Share this article on:

New Methods of Gene Analysis Find Genetic Links Among Apparently Unrelated Conditions


doi: 10.1097/01.NT.0000333580.42351.b1
Best of the Field

New methods of gene analysis are affording a deeper understanding of the genetic foundations of many diseases and suggest that many disorders result from subtle variations in the expression of several genes, rather than a problem with just one.



These new technologies are uncovering surprising genetic links among apparently unrelated conditions. For example, as Andrew Pollack wrote in the May 6, 2008, Science section of The New York Times, “a similar set of genes is active in boys with Duchenne [muscular dystrophy] and adults who have heart attacks.”

These discoveries are prompting the development of a new disease classification in which disorders are grouped according to their genetic underpinnings. Some of the earliest research in this field has involved neurological diseases. Investigators began using the new technology as soon as it was available to unravel the causes of Alzheimer disease, Parkinson disease, and multiple sclerosis.

Back to Top | Article Outline


The relationship among genetic variants is known as genome-wide association (GWA), and GWA research has “exploded in the last 18 to 24 months,” said Bryan Traynor, MD, clinical associate at the NINDS and the National Institute on Aging. GWA research involves the study of variations of individual bases in a given position on a chromosome. These variations are known as single nucleotide polymorphisms (or SNPS, pronounced as snips).

“We usually assume that the affected gene is the one closest to a given SNP. This is often but not always true; sometimes a SNP may affect a gene that is more distant on the chromosome or even on a different chromosome.” Still, he said, when a SNP is affiliated with a particular gene, an individual's risk of expressing a certain phenotype or developing a particular disease may increase by 20 to 40 percent.

Several events accelerated research in this area, said Philip De Jager, MD, PhD, assistant professor of neurology at Brigham and Women's Hospital and Harvard Medical School in Boston. One was the creation of the “HapMap,” a haplotype map of the human genome that describes common patterns of genetic variation in several human populations. The HapMap “permits the understanding of how [genetic] variants are inherited,” he explained. “Many variants have traveled together through time, so we can sample a few and infer that others will be associated with them.” The sampled variants may serve as surrogate markers for other variants that are not directly tested by investigators.

Another advance was the development of high-throughput genotyping arrays that can analyze up to a million SNPs at a time. Combined with new software and the “development of novel statistical methods,” the new technologies have allowed Dr. De Jager and others to perform studies involving thousands of people, each with a million genetic variations, giving them several billion data points' worth of information to explore.

Back to Top | Article Outline


The power of SNP analysis was demonstrated in a paper that appeared in the Nov. 4, 2007, online edition of Nature Genetics, in which lead author Amanda J. Myers, MD, and colleagues in Dr. Traynor's laboratory performed whole-genome analysis on postmortem cortical tissue samples from 193 neurologically normal people.

Among other things, they found more than 17,000 SNPs that correlated significantly with genetic expression in these samples. Needless to say, not all of these associations will prove to be biologically relevant. Still, they concluded that findings like these will permit future investigators to study the factors that control normal genetic expression in greater depth. Also, some day investigators may be able to use known associations between specific SNPs and neurologic or psychiatric disorders to estimate the likelihood that a certain genetic variation will be expressed.

Such studies already are under way. For example, Michael A. van Es, MD, of the University Medical Center of Utrecht, the Netherlands, and colleagues analyzed more than 300,000 SNPs from 461 patients with amyotrophic lateral sclerosis (ALS) and 450 controls to identify the SNPs most closely associated with ALS. With that information, they analyzed samples from another 876 ALS patients and 906 controls. They found an association between ALS and a genetic variation in the 1,4,5-triphosphate receptor 2 (ITPR2) gene. Writing in the October 2007 issue of Lancet Neurology, they concluded that “genetic variation in ITPR2 is a susceptibility factor for ALS.” In an editorial appearing in the same issue, Dr. Traynor and co-author Andrew Singleton, MD, applauded this effort but warned that “more genetic data are required to validate this finding.”



Back to Top | Article Outline


The clinical value of the genetic loci identified by SNP arrays most likely will fall into two basic categories, Dr. De Jager said. First, they may help in the development of diagnostic or prognostic algorithms — although, he noted, the genetic information alone probably will not be sufficient. Additional information from other diagnostic approaches such as serum tests and imaging studies may be necessary.

Second, these disease-associated loci may highlight good targets for drug development. Dr. De Jager explained that “in particular, certain loci are associated with multiple autoimmune diseases such as multiple sclerosis, rheumatoid arthritis, and type I diabetes. These shared ‘autoimmune disease susceptibility loci’ are an emerging feature of genome-wide scans and identify regions of the genome that will prove important in modulating the risk of autoimmune reactions.

“These regions are attractive targets for drug development, because multiple different diseases might respond to a single agent.”

This is where the genetic link among seemingly disparate conditions may be particularly useful, said Stefan Pulst, MD, chairman of neurology at the University of Utah School of Medicine. “It's hard to develop truly new compounds for diseases, but if one can use existing drugs to treat [other] diseases, you could cut five to 10 years off the drug development time.”

Back to Top | Article Outline


Dr. Pulst warned that focusing excessively on a genetically based disease classification system could slow the recognition of environmental and developmental influences on disease manifestations and classifications. Dr. De Jager pointed out that genetics also may influence a person's response to external insults “and thus may play a role in multiple different neurodegenerative diseases.”

All in all, Dr. Pulst acknowledged that “it is clearly important to look at genetic variations, which may reveal common building blocks or shared pathways.” The ability to analyze millions of genetic variants on a single chip has elicited a “major paradigm shift” in research, he added. “This will give rise to important advancements in drug development. That's where I see the greatest promise.”•

Back to Top | Article Outline


• Pollack A. Redefining disease, genes and all. The New York Times 2008;May 6:D1.
    • Myers AJ, et al. A survey of genetic human cortical gene expression. Nature Genet 2007;39(12):1494–1499.
      • Van Es MA, et al. ITPR2 as a susceptibility gene in sporadic amyotrophic lateral sclerosis: a genome-wide association study. Lancet Neurol 2007;6(10):869–877.
        • Traynor BJ, Singleton A. Genome-wide association studies and ALS: are we there yet? Lancet Neurology 2007;6(10):841–843.
          ©2008 American Academy of Neurology