It has recently been demonstrated that inference methods based on genealogical processes with recombination can reveal past population history in unprecedented detail. However, these methods scale poorly with sample size, which limits resolution in the recent past, and they require phased genomes, which contain switch errors that can catastrophically distort the inferred history. Here, we present SMC++, a new statistical tool capable of analyzing orders of magnitude more samples than existing methods, while requiring only unphased genomes (its results are independent of phasing). SMC++ can jointly infer population size histories and split times in diverged populations, and it employs a novel spline regularization scheme that greatly reduces estimation error. We apply SMC++ to analyze sequence data from over a thousand human genomes in Africa and Eurasia, hundreds of genomes from a Drosophila population in Africa, and tens of genomes from zebra finch and long-tailed finch populations in Australia.

Genome sequences from diverse human groups are needed to understand the structure of genetic variation in our species and the history of, and relationships between, different populations. We present 929 high-coverage genome sequences from 54 diverse human populations, 26 of which are physically phased using linked-read sequencing. Analyses of these genomes reveal an excess of previously undocumented common genetic variation private to southern Africa, central Africa, Oceania, and the Americas, but an absence of such variants fixed between major geographical regions. We also find deep and gradual population separations within Africa, contrasting population size histories between hunter-gatherer and agriculturalist groups in the past 10,000 years, and a contrast between single Neanderthal but multiple Denisovan source populations contributing to present-day human populations.

30Genome sequences from diverse human groups are needed to understand the structure of genetic variation in our species and the history of, and relationships between, different populations. We present 929 high-coverage genome sequences from 54 diverse human populations, 26 of which are physically phased using linked-read sequencing. Analyses of these genomes reveal an excess of previously undocumented private genetic variation in 35 southern and central Africa and in Oceania and the Americas, but an absence of fixed, private variants between major geographical regions. We also find deep and gradual population separations within Africa, contrasting population size histories between hunter-gatherer and agriculturalist groups in the last 10,000 years, a potentially major population growth episode after the peopling of the Americas, and a contrast between single Neanderthal but multiple 40Denisovan source populations contributing to present-day human populations. We also demonstrate benefits to the study of population relationships of genome sequences over ascertained array genotypes. These genome sequences are freely available as a resource with no access or analysis restrictions.

Despite broad agreement that the Americas were initially populated via Beringia, the land bridge that connected far northeast Asia with northwestern North America during the Pleistocene epoch, when and how the peopling of the Americas occurred remains unresolved. Analyses of human remains from Late Pleistocene Alaska are important to resolving the timing and dispersal of these populations. The remains of two infants were recovered at Upward Sun River (USR), and have been dated to around 11.5 thousand years ago (ka). Here, by sequencing the USR1 genome to an average coverage of approximately 17 times, we show that USR1 is most closely related to Native Americans, but falls basal to all previously sequenced contemporary and ancient Native Americans. As such, USR1 represents a distinct Ancient Beringian population. Using demographic modelling, we infer that the Ancient Beringian population and ancestors of other Native Americans descended from a single founding population that initially split from East Asians around 36 ± 1.5 ka, with gene flow persisting until around 25 ± 1.1 ka. Gene flow from ancient north Eurasians into all Native Americans took place 25-20 ka, with Ancient Beringians branching off around 22-18.1 ka. Our findings support a long-term genetic structure in ancestral Native Americans, consistent with the Beringian 'standstill model'. We show that the basal northern and southern Native American branches, to which all other Native Americans belong, diverged around 17.5-14.6 ka, and that this probably occurred south of the North American ice sheets. We also show that after 11.5 ka, some of the northern Native American populations received gene flow from a Siberian population most closely related to Koryaks, but not Palaeo-Eskimos, Inuits or Kets, and that Native American gene flow into Inuits was through northern and not southern Native American groups. Our findings further suggest that the far-northern North American presence of northern Native Americans is from a back migration that replaced or absorbed the initial founding population of Ancient Beringians.

