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Whole-genome association mapping of gene expression in the human prefrontal cortex

Molecular Psychiatry volume 15, pages 779–784 (2010)Cite this article

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Variations in gene expression among individuals may have multiple downstream implications, including an effect on disease risk. ‘Genetical genomics’ (or expression genetics) uses linkage and association methods to map gene expression phenotypes, connecting genetic variants to expression quantitative trait loci (eQTLs). It represents a promising approach to identifying novel expression-regulatory elements in the genome. Studies of human lymphoblastoid cell lines,1 liver2 and brain3 have also been reported. Meyers et al.3 studied 193 neuropathologically normal human brain samples from three cortical regions using the Affymetrix 500K Array for genotyping and the Illumina HumanRefseq-8 Expression Array for gene expression measurements. They assessed association between 366 140 single-nucleotide polymorphisms (SNPs) and the expression of 14 078 transcripts, and identified 433 SNP–transcript pairs (99 transcripts) that showed significant cis-association (transcript-specific empirical P-value ⩽0.05); but only 25 of them (involving two genes, KIF1B and IPP) appear significant after correcting for all the SNPs and phenotypes (transcripts) tested (Sidak multi-transcript-corrected empirical P-values ⩽0.05). We would consider only the two genes truly significant cis-associations as they were the ones surviving correction for all the statistical tests.

There are several major limitations in the Myers et al. study, including sample heterogeneity (pooled samples from three different cortical regions: frontal, temporal and parietal), expression data confounded by uncontrolled covariates, particularly brain pH value, and microarray batch effects. In any case, it would be reasonable to attempt a replication study. We performed a new brain eQTL mapping using psychiatric patients and control brains, focusing on the prefrontal cortex and with a statistical procedure optimized for covariates and microarray batch effects. We used Surrogate Variable Analysis (SVA)4 to remove covariate effects and ComBat5 to remove batch effects on gene expression before the SNP-expression association tests. These procedures, we hoped, would improve the power of detecting associations by removing sources of non-genetic variation from the data.

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Authors and Affiliations

  1. Department of Psychiatry, The University of Chicago, Chicago, IL, USA

    C Liu, L Cheng, J A Badner, D Zhang & E S Gershon

  2. Department of Human Genetics, The University of Chicago, Chicago, IL, USA

    E S Gershon

  3. The Translational Genomic Research Institute, Phoenix, AZ, USA

    D W Craig & M Redman

Authors
  1. C Liu
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  2. L Cheng
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  3. J A Badner
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  4. D Zhang
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  5. D W Craig
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  6. M Redman
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  7. E S Gershon
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Correspondence to C Liu.

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Supplementary Information accompanies the paper on the Molecular Psychiatry website

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Liu, C., Cheng, L., Badner, J. et al. Whole-genome association mapping of gene expression in the human prefrontal cortex. Mol Psychiatry 15, 779–784 (2010). https://doi.org/10.1038/mp.2009.128

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