Summary of contributions to GAW15 Group 16: Processing/normalization of expression traits
Autor: | Aurélie, Labbe, Jeanette, McClintick, Maria, Martinez, J, Beyenne, L, Chen, M, de Andrade, H J, Edenberg, Y, Kim, K Y, Liu, Y, Liu, J, Ma, B, Peng, J, Peng, J M, Peralta, S, Qin, M P, Roth, P, Wang |
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Rok vydání: | 2007 |
Předmět: |
Genetics
Microarray Genetic Linkage Epidemiology Microarray analysis techniques Quantitative Trait Loci Computational biology Biology Heritability Quantitative trait locus Genetic analysis Phenotype Expression quantitative trait loci Humans DNA microarray Gene Algorithms Genetics (clinical) Oligonucleotide Array Sequence Analysis |
Zdroj: | Genetic Epidemiology. 31:S132-S138 |
ISSN: | 1098-2272 0741-0395 |
Popis: | Here, we summarize the contributions to group 16 of Genetic Analysis Workshop 15, held in Florida, U.S.A. The theme of this group was preprocessing of expression quantitative trait loci (eQTL) studies using the Affymetrix platform. The objective of the Genetic Analysis Workshop 15 problem 1 dataset was to use transcript levels that are measured using DNA microarrays as quantitative traits and localize the genes or other features of the DNA that control gene expression by quantitative trait loci linkage analyses. All contributors of this group used the microarray expression profiles (problem 1) data. Various approaches and questions were examined to investigate the effects of preprocessing methods and/or gene filtering on the interpretation of data, specifically on heritability estimates of gene expression and on linkage results. In addition, some contributors focused on the statistical issues involved in large-scale genetic analyses of quantitative traits that account for or build composite phenotypes from a large number of correlated traits. Since the true eQTLs are not known in the problem 1 data, results from the 11 studies cannot be fully evaluated for the methods employed. However, several common trends were found. All reports concluded that preprocessing statistical analyses may have an important impact on eQTL analyses and on the identification of cis-/trans-regulators and/or major biological pathways. Genet. Epidemiol. 31(Suppl. 1):S132–S138, 2007. © 2007 Wiley-Liss, Inc. |
Databáze: | OpenAIRE |
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