Suchergebnisse

Acknowledgements: We thank the LBC1936 members and project staff. Study Funding: The LBC1936 and this research are supported by Age UK (Disconnected Mind project), the UK Medical Research Council [MRC; G0701120, G1001245, MR/M013111/1, MR/R024065/1], and the University of Edinburgh. SRC, MEB, and IJD were also supported by a National Institutes of Health (NIH) research grant R01AG054628. JMW, IJD are also supported by a Wellcome Trust Strategic Award (Ref 104036/Z/14/Z). MCVH is funded by the Row Fogo Charitable Trust (grant No. BROD.FID3668413). SRC was also supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 221890/Z/20/Z). Imaging and image analysis was performed at the Brain Research Imaging Centre (http://www.sbirc.ed.ac.uk/), Edinburgh, supported by the Scottish Funding Council SINAPSE Collaboration. LCAC acknowledges 4 Luciano funding from the Scottish Government's Rural and Environment Science and Analytical Services (RESAS) division. MVH is funded by the Row Fogo Charitable Trust Centre for Research into Ageing and the Brain (Ref No: AD.ROW4.35. BRO-D.FID3668413). JMW received funding from the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society and Alzheimer's Research UK. ; Peer reviewed ; Publisher PDF

Acknowledgements This work is supported by the MRC Doctoral Training Programme in Precision Medicine (JB); the Wellcome Trust (patient recruitment, scanning, and primary study - Reference No. WT088134/Z/09/A); the UK Dementia Research Institute, which receives its funding from DRI Ltd, funded by the UK MRC, Alzheimer's Society, and Alzheimer's Research UK; the Fondation Leducq Network for the Study of Perivascular Spaces in Small Vessel Disease (16 CVD 05); The Row Fogo Charitable Trust Centre for Research into ageing and the Brain (MVH) (BRO-D.FID3668413); a British Heart Foundation Chair award (RMT) (CH/12/4/29762); and NHS Lothian Research and Development Office (MJT); European Union Horizon 2020, PHC-03-15, project No666881. We thank the participants, their families, radiographers at Edinburgh Imaging, and the Stroke Research Network at the University of Edinburgh. ; Peer reviewed ; Publisher PDF

Funding Information: The authors disclosed receipt of the following financial support for the research, authorship and/or publication of this article: Wellcome Trust [grant number WT088134/Z/09/A ; SDJM, FC]; Row Fogo Charitable Trust (MCVH, FC, AKH, PAA); Scottish Funding Council Scottish Imaging Network A Platform for Scientific Excellence collaboration (JMW); NHS Lothian R + D Department (MJT); the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK MRC, Alzheimer's Research UK and the Alzheimer's Society (MS, FC, ES, JMW); the Fondation Leducq Transatlantic Network of Excellence for the Study of Perivascular Spaces in Small Vessel Disease [reference number 16 CVD 05] (MS); and European Union Horizon 2020 [project number 666881, SVDs@Target] (MS, FC). We acknowledge the participants, their relatives, and carers for their participation in this study, and the staff of NHS Lothian Stroke Services and Brain Research Imaging Centre Edinburgh for their assistance in recruiting and assessing the patients. ; Peer reviewed ; Publisher PDF

Structural brain magnetic resonance imaging (MRI) traits share part of their genetic variance with cognitive traits. Here, we use genetic association results from large meta-analytic studies of genome-wide association (GWA) for brain infarcts (BI), white matter hyperintensities, intracranial, hippocampal, and total brain volumes to estimate polygenic scores for these traits in three Scottish samples: Generation Scotland: Scottish Family Health Study (GS:SFHS), and the Lothian Birth Cohorts of 1936 (LBC1936) and 1921 (LBC1921). These five brain MRI trait polygenic scores were then used to: (1) predict corresponding MRI traits in the LBC1936 (numbers ranged 573 to 630 across traits), and (2) predict cognitive traits in all three cohorts (in 8,115–8,250 persons). In the LBC1936, all MRI phenotypic traits were correlated with at least one cognitive measure, and polygenic prediction of MRI traits was observed for intracranial volume. Meta-analysis of the correlations between MRI polygenic scores and cognitive traits revealed a significant negative correlation (maximal r = 0.08) between the HV polygenic score and measures of global cognitive ability collected in childhood and in old age in the Lothian Birth Cohorts. The lack of association to a related general cognitive measure when including the GS:SFHS points to either type 1 error or the importance of using prediction samples that closely match the demographics of the GWA samples from which prediction is based. Ideally, these analyses should be repeated in larger samples with data on both MRI and cognition, and using MRI GWA results from even larger meta-analysis studies.