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Jiang J, Xing F, Wang C, Zeng X, Zou Q. Investigation and development of maize fused network analysis with multi-omics. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2019; 141:380-387. [PMID: 31220804 DOI: 10.1016/j.plaphy.2019.06.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 06/12/2019] [Accepted: 06/12/2019] [Indexed: 05/19/2023]
Abstract
Maize is a critically important staple crop in the whole world, which has contributed to both economic security and food in planting areas. The main target for researchers and breeding is the improvement of maize quality and yield. The use of computational biology methods combined with multi-omics for selecting biomolecules of interest for maize breeding has been receiving more attention. Moreover, the rapid growth of high-throughput sequencing data provides the opportunity to explore biomolecules of interest at the molecular level in maize. Furthermore, we constructed weighted networks for each of the omics and then integrated them into a final fused weighted network based on a nonlinear combination method. We also analyzed the final fused network and mined the orphan nodes, some of which were shown to be transcription factors that played a key role in maize development. This study could help to improve maize production via insights at the multi-omics level and provide a new perspective for maize researchers. All related data have been released at http://lab.malab.cn/∼jj/maize.htm.
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Affiliation(s)
- Jing Jiang
- School of Aerospace Engineering, Xiamen University, Xiamen, 361001, China
| | - Fei Xing
- School of Aerospace Engineering, Xiamen University, Xiamen, 361001, China
| | - Chunyu Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Xiangxiang Zeng
- School of Information Science and Engineering, Hunan University, 410082, Changsha, China.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610000, China.
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Lea A, Subramaniam M, Ko A, Lehtimäki T, Raitoharju E, Kähönen M, Seppälä I, Mononen N, Raitakari OT, Ala-Korpela M, Pajukanta P, Zaitlen N, Ayroles JF. Genetic and environmental perturbations lead to regulatory decoherence. eLife 2019; 8:e40538. [PMID: 30834892 PMCID: PMC6400502 DOI: 10.7554/elife.40538] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Accepted: 02/14/2019] [Indexed: 01/24/2023] Open
Abstract
Correlation among traits is a fundamental feature of biological systems that remains difficult to study. To address this problem, we developed a flexible approach that allows us to identify factors associated with inter-individual variation in correlation. We use data from three human cohorts to study the effects of genetic and environmental variation on correlations among mRNA transcripts and among NMR metabolites. We first show that environmental exposures (infection and disease) lead to a systematic loss of correlation, which we define as 'decoherence'. Using longitudinal data, we show that decoherent metabolites are better predictors of whether someone will develop metabolic syndrome than metabolites commonly used as biomarkers of this disease. Finally, we demonstrate that correlation itself is under genetic control by mapping hundreds of 'correlation quantitative trait loci (QTLs)'. Together, this work furthers our understanding of how and why coordinated biological processes break down, and points to a potential role for decoherence in disease. Editorial note This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
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Affiliation(s)
- Amanda Lea
- Department of Ecology and EvolutionPrinceton UniversityPrincetonUnited States
- Lewis-Sigler Institute for Integrative GenomicsPrinceton UniversityPrincetonUnited States
| | - Meena Subramaniam
- Department of Medicine, Lung Biology CenterUniversity of California, San FranciscoSan FranciscoUnited States
| | - Arthur Ko
- Department of Medicine, David Geffen School of Medicine at UCLAUniversity of California, Los AngelesLos AngelesUnited States
