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Roberts AGK, Catchpoole DR, Kennedy PJ. Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability. NAR Genom Bioinform 2022; 4:lqab124. [PMID: 35047816 PMCID: PMC8759562 DOI: 10.1093/nargab/lqab124] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 11/19/2021] [Accepted: 12/16/2021] [Indexed: 12/13/2022] Open
Abstract
There is increasing evidence that changes in the variability or overall distribution of gene expression are important both in normal biology and in diseases, particularly cancer. Genes whose expression differs in variability or distribution without a difference in mean are ignored by traditional differential expression-based analyses. Using a Bayesian hierarchical model that provides tests for both differential variability and differential distribution for bulk RNA-seq data, we report here an investigation into differential variability and distribution in cancer. Analysis of eight paired tumour-normal datasets from The Cancer Genome Atlas confirms that differential variability and distribution analyses are able to identify cancer-related genes. We further demonstrate that differential variability identifies cancer-related genes that are missed by differential expression analysis, and that differential expression and differential variability identify functionally distinct sets of potentially cancer-related genes. These results suggest that differential variability analysis may provide insights into genetic aspects of cancer that would not be revealed by differential expression, and that differential distribution analysis may allow for more comprehensive identification of cancer-related genes than analyses based on changes in mean or variability alone.
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Balliu B, Durrant M, Goede OD, Abell N, Li X, Liu B, Gloudemans MJ, Cook NL, Smith KS, Knowles DA, Pala M, Cucca F, Schlessinger D, Jaiswal S, Sabatti C, Lind L, Ingelsson E, Montgomery SB. Genetic regulation of gene expression and splicing during a 10-year period of human aging. Genome Biol 2019; 20:230. [PMID: 31684996 PMCID: PMC6827221 DOI: 10.1186/s13059-019-1840-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 09/27/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Molecular and cellular changes are intrinsic to aging and age-related diseases. Prior cross-sectional studies have investigated the combined effects of age and genetics on gene expression and alternative splicing; however, there has been no long-term, longitudinal characterization of these molecular changes, especially in older age. RESULTS We perform RNA sequencing in whole blood from the same individuals at ages 70 and 80 to quantify how gene expression, alternative splicing, and their genetic regulation are altered during this 10-year period of advanced aging at a population and individual level. We observe that individuals are more similar to their own expression profiles later in life than profiles of other individuals their own age. We identify 1291 and 294 genes differentially expressed and alternatively spliced with age, as well as 529 genes with outlying individual trajectories. Further, we observe a strong correlation of genetic effects on expression and splicing between the two ages, with a small subset of tested genes showing a reduction in genetic associations with expression and splicing in older age. CONCLUSIONS These findings demonstrate that, although the transcriptome and its genetic regulation is mostly stable late in life, a small subset of genes is dynamic and is characterized by a reduction in genetic regulation, most likely due to increasing environmental variance with age.
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Affiliation(s)
- Brunilda Balliu
- Department of Pathology, Stanford University School of Medicine, Stanford, USA.
| | - Matthew Durrant
- Department of Genetics, Stanford University School of Medicine, Stanford, USA
| | - Olivia de Goede
- Department of Genetics, Stanford University School of Medicine, Stanford, USA
| | - Nathan Abell
- Department of Genetics, Stanford University School of Medicine, Stanford, USA
| | - Xin Li
- Department of Pathology, Stanford University School of Medicine, Stanford, USA
| | - Boxiang Liu
- Department of Biology, Stanford University School of Medicine, Stanford, USA
| | | | - Naomi L Cook
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Kevin S Smith
- Department of Pathology, Stanford University School of Medicine, Stanford, USA
| | | | - Mauro Pala
- Dipartimento di Scienze Biomediche, Universita di Sassari, Sassari, Italy
| | - Francesco Cucca
- Dipartimento di Scienze Biomediche, Universita di Sassari, Sassari, Italy
| | | | - Siddhartha Jaiswal
- Department of Pathology, Stanford University School of Medicine, Stanford, USA
| | - Chiara Sabatti
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, USA
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Erik Ingelsson
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, USA.
- Stanford Cardiovascular Institute, Stanford University, Stanford, USA.
