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Population genetic models only provide coarse representations of real-world ancestry. We used a pedigree compiled from 4 million parish records and genotype data from 2276 French and 20,451 French Canadian individuals to finely model and trace French Canadian ancestry through space and time. The loss of ancestral French population structure and the appearance of spatial and regional structure highlights a wide range of population expansion models. Geographic features shaped migrations, and we find enrichments for migration, genetic, and genealogical relatedness patterns within river networks across regions of Quebec. Finally, we provide a freely accessible simulated whole-genome sequence dataset with spatiotemporal metadata for 1,426,749 individuals reflecting intricate French Canadian population structure. Such realistic population-scale simulations provide opportunities to investigate population genetics at an unprecedented resolution.

The recent proliferation of large scale genome-wide association studies (GWASs) has motivated the development of statistical methods for phenotype prediction using single nucleotide polymorphism (SNP) array data. These polygenic risk score (PRS) methods formulate the task of polygenic prediction in terms of a multiple linear regression framework, where the goal is to infer the joint effect sizes of all genetic variants on the trait. Among the subset of PRS methods that operate on GWAS summary statistics, sparse Bayesian methods have shown competitive predictive ability. However, most existing Bayesian approaches employ Markov Chain Monte Carlo (MCMC) algorithms for posterior inference, which are computationally inefficient and do not scale favorably with the number of SNPs included in the analysis. Here, we introduce Variational Inference of Polygenic Risk Scores (VIPRS), a Bayesian summary statistics-based PRS method that utilizes Variational Inference (VI) techniques to efficiently approximate the posterior distribution for the effect sizes. Our experiments with genome-wide simulations and real phenotypes from the UK Biobank (UKB) dataset demonstrated that variational approximations to the posterior are competitively accurate and highly efficient. When compared to state-of-the-art PRS methods, VIPRS consistently achieves the best or second best predictive accuracy in our analyses of 36 simulation configurations as well as 12 real phenotypes measured among the UKB participants of “White British” background. This performance advantage was higher among individuals from other ethnic groups, with an increase in R2 of up to 1.7-fold among participants of Nigerian ancestry for Low-Density Lipoprotein (LDL) cholesterol. Furthermore, given its computational efficiency, we applied VIPRS to a dataset of up to 10 million genetic markers, an order of magnitude greater than the standard HapMap3 subset used to train existing PRS methods. Modeling this expanded set of variants conferred significant improvements in prediction accuracy for a number of highly polygenic traits, such as standing height.

Uniform manifold approximation and projection (UMAP) has been rapidly adopted by the population genetics community to study population structure. It has become common in visualizing the ancestral composition of human genetic datasets, as well as searching for unique clusters of data, and for identifying geographic patterns. Here we give an overview of applications of UMAP in population genetics, provide recommendations for best practices, and offer insights on optimal uses for the technique.

Melanoma is an immunogenic cancer with a high response rate to immune checkpoint inhibitors (ICIs). It harbors a high mutation burden compared with other cancers and, as a result, has abundant tumor-infiltrating lymphocytes (TILs) within its microenvironment. However, understanding the complex interplay between the stroma, tumor cells, and distinct TIL subsets remains a substantial challenge in immune oncology. To properly study this interplay, quantifying spatial relationships of multiple cell types within the tumor microenvironment is crucial. To address this, we used cytometry time-of-flight (CyTOF) imaging mass cytometry (IMC) to simultaneously quantify the expression of 35 protein markers, characterizing the microenvironment of 5 benign nevi and 67 melanomas. We profiled more than 220,000 individual cells to identify melanoma, lymphocyte subsets, macrophage/monocyte, and stromal cell populations, allowing for in-depth spatial quantification of the melanoma microenvironment. We found that within pretreatment melanomas, the abundance of proliferating antigen-experienced cytotoxic T cells (CD8 + CD45RO + Ki67 + ) and the proximity of antigen-experienced cytotoxic T cells to melanoma cells were associated with positive response to ICIs. Our study highlights the potential of multiplexed single-cell technology to quantify spatial cell-cell interactions within the tumor microenvironment to understand immune therapy responses.

