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The Journal of Nutrition Vol. 127 No. 9 September 1997, pp. 1909S-1916S
Copyright ©1997 by the American Society for Nutritional Sciences

Mapping of Mouse Obesity Genes: A Generic Approach to a Complex Trait

Janis S. Fisler*, and Craig H. Wardendagger

* Department of Medicine, Division of Cardiology, University of California, Los Angeles, CA 90095 and dagger  Department of Pediatrics and Rowe Program in Genetics, University of California, Davis, CA 95616

Identification of genes underlying any complex trait such as obesity is an important and difficult problem in genetics. Traditional candidate gene approaches cannot be relied on to identify all of the genes influencing a complex trait, and positional cloning is very laborious. With the advent of new tools and methods, however, comprehensive approaches to the identification of any genes underlying complex traits are now available. Quantitative trait locus (QTL) mapping is a general technique to map Mendelian factors influencing complex traits. The QTL approach involves the crossing of two strains that differ in the trait of interest to produce F2 or back-cross progeny, individually phenotyping and genotyping each progeny, and statistically associating the typed markers and the phenotype. QTL mapping has been used in the last 4 years to map genes for a wide variety of traits, including body weight and growth, obesity, atherosclerosis and susceptibility to cancer in the mouse, and hypertension, hyperactivity and arthritis in the rat. QTL mapping has also been used to map genes in pigs, poultry, cows, fish and plants. Once a trait has been located in a chromosomal subregion, identifying the underlying gene remains a significant problem. A monogenic model must be developed, isolating one gene influencing a trait from other genes affecting the same phenotype. Then the positional candidate strategy, which relies on a combination of mapping to a chromosomal subregion followed by a survey of the interval to see if attractive candidates reside there, becomes practical.

Key words: genetics, percentage body fat.







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