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Genotator: A disease-agnostic tool for genetic annotation of disease
© Wall et al; licensee BioMed Central Ltd. 2010
Received: 2 July 2010
Accepted: 29 October 2010
Published: 29 October 2010
Disease-specific genetic information has been increasing at rapid rates as a consequence of recent improvements and massive cost reductions in sequencing technologies. Numerous systems designed to capture and organize this mounting sea of genetic data have emerged, but these resources differ dramatically in their disease coverage and genetic depth. With few exceptions, researchers must manually search a variety of sites to assemble a complete set of genetic evidence for a particular disease of interest, a process that is both time-consuming and error-prone.
We designed a real-time aggregation tool that provides both comprehensive coverage and reliable gene-to-disease rankings for any disease. Our tool, called Genotator, automatically integrates data from 11 externally accessible clinical genetics resources and uses these data in a straightforward formula to rank genes in order of disease relevance. We tested the accuracy of coverage of Genotator in three separate diseases for which there exist specialty curated databases, Autism Spectrum Disorder, Parkinson's Disease, and Alzheimer Disease. Genotator is freely available at http://genotator.hms.harvard.edu.
Genotator demonstrated that most of the 11 selected databases contain unique information about the genetic composition of disease, with 2514 genes found in only one of the 11 databases. These findings confirm that the integration of these databases provides a more complete picture than would be possible from any one database alone. Genotator successfully identified at least 75% of the top ranked genes for all three of our use cases, including a 90% concordance with the top 40 ranked candidates for Alzheimer Disease.
As a meta-query engine, Genotator provides high coverage of both historical genetic research as well as recent advances in the genetic understanding of specific diseases. As such, Genotator provides a real-time aggregation of ranked data that remains current with the pace of research in the disease fields. Genotator's algorithm appropriately transforms query terms to match the input requirements of each targeted databases and accurately resolves named synonyms to ensure full coverage of the genetic results with official nomenclature. Genotator generates an excel-style output that is consistent across disease queries and readily importable to other applications.
The promise of personalized and genetic-based medicine has encouraged researchers to develop new technologies to search for genetic causes of common disease. More and more genome data are becoming available due to these technological advances in genotyping and increasing numbers of genome-wide association studies (GWAS). Concomitant with this rise in disease genomics is the rise of publicly accessible databases to report clinical relevance of mutations and provide rankings of genes in terms of their roles in disease. Top among these include several large and established resources like Online Mendelian Inheritance in Man (OMIM) , The Human Gene Mutation Database (HGMD) , and GeneCards  that attempt to cover a wide range of human disease using semi-automated approaches, as well as smaller resources devoted to manual reports of genetic association to high profile diseases, including SFARI Gene for Autism , PDGene for Parkinson's Disease (PD) , and AlzGene for Alzheimer Disease . The former tend to cover large numbers of human diseases, but also tend to differ, sometimes dramatically, in their concepts of disease-gene association. The latter tend to be rich in content and reliability, but also have a tendency to be incomplete in coverage and lag behind the emerging research trends, at least in part because much of their content is added manually. In addition, the number of such manually curated databases remains restricted to priority diseases for which federal or other funding is available. Many databases are also rarely updated, difficult to navigate, provide information without rankings, maintain inconsistent coverage of information and/or lack a description of their methodology. While all of these resources have merit, a researcher interested in the complete set of the genetic knowledge for a disease or set of diseases is left having to search through a variety of databases to manually compile the results. A resource that automates this process by computationally integrating results from a host of resources to provide best-of-breed genetic knowledge would be a tremendous boon to the field.
Motivated by the need for an integrated system for gene-disease knowledge, we built an engine called Genotator that integrates a wide array of trusted databases and provides a rich set of annotations relating genes to disease. We designed Genotator as a fully automated real-time aggregator of all relevant and up-to-date information about each gene linked to a particular disease. Every disease term queried in the Genotator returns with a list of genes and relevant attributes and annotations for each gene. In the present manuscript we describe the Genotator algorithm and demonstrate its functionality using three use cases, Autism Spectrum Disorder (ASD), Parkinson's Disease (PD), and Alzheimer Disease (AD).
