Direct power comparisons between simple LOD scores and NPL scores for linkage analysis in complex diseases

Article Abstract:

Direct power comparisons between nonparametric linkage (NPL) scores and simple maximum likelihood-based methods (LOD scores) for linkage analysis in complex diseases are discussed. LOD score maximized over disease models (MMLS) and NPL are compared directly and 100 data sets with 20 families each have been simulated using 26 generating models. More robustness was found for the MMLS-C vs NPL in affecteds-only analysis. Use of two simple modes of inheritance at a fixed penetrance can be more effective than NPL when the trait mode of inheritance is complex and when heterogeneity is in the data set.

Statistical Data Included, Methods, Analysis, Evaluation, Mathematical models, Simulation methods, Simulation, Biological models, Probabilities, Probability theory, Medical statistics, Biometric research, Biomathematics

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Evidence for linkage of adolescent-onset idiopathic generalized epilepsies to chromosome 8--and genetic heterogeneity

Article Abstract:

Families with various adolescent-onset idiopathic generalized epilepsies (IGEs) have been investigated for linkage to markers across chromosome 8. A gene common to all of the IGEs was sought, and linkage to specific subforms of IGE was investigated. No such common gene was found. Evidence for linkage to chromosome 8 was found in IGE families in which juvenile myoclonic epilepsy did not exist. Evidence for genetic heterogeneity in IGEs overall exists.

Genetic aspects, Epilepsy in children, Pediatric epilepsy

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The power to detect linkage in complex disease by means of simple LOD-score analyses

Article Abstract:

The 'maximized maximum LOD score' (MMLS) can be used to detect linkage when the true mode of inheritance (MOI) is complex but simple models are assumed for LOD analysis. The MOI at the linked locus is critical, but not that of the disease or trait. The power to determine linkage is not significantly diminished, even when a simple Mendelian model is assumed while the true model is fairly complex.

Maximum likelihood estimates (Statistics), Maximum likelihood (Statistics)

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