Genetic elements influencing risk of type 2 diabetes identified

ScienceDaily (Nov. 2, 2010) ? A team led by researchers at the National Human Genome Research Institute (NHGRI), part of the National Institutes of Health, has captured the most comprehensive snapshot to date of DNA regions that regulate genes in human pancreatic islet cells, a subset of which produces insulin.

The study highlights the importance of genome regulatory sequences in human health and disease, particularly type 2 diabetes, which affects more than 20 million people in the United States and 200 million people worldwide. The findings appear Nov. 3 in Cell Metabolism.

"This study applies the power of epigenomics to a common disease with both inherited and environmental causes," said NHGRI Scientific Director Daniel Kastner, M.D., Ph.D. "Epigenomic studies are exciting new avenues for genomic analysis, providing the opportunity to peer deeper into genome function, and giving rise to new insights about our genome's adaptability and potential."

Epigenomic research focuses on the mechanisms that regulate the expression of genes in the human genome. Genetic information is written in the chemical language of DNA, a long molecule of nucleic acid wound around specialized proteins called histones. Together, they constitute chromatin, the DNA-protein complex that forms chromosomes during cell division.

The researchers used DNA sequencing technology to search the chromatin of islet cells for specific histone modifications and other signals marking regulatory DNA.

Computational analysis of the large amounts of DNA sequence data generated in this study identified different classes of regulatory DNA.

"This study gives us an encyclopedia of regulatory elements in islet cells of the human pancreas that may be important for normal function and whose potential dysfunction can contribute to disease," said senior author and NIH Director Francis S. Collins, M.D., Ph.D. "These elements represent an important component of the uncharted genetic underpinnings of type-2 diabetes that is outside of protein-coding genes."

Among the results, the researchers detected about 18,000 promoters, which are regulatory sequences immediately adjacent to the start of genes. Promoters are like molecular on-off switches and more than one switch can control a gene. Several hundred of these were previously unknown and found to be highly active in the islet cells.

"Along the way, we also hit upon some unexpected but fascinating findings," said co-lead author Praveen Sethupathy, Ph.D., NHGRI postdoctoral fellow. "For example, some of the most important regulatory DNA in the islet, involved in controlling hormones such as insulin, completely lacked typical histone modifications, suggesting an unconventional mode of gene regulation."

The researchers also identified at least 34,000 distal regulatory elements, so called because they are farther away from the genes. Many of these were bunched together, suggesting they may cooperate to form regulatory modules. These modules may be unique to islets and play an important role in the maintenance of blood glucose levels.

"Genome-wide association studies have told us there are genetic differences between type 2 diabetic and non-diabetic individuals in specific regions of the genome, but substantial efforts are required to understand how these differences contribute to disease," said co-lead author Michael Stitzel, Ph.D., NHGRI postdoctoral fellow. "Defining regulatory elements in human islets is a critical first step to understanding the molecular and biological effects for some of the genetic variants statistically associated with type 2 diabetes."

The researchers also found that 50 single nucleotide polymorphisms, or genetic variants, associated with islet-related traits or diseases are located within or very close to non-promoter regulatory elements. Variants associated with type 2 diabetes are present in six such elements that function to boost gene activity. These results suggest that regulatory elements may be a key component to understanding the molecular defects that contribute to type 2 diabetes.

Genetic association data pertaining to diabetes or other measures of islet function continue to be generated. The catalog of islet regulatory elements generated in the study provides an openly accessible resource for anyone to reference and ask whether newly emerging, statistically-associated variants are falling within these regulatory elements. The raw data can be found at the National Center for Biotechnology Information's Gene Expression Omnibus using accession number GSE23784.

"These findings represent important strides that were not possible just five years ago, but that are now realized with advances in genome sequencing technologies," said NHGRI Director Eric D. Green, M.D., Ph.D. "The power of DNA sequencing is allowing us to go from studies of a few genes at a time to profiling the entire genome. The scale is tremendously expanded. "

In addition to the NHGRI and the NIH Intramural Sequencing Center, researchers from Duke University, Durham, N.C. and the University of Michigan, Ann Arbor, contributed to the study.

Previously known as adult-onset, or non-insulin dependent diabetes mellitus, type 2 diabetes usually appears after age 40, often in overweight, sedentary people. However, a growing number of younger people -- and even children -- are developing the disease.

Editor's Note: This article is not intended to provide medical advice, diagnosis or treatment.

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by NIH/National Human Genome Research Institute.

