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Two general classes of model‐free linkage methods are discussed in this chapter, relative pair methods designed primarily for analysis of discrete traits and variance component methods designed primarily for analysis of quantitative traits.
Model‐free linkage methods are based on identifying regions of the genome in which patterns of allele sharing among family members correspond to patterns of phenotype correlation among family members.
Another alternative to deterministic linkage methods are probabilistic methods.
Data custodians, extraction criteria and linkage methods are described elsewhere.
In hierarchical clustering, the complete linkage and the average linkage methods are more widely used than the single linkage method (Quince et al., 2009).
Traditional linkage methods are exponential either in terms of the number of markers [i.e. Elston Stewart i.e. Elston and Stewart, 1971)], or in terms of the size of a pedigree [Lander Green (Lander and Green, 1987)], therefore cannot efficiently deal with new data.
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Parametric and non-parametric parent-of-origin linkage methods were applied.
City Block similarity metric and average linkage methods were used for hierarchical clustering of arrays.
Consequently, probabilistic linkage methods were utilised.
Different dissimilarity and linkage methods were tested and evaluated.
Recently, the validity of the registries and linkage methods was studied.
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