Researchers at Baylor College of Medicine have published new findings that outline the best practices for using a newly developed computer program that was developed specifically to analyze genetic sequence data in order to discover the underlying genetic causes of complex traits and diseases, such as coronary heart disease and cholesterol levels.

The research led by Dr. Suzanne Leal, professor of molecular and human genetics at Baylor and Director of the Center for Statistical Genetics, was published today online in the American Journal of Human Genetics. The report provides information on a new tool for researchers to use to detect complex trait and disease rare variant associations in order to better understand the genes involved in human diseases.

“Currently there are many studies on complex diseases such as type 2 diabetes, hypertension and schizophrenia which sequence the DNA from affected and unaffected individuals,” said Leal. “Additionally some studies instead of examining diseases wish to find the underlying cause of quantitative traits, such as cholesterol levels – a disease risk factor for heart disease.”

Complex disease and quantitative traits are the inheritance of a phenotypic characteristic that is attributable to two or more genes.

“Analysis to find the underlying genetic cause of these diseases and traits is not easy,” said Leal. “Researchers need programs in order to be able to analyze their data to find genes which explain the inherited units which make individuals ill.” It is particularly challenging to find mutations which cause disease and traits which have extremely low frequencies.

The newly developed program called the Variant Association Tools or VAT provides a pipeline which allows investigators to readily analyze and find disease causing genes, she said.

In the report, Leal and colleagues use interpretation of the 1,000 Genomes project as an example of how VAT works. The 1,000 Genomes project is an international collaboration to establish the most detailed catalogue of human genetic variation.

“Analysis tools like VAT will be even more important in the near feature when sequence data is generated on hundreds of thousands of individuals,” said Leal.

Co-authors on the report include Gao T. Wang of Baylor and Dr. Bo Peng of The University of Texas MD Anderson Cancer Center.

This work was supported by National Institute of Health grants HL102926, MD005964 and HG006493 to Leal and 1R01HG005859 to Peng and the Lyda Hill Foundation.