Genetic markers for brain plasticity: Neuroimaging: Imaging genetics. (7th December 2020)

• Record Type: Journal Article
• Authors: Brouwer, Rachel M.; Klein, Marieke; Grasby, Katrina L; Schnack, Hugo G.; Jahanshad, Neda; Teeuw, Jalmar; Medland, Sarah E.; Franke, Barbara; Thompson, Paul; Pol, Hilleke E. Hulshoff
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 5
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.042812
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Under the influence of genes and a varying environment, human brain structure changes continuously throughout the lifespan. The quest to identify genetic variants that affect the speed of brain aging is of crucial importance for public health and to guide drug development. As structural brain changes vary considerably across individuals, longitudinal studies are crucial to identify genetic and environmental factors that influence development and aging. Brain changes have been shown to be a heritable phenotype (Brouwer et al., 2017). Yet, we still completely lack information on specific genetic variants that influence brain changes throughout life. Methods: Through the ENIGMA plasticity working group as part of the ENIGMA consortium (Thompson et al., 2019), we recruited 10, 163 subjects [age 4 – 99 year] from 37 cohorts worldwide who had longitudinal structural MRI and whole‐genome genotyping data available in a prospective meta‐analysis design. Longitudinal image processing (Reuter et al., 2012), genomic imputation (Das et al., 2016), quality control and genome‐wide association procedures (Feng et al., 2014)followed a harmonized protocol in each cohort separately. Meta‐analysis and genome‐wide meta‐regressions accounting for age‐dependent effects of genetic variants were performed for global and subcortical structures. Results: Change rates of global brain measures showed different patterns with age. We identified 5 genome‐wide significant loci and 15 genome‐wide significant genes, associated with longitudinal brain changes, including an age‐dependent effect of Apolipoprotein E ( APOE ) in the hippocampus and amygdala. Our results suggest a role for metabolic and immune related processes‐shaping brain change throughout our lives. Conclusion: We discovered novel genetic effects that influence the speed of both development and aging of brain structures. Our findings are consistent with the changing expression of genes during development and aging (Kang et al., 2011). In conclusion, our study implicates a specific genetic background for inter‐individual differences in human brain plasticity throughout life.