Biology subjects

Harris, A. M.

Publications and source records attributed to Harris, A. M..

17 recordsLinked to original sources

bioRxiv2026

Polysynaptic signal propagation in networked neural masses

The routing of information across the brain's structural network is central to its wide range of functional capabilities. However, the mechanisms underlying information routing in complex brain networks, particularly between regions that do not share a direct anatomical connection, remain poorly understood. Neural mass models (NMMs), a computational modelling framework capable of capturing complex neural dynamics across scales, can potentially be used to study the dynamical and network bases of these vital polysynaptic routing processes. In this study, we investigate polysynaptic signalling in three widely used NMMs, obeying Ornstein-Uhlenbeck, Stuart-Landau, and Jansen-Rit dynamics, by tracking the propagation of a discrete, focal, high-amplitude perturbation across the underlying network. We find that polysynaptic propagation emerges in all tested NMMs when configured within dynamical regimes that effectively enhance the persistence of perturbations. We also find distinct parameter domains that maximise signal propagation to directly connected regions and to those separated from the source by at least two hops. Finally, we benchmark in silico stimulus propagation in the brain network against an empirical dataset of direct electrical stimulation trials, to explore the relative capabilities of the NMMs in capturing signal propagation to connected versus unconnected regions. This analysis highlights the significance of dynamical repertoire in capturing stimulus propagation outcomes. Overall, this study provides insights into how dynamical and network features shape signal propagation over complex brain networks.

neuroscience↗

bioRxiv2026

A joint alpha power-phase dynamic shapes visual sensitivity

Perceptual experience is influenced by alpha-band oscillations (8-14 Hz) that dominate parietal and sensory cortices. However, there is uncertainty around the perceptual mechanisms that are affected by oscillatory power and phase. Previous work has linked power and phase to behaviour separately despite their theorised common effects via pulsed-inhibition, potentially misrepresenting or conflating their effects. Here we recorded brain activity using electroencephalography to investigate how alpha oscillations affect the psychometric function over visual contrast in both detection and discrimination tasks. We found that prestimulus power and phase predicted the strength of subsequent evoked neural responses and behavioural accuracy. We then combined power and phase into a joint model of pulsed-inhibition and estimated its effects within a signal detection model of behaviour. The model revealed response gain modulation of visual sensitivity in both tasks, and perceptual bias modulation in detection. Critically, oscillatory power suppressed visual sensitivity more strongly than phase, suggesting a sustained effect of alpha oscillations that is not accounted for by the pulsed-inhibition hypothesis. We conclude that alpha-band activity shapes visual perception by divisively suppressing sensory evidence and baseline sensory noise, with joint power-phase modelling revealing asymmetric contributions to visual sensitivity.

neuroscience↗

bioRxiv2025

Evaluating oscillatory mechanisms underlying flexible neural communication in the human brain

How the brain orchestrates the flow of information between its multiple functional units flexibly, quickly, and accurately, remains a fundamental question in neuroscience. Multiple theories identify neural oscillations as a likely basis for this process. However, a lack of empirical validation of proposed theories, particularly at the whole-brain scale, has hampered consensus on oscillatory principles governing neural communication, limiting our understanding of a process central to perception and cognition and its integration into experiments and clinical applications. Here, we empirically validate previously proposed neural-oscillatory communication mechanisms in the human brain - specifically those involving power and interareal phase coherence - at the whole-brain scale. We do this by estimating the dependence of inferred communication on oscillatory measures that have been theorised to facilitate communication, in source-localised resting-state magnetoencephalography (MEG) recordings. We find that power and phase coherence in the alpha, beta, and high-gamma bands track communication better than others. Crucially, the relation between communication and oscillatory measures varied across regions, indicating spatial heterogeneity in routing mechanisms. Notably, power and coherence-based principles tracked communication patterns of unimodal regions better than those of transmodal regions. In sum, these findings suggest that the human brain implements regionally specific communication mechanisms with complex neural-oscillatory dependence. Significance statementFlexible and dynamic routing of information between neural elements is vital for human brain function. Despite its importance, the mechanisms underlying flexible communication in the brain are not fully understood. Several lines of evidence suggest that neural oscillations might mediate flexible communication, but whether such mechanisms operate at whole-brain scales remains uncertain. Here, we investigate the oscillatory underpinnings of communication by assessing how well observed communication patterns from magnetoencephalography data align with the predictions of established theories. Our findings suggest that the brain follows complex region-specific routing principles to realise flexible communication. Knowledge of these principles can develop our fundamental understanding of functions like perception and cognition, refine models of interareal interactions, and help develop more effective stimulation-based clinical interventions.