- Stanford Diabetes Research Center, Stanford University, Stanford, USA.
| | - Stephen B Montgomery
- Department of Pathology, Stanford University School of Medicine, Stanford, USA.
- Department of Genetics, Stanford University School of Medicine, Stanford, USA.
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3
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Kedlian VR, Donertas HM, Thornton JM. The widespread increase in inter-individual variability of gene expression in the human brain with age. Aging (Albany NY) 2019; 11:2253-2280. [PMID: 31003228 PMCID: PMC6520006 DOI: 10.18632/aging.101912] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 04/05/2019] [Indexed: 11/25/2022]
Abstract
Aging is broadly defined as a time-dependent progressive decline in the functional and physiological integrity of organisms. Previous studies and evolutionary theories of aging suggest that aging is not a programmed process but reflects dynamic stochastic events. In this study, we test whether transcriptional noise shows an increase with age, which would be expected from stochastic theories. Using human brain transcriptome dataset, we analyzed the heterogeneity in the transcriptome for individual genes and functional pathways, employing different analysis methods and pre-processing steps. We show that unlike expression level changes, changes in heterogeneity are highly dependent on the methodology and the underlying assumptions. Although the particular set of genes that can be characterized as differentially variable is highly dependent on the methods, we observe a consistent increase in heterogeneity at every level, independent of the method. In particular, we demonstrate a weak but reproducible transcriptome-wide shift towards an increase in heterogeneity, with twice as many genes significantly increasing as opposed to decreasing their heterogeneity. Furthermore, this pattern of increasing heterogeneity is not specific but is associated with a wide range of pathways.
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Affiliation(s)
- Veronika R. Kedlian
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton CB10 1SD, UK
- Current Address - Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK
- Equal contribution
| | - Handan Melike Donertas
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton CB10 1SD, UK
- Equal contribution
| | - Janet M. Thornton
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton CB10 1SD, UK
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4
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Viñuela A, Brown AA, Buil A, Tsai PC, Davies MN, Bell JT, Dermitzakis ET, Spector TD, Small KS. Age-dependent changes in mean and variance of gene expression across tissues in a twin cohort. Hum Mol Genet 2018; 27:732-741. [PMID: 29228364 PMCID: PMC5886097 DOI: 10.1093/hmg/ddx424] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 11/10/2017] [Accepted: 11/29/2017] [Indexed: 12/13/2022] Open
Abstract
Changes in the mean and variance of gene expression with age have consequences for healthy aging and disease development. Age-dependent changes in phenotypic variance have been associated with a decline in regulatory functions leading to increase in disease risk. Here, we investigate age-related mean and variance changes in gene expression measured by RNA-seq of fat, skin, whole blood and derived lymphoblastoid cell lines (LCLs) expression from 855 adult female twins. We see evidence of up to 60% of age effects on transcription levels shared across tissues, and 47% of those on splicing. Using gene expression variance and discordance between genetically identical MZ twin pairs, we identify 137 genes with age-related changes in variance and 42 genes with age-related discordance between co-twins; implying the latter are driven by environmental effects. We identify four eQTLs whose effect on expression is age-dependent (FDR 5%). Combined, these results show a complicated mix of environmental and genetically driven changes in expression with age. Using the twin structure in our data, we show that additive genetic effects explain considerably more of the variance in gene expression than aging, but less that other environmental factors, potentially explaining why reliable expression-derived biomarkers for healthy-aging have proved elusive compared with those derived from methylation.