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Faculty of Medicine and Health TechnologyTampere UniversityTampereFinland
- Finnish Cardiovascular Research Center, Faculty of Medicine and Health TechnologyTampere UniversityTampereFinland
| | - Emma Raitoharju
- Finnish Cardiovascular Research Center, Faculty of Medicine and Health TechnologyTampere UniversityTampereFinland
| | - Mika Kähönen
- Finnish Cardiovascular Research Center, Faculty of Medicine and Health TechnologyTampere UniversityTampereFinland
- Department of Clinical PhysiologyTampere University, Tampere University HospitalTampereFinland
| | - Ilkka Seppälä
- Finnish Cardiovascular Research Center, Faculty of Medicine and Health TechnologyTampere UniversityTampereFinland
| | - Nina Mononen
- Finnish Cardiovascular Research Center, Faculty of Medicine and Health TechnologyTampere UniversityTampereFinland
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular MedicineUniversity of TurkuTurkuFinland
- Department of Clinical Physiology and Nuclear MedicineTurku University HospitalTurkuFinland
| | - Mika Ala-Korpela
- Systems Epidemiology, Baker Heart and Diabetes InstituteMelbourneAustralia
- Computational Medicine, Faculty of Medicine, Biocenter OuluUniversity of OuluOuluFinland
- NMR Metabolomics Laboratory, School of PharmacyUniversity of Eastern FinlandKuopioFinland
- Population Health Science, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
- Medical Research Council Integrative Epidemiology UnitUniversity of BristolBristolUnited Kingdom
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health SciencesThe Alfred Hospital, Monash UniversityMelbourneAustralia
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLAUniversity of California, Los AngelesLos AngelesUnited States
| | - Noah Zaitlen
- Department of Medicine, Lung Biology CenterUniversity of California, San FranciscoSan FranciscoUnited States
| | - Julien F Ayroles
- Department of Ecology and EvolutionPrinceton UniversityPrincetonUnited States
- Lewis-Sigler Institute for Integrative GenomicsPrinceton UniversityPrincetonUnited States
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Kinzy TG, Starr TK, Tseng GC, Ho YY. Meta-analytic framework for modeling genetic coexpression dynamics. Stat Appl Genet Mol Biol 2019; 18:/j/sagmb.ahead-of-print/sagmb-2017-0052/sagmb-2017-0052.xml. [DOI: 10.1515/sagmb-2017-0052] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Methods for exploring genetic interactions have been developed in an attempt to move beyond single gene analyses. Because biological molecules frequently participate in different processes under various cellular conditions, investigating the changes in gene coexpression patterns under various biological conditions could reveal important regulatory mechanisms. One of the methods for capturing gene coexpression dynamics, named liquid association (LA), quantifies the relationship where the coexpression between two genes is modulated by a third “coordinator” gene. This LA measure offers a natural framework for studying gene coexpression changes and has been applied increasingly to study regulatory networks among genes. With a wealth of publicly available gene expression data, there is a need to develop a meta-analytic framework for LA analysis. In this paper, we incorporated mixed effects when modeling correlation to account for between-studies heterogeneity. For statistical inference about LA, we developed a Markov chain Monte Carlo (MCMC) estimation procedure through a Bayesian hierarchical framework. We evaluated the proposed methods in a set of simulations and illustrated their use in two collections of experimental data sets. The first data set combined 10 pancreatic ductal adenocarcinoma gene expression studies to determine the role of possible coordinator gene USP9X in the Hippo pathway. The second experimental data set consisted of 907 gene expression microarray Escherichia coli experiments from multiple studies publicly available through the Many Microbe Microarray Database website (http://m3d.bu.edu/) and examined genes that coexpress with serA in the presence of coordinator gene Lrp.