neuroscience↗

bioRxiv2025

Individual differences in perceptual capacity depend on aperiodic slope, not alpha oscillations

Visual processing is subject to strict capacity limitations that restrict the number of stimuli that can be individuated, that is, distinguished as separate objects, to around 3-4 items at a time. The neural mechanisms that give rise to this subitizing limit are unclear. Here, we tested an account of the subitizing limit that attributes it to the amount of information that can be processed within one cycle of the 8-14 Hz alpha oscillation. This account proposes that the subitizing limit should be correlated with an individuals alpha oscillation frequency. We pitted this account against one based on neural excitatory-inhibitory balance, as indexed by the slope of the aperiodic component of the electrophysiological power spectrum. To test these accounts, we had human participants (N=51; 37 females) complete a visual enumeration task while we measured their brain activity with electroencephalography (EEG). We extracted aperiodic and alpha-band activity from the pre-stimulus period and correlated these with model-based estimates of individuals subitizing capacity. Alpha oscillations were not correlated with subitizing capacity within-participants or as individual differences, but they were associated with other elements of enumeration performance, including baseline reaction times and the slope of the performance decrement outside the subitizing range. By contrast, individual differences in aperiodic slope reliably predicted individuals subitizing capacity. These results suggest that differences in neural excitatory:inhibitory balance are the source of individuation-related capacity limits, not alpha-frequency sampling. Significance StatementBusy, multi-element visual scenes are common in daily life. Human visual perception, however, is capacity limited, individuating only 3-4 objects at a time. The source of these capacity limitations is currently unknown. This work examines the neural correlates of perceptual capacity within and between individuals, comparing a rhythmic pulsed inhibition account with one based on the arhythmic balance of neural excitation and inhibition. Our results demonstrate that individuals with more arhythmic inhibition demonstrate greater perceptual capacity. These results help to explain how excitation and inhibition across populations of neurons sculpts the capacity of the neural system and suggest targets for future interventions to improve perceptual capacity with arhythmic brain stimulation.

neuroscience↗

bioRxiv2024

A novel mouse model for developmental and epileptic encephalopathy by Purkinje cell-specific deletion of Scn1b

Loss of function variants of SCN1B are associated with a range of developmental and epileptic encephalopathies (DEEs), including Dravet syndrome. These DEEs feature a wide range of severe neurological disabilities, including changes to social, motor, mood, sleep, and cognitive function which are notoriously difficult to treat, and high rates of early mortality. While the symptomology of SCN1B-associated DEEs indicates broad changes in neural function, most research has focused on epilepsy-related brain structures and function. Mechanistic studies of SCN1B/Scn1b have delineated diverse roles in development and adult maintenance of neural function, via cell adhesion, ion channel regulation, and other intra- and extra-cellular actions. However, use of mouse models is limited as knockout of Scn1b, globally and even in some cell-specific models (e.g., Parvalbumin+ interneuron-specific knockout) in adult mice, leads to severe and progressive epilepsy, health deterioration, and 100% mortality within weeks. Here, we report findings of a novel transgenic mouse line in which Scn1b was specifically deleted in cerebellar Purkinje cells. Unlike most existing models, these mice did not show failure to thrive or early mortality. However, we quantified marked decrements to Purkinje cell physiology as well as motor, social, and cognitive dysfunction. Our data indicate that cerebellar Purkinje cells are an important node for dysfunction and neural disabilities in SCN1B-related DEEs, and combined with previous work identify this as a potentially vital site for understanding mechanisms of DEEs and developing therapies that can treat these disorders holistically.

