On the genes, genealogies, and geographies of Quebec.

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.

A weakly structured stem for human origins in Africa.

Despite broad agreement that Homo sapiens originated in Africa, considerable uncertainty surrounds specific models of divergence and migration across the continent1. Progress is hampered by a shortage of fossil and genomic data, as well as variability in previous estimates of divergence times1. Here we seek to discriminate among such models by considering linkage disequilibrium and diversity-based statistics, optimized for rapid, complex demographic inference2. We infer detailed demographic models for populations across Africa, including eastern and western representatives, and newly sequenced whole genomes from 44 Nama (Khoe-San) individuals from southern Africa. We infer a reticulated African population history in which present-day population structure dates back to Marine Isotope Stage 5. The earliest population divergence among contemporary populations occurred 120,000 to 135,000 years ago and was preceded by links between two or more weakly differentiated ancestral Homo populations connected by gene flow over hundreds of thousands of years. Such weakly structured stem models explain patterns of polymorphism that had previously been attributed to contributions from archaic hominins in Africa2-7. In contrast to models with archaic introgression, we predict that fossil remains from coexisting ancestral populations should be genetically and morphologically similar, and that only an inferred 1-4% of genetic differentiation among contemporary human populations can be attributed to genetic drift between stem populations. We show that model misspecification explains the variation in previous estimates of divergence times, and argue that studying a range of models is key to making robust inferences about deep history.

Fast and accurate Bayesian polygenic risk modeling with variational inference.

The advent of large-scale genome-wide association studies (GWASs) has motivated the development of statistical methods for phenotype prediction with single-nucleotide polymorphism (SNP) array data. These polygenic risk score (PRS) methods use a multiple linear regression framework to infer 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, which are computationally inefficient and do not scale favorably to higher dimensions, for posterior inference. Here, we introduce variational inference of polygenic risk scores (VIPRS), a Bayesian summary statistics-based PRS method that utilizes variational inference techniques to approximate the posterior distribution for the effect sizes. Our experiments with 36 simulation configurations and 12 real phenotypes from the UK Biobank dataset demonstrated that VIPRS is consistently competitive with the state-of-the-art in prediction accuracy while being more than twice as fast as popular MCMC-based approaches. This performance advantage is robust across a variety of genetic architectures, SNP heritabilities, and independent GWAS cohorts. In addition to its competitive accuracy on the "White British" samples, VIPRS showed improved transferability when applied to other ethnic groups, with up to 1.7-fold increase in R2 among individuals of Nigerian ancestry for low-density lipoprotein (LDL) cholesterol. To illustrate its scalability, we applied VIPRS to a dataset of 9.6 million genetic markers, which conferred further improvements in prediction accuracy for highly polygenic traits, such as height.

PGC-1s shape epidermal physiology by modulating keratinocyte proliferation and terminal differentiation.

Skin plays central roles in systemic physiology, and it undergoes significant functional changes during aging. Members of the peroxisome proliferator-activated receptor-gamma coactivator (PGC-1) family (PGC-1s) are key regulators of the biology of numerous tissues, yet we know very little about their impact on skin functions. Global gene expression profiling and gene silencing in keratinocytes uncovered that PGC-1s control the expression of metabolic genes as well as that of terminal differentiation programs. Glutamine emerged as a key substrate promoting mitochondrial respiration, keratinocyte proliferation, and the expression of PGC-1s and terminal differentiation programs. Importantly, gene silencing of PGC-1s reduced the thickness of a reconstructed living human epidermal equivalent. Exposure of keratinocytes to a salicylic acid derivative potentiated the expression of PGC-1s and terminal differentiation genes and increased mitochondrial respiration. Overall, our results show that the PGC-1s are essential effectors of epidermal physiology, revealing an axis that could be targeted in skin conditions and aging.

Current knowledge on the tissue distribution of mRNA nanocarriers for therapeutic protein expression.

Exogenously delivered mRNA-based drugs are emerging as a new class of therapeutics with the potential to treat several diseases. Over the last decade, advancements in the design of non-viral delivery tools have enabled mRNA to be evaluated for several therapeutic purposes including protein replacement therapies, gene editing, and vaccines. However, in vivo delivery of mRNA to targeted organs and cells remains a critical challenge. Evaluation of the biodistribution of mRNA vehicles is of utmost importance for the development of effective pharmaceutical candidates. In this review, we discuss the recent advances in the design of nanoparticles loaded with mRNA and extrapolate the key factors influencing their biodistribution following administration. Finally, we highlight the latest developments in the preclinical and clinical translation of mRNA therapeutics for protein supplementation therapy.

The Genetic and Molecular Analyses of RAD51C and RAD51D Identifies Rare Variants Implicated in Hereditary Ovarian Cancer from a Genetically Unique Population.

