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

Wang, Quan; Chen, Rui; Cheng, Feixiong; Wei, Qiang; Ji, Ying; Hai, Yang; Zhong, Xue; Tao, Ran; Wen, Zhexing; Sutcliffe, James S.; Liu, Chunyu; Cook, Edwin H.; Cox, Nancy J.; Li, Bingshan

doi:10.1038/s41593-019-0382-7

Cited by 128 publications

(132 citation statements)

References 74 publications

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“…Our findings that ASD-linked dnLoF mutations as well as SCZ GWAS signal enrich in brain-wide neuronal and neurogenesis modules underscore previous findings linking both common and de novo variation to synaptic genes, 160,161 neuronal genes, 162 developmentally-expressed genes, 163 and neurogenesis pathways. 164 Even though cortical regions contain a much higher proportion of neurons than other brain regions, the pattern of enrichment is not cortex-specific, and enrichments in the BW-M1 and BW-M4 module sets are significant across the brain, implying likely widespread effects of these genetic risk variants on brain function.…”

Section: Discussionsupporting

confidence: 82%

The architecture of brain co-expression reveals the brain-wide basis of disease susceptibility

Hartl¹,

Ramaswami²,

Pembroke³

et al. 2020

Preprint

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Gene networks have proven their utility for elucidating transcriptome structure in the brain, yielding numerous biological insights. Most analyses have focused on expression relationships within a circumspect number of regions -how these relationships vary across a broad array of brain regions is largely unknown. By leveraging RNAsequencing in 864 samples representing 12 brain regions in a cohort of 131 phenotypically normal individuals, we identify 12 brain-wide, 114 region-specific, and 50 cross-regional co-expression modules. We replicate the majority (81%) of modules in regional microarray datasets. Nearly 40% of expressed genes fall into brain-wide modules corresponding to major cell classes and conserved biological processes.Region-specific modules comprise 25% of expressed genes and correspond to regionspecific cell types and processes, such as oxytocin signaling in the hypothalamus, or addiction pathways in the nucleus accumbens. We further leverage these modules to capture cell-type-specific lncRNA and gene isoforms, both of which contribute substantially to regional synaptic diversity. We identify enrichment of neuropsychiatric disease risk variants in brain wide and multi-regional modules, consistent with their broad impact on cell classes, and highlight specific roles in neuronal proliferation and activity-dependent processes. Finally, we examine the manner in which gene coexpression and gene regulatory networks reflect genetic risk, including the recently framed omnigenic model of disease architecture.Using this hierarchy, we form a tree of consensus co-expression networks for each split, thereby generating co-expression modules for 20 hierarchical expression categories: 12 brain region specific categories (corresponding to each sampled region), 7 multiregional categories (corresponding to multiple, structurally-linked regions, figure 1b), and a brain-wide category. The majority of the resulting modules are highly overlapping, therefore we group these modules hierarchically into groups of highly similar modules which we term "module sets" (Methods). In total, we identify 311 modules at all levels, of which 173/199 (87%) of the tissue-level modules are replicated with strong support in at least one other independent dataset (figure 1c; Methods). Finally, by using network preservation statistics on all samples within regions and meta regions, we verify that module sets are supported by strong evidence within their own regions and little evidence outside of them (figure 1d).To test whether co-expression modules vary substantially by the method used for co-expression network construction, we build modules at the tissue level using three alternative approaches: ARACNe, 34 PAM-guided graphical LASSO, 35 and Fisher-von-Mises mixture modeling. 36 We find that all methods show high pairwise clustering coefficients (figure S1e), and differ predominantly by module splitting (figure 1e). For instance, most of the differences between ARACNe clusters and WGCNA clusters come from large ARACNe modules represented as...