neuroscience↗

bioRxiv2024

Oscillatory dynamics of sustained attention states

Sustained attention allows concentration on a task over long periods of time. This ability fluctuates, with periods of effective focus ( in-the-zone) and periods of increased performance variability and susceptibility to errors ( out-of-the-zone). Little is known about the neural dynamics underlying these states and their fluctuations during sustained attention tasks. To address this, we had thirty young adults perform the gradual onset continuous performance task (gradCPT), during which their EEG and responses were recorded. States of sustained attention (out-vs. in-the-zone) were identified based on the variance time course of participants RT. Out-of-the-zone states were associated with increased errors of commission and reduced perceptual sensitivity compared to in-the-zone states, as expected. Importantly, a significant decline in theta oscillations at mid-prefrontal regions was found during out-of-the-zone (vs. in-the-zone) states over a [~]400 ms period around the transition point between stimuli, and the extent of this decline predicted commission errors and response bias. In addition, individual differences in the variability of midfrontal theta along the task were associated with RT variability. Finally, participants exhibiting greater theta variability showed a more pronounced decline in perceptual sensitivity when being out-of-the-zone and less stable RTs compared to those with lower variability. Our results suggest that states of diminished sustained attention, even during short lapses, are characterized by a reduction in midfrontal theta activity, and that fluctuations in this rhythm covary with fluctuations in attentional control.

neuroscience↗

bioRxiv2024

Detecting behavioural oscillations with increased sensitivity: A modification of Brookshire's (2022) AR-surrogate method

A core challenge of cognitive neuroscience is to understand how cognition changes over time within the same individual. For example, the tendency for behavioural responses in a range of cognitive domains to oscillate over time has been studied extensively. Recently, however, the phenomenon of behavioural oscillations has been called into question by indications that past findings might reflect aperiodic temporal structure rather than true oscillations. Brookshire (2022) proposed methods to control for aperiodic temporal structure while examining oscillations in behavioural time-courses and found no evidence of behavioural oscillations in reanalyses of four published datasets. However, Brookshires (2022) method has been criticised for having low sensitivity to detect effects of realistic magnitude, so it is currently unclear whether these findings suggest that behavioural oscillations are not present in these and perhaps many other datasets, or whether they are false negatives. Here, we present a modification of Brookshires (2022) AR-surrogate method with increased sensitivity to detect effects of realistic magnitude, adequate control of false positives, and other desirable properties such as the ability to increase statistical power by adding more participants. Using this method, we reanalyse the same publicly available datasets and show significant behavioural oscillations in each of them, suggesting oscillations in behaviour are a robust phenomenon upon which to draw theoretical inferences. The participant-level AR-surrogate method is currently the most sensitive method available for analysing behavioural oscillations while controlling for the contribution of aperiodic data fluctuations.

neuroscience↗

bioRxiv2023

Effects of oscillation phase on discrimination performance in a visual tilt illusion

Neural oscillations reflect fluctuations in the relative excitation/inhibition of neural systems1-5 and are theorised to play a critical role in several canonical neural computations6-9 and cognitive processes10-14. These theories have been supported by findings that detection of visual stimuli fluctuates with the phase of oscillations at the time of stimulus onset15-23. However, null results have emerged in studies seeking to demonstrate these effects in visual discrimination tasks24-27, raising questions about the generalisability of these phenomena to wider neural processes. Recently, we suggested that methodological limitations may mask effects of oscillation phase in higher-level sensory processing28. Thus, to test the generality of phasic influences requires a task that requires stimulus discrimination but depends on early sensory processing. Here, we examined the influence of oscillation phase in the visual tilt illusion, in which an oriented centre grating is perceived titled away from the orientation of a surround grating29. This illusion is produced by lateral inhibitory interactions in early visual processing30-32. We presented centre gratings at participants titrated subjective vertical angle and had participants report whether the grating appeared tilted leftward or rightward of vertical on each trial while measuring their brain activity with EEG. We observed a robust fluctuation in orientation perception across different phases of posterior alpha and theta oscillations, consistent with fluctuating illusion magnitude across the oscillatory cycle. These results confirm that oscillation phase affects complex processing involved in stimulus discrimination, consistent with their purported role in canonical computations that underpin cognition.

neuroscience↗

bioRxiv2022

Different genetic architectures of complex traits and their relevance to polygenic score performance

