Stanford University School of Medicine scientists have played a major role in an international effort that has shown, for the first time, that modern genetic technologies can solve the riddle of how gene variations lead to schizophrenia.
Researchers at Stanford and 14 other institutions carried out a study of common DNA variations throughout the genome, and then combined forces with two independent studies to complete a pooled analysis of 27,000 individuals. The largest genetic differences between the study participants with and without schizophrenia were found on a stretch of chromosome 6 containing numerous genes associated with immune response (and some with other roles). This raises the possibility that immune function plays a role in schizophrenia.
Stanford’s Jianxin Shi, PhD, and Douglas Levinson, MD, are first and second authors of one of three linked papers to be published online together in Nature on July 1. Their paper reports on the Molecular Genetics of Schizophrenia Project. This undertaking implicated a region of the human genome not previously suspected as a risk factor for schizophrenia. That finding was bolstered by another of the simultaneously published papers, which showed an even stronger association when the number of subjects was increased to almost 48,000, and identified significant association in two additional genes. The third paper shows that there are likely to be many common gene variations, perhaps hundreds or more, that have small effects in the risk of schizophrenia.
Taken together, “the papers present the first highly significant findings of gene regions associated with schizophrenia risk,” said Levinson, professor of psychiatry and behavioral sciences, director of that department’s Program on the Genetics of Brain Function, and the Walter E. Nichols, MD, Professor in the School of Medicine.
It is already known that schizophrenia – which strikes close to one in every 100 people – has a very strong genetic component, probably accounting for at least 80 percent of risk for this disease. However, unlike sickle-cell anemia or Huntington’s disease, in which a defect at a single genetic location is responsible, most cases of schizophrenia are believed to involve interactions among a multitude of genes, with a variant of any single gene contributing only a tiny bit to a person’s risk.
“That makes it hard to tease out, in a statistically significant way, any of these schizophrenia-associated genes,” said Levinson. But it is feasible with very large numbers of subjects, he said. Finding genes involved in a multigenic trait can, at least in theory, be accomplished by means of so-called genome-wide association studies, in which DNA variations are measured in two large groups of people, one with a common pathology and the other without it.
To achieve the needed sample size, data from three independent studies were pooled and analyzed in a special way that corrected for differences in how those disparate studies were designed and run. Such a methodology is called a meta-analysis. Shi, a research scientist in Levinson’s laboratory, designed and performed the meta-analysis on the resulting pooled-subject group, some 8,000 individuals with schizophrenia and 19,000 normal controls of European ancestry. (Restricting the study population to people of similar ancestry excludes numerous non-disease-related genetic differences that would otherwise be observed, Shi said.)
In 1999, when Levinson and Shi’s study began, genomic technologies were nowhere near as advanced as they are today. But the recent hybridization of Silicon Valley-style microelectronics with biotechnology-bred DNA assay techniques has resulted in powerful new microarrays capable of scanning entire genomes for tiny variations called “single base-pair polymorphisms,” or SNPs.
A DNA base pair is effectively the genome’s smallest possible accounting unit – the penny, as it were, of genetic variation. As a simplified analogy, think of your genetic inheritance as a stack of 3 billion pennies, with each coin bearing one of four mint marks. If you set two such stacks (representing two individuals’ genomes) side by side and compare two adjacent pennies’ mint marks at any given height, they’ll usually be the same. We’re all descendants of a common ancestor, so the similarities in our genomic sequences shouldn’t surprise anyone.
But evolution happens. Every few hundred “pennies” or so, you will observe a divergence, or SNP – one chemical “mint mark” on this genome, another on that one. With the human genome being so huge, this comes to something like 10 million SNPs, of which about a million occur with frequencies of at least 5 percent.
Using commercially available “SNP chips” designed to detect those more-common variants, the investigators looked for differences between the DNA of people with schizophrenia versus the DNA of those without the disease. The scientists required that such differences achieve “genome-wide statistical significance.” Here’s why: If you flip a million coins, one at a time, you’re going to see all kinds of seemingly miraculous events – say, 15 heads in a row – that may seem significant but are typical when you toss even a perfectly balanced coin so many times.
Shi’s job was to devise analytical techniques to determine whether the “finding” of a SNP’s greater likelihood among schizophrenics was real or spurious. The genomic region on chromosome 6 survived this rigorous statistical test.
“These findings show that our genetic methods are working, and that the genetic underpinnings of schizophrenia can be understood,” said Levinson. “Similar methods have produced critical new discoveries in many other common diseases, once very large numbers of people could be studied. Now we see that the same approach works for psychiatric disorders like schizophrenia.”
Pablo Gejman, MD, of Northwestern University was the senior author of the paper. Stanford co-author Alice Whittemore, PhD, professor of health research and policy, consulted on the study’s meta-analytic methodology. The study was funded by the National Institute of Mental Health and by the National Alliance for Research on Schizophrenia and Depression.
Stanford University Medical Center