Genome-Wide Associations of Gene Expression Variation in Humans

Barbara E. Stranger(Wellcome Sanger Institute), Matthew S. Forrest(Wellcome Sanger Institute), Andrew G. Clark(Cornell University), Mark J Minichiello(Wellcome Sanger Institute), Samuel Deutsch(University of Geneva), Robert Lyle(University of Geneva), Sarah Hunt(Wellcome Sanger Institute), Brenda F Kahl(Illumina (United States)), Stylianos E. Antonarakis(University of Geneva), Simon Tavaré(University of Southern California), Panagiotis Deloukas(Wellcome Sanger Institute), Emmanouil T. Dermitzakis(Wellcome Sanger Institute)
PLoS Genetics
December 13, 2005
Cited by 518Open Access
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Abstract

The exploration of quantitative variation in human populations has become one of the major priorities for medical genetics. The successful identification of variants that contribute to complex traits is highly dependent on reliable assays and genetic maps. We have performed a genome-wide quantitative trait analysis of 630 genes in 60 unrelated Utah residents with ancestry from Northern and Western Europe using the publicly available phase I data of the International HapMap project. The genes are located in regions of the human genome with elevated functional annotation and disease interest including the ENCODE regions spanning 1% of the genome, Chromosome 21 and Chromosome 20q12-13.2. We apply three different methods of multiple test correction, including Bonferroni, false discovery rate, and permutations. For the 374 expressed genes, we find many regions with statistically significant association of single nucleotide polymorphisms (SNPs) with expression variation in lymphoblastoid cell lines after correcting for multiple tests. Based on our analyses, the signal proximal (cis-) to the genes of interest is more abundant and more stable than distal and trans across statistical methodologies. Our results suggest that regulatory polymorphism is widespread in the human genome and show that the 5-kb (phase I) HapMap has sufficient density to enable linkage disequilibrium mapping in humans. Such studies will significantly enhance our ability to annotate the non-coding part of the genome and interpret functional variation. In addition, we demonstrate that the HapMap cell lines themselves may serve as a useful resource for quantitative measurements at the cellular level.


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