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

Estimating between-country migration in pneumococcal populations

Streptococcus pneumoniae (the pneumococcus) is a globally distributed, human obligate opportunistic bacterial pathogen which, although often carried commensally, is also a significant cause of invasive disease. Apart from multi-drug resistant and virulent clones, the rate and direction of pneumococcal dissemination between different countries remains largely unknown. The ability for the pneumococcus to take a foothold in a country depends on existing population configuration, the extent of vaccine implementation, as well as human mobility since it is a human obligate bacterium. To shed light on its international movement, we used extensive genome data from the Global Pneumococcal Sequencing project and estimated migration parameters between multiple countries in Africa. Data on allele frequencies of polymorphisms at housekeeping-like loci for multiple different lineages circulating in the populations of South Africa, Malawi, Kenya, and The Gambia were used to calculate the fixation index (Fst) between countries. We then further used these summaries to fit migration coalescent models with the likelihood-free inference algorithms available in the ELFI software package. Synthetic datawere additionally used to validate the inference approach. Our results demonstrate country-pair specific migration patterns and heterogeneity in the extent of migration between different lineages. Our approach demonstrates that coalescent models can be effectively used for inferring migration rates for bacterial species and lineages provided sufficiently granular population genomics surveillance data. Further, it can demonstrate the connectivity of respiratory disease agents between countries to inform intervention policy in the longer term.

A general and efficient representation of ancestral recombination graphs

As a result of recombination, adjacent nucleotides can have different paths of genetic inheritance and therefore the genealogical trees for a sample of DNA sequences vary along the genome. The structure capturing the details of these intricately interwoven paths of inheritance is referred to as an ancestral recombination graph (ARG). Classical formalisms have focused on mapping coalescence and recombination events to the nodes in an ARG. This approach is out of step with modern developments, which do not represent genetic inheritance in terms of these events or explicitly infer them. We present a simple formalism that defines an ARG in terms of specific genomes and their intervals of genetic inheritance, and show how it generalises these classical treatments and encompasses the outputs of recent methods. We discuss nuances arising from this more general structure, and argue that it forms an appropriate basis for a software standard in this rapidly growing field.

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

Paleogenomics has expanded our knowledge of human evolutionary history. Since the 2020s, the study of ancient DNA has increased its focus on reconstructing the recent past. However, the accuracy of paleogenomic methods in answering questions of historical and archaeological importance amidst the increased demographic complexity and decreased genetic differentiation within the historical period remains an open question. We used two simulation approaches to evaluate the limitations and behavior of commonly used methods, qpAdm and the f 3 -statistic, on admixture inference. The first is based on branch-length data simulated from four simple demographic models of varying complexities and configurations. The second, an analysis of Eurasian history composed of 59 populations using whole-genome data modified with ancient DNA conditions such as SNP ascertainment, data missingness, and pseudo-haploidization. We show that under conditions resembling historical populations, qpAdm can identify a small candidate set of true sources and populations closely related to them. However, in typical ancient DNA conditions, qpAdm is unable to further distinguish between them, limiting its utility for resolving fine-scaled hypotheses. Notably, we find that complex gene-flow histories generally lead to improvements in the performance of qpAdm and observe no bias in the estimation of admixture weights. We offer a heuristic for admixture inference that incorporates admixture weight estimate and P -values of qpAdm models, and f 3 -statistics to enhance the power to distinguish between multiple plausible candidates. Finally, we highlight the future potential of qpAdm through whole-genome branch-length f 2 -statistics, demonstrating the improved demographic inference that could be achieved with advancements in f -statistic estimations.

False discovery rates of qpAdm-based screens for genetic admixture

Although a broad range of methods exists for reconstructing population history from genome-wide single nucleotide polymorphism data, just a few methods gained popularity in archaeogenetics: principal component analysis (PCA); ADMIXTURE, an algorithm that models individuals as mixtures of multiple ancestral sources represented by actual or inferred populations; formal tests for admixture such as f3-statistics and D/f4-statistics; and qpAdm, a tool for fitting two-component and more complex admixture models to groups or individuals. Despite their popularity in archaeogenetics, which is explained by modest computational requirements and ability to analyze data of various types and qualities, protocols relying on qpAdm that screen numerous alternative models of varying complexity and find "fitting" models (often considering both estimated admixture proportions and p-values as a composite criterion of model fit) remain untested on complex simulated population histories in the form of admixture graphs of random topology. We analyzed genotype data extracted from such simulations and tested various types of high-throughput qpAdm protocols ("rotating" and "non-rotating", with or without temporal stratification of target groups and proxy ancestry sources, and with or without a "model competition" step). We caution that high-throughput qpAdm protocols may be inappropriate for exploratory analyses in poorly studied regions/periods since their false discovery rates varied between 12% and 68% depending on the details of the protocol and on the amount and quality of simulated data (i.e., >12% of fitting two-way admixture models imply gene flows that were not simulated). We demonstrate that for reducing false discovery rates of qpAdm protocols to nearly 0% it is advisable to use large SNP sets with low missing data rates, the rotating qpAdm protocol with a strictly enforced rule that target groups do not pre-date their proxy sources, and an unsupervised ADMIXTURE analysis as a way to verify feasible qpAdm models. Our study has a number of limitations: for instance, these recommendations depend on the assumption that the underlying genetic history is a complex admixture graph and not a stepping-stone model.

