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Expanded high-resolution genetic study of 109 Swedish families with Alzheimer's disease
Karolinska Institutet.
KTH, School of Biotechnology (BIO), Gene Technology.
Karolinska Institutet.
Karolinska Institutet.
Show others and affiliations
2008 (English)In: European Journal of Human Genetics, ISSN 1018-4813, E-ISSN 1476-5438, Vol. 16, no 2, 202-208 p.Article in journal (Refereed) Published
Abstract [en]

Alzheimer's disease (AD) is a neurodegenerative disease that affects approximately 20 million persons all over the world. There are both sporadic and familial forms of AD. We have previously reported a genome-wide linkage analysis on 71 Swedish AD families using 365 genotyped microsatellite markers. In this study, we increased the number of individuals included in the original 71 analysed families besides adding 38 new families. These 109 families were genotyped for 1100 novel microsatellite markers. The present study reports on the linkage data generated from the non-overlapping genotypes from the first genome scan and the genotypes of the present scan, which results in a total of 1289 successfully genotyped markers at an average density of 2.85 cM on 468 individuals from 109 AD families. Non-parametric linkage analysis yielded a significant multipoint LOD score in chromosome 19q13, the region harbouring the major susceptibility gene APOE, both for the whole set of families (LOD = 5.0) and the APOE epsilon 4-positive subgroup made up of 63 families (LOD = 5.3). Other suggestive linkage peaks that were observed in the original genome scan of 71 Swedish AD families were not detected in this extended analysis, and the previously reported linkage signals in chromosomes 9, 10 and 12 were not replicated.

Place, publisher, year, edition, pages
2008. Vol. 16, no 2, 202-208 p.
Keyword [en]
linkage analysis; familial dementia; APOE; Alzheimer's disease; genome scan; lod score
National Category
Industrial Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-7798DOI: 10.1038/sj.ejhg.5201946ISI: 000252426500011Scopus ID: 2-s2.0-38349170135OAI: oai:DiVA.org:kth-7798DiVA: diva2:12927
Note
QC 20100622Available from: 2007-12-10 Created: 2007-12-10 Last updated: 2012-03-21Bibliographically approved
In thesis
1. Grid and High-Performance Computing for Applied Bioinformatics
Open this publication in new window or tab >>Grid and High-Performance Computing for Applied Bioinformatics
2007 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

The beginning of the twenty-first century has been characterized by an explosion of biological information. The avalanche of data grows daily and arises as a consequence of advances in the fields of molecular biology and genomics and proteomics. The challenge for nowadays biologist lies in the de-codification of this huge and complex data, in order to achieve a better understanding of how our genes shape who we are, how our genome evolved, and how we function.

Without the annotation and data mining, the information provided by for example high throughput genomic sequencing projects is not very useful. Bioinformatics is the application of computer science and technology to the management and analysis of biological data, in an effort to address biological questions. The work presented in this thesis has focused on the use of Grid and High Performance Computing for solving computationally expensive bioinformatics tasks, where, due to the very large amount of available data and the complexity of the tasks, new solutions are required for efficient data analysis and interpretation.

Three major research topics are addressed; First, the use of grids for distributing the execution of sequence based proteomic analysis, its application in optimal epitope selection and in a proteome-wide effort to map the linear epitopes in the human proteome. Second, the application of grid technology in genetic association studies, which enabled the analysis of thousand of simulated genotypes, and finally the development and application of a economic based model for grid-job scheduling and resource administration.

The applications of the grid based technology developed in the present investigation, results in successfully tagging and linking chromosomes regions in Alzheimer disease, proteome-wide mapping of the linear epitopes, and the development of a Market-Based Resource Allocation in Grid for Scientific Applications.

Place, publisher, year, edition, pages
Stockholm: KTH, 2007
Series
Trita-BIO-Report, ISSN 1654-2312 ; 2007:9
Keyword
Grid computing, bioinformatics, genomics, proteomics
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:kth:diva-4573 (URN)978-91-7178-782-8 (ISBN)
Public defence
2007-12-21, FD5, AlbaNova, oslagstullsbacken 21, Stockholm, 10:00
Opponent
Supervisors
Note
QC 20100622Available from: 2007-12-10 Created: 2007-12-10 Last updated: 2012-03-20Bibliographically approved

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