BackgroundDespite the many insights gleaned from GWAS, polygenic predictions of complex traits have had limited success, particularly when these predictions are applied to individuals of non-European descent. A deeper understanding of the genetic architecture of complex traits may inform why some traits are easier to predict than others. MethodsExamining 163 complex traits from the UK Biobank, we compared and contrasted three aspects of genetic architecture (SNP heritability, LD variability, and genomic inequality) with three aspects of polygenic score performance (prediction accuracy in the source population, portability across populations, and trait divergence across populations). Here, genomic inequality refers to how unequally the genetic variance of each trait is distributed across the top trait-associated SNPs, as quantified via a novel application of Gini coefficients. ResultsConsistent with reduced statistical power, polygenic predictions of binary traits performed worse than predictions of quantitative traits. Traits with low Gini coefficients (i.e., highly polygenic architectures) include hip circumference as well as systolic and diastolic blood pressure. Traits with large population-level differences in polygenic scores include skin pigmentation and hair color. Focusing on 96 quantitative traits, we found that highly heritable traits were easier to predict and had predictions that were more portable to other ancestries. Traits with highly divergent polygenic score distributions across populations were less likely to have portable predictions. Intriguingly, LD variability was largely uninformative regarding the portability of polygenic predictions. This suggests that factors other than the differential tagging of causal SNPs drive the reduction in polygenic score accuracy across populations. Subsequent analyses identified suites of traits with similar genetic architecture and polygenic score performance profiles. Importantly, lifestyle and psychological traits tended to have low heritability, as well as poor predictability and portability. ConclusionsNovel metrics capture different aspects of trait-specific genetic architectures and polygenic score performance. Our findings also caution against the application of polygenic scores to traits like general happiness, alcohol frequency, and average income, especially when polygenic scores are applied to individuals who have an ancestry that differs from the original source population.

genomics↗

bioRxiv2022

Diminished distractor filtering with increased perceptual load and sustained effort explains attention deficit in post-stroke fatigue

ObjectivesPost-stroke fatigue (PSF) is a prevalent symptom associated with attention deficits. However, it is currently unclear what drives these. Here we applied Load Theory of Attention to investigate the role of perceptual load in the relationship between attention, distraction, and fatigue levels in PSF. MethodsThirty-two chronic stroke survivors performed a selective attention task under different perceptual load levels. Neural responses to targets and distractors were measured with frequency-tagged EEG responses. ResultsWe showed that Fatigue Severity Scale-7 (FSS-7) scores are predictive of slower responses, irrespective of the task level of perceptual load. Although average distractor frequency (10Hz) power was reduced with higher perceptual load, the reduction in 10Hz power with increasing task load, was smaller in those with higher FSS-7 scores. Higher FSS-7 scores were also associated with increased 10Hz distractor response as time progressed, both within task-trials and across the experiment duration. ConclusionThe results are consistent with previous reports of slower processing speed and sustained selective attention deficits. Importantly they demonstrate that higher fatigue severity is associated with a reduced ability to suppress neural response to irrelevant distractors stimuli when the task demands more attention due to its increased perceptual load. An account for attention deficit in PSF based on a distractor filtering deficit that emerges with increased perceptual load and sustained effort can explain a myriad of PSF symptoms (e.g. sensory perceptual overload, and difficulties to concentrate). Key messagesO_LIWhat is already known on this topic [tpltrtarr]Post-stroke fatigue involves mental fatigue which is exacerbated in cognitively demanding task, as well as difficulties in concentrating and sustaining attention on a task. [tpltrtarr]However previous research has not converged on a clear account of the nature of attention deficits in post-stroke fatigue, as well as on how these relate to the level of perceptual demand in a cognitive task C_LIO_LIWhat this study adds [tpltrtarr]The study suggests that attention deficits in post-stroke fatigue are attributed to a failure to attenuate neural responses to distractors with increased perceptual load in the task, as well as with increased demands on sustained attention with a longer task engagement [tpltrtarr]This pattern points to a distractor filtering deficit that is magnified by perceptual load and sustained mental effort as main drivers of attention deficits in post-stroke fatigue How this study might affect research, practice or policy [tpltrtarr]This study enhances our understanding of attentional deficit in post-stroke fatigue, within an integrative account that explains mental fatigue symptomology C_LI

neuroscience↗

bioRxiv2021

Sensitizing solid tumors to CAR-mediated cytotoxicity using synthetic antigens

CAR T cell immunotherapy relies on CAR targeting of tumor-associated antigens, yet heterogenous antigen expression, interpatient variation, and off-tumor expression by healthy cells remain barriers. Here, we develop synthetic antigens to sensitize solid tumors for recognition and elimination by CAR T cells. Unlike tumor-associated antigens, we design synthetic antigens that are orthogonal to endogenous proteins to eliminate off-tumor targeting and that have a small genetic footprint to facilitate efficient tumor delivery to tumors by viral vectors. Using the RSV-F camelid single-domain antibody (VHH) as a synthetic antigen, we show that adoptive transfer of VHH CAR T cells to mice bearing VHH expressing tumors reduced tumor burden in multiple syngeneic mouse models of cancer, improved survival, induced epitope spread, and protected against tumor rechallenge. Our work supports in situ delivery of synthetic antigens to treat antigen low or negative tumors with CAR T cells.