Demographic modeling of admixed Latin American populations from whole genomes

Demographic models of Latin American populations often fail to fully capture their complex evolutionary history, which has been shaped by both recent admixture and deeper-in-time demographic events. To address this gap, we used high-coverage whole-genome data from Indigenous American ancestries in present-day Mexico and existing genomes from across Latin America to infer multiple demographic models that capture the impact of different timescales on genetic diversity. Our approach, which combines analyses of allele frequencies and ancestry tract length distributions, represents a significant improvement over current models in predicting patterns of genetic variation in admixed Latin American populations. We jointly modeled the contribution of European, African, East Asian, and Indigenous American ancestries into present-day Latin American populations. We infer that the ancestors of Indigenous Americans and East Asians diverged ∼30 thousand years ago, and we characterize genetic contributions of recent migrations from East and Southeast Asia to Peru and Mexico. Our inferred demographic histories are consistent across different genomic regions and annotations, suggesting that our inferences are robust to the potential effects of linked selection. In conjunction with published distributions of fitness effects for new nonsynonymous mutations in humans, we show in large-scale simulations that our models recover important features of both neutral and deleterious variation. By providing a more realistic framework for understanding the evolutionary history of Latin American populations, our models can help address the historical under-representation of admixed groups in genomics research and can be a valuable resource for future studies of populations with complex admixture and demographic histories.

Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic datasets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than threefold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed the best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

RaPID-Query for fast identity by descent search and genealogical analysis

MOTIVATION

Due to the rapid growth of the genetic database size, genealogical search, a process of inferring familial relatedness by identifying DNA matches, has become a viable approach to help individuals finding missing family members or law enforcement agencies locating suspects. A fast and accurate method is needed to search an out-of-database individual against millions of individuals. Most existing approaches only offer all-versus-all within panel match. Some prototype algorithms offer one-versus-all query from out-of-panel individual, but they do not tolerate errors.

RESULTS

A new method, random projection-based identity-by-descent (IBD) detection (RaPID) query, is introduced to make fast genealogical search possible. RaPID-Query identifies IBD segments between a query haplotype and a panel of haplotypes. By integrating matches over multiple PBWT indexes, RaPID-Query manages to locate IBD segments quickly with a given cutoff length while allowing mismatched sites. A single query against all UK biobank autosomal chromosomes was completed within 2.76 seconds on average, with the minimum length 7 cM and 700 markers. RaPID-Query achieved a 0.016 false negative rate and a 0.012 false positive rate simultaneously on a chromosome 20 sequencing panel having 86 265 sites. This is comparable to the state-of-the-art IBD detection method TPBWT(out-of-sample) and Hap-IBD. The high-quality IBD segments yielded by RaPID-Query were able to distinguish up to fourth degree of the familial relatedness for a given individual pair, and the area under the receiver operating characteristic curve values are at least 97.28%.

AVAILABILITY AND IMPLEMENTATION

The RaPID-Query program is available at https://github.com/ucfcbb/RaPID-Query.

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.

An exploration of linkage fine-mapping on sequences from case-control studies

Linkage analysis maps genetic loci for a heritable trait by identifying genomic regions with excess relatedness among individuals with similar trait values. Analysis may be conducted on related individuals from families, or on samples of unrelated individuals from a population. For allelically heterogeneous traits, population-based linkage analysis can be more powerful than genotypic-association analysis. Here, we focus on linkage analysis in a population sample, but use sequences rather than individuals as our unit of observation. Earlier investigations of sequence-based linkage mapping relied on known sequence relatedness, whereas we infer relatedness from the sequence data. We propose two ways to associate similarity in relatedness of sequences with similarity in their trait values and compare the resulting linkage methods to two genotypic-association methods. We also introduce a procedure to label case sequences as potential carriers or noncarriers of causal variants after an association has been found. This post hoc labeling of case sequences is based on inferred relatedness to other case sequences. Our simulation results indicate that methods based on sequence relatedness improve localization and perform as well as genotypic-association methods for detecting rare causal variants. Sequence-based linkage analysis therefore has potential to fine-map allelically heterogeneous disease traits.

