Mechanisms of tissue and cell-type specificity in heritable traits and diseases

PCGA: a comprehensive web server for phenotype-cell-gene association analysis

Most complex disease-associated loci mapped by genome-wide association studies (GWAS) are located in non-coding regions. It remains elusive which genes the associated loci regulate and in which tissues/cell types the regulation occurs. Here, we present PCGA (https://pmglab.top/pcga), a comprehensive web server for jointly estimating both associated tissues/cell types and susceptibility genes for complex phenotypes by GWAS summary statistics. The web server is built on our published method, DESE, which represents an effective method to mutually estimate driver tissues and genes by integrating GWAS summary statistics and transcriptome data. By collecting and processing extensive bulk and single-cell RNA sequencing datasets, PCGA has included expression profiles of 54 human tissues, 2,214 human cell types and 4,384 mouse cell types, which provide the basis for estimating associated tissues/cell types and genes for complex phenotypes. We develop a framework to sequentially estimate associated tissues and cell types of a complex phenotype according to their hierarchical relationships we curated. Meanwhile, we construct a phenotype-cell-gene association landscape by estimating the associated tissues/cell types and genes of 1,871 public GWASs. The association landscape is generally consistent with biological knowledge and can be searched and browsed at the PCGA website.

Balancing the transcriptome: leveraging sample similarity to improve measures of gene specificity

The spatial and temporal domain of a gene's expression can range from ubiquitous to highly specific. Quantifying the degree to which this expression is unique to a specific tissue or developmental timepoint can provide insight into the etiology of genetic diseases. However, quantifying specificity remains challenging as measures of specificity are sensitive to similarity between samples in the sample set. For example, in the Gene-Tissue Expression project (GTEx), brain subregions are overrepresented at 13 of 54 (24%) unique tissues sampled. In this dataset, existing specificity measures have a decreased ability to identify genes specific to the brain relative to other organs. To solve this problem, we leverage sample similarity information to weight samples such that overrepresented tissues do not have an outsized effect on specificity estimates. We test this reweighting procedure on 4 measures of specificity, Z-score, Tau, Tsi and Gini, in the GTEx data and in single cell datasets for zebrafish and mouse. For all of these measures, incorporating sample similarity information to weight samples results in greater stability of sets of genes called as specific and decreases the overall variance in the change of specificity estimates as sample sets become more unbalanced. Furthermore, the genes with the largest improvement in their specificity estimate's stability are those with functions related to the overrepresented sample types. Our results demonstrate that incorporating similarity information improves specificity estimates' stability to the choice of the sample set used to define the transcriptome, providing more robust and reproducible measures of specificity for downstream analyses.

Applying Mendelian randomization to appraise causality in relationships between nutrition and cancer

Dietary factors are assumed to play an important role in cancer risk, apparent in consensus recommendations for cancer prevention that promote nutritional changes. However, the evidence in this field has been generated predominantly through observational studies, which may result in biased effect estimates because of confounding, exposure misclassification, and reverse causality. With major geographical differences and rapid changes in cancer incidence over time, it is crucial to establish which of the observational associations reflect causality and to identify novel risk factors as these may be modified to prevent the onset of cancer and reduce its progression. Mendelian randomization (MR) uses the special properties of germline genetic variation to strengthen causal inference regarding potentially modifiable exposures and disease risk. MR can be implemented through instrumental variable (IV) analysis and, when robustly performed, is generally less prone to confounding, reverse causation and measurement error than conventional observational methods and has different sources of bias (discussed in detail below). It is increasingly used to facilitate causal inference in epidemiology and provides an opportunity to explore the effects of nutritional exposures on cancer incidence and progression in a cost-effective and timely manner. Here, we introduce the concept of MR and discuss its current application in understanding the impact of nutritional factors (e.g., any measure of diet and nutritional intake, circulating biomarkers, patterns, preference or behaviour) on cancer aetiology and, thus, opportunities for MR to contribute to the development of nutritional recommendations and policies for cancer prevention. We provide applied examples of MR studies examining the role of nutritional factors in cancer to illustrate how this method can be used to help prioritise or deprioritise the evaluation of specific nutritional factors as intervention targets in randomised controlled trials. We describe possible biases when using MR, and methodological developments aimed at investigating and potentially overcoming these biases when present. Lastly, we consider the use of MR in identifying causally relevant nutritional risk factors for various cancers in different regions across the world, given notable geographical differences in some cancers. We also discuss how MR results could be translated into further research and policy. We conclude that findings from MR studies, which corroborate those from other well-conducted studies with different and orthogonal biases, are poised to substantially improve our understanding of nutritional influences on cancer. For such corroboration, there is a requirement for an interdisciplinary and collaborative approach to investigate risk factors for cancer incidence and progression.

The Organ-Disease Annotations (ODiseA) database of hereditary diseases and inflicted tissues

Hereditary diseases tend to manifest clinically in few selected tissues. Knowledge of those tissues is important for better understanding of disease mechanisms, which often remain elusive. However, information on the tissues inflicted by each disease is not easily obtainable. Well-established resources, such as the Online Mendelian Inheritance in Man (OMIM) database and Human Phenotype Ontology (HPO), report on a spectrum of disease manifestations, yet do not highlight the main inflicted tissues. The Organ-Disease Annotations (ODiseA) database contains 4,357 thoroughly-curated annotations for 2,181 hereditary diseases and 45 inflicted tissues. Additionally, ODiseA reports 692 annotations of 635 diseases and the pathogenic tissues where they emerge. ODiseA can be queried by disease, disease gene, or inflicted tissue. Owing to its expansive, high-quality annotations, ODiseA serves as a valuable and unique tool for biomedical and computational researchers studying genotype-phenotype relationships of hereditary diseases. ODiseA is available at https://netbio.bgu.ac.il/odisea.

