New Study Should Speed Discovery of Gene Function

By Jason Socrates Bardi

Even though the complete and final sequence of the human genome will be published next month, it should come as a shock to nobody that we still have a long way to go in terms of identifying and understanding how our genes actually function in life.

In biology, this is referred to as the phenotype gap—the discrepancy between the 30,000 to 40,000 genes we believe are present in the human genome and the mere total of 5,000 distinguishable traits that have been identified through studies of inherited diseases and knockout mutations produced by gene targeting. The inference to be made is that the critical function of most genes remains unknown—we know the genome sequence, but we still have yet to discover all of its consequences.

Now a new study by a team of scientists, which was recently published in the online edition of the journal Proceedings of the National Academy of Sciences, could help to fill in this gap, thanks to a team of scientists from the non-profit Institute for Childhood and Neglected Diseases (ICND) at The Scripps Research Institute (TSRI) in La Jolla; the non-profit Genomics Institute of the Novartis Research Foundation; Phenomix and Sequenom, Inc., both San Diego-based biotechnology companies; the National Cancer Institute; and the Rockville, Maryland-based company Celera Genomics.

The team conducted a massive study of the mouse genome to examine the genomic variations within individual strains. This analysis should help scientists decide which particular mouse strains to breed in experiments aimed at mapping phenotypes to genes, greatly increasing the speed with which the function of genes are discovered.

"We now have a much clearer picture of the distribution of DNA polymorphisms around the genome," says TSRI Assistant Professor Colin Fletcher, one of the lead authors on the study.

Haplotype Patterns in Mouse Defined

Basically, explains TSRI Professor Steve Kay, the new tool is like a global positioning system for mouse genetics—a set of coordinates that scientists can use to "navigate" the genome the same way that a sailboat pilot uses a constellation of GPS satellites to navigate the Pacific Ocean.

The biological coordinates used in the new method are, in actuality, what are known as single nucleotide polymorphisms (SNPs). SNPs are locations in the genome where a particular base can vary among individuals. Occasionally, these changes occur in the middle of a gene and sometimes even alter the function of the product of that gene, although the majority of SNPs are not themselves responsible for disease.

Nevertheless, SNPs are extremely valuable for research if they are located close to genes linked to a particular biological trait—like susceptibility to a specific disease, for instance. In these cases, the SNPs serve as biological markers that scientists can use to identify and "positionally" clone those linked genes.

In the study, the scientists analyzed the haplotypes, or arrangements of these SNPs, throughout the mouse genome. In fact, the team used technology developed by Sequenom, Inc. and data generated by Celera Genomics to discover and identify some 80,000 SNPs, a broad sampling of the mouse genome.

"We tried to get a set of SNPs that were spaced all over the chromosomes," says GNF's Tim Wiltshire, who was the first author on the paper.

What the team found was that the SNPs of the eight inbred mouse species they surveyed tended to appear in clumps. That is, certain regions had almost no SNPs, while other regions were rich with the markers.

"You can go 50 million bases and find only a handful, and then the next 20 million base pairs, there will be a SNP every few hundred bases," says Fletcher.

This finding is intriguing because many scientists had assumed SNPs were more or less randomly distributed throughout the genome. But this analysis demonstrated that there is a structure to the distribution of SNPs.

The findings also probably indicate something about the nature of the laboratory mice—that the ancestors of the various inbred strains of mice were themselves inbred when the strains were first established around the turn of the last century.

Because of this inbreeding, no one strain can represent the sum total of the biology of a mouse any more than one family would represent the sum of all human biology.

Help for Cloning Genes

Based on this analysis of the haplotype patterns of the different mouse strains, scientists should be able to compare different strains of mice and determine which two would be appropriate to breed in order to create a mouse model or attempt to positionally clone a particular gene.

Positional cloning traditionally entails the use of classical genetic mapping methods to confine the location of the gene to a particular area in the genome, extensive sequencing of the region in question, and the performance of computer-aided searches through databases to find homology between sequences in that region and known genes.

But with the new haplotype analysis and set of SNPs, researchers could narrow the search dramatically. If a SNP is located near a gene found to be associated with a particular trait, scientists can recognize its proximity and use its known location to identify nearby genes that may be linked to that trait.

Says Kay, "This [work] has allowed us to start cloning mouse genes 100-fold faster than before."

To read the article "Genome-wide single-nucleotide polymorphism analysis defines haplotype patterns in mouse" by Tim Wiltshire, Mathew T. Pletcher, Serge Batalov, S. Whitney Barnes, Lisa M. Tarantino, Michael P. Cooke, Hua Wu, Kevin Smylie, Andrey Santrosyan, Neal G. Copeland, Nancy A. Jenkins, Francis Kalush, Richard J. Mural, Richard J. Glynne, Steve A. Kay, Mark D. Adams, and Colin F. Fletcher, see:

http://www.pnas.org/cgi/content/abstract/0130101100v1

 

 

 


The "SNPview" browser was developed to allow visualization of genomic features in the context of each chromosome. It can be viewed at http://www.gnf.org/SNP. Click to enlarge.

 


One of the multiple views of the genome sample from the browser. Click to enlarge.