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Combined linkage and family-based association analysis improves candidate gene detection in Genetic Analysis Workshop 18 simulation data

Because the genotype-phenotype correlation information is investigated differently by linkage and association analyses, various efforts have been made to model linkage and association jointly. However, joint modeling methods are usually computationally intensive; hence they cannot currently accommod...

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Detalles Bibliográficos
Autores principales: Li, Yi, Foo, Jia Nee, Liany, Herty, Low, Hui-Qi, Liu, Jianjun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4143774/
https://www.ncbi.nlm.nih.gov/pubmed/25519379
http://dx.doi.org/10.1186/1753-6561-8-S1-S29
Descripción
Sumario:Because the genotype-phenotype correlation information is investigated differently by linkage and association analyses, various efforts have been made to model linkage and association jointly. However, joint modeling methods are usually computationally intensive; hence they cannot currently accommodate large pedigrees with dense markers. This article proposes a simple method to combine the linkage and association evidence with the aim of improving the detection power of disease susceptibility genes. Our detection power comparisons show that the combined linkage-association p values can improve remarkably the causal gene detection power in Genetic Analysis Workshop 18 simulation data.