A conditional gene-based association framework integrating isoform-level eQTL data reveals new susceptibility genes for schizophrenia

Linkage disequilibrium and disease-associated variants in the non-coding regions make it difficult to distinguish the truly associated genes from the redundantly associated genes for complex diseases. In this study, we proposed a new conditional gene-based framework called eDESE that leveraged an improved effective chi-squared statistic to control the type I error rates and remove the redundant associations. eDESE initially performed the association analysis by mapping variants to genes according to their physical distance. We further demonstrated that the isoform-level eQTLs could be more powerful than the gene-level eQTLs in the association analysis using a simulation study. Then the eQTL-guided strategies, that is, mapping variants to genes according to their gene/isoform-level variant-gene cis-eQTLs associations, were also integrated with eDESE. We then applied eDESE to predict the potential susceptibility genes of schizophrenia and found that the potential susceptibility genes were enriched with many neuronal or synaptic signaling-related terms in the Gene Ontology knowledgebase and antipsychotics-gene interaction terms in the drug-gene interaction database (DGIdb). More importantly, seven potential susceptibility genes identified by eDESE were the target genes of multiple antipsychotics in DrugBank. Comparing the potential susceptibility genes identified by eDESE and other benchmark approaches (i.e., MAGMA and S-PrediXcan) implied that strategy based on the isoform-level eQTLs could be an important supplement for the other two strategies (physical distance and gene-level eQTLs). We have implemented eDESE in our integrative platform KGGSEE (http://pmglab.top/kggsee/#/) and hope that eDESE can facilitate the prediction of candidate susceptibility genes and isoforms for complex diseases in a multi-tissue context.

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.

Transcription factor protein interactomes reveal genetic determinants in heart disease

Congenital heart disease (CHD) is present in 1% of live births, yet identification of causal mutations remains challenging. We hypothesized that genetic determinants for CHDs may lie in the protein interactomes of transcription factors whose mutations cause CHDs. Defining the interactomes of two transcription factors haplo-insufficient in CHD, GATA4 and TBX5, within human cardiac progenitors, and integrating the results with nearly 9,000 exomes from proband-parent trios revealed an enrichment of de novo missense variants associated with CHD within the interactomes. Scoring variants of interactome members based on residue, gene, and proband features identified likely CHD-causing genes, including the epigenetic reader GLYR1. GLYR1 and GATA4 widely co-occupied and co-activated cardiac developmental genes, and the identified GLYR1 missense variant disrupted interaction with GATA4, impairing in vitro and in vivo function in mice. This integrative proteomic and genetic approach provides a framework for prioritizing and interrogating genetic variants in heart disease.

Integrative multiomics analysis highlights immune-cell regulatory mechanisms and shared genetic architecture for 14 immune-associated diseases and cancer outcomes

Developing functional insight into the causal molecular drivers of immunological disease is a critical challenge in genomic medicine. Here, we systematically apply Mendelian randomization (MR), genetic colocalization, immune-cell-type enrichment, and phenome-wide association methods to investigate the effects of genetically predicted gene expression on ten immune-associated diseases and four cancer outcomes. Using whole blood-derived estimates for regulatory variants from the eQTLGen consortium (n = 31,684), we constructed genetic risk scores for 10,104 genes. Applying the inverse-variance-weighted MR method transcriptome wide while accounting for linkage disequilibrium structure identified 664 unique genes with evidence of a genetically predicted effect on at least one disease outcome (p < 4.81 × 10-5). We next undertook genetic colocalization to investigate cell-type-specific effects at these loci by using gene expression data derived from 18 types of immune cells. This highlighted many cell-type-dependent effects, such as PRKCQ expression and asthma risk (posterior probability = 0.998), which was T cell specific. Phenome-wide analyses on 311 complex traits and endpoints allowed us to explore shared genetic architecture and prioritize key drivers of disease risk, such as CASP10, which provided evidence of an effect on seven cancer-related outcomes. Our atlas of results can be used to characterize known and novel loci in immune-associated disease and cancer susceptibility, both in terms of elucidating cell-type-dependent effects as well as dissecting shared disease pathways and pervasive pleiotropy. As an exemplar, we have highlighted several key findings in this study, although similar evaluations can be conducted via our interactive web platform.

Chaperone networks are shaped by cellular differentiation and identity

Chaperone expression is developmentally regulated, establishing tissue-specific networks. However, the molecular basis underlying this specificity is mainly unknown. Recent evidence suggests that chaperone network rewiring is mediated, in part, by differentiation transcription factors to fit the proteome folding demands, with implications for the tissue-specific manifestation of protein misfolding diseases.

Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics

Genome-wide association studies (GWAS) provide a powerful means to identify loci and genes contributing to disease, but in many cases the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important for identifying pathogenic processes and developing therapeutics. Here, we introduce sc-linker, a framework for integrating single-cell RNA-seq (scRNA-seq), epigenomic maps and GWAS summary statistics to infer the underlying cell types and processes by which genetic variants influence disease. We analyzed 1.6 million scRNA-seq profiles from 209 individuals spanning 11 tissue types and 6 disease conditions, and constructed gene programs capturing cell types, disease progression, and cellular processes both within and across cell types. We evaluated these gene programs for disease enrichment by transforming them to SNP annotations with tissue-specific epigenomic maps and computing enrichment scores across 60 diseases and complex traits (average N= 297K). Cell type, disease progression, and cellular process programs captured distinct heritability signals even within the same cell type, as we show in multiple complex diseases that affect the brain (Alzheimer’s disease, multiple sclerosis), colon (ulcerative colitis) and lung (asthma, idiopathic pulmonary fibrosis, severe COVID-19). The inferred disease enrichments recapitulated known biology and highlighted novel cell-disease relationships, including GABAergic neurons in major depressive disorder (MDD), a disease progression M cell program in ulcerative colitis, and a disease-specific complement cascade process in multiple sclerosis. In autoimmune disease, both healthy and disease progression immune cell type programs were associated, whereas for epithelial cells, disease progression programs were most prominent, perhaps suggesting a role in disease progression over initiation. Our framework provides a powerful approach for identifying the cell types and cellular processes by which genetic variants influence disease.

Desmoplakin and periplakin genetically and functionally contribute to eosinophilic esophagitis

Eosinophilic esophagitis (EoE) is a chronic allergic inflammatory disease with a complex underlying genetic etiology. Herein, we conduct whole-exome sequencing of a multigeneration EoE pedigree (discovery set) and 61 additional multiplex families with EoE (replication set). A series of rare, heterozygous, missense variants are identified in the genes encoding the desmosome-associated proteins DSP and PPL in 21% of the multiplex families. Esophageal biopsies from patients with these variants retain dilated intercellular spaces and decrease DSP and PPL expression even during disease remission. These variants affect barrier integrity, cell motility and RhoGTPase activity in esophageal epithelial cells and have increased susceptibility to calpain-14-mediated degradation. An acquired loss of esophageal DSP and PPL is present in non-familial EoE. Taken together, herein, we uncover a pathogenic role for desmosomal dysfunction in EoE, providing a deeper mechanistic understanding of tissue-specific allergic responses.

Network analysis reveals rare disease signatures across multiple levels of biological organization

Rare genetic diseases are typically caused by a single gene defect. Despite this clear causal relationship between genotype and phenotype, identifying the pathobiological mechanisms at various levels of biological organization remains a practical and conceptual challenge. Here, we introduce a network approach for evaluating the impact of rare gene defects across biological scales. We construct a multiplex network consisting of over 20 million gene relationships that are organized into 46 network layers spanning six major biological scales between genotype and phenotype. A comprehensive analysis of 3,771 rare diseases reveals distinct phenotypic modules within individual layers. These modules can be exploited to mechanistically dissect the impact of gene defects and accurately predict rare disease gene candidates. Our results show that the disease module formalism can be applied to rare diseases and generalized beyond physical interaction networks. These findings open up new venues to apply network-based tools for cross-scale data integration.

Triangulating Evidence through the Inclusion of Genetically Informed Designs

Much research effort is invested in attempting to determine causal influences on disease onset and progression to inform prevention and treatment efforts. However, this is often dependent on observational data that are prone to well-known limitations, particularly residual confounding and reverse causality. Several statistical methods have been developed to support stronger causal inference. However, a complementary approach is to use design-based methods for causal inference, which acknowledge sources of bias and attempt to mitigate these through the design of the study rather than solely through statistical adjustment. Genetically informed methods provide a novel and potentially powerful extension to this approach, accounting by design for unobserved genetic and environmental confounding. No single approach will be absent from bias. Instead, we should seek and combine evidence from multiple methodologies that each bring different (and ideally uncorrelated) sources of bias. If the results of these different methodologies align-or triangulate-then we can be more confident in our causal inference. To be truly effective, this should ideally be done prospectively, with the sources of evidence specified in advance, to protect against one final source of bias-our own cognitions, expectations, and fondly held beliefs.

Computational Methods for Prediction of Human Protein-Phenotype Associations: A Review

Deciphering the relationship between human proteins (genes) and phenotypes is one of the fundamental tasks in phenomics research. The Human Phenotype Ontology (HPO) builds upon a standardized logical vocabulary to describe the abnormal phenotypes encountered in human diseases and paves the way towards the computational analysis of their genetic causes. To date, many computational methods have been proposed to predict the HPO annotations of proteins. In this paper, we conduct a comprehensive review of the existing approaches to predicting HPO annotations of novel proteins, identifying missing HPO annotations, and prioritizing candidate proteins with respect to a certain HPO term. For each topic, we first give the formalized description of the problem, and then systematically revisit the published literatures highlighting their advantages and disadvantages, followed by the discussion on the challenges and promising future directions. In addition, we point out several potential topics to be worthy of exploration including the selection of negative HPO annotations and detecting HPO misannotations. We believe that this review will provide insight to the researchers in the field of computational phenotype analyses in terms of comprehending and developing novel prediction algorithms.