bioengineering↗

bioRxiv2020

Activity-based urinary biomarkers of response and resistance to checkpoint blockade immunotherapy

Immune checkpoint blockade (ICB) therapy has transformed cancer treatment, yet most patients do not derive clinical benefit and responders can acquire resistance to therapy. Noninvasive biomarkers are needed to indicate early on-treatment response and resistance mechanisms. Here we developed ImmuNe Sensors for monItorinG cHeckpoint blockade Therapy (INSIGHT), which comprises a library of mass-barcoded peptide substrates conjugated to PD1 antibodies, as therapeutic sensors. Following systemic administration, INSIGHT carries out the dual role of reinvigorating T cell function and profiling T cell and tumor proteases by the release of cleaved peptides into urine for noninvasive detection by mass spectrometry. We show that an PD1 therapeutic sensor for Granzyme B discriminates early treatment responses before tumor volumes significantly diverge from isotype controls in murine models of colorectal cancer. To differentiate mechanisms of resistance by multivariate analysis, we design a mass-barcoded, 14-plex INSIGHT library to profile proteases differentially expressed by tumors harboring B2m or Jak1 loss-of-function mutations. We find that binary classifiers trained on urine samples indicate response to PD-1 therapy as early as the start of the second dose, and discriminate B2m from Jak1 resistance with high sensitivity and specificity (AUROCs > 0.9). Our data supports the use of activity-based biomarkers for early on-treatment response assessment and classification of refractory tumors based on resistance mechanisms. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/420265v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@12ce799org.highwire.dtl.DTLVardef@118fcc5org.highwire.dtl.DTLVardef@17b227eorg.highwire.dtl.DTLVardef@115a7fc_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2020

Remote control of CAR T cell therapies by thermal targeting

The limited ability to control anti-tumor activity within tumor sites contributes to poor CAR T cell responses against solid malignancies. Systemic delivery of biologic drugs such as cytokines can augment CAR T cell activity despite off-target toxicity in healthy tissues that narrows their therapeutic window. Here we develop a platform for remote control of CAR T therapies by thermal targeting. To enable CAR T cells to respond to heat, we construct synthetic thermal gene switches that trigger expression of transgenes in response to mild elevations in local temperature (40-42 {degrees}C) but not to orthogonal cellular stresses such as hypoxia. We show that short pulses of heat (15-30 min) lead to more than 60-fold increases in gene expression without affecting key T cell functions including proliferation, migration, and cytotoxicity. We demonstrate thermal control of broad classes of immunostimulatory agents including CARs, Bispecific T cell Engagers (BiTEs), and cytokine superagonists to enhance proliferation and cell targeting. In mouse models of adoptive transfer, photothermal targeting of intratumoral CAR T cells to control the production of an IL-15 superagonist significantly enhances anti-tumor activity and overall survival. We envision that thermal targeting could improve the safety and efficacy of next-generation therapies by allowing remote control of CAR T cell activity.

bioengineering↗

bioRxiv2019

A likelihood approach for uncovering selective sweep signatures from haplotype data

Selective sweeps are frequent and varied signatures in the genomes of natural populations, and detecting them is consequently important in understanding mechanisms of adaptation by natural selection. Following a selective sweep, haplotypic diversity surrounding the site under selection decreases, and this deviation from the background pattern of variation can be applied to identify sweeps. Multiple methods exist to locate selective sweeps in the genome from haplotype data, but none leverage the power of a model-based approach to make their inference. Here, we propose a likelihood ratio test statistic T to probe whole genome polymorphism datasets for selective sweep signatures. Our framework uses a simple but powerful model of haplotype frequency spectrum distortion to find sweeps and additionally make an inference on the number of presently sweeping haplotypes in a population. We found that the T statistic is suitable for detecting both hard and soft sweeps across a variety of demographic models, selection strengths, and ages of the beneficial allele. Accordingly, we applied the T statistic to variant calls from European and sub-Saharan African human populations, yielding primarily literature-supported candidates, including LCT, RSPH3, and ZNF211 in CEU, SYT1, RGS18, and NNT in YRI, and HLA genes in both populations. We also searched for sweep signatures in Drosophila melanogaster, finding expected candidates at Ace, Uhg1, and Pimet. Finally, we provide open-source software to compute the T statistic and the inferred number of presently sweeping haplotypes from whole-genome data.

