Discovery of Ongoing Selective Sweeps within Anopheles Mosquito Populations Using Deep Learning

Identification of partial sweeps, which include both hard and soft sweeps that have not currently reached fixation, provides crucial information about ongoing evolutionary responses. To this end, we introduce partialS/HIC, a deep learning method to discover selective sweeps from population genomic data. partialS/HIC uses a convolutional neural network for image processing, which is trained with a large suite of summary statistics derived from coalescent simulations incorporating population-specific history, to distinguish between completed versus partial sweeps, hard versus soft sweeps, and regions directly affected by selection versus those merely linked to nearby selective sweeps. We perform several simulation experiments under various demographic scenarios to demonstrate partialS/HIC’s performance, which exhibits excellent resolution for detecting partial sweeps. We also apply our classifier to whole genomes from eight mosquito populations sampled across sub-Saharan Africa by the Anopheles gambiae 1000 Genomes Consortium, elucidating both continent-wide patterns as well as sweeps unique to specific geographic regions. These populations have experienced intense insecticide exposure over the past two decades, and we observe a strong overrepresentation of sweeps at insecticide resistance loci. Our analysis thus provides a list of candidate adaptive loci that may be relevant to mosquito control efforts. More broadly, our supervised machine learning approach introduces a method to distinguish between completed and partial sweeps, as well as between hard and soft sweeps, under a variety of demographic scenarios. As whole-genome data rapidly accumulate for a greater diversity of organisms, partialS/HIC addresses an increasing demand for useful selection scan tools that can track in-progress evolutionary dynamics.     

The Effect of Selective Sweeps on the Variance of the Allele Distribution of a Linked Multiallele Locus: Hitchhiking of Microsatellites

Microsatellite variation and the mechanisms which are responsible for this variation have received much attention in the last few years. Most theoretical studies of microsatellite allele distributions, however, did not incorporate the evolutionary dynamics of linked sites. The dynamics is usually modeled by invoking a special mutation mechanism such as stepwise mutation, which leads to a stepwise increase or decrease of the number of motif repeats on the occasion of mutation. It is shown here that selection at a locus, which itself is not subject to mutation, but which is adjacent to a microsatellite locus has an influence on statistics of the microsatellite allele distribution, provided that mutation rates are low to intermediate, when compared to 1/t1, the inverse of the time to fixation of a linked favorable substitution. If mutation rates are high, as for example in humans, a selective effect upon the microsatellite locus, such as hitchhiking, will quickly be obscured by mutations. In particular, in the latter case, the model shows that no correlation is to be expected between recombination rates and variability of microsatellites—such as had been predicted and experimentally demonstrated for nucleotide variability and recombination rates inDrosophila. The presented model is a generalization of the two locus two allele hitchhiking model which had been studied by Stephan and co-workers.     

Recent Selective Sweeps in North American Drosophila melanogaster Show Signatures of Soft Sweeps

Adaptation from standing genetic variation or recurrent de novo mutation in large populations should commonly generate soft rather than hard selective sweeps. In contrast to a hard selective sweep, in which a single adaptive haplotype rises to high population frequency, in a soft selective sweep multiple adaptive haplotypes sweep through the population simultaneously, producing distinct patterns of genetic variation in the vicinity of the adaptive site. Current statistical methods were expressly designed to detect hard sweeps and most lack power to detect soft sweeps. This is particularly unfortunate for the study of adaptation in species such as Drosophila melanogaster, where all three confirmed cases of recent adaptation resulted in soft selective sweeps and where there is evidence that the effective population size relevant for recent and strong adaptation is large enough to generate soft sweeps even when adaptation requires mutation at a specific single site at a locus. Here, we develop a statistical test based on a measure of haplotype homozygosity (H12) that is capable of detecting both hard and soft sweeps with similar power. We use H12 to identify multiple genomic regions that have undergone recent and strong adaptation in a large population sample of fully sequenced Drosophila melanogaster strains from the Drosophila Genetic Reference Panel (DGRP). Visual inspection of the top 50 candidates reveals that in all cases multiple haplotypes are present at high frequencies, consistent with signatures of soft sweeps. We further develop a second haplotype homozygosity statistic (H2/H1) that, in combination with H12, is capable of differentiating hard from soft sweeps. Surprisingly, we find that the H12 and H2/H1 values for all top 50 peaks are much more easily generated by soft rather than hard sweeps. We discuss the implications of these results for the study of adaptation in Drosophila and in species with large census population sizes.     