An atlas of protein-protein interactions across mouse tissues

Cellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the interactome has therefore been a central objective of high-throughput biology. However, the dynamics of protein interactions across physiological contexts remain poorly understood. Here, we develop a quantitative proteomic approach combining protein correlation profiling with stable isotope labeling of mammals (PCP-SILAM) to map the interactomes of seven mouse tissues. The resulting maps provide a proteome-scale survey of interactome rewiring across mammalian tissues, revealing more than 125,000 unique interactions at a quality comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewired proteins are tightly regulated by multiple cellular mechanisms and are implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape in vivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.

A 12 kb multi-allelic copy number variation encompassing a GC gene enhancer is associated with mastitis resistance in dairy cattle

Clinical mastitis (CM) is an inflammatory disease occurring in the mammary glands of lactating cows. CM is under genetic control, and a prominent CM resistance QTL located on chromosome 6 was reported in various dairy cattle breeds. Nevertheless, the biological mechanism underpinning this QTL has been lacking. Herein, we mapped, fine-mapped, and discovered the putative causal variant underlying this CM resistance QTL in the Dutch dairy cattle population. We identified a ~12 kb multi-allelic copy number variant (CNV), that is in perfect linkage disequilibrium with a lead SNP, as a promising candidate variant. By implementing a fine-mapping and through expression QTL mapping, we showed that the group-specific component gene (GC), a gene encoding a vitamin D binding protein, is an excellent candidate causal gene for the QTL. The multiplicated alleles are associated with increased GC expression and low CM resistance. Ample evidence from functional genomics data supports the presence of an enhancer within this CNV, which would exert cis-regulatory effect on GC. We observed that strong positive selection swept the region near the CNV, and haplotypes associated with the multiplicated allele were strongly selected for. Moreover, the multiplicated allele showed pleiotropic effects for increased milk yield and reduced fertility, hinting that a shared underlying biology for these effects may revolve around the vitamin D pathway. These findings together suggest a putative causal variant of a CM resistance QTL, where a cis-regulatory element located within a CNV can alter gene expression and affect multiple economically important traits.

Patient derived stem cells for discovery and validation of novel pathogenic variants in inherited retinal disease

Our understanding of inherited retinal disease has benefited immensely from molecular genetic analysis over the past several decades. New technologies that allow for increasingly detailed examination of a patient's DNA have expanded the catalog of genes and specific variants that cause retinal disease. In turn, the identification of pathogenic variants has allowed the development of gene therapies and low-cost, clinically focused genetic testing. Despite this progress, a relatively large fraction (at least 20%) of patients with clinical features suggestive of an inherited retinal disease still do not have a molecular diagnosis today. Variants that are not obviously disruptive to the codon sequence of exons can be difficult to distinguish from the background of benign human genetic variations. Some of these variants exert their pathogenic effect not by altering the primary amino acid sequence, but by modulating gene expression, isoform splicing, or other transcript-level mechanisms. While not discoverable by DNA sequencing methods alone, these variants are excellent targets for studies of the retinal transcriptome. In this review, we present an overview of the current state of pathogenic variant discovery in retinal disease and identify some of the remaining barriers. We also explore the utility of new technologies, specifically patient-derived induced pluripotent stem cell (iPSC)-based modeling, in further expanding the catalog of disease-causing variants using transcriptome-focused methods. Finally, we outline bioinformatic analysis techniques that will allow this new method of variant discovery in retinal disease. As the knowledge gleaned from previous technologies is informing targets for therapies today, we believe that integrating new technologies, such as iPSC-based modeling, into the molecular diagnosis pipeline will enable a new wave of variant discovery and expanded treatment of inherited retinal disease.

A cis-regulatory atlas in maize at single-cell resolution

cis-regulatory elements (CREs) encode the genomic blueprints of spatiotemporal gene expression programs enabling highly specialized cell functions. Using single-cell genomics in six maize organs, we determined the cis- and trans-regulatory factors defining diverse cell identities and coordinating chromatin organization by profiling transcription factor (TF) combinatorics, identifying TFs with non-cell-autonomous activity, and uncovering TFs underlying higher-order chromatin interactions. Cell-type-specific CREs were enriched for enhancer activity and within unmethylated long terminal repeat retrotransposons. Moreover, we found cell-type-specific CREs are hotspots for phenotype-associated genetic variants and were targeted by selection during modern maize breeding, highlighting the biological implications of this CRE atlas. Through comparison of maize and Arabidopsis thaliana developmental trajectories, we identified TFs and CREs with conserved and divergent chromatin dynamics, showcasing extensive evolution of gene regulatory networks. In addition to this rich dataset, we developed single-cell analysis software, Socrates, which can be used to understand cis-regulatory variation in any species.

Disease-specific interactome alterations via epichaperomics: the case for Alzheimer's disease

The increasingly appreciated prevalence of complicated stressor-to-phenotype associations in human disease requires a greater understanding of how specific stressors affect systems or interactome properties. Many currently untreatable diseases arise due to variations in, and through a combination of, multiple stressors of genetic, epigenetic, and environmental nature. Unfortunately, how such stressors lead to a specific disease phenotype or inflict a vulnerability to some cells and tissues but not others remains largely unknown and unsatisfactorily addressed. Analysis of cell- and tissue-specific interactome networks may shed light on organization of biological systems and subsequently to disease vulnerabilities. However, deriving human interactomes across different cell and disease contexts remains a challenge. To this end, this opinion article links stressor-induced protein interactome network perturbations to the formation of pathologic scaffolds termed epichaperomes, revealing a viable and reproducible experimental solution to obtaining rigorous context-dependent interactomes. This article presents our views on how a specialized 'omics platform called epichaperomics may complement and enhance the currently available conventional approaches and aid the scientific community in defining, understanding, and ultimately controlling interactome networks of complex diseases such as Alzheimer's disease. Ultimately, this approach may aid the transition from a limited single-alteration perspective in disease to a comprehensive network-based mindset, which we posit will result in precision medicine paradigms for disease diagnosis and treatment.

