Tissue-specific pathway association analysis using genome-wide association study summaries

Tissue-adjusted pathway analysis of cancer (TPAC): A novel approach for quantifying tumor-specific gene set dysregulation relative to normal tissue

We describe a novel single sample gene set testing method for cancer transcriptomics data named tissue-adjusted pathway analysis of cancer (TPAC). The TPAC method leverages information about the normal tissue-specificity of human genes to compute a robust multivariate distance score that quantifies gene set dysregulation in each profiled tumor. Because the null distribution of the TPAC scores has an accurate gamma approximation, both population and sample-level inference is supported. As we demonstrate through an analysis of gene expression data for 21 solid human cancers from The Cancer Genome Atlas (TCGA) and associated normal tissue expression data from the Human Protein Atlas (HPA), TPAC gene set scores are more strongly associated with patient prognosis than the scores generated by existing single sample gene set testing methods.

ProAct: quantifying the differential activity of biological processes in tissues, cells, and user-defined contexts

The distinct functions and phenotypes of human tissues and cells derive from the activity of biological processes that varies in a context-dependent manner. Here, we present the Process Activity (ProAct) webserver that estimates the preferential activity of biological processes in tissues, cells, and other contexts. Users can upload a differential gene expression matrix measured across contexts or cells, or use a built-in matrix of differential gene expression in 34 human tissues. Per context, ProAct associates gene ontology (GO) biological processes with estimated preferential activity scores, which are inferred from the input matrix. ProAct visualizes these scores across processes, contexts, and process-associated genes. ProAct also offers potential cell-type annotations for cell subsets, by inferring them from the preferential activity of 2001 cell-type-specific processes. Thus, ProAct output can highlight the distinct functions of tissues and cell types in various contexts, and can enhance cell-type annotation efforts. The ProAct webserver is available at https://netbio.bgu.ac.il/ProAct/.

Gene Set Enrichment Analysis Detected Immune Cell-Related Pathways Associated with Primary Sclerosing Cholangitis

Aim

To explore various immune cell-related causal pathways for primary sclerosing cholangitis (PSC).

Methods

Immune cell-related pathway association study was conducted via integrative analysis of PSC GWAS summary and five immune cell-related eQTL datasets. The GWAS summary data of PSC was driven from 4,796 PSC cases and 19,955 healthy controls. The eQTL datasets of CD4+ T cells, CD8+ T cells, B cells, natural killer cells (NK), monocytes, and peripheral blood cells (PB) were collected from recently eQTL study. The PSC GWAS summary dataset was first aligned with eQTL datasets of six blood cells to obtain the GWAS summary data at overlapped eQTL loci, separately. For each type of cell, the obtained PSC GWAS summary dataset of eQTLs was subjected to pathway enrichment analysis. 853 biological pathways from Kyoto Encyclopedia of Genes and Genomes, BioCarta, and Reactome pathway databases were analyzed.

Results

We identified 36 pathways for B cells, 33 pathways for CD4+ T cells, 28 pathways for CD8+ T cells, 33 pathways for monocytes (MN), 35 pathways for NK cells, and 33 for PB cells (all empirical P values <5.0 × 10⁻⁵). Comparing the pathway analysis results detected 25 pathways shared by five immune cells, such as KEGG_CELL_ADHESION_MOLECULES_CAMS (P value <5.0 × 10⁻⁵) and REACTOME_MHC_CLASS_II_ANTIGEN_ PRESENTATION (P value <5.0 × 10⁻⁵). Several cell-specific pathways were also identified, including BIOCARTA_INFLAM_PATHWAY (P value <5 × 10⁻⁵) for B cell.

Conclusion

Our study holds potential to identify novel candidate causal pathways and provides clues for revealing the complex genetic mechanism of PSC.

The differential activity of biological processes in tissues and cell subsets can illuminate disease-related processes and cell-type identities

MOTIVATION

The distinct functionalities of human tissues and cell types underlie complex phenotype-genotype relationships, yet often remain elusive. Harnessing the multitude of bulk and single-cell human transcriptomes while focusing on processes can help reveal these distinct functionalities.

