Parietal–prefrontal feedforward connectivity in association with schizophrenia genetic risk and delusions

2019 ◽  
Author(s):  
Danielle Borrajo ◽  
Michelle La ◽  
Shefali Shah ◽  
Qiang Chen ◽  
Karen F Berman ◽  
...  
Keyword(s):  
Genetic Risk ◽  
Regression Models ◽  
Information Transfer ◽  
Effective Connectivity ◽  
Support Vector ◽  
Prior Beliefs ◽  
Feed Forward ◽  
Polygenic Risk ◽  
New Information ◽  
Information Processing Deficits

AbstractBackgroundConceptualizations of delusion formation implicate, in part, deficits at feed-forward information transfer across posterior to prefrontal cortices, resulting in dysfunctional integration of new information in favor of over-familiar prior beliefs. Here, we used functional MRI and machine learning models to examine feedforward parietal-prefrontal information transfer in schizophrenia patients in relation to delusional thinking, and polygenic risk for schizophrenia.MethodsWe studied 66 schizophrenia patients and 143 healthy controls as they performed context updating during working memory (WM). Dynamic causal models of effective connectivity were focused on prefrontal and parietal cortex, where we examined parietal-prefrontal connectivity in relation to delusions in patients. We further tested for an effect of polygenic risk for schizophrenia on connectivity in healthy individuals. We then leveraged support vector regression models to define optimal normalized target connectivity tailored for each patient, and tested the extent to which deviation from this target predicted individual variation in delusion severity.ResultsIn schizophrenia patients, updating and manipulating context information was disproportionately less accurate than was WM maintenance, with a task accuracy-by-diagnosis interaction. Also, patients with delusions tended to have relatively reduced feedforward effective connectivity during context updating in WM manipulation. The same parietal-prefrontal feedforward prefrontal effective connectivity was adversely influenced by polygenic risk for schizophrenia in healthy subjects. Individual patients’ deviation from predicted ‘normal’ feedforward connectivity based on the support vector models correlated with delusional severity.ConclusionsThese computationally-derived observations support a role for feed-forward parietal-prefrontal information processing deficits in delusional psychopathology, and in genetic risk for schizophrenia.

Air pollution interacts with genetic risk to influence cortical networks implicated in depression

2021 ◽  
Vol 118 (46) ◽  
pp. e2109310118
Author(s):  
Zhi Li ◽  
Hao Yan ◽  
Xiao Zhang ◽  
Shefali Shah ◽  
Guang Yang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Genetic Risk ◽  
Information Transfer ◽  
Depressive Disorders ◽  
Effective Connectivity ◽  
Brain Network ◽  
Brain Atlas ◽  
Polygenic Risk ◽  
Increased Risk ◽  
Risk For Depression

Air pollution is a reversible cause of significant global mortality and morbidity. Epidemiological evidence suggests associations between air pollution exposure and impaired cognition and increased risk for major depressive disorders. However, the neural bases of these associations have been unclear. Here, in healthy human subjects exposed to relatively high air pollution and controlling for socioeconomic, genomic, and other confounders, we examine across multiple levels of brain network function the extent to which particulate matter (PM2.5) exposure influences putative genetic risk mechanisms associated with depression. Increased ambient PM2.5 exposure was associated with poorer reasoning and problem solving and higher-trait anxiety/depression. Working memory and stress-related information transfer (effective connectivity) across cortical and subcortical brain networks were influenced by PM2.5 exposure to differing extents depending on the polygenic risk for depression in gene-by-environment interactions. Effective connectivity patterns from individuals with higher polygenic risk for depression and higher exposures with PM2.5, but not from those with lower genetic risk or lower exposures, correlated spatially with the coexpression of depression-associated genes across corresponding brain regions in the Allen Brain Atlas. These converging data suggest that PM2.5 exposure affects brain network functions implicated in the genetic mechanisms of depression.

