Functional Relationship Networks Research Articles

Genome-wide Association Studies of REST Gene Associated Neurological Diseases/traits with Related Single Nucleotide Polymorphisms.

Genome-wide association studies (GWAS) have been used to explore the connections between genotypes and phenotypes by comparing the genotype frequencies of genetic changes in individuals with similar origins but distinct traits. The aim is to employ the GWAS catalog to identify and investigate the various correlations between genotypes and phenotypes of the REST gene. In this study, we utilized a large dataset of GWAS comprising 62,218,976 individuals in 112 studies and 122 associations with 122 traits (www.ebi.ac.uk/gwas/genes/REST) from European, Asian, Hispanic, African ancestry up to 28 February 2023. Protein-association network evaluation and gene ontology enrichment study was utilized to evaluate the biological function of the discovered gene modules. We identified several associations for both neurodevelopmental and neurodegenerative disorders linked to REST, as well as its mapped gene modules and their functional relationship networks. This work offers fresh insights into identifying risk loci of neurological disorders caused by REST.

Identification of Alzheimer associated differentially expressed gene through microarray data and transfer learning-based image analysis

Major factors contribute to mental stress and enhance the progression of late-onset Alzheimer’s disease (AD). The factors that lead to neurodegeneration, such as tau protein hyperphosphorylation and increased amyloid-beta production, can be mimicked in animal stress models. The present study identifies differentially expressed genes (DEGs) data and its corresponding predictive image analysis in rat models. The gene expression profile of GSE72062, GSE85162, GSE143951 and GSE85238 was downloaded from NCBI, GEO archive to analyse DEGs. Functional enrichment and pathway relationship networks, gene signal, protein interaction and micro-RNA interaction DEGs networks were constructed and investigated. The image analysis of histopathological slides of rat brain images corresponding to AD microarray-based DEGs profile was undertaken using the convolution neural networks (ConvNets) model. Enrichment of network in terms of GO concluded with 10 DEGs, namely ARHGAP32, GNA11, NR5A1, GNAT3, FOSL1, HELZ2, NMUR2, BDKRB1, RPL3L and RPL39L as potential gene targets to control neurodegeneration and progression of sporadic AD. The image analysis of AD microarray-based DEGs profile builds a successful predictive model of 89% and 61% training and test accuracy with a minimum of 2.480% loss using transfer learning, VGG16 model. Interestingly, the ARHGAP32 gene, a Rho GTPase activating class, was identified to have a functional relationship with two significant genes BCL2 and MMP9, that are well explored in AD. The current investigation upgrades the traditional pre-clinical AD research using microarray data analysis and ConvNets. The model successfully predicts DEG from histopathology slides of rat brain samples, paving the way for image analysis to determine the underlying molecular makeup of the test samples.

Brain-specific functional relationship networks inform autism spectrum disorder gene prediction

Autism spectrum disorder (ASD) is a neuropsychiatric disorder with strong evidence of genetic contribution, and increased research efforts have resulted in an ever-growing list of ASD candidate genes. However, only a fraction of the hundreds of nominated ASD-related genes have identified de novo or transmitted loss of function (LOF) mutations that can be directly attributed to the disorder. For this reason, a means of prioritizing candidate genes for ASD would help filter out false-positive results and allow researchers to focus on genes that are more likely to be causative. Here we constructed a machine learning model by leveraging a brain-specific functional relationship network (FRN) of genes to produce a genome-wide ranking of ASD risk genes. We rigorously validated our gene ranking using results from two independent sequencing experiments, together representing over 5000 simplex and multiplex ASD families. Finally, through functional enrichment analysis on our highly prioritized candidate gene network, we identified a small number of pathways that are key in early neural development, providing further support for their potential role in ASD.

Machine Learning Analysis Identifies Drosophila Grunge/Atrophin as an Important Learning and Memory Gene Required for Memory Retention and Social Learning.

High-throughput experiments are becoming increasingly common, and scientists must balance hypothesis-driven experiments with genome-wide data acquisition. We sought to predict novel genes involved in Drosophila learning and long-term memory from existing public high-throughput data. We performed an analysis using PILGRM, which analyzes public gene expression compendia using machine learning. We evaluated the top prediction alongside genes involved in learning and memory in IMP, an interface for functional relationship networks. We identified Grunge/Atrophin (Gug/Atro), a transcriptional repressor, histone deacetylase, as our top candidate. We find, through multiple, distinct assays, that Gug has an active role as a modulator of memory retention in the fly and its function is required in the adult mushroom body. Depletion of Gug specifically in neurons of the adult mushroom body, after cell division and neuronal development is complete, suggests that Gug function is important for memory retention through regulation of neuronal activity, and not by altering neurodevelopment. Our study provides a previously uncharacterized role for Gug as a possible regulator of neuronal plasticity at the interface of memory retention and memory extinction.

