available from: 2016-04-24 Created: 2016-04-18 Last updated: 2016-05-25Bibliographically approved
In thesis
1. Methods from Statistical Computing for Genetic Analysis of Complex Traits
Open this publication in new window or tab >>Methods from Statistical Computing for Genetic Analysis of Complex Traits
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The goal of this thesis is to explore, improve and implement some advanced modern computational methods in statistics, focusing on applications in genetics. The thesis has three major directions.

First, we study likelihoods for genetics analysis of experimental populations. Here, the maximum likelihood can be viewed as a computational global optimization problem. We introduce a faster optimization algorithm called PruneDIRECT, and explain how it can be parallelized for permutation testing using the Map-Reduce framework. We have implemented PruneDIRECT as an open source R package, and also Software as a Service for cloud infrastructures (QTLaaS).

The second part of the thesis focusses on using sparse matrix methods for solving linear mixed models with large correlation matrices. For populations with known pedigrees, we show that the inverse of covariance matrix is sparse. We describe how to use this sparsity to develop a new method to maximize the likelihood and calculate the variance components.

In the final part of the thesis we study computational challenges of psychiatric genetics, using only pedigree information. The aim is to investigate existence of maternal effects in obsessive compulsive behavior. We add the maternal effects to the linear mixed model, used in the second part of this thesis, and we describe the computational challenges of working with binary traits.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. 42 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1373
Keyword
Statistical Computing, QTL mapping, Global Optimization, Linear Mixed Models
National Category
Computational Mathematics Genetics
Research subject
Scientific Computing
Identifiers
urn:nbn:se:uu:diva-284378 (URN)978-91-554-9574-9 (ISBN)
Public defence
2016-06-07, 2446, Lägerhyddsvägen 2, Uppsala, 13:15 (English)
Opponent
Supervisors
Projects
eSSENCE
Available from: 2016-05-17 Created: 2016-04-18 Last updated: 2016-06-01Bibliographically approved

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Mahjani, Behrang
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