| 1. |
- Ahlqvist, Emma, et al.
(författare)
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High-resolution mapping of a complex disease, a model for rheumatoid arthritis, using heterogeneous stock mice
- 2011
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Ingår i: Human Molecular Genetics. - Oxford : Oxford University Press. - 0964-6906 .- 1460-2083. ; 20:15, s. 3031-3041
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Tidskriftsartikel (refereegranskat)abstract
- Resolving the genetic basis of complex diseases like rheumatoid arthritis will require knowledge of the corresponding diseases in experimental animals to enable translational functional studies. Mapping of quantitative trait loci in mouse models of arthritis, such as collagen-induced arthritis (CIA), using F(2) crosses has been successful, but can resolve loci only to large chromosomal regions. Using an inbred-outbred cross design, we identified and fine-mapped CIA loci on a genome-wide scale. Heterogeneous stock mice were first intercrossed with an inbred strain, B10.Q, to introduce an arthritis permitting MHCII haplotype. Homozygous H2(q) mice were then selected to set up an F(3) generation with fixed major histocompatibility complex that was used for arthritis experiments. We identified 26 loci, 18 of which are novel, controlling arthritis traits such as incidence of disease, severity and time of onset and fine-mapped a number of previously mapped loci. © The Author 2011. Published by Oxford University Press. All rights reserved.
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| 2. |
- Rönnegård, Lars, et al.
(författare)
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Detecting major genetic loci controlling phenotypic variability in experimental crosses
- 2011
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Ingår i: Genetics. - London : Biomed Central. - 0016-6731 .- 1943-2631. ; 188:2, s. 435-447
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Tidskriftsartikel (refereegranskat)abstract
- Traditional methods for detecting genes that affect complex diseases in humans or animal models, milk production in livestock, or other traits of interest, have asked whether variation in genotype produces a change in that trait’s average value. But focusing on differences in the mean ignores differences in variability about that mean. The robustness, or uniformity, of an individual’s character is not only of great practical importance in medical genetics and food production but is also of scienti?c and evolutionary interest (e.g., blood pressure in animal models of heart disease, litter size in pigs, ?owering time in plants). We describe a method for detecting major genes controlling the phenotypic variance, referring to these as vQTL. Our method uses a double generalized linear model with linear predictors based on probabilities of line origin. We evaluate our method on simulated F2 and collaborative cross data, and on a real F2 intercross, demonstrating its accuracy and robustness to the presence of ordinary mean-controlling QTL. We also illustrate the connection between vQTL and QTL involved in epistasis, explaining how these concepts overlap. Our method can be applied to a wide range of commonly used experimental crosses and may be extended to genetic association more generally.
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| 3. |
- Rönnegård, Lars, et al.
(författare)
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Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability
- 2012
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Ingår i: BMC Genetics. - : Springer Science and Business Media LLC. - 1471-2156. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- A number of recent works have introduced statistical methods for detecting genetic loci that affect phenotypic variability, which we refer to as variability-controlling quantitative trait loci (vQTL). These are genetic variants whose allelic state predicts how much phenotype values will vary about their expected means. Such loci are of great potential interest in both human and non-human genetic studies, one reason being that a detected vQTL could represent a previously undetected interaction with other genes or environmental factors. The simultaneous publication of these new methods in different journals has in many cases precluded opportunity for comparison. We survey some of these methods, the respective trade-offs they imply, and the connections between them. The methods fall into three main groups: classical non-parametric, fully parametric, and semi-parametric two-stage approximations. Choosing between alternatives involves balancing the need for robustness, flexibility, and speed. For each method, we identify important assumptions and limitations, including those of practical importance, such as their scope for including covariates and random effects. We show in simulations that both parametric methods and their semi-parametric approximations can give elevated false positive rates when they ignore mean-variance relationships intrinsic to the data generation process. We conclude that choice of method depends on the trait distribution, the need to include non-genetic covariates, and the population size and structure, coupled with a critical evaluation of how these fit with the assumptions of the statistical model.
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