Genetic and Environmental Effects on Complex Traits in Mice

Valdar, William; Solberg, Leah C.; Guyader, Dominique; Cookson, William; Rawlins, J. N. P.; Mott, Richard; Flint, Jonathan

doi:10.1534/genetics.106.060004

Cited by 173 publications

(205 citation statements)

References 31 publications

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“…Again, it is abundantly clear from the model organism work that these context-dependent genetic effects are pervasive. In mice, quantitative analyses indicate that genotype by environment interaction (GEI) effects occur for all phenotypes, and often makes as large, or larger, contribution to the phenotypic variation than the environmental effects (Valdar et al 2006b). In flies, whenever QTLs have been mapped in different environments, GEI has been observed: for numbers of sensory bristles (Gurganus et al 1998(Gurganus et al , 1999Dilda and Mackay 2002;Geiger-Thornsberry and Mackay 2002), longevity Mackay 2000, 2002;Vieira et al 2000), competitive fitness , and immune response to different bacteria (Lazzaro et al 2006).…”

Section: Genetic Architecture: Context-dependent Effectsmentioning

confidence: 99%

Genetic architecture of quantitative traits in mice, flies, and humans

Flint¹,

Mackay²

2009

Genome Res.

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409

364

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We compare and contrast the genetic architecture of quantitative phenotypes in two genetically well-characterized model organisms, the laboratory mouse, Mus musculus, and the fruit fly, Drosophila melanogaster, with that found in our own species from recent successes in genome-wide association studies. We show that the current model of large numbers of loci, each of small effect, is true for all species examined, and that discrepancies can be largely explained by differences in the experimental designs used. We argue that the distribution of effect size of common variants is the same for all phenotypes regardless of species, and we discuss the importance of epistasis, pleiotropy, and gene by environment interactions. Despite substantial advances in mapping quantitative trait loci, the identification of the quantitative trait genes and ultimately the sequence variants has proved more difficult, so that our information on the molecular basis of quantitative variation remains limited. Nevertheless, available data indicate that many variants lie outside genes, presumably in regulatory regions of the genome, where they act by altering gene expression. As yet there are very few instances where homologous quantitative trait loci, or quantitative trait genes, have been identified in multiple species, but the availability of high-resolution mapping data will soon make it possible to test the degree of overlap between species.Recent successes in mapping quantitative trait loci (QTLs) that contribute to phenotypic variation in humans and model organisms make it possible to address important questions about the genetic architecture of quantitative phenotypes, such as the likely number of loci, their mode of genetic action, and interaction with each other and with the environment. An understanding of the genetic architecture of quantitative traits is not only important in its own right, it will also inform studies that seek to proceed to the identification of the quantitative trait genes (QTGs) and the quantitative trait nucleotide (QTN) variants, the functional changes in DNA sequence that contribute to trait variation.In this work we use examples from two genetically wellcharacterized model organisms, the laboratory mouse, Mus musculus, and the fruit fly, Drosophila melanogaster, to discuss how an understanding of the genetic architecture of quantitative phenotypes in model organisms informs mapping experiments in human genetics. In our discussion of the latter, we draw upon the recent successful application of genome-wide association studies (GWAS) to both quantitative phenotypes (such as height and weight) and disease phenotypes (assuming that the genetic architecture of common disease is similar, if not identical, to that of quantitative phenotypes).Genetic architecture has been the subject of a number of recent reviews, most of which deal with information from a single model organism (Mackay 2004) or review a small number of phenotypes (Kendler and Greenspan 2006). Here, our purpose is different. We tackle three relate...

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Section: Genetic Architecture: Context-dependent Effectsmentioning

confidence: 99%

Genetic architecture of quantitative traits in mice, flies, and humans

Flint¹,

Mackay²

2009

Genome Res.

Self Cite

409

364

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“…Analyses of weight traits based on model (1), using polygenic effects only, showed slightly lower heritabilities (Table 1) compared to those reported by Valdar et al [11]. The differences are likely because of different fixed effects fitted in the models.…”

Section: Resultsmentioning

confidence: 60%

Efficiency of genomic selection using Bayesian multi-marker models for traits selected to reflect a wide range of heritabilities and frequencies of detected quantitative traits loci in mice

Kapell

Sörensen

et al. 2012

BMC Genet

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BackgroundGenomic selection uses dense single nucleotide polymorphisms (SNP) markers to predict breeding values, as compared to conventional evaluations which estimate polygenic effects based on phenotypic records and pedigree information. The objective of this study was to compare polygenic, genomic and combined polygenic-genomic models, including mixture models (labelled according to the percentage of genotyped SNP markers considered to have a substantial effect, ranging from 2.5% to 100%). The data consisted of phenotypes and SNP genotypes (10,946 SNPs) of 2,188 mice. Various growth, behavioural and physiological traits were selected for the analysis to reflect a wide range of heritabilities (0.10 to 0.74) and numbers of detected quantitative traits loci (QTL) (1 to 20) affecting those traits. The analysis included estimation of variance components and cross-validation within and between families.ResultsGenomic selection showed a high predictive ability (PA) in comparison to traditional polygenic selection, especially for traits of moderate heritability and when cross-validation was between families. This occurred although the proportion of genomic variance of traits using genomic models was 22 to 33% smaller than using polygenic models. Using a 2.5% mixture genomic model, the proportion of genomic variance was 79% smaller relative to the polygenic model. Although the proportion of variance explained by the markers was reduced further when a smaller number of SNPs was assumed to have a substantial effect on the trait, PA of genomic selection for most traits was little affected. These low mixture percentages resulted in improved estimates of single SNP effects. Genomic models implemented for traits with fewer QTLs showed even lower PA than the polygenic models.ConclusionsGenomic selection generally performed better than traditional polygenic selection, especially in the context of between family cross-validation. Reducing the number of markers considered to affect the trait did not significantly change PA for most traits, particularly in the case of within family cross-validation, but increased the number of markers found to be associated with QTLs. The underlying number of QTLs affecting the trait has an effect on PA, with a smaller number of QTLs resulting in lower PA using the genomic model compared to the polygenic model.

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“…19 for protocol on these topics), potentially have large influences on the outcomes 20 . There is ample evidence to support this belief in the outcomes obtained in mouse behavioral tests [21][22][23] . …”

Section: Anticipated Resultsmentioning

confidence: 91%

The parallel rod floor test: a measure of ataxia in mice

Kamens

Crabbe

2007

Nat Protoc

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The parallel rod floor test is a new model of ataxia in mice. It allows the simultaneous measurement of ataxia and locomotor activity. This protocol is designed for researchers examining ethanol-induced motor incoordination in mice, but it should be applicable to other sedative/hypnotic drugs and to testing cerebellar mutant mice or mice with engineered genetic defects. This protocol takes 3 d, with the time per day depending on how many animals are tested. The test allows researchers to quantify differences in motor coordination among genotypes of mice that may differ in locomotor activity. Unlike many other methods for assessing incoordination, the parallel rod floor test yields similar patterns of genetic sensitivity across a range of variant forms of the apparatus.

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Genetic and Environmental Effects on Complex Traits in Mice

Cited by 173 publications

References 31 publications

Genetic architecture of quantitative traits in mice, flies, and humans

Genetic architecture of quantitative traits in mice, flies, and humans

Efficiency of genomic selection using Bayesian multi-marker models for traits selected to reflect a wide range of heritabilities and frequencies of detected quantitative traits loci in mice

The parallel rod floor test: a measure of ataxia in mice

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