Estimating dispersal rates and locating genetic ancestors with genome-wide genealogies DOI Creative Commons
Matthew M. Osmond, Graham Coop

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2021, Номер unknown

Опубликована: Июль 14, 2021

Abstract Spatial patterns in genetic diversity are shaped by individuals dispersing from their parents and larger-scale population movements. It has long been appreciated that these of movement shape the underlying genealogies along genome leading to geographic isolation distance contemporary data. However, extracting enormous amount information contained recombining sequences has, until recently, not computationally feasible. Here we capitalize on important recent advances genome-wide gene-genealogy reconstruction develop methods use thousands trees estimate per-generation dispersal rates locate ancestors a sample back through time. We take likelihood approach continuous space using simple approximate model (branching Brownian motion) as our prior distribution spatial genealogies. After testing method with simulations apply it Arabidopsis thaliana . rate roughly 60km 2 per generation, slightly higher across latitude than longitude, potentially reflecting northward post-glacial expansion. Locating allows us visualize major movements, alternative histories, admixture. Our highlights huge about past events movements

Язык: Английский

Inference and applications of ancestral recombination graphs DOI
Rasmus Nielsen, Andrew H. Vaughn, Yun Deng

и другие.

Nature Reviews Genetics, Год журнала: 2024, Номер unknown

Опубликована: Сен. 30, 2024

Язык: Английский

Процитировано

14
A geographic history of human genetic ancestry DOI Creative Commons
Michael C. Gründler, Jonathan Terhorst, Gideon S. Bradburd

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Март 29, 2024

Describing the distribution of genetic variation across individuals is a fundamental goal population genetics. In humans, traditional approaches for describing often rely on discrete ancestry labels, which, despite their utility, can obscure complex, multi-faceted nature human history. These labels risk oversimplifying by ignoring its temporal depth and geographic continuity, may therefore conflate notions race, ethnicity, geography, ancestry. Here, we present method that capitalizes rich genealogical information encoded in genomic tree sequences to infer locations shared ancestors sample sequenced individuals. We use this history set genomes sampled from Europe, Asia, Africa, accurately recovering major movements those continents. Our findings demonstrate importance defining spatial-temporal context caution against oversimplified interpretations data prevalent contemporary discussions race

Язык: Английский

Процитировано

9
A general and efficient representation of ancestral recombination graphs DOI Creative Commons
Yan Wong, Anastasia Ignatieva, Jere Koskela

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Ноя. 4, 2023

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

Язык: Английский

Процитировано

15
Estimating dispersal rates and locating genetic ancestors with genome-wide genealogies DOI Creative Commons
Matthew M. Osmond, Graham Coop

eLife, Год журнала: 2024, Номер 13

Опубликована: Ноя. 26, 2024

Spatial patterns in genetic diversity are shaped by individuals dispersing from their parents and larger-scale population movements. It has long been appreciated that these of movement shape the underlying genealogies along genome leading to geographic isolation-by-distance contemporary data. However, extracting enormous amount information contained recombining sequences has, until recently, not computationally feasible. Here, we capitalize on important recent advances genome-wide gene-genealogy reconstruction develop methods use thousands trees estimate per-generation dispersal rates locate ancestors a sample back through time. We take likelihood approach continuous space using simple approximate model (branching Brownian motion) as our prior distribution spatial genealogies. After testing method with simulations apply it Arabidopsis thaliana. rate roughly 60 km2/generation, slightly higher across latitude than longitude, potentially reflecting northward post-glacial expansion. Locating allows us visualize major movements, alternative histories, admixture. Our highlights huge about past events movements

Язык: Английский

Процитировано

5
Likelihoods for a general class of ARGs under the SMC DOI Creative Commons
Gertjan Bisschop, Jerome Kelleher, Peter L. Ralph

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Фев. 27, 2025

Ancestral recombination graphs (ARGs) are the focus of much ongoing research interest. Recent progress in inference has made ARG-based approaches feasible across range applications, and many new methods using inferred ARGs as input have appeared. This on long-standing problem ARG proceeded two distinct directions. First, Bayesian under Sequentially Markov Coalescent (SMC), is now practical for tens-to-hundreds samples. Second, approximate models heuristics can scale to sample sizes three orders magnitude larger. Although these heuristic reasonably accurate metrics, one significant drawback that they estimate do not topological properties required compute a likelihood such SMC present-day formulations. In particular, typically precise details about events, which currently likelihood. this paper we present backwards-time formulation derive straightforward definition general class model. We show does require events be estimated, robust presence polytomies. discuss possibilities opens.

Язык: Английский

Процитировано

0
Estimating dispersal rates and locating genetic ancestors with genome-wide genealogies DOI Creative Commons
Matthew M. Osmond, Graham Coop

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2021, Номер unknown

Опубликована: Июль 14, 2021

Abstract Spatial patterns in genetic diversity are shaped by individuals dispersing from their parents and larger-scale population movements. It has long been appreciated that these of movement shape the underlying genealogies along genome leading to geographic isolation distance contemporary data. However, extracting enormous amount information contained recombining sequences has, until recently, not computationally feasible. Here we capitalize on important recent advances genome-wide gene-genealogy reconstruction develop methods use thousands trees estimate per-generation dispersal rates locate ancestors a sample back through time. We take likelihood approach continuous space using simple approximate model (branching Brownian motion) as our prior distribution spatial genealogies. After testing method with simulations apply it Arabidopsis thaliana . rate roughly 60km 2 per generation, slightly higher across latitude than longitude, potentially reflecting northward post-glacial expansion. Locating allows us visualize major movements, alternative histories, admixture. Our highlights huge about past events movements

Язык: Английский

Процитировано

22