To identify candidate variants in RAD51C and RAD51D ovarian cancer (OC) predisposing genes by investigating French Canadians (FC) exhibiting unique genetic architecture. Candidates were identified by whole exome sequencing analysis of 17 OC families and 53 early-onset OC cases. Carrier frequencies were determined by the genetic analysis of 100 OC or HBOC families, 438 sporadic OC cases and 1025 controls. Variants of unknown function were assayed for their biological impact and/or cellular sensitivity to olaparib. RAD51C c.414G>C;p.Leu138Phe and c.705G>T;p.Lys235Asn and RAD51D c.137C>G;p.Ser46Cys, c.620C>T;p.Ser207Leu and c.694C>T;p.Arg232Ter were identified in 17.6% of families and 11.3% of early-onset cases. The highest carrier frequency was observed in OC families (1/44, 2.3%) and sporadic cases (15/438, 3.4%) harbouring RAD51D c.620C>T versus controls (1/1025, 0.1%). Carriers of c.620C>T (n = 7), c.705G>T (n = 2) and c.137C>G (n = 1) were identified in another 538 FC OC cases. RAD51C c.705G>T affected splicing by skipping exon four, while RAD51D p.Ser46Cys affected protein stability and conferred olaparib sensitivity. Genetic and functional assays implicate RAD51C c.705G>T and RAD51D c.137C>G as likely pathogenic variants in OC. The high carrier frequency of RAD51D c.620C>T in FC OC cases validates previous findings. Our findings further support the role of RAD51C and RAD51D in hereditary OC.

Low expression of PGC-1ß and other mitochondrial biogenesis modulators in melanoma is associated with growth arrest and the induction of an immunosuppressive gene expression program dependent on MEK and IRF-1.

Mitochondria are specialized metabolic and immune organelles that have important roles in tumor progression, metastasis, and response to chemotherapy and immunotherapy. Mitochondrial biogenesis and functions are under the control of the peroxisome-proliferator activated receptor-gamma (PGC-1) transcriptional coactivators. Recent research unveiled the role of PGC-1α in bolstering mitochondrial oxidative functions and in the suppression of metastasis in melanoma, but the role of PGC-1s in tumor immunology remains elusive. Herein, we show that low PGC-1s expression in human melanoma tumors is associated with increased expression of a repertoire of immunosuppressive (CD73, PD-L2, Galectin-9) and pro-inflammatory (IL-8, TNF, IL-1ß) transcripts, and that experimental depletion of PGC-1ß recapitulates this signature in human melanoma cell lines. The depletion of PGC-1ß reduces the expression of HSPA9, impairs mitochondrial activity, and leads to cell cycle arrest. Using pharmacological and gene silencing approaches, we further show that MEK1/2 and IRF-1 mediate the observed immune transcriptional response. Overall, this research suggests that mitochondrial biogenesis modulators can modulate tumor progression, immune evasion, and response to therapeutics through transcriptional control of immune pathways.

Spatially mapping the immune landscape of melanoma using imaging mass cytometry.

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.

Efficient ancestry and mutation simulation with msprime 1.0.

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.

Assumptions about frequency-dependent architectures of complex traits bias measures of functional enrichment.

Linkage-Disequilibrium Score Regression (LDSC) is a popular framework for analyzing Genome-wide Association Studies (GWAS) summary statistics that allows for estimating single nucleotide polymorphism 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 study, 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.

A review of UMAP in population genetics.

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.

Recent shifts in the genomic ancestry of Mexican Americans may alter the genetic architecture of biomedical traits.

People in the Americas represent a diverse continuum of populations with varying degrees of admixture among African, European, and Amerindigenous ancestries. In the United States, populations with non-European ancestry remain understudied, and thus little is known about the genetic architecture of phenotypic variation in these populations. Using genotype data from the Hispanic Community Health Study/Study of Latinos, we find that Amerindigenous ancestry increased by an average of ~20% spanning 1940s-1990s in Mexican Americans. These patterns result from complex interactions between several population and cultural factors which shaped patterns of genetic variation and influenced the genetic architecture of complex traits in Mexican Americans. We show for height how 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 need to improve our understanding of admixed populations.

Lessons Learned from Bugs in Models of Human History.

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.

A community-maintained standard library of population genetic models.

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. Stdpopsim is 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 how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

Accounting for long-range correlations in genome-wide simulations of large cohorts.

Coalescent simulations are widely used to examine the effects of evolution and demographic history on the genetic makeup of populations. Thanks to recent progress in algorithms and data structures, simulators such as the widely-used msprime now provide genome-wide simulations for millions of individuals. However, this software relies on classic coalescent theory and its assumptions that sample sizes are small and that the region being simulated is short. Here we show that coalescent simulations of long regions of the genome exhibit large biases in identity-by-descent (IBD), long-range linkage disequilibrium (LD), and ancestry patterns, particularly when the sample size is large. We present a Wright-Fisher extension to msprime, and show that it produces more realistic distributions of IBD, LD, and ancestry proportions, while also addressing more subtle biases of the coalescent. Further, these extensions are more computationally efficient than state-of-the-art coalescent simulations when simulating long regions, including whole-genome data. For shorter regions, efficiency can be maintained via a hybrid model which simulates the recent past under the Wright-Fisher model and uses coalescent simulations in the distant past.

Legacy Data Confound Genomics Studies.

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.

Unbiased Estimation of Linkage Disequilibrium from Unphased Data.

Linkage disequilibrium (LD) is used to infer evolutionary history, to identify genomic regions under selection, and to dissect the relationship between genotype and phenotype. In each case, we require accurate estimates of LD statistics from sequencing data. Unphased data present a challenge because multilocus haplotypes cannot be inferred exactly. Widely used estimators for the common statistics r2 and D2 exhibit 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, including D2, for both single and multiple randomly mating populations. These unbiased statistics are particularly well suited to estimate effective population sizes from unlinked loci in small populations. We develop a simple inference pipeline and use it to refine estimates of recent effective population sizes of the threatened Channel Island Fox populations.