Biology subjects

Huber, C. D.

Publications and source records attributed to Huber, C. D..

At least 19 recordsLinked to original sources

bioRxiv2026

Variation and selection at predicted G-quadruplexes across the human pangenome

G-quadruplexes (G4s), non-canonical DNA structures whose sequence motifs occupy approximately 1% of the human genome, are important for myriad cellular functions, including regulating transcription and replication. Yet they also contribute to genomic instability by increasing mutations and structural variation. Despite their significance, G4 motifs have not been studied in detail across multiple human genomes. Here, we conducted a comprehensive analysis of presence/absence and sequence variation, measured selection strength, and evaluated gene expression regulation potential for predicted G4s (pG4s) across population groups in the second release of the Human Pangenome Reference Consortium dataset, comprising high-quality, near-telomere-to-telomere diploid genomes from 231 individuals worldwide, along with three reference assemblies. Across the human pangenome, we identified over 353 million pG4s, including 1.15 million pG4s absent from reference assemblies but shared across other haplotypes. Our analysis revealed that pG4 sharing patterns recapitulate human population structure: African individuals displayed lower levels of pG4 sharing than non-Africans, whereas East Asian individuals exhibited higher levels of sharing. By analyzing the site frequency spectrum across various genomic annotations, we computed and compared selection coefficients (Sd) at pG4 vs. non-pG4 sites. As expected, the strongest purifying selection (Sd [≥] 10) was detected at protein-coding exons, where pG4 sites had similar or lower selection coefficients compared with those for pG4 sites. Strikingly, this pattern reversed at regulatory regions: although purifying selection was weaker overall at promoters, introns, enhancers, and replication origins (1 [≤] Sd < 10), pG4 sites at these regions experienced stronger selection than non-pG4 sites--suggesting that pG4s play functional roles outside coding sequences. Additionally, by integrating pG4 data with long-read transcriptome data profiles from this large cohort, we found that pG4s located at promoters and at (or near) exon-intron junctions may influence variation in gene expression levels and transcript isoforms, respectively, across the human pangenome individuals. Leveraging extensive population-scale data, our research illuminates the fundamental importance and functional relevance of G4s across human genomes.

genomics↗

bioRxiv2026

Haplotype-based models improve sweep detection in ancient populations with complex demography

Identifying signatures of positive selection in humans is complicated by demographic processes such as bottlenecks, migration and admixture, all of which can distort or obscure the genomic patterns produced by selective sweeps. Ancient DNA offers a direct window into past allele and haplotype frequencies, yet most sweep scans in ancient populations rely on allele-frequency or site frequency spectrum (SFS) summaries, with limited use of haplotype-based approaches. Here, we evaluate the performance of haplotype and SFS-based methods for detecting selective sweeps under demographic scenarios that reflect the complex history of ancient and modern Europeans. We extend the haplotype-based likelihood framework saltiLASSI to accommodate pseudohaploid ancient genomes, enabling the use of truncated haplotype frequency spectra and their spatial decay to detect sweeps without requiring phased data. Using forward-in-time simulations, we examine sweeps of varying ages, two pulses of admixture with different source proportions, and cases where selection continues or ceases after admixture. We compare saltiLASSI to a widely used SFS-based approach (SweepFinder2). Our results show that haplotype-based likelihood models retain higher power than SFS methods in admixed populations, particularly when sweep haplotypes are introduced through migration or when selection has not had sufficient time to regenerate a clear SFS signature after admixture. These findings highlight the promise of haplotype-based inference for ancient DNA and demonstrate how model-based approaches can improve the detection of historical selective sweeps in populations with complex demographic histories.

genetics↗

bioRxiv2026

Seasonal fluctuations in fitness result in severe reductions in effective population size