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Section: Discussionsupporting

confidence: 82%

The architecture of brain co-expression reveals the brain-wide basis of disease susceptibility

Hartl¹,

Ramaswami²,

Pembroke³

et al. 2020

Preprint

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“…We carried out drug-target lookups using a recently published drug-target database 99 to see whether any of the TWAS and GWAS-significant genes were known targets of existing drugs. We focused on nervous system drugs with Anatomical Therapeutic Chemical (ATC) code started with “N”, yielding 2,285 drug-gene pairs between 273 drugs and 241 targeted genes.…”

Section: Resultsmentioning

confidence: 99%

“…For the high confident interactions of adult and fetal cortex, the enrichment of each gene was measured as the sum of –log 10 (P-value) of all significant interactions overlapping the gene 98 . The drug-target lookups were conducted using the drug-gene associations reported in Wang, et al 99 . We focused on nervous system drugs whose Anatomical Therapeutic Chemical code starts with “N” according to the DrugBank database (version 2019-07-02, https://www.drugbank.ca/atc).…”

Section: Methodsmentioning

confidence: 99%

Transcriptome-wide association analysis of 211 neuroimaging traits identifies new genes for brain structures and yields insights into the gene-level pleiotropy with other complex traits

Zhao

Shan

Yang

et al. 2019

Preprint

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Structural and microstructural variations of human brain are heritable and highly polygenic traits, with hundreds of associated genes founded in recent genome-wide association studies (GWAS). Using gene expression data, transcriptome-wide association studies (TWAS) can prioritize these GWAS findings and also identify novel gene-trait associations. Here we performed TWAS analysis of 211 structural neuroimaging phenotypes in a discovery-validation analysis of six datasets. Using a cross-tissue approach, TWAS discovered 204 associated genes (86 new) exceeding Bonferroni significance threshold of 1.37*10−8 (adjusted for testing multiple phenotypes) in the UK Biobank (UKB) cohort, and validated 18 TWAS or previous GWAS-detected genes. The TWAS-significant genes of brain structures had been linked to a wide range of complex traits in different domains. Additional TWAS analysis of 11 cognitive and mental health traits detected 69 overlapping significant genes with brain structures, further characterizing the genetic overlaps among these brain-related traits. Through TWAS gene-based polygenic risk scores (PRS) prediction, we found that TWAS PRS gained substantial power in association analysis compared to conventional variant-based PRS, and up to 6.97% of phenotypic variance (p-value=7.56*10−31) in testing datasets can be explained by UKB TWAS-derived PRS. In conclusion, our study illustrates that TWAS can be a powerful supplement to traditional GWAS in imaging genetics studies for gene discovery-validation, genetic co-architecture analysis, and polygenic risk prediction.

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“…Integration of multi-omics data and gene networks has been used in schizophrenia, to identify risk genes which enrich in brain tissue for potential drug targeting 75 identifying new genes in linkage disequilibrium with SNPs. By doing this, we were able to identify potential novel protein drug targets for specific patient cohorts.…”

Section: Discussionmentioning

confidence: 99%

A systems genomics approach to uncover patient-specific pathogenic pathways and proteins in a complex disease

Brooks

Módos

Sudhakar

et al. 2019

Preprint

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We describe a novel precision medicine workflow, the integrated single nucleotide polymorphism network platform (iSNP), designed to identify the exact mechanisms of how SNPs affect cellular regulatory networks, and how SNP co-occurrences contribute to disease pathogenesis in ulcerative colitis (UC). Using SNP profiles of 377 UC patients, we mapped the regulatory effects of the SNPs to a human signalling network containing protein-protein, miRNA-mRNA and transcription factor binding interactions. Unsupervised clustering algorithms grouped these patient-specific networks into four distinct clusters based on two large disease hubs, NFKB1 and PKCB. Pathway analysis identified the epigenetic modification as common and the T-cell specific responses as differing signalling pathways in the clusters. By integrating individual transcriptomes in active and quiescent disease setting to the patient networks, we validated the impact of non-coding SNPs. The iSNP approach identified regulatory effects of disease-associated non-coding SNPs, and identified how pathogenesis pathways are activated via different genetic modifications.

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

Cited by 128 publications

References 74 publications

The architecture of brain co-expression reveals the brain-wide basis of disease susceptibility

The architecture of brain co-expression reveals the brain-wide basis of disease susceptibility

Transcriptome-wide association analysis of 211 neuroimaging traits identifies new genes for brain structures and yields insights into the gene-level pleiotropy with other complex traits

A systems genomics approach to uncover patient-specific pathogenic pathways and proteins in a complex disease

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