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

Open Access Proceedings

Using linkage analysis of large pedigrees to guide association analyses

Seung-Hoan Choi1, Chunyu Liu2, Josée Dupuis1, Mark W Logue13 and Gyungah Jun134*

Author Affiliations

1 Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Avenue, Boston, MA 02118, USA

2 Framingham Heart Study, National Heart, Lung, and Blood Institute, 73 Mount Wayte Avenue, Suite 2, Framingham, MA 01702, USA

3 Department of Medicine, Boston University School of Medicine, 72 East Concord Street, L310, Boston, MA 02118, USA

4 Department of Ophthalmology, Boston University School of Medicine, 72 East Concord Street, L310, Boston, MA 02118, USA

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BMC Proceedings 2011, 5(Suppl 9):S79  doi:10.1186/1753-6561-5-S9-S79

Published: 29 November 2011

Abstract

To date, genome-wide association studies have yielded discoveries of common variants that partly explain familial aggregation of diseases and traits. Researchers are now turning their attention to less common variants because the price of sequencing has dropped drastically. However, because sequencing of the whole genome in large samples is costly, great care must be taken to prioritize which samples and which genomic regions are selected for sequencing. We are interested in identifying genomic regions for deep sequencing using large multiplex families collected as part of earlier linkage studies. We incorporate linkage analysis into our search for Q1-associated alleles. Overall, we found that power was low for both whole-exome and linkage-guided sequencing analysis. By restricting sequencing to regions with high LOD peaks, we found fewer associated single-nucleotide polymorphisms than by using whole-exome sequencing. However, incorporating linkage analysis enabled us to detect more than half of the associated susceptibility loci (52%) that would have been identified by whole-exome sequencing while examining only 2.5% of the exome. This result suggests that incorporating linkage results from large multiplex families might greatly increase the efficiency of sequencing to detect trait-associated alleles in complex disease.