Age-dependent genetic variants associated with longitudinal changes in brain structure across the lifespan

Abstract

We identified common genetic variants associated with the rate of brain development and aging, in longitudinal MRI scans worldwide. AbstractHuman brain structure changes throughout our lives. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental, and neurodegenerative diseases. While heritable, specific loci in the genome that influence these rates are largely unknown. Here, we sought to find common genetic variants that affect rates of brain growth or atrophy, in the first genome-wide association analysis of longitudinal changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 10,163 individuals aged 4 to 99 years, on average 3.5 years apart, were used to compute rates of morphological change for 15 brain structures. We discovered 5 genome-wide significant loci and 15 genes associated with brain structural changes. Most individual variants exerted age-dependent effects. All identified genes are expressed in fetal and adult brain tissue, and some exhibit developmentally regulated expression across the lifespan. We demonstrate genetic overlap with depression, schizophrenia, cognitive functioning, height, body mass index and smoking. Several of the discovered loci are implicated in early brain development and point to involvement of metabolic processes. Gene-set findings also implicate immune processes in the rates of brain changes. Taken together, in the world's largest longitudinal imaging genetics dataset we identified genetic variants that alter agedependent brain growth and atrophy throughout our lives. a Position based on build hg19. Study-wide significant hits are displayed in bold. *This gene also showed a genome-wide significant quadratic age effect. The most parsimonious model is listed in this table.Genome-wide significant gene sets based on gene ontology. Study-wide significant gene sets are displayed in bold. a See Supplementary Table S9 for genes included in the gene set. Genes included in GO_INTERLEUKIN_1_RECEPTOR_ACTIVITY and GO_RESPONSE_TO_INTERLEUKIN_2 do not overlap.

The Speed of Development of Adolescent Brain Age Depends on Sex and Is Genetically Determined

“…2010 ). We recently found genetic variants that influence longitudinal structural brain changes in an age-dependent manner ( Brouwer et al. 2020 ).…”

Associations between ADHD symptom remission and white matter microstructure: a longitudinal analysis

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“…Healthy brain development has been characterized using structural and functional magnetic resonance imaging (MRI), showing trajectories across the lifespan of regional volumes and activity/connectivity, respectively. [13][14][15] Those that have applied diffusion, magnetization transfer, relaxometry, and myelin water imaging methods have also demonstrated consistent, rapid white matter (WM) microstructural changes in the first three years of life, reflecting increased myelination or axonal packing. 10 These changes continue throughout adolescence and are associated with corresponding age-related changes in gray matter.…”

“…Neurodevelopmental mechanisms underlying the variable long-term course of ADHD may be partly traceable using neuroimaging. Healthy brain development has been characterized using structural and functional magnetic resonance imaging (MRI), showing trajectories across the lifespan of regional volumes and activity/connectivity, respectively (Brouwer et al, 2020;van Duijvenvoorde, Westhoff, de Vos, Wierenga, & Crone, 2019). Those that have applied diffusion, magnetization transfer, relaxometry, and myelin water imaging methods have also demonstrated consistent, rapid white matter (WM) microstructural changes in the first three years of life, reflecting increased myelination or axonal packing (Lebel & Deoni, 2018).…”

Alzheimer’s genetic risk effects on cerebral blood flow are spatially consistent and proximal to gene expression across the lifespan

“…First, while we observed consistent AD-GRS effects across the lifespan, we did not observe APOE related effects in our older sample. This may be explained by dynamic APOE effects that have recently been discovered in recent structural MRI studies (Brouwer et al, 2020) or lower statistical power afforded in smaller samples. Third, while we aimed to generate a single regional metric of AD gene expression, AD risk SNPs may confer risk by increasing or decreasing proximal gene expression, so generating regional expression profiles that consider this variation (rather than using average AD gene expression) will be useful in refining the impact of AD risk SNPs on regional gene expression,…”