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News

Boston University researchers have developed a better method for identifying genes associated with diseases

Boston University : 18 June, 2003  (Technical Article)
Two Boston University researchers have developed a better method for identifying genes associated with diseases. Where previous methods could only locate disease genes by looking at individual mutations separately, this new technique can simultaneously scan several disease genes. This new method gives greater statistical power to disease association studies.
If one thinks of finding a disease gene as like finding a single word, this method will provide the equivalent of sentences of meaningful information, a boon to the field of genetic scanning.

Cost-effective, accurate, and efficient, the novel technique will provide vital information to physicians and others who need to diagnose, counsel, or treat people who are at risk for gene-based diseases or disorders.

The new technique was developed by Chunming Ding, researcher in Boston University’s Bioinformatics Program in the Center for Advanced Biotechnology, and Charles Cantor, professor of biomedical engineering, CAB director, and chief scientific officer of Sequenom, Inc., a DNA analysis company based in San Diego, Calif. It is reported in the June 24 issue of Proceedings of the National Academy of Sciences.

Building on the Human Genome Project, biomedical researchers have been working to identify genes that are associated with predisposition to disease. Research to link a single gene with a single disease was the initial paradigm, borne out by the linking of specific gene mutations to the development of specific diseases, such as cystic fibrosis. These pinpoint, disease-related genetic changes, known as single-nucleotide polymorphisms, could readily be identified using a DNA testing method known as genotyping.

To develop a better tool, the researchers chose to take a fresh look at haplotyping, a DNA-scanning technique that had shown limited success when used in studies of disease-related genes and chromosomal change.

Haplotypes are genes that are located closely together on a given region of a chromosome and are usually inherited as a group from one parent. Although haplotyping held the promise of showing how genetic interplay could contribute to disease development, available methods were labor-intensive and limited to describing only very short sections of genomic DNA, the DNA found in chromosomes. In addition, accurate interpretation of results required knowing a person’s inherited genetic profile, something that is not always readily available.

The researchers achieved three significant refinements to the technique. They reduced the amount of sample needed for testing, requiring quantities that were a tiny fraction of that used in existing genotyping methods. They also included an automated process to allow for a high throughput analysis of samples. Most importantly, they made the technique work for longer ranges of genes.

Using a “multiplex” analysis approach, the researchers first amplified, made copies of, a 100-base-pair region of genomic DNA surrounding each SNP. A base describes any of four chemical units that make up DNA. Bases bind with one another selectively, pairing to form the characteristic “ladder” structure of the DNA molecule.

By performing the amplification procedure simultaneously on several SNPs along a long stretch of DNA, the researchers showed they could build accurate pictures of haplotypes of potential disease genes along distances that were 10-fold greater than those described by any existing testing method. Their technique has essentially placed SNP “words” within the equivalent of a full sentence, providing context to disease-related analyses of genetic text.

Sequenom, Inc. has business units that focus on developing hardware and software for DNA analysis and on applying these products to the systematic identification of disease-related genes within the population.

CAB researchers at Boston University develop new methodologies and new biological materials for the analysis of DNA. The University’s multidisciplinary Bioinformatics Program integrates advanced mathematics and computational methods with investigations into the molecular biology and physics of the cell.
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