Shared Genetic Risk Factors of Intracranial, Abdominal, and Thoracic Aneurysms

Background Intracranial aneurysms (IAs), abdominal aortic aneurysms (AAAs), and thoracic aortic aneurysms (TAAs) all have a familial predisposition. Given that aneurysm types are known to co‐occur, we hypothesized that there may be shared genetic risk factors for IAs, AAAs, and TAAs. Methods and Results We performed a mega‐analysis of 1000 Genomes Project‐imputed genome‐wide association study (GWAS) data of 4 previously published aneurysm cohorts: 2 IA cohorts (in total 1516 cases, 4305 controls), 1 AAA cohort (818 cases, 3004 controls), and 1 TAA cohort (760 cases, 2212 controls), and observed associations of 4 known IA, AAA, and/or TAA risk loci (9p21, 18q11, 15q21, and 2q33) with consistent effect directions in all 4 cohorts. We calculated polygenic scores based on IA‐, AAA‐, and TAA‐associated SNPs and tested these scores for association to case‐control status in the other aneurysm cohorts; this revealed no shared polygenic effects. Similarly, linkage disequilibrium–score regression analyses did not show significant correlations between any pair of aneurysm subtypes. Last, we evaluated the evidence for 14 previously published aneurysm risk single‐nucleotide polymorphisms through collaboration in extended aneurysm cohorts, with a total of 6548 cases and 16 843 controls (IA) and 4391 cases and 37 904 controls (AAA), and found nominally significant associations for IA risk locus 18q11 near RBBP8 to AAA (odds ratio [OR]=1.11; P=4.1×10−5) and for TAA risk locus 15q21 near FBN1 to AAA (OR=1.07; P=1.1×10−3). Conclusions Although there was no evidence for polygenic overlap between IAs, AAAs, and TAAs, we found nominally significant effects of two established risk loci for IAs and TAAs in AAAs. These two loci will require further replicatio

Comparison of Two Meta-Analysis Methods: Inverse-Variance-Weighted Average and Weighted Sum of Z-Scores

The meta-analysis has become a widely used tool for many applications in bioinformatics, including genome-wide association studies. A commonly used approach for meta-analysis is the fixed effects model approach, for which there are two popular methods: the inverse variance-weighted average method and weighted sum of z-scores method. Although previous studies have shown that the two methods perform similarly, their characteristics and their relationship have not been thoroughly investigated. In this paper, we investigate the optimal characteristics of the two methods and show the connection between the two methods. We demonstrate that the each method is optimized for a unique goal, which gives us insight into the optimal weights for the weighted sum of z-scores method. We examine the connection between the two methods both analytically and empirically and show that their resulting statistics become equivalent under certain assumptions. Finally, we apply both methods to the Wellcome Trust Case Control Consortium data and demonstrate that the two methods can give distinct results in certain study designs

A Compact Wideband Crossover Coupler with Lumped Elements

A compact wideband crossover coupler with fully lumped elements is presented. To achieve a wideband operation, a three-section branch-line structure is employed for the crossover coupler. The size is significantly minimized by replacing transmission lines with lumped elements. The measurement shows that the insertion loss, isolation, and return loss are 1.7 dB, 24 dB, and 14.5 dB, respectively, at 2 GHz. The fractional bandwidth of 20-dB isolation and 3-dB insertion loss is 27%. The size of the crossover coupler is 11 mm × 9 mm, which corresponds to 0.07λ × 0.06λ at 2 GHz. This is significantly smaller than a conventional three-section branch-line crossover coupler by 95%

Examining neural correlates of skill acquisition in a complex videogame training program

Acquisition of complex skills is a universal feature of human behavior that has been conceptualized as a process that starts with intense resource dependency, requires effortful cognitive control, and ends in relative automaticity on the multi-faceted task. The present study examined the effects of different theoretically based training strategies on cortical recruitment during acquisition of complex video game skills. Seventy-five participants were recruited and assigned to one of three training groups: (1) Fixed Emphasis Training (FET), in which participants practiced the game, (2) Hybrid Variable-Priority Training (HVT), in which participants practiced using a combination of part-task training and variable priority training, or (3) a Control group that received limited game play. After 30 h of training, game data indicated a significant advantage for the two training groups relative to the control group. The HVT group demonstrated enhanced benefits of training, as indexed by an improvement in overall game score and a reduction in cortical recruitment post-training. Specifically, while both groups demonstrated a significant reduction of activation in attentional control areas, namely the right middle frontal gyrus, right superior frontal gyrus, and the ventral medial prefrontal cortex, participants in the control group continued to engage these areas post-training, suggesting a sustained reliance on attentional regions during challenging task demands. The HVT group showed a further reduction in neural resources post-training compared to the FET group in these cognitive control regions, along with reduced activation in the motor and sensory cortices and the posteromedial cortex. Findings suggest that training, specifically one that emphasizes cognitive flexibility can reduce the attentional demands of a complex cognitive task, along with reduced reliance on the motor network

Predicting Individuals' Learning Success from Patterns of Pre-Learning MRI Activity