Context-sensitive network-based disease genetics prediction and its implications in drug discovery.

Disease phenotype networks play an important role in computational approaches to identifying new disease-gene associations. Current disease phenotype networks often model disease relationships based on pairwise similarities, therefore ignore the specific context on how two diseases are connected. In this study, we propose a new strategy to model disease associations using context-sensitive networks (CSNs). We developed a CSN-based phenome-driven approach for disease genetics prediction, and investigated the translational potential of the predicted genes in drug discovery. We constructed CSNs by directly connecting diseases with associated phenotypes. Here, we constructed two CSNs using different data sources; the two networks contain 26 790 and 13 822 nodes respectively. We integrated the CSNs with a genetic functional relationship network and predicted disease genes using a network-based ranking algorithm. For comparison, we built Similarity-Based disease Networks (SBN) using the same disease phenotype data. In a de novo cross validation for 3324 diseases, the CSN-based approach significantly increased the average rank from top 12.6 to top 8.8% for all tested genes comparing with the SBN-based approach ( p<e-22 ). The area under the receiver operating characteristic curve for the CSN approach was also significantly higher than the SBN approach (0.91 versus 0.87, p<e-3 ). In addition, we predicted genes for Parkinson's disease using CSNs, and demonstrated that the top-ranked genes are highly relevant to PD pathologenesis. We pin-pointed a top-ranked drug target gene for PD, and found its association with neurodegeneration supported by literature. In summary, CSNs lead to significantly improve the disease genetics prediction comparing with SBNs and provide leads for potential drug targets. nlp.case.edu/public/data/. rxx@case.edu.

COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer's disease.

We present COMPASS, a COmputational Model to Predict the development of Alzheimer’s diSease Spectrum, to model Alzheimer’s disease (AD) progression. This was the best-performing method in recent crowdsourcing benchmark study, DREAM Alzheimer’s Disease Big Data challenge to predict changes in Mini-Mental State Examination (MMSE) scores over 24-months using standardized data. In the present study, we conducted three additional analyses beyond the DREAM challenge question to improve the clinical contribution of our approach, including: (1) adding pre-validated baseline cognitive composite scores of ADNI-MEM and ADNI-EF, (2) identifying subjects with significant declines in MMSE scores, and (3) incorporating SNPs of top 10 genes connected to APOE identified from functional-relationship network. For (1) above, we significantly improved predictive accuracy, especially for the Mild Cognitive Impairment (MCI) group. For (2), we achieved an area under ROC of 0.814 in predicting significant MMSE decline: our model has 100% precision at 5% recall, and 91% accuracy at 10% recall. For (3), “genetic only” model has Pearson’s correlation of 0.15 to predict progression in the MCI group. Even though addition of this limited genetic model to COMPASS did not improve prediction of progression of MCI group, the predictive ability of SNP information extended beyond well-known APOE allele.

Phenome-based gene discovery provides information about Parkinson's disease drug targets.

BackgroundParkinson disease (PD) is a severe neurodegenerative disease without curative drugs. The highly complex and heterogeneous disease mechanisms are still unclear. Detecting novel PD associated genes not only contributes in revealing the disease pathogenesis, but also facilitates discovering new targets for drugs.MethodsWe propose a phenome-based gene prediction strategy to identify disease-associated genes for PD. We integrated multiple disease phenotype networks, a gene functional relationship network, and known PD genes to predict novel candidate genes. Then we investigated the translational potential of the predicted genes in drug discovery.ResultsIn a cross validation analysis, the average rank for 15 known PD genes is within top 0.8 %. We also tested the algorithm with an independent validation set of 669 PD-associated genes detected by genome-wide association studies. The top ranked genes predicted by our approach are enriched for these validation genes. In addition, our approach prioritized the target genes for FDA-approved PD drugs and the drugs that have been tested for PD in clinical trials. Pathway analysis shows that the prioritized drug target genes are closely associated with PD pathogenesis. The result provides empirical evidence that our computational gene prediction approach identifies novel candidate genes for PD, and has the potential to lead to rapid drug discovery.

