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BioMed Central, BMC Proceedings, S9(5), 2011

DOI: 10.1186/1753-6561-5-s9-s79



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Using linkage analysis of large pedigrees to guide association analyses

Journal article published in 2011 by Seung-Hoan Choi, Chunyu Liu, Josée Dupuis, Mark W. Logue ORCID, Gyungah Jun ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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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.