By Kevin Davies
June 23, 2011 | Scientists at the University of Utah and a Bay Area software company, Omicia, have released details of a powerful computational tool for identifying disease-causing mutations by individual genome sequencing. The new software is described in a paper in the journal Genome Research. It’s potential for rapidly screening genome data to identify a deleterious mutation is illustrated in a companion paper in the American Journal of Human Genetics, in which researchers rapidly identified novel mutations in a fatal X-linked disorder.
VAAST -- the Variant Annotation, Analysis and Selection Tool – was developed by Mark Yandell’s group at the University of Utah School of Medicine in collaboration with a team at Omicia in San Francisco, led by CEO Martin Reese. It was developed using stimulus funding from the National Human Genome Research Institute (NHGRI), specifically a ‘Grand Opportunities’ (GO) grant, awarded to the University of Utah and Omica.
“VAAST is an integrative tool that uses a number of inputs to rank the [DNA] variants based on clinical gene importance in an automatic way,” Reese told Bio-IT World. The mutation tracking software is designed to screen individual human genome sequences for clinically significant mutations.
The ability to computationally screen exome data to identify Mendelian mutations using programs with the aid of programs such as SIFT and PolyPhen, which predict the likely phenotypic effect of a given mutation, has been well established for the past 18 months. A recent example involving the twin children of the CIO of Life Technologies, Joe Beery, using whole-genome sequencing was published just last week. But these studies do not disguise the challenges involved in routinely sifting through thousands of potentially deleterious base changes to identify the mutational needle in the haystack.
In the new Genome Research paper, Yandell, Reese and colleagues show that VAAST can accurately and switfly analyze the variations in a handful of personal genome sequences to identify the causative mutation. In fact, this can be done in as few as three genomes from unrelated children, or the parents and two children.
The program works like the classic sequence homology program BLAST – hence the name. “In BLAST, you take a sequence and run it against a background database, asking: How similar is my sequence to the other databases? VAAST does the same thing for personal genomes, but does it for dissimilarities,” says Reese.
“SIFT and Polyphen, and other academic [mutation prediction] platforms -- they’re all a collection you have to run,” says Reese. “But VAAST does it integrated in one program. It looks at a mutation and its [putative] physiological function. Then it also looks at the frequency of that mutation in a background distribution. You can use the frequency from the 1000 Genomes project or other sources, or you can put in your own background distributions.”
By incorporating variant frequency data in the algorithm, VAAST is able to rapidly zero in on the likely causative mutation, which would be expected to be present very rarely in the population. The program compares variations from a patient against dozens or hundreds of healthy genomes, and automatically scores those mutations in the form of a gene-by-gene ranking summary.
In the Genome Research paper, Yandell and colleagues describe a proof-of-principle studying patients with Miller syndrome, a rare genetic disorder whose genetic basis was uncovered last year. The group looked at six Miller patients, each of whom is a compound heterozygote (harboring two different mutations in the same gene, one inherited from each parent).
When VAAST was run on a single patient, the known Miller syndrome gene ranked #86 out of 20,000 genes in the human genome. Adding a second patient, the gene rose to #2 in the list, and jumped to the top with just three or more patients. (Like BLAST, VAAST provides a P value of statistical significance in the results.)
Reese adds that running the VAAST program retrospectively on the family that was sequenced last year to discover the Miller syndrome gene, the analysis took about a day, compared to several months.
“VAAST solves many of the practical and theoretical problems that currently plague mutation hunts using personal genome sequences,” said Yandell. “This tool substantially improves upon existing methods with regard to statistical power, flexibility, and scope of use. Further, VAAST is automated, fast, works across all variant population frequencies and is sequencing platform independent.”
X Marks Spot
Writing in the American Journal of Human Genetics, Gholson Lyon at the Children’s Hospital of Philadelphia, Yandell and colleagues show how VAAST can be applied to tease out the mutation responsible for a devastating childhood syndrome of unknown etiology.
Lyon, who was formerly at the University of Utah, had been working with a family in the area in which four affected boys had severe neurological damage and signs of progeria (premature aging), and died by the age of 4. Recognizing that the disorder was X-linked, Lyon restricted the next-generation sequencing to the coding regions of the X chromosome. But even then, using traditional tools, he was only able to narrow down the list of candidates to five genes.
Lyon gave Yandell the data, which he loaded the data onto his computer, converted it into the appropriate file format, and ran through VAAST. Within an hour, he was certain he had found the gene, NAA10, which had been one of Lyon’s original candidates. A few weeks after the manuscript was originally submitted, one of the reviewers contacted the authors, as he had seen a family with similar characteristics. Affected members in that family proved to have the same gene mutation. The disorder has been preliminarily called Ogden syndrome.
“One of most important and exciting opportunities in genomic medicine is the newfound ability to pinpoint the root cause of an unknown idiopathic disease in an individual,” commented Eric Topol, director of the Scripps Translational Science Institute. The VAAST tool will markedly facilitate this and represents a major advance in the field.”
VAAST was developed jointly by the University of Utah and Omicia. “It’s joint IP,” says Reese. “Omicia has the exclusive license and we will put it into our suite of tools for whole-genome interpretation. We’re offering it for clinical and commercial applications. Yandell is continuing to use and offer it for academic research through collaborations with the University of Utah.”
Reese says his team is starting to apply VAAST to cancer and other areas. “VAAST works really well right now on rare genetic diseases, but we need more feeling on it. There’s a whole bunch of applications where VAAST can work – we just need to run it through and improve it in the next 6-12 months.”
Omicia will offer VAAST as a tool in its Genome Analysis System, which is scheduled for release in the third quarter of this year.
Yandell, M. et al. 2011. “A probabilistic disease-gene finder for personal genomes.” Genome Res. 21 (2011). doi:10.1101/gr.123158.111.
Rope, A. et al. “The use of VAAST to identify an X-linked disorder resulting in lethality in male infants due to N-terminal acetyltransferase deficiency.” American Journal of Human Genetics, June 23 , 2011. doi: 10.1016/j.ajhg.2011.05.017.