Abstract
"Genetical genomics", the study of natural genetic variation combining data from genetic marker-based studies with gene expression analyses, has exploded with the recent development of advanced microarray technologies. To account for systematic variation known to exist in microarray data, it is critical to properly normalize gene expression traits before performing genetic linkage analyses. However, imposing equal means and variances across pedigrees can over-correct for the true biological variation by ignoring familial correlations in expression values. We applied the robust multiarray average (RMA) method to gene expression trait data from 14 Centre d'Etude du Polymorphisme Humain (CEPH) Utah pedigrees provided by GAW15 (Genetic Analysis Workshop 15). We compared the RMA normalization method using within-pedigree pools to RMA normalization using all individuals in a single pool, which ignores pedigree membership, and investigated the effects of these different methods on 18 gene expression traits previously found to be linked to regions containing the corresponding structural locus. Familial correlation coefficients of the expressed traits were stronger when traits were normalized within pedigrees. Surprisingly, the linkage plots for these traits were similar, suggesting that although heritability increases when traits are normalized within pedigrees, the strength of linkage evidence does not necessarily change substantially.
Highlights
Genetical genomics [1] integrates genome-wide expression profile data of microarray experiments and markerbased measures of genetic variation
Among the normalization methods used for the common Affymetrix GeneChip, the robust multiarray average (RMA) method and the statistical algorithm implemented in Affymetrix's Microarray Suite (MAS5) program are the gold standard to control for systematic variation in samples of unrelated individuals [4,5]
RMA adjusts for systematic variation by performing a quantile normalization procedure, which assumes that the data for the variable considered all are sampled from the same or similar distributions and the values for that variable are normalized to a standard distribution
Summary
Genetical genomics [1] integrates genome-wide expression profile data of microarray experiments and markerbased measures of genetic variation. RMA adjusts for systematic variation by performing a quantile normalization procedure, which assumes that the data for the variable considered (such as study sample) all are sampled from the same or similar distributions and the values for that variable are normalized to a standard distribution. It is not yet known which standard distribution is the best to use for family data in genetical genomics studies.
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