A Network of Splice Isoforms for the Mouse.

The laboratory mouse is the primary mammalian species used for studying alternative splicing events. Recent studies have generated computational models to predict functions for splice isoforms in the mouse. However, the functional relationship network, describing the probability of splice isoforms participating in the same biological process or pathway, has not yet been studied in the mouse. Here we describe a rich genome-wide resource of mouse networks at the isoform level, which was generated using a unique framework that was originally developed to infer isoform functions. This network was built through integrating heterogeneous genomic and protein data, including RNA-seq, exon array, protein docking and pseudo-amino acid composition. Through simulation and cross-validation studies, we demonstrated the accuracy of the algorithm in predicting isoform-level functional relationships. We showed that this network enables the users to reveal functional differences of the isoforms of the same gene, as illustrated by literature evidence with Anxa6 (annexin a6) as an example. We expect this work will become a useful resource for the mouse genetics community to understand gene functions. The network is publicly available at: http://guanlab.ccmb.med.umich.edu/isoformnetwork.

Genetic Association-Guided Analysis of Gene Networks for the Study of Complex Traits.

The wealth of data measuring genetic variations and complex phenotypes in large cohorts provides a substantial resource, but it also raises significant challenges. The number of loci with strong statistical support for an association with one or more phenotypes has grown, but we are still not able to account for the genetic basis of many common phenotypes. New methods use gene networks to identify additional genes associated with phenotypes. Here we briefly review recently developed methods and categorize them by their analytic approach. We also highlight a factor that can confound network-based approaches: genes with more measured single-nucleotide polymorphisms (SNPs) tend to also be more connected across many types of networks. Finally, we highlight new techniques that use nominally significant, as opposed to genome-wide significant, associations to guide the analysis of functional relationship networks. In contrast with network-based methods that use documented associations to reallocate weight or to adjust P values, these methods do not require documented associations with the underlying phenotype. We provide an example of such techniques, the network-wide association study (NetWAS), and discuss how such methods complement the analytic toolkit available to modern geneticists. We conclude by discussing remaining challenges in the field and new application areas for network-based methods for analysis of association data. In a landmark study in 2005, Klein et al1 reported the discovery of a variant in the complement factor H gene that was associated with age-related macular degeneration via a genome-wide association study (GWAS). Since that time, GWAS has been used to identify variants associated with numerous traits from complex diseases2 to preferences for cilantro.3 Recent work has demonstrated that GWAS discoveries provide a fruitful means to reposition drugs4 and that the presence of a GWAS hit in a drug target is positively associated with the chance that …

Algorithms for modeling global and context-specific functional relationship networks.

Functional genomics has enormous potential to facilitate our understanding of normal and disease-specific physiology. In the past decade, intensive research efforts have been focused on modeling functional relationship networks, which summarize the probability of gene co-functionality relationships. Such modeling can be based on either expression data only or heterogeneous data integration. Numerous methods have been deployed to infer the functional relationship networks, while most of them target the global (non-context-specific) functional relationship networks. However, it is expected that functional relationships consistently reprogram under different tissues or biological processes. Thus, advanced methods have been developed targeting tissue-specific or developmental stage-specific networks. This article brings together the state-of-the-art functional relationship network modeling methods, emphasizes the need for heterogeneous genomic data integration and context-specific network modeling and outlines future directions for functional relationship networks.

Proteomic study reveals a functional network of cancer markers in the G1-Stage of the breast cancer cell cycle.

