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Million CR, Wijeratne S, Karhoff S, Cassone BJ, McHale LK, Dorrance AE. Molecular mechanisms underpinning quantitative resistance to Phytophthora sojae in Glycine max using a systems genomics approach. FRONTIERS IN PLANT SCIENCE 2023; 14:1277585. [PMID: 38023885 PMCID: PMC10662313 DOI: 10.3389/fpls.2023.1277585] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023]
Abstract
Expression of quantitative disease resistance in many host-pathogen systems is controlled by genes at multiple loci, each contributing a small effect to the overall response. We used a systems genomics approach to study the molecular underpinnings of quantitative disease resistance in the soybean-Phytophthora sojae pathosystem, incorporating expression quantitative trait loci (eQTL) mapping and gene co-expression network analysis to identify the genes putatively regulating transcriptional changes in response to inoculation. These findings were compared to previously mapped phenotypic (phQTL) to identify the molecular mechanisms contributing to the expression of this resistance. A subset of 93 recombinant inbred lines (RILs) from a Conrad × Sloan population were inoculated with P. sojae isolate 1.S.1.1 using the tray-test method; RNA was extracted, sequenced, and the normalized read counts were genetically mapped from tissue collected at the inoculation site 24 h after inoculation from both mock and inoculated samples. In total, more than 100,000 eQTLs were mapped. There was a switch from predominantly cis-eQTLs in the mock treatment to an almost entirely nonoverlapping set of predominantly trans-eQTLs in the inoculated treatment, where greater than 100-fold more eQTLs were mapped relative to mock, indicating vast transcriptional reprogramming due to P. sojae infection occurred. The eQTLs were organized into 36 hotspots, with the four largest hotspots from the inoculated treatment corresponding to more than 70% of the eQTLs, each enriched for genes within plant-pathogen interaction pathways. Genetic regulation of trans-eQTLs in response to the pathogen was predicted to occur through transcription factors and signaling molecules involved in plant-pathogen interactions, plant hormone signal transduction, and MAPK pathways. Network analysis identified three co-expression modules that were correlated with susceptibility to P. sojae and associated with three eQTL hotspots. Among the eQTLs co-localized with phQTLs, two cis-eQTLs with putative functions in the regulation of root architecture or jasmonic acid, as well as the putative master regulators of an eQTL hotspot nearby a phQTL, represent candidates potentially underpinning the molecular control of these phQTLs for resistance.
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Affiliation(s)
- Cassidy R. Million
- Department of Plant Pathology, The Ohio State University, Wooster, OH, United States
- Center for Soybean Research and Center for Applied Plant Sciences, The Ohio State University, Columbus, OH, United States
| | - Saranga Wijeratne
- Molecular and Cellular Imaging Center, The Ohio State University, Wooster, OH, United States
| | - Stephanie Karhoff
- Center for Soybean Research and Center for Applied Plant Sciences, The Ohio State University, Columbus, OH, United States
- Translational Plant Sciences Graduate Program, The Ohio State University, Columbus, OH, United States
| | - Bryan J. Cassone
- Center for Soybean Research and Center for Applied Plant Sciences, The Ohio State University, Columbus, OH, United States
- Department of Biology, Brandon University, Brandon, Manitoba, MB, Canada
| | - Leah K. McHale
- Center for Soybean Research and Center for Applied Plant Sciences, The Ohio State University, Columbus, OH, United States
- Department of Horticulture and Crop Science, The Ohio State University, Columbus, OH, United States
| | - Anne E. Dorrance
- Department of Plant Pathology, The Ohio State University, Wooster, OH, United States
- Center for Soybean Research and Center for Applied Plant Sciences, The Ohio State University, Columbus, OH, United States
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Bertero A. RNA Biogenesis Instructs Functional Inter-Chromosomal Genome Architecture. Front Genet 2021; 12:645863. [PMID: 33732290 PMCID: PMC7957078 DOI: 10.3389/fgene.2021.645863] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 02/08/2021] [Indexed: 12/12/2022] Open
Abstract
Three-dimensional (3D) genome organization has emerged as an important layer of gene regulation in development and disease. The functional properties of chromatin folding within individual chromosomes (i.e., intra-chromosomal or in cis) have been studied extensively. On the other hand, interactions across different chromosomes (i.e., inter-chromosomal or in trans) have received less attention, being often regarded as background noise or technical artifacts. This viewpoint has been challenged by emerging evidence of functional relationships between specific trans chromatin interactions and epigenetic control, transcription, and splicing. Therefore, it is an intriguing possibility that the key processes involved in the biogenesis of RNAs may both shape and be in turn influenced by inter-chromosomal genome architecture. Here I present the rationale behind this hypothesis, and discuss a potential experimental framework aimed at its formal testing. I present a specific example in the cardiac myocyte, a well-studied post-mitotic cell whose development and response to stress are associated with marked rearrangements of chromatin topology both in cis and in trans. I argue that RNA polymerase II clusters (i.e., transcription factories) and foci of the cardiac-specific splicing regulator RBM20 (i.e., splicing factories) exemplify the existence of trans-interacting chromatin domains (TIDs) with important roles in cellular homeostasis. Overall, I propose that inter-molecular 3D proximity between co-regulated nucleic acids may be a pervasive functional mechanism in biology.
