On the unfounded enthusiasm for soft selective sweeps II: examining recent evidence from humans, flies, and viruses

ABSTRACTSince the initial description of the genomic patterns expected under models of positive selection acting on standing genetic variation and on multiple beneficial mutations—so-called soft selective sweeps—researchers have sought to identify these patterns in natural population data. Indeed, over the past two years, large-scale data analyses have argued that soft sweeps are pervasive across organisms of very different effective population size and mutation rate—humans, Drosophila, and HIV. Yet, others have evaluated the relevance of these models to natural populations, as well as the identifiability of the models relative to other known population-level processes, arguing that soft sweeps are likely to be rare. Here, we look to reconcile these opposing results by carefully evaluating three recent studies and their underlying methodologies. Using population genetic theory, as well as extensive simulation, we find that all three examples are prone to extremely high false-positive rates, incorrectly identifying soft sweeps under both hard sweep and neutral models. Furthermore, we demonstrate that well-fit demographic histories combined with rare hard sweeps serve as the more parsimonious explanation. These findings represent a necessary response to the growing tendency of invoking parameter-heavy, assumption-laden models of pervasive positive selection, and neglecting best practices regarding the construction of proper demographic null models.

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From Drift to Draft: How Much Do Beneficial Mutations Actually Contribute to Predictions of Ohta’s Slightly Deleterious Model of Molecular Evolution?

Genetics ◽

10.1534/genetics.119.302869 ◽

2020 ◽

Vol 214 (4) ◽

pp. 1005-1018 ◽

Cited By ~ 5

Author(s):

Jun Chen ◽

Sylvain Glémin ◽

Martin Lascoux

Keyword(s):

Molecular Evolution ◽

Positive Selection ◽

Neutral Theory ◽

Natural Populations ◽

Genomic Data ◽

Effective Population ◽

High Quality ◽

Beneficial Mutations ◽

Nearly Neutral Theory ◽

Neutral Mutations

Since its inception in 1973, the slightly deleterious model of molecular evolution, also known as the nearly neutral theory of molecular evolution, remains a central model to explain the main patterns of DNA polymorphism in natural populations. This is not to say that the quantitative fit to data are perfect. A recent study used polymorphism data from Drosophila melanogaster to test whether, as predicted by the nearly neutral theory, the proportion of effectively neutral mutations depends on the effective population size (Ne). It showed that a nearly neutral model simply scaling with Ne variation across the genome could not alone explain the data, but that consideration of linked positive selection improves the fit between observations and predictions. In the present article, we extended the work in two main directions. First, we confirmed the observed pattern on a set of 59 species, including high-quality genomic data from 11 animal and plant species with different mating systems and effective population sizes, hence a priori different levels of linked selection. Second, for the 11 species with high-quality genomic data we also estimated the full distribution of fitness effects (DFE) of mutations, and not solely the DFE of deleterious mutations. Both Ne and beneficial mutations contributed to the relationship between the proportion of effectively neutral mutations and local Ne across the genome. In conclusion, the predictions of the slightly deleterious model of molecular evolution hold well for species with small Ne, but for species with large Ne, the fit is improved by incorporating linked positive selection to the model.

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Examination of signatures of recent positive selection on genes involved in human sialic acid biology

10.1101/137034 ◽

2017 ◽

Author(s):

Jiyun M. Moon ◽

David M. Aronoff ◽

John A. Capra ◽

Patrick Abbot ◽

Antonis Rokas

Keyword(s):