Soft Shoulders Ahead: Spurious Signatures of Soft and Partial Selective Sweeps Result from Linked Hard Sweeps

Characterizing the nature of the adaptive process at the genetic level is a central goal for population genetics. In particular, we know little about the sources of adaptive substitution or about the number of adaptive variants currently segregating in nature. Historically, population geneticists have focused attention on the hard-sweep model of adaptation in which a de novo beneficial mutation arises and rapidly fixes in a population. Recently more attention has been given to soft-sweep models, in which alleles that were previously neutral, or nearly so, drift until such a time as the environment shifts and their selection coefficient changes to become beneficial. It remains an active and difficult problem, however, to tease apart the telltale signatures of hard vs. soft sweeps in genomic polymorphism data. Through extensive simulations of hard- and soft-sweep models, here we show that indeed the two might not be separable through the use of simple summary statistics. In particular, it seems that recombination in regions linked to, but distant from, sites of hard sweeps can create patterns of polymorphism that closely mirror what is expected to be found near soft sweeps. We find that a very similar situation arises when using haplotype-based statistics that are aimed at detecting partial or ongoing selective sweeps, such that it is difficult to distinguish the shoulder of a hard sweep from the center of a partial sweep. While knowing the location of the selected site mitigates this problem slightly, we show that stochasticity in signatures of natural selection will frequently cause the signal to reach its zenith far from this site and that this effect is more severe for soft sweeps; thus inferences of the target as well as the mode of positive selection may be inaccurate. In addition, both the time since a sweep ends and biologically realistic levels of allelic gene conversion lead to errors in the classification and identification of selective sweeps. This general problem of “soft shoulders” underscores the difficulty in differentiating soft and partial sweeps from hard-sweep scenarios in molecular population genomics data. The soft-shoulder effect also implies that the more common hard sweeps have been in recent evolutionary history, the more prevalent spurious signatures of soft or partial sweeps may appear in some genome-wide scans.     

A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph

Detecting signals of selection from genomic data is a central problem in population genetics. Coupling the rich information in the ancestral recombination graph (ARG) with a powerful and scalable deep-learning framework, we developed a novel method to detect and quantify positive selection: Selection Inference using the Ancestral recombination graph (SIA). Built on a Long Short-Term Memory (LSTM) architecture, a particular type of a Recurrent Neural Network (RNN), SIA can be trained to explicitly infer a full range of selection coefficients, as well as the allele frequency trajectory and time of selection onset. We benchmarked SIA extensively on simulations under a European human demographic model, and found that it performs as well or better as some of the best available methods, including state-of-the-art machine-learning and ARG-based methods. In addition, we used SIA to estimate selection coefficients at several loci associated with human phenotypes of interest. SIA detected novel signals of selection particular to the European (CEU) population at the MC1R and ABCC11 loci. In addition, it recapitulated signals of selection at the LCT locus and several pigmentation-related genes. Finally, we reanalyzed polymorphism data of a collection of recently radiated southern capuchino seedeater taxa in the genus Sporophila to quantify the strength of selection and improved the power of our previous methods to detect partial soft sweeps. Overall, SIA uses deep learning to leverage the ARG and thereby provides new insight into how selective sweeps shape genomic diversity.     

An Analysis of Signatures of Selective Sweeps in Natural Populations of the House Mouse

doi:10.1093/molbev/msj096 Sonja Ihle, Iary Ravaoarimanana, Meike Thomas, and DiethardTautz      

Strong Selective Sweeps on the X Chromosome in the Human-Chimpanzee Ancestor Explain Its Low Divergence

The human and chimpanzee X chromosomes are less divergent than expected based on autosomal divergence. We study incomplete lineage sorting patterns between humans, chimpanzees and gorillas to show that this low divergence can be entirely explained by megabase-sized regions comprising one-third of the X chromosome, where polymorphism in the human-chimpanzee ancestral species was severely reduced. We show that background selection can explain at most 10% of this reduction of diversity in the ancestor. Instead, we show that several strong selective sweeps in the ancestral species can explain it. We also report evidence of population specific sweeps in extant humans that overlap the regions of low diversity in the ancestral species. These regions further correspond to chromosomal sections shown to be devoid of Neanderthal introgression into modern humans. This suggests that the same X-linked regions that undergo selective sweeps are among the first to form reproductive barriers between diverging species. We hypothesize that meiotic drive is the underlying mechanism causing these two observations.     

