Efficient Reconstruction of Stochastic Pedigrees

TL;DR

The paper presents { extsc{Rec-Gen}}, an iterative algorithm for reconstructing pedigrees from genetic data, with proven effectiveness on idealized models and potential implications for genomic privacy.

Contribution

Introduces { extsc{Rec-Gen}}, a novel iterative algorithm with proven accuracy and low sample complexity for pedigree reconstruction from genetic data.

Findings

Effective reconstruction of pedigrees in idealized models

Low sample complexity required for accurate results

Potential applications to genomic privacy

Abstract

We introduce a new algorithm called {\sc Rec-Gen} for reconstructing the genealogy or \textit{pedigree} of an extant population purely from its genetic data. We justify our approach by giving a mathematical proof of the effectiveness of {\sc Rec-Gen} when applied to pedigrees from an idealized generative model that replicates some of the features of real-world pedigrees. Our algorithm is iterative and provides an accurate reconstruction of a large fraction of the pedigree while having relatively low \emph{sample complexity}, measured in terms of the length of the genetic sequences of the population. We propose our approach as a prototype for further investigation of the pedigree reconstruction problem toward the goal of applications to real-world examples. As such, our results have some conceptual bearing on the increasingly important issue of genomic privacy.