Databases integrated within Genotator (Statistics Gathered: June 19, 2010)
Searchable database of human genes that provides concise genomic, genetic and functional information
73,017 Entries, 28,656 of them with symbols approved by the HUGO gene nomenclature committee
Genetic Association Database 
Archive of human genetic association studies of complex diseases and disorders
2673 genes for 5636 diseases/phenotypes
HuGE Navigator 
Knowledge base including information on gene-disease and gene-gene associations
9429 genes for 2215 diseases
Human Gene Mutation Database 
Database established for the study of mutational mechanisms in human genes
72414 mutations in public release
Online Mendelian Inheritance in Man 
NCBI's compendium of human genes and genetic phenotypes
13, 158 genes for 2799 phenotypes/diseases
Your Favorite Gene
Database containing scientific descriptions and overviews of genes and their corresponding proteins with links to the most used gene data-banks
~ 8595 genes
Central database of protein sequences with accurate, consistent, rich sequence and functional annotation
UniProtKB/Swiss-Prot Release 2010_06 of 18-May-10 of contains 517100 sequence entries
UniProtKB/TrEMBL Release 2010_07 of 15-Jun-2010 of contains 11109684 sequence entries
Database of primary genotype and phenotype data, annotate gene variants and gene-drug-disease relationships
3,197 diseases, 26,216 genes
Searchable database of genes, from RefSeq genomes, and defined by sequence and/or located in the NCBI Map Viewer
Non-profit, open access database of articles on genes, proteins and chemical compounds
Database containing relevant information with respect to gene mapping and genetic diseases
22466 genes/4434 phenotypes
The characteristics included in Genotator from select resources.
Genetic Association Database
List of Genes
"Yes" Association (published statement of link between gene and disease phenotype)
"No" Association (published statement of no link between gene and disease)
p-value (from genome wide association studies)
List of Genes (official gene symbols)
Gene Prospector Score 
List of PubMed References
List of Genes (official gene symbols)
Official Full Name
Location on Chromosome
In addition, we included a select group of high-value parameters to enrich the quality of the data with regards to clinical and experimental relevance of each gene. Specifically, we included from HuGE Navigator  and the Genetic Association Database  the manually assigned labels "Yes Association", "No Association", "Unknown Association" (where yes association indicates positive support for a gene's role in the disease phenotype) for each gene-article pair, p-values from genome-wide association studies, and the Gene Prospector score of association . All key parameters were then compiled into an attribute vector for scoring and ranking of genes (described in the section immediately below). Finally, to obtain a list of publications supporting a gene-disease pair, we queried HuGE Navigator first with the disease of interest and then separately with the gene name (in this case the official gene symbol) and reported the intersecting publications in the final results.
GAD Y = Total number of "Yes" labeled associations for the gene and disease in the Genetic Association Database
GAD N = Total number of "No" labeled associations for the gene and disease in the Genetic Association Database
GPS = Gene Prospector's score of gene-disease association
DB = Number of total databases (out of 11) that the gene appeared in
REF = Number of total references for the given gene
The constants ϕ , γ , and κ were used to weight the contributions of the GPS, DB, and REF parameters. We elected to set these values at 100, 10, and 5, respectively, as these numbers provided the best overall performance. However, we designed the Genotator software to enable users the ability to alter these parameters should alternative weighting schemes be desired. In addition, Genotator reports the complete set of attributes together with the GS score, thus enabling users the flexibility to retain the original scoring scheme or to define an alternative strategy. We devised the GS to serve as guide for gene prioritization that best captures the history of biomedical research findings while also ensuring adjustability of the threshold for inclusion/exclusion of genes.