RESULTS

The Tissue-Process Activity (TiPA) method aims to identify processes that are preferentially active or under-expressed in specific contexts, by comparing the expression levels of process genes between contexts. We tested TiPA on 1579 tissue-specific processes and bulk tissue transcriptomes, finding that it performed better than another method. Next, we used TiPA to ask whether the activity of certain processes could underlie the tissue-specific manifestation of 1233 hereditary diseases. We found that 21% of the disease-causing genes indeed participated in such processes, thereby illuminating their genotype-phenotype relationships. Lastly, we applied TiPA to single-cell transcriptomes of 108 human cell types, revealing that process activities often match cell-type identities and can thus aid annotation efforts. Hence, differential activity of processes can highlight the distinct functionality of tissues and cells in a robust and meaningful manner.

AVAILABILITY AND IMPLEMENTATION

TiPA code is available in GitHub (https://github.com/moranshar/TiPA). In addition, all data are available as part of the Supplementary Material.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A new algorithm for DNA motif discovery using multiple sample sequence sets

DNA motif discovery plays an important role in understanding the mechanisms of gene regulation. Most existing motif discovery algorithms can identify motifs in an efficient and effective manner when dealing with small datasets. However, large datasets generated by high-throughput sequencing technologies pose a huge challenge: it is too time-consuming to process the entire dataset, but if only a small sample sequence set is processed, it is difficult to identify infrequent motifs. In this paper, we propose a new DNA motif discovery algorithm: first divide the input dataset into multiple sample sequence sets, then refine initial motifs of each sample sequence set with the expectation maximization method, and finally combine all the results from each sample sequence set. Besides, we design a new initial motif generation method with the utilization of the entire dataset, which helps to identify infrequent motifs. The experimental results on the simulated data show that the proposed algorithm has better time performance for large datasets and better accuracy of identifying infrequent motifs than the compared algorithms. Also, we have verified the validity of the proposed algorithm on the real data.

eQTLs Weighted Genetic Correlation Analysis Detected Brain Region Differences in Genetic Correlations for Complex Psychiatric Disorders

BACKGROUND

Psychiatric disorders are usually caused by the dysfunction of various brain regions. Incorporating the genetic information of brain regions into correlation analysis can provide novel clues for pathogenetic and therapeutic studies of psychiatric disorders.

METHODS

The latest genome-wide association study (GWAS) summary data of schizophrenia (SCZ), bipolar disorder (BIP), autism spectrum disorder (AUT), major depression disorder (MDD), and attention-deficit/hyperactivity disorder (ADHD) were obtained from the Psychiatric GWAS Consortium (PGC). The expression quantitative trait loci (eQTLs) datasets of 10 brain regions were driven from the genotype-tissue expression (GTEx) database. The PGC GWAS summaries were first weighted by the GTEx eQTLs summaries for each brain region. Linkage disequilibrium score regression was applied to the weighted GWAS summary data to detect genetic correlation for each pair of 5 disorders.

RESULTS

Without considering brain region difference, significant genetic correlations were observed for BIP vs SCZ (P = 1.68 × 10-63), MDD vs SCZ (P = 5.08 × 10-45), ADHD vs MDD (P = 1.93 × 10-44), BIP vs MDD (P = 6.39 × 10-9), AUT vs SCZ (P = .0002), and ADHD vs SCZ (P = .0002). Utilizing brain region related eQTLs weighted LD score regression, different strengths of genetic correlations were observed within various brain regions for BIP vs SCZ, MDD vs SCZ, ADHD vs MDD, and SCZ vs ADHD. For example, the most significant genetic correlations were observed at anterior cingulate cortex (P = 1.13 × 10-34) for BIP vs SCZ.

CONCLUSIONS

This study provides new clues for elucidating the mechanism of genetic correlations among various psychiatric disorders.

