Genetic mapping of complex discrete human diseases by discriminant analysis

The objective of the present study is to propose and evaluate a novel multivariate approach for genetic mapping of complex categorical diseases. This approach results from an application of standard stepwise discriminant analysis to detect linkage based on the differential marker identity-by-descent (IBD) distributions among the different groups of sib pairs. Two major advantages of this method are that it allows for simultaneously testing all markers, together with other genetic and environmental factors in a single multivariate setting and it avoids explicitly modeling the complex relationship between the affection status of sib pairs and the underlying genetic determinants. The efficiency and properties of the method are demonstrated via simulations. The proposed multivariate approach has successfully located the true position(s) under various genetic scenarios. The more important finding is that using highly densely spaced markers (1～2 cM) leads to only a marginal loss of statistical efficiency of the proposed methods in terms of gene localization and statistical power. These results have well established its utility and advantages as a fine-mapping tool. A unique property of the proposed method is the ability to map multiple linked trait loci to their precise positions due to its sequential nature, as demonstrated via simulations.

Genetic mapping of complex discrete human diseases by discriminant analysis

The objective of the present study is to propose and evaluate a novel multivariate approach for genetic mapping of complex categorical diseases. This approach results from an application of standard stepwise discriminant analysis to detect linkage based on the differential marker identity-by-descent (IBD) distributions among the different groups of sib pairs. Two major advantages of this method are that it allows for simultaneously testing all markers, together with other genetic and environmental factors in a single multivariate setting and it avoids explicitly modeling the complex relationship between the affection status of sib pairs and the underlying genetic determinants. The efficiency and properties of the method are demonstrated via simulations. The proposed multivariate approach has successfully located the true position(s) under various genetic scenarios. The more important finding is that using highly densely spaced markers (1～2 cM) leads to only a marginal loss of statistical efficiency of the proposed methods in terms of gene localization and statistical power. These results have well established its utility and advantages as a fine-mapping tool. A unique property of the proposed method is the ability to map multiple linked trait loci to their precise positions due to its sequential nature, as demonstrated via simulations.