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A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data


Genome-wide association studies (GWAS) have identified more than 100 schizophrenia (SCZ)-associated loci, but using these findings to illuminate disease biology remains a challenge. Here we present integrative risk gene selector (iRIGS), a Bayesian framework that integrates multi-omics data and gene networks to infer risk genes in GWAS loci. By applying iRIGS to SCZ GWAS data, we predicted a set of high-confidence risk genes, most of which are not the nearest genes to the GWAS index variants. High-confidence risk genes account for a significantly enriched heritability, as estimated by stratified linkage disequilibrium score regression. Moreover, high-confidence risk genes are predominantly expressed in brain tissues, especially prenatally, and are enriched for targets of approved drugs, suggesting opportunities to reposition existing drugs for SCZ. Thus, iRIGS can leverage accumulating functional genomics and GWAS data to advance our understanding of SCZ etiology and potential therapeutics.

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Fig. 1: Schematic of the iRIGS framework.
Fig. 2: Discovery of genomic features characteristic of SCZ risk genes.
Fig. 3: Characteristics of predicted risk genes.

Data availability

All the data used in this study are from public resources that are specified in the Methods and the Supplementary Note.

Code availability

The source code and the companying genomics datasets used in this study are available at


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The authors are grateful to C. Niswender, B. Stansley, and J. Conn at Vanderbilt University for critical input on portions of this manuscript. This study is supported by US NIH/NHGRI grants U01HG009086 (to Q. Wang, R.C., Q. Wei, Y.J., H.Y., X.Z., R.T., N.J.C., and B.L.), U24HG008956, and R01MH113362 (to J.S., N.J.C., and B.L.). The grant U01HG009086 supports the Vanderbilt Analysis Center for the Genome Sequencing Project (GSP) and U24 HG008956 supports the GSP Coordinating Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information




B.L. conceived the overall design of the study, with Q. Wang and R.C. providing input. Q. Wang and R.C. implemented the algorithm and performed most of the analyses. F.C., Q. Wei, Y.J., H.Y., X.Z., and R.T. provided data integration and analyses. Z.W., J.S.S., C.L., E.H.C., and N.J.C. contributed to the interpretation of the results. Q. Wang, R.C., F.C., and B.L. wrote the manuscript, and all authors participated in the review and revision of the manuscript.

Corresponding author

Correspondence to Bingshan Li.

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The authors declare no competing interests.

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Journal peer review information: Nature Neuroscience thanks Vincent Mooser, Hon-Cheong So, and other anonymous reviewer(s) for their contribution to the peer review of this work.

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Integrated supplementary information

Supplementary Figure 1 The DRE-promoter links of NRGs and LBGs in brain specific Hi-C before incorporating capHiC and FANTOM5 data.

One-sided Wilcoxon rank sum test was used for comparison (n = 104 and 831 for NRGs and LBGs respectively). The box plots show median and the 25th and 75th percentiles. The whiskers extend from the box to the largest and smallest values no further than 1.5 * IQR from the box (where IQR is the inter-quartile range, or distance between the 25th and 75th percentiles).

Supplementary Figure 2 The temporal expression pattern of whole genome background genes (WBGs) in BrainSpan data.

WBGs are not differentially expressed across development stages in BrainSpan data (one-sided Wilcoxon rank sum test using medians of expression at prenatal (n = 3) and medians of expression at postnatal (n = 4) stages). The error bar plot shows the median and the 25th and 75th percentiles.

Supplementary Figure 3 Tissue specificity of nearest non-HRG genes and non-nearest HRGs.

Nearest non-HRG genes are highly expressed in a majority of brain tissues (a) while non-nearest HRGs are not (b) in GTEx data (one-sided Wilcoxon rank sum test, n = 65 and 817 for non-nearest HRGs/nearest non-HRG genes and LBGs respectively). For LBGs, we excluded all the nearest genes, thus the sample size is a little smaller than LBGs (n = 830) used for all HRGs in the main text.

Supplementary Figure 4 The Hi-C links of genes CACNA1C, CACNB2, SOX2, SATB2, GRIN2A, and TCF4 in BrainGZ data.

For each GWAS locus, we colored 3 genes with the highest PPs in the bottom panel and used the same coloring scheme for Hi-C links in the middle panel. The r2 values in the top panel indicate the LD between nearby SNPs and the index SNPs. The pattern is very similar in BrainCP data (figures not shown here).

Supplementary Figure 5 The Hi-C links of NCAM1 and DRD2 in BrainGZ data.

The pattern is very similar in BrainCP data (figure not shown here).

Supplementary Figure 6 Tissue-specific expression pattern of NCAM1 and DRD2 in GTEx data.

DRD2 is highly expressed in basal ganglia caudate, hypothalamus, basal ganglia nucleus accumbens, basal ganglia putamen and substantia nigra, but the expression in cortex and frontal cortex is rather low. NCAM1 is uniformly and highly expressed in all brain tissues. The red vertical lines indicate the brain tissues.

Supplementary Figure 7 The temporal expression pattern of NCAM1 and DRD2 in BrainSpan data.

NACAM1 shows consecutively high expression at all developmental stages and extremely high expression at prenatal stages. While DRD2 shows no obvious pattern of transition between prenatal and postnatal stages. The red vertical lines indicate the prenatal stages.

Supplementary Figure 8 The Hi-C links of PTK2B in BrainGZ data.

As a non-nearest gene, PTK2B has a lot of physical interactions with the index SNP region. The pattern is very similar in BrainCP data (figure not shown here).

Supplementary Figure 9 The identified SCZ risk genes and the drugs that target these genes.

The circles and squares indicate the drug targets and drugs respectively. Different colors show the drug first-level ATC classification and the size of circle shows gene’s expression level in brain.

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Wang, Q., Chen, R., Cheng, F. et al. A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data. Nat Neurosci 22, 691–699 (2019).

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