Biology subjects

Novembre, J.

Publications and source records attributed to Novembre, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Understanding genetic and environmental complexity in phenotypic resemblance across relatedness in population-scale data

A fundamental feature of a trait's variation in a population is how its phenotypic similarity scales with genetic relatedness. Under a strictly additive model of genetic architecture, phenotypic similarity increases linearly with genetic relatedness, with a slope equal to the heritability; this underpins approaches to heritability estimation from sibling IBD regression to Haseman--Elston regression and GREML. Empirically, however, the relationship is not linear for many traits (Kemper et al., 2021). Biobank-scale measurements make fine-grain observations of the phenotypic covariance versus genetic relatedness relationship increasingly feasible, motivating new theory to understand these new empirical observations. Here we evaluate how three processes might introduce non-linearities to the relationship: imperfect tagging of causal variants, assortative mating, and multi-generational shared environments. Through theoretical modeling and simulation, we find each process acts within pedigree classes differently, introducing non-linearities through impacts on the local slope and intercept of phenotypic covariance on genetic relatedness: imperfect tagging lowers the slope among distant relatives, shared environments elevates the intercepts for closer relatives, and assortative mating changes both the slope and intercept. Within these models, we explore a variety of scenarios, finding cases where the local slope of covariance on relatedness values among unrelated pairs versus among siblings can reach nearly three times the raw heritability, and building insight to where heritability estimates are likely to be biased. We then examine these patterns across a range of traits in the UK Biobank, with a supplementary analysis in All of Us. The shape of the relationship varies widely across traits, with profiles exhibiting features found under each of the theoretical mechanisms we explored. Overall, we conclude disentangling these multiple overlaid processes will be necessary to fully understand how phenotypic similarity increases with genetic relatedness in large cohorts.

genetics↗

bioRxiv2025

When migration leaves a clean trace: Decoupling migration from coalescence in the Structured Serial Coalescent

With the rapid accumulation of population genomic data across space and time, there is an urgent need for demographic inference methods that incorporate explicit time-series modeling, achieve high spatial scalability, and ensure clear identifiability between migration and coalescence rates. To address this need, we investigate pairwise genealogical processes under the structured serial coalescent, deriving evolution equations for pairwise branch length distributions and related statistics. By classifying the resulting identities according to their parameter dependencies and computational complexity, we identify a class that is not only computationally tractable but also determined exclusively by migration rates. Building on this theoretical basis, we propose a scalable framework for inferring time-varying migration rates and demonstrate its feasibility through simulation. We further outline how this framework can be extended to the joint estimation of migration and coalescence rates.

evolutionary biology↗

bioRxiv2025

Fine-Resolution Asymmetric Migration Estimation

The genetic structure of populations is often shaped by processes and events that introduce asymmetries to gene flow between geographic locations. Here, we first develop an algorithm that allows efficient computation of pairwise coalescent times in time-homogeneous models of population structure at migration-drift equilibrium. We then use the algorithm as the foundation for a new method to infer asymmetric migration rates in spatial models of population structure. The inferred equilibrium migration rates provide a novel representation of the geographic structure of genetic variation. We assess the method using a variety of simulated histories of gene flow, and apply the method to datasets from poplar trees, North American gray wolves, and human archaeogenetic samples, revealing complex asymmetric migration signals and providing a more refined view of the geographic structure of genetic variation.

genetics↗

bioRxiv2025

Jointly representing long-range genetic similarity and spatially heterogeneous isolation-by-distance