We elected to use three trusted, manually updated, and disease-specific databases to provide (1) a list of vetted candidate genes that have clear association to the disease based on published research (linkage studies, genome-wide association studies, etc.), and (2) an independent ranking of the strength of association between the genes and the disease of interest against which to compare ranked results from Genotator. The three databases, SFARI Gene, PDGene, and AlzGene target Autism Spectrum Disorder (ASD), Parkinson Disease (PD), and Alzheimer's Disease (AD), respectively. SFARI gene is a leading repository of genetic information for ASD that is updated with high frequency by manual curators. PDGene is one of the most trusted web resources for genes associated with PD and has previously been used to validate the Gene Prospector scoring algorithm in Yu et al. . AlzGene  has a similar structure to PDGene, and is also widely regarded as an authoritative source of information on the genes associated with AD. To generate a metric for ranking the genes reported by SFARI, we subtracted the total number of negative from the total number of positive associations that were directly reported in the database. PDGene and AlzGene provided a ranking of the top gene candidates in section of their websites called "TopGenes". At the time of writing, PDGene listed 32 and AlzGene listed 40 top genes. In addition, both resources assigned a label of "strong", "moderate", or "weak" to each top gene candidate based upon amount of evidence, extent of replication and protection from bias . By utilizing these resources as a baseline for comparison, we could determine how well the automated procedures employed by Genotator compared to standards of manual annotation, both in terms of coverage of published research as well as in terms of prioritization of gene candidates.
The percent contribution of disease candidate genes from each of the 11 databases totaled over autism, Parkinson's Disease, and Alzheimer Disease.
Genetic Association Database
Your Favorite Gene
Human Gene Mutation Database
Online Mendelian Inheritance in Man
Top scoring autism genes ranked on Genotator score.
Linkage and association analysis at the serotonin transporter (SLC6A4) locus in a rigid-compulsive subset of autism.
Intragenic rearrangements in NRXN1 in three families with autism spectrum disorder, developmental delay, and speech delay.
Association and transmission analysis of the FMR1 IVS10 + 14C-T variant in autism
Subset of individuals with autism spectrum disorders and extreme macrocephaly associated with germline PTEN tumour suppressor gene mutations.
Point mutation analysis of the FMR-1 gene in autism.
Cognitive, behavioral, and neuroanatomical assessment of two unrelated male children expressing FRAXE.
Mental impairment and attention deficit hyperactivity disorder in a family with FRAXF.
Linkage, association, and gene-expression analyses identify CNTNAP2 as an autism-susceptibility gene.
Linkage disequilibrium at the Angelman syndrome gene UBE3A in autism families.
Common genetic variants on 5p14.1 associate with autism spectrum disorders.
Top 10 Parkinson's Disease Genes ranked on Genotator score.
Frequency of LRRK2 mutations in early-and late-onset Parkinson disease.
Different MAPT haplotypes are associated with Parkinson's disease and progressive supranuclear palsy.
Genome-wide association study confirms SNPs in SNCA and the MAPT region as common risk factors for Parkinson disease.
Case-control study of the parkin gene in early-onset Parkinson disease.
Phenotypic associations of tau and ApoE in Parkinson's disease.
Genotype-phenotype correlations between GBA mutations and Parkinson disease risk and onset.
BDNF Val66Met polymorphism is associated with cognitive impairment in Italian patients with Parkinson's disease.
Association of DRD3 and GRIN2B with impulse control and related behaviors in Parkinson's disease.
Association of variations in monoamine oxidases A and B with Parkinson's disease subgroups.
Parkin and PINK1 mutations in early-onset Parkinson's disease: comprehensive screening in publicly available cases and control.
Top 10 Alzheimer Disease Genes ranked on Genotator score.
Effect of APOE genotype on amyloid plaque load and gray matter volume in Alzheimer disease.
Early Onset Alzheimer's Disease with Spastic Paraparesis, Dysarthria and Seizures and N135 S Mutation in PSEN.
Amyloid-beta-Related Genes SORL1 and ACE are Genetically Associated With Risk for Late-onset Alzheimer Disease in the Chinese Population.
Earlier onset of Alzheimer's disease: risk polymorphisms within PRNP, PRND, CYP46, and APOE genes.
Dipeptidyl carboxypeptidase 1 (DCP1) and butyrylcholinesterase (BCHE) gene interactions with the apolipoprotein E epsilon4 allele as risk factors in Alzheimer's disease and in Parkinson's disease with coexisting Alzheimer pathology.
