Reconstructing pedigrees: a stochastic perspective

TL;DR

This paper investigates the theoretical possibility of reconstructing pedigrees from genomic sequence data, demonstrating that under certain stochastic models, pedigrees can be uniquely identified given sufficiently long sequences.

Contribution

It provides a theoretical framework showing pedigree reconstruction is possible under specific stochastic models, extending prior work on relatedness limitations.

Findings

Pedigrees can be reconstructed up to isomorphism from long sequences under certain models.

Reconstruction is impossible if only relatedness degrees are used.

The work complements existing results by identifying conditions for successful pedigree inference.

Abstract

A pedigree is a directed graph that describes how individuals are related through ancestry in a sexually-reproducing population. In this paper we explore the question of whether one can reconstruct a pedigree by just observing sequence data for present day individuals. This is motivated by the increasing availability of genomic sequences, but in this paper we take a more theoretical approach and consider what models of sequence evolution might allow pedigree reconstruction (given sufficiently long sequences). Our results complement recent work that showed that pedigree reconstruction may be fundamentally impossible if one uses just the degrees of relatedness between different extant individuals. We find that for certain stochastic processes, pedigrees can be recovered up to isomorphism from sufficiently long sequences.