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Affiliation(s)
- Ana Viñuela
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, SE1 7EH London, UK
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland
- Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland
| | - Andrew A Brown
- Wellcome Trust Sanger Institute, Hinxton CB10 1SA, Cambridge, UK
- Division of Mental Health and Addiction, NORMENT, KG Jebsen Centre for Psychosis Research, Oslo University Hospital, Oslo 0450, Norway
| | - Alfonso Buil
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland
- Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland
| | - Pei-Chien Tsai
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, SE1 7EH London, UK
| | - Matthew N Davies
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, SE1 7EH London, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, SE1 7EH London, UK
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland
- Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, SE1 7EH London, UK
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, SE1 7EH London, UK
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5
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Mangold CA, Wronowski B, Du M, Masser DR, Hadad N, Bixler GV, Brucklacher RM, Ford MM, Sonntag WE, Freeman WM. Sexually divergent induction of microglial-associated neuroinflammation with hippocampal aging. J Neuroinflammation 2017; 14:141. [PMID: 28732515 PMCID: PMC5521082 DOI: 10.1186/s12974-017-0920-8] [Citation(s) in RCA: 116] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 07/13/2017] [Indexed: 01/11/2023] Open
Abstract
Background The necessity of including both males and females in molecular neuroscience research is now well understood. However, there is relatively limited basic biological data on brain sex differences across the lifespan despite the differences in age-related neurological dysfunction and disease between males and females. Methods Whole genome gene expression of young (3 months), adult (12 months), and old (24 months) male and female C57BL6 mice hippocampus was analyzed. Subsequent bioinformatic analyses and confirmations of age-related changes and sex differences in hippocampal gene and protein expression were performed. Results Males and females demonstrate both common expression changes with aging and marked sex differences in the nature and magnitude of the aging responses. Age-related hippocampal induction of neuroinflammatory gene expression was sexually divergent and enriched for microglia-specific genes such as complement pathway components. Sexually divergent C1q protein expression was confirmed by immunoblotting and immunohistochemistry. Similar patterns of cortical sexually divergent gene expression were also evident. Additionally, inter-animal gene expression variability increased with aging in males, but not females. Conclusions These findings demonstrate sexually divergent neuroinflammation with aging that may contribute to sex differences in age-related neurological diseases such as stroke and Alzheimer’s, specifically in the complement system. The increased expression variability in males suggests a loss of fidelity in gene expression regulation with aging. These findings reveal a central role of sex in the transcriptomic response of the hippocampus to aging that warrants further, in depth, investigations. Electronic supplementary material The online version of this article (doi:10.1186/s12974-017-0920-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Colleen A Mangold
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, State College, PA, USA
| | - Benjamin Wronowski
- Department of Physiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Mei Du
- Department of Physiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Dustin R Masser
- Department of Physiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.,Reynolds Oklahoma Center on Aging & Nathan Shock Center of Excellence in the Biology of Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Niran Hadad
- Reynolds Oklahoma Center on Aging & Nathan Shock Center of Excellence in the Biology of Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.,Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Georgina V Bixler
- Genome Sciences Facility, Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Robert M Brucklacher
- Genome Sciences Facility, Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Matthew M Ford
- Division of Neuroscience, Oregon National Primate Research Center, Beaverton, Oregon, USA
| | - William E Sonntag
- Department of Physiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.,Reynolds Oklahoma Center on Aging & Nathan Shock Center of Excellence in the Biology of Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.,Department of Geriatric Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, USA
| | - Willard M Freeman
- Department of Physiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA. .,Reynolds Oklahoma Center on Aging & Nathan Shock Center of Excellence in the Biology of Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA. .,Department of Geriatric Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, USA. .,, SLY-BRC 1370, 975 NE 10th St, Oklahoma City, OK, 73104, USA.
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6
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Brinkmeyer-Langford CL, Guan J, Ji G, Cai JJ. Aging Shapes the Population-Mean and -Dispersion of Gene Expression in Human Brains. Front Aging Neurosci 2016; 8:183. [PMID: 27536236 PMCID: PMC4971101 DOI: 10.3389/fnagi.2016.00183] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Accepted: 07/15/2016] [Indexed: 11/13/2022] Open
Abstract
Human aging is associated with cognitive decline and an increased risk of neurodegenerative disease. Our objective for this study was to evaluate potential relationships between age and variation in gene expression across different regions of the brain. We analyzed the Genotype-Tissue Expression (GTEx) data from 54 to 101 tissue samples across 13 brain regions in post-mortem donors of European descent aged between 20 and 70 years at death. After accounting for the effects of covariates and hidden confounding factors, we identified 1446 protein-coding genes whose expression in one or more brain regions is correlated with chronological age at a false discovery rate of 5%. These genes are involved in various biological processes including apoptosis, mRNA splicing, amino acid biosynthesis, and neurotransmitter transport. The distribution of these genes among brain regions is uneven, suggesting variable regional responses to aging. We also found that the aging response of many genes, e.g., TP37 and C1QA, depends on individuals' genotypic backgrounds. Finally, using dispersion-specific analysis, we identified genes such as IL7R, MS4A4E, and TERF1/TERF2 whose expressions are differentially dispersed by aging, i.e., variances differ between age groups. Our results demonstrate that age-related gene expression is brain region-specific, genotype-dependent, and associated with both mean and dispersion changes. Our findings provide a foundation for more sophisticated gene expression modeling in the studies of age-related neurodegenerative diseases.
