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- Beecham, Ashley H, et al.
(author)
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Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis.
- 2013
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In: Nature genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 45:11, s. 1353-60
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Journal article (peer-reviewed)abstract
- Using the ImmunoChip custom genotyping array, we analyzed 14,498 subjects with multiple sclerosis and 24,091 healthy controls for 161,311 autosomal variants and identified 135 potentially associated regions (P < 1.0 × 10(-4)). In a replication phase, we combined these data with previous genome-wide association study (GWAS) data from an independent 14,802 subjects with multiple sclerosis and 26,703 healthy controls. In these 80,094 individuals of European ancestry, we identified 48 new susceptibility variants (P < 5.0 × 10(-8)), 3 of which we found after conditioning on previously identified variants. Thus, there are now 110 established multiple sclerosis risk variants at 103 discrete loci outside of the major histocompatibility complex. With high-resolution Bayesian fine mapping, we identified five regions where one variant accounted for more than 50% of the posterior probability of association. This study enhances the catalog of multiple sclerosis risk variants and illustrates the value of fine mapping in the resolution of GWAS signals.
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- Piper, I, et al.
(author)
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The BrainIT group: concept and core dataset definition
- 2003
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In: Acta Neurochirurgica. - : Springer Science and Business Media LLC. - 0001-6268 .- 0942-0940. ; 145:8, s. 615-629
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Journal article (peer-reviewed)abstract
- Introduction. An open collaborative international network has been established which aims to improve inter-centre standards for collection of high-resolution, neurointensive care data on patients with traumatic brain injury. The group is also working towards the creation of an open access, detailed and validated database that will be useful for post-hoc hypothesis testing. In Part A, the underlying concept, the group coordination structure, membership guidelines and database access and publication criteria are described. Secondly, in part B, we describe a set of meetings funded by the EEC that allowed us to define a "Core Dataset" and we present the results of a feasibility exercise for collection of this core dataset. Methods. Four group meetings funded by the EEC have enabled definition of a "Core Dataset" to be collected from all centres regardless of specific project aim. A paper based pilot collection of data was conducted to determine the feasibility for collection of the core dataset. Specially designed forms to collect the core dataset demographic and clinical information as well as sample the time-series data elements were distributed by both email and standard mail to 22 BrainIT centres. A deadline of two months was set to receive completed forms back from centres. A pilot data collection of minute by minute physiological monitoring data was also performed. Findings. A core-dataset was defined and can be downloaded from the BrainIT web-site (go to "Core dataset" link at: www.brainit.org). Eighteen centres (82%) returned completed forms by the set deadline. Overall the feasibility for collection of the core data elements was high with only 10 of the 64 questions (16%) showing missing data. Of those 10 fields with missing data, the average number of centres not responding was 12% and the median 6%. An SQL database to hold the data has been designed and is being tested. Software tools for collection of the core dataset have been developed. Ethics approval has been granted for collection of multi-centre data as part of a pilot data collection study. Interpretation. The BrainIT network provides a more standardised and higher resolution data collection mechanism for research groups, organisations and the device industry to conduct multi-centre trials of new health care technology in patients with traumatic brain injury.
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