Utility of Expert-Based Knowledge for Predicting Wildlife-Vehicle Collisions

ABSTRACT Wildlife-vehicle collisions have important ecological, economic, and social effects. In North America and across northern Europe, moose (Alces alces) are one of the largest ungulates hit by motor vehicles. The force and increasing frequency of these collisions has resulted in a commitment by wildlife and transportation agencies to limit or reduce causal factors. In an effort to improve these mitigation strategies, we used the most readily available source of knowledge of collision factors, expert opinion, to develop a series of models that explained and predicted location of moose-vehicle collisions (MVC). We developed expert-based models using the Analytical Hierarchy Process (AHP) and we used a structured survey approach where experts could assess criteria relevancy, weight criteria, and review weights for consistency. We hypothesized that collisions were the product of habitat- or driver-related factors and we formulated the survey accordingly. We used the receiver operating characteristic to validate the resulting models and the Kappa index of agreement to quantify differences among spatial predictions originating from the experts. Local and nonlocal experts weighted the moose habitat classification as the most important criterion for identifying MVC. Among driver-related criteria, speed limit was weighted as the most important factor. Overall, habitat-based models were more proficient than driver-based models in predicting MVC within Mount Revelstoke and Glacier National Parks, Canada. Both local and nonlocal expert models were excellent predictors of MVC, with local experts slightly outperforming nonlocal experts. Considering that habitat-related criteria were more powerful for predicting MVC, and that habitat can vary considerably across study areas, we suggest that local experts be used when possible. The AHP is a valuable tool for wildlife, highway, and park managers to better understand why and where wildlife-vehicle collisions occur. Adopting this process, our data suggested that MVC were most strongly correlated with highway attractants associated with habitat. Vegetation management or alternative routing could minimize spatial juxtaposition of moose and motor vehicles.     

The Sampling Distribution of Linkage Disequilibrium under an Infinite Allele Model without Selection

The sampling distributions of several statistics that measure the association of alleles on gametes (linkage disequilibrium) are estimated under a two-locus neutral infinite allele model using an efficient Monte Carlo method. An often used approximation for the mean squared linkage disequilibrium is shown to be inaccurate unless the proper statistical conditioning is used. The joint distribution of linkage disequilibrium and the allele frequencies in the sample is studied. This estimated joint distribution is sufficient for obtaining an approximate maximum likelihood estimate of C = 4Nc, where N is the population size and c is the recombination rate. It has been suggested that observations of high linkage disequilibrium might be a good basis for rejecting a neutral model in favor of a model in which natural selection maintains genetic variation. It is found that a single sample of chromosomes, examined at two loci cannot provide sufficient information for such a test if C less than 10, because with C this small, very high levels of linkage disequilibrium are not unexpected under the neutral model. In samples of size 50, it is found that, even when C is as large as 50, the distribution of linkage disequilibrium conditional on the allele frequencies is substantially different from the distribution when there is no linkage between the loci. When conditioned on the number of alleles at each locus in the sample, all of the sample statistics examined are nearly independent of theta = 4N mu, where mu is the neutral mutation rate.     

The Emergence of Resistance to the Benzimidazole Anthlemintics in Parasitic Nematodes of Livestock Is Characterised by Multiple Independent Hard and Soft Selective Sweeps

Anthelmintic resistance is a major problem for the control of parasitic nematodes of livestock and of growing concern for human parasite control. However, there is little understanding of how resistance arises and spreads or of the “genetic signature” of selection for this group of important pathogens. We have investigated these questions in the system for which anthelmintic resistance is most advanced; benzimidazole resistance in the sheep parasites Haemonchus contortus and Teladorsagia circumcincta. Population genetic analysis with neutral microsatellite markers reveals that T. circumcincta has higher genetic diversity but lower genetic differentiation between farms than H. contortus in the UK. We propose that this is due to epidemiological differences between the two parasites resulting in greater seasonal bottlenecking of H. contortus. There is a remarkably high level of resistance haplotype diversity in both parasites compared with drug resistance studies in other eukaryotic systems. Our analysis suggests a minimum of four independent origins of resistance mutations on just seven farms for H. contortus, and even more for T. circumincta. Both hard and soft selective sweeps have occurred with striking differences between individual farms. The sweeps are generally softer for T. circumcincta than H. contortus, consistent with its higher level of genetic diversity and consequent greater availability of new mutations. We propose a model in which multiple independent resistance mutations recurrently arise and spread by migration to explain the widespread occurrence of resistance in these parasites. Finally, in spite of the complex haplotypic diversity, we show that selection can be detected at the target locus using simple measures of genetic diversity and departures from neutrality. This work has important implications for the application of genome-wide approaches to identify new anthelmintic resistance loci and the likelihood of anthelmintic resistance emerging as selection pressure is increased in human soil-transmitted nematodes by community wide treatment programs.     