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Affiliation(s)
| | - Jinting Guan
- Department of Automation, Xiamen UniversityXiamen, China
| | - Guoli Ji
- Department of Automation, Xiamen UniversityXiamen, China
- Innovation Center for Cell Signaling Network, Xiamen UniversityXiamen, China
| | - James J. Cai
- Department of Veterinary Integrative Biosciences, Texas A&M UniversityCollege Station, TX, USA
- Interdisciplinary Program in Genetics, Texas A&M UniversityCollege Station, TX, USA
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7
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Zhang Q, Nogales-Cadenas R, Lin JR, Zhang W, Cai Y, Vijg J, Zhang ZD. Systems-level analysis of human aging genes shed new light on mechanisms of aging. Hum Mol Genet 2016; 25:2934-2947. [PMID: 27179790 DOI: 10.1093/hmg/ddw145] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Revised: 04/07/2016] [Accepted: 05/09/2016] [Indexed: 11/13/2022] Open
Abstract
Although studies over the last decades have firmly connected a number of genes and molecular pathways to aging, the aging process as a whole still remains poorly understood. To gain novel insights into the mechanisms underlying aging, instead of considering aging genes individually, we studied their characteristics at the systems level in the context of biological networks. We calculated a comprehensive set of network characteristics for human aging-related genes from the GenAge database. By comparing them with other functional groups of genes, we identified a robust group of aging-specific network characteristics. To find the structural basis and the molecular mechanisms underlying this aging-related network specificity, we also analyzed protein domain interactions and gene expression patterns across different tissues. Our study revealed that aging genes not only tend to be network hubs, playing important roles in communication among different functional modules or pathways, but also are more likely to physically interact and be co-expressed with essential genes. The high expression of aging genes across a large number of tissue types also points to a high level of connectivity among aging genes. Unexpectedly, contrary to the depletion of interactions among hub genes in biological networks, we observed close interactions among aging hubs, which renders the aging subnetworks vulnerable to random attacks and thus may contribute to the aging process. Comparison across species reveals the evolution process of the aging subnetwork. As the organisms become more complex, the complexity of its aging mechanisms increases and their aging hub genes are more functionally connected.
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Affiliation(s)
| | | | | | | | | | - Jan Vijg
- Department of Genetics.,Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, NY, USA
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8
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Molecular analyses provide insight into mechanisms underlying sarcopenia and myofibre denervation in old skeletal muscles of mice. Int J Biochem Cell Biol 2014; 53:174-85. [DOI: 10.1016/j.biocel.2014.04.025] [Citation(s) in RCA: 54] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2014] [Revised: 04/20/2014] [Accepted: 04/29/2014] [Indexed: 12/23/2022]
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9
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Serum biomarkers of aging in the Brown Norway rat. Exp Gerontol 2011; 46:953-7. [DOI: 10.1016/j.exger.2011.07.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2011] [Revised: 05/09/2011] [Accepted: 07/19/2011] [Indexed: 12/21/2022]
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10
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Southworth LK, Owen AB, Kim SK. Aging mice show a decreasing correlation of gene expression within genetic modules. PLoS Genet 2009; 5:e1000776. [PMID: 20019809 PMCID: PMC2788246 DOI: 10.1371/journal.pgen.1000776] [Citation(s) in RCA: 121] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2009] [Accepted: 11/18/2009] [Indexed: 12/22/2022] Open
Abstract
In this work we present a method for the differential analysis of gene co-expression networks and apply this method to look for large-scale transcriptional changes in aging. We derived synonymous gene co-expression networks from AGEMAP expression data for 16-month-old and 24-month-old mice. We identified a number of functional gene groups that change co-expression with age. Among these changing groups we found a trend towards declining correlation with age. In particular, we identified a modular (as opposed to uniform) decline in general correlation with age. We identified potential transcriptional mechanisms that may aid in modular correlation decline. We found that computationally identified targets of the NF-ΚB transcription factor decrease expression correlation with age. Finally, we found that genes that are prone