Modeling regulatory network topology improves genome-wide analyses of complex human traits

Genome-wide association studies (GWAS) have cataloged many significant associations between genetic variants and complex traits. However, most of these findings have unclear biological significance, because they often have small effects and occur in non-coding regions. Integration of GWAS with gene regulatory networks addresses both issues by aggregating weak genetic signals within regulatory programs. Here we develop a Bayesian framework that integrates GWAS summary statistics with regulatory networks to infer genetic enrichments and associations simultaneously. Our method improves upon existing approaches by explicitly modeling network topology to assess enrichments, and by automatically leveraging enrichments to identify associations. Applying this method to 18 human traits and 38 regulatory networks shows that genetic signals of complex traits are often enriched in interconnections specific to trait-relevant cell types or tissues. Prioritizing variants within enriched networks identifies known and previously undescribed trait-associated genes revealing biological and therapeutic insights.

A cis-regulatory atlas in maize at single-cell resolution

cis-regulatory elements (CREs) encode the genomic blueprints of spatiotemporal gene expression programs enabling highly specialized cell functions. Using single-cell genomics in six maize organs, we determined the cis- and trans-regulatory factors defining diverse cell identities and coordinating chromatin organization by profiling transcription factor (TF) combinatorics, identifying TFs with non-cell-autonomous activity, and uncovering TFs underlying higher-order chromatin interactions. Cell-type-specific CREs were enriched for enhancer activity and within unmethylated long terminal repeat retrotransposons. Moreover, we found cell-type-specific CREs are hotspots for phenotype-associated genetic variants and were targeted by selection during modern maize breeding, highlighting the biological implications of this CRE atlas. Through comparison of maize and Arabidopsis thaliana developmental trajectories, we identified TFs and CREs with conserved and divergent chromatin dynamics, showcasing extensive evolution of gene regulatory networks. In addition to this rich dataset, we developed single-cell analysis software, Socrates, which can be used to understand cis-regulatory variation in any species.

Decoding Neuromuscular Disorders Using Phenotypic Clusters Obtained From Co-Occurrence Networks

Neuromuscular disorders (NMDs) represent an important subset of rare diseases associated with elevated morbidity and mortality whose diagnosis can take years. Here we present a novel approach using systems biology to produce functionally-coherent phenotype clusters that provide insight into the cellular functions and phenotypic patterns underlying NMDs, using the Human Phenotype Ontology as a common framework. Gene and phenotype information was obtained for 424 NMDs in OMIM and 126 NMDs in Orphanet, and 335 and 216 phenotypes were identified as typical for NMDs, respectively. ‘Elevated serum creatine kinase’ was the most specific to NMDs, in agreement with the clinical test of elevated serum creatinine kinase that is conducted on NMD patients. The approach to obtain co-occurring NMD phenotypes was validated based on co-mention in PubMed abstracts. A total of 231 (OMIM) and 150 (Orphanet) clusters of highly connected co-occurrent NMD phenotypes were obtained. In parallel, a tripartite network based on phenotypes, diseases and genes was used to associate NMD phenotypes with functions, an approach also validated by literature co-mention, with KEGG pathways showing proportionally higher overlap than Gene Ontology and Reactome. Phenotype-function pairs were crossed with the co-occurrent NMD phenotype clusters to obtain 40 (OMIM) and 72 (Orphanet) functionally coherent phenotype clusters. As expected, many of these overlapped with known diseases and confirmed existing knowledge. Other clusters revealed interesting new findings, indicating informative phenotypes for differential diagnosis, providing deeper knowledge of NMDs, and pointing towards specific cell dysfunction caused by pleiotropic genes. This work is an example of reproducible research that i) can help better understand NMDs and support their diagnosis by providing a new tool that exploits existing information to obtain novel clusters of functionally-related phenotypes, and ii) takes us another step towards personalised medicine for NMDs.

The landscape of molecular chaperones across human tissues reveals a layered architecture of core and variable chaperones

The sensitivity of the protein-folding environment to chaperone disruption can be highly tissue-specific. Yet, the organization of the chaperone system across physiological human tissues has received little attention. Through computational analyses of large-scale tissue transcriptomes, we unveil that the chaperone system is composed of core elements that are uniformly expressed across tissues, and variable elements that are differentially expressed to fit with tissue-specific requirements. We demonstrate via a proteomic analysis that the muscle-specific signature is functional and conserved. Core chaperones are significantly more abundant across tissues and more important for cell survival than variable chaperones. Together with variable chaperones, they form tissue-specific functional networks. Analysis of human organ development and aging brain transcriptomes reveals that these functional networks are established in development and decline with age. In this work, we expand the known functional organization of de novo versus stress-inducible eukaryotic chaperones into a layered core-variable architecture in multi-cellular organisms.

