An entropy-based genome-wide transmission/disequilibrium test.

TitleAn entropy-based genome-wide transmission/disequilibrium test.
Publication TypeJournal Article
Year of Publication2007
AuthorsZhao, J, Boerwinkle, E, Xiong, M
JournalHum Genet
Volume121
Issue3-4
Pagination357-67
Date Published2007 May
ISSN0340-6717
KeywordsEntropy, Gene Frequency, Genetic Techniques, Genome, Human, Haplotypes, Hirschsprung Disease, Humans, Linkage Disequilibrium, Lupus Erythematosus, Systemic, Models, Genetic, Models, Statistical, Proto-Oncogene Proteins c-ret, Receptors, IgG
Abstract

Availability of a large collection of single nucleotide polymorphisms (SNPs) and efficient genotyping methods enable the extension of linkage and association studies for complex diseases from small genomic regions to the whole genome. Establishing global significance for linkage or association requires small P-values of the test. The original TDT statistic compares the difference in linear functions of the number of transmitted and nontransmitted alleles or haplotypes. In this report, we introduce a novel TDT statistic, which uses Shannon entropy as a nonlinear transformation of the frequencies of the transmitted or nontransmitted alleles (or haplotypes), to amplify the difference in the number of transmitted and nontransmitted alleles or haplotypes in order to increase statistical power with large number of marker loci. The null distribution of the entropy-based TDT statistic and the type I error rates in both homogeneous and admixture populations are validated using a series of simulation studies. By analytical methods, we show that the power of the entropy-based TDT statistic is higher than the original TDT, and this difference increases with the number of marker loci. Finally, the new entropy-based TDT statistic is applied to two real data sets to test the association of the RET gene with Hirschsprung disease and the Fcgamma receptor genes with systemic lupus erythematosus. Results show that the entropy-based TDT statistic can reach p-values that are small enough to establish genome-wide linkage or association analyses.

DOI10.1007/s00439-007-0322-6
Alternate JournalHum. Genet.
PubMed ID17297624
Grant ListES09912 / ES / NIEHS NIH HHS / United States
IP50AR44888 / AR / NIAMS NIH HHS / United States