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Summary ParagraphAlthough germline mutation rates and spectra can vary within and between species, genetic modifiers of these traits have long eluded detection. In this study, we searched for loci that influence germline mutagenesis using a uniquely powerful resource: a panel of recombinant inbred mouse lines known as the BXD, descended from the laboratory mouse strains C57BL/6J (B) and DBA/2J (D). Each BXD lineage has been maintained by brother-sister mating in the near absence of natural selection, accumulating de novo mutations for up to 50 years on a known genetic background that is a unique linear mosaic of B and D haplotypes. We show that mice inheriting D haplotypes at a quantitative trait locus (QTL) on chromosome 4 accumulate C>A germline mutations at a 50% higher rate than those inheriting B haplotypes, primarily due to the activity of a C>A-dominated mutational signature known as SBS18. The B and D QTL haplotypes encode different alleles of the DNA repair gene Mutyh, which underlies the heritable colorectal cancer syndrome in which SBS18 was first identified. The B and D Mutyh alleles are present in wild populations of Mus musculus domesticus, providing evidence that common genetic variation modulates germline mutagenesis in a model mammalian species.

Maintaining germline genome integrity is essential and enormously complex. Hundreds of proteins are involved in DNA replication and proofreading, and hundreds more are mobilized to repair DNA damage [1]. While loss-of-function mutations in any of the genes encoding these proteins might lead to elevated mutation rates,mutator alleleshave largely eluded detection in mammals.DNA replication and repair proteins often recognize particular sequence motifs or excise lesions at specific nucleotides. Thus, we might expect that the spectrum ofde novomutations — that is, the frequency of each individual mutation type (C>T, A>G, etc.) — will differ between genomes that harbor either a mutator or wild-type allele at a given locus. Previously, we used quantitative trait locus mapping to discover candidate mutator alleles in the DNA repair geneMutyhthat increased the C>A germline mutation rate in a family of inbred mice known as the BXDs [2, 3].In this study we developed a new method, called “inter-haplotype distance,” to detect alleles associated with mutation spectrum variation. By applying this approach to mutation data from the BXDs, we confirmed the presence of the germline mutator locus nearMutyhand discovered an additional C>A mutator locus on chromosome 6 that overlapsOgg1andMbd4, two DNA glycosylases involved in base-excision repair [4, 5]. The effect of a chromosome 6 mutator allele depended on the presence of a mutator allele nearMutyh, and BXDs with mutator alleles at both loci had even greater numbers of C>A mutations than those with mutator alleles at either locus alone.Our new methods for analyzing mutation spectra reveal evidence of epistasis between germline mutator alleles, and may be applicable to mutation data from humans and other model organisms.

SARS-CoV-2 evolves rapidly in part because of its high mutation rate. Here, we examine whether this mutational process itself has changed during viral evolution. To do this, we quantify the relative rates of different types of single-nucleotide mutations at 4-fold degenerate sites in the viral genome across millions of human SARS-CoV-2 sequences. We find clear shifts in the relative rates of several types of mutations during SARS-CoV-2 evolution. The most striking trend is a roughly 2-fold decrease in the relative rate of G→T mutations in Omicron versus early clades, as was recently noted by Ruis et al. (2022. Mutational spectra distinguish SARS-CoV-2 replication niches. bioRxiv, doi:10.1101/2022.09.27.509649). There is also a decrease in the relative rate of C→T mutations in Delta, and other subtle changes in the mutation spectrum along the phylogeny. We speculate that these changes in the mutation spectrum could arise from viral mutations that affect genome replication, packaging, and antagonization of host innate-immune factors, although environmental factors could also play a role. Interestingly, the mutation spectrum of Omicron is more similar than that of earlier SARS-CoV-2 clades to the spectrum that shaped the long-term evolution of sarbecoviruses. Overall, our work shows that the mutation process is itself a dynamic variable during SARS-CoV-2 evolution and suggests that human SARS-CoV-2 may be trending toward a mutation spectrum more similar to that of other animal sarbecoviruses.

