Quantitative comparison of mapping methods between Human and Mammalian Phenotype Ontology

Researchers use animal studies to better understand human diseases. In recent years, large-scale phenotype studies such as Phenoscape and EuroPhenome have been initiated to identify genetic causes of a species' phenome. Species-specific phenotype ontologies are required to capture and report about all findings and to automatically infer results relevant to human diseases. The integration of the different phenotype ontologies into a coherent framework is necessary to achieve interoperability for cross-species research. Here, we investigate the quality and completeness of two different methods to align the Human Phenotype Ontology and the Mammalian Phenotype Ontology. The first method combines lexical matching with inference over the ontologies' taxonomic structures, while the second method uses a mapping algorithm based on the formal definitions of the ontologies. Neither method could map all concepts. Despite the formal definitions method provides mappings for more concepts than does the lexical matching method, it does not outperform the lexical matching in a biological use case. Our results suggest that combining both approaches will yield a better mappings in terms of completeness, specificity and application purposes

Mapping genetic interactions in cancer: a road to rational combination therapies.

The discovery of synthetic lethal interactions between poly (ADP-ribose) polymerase (PARP) inhibitors and BRCA genes, which are involved in homologous recombination, led to the approval of PARP inhibition as a monotherapy for patients with BRCA1/2-mutated breast or ovarian cancer. Studies following the initial observation of synthetic lethality demonstrated that the reach of PARP inhibitors is well beyond just BRCA1/2 mutants. Insights into the mechanisms of action of anticancer drugs are fundamental for the development of targeted monotherapies or rational combination treatments that will synergize to promote cancer cell death and overcome mechanisms of resistance. The development of targeted therapeutic agents is premised on mapping the physical and functional dependencies of mutated genes in cancer. An important part of this effort is the systematic screening of genetic interactions in a variety of cancer types. Until recently, genetic-interaction screens have relied either on the pairwise perturbations of two genes or on the perturbation of genes of interest combined with inhibition by commonly used anticancer drugs. Here, we summarize recent advances in mapping genetic interactions using targeted, genome-wide, and high-throughput genetic screens, and we discuss the therapeutic insights obtained through such screens. We further focus on factors that should be considered in order to develop a robust analysis pipeline. Finally, we discuss the integration of functional interaction data with orthogonal methods and suggest that such approaches will increase the reach of genetic-interaction screens for the development of rational combination therapies

Automated data integration for developmental biological research

In an era exploding with genome-scale data, a major challenge for developmental biologists is how to extract significant clues from these publicly available data to benefit our studies of individual genes, and how to use them to improve our understanding of development at a systems level. Several studies have successfully demonstrated new approaches to classic developmental questions by computationally integrating various genome-wide data sets. Such computational approaches have shown great potential for facilitating research: instead of testing 20,000 genes, researchers might test 200 to the same effect. We discuss the nature and state of this art as it applies to developmental research

Functional Genomics Complements Quantitative Genetics in Identifying Disease-Gene Associations

An ultimate goal of genetic research is to understand the connection between genotype and phenotype in order to improve the diagnosis and treatment of diseases. The quantitative genetics field has developed a suite of statistical methods to associate genetic loci with diseases and phenotypes, including quantitative trait loci (QTL) linkage mapping and genome-wide association studies (GWAS). However, each of these approaches have technical and biological shortcomings. For example, the amount of heritable variation explained by GWAS is often surprisingly small and the resolution of many QTL linkage mapping studies is poor. The predictive power and interpretation of QTL and GWAS results are consequently limited. In this study, we propose a complementary approach to quantitative genetics by interrogating the vast amount of high-throughput genomic data in model organisms to functionally associate genes with phenotypes and diseases. Our algorithm combines the genome-wide functional relationship network for the laboratory mouse and a state-of-the-art machine learning method. We demonstrate the superior accuracy of this algorithm through predicting genes associated with each of 1157 diverse phenotype ontology terms. Comparison between our prediction results and a meta-analysis of quantitative genetic studies reveals both overlapping candidates and distinct, accurate predictions uniquely identified by our approach. Focusing on bone mineral density (BMD), a phenotype related to osteoporotic fracture, we experimentally validated two of our novel predictions (not observed in any previous GWAS/QTL studies) and found significant bone density defects for both Timp2 and Abcg8 deficient mice. Our results suggest that the integration of functional genomics data into networks, which itself is informative of protein function and interactions, can successfully be utilized as a complementary approach to quantitative genetics to predict disease risks. All supplementary material is available at http://cbfg.jax.org/phenotype

