Selective Phenotyping for Increased Efficiency in Genetic Mapping Studies

• Activity Number: 326
• Type: Topic Contributed
• Date/Time: Wednesday, August 11, 2004 : 10:30 AM to 12:20 PM
• Sponsor: ENAR
• Author(s): Chunfang (Amy) Jin*+ and Hong Lan and Alan Attie and Dursun Bulutuglo and Gary A. Churchill and Brian S. Yandell
• Companies: University of Wisconsin, Madison and University of Wisconsin, Madison and University of Wisconsin, Madison and University of Wisconsin, Madison and University of Wisconsin, Madison and University of Wisconsin, Madison
• Keywords: quantitative trait loci ; interval-mapping ; power ; minimum moment aberration ; diabetes

Abstract

The power of a genetic-mapping study depends on the heritability of the trait, the number of individuals included in the analysis, and the genetic dissimilarity among them. In experiments that involve microarrays or other complex physiological assays, phenotyping can be expensive and time consuming and may impose limits on the sample size. A random selection of individuals may not provide sufficient power to detect linkage until a large sample size is reached. We present an algorithm for selecting a subset of individuals based solely on genotype data that can achieve substantial improvements in sensitivity compared to a random sample of the same size. The selective phenotyping method involves preferentially selecting individuals to maximize their genotypic dissimilarity. Selective phenotyping is most effective when prior knowledge of genetic architecture allows us to focus on specific genetic regions. However, it can also provide modest improvements in efficiency when applied on a whole genome basis. Importantly, selective phenotyping does not reduce the efficiency of mapping as compared to a random sample in regions that are not considered in the selection process.