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Fast and accurate detection of multiple quantitative trait loci
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.
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.
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.
2013 (English)In: Journal of Computational Biology, ISSN 1066-5277, E-ISSN 1557-8666, Vol. 20, 687-702 p.Article in journal (Refereed) Published
Place, publisher, year, edition, pages
2013. Vol. 20, 687-702 p.
National Category
Bioinformatics and Systems Biology Genetics Computational Mathematics Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-180916DOI: 10.1089/cmb.2012.0242ISI: 000323822000006OAI: oai:DiVA.org:uu-180916DiVA: diva2:552117
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eSSENCE
Available from: 2013-08-06 Created: 2012-09-13 Last updated: 2017-12-07Bibliographically approved
In thesis
1. Two Optimization Problems in Genetics: Multi-dimensional QTL Analysis and Haplotype Inference
Open this publication in new window or tab >>Two Optimization Problems in Genetics: Multi-dimensional QTL Analysis and Haplotype Inference
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The existence of new technologies, implemented in efficient platforms and workflows has made massive genotyping available to all fields of biology and medicine. Genetic analyses are no longer dominated by experimental work in laboratories, but rather the interpretation of the resulting data. When billions of data points representing thousands of individuals are available, efficient computational tools are required. The focus of this thesis is on developing models, methods and implementations for such tools.

The first theme of the thesis is multi-dimensional scans for quantitative trait loci (QTL) in experimental crosses. By mating individuals from different lines, it is possible to gather data that can be used to pinpoint the genetic variation that influences specific traits to specific genome loci. However, it is natural to expect multiple genes influencing a single trait to interact. The thesis discusses model structure and model selection, giving new insight regarding under what conditions orthogonal models can be devised. The thesis also presents a new optimization method for efficiently and accurately locating QTL, and performing the permuted data searches needed for significance testing. This method has been implemented in a software package that can seamlessly perform the searches on grid computing infrastructures.

The other theme in the thesis is the development of adapted optimization schemes for using hidden Markov models in tracing allele inheritance pathways, and specifically inferring haplotypes. The advances presented form the basis for more accurate and non-biased line origin probabilities in experimental crosses, especially multi-generational ones. We show that the new tools are able to reconstruct haplotypes and even genotypes in founder individuals and offspring alike, based on only unordered offspring genotypes. The tools can also handle larger populations than competing methods, resolving inheritance pathways and phase in much larger and more complex populations. Finally, the methods presented are also applicable to datasets where individual relationships are not known, which is frequently the case in human genetics studies. One immediate application for this would be improved accuracy for imputation of SNP markers within genome-wide association studies (GWAS).

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2012. 57 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 973
Keyword
quantitative trait loci, genome-wide association studies, hidden Markov models, numerical optimization, linkage analysis, haplotype inference, genotype imputation, high performance computing
National Category
Computational Mathematics Probability Theory and Statistics Bioinformatics and Systems Biology Genetics Bioinformatics (Computational Biology) Software Engineering
Identifiers
urn:nbn:se:uu:diva-180920 (URN)978-91-554-8473-6 (ISBN)
Public defence
2012-10-26, Room 2446, Polacksbacken, Lägerhyddsvägen 2D, Uppsala, 13:15 (English)
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eSSENCE
Available from: 2012-10-04 Created: 2012-09-13 Last updated: 2017-01-25Bibliographically approved
2. 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
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eSSENCE
Available from: 2016-05-17 Created: 2016-04-18 Last updated: 2016-06-01Bibliographically approved

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Nettelblad, CarlMahjani, BehrangHolmgren, Sverker

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