APOE and APOC1 promoter polymorphisms and the risk of Alzheimer disease in African American and Caribbean Hispanic individuals
Assessment of Alzheimer's disease case-control associations using family-based methods.
Pharmacogenomics in Alzheimer's disease.
Association of MTHFR gene polymorphism C677T with susceptibility to late-onset Alzheimer's disease.
Association between BDNF Val66Met polymorphism and Alzheimer disease, dementia with Lewy bodies, and Pick disease.
As we advance into the era of personalized medicine, our ability to annotate the human genome with clinically actionable information is paramount. An important step in that annotation process is accurate characterization of the genetic etiology of any human disease. Numerous informatics approaches have been and are being developed to assist in this process, including methods for filtering biomedical knowledge for gene-disease association (e.g. , ), as well as full scale natural language processing approaches [15, 16], although the corpora necessary for both high precision and recall remain incomplete . As these strategies have matured, an abundance of databases have emerged to provide summaries of recent and historical advances in human disease research. However, because these databases differ in their coverage of genes and annotation content, it is challenging for a researcher to develop a complete picture for a single disease or set of diseases of interest. In order to facilitate multi-database searching and to provide a more complete picture of advances in genetic research of human diseases, we developed a software tool called "Genotator". Genotator generates a comprehensive set of results for any disease by integrating gene and annotation data from 11 externally accessible and best-of-breed genetic resources. The results from Genotator are ranked using a scoring system that integrates bibliomic and genomic data and provides a preliminary likelihood of strength of association for use in future thesis testing.
To test the content of Genotator and assess the efficacy of its scoring system, we applied Genotator to three separate diseases: Autism Spectrum Disorder (ASD), Parkinson's Disease (PD) and Alzheimer Disease (AD), and compared our results to the three web resources devoted to manual curation of these diseases, SFARI gene, PDgene, and AlzGene, respectively. Genotator's results were in high agreement, with over 75% in common with the gene lists provided by SFARI gene for autism, nearly 60% in common with the PDGene for PD and almost 75% in common with AlzGene for AD. Furthermore, the rank order provided by Genotator matched the prioritizations by these resources, especially among the most highly 12 ranked cohort of genes, supporting the notion that Genotator provides similar coverage and quality to that available from manually curated, well-funded resources that are under active development.
In addition, we were able to demonstrate the value-add provided through integration of the 11 different resources used by Genotator. Nearly every database reported genes not reported by any of the other databases, replete with sufficient justification for the gene-disease link (Figure 3). Thus, Genotator can achieve algorithmically what would otherwise require extensive manual labor. Genotator also provides an enriched output with features often lacking from other disease annotation including synonym disambiguation, standard HGNC nomenclature, and the ability to download the entire dataset including annotations, pubmed ids, and scores.
As the boundaries between diseases become more obscured, and as our definitions evolve in the wake of new genetic information, resources that provide high coverage of human disease are becoming increasingly more important. While Genotator will not obviate the need for manually curated disease-specific databases going forward, it will enable researchers to keep pace with the research being done on their disease of interest, including those for which devoted websites do not currently exist.
Genotator is a comprehensive biomedical informatics tool that integrates over a host of mainstream databases to provide automatic and up-to-date information on any human disease. Based on our analysis using three well-studied disorders, we confirmed that the results generated by Genotator match the quality and coverage of manually curated and disease-specific databases. This outcome, coupled with the highly flexible and detailed output, make Genotator a novel and valuable contribution to the field.
Availability and Requirements
Software Name: Genotator
Project home page: http://genotator.hms.harvard.edu
Programming Languages: Java, Python; source code available by request
We thank Gisela Gonzalez for work with Genotator validation, Jack Kosmicki for his improvements to the Genotator website, Isaac S. Kohane and Mark Boguski for useful discussions and ideas, and Russ Altman and Wei Yu for constructive critiques of an earlier version of the manuscript. This research was facilitated through funding from NIMH under grant number 1R01MH090611-01A1 awarded to DPW.
