Periodic Reporting for period 3 - SEGWAY (Study on Environmental and GenomeWide predictors of early structural brain Alterations in Young students)
Reporting period: 2018-12-01 to 2020-05-31
-Duperron MG, et al. Burden of Dilated Perivascular Spaces, an Emerging Marker of Cerebral Small Vessel Disease, Is Highly Heritable. Stroke 2018 PMID: 29311265
-Malik R, et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat Genet 2018 PMID: 29531354
-Chauhan G, et al. Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting. Neurology 2019 PMID: 30651383
-Debette S, et al. Clinical Significance of Magnetic Resonance Imaging Markers of Vascular Brain Injury: A Systematic Review and Meta-analysis. JAMA Neurol 2019 PMID: 30422209
-Jian X, et al. Exome Chip Analysis Identifies Low-Frequency and Rare Variants in MRPL38 for White Matter Hyperintensities on Brain Magnetic Resonance Imaging.Stroke 2018 PMID: 30002152
-Mishra A, et al. Association of variants in HTRA1 and NOTCH3 with MRI-defined extremes of cerebral small vessel disease in older subjec. Brain 2019 PMID: 30859180
-Duperron MG et al. High dilated perivascular space burden: a new MRI marker for risk of intracerebral hemorrhage.Neurobiol Aging 2019 PMDI: 31629114
-Satizabal C et al. Genetic architecture of subcortical brain structures in 38,851 individuals. Nat Genet 2019 PMID: 31636452
-Beaudet G et al. Age-Related Changes of Peak Width Skeletonized Mean Diffusivity (PSMD) Across the Adult Lifespan: A Multi-Cohort Study. Front Psychiatry 2020 PMID: 32425831
-Sargurupremraj M et al. Cerebral small vessel disease genomics: implications across the lifespan. Nat Comm 2020, accepted in principle
There is a crucial need to improve our understanding of the genetic basis and temporal sequence that lead from structural brain changes in early adulthood to accelerated brain aging in late life, portending an increased risk of common late-life neurological diseases, such as dementia. Indeed, no efficient strategies are currently available for the prevention of dementia, and identifying the molecular underpinnings of lifetime changes in brain structure could provide invaluable information to identify novel therapeutic targets and to detect populations at highest risk of accelerated brain aging and dementia who would be most likely to benefit from early, intensive interventions. Cognitive decline and dementia represent a major public health concern, and exploring their risk factors and mechanisms is of the utmost importance at the community level.
Bringing genetic association findings into clinical use, e.g. for identification of drugable molecular targets, requires multiple additional steps, including identification of the causal variant(s) and gene(s). We have set up innovative in silico bioinformatics pipelines to functionally explore identified association signals and facilitate the subsequent design of functional experiments, that we have also started conducting through complementary funding.