Sialic Acid ◽

Positive Selection ◽

Transfer Functions ◽

Demographic History ◽

Neutral Evolution ◽

Human Lineage ◽

Selective Sweeps ◽

Frequency Spectra ◽

Recent Positive Selection ◽

Soft Selective Sweeps

AbstractSialic acids are nine carbon sugars ubiquitously found on the surfaces of vertebrate cells and are involved in various immune response-related processes. In humans, at least 58 genes spanning diverse functions, from biosynthesis and activation to recycling and degradation, are involved in sialic acid biology. Because of their role in immunity, sialic acid biology genes have been hypothesized to exhibit elevated rates of evolutionary change. Consistent with this hypothesis, several genes involved in sialic acid biology have experienced higher rates of non-synonymous substitutions in the human lineage than their counterparts in other great apes, perhaps in response to ancient pathogens that infected hominins millions of years ago (paleopathogens). To test whether sialic acid biology genes have also experienced more recent positive selection during the evolution of the modern human lineage, reflecting adaptation to contemporary cosmopolitan or geographically-restricted pathogens, we examined whether their protein-coding regions showed evidence of recent hard and soft selective sweeps. This examination involved the calculation of four measures that quantify changes in allele frequency spectra, extent of population differentiation, and haplotype homozygosity caused by recent hard and soft selective sweeps for 55 sialic acid biology genes using publicly available whole genome sequencing data from 1,668 humans from three ethnic groups. To disentangle evidence for selection from confounding demographic effects, we compared the observed patterns in sialic acid biology genes to simulated sequences of the same length under a model of neutral evolution that takes into account human demographic history. We found that the patterns of genetic variation of most sialic acid biology genes did not significantly deviate from neutral expectations and were not significantly different among genes belonging to different functional categories. Those few sialic acid biology genes that significantly deviated from neutrality either experienced soft sweeps or population-specific hard sweeps. Interestingly, while most hard sweeps occurred on genes involved in sialic acid recognition, most soft sweeps involved genes associated with recycling, degradation and activation, transport, and transfer functions. We propose that the lack of signatures of recent positive selection for the majority of the sialic acid biology genes is consistent with the view that these genes regulate immune responses against ancient rather than contemporary cosmopolitan or geographically restricted pathogens.

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A geometric characterization of population coding in the prefrontal cortex and hippocampus during a paired-associate learning task

10.1101/578849 ◽

2019 ◽

Author(s):

Yue Liu ◽

Scott L Brincat ◽

Earl K Miller ◽

Michael E Hasselmo

Keyword(s):

Prefrontal Cortex ◽

Large Scale ◽

Paired Associate ◽

Population Data ◽

Population Level ◽

Population Coding ◽

Population Activity ◽

Associate Learning ◽

Task Variables ◽

Monkey Prefrontal Cortex

Large-scale neuronal recording techniques have enabled discoveries of population-level mechanisms for neural computation. However it is not clear how these mechanisms form by trial and error learning. In this paper we present an initial effort to characterize the population activity in monkey prefrontal cortex (PFC) and hippocampus (HPC) during the learning phase of a paired-associate task. To analyze the population data, we introduce the normalized distance, a dimensionless metric that describes the encoding of cognitive variables from the geometrical relationship among neural trajectories in state space. It is found that PFC exhibits a more sustained encoding of task-relevant variables whereas HPC only transiently encodes the identity of the stimuli. We also found partial evidence on the learning-dependent changes for some of the task variables. This study shows the feasibility of using normalized distance as a metric to characterize and compare population level encoding of task variables, and suggests further directions to explore the learning-dependent changes in the population activity.

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From drift to draft: How much do beneficial mutations actually contribute to predictions of Ohta’s slightly deleterious model of molecular evolution?

10.1101/681866 ◽

2019 ◽

Cited By ~ 1

Author(s):

Jun Chen ◽

Sylvain Glémin ◽

Martin Lascoux

Keyword(s):

