This story was originally posted on May 20.
Baylor College of Medicine and bioinformatics company Golden Helix recently partnered to begin selling new analysis tools for next-generation sequencing and array-based genome-wide association studies.
As part of the collaboration, Golden Helix has added two new methods to its SNP & Variation Suite, or SVS: combined multivariate and collapsing, or CMC, and kernel based adaptive cluster, or KBAC. Both methods were developed at Baylor under the direction of Suzanne Leal, a professor in Baylor's department of molecular and human genetics.
Golden Helix debuted version 7.4 of the suite earlier this year (BAN 2/1/2011).
In a statement, Baylor and Golden Helix said the methods "allow researchers to assess the combined effect of multiple independent rare and common sequence variants on disease phenotypes."
Christophe Lambert, CEO of Bozeman, Mont.-based Golden Helix, said the new tools are designed to help researchers using next-gen sequencing.
"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," he said. Integrating CMC and KBAC with SVS offers "opportunities for researchers to statistically explore the importance of rare variants, in addition to common variants, and the role they play in disease."
Andy Ferrin, Golden Helix's executive vice president of business development, said the new tools can also be used by array researchers.
"The new generation of arrays, such as Illumina’s HumanOmni2.5 product, includes substantial rare-variant content in addition to common SNPs used for genome-wide association studies," Ferrin told BioArray News this week. "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."
Ferrin noted that the integration is the "first supported implementation [of the tools] available in any form, whether free, open-source, or commercial."
Additionally, Golden Helix and Baylor College of Medicine worked together to "extend both the CMC and KBAC methods" beyond their original specifications. For instance, they created tools that "leverage a regression framework to allow users to correct for confounding variables."
The CMC method collapses genotypes across variants and applies a univariate test, rather than analyzing each variant individually with univariate tests. Leal described the method in a 2008 American Journal of Human Genetics paper.
By comparison, the KBAC method combines variant classification and association testing in a single framework. Covariates can also be incorporated in the analysis to control for potential confounders such as age, gender, and population substructure, according to the paper abstract. Leal and colleagues discussed the method in a PLoS Genetics paper last year.