Author Archives: cooplab

Selection signatures in worldwide Sheep populations

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Selection signatures in worldwide Sheep populations
Maria-Ines FarielloBertrand ServinGwenola Tosser-KloppRachelle RuppCarole MorenoMagali San Cristobals imon boitard

The diversity of populations in domestic species offer great opportunities to study genome response to selection. The recently published Sheep Hapmap dataset is a great example of characterization of the world wide genetic diversity in the Sheep. In this study, we re-analyzed the Sheep Hapmap dataset to identify selection signatures in worldwide Sheep populations. Compared to previous analyses, we make use of statistical methods that (i) take account of the hierarchical structure of sheep populations, (ii) make use of Linkage Disequilibrium information and (iii) focus specifically on either recent or older selection signatures. We show that this allows to pinpoint several new selection signatures in the sheep genome and to distinguish those related to modern breeding objectives and to earlier post-domestication constraints. The newly identified regions, together with the one previously identified, reveal the extensive genome response to selection on morphology, color and adaptation to new environments.

piBUSS: a parallel BEAST/BEAGLE utility for sequence simulation under complex evolutionary scenarios

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piBUSS: a parallel BEAST/BEAGLE utility for sequence simulation under complex evolutionary scenarios
Filip Bielejec, Philippe Lemey, Luiz Max Carvalho, Guy Baele, Andrew Rambaut, Marc A. Suchard
Comments: 13 pages, 2 figures, 1 table
Subjects: Populations and Evolution (q-bio.PE)

Background: Simulated nucleotide or amino acid sequences are frequently used to assess the performance of phylogenetic reconstruction methods. BEAST, a Bayesian statistical framework that focuses on reconstructing time-calibrated molecular evolutionary processes, supports a wide array of evolutionary models, but lacked matching machinery for simulation of character evolution along phylogenies.
Results: We present a flexible Monte Carlo simulation tool, called piBUSS, that employs the BEAGLE high performance library for phylogenetic computations within BEAST to rapidly generate large sequence alignments under complex evolutionary models. piBUSS sports a user-friendly graphical user interface (GUI) that allows combining a rich array of models across an arbitrary number of partitions. A command-line interface mirrors the options available through the GUI and facilitates scripting in large-scale simulation studies. Analogous to BEAST model and analysis setup, more advanced simulation options are supported through an extensible markup language (XML) specification, which in addition to generating sequence output, also allows users to combine simulation and analysis in a single BEAST run.
Conclusions: piBUSS offers a unique combination of flexibility and ease-of-use for sequence simulation under realistic evolutionary scenarios. Through different interfaces, piBUSS supports simulation studies ranging from modest endeavors for illustrative purposes to complex and large-scale assessments of evolutionary inference procedures. The software aims at implementing new models and data types that are continuously being developed as part of BEAST/BEAGLE.

genomic architecture of human neuroanatomical diversity

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Genomic architecture of human neuroanatomical diversity
Roberto Toro, Jean-Baptiste Poline, Guillaume Huguet, Eva Loth, Vincent Frouin, Tobias Banaschewski, Gareth J Barker, Arun Bokde, Christian Büchel, Fabiana Carvalho, Patricia Conrod, Mira Fauth-Bühler, Herta Flor, Jürgen Gallinat, Hugh Garavan, Penny Gowloan, Andreas Heinz, Bernd Ittermann, Claire Lawrence, Hervé Lemaître, Karl Mann, Frauke Nees, TomᚠPaus, Zdenka Pausova, Marcella Rietschel, Trevor Robbins, Michael Smolka, Andreas Ströhle, Gunter Schumann, Thomas Bourgeron

Human brain anatomy is strikingly diverse and highly inheritable: genetic factors may explain up to 80% of its variability. Prior studies have tried to detect genetic variants with a large effect on neuroanatomical diversity, but those currently identified account for <5% of the variance. Here we show, based on our analyses of neuroimaging and whole-genome genotyping data from 1,765 subjects, that up to 54% of this heritability is captured by large numbers of single nucleotide polymorphisms of small effect spread throughout the genome, especially within genes and close regulatory regions. The genetic bases of neuroanatomical diversity appear to be relatively independent of those of body size (height), but shared with those of verbal intelligence scores. The study of this genomic architecture should help us better understand brain evolution and disease.

