Uncovering Proximity of Chromosome Territories using Classical Algebraic Statistics

TL;DR

This paper introduces an algebraic statistical approach to analyze the three-dimensional genome organization by examining chromosome aberration data, revealing specific chromosome pairs with significant spatial proximity in irradiated human cells.

Contribution

The study presents a novel algebraic statistical method for assessing genome organization from sparse chromosome aberration data, validated on irradiated human blood lymphocytes.

Findings

Chromosome pairs {1,22} and {13,14} show significant proximity.

The overall genome organization appears largely random.

Algebraic methods are effective for analyzing genetic spatial data.

Abstract

Exchange type chromosome aberrations (ETCAs) are rearrangements of the genome that occur when chromosomes break and the resulting fragments rejoin with other fragments from other chromosomes. ETCAs are commonly observed in cancer cells and in cells exposed to radiation. The frequency of these chromosome rearrangements is correlated with their spatial proximity, therefore it can be used to infer the three dimensional organization of the genome. Extracting statistical significance of spatial proximity from cancer and radiation data has remained somewhat elusive because of the sparsity of the data. We here propose a new approach to study the three dimensional organization of the genome using algebraic statistics. We test our method on a published data set of irradiated human blood lymphocyte cells. We provide a rigorous method for testing the overall organization of the genome, and in agreement with previous results we find a random relative positioning of chromosomes with the exception of the chromosome pairs \{1,22\} and \{13,14\} that have a significantly larger number of ETCAs than the rest of the chromosome pairs suggesting their spatial proximity. We conclude that algebraic methods can successfully be used to analyze genetic data and have potential applications to larger and more complex data sets.