The anatomical distribution of genetic associations

The anatomical distribution of genetic associations

Alan B Wells, Nathan Kopp, Xiaoxiao Xu, David R O’Brien, Wei Yang, Arye Nehorai, Tracy L. Adair-Kirk, Raphael Kopan, Joseph D Dougherty
doi: http://dx.doi.org/10.1101/021824

Deeper understanding of the anatomical intermediaries for disease and other complex genetic traits is essential to understanding mechanisms and developing new interventions. Existing ontology tools provide functional annotations for many genes in the genome and they are widely used to develop mechanistic hypotheses based on genetic and transcriptomic data. Yet, information about where a set of genes is expressed may be equally useful in interpreting results and forming novel mechanistic hypotheses for a trait. Therefore, we developed a framework for statistically testing the relationship between gene expression across the body and sets of candidate genes from across the genome. We validated this tool and tested its utility on three applications. First, using thousands of loci identified by GWA studies, our framework identifies the number of disease-associated genes that have enriched expression in the disease-affected tissue. Second, we experimentally confirmed an underappreciated prediction highlighted by our tool: variation in skin expressed genes are a major quantitative genetic modulator of white blood cell count – a trait considered to be a feature of the immune system. Finally, using gene lists derived from sequencing data, we show that human genes under constrained selective pressure are disproportionately expressed in nervous system tissues.

The two-speed genomes of filamentous pathogens: waltz with plants

The two-speed genomes of filamentous pathogens: waltz with plants

Suomeng Dong, Sylvain Raffaele, Sophien Kamoun
doi: http://dx.doi.org/10.1101/021774

Fungi and oomycetes include deep and diverse lineages of eukaryotic plant pathogens. The last 10 years have seen the sequencing of the genomes of a multitude of species of these so-called filamentous plant pathogens. Already, fundamental concepts have emerged. Filamentous plant pathogen genomes tend to harbor large repertoires of genes encoding virulence effectors that modulate host plant processes. Effector genes are not randomly distributed across the genomes but tend to be associated with compartments enriched in repetitive sequences and transposable elements. These findings have led to the “two-speed genome” model in which filamentous pathogen genomes have a bipartite architecture with gene sparse, repeat rich compartments serving as a cradle for adaptive evolution. Here, we review this concept and discuss how plant pathogens are great model systems to study evolutionary adaptations at multiple time scales. We will also introduce the next phase of research on this topic.

Most viewed on Haldane’s Sieve: June 2015

The most viewed preprints on Haldane’s Sieve in June 2015 were:

SimPhy: Phylogenomic Simulation of Gene, Locus and Species Trees

SimPhy: Phylogenomic Simulation of Gene, Locus and Species Trees
Diego Mallo, Leonardo de Oliveira Martins, David Posada
doi: http://dx.doi.org/10.1101/021709
We present here a fast and flexible software–SimPhy–for the simulation of multiple gene families evolving under incomplete lineage sorting, gene duplication and loss, horizontal gene transfer—all three potentially leading to the species tree/gene tree discordance—and gene conversion. SimPhy implements a hierarchical phylogenetic model in which the evolution of species, locus and gene trees is governed by global and local parameters (e.g., genome-wide, species-specific, locus-specific), that can be fixed or be sampled from a priori statistical distributions. SimPhy also incorporates comprehensive models of substitution rate variation among lineages (uncorrelated relaxed clocks) and the capability of simulating partitioned nucleotide, codon and protein multilocus sequence alignments under a plethora of substitution models using the program INDELible. We validate SimPhy’s output using theoretical expectations and other programs, and show that it scales extremely well with complex models and/or large trees, being an order of magnitude faster than the most similar program (DLCoal-Sim). In addition, we demonstrate how SimPhy can be useful to understand interactions among different evolutionary processes, conducting a simulation study to characterize the systematic overestimation of the duplication time when using standard reconciliation methods. SimPhy is available at https://github.com/adamallo/SimPhy, where users can find the source code, pre-compiled executables, a detailed manual and example cases.

Mendelian randomization: a premature burial?

Mendelian randomization: a premature burial?
George Davey Smith
doi: http://dx.doi.org/10.1101/021386
Mendelian randomization is a promising approach to help improve causal inference in observational studies, with widespread potential applications, including to prioritization of pharmacotherapeutic targets for evaluation in RCTs. From its initial proposal the limitations of Mendelian randomization approaches have been widely recognised and discussed, and recently Pickrell has reiterated these1. However this critique did not acknowledge recent developments in both methodological and empirical research, nor did it recognise many future opportunities for application of the Mendelian randomization approach. These issues are briefly reviewed here.

Evolution in spatial and spatiotemporal variable metapopulations changes a herbivore’s host plant range

Evolution in spatial and spatiotemporal variable metapopulations changes a herbivore’s host plant rangeAnnelies De Roissart, Nicky Wybouw, David Renault, Thomas Van Leeuwen, Dries Bonte
doi: http://dx.doi.org/10.1101/021683

The persistence and dynamics of populations largely depends on the way they are configured and integrated into space and the ensuing eco-evolutionary dynamics. We manipulated spatial and temporal variation in patch size in replicated experimental metapopulations of the herbivore mite Tetranychus urticae. Evolution over approximately 30 generations in the spatially and spatiotemporally variable metapopulations induced a significant divergence in life history traits, physiological endpoints and gene expression, but also a remarkable convergence relative to the stable reference patchy metapopulation in traits related to size and fecundity and in its transcriptional regulation. The observed evolutionary dynamics are tightly linked to demographic changes, more specifically frequent episodes of resource shortage, and increased the reproductive performance of mites on tomato, a challenging host plant. This points towards a general, adaptive stress response in stable spatial variable and spatiotemporal variable metapopulations that pre-adapts a herbivore arthropod to novel environmental stressors.

Collective Fluctuations in models of adaptation

Collective Fluctuations in models of adaptation
Oskar Hallatschek, Lukas Geyrhofer
Subjects: Populations and Evolution (q-bio.PE); Statistical Mechanics (cond-mat.stat-mech); Biological Physics (physics.bio-ph)

The dynamics of adaptation is difficult to predict because it is highly stochastic even in large populations. The uncertainty emerges from number fluctuations, called genetic drift, arising in the small number of particularly fit individuals of the population. Random genetic drift in this evolutionary vanguard also limits the speed of adaptation, which diverges in deterministic models that ignore these chance effects. Several approaches have been developed to analyze the crucial role of noise on the expected dynamics of adaptation, including the mean fitness of the entire population, or the fate of newly arising beneficial deleterious mutations. However, very little is known about how genetic drift causes fluctuations to emerge on the population level, including fitness distribution variations and speed variations. Yet, these phenomena control the replicability of experimental evolution experiments and are key to a truly predictive understanding of evolutionary processes. Here, we develop an exact approach to these emergent fluctuations by a combination of computational and analytical methods. We show, analytically, that the infinite hierarchy of moment equations can be closed at any arbitrary order by a suitable choice of a dynamical constraint. This constraint regulates (rather than fixes) the population size, accounting for resource limitations. The resulting linear equations, which can be accurately solved numerically, exhibit fluctuation-induced terms that amplify short-distance correlations and suppress long-distance ones. Importantly, by accounting for the dynamics of sub-populations, we provide a systematic route to key population genetic quantities, such as fixation probabilities and decay rates of the genetic diversity.