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      A Genomic Pathway Approach to a Complex Disease: Axon Guidance and Parkinson Disease

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          Abstract

          While major inroads have been made in identifying the genetic causes of rare Mendelian disorders, little progress has been made in the discovery of common gene variations that predispose to complex diseases. The single gene variants that have been shown to associate reproducibly with complex diseases typically have small effect sizes or attributable risks. However, the joint actions of common gene variants within pathways may play a major role in predisposing to complex diseases (the paradigm of complex genetics). The goal of this study was to determine whether polymorphism in a candidate pathway (axon guidance) predisposed to a complex disease (Parkinson disease [PD]). We mined a whole-genome association dataset and identified single nucleotide polymorphisms (SNPs) that were within axon-guidance pathway genes. We then constructed models of axon-guidance pathway SNPs that predicted three outcomes: PD susceptibility (odds ratio = 90.8, p = 4.64 × 10 −38), survival free of PD (hazards ratio = 19.0, p = 5.43 × 10 −48), and PD age at onset ( R 2 = 0.68, p = 1.68 × 10 −51). By contrast, models constructed from thousands of random selections of genomic SNPs predicted the three PD outcomes poorly. Mining of a second whole-genome association dataset and mining of an expression profiling dataset also supported a role for many axon-guidance pathway genes in PD. These findings could have important implications regarding the pathogenesis of PD. This genomic pathway approach may also offer insights into other complex diseases such as Alzheimer disease, diabetes mellitus, nicotine and alcohol dependence, and several cancers.

          Author Summary

          Complex diseases are common disorders that are believed to have many causes. Examples include Alzheimer disease, diabetes mellitus, nicotine and alcohol dependence, and several cancers. This study represents a paradigm shift from single gene to pathway studies of complex diseases. We present the example of Parkinson disease (PD) and a complex array of chemical signals that wires the brain during fetal development (the axon guidance pathway). We mined a dataset that studied hundreds of thousands of DNA variations (single nucleotide polymorphisms [SNPs]) in persons with and without PD and identified SNPs that were assigned to axon-guidance pathway genes. We then identified sets of SNPs that were highly predictive of PD susceptibility, survival free of PD, and age at onset of PD. The effect sizes and the statistical significance observed for the pathway were far greater than for any single gene. We validated our findings for the pathway using a second SNP dataset for PD and also a dataset for PD that studied RNA variations. There is prior evidence that the axon guidance pathway might play a role in other brain disorders (e.g., Alzheimer disease, Tourette syndrome, dyslexia, epilepsy, and schizophrenia). A genomic pathway approach may lead to important breakthroughs for many complex diseases.

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          Most cited references51

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          The future of genetic studies of complex human diseases.

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            A vision for the future of genomics research.

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              Genome-wide strategies for detecting multiple loci that influence complex diseases.

              After nearly 10 years of intense academic and commercial research effort, large genome-wide association studies for common complex diseases are now imminent. Although these conditions involve a complex relationship between genotype and phenotype, including interactions between unlinked loci, the prevailing strategies for analysis of such studies focus on the locus-by-locus paradigm. Here we consider analytical methods that explicitly look for statistical interactions between loci. We show first that they are computationally feasible, even for studies of hundreds of thousands of loci, and second that even with a conservative correction for multiple testing, they can be more powerful than traditional analyses under a range of models for interlocus interactions. We also show that plausible variations across populations in allele frequencies among interacting loci can markedly affect the power to detect their marginal effects, which may account in part for the well-known difficulties in replicating association results. These results suggest that searching for interactions among genetic loci can be fruitfully incorporated into analysis strategies for genome-wide association studies.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                pgen
                plge
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                June 2007
                15 June 2007
                : 3
                : 6
                : e98
                Affiliations
                [1 ] Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota, United States of America
                [2 ] Department of Neurology, Miller School of Medicine, University of Miami, Miami, Florida, United States of America
                [3 ] Gene Logic, Gaithersburg, Maryland, United States of America
                [4 ] Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, Florida, United States of America
                [5 ] Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine, Rochester, Minnesota, United States of America
                [6 ] Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota, United States of America
                [7 ] Department of Neurology, Mayo Clinic College of Medicine, Rochester, Minnesota, United States of America
                Baylor College of Medicine, United States of America
                Author notes
                * To whom correspondence should be addressed. E-mail: dmaraganore@ 123456mayo.edu
                Article
                06-PLGE-RA-0572R2 plge-03-06-10
                10.1371/journal.pgen.0030098
                1904362
                17571925
                fbd2cba8-ef1a-4f27-b158-93f9547711e1
                Copyright: © 2007 Lesnick et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 29 December 2006
                : 2 May 2007
                Page count
                Pages: 12
                Categories
                Research Article
                Cell Biology
                Cell Biology
                Developmental Biology
                Genetics and Genomics
                Genetics and Genomics
                Genetics and Genomics
                Genetics and Genomics
                Neurological Disorders
                Neuroscience
                Neuroscience
                Neuroscience
                Neuroscience
                Homo (Human)
                Custom metadata
                Lesnick TG, Papapetropoulos S, Mash DC, Ffrench-Mullen J, Shehadeh L, et al. (2007) A genomic pathway approach to a complex disease: Axon guidance and Parkinson disease. PLoS Genet 3(6): e98. doi: 10.1371/journal.pgen.0030098

                Genetics
                Genetics

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