Sex-biased expression of microRNAs in Drosophila melanogaster

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Sex-biased expression of microRNAs in Drosophila melanogaster
Antonio Marco
(Submitted on 11 Dec 2013)

Most animals have separate sexes. The differential expression of gene products, in particular that of gene regulators, is underlying sexual dimorphism. Analyses of sex-biased expression have focused mostly in protein coding genes. Several lines of evidence indicate that microRNAs, a class of major gene regulators, are likely to have a significant role in sexual dimorphism. This role has not been systematically explored so far. Here I study the sex-biased expression pattern of microRNAs in the model species Drosophila melanogaster. As with protein coding genes, sex biased microRNAs are associated with the reproductive function. Strikingly, contrary to protein-coding genes, male biased microRNAs are enriched in the X chromosome whilst female microRNAs are mostly autosomal. I propose that the chromosomal distribution is a consequence of high rates of de novo emergence, and a preference of new microRNAs to be expressed in the testis. I also suggest that demasculinization of the X chromosome may not affect microRNAs. Interestingly, female biased microRNAs are often encoded within protein coding genes that are also expressed in females. These results strongly suggest that the sex-biased expression of microRNAs is mainly a consequence of high rates of microRNA emergence in the X (male bias) or hitch-hiked expression by host genes (female bias).

Evolution of female choice and age-dependent male traits with paternal germ-line mutation

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Evolution of female choice and age-dependent male traits with paternal germ-line mutation
Joel James Adamson
(Submitted on 11 Dec 2013)

Several studies question the adaptive value of female preferences for older males. Theory and evidence show that older males carry more deleterious mutations in their sperm than younger males carry. These mutations are not visible to females choosing mates. Germ-line mutations could oppose preferences for “good genes.” Choosy females run the risk that offspring of older males will be no more attractive or healthy than offspring of younger males. Germ-line mutations could pose a particular problem when females can only judge male trait size, rather than assessing age directly. I ask whether or not females will prefer extreme traits, despite reduced offspring survival due to age-dependent mutation. I use a quantitative genetic model to examine the evolution of female preferences, an age-dependent male trait, and overall health (“condition”). My dynamical equation includes mutation bias that depends on the generation time of the population. I focus on the case where females form preferences for older males because male trait size depends on male age. My findings agree with good genes theory. Females at equilibrium always select above-average males. The trait size preferred by females directly correlates with the direct costs of the preference. Direct costs can accentuate the equilibrium preference at a higher rate than mutational parameters. Females can always offset direct costs by mating with older, more ornamented males. Age-dependent mutation in condition maintains genetic variation in condition and thereby maintains the selective value of female preferences. Rather than eliminating female preferences, germ-line mutations provide an essential ingredient in sexual selection.

Response to a population bottleneck can be used to infer recessive selection

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Response to a population bottleneck can be used to infer recessive selection
Daniel J. Balick, Ron Do, David Reich, Shamil R. Sunyaev
(Submitted on 11 Dec 2013)

Here we present the first genome wide statistical test for recessive selection. This test uses explicitly non-equilibrium demographic differences between populations to infer the mode of selection. By analyzing the transient response to a population bottleneck and subsequent re-expansion, we qualitatively distinguish between alleles under additive and recessive selection. We analyze the response of the average number of deleterious mutations per haploid individual and describe time dependence of this quantity. We introduce a statistic, BR, to compare the number of mutations in different populations and detail its functional dependence on the strength of selection and the intensity of the population bottleneck. This test can be used to detect the predominant mode of selection on the genome wide or regional level, as well as among a sufficiently large set of medically or functionally relevant alleles.

