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. 2010 Sep 9;6(9):e1001097.
doi: 10.1371/journal.pgen.1001097.

Accurately assessing the risk of schizophrenia conferred by rare copy-number variation affecting genes with brain function

Soumya Raychaudhuri  1 , Joshua M Korn, Steven A McCarroll; International Schizophrenia Consortium; David Altshuler, Pamela Sklar, Shaun Purcell, Mark J Daly
Collaborators, Affiliations

Accurately assessing the risk of schizophrenia conferred by rare copy-number variation affecting genes with brain function

Soumya Raychaudhuri et al. PLoS Genet. .

Abstract

Investigators have linked rare copy number variation (CNVs) to neuropsychiatric diseases, such as schizophrenia. One hypothesis is that CNV events cause disease by affecting genes with specific brain functions. Under these circumstances, we expect that CNV events in cases should impact brain-function genes more frequently than those events in controls. Previous publications have applied "pathway" analyses to genes within neuropsychiatric case CNVs to show enrichment for brain-functions. While such analyses have been suggestive, they often have not rigorously compared the rates of CNVs impacting genes with brain function in cases to controls, and therefore do not address important confounders such as the large size of brain genes and overall differences in rates and sizes of CNVs. To demonstrate the potential impact of confounders, we genotyped rare CNV events in 2,415 unaffected controls with Affymetrix 6.0; we then applied standard pathway analyses using four sets of brain-function genes and observed an apparently highly significant enrichment for each set. The enrichment is simply driven by the large size of brain-function genes. Instead, we propose a case-control statistical test, cnv-enrichment-test, to compare the rate of CNVs impacting specific gene sets in cases versus controls. With simulations, we demonstrate that cnv-enrichment-test is robust to case-control differences in CNV size, CNV rate, and systematic differences in gene size. Finally, we apply cnv-enrichment-test to rare CNV events published by the International Schizophrenia Consortium (ISC). This approach reveals nominal evidence of case-association in neuronal-activity and the learning gene sets, but not the other two examined gene sets. The neuronal-activity genes have been associated in a separate set of schizophrenia cases and controls; however, testing in independent samples is necessary to definitively confirm this association. Our method is implemented in the PLINK software package.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Performance of each of the five proposed models (M0–M4) across five hypothetical scenarios.
For each of the scenarios (S0–S4) outlined in Table S2 we simulated 10,000 datasets to calculate the type I error rate for the enrichment test, for a nominal rate of 0.05. Only the model M4, controlling for CNV rate and average size, obtains an appropriate type I error rate under all scenarios where case-control differences in size and rate are presence. Other models, fail to adequately control for these confounders. The M4 model is presented in the main text as the cnv-enrichment-test.
Figure 2
Figure 2. Neurodevelopmental gene sets are enriched in CNVs for affected and unaffected individuals.
Here we present results from three gene sets – neuronal-activity genes (A), brain-expressed genes (B), learning genes (C), and synapse genes (D). For each set we calculate enrichment among genes disrupted by rare CNVs with a Fisher's exact test in meta-controls and also within the affected and unaffected individuals in the Walsh et al. study and the ISC study. We explicitly list all p-values <0.1. Each point represents an odds ratio and is plotted with a 95% confidence interval. A comparable degree of enrichment was observed across all data sets for each of the gene sets.
Figure 3
Figure 3. Genes with brain function and genes impacted by CNVs are large.
A. Brain-expressed, neuronal-activity, learning, and synapse gene sets consist of large genes. For each gene set we plot the length of each gene, relative to the median length of human genes (28.2 kb). Median gene length is labeled and represented by a horizontal line. Box indicates the range of gene lengths (2.5%–97.5%). Outliers are plotted as dark points outside the box. For each gene set we compared the length of genes within the set and outside the set with a two-tailed rank-sum p-value. B. Genes overlapping and disrupted by rare CNVs are large. For each data set we plot the length of genes overlapping (left) and disrupted by (right) rare CNVs, relative to the median length of human genes (28.2 kb). Median gene length is labeled and represented by a horizontal line. Box indicates the range of gene lengths (2.5%–97.5%). Outliers are plotted as dark points outside the box. For each set we compared the length of genes affected and not affected by CNVs with a two-tailed rank-sum p-value. To allow for consistent comparisons, we restricted meta-control events to those >100 kb.
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