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Power calculations for selective genotyping in QTL mapping in backcross mice

Published online by Cambridge University Press:  26 November 2004

NUSRAT RABBEE ,
DAVID SPECA ,
NICOLA J. ARMSTRONG  and
TERENCE P. SPEED
NUSRAT RABBEE
Affiliation:
Department of Statistics, University of California, Berkeley, 367 Evans Hall, Berkeley, CA 94720, USA
DAVID SPECA
Affiliation:
Department of Neurology and the Ernest Gallo Clinic and Research Center, University of California at San Francisco, 5858 Horton Street, Emeryville, CA 94608, USA
NICOLA J. ARMSTRONG
Affiliation:
Eurandom, Eindhoven, The Netherlands
TERENCE P. SPEED
Affiliation:
Department of Statistics, University of California, Berkeley, 367 Evans Hall, Berkeley, CA 94720, USA Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
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Abstract

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Selective genotyping concerns the genotyping of a portion of individuals chosen on the basis of their phenotypic values. Often individuals are selected for genotyping from the high and low extremes of the phenotypic distribution. This procedure yields savings in cost and time by decreasing the total number of individuals genotyped. Previous work by Darvasi et al. (1993) has shown that the power to detect a QTL by genotyping 40–50% of a population is roughly equivalent to genotyping the entire sample. However, these power studies have not accounted for different strategies of analysing the data when phenotypes of individuals in the middle are excluded, nor have they investigated the genome-wide type I error rate under these different strategies or different selection percentages. Further, these simulation studies have not considered markers over the entire genome. In this paper, we present simulation studies of power for the maximum likelihood approach to QTL mapping by Lander & Botstein (1989) in the context of selective genotyping. We calculate the power of selectively genotyping the individuals from the middle of the phenotypic distribution when performing QTL mapping over the whole mouse genome.

Type
Research Article
Information
Genetics Research , Volume 84 , Issue 2 , October 2004 , pp. 103 - 108
Copyright
© 2004 Cambridge University Press