To identify candidate variants in

Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this, a large number of specialized simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and the tskit library. We summarize msprime’s many features, and show that its performance is excellent, often many times faster and more memory efficient than specialized alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.

Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this necessity, a large number of specialised simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introducemsprimeversion 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure andtskitlibrary. We summarisemsprime’s many features, and show that its performance is excellent, often many times faster and more memory efficient than specialised alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.

Efforts to build representative studies are defeated when scientists discard data from certain groups. Instead, researchers should work to balance statistical needs with fairness. Efforts to build representative studies are defeated when scientists discard data from certain groups. Instead, researchers should work to balance statistical needs with fairness.

Linkage disequilibrium is used to infer evolutionary history and to identify regions under selection or associated with a given trait. In each case, we require accurate estimates of linkage disequilibrium from sequencing data. Unphased data presents a challenge because the co-occurrence of alleles at different loci is ambiguous. Commonly used estimators for the common statisticsr2andD2exhibit large and variable upward biases that complicate interpretation and comparison across cohorts. Here, we show how to find unbiased estimators for a wide range of two-locus statistics, includingD2, for both single and multiple randomly mating populations. These provide accurate estimates over three orders of magnitude in LD. We also use these estimators to construct an estimator forr2that is less biased than commonly used estimators, but nevertheless argue for usingrather thanr2for population size estimates.

Recent reports have identified differences in the mutational spectra across human populations. Although some of these reports have been replicated in other cohorts, most have been reported only in the 1000 Genomes Project (1kGP) data. While investigating an intriguing putative population stratification within the Japanese population, we identified a previously unreported batch effect leading to spurious mutation calls in the 1kGP data and to the apparent population stratification. Because the 1kGP data are used extensively, we find that the batch effects also lead to incorrect imputation by leading imputation servers and a small number of suspicious GWAS associations. Lower quality data from the early phases of the 1kGP thus continue to contaminate modern studies in hidden ways. It may be time to retire or upgrade such legacy sequencing data.

Linkage-Disequilibrium Score Regression (LDSC) is a popular framework for analyzing GWAS summary statistics that allows for estimating SNP heritability, confounding, and functional enrichment of genetic variants with different annotations. Recent work has highlighted the influence of implicit and explicit assumptions of the model on the biological interpretation of the results. In this work, we explored a formulation of LDSC that replaces the r2 measure of LD with a recently-proposed unbiased estimator of the D2 statistic. In addition to modest statistical difference across estimators, this derivation highlighted implicit and unrealistic assumptions about the relationship between allele frequency, effect size, and annotation status. We carry out a systematic comparison of alternative LDSC formulations by applying them to summary statistics from 47 GWAS traits. Our results show that commonly used models likely underestimate functional enrichment. These results highlight the importance of calibrating the LDSC model to achieve a more robust understanding of polygenic traits.

1AbstractThe fate of mutations and the genetic load of populations depend on the relative importance of genetic drift and natural selection. In addition, the accuracy of numerical models of evolution depends on the strength of both selection and drift: strong selection breaks the assumptions of the nearly neutral model, and drift coupled with large sample sizes breaks Kingman’s coalescent model.Thus, the regime with strong selection and large sample sizes, relevant to the study of pathogenic variation, appears particularly daunting. Surprisingly, we find that the interplay of drift and selection in that regime can be used to define asymptotically closed recursions for the distribution of allele frequencies that are accurate well beyond the strong selection limit.Selection becomes more analytically tractable when the sample size n is larger than twice the population-scaled selection coefficient: n ≥ 2Ns (4Ns in diploids). That is, when the expected number of coalescent events in the sample is larger than the number of selective events. We construct the relevant transition matrices, show how they can be used to accurately compute distributions of allele frequencies, and show that the distribution of deleterious allele frequencies is sensitive to details of the evolutionary model.

We thank Aaron Ragsdale, Dominic Nelson, and Simon Gravel for identifying a coding errorin the setup of the demographic simulations shown in Figure 5 that changed the magnitude of simulation results regarding population differences. The interpretation of Figure 5 that polygenic risk scores do not generalize well across populations remains the same, although the magnitude of differences shown in Figure 5C are considerably diminished (see Ragsdale et al Figure 2 for an update).