Genetic evidence for fluctuating selection has begun to accumulate for different species over the past few decades, especially for the Drosophila genus where studies have reported hundreds of loci undergoing putatively adaptive oscillations across successive seasons. However, most theoretical and simulation studies of fluctuating selection have relied on abstract or weakly parameterized models, making it difficult to assess their relevance for natural populations. In this study, we simulate multilocus seasonally fluctuating selection under a recently developed model and examine its effect on the variance effective population size (Ne) at a genome-wide scale. By recapitulating genomic, demographic, and evolutionary parameters from natural Drosophila populations in our simulations, we were able to reproduce allele frequency oscillations reported in recent studies and show that these lead to [~]50% genome-wide reductions in Ne. We also demonstrate that Ne reductions are well predicted by the maximum frequency amplitude among all adaptively fluctuating loci, and that the frequency amplitudes are largely determined by the number of adaptively fluctuating loci and the strength of their epistatic interactions. Our results demonstrate that fluctuating selection can substantially reduce effective population size and underscore the importance of temporally variable selection in shaping genome-wide patterns of variation beyond classical models. Article SummaryGenetic studies of fluctuating selection in natural populations have grown steadily over the past decade, with reports suggesting that hundreds of loci undergo adaptive oscillations over seasonal timescales in cosmopolitan Drosophila populations. By simulating seasonally fluctuating selection under a recently developed model and ecological scenarios informed by published studies, the authors show that this mode of selection can reduce effective population size by [~]50%, with the magnitude of the reduction correlated with the locus exhibiting the largest allele frequency fluctuations. These findings highlight fluctuating selection as an important factor shaping genome-wide patterns of genetic variation and effective population size.

evolutionary biology↗

bioRxiv2026

Simulations reveal hybridization in Caribbean Acropora restoration poses low risk of genetic swamping but limited potential for adaptive introgression

Severe global declines in coral populations have driven growing demand for human intervention and restoration. One goal of restoration is to repopulate reef ecosystems through outplanting, which requires detailed understanding of target systems. However, long term ecological and reproductive data from interventions remain scarce. An exception to this are the critically endangered Caribbean corals, Acropora palmata and A. cervicornis, which have been central to restoration efforts in the region. These species serve as a unique case study due to the abundance of published data spanning ecology, and reproductive biology. In the wild, these species can cross to form an F1 hybrid, A. prolifera, though it is rarely used in restoration. It remains unclear whether A. prolifera is an evolutionary dead-end competing with its parents, or a potential bridge enabling genetic exchange via backcrossing. To evaluate benefits and risks of restoration among Caribbean Acropora, we developed a two-dimensional agent-based simulation using reproductive and ecological data to model realistic reef dynamics. Our model suggests the hybrid can facilitate introgression between parentals without outcompeting them. Yet, such introgression is too limited for large-scale or beneficial ancestry transfer except under ecologically unrealistic conditions or timescales significantly longer than those relevant for management. Thus, our model suggests that the risks of genetic swamping may be overstated, whereas hopes for adaptive introgression are also low, underscoring the value of simulations for generating long-term ecological and evolutionary insights relevant to coral restoration.

bioinformatics↗

bioRxiv2025

The distribution of fitness effects varies phylogenetically across animals

1The distribution of fitness effects (DFE) describes the selection coefficients (s) of newly arising mutations and fundamentally influences population genetic processes. However, the extent and mechanisms of DFE variation have not been systematically investigated across species with divergent phylogenetic histories and ecological functions. Here, we inferred the DFE in natural populations of eleven animal (sub)species, including humans, mice, fin whales, vaquitas, wolves, collared flycatchers, pied flycatchers, halictid bees, Drosophila, and mosquitoes. We find that the DFE co-varies with phylogeny, where the expected mutation effects are more similar in closely related species (Pagels{lambda} = 0.84, P = 0.01). Additionally, mammals have a higher proportion of strongly deleterious mutations (22% to 47% in mammals; 0.0% to 5.4% in insects and birds) and a lower proportion of weakly deleterious mutations than insects and birds. Population size is significantly negatively correlated with the expected impact of new deleterious mutations (PGLS{lambda}, P = 0.03), and the proportion of new beneficial mutations ([Formula], P < 0.001). These findings align with Fishers Geometric Model (FGM), which defines organismal complexity as the number of phenotypes under selection. Consistent with the FGMs predictions, we observe that mutations are more deleterious in complex organisms, while beneficial mutations occur more frequently in smaller populations to compensate for the drift load. Our study demonstrates strong phylogenetic constraints in the evolution of a fundamental population genetics parameter, and proposes that, through mechanisms of global epistasis, long-term population size and organismal complexity drive variation in the DFE across animals. 2 Significance StatementUnderstanding how mutations affect fitness is fundamental in evolution, but little is known about how and why the distribution of fitness effects (DFE) varies across species. In this study, we examine the DFE in diverse animal populations and show that closely related species exhibit similar patterns of mutation effects, with new mutations being more deleterious in mammals compared with birds and insects. Our findings corroborate Fishers Geometric Model, which explains the variation in the DFE across species as a function of organismal complexity and long-term population size. By connecting organismal complexity and population size with the DFE, we offer a phylogenetic view into the selective forces shaping species adaptation and evolution.

