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

Why technology needs feminism

What is good technology? Is ‘good’ technology even possible? And how can feminism contribute towards it? Those questions and more are at the heart of a new book co-edited by Gates Cambridge Scholar Dr Kerry McInerney and based on the popular podcast series she co-hosts. The Good Robot: Why technology needs feminism gathers together the thoughts of leading […]

‘Knowledge alone isn’t enough’

The summer before starting his PhD at the University of Cambridge, Rob Henderson was working up a book proposal with his literary agent. That book, Troubled, is published next month by Simon & Schuster and is part memoir, part social commentary.  In it Rob recounts his life growing up in foster care and his time […]

Connecting climate change and mental health

A Gates Cambridge Scholar is organising a webinar to publicise the formal launch of a public, online information hub on the intersection between climate change and mental health. Colleen Rollins [2017], editorial and project manager at the Climate Psychiatry Alliance, is working on the Ecopsychepedia (“EcoPsy”) project which will be the subject of a webinar […]

Reconnecting through music

When José Izquierdo [2013] was working on his PhD at Cambridge on how Latin American composers united European and local influences in the 19th century, he found a way to make his academic work come to life. Much of the music he was researching had never been heard before and he was also discovering old scores […]