SWEEPFINDER2: Increased sensitivity, robustness, and flexibility
Michael DeGiorgio, Christian D. Huber, Melissa J. Hubisz, Ines Hellmann, Rasmus Nielsen
Subjects: Populations and Evolution (q-bio.PE)
SweepFinder is a popular program that implements a powerful likelihood-based method for detecting recent positive selection, or selective sweeps. Here, we present SweepFinder2, an extension of SweepFinder with increased sensitivity and robustness to the confounding effects of mutation rate variation and background selection, as well as increased flexibility that enables the user to examine genomic regions in greater detail and to specify a fixed distance between test sites. Moreover, SweepFinder2 enables the use of invariant sites for sweep detection, increasing both its power and precision relative to SweepFinder.
Detection and interpretation of shared genetic influences on 40 human traits
Joseph Pickrell, Tomaz Berisa, Laure Segurel, Joyce Y Tung, David Hinds
We performed a genome-wide scan for genetic variants that influence multiple human phenotypes by comparing large genome-wide association studies (GWAS) of 40 traits or diseases, including anthropometric traits (e.g. nose size and male pattern baldness), immune traits (e.g. susceptibility to childhood ear infections and Crohn’s disease), metabolic phenotypes (e.g. type 2 diabetes and lipid levels), and psychiatric diseases (e.g. schizophrenia and Parkinson’s disease). First, we identified 307 loci (at a false discovery rate of 10%) that influence multiple traits (excluding “trivial” phenotype pairs like type 2 diabetes and fasting glucose). Several loci influence a large number of phenotypes; for example, variants near the blood group gene ABO influence eleven of these traits, including risk of childhood ear infections (rs635634: log-odds ratio = 0.06, P = 1.4 × 10−8) and allergies (log-odds ratio = 0.05, P = 2.5 × 10−8), among others. Similarly, a nonsynonymous variant in the zinc transporter SLC39A8 influences seven of these traits, including risk of schizophrenia (rs13107325: log-odds ratio = 0.15, P = 2 × 10−12) and Parkinson’s disease (log-odds ratio = -0.15, P = 1.6 × 10−7), among others. Second, we used these loci to identify traits that share multiple genetic causes in common. For example, genetic variants that delay age of menarche in women also, on average, delay age of voice drop in men, decrease body mass index (BMI), increase adult height, and decrease risk of male pattern baldness. Finally, we identified four pairs of traits that show evidence of a causal relationship. For example, we show evidence that increased BMI causally increases triglyceride levels, and that increased liability to hypothyroidism causally decreases adult height.
General formulation of Luria-Delbrück distribution of the number of mutants
Abstract The Luria-Delbrück experiment is a cornerstone of evolutionary theory, demonstrating the randomness of mutations before selection. The distribution of the number of mutants in this experiment has been the subject of intense investigation during the last 70 years. Despite this considerable effort, most of the results have been obtained under the assumption of constant growth rate, which is far from the experimental condition. We derive here the properties of this distribution for arbitrary growth function, for both the deterministic and stochastic growth of the mutants. The derivation we propose is surprisingly simple and versatile, allowing many generalizations to be taken easily into account.
Stable eusociality via maternal manipulation when resistance is costless
In many eusocial species, workers develop or maintain their non-reproductive condition following maternal influence through aggression, differential feeding, or pheromones. This observation has suggested that eusociality may evolve from maternal manipulation where the mother induces offspring to take worker roles against their inclusive fitness interests. If manipulation is executed via aggression or poor feeding, offspring resistance to manipulation could be costly enough to be disfavored, allowing eusociality via manipulation to be evolutionarily stable. However, if manipulation is executed via pheromones, resistance could be less costly, in principle leading to evolutionarily unstable eusociality. Here I show that maternal manipulation can generate evolutionarily stable eusociality even if resistance has no direct costs provided that maternally neglected offspring use help more efficiently than maternally provisioned offspring (e.g., to regain survival). Manipulation temporarily creates ineffectively resisting helpers that allow the mother to reduce maternal care toward helped offspring. If maternally neglected offspring use help more efficiently, maternal care reduction produces offspring that benefit more from the ineffectively resisting helpers. Thus, maternal care reduction increases the average benefit received by helped offspring, bringing Hamilton’s rule to satisfaction and eliminating selection for resistance. Manipulation can then generate stable eusociality under smaller benefit-cost ratios than when manipulation is absent although resistance is costless. These results predict that eusociality where ignoring maternal influence is rather costless is likely to have originated from maternal manipulation if (1) maternally neglected offspring are highly efficient help users and (2) maternally provisioned offspring can only moderately increase their survival by being helped.
