Mosley, Thomas H.

Background Atrial fibrillation (AF) is a risk factor for cognitive decline, possibly from silent brain infarction. Left atrial changes in structure or function (atrial cardiopathy) can lead to AF but may impact cognition independently. It is unknown if AF or atrial cardiopathy also acts on Alzheimer disease–specific mechanisms, such as deposition of β‐amyloid. Methods and Results A total of 316 dementia‐free participants from the ARIC (Atherosclerosis Risk in Communities) study underwent florbetapir positron emission tomography, electrocardiography, and 2‐dimensional echocardiography. Atrial cardiopathy was defined as ≥1: (1) left atrial volume index >34 mL/m 2 ; (2) P‐wave terminal force >5000 µV×ms; and (3) serum NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide) >250 pg/mL. Cross‐sectional associations between global cortical β‐amyloid (>1.2 standardized uptake value ratio) and adjudicated history of AF and atrial cardiopathy, each, were evaluated using multivariable logistic regression. Participants (mean age, 76 years) were 56% women and 42% Black individuals. Odds of elevated florbetapir standardized uptake value ratio were significantly increased among those with atrial cardiopathy (odds ratio, 1.81; 95% CI, 1.02–3.22) and doubled for those with enlarged left atrial volume index after adjustment for demographics/risk factors (95% CI, 1.04–4.61). There was no association between P‐wave terminal force or NT‐proBNP and elevated florbetapir standardized uptake value ratio, nor between AF and elevated standardized uptake value ratio. Conclusions Among healthy, nondemented community‐dwelling older individuals, we report an association between atrial cardiopathy, left atrial volume index, and elevated brain amyloid, by positron emission tomography, without a similar association in individuals with AF. Potential limitations include reverse causation and survival bias. Ongoing work will help determine if changes in cardiac structure and function precede or occur simultaneously with amyloid deposition.

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

Background and Purpose— White matter hyperintensities (WMH) on brain magnetic resonance imaging are typical signs of cerebral small vessel disease and may indicate various preclinical, age-related neurological disorders, such as stroke. Though WMH are highly heritable, known common variants explain a small proportion of the WMH variance. The contribution of low-frequency/rare coding variants to WMH burden has not been explored. Methods— In the discovery sample we recruited 20 719 stroke/dementia-free adults from 13 population-based cohort studies within the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, among which 17 790 were of European ancestry and 2929 of African ancestry. We genotyped these participants at ≈250 000 mostly exonic variants with Illumina HumanExome BeadChip arrays. We performed ethnicity-specific linear regression on rank-normalized WMH in each study separately, which were then combined in meta-analyses to test for association with single variants and genes aggregating the effects of putatively functional low-frequency/rare variants. We then sought replication of the top findings in 1192 adults (European ancestry) with whole exome/genome sequencing data from 2 independent studies. Results— At 17q25, we confirmed the association of multiple common variants in TRIM65 , FBF1 , and ACOX1 ( P <6×10 −7 ). We also identified a novel association with 2 low-frequency nonsynonymous variants in MRPL38 (lead, rs34136221; P EA =4.5×10 −8 ) partially independent of known common signal ( P EA(conditional) =1.4×10 −3 ). We further identified a locus at 2q33 containing common variants in NBEAL1 , CARF , and WDR12 (lead, rs2351524; P all =1.9×10 −10 ). Although our novel findings were not replicated because of limited power and possible differences in study design, meta-analysis of the discovery and replication samples yielded stronger association for the 2 low-frequency MRPL38 variants ( P rs34136221 =2.8×10 −8 ). Conclusions— Both common and low-frequency/rare functional variants influence WMH. Larger replication and experimental follow-up are essential to confirm our findings and uncover the biological causal mechanisms of age-related WMH.

Background and Purpose— Genetic factors that influence interindividual variation in levels of plasma homocysteine, a risk factor for vascular disease, are not fully understood. We performed linkage analyses to identify genomic regions that influence homocysteine levels in blacks and non-Hispanic whites. Methods— Subjects (n=2283) belonged to hypertensive sibships and included 1319 blacks (63±10 years, 70% women) and 964 non-Hispanic whites (61±7 years, 57% women). Fasting plasma homocysteine was measured by high-pressure liquid chromatography. Genotypes were measured at 366 microsatellite marker loci distributed across the 22 autosomes. Plasma homocysteine adjusted for age, sex, body mass index, serum creatinine, and estrogen use (in women) was used in the genetic analyses. Heritability and linkage analyses were performed using a variance components approach. Results— Mean (±SD) homocysteine levels were 10.4±5.27 μmol/L in blacks and 10.0±2.84 μmol/L in non-Hispanic whites ( P =0.58 for difference). Homocysteine levels were significantly ( P <0.0001) heritable in blacks ( h 2 =0.70) and in non-Hispanic whites ( h 2 =0.49). Linkage analyses demonstrated significant evidence of linkage (multipoint logarithm of odds≥3.0) for homocysteine on chromosomes 1q42, 14q32, and 19p13 in blacks and on chromosomes 9q34 and 12q24 in non-Hispanic whites. Tentative evidence of linkage (logarithm of odds 1.3 to 2.0) was present on chromosomes 2q32, 7p15, 8q24, 18q21, and 20p12 in blacks and chromosomes 6q26 and 18q21 in non-Hispanic whites. Four genes in the homocysteine metabolism pathway ( MTR , DNMT1 , GAMT , and CARM1 ) were present under 2 of the significant linkage signals in blacks (chromosomes 1q42 and 19p13). Conclusions— Plasma homocysteine is a significantly heritable trait. Linkage analyses reveal several unique genomic loci that may influence circulating levels of homocysteine and therefore susceptibility to vascular diseases including stroke.