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Affiliation(s)
- Alessandro Bertero
- Department of Laboratory Medicine and Pathology, Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, United States
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A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine. Trends Genet 2020; 36:318-336. [PMID: 32294413 DOI: 10.1016/j.tig.2020.01.009] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 01/05/2020] [Accepted: 01/21/2020] [Indexed: 02/07/2023]
Abstract
Quantitative trait loci (QTL) analysis is an important approach to investigate the effects of genetic variants identified through an increasing number of large-scale, multidimensional 'omics data sets. In this 'big data' era, the research community has identified a significant number of molecular QTLs (molQTLs) and increased our understanding of their effects. Herein, we review multiple categories of molQTLs, including those associated with transcriptome, post-transcriptional regulation, epigenetics, proteomics, metabolomics, and the microbiome. We summarize approaches to identify molQTLs and to infer their causal effects. We further discuss the integrative analysis of molQTLs through a multi-omics perspective. Our review highlights future opportunities to better understand the functional significance of genetic variants and to utilize the discovery of molQTLs in precision medicine.
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Fischer D. Performing QTL and eQTL Analyses with the R-Package GenomicTools. Methods Mol Biol 2020; 2082:15-38. [PMID: 31849005 DOI: 10.1007/978-1-0716-0026-9_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
We present the R-package GenomicTools that can be used to perform QTL and eQTL analyses in a user-friendly way. First, the theoretical backgrounds of both implemented methods are explained. These are (a) the linear model approach that is commonly used in the standard QTL/eQTL testing as well as (b) a non-parametrical directional testing method implemented in GenomicTools. The directional test overcomes some of the drawbacks of the standard way and is robust against outliers in the data. The main focus, however, is on a detailed explanation, how the R-package is used in practice. Starting from the installation of the package, followed by the data import and also the required steps to perform the analyses, all necessary steps are explained in detail with examples. Also, the commands to create publication-ready figures are presented. The last chapter concludes and discusses general topics related to the analysis of QTL and eQTL data in particular and genomic data in general.
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Affiliation(s)
- Daniel Fischer
- Applied Statistical Methods, Natural Resources Institute Finland (Luke), Jokioinen, Finland.
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Zhuang Y, Wade K, Saba LM, Kechris K. Development of a tissue augmented Bayesian model for expression quantitative trait loci analysis. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2019; 17:122-143. [PMID: 31731343 PMCID: PMC7384761 DOI: 10.3934/mbe.2020007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
Expression quantitative trait loci (eQTL) analyses detect genetic variants (SNPs) associated with RNA expression levels of genes. The conventional eQTL analysis is to perform individual tests for each gene-SNP pair using simple linear regression and to perform the test on each tissue separately ignoring the extensive information known about RNA expression in other tissue(s). Although Bayesian models have been recently developed to improve eQTL prediction on multiple tissues, they are often based on uninformative priors or treat all tissues equally. In this study, we develop a novel tissue augmented Bayesian model for eQTL analysis (TA-eQTL), which takes prior eQTL information from a different tissue into account to better predict eQTL for another tissue. We demonstrate that our modified Bayesian model has comparable performance to several existing methods in terms of sensitivity and specificity using allele-specific expression (ASE) as the gold standard. Furthermore, the tissue augmented Bayesian model improves the power and accuracy for local-eQTL prediction especially when the sample size is small. In summary, TA-eQTL's performance is comparable to existing methods but has additional flexibility to evaluate data from different platforms, can focus prediction on one tissue using only summary statistics from the secondary tissue(s), and provides a closed form solution for estimation.