Integrative analysis of genome-wide association study and chromosomal enhancer maps identified brain region related pathways associated with ADHD

Attention deficit/hyperactivity disorder (ADHD) is among the most common childhood onset psychiatric behavioral disorders, and the pathogenesis of ADHD is still unclear. Utilizing the latest genome wide association studies (GWAS) data and enhancer map, we explored the brain region related biological pathways associated with ADHD. The GWAS summary data of ADHD was driven from a published study, involving 20,183 ADHD cases and 35,191 healthy controls. The brain-related enhancer map was collected from ENCODE and Roadmap Epigenomics (ENCODE + Roadmap) including 489,581 enhancers. Firstly, the chromosomal enhancer maps of four brain regions were aligned with the ADHD GWAS summary data in order to obtain enhancer SNPs. Then the significant enhancers SNPs were subjected to the gene set enrichment analysis (GSEA) for identifying ADHD associated gene sets. A total of 866 pathways and 4 brain tissues were analyzed in this study. We detected several candidate genes for ADHD, such as AHI1, ALG2 and DNM1. We also detected several candidate biological pathways associated with ADHD, such as Reactome SEMA4D in semaphorin signaling and Reactome NCAM1 interactions. Our findings may provide a novel insight into the complex genetic mechanism of ADHD.

A genome-wide pathway enrichment analysis identifies brain region related biological pathways associated with intelligence

Intelligence is an important quantitative trait associated with human cognitive ability. The genetic basis of intelligence remains unclear now. Utilizing the latest chromosomal enhancer maps of brain regions, we explored brain region related biological pathways associated with intelligence. Summary data was derived from a large scale genome-wide association study (GWAS) of human, involving 78,308 unrelated individuals from 13 cohorts. The chromosomal enhancer maps of 8 brain regions were then aligned with the GWAS summary data to obtain the association testing results of enhancer regions for intelligence. Gene set enrichment analysis was then conducted to identify the biological pathways associated with intelligence for 8 brain regions, respectively. A total of 178 KEGG pathways was analyzed in this study. We detected multiple biological pathways showing cross brain regions or brain region specific association signals for human intelligence. For instance, KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS pathway presented association signals for intelligence across 8 brain regions (all P value < 0.01). KEGG_GLYCOSPHINGOLIPID_BIOSYNTHESIS_GANGLIO_SERIES was detected for 5 brain regions. We also identified several brain region specific pathways, such as AMINO_SUGAR_AND_NUCLEOTIDE_SUGAR_METABOLISM for Germinal Matrix (P value = 0.009) and FRUCTOSE_AND_MANNOSE_METABOLISM for Anterior Caudate (P value = 0.005). Our study results provided novel clues for understanding the genetic mechanism of intelligence.

Predicting multicellular function through multi-layer tissue networks

Motivation

Understanding functions of proteins in specific human tissues is essential for insights into disease diagnostics and therapeutics, yet prediction of tissue-specific cellular function remains a critical challenge for biomedicine.

Results

Here, we present OhmNet, a hierarchy-aware unsupervised node feature learning approach for multi-layer networks. We build a multi-layer network, where each layer represents molecular interactions in a different human tissue. OhmNet then automatically learns a mapping of proteins, represented as nodes, to a neural embedding-based low-dimensional space of features. OhmNet encourages sharing of similar features among proteins with similar network neighborhoods and among proteins activated in similar tissues. The algorithm generalizes prior work, which generally ignores relationships between tissues, by modeling tissue organization with a rich multiscale tissue hierarchy. We use OhmNet to study multicellular function in a multi-layer protein interaction network of 107 human tissues. In 48 tissues with known tissue-specific cellular functions, OhmNet provides more accurate predictions of cellular function than alternative approaches, and also generates more accurate hypotheses about tissue-specific protein actions. We show that taking into account the tissue hierarchy leads to improved predictive power. Remarkably, we also demonstrate that it is possible to leverage the tissue hierarchy in order to effectively transfer cellular functions to a functionally uncharacterized tissue. Overall, OhmNet moves from flat networks to multiscale models able to predict a range of phenotypes spanning cellular subsystems.

Availability and implementation

Source code and datasets are available at http://snap.stanford.edu/ohmnet.