evolutionary biology↗

bioRxiv2025

Accessible, realistic genome simulation with selection using stdpopsim

Selection is a fundamental evolutionary force that shapes patterns of genetic variation across species. However, simulations incorporating realistic selection along heterogeneous genomes in complex demographic histories are challenging, limiting our ability to benchmark statistical methods aimed at detecting selection and to explore theoretical predictions. stdpopsim is a community-maintained simulation library that already provides an extensive catalog of species-specific population genetic models. Here we present a major extension to the stdpopsim framework that enables simulation of various modes of selection, including background selection, selective sweeps, and arbitrary distributions of fitness effects (DFE) acting on annotated subsets of the genome (for instance, exons). This extension maintains stdpopsims core principles of reproducibility and accessibility while adding support for species-specific genomic annotations and published DFE estimates. We demonstrate the utility of this framework by comparing methods for demographic inference, DFE estimation, and selective sweep detection across several species and scenarios. Our results demonstrate the robustness of demographic inference methods to selection on linked sites, reveal the sensitivity of DFE-inference methods to model assumptions, and show how genomic features, like recombination rate and functional sequence density, influence power to detect selective sweeps. This extension to stdpopsim provides a powerful new resource for the population genetics community to explore the interplay between selection and other evolutionary forces in a reproducible, user-friendly framework.

evolutionary biology↗

bioRxiv2025

Patterns of population structure and genetic variation within the Saudi Arabian population

Both the demographic history and cultural practices influence the pattern of genetic variation, sometimes in opposing manners. For Saudi Arabians, being situated at the hub connecting multiple continents is expected to increase heterogeneity and diversity due to cross-continental migrations. On the other hand, cultural practices such as endogamy to promote social stability may have also promoted regional isolations and reduced heterogeneity. To better understand the genomic impact of these potentially opposing forces, we genotyped and sequenced 3,352 and 302 individuals, respectively, from Saudi Arabians to study their population structure and admixture history, and patterns of genetic variation. We identified twelve genetic sub-clusters that correlated with geographical regions, differentiated by distinct components of ancestry based on comparisons to modern and ancient DNA references. These sub-clusters also showed variation across ranges of the genome covered in runs of homozygosity, reflecting potential differences in kinship or marital practices, as well as differences in population size changes over time. Using 25,488,981 variants found in whole genome sequencing, we found that the Saudi do not show the depletion of rare alleles typically observed in isolated populations, though they do show the expected pattern of enrichment of alleles bioinformatically annotated as deleterious when compared to Africans/African Americans and Non-Finnish Europeans from gnomAD. Saudi sub-clusters with greater inbreeding and lower effective population sizes showed greater enrichment of deleterious alleles as well. Taken together, our results suggest that Saudis history and culture impact its pattern of genetic variation and potentially to the population health. We also made available the allele frequency estimates of alleles discovered in our samples so to start a foundation on which to interpret medical- and pharmaco-genomic findings from these populations.

evolutionary biology↗

bioRxiv2024

Apollo: A comprehensive GPU-powered within-host simulator for viral evolution and infection dynamics across population, tissue, and cell

Modern sequencing instruments bring unprecedented opportunity to study within-host viral evolution in conjunction with viral transmissions between hosts. However, no computational simulators are available to assist the characterization of within-host dynamics. This limits our ability to interpret epidemiological predictions incorporating within-host evolution and to validate computational inference tools. To fill this need we developed Apollo, a GPU-accelerated, out-of-core tool for within-host simulation of viral evolution and infection dynamics across population, tissue, and cellular levels. Apollo is scalable to hundreds of millions of viral genomes and can handle complex demographic and population genetic models. Apollo can replicate real within-host viral evolution; accurately recapturing observed viral sequences from an HIV cohort derived from initial population-genetic configurations. For practical applications, using Apollo-simulated viral genomes and transmission networks, we validated and uncovered the limitations of a widely used viral transmission inference tool.

bioinformatics↗

bioRxiv2024

The European Neolithic Expansion: A Model Revealing Intense Assortative Mating and Restricted Cultural Transmission

