Journal
AMERICAN JOURNAL OF HUMAN GENETICS
Volume 68, Issue 6, Pages 1527-1532Publisher
CELL PRESS
DOI: 10.1086/320593
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Funding
- NEI NIH HHS [EY12562, R01 EY012562] Funding Source: Medline
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The Haseman-Elston regression method offers a simpler alternative to variance-components (VC) models, for the linkage analysis of quantitative traits. However, even the revisited method, which uses the cross-product-rather than the squared difference-in sib trait values, is, in general, less powerful than VC models. In this report, we clarify the relative efficiencies of existing Haseman-Elston methods and show how a new Haseman-Elston method can be constructed to have power equivalent to that of VC models. This method uses as the dependent variable a linear combination of squared sums and squared differences, in which the weights are determined by the overall trait correlation between sibs in a population. We show how this method can be used for both the selection of maximally informative sib pairs for genotyping and the subsequent analysis of such selected samples.
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