‘The apportionment of human diversity’ (1972) is the most highly cited research article published by geneticist Richard Lewontin in his career. This study's primary result—that most genetic diversity in humans can be accounted for by within-population differences, not between-population differences—along with Lewontin's outspoken, politically charged interpretations thereof, has become foundational to the scientific and cultural discourse pertaining to human genetic variation. The article has an unusual bibliometric trajectory in that it is much more salient in the bibliographic record today compared to the first 20 years after its publication. Here, we highlight four factors that may have played a role in shaping the paper's fame: (i) citations in influential publications across several disciplines; (ii) Lewontin's own popular books and media appearances; (iii) the renaissance of population genetics research of the early 1990s; and (iv) the serendipitous collision of scientific progress, influential books and papers, and heated controversies around the year 1994. We conclude with an analysis of Twitter data to characterize the communities and conversations that continue to keep this study at the centre of discussions about race and genetics, prompting new challenges for scientists who have inherited Lewontin's legacy. This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’.

“The Apportionment of Human Diversity” (1972) is the most highly cited research article published by geneticist Richard Lewontin in his career. This study’s primary result—that most genetic diversity in humans can be accounted for by within-population differences, not between-population differences—along with Lewontin’s outspoken, politically-charged interpretations thereof, has become foundational to the scientific and cultural discourse pertaining to human genetic variation. The article has an unusual bibliometric trajectory in that it is much more salient in the bibliographic record today compared to the first 20 years after its publication. Here, we show how the paper’s fame was shaped by four factors: 1) citations in influential publications across several disciplines; 2) Lewontin’s own popular books and media appearances; 3) the renaissance of population genetics research of the early 1990s; and 4) the serendipitous collision of scientific progress, influential books/papers, and heated controversies in the year 1994. We conclude with an analysis of Twitter data to characterize the communities and conversations that continue to keep this study at the epicenter of discussions about race and genetics, prompting new challenges for scientists who have inherited Lewontin’s legacy.

In the past decade, several studies have estimated the human per-generation germline mutation rate using large pedigrees. More recently, estimates for various non-human species have been published. However, methodological differences among studies in detecting germline mutations and estimating mutation rates make direct comparisons difficult. Here, we describe the many different steps involved in estimating pedigree-based mutation rates, including sampling, sequencing, mapping, variant calling, filtering, and how to appropriately account for false-positive and false-negative rates. For each step, we review the different methods and parameter choices that have been used in the recent literature. Additionally, we present the results from a “Mutationathon”, a competition organized among five research labs to compare germline mutation rate estimates for a single pedigree of rhesus macaques. We report almost a two-fold variation in the final estimated rate among groups using different post-alignment processing, calling, and filtering criteria and provide details into the sources of variation across studies. Though the difference among estimates is not statistically significant, this discrepancy emphasizes the need for standardized methods in mutation rate estimations and the difficulty in comparing rates from different studies. Finally, this work aims to provide guidelines for computational and statistical benchmarks for future studies interested in identifying germline mutations from pedigrees.

Significance Population histories are encoded by genomic variation among modern individuals. Population genetic inference methods, all theoretically rooted in probabilistic population models, can recover complex demographic histories from genomic variation data. However, the mutation process is treated very simply in these models—usually as a single constant. Recent empirical findings show that the mutation process is complex and dynamic over a range of evolutionary timescales and thus, deserving of richer descriptions in population genetic models. Here, we show that complex mutation spectrum histories can be accommodated by extending classical theoretical tools. We develop mathematical optimization methods and software to infer both demographic history and mutation spectrum history, revealing human mutation signatures varying through time and global divergence of mutational processes., As populations boom and bust, the accumulation of genetic diversity is modulated, encoding histories of living populations in present-day variation. Many methods exist to decode these histories, and all must make strong model assumptions. It is typical to assume that mutations accumulate uniformly across the genome at a constant rate that does not vary between closely related populations. However, recent work shows that mutational processes in human and great ape populations vary across genomic regions and evolve over time. This perturbs the mutation spectrum (relative mutation rates in different local nucleotide contexts). Here, we develop theoretical tools in the framework of Kingman’s coalescent to accommodate mutation spectrum dynamics. We present mutation spectrum history inference (mushi), a method to perform nonparametric inference of demographic and mutation spectrum histories from allele frequency data. We use mushi to reconstruct trajectories of effective population size and mutation spectrum divergence between human populations, identify mutation signatures and their dynamics in different human populations, and calibrate the timing of a previously reported mutational pulse in the ancestors of Europeans. We show that mutation spectrum histories can be placed in a well-studied theoretical setting and rigorously inferred from genomic variation data, like other features of evolutionary history.

