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Methods from Statistical Computing for Genetic Analysis of Complex TraitsPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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2016 (English)Doctoral thesis, comprehensive summary (Other academic)
##### Abstract [en]

##### Place, publisher, year, edition, pages

Uppsala: Acta Universitatis Upsaliensis, 2016. , p. 42
##### Series

Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1373
##### Keywords [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, id: diva2:920245
##### Public defence

2016-06-07, 2446, Lägerhyddsvägen 2, Uppsala, 13:15 (English)
##### Opponent

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##### Supervisors

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#####

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##### Projects

eSSENCEAvailable from: 2016-05-17 Created: 2016-04-18 Last updated: 2016-06-01Bibliographically approved
##### List of papers

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.

1. Fast and accurate detection of multiple quantitative trait loci$(function(){PrimeFaces.cw("OverlayPanel","overlay552117",{id:"formSmash:j_idt1002:0:j_idt1010",widgetVar:"overlay552117",target:"formSmash:j_idt1002:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. Global optimization algorithm PruneDIRECT as an R package$(function(){PrimeFaces.cw("OverlayPanel","overlay920237",{id:"formSmash:j_idt1002:1:j_idt1010",widgetVar:"overlay920237",target:"formSmash:j_idt1002:1:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

3. A flexible computational framework using R and Map-Reduce for permutation tests of massive genetic analysis of complex traits$(function(){PrimeFaces.cw("OverlayPanel","overlay920235",{id:"formSmash:j_idt1002:2:j_idt1010",widgetVar:"overlay920235",target:"formSmash:j_idt1002:2:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

4. QTL as a service: PruneDIRECT for multi-dimensional QTL scans in cloud settings$(function(){PrimeFaces.cw("OverlayPanel","overlay920239",{id:"formSmash:j_idt1002:3:j_idt1010",widgetVar:"overlay920239",target:"formSmash:j_idt1002:3:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

5. Software as a service in analysis of quantitative trait loci$(function(){PrimeFaces.cw("OverlayPanel","overlay920238",{id:"formSmash:j_idt1002:4:j_idt1010",widgetVar:"overlay920238",target:"formSmash:j_idt1002:4:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

6. Fitting linear mixed models using sparse matrix methods and Lanczos factorization$(function(){PrimeFaces.cw("OverlayPanel","overlay920236",{id:"formSmash:j_idt1002:5:j_idt1010",widgetVar:"overlay920236",target:"formSmash:j_idt1002:5:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

7. Computational challenges in modeling maternal effects in psychiatric disorders$(function(){PrimeFaces.cw("OverlayPanel","overlay920241",{id:"formSmash:j_idt1002:6:j_idt1010",widgetVar:"overlay920241",target:"formSmash:j_idt1002:6:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

isbn
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