evolutionary biology↗

bioRxiv2019

A response to Zhang et al. (2018), \"Can Mouse-tracking Reveal Attribute Processing Speeds in Dietary Self-control? Commentary on Sullivan et al. (2015) and Lim et al. (2018)\"

Introduction Introduction References In Sullivan et al. (2015), mouse-tracking was used in a food choice paradigm to test two related hypotheses: 1) that there are differences in the relative speed with which the decision-making circuitry computes and weights the value of attributes like health and taste; and, 2) that individual differences in these relative speeds are associated with individual differences in the ability to make healthy dietary choices. Consistent with these hypotheses, a regression analysis of the mouse-tracking paths found that health became significantly predictive of the mouses angle of movement ~195 ms later than taste, on average. Moreover, individual dietary self-control varied with estimates of the individual differences in the relative speed with which taste (tTaste) and health (tHealth) entered the decision-making process. Similar results have ...

neuroscience↗

bioRxiv2018

Identifying and classifying shared selective sweeps from multilocus data

Positive selection causes beneficial alleles to rise to high frequency, resulting in a selective sweep of the diversity surrounding the selected sites. Accordingly, the signature of a selective sweep in an ancestral population may still remain in its descendants. Identifying genomic regions under selection in the ancestor is important to contextualize the timing of a sweep, but few methods exist for this purpose. To uncover genomic regions under shared positive selection across populations, we apply the theory of the expected haplotype homozygosity statistic H12, which detects recent hard and soft sweeps from the presence of high-frequency haplotypes. Our statistic, SS-H12, is distinct from other statistics that detect shared sweeps because it requires a minimum of only two populations, and properly identifies independent convergent sweeps and true ancestral sweeps, with high power. Furthermore, we can apply SS-H12 in conjunction with the ratio of a different set of expected haplotype homozygosity statistics to further classify identified shared sweeps as hard or soft. Finally, we identified both previously-reported and novel shared sweep candidates from whole-genome sequences of global human populations. Previously-reported candidates include the well-characterized ancestral sweeps at LCT and SLC24A5 in Indo-European populations, as well as GPHN worldwide. Novel candidates include an ancestral sweep at RGS18 in sub-Saharan African populations involved in regulating the platelet response and implicated in sudden cardiac death, and a convergent sweep at C2CD5 between European and East Asian populations that may explain their different insulin responses.

evolutionary biology↗

bioRxiv2018

Detection and classification of hard and soft sweeps from unphased genotypes by multilocus genotype identity

Positive natural selection can lead to a decrease in genomic diversity at the selected site and at linked sites, producing a characteristic signature of elevated expected haplotype homozygosity. These selective sweeps can be hard or soft. In the case of a hard selective sweep, a single adaptive haplotype rises to high population frequency, whereas multiple adaptive haplotypes sweep through the population simultaneously in a soft sweep, producing distinct patterns of genetic variation in the vicinity of the selected site. Measures of expected haplotype homozygosity have previously been used to detect sweeps in multiple study systems. However, these methods are formulated for phased haplotype data, typically unavailable for nonmodel organisms, and may have reduced power to detect soft sweeps due to their increased genetic diversity relative to hard sweeps. To address these limitations, we applied the H12 and H2/H1 statistics of Garud et al. [2015] to unphased multilocus genotypes, denoting them as G12 and G2/G1. G12 (and the more direct expected homozygosity analogue to H12, denoted G123) has comparable power to H12 for detecting both hard and soft sweeps. G2/G1 can be used to classify hard and soft sweeps analogously to H2/H1, conditional on a genomic region having high G12 or G123 values. The reason for this power is that under random mating, the most frequent haplotypes will yield the most frequent multilocus genotypes. Simulations based on parameters compatible with our recent understanding of human demographic history suggest that expected homozygosity methods are best suited for detecting recent sweeps, and increase in power under recent population expansions. Finally, we find candidates for selective sweeps within the 1000 Genomes CEU, YRI, GIH, and CHB populations, which corroborate and complement existing studies.

evolutionary biology↗