New software tool can identify genetic mutations associated with an increased risk of cancer -
Unique, robust method uses the family data to find mutations for diseases common and rare
researchers from the University of Texas MD Anderson cancer Center and other institutions applied a newly developed software tool to identify genetic mutations that contribute to increasing the risk of a person for the development, common complex diseases, such as cancer. The research is published in the May 2014 journal Nature Biotechnology .
The technology, known as pVAAST (pedigree Variant Annotation, Analysis and Search Tool), combines two different statistical methods used to identify mutations of pathogenic genes. This combined approach outperforms individual methods of family analysis by the power or speed in which the mutations are identified more and more, and reducing complications through the design and analysis study.
"This method allows faster, identification and validation of genetic variants more effective than the risk of influence disease," Chad Huff, Ph.D., professor of epidemiology at MD Anderson . "This will ultimately enable clinical laboratories to develop genetic tests that provide better forecasts of individual risk of developing cancer of a person."
The tool pVAAST combines two methods for identifying genetic diseases commonly used analysis of linkage and association tests. Linkage analysis follows the inheritance of genetic mutations in families to identify possible causal mutations. Association of tests compare unrelated individuals with a specific disease to healthy people looking for a common mutation in either group.
"Analysis and the Association of binding assays were originally designed for rare genetic markers available from earlier genotyping techniques," said lead author of the study, Hao Hu, Ph.D. .D., a postdoctoral fellow in epidemiology at MD Anderson. "PVAAST integrates both methods and reallocates them to the next generation of DNA sequencing data, which is the technical state of the art for genetic research. It fills a real gap between molecular and computational tools Technical in family studies diseases. "
the researchers also incorporated in functional variant prioritization tool that predicts whether a particular mutation in the family is damaging
for this study, pVAAST analyzed data to identify genetic causes the three diseases. enteropathy - a chronic inflammation of the intestine, abnormal heart septum and Miller's syndrome - a developmental defect of the face and several members. The tool was able to identify the exact mutations that cause these diseases from DNA data from one family. In the defects of the cardiac septum and families Syndrome Miller, occasional mutations had already been identified and the results served as a proof of concept. Enteropathy in the family, the causative mutation in the family was unknown prior to analysis
In addition, the researchers applied pVAAST and three other statistical methods for three genetic disease models :. Dominant, in which one copy has defected gene is inherited from a parent; recessive, wherein the two copies of the gene must have the defect; or dominant caused by a new mutation not inherited from either parent. In each case, pVAAST necessary a fraction of the size of the sample of families to detect the risk of disease than other methods did.
"For most rare diseases, it is difficult to collect DNA samples from several patients," said Huff. "It is therefore essential to be able to integrate parents in a study to improve the success rate. "
in this study, the combined methodology using multiple statistical methods has increased the power of the results and reduces the complexity of the analysis." this provides a gateway to identify genetic variants that influence the risk of developing certain cancers, "Huff said.
Many current Genetic studies recruiting patients with a family history of cancer to look for inherited mutations that increase the risk of developing cancer. Huff says this tool will allow researchers to analyze the sequence data of these families to identify genetic variants that are most likely responsible for the history of cancer in families.
Huff said the major objective to advance the software will discover new cancer genes sensitivity. In a separate paper published in Cancer Discovery, the software was used to support the discovery of RINT1 as a new susceptibility gene breast cancer.
"The identification of potential susceptibility genes to cancer is only a first step, and years more research is needed to characterize these variants to establish conclusively the extent to which they influence the risk of cancer "Huff said.
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