DeepAI AI Chat
Log In Sign Up

Efficient Reconstruction of Stochastic Pedigrees

by   Younhun Kim, et al.

We introduce a new algorithm called Rec-Gen for reconstructing the genealogy or pedigree of an extant population purely from its genetic data. We justify our approach by giving a mathematical proof of the effectiveness of 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 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.


page 1

page 2

page 3

page 4


Efficient Reconstruction of Stochastic Pedigrees: Some Steps From Theory to Practice

In an extant population, how much information do extant individuals prov...

On the Effectiveness of Genetic Operations in Symbolic Regression

This paper describes a methodology for analyzing the evolutionary dynami...

The quasispecies regime for the simple genetic algorithm with ranking selection

We study the simple genetic algorithm with a ranking selection mechanism...

Mutual Information Maximization for Robust Plannable Representations

Extending the capabilities of robotics to real-world complex, unstructur...

A Fast, Accurate Two-Step Linear Mixed Model for Genetic Analysis Applied to Repeat MRI Measurements

Large-scale biobanks are being collected around the world in efforts to ...

Lagged Exact Bayesian Online Changepoint Detection

Identifying changes in the generative process of sequential data, known ...

Improved Active Multi-Task Representation Learning via Lasso

To leverage the copious amount of data from source tasks and overcome th...