The Neolithic revolution initiated a pivotal change in human society, marking the shift from foraging to farming. The underlying mechanisms of agricultural expansion are debated, primarily between cultural diffusion (knowledge and practices transfer) and demic diffusion, or people migration and replacement. Ancient DNA analyses reveal significant ancestry changes during Europes Neolithic transition, suggesting primarily demic expansion. However, the presence of 10-15% hunter-gatherer ancestry in modern Europeans indicates cultural transmission and non-assortative mating were additional contributing factors. We integrate mathematical models, agent-based simulations, and ancient DNA analysis to dissect and quantify the roles of cultural diffusion and assortative mating in farmings expansion. Our findings indicate limited cultural transmission and predominantly within-group mating. Additionally, we challenge the assumption that demic spread always leads to ancestry turnover. This underscores the need to reassess prehistoric cultural expansions and offers new insights into early agricultural society through the integration of ancient DNA with archaeological models.

genomics↗

bioRxiv2023

Testing Times: Challenges in Disentangling Admixture Histories in Recent and Complex Demographies

Paleogenomics has expanded our knowledge of human evolutionary history. Since the 2020s, the study of ancient DNA has increased its focus on reconstructing the recent past. However, the accuracy of paleogenomic methods in answering questions of historical and archaeological importance amidst the increased demographic complexity and decreased genetic differentiation within the historical period remains an open question. We used two simulation approaches to evaluate the limitations and behavior of commonly used methods, qpAdm and the f3-statistic, on admixture inference. The first is based on branch-length data simulated from four simple demographic models of varying complexities and configurations. The second, an analysis of Eurasian history composed of 59 populations using whole-genome data modified with ancient DNA conditions such as SNP ascertainment, data missingness, and pseudo-haploidization. We show that under conditions resembling historical populations, qpAdm can identify a small candidate set of true sources and populations closely related to them. However, in typical ancient DNA conditions, qpAdm is unable to further distinguish between them, limiting its utility for resolving fine-scaled hypotheses. Notably, we find that complex gene-flow histories generally lead to improvements in the performance of qpAdm and observe no bias in the estimation of admixture weights. We offer a heuristic for admixture inference that incorporates admixture weight estimate and P-values of qpAdm models, and f3-statistics to enhance the power to distinguish between multiple plausible candidates. Finally, we highlight the future potential of qpAdm through whole-genome branch-length f2-statistics, demonstrating the improved demographic inference that could be achieved with advancements in f-statistic estimations.

genomics↗

bioRxiv2023

A likelihood-based framework for demographic inference from genealogical trees

The demographic history of a population drives the pattern of genetic variation and is encoded in the gene-genealogical trees of the sampled alleles. However, existing methods to infer demographic history from genetic data tend to use relatively low-dimensional summaries of the genealogy, such as allele frequency spectra. As a step toward capturing more of the information encoded in the genome-wide sequence of genealogical trees, here we propose a novel framework called the genealogical likelihood (gLike), which derives the full likelihood of a genealogical tree under any hypothesized demographic history. Employing a graph-based structure, gLike summarizes across independent trees the relationships among all lineages in a tree with all possible trajectories of population memberships through time and efficiently computes the exact marginal probability under a parameterized demographic model. Through extensive simulations and empirical applications on populations that have experienced multiple admixtures, we showed that gLike can accurately estimate dozens of demographic parameters when the true genealogy is known, including ancestral population sizes, admixture timing, and admixture proportions. Moreover, when using genealogical trees inferred from genetic data, we showed that gLike outperformed conventional demographic inference methods that leverage only the allele-frequency spectrum and yielded parameter estimates that align with established historical knowledge of the past demographic histories for populations like Latino Americans and Native Hawaiians. Furthermore, our framework can trace ancestral histories by analyzing a sample from the admixed population without proxies for its source populations, removing the need to sample ancestral populations that may no longer exist. Taken together, our proposed gLike framework harnesses underutilized genealogical information to offer exceptional sensitivity and accuracy in inferring complex demographies for humans and other species, particularly as estimation of genome-wide genealogies improves.