Simulation plays a central role in population genomics studies. Recent years have seen rapid improvements in software efficiency that make it possible to simulate large genomic regions for many individuals sampled from large numbers of populations. As the complexity of the demographic models we study grows, however, there is an ever-increasing opportunity to introduce bugs in their implementation. Here we describe two errors made in defining population genetic models using the msprime coalescent simulator that have found their way into the published record. We discuss how these errors have affected downstream analyses and give recommendations for software developers and users to reduce the risk of such errors.

Learning the transmission history of alleles through a family or population plays an important role in evolutionary, demographic, and medical genetic studies. Most classical models of population genetics have attempted to do so under the assumption that the genealogy of a population is unavailable and that its idiosyncrasies can be described by a small number of parameters describing population size and mate choice dynamics. Large genetic samples have increased sensitivity to such modeling assumptions, and large-scale genealogical datasets become a useful tool to investigate realistic genealogies. However, analyses in such large datasets are often intractable using conventional methods. We present an efficient method to infer transmission paths of rare alleles through population-scale genealogies. Based on backward-time Monte Carlo simulations of genetic inheritance, we use an importance sampling scheme to dramatically speed up convergence. The approach can take advantage of available genotypes of subsets of individuals in the genealogy including haplotype structure as well as information about the mode of inheritance and general prevalence of a mutation or disease in the population. Using a high-quality genealogical dataset of more than three million married individuals in the Quebec founder population, we apply the method to reconstruct the transmission history of chronic atrial and intestinal dysrhythmia (CAID), a rare recessive disease. We identify the most likely early carriers of the mutation and geographically map the expected carrier rate in the present-day French-Canadian population of Quebec.

Understanding the historical events that shaped current genomic diversity has applications in historical, biological, and medical research. However, the amount of historical information that can be inferred from genetic data is finite, which leads to an identifiability problem. For example, different historical processes can lead to identical distribution of allele frequencies. This identifiability issue casts a shadow of uncertainty over the results of any study which uses the frequency spectrum to infer past demography. It has been argued that imposing mild ‘reasonableness’ constraints on demographic histories can enable unique reconstruction, at least in an idealized setting where the length of the genome is nearly infinite. Here, we discuss this problem for finite sample size and genome length. Using the diffusion approximation, we obtain bounds on likelihood differences between similar demographic histories, and use them to construct pairs of very different reasonable histories that produce almost-identical frequency distributions. The finite-genome problem therefore remains poorly determined even among reasonable histories. Where fits to few-parameter models produce narrow parameter confidence intervals, large uncertainties lurk hidden by model assumption.

Patterns of genetic variation across populations are influenced by mutation, selection, genetic drift, and migrations. Building models of evolution... Understanding variation in allele frequencies across populations is a central goal of population genetics. Classical models for the distribution of allele frequencies, using forward simulation, coalescent theory, or the diffusion approximation, have been applied extensively for demographic inference, medical study design, and evolutionary studies. Here we propose a tractable model of ordinary differential equations for the evolution of allele frequencies that is closely related to the diffusion approximation but avoids many of its limitations and approximations. We show that the approach is typically faster, more numerically stable, and more easily generalizable than the state-of-the-art software implementation of the diffusion approximation. We present a number of applications to human sequence data, including demographic inference with a five-population joint frequency spectrum and a discussion of the robustness of the out-of-Africa model inference to the choice of modern population.

People in the Americas represent a diverse group of populations with varying degrees of admixture among African, European, and Amerindigenous ancestries. In the United States, many populations with non-European ancestry remain understudied, and thus little is known about the genetic architecture of phenotypic variation in these populations. Using genome-wide genotype data from the Hispanic Community Health Study/Study of Latinos, we find that Amerindigenous ancestry has increased over time across Hispanic/Latino populations, particularly in Mexican Americans where Amerindigenous ancestry increased by an average of ∼20% over the 50-year period spanning 1940s-1990s. We find similar patterns across American cities, and replicate our observations in an independent sample of Mexican Americans. These dynamic ancestry patterns are a result of a complex interaction of several population and cultural factors, including strong ancestry-related assortative mating and subtle shifts in migration with differences in subcontinental Amerindigenous ancestry over time. These factors have shaped patterns of genetic variation, including an increase in runs of homozygosity in Amerindigenous ancestral tracts, and also influenced the genetic architecture of complex traits within the Mexican American population. We show for height, a trait correlated with ancestry, polygenic risk scores based on summary statistics from a European-based genome-wide association study perform poorly in Mexican Americans. Our findings reveal temporal changes in population structure within Hispanics/Latinos that may influence biomedical traits, demonstrating a crucial need to improve our understanding of the genetic diversity of admixed populations.