Modeling of African population history using f -statistics can be highly biased and is not addressed by previously suggested SNP ascertainment schemes

f -statistics have emerged as a first line of analysis for making inferences about demographic history from genome-wide data. These statistics can provide strong evidence for either admixture or cladality, which can be robust to substantial rates of errors or missing data. f -statistics are guaranteed to be unbiased under "SNP ascertainment" (analyzing non-randomly chosen subsets of single nucleotide polymorphisms) only if it relies on a population that is an outgroup for all groups analyzed. However, ascertainment on a true outgroup that is not co-analyzed with other populations is often impractical and uncommon in the literature. In this study focused on practical rather than theoretical aspects of SNP ascertainment, we show that many non-outgroup ascertainment schemes lead to false rejection of true demographic histories, as well as to failure to reject incorrect models. But the bias introduced by common ascertainments such as the 1240K panel is mostly limited to situations when more than one sub-Saharan African and/or archaic human groups (Neanderthals and Denisovans) or non-human outgroups are co-modelled, for example, f 4 -statistics involving one non-African group, two African groups, and one archaic group. Analyzing panels of SNPs polymorphic in archaic humans, which has been suggested as a solution for the ascertainment problem, cannot fix all these problems since for some classes of f -statistics it is not a clean outgroup ascertainment, and in other cases it demonstrates relatively low power to reject incorrect demographic models since it provides a relatively small number of variants common in anatomically modern humans. And due to the paucity of high-coverage archaic genomes, archaic individuals used for ascertainment often act as sole representatives of the respective groups in an analysis, and we show that this approach is highly problematic. By carrying out large numbers of simulations of diverse demographic histories, we find that bias in inferences based on f -statistics introduced by non-outgroup ascertainment can be minimized if the derived allele frequency spectrum in the population used for ascertainment approaches the spectrum that existed at the root of all groups being co-analyzed. Ascertaining on sites with variants common in a diverse group of African individuals provides a good approximation to such a set of SNPs, addressing the great majority of biases and also retaining high statistical power for studying population history. Such a "pan-African" ascertainment, although not completely problem-free, allows unbiased exploration of demographic models for the widest set of archaic and modern human populations, as compared to the other ascertainment schemes we explored.

Sex-biased admixture and assortative mating shape genetic variation and influence demographic inference in admixed Cabo Verdeans

Genetic data can provide insights into population history, but first, we must understand the patterns that complex histories leave in genomes. Here, we consider the admixed human population of Cabo Verde to understand the patterns of genetic variation left by social and demographic processes. First settled in the late 1400s, Cabo Verdeans are admixed descendants of Portuguese colonizers and enslaved West African people. We consider Cabo Verde’s well-studied historical record alongside genome-wide SNP data from 563 individuals from 4 regions within the archipelago. We use genetic ancestry to test for patterns of nonrandom mating and sex-specific gene flow, and we examine the consequences of these processes for common demographic inference methods and genetic patterns. Notably, multiple population genetic tools that assume random mating underestimate the timing of admixture, but incorporating nonrandom mating produces estimates more consistent with historical records. We consider how admixture interrupts common summaries of genomic variation such as runs of homozygosity. While summaries of runs of homozygosity may be difficult to interpret in admixed populations, differentiating runs of homozygosity by length class shows that runs of homozygosity reflect historical differences between the islands in their contributions from the source populations and postadmixture population dynamics. Finally, we find higher African ancestry on the X chromosome than on the autosomes, consistent with an excess of European males and African females contributing to the gene pool. Considering these genomic insights into population history in the context of Cabo Verde’s historical record, we can identify how assumptions in genetic models impact inference of population history more broadly.