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Xu X, Wang M, Li L, Che R, Li P, Pei L, Li H. Genome-wide trait-trait dynamics correlation study dissects the gene regulation pattern in maize kernels. BMC PLANT BIOLOGY 2017; 17:163. [PMID: 29037150 PMCID: PMC5644097 DOI: 10.1186/s12870-017-1119-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Accepted: 10/09/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND Dissecting the genetic basis and regulatory mechanisms for the biosynthesis and accumulation of nutrients in maize could lead to the improved nutritional quality of this crop. Gene expression is regulated at the genomic, transcriptional, and post-transcriptional levels, all of which can produce diversity among traits. However, the expression of most genes connected with a particular trait usually does not have a direct association with the variation of that trait. In addition, expression profiles of genes involved in a single pathway may vary as the intrinsic cellular state changes. To work around these issues, we utilized a statistical method, liquid association (LA) to investigate the complex pattern of gene regulation in maize kernels. RESULTS We applied LA to the expression profiles of 28,769 genes to dissect dynamic trait-trait correlation patterns in maize kernels. Among the 1000 LA pairs (LAPs) with the largest LA scores, 686 LAPs were identified conditional correlation. We also identified 830 and 215 LA-scouting leaders based on the positive and negative LA scores, which were significantly enriched for some biological processes and molecular functions. Our analysis of the dynamic co-expression patterns in the carotene biosynthetic pathway clearly indicated the important role of lcyE, CYP97A, ZEP1, and VDE in this pathway, which may change the direction of carotene biosynthesis by controlling the influx and efflux of the substrate. The dynamic trait-trait correlation patterns between gene expression and oil concentration in the fatty acid metabolic pathway and its complex regulatory network were also assessed. 23 of 26 oil-associated genes were correlated with oil concentration conditioning on 580 LA-scoutinggenes, and 5% of these LA-scouting genes were annotated as enzymes in the oil metabolic pathway. CONCLUSIONS By focusing on the carotenoid and oil biosynthetic pathways in maize, we showed that a genome-wide LA analysis provides a novel and effective way to detect transcriptional regulatory relationships. This method will help us understand the biological role of maize kernel genes and will benefit maize breeding programs.
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Affiliation(s)
- Xiuqin Xu
- School of Biological and Science Technology, University of Jinan, Jinan, 250022 China
| | - Min Wang
- National Maize Improvement Center of China, Key Laboratory of Crop Genomics and Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Lianbo Li
- School of Biological and Science Technology, University of Jinan, Jinan, 250022 China
| | - Ronghui Che
- School of Biological and Science Technology, University of Jinan, Jinan, 250022 China
| | - Peng Li
- School of Biological and Science Technology, University of Jinan, Jinan, 250022 China
| | - Laming Pei
- School of Biological and Science Technology, University of Jinan, Jinan, 250022 China
| | - Hui Li
- School of Biological and Science Technology, University of Jinan, Jinan, 250022 China
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Yuan H, Li Z, Tang NLS, Deng M. A network based covariance test for detecting multivariate eQTL in saccharomyces cerevisiae. BMC SYSTEMS BIOLOGY 2016; 10 Suppl 1:8. [PMID: 26818242 PMCID: PMC4895706 DOI: 10.1186/s12918-015-0245-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Background Expression quantitative trait locus (eQTL) analysis has been widely used to understand how genetic variations affect gene expressions in the biological systems. Traditional eQTL is investigated in a pair-wise manner in which one SNP affects the expression of one gene. In this way, some associated markers found in GWAS have been related to disease mechanism by eQTL study. However, in real life, biological process is usually performed by a group of genes. Although some methods have been proposed to identify a group of SNPs that affect the mean of gene expressions in the network, the change of co-expression pattern has not been considered. So we propose a process and algorithm to identify the marker which affects the co-expression pattern of a pathway. Considering two genes may have different correlations under different isoforms which is hard to detect by the linear test, we also consider the nonlinear test. Results When we applied our method to yeast eQTL dataset profiled under both the glucose and ethanol conditions, we identified a total of 166 modules, with each module consisting of a group of genes and one eQTL where the eQTL regulate the co-expression patterns of the group of genes. We found that many of these modules have biological significance. Conclusions We propose a network based covariance test to identify the SNP which affects the structure of a pathway. We also consider the nonlinear test as considering two genes may have different correlations under different isoforms which is hard to detect by linear test. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0245-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Huili Yuan
- LMAM, School of Mathematical Sciences, Peking University, Yiheyuan Road, Beijing, 100871, China.
| | - Zhenye Li
- LMAM, School of Mathematical Sciences, Peking University, Yiheyuan Road, Beijing, 100871, China.
| | - Nelson L S Tang
- Department of Chemical Pathology, Prince of Wales Hospital, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China.
| | - Minghua Deng
- LMAM, School of Mathematical Sciences, Peking University, Yiheyuan Road, Beijing, 100871, China. .,Center for Quantitative Biology, Peking University, Yiheyuan Road, Beijing, 100871, China. .,Center for Statistical Sciences, Peking University, Yiheyuan Road, Beijing, 100871, China.