BackgroundCancer cells are characterized by a deregulated cell cycle that facilitates abnormal proliferation by allowing cells to by-pass tightly regulated molecular checkpoints such as the G1/S restriction point. To facilitate early diagnosis and the identification of new drug targets, current research efforts focus on studies that could lead to the development of protein panels that collectively can improve the effectiveness of our response to the detection of a life-threatening disease.MethodsEstrogen-responsive MCF-7 cells were cultured and arrested by serum deprivation in the G1-stage of the cell cycle, and fractionated into nuclear and cytoplasmic fractions. The protein extracts were trypsinized and analyzed by liquid chromatography - mass spectrometry (MS), and the data were interpreted with the Thermo Electron Bioworks software. Biological characterization of the data, selection of cancer markers, and identification of protein interaction networks was accomplished with a combination of bioinformatics tools provided by GoMiner, DAVID and STRING.ResultsThe objective of this work was to explore via MS proteomic profiling technologies and bioinformatics data mining whether randomly identified cancer markers can be associated with the G1-stage of the cell cycle, i.e., the stage in which cancer cells differ most from normal cells, and whether any functional networks can be identified between these markers and placed in the broader context of cell regulatory pathways. The study enabled the identification of over 2000 proteins and 153 cancer markers, and revealed for the first time that the G1-stage of the cell cycle is not only a rich source of cancer markers, but also a host to an intricate network of functional relationships within the majority of these markers. Three major clusters of interacting proteins emerged: (a) signaling, (b) DNA repair, and (c) oxidative phosphorylation.ConclusionsThe identification of cancer marker regulatory components that act not alone, but within networks, represents an invaluable resource for elucidating the moxlecular mechanisms that govern the uncontrolled proliferation of cancer cells, as well as for catalyzing the development of protein panels with biomarker and drug target potential, screening tests with improved sensitivity and specificity, and novel cancer therapies aimed at pursuing multiple drug targets.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2407-14-710) contains supplementary material, which is available to authorized users.

Modeling dynamic functional relationship networks and application to ex vivo human erythroid differentiation.

Functional relationship networks, which summarize the probability of co-functionality between any two genes in the genome, could complement the reductionist focus of modern biology for understanding diverse biological processes in an organism. One major limitation of the current networks is that they are static, while one might expect functional relationships to consistently reprogram during the differentiation of a cell lineage. To address this potential limitation, we developed a novel algorithm that leverages both differentiation stage-specific expression data and large-scale heterogeneous functional genomic data to model such dynamic changes. We then applied this algorithm to the time-course RNA-Seq data we collected for ex vivo human erythroid cell differentiation. Through computational cross-validation and literature validation, we show that the resulting networks correctly predict the (de)-activated functional connections between genes during erythropoiesis. We identified known critical genes, such as HBD and GATA1, and functional connections during erythropoiesis using these dynamic networks, while the traditional static network was not able to provide such information. Furthermore, by comparing the static and the dynamic networks, we identified novel genes (such as OSBP2 and PDZK1IP1) that are potential drivers of erythroid cell differentiation. This novel method of modeling dynamic networks is applicable to other differentiation processes where time-course genome-scale expression data are available, and should assist in generating greater understanding of the functional dynamics at play across the genome during development. The network described in this article is available at http://guanlab.ccmb.med.umich.edu/stageSpecificNetwork.

Investigating immunological pathways and diseases with a comprehensive compendium of human data (HUM8P.347)

Abstract The exponential growth of high throughput immunological data motivates the need to leverage this global effort to guide new investigation and to contextualize domain-specific results. Improved specificity and accuracy over currently utilized approaches such as ontological analysis would be advantageous. To develop such a resource (ImmuNet), we used immunological pathway-guided Bayesian integration of a comprehensive, heterogeneous compendium of human data to assemble functional relationship networks. The utility of ImmuNet was evaluated in various applications. We studied the cellular gene response signature to virus infection, using ImmuNet to guide confirmatory experiments into functionally relevant responses to different viruses. We also demonstrated that ImmuNet can accurately predict disease associated genes in the vicinity of GWAS loci and to explore processes and pathways underlying immune-mediated diseases. By unlocking immunological information captured within the global biomedical research effort, ImmuNet should be widely beneficial to future studies investigating the mechanisms of the human immune system and immunological disease. An interactive user-friendly web interface for investigators is publicly available at immunet.princeton.edu.

Functional craniology and brain evolution: from paleontology to biomedicine.

Anatomical systems are organized through a network of structural and functional relationships among their elements. This network of relationships is the result of evolution, it represents the actual target of selection, and it generates the set of rules orienting and constraining the morphogenetic processes. Understanding the relationship among cranial and cerebral components is necessary to investigate the factors that have influenced and characterized our neuroanatomy, and possible drawbacks associated with the evolution of large brains. The study of the spatial relationships between skull and brain in the human genus has direct relevance in cranial surgery. Geometrical modeling can provide functional perspectives in evolution and brain physiology, like in simulations to investigate metabolic heat production and dissipation in the endocranial form. Analysis of the evolutionary constraints between facial and neural blocks can provide new information on visual impairment. The study of brain form variation in fossil humans can supply a different perspective for interpreting the processes behind neurodegeneration and Alzheimer’s disease. Following these examples, it is apparent that paleontology and biomedicine can exchange relevant information and contribute at the same time to the development of robust evolutionary hypotheses on brain evolution, while offering more comprehensive biological perspectives with regard to the interpretation of pathological processes.