Evaluating the use of ABBA-BABA statistics to locate introgressed loci

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Evaluating the use of ABBA-BABA statistics to locate introgressed loci
Simon Henry Martin, John William Davey, Chris D Jiggins

Several methods have been proposed to test for introgression across genomes. One method identifies an excess of shared derived alleles between taxa using Patterson’s D statistic, but does not establish which loci show such an excess or whether the excess is due to introgression or ancestral population structure. Smith and Kronforst (2013) propose that, at loci identified as outliers for the D statistic, introgression is indicated by a reduction in absolute genetic divergence (dXY) between taxa with shared ancestry, whereas ancestral structure produces no reduction in dXY at these loci. Here, we use simulations and Heliconius butterfly data to investigate the behavior of D when applied to small genomic regions. We find that D imperfectly identifies loci with shared ancestry in many scenarios due to a bias in regions with few segregating sites. A related statistic, f, is mostly robust to this bias but becomes less accurate as gene flow becomes more ancient. Although reduced dXY does indicate introgression when loci with shared ancestry can be accurately detected, both D and f systematically identify regions of lower dXY in the presence of both gene flow and ancestral structure, so detecting a reduction in dXY at D or f outliers is not sufficient to infer introgression. However, models including gene flow produced a larger reduction in dXY than models including ancestral structure in almost all cases, so this reduction may be suggestive, but not conclusive, evidence for introgression.

Author post: Ploidy and the Predictability of Evolution in Fisher’s Geometric Model

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This guest post is by Sandeep Venkataram and Dmitri A Petrov on their paper (with Diamantis Sellis) Venkataram et al. Ploidy and the Predictability of Evolution in Fisher’s Geometric Model

Since Gould’s famous thought-experiment (Gould, 1990) on “replaying the tape of life”, scientists have been interested in the predictability of evolution. Gould wondered whether it is possible to forecast evolution, and determine the path or the final destination of the evolutionary process from a given starting population. It is also possible, however, to ask whether we can retrocast evolution, and reconstruct the true evolutionary trajectory given the final state and possibly the ancestral state. Forward predictability analysis tries to predict the future evolutionary trajectory or future adapted state of an evolving population, while backwards predictability analysis tries to determine the likelihood of the possible alternative adaptive trajectories that lead to the observed adapted state.

Predictability has been empirically studied to a limited extent due to the laborious nature of such studies (e.g. Ferea et al 1999, Weinreich et al 2005 and Tenaillon et al 2012). We overcome these limitations by analyzing simulated adaptive walks under Fisher’s geometric model. To our knowledge, we are the first to study both of these types of predictability in a single system. We compare the predictabilities of haploid and diploid simulations, and find that forward and backward predictability are inversely correlated in this model. We attribute this inverse correlation to the presence of overdominant mutations and balanced polymorphisms in our diploid simulations and the lack of such mutations in the haploids (Sellis et al 2011).

We observe that the presence of balanced polymorphisms in diploids leads to a number of novel dynamics when studying predictability. It greatly increases the phenotypic diversity in diploid adaptive walks, leading to low forward predictability relative to haploids. We also detect mutations which are stably maintained but subsequently lost in diploid adaptive walks, and are thus hidden from sampling at the end of the simulation. We show that these hidden mutations, which also go unobserved in almost all empirical studies, strongly limit the inferences that can be made when analyzing backward predictability. Finally, we observe that when the same set of mutations is introduced into a diploid population in different orders, the final adapted allele is often balanced against different intermediate alleles, resulting in different adapted population states.

Our results show the importance of considering stable polymorphisms when analyzing adaptive trajectories, and detail, for the first time, some of the limitations in conducting such analysis using empirical data. In natural population, stable polymorphisms can be generated in both haploids and diploids by a wide range of mechanisms, including niche construction, frequency dependent selection, balancing selection and spatially and temporally fluctuating selection pressures. Therefore, our results should be relevant for all natural populations, regardless of ploidy.

Ferea, T., Botstein, D., Brown, P. O., & Rosenzweig, R. F. (1999). Systematic changes in gene expression patterns following. Proceedings of the National Academy of Sciences of the United States of America, 96(August), 9721–9726.

Gould, S. J. (1990). Wonderful Life: The Burgess Shale and the Nature of History (p. 352). W. W. Norton & Company.

