A faster and cheaper way to find disease genes in the human genome that is being developed by researchers in the US has passed its initial “proof of concept” test by finding previously unknown gene mutations for Freeman-Sheldon syndrome, a rare Mendelian disorder, in unrelated affected individuals.
Researchers from the University of Washington (UW) in Seattle wrote about their work in a paper that appears in the 16 August issue of Nature.
With the new genome-analysis strategy they are developing, senior investigator Jay Shendure, UW assistant professor of genome sciences, and colleagues, expect to significantly reduce the cost of and the time it takes to find links between genes and diseases among unrelated people with the same inherited disorder.
At the moment, very few scientists can undertake this kind of investigation because current methods for sequencing and comparing human genomes are too complex, cost too much and take too long.
Shendure explained in a statement why this work is important:
“The genetics of thousands of rare diseases remains unsolved because sufficient numbers of families with individuals affected by those disorders are not easily available. Even with such families, mapping and identifying the causative gene can take many years.”
Together with lead author Sarah B. Ng, a graduate student in the UW Department of Genome Sciences, Shendure and colleagues conducted a proof of concept study to see if a more targeted analysis using newer technology could find candidate genes for a Mendelian disorder called Freeman-Sheldon syndrome in unrelated affected individuals.
Mendelian disorders like Freeman-Sheldon syndrome, cystic fibrosis and sickle cell anemia, are caused by a mutation in a single gene and pass down through generations in a simple inheritance pattern.
We already know from work done on the genetics of cancer, diabetes and heart disease that common variations in the human genome are only linked to a very small part of the risk for these common diseases.
The new strategy that Shendure and colleagues have designed allows scientists to look for rare variants, but it could be extended for use in large population studies looking at the complex genetics behind leading causes of death and disability, they said.
The strategy does not entail looking at the whole genome. Shendure said they decided to focus only on the exome: the 1 per cent of the genome that codes for proteins.
The team captured the exomes of 12 people, 4 of whom had the same Mendelian disorder, Freeman-Sheldon syndrome. The participants were not related.
“We then decoded these selected parts of the genome through massively parallel DNA sequencing, a technology that allows one to sequence hundreds of millions of DNA fragments in parallel,” explained Shendure.
When they analysed the data across the participants they found that only one gene, MYH3, had previously undiscovered mutations in the exomes of all 4 affected participants.
The limitation of ignoring the 99 per cent of the rest of the genome is that you don’t find out about the non-coding differences among the genomes, such as the regulatory and structural functions, said the researchers.
However, despite this limitation, the strategy still has some significant advantages, as Shendure explained:
“Our focus on the protein-coding subset of the genome enables us to do at least 20 times more samples than could be done with whole genome sequencing with equivalent effort.”
The researchers started gathering the data in November 2008, and finished in February 2009. They suggest with the lessons learned on this study, a similar rare disease type could be analysed in a matter of weeks, and this timescale could go down even further in the future.
As these “second-generation” DNA sequencing technologies become more common, they present new data management challenges. For example, the amount of raw data collected by the DNA sequencing instruments at the UW alone soon will soon be measured in petabytes. One petabyte is a quadrillion bytes or 1,000 terabytes, or 1,000,000 gigabytes, or about 6 billion web photos.
“Massively parallel technologies that make it possible to study individual genomes have only recently emerged, but hold significant promise for gaining new insights in human biology and medicine,” said Shendure.
“This approach to human exome sequencing will be the key in scaling everyone’s efforts to explore the genetics of both susceptibility and resistance to more complex human diseases such as heart disease, cancer, and infectious diseases,” he added.
As well as Shendure’s lab in Genome Sciences, the key work was done in collaboration with three other UW labs: Deborah Nickerson’s in Genome Sciences, Michael Bamshad’s in Pediatrics; and Evan Eichler’s in Genome Sciences, plus Arindam Bhattacharjee from Agilent Technologies in Santa Clara, California.
The study was funded by the National Institutes of Health, the Washington Research Foundation, and the Agency for Science, Technology, and Research in Singapore.
“Targeted capture and massively parallel sequencing of 12 human exomes.”
Sarah B. Ng, Emily H. Turner, Peggy D. Robertson, Steven D. Flygare, Abigail W. Bigham, Choli Lee, Tristan Shaffer, Michelle Wong, Arindam Bhattacharjee, Evan E. Eichler, Michael Bamshad, Deborah A. Nickerson and Jay Shendure.
Source: University of Washington News.
Written by: Catharine Paddock, PhD