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here we describe a new resource,stdpopsim, that attempts to rectify this situation.Stdpopsimis a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating howstdpopsimcan be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

Introduction Sub-cutaneous panniculitis T-cell lymphomas (SPTCL), a rare non-Hodgkin lymphoma, can be associated with hemophagocytic lymphohistiocytosis (HLH), a life-threatening activation of the immune system which adversely impacts survival. T-cell immunoglobulin mucin 3 (TIM-3) is a modulator of immune responses expressed on subgroups of T- and innate immune cells. In this work we describe the first germline variants associated with SPTCL, which are in the TIM-3 gene. Methods We sequenced 27 SPTCL cases to identify gene variants. We performed TIM-3 functional analysis on immune cells from patients and HEK293 cells engineered to overexpress wild-type or mutant TIM-3. Results We identified homozygous, germline, loss-of-function, missense variants in highly conserved residues of TIM-3, namely p.Y82C and p.I97M in about 60% (16/27) of SPTCL cases. These samples were drawn from cases series across 3 continents. Patients with bi-allelic TIM-3 mutations were younger at diagnosis, and several had life-threatening HLH and severe disease course. TIM-3 mutations show specific geographic distribution. Y82C TIM-3 mutations occur on a founder chromosome in patients with East-Asian and Polynesian ancestry, while I97M TIM-3 is observed in Caucasians. Both variants induce protein misfolding and cytoplasmic retention of TIM-3. Loss of TIM-3 membrane expression in TIM-3 mutants abrogates the PD-1/PDL-1 checkpoint and prevents the termination of a Th1-immune response. In HEK293 cells, mutant TIM-3 was not expressed on the cell surface. Defective TIM-3 expression leads to persistent immune activation with increased production of inflammatory cytokines including TNF-alpha and IL-1beta by innate immune cells. Conclusion Our findings highlight HLH/SPTCL as a new genetic entity where loss of the TIM-3 immune checkpoint is associated with T-cell infiltration of adipose tissue and inflammasome activation. This is the first causative germline defect identified in SPTCL. While our findings indicate that TIM-3-mutant HLH/SPTCL benefit from immunomodulation, therapeutic repression of the TIM-3 checkpoint could have serious adverse consequences. Disclosures Prince: Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen Cilag: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Human populations feature both discrete and continuous patterns of variation. Current analysis approaches struggle to jointly identify these patterns because of modelling assumptions, mathematical constraints, or numerical challenges. Here we apply uniform manifold approximation and projection (UMAP), a non-linear dimension reduction tool, to three well-studied genotype datasets and discover overlooked subpopulations within the American Hispanic population, fine-scale relationships between geography, genotypes, and phenotypes in the UK population, and cryptic structure in the Thousand Genomes Project data. This approach is well-suited to the influx of large and diverse data and opens new lines of inquiry in population-scale datasets., Author summary The demographic history of human populations features varying geographic and social barriers to mating. Over time, these barriers have led to varying levels of genetic relatedness among individuals. This population structure is informative about human history, and can have a significant impact on studies of medical genetics. Because population structure depends on myriad demographic, ecological, and social forces, a priori visualization is useful to identify subtle patterns of population structure. We use a dimension reduction method—UMAP—to visualize population structure in three genomic datasets and find previously unobserved patterns, revealing fine-scale population structure and illustrating differences between groups in traits such as white blood cell count, height, and FEV1, a measure of lung function. Using UMAP is computationally efficient and can identify fine-scale population structure in large population datasets. We find it particularly useful to reveal phenotypic variation among genetically related populations, and recommend it is a complement to principal component analysis in primary data visualization.