Sweeps in time: leveraging the joint distribution of branch lengths

Current methods of identifying positively selected regions in the genome are limited in two key ways: the underlying models cannot account for the timing of adaptive events and the comparison between models of selective sweeps and sequence data is generally made via simple summaries of genetic diversity. Here, we develop a tractable method of describing the effect of positive selection on the genealogical histories in the surrounding genome, explicitly modeling both the timing and context of an adaptive event. In addition, our framework allows us to go beyond analyzing polymorphism data via the site frequency spectrum or summaries thereof and instead leverage information contained in patterns of linked variants. Tests on both simulations and a human data example, as well as a comparison to SweepFinder2, show that even with very small sample sizes, our analytic framework has higher power to identify old selective sweeps and to correctly infer both the time and strength of selection. Finally, we derived the marginal distribution of genealogical branch lengths at a locus affected by selection acting at a linked site. This provides a much-needed link between our analytic understanding of the effects of sweeps on sequence variation and recent advances in simulation and heuristic inference procedures that allow researchers to examine the sequence of genealogical histories along the genome.     

Predicting metabolic cost of running with and without backpack loads

In the past, a mathematical equation to predict the metabolic cost of standing or walking (Mw) was developed. However, this equation was limited to speeds less than 2.2 m.s-1 and overestimated the metabolic cost of walking or running at higher speeds. The purpose of this study was, therefore, to develop a mathematical model for the metabolic cost of running (Mr), in order to be able to predict the metabolic cost under a wide range of speeds, external loads and grades. Twelve male subjects were tested on a level treadmill under different combinations of speed and external load. Speed varied between 2.2 to 3.2 m.s-1 using 0.2 m.s-1 intervals and external loads between 0-30 kg with 10 kg intervals. Four of the subjects were also tested at 2 and 4% incline while speed and load remained constant (2.4 m.s-1, 20 kg). The model developed is based on Mw and is proportionately linear with external load (L) carried as follows: Mr = Mw-0.5 (1-0.01L)(Mw -15L-850), (watt) The correlation coefficient between predicted and observed values was 0.99 (P less than 0.01) with SER of 7.7%. The accuracy of the model was validated by its ability to predict the metabolic cost of running under different conditions extracted from the literature. A highly significant correlation (r = 0.95, P less than 0.02, SER = 6.5%) was found between our predicted and the reported values. In conclusion, the new equation permits accurate calculation of energy cost of running under a large range of speeds, external loads and inclines.     

Ongoing challenges in the management of malaria

Background

Mosquitoes transmit serious human diseases, causing millions of deaths every year. Use of synthetic insecticides to control vector mosquitoes has caused physiological resistance and adverse environmental effects in addition to high operational cost. Insecticides of botanical origin have been reported as useful for control of mosquitoes. Azadirachta indica (Meliaceae) and its derived products have shown a variety of insecticidal properties. The present paper discusses the larvicidal activity of neem-based biopesticide for the control of mosquitoes.

Methods

Larvicidal efficacy of an emulsified concentrate of neem oil formulation (neem oil with polyoxyethylene ether, sorbitan dioleate and epichlorohydrin) developed by BMR & Company, Pune, India, was evaluated against late 3^rd and early 4^th instar larvae of different genera of mosquitoes. The larvae were exposed to different concentrations (0.5–5.0 ppm) of the formulation along with untreated control. Larvicidal activity of the formulation was also evaluated in field against Anopheles, Culex, and Aedes mosquitoes. The formulation was diluted with equal volumes of water and applied @ 140 mg a.i./m² to different mosquito breeding sites with the help of pre calibrated knapsack sprayer. Larval density was determined at pre and post application of the formulation using a standard dipper.

Results

Median lethal concentration (LC₅₀) of the formulation against Anopheles stephensi, Culex quinquefasciatus and Aedes aegypti was found to be 1.6, 1.8 and 1.7 ppm respectively. LC₅₀ values of the formulation stored at 26°C, 40°C and 45°C for 48 hours against Ae. aegypti were 1.7, 1.7, 1.8 ppm while LC₉₀ values were 3.7, 3.7 and 3.8 ppm respectively. Further no significant difference in LC₅₀ and LC₉₀ values of the formulation was observed against Ae. aegypti during 18 months storage period at room temperature. An application of the formulation at the rate of 140 mg a.i./m² in different breeding sites under natural field conditions provided 98.1% reduction of Anopheles larvae on day 1; thereafter 100% reduction was recorded up to week 1 and more than 80% reduction up to week 3, while percent reduction against Culex larvae was 95.5% on day 1, and thereafter 80% reduction was achieved up to week 3. The formulation also showed 95.1% and, 99.7% reduction of Aedes larvae on day 1 and day 2 respectively; thereafter 100% larval control was observed up to day 7.

