Understanding the genomic basis of memory processes may help in combating neurodegenerative disorders. Hence, we examined the associations of common genetic variants with verbal short-term memory... Show moreUnderstanding the genomic basis of memory processes may help in combating neurodegenerative disorders. Hence, we examined the associations of common genetic variants with verbal short-term memory and verbal learning in adults without dementia or stroke (N = 53,637). We identified novel loci in the intronic region of CDH18, and at 13q21 and 3p21.1, as well as an expected signal in the APOE/APOC1/TOMM40 region. These results replicated in an independent sample. Functional and bioinformatic analyses supported many of these loci and further implicated POC1. We showed that polygenic score for verbal learning associated with brain activation in right parieto-occipital region during working memory task. Finally, we showed genetic correlations of these memory traits with several neurocognitive and health outcomes. Our findings suggest a role of several genomic loci in verbal memory processes. Show less
Human brain structure changes throughout the lifespan. Brouwer et al. identified genetic variants that affect rates of brain growth and atrophy. The genes are linked to early brain development and... Show moreHuman brain structure changes throughout the lifespan. Brouwer et al. identified genetic variants that affect rates of brain growth and atrophy. The genes are linked to early brain development and neurodegeneration and suggest involvement of metabolic processes.Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging. Show less
Chatterjee, P.; Tegg, M.; Pedrini, S.; Fagan, A.M.; Xiong, C.J.; Singh, A.K.; ... ; Dominantly Inherited Alzheimer Net 2021
Plasma amyloid-beta (A beta) has long been investigated as a blood biomarker candidate for Cerebral Amyloid Angiopathy (CAA), however previous findings have been inconsistent which could be... Show morePlasma amyloid-beta (A beta) has long been investigated as a blood biomarker candidate for Cerebral Amyloid Angiopathy (CAA), however previous findings have been inconsistent which could be attributed to the use of less sensitive assays. This study investigates plasma A beta alterations between pre-symptomatic Dutch-type hereditary CAA (D-CAA) mutation-carriers (MC) and non-carriers (NC) using two A beta measurement platforms. Seventeen pre-symptomatic members of a D-CAA pedigree were assembled and followed up 3-4 years later (NC = 8; MC = 9). Plasma A beta 1-40 and A beta 1-42 were cross-sectionally and longitudinally analysed at baseline (T1) and follow-up (T2) and were found to be lower in MCs compared to NCs, cross-sectionally after adjusting for covariates, at both T1(A beta 1-40: p = 0.001; A beta 1-42: p = 0.0004) and T2 (A beta 1-40: p = 0.001; A beta 1-42: p = 0.016) employing the Single Molecule Array (Simoa) platform, however no significant differences were observed using the xMAP platform. Further, pairwise longitudinal analyses of plasma A beta 1-40 revealed decreased levels in MCs using data from the Simoa platform (p = 0.041) and pairwise longitudinal analyses of plasma A beta 1-42 revealed decreased levels in MCs using data from the xMAP platform (p = 0.041). Findings from the Simoa platform suggest that plasma A beta may add value to a panel of biomarkers for the diagnosis of pre-symptomatic CAA, however, further validation studies in larger sample sets are required. Show less
Importance Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well... Show moreImportance Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood. Objective To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia. Design, Setting, and Participants Profiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Analysis for gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The numbers of cases and controls in each of the 6 disorders were as follows: ADHD: 1814 and 1602; ASD: 1748 and 1770; BD: 1547 and 3405; MDD: 2658 and 3572; OCD: 2266 and 2007; and schizophrenia: 2688 and 3244. Main Outcomes and Measures Interregional profiles of group difference in cortical thickness between cases and controls. Results A total of 12 721 cases and 15 600 controls, ranging from ages 2 to 89 years, were included in this study. Interregional profiles of group differences in cortical thickness for each of the 6 psychiatric disorders were associated with profiles of gene expression specific to pyramidal (CA1) cells, astrocytes (except for BD), and microglia (except for OCD); collectively, gene-expression profiles of the 3 cell types explain between 25% and 54% of variance in interregional profiles of group differences in cortical thickness. Principal component analysis revealed a shared profile of difference in cortical thickness across the 6 disorders (48% variance explained); interregional profile of this principal component 1 was associated with that of the pyramidal-cell gene expression (explaining 56% of interregional variation). Coexpression analyses of these genes revealed 2 clusters: (1) a prenatal cluster enriched with genes involved in neurodevelopmental (axon guidance) processes and (2) a postnatal cluster enriched with genes involved in synaptic activity and plasticity-related processes. These clusters were enriched with genes associated