So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Human biochemical genetics

Genetics and analysis of quantitative traits

Many criteria for statistical parameter estimation, such as maximum likelihood, are formulated as a nonlinear optimization problem. Automatic Differentiation Model Builder (ADMB) is a programming framework based on automatic differentiation, aimed at highly nonlinear models with a large number of parameters. The benefits of using AD are computational efficiency and high numerical accuracy, both crucial in many practical problems. We describe the basic components and the underlying philosophy of ADMB, with an emphasis on functionality found in no other statistical software. One example of such a feature is the generic implementation of Laplace approximation of high-dimensional integrals for use in latent variable models. We also review the literature in which ADMB has been used, and discuss future development of ADMB as an open source project. Overall, the main advantages of ADMB are flexibility, speed, precision, stability and built-in methods to quantify uncertainty.

/pdf/ad-model-builder-using-automatic-differentiation-for-18b5wunetc.pdf

AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models

A positive temperature coefficient is the term which has been used to indicate that an increase in solubility occurs as the temperature is raised, whereas a negative coefficient indicates a decrease in solubility with rise in temperature.

Effect of Temperature

Repetitive DNA sequences are abundant in a broad range of species, from bacteria to mammals, and they cover nearly half of the human genome. Repeats have always presented technical challenges for sequence alignment and assembly programs. Next-generation sequencing projects, with their short read lengths and high data volumes, have made these challenges more difficult. From a computational perspective, repeats create ambiguities in alignment and assembly, which, in turn, can produce biases and errors when interpreting results. Simply ignoring repeats is not an option, as this creates problems of its own and may mean that important biological phenomena are missed. We discuss the computational problems surrounding repeats and describe strategies used by current bioinformatics systems to solve them.

https://www.csie.ntu.edu.tw/~kmchao/bioinformatics12spr/presentation/Report_20120604%236.pdf

Repetitive DNA and next-generation sequencing: computational challenges and solutions.

The glycolytic/lipogenic axis promotes the synthesis of energetic molecules and building blocks necessary to support cell growth, although the absolute requirement of this metabolic axis must be deeply investigated. Here, we used Drosophila genetics and focus on the mTOR signaling network that controls cell growth and homeostasis. mTOR is present in two distinct complexes, mTORC1 and mTORC2. The former directly responds to amino acids and energetic levels, whereas the latter is required to sustain the signaling response downstream of insulin-like-peptide (Ilp) stimulation. Either signaling branch can be independently modulated in most Drosophila tissues. We confirm this independency in the fat tissue. We show that ubiquitous over-activation of mTORC1 or Ilp signaling affects carbohydrate and lipid metabolism, supporting the use of Drosophila as a powerful model to study the link between growth and metabolism. We show that cell-autonomous restriction of glycolysis or lipogenesis in fat cells impedes overgrowth dependent on Ilp-but not mTORC1-signaling. Additionally, ubiquitous deficiency of lipogenesis (FASN mutants) results in a drop in mTORC1 but not Ilp signaling, whereas, at the cell-autonomous level, lipogenesis deficiency affects none of these signals in fat cells. These findings thus, reveal differential metabolic sensitivity of mTORC1- and Ilp-dependent overgrowth. Furthermore, they suggest that local metabolic defects may elicit compensatory pathways between neighboring cells, whereas enzyme knockdown in the whole organism results in animal death. Importantly, our study weakens the use of single inhibitors to fight mTOR-related diseases and strengthens the use of drug combination and selective tissue-targeting.

/pdf/differential-metabolic-sensitivity-of-insulin-like-response-3kcexzrnlg.pdf

Differential Metabolic Sensitivity of Insulin-like-response- and mTORC1-Dependent Overgrowth in Drosophila Fat Cells