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Ballini E, Lauter N, Wise R. Prospects for advancing defense to cereal rusts through genetical genomics. FRONTIERS IN PLANT SCIENCE 2013; 4:117. [PMID: 23641250 PMCID: PMC3640194 DOI: 10.3389/fpls.2013.00117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2013] [Accepted: 04/15/2013] [Indexed: 05/03/2023]
Abstract
Rusts are one of the most severe threats to cereal crops because new pathogen races emerge regularly, resulting in infestations that lead to large yield losses. In 1999, a new race of stem rust, Puccinia graminis f. sp. tritici (Pgt TTKSK or Ug99), was discovered in Uganda. Most of the wheat and barley cultivars grown currently worldwide are susceptible to this new race. Pgt TTKSK has already spread northward into Iran and will likely spread eastward throughout the Indian subcontinent in the near future. This scenario is not unique to stem rust; new races of leaf rust (Puccinia triticina) and stripe rust (Puccinia striiformis) have also emerged recently. One strategy for countering the persistent adaptability of these pathogens is to stack complete- and partial-resistance genes, which requires significant breeding efforts in order to reduce deleterious effects of linkage drag. These varied resistance combinations are typically more difficult for the pathogen to defeat, since they would be predicted to apply lower selection pressure. Genetical genomics or expression Quantitative Trait Locus (eQTL) analysis enables the identification of regulatory loci that control the expression of many to hundreds of genes. Integrated deployment of these technologies coupled with efficient phenotyping offers significant potential to elucidate the regulatory nodes in genetic networks that orchestrate host defense responses. The focus of this review will be to present advances in genetical genomic experimental designs and analysis, particularly as they apply to the prospects for discovering partial disease resistance alleles in cereals.
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Affiliation(s)
| | | | - Roger Wise
- Corn Insects and Crop Genetics Research, Department of Plant Pathology and Microbiology, US Department of Agriculture - Agricultural Research Service, Center for Plant Responses to Environmental Stresses, Iowa State UniversityAmes, IA, USA
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Wang L, Zheng W, Zhao H, Deng M. Statistical analysis reveals co-expression patterns of many pairs of genes in yeast are jointly regulated by interacting loci. PLoS Genet 2013; 9:e1003414. [PMID: 23555313 PMCID: PMC3610942 DOI: 10.1371/journal.pgen.1003414] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2012] [Accepted: 02/11/2013] [Indexed: 11/30/2022] Open
Abstract
Expression quantitative trait loci (eQTL) studies have generated large amounts of data in different organisms. The analyses of these data have led to many novel findings and biological insights on expression regulations. However, the role of epistasis in the joint regulation of multiple genes has not been explored. This is largely due to the computational complexity involved when multiple traits are simultaneously considered against multiple markers if an exhaustive search strategy is adopted. In this article, we propose a computationally feasible approach to identify pairs of chromosomal regions that interact to regulate co-expression patterns of pairs of genes. Our approach is built on a bivariate model whose covariance matrix depends on the joint genotypes at the candidate loci. We also propose a filtering process to reduce the computational burden. When we applied our method to a yeast eQTL dataset profiled under both the glucose and ethanol conditions, we identified a total of 225 and 224 modules, with each module consisting of two genes and two eQTLs where the two eQTLs epistatically regulate the co-expression patterns of the two genes. We found that many of these modules have biological interpretations. Under the glucose condition, ribosome biogenesis was co-regulated with the signaling and carbohydrate catabolic processes, whereas silencing and aging related genes were co-regulated under the ethanol condition with the eQTLs containing genes involved in oxidative stress response process. eQTL studies collect both gene expression and genotype data, and they are highly informative as to how genes regulate expressions. Although much progress has been made in the analysis of such data, most studies have considered one marker at a time. As a result, those markers with weak marginal yet strong interactive effects may not be inferred from these single-marker-based analyses. In this article, using joint expression patterns between two genes (versus one gene) as the primary phenotype, we propose a novel statistical method to conduct an exhaustive search for joint marker analysis. When our method is applied to a well-studied dataset, we were able to identify many novel features that were overlooked by existing methods. Our general strategy has general applicability to other scientific problems.
