You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases 
Chapter Title:  Other Ways of Establishing Gene-Disease Associations 
1. Abstract of Cited Paper (BIB001):  The mouse has long been an important model for the study of human genetic disease. Through the application of genetic engineering and mutagenesis techniques, the number of unique mutant mouse models and the amount of phenotypic data describing them are growing exponentially. Describing phenotypes of mutant mice in a computationally useful manner that will facilitate data mining is a major challenge for bioinformatics. Here we describe a tool, the Mammalian Phenotype Ontology (MP), for classifying and organizing phenotypic information related to the mouse and other mammalian species. The MP Ontology has been applied to mouse phenotype descriptions in the Mouse Genome Informatics Database (MGI, http://www.informatics.jax.org/), the Rat Genome Database (RGD, http://rgd.mcw.edu), the Online Mendelian Inheritance in Animals (OMIA, http://omia.angis.org.au/) and elsewhere. Use of this ontology allows comparisons of data from diverse sources, can facilitate comparisons across mammalian species, assists in identifying appropriate experimental disease models, and aids in the discovery of candidate disease genes and molecular signaling pathways. 
2. Abstract of Cited Paper (BIB002):  The first decade of Genome Wide Association Studies (GWAS) has uncovered a wealth of disease-associated variants. Two important derivations will be the translation of this information into a multiscale understanding of pathogenic variants, and leveraging existing data to increase the power of existing and future studies through prioritization. We explore edge prediction on heterogeneous networks—graphs with multiple node and edge types—for accomplishing both tasks. First we constructed a network with 18 node types—genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures database) collections—and 19 edge types from high-throughput publicly-available resources. From this network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the topology between specific genes and diseases. Next, we trained a model from GWAS associations and predicted the probability of association between each protein-coding gene and each of 29 well-studied complex diseases. The model, which achieved 132-fold enrichment in precision at 10% recall, outperformed any individual domain, highlighting the benefit of integrative approaches. We identified pleiotropy, transcriptional signatures of perturbations, pathways, and protein interactions as fundamental mechanisms explaining pathogenesis. Our method successfully predicted the results (with AUROC = 0.79) from a withheld multiple sclerosis (MS) GWAS despite starting with only 13 previously associated genes. Finally, we combined our network predictions with statistical evidence of association to propose four novel MS genes, three of which (JAK2, REL, RUNX3) validated on the masked GWAS. Furthermore, our predictions provide biological support highlighting REL as the causal gene within its gene-rich locus. Users can browse all predictions online (http://het.io). Heterogeneous network edge prediction effectively prioritized genetic associations and provides a powerful new approach for data integration across multiple domains. Author Summary For complex human diseases, identifying the genes harboring susceptibility variants has taken on medical importance. Disease-associated genes provide clues for elucidating disease etiology, predicting disease risk, and highlighting therapeutic targets. Here, we develop a method to predict whether a given gene and disease are associated. To capture the multitude of biological entities underlying pathogenesis, we constructed a heterogeneous network, containing multiple node and edge types. We built on a technique developed for social network analysis, which embraces disparate sources of data to make predictions from heterogeneous networks. Using the compendium of associations from genome-wide studies, we learned the influential mechanisms underlying pathogenesis. Our findings provide a novel perspective about the existence of pervasive pleiotropy across complex diseases. Furthermore, we suggest transcriptional signatures of perturbations are an underutilized resource amongst prioritization approaches. For multiple sclerosis, we demonstrated our ability to prioritize future studies and discover novel susceptibility genes. Researchers can use these predictions to increase the statistical power of their studies, to suggest the causal genes from a set of candidates, or to generate evidence-based experimental hypothesis. 
3. Abstract of Cited Paper (BIB003):  In the post-genomic era, the elucidation of the relationship between the molecular origins of diseases and their resulting phenotypes is a crucial task for medical research. Here, we use a large-scale biomedical literature database to construct a symptom-based human disease network and investigate the connection between clinical manifestations of diseases and their underlying molecular interactions. We find that the symptom-based similarity of two diseases correlates strongly with the number of shared genetic associations and the extent to which their associated proteins interact. Moreover, the diversity of the clinical manifestations of a disease can be related to the connectivity patterns of the underlying protein interaction network. The comprehensive, high-quality map of disease-symptom relations can further be used as a resource helping to address important questions in the field of systems medicine, for example, the identification of unexpected associations between diseases, disease etiology research or drug design. 
4. Abstract of Cited Paper (BIB004):  Cardiovascular disease is the most common cause of death worldwide, and hypertension is the major risk factor. Mendelian hypertension elucidates mechanisms of blood pressure regulation. Here we report six missense mutations in PDE3A (encoding phosphodiesterase 3A) in six unrelated families with mendelian hypertension and brachydactyly type E (HTNB). The syndrome features brachydactyly type E (BDE), severe salt-independent but age-dependent hypertension, an increased fibroblast growth rate, neurovascular contact at the rostral-ventrolateral medulla, altered baroreflex blood pressure regulation and death from stroke before age 50 years when untreated. In vitro analyses of mesenchymal stem cell-derived vascular smooth muscle cells (VSMCs) and chondrocytes provided insights into molecular pathogenesis. The mutations increased protein kinase A-mediated PDE3A phosphorylation and resulted in gain of function, with increased cAMP-hydrolytic activity and enhanced cell proliferation. Levels of phosphorylated VASP were diminished, and PTHrP levels were dysregulated. We suggest that the identified PDE3A mutations cause the syndrome. VSMC-expressed PDE3A deserves scrutiny as a therapeutic target for the treatment of hypertension. 