Modeling multifunctionality of genes with secondary gene co-expression networks in human brain provides novel disease insights

MOTIVATION

Co-expression networks are a powerful gene expression analysis method to study how genes co-express together in clusters with functional coherence that usually resemble specific cell type behavior for the genes involved. They can be applied to bulk-tissue gene expression profiling and assign function, and usually cell type specificity, to a high percentage of the gene pool used to construct the network. One of the limitations of this method is that each gene is predicted to play a role in a specific set of coherent functions in a single cell type (i.e. at most we get a single <gene, function, cell type> for each gene). We present here GMSCA (Gene Multifunctionality Secondary Co-expression Analysis), a software tool that exploits the co-expression paradigm to increase the number of functions and cell types ascribed to a gene in bulk-tissue co-expression networks.

RESULTS

We applied GMSCA to 27 co-expression networks derived from bulk-tissue gene expression profiling of a variety of brain tissues. Neurons and glial cells (microglia, astrocytes and oligodendrocytes) were considered the main cell types. Applying this approach, we increase the overall number of predicted triplets <gene, function, cell type> by 46.73%. Moreover, GMSCA predicts that the SNCA gene, traditionally associated to work mainly in neurons, also plays a relevant function in oligodendrocytes.

AVAILABILITYAND IMPLEMENTATION

The tool is available at GitHub, https://github.com/drlaguna/GMSCA as open-source software.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Causal inference with observational data: the need for triangulation of evidence

The goal of much observational research is to identify risk factors that have a causal effect on health and social outcomes. However, observational data are subject to biases from confounding, selection and measurement, which can result in an underestimate or overestimate of the effect of interest. Various advanced statistical approaches exist that offer certain advantages in terms of addressing these potential biases. However, although these statistical approaches have different underlying statistical assumptions, in practice they cannot always completely remove key sources of bias; therefore, using design-based approaches to improve causal inference is also important. Here it is the design of the study that addresses the problem of potential bias - either by ensuring it is not present (under certain assumptions) or by comparing results across methods with different sources and direction of potential bias. The distinction between statistical and design-based approaches is not an absolute one, but it provides a framework for triangulation - the thoughtful application of multiple approaches (e.g. statistical and design based), each with their own strengths and weaknesses, and in particular sources and directions of bias. It is unlikely that any single method can provide a definite answer to a causal question, but the triangulation of evidence provided by different approaches can provide a stronger basis for causal inference. Triangulation can be considered part of wider efforts to improve the transparency and robustness of scientific research, and the wider scientific infrastructure and system of incentives.

The expanding genetic landscape of hereditary motor neuropathies

Hereditary motor neuropathies are clinically and genetically diverse disorders characterized by length-dependent axonal degeneration of lower motor neurons. Although currently as many as 26 causal genes are known, there is considerable missing heritability compared to other inherited neuropathies such as Charcot-Marie-Tooth disease. Intriguingly, this genetic landscape spans a discrete number of key biological processes within the peripheral nerve. Also, in terms of underlying pathophysiology, hereditary motor neuropathies show striking overlap with several other neuromuscular and neurological disorders. In this review, we provide a current overview of the genetic spectrum of hereditary motor neuropathies highlighting recent reports of novel genes and mutations or recent discoveries in the underlying disease mechanisms. In addition, we link hereditary motor neuropathies with various related disorders by addressing the main affected pathways of disease divided into five major processes: axonal transport, tRNA aminoacylation, RNA metabolism and DNA integrity, ion channels and transporters and endoplasmic reticulum.

Dosage-sensitive molecular mechanisms are associated with the tissue-specificity of traits and diseases

Hereditary diseases and complex traits often manifest in specific tissues, whereas their causal genes are expressed in many tissues that remain unaffected. Among the mechanisms that have been suggested for this enigmatic phenomenon is dosage-sensitive compensation by paralogs of causal genes. Accordingly, tissue-selectivity stems from dosage imbalance between causal genes and paralogs that occurs particularly in disease-susceptible tissues. Here, we used a large-scale dataset of thousands of tissue transcriptomes and applied a linear mixed model (LMM) framework to assess this and other dosage-sensitive mechanisms. LMM analysis of 382 hereditary diseases consistently showed evidence for dosage-sensitive compensation by paralogs across diseases subsets and susceptible tissues. LMM analysis of 135 candidate genes that are strongly associated with 16 tissue-selective complex traits revealed a similar tendency among half of the trait-associated genes. This suggests that dosage-sensitive compensation by paralogs affects the tissue-selectivity of complex traits, and can be used to illuminate candidate genes' modes of action. Next, we applied LMM to analyze dosage imbalance between causal genes and three classes of genetic modifiers, including regulatory micro-RNAs, pseudogenes, and genetic interactors. Our results propose modifiers as a fundamental axis in tissue-selectivity of diseases and traits, and demonstrates the power of LMM as a statistical framework for discovering treatment avenues.

