A core collection of pan-schizophrenia genes allows building cohort-specific signatures of affected brain

Abstract

To investigate whether pan-schizophrenia genes could be leveraged for building cohort-specific signatures reflecting the functioning of the affected brain, we first collected 1,518 schizophrenia-related genes upon analysis of 12,316 independent peer-reviewed literature sources. More than half of these genes have been reported in at least 3 independent studies, and a majority (81.4%) were enriched within 156 functional pathways (p-values < 1e-15). Gene expression profiles of brain tissues were extracted from 14 publicly available independent datasets, and classified into "schizophrenia" and "normal" bins using dataset-specific subsets of core schizophrenia collection genes built with either a sparse representation-based variable selection (SRVS) approach or with analysis of variance (ANOVA)-based gene selection approach. Results showed that cohort-specific classifiers by both SRVS and ANOVA methods are capable of providing significantly higher accuracy in the diagnosis of schizophrenia than using the whole core genes (p < 3.38e-6), with relatively low sensitivity to the ethnic backgrounds or areas of brain biopsies. Our results suggest that the formation of consensus collection of pan-schizophrenia genes and its dissection into the functional components could be a feasible alternative to the expansion of sample size, which is needed for further in-depth studies of the pathophysiology of the human brain.

Figure 1

Knowledge-based connections between each of schizophrenia-associated genes and schizophrenia. (a) Bar graph which reflects relative shares of schizophrenia-associated genes supported by varying amount of references. (b) Top 15 research institutions which contributed to uncovering the relationships between each gene and schizophrenia.

Figure 2

Bar graphs presenting the performance of SRVS, ANOVA and pan-signature classifiers in 14 expression datasets. (a) Classification ratio; (b) Permutation p-value (−10 * log (p-value)).

Figure 3

Box-plot of the performance of classifiers built by SRVS and ANOVA ranking procedures as well as by pan-signature classifiers across 14 datasets.

Figure 4

Jaccard Similarity of the dataset-specific classifier gene sets selected by SRVS and ANOVA techniques. SRVS outputs are labeled as ‘SRVS with respective GSE ID’; ANOVA outputs are labeled as ‘PVal with respective GSE ID’. Diagonal entries represent the Jaccard Similarity of a study with itself, which is always equal to one. The ones on the diagonal line were set to zeros.

Figure 5

Diagram of the workflow for building a “core” model of schizophrenia.