Molecular Evolution ◽

Positive Selection ◽

Neutral Theory ◽

Natural Populations ◽

Genomic Data ◽

Effective Population ◽

High Quality ◽

Beneficial Mutations ◽

Nearly Neutral Theory ◽

Neutral Mutations

AbstractSince its inception in 1973 the slightly deleterious model of molecular evolution, aka the Nearly Neutral Theory of molecular evolution, remains a central model to explain the main patterns of DNA polymorphism in natural populations. This is not to say that the quantitative fit to data is perfect. In a recent study Castellanoet al. (2018) used polymorphism data from D. melanogaster to test whether, as predicted by the Nearly Neutral Theory, the proportion of effectively neutral mutations depends on the effective population size (Ne). They showed that a nearly neutral model simply scaling with Ne variation across the genome could not explain alone the data but that consideration of linked positive selection improves the fit between observations and predictions. In the present article we extended their work in two main directions. First, we confirmed the observed pattern on a set of 59 species, including high quality genomic data from 11 animal and plant species with different mating systems and effective population sizes, hence a priori different levels of linked selection. Second, for the 11 species with high quality genomic data we also estimated the full Distribution of Fitness Effects (DFE) of mutations, and not solely the DFE of deleterious mutations. Both Ne and beneficial mutations contributed to the relationship between the proportion of effectively neutral mutations and local Ne across the genome. In conclusion, the predictions of the slightly deleterious model of molecular evolution hold well for species with small Ne. But for species with large Ne the fit is improved by incorporating linked positive selection to the model.

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Soft selective sweeps in complex demographic scenarios

10.1101/004424 ◽

2014 ◽

Cited By ~ 1

Author(s):

Benjamin A Wilson ◽

Dmitri Petrov ◽

Philipp W Messer

Keyword(s):

Population Size ◽

Effective Population Size ◽

De Novo ◽

Analytical Framework ◽

De Novo Mutation ◽

Selective Sweeps ◽

Effective Population ◽

Weak Selection ◽

Adaptive Mutations ◽

Soft Selective Sweeps

Recent studies have shown that adaptation from de novo mutation often produces so-called soft selective sweeps, where adaptive mutations of independent mutational origin sweep through the population at the same time. Population genetic theory predicts that soft sweeps should be likely if the product of the population size and the mutation rate towards the adaptive allele is sufficiently large, such that multiple adaptive mutations can establish before one has reached fixation; however, it remains unclear how demographic processes affect the probability of observing soft sweeps. Here we extend the theory of soft selective sweeps to realistic demographic scenarios that allow for changes in population size over time. We first show that population bottlenecks can lead to the removal of all but one adaptive lineage from an initially soft selective sweep. The parameter regime under which such 'hardening' of soft selective sweeps is likely is determined by a simple heuristic condition. We further develop a generalized analytical framework, based on an extension of the coalescent process, for calculating the probability of soft sweeps under arbitrary demographic scenarios. Two important limits emerge within this analytical framework: In the limit where population size fluctuations are fast compared to the duration of the sweep, the likelihood of soft sweeps is determined by the harmonic mean of the variance effective population size estimated over the duration of the sweep; in the opposing slow fluctuation limit, the likelihood of soft sweeps is determined by the instantaneous variance effective population size at the onset of the sweep. We show that as a consequence of this finding the probability of observing soft sweeps becomes a function of the strength of selection. Specifically, in species with sharply fluctuating population size, strong selection is more likely to produce soft sweeps than weak selection. Our results highlight the importance of accurate demographic estimates over short evolutionary timescales for understanding the population genetics of adaptation from de novo mutation.

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Effects of linked selective sweeps on demographic inference and model selection

10.1101/047019 ◽

2016 ◽

Cited By ~ 2

Author(s):

Daniel R. Schrider ◽

Alexander G. Shanku ◽

Andrew D. Kern

Keyword(s):

Genetic Variation ◽

Natural Selection ◽

Positive Selection ◽

Population Size ◽

Natural Populations ◽

Parameter Estimates ◽

Selective Sweeps ◽

Recent Positive Selection ◽

Demographic Inference ◽

The Impact

AbstractThe availability of large-scale population genomic sequence data has resulted in an explosion in efforts to infer the demographic histories of natural populations across a broad range of organisms. As demographic events alter coalescent genealogies they leave detectable signatures in patterns of genetic variation within and between populations. Accordingly, a variety of approaches have been designed to leverage population genetic data to uncover the footprints of demographic change in the genome. The vast majority of these methods make the simplifying assumption that the measures of genetic variation used as their input are unaffected by natural selection. However, natural selection can dramatically skew patterns of variation not only at selected sites, but at linked, neutral loci as well. Here we assess the impact of recent positive selection on demographic inference by characterizing the performance of three popular methods through extensive simulation of datasets with varying numbers of linked selective sweeps. In particular, we examined three different demographic models relevant to a number of species, finding that positive selection can bias parameter estimates of each of these models—often severely. Moreover, we find that selection can lead to incorrect inferences of population size changes when none have occurred. We argue that the amount of recent positive selection required to skew inferences may often be acting in natural populations. These results suggest that demographic studies conducted in many species to date may have exaggerated the extent and frequency of population size changes.