Genetic rescue (GR) is an important tool in conservation biology for the management of highly inbred populations. However, GR comes at a potential cost: the reduction in native genomic content. The degree to which adaptive evolution for new immigrant alleles lead to complete or partial replacement of the native genome, depends on recombination rates, genome size, effective population sizes, and distributions of fitness effects. As one of the most well-studied examples of an inbred species that exchanged genetic material with a less inbred species, we use Neanderthals and humans as an example. Using simulations, we show that for parameters realistic for these species, the increase in fitness caused by GR is mirrored by an almost proportional reduction in the fraction of the native genome preserved. The main reason is that the effect of selection will be strongest in the first few generations during which time there is only little break-up, by recombination, of the immigrant chromosomes. We also show that in the presence of a strong and short-lived bottleneck with recessive mutations, GR leads to a stronger increase in fitness than in the presence of additive effects or a more prolonged bottleneck.

Engagement with scientific manuscripts is frequently facilitated by Twitter and other social media platforms. As such, the demographics of a paper's social media audience provide a wealth of information about how scholarly research is transmitted, consumed, and interpreted by online communities. By paying attention to public perceptions of their publications, scientists can learn whether their research is stimulating positive scholarly and public thought. They can also become aware of potentially negative patterns of interest from groups that misinterpret their work in harmful ways, either willfully or unintentionally, and devise strategies for altering their messaging to mitigate these impacts. In this study, we collected 331,696 Twitter posts referencing 1,800 highly tweeted bioRxiv preprints and leveraged topic modeling to infer the characteristics of various communities engaging with each preprint on Twitter. We agnostically learned the characteristics of these audience sectors from keywords each user’s followers provide in their Twitter biographies. We estimate that 96% of the preprints analyzed are dominated by academic audiences on Twitter, suggesting that social media attention does not always correspond to greater public exposure. We further demonstrate how our audience segmentation method can quantify the level of interest from nonspecialist audience sectors such as mental health advocates, dog lovers, video game developers, vegans, bitcoin investors, conspiracy theorists, journalists, religious groups, and political constituencies. Surprisingly, we also found that 10% of the preprints analyzed have sizable (>5%) audience sectors that are associated with right-wing white nationalist communities. Although none of these preprints appear to intentionally espouse any right-wing extremist messages, cases exist in which extremist appropriation comprises more than 50% of the tweets referencing a given preprint. These results present unique opportunities for improving and contextualizing the public discourse surrounding scientific research., Analysis of Twitter data for 1,800 bioRxiv preprints to classify the online communities engaging with biological research enables the tracing of each preprint's exposure to various academic disciplines and non-specialist audiences, potentially indicating downstream scientific and societal impacts, and revealing that some preprints receive extraordinarily high engagement from white nationalist communities.

Booming and busting populations modulate the accumulation of genetic diversity, encoding histories of living populations in present-day variation. Many methods exist to decode these histories, and all must make strong model assumptions. It is typical to assume that mutations accumulate uniformly across the genome at a constant rate that does not vary between closely related populations. However, recent work shows that mutational processes in human and great ape populations vary across genomic regions and evolve over time. This perturbs themutation spectrum: the relative mutation rates in different local nucleotide contexts. Here, we develop theoretical tools in the framework of Kingman’s coalescent to accommodate mutation spectrum dynamics. We describemushi: a method to perform fast, nonparametric joint inference of demographic and mutation spectrum histories from allele frequency data. We usemushito reconstruct trajectories of effective population size and mutation spectrum divergence between human populations, identify mutation signatures and their dynamics in different human populations, and produce more accurate time calibration for a previously-reported mutational pulse in the ancestors of Europeans. We show that mutation spectrum histories can be productively incorporated in a well-studied theoretical setting, and rigorously inferred from genomic variation data like other features of evolutionary history.