to declining co-expression tend to be co-located on the chromosome. Our results conclude that there is a modular decline in co-expression with age in mice. They also indicate that factors relating to both chromosome domains and specific transcription factors may contribute to the decline. There is mounting evidence that mammalian aging is marked by increased gene transcriptional variation. This trend was shown not only by studying gene expression in single cells (Bahar et al. 2006), but at the coarse tissue resolution as well (Somel et al. 2006; Li et al. 2009). These led us to believe that looking at absolute changes in expression level alone may not tell the whole story of transcriptional changes in age. Instead the story may be in the more subtle changes in the coordination of expression among multiple genes. For this reason, we decided to look at changes in co-expression relationships with age. To this end, we developed a methodology for differential co-expression network analysis for the comparison gene co-expression on a global scale. We applied this methodology to compare co-expression between young (16-month) and old (24-month) mice. This allowed us to find both gene groups whose coordination appear to be affected by age and to propose potential mechanisms for the change. We believe our work is of broad importance because it represents a different paradigm for looking not only at aging but also at any complex condition or disease—away from changes in individual genes towards changes in gene relationships.
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Affiliation(s)
- Lucinda K. Southworth
- Biomedical Informatics, Stanford University, Stanford, California, United States of America
| | - Art B. Owen
- Statistics, Stanford University, Stanford, California, United States of America
| | - Stuart K. Kim
- Developmental Biology, Stanford University, Stanford, California, United States of America
- * E-mail:
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11
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Ho JWK, Stefani M, dos Remedios CG, Charleston MA. A model selection approach to discover age-dependent gene expression patterns using quantile regression models. BMC Genomics 2009; 10 Suppl 3:S16. [PMID: 19958479 PMCID: PMC2788368 DOI: 10.1186/1471-2164-10-s3-s16] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background It has been a long-standing biological challenge to understand the molecular regulatory mechanisms behind mammalian ageing. Harnessing the availability of many ageing microarray datasets, a number of studies have shown that it is possible to identify genes that have age-dependent differential expression (DE) or differential variability (DV) patterns. The majority of the studies identify "interesting" genes using a linear regression approach, which is known to perform poorly in the presence of outliers or if the underlying age-dependent pattern is non-linear. Clearly a more robust and flexible approach is needed to identify genes with various age-dependent gene expression patterns. Results Here we present a novel model selection approach to discover genes with linear or non-linear age-dependent gene expression patterns from microarray data. To identify DE genes, our method fits three quantile regression models (constant, linear and piecewise linear models) to the expression profile of each gene, and selects the least complex model that best fits the available data. Similarly, DV genes are identified by fitting and comparing two quantile regression models (non-DV and the DV models) to the expression profile of each gene. We show that our approach is much more robust than the standard linear regression approach in discovering age-dependent patterns. We also applied our approach to analyze two human brain ageing datasets and found many biologically interesting gene expression patterns, including some very interesting DV patterns, that have been overlooked in the original studies. Furthermore, we propose that our model selection approach can be extended to discover DE and DV genes from microarray datasets with discrete class labels, by considering different quantile regression models. Conclusion In this paper, we present a novel application of quantile regression models to identify genes that have interesting linear or non-linear age-dependent expression patterns. One important contribution of this paper is to introduce a model selection approach to DE and DV gene identification, which is most commonly tackled by null hypothesis testing approaches. We show that our approach is robust in analyzing real and simulated datasets. We believe that our approach is applicable in many ageing or time-series data analysis tasks.
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Affiliation(s)
- Joshua W K Ho
- School of Information Technologies, The University of Sydney, NSW 2006, Australia.
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