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Affiliation(s)
- Yonghua Zhuang
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver Anschutz Medical Campus, Mail Stop B119, 13001 E. 17th Place, Aurora, 80045, USA
| | - Kristen Wade
- Human Medical Genetics and Genomics Program, School of Medicine, University of Colorado Denver Anschutz Medical Campus, 80045, Aurora, USA
| | - Laura M. Saba
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Denver Anschutz Medical Campus, 80045, Aurora, USA
| | - Katerina Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver Anschutz Medical Campus, Mail Stop B119, 13001 E. 17th Place, Aurora, 80045, USA
- Correspondence:, ; Tel: +13037244363, +13037249697
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Fang YH, Wang JH, Hsiung CA. TSGSIS: a high-dimensional grouped variable selection approach for detection of whole-genome SNP-SNP interactions. Bioinformatics 2018. [PMID: 28651334 DOI: 10.1093/bioinformatics/btx409] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Motivation Identification of single nucleotide polymorphism (SNP) interactions is an important and challenging topic in genome-wide association studies (GWAS). Many approaches have been applied to detecting whole-genome interactions. However, these approaches to interaction analysis tend to miss causal interaction effects when the individual marginal effects are uncorrelated to trait, while their interaction effects are highly associated with the trait. Results A grouped variable selection technique, called two-stage grouped sure independence screening (TS-GSIS), is developed to study interactions that may not have marginal effects. The proposed TS-GSIS is shown to be very helpful in identifying not only causal SNP effects that are uncorrelated to trait but also their corresponding SNP-SNP interaction effects. The benefit of TS-GSIS are gaining detection of interaction effects by taking the joint information among the SNPs and determining the size of candidate sets in the model. Simulation studies under various scenarios are performed to compare performance of TS-GSIS and current approaches. We also apply our approach to a real rheumatoid arthritis (RA) dataset. Both the simulation and real data studies show that the TS-GSIS performs very well in detecting SNP-SNP interactions. Availability and implementation R-package is delivered through CRAN and is available at: https://cran.r-project.org/web/packages/TSGSIS/index.html. Contact hsiung@nhri.org.tw. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yao-Hwei Fang
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan 35053, Taiwan
| | - Jie-Huei Wang
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan 35053, Taiwan
| | - Chao A Hsiung
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan 35053, Taiwan
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Kel I, Chang Z, Galluccio N, Romeo M, Beretta S, Diomede L, Mezzelani A, Milanesi L, Dieterich C, Merelli I. SPIRE, a modular pipeline for eQTL analysis of RNA-Seq data, reveals a regulatory hotspot controlling miRNA expression in C. elegans. MOLECULAR BIOSYSTEMS 2017; 12:3447-3458. [PMID: 27722582 DOI: 10.1039/c6mb00453a] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The interpretation of genome-wide association study is difficult, as it is hard to understand how polymorphisms can affect gene regulation, in particular for trans-regulatory elements located far from their controlling gene. Using RNA or protein expression data as phenotypes, it is possible to correlate their variations with specific genotypes. This technique is usually referred to as expression Quantitative Trait Loci (eQTLs) analysis and only few packages exist for the integration of genotype patterns and expression profiles. In particular, tools are needed for the analysis of next-generation sequencing (NGS) data on a genome-wide scale, which is essential to identify eQTLs able to control a large number of genes (hotspots). Here we present SPIRE (Software for Polymorphism Identification Regulating Expression), a generic, modular and functionally highly flexible pipeline for eQTL processing. SPIRE integrates different univariate and multivariate approaches for eQTL analysis, paying particular attention to the scalability of the procedure in order to support cis- as well as trans-mapping, thus allowing the identification of hotspots in NGS data. In particular, we demonstrated how SPIRE can handle big association study datasets, reproducing published results and improving the identification of trans-eQTLs. Furthermore, we employed the pipeline to analyse novel data concerning the genotypes of two different C. elegans strains (N2 and Hawaii) and related miRNA expression data, obtained using RNA-Seq. A miRNA regulatory hotspot was identified in chromosome 1, overlapping the transcription factor grh-1, known to be involved in the early phases of embryonic development of C. elegans. In a follow-up qPCR experiment we were able to verify most of the predicted eQTLs, as well as to show, for a novel miRNA, a significant difference in the sequences of the two analysed strains of C. elegans. SPIRE is publicly available as open source software at , together with some example data, a readme file, supplementary material and a short tutorial.