scholarly journals Two schizophrenia imaging signatures and their associations with cognition, psychopathology, and genetics in the general population

2022 ◽  
Author(s):  
Ganesh B Chand ◽  
Pankhuri Singhal ◽  
Dominic B Dwyer ◽  
Junhao Wen ◽  
Guray Erus ◽  
...  
Keyword(s):  
Genetic Risk ◽  
Gray Matter Volume ◽  
Control Sample ◽  
Population Level ◽  
Population Based ◽  
Risk Scores ◽  
Support Vector ◽  
Cross Sectional ◽  
Polygenic Risk ◽  
Independent Population

The prevalence and significance of schizophrenia-related phenotypes at the population-level are debated in the literature. Here we assess whether two recently reported neuroanatomical signatures of schizophrenia, signature 1 with widespread reduction of gray matter volume, and signature 2 with increased striatal volume, could be replicated in an independent schizophrenia sample, and investigate whether expression of these signatures can be detected at the population-level and how they relate to cognition, psychosis spectrum symptoms, and schizophrenia genetic risk. This cross-sectional study used an independent schizophrenia-control sample (n=347; age 16-57 years) for replication of imaging signatures, and then examined two independent population-level datasets: Philadelphia Neurodevelopmental Cohort [PNC; n=359 typically developing (TD) and psychosis-spectrum symptoms (PS) youth] and UK Biobank (UKBB; n=836; age 44-50 years) adults. We quantified signature expression using support-vector machine learning, and compared cognition, psychopathology, and polygenic risk between signatures. Two neuroanatomical signatures of schizophrenia were replicated. Signature 1 but not signature 2 was significantly more common in youth with PS than TD youth, whereas signature 2 frequency was similar. In both youth and adults, signature 1 had worse cognitive performance than signature 2. Compared to adults with neither signature, adults expressing signature 1 had elevated schizophrenia polygenic risk scores, but this was not seen for signature 2. We successfully replicate two neuroanatomical signatures of schizophrenia, and describe their prevalence in population-based samples of youth and adults. We further demonstrate distinct relationships of these signatures with psychosis symptoms, cognition, and genetic risk, potentially reflecting underlying neurobiological vulnerability.

Abstract P879: Differences in Statistical Performance of Polygenic Risk Scores for Cardiovascular Disease Across Different Race/Ethnicities

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Julian N Acosta ◽  
Cameron Both ◽  
Natalia Szejko ◽  
Stacy Brown ◽  
Kevin N Sheth ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Logistic Regression ◽  
Genetic Risk ◽  
Regression Models ◽  
Risk Scores ◽  
Uk Biobank ◽  
Polygenic Risk ◽  
Logistic Regression Models ◽  
The Uk ◽  
Significant Health

Introduction: Genome-wide association studies have identified numerous genetic risk variants for stroke and myocardial infarction (MI) in Europeans. However, the limited applicability of these results to non-Europeans due to racial/ethnic differences in the genetic architecture of cardiovascular disease (CVD), coupled with the limited availability of genomic data in non-Europeans, may create significant health disparities now that genomic-based precision medicine is a reality. We tested the hypothesis that the performance of polygenic risk scores (PRS) for CVD differ in Europeans versus non-Europeans. Methods: We conducted a nested study within the UK Biobank, a prospective, population-based study that enrolled ~500,000 participants across the UK. For this study, we identified self-reported black participants and randomly matched them 1:1 by age and sex with white participants. We created a PRS using previously discovered loci for stroke and MI. We then tested whether this PRS representing the aggregate polygenic susceptibility to CVD yielded similar precision in black versus white participants in logistic regression models. Results: Of the 502,536 participants enrolled in the UK Biobank, 8,061 were self-reported blacks, with 7,644 having available data for our analyses. We randomly matched these participants with white individuals, leading to a total sample size of 15,288 (mean age 51.9 [SD 8.1], female 8,722 [57%]). The total number of events was 741 overall, with 363 happening in blacks and 378 happening in whites. In logistic regression models including age, sex, and 5 principal components, the statistical precision (e.g. narrower confidence intervals) for the PRS was substantially higher for whites (OR 1.22, 95%CI 1.08 - 1.37; p<0.0001) compared to blacks (OR 1.24, 95%CI 1.05-1.47; p=0.01). Secondary analyses using genetically-determined ancestry yielded similar results. Conclusion: Because CVD-related PRSs are derived mainly using genetic risk factors identified in populations of European ancestry, their statistical performance is lower in non-European populations. This asymmetry can lead to significant health disparities now that these tools are being evaluated in multiple precision medicine approaches.

