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

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

и другие.

Science, Год журнала: 2025, Номер 387(6741), С. 1391 - 1397

Опубликована: Март 27, 2025

Describing the distribution of genetic variation across individuals is a fundamental goal population genetics. We present method that capitalizes on rich genealogical information encoded in genomic tree sequences to infer geographic locations shared ancestors sample sequenced individuals. used this history ancestry set human genomes sampled from Europe, Asia, and Africa, accurately recovering major movements those continents. Our findings demonstrate importance defining spatiotemporal context when describing caution against oversimplified interpretations data prevalent contemporary discussions race ancestry.

Язык: Английский

Процитировано

1
Inferring the geographic history of recombinant lineages using the full ancestral recombination graph DOI
Puneeth Deraje, James Kitchens, Graham Coop

и другие.

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

Опубликована: Апрель 14, 2024

Abstract Spatial patterns of genetic relatedness among samples reflect the past movements their ancestors. Our ability to untangle this history has potential improve dramatically given that we can now infer ultimate description relatedness, ancestral recombination graph (ARG). By extending spatial theory previously applied trees, generalize common model Brownian motion full ARGs, thereby accounting for correlations in trees along a chromosome while efficiently computing likelihood-based estimates dispersal rate and ancestor locations, with associated uncertainties. We evaluate model’s reconstruct histories using individual-based simulations unfortunately find clear bias locations. investigate causes bias, pinpointing discrepancy between true process at events. This highlights key hurdle ubiquitous analytically-tractable from which otherwise provide an efficient method inference, uncertainties, all information available ARG.

Язык: Английский

Процитировано

6
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