A combinatorial approach to angiosperm pollen morphology

Angiosperms (flowering plants) are strikingly diverse. This is clearly expressed in the morphology of their pollen grains, which are characterized by enormous variety in their shape and patterning. In this paper, I approach angiosperm pollen morphology from the perspective of enumerative combinatorics. This involves generating angiosperm pollen morphotypes by algorithmically combining character states and enumerating the results of these combinations. I use this approach to generate 3 643 200 pollen morphotypes, which I visualize using a parallel-coordinates plot. This represents a raw morphospace. To compare real-world and theoretical morphologies, I map the pollen of 1008 species of Neotropical angiosperms growing on Barro Colorado Island (BCI), Panama, onto this raw morphospace. This highlights that, in addition to their well-documented taxonomic diversity, Neotropical rainforests also represent an enormous reservoir of morphological diversity. Angiosperm pollen morphospace at BCI has been filled mostly by pollen morphotypes that are unique to single plant species. Repetition of pollen morphotypes among higher taxa at BCI reflects both constraint and convergence. This combinatorial approach to morphology addresses the complexity that results from large numbers of discrete character combinations and could be employed in any situation where organismal form can be captured by discrete morphological characters

Detection of selective sweeps in structured populations : a comparison of recent methods

This work was supported by the Marie-Curie Initial Training Network INTERCROSSING (European Commission FP7). OEG was further supported by the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland). Date of Acceptance: 25/08/2015Identifying genomic regions targeted by positive selection has been a longstanding interest of evolutionary biologists. This objective was difficult to achieve until the recent emergence of Next Generation Sequencing, which is fostering the development of large-scale catalogs of genetic variation for increasing number of species. Several statistical methods have been recently developed to analyze these rich datasets but there is still a poor understanding of the conditions under which these methods produce reliable results. This study aims at filling this gap by assessing the performance of genome-scan methods that consider explicitly the physical linkage among SNPs surrounding a selected variant. Our study compares the performance of seven recent methods for the detection of selective sweeps (iHS, nSL, EHHST, xp-EHH, XP-EHHST, XPCLR and hapFLK). We use an individual-based simulation approach to investigate the power and accuracy of these methods under a wide range of population models under both hard and soft sweeps. Our results indicate that XPCLR and hapFLK perform best and can detect soft sweeps under simple population structure scenarios if migration rate is low. All methods perform poorly with moderate to high migration rates, or with weak selection and very poorly under a hierarchical population structure. Finally, no single method is able to detect both starting and nearly completed selective sweeps. However, combining several methods (XPCLR or hapFLK with iHS or nSL) can greatly increase the power to pinpoint the selected region.PostprintPeer reviewe

Characterizing web pornography consumption from passive measurements

Web pornography represents a large fraction of the Internet traffic, with thousands of websites and millions of users. Studying web pornography consumption allows understanding human behaviors and it is crucial for medical and psychological research. However, given the lack of public data, these works typically build on surveys, limited by different factors, e.g. unreliable answers that volunteers may (involuntarily) provide. In this work, we collect anonymized accesses to pornography websites using HTTP-level passive traces. Our dataset includes about $15\,000$ broadband subscribers over a period of 3 years. We use it to provide quantitative information about the interactions of users with pornographic websites, focusing on time and frequency of use, habits, and trends. We distribute our anonymized dataset to the community to ease reproducibility and allow further studies.Comment: Passive and Active Measurements Conference 2019 (PAM 2019). 14 pages, 7 figure

Evolutionary and ecological insights from herbicide‐resistant weeds: what have we learned about plant adaptation, and what is left to uncover?

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149516/1/nph15723_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149516/2/nph15723.pd

Bayesian estimates of linkage disequilibrium

[Background] The maximum likelihood estimator of D' – a standard measure of linkage disequilibrium – is biased toward disequilibrium, and the bias is particularly evident in small samples and rare haplotypes. [Results] This paper proposes a Bayesian estimation of D' to address this problem. The reduction of the bias is achieved by using a prior distribution on the pair-wise associations between single nucleotide polymorphisms (SNP)s that increases the likelihood of equilibrium with increasing physical distances between pairs of SNPs. We show how to compute the Bayesian estimate using a stochastic estimation based on MCMC methods, and also propose a numerical approximation to the Bayesian estimates that can be used to estimate patterns of LD in large datasets of SNPs. [Conclusion] Our Bayesian estimator of D' corrects the bias toward disequilibrium that affects the maximum likelihood estimator. A consequence of this feature is a more objective view about the extent of linkage disequilibrium in the human genome, and a more realistic number of tagging SNPs to fully exploit the power of genome wide association studies.Research supported by NIH/NHLBI grant R21 HL080463-01, NIH/NIDDK 1R01DK069646-01A1 and the Spanish research program [projects TIN2004-06204-C03-02 and TIN2005-02516]

Two Planets, One Species: Does a Mission to Mars Alter the Balance in Favour of Human Enhancement?