Sellis, D., Callahan, B., Petrov, D. A., & Messer, P. W. (2011). Heterozygote advantage as a natural consequence of adaptation in diploids. Proceedings of the National Academy of Sciences of the United States of America, 2011, 1–6. doi:10.1073/pnas.1114573108

Tenaillon, O., Rodriguez-Verdugo, a., Gaut, R. L., McDonald, P., Bennett, a. F., Long, a. D., & Gaut, B. S. (2012). The Molecular Diversity of Adaptive Convergence. Science, 335(6067), 457–461. doi:10.1126/science.1212986

Weinreich, D. M., Delaney, N. F., Depristo, M. a, & Hartl, D. L. (2006). Darwinian evolution can follow only very few mutational paths to fitter proteins. Science, 312(5770), 111–4. doi:10.1126/science.1123539

Author post: The evolution of sex differences in disease genetics

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This guest post is by Ted Morrow, Jessica Abbott, and Will Gilks on their review paper Gilks et al. “The evolution of sex differences in disease genetics”

Our paper forms part of a research project (2Sexes_1Genome, 2012-16) devoted to investigating how sex-specific and sexually antagonistic selection influences the genome, and in particular whether genetic variants that are maintained as a result of these forms of selection could contribute to disease risk. We had three main aims with our paper, which we outline below together with a motivation for each.

Our first aim was to summarise evidence for sex-dependent genetic architecture in complex traits that were otherwise shared between the sexes. We focused particularly on disease phenotypes in humans, although a range of complex traits from diverse taxa were considered. The motivation for this was to establish a baseline for how widespread or rare sex-specific genetic architecture is. An important paper in this respect, published in Nature Reviews Genetics (Ober et al., 2008) specifically addressed the question of sex-specific genetic architecture in human diseases. It reviewed selected examples within the human disease genetics literature for sex-specific effects on a range of phenotypes. They concluded that studies where sex was ignored would miss some important variants that contribute to disease risk. While the Ober et al. (2008) paper makes a robust case for investigating sex as a factor in genetic analyses, several other genome-wide association studies in the primary literature have been published since, suggesting that an up to date review of these would be worthwhile. We did not intend to conduct a full-scale meta-analysis, although that would probably be a very informative exercise given potential problems in terms of reporting bias, non-independence of traits, and selection of traits with known sexual dimorphism. Nonetheless, a clear pattern emerges of widespread evidence of sex-specific genetic architecture based on heritability estimates (see Figure 1 in our paper), eQTLs, gene manipulations, expression studies, and SNPs with sex-by-genotype effects (see Table 1 in our paper). A recently published paper (not included in our review) even reports 10 out of 13 loci reaching genome-wide significance for recombination rate having sex-specific effects (Kong et al., 2013).

The second aim was to show how evolutionary theory could provide ultimate explanations for the origins of sex-specific genetic architecture. In this way, we propose that a deeper understanding of why genes cause disease, and why some common diseases show sexually dimorphic expression, may emerge. The evolutionary theory of why the sexes may differ phenotypically goes back to Darwin’s observations (1871) of how selection acts in males and females. He characterized males as active competitors, engaging in physical battles with rivals or investing in costly signals with which to woo potential mates. Females, on the other hand were characterized as being coy and choosy. There is now good evidence that mate choice is something not only limited to females, and that sexual selection also operates well after copulation (i.e. sperm competition and cryptic female choice). The key point is that fundamental differences between the sexes occur in terms of investment in reproduction, and as a consequence the routes by which males and females may maximize their fitness are often different. In other words, both natural and sexual selection frequently take sex-specific forms in terms of strength and/or direction. The latter possibility that selection acts antagonistically between the sexes is well established in several laboratory and wild populations, including humans. From a human disease perspective, disease may occur as a result of an individual’s phenotypic difference (or departure) from an optimal phenotype (where a particular trait value has the greatest fitness). This difference could be the result of a genetic constraint imposed by an intersexual genetic correlation for that trait, or indirectly (i.e. pleiotropically) though genetic correlations with other traits. Sex-specific or sexually antagonistic selection could therefore maintain genetic variation within a population that is either less favourable or actually deleterious for one sex. A recent model (Morrow & Connallon, 2013) shows how alleles with sex-specific or sexually antagonistic effects will contribute more to genetic variation for disease predisposition than alleles that are deleterious to both sexes in equal measure, and achieve higher allele frequencies. As a result, sexual dimorphism in the genetic architecture of complex polygenic diseases would emerge within the population. This evolutionary model clearly indicates that the search for loci contributing to disease risk in humans would benefit from exploring sex-specific genetic effects.

