Gaussian models for genetic linkage analysis using complete high-resolution maps of identity by descent.
Gaussian-process models are developed to detect genetic linkage using complete high-resolution maps of identity by descent between affected relative pairs. Approximations are given for the significance level and power of the likelihood-ratio test of no linkage and for likelihood-ratio confidence regions for trait loci. The sample sizes required to detect linkage by using different classes of affected relative pairs are compared, and the problem of combining data from different classes of relatives is discussed.
Feingold
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Department of Statistics, Stanford University, CA 94305.
Brown
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Siegmund
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D008040:Genetic Linkage; D006801:Humans; D008957:Models, Genetic