Sib-pair linkage studies are widely used to investigate the genetic factors implicated in complex quantitative traits. To analyze these data, we propose a Maximum-Likelihood-Binomial (MLB) approach, which considers the sibship as a whole and relies on the idea of binomial distributions of parental a
Foundations of the likelihood linkage analysis (LLA) classification method
β Scribed by Lerman, I. C.
- Book ID
- 102754663
- Publisher
- John Wiley and Sons
- Year
- 1991
- Tongue
- English
- Weight
- 843 KB
- Volume
- 7
- Category
- Article
- ISSN
- 8755-0024
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β¦ Synopsis
The aim of this paper is to present the concepts underlying an approach to data analysis using a hierarchical classification. The data can be provided by observation, experiment or knowledge. We begin by presenting the classical view of the context of data representation, in which the algorithm of hierarchical ascendant construction of the classification tree is set. The main notion in our method is one of 'similarity'. The latter must be elaborated in the best way, taking into account the mathematical nature of the objects to be compared. In this elaboration, we adopt a set theoretic and combinatoric representation of the descriptive attributes, which are interpreted in terms of relations. On the other hand, we introduce a probability scale for similarity measurement by using a likelihood concept.
π SIMILAR VOLUMES
Model-free linkage analysis methods, based on identity-by-descent allele sharing, are commonly used for complex trait analysis. The Maximum-Likelihood-Binomial (MLB) approach, which is based on the hypothesis that parental alleles are binomially distributed among affected sibs, is particularly popul