Beyond Common Variations
Genomic medicine began in earnest in the 1980s, when scientists identified genes linked to diseases such as Duchenne muscular dystrophy and cystic fibrosis. Both are so-called Mendelian diseases, meaning that they’re caused by mutations in a single gene; anyone who inherits either one or two copies of the mutated gene, depending on the disease, will be afflicted. Over the last 20 years, researchers have identified genes for a number of Mendelian disorders, and screening tests based on these discoveries have led to earlier diagnoses. In the case of disorders that develop only when a person inherits two copies of the mutation, the tests can identify healthy carriers, helping them make better-informed decisions about having children. Single-gene disorders, however, make up a very small percentage of human diseases. For most diseases, it’s much harder to pinpoint the genetic culprits.
As scientists began assembling a rough draft of the genome sequence in the late 1990s, they uncovered a useful phenomenon. Large blocks of DNA, known as haplotype blocks, tended to be passed down intact through generations. Different versions of these blocks, which were linked to an individual’s ancestral origins, had characteristic patterns of common genetic variations known as single-nucleotide polymorphisms (SNPs), in which the genetic sequence varies by just one DNA letter. Thus, a telltale SNP could serve as a marker for its surrounding DNA. The discovery was a boon to geneticists–if each block tended to occur in a limited number of varieties within the human population, it would be unnecessary to check every base in the genome for variations linked to common diseases such as asthma or schizophrenia. The presence of a particular SNP would indicate which haplotype block an individual carried.
Researchers developed genetic microarrays that could quickly detect the presence of these common SNPs throughout the genome; by scanning for the telltale variations, a relatively inexpensive process, the microarrays have enabled the largest genomic studies to date. Scientists have used them to efficiently search tens of thousands of human genomes for SNPs more common in people with autism or Alzheimer’s, for example, than in healthy people. Over the last two years, a flood of studies have been published, identifying more than 300 genetic variations linked to an assortment of common traits and diseases.
But finding these variations has not led to the breakthrough that some scientists had hoped for in understanding the genetic basis of common diseases. That’s because they turn out to account for only a small fraction of the genetic risk for many illnesses. Researchers have identified 18 genes linked to type 2 diabetes, for example, and tests to identify the variations have been introduced. Yet many other heritable risk factors for the disease remain unidentified. That means that the new tests give an incomplete picture of how likely someone is to develop diabetes, making it difficult to use them to tailor medical decisions. “There is very little reason to be encouraged that prevention strategies can be revolutionized with what we’ve discovered so far [on the genetic basis of common diseases],” says David Goldstein, director of the Center for Population Genomics and Pharmacogeneticsat Duke University in Durham, NC.
The hunt for SNPs makes sense if the inherited risk for diseases like type 2 diabetes results from a combination of many common genetic variations, each exerting a small effect. But what if that is only part of the story? What if other, rarer types of genetic mutations are also playing a role? Because microarrays were designed to detect common SNPs, they miss variations that appear in less than 1 percent of the population. These mutations are the focus of an alternative hypothesis, in which–as in the Mendelian model–high-impact individual variations contribute heavily to a disease. Any one of the variations may occur infrequently, according to this thinking, but if they affect the same or related biochemical pathways, they may produce similar outcomes. Collectively, they could make a disorder relatively common.