Isolation-by-distance patterns in genetic variation are a widespread feature of the geo-graphic structure of genetic variation in many species, and many methods have been developed to illuminate such patterns in genetic data. However, long-range genetic similarities also exist, often as a result of rare or episodic long-range gene flow. Jointly characterizing patterns of isolation-by-distance and long-range genetic similarity in genetic data is an open data analysis challenge that, if resolved, could help produce more complete representations of the geographic structure of genetic data in any given species. Here, we present a computationally tractable method that identifies long-range genetic similarities in a background of spatially heterogeneous isolation-by-distance variation. The method uses a coalescent-based framework, and models long-range genetic similarity in terms of directional events with source fractions describing the fraction of ancestry at a location tracing back to a remote source. The method produces geographic maps annotated with inferred long-range edges, as well as maps of uncertainty in the geographic location of each source of long-range gene flow. We have implemented the method in a package called FEEMSmix (an extension to FEEMS from Marcus et al., 2021), and validated its implementation using simulations representative of typical data applications. We also apply this method to two empirical data sets. In a data set of over 4,000 humans (Homo sapiens) across Afro-Eurasia, we recover many known signals of long-distance dispersal from recent centuries. Similarly, in a data set of over 100 gray wolves (Canis lupus) across North America, we identify several previously unknown long-range connections, some of which were attributable to recording errors in sampling locations. Therefore, beyond identifying genuine long-range dispersals, our approach also serves as a useful tool for quality control in spatial genetic studies. Author SummaryThe movement of individuals across landscapes shapes genetic diversity and has significant implications for both evolutionary studies and conservation efforts. Advances in sequencing now allow researchers to analyze thousands of samples from broad geographic areas, helping to estimate local gene flow. However, long-range genetic flow can occur due to a host of reasons (e.g. natural weather patterns, migration for resources, etc.), and existing methods struggle to represent these patterns. In this study, we developed a method to identify and model these long-range genetic similarities as dispersals from a source to a destination over a landscape. In applying this method to over 4,000 human samples from Afro-Eurasia, we detected signatures of known long-distance dispersals from recent centuries. In applying this method to 100 gray wolf samples from North America, we found many unexpected long-range genetic connections, some of which turned out to be recording errors in sample locations. Thus, beyond detecting real long-range dispersal, our approach also serves as a useful tool for quality control in spatial genetic studies.

evolutionary biology↗

bioRxiv2024

Study design and the sampling of deleterious rare variants in biobank-scale datasets

One key component of study design in population genetics is the "geographic breadth" of a sample (i.e., how broad a region across which individuals are sampled). How the geographic breadth of a sample impacts observations of rare, deleterious variants is unclear, even though such variants are of particular interest for biomedical and evolutionary applications. Here, in order to gain insight into the effects of sample design on ascertained genetic variants, we formulate a stochastic model of dispersal, genetic drift, selection, mutation, and geographically concentrated sampling. We use this model to understand the effects of the geographic breadth of sampling effort on the discovery of negatively selected variants. We find that samples which are more geographically broad will discover a greater number variants as compared geographically narrow samples (an effect we label "discovery"); though the variants will be detected at lower average frequency than in narrow samples (e.g. as singletons, an effect we label "dilution"). Importantly, these effects are amplified for larger sample sizes and moderated by the magnitude of fitness effects. We validate these results using both population genetic simulations and empirical analyses in the UK Biobank. Our results are particularly important in two contexts: the association of large-effect rare variants with particular phenotypes and the inference of negative selection from allele frequency data. Overall, our findings emphasize the importance of considering geographic breadth when designing and carrying out genetic studies, especially at biobank scale. SignificanceAs genetic studies grow, researchers are increasingly seeking to identify rare genetic variants with large impacts on traits. In this paper, we combine theoretical methods and data analysis to show how differences in sampling with respect to geographic location can influence the number and frequency of genetic variants that are found. Our results suggest that geographically broad samples will include more distinct genetic variants, though each variant will be found at a lower frequency, as compared to geographically narrow samples. Our results can help researchers to consider the implications of study design on expected results when constructing new genetic samples.

genetics↗

bioRxiv2022

Nationwide genomic biobank in Mexico unravels demographic history and complex trait architecture from 6,057 individuals

Latin America continues to be severely underrepresented in genomics research, and fine-scale genetic histories as well as complex trait architectures remain hidden due to the lack of Big Data. To fill this gap, the Mexican Biobank project genotyped 1.8 million markers in 6,057 individuals from 32 states and 898 sampling localities across Mexico with linked complex trait and disease information creating a valuable nationwide genotype-phenotype database. Through a suite of state-of-the-art methods for ancestry deconvolution and inference of identity-by-descent (IBD) segments, we inferred detailed ancestral histories for the last 200 generations in different Mesoamerican regions, unraveling native and colonial/post-colonial demographic dynamics. We observed large variations in runs of homozygosity (ROH) among genomic regions with different ancestral origins reflecting their demographic histories, which also affect the distribution of rare deleterious variants across Mexico. We analyzed a range of biomedical complex traits and identified significant genetic and environmental factors explaining their variation, such as ROH found to be significant predictors for trait variation in BMI and triglycerides.