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On the unfounded enthusiasm for soft selective sweeps

10.1101/009563 ◽

2014 ◽

Cited By ~ 1

Author(s):

Jeffrey D. Jensen

Keyword(s):

Experimental Evidence ◽

Population Genetic ◽

Natural Populations ◽

Genetic Models ◽

Relative Importance ◽

Selective Sweeps ◽

Beneficial Mutations ◽

Adaptive Process ◽

Soft Selective Sweeps

Underlying any understanding of the mode, tempo, and relative importance of the adaptive process in the evolution of natural populations is the notion of whether adaptation is mutation-limited. Two very different population genetic models have recently been proposed in which the rate of adaptation is not strongly limited by the rate at which newly arising beneficial mutations enter the population. This review discusses the theoretical underpinnings and requirements of these models, as well as the experimental insights on the parameters of relevance. Importantly, empirical and experimental evidence to date challenges the recent enthusiasm for invoking these models to explain observed patterns of variation in humans and Drosophila.

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Understanding and responding to COVID-19 in Wales: protocol for a privacy-protecting data platform for enhanced epidemiology and evaluation of interventions

BMJ Open ◽

10.1136/bmjopen-2020-043010 ◽

2020 ◽

Vol 10 (10) ◽

pp. e043010

Author(s):

Jane Lyons ◽

Ashley Akbari ◽

Fatemeh Torabi ◽

Gareth I Davies ◽

Laura North ◽

...

Keyword(s):

Policy Development ◽

Healthcare Utilisation ◽

Population Data ◽

Population Level ◽

Risk Groups ◽

Service Utilisation ◽

Natural Experiments ◽

Spread Of Infection ◽

The Impact

IntroductionThe emergence of the novel respiratory SARS-CoV-2 and subsequent COVID-19 pandemic have required rapid assimilation of population-level data to understand and control the spread of infection in the general and vulnerable populations. Rapid analyses are needed to inform policy development and target interventions to at-risk groups to prevent serious health outcomes. We aim to provide an accessible research platform to determine demographic, socioeconomic and clinical risk factors for infection, morbidity and mortality of COVID-19, to measure the impact of COVID-19 on healthcare utilisation and long-term health, and to enable the evaluation of natural experiments of policy interventions.Methods and analysisTwo privacy-protecting population-level cohorts have been created and derived from multisourced demographic and healthcare data. The C20 cohort consists of 3.2 million people in Wales on the 1 January 2020 with follow-up until 31 May 2020. The complete cohort dataset will be updated monthly with some individual datasets available daily. The C16 cohort consists of 3 million people in Wales on the 1 January 2016 with follow-up to 31 December 2019. C16 is designed as a counterfactual cohort to provide contextual comparative population data on disease, health service utilisation and mortality. Study outcomes will: (a) characterise the epidemiology of COVID-19, (b) assess socioeconomic and demographic influences on infection and outcomes, (c) measure the impact of COVID-19 on short -term and longer-term population outcomes and (d) undertake studies on the transmission and spatial spread of infection.Ethics and disseminationThe Secure Anonymised Information Linkage-independent Information Governance Review Panel has approved this study. The study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals.