1st INCF Workshop on Genetic Animal Models for Brain Diseases

The INCF Secretariat organized a workshop to focus on the “role of neuroinformatics in the processes of building, evaluating, and using genetic animal models for brain diseases” in Stockholm, December 13–14, 2009. Eight scientists specialized in the fields of neuroinformatics, database, ontologies, and brain disease participated together with two representatives of the National Institutes of Health and the European Union, as well as three observers of the national INCF nodes of Norway, Poland, and the United Kingdom

Modelling genetic networks involved in the activity-dependent modulation of adult neurogenesis

Die Bildung neuen Nervenzellen im erwachsenen Gehirn—adulte Neurogenese—ist bei Säugetieren auf spezifische Regionen beschränkt. Eine der beiden bekannten ist der Hippokampus, eine Gehirnstruktur, die eine wichtige Rolle beim Lernen sowie der Gedächtnisbildung spielt. Ein Reservoir von neuralen Stammzellen befindet sich in der subgranulären Zone des hippokampalen Gyrus dentatus. Diese Zellen teilen sich fortwährend und bilden neue Nervenzellen. Die Regulation adulter hippokampaler Neurogenese wird sowohl von der Umgebung beeinflusst als auch von mehreren Genen gesteuert. In der vorliegenden Arbeit wurden mittels Hochdurchsatz- Genexpressionsverfahren die an der Neurogenese beteiligten Gene identifiziert und ihr Zusammenspiel untersucht. Anhand von genetischen, umgebungsbedingten und zeitlichen Angaben und Variationen wurde ein vielseitiger Datensatz erstellt, der einen multidimensionalen Blick auf den proliferativen Phänotyp verschafft. Netzwerke aus Gen-Gen und Gen-Phänotyp Interaktionen wurden beschrieben und in einer mehrschichtigen Ressource zusammengefasst. Ein Kern-Netzwerk bestehend aus immerwiederkehrenden Modulen aus verschiedenen Ebenen wurde anhand von Proliferation als Keim-Phänotyp identifiziert. Aus diesem Kern-Netzwerk sind neue Gene und ihre Interaktionen hervorgegangen, die potentiell bei der Regulierung adulter Neurogenesis beteiligt sind.:Zusammenfassung i Abstract iii Acknowledgements vii Contents ix Preface xiii General Introduction 1 Adult Neurogenesis 1 Historical setting 1 Neurogenesis exists in two regions of the adult mammalian brain 1 Implications of neurogenesis in the hippocampus 1 The Hippocampal Formation 2 Function of the hippocampus in learning and memory 2 The functional role of adult neurogenesis 2 Anatomy of the hippocampal formation 2 Neural Precursor Biology 3 The subgranular zone as a neurogenic niche 3 Neuronal maturation is a multi-step pathway 3 Regulation of Adult Neurogenesis 3 Neurogenesis is modulated by age 3 Neurogenesis is modulated by environmental factors 4 Neurogenesis is modulated by genetic background 4 Genetics of the BXD RI Cross 5 C57BL/6 and DBA/2 5 Recombinant Inbred Lines 5 The BXD panel 6 Quantitative genetics 6 Microarray Analysis 7 The concept of ‘whole genome’ expression analysis 7 Technical considerations 8 Theoretical considerations 9 Current Analytical Methods 9 Network Analysis 10 Network Description and Terminology 10 Graph Theory 10 Multiple-Network Comparison 11 Biological networks 11 Types of Biological Network 11 Sources of Network Data 12 Biological Significance of Networks 12 Aim of the current work 13 Methods and Materials 15 Animals 15 BXD panel 15 Progenitor strains 15 Animal behaviour 15 Running wheel activity 15 Enriched environment 16 Morris water maze 16 Open field test 16 Corticosterone assay 16 Histology 17 Tissue collection 17 BrdU staining 17 Statistics 17 Cell culture 18 Maintenance and differentiation 18 Immunostaining 18 RNA isolation 18 Microarray processing 18 Affymetrix arrays 18 M430v2 probe reannotation 19 Illumina arrays 19 Illumina probe reannotation 19 Bioinformatics 19 Translating the STRING network 19 QTL mapping 20 Network graph layout 20 Triplot 20 Enrichment analysis 20 Mammalian Adult Neurogenesis Gene Ontology 21 Introduction 21 Results 25 The cell stage ontology 25 The process ontology 25 Genes known to regulate hippocampal adult neurogenesis 26 Enrichment analysis 27 The MANGO gene network 27 Discussion 28 Hippocampal Coexpression Networks from the BXD Panel 31 Introduction 31 Results 32 Variation and covariation of gene expression across a panel of inbred lines 32 A hippocampal expression correlation network 32 Diverse neurogenesis phenotypes associate with discrete transcript networks 34 Discussion 34 Interactions Between Gene Expression Phenotypes and Genotype 37 Introduction 37 Results 39 QTL analysis and interval definitions 39 Pleiotropic loci and ‘trans-bands’ 39 Transcript expression proxy-QTLs can help in dissection of complex phenotypes 41 Interaction network 43 Discussion 43 Strain-Dependent Effects of Environment 47 Introduction 47 Results 48 Effects of strain and environment on precursor cell proliferation 48 Effects of strain and environment on learning behaviour 52 Transcript expression associated with different housing environments 53 Strain differences in transcript regulation 55 Distance-weighted coexpression networks 57 Discussion 58 Expression Time Course from Differentiating Cell Culture 61 Introduction 61 Results 63 Differentiation of proliferating precursors into neurons in vitro 63 Transcripts associated with stages of differentiation 63 Early events in NPC differentiation 64 A network of transcript coexpression during in vitro differentiation 66 Discussion 67 Integrated Gene Interaction Networks 71 