Monday, December 24, 2012

Genetic clues to insulin production: Genomic analysis method helps track genetic contributors relevant to diabetes

Dec. 23, 2012 ? In research published online Dec. 23, 2012 in the journal Nature Genetics, scientists have found three new and relatively rare genetic variants that influence insulin production, offering new clues about the genetic factors behind diabetes.

"Studying genetic variants -- even rare ones -- helps us learn how genes affect health and disease," said Karen Mohlke, PhD, one of the study's senior authors and associate professor of genetics at the University of North Carolina School of Medicine. "In this study, we've implicated new genes as playing a role in insulin processing and secretion."

The study is also the first time genetic insights have been reported using exome array genotyping, a new tool that is less costly than genetic sequencing. This analysis allows scientists to quickly screen DNA samples for known variants in specific genes. It is especially helpful for testing variants that are rare.

"The exome array allowed us to test a large number of individuals -- in this case, more than 8,000 people -- very efficiently," said Mohlke. "We expect that this type of analysis will be useful for finding low-frequency variants associated with many complex traits, including obesity or cancer."

The scientists pulled data from a large health study directed by researchers at the University of Eastern Finland. A research team including postdoctoral scientist Jeroen Huyghe at the University of Michigan, Ann Arbor led the statistical analysis, which integrated genetic data and detailed health records for a sample of 8,229 Finnish males.

Diabetes, which affects more than 25 million people in the United States, results from problems with the body's ability to produce or use insulin. Rather than pinpointing one gene behind the disease, scientists believe there are a whole host of genes that interact with health and lifestyle factors to influence a person's chances of getting the disease.

The study revealed that certain variants of three genes -- called TBC1D30, KANK1 and PAM -- are associated with abnormal insulin production or processing, even in people without diabetes. The genes may predispose such individuals to developing the disease.

As a next step, the researchers plan to continue to investigate how these genes may lead to diabetes. They also expect the results will inspire other scientists to use exome analysis to look at the genetic factors behind other complex diseases.

In addition to Mohlke, the study's co-Principal Investigators included Michael Boehnke, PhD, of the University of Michigan, Ann Arbor and Markku Laakso, MD, of the University of Eastern Finland and Kuopio University Hospital.

In the U.S., funding for the research came from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and the National Human Genome Research Institute (NHGRI), components of the National Institutes of Health.

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The above story is reprinted from materials provided by University of North Carolina School of Medicine, via Newswise.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Journal Reference:

  1. Jeroen R Huyghe, Anne U Jackson, Marie P Fogarty, Martin L Buchkovich, Alena Stan??kov?, Heather M Stringham, Xueling Sim, Lingyao Yang, Christian Fuchsberger, Henna Cederberg, Peter S Chines, Tanya M Teslovich, Jane M Romm, Hua Ling, Ivy McMullen, Roxann Ingersoll, Elizabeth W Pugh, Kimberly F Doheny, Benjamin M Neale, Mark J Daly, Johanna Kuusisto, Laura J Scott, Hyun Min Kang, Francis S Collins, Gon?alo R Abecasis, Richard M Watanabe, Michael Boehnke, Markku Laakso, Karen L Mohlke. Exome array analysis identifies new loci and low-frequency variants influencing insulin processing and secretion. Nature Genetics, 2012; DOI: 10.1038/ng.2507

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

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.


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