- Online Mendelian Inheritance in Man, OMIM (TM). McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University (Baltimore, MD) and National Center for Biotechnology Information, National Library of Medicine (Bethesda, MD), September 7, 2010. [http://www.ncbi.nlm.nih.gov/omim/]
- Stenson PD, Mort M, Ball EV, Howells K, Phillips AD, Thomas NS, Cooper DN: The Human Gene Mutation Database: 2008 update. Genome Med. 2009, 1 (1): 13-10.1186/gm13.View ArticlePubMedPubMed CentralGoogle Scholar
- Rebhan M, Chalifa-Caspi V, Prilusky J, Lancet D: GeneCards: integrating information about genes, proteins and diseases. Trends Genet. 1997, 13 (4): 163-10.1016/S0168-9525(97)01103-7.View ArticlePubMedGoogle Scholar
- Banerjee-Basu S, Packer A: SFARI Gene: an evolving database for the autism research community. Dis Model Mech. 2010, 3 (3-4): 133-135. 10.1242/dmm.005439.View ArticlePubMedGoogle Scholar
- The PDGene Database. Alzheimer Research Forum. [http://www.pdgene.org/]
- Bertram L, McQueen M, Mullin K, Blacker D, Tanzi R: Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database. Nat Genet. 2007, 39 (1): 17-23. 10.1038/ng1934.View ArticlePubMedGoogle Scholar
- Yu W, Gwinn M, Clyne M, Yesupriya A, Khoury MJ: A navigator for human genome epidemiology. Nat Genet. 2008, 40 (2): 124-125. 10.1038/ng0208-124.View ArticlePubMedGoogle Scholar
- Becker KG, Barnes KC, Bright TJ, Wang SA: The genetic association database. Nat Genet. 2004, 36 (5): 431-432. 10.1038/ng0504-431.View ArticlePubMedGoogle Scholar
- Yu W, Wulf A, Liu T, Khoury MJ, Gwinn M: Gene Prospector: An evidence gateway for evaluating potential susceptibility genes and interacting risk factors for human diseases. 2009, 1-8.Google Scholar
- Ioannidis JPA, Boffetta P, Little J, O'Brien TR, Uitterlinden AG, Vineis P, Balding DJ, Chokkalingam A, Dolan SM, Flanders WD, et al: Assessment of cumulative evidence on genetic associations: interim guidelines. Int J Epidemiol. 2008, 37 (1): 120-132. 10.1093/ije/dym159.View ArticlePubMedGoogle Scholar
- Abrahams BS, Geschwind DH: Advances in autism genetics: on the threshold of a new neurobiology. Nat Rev Genet. 2008, 9 (5): 341-355. 10.1038/nrg2346.View ArticlePubMedPubMed CentralGoogle Scholar
- Wider C, Ross OA, Wszolek ZK: Genetics of Parkinson disease and essential tremor. Current Opinion in Neurology. 2010Google Scholar
- Hoffmann R, Valencia A: Implementing the iHOP concept for navigation of biomedical literature. Bioinformatics. 2005, 21 (Suppl 2): ii252-258. 10.1093/bioinformatics/bti1142.View ArticlePubMedGoogle Scholar
- Yue P, Melamud E, Moult J: SNPs3D: candidate gene and SNP selection for association studies. BMC Bioinformatics. 2006, 7: 166-10.1186/1471-2105-7-166.View ArticlePubMedPubMed CentralGoogle Scholar
- Krallinger M, Leitner F, Valencia A: Analysis of biological processes and diseases using text mining approaches. Methods Mol Biol. 2010, 593: 341-382. full_text.View ArticlePubMedGoogle Scholar
- Cano C, Monaghan T, Blanco A, Wall DP, Peshkin L: Collaborative text-annotation resource for disease-centered relation extraction from biomedical text. J Biomed Inform. 2009, 42 (5): 967-977. 10.1016/j.jbi.2009.02.001.View ArticlePubMedPubMed CentralGoogle Scholar
