Pathway analysis for genome-wide genetic variation data: Analytic principles,latest developments,and new opportunities |
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| Authors: | Micah Silberstein Nicholas Nesbit Jacquelyn Cai Phil H Lee |
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| Institution: | 1. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA;2. Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA;3. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA;1. Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410083, China;2. Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China;3. DIAGenes Precision Medicine, Beijing 102600, China;4. National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410083, China;5. Shanghai Adeptus Biotechnology, Shanghai 200126, China;6. Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325035, China;7. Department of Neurology, Xiangya Hospital, Central South University, Changsha Hunan, 410083, China;8. CAS Center for Excellence in Brain Science and Intelligences Technology (CEBSIT), Shanghai 200031, China;9. School of Basic Medical Science, Central South University, Changsha, Hunan, 410083, China;10. CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing 100049, China;11. State Key Laboratory of Integrated Management of Pest Insects and Rodents, Chinese Academy of Sciences, Beijing 100101, China;1. Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA, 22904 USA;2. Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, 22904 USA;1. Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China;2. The Institute of Translational Medicine, Zhejiang University, Hangzhou 310058, China;3. Department of Cardiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China;4. Department of Integrated Biosciences, Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba 277-8562, Japan;5. Zhejiang Provincial Key Lab of Ophthalmology, Hangzhou 310058, China;6. MOE Laboratory of Biosystems Homeostasis & Protection and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou 310058, China;1. Hunan Provincial Key Laboratory of Phytohormones and Growth Development, College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128, China;2. Department of Botany, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada;3. Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada;1. State Key Laboratory of Genetic Engineering, Ministry of Education Key Laboratory of Biodiversity Sciences and Ecological Engineering, Institute of Plant Biology, School of Life Sciences, Fudan University, Shanghai 200438, China;2. National Centre for Plant Gene Research (Beijing), Innovation Academy for Seed Design, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China;3. College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing 100039, China |
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| Abstract: | Pathway analysis, also known as gene-set enrichment analysis, is a multilocus analytic strategy that integrates a priori, biological knowledge into the statistical analysis of high-throughput genetics data. Originally developed for the studies of gene expression data, it has become a powerful analytic procedure for indepth mining of genome-wide genetic variation data. Astonishing discoveries were made in the past years,uncovering genes and biological mechanisms underlying common and complex disorders. However, as massive amounts of diverse functional genomics data accrue, there is a pressing need for newer generations of pathway analysis methods that can utilize multiple layers of high-throughput genomics data. In this review, we provide an intellectual foundation of this powerful analytic strategy, as well as an update of the state-of-the-art in recent method developments. The goal of this review is threefold:(1) introduce the motivation and basic steps of pathway analysis for genome-wide genetic variation data;(2) review the merits and the shortcomings of classic and newly emerging integrative pathway analysis tools; and(3)discuss remaining challenges and future directions for further method developments. |
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| Keywords: | Pathway analysis Set-based association analysis Gene-set enrichment analysis Genome-wide association study Multilocus association analysis |
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