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Gene Network Predicts Stroke Risk in Sickle Cell Anemia
Bayesian Statistics Identifies DNA Variations Associated with Patients’ Brain Attacks
Knowing certain variations in 11 genes may dramatically improve the ability of doctors to predict the risk of stroke in children and teenagers with sickle cell anemia, report researchers at HMS and Boston University in the April Nature Genetics. The team identified these genetic alterations and clinical factors that influenced the risk of stroke in one group of patients and predicted brain attacks in another group of patients with 98.2 percent accuracy. The findings could be a prognostic boon for a major complication of the disease.
Photo by Steve Gilbert
Twenty-five variations in 11 genes can predict the risk of stroke in sickle cell anemia patients more accurately than current methods, according to a new model developed by (from left) Paola Sebastiani, Marco Ramoni, Martin Steinberg, and their colleagues.
The promising genetic test arose from a statistical approach that is said to be revolutionizing genetics. This is the first time it has been used to analyze the associations between human genetic variations and phenotypes—stroke, in this case, said Marco Ramoni, co–lead author of the paper and an HMS assistant professor of pediatrics and medicine at Children’s Hospital Boston.
The researchers hope to broaden the predictive scope and apply the technique to other conditions. “We would like to use this model to predict the stroke risk in the general population,” said Ramoni, who is also associate director of bioinformatics at the Harvard Partners Center for Genetics and Genomics.
Web of Health and Disease
Many people think of sickle cell disease as a problem with a single gene.
After all, the cascade of serious clinical complications begins when
a person inherits two copies of the mutated gene.
That one nucleotide substitution (GTG for GAG), which makes a single amino acid difference in the hemoglobin molecule, is enough to turn soft doughnut-shaped red blood cells into hard, sticky, boomerang-shaped cells that clog small and large vessels, causing stroke, pulmonary failure, organ damage, and acute and chronic pain in organs, joints, and bones.
But more complex genetics explains why the severity of the disease varies greatly among individuals with the same single mutation. Scientists have discovered that other genes, unlinked to the sickle-cell defect, can either ameliorate or exacerbate the phenotype. 7
“Symptomatic stroke can occur in as many as 11 percent of affected individuals by the age of 20 years, and many more will show evidence of silent infarction by magnetic resonance imaging,” wrote James Meschia and V. Shane Pankratz of the Mayo Clinic in an accompanying perspective in the same issue of the journal. “Testing for risk-modifying genes could identify individuals at risk for stroke early in the course of their disease.”
For this study, Ramoni teamed up with longtime collaborator Paola Sebastiani, associate professor of biostatistics at the Boston University School of Public Health. Ramoni, an expert in artificial intelligence, and Sebastiani, who specializes in Bayesian statistical methodology, have recently turned their proficiency with Bayesian network modeling to genomics, courtesy of the National Science Foundation. The statistical technique is particularly adept at extracting meaning from large data sets and can incorporate other useful information.
| “Testing for risk-modifying genes could identify individuals at risk for stroke early in the course of their disease.” |
“We would like to pick out interesting therapeutic targets, use genetic screening prognostically, and see if patients with certain genetic variants are more or less likely to respond to hydroxyurea,” the only approved drug for adults with sickle cell anemia, said senior author Martin Steinberg, director of the Center of Excellence in Sickle Cell Disease at Boston Medical Center.
Steinberg selected 80 candidate genes involved in regulating vessels, inflammation, cell adhesion, coagulation, hemostasis, cell proliferation, oxidative biology, and other functions. Ramoni, Sebastiani, and their colleagues analyzed 108 single nucleotide polymorphisms (SNPs) in these genes and related clinical data from 1,398 African Americans enrolled in the national Cooperative Study of Sickle Cell Disease, 92 of whom had suffered a stroke.
Stroke Predictors
The analysis
revealed a likely model of 31 SNPs in 12 genes that interacted with the
protective molecule fetal hemoglobin. Twenty-five
of these
SNPs in 11 genes had the largest effect on the prediction of
stroke risk.
Three of the genes belong to the well-studied TGF-beta pathway,
and one gene,
selectin P, has been previously associated with stroke in the
general population.
Though there is no way to prove a model always works, it can be validated by comparing results from different populations. The researchers tested the results in another group of 114 individuals not included in the original study, for an overall predictive accuracy of 98.2 percent.
This is a huge potential improvement on the current state of the art, with substantial implications for treatment. Narrowed arteries detected by transcranial Doppler (TCD) now define “high risk” for stroke in sickle cell disease. But only 10 percent of the high-risk children with abnormal readings are likely to have a stroke in the next year, while 19 percent of those with normal readings are likely to have disabling strokes. Doctors would like to improve the precision, in part because the most effective treatment—blood transfusions every month or two—adds a new set of risks.
“In the future, multiplex genetic testing may prove superior to TCD for stratifying risk of stroke in sickle cell anemia,” write Meschia and Pankratz, who also recommend further validation of the analytical techniques. “The complex data analytical techniques that can be applied to large data sets make it possible to obtain persuasive results for any given data set,” they write.
In collaboration with others, Ramoni and his colleagues are also using the method to root out the genetic factors that determine which people with asthma will respond to steroid therapy and to determine a protein signature that can detect ovarian cancer.