Journal Reference:

Michael L. Stitzel, Praveen Sethupathy, Daniel S. Pearson, Peter S. Chines, Lingyun Song, Michael R. Erdos, Ryan Welch, Stephen C.J. Parker, Alan P. Boyle, Laura J. Scott, Elliott H. Margulies, Michael Boehnke, Terrence S. Furey, Gregory E. Crawford, Francis S. Collins. Global Epigenomic Analysis of Primary Human Pancreatic Islets Provides Insights into Type 2 Diabetes Susceptibility Loci. Cell Metabolism, 2010; 12 (5): 443-455 DOI: 10.1016/j.cmet.2010.09.012

Note: If no author is given, the source is cited instead.

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Scientists Describe New Approach For Identifying Genetic Markers For Common Diseases

Main Category: Genetics
Also Included In: Diabetes;??Cancer / Oncology
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A group of researchers at The Scripps Research Institute and the Scripps Translational Science Institute has published a paper that reviews new strategies for identifying collections of rare genetic variations that reveal whether people are predisposed to developing common conditions like diabetes and cancer.

In our modern genetic age, the entire DNA sequences, or "genomes," of humans and thousands of other animals, plants, and microbial life forms have been completely decoded and are publicly available to scientists worldwide. One of the hopes now that this data is available is that scientists will be able to find genetic markers of diseases - particular bits of DNA that would identify someone as being at risk for developing a particular disease.

Knowing that a person has such a genetic predisposition could be a powerful tool for preventative medicine because, depending on the disease in question, there may be specific drugs or behavioral modifications like diet or exercise that doctors could prescribe to their patients early on to prevent or significantly lessen the impact of those diseases later in life.

Finding these genetic markers has proven to be difficult, however, and despite the fact that the human genome has been available to researchers for years, scientists have only discovered the underlying genetic determinants for about five to ten percent of the heritable component of most common human diseases.

"There's a long way to go," says Nicholas J. Schork, Ph.D., who is a professor at Scripps Research and director of biostatistics and bioinformatics at the Scripps Translational Science Institute. In the November 2010 issue of Nature Reviews Genetics, Schork and his colleagues outline new statistical strategies that may help to close the gap in the coming years.

Part of the problem, Schork says, is that most studies up to now have focused on identifying common genetic markers of diseases - those definitive DNA signatures that are unmistakably linked to diseases because they are shared by large groups of people who have those diseases.

Such investigations, typically referred to as "genome-wide association studies," use statistical algorithms to sift through DNA samples and pull out whatever common variations exist that exhibit signs of association with a condition. While powerful, these statistical methods may not shed light on many diseases, says Schork, because not all diseases have such definitive DNA signatures. Many of the most common diseases are more complex. They are associated with multiple genes and multiple environmental factors.

According to Schork, the key to identifying the genetic components of these complex diseases is not to focus on finding single common genetic signatures that people share - but rather to identify whole collections of rare genetic signatures, any one of which may indicate a predisposition toward a disease.

The situation is analogous to asking how someone from outside New York City could get to Times Square in Manhattan. There is no single answer to that question because there are any number of approaches and modes of transportation - from New Jersey, from Brooklyn, from Wall Street, or from the Bronx, and via plane, bus, train, taxi, ferry, bridge, tunnel, subway, or sidewalk.

Regardless of where they start or how they get there, it is possible for many people to wind up at exactly the same spot, though, and Schork says the same is true for many human diseases. There may not be one single genetic marker for many diseases, but multiple markers involving any number of genes, even among people who share the same disease.

Finding these rare signatures requires a great deal more scientific sleuthing, says Schork, and in their Nature Review Genetics article Schork and his colleagues suggest a new approach to discover all the possible combinations.

This approach will require collaborations between mathematicians and computer scientists, who have the skills needed to tease out these elusive genetic markers, and biologists who can shed light on what those genes do.

"Mathematics, statistics, and fancy computers alone won't do it," Schork says. "A much more integrative approach has to occur in order to make sense of DNA sequence data."

The article, "Statistical analysis strategies for association studies involving rare variants," is authored by Vikas Bansal, Ondrej Libiger, Ali Torkamani, and Nicholas J. Schork. It appears in the November issue of Nature Reviews Genetics. See http://www.nature.com/nrg/journal/v11/n11/abs/nrg2867.html

This work was funded by grants and support from the National Institutes of Health, the Price Foundation, Scripps Genomic Medicine, and Charles University.

Source:
Mika Ono
Scripps Research Institute

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