genetics↗

bioRxiv2023

False discovery rates of qpAdm-based screens for genetic admixture

qpAdm is a statistical tool that is often used for testing large sets of alternative admixture models for a target population. Despite its popularity, qpAdm remains untested on two-dimensional stepping-stone landscapes and in situations with low pre-study odds (low ratio of true to false models). We tested high-throughput qpAdm protocols with typical properties such as number of source combinations per target, model complexity, model feasibility criteria, etc. Those protocols were applied to admixture-graph-shaped and stepping-stone simulated histories sampled randomly or systematically. We demonstrate that false discovery rates of high-throughput qpAdm protocols exceed 50% for many parameter combinations since: 1) pre-study odds are low and fall rapidly with increasing model complexity; 2) complex migration networks violate the assumptions of the method, hence there is poor correlation between qpAdm p-values and model optimality, contributing to low but non-zero false positive rate and low power; 3) although admixture fraction estimates between 0 and 1 are largely restricted to symmetric configurations of sources around a target, a small fraction of asymmetric highly non-optimal models have estimates in the same interval, contributing to the false positive rate. We also re-interpret large sets of qpAdm models from two studies in terms of source-target distance and symmetry and suggest improvements to qpAdm protocols: 1) temporal stratification of targets and proxy sources in the case of admixture-graph-shaped histories; 2) focused exploration of few models for increasing pre-study odds; dense landscape sampling for increasing power and stringent conditions on estimated admixture fractions for decreasing the false positive rate. Article SummaryProliferation in the archaeogenetic literature of protocols for detection of admixed groups based a so-called qpAdm algorithm became disconnected from performance testing: the only extensive study of qpAdm on simulated data showed that it performs well under an unrealistically simple demographic scenario. We found that false discoveries of gene flows by qpAdm on a collection of random admixture-graph-shaped histories and on complex stepping-stone landscapes are very common and provide guidelines for design of qpAdm protocols in archaeogenetic studies.

genetics↗

bioRxiv2023

CATE: A fast and scalable CUDA implementation to conduct highly parallelized evolutionary tests on large scale genomic data.

Statistical tests for molecular evolution provide quantifiable insights into the selection pressures that govern a genomes evolution. Increasing sample sizes used for analysis leads to higher statistical power. However, this requires more computational nodes or longer computational time. CATE (CUDA Accelerated Testing of Evolution) is a computational solution to this problem comprised of two main innovations. The first is a file organization system coupled with a novel search algorithm and the second is a large-scale parallelization of algorithms using both GPU and CPU. CATE is capable of conducting evolutionary tests such as Tajimas D, Fu and Lis, and Fay and Wus test statistics, McDonald-Kreitman Neutrality Index, Fixation Index, and Extended Haplotype Homozygosity. CATE is magnitudes faster than standard tools with benchmarks estimating it being on average over 180 times faster. For instance, CATE processes all 54,849 human genes for all 22 autosomal chromosomes across the five super populations present in the 1000 Genomes Project in less than thirty minutes while counterpart software took 3.62 days. This proven framework has the potential to be adapted for GPU-accelerated large-scale parallel analyses of many evolutionary and genomic analyses.

bioinformatics↗

bioRxiv2021

Standing genetic variation fuels rapid evolution of herbicide resistance in blackgrass

Repeated herbicide applications exert enormous selection on blackgrass (Alopecurus myosuroides), a major weed in cereal crops of the temperate climate zone including Europe. This inadvertent large-scale experiment gives us the opportunity to look into the underlying genetic mechanisms and evolutionary processes of rapid adaptation, which can occur both through mutations in the direct targets of herbicides and through changes in other, often metabolic, pathways, known as non-target-site resistance. How much either type of adaptation relies on de novo mutations versus pre-existing standing variation is important for developing strategies to manage herbicide resistance. We generated a chromosome-level reference genome for A. myosuroides for population genomic studies of herbicide resistance and genome-wide diversity across Europe in this species. Bulked-segregant analysis evidenced that non-target-site resistance has a complex genetic architecture. Through empirical data and simulations, we showed that, despite its simple genetics, target-site resistance mainly results from standing genetic variation, with only a minor role for de novo mutations.

evolutionary biology↗

bioRxiv2020

An ancient coronavirus-like epidemic drove adaptation in East Asians from 25,000 to 5,000 years ago