There are many possible failure points in the transmission of genetic information that can produce heritable germline mutations. Once a mutation has been passed from parents to offspring for several generations, it can be difficult or impossible to identify its root cause; however, sometimes the nature of the ancestral and derived DNA sequences can provide mechanistic clues about a genetic change that happened hundreds or thousands of generations ago. Here, we review evidence that the sequence context 'spectrum' of germline mutagenesis has been evolving surprisingly rapidly over the history of humans and other species. We go on to discuss possible causal factors that might underlie rapid mutation spectrum evolution.

This article has been accepted for publication in Molecular Biology and Evolution. Published by Oxford University Press. Submitted manuscript version. Published version available at https://doi.org/10.1093/molbev/msy018. Recent genomic analyses have provided substantial evidence for past periods of gene flow from polar bears (Ursus maritimus) into Alaskan brown bears (Ursus arctos), with some analyses suggesting a link between climate change and genomic introgression. However, because it has mainly been possible to sample bears from the present day, the timing, frequency, and evolutionary significance of this admixture remains unknown. Here, we analyze genomic DNA from three additional and geographically distinct brown bear populations, including two that lived temporally close to the peak of the last ice age. We find evidence of admixture in all three populations, suggesting that admixture between these species has been common in their recent evolutionary history. In addition, analyses of ten fossil bears from the now-extinct Irish population indicate that admixture peaked during the last ice age, whereas brown bear and polar bear ranges overlapped. Following this peak, the proportion of polar bear ancestry in Irish brown bears declined rapidly until their extinction. Our results support a model in which ice age climate change created geographically widespread conditions conducive to admixture between polar bears and brown bears, as is again occurring today. We postulate that this model will be informative for many admixing species pairs impacted by climate change. Our results highlight the power of paleogenomics to reveal patterns of evolutionary change that are otherwise masked in contemporary data.

Great ape clades exhibit variation in the relative mutation rates of different three-base-pair genomic motifs, with closely related species having more similar mutation spectra than distantly related species. This pattern cannot be explained by classical demographic or selective forces, but imply that DNA replication fidelity has been perturbed in different ways on each branch of the great ape phylogeny. Here, we use whole-genome variation from 88 great apes to investigate whether these species’ mutation spectra are broadly differentiated across the entire genome, or whether mutation spectrum differences are driven by DNA compartments that have particular functional features or chromatin states. We perform principal component analysis and mutational signature deconvolution on mutation spectra ascertained from compartments defined by features including replication timing and ancient repeat content, finding evidence for consistent species-specific mutational signatures that do not depend on which functional compartments the spectra are ascertained from. At the same time, we find that many compartments have their own characteristic mutational signatures that appear stable across the great ape phylogeny. For example, in a mutation spectrum PCA compartmentalized by replication timing, the second PC explaining 21.2% of variation separates all species’ late-replicating regions from their early-replicating regions. Our results suggest that great ape mutation spectrum evolution is not driven by epigenetic changes that modify mutation rates in specific genomic regions, but instead by trans-acting mutational modifiers that affect mutagenesis across the whole genome fairly uniformly.SIGNIFICANCE STATEMENTAll heritable variation begins with damage or copying mistakes affecting the DNA of sperm, eggs, or embryos. Different DNA motifs can have different mutation rates, and these rates can evolve over time: the spectrum of mutability of three-base-pair motifs has evolved rapidly during great ape diversification. Here, we show that even as ape mutation spectra diverged from each other, ape genomes preserved a landscape of spatial mutation spectrum variation. We can thus deconvolute the mutational process into a mixture of fast-evolving signatures with uniform spatial distributions and conserved signatures that target specific regions. Our findings may ultimately help determine the factors, either genetic or environmental, that contribute to temporal and spatial variation in germline mutagenesis.