Recent reports have identified differences in the mutational spectra across human populations. While some of these reports have been replicated in other cohorts, most have been reported only in the 1000 Genomes Project (1kGP) data. While investigating an intriguing putative population stratification within the Japanese population, we identified a previously unreported batch effect leading to spurious mutation calls in the 1kGP data and to the apparent population stratification. Because the 1kGP data is used extensively, we find that the batch effects also lead to incorrect imputation by leading imputation servers and a small number of suspicious GWAS associations. Lower-quality data from the early phases of the 1kGP thus continues to contaminate modern studies in hidden ways. It may be time to retire or upgrade such legacy sequencing data.

Genetic variation in melanocortin-1 receptor (MC1R) is a known contributor to disease-free red hair in humans. Three loss-of-function single-nucleotide variants (rs1805007, rs1805008 and rs1805009) have been established as strongly correlated with red hair. The contribution of other loss-of-function MC1R variants (in particular rs1805005, rs2228479 and rs885479) and the extent to which other genetic loci are involved in red hair colour is less well understood. Here, we used the UK Biobank cohort to capture a comprehensive list of MC1R variants contributing to red hair colour. We report a correlation with red hair for both strong-effect variants (rs1805007, rs1805008 and rs1805009) and weak-effect variants (rs1805005, rs2228479 and rs885479) and show that their coefficients differ by two orders of magnitude. On the haplotype level, both strong- and weak-effect variants contribute to the red hair phenotype, but when considered individually, weak-effect variants show a reverse, negative association with red hair. The reversal of association direction in the single-variant analysis is facilitated by a distinguishing structure of MC1R, in which loss-of-function variants are never found to co-occur on the same haplotype. The other previously reported hair colour genes' variants do not substantially improve the MC1R red hair colour predictive model. Our best model for predicting red versus other hair colours yields an unparalleled area under the receiver operating characteristic of 0.96 using only MC1R variants. In summary, we present a comprehensive statistically derived characterization of the role of MC1R variants in red hair colour and offer a powerful, economical and parsimonious model that achieves unsurpassed performance.

International audience; We identified individuals with variations in ACTL6B, a component of the chromatin remodeling machinery including the BAF complex. Ten individuals harbored bi-allelic mutations and presented with global developmental delay, epileptic encephalopathy, and spasticity, and ten individuals with de novo heterozygous mutations displayed intellectual disability, ambulation deficits, severe language impairment, hypotonia, Rett-like stereotypies, and minor facial dysmorphisms (wide mouth, diastema, bulbous nose). Nine of these ten unrelated individuals had the identical de novo c.1027G>A (p.Gly343Arg) mutation. Human-derived neurons were generated that recaptured ACTL6B expression patterns in development from progenitor cell to post-mitotic neuron, validating the use of this model. Engineered knock-out of ACTL6B in wild-type human neurons resulted in profound deficits in dendrite development, a result recapitulated in two individuals with different bi-allelic mutations, and reversed on clonal genetic repair or exogenous expression of ACTL6B. Whole-transcriptome analyses and whole-genomic profiling of the BAF complex in wild-type and bi-allelic mutant ACTL6B neural progenitor cells and neurons revealed increased genomic binding of the BAF complex in ACTL6B mutants, with corresponding transcriptional changes in several genes including TPPP and FSCN1, suggesting that altered regulation of some cytoskeletal genes contribute to altered dendrite development. Assessment of bi-alleic and heterozygous ACTL6B mutations on an ACTL6B knock-out human background demonstrated that bi-allelic mutations mimic engineered deletion deficits while heterozygous mutations do not, suggesting that the former are loss of function and the latter are gain of function. These results reveal a role for ACTL6B in neurodevelopment and implicate another component of chromatin remodeling machinery in brain disease.