Pharmacological Validation Of Potentiating Targets From SAHA RNA-Interference Modifier Screens In Acute Myeloid Leukemia

The lysine deacetylase inhibitor suberoylanilide acid (SAHA) exhibits promising but limited activity in the treatment of acute myeloid leukemia (AML). To identify potential targets for rational combination therapies that increase the efficacy of SAHA in AML, we performed a functional RNA-interference (RNAi) drug modifier screen to identify genes that, when inhibited, potentiate or antagonize the in vitro anti-leukemic activity of SAHA. A total of 901 kinase, phosphatase and closely associated signaling genes were silenced in TF-1, HEL and THP-1 cells, with four different siRNA sequences per gene, both alone and in combination with SAHA treatment. Screen hit lists for each cell line were over-laid on an integrated functional relationship network. A community detection algorithm was then applied to this sub-network and siRNA sensitive modules were identified. Each module represents a highly connected set of genes in the integrated network. To identify pathways represented by each module, enrichment was evaluated using the National Cancer Institute (NCI) Protein Interaction Database (PID) pathways. Both cell line-specific and universal sensitizing targets, grouped into a small number of pathways, emerged from these screens. Seven pharmacologic inhibitors interfering with these pathways have been assessed in a panel of four or five AML cell lines, including SET-2, TF-1, HEL, THP-1, and OCI-AML3. Of the seven tested inhibitors targeting SAHA sensitizing networks, one combination exhibiting universal SAHA sensitization in all AML cell lines tested was further assessed in drug dose response assays using ex vivo cultures of bone marrow mononuclear cell populations derived from patients with various myeloid malignancies. The combination demonstrated “one-sided” SAHA EC50 fold-enhancement, as well as strong synergy by “two-sided” CalcuSyn combination index determination in all ex vivo specimens examined to date (N=6), including polycythemia vera (PV), chronic myelomonocytic leukemia (CMML), myelodysplastic syndrome/myeloproliferative neoplasm (MDS/MPN) overlap syndrome, MDS transformed to AML, and de novo AML samples. Thus, this first reported large-scale SAHA RNAi modifier screen in AML has identified a specific pathway/target whose inhibition broadly synergizes with HDAC inhibition in myeloid malignancies. A novel compound putatively targeting this respective pathway/target is currently in early clinical development and has shown limited single-agent activity. With the full dataset presented, we will propose a novel rational combination to enhance the activity of HDAC inhibition in AML and other myeloid malignancies that could be translated into design of a clinical trial. Disclosures:No relevant conflicts of interest to declare.

Abstract A28: Identification of HDAC inhibitor potentiating targets in acute myeloid leukemia cells by large-scale RNA-interference

Abstract The lysine deacetylase inhibitor suberoylanilide acid (SAHA) has shown promising but limited activity in the treatment of acute myeloid leukemia (AML). To identify potential targets for rational combination therapies that increase the efficacy of SAHA in AML, we used a functional RNA-interference (RNAi) screening approach to identify genes, that when inhibited, potentiate the in vitro anti-leukemic activity of SAHA. A total of 901 kinase, phosphatase and associated signaling genes were silenced, with four different siRNA sequences per gene, in combination with SAHA treatment. Log2 values of the ratio [(siRNA + SAHA)/(siRNA alone)] were calculated, with median and standard deviation determined on a per-plate basis. Hits were defined as ≥ 2 standard deviations from the log2 ratio median. Hit lists for each cell line were over-laid on an integrated functional relationship network. We applied a community detection algorithm to this sub-network and identified siRNA sensitive modules. Each module represents a highly connected set of genes in the integrated network. To identify the pathways represented by each module, we evaluated enrichment using the National Cancer Institute (NCI) Protein Interaction Database (PID) pathways. Several novel sensitizing targets, grouped into a small number of pathways, emerged from these screens. Some hits exhibit little to no anti-leukemic activity when silenced alone, indicative of synthetic lethal interaction with SAHA treatment. Initial validation experiments with siRNA and novel small molecule inhibitors confirm RNAi screen results and pharmacological sensitization is observed. The first reported large-scale HDAC inhibitor RNAi screen in leukemias has identified a novel rational combination that can be translated into design of a clinical trial. Citation Format: James M. Bogenberger, James E. Rudd, Donald Chow, Michelle Kassner, Holly Yin, Casey S. Greene, Raoul Tibes. Identification of HDAC inhibitor potentiating targets in acute myeloid leukemia cells by large-scale RNA-interference. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities; May 17-20, 2013; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(5 Suppl):Abstract nr A28.