- Consortium U: The Universal Protein Resource (UniProt) in 2010. Nucleic Acids Research. 2010, D142-148. 38 DatabaseGoogle Scholar
- Klein TE, Chang JT, Cho MK, Easton KL, Fergerson R, Hewett M, Lin Z, Liu Y, Liu S, Oliver DE, et al: Integrating genotype and phenotype information: an overview of the PharmGKB project. Pharmacogenetics Research Network and Knowledge Base. Pharmacogenomics J. 2001, 1 (3): 167-170.View ArticlePubMedGoogle Scholar
- Hoffmann R: A wiki for the life sciences where authorship matters. Nat Genet. 2008, 40 (9): 1047-1051. 10.1038/ng.f.217.View ArticlePubMedGoogle Scholar
- Frézal J: Genatlas database, genes and development defects. C R Acad Sci III, Sci Vie. 1998, 321 (10): 805-817.View ArticlePubMedGoogle Scholar
- McCauley JL, Olson LM, Dowd M, Amin T, Steele A, Blakely RD, Folstein SE, Haines JL, Sutcliffe JS: Linkage and association analysis at the serotonin transporter (SLC6A4) locus in a rigid-compulsive subset of autism. Am J Med Genet B Neuropsychiatr Genet. 2004, 127B (1): 104-112. 10.1002/ajmg.b.20151.View ArticlePubMedGoogle Scholar
- Wiśniowiecka-Kowalnik B, Nesteruk M, Peters SU, Xia Z, Cooper ML, Savage S, Amato RS, Bader P, Browning MF, Haun CL, et al: Intragenic rearrangements in NRXN1 in three families with autism spectrum disorder, developmental delay, and speech delay. Am J Med Genet B Neuropsychiatr Genet. 2010Google Scholar
- Vincent JB, Thevarkunnel S, Kolozsvari D, Paterson AD, Roberts W, Scherer SW: Association and transmission analysis of the FMR1 IVS10 + 14C-T variant in autism. Am J Med Genet B Neuropsychiatr Genet. 2004, 125B (1): 54-56. 10.1002/ajmg.b.20088.View ArticlePubMedGoogle Scholar
- Butler MG, Dasouki MJ, Zhou X-P, Talebizadeh Z, Brown M, Takahashi TN, Miles JH, Wang CH, Stratton R, Pilarski R, et al: Subset of individuals with autism spectrum disorders and extreme macrocephaly associated with germline PTEN tumour suppressor gene mutations. J Med Genet. 2005, 42 (4): 318-321. 10.1136/jmg.2004.024646.View ArticlePubMedPubMed CentralGoogle Scholar
- Vincent JB, Konecki DS, Munstermann E, Bolton P, Poustka A, Poustka F, Gurling HM: Point mutation analysis of the FMR-1 gene in autism. Mol Psychiatry. 1996, 1 (3): 227-231.PubMedGoogle Scholar
- Abrams MT, Doheny KF, Mazzocco MM, Knight SJ, Baumgardner TL, Freund LS, Davies KE, Reiss AL: Cognitive, behavioral, and neuroanatomical assessment of two unrelated male children expressing FRAXE. Am J Med Genet. 1997, 74 (1): 73-81. 10.1002/(SICI)1096-8628(19970221)74:1<73::AID-AJMG16>3.0.CO;2-O.View ArticlePubMedGoogle Scholar
- Ehlers S, Billstedt E, Wahlstrom J: Mental impairment and attention deficit hyperactivity disorder in a family with FRAXF. Neurocase. 1999Google Scholar
- Alarcón M, Abrahams BS, Stone JL, Duvall JA, Perederiy JV, Bomar JM, Sebat J, Wigler M, Martin CL, Ledbetter DH, et al: Linkage, association, and gene-expression analyses identify CNTNAP2 as an autism-susceptibility gene. Am J Hum Genet. 2008, 82 (1): 150-159. 10.1016/j.ajhg.2007.09.005.View ArticlePubMedPubMed CentralGoogle Scholar
