Schmidt, Reinhold

Background: Intracerebral hemorrhage (ICH) is the stroke subtype with the worst prognosis and has no established acute treatment. ICH is classified as lobar and non-lobar based on the location of the hemorrhage, with genetic variation contributing to risk of both subtypes. We report the first genome wide association study (GWAS) of this condition. Methods: The discovery phase included GWAS data (Illumina 610 or Affymetrix 6.0) from 6 ICH studies that enrolled subjects of European ancestry in the US and Europe. Cases were classified as lobar or non-lobar ICH based on head CT. ICH-free controls were selected from ambulatory clinics or by random digit dialing. Standard quality controls filters were applied and principal component analysis was used to account for population structure. Unobserved genotypes were inferred by imputation to 1000 genomes references panels. Association testing was carried out using logistic regression, assuming additive effects, and including age, gender and principal components in the model. Meta-analyses were completed using the inverse normal method, weighting by sample size. Replication of signals with p<1x10E-6 was pursued in an independent multi-ethnic study utilizing direct genotyping. Results: The discovery phase included 1545 cases (664 lobar and 881 non-lobar) and 1481 controls, and tested 5,358,103 SNPs. Two loci were identified: for lobar ICH, an intergenic region located on chromosome 12q21.1 (rs11179580: minor allele frequency [MAF] 0.22, OR 1.54, 95% CI 1.33 - 1.84; p = 6.7x10E-8); and for non-lobar ICH, a region on chromosome 1q22 encompassing the PMF1 and SLC25A44 genes (rs2984613: MAF 0.31, OR 1.45, 95% CI 1.2 - 1.64; p = 8.9x10E-9). Replication included 1256 cases (329 whites, 506 blacks and 421 Hispanics) and 760 controls (237 whites, 346 blacks and 177 Hispanics) and confirmed both loci: 12q21.1 (p = 0.037, meta-analysis p = 2.3x10E-8) and 1q22 (p = 3.0x10E-4, meta-analysis p = 2.2x10E-11). Conclusions: Two new susceptibility loci for ICH were identified, both showing location-specificity. These results confirm the presence of biological heterogeneity across ICH subtypes and highlight that different genetic risk factors may contribute to the small vessel diseases that underlie each ICH subtype.

Introduction: Genetic studies of early-onset disease have been an effective strategy to identify novel pathways and drug targets relevant to later-onset disease. Few studies have investigated the role of common genetic variation in the etiology of early-onset ischemic stroke (IS). Methods: We performed a GWAS meta-analysis of 38 studies from 10 countries, comprised of 5,847 IS cases of European ancestry under age 60 and 32,533 controls. Results: We identified two genome-wide significant (p< 5 x 10 -8 ) loci (see Figure). The ABO locus has previously been associated with venous thrombosis and ischemic stroke in predominantly older adults, but the effect size of our top SNP (OR 1.18; p = 9.1 x 10 -12 ) is larger than the effect size for this same SNP in MEGASTROKE (OR: 1.05; p = 6.5 x 10 -5 ). The lead SNP at the BCL7A/MLXIP locus is a novel GWAS finding for stroke (OR 1.14, 95% CI 1.08-1.19; p = 1.7 x 10 -8 ) and is noteworthy because of prior reports linking SNPs in these genes to BMI and blood pressure. Conclusions: We identified a novel locus that is near variants associated with BMI and blood pressure. Further studies are needed to confirm this locus, examine subtype specificity, and determine its function. The larger effect size observed at the ABO in this early-onset IS sample compared to older-onset IS samples is consistent with a larger role for prothrombotic mechanisms in early-onset IS.

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.

Objective Genome‐wide association studies (GWAS) have been successful at identifying associations with stroke and stroke subtypes, but have not yet identified any associations solely with small vessel stroke (SVS). SVS comprises one quarter of all ischemic stroke and is a major manifestation of cerebral small vessel disease, the primary cause of vascular cognitive impairment. Studies across neurological traits have shown that younger‐onset cases have an increased genetic burden. We leveraged this increased genetic burden by performing an age‐at‐onset informed GWAS meta‐analysis, including a large younger‐onset SVS population, to identify novel associations with stroke. Methods We used a three‐stage age‐at‐onset informed GWAS to identify novel genetic variants associated with stroke. On identifying a novel locus associated with SVS, we assessed its influence on other small vessel disease phenotypes, as well as on messenger RNA (mRNA) expression of nearby genes, and on DNA methylation of nearby CpG sites in whole blood and in the fetal brain. Results We identified an association with SVS in 4,203 cases and 50,728 controls on chromosome 16q24.2 (odds ratio [OR; 95% confidence interval {CI}] = 1.16 [1.10–1.22]; p = 3.2 × 10 −9 ). The lead single‐nucleotide polymorphism (rs12445022) was also associated with cerebral white matter hyperintensities (OR [95% CI] = 1.10 [1.05–1.16]; p = 5.3 × 10 −5 ; N = 3,670), but not intracerebral hemorrhage (OR [95% CI] = 0.97 [0.84–1.12]; p = 0.71; 1,545 cases, 1,481 controls). rs12445022 is associated with mRNA expression of ZCCHC14 in arterial tissues ( p = 9.4 × 10 −7 ) and DNA methylation at probe cg16596957 in whole blood ( p = 5.3 × 10 −6 ). Interpretation 16q24.2 is associated with SVS. Associations of the locus with expression of ZCCHC14 and DNA methylation suggest the locus acts through changes to regulatory elements. Ann Neurol 2017;81:383–394

Genetic College Many genomic elements in humans are associated with behavior, including educational attainment. In a genome-wide association study including more than 100,000 samples, Rietveld et al. (p. 1467 , published online 30 May; see the Perspective by Flint and Munafò ) looked for genes related to educational attainment in Caucasians. Small genetic effects at three loci appeared to impact educational attainment.