In this chapter we examine the implications of a crewed mission to Mars, possible colonisation of the planet, and the wider implications this may have on genetic enhancement in both a terrestrial and space context. We consider the usage of both somatic and germ-line genetic engineering, and its potential impact on the evolution of Homo sapiens. We acknowledge that a mission to Mars may require the usage of such technologies if it is to be successful. Our investigation suggests that the use of such technologies might ultimately be linked with the transformation of our own species. We also consider projected timescales for the development of these genetic enhancements and the ethical questions raised by the possibility of speciation. Cooperation among spacefaring nations in this context and the development of norms for the use of such technologies is desirable

Genome scan of Diabrotica virgifera virgifera for genetic variation associated with crop rotation tolerance

Crop rotation has been a valuable technique for control of Diabrotica virgifera virgifera for almost a century. However, during the last two decades, crop rotation has ceased to be effective in an expanding area of the US corn belt. This failure appears to be due to a change in the insect's oviposition behaviour, which, in all probability, has an underlying genetic basis. A preliminary genome scan using 253 amplified fragment-length polymorphism (AFLP) markers sought to identify genetic variation associated with the circumvention of crop rotation. Samples of D. v. virgifera from east-central Illinois, where crop rotation is ineffective, were compared with samples from Iowa at locations that the behavioural variant has yet to reach. A single AFLP marker showed signs of having been influenced by selection for the circumvention of crop rotation. However, this marker was not diagnostic. The lack of markers strongly associated with the trait may be due to an insufficient density of marker coverage throughout the genome. A weak but significant general heterogeneity was observed between the Illinois and Iowa samples at microsatellite loci and AFLP markers. This has not been detected in previous population genetic studies of D. v. virgifera and may indicate a reduction in gene flow between variant and wild-type beetles

Evaluating methods for the analysis of rare variants in sequence data

A number of rare variant statistical methods have been proposed for analysis of the impending wave of next-generation sequencing data. To date, there are few direct comparisons of these methods on real sequence data. Furthermore, there is a strong need for practical advice on the proper analytic strategies for rare variant analysis. We compare four recently proposed rare variant methods (combined multivariate and collapsing, weighted sum, proportion regression, and cumulative minor allele test) on simulated phenotype and next-generation sequencing data as part of Genetic Analysis Workshop 17. Overall, we find that all analyzed methods have serious practical limitations on identifying causal genes. Specifically, no method has more than a 5% true discovery rate (percentage of truly causal genes among all those identified as significantly associated with the phenotype). Further exploration shows that all methods suffer from inflated false-positive error rates (chance that a noncausal gene will be identified as associated with the phenotype) because of population stratification and gametic phase disequilibrium between noncausal SNPs and causal SNPs. Furthermore, observed true-positive rates (chance that a truly causal gene will be identified as significantly associated with the phenotype) for each of the four methods was very low (<19%). The combination of larger than anticipated false-positive rates, low true-positive rates, and only about 1% of all genes being causal yields poor discriminatory ability for all four methods. Gametic phase disequilibrium and population stratification are important areas for further research in the analysis of rare variant data

ENGINES: exploring single nucleotide variation in entire human genomes

<p>Abstract</p> <p>Background</p> <p>Next generation ultra-sequencing technologies are starting to produce extensive quantities of data from entire human genome or exome sequences, and therefore new software is needed to present and analyse this vast amount of information. The 1000 Genomes project has recently released raw data for 629 complete genomes representing several human populations through their Phase I interim analysis and, although there are certain public tools available that allow exploration of these genomes, to date there is no tool that permits comprehensive population analysis of the variation catalogued by such data.</p> <p>Description</p> <p>We have developed a genetic variant site explorer able to retrieve data for Single Nucleotide Variation (SNVs), population by population, from entire genomes without compromising future scalability and agility. ENGINES (ENtire Genome INterface for Exploring SNVs) uses data from the 1000 Genomes Phase I to demonstrate its capacity to handle large amounts of genetic variation (>7.3 billion genotypes and 28 million SNVs), as well as deriving summary statistics of interest for medical and population genetics applications. The whole dataset is pre-processed and summarized into a data mart accessible through a web interface. The query system allows the combination and comparison of each available population sample, while searching by rs-number list, chromosome region, or genes of interest. Frequency and F_STfilters are available to further refine queries, while results can be visually compared with other large-scale Single Nucleotide Polymorphism (SNP) repositories such as HapMap or Perlegen.</p> <p>Conclusions</p> <p>ENGINES is capable of accessing large-scale variation data repositories in a fast and comprehensive manner. It allows quick browsing of whole genome variation, while providing statistical information for each variant site such as allele frequency, heterozygosity or F_STvalues for genetic differentiation. Access to the data mart generating scripts and to the web interface is granted from <url>http://spsmart.cesga.es/engines.php</url></p

Evidence for variation in the effective population size of animal mitochondrial DNA

Background: It has recently been shown that levels of diversity in mitochondrial DNA are remarkably constant across animals of diverse census population sizes and ecologies, which has led to the suggestion that the effective population of mitochondrial DNA may be relatively constant. Results: Here we present several lines of evidence that suggest, to the contrary, that the effective population size of mtDNA does vary, and that the variation can be substantial. First, we show that levels of mitochondrial and nuclear diversity are correlated within all groups of animals we surveyed. Second, we show that the effectiveness of selection on non-synonymous mutations, as measured by the ratio of the numbers of non-synonymous and synonymous polymorphisms, is negatively correlated to levels of mitochondrial diversity. Finally, we estimate the effective population size of mitochondrial DNA in selected mammalian groups and show that it varies by at least an order of magnitude. Conclusions: We conclude that there is variation in the effective population size of mitochondria. Furthermore we suggest that the relative constancy of DNA diversity may be due to a negative correlation between the effective population size and the mutation rate per generation