Epigenome breakthrough

  • February 6, 2014
Epigenome breakthrough

A new computational method which can identify changes in the epigenome across human populations that are associated with diseases such as arthritis has been developed by Gates Cambridge Alumnus James Zou.

A new computational method which can identify changes in the epigenome across human populations that are associated with diseases such as arthritis has been developed by a Gates Cambridge Scholar.

James Zou leads a study on the method which has just been published in Nature Methods.

There has been a lot of interest in finding genetic mutations associated with diseases, but finding epigenetic changes has been challenging up until now.

It is well known that genetic changes, i.e. mutations to DNA, can increase or decrease our risk for a variety of diseases. However, increasing interest is now focusing on how epigenetic changes – changes in the 3D packaging of DNA inside the cell and which part of the genome is accessible – can also be a significant driver for many diseases, including many forms of cancer.

The standard way to identify genetic mutations is to compare the genomes of people with a disease and healthy people to see which mutations are specific to the patients. However, it is very difficult to use this approach to identify epigenetic changes since different cells in the human body have very different epigenomes.

When the epigenome of an individual is measured, a heterogeneous sample of cells is used. So most of the differences highlighted in the epigenomes are due to the fact that samples from different individuals contain different mixtures of cells. This gives rise to many false signals and makes it difficult to identify the true epigenetic changes associated with disease.

The new research proposes a method, FaST-LMM-EWASher, that automatically corrects for cell-type composition without the need for explicit knowledge of it. It was done in collaboration with scientists at Microsoft Research.

James [2007], who did Part III Applied Mathematics at Cambridge, says: “The algorithm we developed for the paper solves this problem by modelling the heterogeneity of samples.”

James is currently a research fellow at the Simons Institute for the Theory of Computing at UC Berkeley. He says some of the mathematical techniques he learnt at Cambridge were essential for developing the algorithm.

Picture credit: www.freedigitalphotos.net and Photokanok.

 

 

Latest News

New app aims to help women through the menopause

A new app which helps women to manage the menopause was soft launched last month in collaboration with Mumsnet. Stella is the first product by Vira Health, a company which was co-founded in 2019 by Gates Cambridge Scholar Rebecca Love. Stella offers women relief from the most common symptoms of menopause, including sleep disturbances, hot […]

A global perspective on gender and health

The middle of a global pandemic may not seem the ideal time to move country with a new baby, but Johanna Riha [2011] took up her new role as a research fellow at the United Nations University International Institute for Global Health (UNU-IIGH) in Malaysia during the pandemic and moved to Kuala Lumpur around a […]

Scholars share 2021 Bill Gates Sr. Prize

Two Gates Cambridge Scholars are sharing the 2021 Bill Gates Sr. Prize in recognition of their outstanding research and social leadership. Emma Soneson and Maša Josipović have been selected for the prize which was established by the Gates Cambridge Trustees in June 2012 in recognition of the late Bill Gates Sr.’s role in establishing the […]

The censoring effect of populist anti-media messages

Populist attacks on the press should be viewed as a form of soft censorship which uses journalistic norms regarding objectivity to undermine the media, according to a new study by a Gates Cambridge Scholar. The study, Covering populist media criticism: When journalists’ professional norms turn against them, by Ayala Panievsky, is published in the International […]