Simultaneous inference of parental admixture proportions and admixture times from unphased local ancestry calls

Population genetic analyses of local ancestry tracts routinely assume that the ancestral admixture process is identical for both parents of an individual, an assumption that may be invalid when considering recent admixture. Here, we present Parental Admixture Proportion Inference (PAPI), a Bayesian tool for inferring the admixture proportions and admixture times for each parent of a single admixed individual. PAPI analyzes unphased local ancestry tracts and has two components: a binomial model that leverages genome-wide ancestry fractions to infer parental admixture proportions and a hidden Markov model (HMM) that infers admixture times from tract lengths. Crucially, the HMM accounts for unobserved within-ancestry recombination by approximating the pedigree crossover dynamics, enabling inference of parental admixture times. In simulations, we find that PAPI's admixture proportion estimates deviate from the truth by 0.047 on average, outperforming ANCESTOR and PedMix by 46.0% and 57.6%, respectively. Moreover, PAPI's admixture time estimates were strongly correlated with the truth (R=0.76) but have an average downward bias of 1.01 generations that is partly attributable to inaccuracies in local ancestry inference. As an illustration of its utility, we ran PAPI on African American genotypes from the PAGE study (N = 5,786) and found strong evidence of assortative mating by ancestry proportion: couples' ancestry proportions are highly correlated (R = 0.87) and are closer to each other than expected under random mating (p < 10^-6). We anticipate that PAPI will be useful in studying the population dynamics of admixture and will also be of interest to individuals seeking to learn about their personal genealogies.

Hunter-gatherer genomes reveal diverse demographic trajectories during the rise of farming in Eastern Africa

The fate of hunting and gathering populations following the rise of agriculture and pastoralism remains a topic of debate in the study of human prehistory. Studies of ancient and modern genomes have found that autochthonous groups were largely replaced by expanding farmer populations with varying levels of gene flow, a characterization that is influenced by the almost universal focus on the European Neolithic.^1-5 We sought to understand the demographic impact of an ongoing cultural transition to farming in Southwest Ethiopia, one of the last regions in Africa to experience such shifts.⁶ Importantly, Southwest Ethiopia is home to several of the world's remaining hunter-gatherer groups, including the Chabu people, who are currently transitioning away from their traditional mode of subsistence.⁷ We generated genome-wide data from the Chabu and four neighboring populations, the Majang, Shekkacho, Bench, and Sheko, to characterize their genetic ancestry and estimate their effective population sizes over the last 60 generations. We show that the Chabu are a distinct population closely related to ancient people who occupied Southwest Ethiopia >4,500 years ago. Furthermore, the Chabu are undergoing a severe population bottleneck, which began approximately 1,400 years ago. By analyzing eleven Eastern African populations, we find evidence for divergent demographic trajectories among hunter-gatherer-descendant groups. Our results illustrate that although foragers respond to encroaching agriculture and pastoralism with multiple strategies, including cultural adoption of agropastoralism, gene flow, and economic specialization, they often face population decline.

The "Golden Age" of Behavior Genetics?

The search for genetic risk factors underlying the presumed heritability of all human behavior has unfolded in two phases. The first phase, characterized by candidate-gene-association (CGA) studies, has fallen out of favor in the behavior-genetics community, so much so that it has been referred to as a "cautionary tale." The second and current iteration is characterized by genome-wide association studies (GWASs), single-nucleotide polymorphism (SNP) heritability estimates, and polygenic risk scores. This research is guided by the resurrection of, or reemphasis on, Fisher's "infinite infinitesimal allele" model of the heritability of complex phenotypes, first proposed over 100 years ago. Despite seemingly significant differences between the two iterations, they are united in viewing the discovery of risk alleles underlying heritability as a matter of finding differences in allele frequencies. Many of the infirmities that beset CGA studies persist in the era of GWASs, accompanied by a host of new difficulties due to the human genome's underlying complexities and the limitations of Fisher's model in the postgenomics era.

Gene-Level Germline Contributions to Clinical Risk of Recurrence Scores in Black and White Patients with Breast Cancer

Continuous risk of recurrence scores (CRS) based on tumor gene expression are vital prognostic tools for breast cancer. Studies have shown that Black women (BW) have higher CRS than White women (WW). Although systemic injustices contribute substantially to breast cancer disparities, evidence of biological and germline contributions is emerging. In this study, we investigated germline genetic associations with CRS and CRS disparity using approaches modeled after transcriptome-wide association studies (TWAS). In the Carolina Breast Cancer Study, using race-specific predictive models of tumor expression from germline genetics, we performed race-stratified (N = 1,043 WW, 1,083 BW) linear regressions of three CRS (ROR-S: PAM50 subtype score; proliferation score; ROR-P: ROR-S plus proliferation score) on imputed tumor genetically regulated tumor expression (GReX). Bayesian multivariate regression and adaptive shrinkage tested GReX-prioritized genes for associations with tumor PAM50 expression and subtype to elucidate patterns of germline regulation underlying GReX-CRS associations. At FDR-adjusted P < 0.10, 7 and 1 GReX prioritized genes among WW and BW, respectively. Among WW, CRS were positively associated with MCM10, FAM64A, CCNB2, and MMP1 GReX and negatively associated with VAV3, PCSK6, and GNG11 GReX. Among BW, higher MMP1 GReX predicted lower proliferation score and ROR-P. GReX-prioritized gene and PAM50 tumor expression associations highlighted potential mechanisms for GReX-prioritized gene to CRS associations. Among patients with breast cancer, differential germline associations with CRS were found by race, underscoring the need for larger, diverse datasets in molecular studies of breast cancer. These findings also suggest possible germline trans-regulation of PAM50 tumor expression, with potential implications for CRS interpretation in clinical settings. SIGNIFICANCE: This study identifies race-specific genetic associations with breast cancer risk of recurrence scores and suggests mediation of these associations by PAM50 subtype and expression, with implications for clinical interpretation of these scores.