Traces of archaic hominin DNA persist in the human gene pool, but are systematically depleted around genes and other functionally important genomic regions. This suggests that many Neandertal and Denisovan alleles had harmful effects on hybrid fitness. We hypothesized that if some harmful effects were mediated by gene dysregulation in specific tissues, alleles previously flagged as archaic using a conditional random field (CRF) should be depleted from those tissues’ regulatory enhancers compared to “control” alleles matched for allele frequency and the strength of background selection. By this metric, both Neandertal and Denisovan variation appear depleted from enhancers, particularly enhancers that show pleiotropic activity across tissues. This depletion is driven by young archaic SNPs that the CRF confidently identifies as private to Neandertals or Denisovans; older variants that were likely present in both archaic species are not depleted from enhancers. We found that enhancer pleiotropy is not only a predictor of archaic SNP depletion, but also a predictor of intolerance to new mutations as measured by both phastCons scores and the frequency spectrum of African variation. In other respects, however, the landscape of selection against young archaic alleles appears qualitatively different from the landscape of ordinary purifying selection, suggesting that archaic alleles had a different distribution of fitness effects from ordinary new mutations. Most strikingly, fetal brain and muscle are the tissues most depleted of young archaic variation in their regulatory regions, but only brain enhancers appear commensurately intolerant to new mutations. In contrast, fetal muscle enhancers show no evidence of elevated purifying selection relative to other enhancers. This suggests that epistatic incompatibility between human and archaic alleles is needed to explain the degree of archaic variant depletion from fetal muscle enhancers, perhaps due to divergent selection for higher muscle mass in archaic hominins compared to humans.

Approximately 2-4% of the human genome is in non-Africans comprised of DNA intro- gressed from Neanderthals. Recent studies have shown that there is a paucity of introgressed DNA around functional regions, presumably caused by selection after introgression. This observation has been suggested to be a possible consequence of the accumulation of a large amount of Dobzhansky-Muller incompatibilities, i.e. epistatic effects between human and Neanderthal specific mutations, since the divergence of humans and Neanderthals approx. 400-600 kya. However, using previously published estimates of inbreeding in Neanderthals, and of the distribution of fitness effects from human protein coding genes, we show that the average Neanderthal would have had at least 40% lower fitness than the average human due to higher levels of inbreeding and an increased mutational load, regardless of the dominance coefficients of new mutations. Using simulations, we show that under the assumption of additive dominance effects, early Neanderthal/human hybrids would have experienced strong negative selection, though not so strong that it would prevent Neanderthal DNA from entering the human population. In fact, the increased mutational load in Neanderthals predicts the observed reduction in Neanderthal introgressed segments around protein coding genes, without any need to invoke epistasis. The simulations also predict that there is a residual Neanderthal derived mutational load in non-African humans, leading to an average fitness reduction of at least 0.5%. Although there has been much previous debate about the effects of the out-of-Africa bottleneck on mutational loads in non-Africans, the significant deleterious effects of Neanderthal introgression have hitherto been left out of this discussion, but might be just as important for understanding fitness differences among human populations. We also show that if deleterious mutations are recessive, the Neanderthal admixture fraction would gradually increase over time due to selection for Neanderthal haplotypes that mask human deleterious mutations in the heterozygous state. This effect of dominance heterosis might partially explain why adaptive introgression appears to be widespread in nature.

Inferring adaptation, migration and population history would be profoundly easier if we could use the genomes that we sequence to infer the true genealogical history of each locus. Two new papers bring us close to achieving this goal.

One way to study a genome is to read it like an instruction manual. It contains genes that are easily decoded into the protein building blocks of cells, as well as much more cryptic regulatory codes that dictate when and where each protein should be produced.

