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HuGENet™ Case Study

Using whole genome scans to discover obesity genes: Implications for the Clinical Utility of Genetic Testing in Obesity

 

Katrina A.B. Goddard
American Society of Human Genetics Fellow
Office of Public Health Genomics
Centers for Disease Control and Prevention

Educational objectives

After reading this case study, you should be able to:

  • Evaluate findings from a GWAS approach
  • Calculate measures of clinical validity and utility
  • Understand the underlying components that contribute to the ‘value added’ of a genetic test
  • Discuss potential clinical or public health benefits of genetic testing

Introduction

Approximately 30% of US adults are now obese (Body Mass Index (BMI)≥30 kg/m2) [Hedley et al., 2004], representing a significant health problem in the US and other developed countries. Obesity is associated with considerable morbidity and mortality through its association with type 2 diabetes, heart disease, metabolic syndrome, hypertension, stroke, and cancer. Obesity is commonly measured using the body mass index (BMI) [weight/height2 in kg/m2], although recently alternative measures, such as the waist-to-hip ratio, have been suggested that may be better predictors of mortality [Welborn, 2007]. Although lifestyle factors, such as diet and exercise, are important determinants of obesity, genetic studies have produced estimates of heritability for BMI between 30-70% [Bell et al., 2005; Farooqi et al., 2005; Hebebrand et al., 2003; Schousboe et al., 2003].

Genome-wide Association Studies (GWAS) are an increasingly popular tool to search for genetic risk factors that contribute to susceptibility to disease. The advantages of the approach include the ability to conduct population-based studies, which may be easier and less expensive than family studies. They may also have increased power to detect loci with small effect, a likely circumstance for complex traits, and allow finer localization of the signal. The drawbacks of this approach include sensitivity to alternative causes of allelic association, including population stratification and chance (type II error), as well as reduced power in the presence of locus and allelic heterogeneity or mutations that arise more than once.

Case Study

In April 2006, Herbert et al. reported that a common variant near the insulin-induced gene 2 (INSIG2) is associated with obesity, which was identified through a genome wide association approach. The primary study population was the NHLBI Framingham Heart Study, where individuals were enrolled from the community, and were not selected for a particular trait or disease. Herbert, and coworkers, then genotyped the variant in five additional studies including the Nurses Health Study; the KORA S4 cohort from a town near Munich, Germany; a case-control study of subjects from Poland and the United States, in which cases were selected based on BMI; a sample of African-American families and unrelated individuals from Maywood, Illinois, who were selected based on BMI; and a sample of Western European parent-child trios who were selected because of obesity in the child. Association was detected in four of the five replication studies. The high-risk genotype is present in approximately 10% of the population, and confers a risk approximately 1.22-1.33 times the risk among persons without the high risk genotype.

Herbert A, et al., A Common Genetic Variant is Associated with Adult and Childhood Obesity. Science 312:279-283, 2006.

 

References

  1. A. A. Hedley et al., JAMA 291, 2847 (2004).
  2. C. G. Bell, A. J. Walley, P. Froguel, Nat. Rev. Genet. 6, 221 (2005).
  3. I. S. Farooqi, S. O'Rahilly, Int. J. Obes. 29, 1149 (2005).
  4. J. Hebebrand, S. Friedel, N. Schauble, F. Geller, A. Hinney, Obes. Rev. 4, 139 (2003).
  5. K. Schousboe et al., Twin Res. 6, 409 (2003).
  6. C. Dina et al., Science 315, 187b (2007).
  7. R. J. F. Loos et al., Science 315, 187c (2007).
  8. D. Rosskopf et al., Science 315, 187d (2007).
  9. A. Herbert et al., Science 315, 187e (2007).

 

 

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