genomics↗

bioRxiv2021

Modelling the spatiotemporal spread of beneficial alleles using ancient genomes

Ancient genome sequencing technologies now provide the opportunity to study natural selection in unprecedented detail. Rather than making inferences from indirect footprints left by selection in present-day genomes, we can directly observe whether a given allele was present or absent in a particular region of the world at almost any period of human history within the last 10,000 years. Methods for studying selection using ancient genomes often rely on partitioning individuals into discrete time periods or regions of the world. However, a complete understanding of natural selection requires more nuanced statistical methods which can explicitly model allele frequency changes in a continuum across space and time. Here we introduce a method for inferring the spread of a beneficial allele across a landscape using two-dimensional partial differential equations. Unlike previous approaches, our framework can handle time-stamped ancient samples, as well as genotype likelihoods and pseudohaploid sequences from low-coverage genomes. We apply the method to a panel of published ancient West Eurasian genomes to produce dynamic maps showcasing the inferred spread of candidate beneficial alleles over time and space. We also provide estimates for the strength of selection and diffusion rate for each of these alleles. Finally, we highlight possible avenues of improvement for accurately tracing the spread of beneficial alleles in more complex scenarios.

evolutionary biology↗

bioRxiv2021

Properties of Two-Locus Genealogies and Linkage Disequilibrium in Temporally Structured Samples

Archaeogenetics has been revolutionary, revealing insights into demographic history and recent positive selection in many organisms. However, most studies to date have ignored the non-random association of genetic variants at different loci (i.e., linkage disequilibrium, LD). This may be in part because basic properties of LD in samples from different times are still not well understood. Here, we derive several results for summary statistics of haplotypic variation under a model with time-stratified sampling: 1) The correlation between the number of pairwise differences observed between time-staggered samples ({pi}{Delta}t) in models with and without strict population continuity; 2) The product of the LD coefficient, D, between ancient and modern samples, which is a measure of haplotypic similarity between modern and ancient samples; and 3) The expected switch rate in the Li and Stephens haplotype copying model. The latter has implications for genotype imputation and phasing in ancient samples with modern reference panels. Overall, these results provide a characterization of how haplotype patterns are affected by sample age, recombination rates, and population sizes. We expect these results will help guide the interpretation and analysis of haplotype data from ancient and modern samples.

genetics↗

bioRxiv2021

Indigenous ancestry and admixture in the Uruguayan population

The Amerindian group known as the Charruas inhabited Uruguay at the timing of European colonial contact. Even though they were extinguished as an ethnic group as a result of a genocide, Charruan heritage is part of the Uruguayan identity both culturally and genetically. While mitochondrial DNA studies have shown evidence of Amerindian ancestry in living Uruguayans, here we undertake whole-genome sequencing of 10 Uruguayan individuals with Charruan heritage. We detect chromosomal segments of Amerindian ancestry supporting the presence of indigenous genetic ancestry in living descendants. Specific haplotypes were found to be enriched in Charruas and rare in the rest of the Amerindian groups studied. Some of these we interpret as the result of positive selection, as we identified selection signatures and they were located mostly within genes related to the infectivity of specific viruses. Historical records describe contacts of the Charruas with other Amerindians, such as Guarani, and patterns of genomic similarity observed here concur with genomic similarity between these groups. Less expected, we found a high genomic similarity of the Charruas to Diaguita from Argentinian and Chile, which could be explained by geographically proximity. Finally, by fitting admixture models of Amerindian and European ancestry for the Uruguayan population, we were able to estimate the timing of the first pulse of admixture between European and Uruguayan indigenous peoples in 1658 and the second migration pulse in 1683. Both dates roughly concurring with the Franciscan missions in 1662 and the foundation of the city of Colonia in 1680 by the Spanish.

genomics↗

bioRxiv2020

Fast and Flexible Estimation of Effective Migration Surfaces

An important feature in spatial population genetic data is often "isolation-by-distance," where genetic differentiation tends to increase as individuals become more geographically distant. Recently, Petkova et al. (2016) developed a statistical method called Estimating Effective Migration Surfaces (EEMS) for visualizing spatially heterogeneous isolation-by-distance on a geographic map. While EEMS is a powerful tool for depicting spatial population structure, it can suffer from slow runtimes. Here we develop a related method called Fast Estimation of Effective Migration Surfaces (FEEMS). FEEMS uses a Gaussian Markov Random Field in a penalized likelihood framework that allows for efficient optimization and output of effective migration surfaces. Further, the efficient optimization facilitates the inference of migration parameters per edge in the graph, rather than per node (as in EEMS). When tested with coalescent simulations, FEEMS accurately recovers effective migration surfaces with complex gene-flow histories, including those with anisotropy. Applications of FEEMS to population genetic data from North American gray wolves shows it to perform comparably to EEMS, but with solutions obtained orders of magnitude faster. Overall, FEEMS expands the ability of users to quickly visualize and interpret spatial structure in their data.

genetics↗

bioRxiv2020

Geographic patterns of human allele frequency variation: a variant-centric perspective