The current SARS-CoV-2 pandemic has emphasized the vulnerability of human populations to novel viral pressures, despite the vast array of epidemiological and biomedical tools now available. Notably, modern human genomes contain evolutionary information tracing back tens of thousands of years, which may help identify the viruses that have impacted our ancestors - pointing to which viruses have future pandemic potential. Here, we apply evolutionary analyses to human genomic datasets to recover selection events involving tens of human genes that interact with coronaviruses, including SARS-CoV-2, that likely started more than 20,000 years ago. These adaptive events were limited to the population ancestral to East Asian populations. Multiple lines of functional evidence support an ancient viral selective pressure, and East Asia is the geographical origin of several modern coronavirus epidemics. An arms race with an ancient coronavirus, or with a different virus that happened to use similar interactions as coronaviruses with human hosts, may thus have taken place in ancestral East Asian populations. By learning more about our ancient viral foes, our study highlights the promise of evolutionary information to better predict the pandemics of the future. Importantly, adaptation to ancient viral epidemics in specific human populations does not necessarily imply any difference in genetic susceptibility between different human populations, and the current evidence points toward an overwhelming impact of socioeconomic factors in the case of COVID-19.

evolutionary biology↗

bioRxiv2020

Ancient human genomes reveal a hidden history of strong selection in Eurasia

The role of natural selection in shaping biological diversity is an area of intense interest in modern biology. To date, studies of positive selection have primarily relied upon genomic datasets from contemporary populations, which are susceptible to confounding factors associated with complex and often unknown aspects of population history. In particular, admixture between diverged populations can distort or hide prior selection events in modern genomes, though this process is not explicitly accounted for in most selection studies despite its apparent ubiquity in humans and other species. Through analyses of ancient and modern human genomes, we show that previously reported Holocene-era admixture has masked more than 50 historic hard sweeps in modern European genomes. Our results imply that this canonical mode of selection has likely been underappreciated in the evolutionary history of humans and suggests that our current understanding of the tempo and mode of selection in natural populations may be quite inaccurate.

genomics↗

bioRxiv2019

Population genetic models of GERP scores suggest pervasive turnover of constrained sites across mammalian evolution

Comparative genomic approaches have been used to identify sites where mutations are under purifying selection and of functional consequence by searching for sequences that are conserved across distantly related species. However, the performance of these approaches has not been rigorously evaluated under population genetic models. Further, short-lived functional elements may not leave a footprint of sequence conservation across many species. Here, we use simulations to study how one measure of conservation, the GERP score, relates to the strength of selection (Nes). We show that the GERP score is related to the strength of purifying selection. However, changes in selection coefficients or functional elements over time (i.e. functional turnover) can strongly affect the GERP distribution, leading to unexpected relationships between GERP and Nes. Further, we show that for functional elements that have a high turnover rate, the optimal tree size is not necessarily the largest possible tree, and more turnover reduces the optimal tree size. Finally, we use the distribution of GERP scores across the human genome to compare models with and without turnover of sites where mutations under purifying selection. We show that mutations in 4.51% of the noncoding human genome are under purifying selection and that most of this sequence has likely experienced changes in selection coefficients throughout mammalian evolution.

genetics↗

bioRxiv2018

Stronger and higher proportion of beneficial amino acid changing mutations in humans compared to mice and flies

Quantifying and comparing the amount of adaptive evolution among different species is key to understanding evolutionary processes. Previous studies have shown differences in adaptive evolution across species; however, their specific causes remain elusive. Here, we use improved modeling of weakly deleterious mutations and the demographic history of the outgroup species and ancestral population and estimate that at least 20% of nonsynonymous substitutions between humans and an outgroup species were fixed by positive selection. This estimate is much higher than previous estimates, which did not correct for the sizes of the outgroup species and ancestral population. Next, we directly estimate the proportion and selection coefficients (p+ and s+, respectively) of newly arising beneficial nonsynonymous mutations in humans, mice, and Drosophila melanogaster by examining patterns of polymorphism and divergence. We develop a novel composite likelihood framework to test whether these parameters differ across species. Overall, we reject a model with the same p+ and s+ of beneficial mutations across species, and estimate that humans have a higher p+s+ compared to D. melanogaster and mice. We demonstrate that this result cannot be caused by biased gene conversion or hypermutable CpG sites. In summary, we find the proportion of beneficial mutations to be higher in humans than in D. melanogaster or mice, suggesting that organismal complexity, which increases the number of steps required in adaptive walks, may be a key predictor of the amount of adaptive evolution within a species.

evolutionary biology↗