A new software tool helps identify mu-
tations in the human genome and more accurately pinpoint their source and their possible significance for health; © panthermedia.net/Radu Razvan
A researcher at Arizona State University reports on the new software DeNovoGear. It uses statistical probabilities to help identify mutations in the human genome and more accurately pinpoint their source and their possible significance for health.
Improvements in the accuracy of mutation identification and validation could have a profound impact on the diagnosis and treatment of mutation-related diseases. "These techniques are being considered in two different realms," Reed Cartwright from the Arizona State University's Biodesign Institute says. "The first is for pediatric diseases." Here, a child with an unusual genetic disease may undergo genomic sequencing to see if the mutations observed have been acquired from the parents or are instead, unique to the child. "We can identify these mutations and try to detect which gene may be broken," he says.
The second application is for cancer research, where tumor tissues are genetically compared with normal tissue. Many now believe that the identification of a specific cancer mutation may eventually permit clinicians to customize a treatment for that tissue type. "We are developing methods to allow researchers to make those types of analyses, using advanced, probabilistic methods," Cartwright says. "We actually model the whole process."
The current study focuses on a class of mutations that play a critical role in human disease, namely de novo mutations, which arise spontaneously and are not derived from the genomes of either parent. Traditionally, two approaches for identifying de novo mutation rates in humans have been applied, each involving estimates of average mutations over multiple generations. In the first, such rates are measured directly through an estimation of the number of mutations occurring over a known number of generations. In the second or indirect method, mutation rates are inferred by estimating levels of genetic variation within or between species.
In the new study, a novel approach is used. The strategy, pioneered in part by Donald Conrad, professor in the Department of Genetics at Washington University School of Medicine, takes advantage of high throughput genetic sequencing to examine whole genome data in search of de novo mutations. A seemingly simple approach to pinpointing mutations is to compare sequence data from each parent with sequence data from their offspring. Where changes exist at a given site in the offspring, de novo mutations can be inferred and their potential affect on human health, assessed.
In addition to further refining the DeNovoGear software, Cartwright's group plans to more closely examine normal human tissue in order to establish rates of somatic mutation. Some of the specific mutations currently associated with cancer for example, may actually be part of normal variability, which appears to be much greater than originally assumed. "No one has really looked at this at the level we are interested in."
COMPAMED.de; Source: Arizona State University