60,385 research outputs found
A genomic map of the effects of linked selection in Drosophila
Natural selection at one site shapes patterns of genetic variation at linked
sites. Quantifying the effects of 'linked selection' on levels of genetic
diversity is key to making reliable inference about demography, building a null
model in scans for targets of adaptation, and learning about the dynamics of
natural selection. Here, we introduce the first method that jointly infers
parameters of distinct modes of linked selection, notably background selection
and selective sweeps, from genome-wide diversity data, functional annotations
and genetic maps. The central idea is to calculate the probability that a
neutral site is polymorphic given local annotations, substitution patterns, and
recombination rates. Information is then combined across sites and samples
using composite likelihood in order to estimate genome-wide parameters of
distinct modes of selection. In addition to parameter estimation, this approach
yields a map of the expected neutral diversity levels along the genome. To
illustrate the utility of our approach, we apply it to genome-wide resequencing
data from 125 lines in Drosophila melanogaster and reliably predict diversity
levels at the 1Mb scale. Our results corroborate estimates of a high fraction
of beneficial substitutions in proteins and untranslated regions (UTR). They
allow us to distinguish between the contribution of sweeps and other modes of
selection around amino acid substitutions and to uncover evidence for pervasive
sweeps in untranslated regions (UTRs). Our inference further suggests a
substantial effect of linked selection from non-classic sweeps. More generally,
we demonstrate that linked selection has had a larger effect in reducing
diversity levels and increasing their variance in D. melanogaster than
previously appreciated
The asexual genome of Drosophila
The rate of recombination affects the mode of molecular evolution. In
high-recombining sequence, the targets of selection are individual genetic
loci; under low recombination, selection collectively acts on large,
genetically linked genomic segments. Selection under linkage can induce clonal
interference, a specific mode of evolution by competition of genetic clades
within a population. This mode is well known in asexually evolving microbes,
but has not been traced systematically in an obligate sexual organism. Here we
show that the Drosophila genome is partitioned into two modes of evolution: a
local interference regime with limited effects of genetic linkage, and an
interference condensate with clonal competition. We map these modes by
differences in mutation frequency spectra, and we show that the transition
between them occurs at a threshold recombination rate that is predictable from
genomic summary statistics. We find the interference condensate in segments of
low-recombining sequence that are located primarily in chromosomal regions
flanking the centromeres and cover about 20% of the Drosophila genome.
Condensate regions have characteristics of asexual evolution that impact gene
function: the efficacy of selection and the speed of evolution are lower and
the genetic load is higher than in regions of local interference. Our results
suggest that multicellular eukaryotes can harbor heterogeneous modes and tempi
of evolution within one genome. We argue that this variation generates
selection on genome architecture
On the accumulation of deleterious mutations during range expansions
We investigate the effect of spatial range expansions on the evolution of
fitness when beneficial and deleterious mutations co-segregate. We perform
individual-based simulations of a uniform linear habitat and complement them
with analytical approximations for the evolution of mean fitness at the edge of
the expansion. We find that deleterious mutations accumulate steadily on the
wave front during range expansions, thus creating an expansion load. Reduced
fitness due to the expansion load is not restricted to the wave front but
occurs over a large proportion of newly colonized habitats. The expansion load
can persist and represent a major fraction of the total mutation load thousands
of generations after the expansion. Our results extend qualitatively and
quantitatively to two-dimensional expansions. The phenomenon of expansion load
may explain growing evidence that populations that have recently expanded,
including humans, show an excess of deleterious mutations. To test the
predictions of our model, we analyze patterns of neutral and non-neutral
genetic diversity in humans and find an excellent fit between theory and data
Tracking moving optima using Kalman-based predictions
The dynamic optimization problem concerns finding an optimum in a changing environment. In the field of evolutionary algorithms, this implies dealing with a timechanging fitness landscape. In this paper we compare different techniques for integrating motion information into an evolutionary algorithm, in the case it has to follow a time-changing optimum, under the assumption that the changes follow a nonrandom law. Such a law can be estimated in order to improve the optimum tracking capabilities of the algorithm. In particular, we will focus on first order dynamical laws to track moving objects. A vision-based tracking robotic application is used as testbed for experimental comparison
Beyond Biomass: Valuing Genetic Diversity in Natural Resource Management
Strategies for increasing production of goods from working and natural systems have raised concerns that the diversity of species on which these services depend may be eroding. This loss of natural capital threatens to homogenize global food supplies and compromise the stability of human welfare. We assess the trade off between artificial augmentation of biomass and degradation of biodiversity underlying a populations' ability to adapt to shocks. Our application involves the augmentation of wild stocks of salmon. Practices in this system have generated warnings that genetic erosion may lead to a loss of the âportfolio effectâ and the value of this loss is not accounted for in decision making. We construct an integrated bioeconomic model of salmon biomass and genetic diversity. Our results show how practices that homogenize natural systems can still generate positive returns. However, the substitution of more physical capital and labor for natural capital must be maintained for gains to persist, weakens the capacity for adaptation should this investment cease, and can cause substantial loss of population wildness. We apply an emerging optimization methodâapproximate dynamic programmingâto solve the model without simplifying restrictions imposed previously
Multi-population methods with adaptive mutation for multi-modal optimization problems
open access journalThis paper presents an efficient scheme to locate multiple peaks on multi-modal optimization problems by using genetic algorithms (GAs). The premature convergence problem shows due to the loss of diversity, the multi-population technique can be applied to maintain the diversity in the population and the convergence capacity of GAs. The proposed scheme is the combination of multi-population with adaptive mutation operator, which determines two different mutation probabilities for different sites of the solutions. The probabilities are updated by the fitness and distribution of solutions in the search space during the evolution process. The experimental results demonstrate the performance of the proposed algorithm based on a set of benchmark problems in comparison with relevant algorithms
Interplay of spatial dynamics and local adaptation shapes species lifetime distributions and species-area relationships
The distributions of species lifetimes and species in space are related,
since species with good local survival chances have more time to colonize new
habitats and species inhabiting large areas have higher chances to survive
local disturbances. Yet, both distributions have been discussed in mostly
separate communities. Here, we study both patterns simultaneously using a
spatially explicit, evolutionary community assembly approach. We present and
investigate a metacommunity model, consisting of a grid of patches, where each
patch contains a local food web. Species survival depends on predation and
competition interactions, which in turn depend on species body masses as the
key traits. The system evolves due to the migration of species to neighboring
patches, the addition of new species as modifications of existing species, and
local extinction events. The structure of each local food web thus emerges in a
self-organized manner as the highly non-trivial outcome of the relative time
scales of these processes. Our model generates a large variety of complex,
multi-trophic networks and therefore serves as a powerful tool to investigate
ecosystems on long temporal and large spatial scales. We find that the observed
lifetime distributions and species-area relations resemble power laws over
appropriately chosen parameter ranges and thus agree qualitatively with
empirical findings. Moreover, we observe strong finite-size effects, and a
dependence of the relationships on the trophic level of the species. By
comparing our results to simple neutral models found in the literature, we
identify the features that are responsible for the values of the exponents.Comment: Theor Ecol (2019
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