On the genes, genealogies, and geographies of Quebec

Anderson-Trocmé, Luke; Nelson, Dominic; Zabad, Shadi; Diaz-Papkovich, Alex; Kryukov, Ivan; Baya, Nikolas; Touvier, Mathilde; Jeffery, Ben; Dina, Christian; Vézina, Hélène; Kelleher, Jerome; Gravel, Simon

doi:10.1126/science.add5300

Cited by 15 publications

(6 citation statements)

References 60 publications

(55 reference statements)

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“…The development of ARG evaluation metrics that take into account more of the global topology and can be applied to large ARGs would be a valuable and timely addition to the field. Using ARGs simulated from observed pedigree data (Anderson-Trocmé et al, 2023) as ground-truth would also add a valuable dimension to our understanding of how well methods perform when faced with realistic population and family structure.…”

Section: Discussionmentioning

confidence: 99%

“…The tskit library (Section 10) is a high-quality open-source gARG implementation, with proven efficiency and scalability (e.g. Anderson-Trocmé et al, 2023;Zhan et al, 2023), that is already in widespread use. Adopting it as a community standard may ease software implementation burden on researchers, freeing their time to address the many fascinating open questions and challenges that exist.…”

Section: Discussionmentioning

confidence: 99%

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A general and efficient representation of ancestral recombination graphs

Wong,

Ignatieva,

Koskela

et al. 2023

Preprint

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As a result of recombination, adjacent nucleotides can have different paths of genetic inheri-tance 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). New developments have made it possible to infer ARGs at scale, enabling many new applications in population and statistical genetics. This rapid progress, however, has led to a substantial gap opening between theory and practice. Standard mathematical formalisms, based on exhaustively detailing the “events” that occur in the history of a sample, are insufficient to describe the outputs of current methods. Moreover, we argue that the underlying assumption that all events can be known and precisely estimated is fundamentally unsuited to the realities of modern, population-scale datasets. We propose an alternative mathematical formulation that encompasses the outputs of recent methods and can capture the full richness of modern large-scale datasets. By defining this ARG encoding in terms of specific genomes and their intervals of genetic inheritance, we avoid the need to exhaustively list (and estimate)allevents. The effects of multiple events can be aggregated in different ways, providing a natural way to express many forms of approximate and partial knowledge about the recombinant ancestry of a sample.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A general and efficient representation of ancestral recombination graphs

Wong,

Ignatieva,

Koskela

et al. 2023

Preprint

Self Cite

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“…The notable innovation of msprime relative to previous coalescent programs is the speed at which it can perform simulations at biologically realistic scales under a variety of models and with recombination. For example, msprime has been used to simulate realistic whole genome sequences based on genealogical information for approximately 1.4 million people inhabiting Quebec, Canada [104].…”

Section: Box 2: Simulationmentioning

confidence: 99%

The era of the ARG: An introduction to ancestral recombination graphs and their significance in empirical evolutionary genomics

Lewanski,

Grundler,

Bradburd

2024

PLoS Genet

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In the presence of recombination, the evolutionary relationships between a set of sampled genomes cannot be described by a single genealogical tree. Instead, the genomes are related by a complex, interwoven collection of genealogies formalized in a structure called an ancestral recombination graph (ARG). An ARG extensively encodes the ancestry of the genome(s) and thus is replete with valuable information for addressing diverse questions in evolutionary biology. Despite its potential utility, technological and methodological limitations, along with a lack of approachable literature, have severely restricted awareness and application of ARGs in evolution research. Excitingly, recent progress in ARG reconstruction and simulation have made ARG-based approaches feasible for many questions and systems. In this review, we provide an accessible introduction and exploration of ARGs, survey recent methodological breakthroughs, and describe the potential for ARGs to further existing goals and open avenues of inquiry that were previously inaccessible in evolutionary genomics. Through this discussion, we aim to more widely disseminate the promise of ARGs in evolutionary genomics and encourage the broader development and adoption of ARG-based inference.

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“…Moreover, we could learn about the history of dispersal in a species, identifying major population movements, demographic events, and barriers to migration. Although such detailed pedigree information is rarely available (but see Aguillon et al (2017) and Anderson-Trocmé et al (2023)), it nevertheless is possible to learn about the pedigree ancestors that are shared among individuals in a sample. This is because genetic relationships between samples, as well as the identities of the shared ancestors via whom they are related, are encoded in an interwoven collection of gene genealogies called an ancestral recombination graph (ARG) (Griffiths and Marjoram, 1997; Lewanski et al, 2024; Wong et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

A geographic history of human genetic ancestry

Grundler,

Terhorst,

Bradburd

2024

Preprint

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

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On the genes, genealogies, and geographies of Quebec

Cited by 15 publications

References 60 publications

A general and efficient representation of ancestral recombination graphs

A general and efficient representation of ancestral recombination graphs

The era of the ARG: An introduction to ancestral recombination graphs and their significance in empirical evolutionary genomics

A geographic history of human genetic ancestry

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