A pioneering statistical technique developed at the Harvard School of Public Health (HSPH) has helped identify a common gene variation associated with an increased risk for obesity. The finding, which has been replicated in four other samples of children and of adults of European and African ancestry, provides an unusually strong association between a gene variant and a complex disease in the field of association mapping.
The study involved an international team led by researchers at the Boston University School of Medicine (BUSM) in collaboration with researchers at HSPH, Children's Hospital Boston, GSF National Research Center, Technical University Munich, Ludwig Maximilians University, Channing Laboratory, University of Duisburg-Essen, Loyola University Medical Center, and SeraCare Life Sciences Inc. The paper will appear in the April 14 issue of Science.
The scientists identified the variant by testing 86,604 single nucleotide polymorphisms (SNPs), or DNA sequence variations, for an association to body mass index (BMI), a surrogate measurement for obesity. The SNPs came from blood samples from participants in the Framingham Heart Study (FHS). BMI measurements were gathered by FHS investigators under a contract from the National Heart Lung and Blood Institute administered by Boston University.
Researchers at the BUSM Genetics and Genomics Department, led by lead author and Assistant Professor Alan Herbert and senior author and Department Chair Michael Christman, genotyped the samples, identifying genetic variations within them for comparison. The large-scale genotyping was enabled by 100K DNA microarray technology developed by Affymetrix, a company based in Santa Clara, Calif. Data management and analysis was done by Drs. Norman Gerry and Marc Lenburg at BUSM and HSPH research fellow Matthew McQueen.
The identification of the SNP was made possible by a new statistical technique developed by HSPH Assistant Professor Christoph Lange, who is a senior author on the Science paper, in collaboration with Professor and co-author Nan Laird, and Kristel Van Steen, former postdoctoral fellow.
The technique averts a major statistical difficulty called the multiple comparison problem, which is exacerbated when crunching huge data sets. The more SNPs in a study, the more likelihood of false-positive signals, the increased risk that some SNPs are not tested -- even though they may be viable candidates.
Typically, a study would demand a two-step process of 1) initial testing of all SNPs for associations to specific traits, such as body mass index, and 2) testing only the most promising SNPs in a second data set.
The HSPH analysis method handles the multiple comparison problem with just one data set by estimating the extent to which genetics can explain variations in a specific trait, and then identifying the most promising candidate SNPs for that genetic effect. For more information on the technique, see http://www.hsph.harvard.edu/press/releases/press06052005.html.
In the Science paper, the HSPH method was applied to identify exactly one SNP that emerged from the tens of thousands tested. It lies near the INSIG2 gene (insulin-induced-gene). The variant was present in ten percent of the populations studied and required two copies to be active.
Importantly, the scientists then collaborated with other institutions and replicated the association between the SNP and obesity risk in four other populations. The populations included both families and non-related subjects of different ages and of varying ethnic backgrounds:
The association was not found in a sample from the Nurses' Health Study. The authors suggest the result may reflect a different distribution of BMI rates among the Nurses' Health Study subjects compared to the other samples, or differences in their environments and lifestyles.
"Several features make the study in Science especially exciting: the large-scale genotyping, the use of a population sample of families, the long-term follow up of the subjects, the replication of the finding in independent samples, and a novel statistical methodology which makes maximal use of the data," said Laird. "Most studies of this type use clinical samples and unrelated subjects that are not measured repeatedly over time, and obtaining replications in independent samples is very difficult."
Approximately 65 percent of Americans are overweight and 30 percent are obese. Obesity is a risk factor for numerous diseases, including hypertension, diabetes, and stroke. Genetics and environmental factors such as poor diet and lack of exercise influence obesity risk. Understanding the genetics underlying obesity is an important piece in the puzzle of how to manage the obesity epidemic.
Components of this work received support from a number of sources: Leadership Award from the Whitaker Foundation, grants from the American Diabetes Association, an ADA Smith Family Pinnacle Program Project, NIH grants, German Ministry of Education and Research through the National Genome Research Network, Bioinformatics for the Analysis of Mammalian Genomes, and National Cancer Institute. DNA samples and phenotypic data were provided by NHLBI-Framingham Heart Study investigators.
Cite This Page: