Golden Helix integrates CMC and KBAC methods of analyzing rare DNA sequence variants in SVS product

May 21 2011

Though two of the best-known methods for analyzing rare variants in sequencing data were published in 2008 and 2010, they have never been available to the public as a supported package.

“Golden Helix' integration of CMC and KBAC demonstrates their commitment to working with academia to make cutting edge methodologies available to researchers studying the genetic causes of disease and other traits”

Baylor College of Medicine and Golden Helix announce the availability of the Combined Multivariate and Collapsing (CMC) and the Kernel Based Adaptive Cluster (KBAC) methods in Golden Helix' flagship product, SNP & Variation Suite (SVS).

CMC and KBAC were developed under the direction of Suzanne Leal, PhD, a professor in Baylor's Department of Molecular and Human Genetics, and are powerful and flexible methods of analyzing rare DNA sequence variants. Leal's methods allow researchers to assess the combined effect of multiple independent rare and common sequence variants on disease phenotypes. Rare variants are hypothesized to have greater effect sizes than common ones for some diseases, but standard GWAS methodologies that test each variant individually lack sufficient statistical power to detect these associations.

Christophe Lambert, President and CEO of Golden Helix, explained, "The move to next-generation sequencing and the study of rare variants has caused us to rethink analytically how best to assess the effect of genetic variation on disease. The standard methods to analyze common variants in GWAS data are underpowered to test for rare variant complex trait associations. Dr. Leal's methods open exciting opportunities for researchers to statistically explore the importance of rare variants, in addition to common variants, and the role they play on disease."

The SVS integration of CMC and KBAC is the first supported implementation available in any form, whether free, open-source, or commercial. Furthermore, Golden Helix and Baylor worked together to extend both the CMC and KBAC methods beyond the original paper's specifications, creating a version that leverages a regression framework. The regression-based adaptations allow users to correct for confounding variables that might otherwise result in spurious results.

"Golden Helix' integration of CMC and KBAC demonstrates their commitment to working with academia to make cutting edge methodologies available to researchers studying the genetic causes of disease and other traits," said Dr. Leal. "I am very happy with their implementation of our methods and look forward to working with Golden Helix further."

SVS is a powerful and integrated collection of high-performance analytic tools for managing, analyzing, and visualizing large-scale, complex genomic data.