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Inheritance beyond plain heritability: variance controlling genes in Arabidopsis thaliana
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology. Swedish University of Agricultural Sciences, Uppsala, Sweden.ORCID iD: 0000-0002-2722-5264
2012 (English)In: PLOS Genetics, ISSN 1553-7404, Vol. 8, no 8, e1002839- p.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.

Place, publisher, year, edition, pages
2012. Vol. 8, no 8, e1002839- p.
National Category
URN: urn:nbn:se:uu:diva-170089DOI: 10.1371/journal.pgen.1002839ISI: 000308529300006OAI: diva2:508273
Available from: 2012-03-07 Created: 2012-03-07 Last updated: 2016-05-25Bibliographically 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.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 908
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)
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)
Available from: 2012-04-04 Created: 2012-03-07 Last updated: 2012-04-19Bibliographically approved

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Carlborg, Örjan
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