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How to deal with genotype uncertainty in variance component quantitative trait loci analyses
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology. SLU.ORCID iD: 0000-0002-2722-5264
2011 (English)In: Genetical Research, ISSN 0016-6723, E-ISSN 1469-5073, Vol. 93, no 5, 333-342 p.Article 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.

Place, publisher, year, edition, pages
2011. Vol. 93, no 5, 333-342 p.
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:uu:diva-161047DOI: 10.1017/S0016672311000152ISI: 000295808300002OAI: oai:DiVA.org:uu-161047DiVA: diva2:456545
Available from: 2011-11-15 Created: 2011-11-07 Last updated: 2017-12-08
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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Shen, XiaCarlborg, Örjan

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