Cell-Type-Specific Predictive Network Yields Novel Insights into Mouse Embryonic Stem Cell Self-Renewal and Cell Fate

Self-renewal, the ability of a stem cell to divide repeatedly while maintaining an undifferentiated state, is a defining characteristic of all stem cells. Here, we clarify the molecular foundations of mouse embryonic stem cell (mESC) self-renewal by applying a proven Bayesian network machine learning approach to integrate high-throughput data for protein function discovery. By focusing on a single stem-cell system, at a specific developmental stage, within the context of well-defined biological processes known to be active in that cell type, we produce a consensus predictive network that reflects biological reality more closely than those made by prior efforts using more generalized, context-independent methods. In addition, we show how machine learning efforts may be misled if the tissue specific role of mammalian proteins is not defined in the training set and circumscribed in the evidential data. For this study, we assembled an extensive compendium of mESC data: ∼2.2 million data points, collected from 60 different studies, under 992 conditions. We then integrated these data into a consensus mESC functional relationship network focused on biological processes associated with embryonic stem cell self-renewal and cell fate determination. Computational evaluations, literature validation, and analyses of predicted functional linkages show that our results are highly accurate and biologically relevant. Our mESC network predicts many novel players involved in self-renewal and serves as the foundation for future pluripotent stem cell studies. This network can be used by stem cell researchers (at http://StemSight.org) to explore hypotheses about gene function in the context of self-renewal and to prioritize genes of interest for experimental validation.

Tissue-Specific Functional Networks for Prioritizing Phenotype and Disease Genes

Integrated analyses of functional genomics data have enormous potential for identifying phenotype-associated genes. Tissue-specificity is an important aspect of many genetic diseases, reflecting the potentially different roles of proteins and pathways in diverse cell lineages. Accounting for tissue specificity in global integration of functional genomics data is challenging, as “functionality” and “functional relationships” are often not resolved for specific tissue types. We address this challenge by generating tissue-specific functional networks, which can effectively represent the diversity of protein function for more accurate identification of phenotype-associated genes in the laboratory mouse. Specifically, we created 107 tissue-specific functional relationship networks through integration of genomic data utilizing knowledge of tissue-specific gene expression patterns. Cross-network comparison revealed significantly changed genes enriched for functions related to specific tissue development. We then utilized these tissue-specific networks to predict genes associated with different phenotypes. Our results demonstrate that prediction performance is significantly improved through using the tissue-specific networks as compared to the global functional network. We used a testis-specific functional relationship network to predict genes associated with male fertility and spermatogenesis phenotypes, and experimentally confirmed one top prediction, Mbyl1. We then focused on a less-common genetic disease, ataxia, and identified candidates uniquely predicted by the cerebellum network, which are supported by both literature and experimental evidence. Our systems-level, tissue-specific scheme advances over traditional global integration and analyses and establishes a prototype to address the tissue-specific effects of genetic perturbations, diseases and drugs.

Integrated functional networks of process, tissue, and developmental stage specific interactions in Arabidopsis thaliana

BackgroundRecent years have seen an explosion in plant genomics, as the difficulties inherent in sequencing and functionally analyzing these biologically and economically significant organisms have been overcome. Arabidopsis thaliana, a versatile model organism, represents an opportunity to evaluate the predictive power of biological network inference for plant functional genomics.ResultsHere, we provide a compendium of functional relationship networks for Arabidopsis thaliana leveraging data integration based on over 60 microarray, physical and genetic interaction, and literature curation datasets. These include tissue, biological process, and development stage specific networks, each predicting relationships specific to an individual biological context. These biological networks enable the rapid investigation of uncharacterized genes in specific tissues and developmental stages of interest and summarize a very large collection of A. thaliana data for biological examination. We found validation in the literature for many of our predicted networks, including those involved in disease resistance, root hair patterning, and auxin homeostasis.ConclusionsThese context-specific networks demonstrate that highly specific biological hypotheses can be generated for a diversity of individual processes, developmental stages, and plant tissues in A. thaliana. All predicted functional networks are available online at http://function.princeton.edu/arathGraphle.

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.