Abstract
To explore the mapping of factors regulating gene expression, we have carried out linkage studies using expression data from individual transcripts (from Affymetrix microarrays; Genetic Analysis Workshop 15 Problem 1) and composite data on correlated groups of transcripts. Quality measures for the arrays were used to remove outliers, and arrays with sex mismatches were also removed. Data likely to represent noise were removed by setting a minimum threshold of present calls among the non-redundant set of 190 arrays. SOLAR was used for genetic analysis, with MAS5 signal as the measure of expression. Probe sets with larger CVs generated more linkages (LOD > 2.0). While trans linkages predominated, linkages with the largest LOD scores (>4) were mostly cis. Hierarchical clustering was used to generate correlated groups of genes. We tested four composite measures of expression for the clusters. The average signal, average normalized signal, and the first principal component of the data behaved similarly; in 8/19 clusters tested, the composite measures linked to a region to which some individual probe sets within the cluster also linked. The second principal component only produced one linkage with LOD > 2. One cluster based upon chromosomal location, containing histone genes, linked to two trans regions. This work demonstrates that composite measures for genes with correlated expression can be used to identify loci that affect multiple co-expressed genes.
Highlights
There is a genetic component to the differences between individuals in gene expression
The initial step was to check the quality of the array data and remove outlier arrays and arrays in which the gene expression did not match the gender indicated in the pedigree
Data MAS5 signals, detection calls, and quality control (QC) information were generated from the 267 Affymetrix HG focus array CEL files (Affymetrix feature intensity files) in the Genetic Analysis Workshop 15 (GAW15) Problem 1 using R/Bioconductor [1]
Summary
There is a genetic component to the differences between individuals in gene expression. The confluence of techniques that allow genome-wide measurements of gene expression and the technology to examine genomic variations, single-nucleotide polymorphisms (SNPs), on a large scale allows one to map the genetic determinants of differences in gene expression. Problem 1 in Genetic Analysis Workshop 15 (GAW15) provides expression data for approximately 8800 genes, along with SNP genotypes at 2883 sites-sufficient for linkage mapping but too low a density for genome-wide association studies. We have examined several parameters and strategies that could be used to localize regulatory elements from such data. We are interested in detecting trans-acting loci that regulate correlated groups of genes, because such loci should be master regulatory elements integrating expression of many genes, and have tested several strategies for detecting them
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