title: Combined linkage and family-based association analysis improves candidate gene detection in Genetic Analysis Workshop 18 simulation data.
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.
authors.affiliation: Human Genetics, Genome Institute of Singapore, #02-01, Genome, 60 Biopolis Street, 138672, Republic of Singapore.
authors.forename: Yi
authors.initials: Y
authors.lastname: Li
authors.affiliation: Human Genetics, Genome Institute of Singapore, #02-01, Genome, 60 Biopolis Street, 138672, Republic of Singapore.
authors.forename: Jia Nee
authors.initials: JN
authors.lastname: Foo
authors.affiliation: Human Genetics, Genome Institute of Singapore, #02-01, Genome, 60 Biopolis Street, 138672, Republic of Singapore.
authors.forename: Herty
authors.initials: H
authors.lastname: Liany
authors.affiliation: Human Genetics, Genome Institute of Singapore, #02-01, Genome, 60 Biopolis Street, 138672, Republic of Singapore.
authors.forename: Hui-Qi
authors.initials: HQ
authors.lastname: Low
authors.affiliation: Human Genetics, Genome Institute of Singapore, #02-01, Genome, 60 Biopolis Street, 138672, Republic of Singapore.
authors.forename: Jianjun
authors.initials: J
authors.lastname: Liu