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hglm: a package for fitting hierarchical generalized linear models
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology.
2010 (English)In: The R Journal, ISSN 2073-4859, E-ISSN 2073-4859, Vol. 2, no 2, 20-28 p.Article 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.

Place, publisher, year, edition, pages
2010. Vol. 2, no 2, 20-28 p.
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-170083ISI: 000208590000004OAI: oai:DiVA.org:uu-170083DiVA: diva2:508270
Available from: 2012-03-07 Created: 2012-03-07 Last updated: 2017-12-07Bibliographically approved
In thesis
1. Novel Statistical Methods in Quantitative Genetics: Modeling Genetic Variance for Quantitative Trait Loci Mapping and Genomic Evaluation
Open this publication in new window or tab >>Novel Statistical Methods in Quantitative Genetics: Modeling Genetic Variance for Quantitative Trait Loci Mapping and Genomic Evaluation
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. 67 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 908
Keyword
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:nbn:se:uu:diva-170091 (URN)978-91-554-8298-5 (ISBN)
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: 2012-04-19Bibliographically approved

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