Team Science helps Identify Genetic Influence in Obesity for People of African Ancestry
Genetic variations work with environmental factors to influence body size and obesity
Researchers from Norris Cotton Cancer Center and Dartmouth's Institute for Quantitative Biomedical Sciences (iQBS) brought together separate areas of expertise to identify three unique genetic variations that influence body size and obesity in men and women of African ancestry.
The study, a meta-analysis that examined 3.2 million genetic variants in over 30,000 people with African heritage for links to body-mass index (BMI), was the largest ever done on this population to date. The paper describing the study (published online in April by Nature Genetics) was written by professors Jason H. Moore, PhD, Third Century Professor, professor of genetics, and director of the iQBS; Christopher Amos, PhD, director of the Center for Genomic Medicine, associate director for Population Sciences at NCCC, and professor of community and family medicine; and Scott Williams, PhD, professor of genetics and founding director of the Center for Integrative Biomedical Sciences in iQBS. (Read more: Dartmouth iQBS Researchers Help Discover Three Unique Gene Variants That Influence Body Size and Obesity in People of African Ancestry)
Each scientist brought unique research expertise to the table:"Christopher Amos brings statistical genetics and genetic epidemiology; Scott Williams brings population and evolutionary genetics, and I bring computational genetics and bioinformatics." Moore explains. "Each of these pieces is critical for carrying out the kind of studies that are represented in this paper."
Obesity, a worldwide health epidemic, increases risks for heart disease and diabetes
Obesity is a worldwide health epidemic and associated with higher cardiovascular disease, diabetes and mortality , and lower quality of life. Nearly 50 percent of African-American adults in the U.S. are clinically obese. The study revealed that people with African ancestry possess three genetic variations that work with environmental factors to impact BMI, knowledge that may help clinicians to better prevent or treat obesity in this population.
The authors agree that both genes and environment play a role in obesity: "This paper is really just a start or a foundation for understanding the role of genetic variation in obesity," said Moore. "We expect obesity to be influenced by hundreds, if not thousands of genes and many, many environmental factors. While some genetic variants are likely to increase or decrease weight in all people, most are likely to influence weight in specific people depending on their genetic background and their unique environmental history including diet, toxic metal exposure, exercise, etc. We will not fully understand the genetics of obesity until we can fully investigate these context-dependent genetic effects."
Team Science: shared areas of expertise create new approaches and insights
The context-dependent nature of the large-scale genetic analysis involved in this study lends itself to a model of collaborative team science that iQBS was created to foster.
Established in 2010, the institute brings together scientists studying similar issues but approaching them from different perspectives. The institute creates an environment that supports interdisciplinary collaboration in the quantitative biomedical sciences, including bioengineering, bioinformatics, biophysics, biostatistics, computational biology, genomics, epidemiology, proteomics, structural biology, systems biology and related areas.
It also brings together Dartmouth faculty from across the sciences to teach in the Graduate Program in Quantitative Biomedical Sciences (QBS), which cross-trains students in bioinformatics, biostatistics, and epidemiology.
"This is really the glue that holds iQBS together," Moore says, noting that faculty collaborations are formed as they work together on student projects. "We assume complexity rather than simplicity when approaching a problem, and have created what we think is a model of the future—training computational biology students to speak multiple languages beyond bioinformatics."
 Association of All-Cause Mortality with Overweight and Obesity Using Standard Body Mass Index Categories: A Systematic Review and Meta-analysis. Katherine M. Flegal, PhD; Brian K. Kit, MD; Heather Orpana, PhD; Barry I. Graubard, PhD. JAMA. 2013;309(1):71-82. doi:10.1001/jama.2012.113905.
May 13, 2013
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