Recent genomic analyses have provided substantial evidence for past periods of gene flow from polar bears (Ursus maritimus) into Alaskan brown bears (Ursus arctos), with some analyses suggesting a link between climate change and genomic introgression. However, because it has only been possible to sample bears from the present day, the timing, frequency, and evolutionary significance of this admixture remains unknown. Here, we analyze genomic DNA from three additional and geographically distinct brown bear populations, including two that lived temporally close to the peak of the last ice age. We find evidence of admixture in all three populations, suggesting that admixture between these species has been common in their recent evolutionary history. In addition, analyses of ten fossil bears from the now-extinct Irish population indicate that admixture peaked during the last ice age, when brown bear and polar bear ranges overlapped. Following this peak, the proportion of polar bear ancestry in Irish brown bears declined rapidly until their extinction. Our results support a model in which ice age climate change created geographically widespread conditions conducive to admixture between polar bears and brown bears, as is again occurring today. We postulate that this model will be informative for many admixing species pairs impacted by climate change. Our results highlight the power of paleogenomes to reveal patterns of evolutionary change that are otherwise masked with only contemporary data.

DNA is a molecule that contains the information needed to build an organism. This information is stored as a code made up of four chemicals: adenine (A), guanine (G), cytosine (C), and thymine (T). Every time a cell divides and copies its DNA, it accidentally introduces ‘typos’ into the code, known as mutations. Most mutations are harmless, but some can cause damage. All cells have ways to proofread DNA, and the more resources are devoted to proofreading, the less mutations occur. Simple organisms such as bacteria use less energy to reduce mutations, because their genomes may tolerate more damage. More complex organisms, from yeast to humans, instead need to proofread their genomes more thoroughly. Recent research has shown that humans have a lower mutation rate than chimpanzees and gorillas, their closest living relatives. Humans and other apes copy and proofread their DNA with basically the same biological machinery as yeast, which is about a billion years old. Yet, humans and apes have only existed for a small fraction of this time, a few million years. Why then do humans need to replicate and proofread their DNA differently from apes, and could it be that the way mutations arise is still evolving? Previous research revealed that European people experience more mutations within certain DNA motifs (specifically, the DNA sequences ‘TCC’, ‘TCT’, ‘CCC’ and ‘ACC’) than Africans or East Asians do. Now, Harris (who conducted the previous research) and Pritchard have compared how various human ethnic groups accumulate mutations and how these processes differ in different groups. Statistical analysis of the genomes of thousands of people from all over the world did indeed show that the mutation rates of many different three-letter DNA motifs have changed during the past 20,000 years of human evolution. Harris and Pritchard report that when groups of humans left Africa and settled in isolated populations across different continents, each population quickly became better at avoiding mutations in some genomic contexts, but worse in others. This suggests that the risk of passing on harmful mutations to future generations is changing and evolving at an even faster rate than was originally suspected. The results suggest that every human ethnic group carries specific variants of the genes which ensure that DNA replication and repair are accurate. These differences appear to influence which types of mutations are frequently passed down to future generations. An important next step will be to identify the genetic variants that could be controlling mutational patterns and how they affect human health.

We introduce a method for comparing a test genome with numerous genomes from a reference population. Sites in the test genome are given a weight, w, that depends on the allele frequency, x, in the reference population. The projection of the test genome onto the reference population is the average weight for each x, w¯(x). The weight is assigned in such a way that, if the test genome is a random sample from the reference population, then w¯(x)=1. Using analytic theory, numerical analysis, and simulations, we show how the projection depends on the time of population splitting, the history of admixture, and changes in past population size. The projection is sensitive to small amounts of past admixture, the direction of admixture, and admixture from a population not sampled (a ghost population). We compute the projections of several human and two archaic genomes onto three reference populations from the 1000 Genomes project—Europeans, Han Chinese, and Yoruba—and discuss the consistency of our analysis with previously published results for European and Yoruba demographic history. Including higher amounts of admixture between Europeans and Yoruba soon after their separation and low amounts of admixture more recently can resolve discrepancies between the projections and demographic inferences from some previous studies.

