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Novel Statistical Methods in Quantitative Genetics: Modeling Genetic Variance for Quantitative Trait Loci Mapping and Genomic Evaluation
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis develops and evaluates statistical methods for different types of genetic analyses, including quantitative trait loci (QTL) analysis, genome-wide association study (GWAS), and genomic evaluation. The main contribution of the thesis is to provide novel insights in modeling genetic variance, especially via random effects models.

In variance component QTL analysis, a full likelihood model accounting for uncertainty in the identity-by-descent (IBD) matrix was developed. It was found to be able to correctly adjust the bias in genetic variance component estimation and gain power in QTL mapping in terms of precision. 

Double hierarchical generalized linear models, and a non-iterative simplified version, were implemented and applied to fit data of an entire genome. These whole genome models were shown to have good performance in both QTL mapping and genomic prediction.

A re-analysis of a publicly available GWAS data set identified significant loci in Arabidopsis that control phenotypic variance instead of mean, which validated the idea of variance-controlling genes. 

The works in the thesis are accompanied by R packages available online, including a general statistical tool for fitting random effects models (hglm), an efficient generalized ridge regression for high-dimensional data (bigRR), a double-layer mixed model for genomic data analysis (iQTL), a stochastic IBD matrix calculator (MCIBD), a computational interface for QTL mapping (qtl.outbred), and a GWAS analysis tool for mapping variance-controlling loci (vGWAS).

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2012. , p. 67
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 908
Keywords [en]
statistical genetics, quantitative trait loci, genome-wide association study, genomic selection, genetic variance, hierarchical generalized linear model, linear mixed model, random effect, heteroscedastic effects model, variance-controlling genes
National Category
Genetics Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:uu:diva-170091ISBN: 978-91-554-8298-5 (print)OAI: oai:DiVA.org:uu-170091DiVA, id: diva2:508277
Public defence
2012-04-27, C10:305, BMC, Husargatan 3, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2012-04-04 Created: 2012-03-07 Last updated: 2018-01-12Bibliographically approved
List of papers
1. hglm: a package for fitting hierarchical generalized linear models
Open this publication in new window or tab >>hglm: a package for fitting hierarchical generalized linear models
2010 (English)In: The R Journal, ISSN 2073-4859, E-ISSN 2073-4859, Vol. 2, no 2, p. 20-28Article in journal (Refereed) Published
Abstract [en]

We present the hglm package for fitting hierarchical generalized linear models. It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the model.

National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-170083 (URN)000208590000004 ()
Available from: 2012-03-07 Created: 2012-03-07 Last updated: 2017-12-07Bibliographically approved
2. How to deal with genotype uncertainty in variance component quantitative trait loci analyses
Open this publication in new window or tab >>How to deal with genotype uncertainty in variance component quantitative trait loci analyses
2011 (English)In: Genetical Research, ISSN 0016-6723, E-ISSN 1469-5073, Vol. 93, no 5, p. 333-342Article in journal (Refereed) Published
Abstract [en]

Dealing with genotype uncertainty is an ongoing issue in genetic analyses of complex traits. Here we consider genotype uncertainty in quantitative trait loci (QTL) analyses for large crosses in variance component models, where the genetic information is included in identity-by-descent (IBD) matrices. An IBD matrix is one realization from a distribution of potential IBD matrices given available marker information. In QTL analyses, its expectation is normally used resulting in potentially reduced accuracy and loss of power. Previously, IBD distributions have been included in models for small human full-sib families. We develop an Expectation-Maximization (EM) algorithm for estimating a full model based on Monte Carlo imputation for applications in large animal pedigrees. Our simulations show that the bias of variance component estimates using traditional expected IBD matrix can be adjusted by accounting for the distribution and that the calculations are computationally feasible for large pedigrees.

National Category
Natural Sciences
Identifiers
urn:nbn:se:uu:diva-161047 (URN)10.1017/S0016672311000152 (DOI)000295808300002 ()
Available from: 2011-11-15 Created: 2011-11-07 Last updated: 2017-12-08
3. Hierarchical likelihood opens a new way of estimating genetic values using genome-wide dense marker maps
Open this publication in new window or tab >>Hierarchical likelihood opens a new way of estimating genetic values using genome-wide dense marker maps
2011 (English)In: BMC Proceedings, ISSN 1753-6561, E-ISSN 1753-6561, Vol. 5, no Suppl 3, p. S14-Article in journal (Refereed) Published
National Category
Genetics
Identifiers
urn:nbn:se:uu:diva-170085 (URN)
Available from: 2012-03-07 Created: 2012-03-07 Last updated: 2017-12-07
4. qtl.outbred: interfacing outbred line cross data with the R/qtl mapping software
Open this publication in new window or tab >>qtl.outbred: interfacing outbred line cross data with the R/qtl mapping software
2011 (English)In: BMC Research Notes, ISSN 1756-0500, E-ISSN 1756-0500, Vol. 4, no 154Article in journal (Refereed) Published
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-170087 (URN)
Available from: 2012-03-07 Created: 2012-03-07 Last updated: 2018-01-12
5. Inheritance beyond plain heritability: variance controlling genes in Arabidopsis thaliana
Open this publication in new window or tab >>Inheritance beyond plain heritability: variance controlling genes in Arabidopsis thaliana
2012 (English)In: PLOS Genetics, ISSN 1553-7404, Vol. 8, no 8, p. e1002839-Article in journal (Refereed) Published
Abstract [en]

The phenotypic effect of a gene is normally described by the mean-difference between alternative genotypes. A gene may, however, also influence the phenotype by causing a difference in variance between genotypes. Here, we reanalyze a publicly available Arabidopsis thaliana dataset [1] and show that genetic variance heterogeneity appears to be as common as normal additive effects on a genomewide scale. The study also develops theory to estimate the contributions of variance differences between genotypes to the phenotypic variance, and this is used to show that individual loci can explain more than 20% of the phenotypic variance. Two well-studied systems, cellular control of molybdenum level by the ion-transporter MOT1 and flowering-time regulation by the FRI-FLC expression network, and a novel association for Leaf serration are used to illustrate the contribution of major individual loci, expression pathways, and gene-by-environment interactions to the genetic variance heterogeneity.

National Category
Genetics
Identifiers
urn:nbn:se:uu:diva-170089 (URN)10.1371/journal.pgen.1002839 (DOI)000308529300006 ()
Available from: 2012-03-07 Created: 2012-03-07 Last updated: 2016-05-25Bibliographically approved
6. Fast generalized ridge regression for models including heteroscedastic effects in quantitative genetics
Open this publication in new window or tab >>Fast generalized ridge regression for models including heteroscedastic effects in quantitative genetics
(English)Manuscript (preprint) (Other academic)
National Category
Genetics
Identifiers
urn:nbn:se:uu:diva-170090 (URN)
Available from: 2012-03-07 Created: 2012-03-07 Last updated: 2012-04-19

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