Phyloseminar

Scalable approaches to inference and analysis of genome-wide genealogies
The ancestral recombination graph (ARG) is a graph-like structure that encodes a detailed genealogical history of a set of individuals along the genome. ARGs that are accurately reconstructed from genomic data sets are useful for a range of applications in statistical and population genetics, but inference from data sets comprising millions of samples and variants remains computationally challenging. In this talk, I will introduce a novel ARG inference algorithm, called Threads, and show how ARG inference can be applied to bionbank-scale data sets using the algorithmic paradigm of “threading”. Using inferred ARGs, I will then explore applications of inferred ARGs to three familiar tasks in statistical genetics. First, I will show how threading algorithms can be used to improve upon traditional genotype compression methods by identifying long identical-by-descent segments. Second, I will show how careful modeling of allele ages can help improve imputation of ultra-rare variants. Finally, I will discuss how inferred ARGs can complement or improve upon traditional genetic association studies.