About 2% of human genetic polymorphisms have been hypothesized to arise via multinucleotide mutations (MNMs), complex events that generate SNPs at multiple sites in a single generation. MNMs have the potential to accelerate the pace at which single genes evolve and to confound studies of demography and selection that assume all SNPs arise independently. In this paper, we examine clustered mutations that are segregating in a set of 1,092 human genomes, demonstrating that MNMs become enriched as large numbers of individuals are sampled. We leverage the size of the dataset to deduce new information about the allelic spectrum of MNMs, estimating the percentage of linked SNP pairs that were generated by simultaneous mutation as a function of the distance between the affected sites and showing that MNMs exhibit a high percentage of transversions relative to transitions. These findings are reproducible in data from multiple sequencing platforms. Among tandem mutations that occur simultaneously at adjacent sites, we find an especially skewed distribution of ancestral and derived dinucleotides, with $\textrm{GC}\to \textrm{AA}$, $\textrm{GA}\to \textrm{TT}$ and their reverse complements making up 36% of the total. These same mutations dominate the spectrum of tandem mutations produced by the upregulation of low-fidelity Polymerase $\zeta$ in mutator strains of S. cerevisiae that have impaired DNA excision repair machinery. This suggests that low-fidelity DNA replication by Pol $\zeta$ is at least partly responsible for the MNMs that are segregating in the human population, and that useful information about the biochemistry of MNM can be extracted from ordinary population genomic data. We incorporate our findings into a mathematical model of the multinucleotide mutation process that can be used to correct phylogenetic and population genetic methods for the presence of MNMs.

Throughout history, the population size of modern humans has varied considerably due to changes in environment, culture, and technology. More accurate estimates of population size changes, and when they occurred, should provide a clearer picture of human colonization history and help remove confounding effects from natural selection inference. Demography influences the pattern of genetic variation in a population, and thus genomic data of multiple individuals sampled from one or more present-day populations contain valuable information about the past demographic history. Recently, Li and Durbin developed a coalescent-based hidden Markov model, called the pairwise sequentially Markovian coalescent (PSMC), for a pair of chromosomes (or one diploid individual) to estimate past population sizes. This is an efficient, useful approach, but its accuracy in the very recent past is hampered by the fact that, because of the small sample size, only few coalescence events occur in that period. Multiple genomes from the same population contain more information about the recent past, but are also more computationally challenging to study jointly in a coalescent framework. Here, we present a new coalescent-based method that can efficiently infer population size changes from multiple genomes, providing access to a new store of information about the recent past. Our work generalizes the recently developed sequentially Markov conditional sampling distribution framework, which provides an accurate approximation of the probability of observing a newly sampled haplotype given a set of previously sampled haplotypes. Simulation results demonstrate that we can accurately reconstruct the true population histories, with a significant improvement over the PSMC in the recent past. We apply our method, called diCal, to the genomes of multiple human individuals of European and African ancestry to obtain a detailed population size change history during recent times.

As humans dispersed out of Africa they adapted to new environmental challenges, including changes in exposure to mutagenic solar radiation. Humans in temperate latitudes have acquired light skin that is relatively transparent to UV light, and some evidence suggests that their DNA damage response pathways have also experienced local adaptation. This raises the possibility that different populations have experienced different selective pressures affecting genome integrity. Here, I present evidence that the rate of a particular mutation type has recently increased in the European population, rising in frequency by 50% during the 40,000–80,000 y since Europeans began diverging from Asians. A comparison of SNPs private to Africa, Asia, and Europe in the 1000 Genomes data reveals that private European variation is enriched for the transition 5′-TCC-3′ → 5′-TTC-3′. Although it is not clear whether UV played a causal role in changing the European mutational spectrum, 5′-TCC-3′ → 5′-TTC-3′ is known to be the most common somatic mutation present in melanoma skin cancers, as well as the mutation most frequently induced in vitro by UV. Regardless of its causality, this change indicates that DNA replication fidelity has not remained stable even since the origin of modern humans and might have changed numerous times during our recent evolutionary history.

How and when the Americas were populated remains contentious. Using ancient and modern genome-wide data, we find that the ancestors of all present-day Native Americans, including Athabascans and Amerindians, entered the Americas as a single migration wave from Siberia no earlier than 23 thousand years ago (KYA), and after no more than 8,000-year isolation period in Beringia. Following their arrival to the Americas, ancestral Native Americans diversified into two basal genetic branches around 13 KYA, one that is now dispersed across North and South America and the other is restricted to North America. Subsequent gene flow resulted in some Native Americans sharing ancestry with present-day East Asians (including Siberians) and, more distantly, Australo-Melanesians. Putative ‘Paleoamerican’ relict populations, including the historical Mexican Pericúes and South American Fuego-Patagonians, are not directly related to modern Australo-Melanesians as suggested by the Paleoamerican Model.

