"This collaboration started a few years ago, when Donald and I were both working on mutations for the 1,000 Genomes project," Cartwright says, referring to an ambitious project to produce a comprehensive map of human variation using next-gen sequencing.
The mutations under study may take the form of either point mutations - individual nucleotide substitutions, or so-called indel (insertion-deletion) mutations. In the latter case, single nucleotides or nucleotide sequences may be either added or subtracted from the genome.
While point mutations and indel mutations can both have adverse effects on health, indels are significantly more difficult to identify and verify. They have a strong potential to cause havoc when they occur in coding portions of the genome, as the addition or deletion of nucleotides can disrupt the translation process needed to accurately assemble proteins. (The current study is the first paper to use model-based approaches to detect indel 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 reality, such efforts are complicated by a number of potential sources of error, including insufficient sampling of the genome, mistakes in the gene sequencing process and errors of alignment between sequences. The new method uses a probabilistic algorithm to evaluate the likelihood of mutation at each site in the genome, comparing it with actual sequence data.
Human cells contain two copies of the genome - one from each parent. For most positions in the genome, the bases from each parent are the same or homozygous, but occasionally, they are different or heterozygous.
Errors derived from conventional methods can take the form of false negatives, particularly when gene sequencing misses heterozygous sites in the genotype of the child. On the other hand, failure to identify a heterozygous site in one of the parents can lead to a false positive result.
In the current study, data from the 1,000 genomes project was analyzed using DeNovoGear, with markedly improved accuracy. The technique will assist ongoing efforts to better understand which mutations contribute to sporadic disease or cancer in individuals, the distribution of mutations and their characteristics across populations.
The power of this technique comes from its probabilistic model, which calculates the probability of a de novo mutation at a site based on estimations of mutation rates, sequencing error rate and the initial genetic variation in the population from which the parents arise. This model is able to consider multiple explainations for experimental observations and decide between them. The probabilities are used to indentify candidate loci, which are then evaluated using target resequncing.
Given adequate data of genetic pedigree, the method is able to distinguish germline from somatic mutations in an automated manner with high accuracy.
"Our goal is to develop software that will allow researchers and clinicians to estimate a range of mutation types, faster, more accurately and cheaper," Cartwright says.
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're interested in."
TNS 30TagarumaMar-130829-4470053 30TagarumaMar
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