- THIS ARTICLE
- Full Text
- Full Text (PDF)
- Alert me when this article is cited
- Alert me if a correction is posted
- SERVICES
- Email this article to a friend
- Similar articles in this journal
- Similar articles in PubMed
- Alert me to new issues of the journal
- Download to citation manager
- Reprints & Permissions
- CITING ARTICLES
- Citing Articles via HighWire
- Citing Articles via Google Scholar
- GOOGLE SCHOLAR
- Articles by Jin, C.
- Articles by Yandell, B. S.
- Search for Related Content
- PUBMED
- PubMed Citation
- Articles by Jin, C.
- Articles by Yandell, B. S.
Genetics, Vol. 168, 2285-2293, December 2004, Copyright © 2004
doi:10.1534/genetics.104.027524
Selective Phenotyping for Increased Efficiency in Genetic Mapping Studies
Chunfang Jin*,
Hong Lan
,
Alan D. Attie,
Gary A. Churchill
,
Dursun Bulutuglo and
Brian S. Yandell*,
,1
* Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706
Department of Biochemistry, University of Wisconsin, Madison, Wisconsin 53706
The Jackson Laboratory, Bar Harbor, Maine 04609
Department of Horticulture, University of Wisconsin, Madison, Wisconsin 53706
1 Corresponding author: Department of Statistics, University of Wisconsin, 1300 University Ave., MSC-1239, Madison, WI 53706.
E-mail: byandell{at}wisc.edu
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 solely on the basis of 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. In contrast to selective genotyping, inferences based solely on a selectively phenotyped population of individuals are representative of the whole population. The substantial improvement introduced by selective phenotyping is particularly useful when phenotyping is difficult or costly and thus limits the sample size in a genetic mapping study.
This article has been cited by other articles:
 |
|
 |
|
  P. Boddhireddy, J.-L. Jannink, and J. C. Nelson Selective Advance for Accelerated Development of Recombinant Inbred QTL Mapping Populations Crop Sci., June 26, 2009; 49(4): 1284 - 1294. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. Sen, F. Johannes, and K. W. Broman Selective Genotyping and Phenotyping Strategies in a Complex Trait Context Genetics, April 1, 2009; 181(4): 1613 - 1626. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. C. Lam, J. Fu, R. C. Jansen, C. S. Haley, and D.-J. de Koning Optimal Design of Genetic Studies of Gene Expression With Two-Color Microarrays in Outbred Crosses Genetics, November 1, 2008; 180(3): 1691 - 1698. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
F. F. Cardoso, G. J. M. Rosa, J. P. Steibel, C. W. Ernst, R. O. Bates, and R. J. Tempelman Selective Transcriptional Profiling and Data Analysis Strategies for Expression Quantitative Trait Loci Mapping in Outbred F2 Populations Genetics, November 1, 2008; 180(3): 1679 - 1690. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. V. Rockman and L. Kruglyak Breeding Designs for Recombinant Inbred Advanced Intercross Lines Genetics, June 1, 2008; 179(2): 1069 - 1078. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. J. Sillanpaa and F. Hoti Mapping Quantitative Trait Loci From a Single-Tail Sample of the Phenotype Distribution Including Survival Data Genetics, December 1, 2007; 177(4): 2361 - 2377. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Chen and C. Kendziorski A Statistical Framework for Expression Quantitative Trait Loci Mapping Genetics, October 1, 2007; 177(2): 761 - 771. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 G. J. M. Rosa, N. de Leon, and A. J. M. Rosa Review of microarray experimental design strategies for genetical genomics studies Physiol Genomics, December 13, 2006; 28(1): 15 - 23. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. Granhall, H.-B. Park, H. Fakhrai-Rad, and H. Luthman High-Resolution Quantitative Trait Locus Analysis Reveals Multiple Diabetes Susceptibility Loci Mapped to Intervals <800 kb in the Species-Conserved Niddm1i of the GK Rat Genetics, November 1, 2006; 174(3): 1565 - 1572. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. S. S. B. Filho, S. G. Gilmour, and G. J. M. Rosa Design of Microarray Experiments for Genetical Genomics Studies Genetics, October 1, 2006; 174(2): 945 - 957. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
V. Orgogozo, K. W. Broman, and D. L. Stern High-Resolution Quantitative Trait Locus Mapping Reveals Sign Epistasis Controlling Ovariole Number Between Two Drosophila Species Genetics, May 1, 2006; 173(1): 197 - 205. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Fu and R. C. Jansen Optimal Design and Analysis of Genetic Studies on Gene Expression Genetics, March 1, 2006; 172(3): 1993 - 1999. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Sen, J. M. Satagopan, and G. A. Churchill Quantitative Trait Locus Study Design From an Information Perspective Genetics, May 1, 2005; 170(1): 447 - 464. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Z. Xu, F. Zou, and T. J. Vision Improving Quantitative Trait Loci Mapping Resolution in Experimental Crosses by the Use of Genotypically Selected Samples Genetics, May 1, 2005; 170(1): 401 - 408. [Abstract] [Full Text] [PDF]
|
|
|