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Comparison of GIST and LAMP on the GAW15 simulated data

Overview of attention for article published in BMC Proceedings, December 2007
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Comparison of GIST and LAMP on the GAW15 simulated data
Published in
BMC Proceedings, December 2007
DOI 10.1186/1753-6561-1-s1-s41
Pubmed ID

Xuemei Lou, Silke Schmidt, Elizabeth R Hauser


After genetic linkage has been identified for a complex disease, the next step is often fine-mapping by association analysis, using single-nucleotide polymorphisms (SNPs) within a linkage region. If a SNP shows evidence of association, it is useful to know whether the linkage result can be explained in part or in full by the candidate SNP. The genotype identity-by-descent sharing test (GIST) and linkage and association modeling in pedigrees (LAMP) are two methods that were specifically proposed to address this question. GIST determines whether there is significant correlation between family-specific weights, defined by the presence of a tentatively associated allele in affected siblings, and family-specific nonparametric linkage scores. LAMP constructs a pedigree likelihood function of the marker data conditional on the trait data, and implements three likelihood ratio tests to characterize the relationship between the candidate SNP and the disease locus. The goal of our study was to compare the two approaches and evaluate their ability to identify disease-associated SNPs in the Genetic Analysis Workshop 15 (GAW15) simulated data. Our results can be summarized as follows: 1) GIST is simple and fast but, as a test of association, did not perform well in the GAW15 data, especially with adjustment for multiple testing; 2) as a test of association, the LAMP-LE test performs best when the linkage evidence is strong, or when there is at least moderate linkage disequilibrium between the candidate SNP and the trait locus. We conclude that LAMP is more flexible and reliable to use in practice.

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Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 29%
Professor 1 14%
Other 1 14%
Student > Ph. D. Student 1 14%
Professor > Associate Professor 1 14%
Other 0 0%
Unknown 1 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 43%
Biochemistry, Genetics and Molecular Biology 1 14%
Medicine and Dentistry 1 14%
Unknown 2 29%