A general and efficient representation of ancestral recombination graphs

• Published in: Genetics (Austin) Vol. 228; no. 1
• Main Authors: Wong, Yan, Ignatieva, Anastasia, Koskela, Jere, Gorjanc, Gregor, Wohns, Anthony W, Kelleher, Jerome
• Format: Journal Article
• Language: English
• Published: US Oxford University Press 04.09.2024; Genetics Society of America
• Subjects: Coalescence; DNA structure; Evolution, Molecular; Formalism; Gene mapping; Gene sequencing; Genome; Genomes; Graphical representations; Heredity; Humans; Models, Genetic; Nucleotide sequence; Nucleotides; Recombination; Recombination, Genetic; Software; Stand structure; ancestral recombination graphs
• ISSN: 1943-2631; 0016-6731; 1943-2631
• DOI: 10.1093/genetics/iyae100

Abstract 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. However, this approach is out of step with some 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 generalizes 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. Ancestral Recombination Graphs (ARGs) describe the complex web of genetic ancestry that results from individuals inheriting parts of their genomes through different routes. Classical methods of describing ARGs focus on evolutionary events, but this approach is out of step with some modern developments and cannot describe their outputs. The authors provide a simple alternative formulation that generalises this classical view show how it leads to efficient software.