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Methods from Statistical Computing for Genetic Analysis of Complex 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.
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 [en]
Statistical Computing, QTL mapping, Global Optimization, Linear Mixed Models
National Category
Computational Mathematics Genetics
Research subject
Scientific Computing
Identifiers
URN: urn:nbn:se:uu:diva-284378ISBN: 978-91-554-9574-9 (print)OAI: oai:DiVA.org:uu-284378DiVA: diva2:920245
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
List of papers
1. Fast and accurate detection of multiple quantitative trait loci
Open this publication in new window or tab >>Fast and accurate detection of multiple quantitative trait loci
2013 (English)In: Journal of Computational Biology, ISSN 1066-5277, E-ISSN 1557-8666, Vol. 20, 687-702 p.Article in journal (Refereed) Published
National Category
Bioinformatics and Systems Biology Genetics Computational Mathematics Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-180916 (URN)10.1089/cmb.2012.0242 (DOI)000323822000006 ()
Projects
eSSENCE
Available from: 2013-08-06 Created: 2012-09-13 Last updated: 2017-12-07Bibliographically approved
2. Global optimization algorithm PruneDIRECT as an R package
Open this publication in new window or tab >>Global optimization algorithm PruneDIRECT as an R package
2016 (English)Report (Other academic)
National Category
Computational Mathematics Software Engineering
Identifiers
urn:nbn:se:uu:diva-284374 (URN)
Projects
eSSENCE
Available from: 2016-04-24 Created: 2016-04-18 Last updated: 2016-05-25Bibliographically approved
3. A flexible computational framework using R and Map-Reduce for permutation tests of massive genetic analysis of complex traits
Open this publication in new window or tab >>A flexible computational framework using R and Map-Reduce for permutation tests of massive genetic analysis of complex traits
2017 (English)In: IEEE/ACM Transactions on Computational Biology & Bioinformatics, ISSN 1545-5963, E-ISSN 1557-9964, Vol. 14, 381-392 p.Article in journal (Refereed) Published
National Category
Computational Mathematics Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:uu:diva-284372 (URN)10.1109/TCBB.2016.2527639 (DOI)
Projects
eSSENCE
Available from: 2016-02-11 Created: 2016-04-18 Last updated: 2017-11-30Bibliographically approved
4. QTL as a service: PruneDIRECT for multi-dimensional QTL scans in cloud settings
Open this publication in new window or tab >>QTL as a service: PruneDIRECT for multi-dimensional QTL scans in cloud settings
2016 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811Article in journal (Other academic) Submitted
National Category
Bioinformatics and Systems Biology Software Engineering
Identifiers
urn:nbn:se:uu:diva-284376 (URN)
Projects
eSSENCE
Available from: 2016-04-18 Created: 2016-04-18 Last updated: 2017-11-30Bibliographically approved
5. Software as a service in analysis of quantitative trait loci
Open this publication in new window or tab >>Software as a service in analysis of quantitative trait loci
2016 (English)Report (Other academic)
National Category
Bioinformatics and Systems Biology Software Engineering
Identifiers
urn:nbn:se:uu:diva-284375 (URN)
Projects
eSSENCE
Available from: 2016-04-18 Created: 2016-04-18 Last updated: 2016-05-25Bibliographically approved
6. Fitting linear mixed models using sparse matrix methods and Lanczos factorization
Open this publication in new window or tab >>Fitting linear mixed models using sparse matrix methods and Lanczos factorization
2016 (English)In: Computational Statistics & Data Analysis, ISSN 0167-9473, E-ISSN 1872-7352Article in journal (Other academic) Submitted
National Category
Computational Mathematics
Identifiers
urn:nbn:se:uu:diva-284373 (URN)
Projects
eSSENCE
Available from: 2016-04-08 Created: 2016-04-18 Last updated: 2017-11-30Bibliographically approved
7. Computational challenges in modeling maternal effects in psychiatric disorders
Open this publication in new window or tab >>Computational challenges in modeling maternal effects in psychiatric disorders
Show others...
2016 (English)Manuscript (preprint) (Other academic)
National Category
Genetics Computational Mathematics
Identifiers
urn:nbn:se:uu:diva-284377 (URN)
Projects
eSSENCE
Available from: 2016-04-18 Created: 2016-04-18 Last updated: 2017-03-06

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Mahjani, Behrang

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