The genetics of cognitive impairment in schizophrenia: a phenomic perspective

Abstract

Cognitive impairments are central to schizophrenia and could mark underlying biological dysfunction but efforts to detect genetic associations for schizophrenia or cognitive phenotypes have been disappointing. Phenomics strategies emphasizing simultaneous study of multiple phenotypes across biological scales might help, particularly if the high heritabilities of schizophrenia and cognitive impairments are due to large numbers of genetic variants with small effect. Convergent evidence is reviewed, and a new collaborative knowledgebase - CogGene - is introduced to share data about genetic associations with cognitive phenotypes, and enable users to meta-analyze results interactively. CogGene data demonstrate the need for larger studies with broader representation of cognitive phenotypes. Given that meta-analyses will probably be necessary to detect the small association signals linking the genome and cognitive phenotypes, CogGene or similar applications will be needed to enable collaborative knowledge aggregation and specify true effects.

Figure 1. Relations of cognitive phenotypes to neural systems and diagnosis

Path diagrams schematizing different points of view regarding the role of cognitive phenotypes as a) intermediate phenotypes or endophenotypes; or b) as different behavioral effects – paraphenotypes -- that differ primarily in the strength of relations to neural system activity.

Figure 2. Screen-shot illustrating features of the CogGene web service

Users can filter records in the CogGene database either by “gene” or by “task”, and the system will then show all relevant effects in a dynamic forest plot. The example shows results selected for the task “AX-CPT” (the “AX” version of the Continuous Performance Test), filtered to show results only for the gene TPH2 from a single publication (see green bar on right side of figure, which gives the PubMedID (pID), and a single SNP (see purple bar on left side of figure, labeled “refSeq”). In this example, the publication had examined 5 different Indicators (see 5 bars stacked on the left side of the figure). If the user clicks on any of these bars the CogGene system sorts all results based on that column (i.e., by gene, task, indicator, refSeq, or PubMedID). Note also that each bar has a checkbox in its upper left corner; users can check these boxes and then the meta-analysis calculations will be executed over the checked results. The actual entries in the forest plot show the effect size d for the specific indicator and refSeq contrast for that study orange circles (not shown is that the mouse fly-over function reveals exactly which allelic contrast is being represented). At the bottom of the figure, the blue diamond represents the meta-analytic result (sample size weighted average of all selected effects). The gray bars represent 95% confidence intervals around the individual or meta-analytic effect size values. Further details are available at www.CogGene.org.

Figure 3. Top results from CogGene

Effect size statistics from CogGene (expressed in terms of Cohen’s d statistic) for genetic associations with cognitive indicators, based on sample size-weighted mean effect sizes for each SNP. Error bars provide 95% confidence intervals around each mean effect, only effects not including zero are shown. DRD2 and SNAP25 each had two SNP’s satisfying these criteria.