The domestication of plant and animal species lead to repeatable morphological evolution, often referred to as the phenotypic domestication syndrome. Domestication is also associated with important genomic changes, such as the loss of genetic diversity and modifications of gene expression patterns. Here, we explored theoretically the effect of domestication at the genomic level by characterizing the impact of a domestication-like scenario on gene regulatory networks. We ran population genetics simulations in which individuals were featured by their genotype (an interaction matrix encoding a gene regulatory network) and their gene expressions, representing the phenotypic level. Our domestication scenario included a population bottleneck and a selection switch (change in the optimal gene expression level) mimicking canalizing selection, i.e. evolution towards more stable expression to parallel enhanced environmental stability in man-made habitat. We showed that domestication profoundly alters genetic architectures. Based on the well-documented example of the maize (Zea mays ssp. mays) domestication, our simulations predicted (i) a drop in neutral allelic diversity, (ii) a change in gene expression variance that depended upon the domestication scenario, (iii) transient maladaptive plasticity, (iv) a deep rewiring of the gene regulatory networks, with a trend towards gain of regulatory interactions between genes, and (v) a global increase in the genetic correlations among gene expressions, with a loss of modularity in the resulting coexpression patterns and in the underlying networks. Extending the range of parameters, we provide empirically testable predictions on the differences of genetic architectures between wild and domesticated and forms. The characterization of such systematic evolutionary changes in the genetic architecture of traits contributes to define a molecular domestication syndrome.

/pdf/gene-network-simulations-provide-testable-predictions-for-241hz18bfp.pdf

Gene network simulations provide testable predictions for the molecular domestication syndrome

The contribution of non-additive genetic effects in general, and to the evolutionary potential of populations in particular, is a topic of long-standing theoretical and empirical interest, which nevertheless remains controversial. As a consequence, the empirical study of these effects in natural populations remains scarce, which is problematic because non-additive effects are expected to modify both the adaptive potential of populations and the way we should measure it. In this study, we explored the contribution of dominance and epistasis in natural Alpine populations of Arabidopsis thaliana, for two fitness-related traits, the dry weight and the number of siliques. We first found that, on average, crosses between inbred lines of A. thaliana led to heterosis for the dry weight, but outbreeding depression for the number of siliques. We found that heterosis for the dry weight was due to positive directional dominance. For the number of siliques, however, we found that outbreeding depression was due to the breakdown of positive directional epistasis. The implication of these results for the adaptive potential of the studied populations, as well as the use of line-cross analyses to detect directional non-additive genetic effects, are discussed.

/pdf/detecting-directional-epistasis-and-dominance-from-cross-2eygzogv.pdf

Detecting directional epistasis and dominance from cross-line analyses in Alpine populations of Arabidopsis thaliana

The impact of transposable elements (TEs) on genome structure, plasticity, and evolution is still not well understood. The recent availability of complete genome sequences makes it possible to get new insights on the evolutionary dynamics of TEs from the phylogenetic analysis of their multiple copies in a wide range of species. However, this source of information is not always fully exploited. Here, we show how the history of transposition activity may be qualitatively and quantitatively reconstructed by considering the distribution of transposition events in the phylogenetic tree, along with the tree topology. Using statistical models developed to infer speciation and extinction rates in species phylogenies, we demonstrate that it is possible to estimate the past transposition rate of a TE family, as well as how this rate varies with time. This methodological framework may not only facilitate the interpretation of genomic data, but also serve as a basis to develop new theoretical and statistical models.

Reconstructing the Evolutionary History of Transposable

The evolution of gene expression is constrained by the topology of gene regulatory networks, as co-expressed genes are likely to have their expressions affected together by mutations. Conversely, co-expression can also be an advantage when genes are under joint selection. Here, we assessed theoretically whether correlated selection (selection for a combination of traits) was able to affect the pattern of correlated gene expressions and the underlying gene regulatory networks. We ran individual-based simulations, applying a stabilizing correlated fitness function to three genetic architectures: a quantitative genetics (multilinear) model featuring epistasis and pleiotropy, a quantitative genetics model where each genes has an independent mutational structure, and a gene regulatory model, mimicking the mechanisms of gene expression regulation. Simulations showed that correlated mutational effects evolved in the three genetic architectures as a response to correlated selection, but the response in gene networks was specific. The intensity of gene co-expression was mostly explained by the regulatory distance between genes (largest correlations being associated to genes directly interacting with each other), and the sign of co-expression was associated with the nature of the regulation (transcription activation or inhibition). These results concur to the idea that gene network topologies could partly reflects past correlated selection patterns on gene expression.

Arnaud Le Rouzic

Papers

Differential Metabolic Sensitivity of Insulin-like-response- and mTORC1-Dependent Overgrowth in Drosophila Fat Cells

Gene network simulations provide testable predictions for the molecular domestication syndrome

Detecting directional epistasis and dominance from cross-line analyses in Alpine populations of Arabidopsis thaliana

Reconstructing the Evolutionary History of Transposable

Correlated stabilizing selection shapes the topology of gene regulatory networks