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Affiliation(s)
- Ivan Kel
- Instituto di Tecnologie Biomediche - Consiglio Nazionale delle Ricerche, via F.lli Cervi 93, 20090, Segrate, Milano, Italy.
| | - Zisong Chang
- Max Delbrück Center for Molecular Medicine, Berlin Institute for Medical Systems Biology, Robert-Rössle-Straße 10, 13125, Berlin, Germany.
| | - Nadia Galluccio
- Instituto di Tecnologie Biomediche - Consiglio Nazionale delle Ricerche, via F.lli Cervi 93, 20090, Segrate, Milano, Italy.
| | - Margherita Romeo
- Dipartimento di Biochimica e Farmacologia Molecolare, IRCCS - Istituto di Ricerche Farmacologiche "Mario Negri", Via Giuseppe La Masa 19, Milan, Italy.
| | - Stefano Beretta
- Dipartimento di Informatica Sistemistica e Comunicazione, Università degli studi di Milano-Biccoca, Viale Sarca 336, 20125 Milano, Italy.
| | - Luisa Diomede
- Dipartimento di Biochimica e Farmacologia Molecolare, IRCCS - Istituto di Ricerche Farmacologiche "Mario Negri", Via Giuseppe La Masa 19, Milan, Italy.
| | - Alessandra Mezzelani
- Instituto di Tecnologie Biomediche - Consiglio Nazionale delle Ricerche, via F.lli Cervi 93, 20090, Segrate, Milano, Italy.
| | - Luciano Milanesi
- Instituto di Tecnologie Biomediche - Consiglio Nazionale delle Ricerche, via F.lli Cervi 93, 20090, Segrate, Milano, Italy.
| | - Christoph Dieterich
- Section of Bioinformatics and Systems Cardiology, Klaus Tschira Institute for Integrative Computational Cardiology and Department of Internal Medicine III, University of Heidelberg, Grabengasse 1, 69117 Heidelberg, Germany.
| | - Ivan Merelli
- Instituto di Tecnologie Biomediche - Consiglio Nazionale delle Ricerche, via F.lli Cervi 93, 20090, Segrate, Milano, Italy.
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Jeidane S, Scott-Boyer MP, Tremblay N, Cardin S, Picard S, Baril M, Lamarre D, Deschepper CF. Association of a Network of Interferon-Stimulated Genes with a Locus Encoding a Negative Regulator of Non-conventional IKK Kinases and IFNB1. Cell Rep 2016; 17:425-435. [PMID: 27705791 DOI: 10.1016/j.celrep.2016.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2015] [Revised: 08/11/2016] [Accepted: 09/02/2016] [Indexed: 11/25/2022] Open
Abstract
Functional genomic analysis of gene expression in mice allowed us to identify a quantitative trait locus (QTL) linked in trans to the expression of 190 gene transcripts and in cis to the expression of only two genes, one of which was Ypel5. Most of the trans-expression QTL genes were interferon-stimulated genes (ISGs), and their expression in mouse macrophage cell lines was stimulated in an IFNB1-dependent manner by Ypel5 silencing. In human HEK293T cells, YPEL5 silencing enhanced the induction of IFNB1 by pattern recognition receptors and phosphorylation of TBK1/IKBKE kinases, whereas co-immunoprecipitation experiments revealed that YPEL5 interacted physically with IKBKE. We thus found that the Ypel5 gene (contained in a locus linked to a network of ISGs in mice) is a negative regulator of IFNB1 production and innate immune responses that interacts functionally and physically with TBK1/IKBKE kinases.
