A cross-laboratory comparison of expression profiling data from normal human postmortem brain

Abstract

Expression profiling of post-mortem human brain tissue has been widely used to study molecular changes associated with neuropsychiatric diseases as well as normal processes such as aging. Changes in expression associated with factors such as age, gender or postmortem interval are often more pronounced than changes associated with disease. Therefore in addition to being of interest in their own right, careful consideration of these effects are important in the interpretation of disease studies. We performed a large meta-analysis of genome-wide expression studies of normal human cortex to more fully catalogue the effects of age, gender, postmortem interval and brain pH, yielding a "meta-signature" of gene expression changes for each factor. We validated our results by showing a significant overlap with independent gene lists extracted from the literature. Importantly, meta-analysis identifies genes which are not significant in any individual study. Finally, we show that many schizophrenia candidate genes appear in the meta-signatures, reinforcing the idea that studies must be carefully controlled for interactions between these factors and disease. In addition to the inherent value of the meta-signatures, our results provide critical information for future studies of disease effects in the human brain.

Figure 1. Distribution of dataset p-values across meta-signature q-values

For each dataset used, gene p-values were plotted against the corresponding meta-q value and a loess fit was computed to generate a smooth curve between points. The fact that most data sets show a rise in p-values correlated with the meta-q-values indicates the contribution of signals of varying strengths to the meta-signatures. An alternative view of the data using heat maps is available in the supplement. The distorted curves for gender are due to the strong effects of a small number of genes with very small meta-q-values (note the difference in scale of D compared to A–C).

Figure 2. Distribution of dataset p-values for individual genes: a magnified view

For selected genes from each of the meta-signatures we have plotted the log regression p-values from each dataset. Open circles represent the datasets for which the gene was found to be significant after multiple test correction (q < 0.01). Dashed line indicates a per-study p-value significance level of 0.05 for reference.

Figure 3. Top genes down-regulated with age

The top 50 age down-regulated genes were selected based on meta-analysis q-value ranking. For each gene, the corresponding data from each study was extracted and converted to a heat map. Expression values were normalized across samples within each dataset, and ordered by age. Age is plotted at the top of each heat map. Light values in heat map indicate higher expression. Grey bars indicate missing values. All data sets are at approximately the same horizontal scale except the last, which is compressed to fit on the page.

Figure 4. Gene Ontology Enrichment Analysis

For the age meta-signatures, we have displayed the top 10 GO terms identified using a GO over-representation analysis. The primary y-axis displays the number of meta-signature genes that fall in the given ‘biological process’ category, while the secondary axis displays the associated p-value. GO terms were collapsed to parent term if parent and child both appeared in the top ten.