Performance in most complex cognitive and psychomotor tasks improves with training, yet the extent of improvement varies among individuals. Is it possible to forecast the benefit that a person might reap from training? Several behavioral measures have been used to predict individual differences in task improvement, but their predictive power is limited. Here we show that individual differences in patterns of time-averaged T2*-weighted MRI images in the dorsal striatum recorded at the initial stage of training predict subsequent learning success in a complex video game with high accuracy. These predictions explained more than half of the variance in learning success among individuals, suggesting that individual differences in neuroanatomy or persistent physiology predict whether and to what extent people will benefit from training in a complex task. Surprisingly, predictions from white matter were highly accurate, while voxels in the gray matter of the dorsal striatum did not contain any information about future training success. Prediction accuracy was higher in the anterior than the posterior half of the dorsal striatum. The link between trainability and the time-averaged T2*-weighted signal in the dorsal striatum reaffirms the role of this part of the basal ganglia in learning and executive functions, such as task-switching and task coordination processes. The ability to predict who will benefit from training by using neuroimaging data collected in the early training phase may have far-reaching implications for the assessment of candidates for specific training programs as well as the study of populations that show deficiencies in learning new skills

A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases

There is growing evidence of shared risk alleles between complex traits (pleiotropy), including autoimmune and neuropsychiatric diseases. This might be due to sharing between all individuals (whole-group pleiotropy), or a subset of individuals within a genetically heterogeneous cohort (subgroup heterogeneity). BUHMBOX is a well-powered statistic distinguishing between these two situations using genotype data. We observed a shared genetic basis between 11 autoimmune diseases and type 1 diabetes (T1D, p0.2, 6,670 T1D cases and 7,279 RA cases). Genetic sharing between seronegative and seropostive RA (p<10−9) had significant evidence of subgroup heterogeneity, suggesting a subgroup of seropositive-like cases within seronegative cases (pBUHMBOX=0.008, 2,406 seronegative RA cases). We also observed a shared genetic basis between major depressive disorder (MDD) and schizophrenia (p<10−4) that was not explained by subgroup heterogeneity (pBUHMBOX=0.28 in 9,238 MDD cases)

Shared Genetic Risk Factors of Intracranial, Abdominal, and Thoracic Aneurysms

Background-Intracranial aneurysms (IAs), abdominal aortic aneurysms (AAAs), and thoracic aortic aneurysms (TAAs) all have a familial predisposition. Given that aneurysm types are known to co-occur, we hypothesized that there may be shared genetic risk factors for IAs, AAAs, and TAAs. Methods and Results-We performed a mega-analysis of 1000 Genomes Project-imputed genome-wide association study (GWAS) data of 4 previously published aneurysm cohorts: 2 IA cohorts (in total 1516 cases, 4305 controls), 1 AAA cohort (818 cases, 3004 controls), and 1 TAA cohort (760 cases, 2212 controls), and observed associations of 4 known IA, AAA, and/or TAA risk loci (9p21, 18q11, 15q21, and 2q33) with consistent effect directions in all 4 cohorts. We calculated polygenic scores based on IA-, AAA-, and TAA-associated SNPs and tested these scores for association to case-control status in the other aneurysm cohorts; this revealed no shared polygenic effects. Similarly, linkage disequilibrium-score regression analyses did not show significant correlations between any pair of aneurysm subtypes. Last, we evaluated the evidence for 14 previously published aneurysm risk single-nucleotide polymorphisms through collaboration in extended aneurysm cohorts, with a total of 6548 cases and 16 843 controls (IA) and 4391 cases and 37 904 controls (AAA), and found nominally significant associations for IA risk locus 18q11 near RBBP8 to AAA (odds ratio [OR]= 1.11; P=4.1 x 10(-5)) and for TAA risk locus 15q21 near FBN1 to AAA (OR=1.07; P=1.1 x 10(-3)). Conclusions-Although there was no evidence for polygenic overlap between IAs, AAAs, and TAAs, we found nominally significant effects of two established risk loci for IAs and TAAs in AAAs. These two loci will require further replication.Peer reviewe

Shared Genetic Risk Factors of Intracranial, Abdominal, and Thoracic Aneurysms

Background: Intracranial aneurysms (IAs), abdominal aortic aneurysms (AAAs), and thoracic aortic aneurysms (TAAs) all have a familial predisposition. Given that aneurysm types are known to co‐occur, we hypothesized that there may be shared genetic risk factors for IAs, AAAs, and TAAs. Methods and Results: We performed a mega‐analysis of 1000 Genomes Project‐imputed genome‐wide association study (GWAS) data of 4 previously published aneurysm cohorts: 2 IA cohorts (in total 1516 cases, 4305 controls), 1 AAA cohort (818 cases, 3004 controls), and 1 TAA cohort (760 cases, 2212 controls), and observed associations of 4 known IA, AAA, and/or TAA risk loci (9p21, 18q11, 15q21, and 2q33) with consistent effect directions in all 4 cohorts. We calculated polygenic scores based on IA‐, AAA‐, and TAA‐associated SNPs and tested these scores for association to case‐control status in the other aneurysm cohorts; this revealed no shared polygenic effects. Similarly, linkage disequilibrium–score regression analyses did not show significant correlations between any pair of aneurysm subtypes. Last, we evaluated the evidence for 14 previously published aneurysm risk single‐nucleotide polymorphisms through collaboration in extended aneurysm cohorts, with a total of 6548 cases and 16 843 controls (IA) and 4391 cases and 37 904 controls (AAA), and found nominally significant associations for IA risk locus 18q11 near RBBP8 to AAA (odds ratio [OR]=1.11; P=4.1×10−5) and for TAA risk locus 15q21 near FBN1 to AAA (OR=1.07; P=1.1×10−3). Conclusions: Although there was no evidence for polygenic overlap between IAs, AAAs, and TAAs, we found nominally significant effects of two established risk loci for IAs and TAAs in AAAs. These two loci will require further replication