Knol, Maria J.

Abstract Introduction There is increasing interest in plasma amyloid beta (Aβ) as an endophenotype of Alzheimer's disease (AD). Identifying the genetic determinants of plasma Aβ levels may elucidate important biological processes that determine plasma Aβ measures. Methods We included 12,369 non‐demented participants from eight population‐based studies. Imputed genetic data and measured plasma Aβ1‐40, Aβ1‐42 levels and Aβ1‐42/Aβ1‐40 ratio were used to perform genome‐wide association studies, and gene‐based and pathway analyses. Significant variants and genes were followed up for their association with brain positron emission tomography Aβ deposition and AD risk. Results Single‐variant analysis identified associations with apolipoprotein E ( APOE ) for Aβ1‐42 and Aβ1‐42/Aβ1‐40 ratio, and BACE1 for Aβ1‐40. Gene‐based analysis of Aβ1‐40 additionally identified associations for APP , PSEN2 , CCK , and ZNF397 . There was suggestive evidence for interaction between a BACE1 variant and APOE ε4 on brain Aβ deposition. Discussion Identification of variants near/in known major Aβ‐processing genes strengthens the relevance of plasma‐Aβ levels as an endophenotype of AD.

Background and Purpose: Periventricular white matter hyperintensities (WMH; PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first study, to date, we undertook genome-wide association analyses of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings. Methods: Participants were aged 45 years and older, free of stroke and dementia. We conducted genome-wide association analyses of PVWMH and DWMH in 26,654 participants from CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology), ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis), and the UKB (UK Biobank). Regional correlations were investigated using the genome-wide association analyses -pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC. Results: In the discovery and replication analysis, for PVWMH only, we found associations on chromosomes 2 ( NBEAL ), 10q23.1 ( TSPAN14/FAM231A ), and 10q24.33 ( SH3PXD2A). In the much larger combined meta-analysis of all cohorts, we identified ten significant regions for PVWMH: chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1. New loci of interest include 7q36.1 ( NOS3 ) and 16q24.2. In both the discovery/replication and combined analysis, we found genome-wide significant associations for the 17q25.1 locus for both DWMH and PVWMH. Using gene-based association analysis, 19 genes across all regions were identified for PVWMH only, including the new genes: CALCRL (2q32.1), KLHL24 (3q27.1), VCAN (5q27.1), and POLR2F (22q13.1). Thirteen genes in the 17q25.1 locus were significant for both phenotypes. More extensive genetic correlations were observed for PVWMH with small vessel ischemic stroke. There were no associations with dementia for either phenotype. Conclusions: Our study confirms these phenotypes have distinct and also shared genetic architectures. Genetic analyses indicated PVWMH was more associated with ischemic stroke whilst DWMH loci were implicated in vascular, astrocyte, and neuronal function. Our study confirms these phenotypes are distinct neuroimaging classifications and identifies new candidate genes associated with PVWMH only.

INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 369 nominally genome-wide significant loci ( P < 5 × 10 −8 ) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 360 loci for which replication data were available, 241 loci influencing surface area and 66 influencing thickness remained significant after replication, with 237 loci passing multiple testing correction ( P < 8.3 × 10 −10 ; 187 influencing surface area and 50 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation ( r G = −0.32, SE = 0.05, P = 6.5 × 10 −12 ), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 46 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function. Identifying genetic influences on human cortical structure. ( A ) Measurement of cortical surface area and thickness from MRI. ( B ) Genomic locations of common genetic variants that influence global and regional cortical structure. ( C ) Our results support the radial unit hypothesis that the expansion of cortical surface area is driven by proliferating neural progenitor cells. ( D ) Cortical surface area shows genetic correlation with psychiatric and cognitive traits. Error bars indicate SE. IMAGE CREDITS: (A) K. COURTNEY; (C) M. R. GLASS