Origins of major archaeal clades do not correspond to gene acquisitions from bacteria
Mathieu Groussin, Bastien Boussau, Gergely Szöllősi, Laura Eme, Manolo Gouy, Céline Brochier-Armanet, Vincent Daubin
In a recent article, Nelson-Sathi et al. [NS] report that the origins of Major Archaeal Lineages [MAL] correspond to massive group-specific gene acquisitions via horizontal gene transfer (HGT) from bacteria (Nelson-Sathi et al., 2015, Nature 517(7532):77-80). If correct, this would have fundamental implications for the process of diversification in microbes. However, a re-examination of these data and results shows that the methodology used by NS systematically inflates the number of genes acquired at the root of each MAL, and incorrectly assumes bacterial origins for these genes. A re-analysis of their data with appropriate phylogenetic models accounting for the dynamics of gene gain and loss between lineages supports the continuous acquisition of genes over long periods in the evolution of Archaea.
Chromosomal rearrangements as barriers to genetic homogenization between archaic and modern humans
Rebekah L. Rogers
(Submitted on 26 May 2015)
Chromosomal rearrangements, which shuffle DNA across the genome, are an important source of divergence across taxa that can modify gene expression and function. Using a paired-end read approach with Illumina sequence data for archaic humans, I identify changes in genome structure that occurred recently in human evolution. Hundreds of rearrangements indicate genomic trafficking between the sex chromosomes and autosomes, raising the possibility of sex-specific changes. Additionally, genes adjacent to genome structure changes in Neanderthals are associated with testis-specific expression, consistent with evolutionary theory that new genes commonly form with expression in the testes. I identify one case of new-gene creation through transposition from the Y chromosome to chromosome 10 that combines the 5′ end of the testis-specific gene Fank1 with previously untranscribed sequence. This new transcript experienced copy number expansion in archaic genomes, indicating rapid genomic change. Finally, loci containing genome structure changes show diminished rates of introgression from Neanderthals into modern humans, consistent with the hypothesis that rearrangements serve as barriers to gene flow during hybridization. Together, these results suggest that this previously unidentified source of genomic variation has important biological consequences in human evolution.
A Unified Architecture of Transcriptional Regulatory Elements
Robin Andersson, Albin Sandelin, Charles G Danko
Gene expression is precisely controlled in time and space through the integration of signals that act at gene promoters and gene-distal enhancers. Classically, promoters and enhancers are considered separate classes of regulatory elements, often distinguished by histone modifications. However, recent studies have revealed broad similarities between enhancers and promoters, blurring the distinction: active enhancers often initiate transcription, and some gene promoters have the potential of enhancing transcriptional output of other promoters. Here, we propose a model in which promoters and enhancers are considered a single class of functional element, with a unified architecture for transcription initiation. The context of interacting regulatory elements, and surrounding sequences, determine local transcriptional output as well as the enhancer and promoter activities of individual elements.