The Yamnaya expansions from the western steppe into Europe and Asia during the Early Bronze Age (~3000 BCE) are believed to have brought with them Indo-European languages and possibly horse husbandry. We analyzed 74 ancient whole-genome sequences from across Inner Asia and Anatolia and show that the Botai people associated with the earliest horse husbandry derived from a hunter-gatherer population deeply diverged from the Yamnaya. Our results also suggest distinct migrations bringing West Eurasian ancestry into South Asia before and after, but not at the time of, Yamnaya culture. We find no evidence of steppe ancestry in Bronze Age Anatolia from when Indo-European languages are attested there. Thus, in contrast to Europe, Early Bronze Age Yamnaya-related migrations had limited direct genetic impact in Asia.

SARS-CoV-2 infection is characterized by peak viral load in the upper airway prior to or at the time of symptom onset, an unusual feature that has enabled widespread transmission of the virus and precipitated a global pandemic. How SARS-CoV-2 is able to achieve high titer in the absence of symptoms remains unclear. Here, we examine the upper airway host transcriptional response in patients with COVID-19 (n = 93), other viral (n = 41) or non-viral (n = 100) acute respiratory illnesses (ARIs). Compared with other viral ARIs, COVID-19 is characterized by a pronounced interferon response but attenuated activation of other innate immune pathways, including toll-like receptor, interleukin and chemokine signaling. The IL-1 and NLRP3 inflammasome pathways are markedly less responsive to SARS-CoV-2, commensurate with a signature of diminished neutrophil and macrophage recruitment. This pattern resembles previously described distinctions between symptomatic and asymptomatic viral infections and may partly explain the propensity for pre-symptomatic transmission in COVID-19. We further use machine learning to build 27-, 10- and 3-gene classifiers that differentiate COVID-19 from other ARIs with AUROCs of 0.981, 0.954 and 0.885, respectively. Classifier performance is stable across a wide range of viral load, suggesting utility in mitigating false positive or false negative results of direct SARS-CoV-2 tests.

The sample frequency spectrum (SFS), or histogram of allele counts, is an important summary statistic in evolutionary biology, and is often used to infer the history of population size changes, migrations, and other demographic events affecting a set of populations. The expected multipopulation SFS under a given demographic model can be efficiently computed when the populations in the model are related by a tree, scaling to hundreds of populations. Admixture, back-migration, and introgression are common natural processes that violate the assumption of a tree-like population history, however, and until now the expected SFS could be computed for only a handful of populations when the demographic history is not a tree. In this article, we present a new method for efficiently computing the expected SFS and linear functionals of it, for demographies described by general directed acyclic graphs. This method can scale to more populations than previously possible for complex demographic histories including admixture. We apply our method to an 8-population SFS to estimate the timing and strength of a proposed "basal Eurasian" admixture event in human history. We implement and release our method in a new open-source software package momi2.

A wide range of studies in population genetics have employed the sample frequency spectrum (SFS), a summary statistic which describes the distribution of mutant alleles at a polymorphic site in a sample of DNA sequences and provides a highly efficient dimensional reduction of large-scale population genomic variation data. Recently, there has been much interest in analyzing the joint SFS data from multiple populations to infer parameters of complex demographic histories, including variable population sizes, population split times, migration rates, admixture proportions, and so on. SFS-based inference methods require accurate computation of the expected SFS under a given demographic model. Although much methodological progress has been made, existing methods suffer from numerical instability and high computational complexity when multiple populations are involved and the sample size is large. In this paper, we present new analytic formulas and algorithms that enable accurate, efficient computation of the expected joint SFS for thousands of individuals sampled from hundreds of populations related by a complex demographic model with arbitrary population size histories (including piecewise-exponential growth). Our results are implemented in a new software package called momi (MOran Models for Inference). Through an empirical study we demonstrate our improvements to numerical stability and computational complexity.