with all 6 psychiatric disorders. Conclusions and Relevance In this study, shared neurobiologic processes were associated with differences in cortical thickness across multiple psychiatric disorders. These processes implicate a common role of prenatal development and postnatal functioning of the cerebral cortex in these disorders.Question What are the neurobiologic underpinnings of group differences in cortical thickness in various psychiatric disorders? Findings In this consortium analysis of data from 145 cohorts, regions of the cerebral cortex with greater expression of genes specific to pyramidal (CA1) cells were also regions with greater case-control group differences in cortical thickness in all 6 disorders: attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, and schizophrenia. There was a common profile of group differences in cortical thickness shared among these disorders, which was associated with the expression of genes involved in neurodevelopmental processes (prenatally) and processes underlying synaptic activity and plasticity (postnatally). Meaning There are shared neurobiologic and cellular mechanisms associated with differences in cortical thickness across multiple psychiatric disorders, implicating a common role of prenatal development and postnatal functioning of the cerebral cortex.This study evaluates neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 psychiatric disorders. Show less
Chatterjee, P.; Fagan, A.M.; Xiong, C.J.; McKay, M.; Bhatnagar, A.; Wu, Y.Q.; ... ; Dominantly Inherited Alzheimer Net 2021
Objective: Investigation of pre-symptomatic CAA related blood metabolite alterations in Dutch-type hereditary CAA mutation carriers (D-CAA MCs).Methods: Plasma metabolites were measured using mass... Show moreObjective: Investigation of pre-symptomatic CAA related blood metabolite alterations in Dutch-type hereditary CAA mutation carriers (D-CAA MCs).Methods: Plasma metabolites were measured using mass-spectrometry (AbsoluteIDQ (R) p400 HR kit) and were compared between pre-symptomatic D-CAA MCs (n= 9) and non-carriers (D-CAA NCs, n= 8) from the same pedigree. Metabolites that survived correction for multiple comparisons were further compared between D-CAA MCs and additional control groups (cognitively unimpaired adults).Results: 275 metabolites were measured in the plasma, 22 of which were observed to be significantly lower in the D-CAA MCs compared to D-CAA NCs, following adjustment for potential confounding factors age, sex, and APOE epsilon 4 (p < 00.05). After adjusting for multiple comparisons, only spermidine remained significantly lower in the D-CAA MCs compared to the D-CAA NCs (p < 0.00018). Plasma spermidine was also significantly lower in D-CAA MCs compared to the cognitively unimpaired young adult and older adult groups (p < 0.01). Spermidine was also observed to correlate with CSF A beta(40) (r(s) = 0.621, p = 0.024), CSF A beta(42) (r(s) = 0.714, p = 0.006), and brain A beta load (r(s) = -0.527, p = 0.030).Conclusion: The current study provides pilot data on D-CAA linked metabolite signals, that also associated with A beta neuropathology and are involved in several biological pathways that have previously been linked to neurodegeneration and dementia. Show less
Sargurupremraj, M.; Suzuki, H.; Jian, X.Q.; Sarnowski, C.; Evans, T.E.; Bis, J.C.; ... ; Int Headache Genomics Consortium I 2020
White matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide... Show moreWhite matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide loci for WMH-volume in a cohort of 50,970 older individuals, accounting for modification/confounding by hypertension. Aggregated WMH risk variants were associated with altered white matter integrity (p=2.5x10-7) in brain images from 1,738 young healthy adults, providing insight into the lifetime impact of SVD genetic risk. Mendelian randomization suggested causal association of increasing WMH-volume with stroke, Alzheimer-type dementia, and of increasing blood pressure (BP) with larger WMH-volume, notably also in persons without clinical hypertension. Transcriptome-wide colocalization analyses showed association of WMH-volume with expression of 39 genes, of which four encode known drug targets. Finally, we provide insight into BP-independent biological pathways underlying SVD and suggest potential for genetic stratification of high-risk individuals and for genetically-informed prioritization of drug targets for prevention trials. White matter hyperintensities (WMH) are a common brain-imaging feature of cerebral small vessel disease. Here, the authors carry out a GWAS and followup analyses for WMH-volume, implicating several variants with potential for risk stratification and drug targeting. Show less
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... Show moreBackground 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), andPOLR2F(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. Show less
The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that... Show moreThe cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder. Show less
Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala,... Show moreSubcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease. Show less
Davies, G.; Lam, M.; Harris, S.E.; Trampush, J.W.; Luciano, M.; Hill, W.D.; ... ; Deary, I.J. 2019
