http://www.cnr.it/ontology/cnr/individuo/prodotto/ID26354
Meta-analysis of genome-wide linkage studies for multiple sclerosis, using an extended GSMA method. (Articolo in rivista)
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- Meta-analysis of genome-wide linkage studies for multiple sclerosis, using an extended GSMA method. (Articolo in rivista) (literal)
- Anno
- 2007-01-01T00:00:00+01:00 (literal)
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- Hermanowski J; Bouzigon E; Forabosco P; Ng MY; Fisher SA; Lewis CM (literal)
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- Department of Medical and Molecular Genetics, King's College London School of Medicine at Guy's, King's College and St Thomas' Hospitals, London, UK
INSERM, U794, Evry, France
Istituto di Genetica delle Popolazioni-CNR, Alghero, Italy (literal)
- Titolo
- Meta-analysis of genome-wide linkage studies for multiple sclerosis, using an extended GSMA method. (literal)
- Abstract
- Many genome-wide linkage studies in multiple sclerosis (MS) have been performed, but results are disappointing, with linkage confirmed only in the HLA region. We combined results from all available, non-overlapping genome-wide linkage studies in MS using the Genome Search Meta-Analysis method (GSMA). The GSMA is a rank-based analysis, which assesses the strongest evidence for linkage within bins of traditionally 30 cM width on the autosomes and X chromosome. Genome-wide evidence for linkage was confirmed on chromosome 6p (HLA region; P=0.00004). Suggestive evidence for linkage was found on chromosomes 10q (P=0.0077), 18p (P=0.0054) and 20p (P=0.0079). To explore how these results could be affected by bin definition, we analysed the data using different bin widths (20 and 40 cM) and using a shifted 30 cM bin by moving bin boundaries by 15 cM. Consistently significant results were obtained for the 6p region. The regions on 10q and 18p provided suggestive evidence for linkage in some analyses, and, interestingly, a region on 6q, that showed only nominal significance in the original analysis, yielded increased, suggestive significance in two of the additional analyses. These regions may provide targets to focus candidate gene studies or to prioritise results from genome-wide association studies. (literal)
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