We learn about population history and underlying evolutionary biology through patterns of genetic polymorphism. Many approaches to reconstruct evolutionary histories focus on a limited number of informative statistics describing distributions of allele frequencies or patterns of linkage disequilibrium. We show that many commonly used statistics are part of a broad family of two-locus moments whose expectation can be computed jointly and rapidly under a wide range of scenarios, including complex multi-population demographies with continuous migration and admixture events. A full inspection of these statistics reveals that widely used models of human history fail to predict simple patterns of linkage disequilibrium. To jointly capture the information contained in classical and novel statistics, we implemented a tractable likelihood-based inference framework for demographic history. Using this approach, we show that human evolutionary models that include archaic admixture in Africa, Asia, and Europe provide a much better description of patterns of genetic diversity across the human genome. We estimate that an unidentified, deeply diverged population admixed with modern humans within Africa both before and after the split of African and Eurasian populations, contributing 4 − 8% genetic ancestry to individuals in world-wide populations., Author summary Throughout human history, populations have expanded and contracted, split and merged, and exchanged migrants. Because these events affected genetic diversity, we can learn about human history by comparing predictions from evolutionary models to genetic data. Here, we show how to rapidly compute such predictions for a wide range of diversity measures within and across populations under complex demographic scenarios. While widely used models of human history accurately predict common measures of diversity, we show that they strongly underestimate the co-occurence of low frequency mutations within human populations in Asia, Europe, and Africa. Models allowing for archaic admixture, the relatively recent mixing of human populations with deeply diverged human lineages, resolve this discrepancy. We use such models to infer demographic models that include both recent and ancient features of human history. We recover the well-characterized admixture of Neanderthals in Eurasian populations, as well as admixture from an as-yet unknown diverged human population within Africa, further suggesting that admixture with deeply diverged lineages occurred multiple times in human history. By simultaneously testing model predictions for a broad range of diversity statistics, we can assess the robustness of common evolutionary models, identify missing historical events, and build more informed models of human demography.

Subcutaneous panniculitis-like T cell lymphoma (SPTCL), a non-Hodgkin lymphoma, can be associated with hemophagocytic lymphohistiocytosis (HLH), a life-threatening immune activation that adversely affects survival1,2. T cell immunoglobulin mucin 3 (TIM-3) is a modulator of immune responses expressed on subgroups of T and innate immune cells. We identify in ~60% of SPTCL cases germline, loss-of-function, missense variants altering highly conserved residues of TIM-3, c.245A>G (p.Tyr82Cys) and c.291A>G (p.Ile97Met), each with specific geographic distribution. The variant encoding p.Tyr82Cys TIM-3 occurs on a potential founder chromosome in patients with East Asian and Polynesian ancestry, while p.Ile97Met TIM-3 occurs in patients with European ancestry. Both variants induce protein misfolding and abrogate TIM-3’s plasma membrane expression, leading to persistent immune activation and increased production of inflammatory cytokines, including tumor necrosis factor-α and interleukin-1β, promoting HLH and SPTCL. Our findings highlight HLH–SPTCL as a new genetic entity and identify mutations causing TIM-3 alterations as a causative genetic defect in SPTCL. While HLH–SPTCL patients with mutant TIM-3 benefit from immunomodulation, therapeutic repression of the TIM-3 checkpoint may have adverse consequences.

Genetic structure in large cohorts results from technical, sampling and demographic variation. Visualisation is therefore a first step in most genomic analyses. However, existing data exploration methods struggle with unbalanced sampling and the many scales of population structure. We investigate an approach to dimension reduction of genomic data that combines principal components analysis (PCA) with uniform manifold approximation and projection (UMAP) to succinctly illustrate population structure in large cohorts and capture their relationships on local and global scales. Using data from large-scale genomic datasets, we demonstrate that PCA-UMAP effectively clusters closely related individuals while placing them in a global continuum of genetic variation. This approach reveals previously overlooked subpopulations within the American Hispanic population and fine-scale relationships between geography, genotypes, and phenotypes in the UK population. This opens new lines of investigation for demographic research and statistical genetics. Given its small computational cost, PCA-UMAP also provides a general-purpose approach to exploratory analysis in population-scale datasets.Author summaryBecause of geographic isolation, individuals tend to be more genetically related to people living nearby than to people living far. This is an example of population structure, a situation where a large population contains subgroups that share more than the average amount of DNA. This structure can tell us about human history, and it can also have a large effect on medical studies. We use a newly developed method (UMAP) to visualize population structure from three genomic datasets. Using genotype data alone, we reveal numerous subgroups related to ancestry and correlated with traits such as white blood cell count, height, and FEV1, a measure used to detect airway obstruction. We demonstrate that UMAP reveals previously unobserved patterns and fine-scale structure. We show that visualizations work especially well in large datasets containing populations with diverse backgrounds, which are rapidly becoming more common, and that unlike other visualization methods, we can preserve intuitive connections between populations that reflect their shared ancestries. The combination of these results and the effectiveness of the strategy on large and diverse datasets make this an important approach for exploratory analysis for geneticists studying ancestral events and phenotype distributions.