A key challenge in human genetics is to describe and understand the distribution of human genetic variation. Often genetic variation is described by showing relationships among populations or individuals, in each case drawing inferences over a large number of variants. Here, we present an alternative representation of human genetic variation that reveals the relative abundance of different allele frequency patterns across populations. This approach allows viewers to easily see several features of human genetic structure: (1) most variants are rare and geographically localized, (2) variants that are common in a single geographic region are more likely to be shared across the globe than to be private to that region, and (3) where two individuals differ, it is most often due to variants that are common globally, regardless of whether the individuals are from the same region or different regions. To guide interpretation of the results, we also apply the visualization to contrasting theoretical scenarios with varying levels of divergence and gene flow. Our variant-centric visualization clarifies the major geographic patterns of human variation and can be used to help correct potential misconceptions about the extent and nature of genetic differentiation among populations.

evolutionary biology↗

bioRxiv2020

Detecting runs of homozygosity from low-coverage ancient DNA

At present day, human parental relatedness varies substantially across the globe, but little is known about the past. Here we use ancient DNA to provide new insights, leveraging that parental relatedness leaves traces in the offsprings genome in the form of runs of homozygosity. We present a method to identify such runs in low-coverage ancient DNA data using linkage information from a reference panel of modern haplotypes. As a result, the method facilitates analysis of a much larger fraction of the global ancient DNA record than previously possible. Simulation and experiments show that this new method has power to detect runs of homozygosity longer than 4 centimorgan for ancient individuals with at least 0.3x coverage. We used this new method to analyze sequence data from 1,785 humans from the last 45,000 years. Generally, we detect very low rates of first cousin or closer unions across most ancient populations. Moreover, our results evidence a substantial impact of the adoption of agricultural lifestyles: We find a marked decay in background parental relatedness, co-occurring with or shortly after the advent of sedentary agriculture. We observe this signal, likely linked to increasing local population sizes, across several geographic regions worldwide.

genetics↗

bioRxiv2019

Haplotype-based inference of the distribution of fitness effects

Recent genome sequencing studies with large sample sizes in humans have discovered a vast quantity of low-frequency variants, providing an important source of information to analyze how selection is acting on human genetic variation. In order to estimate the strength of natural selection acting on low-frequency variants, we have developed a likelihood-based method that uses the lengths of pairwise identity-by-state between haplotypes carrying low-frequency variants. We show that in some non-equilibrium populations (such as those that have had recent population expansions) it is possible to distinguish between positive or negative selection acting on a set of variants. With our new framework, one can infer a fixed selection intensity acting on a set of variants at a particular frequency, or a distribution of selection coefficients for standing variants and new mutations. We apply our method to the UK10K phased haplotype dataset of 3,781 individuals and find a similar proportion of neutral, moderately deleterious, and deleterious variants compared to previous estimates made using the site frequency spectrum. We discuss several interpretations for this result, including that selective constraints have remained constant over time.

evolutionary biology↗

bioRxiv2019

Population history from the Neolithic to present on the Mediterranean island of Sardinia: An ancient DNA perspective

Recent ancient DNA studies of western Eurasia have revealed a dynamic history of admixture, with evidence for major migrations during the Neolithic and Bronze Age. The population of the Mediterranean island of Sardinia has been notable in these studies - Neolithic individuals from mainland Europe cluster more closely with Sardinian individuals than with all other present-day Europeans. The current model to explain this result is that Sardinia received an initial influx of Neolithic ancestry and then remained relatively isolated from expansions in the later Neolithic and Bronze Age that took place in continental Europe. To test this model, we generated genome-wide capture data (approximately 1.2 million variants) for 43 ancient Sardinian individuals spanning the Neolithic through the Bronze Age, including individuals from Sardinias Nuragic culture, which is known for the construction of numerous large stone towers throughout the island. We analyze these new samples in the context of previously generated genome-wide ancient DNA data from 972 ancient individuals across western Eurasia and whole-genome sequence data from approximately 1,500 modern individuals from Sardinia. The ancient Sardinian individuals show a strong affinity to western Mediterranean Neolithic populations and we infer a high degree of genetic continuity on the island from the Neolithic (around fifth millennium BCE) through the Nuragic period (second millennium BCE). In particular, during the Bronze Age in Sardinia, we do not find significant levels of the \"Steppe\" ancestry that was spreading in many other parts of Europe at that time. We also characterize subsequent genetic influx between the Nuragic period and the present. We detect novel, modest signals of admixture between 1,000 BCE and present-day, from ancestry sources in the eastern and northern Mediterranean. Within Sardinia, we confirm that populations from the more geographically isolated mountainous provinces have experienced elevated levels of genetic drift and that northern and southwestern regions of the island received more gene flow from outside Sardinia. Overall, our genetic analysis sheds new light on the origin of Neolithic settlement on Sardinia, reinforces models of genetic continuity on the island, and provides enhanced power to detect post-Bronze-Age gene flow. Together, these findings offer a refined demographic model for future medical genetic studies in Sardinia.

