The Multiscale Backbone of the Human Phenotype Network Based on Biological Pathways

Background: Networks are commonly used to represent and analyze large and complex systems of interacting elements. In systems biology, human disease networks show interactions between disorders sharing common genetic background. We built pathway-based human phenotype network (PHPN) of over 800 physical attributes, diseases, and behavioral traits; based on about 2,300 genes and 1,200 biological pathways. Using GWAS phenotype-to-genes associations, and pathway data from Reactome, we connect human traits based on the common patterns of human biological pathways, detecting more pleiotropic effects, and expanding previous studies from a gene-centric approach to that of shared cell-processes. Results: The resulting network has a heavily right-skewed degree distribution, placing it in the scale-free region of the network topologies spectrum. We extract the multi-scale information backbone of the PHPN based on the local densities of the network and discarding weak connection. Using a standard community detection algorithm, we construct phenotype modules of similar traits without applying expert biological knowledge. These modules can be assimilated to the disease classes. However, we are able to classify phenotypes according to shared biology, and not arbitrary disease classes. We present examples of expected clinical connections identified by PHPN as proof of principle. Conclusions: We unveil a previously uncharacterized connection between phenotype modules and discuss potential mechanistic connections that are obvious only in retrospect. The PHPN shows tremendous potential to become a useful tool both in the unveiling of the diseases’ common biology, and in the elaboration of diagnosis and treatments

Emerging patterns of genetic overlap across autoimmune disorders.

Most of the recently identified autoimmunity loci are shared among multiple autoimmune diseases. The pattern of genetic association with autoimmune phenotypes varies, suggesting that certain subgroups of autoimmune diseases are likely to share etiological similarities and underlying mechanisms of disease. In this review, we summarize the major findings from recent studies that have sought to refine genotype-phenotype associations in autoimmune disease by identifying both shared and distinct autoimmunity loci. More specifically, we focus on information from recent genome-wide association studies of rheumatoid arthritis, ankylosing spondylitis, celiac disease, multiple sclerosis, systemic lupus erythematosus, type 1 diabetes and inflammatory bowel disease. Additional work in this area is warranted given both the opportunity it provides to elucidate pathogenic mechanisms in autoimmunity and its potential to inform the development of improved diagnostic and therapeutic tools for this group on complex human disorders

Towards a Model of Life and Cognition

What should be the ontology of the world such that life and cognition are possible? In this essay, I undertake to outline an alternative ontological foundation which makes biological and cognitive phenomena possible. The foundation is built by defining a model, which is presented in the form of a description of a hypothetical but a logically possible world with a defined ontological base. Biology rests today on quite a few not so well connected foundations: molecular biology based on the genetic dogma; evolutionary biology based on neo-Darwinian model; ecology based on systems view; developmental biology by morphogenetic models; connectionist models for neurophysiology and cognitive biology; pervasive teleonomic explanations for the goal-directed behavior across the discipline; etc. Can there be an underlying connecting theme or a model which could make these seemingly disparate domains interconnected? I shall atempt to answer this question. By following the semantic view of scientific theories, I tend to believe that the models employed by the present physical sciences are not rich enough to capture biological (and some of the non-biological) systems. A richer theory that could capture biological reality could also capture physical and chemical phenomena as limiting cases, but not vice versa

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch\u27s APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch\u27s data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch\u27s analytic tools by developing a customized plugin for OpenAI\u27s ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app

Bioinformatics challenges for genome-wide association studies

Motivation: The sequencing of the human genome has made it possible to identify an informative set of >1 million single nucleotide polymorphisms (SNPs) across the genome that can be used to carry out genome-wide association studies (GWASs). The availability of massive amounts of GWAS data has necessitated the development of new biostatistical methods for quality control, imputation and analysis issues including multiple testing. This work has been successful and has enabled the discovery of new associations that have been replicated in multiple studies. However, it is now recognized that most SNPs discovered via GWAS have small effects on disease susceptibility and thus may not be suitable for improving health care through genetic testing. One likely explanation for the mixed results of GWAS is that the current biostatistical analysis paradigm is by design agnostic or unbiased in that it ignores all prior knowledge about disease pathobiology. Further, the linear modeling framework that is employed in GWAS often considers only one SNP at a time thus ignoring their genomic and environmental context. There is now a shift away from the biostatistical approach toward a more holistic approach that recognizes the complexity of the genotype–phenotype relationship that is characterized by significant heterogeneity and gene–gene and gene–environment interaction. We argue here that bioinformatics has an important role to play in addressing the complexity of the underlying genetic basis of common human diseases. The goal of this review is to identify and discuss those GWAS challenges that will require computational methods