Conclusion

The neem oil formulation was found effective in controlling mosquito larvae in different breeding sites under natural field conditions. As neem trees are widely distributed in India, their formulations may prove to be an effective and eco-friendly larvicide, which could be used as an alternative for malaria control.     

Ongoing trials of the National Eye Institute

National Eye Institute (NEI) guided trials have a tremendous influence on clinical practice. Ongoing trials in two NEI areas — glaucoma; and strabismus, amblyopia and visual processing — are reviewed.     

The Serotype Distribution among Healthy Carriers before Vaccination Is Essential for Predicting the Impact of Pneumococcal Conjugate Vaccine on Invasive Disease

Pneumococcal conjugate vaccines (PCVs) have substantially reduced morbidity and mortality of pneumococcal disease. The impact of the 7-valent PCV on all-serotype invasive pneumococcal disease (IPD) among children was reported to vary between high-income countries. We investigate the ability to predict this heterogeneity from pre-vaccination data. We propose a parsimonious model that predicts the impact of PCVs from the odds of vaccine serotype (VT) among carriers and IPD cases in the pre-PCV period, assuming that VT are eliminated in a mature PCV programme, that full serotype replacement occurs in carriage and that invasiveness of the NVT group is unchanged. We test model performance against the reported impact of PCV7 on childhood IPD in high-income countries from a recent meta-analysis. The odds of pre-PCV7 VT IPD, PCV schedule, PCV coverage and whether a catch up campaign was used for introduction was gathered from the same analysis. We conducted a literature review and meta-analysis to obtain the odds of pre-PCV7 VT carriage in the respective settings. The model predicted the reported impact on childhood IPD of mature PCV programmes; the ratio of predicted and observed incidence risk ratios was close to 1 in all settings. In the high income settings studied differences in schedule, coverage, and catch up campaigns were not associated with the observed heterogeneity in impact of PCV7 on childhood all-serotype IPD. The pre-PCV7 proportion of VT IPD alone also had limited predictive value. The pre-PCV7 proportion of VT carriage and IPD are the main determinants for the impact of PCV7 on childhood IPD and can be combined in a simple model to provide predictions of the vaccine preventable burden of IPD.     

On the Genealogy of a Rare Allele

The gene genealogy is derived for a rare allele that is descended from a mutant ancestor that arose at a fixed time in the past. Following Thompson (1976,Amer. J. Human Genet.28, 442–452), the fractional linear branching process is used as a model of the demography of a rare allele. The model does not require the total population size to be constant or the mutant class to be neutral; so long as individuals in the class are selectively equivalent, the class as a whole may have a selective advantage, or disadvantage, relative to other alleles in the population. An exact result is given for the joint probability distribution of the coalescence times among a sample of alleles descended from the mutant. A method is described for rapidly simulating these coalescence times. The relationship between the genealogical structure of a discrete generation branching process and a continuous generation birth–death process is elucidated. The theory may be applied to the problem of estimating the ages of rare nonrecurrent mutations.     

Incidence of Dumping after Truncal and Selective Vagotomy with Pyloroplasty and Highly Selective Vagotomy without Drainage Procedure

The incidence of dumping after truncal or selective vagotomy with pyloroplasty and highly selective vagotomy without a drainage procedure was assessed both clinically and experimentally. At a gastric follow-up clinic dumping was found to be significantly less frequent in patients who had undergone highly selective vagotomy without a drainage procedure than in patients who had undergone truncal or selective vagotomy with pyloroplasty (P < 0·05 or < 0·001, respectively). Hypertonic glucose given by mouth provoked the onset of dumping in 20% of patients with duodenal ulcer before operation, in 73% after truncal vagotomy and pyloroplasty, in 80% after selective vagotomy and pyloroplasty, and in 47% after highly selective vagotomy. The test meal also produced significantly greater decreases in blood pressure and increases in pulse rate in patients who had undergone vagotomy with pyloroplasty than in patients who had undergone highly selective vagotomy.