In many areas of computational biology, hidden Markov models (HMMs) have been used to model local genomic features. In particular, coalescent HMMs have been used to infer ancient population sizes, migration rates, divergence times, and other parameters such as mutation and recombination rates. As more loci, sequences, and hidden states are added to the model, however, the runtime of coalescent HMMs can quickly become prohibitive. Here we present a new algorithm for reducing the runtime of coalescent HMMs from quadratic in the number of hidden time states to linear, without making any additional approximations. Our algorithm can be incorporated into various coalescent HMMs, including the popular method PSMC for inferring variable effective population sizes. Here we implement this algorithm to speed up our demographic inference method diCal, which is equivalent to PSMC when applied to a sample of two haplotypes. We demonstrate that the linear-time method can reconstruct a population size change history more accurately than the quadratic-time method, given similar computation resources. We also apply the method to data from the 1000 Genomes project, inferring a high-resolution history of size changes in the European population., 18 pages, 5 figures. To appear in the Proceedings of the 18th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2014). The final publication is available at link.springer.com

Polar bears are uniquely adapted to life in the High Arctic and have undergone drastic physiological changes in response to Arctic climates and a hyperlipid diet of primarily marine mammal prey. We analyzed 89 complete genomes of polar bear and brown bear using population genomic modeling and show that the species diverged only 479-343 thousand years BP. We find that genes on the polar bear lineage have been under stronger positive selection than in brown bears; nine of the top 16 genes under strong positive selection are associated with cardiomyopathy and vascular disease, implying important reorganization of the cardiovascular system. One of the genes showing the strongest evidence of selection, APOB, encodes the primary lipoprotein component of low-density lipoprotein (LDL); functional mutations in APOB may explain how polar bears are able to cope with life-long elevated LDL levels that are associated with high risk of heart disease in humans. PAPERCLIP

The origin of life is a fascinating problem for both theorists and experimentalists. In this chapter, we review some major mathematical ideas (quasispecies, the error threshold and ways around it, hypercycles, and recent work on the transition from chemical kinetics to replicator dynamics) and relevant experimental contexts. Such models are ideal launch points for mutually informative collaborations in this highly interdisciplinary field.

The expressions ϕ ( n ) + σ ( n ) − 3 n and ϕ ( n ) + σ ( n ) − 4 n are unusual among linear combinations of arithmetic functions in that they each vanish on a nonempty set of composite numbers. In 1966, Nicol proved that the set A : = { n | ( ϕ ( n ) + σ ( n ) ) / n ∈ N ⩾ 3 } contains 2 a ⋅ 3 ⋅ ( 2 a − 2 ⋅ 7 − 1 ) if and only if 2 a − 2 ⋅ 7 − 1 is prime and conjectured that A contains no odd integers. A 2008 paper by Luca and Sandor completely classifies the elements of A that have three distinct prime factors and observes that Nicol's conjecture holds for numbers with fewer than six distinct prime factors. In this paper we let A K denote the set of n ∈ A with exactly K distinct prime factors and present a computer-implementable algorithm that decides whether Nicol's conjecture holds for a given A K . Using this algorithm, we verify Nicol's conjecture for A 6 and completely classify the elements of A 4 . We prove that all but finitely many n ∈ A 4 have the form 2 a ⋅ 3 ⋅ p 3 ⋅ p 4 , and that all but finitely many n ∈ A 5 are divisible by 6 and not 9. In addition, we prove that every A K is contained in a finite union of sequences that each have the form { p 1 a 1 i ⋯ p k a k i ⋅ u ⋅ w i } i = 1 ∞ , where k ⩾ 1 , p 1 , … , p k are distinct primes, and each a j i as well as the least prime factor of w i go to infinity as i does.