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Affiliation(s)
- Lin Wang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Wei Zheng
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
- * E-mail: (HZ); (MD)
| | - Minghua Deng
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- LMAM, School of Mathematical Sciences, Peking University, Beijing, China
- Center for Statistical Science, Peking University, Beijing, China
- * E-mail: (HZ); (MD)
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Li S, Cui Y. Gene-centric gene–gene interaction: A model-based kernel machine method. Ann Appl Stat 2012. [DOI: 10.1214/12-aoas545] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Chen J, Xie J, Li H. A penalized likelihood approach for bivariate conditional normal models for dynamic co-expression analysis. Biometrics 2011; 67:299-308. [PMID: 20374241 PMCID: PMC2902622 DOI: 10.1111/j.1541-0420.2010.01413.x] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Gene co-expressions have been widely used in the analysis of microarray gene expression data. However, the co-expression patterns between two genes can be mediated by cellular states, as reflected by expression of other genes, single nucleotide polymorphisms, and activity of protein kinases. In this article, we introduce a bivariate conditional normal model for identifying the variables that can mediate the co-expression patterns between two genes. Based on this model, we introduce a likelihood ratio (LR) test and a penalized likelihood procedure for identifying the mediators that affect gene co-expression patterns. We propose an efficient computational algorithm based on iterative reweighted least squares and cyclic coordinate descent and have shown that when the tuning parameter in the penalized likelihood is appropriately selected, such a procedure has the oracle property in selecting the variables. We present simulation results to compare with existing methods and show that the LR-based approach can perform similarly or better than the existing method of liquid association and the penalized likelihood procedure can be quite effective in selecting the mediators. We apply the proposed method to yeast gene expression data in order to identify the kinases or single nucleotide polymorphisms that mediate the co-expression patterns between genes.
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Affiliation(s)
- Jun Chen
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Jichun Xie
- Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Hongzhe Li
- Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
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Systems genetics, bioinformatics and eQTL mapping. Genetica 2010; 138:915-24. [DOI: 10.1007/s10709-010-9480-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2010] [Accepted: 07/30/2010] [Indexed: 12/15/2022]
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Tai SK, Wu G, Yuan S, Li KC. Genome-wide expression links the electron transfer pathway of Shewanella oneidensis to chemotaxis. BMC Genomics 2010; 11:319. [PMID: 20492688 PMCID: PMC2886065 DOI: 10.1186/1471-2164-11-319] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2009] [Accepted: 05/21/2010] [Indexed: 12/25/2022] Open
Abstract
Background By coupling the oxidation of organic substrates to a broad range of terminal electron acceptors (such as nitrate, metals and radionuclides), Shewanella oneidensis MR-1 has the ability to produce current in microbial fuel cells (MFCs). omcA, mtrA, omcB (also known as mtrC), mtrB, and gspF are some known genes of S. oneidensis MR-1 that participate in the process of electron transfer. How does the cell coordinate the expression of these genes? To shed light on this problem, we obtain the gene expression datasets of MR-1 that are recently public-accessible in Gene Expression Omnibus. We utilize the novel statistical method, liquid association (LA), to investigate the complex pattern of gene regulation. Results Through a web of information obtained by our data analysis, a network of transcriptional regulatory relationship between chemotaxis and electron transfer pathways is revealed, highlighting the important roles of the chemotaxis gene cheA-1, the magnesium transporter gene mgtE-1, and a triheme c-type cytochrome gene SO4572. Conclusion We found previously unknown relationship between chemotaxis and electron transfer using LA system. The study has the potential of helping researchers to overcome the intrinsic metabolic limitation of the microorganisms for improving power density output of an MFC.
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Affiliation(s)
- Shang-Kai Tai
- Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan
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