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Am. J. Hum. Genet. 68:14631474, 2001 The Power to Detect Linkage Disequilibrium with Quantitative Traits

Summary: Am. J. Hum. Genet. 68:1463­1474, 2001
The Power to Detect Linkage Disequilibrium with Quantitative Traits
in Selected Samples
Gonc¸alo R. Abecasis, William O. C. Cookson, and Lon R. Cardon
University of Oxford, Oxford, United Kingdom
Results from power studies for linkage detection have led to many ongoing and planned collections of phenotypically
extreme nuclear families. Given the great expense of collecting these families and the imminent availability of a
dense diallelic marker map, the families are likely to be used in allelic-association as well as linkage studies. However,
optimal selection strategies for linkage may not be equally powerful for association. We examine the power to
detect linkage disequilibrium for quantitative traits after phenotypic selection. The results encompass six selection
strategies that are in widespread use, including single selection (two designs), affected sib pairs, concordant and
discordant pairs, and the extreme-concordant and -discordant design. Selection of sibships on the basis of one
extreme proband with high or low trait scores provides as much power as discordant sib pairs but requires the
screening and phenotyping of substantially fewer initial families from which to select. Analysis of the role of allele
frequencies within each selection design indicates that common trait alleles generally offer the most power, but
similarities between the marker- and trait-allele frequencies are much more important than the trait-locus frequency
alone. Some of the most widespread selection designs, such as single selection, yield power gains only when both
the marker and quantitative trait loci (QTL) are relatively rare in the population. In contrast, discordant pairs and
the extreme-proband design provide power for the broadest range of QTL­marker-allele frequency differences.


Source: Abecasis, Goncalo - Department of Biostatistics, University of Michigan


Collections: Biology and Medicine; Mathematics