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Molecular Evolution of Duplicated Amylase Gene Regions in Drosophila melanogaster: Evidence of Positive Selection in the Coding Regions and Selective Constraints in the cis-Regulatory Regions

Genetics ◽

10.1093/genetics/157.2.667 ◽

2001 ◽

Vol 157 (2) ◽

pp. 667-677

Author(s):

Hitoshi Araki ◽

Nobuyuki Inomata ◽

Tsuneyuki Yamazaki

Keyword(s):

Drosophila Melanogaster ◽

Molecular Evolution ◽

Positive Selection ◽

Natural Populations ◽

Sliding Window ◽

Selective Constraint ◽

Proximal Region ◽

Coding Regions ◽

Asian Populations ◽

Flanking Region

Abstract In this study, we randomly sampled Drosophila melanogaster from Japanese and Kenyan natural populations. We sequenced duplicated (proximal and distal) Amy gene regions to test whether the patterns of polymorphism were consistent with neutral molecular evolution. Fst between the two geographically distant populations, estimated from Amy gene regions, was 0.084, smaller than reported values for other loci, comparing African and Asian populations. Furthermore, little genetic differentiation was found at a microsatellite locus (DROYANETSB) in these samples (Gst′=−0.018). The results of several tests (Tajima's, Fu and Li's, and Wall's tests) were not significantly different from neutrality. However, a significantly higher level of fixed replacement substitutions was detected by a modified McDonald and Kreitman test for both populations. This indicates that positive selection occurred during or immediately after the speciation of D. melanogaster. Sliding-window analysis showed that the proximal region 1, a part of the proximal 5′ flanking region, was conserved between D. melanogaster and its sibling species, D. simulans. An HKA test was significant when the proximal region 1 was compared with the 5′ flanking region of Alcohol dehydrogenase (Adh), indicating a severe selective constraint on the Amy proximal region 1. These results suggest that natural selection has played an important role in the molecular evolution of Amy gene regions in D. melanogaster.

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214 Longitudinal Changes in Sleep Duration, Timing, Variability, and Stages during the COVID-19 Pandemic: Large-Scale Fitbit Data

SLEEP ◽

10.1093/sleep/zsab072.213 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A86-A86

Author(s):

Michael Grandner ◽

Naghmeh Rezaei

Keyword(s):

New York ◽

Los Angeles ◽

San Francisco ◽

Sleep Duration ◽

Large Scale ◽

Age Groups ◽

Population Level ◽

Sleep Stages ◽

Longitudinal Changes ◽

Timing Variability

Abstract Introduction The COVID-19 pandemic has resulted in societal-level changes to sleep and other behavioral patterns. Objective, longitudinal data would allow for a greater understanding of sleep-related changes at the population level. Methods N= 163,524 deidentified active Fitbit users from 6 major US cities contributed data, representing areas particularly hard-hit by the pandemic (Chicago, Houston, Los Angeles, New York, San Francisco, and Miami). Sleep variables extracted include nightly and weekly mean sleep duration and bedtime, variability (standard deviation) of sleep duration and bedtime, and estimated arousals and sleep stages. Deviation from similar timeframes in 2019 were examined. All analyses were performed in Python. Results These data detail how sleep duration and timing changed longitudinally, stratified by age group and gender, relative to previous years’ data. Overall, 2020 represented a significant departure for all age groups and both men and women (P<0.00001). Mean sleep duration increased in nearly all groups (P<0.00001) by 5-11 minutes, compared to a mean decrease of 5-8 minutes seen over the same period in 2019. Categorically, sleep duration increased for some and decreased for others, but more extended than restricted. Sleep phase shifted later for nearly all groups (p<0.00001). Categorically, bedtime was delayed for some and advanced for others, though more delayed than advanced. Duration and bedtime variability decreased, owing largely to decreased weekday-weekend differences. WASO increased, REM% increased, and Deep% decreased. Additional analyses show stratified, longitudinal changes to sleep duration and timing mean and variability distributions by month, as well as effect sizes and correlations to other outcomes. Conclusion The pandemic was associated with increased sleep duration on average, in contrast to 2019 when sleep decreased. The increase was most profound among younger adults, especially women. The youngest adults also experienced the greatest bedtime delay, in line with extensive school-start-times and chronotype data. When given the opportunity, the difference between weekdays and weekends became smaller, with occupational implications. Sleep staging data showed that slightly extending sleep minimally impacted deep sleep but resulted in a proportional increase in REM. Wakefulness during the night also increased, suggesting increased arousal despite greater sleep duration. Support (if any) This research was supported by Fitbit, Inc.

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