TWAS pathway method greatly enhances the number of leads for uncovering the molecular underpinnings of psychiatric disorders

Genetic signal detection in genome-wide association studies (GWAS) is enhanced by pooling small signals from multiple Single Nucleotide Polymorphism (SNP), for example, across genes and pathways. Because genes are believed to influence traits via gene expression, it is of interest to combine information from expression Quantitative Trait Loci (eQTLs) in a gene or genes in the same pathway. Such methods, widely referred to as transcriptomic wide association studies (TWAS), already exist for gene analysis. Due to the possibility of eliminating most of the confounding effects of linkage disequilibrium (LD) from TWAS gene statistics, pathway TWAS methods would be very useful in uncovering the true molecular basis of psychiatric disorders. However, such methods are not yet available for arbitrarily large pathways/gene sets. This is possibly due to the quadratic (as a function of the number of SNPs) computational burden for computing LD across large chromosomal regions. To overcome this obstacle, we propose JEPEGMIX2-P, a novel TWAS pathway method that (a) has a linear computational burden, (b) uses a large and diverse reference panel (33 K subjects), (c) is competitive (adjusts for background enrichment in gene TWAS statistics), and (d) is applicable as-is to ethnically mixed-cohorts. To underline its potential for increasing the power to uncover genetic signals over the commonly used nontranscriptomics methods, for example, MAGMA, we applied JEPEGMIX2-P to summary statistics of most large meta-analyses from Psychiatric Genetics Consortium (PGC). While our work is just the very first step toward clinical translation of psychiatric disorders, PGC anorexia results suggest a possible avenue for treatment.

Developmental Gene Expression Differences between Humans and Mammalian Models

Identifying the molecular programs underlying human organ development and how they differ from model species is key for understanding human health and disease. Developmental gene expression profiles provide a window into the genes underlying organ development and a direct means to compare them across species. We use a transcriptomic resource covering the development of seven organs to characterize the temporal profiles of human genes associated with distinct disease classes and to determine, for each human gene, the similarity of its spatiotemporal expression with its orthologs in rhesus macaque, mouse, rat, and rabbit. We find clear associations between spatiotemporal profiles and the phenotypic manifestations of diseases. We also find that half of human genes differ from their mouse orthologs in their temporal trajectories in at least one of the organs. These include more than 200 genes associated with brain, heart, and liver disease for which mouse models should undergo extra scrutiny.

Deciphering cell-type specific signal transduction in the brain: Challenges and promises

Signal transduction designates the set of molecular events that take place within a cell upon extracellular stimulation to mediate a functional outcome. Decades after the discovery that dopamine triggers opposing signaling pathways in D1- and D2-expressing medium spiny neurons, it is now clear that there are as many different flavors of signaling pathways in the brain as there are neuron types. One of the biggest challenges in molecular neuroscience is to elucidate cell-type specific signaling, in order to understand neurological diseases with regional vulnerability, but also to identify targets for precision drugs devoid of off-target effects. Here, we make a case for the importance of the study of neuron-type specific molecular characteristics. We then review the technologies that exist to study neurons in their full diversity and highlight their disease-relevant idiosyncrasies.

Genetic mapping of etiologic brain cell types for obesity

The underlying cell types mediating predisposition to obesity remain largely obscure. Here, we integrated recently published single-cell RNA-sequencing (scRNA-seq) data from 727 peripheral and nervous system cell types spanning 17 mouse organs with body mass index (BMI) genome-wide association study (GWAS) data from >457,000 individuals. Developing a novel strategy for integrating scRNA-seq data with GWAS data, we identified 26, exclusively neuronal, cell types from the hypothalamus, subthalamus, midbrain, hippocampus, thalamus, cortex, pons, medulla, pallidum that were significantly enriched for BMI heritability (p<1.6×10⁻⁴). Using genes harboring coding mutations associated with obesity, we replicated midbrain cell types from the anterior pretectal nucleus and periaqueductal gray (p<1.2×10⁻⁴). Together, our results suggest that brain nuclei regulating integration of sensory stimuli, learning and memory are likely to play a key role in obesity and provide testable hypotheses for mechanistic follow-up studies.

Elucidation of Biological Networks across Complex Diseases Using Single-Cell Omics

Single-cell multimodal omics (scMulti-omics) technologies have made it possible to trace cellular lineages during differentiation and to identify new cell types in heterogeneous cell populations. The derived information is especially promising for computing cell-type-specific biological networks encoded in complex diseases and improving our understanding of the underlying gene regulatory mechanisms. The integration of these networks could, therefore, give rise to a heterogeneous regulatory landscape (HRL) in support of disease diagnosis and drug therapeutics. In this review, we provide an overview of this field and pay particular attention to how diverse biological networks can be inferred in a specific cell type based on integrative methods. Then, we discuss how HRL can advance our understanding of regulatory mechanisms underlying complex diseases and aid in the prediction of prognosis and therapeutic responses. Finally, we outline challenges and future trends that will be central to bringing the field of HRL in complex diseases forward.

Phenome-wide analyses establish a specific association between aortic valve PALMD expression and calcific aortic valve stenosis

Calcific aortic valve stenosis (CAVS) is a frequent heart disease with significant morbidity and mortality. Recent genomic studies have identified a locus near the gene PALMD (palmdelphin) strongly associated with CAVS. Here, we show that genetically-determined expression of PALMD in the aortic valve is inversely associated with CAVS, with a stronger effect in women, in a meta-analysis of two large cohorts totaling 2359 cases and 350,060 controls. We further demonstrate the specificity of this relationship by showing the absence of other significant association between the genetically-determined expression of PALMD in 9 tissues and 852 phenotypes. Using genome-wide association studies meta-analyses of cardiovascular traits, we identify a significant colocalized positive association between genetically-determined expression of PALMD in four non-cardiac tissues (brain anterior cingulate cortex, esophagus muscularis, tibial nerve and subcutaneous adipose tissue) and atrial fibrillation. The present work further establishes PALMD as a promising molecular target for CAVS.