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.

GSpace: an exact coalescence simulator of recombining genomes under isolation by distance

MOTIVATION

Simulation-based inference can bypass the limitations of statistical methods based on analytical approximations, but software allowing simulation of structured population genetic data without the classical n-coalescent approximations (such as those following from assuming large population size) are scarce or slow.

RESULTS

We present GSpace, a simulator for genomic data, based on a generation-by-generation coalescence algorithm taking into account small population size, recombination and isolation by distance.

AVAILABILITY AND IMPLEMENTATION

Freely available at site web INRAe (http://www1.montpellier.inra.fr/CBGP/software/gspace/download.html).

Pseudoreplication in genomics-scale datasets

In genomics-scale datasets, loci are closely packed within chromosomes and hence provide correlated information. Averaging across loci as if they were independent creates pseudoreplication, which reduces the effective degrees of freedom (df') compared to the nominal degrees of freedom, df. This issue has been known for some time, but consequences have not been systematically quantified across the entire genome. Here we measured pseudoreplication (quantified by the ratio df'/df) for a common metric of genetic differentiation (F_ST ) and a common measure of linkage disequilibrium between pairs of loci (r² ). Based on data simulated using models (SLiM and msprime) that allow efficient forward-in-time and coalescent simulations while precisely controlling population pedigrees, we estimated df' and df'/df by measuring the rate of decline in the variance of mean F_ST and mean r² as more loci were used. For both indices, df' increases with N_e and genome size, as expected. However, even for large N_e and large genomes, df' for mean r² plateaus after a few thousand loci, and a variance components analysis indicates that the limiting factor is uncertainty associated with sampling individuals rather than genes. Pseudoreplication is less extreme for F_ST , but df'/df ≤0.01 can occur in datasets using tens of thousands of loci. Commonly-used block-jackknife methods consistently overestimated var(F_ST ), producing very conservative confidence intervals. Predicting df' based on our modeling results as a function of N_e , L, S, and genome size provides a robust way to quantify precision associated with genomics-scale datasets.

Replicator equations induced by microscopic processes in nonoverlapping population playing bimatrix games

This paper is concerned with exploring the microscopic basis for the discrete versions of the standard replicator equation and the adjusted replicator equation. To this end, we introduce frequency-dependent selection-as a result of competition fashioned by game-theoretic consideration-into the Wright-Fisher process, a stochastic birth-death process. The process is further considered to be active in a generation-wise nonoverlapping finite population where individuals play a two-strategy bimatrix population game. Subsequently, connections among the corresponding master equation, the Fokker-Planck equation, and the Langevin equation are exploited to arrive at the deterministic discrete replicator maps in the limit of infinite population size.

Inclusion of variants discovered from diverse populations improves polygenic risk score transferability

The majority of polygenic risk scores (PRSs) have been developed and optimized in individuals of European ancestry and may have limited generalizability across other ancestral populations. Understanding aspects of PRSs that contribute to this issue and determining solutions is complicated by disease-specific genetic architecture and limited knowledge of sharing of causal variants and effect sizes across populations. Motivated by these challenges, we undertook a simulation study to assess the relationship between ancestry and the potential bias in PRSs developed in European ancestry populations. Our simulations show that the magnitude of this bias increases with increasing divergence from European ancestry, and this is attributed to population differences in linkage disequilibrium and allele frequencies of European-discovered variants, likely as a result of genetic drift. Importantly, we find that including into the PRS variants discovered in African ancestry individuals has the potential to achieve unbiased estimates of genetic risk across global populations and admixed individuals. We confirm our simulation findings in an analysis of hemoglobin A1c (HbA1c), asthma, and prostate cancer in the UK Biobank. Given the demonstrated improvement in PRS prediction accuracy, recruiting larger diverse cohorts will be crucial—and potentially even necessary—for enabling accurate and equitable genetic risk prediction across 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.