title: Identifying causal rare variants of disease through family-based analysis of Genetics Analysis Workshop 17 data set.
abstract: Linkage- and association-based methods have been proposed for mapping disease-causing rare variants. Based on the family information provided in the Genetic Analysis Workshop 17 data set, we formulate a two-pronged approach that combines both methods. Using the identity-by-descent information provided for eight extended pedigrees (n = 697) and the simulated quantitative trait Q1, we explore various traditional nonparametric linkage analysis methods; the best result is obtained by assuming between-family heterogeneity and applying the Haseman-Elston regression to each pedigree separately. We discover strong signals from two genes in two different families and weaker signals for a third gene from two other families. As an exploratory approach, we apply an association test based on a modified family-based association test statistic to all rare variants (frequency < 1% or < 3%) designated as causal for Q1. Family-based association tests correctly identified causal single-nucleotide polymorphisms for four genes (KDR, VEGFA, VEGFC, and FLT1). Our results suggest that both linkage and association tests with families show promise for identifying rare variants.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Building II, Boston, MA 02115, USA.
authors.forename: Wai-Ki
authors.initials: WK
authors.lastname: Yip
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Building II, Boston, MA 02115, USA.
authors.forename: Gourab
authors.initials: G
authors.lastname: De
authors.affiliation: Brigham and Women Hospital, 75 Francis Street, Boston, MA 02115, USA.
authors.forename: Benjamin A
authors.initials: BA
authors.lastname: Raby
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Building II, Boston, MA 02115, USA.
authors.forename: Nan
authors.initials: N
authors.lastname: Laird