Zhonglin Li et al. perform phenome-wide analyses to explore the genetic association between the locus near PALMD and calcific aortic valve stenosis (CAVS). Using previously reported aortic valve expression data and genotypes from large cohorts, they find a strong and specific association between genetically-determined PALMD expression in the aortic valve and CAVS as well as a novel association with atrial fibrillation in non-cardiac tissues.

Integration of GWAS Summary Statistics and Gene Expression Reveals Target Cell Types Underlying Kidney Function Traits

BACKGROUND

Genetic variants identified in genome-wide association studies (GWAS) are often not specific enough to reveal complex underlying physiology. By integrating RNA-seq data and GWAS summary statistics, novel computational methods allow unbiased identification of trait-relevant tissues and cell types.

METHODS

The CKDGen consortium provided GWAS summary data for eGFR, urinary albumin-creatinine ratio (UACR), BUN, and serum urate. Genotype-Tissue Expression Project (GTEx) RNA-seq data were used to construct the top 10% specifically expressed genes for each of 53 tissues followed by linkage disequilibrium (LD) score-based enrichment testing for each trait. Similar procedures were performed for five kidney single-cell RNA-seq datasets from humans and mice and for a microdissected tubule RNA-seq dataset from rat. Gene set enrichment analyses were also conducted for genes implicated in Mendelian kidney diseases.

RESULTS

Across 53 tissues, genes in kidney function-associated GWAS loci were enriched in kidney (P=9.1E-8 for eGFR; P=1.2E-5 for urate) and liver (P=6.8·10^-5 for eGFR). In the kidney, proximal tubule was enriched in humans (P=8.5E-5 for eGFR; P=7.8E-6 for urate) and mice (P=0.0003 for eGFR; P=0.0002 for urate) and confirmed as the primary cell type in microdissected tubules and organoids. Gene set enrichment analysis supported this and showed enrichment of genes implicated in monogenic glomerular diseases in podocytes. A systematic approach generated a comprehensive list of GWAS genes prioritized by cell type-specific expression.

CONCLUSIONS

Integration of GWAS statistics of kidney function traits and gene expression data identified relevant tissues and cell types, as a basis for further mechanistic studies to understand GWAS loci.

Missing heritability in Parkinson's disease: the emerging role of non-coding genetic variation

Parkinson's disease (PD) is a neurodegenerative disorder caused by a complex interplay of genetic and environmental factors. For the stratification of PD patients and the development of advanced clinical trials, including causative treatments, a better understanding of the underlying genetic architecture of PD is required. Despite substantial efforts, genome-wide association studies have not been able to explain most of the observed heritability. The majority of PD-associated genetic variants are located in non-coding regions of the genome. A systematic assessment of their functional role is hampered by our incomplete understanding of genotype-phenotype correlations, for example through differential regulation of gene expression. Here, the recent progress and remaining challenges for the elucidation of the role of non-coding genetic variants is reviewed with a focus on PD as a complex disease with multifactorial origins. The function of gene regulatory elements and the impact of non-coding variants on them, and the means to map these elements on a genome-wide level, will be delineated. Moreover, examples of how the integration of functional genomic annotations can serve to identify disease-associated pathways and to prioritize disease- and cell type-specific regulatory variants will be given. Finally, strategies for functional validation and considerations for suitable model systems are outlined. Together this emphasizes the contribution of rare and common genetic variants to the complex pathogenesis of PD and points to remaining challenges for the dissection of genetic complexity that may allow for better stratification, improved diagnostics and more targeted treatments for PD in the future.

Differential network analysis of multiple human tissue interactomes highlights tissue-selective processes and genetic disorder genes

Motivation

Differential network analysis, designed to highlight network changes between conditions, is an important paradigm in network biology. However, differential network analysis methods have been typically designed to compare between two conditions and were rarely applied to multiple protein interaction networks (interactomes). Importantly, large-scale benchmarks for their evaluation have been lacking.

Results

Here, we present a framework for assessing the ability of differential network analysis of multiple human tissue interactomes to highlight tissue-selective processes and disorders. For this, we created a benchmark of 6499 curated tissue-specific Gene Ontology biological processes. We applied five methods, including four differential network analysis methods, to construct weighted interactomes for 34 tissues. Rigorous assessment of this benchmark revealed that differential analysis methods perform well in revealing tissue-selective processes (AUCs of 0.82–0.9). Next, we applied differential network analysis to illuminate the genes underlying tissue-selective hereditary disorders. For this, we curated a dataset of 1305 tissue-specific hereditary disorders and their manifesting tissues. Focusing on subnetworks containing the top 1% differential interactions in disease-relevant tissue interactomes revealed significant enrichment for disorder-causing genes in 18.6% of the cases, with a significantly high success rate for blood, nerve, muscle and heart diseases.

Summary

Altogether, we offer a framework that includes expansive manually curated datasets of tissue-selective processes and disorders to be used as benchmarks or to illuminate tissue-selective processes and genes. Our results demonstrate that differential analysis of multiple human tissue interactomes is a powerful tool for highlighting processes and genes with tissue-selective functionality and clinical impact.

Availability and implementation

Datasets are available as part of the Supplementary data.

Supplementary information

Supplementary data are available at Bioinformatics online.