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Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology. (Örjan Carlborg)ORCID iD: 0000-0002-7451-9222
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Experiments in model organisms report abundant genetic interactions underlying biologically important traits, whereas quantitative genetics theory predicts, and data support, that most genetic variance in populations is additive. Here we describe networks of capacitating genetic interactions that contribute to quantitative trait variation in a large yeast intercross population. The additive variance explained by individual loci in a network is highly dependent on the allele frequencies of the interacting loci. Modeling of phenotypes for multi-locus genotype classes in the epistatic networks is often improved by accounting for the interactions. We discuss the implications of these results for attempts to dissect genetic architectures and to predict individual phenotypes and long-term responses to selection.

Keyword [en]
genetic interactions, epistasis, yeast, QTL, epistatic networks
National Category
Genetics
Research subject
Genetics
Identifiers
URN: urn:nbn:se:uu:diva-307821DOI: 10.1101/059485OAI: oai:DiVA.org:uu-307821DiVA: diva2:1048703
Available from: 2016-11-22 Created: 2016-11-22 Last updated: 2016-11-30
In thesis
1. Complex Trait Genetics: Beyond Additivity
Open this publication in new window or tab >>Complex Trait Genetics: Beyond Additivity
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The link between the genotype and the phenotype of an organism is immensely complex. Despite this it can, to a great extent, be captured using models that assume that gene variants combine their effects in an additive manner. This thesis explores aspects of genetics that cannot be fully captured using such additive models. Using experimental data from three different model organisms, I study two phenomena that fall outside of the additive paradigm: genetic interactions and genetic variance heterogeneity.

Using the model plant Arabidopsis thaliana, we show how important biological insights can be reached by exploring loci that display genetic variance heterogeneity. In the first study, this approach identified alleles in the gene CMT2 associated with the climate at sampling locations, suggesting a role in climate adaption. These alleles affected the genome wide methylation pattern, and a complete knock down of this gene increased the plants heat tolerance. In the second study, we demonstrate how the observed genetic variance heterogeneity was the result of the partial linkage of many functional alleles near the gene MOT1, all contributing to Molybdenum levels in the leaves.

Further, we explore genetic interactions using data from dogs and budding yeast (Saccharomyces cerevisiae). In the dog population, two interacting loci were associated with fructosamine levels, a biomarker used to monitor blood glucose. One of the loci displayed the pattern of a selective sweep in some of the studied breeds, suggesting that the interaction is important for the phenotypic breed-differences.

In a cross between two strains of yeast, with the advantage of large population size and nearly equal allele frequencies, we identified large epistatic networks. The networks were largely centered on a number of hub-loci and altogether involved hundreds of genetic interactions. Most network hubs had the ability to either suppress or uncover the phenotypic effects of other loci. Many multi-locus allele combinations resulted in phenotypes that deviated significantly from the expectations, had the loci acted in an additive manner.

Critically, this thesis demonstrates that non-additive genetic mechanisms often need to be considered in order to fully understand the genetics of complex traits. 

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. 45 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1278
Keyword
genetic interactions, epistasis, additivity, GWAS, vGWAS, Genetic mapping, yeast, Arabidopsis Thaliana, dog
National Category
Genetics
Research subject
Genetics
Identifiers
urn:nbn:se:uu:diva-307837 (URN)978-91-554-9754-5 (ISBN)
Public defence
2017-01-13, B22, BMC, Husarg. 3, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2016-12-22 Created: 2016-11-22 Last updated: 2016-12-28

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CiteExportLink to record
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Citation style
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