5. Abstract of Cited Paper (BIB005):  The Human Phenotype Ontology (HPO) is widely used in the rare disease community for differential diagnostics, phenotype-driven analysis of next-generation sequence-variation data, and translational research, but a comparable resource has not been available for common disease. Here, we have developed a concept-recognition procedure that analyzes the frequencies of HPO disease annotations as identified in over five million PubMed abstracts by employing an iterative procedure to optimize precision and recall of the identified terms. We derived disease models for 3,145 common human diseases comprising a total of 132,006 HPO annotations. The HPO now comprises over 250,000 phenotypic annotations for over 10,000 rare and common diseases and can be used for examining the phenotypic overlap among common diseases that share risk alleles, as well as between Mendelian diseases and common diseases linked by genomic location. The annotations, as well as the HPO itself, are freely available. 
6. Abstract of Cited Paper (BIB006):  The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) provides information about interactions between chemicals and gene products, and their relationships to diseases. Core CTD content (chemical-gene, chemical-disease and gene-disease interactions manually curated from the literature) are integrated with each other as well as with select external datasets to generate expanded networks and predict novel associations. Today, core CTD includes more than 30.5 million toxicogenomic connections relating chemicals/drugs, genes/proteins, diseases, taxa, Gene Ontology (GO) annotations, pathways, and gene interaction modules. In this update, we report a 33% increase in our core data content since 2015, describe our new exposure module (that harmonizes exposure science information with core toxicogenomic data) and introduce a novel dataset of GO-disease inferences (that identify common molecular underpinnings for seemingly unrelated pathologies). These advancements centralize and contextualize real-world chemical exposures with molecular pathways to help scientists generate testable hypotheses in an effort to understand the etiology and mechanisms underlying environmentally influenced diseases. 
7. Abstract of Cited Paper (BIB007):  Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology. 
8. Abstract of Cited Paper (BIB008):  The principles of genetics apply across the whole tree of life: on a cellular level, we share mechanisms with species from which we diverged millions or even billions of years ago. We can exploit this common ancestry at the level of sequences, but also in terms of observable outcomes (phenotypes), to learn more about health and disease for humans and all other species. Applying the range of available knowledge to solve challenging disease problems requires unified data relating genomics, phenotypes, and disease; it also requires computational tools that leverage these multimodal data to inform interpretations by geneticists and to suggest experiments. However, the distribution and heterogeneity of databases is a major impediment: databases tend to focus either on a single data type across species, or on single species across data types. Although each database provides rich, high-quality information, no single one provides unified data that is comprehensive across species, biological scales, and data types. Without a big-picture view of the data, many questions in genetics are difficult or impossible to answer. The Monarch Initiative (https://monarchinitiative.org) is an international consortium dedicated to providing computational tools that leverage a computational representation of phenotypic data for genotype-phenotype analysis, genomic diagnostics, and precision medicine on the basis of a large-scale platform of multimodal data that is deeply integrated across species and covering broad areas of disease.

Literature Review:
In addition to gene-disease associations identified in case-control experiments, some indirect associations can be inferred through a third biological entity, utilizing guilt-by-association principle. The next paragraph discusses indirect gene-disease associations mediated by known diseases genes in deep details. Moreover, indirect gene-disease associations can be mediated by biological entities other than genes: chemicals BIB007 , tissues BIB003 , and other diseases and phenotypes. For example, a gene targeted by a drug used to treat the disease, it is likely to participate in the mechanism of the disease. The same may be true for targets of drugs and toxic compounds are known to cause negative effects e.g. adverse reactions, similar to the disease. Such gene-disease associations inferred from curated pairwise associations with chemicals, which can be obtained, for example, from Comparative Toxicogenomic Database BIB007 (CTD, http://ctdbase.org/). Similarly, putative gene-disease associations can be established through the second disease, via disease correlation BIB001 or symptom similarities BIB004 . Since co-occurring diseases tend to have more shared genes, than expected by chance BIB001 , at least some of genes, known to be associated with one disease may be involved into the other disease. The same is true for diseases demonstrating phenotype similarities BIB004 . Each human disease can be characterized by a specific combination of multiple phenotypes and, in turn, some phenotypic abnormalities can manifest in many diseases. Thus, brachydactyly syndrome (OMIM:#112410), characterized by age-dependent hypertension, shortening of both phalanges and many other abnormalities BIB005 . At the same time, hypertension is a symptom of many other disorders, e.g. #613677, #614495, #500005, #218030, #602531. In order to annotate diseases with phenotypes and calculate phenotypic similarities between diseases, unified ontologies for diseases  and phenotypes  , BIB006 , BIB008 were developed. Human Phenotype Ontology (HPO) project also provides the results of semiautomatic mapping between phenotypes, disease, and genes BIB006 . Recent the Monarch Initiative BIB009 allows comparing human phenotypes with animal phenotypes BIB002 with a known genetic basis.