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This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data

Open Access Proceedings

Identifying causal rare variants of disease through family-based analysis of Genetics Analysis Workshop 17 data set

Wai-Ki Yip1*, Gourab De1, Benjamin A Raby2 and Nan Laird1

Author Affiliations

1 Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Building II, Boston, MA 02115, USA

2 Brigham and Women Hospital, 75 Francis Street, Boston, MA 02115, USA

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BMC Proceedings 2011, 5(Suppl 9):S21  doi:10.1186/1753-6561-5-S9-S21

Published: 29 November 2011

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.