genetics↗

bioRxiv2018

Distinguishing multiple-merger from Kingman coalescence using two-site frequency spectra

Demographic inference methods in population genetics typically assume that the ancestry of a sample can be modeled by the Kingman coalescent. A defining feature of this stochastic process is that it generates genealogies that are binary trees: no more than two ancestral lineages may coalesce at the same time. However, this assumption breaks down under several scenarios. For example, pervasive natural selection and extreme variation in offspring number can both generate genealogies with "multiple-merger" events in which more than two lineages coalesce instantaneously. Therefore, detecting multiple mergers (and other violations of the Kingman assumptions) is important both for understanding which forces have shaped the diversity of a population and for avoiding fitting misspecified models to data. Current methods to detect multiple mergers in genomic data rely primarily on the site frequency spectrum (SFS). However, the signatures of multiple mergers in the SFS are also consistent with a Kingman coalescent with a time-varying population size. Here, we present a new statistical test for determining whether the Kingman coalescent with any population size history is consistent with population data. Our approach is based on information contained in the two-site joint frequency spectrum (2-SFS) for pairs of linked sites, which has a different dependence on the topologies of genealogies than the SFS. Our statistical test is global in the sense that it can detect when the genome-wide genetic diversity is inconsistent with the Kingman model, rather than detecting outlier regions, as in selection scan methods. We validate this test using simulations, and then apply it to demonstrate that genomic diversity data from Drosophila melanogaster is inconsistent with the Kingman coalescent.

evolutionary biology↗

bioRxiv2018

Expected patterns of local ancestry in a hybrid zone

1The initial drivers of reproductive isolation between species are poorly characterized. In cases where partial reproductive isolation exists, genomic patterns of variation in hybrid zones may provide clues about the barriers to gene flow which arose first during the early stages of speciation. Purifying selection against incompatible substitutions that reduce hybrid fitness has the potential to distort local patterns of ancestry relative to background patterns across the genome. The magnitude and qualitative properties of this pattern are dependent on several factors including migration history and the relative fitnesses for different combinations of incompatible alleles. We present a model which may account for these factors and highlight the potential for its use in verifying the action of natural selection on candidate loci implicated in reducing hybrid fitness.

evolutionary biology↗

bioRxiv2018

The genetic prehistory of the Andean highlands 7,000 Years BP though European contact

The peopling of the Andean highlands above 2500m in elevation was a complex process that included cultural, biological and genetic adaptations. Here we present a time series of ancient whole genomes from the Andes of Peru, dating back to 7,000 calendar years before present (BP), and compare them to 64 new genome-wide genetic variation datasets from both high and lowland populations. We infer three significant features: a split between low and high elevation populations that occurred between 9200-8200 BP; a population collapse after European contact that is significantly more severe in South American lowlanders than in highland populations; and evidence for positive selection at genetic loci related to starch digestion and plausibly pathogen resistance after European contact. Importantly, we do not find selective sweep signals related to known components of the human hypoxia response, which may suggest more complex modes of genetic adaptation to high altitude.\n\nOne Sentence SummaryAncient DNA from the Andes reveals a complex picture of human adaptation from early settlement to the colonial period.

genomics↗

bioRxiv2018

Estimating recent migration and population size surfaces

In many species a fundamental feature of genetic diversity is that genetic similarity decays with geographic distance; however, this relationship is often complex, and may vary across space and time. Methods to uncover and visualize such relationships have widespread use for analyses in molecular ecology, conservation genetics, evolutionary genetics, and human genetics. While several frameworks exist, a promising approach is to infer maps of how migration rates vary across geographic space. Such maps could, in principle, be estimated across time to reveal the full complexity of population histories. Here, we take a step in this direction: we present a method to infer separate maps of population sizes and migration rates for different time periods from a matrix of genetic similarity between every pair of individuals. Specifically, genetic similarity is measured by counting the number of long segments of haplotype sharing (also known as identity-by-descent tracts). By varying the length of these segments we obtain parameter estimates for qualitatively different time periods. Using simulations, we show that the method can reveal time-varying migration rates and population sizes, including changes that are not detectable when ignoring haplotypic structure. We apply the method to a dataset of contemporary European individuals (POPRES), and provide an integrated analysis of recent population structure and growth over the last ~3,000 years in Europe. Software implementing the methods is available at https://github.com/halasadi/MAPS.

bioinformatics↗