Large-scale Machine Learning for Metagenomics Sequence Classification
Kévin Vervier (CBIO), Pierre Mahé, Maud Tournoud, Jean-Baptiste Veyrieras, Jean-Philippe Vert (CBIO)
(Submitted on 26 May 2015)
Metagenomics characterizes the taxonomic diversity of microbial communities by sequencing DNA directly from an environmental sample. One of the main challenges in metagenomics data analysis is the binning step, where each sequenced read is assigned to a taxonomic clade. Due to the large volume of metagenomics datasets, binning methods need fast and accurate algorithms that can operate with reasonable computing requirements. While standard alignment-based methods provide state-of-the-art performance, compositional approaches that assign a taxonomic class to a DNA read based on the k-mers it contains have the potential to provide faster solutions. In this work, we investigate the potential of modern, large-scale machine learning implementations for taxonomic affectation of next-generation sequencing reads based on their k-mers profile. We show that machine learning-based compositional approaches benefit from increasing the number of fragments sampled from reference genome to tune their parameters, up to a coverage of about 10, and from increasing the k-mer size to about 12. Tuning these models involves training a machine learning model on about 10 8 samples in 10 7 dimensions, which is out of reach of standard soft-wares but can be done efficiently with modern implementations for large-scale machine learning. The resulting models are competitive in terms of accuracy with well-established alignment tools for problems involving a small to moderate number of candidate species, and for reasonable amounts of sequencing errors. We show, however, that compositional approaches are still limited in their ability to deal with problems involving a greater number of species, and more sensitive to sequencing errors. We finally confirm that compositional approach achieve faster prediction times, with a gain of 3 to 15 times with respect to the BWA-MEM short read mapper, depending on the number of candidate species and the level of sequencing noise.
On the equivalence of Maximum Parsimony and Maximum Likelihood on phylogenetic networks
Mareike Fischer, Parisa Bazargani
(Submitted on 26 May 2015)
Phylogenetic inference aims at reconstructing the evolutionary relationships of different species given some data (e.g. DNA, RNA or proteins). Traditionally, the relationships between species were assumed to be treelike, so the most frequently used phylogenetic inference methods like e.g. Maximum Parsimony or Maximum Likelihood were originally introduced to reconstruct phylogenetic trees. However, it has been well-known that some evolutionary events like hybridization or horizontal gene transfer cannot be represented by a tree but rather require a phylogenetic network. Therefore, current research seeks to adapt tree inference methods to networks. In the present paper, we analyze Maximum Parsimony and Maximum Likelihood on networks for various network definitions which have recently been introduced, and we investigate the well-known Tuffley and Steel equivalence result concerning these methods under the setting of a phylogenetic network.
RAD sequencing enables unprecedented phylogenetic resolution and objective species delimitation in recalcitrant divergent taxa
Santiago Herrera, Timothy M. Shank
Species delimitation is problematic in many taxa due to the difficulty of evaluating predictions from species delimitation hypotheses, which chiefly relay on subjective interpretations of morphological observations and/or DNA sequence data. This problem is exacerbated in recalcitrant taxa for which genetic resources are scarce and inadequate to resolve questions regarding evolutionary relationships and uniqueness. In this case study we demonstrate the empirical utility of restriction site associated DNA sequencing (RAD-seq) by unambiguously resolving phylogenetic relationships among recalcitrant octocoral taxa with divergences greater than 80 million years. We objectively infer robust species boundaries in the genus Paragorgia, which contains some of the most important ecosystem engineers in the deep-sea, by testing alternative taxonomy-guided or unguided species delimitation hypotheses using the Bayes factors delimitation method (BFD*) with genome-wide single nucleotide polymorphism data. We present conclusive evidence rejecting the current morphological species delimitation model for the genus Paragorgia and indicating the presence of cryptic species boundaries associated with environmental variables. We argue that the suitability limits of RAD-seq for phylogenetic inferences in divergent taxa cannot be assessed in terms of absolute time, but depend on taxon-specific factors such as mutation rate, generation time and effective population size. We show that classic morphological taxonomy can greatly benefit from integrative approaches that provide objective tests to species delimitation hypothesis. Our results pave the way for addressing further questions in biogeography, species ranges, community ecology, population dynamics, conservation, and evolution in octocorals and other marine taxa.