Evolutionary, biological, and demographic processes combine to shape the variation observed in populations. Understanding how these processes are expected to influence variation allows us to infer past demographic events and the nature of selection in human populations. Forward models such as the diffusion approximation provide a powerful tool for analyzing the distribution of allele frequencies in contemporary populations due to their computational tractability and model flexibility. Here, we discuss recent computational developments and their application to reconstructing human demographic history and patterns of selection at new mutations. We also reexamine how some classical assumptions that are still commonly used in inference studies fare when applied to modern data. We use whole-genome sequence data for 797 French Canadian individuals to examine the neutrality of synonymous sites. We find that selection can lead to strong biases in the inferred demography, mutation rate, and distributions of fitness effects. We use these distributions of fitness effects together with demographic and phenotype-fitness models to predict the relationship between effect size and allele frequency, and contrast those predictions to commonly used models in statistical genetics. Thus the simple evolutionary models investigated by Kimura and Ohta still provide important insight into modern genetic research.

Evolutionary, biological, and demographic processes together shape observed variation in populations. Understanding how these processes influence variation allows us to infer past demography and the nature of selection in populations. Forward in time models such as the diffusion approximation provide a powerful tool for performing inference based on the distribution of allele frequencies. Here, we discuss recent computational developments and their application to reconstructing human demographic history. Using whole-genome sequence data for 797 French Canadian individuals, we assess the neutrality of synonymous variants and show that selection can bias inferred demography, mutation rates, and distributions of fitness effects. We argue that the simple evolutionary models investigated by Kimura and Ohta still provide important insight into modern genetic research.

Next-generation sequencing technology has facilitated the discovery of millions of genetic variants in human genomes. A sizeable fraction of these variants are predicted to be deleterious. Here, we review the pattern of deleterious alleles as ascertained in genome sequencing data sets and ask whether human populations differ in their predicted burden of deleterious alleles - a phenomenon known as mutation load. We discuss three demographic models that are predicted to affect mutation load and relate these models to the evidence (or the lack thereof) for variation in the efficacy of purifying selection in diverse human genomes. We also emphasize why accurate estimation of mutation load depends on assumptions regarding the distribution of dominance and selection coefficients - quantities that remain poorly characterized for current genomic data sets.

SummaryGenetic diversity plays a central role in tumor progression, metastasis, and resistance to treatment. Experiments are shedding light on this diversity at ever finer scales, but interpretation is challenging. Using recent progress in numerical models, we simulate macroscopic tumors to investigate the interplay between growth dynamics, microscopic composition, and circulating tumor cell cluster diversity. We find that modest differences in growth parameters can profoundly change microscopic diversity. Simple outwards expansion leads to spatially segregated clones and low diversity, as expected. However, a modest cell turnover can result in an increased number of divisions and mixing among clones resulting in increased microscopic diversity in the tumor core. Using simulations to estimate power to detect such spatial trends, we find that multiregion sequencing data from contemporary studies is marginally powered to detect the predicted effects. Slightly larger samples, improved detection of rare variants, or sequencing of smaller biopsies or circulating tumor cell clusters would allow one to distinguish between leading models of tumor evolution. The genetic composition of circulating tumor cell clusters, which can be obtained from non-invasive blood draws, is therefore informative about tumor evolution and its metastatic potential.HighlightsNumerical and theoretical models show interaction of front expansion, mutation, and clonal mixing in shaping tumor heterogeneity.Cell turnover increases intratumor heterogeneity.Simulated circulating tumor cell clusters and microbiopsies exhibit substantial diversity with strong spatial trends.Simulations suggest attainable sampling schemes able to distinguish between prevalent tumor growth models.