uu.seUppsala University Publications
Change search
ReferencesLink to record
Permanent link

Direct link
MAPfastR: Quantitative trait loci mapping in outbred line crosses
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.
Show others and affiliations
2013 (English)In: G3: Genes, Genomes, Genetics, ISSN 2160-1836, E-ISSN 2160-1836, Vol. 3, 2147-2149 p.Article in journal (Refereed) Published
Place, publisher, year, edition, pages
2013. Vol. 3, 2147-2149 p.
National Category
Computational Mathematics Genetics Bioinformatics and Systems Biology
URN: urn:nbn:se:uu:diva-180917DOI: 10.1534/g3.113.008623ISI: 000328334500005OAI: oai:DiVA.org:uu-180917DiVA: diva2:552118
Available from: 2013-10-11 Created: 2012-09-13 Last updated: 2016-05-25Bibliographically 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.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 973
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
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)
Available from: 2012-10-04 Created: 2012-09-13 Last updated: 2013-01-23Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Nettelblad, CarlHolmgren, SverkerCarlborg, Örjan
By organisation
Division of Scientific ComputingComputational Science
In the same journal
G3: Genes, Genomes, Genetics
Computational MathematicsGeneticsBioinformatics and Systems Biology

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 573 hits
ReferencesLink to record
Permanent link

Direct link