Introduction 71 Results 72 Description of network layers 72 Merging of network layers to a multigraph 74 A network of genes controls neural precursor proliferation in the adult hippocampus 75 Novel candidate regulators of adult hippocampal neurogenesis 77 Novel pathways regulating adult hippocampal neurogenesis 77 Discussion 79 General Discussion 81 References 89 Selbständigkeitserklärung 107Neurogenesis, the production of new neurons, is restricted in the adult brain of mammals to only a few regions. One of these sites of adult neurogenesis is the hippocampus, a structure essential for many types of learning. A pool of stem cells is maintained in the subgranular zone of the hippocampal dentate gyrus which proliferate and can differentiate into new neurons, astrocytes and oligodendroctytes. Regulation of adult hippocampal neurogenesis occurs in response to en- vironmental stimuli and is under the control of many genes. This work employs high-throughput gene expression technologies to identify these genes and their interactions with each other and the neurogenesis phenotype. Harnessing variation from genetic, environmental and temporal sources, a multi-faceted dataset has been generated which offers a multidimensional view of the neural precursor proliferation phenotype. Networks of gene-gene and gene-phenotype interac- tions have been described and merged into a multilayer resource. A core subnetwork derived from modules recurring in the different layers has been identified using the proliferation phenotype as a seed. This subnetwork has suggested novel genes and interactions potentially involved in the regulation of adult hippocampal neurogenesis.:Zusammenfassung i Abstract iii Acknowledgements vii Contents ix Preface xiii General Introduction 1 Adult Neurogenesis 1 Historical setting 1 Neurogenesis exists in two regions of the adult mammalian brain 1 Implications of neurogenesis in the hippocampus 1 The Hippocampal Formation 2 Function of the hippocampus in learning and memory 2 The functional role of adult neurogenesis 2 Anatomy of the hippocampal formation 2 Neural Precursor Biology 3 The subgranular zone as a neurogenic niche 3 Neuronal maturation is a multi-step pathway 3 Regulation of Adult Neurogenesis 3 Neurogenesis is modulated by age 3 Neurogenesis is modulated by environmental factors 4 Neurogenesis is modulated by genetic background 4 Genetics of the BXD RI Cross 5 C57BL/6 and DBA/2 5 Recombinant Inbred Lines 5 The BXD panel 6 Quantitative genetics 6 Microarray Analysis 7 The concept of ‘whole genome’ expression analysis 7 Technical considerations 8 Theoretical considerations 9 Current Analytical Methods 9 Network Analysis 10 Network Description and Terminology 10 Graph Theory 10 Multiple-Network Comparison 11 Biological networks 11 Types of Biological Network 11 Sources of Network Data 12 Biological Significance of Networks 12 Aim of the current work 13 Methods and Materials 15 Animals 15 BXD panel 15 Progenitor strains 15 Animal behaviour 15 Running wheel activity 15 Enriched environment 16 Morris water maze 16 Open field test 16 Corticosterone assay 16 Histology 17 Tissue collection 17 BrdU staining 17 Statistics 17 Cell culture 18 Maintenance and differentiation 18 Immunostaining 18 RNA isolation 18 Microarray processing 18 Affymetrix arrays 18 M430v2 probe reannotation 19 Illumina arrays 19 Illumina probe reannotation 19 Bioinformatics 19 Translating the STRING network 19 QTL mapping 20 Network graph layout 20 Triplot 20 Enrichment analysis 20 Mammalian Adult Neurogenesis Gene Ontology 21 Introduction 21 Results 25 The cell stage ontology 25 The process ontology 25 Genes known to regulate hippocampal adult neurogenesis 26 Enrichment analysis 27 The MANGO gene network 27 Discussion 28 Hippocampal Coexpression Networks from the BXD Panel 31 Introduction 31 Results 32 Variation and covariation of gene expression across a panel of inbred lines 32 A hippocampal expression correlation network 32 Diverse neurogenesis phenotypes associate with discrete transcript networks 34 Discussion 34 Interactions Between Gene Expression Phenotypes and Genotype 37 Introduction 37 Results 39 QTL analysis and interval definitions 39 Pleiotropic loci and ‘trans-bands’ 39 Transcript expression proxy-QTLs can help in dissection of complex phenotypes 41 Interaction network 43 Discussion 43 Strain-Dependent Effects of Environment 47 Introduction 47 Results 48 Effects of strain and environment on precursor cell proliferation 48 Effects of strain and environment on learning behaviour 52 Transcript expression associated with different housing environments 53 Strain differences in transcript regulation 55 Distance-weighted coexpression networks 57 Discussion 58 Expression Time Course from Differentiating Cell Culture 61 Introduction 61 Results 63 Differentiation of proliferating precursors into neurons in vitro 63 Transcripts associated with stages of differentiation 63 Early events in NPC differentiation 64 A network of transcript coexpression during in vitro differentiation 66 Discussion 67 Integrated Gene Interaction Networks 71 Introduction 71 Results 72 Description of network layers 72 Merging of network layers to a multigraph 74 A network of genes controls neural precursor proliferation in the adult hippocampus 75 Novel candidate regulators of adult hippocampal neurogenesis 77 Novel pathways regulating adult hippocampal neurogenesis 77 Discussion 79 General Discussion 81 References 89 Selbständigkeitserklärung 10

