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Combined linkage and family-based association analysis improves candidate gene detection in Genetic Analysis Workshop 18 simulation data.
Li, Yi; Foo, Jia Nee; Liany, Herty; Low, Hui-Qi; Liu, Jianjun.
Afiliação
  • Li Y; Human Genetics, Genome Institute of Singapore, #02-01, Genome, 60 Biopolis Street, 138672, Republic of Singapore.
  • Foo JN; Human Genetics, Genome Institute of Singapore, #02-01, Genome, 60 Biopolis Street, 138672, Republic of Singapore.
  • Liany H; Human Genetics, Genome Institute of Singapore, #02-01, Genome, 60 Biopolis Street, 138672, Republic of Singapore.
  • Low HQ; Human Genetics, Genome Institute of Singapore, #02-01, Genome, 60 Biopolis Street, 138672, Republic of Singapore.
  • Liu J; Human Genetics, Genome Institute of Singapore, #02-01, Genome, 60 Biopolis Street, 138672, Republic of Singapore.
BMC Proc ; 8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo): S29, 2014.
Article em En | MEDLINE | ID: mdl-25519379
ABSTRACT
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.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2014 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2014 Tipo de documento: Article