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Affiliation(s)
- Saloua Jeidane
- Cardiovascular Biology Research Unit, Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC H2W 1R7, Canada; Département de Médecine, Faculté de Médecine, Université de Montréal, Montréal, QC J2S 2M2, Canada
| | - Marie-Pier Scott-Boyer
- Cardiovascular Biology Research Unit, Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC H2W 1R7, Canada; Département de Médecine, Faculté de Médecine, Université de Montréal, Montréal, QC J2S 2M2, Canada
| | - Nicolas Tremblay
- Département de Médecine, Faculté de Médecine, Université de Montréal, Montréal, QC J2S 2M2, Canada; Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CR-CHUM), Montréal, QC H2X 3J4, Canada
| | - Sophie Cardin
- Cardiovascular Biology Research Unit, Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC H2W 1R7, Canada; Département de Médecine, Faculté de Médecine, Université de Montréal, Montréal, QC J2S 2M2, Canada
| | - Sylvie Picard
- Cardiovascular Biology Research Unit, Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC H2W 1R7, Canada
| | - Martin Baril
- Département de Médecine, Faculté de Médecine, Université de Montréal, Montréal, QC J2S 2M2, Canada; Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CR-CHUM), Montréal, QC H2X 3J4, Canada
| | - Daniel Lamarre
- Département de Médecine, Faculté de Médecine, Université de Montréal, Montréal, QC J2S 2M2, Canada; Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CR-CHUM), Montréal, QC H2X 3J4, Canada
| | - Christian F Deschepper
- Cardiovascular Biology Research Unit, Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC H2W 1R7, Canada; Département de Médecine, Faculté de Médecine, Université de Montréal, Montréal, QC J2S 2M2, Canada.
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Richardson S, Tseng GC, Sun W. Statistical Methods in Integrative Genomics. ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION 2016; 3:181-209. [PMID: 27482531 PMCID: PMC4963036 DOI: 10.1146/annurev-statistics-041715-033506] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Statistical methods in integrative genomics aim to answer important biology questions by jointly analyzing multiple types of genomic data (vertical integration) or aggregating the same type of data across multiple studies (horizontal integration). In this article, we introduce different types of genomic data and data resources, and then review statistical methods of integrative genomics, with emphasis on the motivation and rationale of these methods. We conclude with some summary points and future research directions.
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Affiliation(s)
- Sylvia Richardson
- MRC Biostatistics Unit, Cambridge Institute of Public Health, University of Cambridge, CB2 0SR, United Kingdom
| | - George C. Tseng
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261
| | - Wei Sun
- Department of Biostatistics, Department of Genetics, University of North Carolina, Chapel Hill, NC 27599
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 27516
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Jiang B, Liu JS. Bayesian Partition Models for Identifying Expression Quantitative Trait Loci. J Am Stat Assoc 2015; 110:1350-1361. [PMID: 29056798 DOI: 10.1080/01621459.2015.1049746] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Expression quantitative trait loci (eQTLs) are genomic locations associated with changes of expression levels of certain genes. By assaying gene expressions and genetic variations simultaneously on a genome-wide scale, scientists wish to discover genomic loci responsible for expression variations of a set of genes. The task can be viewed as a multivariate regression problem with variable selection on both responses (gene expression) and covariates (genetic variations), including also multi-way interactions among covariates. Instead of learning a predictive model of quantitative trait given combinations of genetic markers, we adopt an inverse modeling perspective to model the distribution of genetic markers conditional on gene expression traits. A particular strength of our method is its ability to detect interactive effects of genetic variations with high power even when their marginal effects are weak, addressing a key weakness of many existing eQTL mapping methods. Furthermore, we introduce a hierarchical model to capture the dependence structure among correlated genes. Through simulation studies and a real data example in yeast, we demonstrate how our Bayesian hierarchical partition model achieves a significantly improved power in detecting eQTLs compared to existing methods.
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Affiliation(s)
- Bo Jiang
- Harvard University, Cambridge, MA 02138
| | - Jun S Liu
- Department of Statistics, Harvard University, Cambridge, MA 02138
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