Researchers have revealed that numerous rare gene variants may be causing new diseases.
And these variants are located in DNA sequences farther away from the original "hot spots" than scientists have been accustomed to look.
Using an approach that detects rare but powerful causal gene variants, the researchers say they have accounted for a significant proportion of the "missing heritability" problem.
This refers to the disappointing fact that, to date, conventional gene-hunting studies have often failed to identify, when searching for gene variants, variants that cause a large proportion of common diseases, such as heart disease, cancers and diabetes.
The new approach draws on existing data from genome-wise association studies (GWAS) that have already been performed, re-analysing the data to pinpoint causal variants that have not been identified previously.
In addition, the technique may allow researchers to identify individuals whose DNA is more likely to carry specific mutations in the causal genes.
"Our approach draws us closer to the goal of personalized medicine, in which treatment will be tailored to an individual's genetic profile. When we can say that a specific gene mutation causes a patient's disease, we have more meaningful diagnostic results. Identifying causal variants in disease genes provides an opportunity to develop drugs to rectify the biological consequences of these mutated genes," said study leader Dr. Hakon Hakonarson, director of the Center for Applied Genomics at The Children's Hospital of Philadelphia.
By applying their methods to real DNA samples from patients with genetic hearing loss, the researchers' approach helped them to select from GWAS data a subset of cases for sequencing analysis that were most likely to carry causative mutations.
Sequencing the DNA in this subset, the study team found that the majority of those patients carried an actual mutation known to cause hearing loss.
"Our technique suggests that when we do our resequencing follow-up studies, we can identify people who are much more likely to carry a causative gene," said Kai Wang, who analyzed the dataset.
Hakonarson added: "We present a more efficient approach for mining GWAS data to find the actual causative gene variants that will have future utility in designing therapies."
The study appears online in The American Journal of Human Genetics.