Lung eQTLs to Help Reveal the Molecular Underpinnings of Asthma

Genome-wide association studies (GWAS) have identified loci reproducibly associated with pulmonary diseases; however, the molecular mechanism underlying these associations are largely unknown. The objectives of this study were to discover genetic variants affecting gene expression in human lung tissue, to refine susceptibility loci for asthma identified in GWAS studies, and to use the genetics of gene expression and network analyses to find key molecular drivers of asthma. We performed a genome-wide search for expression quantitative trait loci (eQTL) in 1,111 human lung samples. The lung eQTL dataset was then used to inform asthma genetic studies reported in the literature. The top ranked lung eQTLs were integrated with the GWAS on asthma reported by the GABRIEL consortium to generate a Bayesian gene expression network for discovery of novel molecular pathways underpinning asthma. We detected 17,178 cis- and 593 trans- lung eQTLs, which can be used to explore the functional consequences of loci associated with lung diseases and traits. Some strong eQTLs are also asthma susceptibility loci. For example, rs3859192 on chr17q21 is robustly associated with the mRNA levels of GSDMA (P = 3.55 × 10(-151)). The genetic-gene expression network identified the SOCS3 pathway as one of the key drivers of asthma. The eQTLs and gene networks identified in this study are powerful tools for elucidating the causal mechanisms underlying pulmonary disease. This data resource offers much-needed support to pinpoint the causal genes and characterize the molecular function of gene variants associated with lung diseases

Myocardial fibrosis as an early manifestation of hypertrophic cardiomyopathy

BACKGROUND: Myocardial fibrosis is a hallmark of hypertrophic cardiomyopathy and a proposed substrate for arrhythmias and heart failure. In animal models, profibrotic genetic pathways are activated early, before hypertrophic remodeling. Data showing early profibrotic responses to sarcomere-gene mutations in patients with hypertrophic cardiomyopathy are lacking. METHODS: We used echocardiography, cardiac magnetic resonance imaging (MRI), and serum biomarkers of collagen metabolism, hemodynamic stress, and myocardial injury to evaluate subjects with hypertrophic cardiomyopathy and a confirmed genotype. RESULTS: The study involved 38 subjects with pathogenic sarcomere mutations and overt hypertrophic cardiomyopathy, 39 subjects with mutations but no left ventricular hypertrophy, and 30 controls who did not have mutations. Levels of serum C-terminal propeptide of type I procollagen (PICP) were significantly higher in mutation carriers without left ventricular hypertrophy and in subjects with overt hypertrophic cardiomyopathy than in controls (31% and 69% higher, respectively; P<0.001). The ratio of PICP to C-terminal telopeptide of type I collagen was increased only in subjects with overt hypertrophic cardiomyopathy, suggesting that collagen synthesis exceeds degradation. Cardiac MRI studies showed late gadolinium enhancement, indicating myocardial fibrosis, in 71% of subjects with overt hypertrophic cardiomyopathy but in none of the mutation carriers without left ventricular hypertrophy. CONCLUSIONS: Elevated levels of serum PICP indicated increased myocardial collagen synthesis in sarcomere-mutation carriers without overt disease. This profibrotic state preceded the development of left ventricular hypertrophy or fibrosis visible on MRI. (Funded by the National Institutes of Health and others.

Modelling the structural, functional and phenotypic consequences of protein coding mutations

Proteins are integral to all cellular processes and underpin the function of all extant organisms, meaning variants impacting them are a primary cause of phenotypic variation. Protein coding variants are a key area of study in biology, with relevance from structural and molecular biology to population genetics. They are also medically important, impacting inherited genetic diseases, cancer and response to pathogens. Recent advances in highthroughput experimental techniques have opened the door to many new approaches in biology, and protein variants are no exception. Deep mutational scanning experiments exhaustively measure the fitness of variants in a protein, which gives us more experimentally validated mutational consequence measurements than ever before. Such advances, together with ever larger sequence and structure databases, have created an opportunity to apply large scale analyses to coding variation, studying the effect on protein structure, function and phenotype. In this thesis I perform three large scale variant analyses. First, I use the consequences of variation to learn about protein structure and function. I compile a dataset from 28 deep mutational scanning studies, covering 6291 positions in 30 proteins, and use the consequences of mutation at each position to define a mutational landscape. I show rich biophysical relationships in this landscape and identify functionally distinct positional subtypes of each amino acid. In the second analysis, I explore genotype to phenotype prediction using a dataset of 1011 S. cerevisiae strains, with genotypes, transcriptomics, proteomics and measured phenotypes, and comprehensive gene deletions in four strains. I show knowledge-based models of mutational consequences and pathway function can be used to associate genes with phenotypes and predict growth phenotypes across 34 growth conditions. However, genetic background is found to have a large effect on variant consequences, to such an extent that the same deletion can be highly significant in one strain and have no effect in another. Finally, I analyse computational variant effect prediction, benchmarking current predictors using deep mutational scanning data. I then develop a new end-to-end deep convolutional neural network predictor that predicts consequences directly from sequence and structure and show it improves on current methods. Together these projects advance our knowledge of protein coding variation and enhance our capacity to link variation to impacts on structure, function and phenotype