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9 results for “mutational variance”
Short-term heritable variation overwhelms two hundred generations of mutational variance for metabolic traits in Caenorhabditis elegans
<p>Metabolic disorders have a large heritable component, and have increased markedly in human populations over the past few generations. Genome-wide association studies of metabolic traits typically find a substantial unexplained fraction of total heritability, suggesting an important role of spontaneous mutation. An alternative explanation is that epigenetic effects contribute significantly to the heritable variation. Here we report a study designed to quantify the cumulative effects of spontaneous mutation on adenosine metabolism in the nematode <i>Caenorhabditis elegans</i>, including both the activity and concentration of two metabolic enzymes and the standing pools of their associated metabolites. The only prior studies on the effects of mutation on metabolic enzyme activity, in <i>Drosophila melanogaster</i>, found that total enzyme activity presents a mutational target similar to that of morphological and life-history traits. However, those studies were not designed to account for short-term heritable effects. We find that the short-term heritable variance for most traits is of similar magnitude as the variance among MA lines. This result suggests that the potential heritable effects of epigenetic variation in metabolic disease warrant additional scrutiny.</p>
Data, sample sizes, and R code for analysis of: Variation in mutation (co)variances
<p>Because of pleiotropy, mutations affect the expression and inheritance of multiple traits and, together with selection, are expected to shape standing genetic covariances between traits and eventual phenotypic divergence between populations. It is therefore important to find if the M matrix, describing mutational variances of each trait and covariances between traits, varies between genotypes. We here estimate the M matrix for six locomotion behavior traits in lines of two genotypes of the nematode <em>Caenorhabditis elegans </em>that accumulated mutations in a nearly-neutral manner for 250 generations. We find significant mutational variance along at least one phenotypic dimension of the M matrices, but neither their size nor their orientation had detectable differences between genotypes. The number of generations of mutation accumulation, or the number of MA lines measured, was likely insufficient to sample enough mutations and detect potentially small differences between the two M matrices. We then tested if the M matrices were similar to one G matrix describing the standing genetic (co)variances of a population derived by the hybridization of several genotypes, including the two measured for M, and domesticated to a lab-defined environment for 140 generations. We found that the M and G were different because the genetic covariances caused by mutational pleiotropy in the two genotypes are smaller than those caused by linkage disequilibrium in the lab population. We further show that M matrices differed in their alignment with the lab population G matrix. If generalized to other founder genotypes of the lab population, these observations indicate that selection does not shape the evolution of the M matrix for locomotion behavior in the short-term of a few tens to hundreds of generations and suggests that the hybridization of <em>C. elegans </em>genotypes allows selection on new phenotypic dimensions of locomotion behavior.</p>
Short-term heritable variation overwhelms two hundred generations of mutational variance for metabolic traits in Caenorhabditis elegans
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Data, sample sizes, and R code for analysis of: Variation in mutation (co)variances
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Data from: Genetic regulatory network motifs constrain adaptation through curvature in the landscape of mutational (co)variance
Systems biology is accumulating a wealth of understanding about the structure of genetic regulatory networks, leading to a more complete picture of the complex genotype-phenotype relationship. However, models of multivariate phenotypic evolution based on quantitative genetics have largely not incorporated a network-based view of genetic variation. Here we model a set of two-node, two-phenotype genetic network motifs, covering a full range of regulatory interactions. We find that network interactions result in different patterns of mutational (co)variance at the phenotypic level (the M-matrix), not only across network motifs but also across phenotypic space within single motifs. This effect is due almost entirely to mutational input of additive genetic (co)variance. Variation in M has the effect of stretching and bending phenotypic space with respect to evolvability, analogous to the curvature of space-time under general relativity, and similar mathematical tools may apply in each case. We explored the consequences of curvature in mutational variation by simulating adaptation under divergent selection with gene flow. Both standing genetic variation (the G-matrix) and rate of adaptation are constrained by M, so that G and adaptive trajectories are curved across phenotypic space. Under weak selection the phenotypic mean at migration-selection balance also depends on M.
Data from: An experimental test of the mutation-selection balance model for the maintenance of genetic variance in fitness components
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Data from: Properties of spontaneous mutational variance and covariance for wing size and shape in Drosophila melanogaster
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Data from: Effects of partial selfing on the equilibrium genetic variance, mutation load and inbreeding depression under stabilizing selection
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Data from: Genetic regulatory network motifs constrain adaptation through curvature in the landscape of mutational (co)variance
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.