Roche Bioscience of Palo Alto, Calif., announced that it plans to make a database of mouse SNPs and a new methodology for genotyping publicly available over the Internet early next year. The project is being supported in part by a $1.2 million, three-year grant from the US National Human Genome Research Institute, making Roche the first pharma to receive such a grant.
Roche officials said this move is a smaller version of the SNP Consortium, where Roche and other large pharma and technology companies banded together to make human SNP data public.
Like the SNP Consortium, the Roche effort is likely to put pressure on Celera Genomics, which has been selling mouse SNP and genomic information to its subscribers. In the past Celera has incorporated publicly available human genome and SNP data into its database.
Tony Kerlavage, Celera’s senior director, bioinformatics and product strategy, declined to comment on Roche’s plan and what it means for Celera’s business.
At Roche, the NHGRI grant is being used to fund four postdoctoral researchers and to pay for sequencing and genotyping equipment. Lisa Brooks, program officer at NHGRI, said that the institute decided to fund the project because of Roche’s intent to make the mouse SNP data and tools available to other researchers.
The pharma is filling its database by using its genotyping method, which enables samples to be pooled for batch processing, a cheaper and more time efficient method, said Gary Peltz, head of genetics at Roche’s inflammatory diseases unit.
This technique has allowed the company’s researchers to identify more than 300 murine SNP genotyping assays in less than six months. The pharma said these results are 30 to 50 times more productive than existing assay methods.
“A genome scan that took six months or so in an average academic lab, we’ve been able to do in about a week,” said Peltz.
Peltz and Russell Higuchi, research leader in human genetics at Roche Molecular Systems, developed the genotyping method.
Roche’s collection of 300 SNPs — which have not been in the public domain before — is expected to grow to 500 by year-end and 1,000 by the end of 2001, said Peltz.
The Oracle-based database, called MSNP, will also have a list of SNPs that have been described in other publicly available repositories. MSNP will enable researchers in the scientific community to get answers to queries in real-time, Peltz added.
MSNP’s availability is contingent on the publication of the paper that Peltz and Higuchi are submitting to Nature. The researchers hope that the paper will be published early in 2001.
Because of Nature’s prepublication restrictions, Peltz said he could not discuss the specifics of the new software tools that Roche developed for analyzing the mouse data.
In addition to making the data publicly available through a Roche website, the company will also deposit its data in DbSNP, a public repository for SNP information that is a collaborative effort by NHGRI and the National Center for Biotechnology Information.
Roche intends to keep its database focused on mouse SNPs rather than sequence or other information. Peltz said Roche wants to provide a research tool but does not want to compete with the NIH or the Jackson Laboratory. “We want to be complementary to what exists already,” he said.
The company originally undertook the genotyping project about a year ago. Until then, the study of mouse genetic models as a way to learn about human genetic analysis was limited to slow and cumbersome genotyping methods, Peltz said.
Roche hopes that by making this data public to other researchers it will help to advance the science more quickly than if it was working alone, said Peltz. Roche also hopes that the new method will help it to develop both therapeutic and diagnostic products.
“We’re trying to develop these tools in order to improve the efficiency of the drug discovery and drug development process. This is an important step to use to develop genetically based medicines,” said Peltz.
Peltz said that the genotyping technique will help researchers check the entire mouse genome for disease-associated risk factors. By extension, such data can then be applied to pick out genetic risk factors in the human genome.
For example, Roche plans to use the technique to study DNA samples from individuals with osteoporosis and other conditions to find genetic factors associated with these diseases.