- Nurmi EL, Bradford Y, Chen Y, Hall J, Arnone B, Gardiner MB, Hutcheson HB, Gilbert JR, Pericak-Vance MA, Copeland-Yates SA, et al: Linkage disequilibrium at the Angelman syndrome gene UBE3A in autism families. Genomics. 2001, 77 (1-2): 105-113. 10.1006/geno.2001.6617.View ArticlePubMedGoogle Scholar
- Wang K, Zhang H, Ma D, Bucan M, Glessner JT, Abrahams BS, Salyakina D, Imielinski M, Bradfield JP, Sleiman PMA, et al: Common genetic variants on 5p14.1 associate with autism spectrum disorders. Nature. 2009, 459 (7246): 528-533. 10.1038/nature07999.View ArticlePubMedPubMed CentralGoogle Scholar
- Clark LN, Wang Y, Karlins E, Saito L, Mejia-Santana H, Harris J, Louis ED, Cote LJ, Andrews H, Fahn S, et al: Frequency of LRRK2 mutations in early- and late-onset Parkinson disease. Neurology. 2006, 67 (10): 1786-1791. 10.1212/01.wnl.0000244345.49809.36.View ArticlePubMedGoogle Scholar
- Ezquerra M, Pastor P, Gaig C, Vidal-Taboada JM, Cruchaga C, Muñoz E, Martí M-J, Valldeoriola F, Aguilar M, Calopa M, et al: Different MAPT haplotypes are associated with Parkinson's disease and progressive supranuclear palsy. Neurobiology of aging. 2009Google Scholar
- Edwards TL, Scott WK, Almonte C, Burt A, Powell EH, Beecham GW, Wang L, Züchner S, Konidari I, Wang G, et al: Genome-wide association study confirms SNPs in SNCA and the MAPT region as common risk factors for Parkinson disease. Ann Hum Genet. 2010, 74 (2): 97-109. 10.1111/j.1469-1809.2009.00560.x.View ArticlePubMedPubMed CentralGoogle Scholar
- Clark LN, Afridi S, Karlins E, Wang Y, Mejia-Santana H, Harris J, Louis ED, Cote LJ, Andrews H, Fahn S, et al: Case-control study of the parkin gene in early-onset Parkinson disease. Arch Neurol. 2006, 63 (4): 548-552. 10.1001/archneur.63.4.548.View ArticlePubMedGoogle Scholar
- Papapetropoulos S, Farrer MJ, Stone JT, Milkovic NM, Ross OA, Calvo L, McQuorquodale D, Mash DC: Phenotypic associations of tau and ApoE in Parkinson's disease. Neurosci Lett. 2007, 414 (2): 141-144. 10.1016/j.neulet.2006.12.008.View ArticlePubMedGoogle Scholar
- Gan-Or Z, Giladi N, Rozovski U, Shifrin C, Rosner S, Gurevich T, Bar-Shira A, Orr-Urtreger A: Genotype-phenotype correlations between GBA mutations and Parkinson disease risk and onset. Neurology. 2008, 70 (24): 2277-2283. 10.1212/01.wnl.0000304039.11891.29.View ArticlePubMedGoogle Scholar
- Guerini FR, Beghi E, Riboldazzi G, Zangaglia R, Pianezzola C, Bono G, Casali C, Di Lorenzo C, Agliardi C, Nappi G, et al: BDNF Val66Met polymorphism is associated with cognitive impairment in Italian patients with Parkinson's disease. Eur J Neurol. 2009, 16 (11): 1240-1245. 10.1111/j.1468-1331.2009.02706.x.View ArticlePubMedGoogle Scholar
- Lee J-Y, Lee EK, Park SS, Lim J-Y, Kim HJ, Kim JS, Jeon BS: Association of DRD3 and GRIN2B with impulse control and related behaviors in Parkinson's disease. Mov Disord. 2009, 24 (12): 1803-1810. 10.1002/mds.22678.View ArticlePubMedGoogle Scholar
- Parsian A, Racette B, Zhang ZH, Rundle M, Perlmutter JS: Association of variations in monoamine oxidases A and B with Parkinson's disease subgroups. Genomics. 2004, 83 (3): 454-460. 10.1016/j.ygeno.2003.09.002.View ArticlePubMedGoogle Scholar
