SALT LAKE CITY, May 30, 2014 /PRNewswire-USNewswire/ -- Scientists at the University of Utah (U of U), the University of Texas MD Anderson Cancer Center in Houston and colleagues have developed a powerful tool called pVAAST that combines linkage analysis with case control association to help identify disease-causing mutations in families faster and more precisely than ever before.
In a study in Nature Biotechnology, the researchers describe cases in which pVAAST (the pedigree Variant Annotation, Analysis and Search Tool) identified mutations in two families with separate diseases and a de novo or new variation in a 12-year-old who was the only one in his family to suffer from a mysterious and life threatening intestinal problem.
"Linkage analysis and case control association traditionally have been used to find gene mutations," says Chad Huff, Ph.D., corresponding author on the study, assistant professor of epidemiology at the MD Anderson Cancer Center and former postdoctoral fellow in human genetics at the U of U. "Bringing those methods together provides a strong increase in the power to find gene variations that cause disease."
Genome sequencing allows researchers to search for disease-causing mutations in the genomes of individual patients, unrelated people or small and large families. The researchers believe the most powerful way to identify these variants is by sequencing the genomes of families that experience unusually high occurrences of a particular illness. By identifying gene variations that family members share, it's possible to identify mutations in a gene that causes the disease, according to Mark Yandell, Ph.D., U of U professor of human genetics and a senior author on the paper.
"The issue with whole genome sequences has been that sequencing one person's genome to find a single disease-causing gene is difficult," Yandell says. "If you can sequence the whole family it gives a fuller picture of the sequence and variations potentially involved in disease."
pVAAST was designed to search sequenced family genomes to find shared mutations and thus identify the gene with the highest probability of causing disease. It can simultaneously search multiple families with the same disease to find mutations, reducing the time and effort needed to find a disease-causing variant. For example, if three families have the same disease, two might have different mutations damaging the same gene, while the third family might have a different damaged gene. "pVAAST has the power to determine the true disease-causing mutations across all those families in one analysis," Yandell says.
SOURCE University of Utah Health Sciences
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