The final aim was to provide readers with an overview of the analytical options available for detecting sex-specific associations in genome-wide studies of complex diseases and phenotypes. As we show, more studies are investigating and discovering sex-dependent effects using GWAS data, Common strategies are to separate or stratify the samples within case and control groups by sex, or to model sex as a covariate. The first approach reduces the statistical power to detect sex-dependent effects, and thus only strong ones will be detected. The second simply controls for any sex-specific effects, it is not intended to identify them. We instead advocate the inclusion of a genotype-by-sex interaction term in statistical models, available as an option in some of the commonly used analytical platforms such as GenABEL and PLINK.

Overall, we hope our article raises the profile of sex-specific genetic effects, a topic that is already apparently receiving increasing interest judging by the recent crop of sex-specific associations appearing in the GWAS literature. This forms a more general theme within the field of human disease genetics, of exploring the impact of interaction effects, such as genotype-by-environment interactions. The identification of strong main effects has had successes but the debate over the ‘missing heritability’ of complex traits has activated researchers to look beyond to more complex processes such as epistasis and environmental effects. We welcome any comments either here on Haldane’s Sieve or in the comments section of biorXiv where are article is currently posted.

References
2Sexes_1Genome. 2012-16. Edward H. Morrow. FP7 ERC Starting Grant – Evolutionary, population and environmental biology. http://www.2020-horizon.com/2SEXES-1GENOME-Sex-specific-genetic-effects-on-fitness-and-human-disease(2SEXES-1GENOME)-s2903.html
Darwin, C. 1871. The Descent of Man. Prometheus Books, New York.
Kong, A., Thorleifsson, G., Frigge, M.L., Masson, G., Gudbjartsson, D.F., Villemoes, R., et al. 2013. Common and low-frequency variants associated with genome-wide recombination rate. Nat. Genet. doi:10.1038/ng.2833.
Morrow, E.H. & Connallon, T. 2013. Implications of sex-specific selection for the genetic basis of disease. Evol. Appl. doi:10.1111/eva.12097.
Ober, C., Loisel, D.A. & Gilad, Y. 2008. Sex-specific genetic architecture of human disease. Nat Rev Genet 9: 911–922.

Biophysical Fitness Landscapes for Transcription Factor Binding Sites

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Biophysical Fitness Landscapes for Transcription Factor Binding Sites
Allan Haldane, Michael Manhart, Alexandre V. Morozov
(Submitted on 3 Dec 2013)

Evolutionary trajectories and phenotypic states available to cell populations are ultimately dictated by intermolecular interactions between DNA, RNA, proteins, and other molecular species. Here we study how evolution of gene regulation in a single-cell eukaryote S. cerevisiae is affected by the interactions between transcription factors (TFs) and their cognate genomic sites. Our study is informed by high-throughput in vitro measurements of TF-DNA binding interactions and by a comprehensive collection of genomic binding sites. Using an evolutionary model for monomorphic populations evolving on a fitness landscape, we infer fitness as a function of TF-DNA binding energy for a collection of 12 yeast TFs, and show that the shape of the predicted fitness functions is in broad agreement with a simple thermodynamic model of two-state TF-DNA binding. However, the effective temperature of the model is not always equal to the physical temperature, indicating selection pressures in addition to biophysical constraints caused by TF-DNA interactions. We find little statistical support for the fitness landscape in which each position in the binding site evolves independently, showing that epistasis is common in evolution of gene regulation. Finally, by correlating TF-DNA binding energies with biological properties of the sites or the genes they regulate, we are able to rule out several scenarios of site-specific selection, under which binding sites of the same TF would experience a spectrum of selection pressures depending on their position in the genome. These findings argue for the existence of universal fitness landscapes which shape evolution of all sites for a given TF, and whose properties are determined in part by the physics of protein-DNA interactions.