Improving Disease Gene Prioritization by Comparing the Semantic Similarity of Phenotypes in Mice with Those of Human Diseases

Despite considerable progress in understanding the molecular origins of hereditary human diseases, the molecular basis of several thousand genetic diseases still remains unknown. High-throughput phenotype studies are underway to systematically assess the phenotype outcome of targeted mutations in model organisms. Thus, comparing the similarity between experimentally identified phenotypes and the phenotypes associated with human diseases can be used to suggest causal genes underlying a disease. In this manuscript, we present a method for disease gene prioritization based on comparing phenotypes of mouse models with those of human diseases. For this purpose, either human disease phenotypes are “translated” into a mouse-based representation (using the Mammalian Phenotype Ontology), or mouse phenotypes are “translated” into a human-based representation (using the Human Phenotype Ontology). We apply a measure of semantic similarity and rank experimentally identified phenotypes in mice with respect to their phenotypic similarity to human diseases. Our method is evaluated on manually curated and experimentally verified gene–disease associations for human and for mouse. We evaluate our approach using a Receiver Operating Characteristic (ROC) analysis and obtain an area under the ROC curve of up to . Furthermore, we are able to confirm previous results that the Vax1 gene is involved in Septo-Optic Dysplasia and suggest Gdf6 and Marcks as further potential candidates. Our method significantly outperforms previous phenotype-based approaches of prioritizing gene–disease associations. To enable the adaption of our method to the analysis of other phenotype data, our software and prioritization results are freely available under a BSD licence at http://code.google.com/p/phenomeblast/wiki/CAMP. Furthermore, our method has been integrated in PhenomeNET and the results can be explored using the PhenomeBrowser at http://phenomebrowser.net

Detection of regulator genes and eQTLs in gene networks

Genetic differences between individuals associated to quantitative phenotypic traits, including disease states, are usually found in non-coding genomic regions. These genetic variants are often also associated to differences in expression levels of nearby genes (they are "expression quantitative trait loci" or eQTLs for short) and presumably play a gene regulatory role, affecting the status of molecular networks of interacting genes, proteins and metabolites. Computational systems biology approaches to reconstruct causal gene networks from large-scale omics data have therefore become essential to understand the structure of networks controlled by eQTLs together with other regulatory genes, and to generate detailed hypotheses about the molecular mechanisms that lead from genotype to phenotype. Here we review the main analytical methods and softwares to identify eQTLs and their associated genes, to reconstruct co-expression networks and modules, to reconstruct causal Bayesian gene and module networks, and to validate predicted networks in silico.Comment: minor revision with typos corrected; review article; 24 pages, 2 figure