- Brooks J, Ding J, Simon-Sanchez J, Paisan-Ruiz C, Singleton AB, Scholz SW: Parkin and PINK1 mutations in early-onset Parkinson's disease: comprehensive screening in publicly available cases and control. J Med Genet. 2009, 46 (6): 375-381. 10.1136/jmg.2008.063917.View ArticlePubMedPubMed CentralGoogle Scholar
- Drzezga A, Grimmer T, Henriksen G, Mühlau M, Perneczky R, Miederer I, Praus C, Sorg C, Wohlschläger A, Riemenschneider M, et al: Effect of APOE genotype on amyloid plaque load and gray matter volume in Alzheimer disease. Neurology. 2009, 72 (17): 1487-1494. 10.1212/WNL.0b013e3181a2e8d0.View ArticlePubMedGoogle Scholar
- Rudzinski L, Fletcher R, Dickson D: Early Onset Alzheimer's Disease with Spastic Paraparesis, Dysarthria and Seizures and N135 S Mutation in PSEN1. Alzheimer disease... 2008Google Scholar
- Ning M, Yang Y, Zhang Z, Chen Z, Zhao T, Zhang D, Zhou D, Xu J, Liu Z, Wang Y, et al: Amyloid-beta-Related Genes SORL1 and ACE are Genetically Associated With Risk for Late-onset Alzheimer Disease in the Chinese Population. Alzheimer disease and associated disorders. 2010Google Scholar
- Golanska E, Hulas-Bigoszewska K, Sieruta M, Zawlik I, Witusik M, Gresner SM, Sobow T, Styczynska M, Peplonska B, Barcikowska M, et al: Earlier onset of Alzheimer's disease: risk polymorphisms within PRNP, PRND, CYP46, and APOE genes. J Alzheimers Dis. 2009, 17 (2): 359-368.PubMedGoogle Scholar
- Mattila KM, Rinne JO, Röyttä M, Laippala P, Pietilä T, Kalimo H, Koivula T, Frey H, Lehtimäki T: Dipeptidyl carboxypeptidase 1 (DCP1) and butyrylcholinesterase (BCHE) gene interactions with the apolipoprotein E epsilon4 allele as risk factors in Alzheimer's disease and in Parkinson's disease with coexisting Alzheimer pathology. J Med Genet. 2000, 37 (10): 766-770. 10.1136/jmg.37.10.766.View ArticlePubMedPubMed CentralGoogle Scholar
- Tycko B, Lee JH, Ciappa A, Saxena A, Li C-M, Feng L, Arriaga A, Stern Y, Lantigua R, Shachter N, et al: APOE and APOC1 promoter polymorphisms and the risk of Alzheimer disease in African American and Caribbean Hispanic individuals. Arch Neurol. 2004, 61 (9): 1434-1439. 10.1001/archneur.61.9.1434.View ArticlePubMedGoogle Scholar
- Schjeide B-MM, McQueen MB, Mullin K, DiVito J, Hogan MF, Parkinson M, Hooli B, Lange C, Blacker D, Tanzi RE, et al: Assessment of Alzheimer's disease case-control associations using family-based methods. Neurogenetics. 2009, 10 (1): 19-25. 10.1007/s10048-008-0151-3.View ArticlePubMedGoogle Scholar
- Cacabelos R: Pharmacogenomics in Alzheimer's disease. Methods in molecular biology (Clifton, NJ). 2008, 448: 213-357. full_text.View ArticleGoogle Scholar
- Wang B, Jin F, Kan R, Ji S, Zhang C, Lu Z, Zheng C, Yang Z, Wang L: Association of MTHFR gene polymorphism C677T with susceptibility to late-onset Alzheimer's disease. J Mol Neurosci. 2005, 27 (1): 23-27. 10.1385/JMN:27:1:023.View ArticlePubMedGoogle Scholar
- Fehér A, Juhász A, Rimanóczy A, Kálmán J, Janka Z: Association between BDNF Val66Met polymorphism and Alzheimer disease, dementia with Lewy bodies, and Pick disease. Alzheimer disease and associated disorders. 2009, 23 (3): 224-228. 10.1097/WAD.0b013e318199dd7d.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/3/50/prepub
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