Polygenic Risk Score for Smoking is associated with Externalizing Psychopathology and Disinhibited Personality Traits but not Internalizing Psychopathology in Adolescence

2020 ◽  
Author(s):  
Brian M. Hicks ◽  
D. Angus Clark ◽  
Joseph D. Deak ◽  
Mengzhen Liu ◽  
C. Emily Durbin ◽  
...  
Keyword(s):  
Personality Traits ◽  
Genetic Risk ◽  
Behavioral Control ◽  
Cumulative Exposure ◽  
Genetic Influences ◽  
Behavioral Disinhibition ◽  
Polygenic Risk ◽  
Externalizing Psychopathology ◽  
Internalizing Psychopathology ◽  
Random Intercept

Importance: Large consortia of genome wide association studies have yielded more accurate polygenic risk scores (PRS) that aggregate the small effects of many genetic variants to characterize the genetic architecture of disorders and provide a personalized measure of genetic risk. Objective: We examined whether a PRS for smoking measured genetic risk for general behavioral disinhibition by estimating its associations with externalizing and internalizing psychopathology and related personality traits. We examined these associations at multiple time points in adolescence using more refined phenotypes defined by stable characteristics across time and at young ages, which reduced potential confounds associated with cumulative exposure to substances and reverse causality. Methods: Random intercept panel models were fit to symptoms of conduct disorder, oppositional defiant disorder, major depressive disorder (MDD), and teacher ratings of externalizing and internalizing problems and personality traits at ages 11, 14, and 17 years-old in the Minnesota Twin Family Study (N = 3225). Results: The smoking PRS had strong associations with the random intercept factors for all the externalizing measures (mean standardized ꞵ = .27), agreeableness (ꞵ=-.22, 95% CI: -.28, -.16), and conscientiousness (ꞵ=-.19, 95% CI: -.24, -.13), but was not significantly associated with the internalizing measures (mean ꞵ = .06) or extraversion (ꞵ=.01, 95% CI: -.05, .07). After controlling for smoking at age 17, the associations with the externalizing measures (mean ꞵ = .13) and personality traits related to behavioral control (mean ꞵ = -.10) remained statistically significant. Conclusions and Relevance: The smoking PRS measures genetic influences that contribute to a spectrum of phenotypes related to behavioral disinhibition including externalizing psychopathology and normal-range personality traits related to behavioral control, but not internalizing psychopathology. Continuing to identify the correlates and delineate the mechanisms of the genetic influences associated with disinhibition could have substantial impact in mitigating a variety of public health problems (e.g., mental health, academic achievement, criminality).

scholarly journals Interaction between dietary branched-chain amino acids and genetic risk score on the risk of type 2 diabetes in Chinese

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Weiqi Wang ◽  
Haiyang Jiang ◽  
Ziwei Zhang ◽  
Wei Duan ◽  
Tianshu Han ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Genetic Predisposition ◽  
Genetic Risk ◽  
Odds Ratio ◽  
Regression Models ◽  
Fasting Glucose ◽  
Genetic Risk Score ◽  
Branched Chain Amino Acids ◽  
Branched Chain

Abstract Background and objectives Previous studies have found the important gene-diet interactions on type 2 diabetes (T2D) incident but have not followed branched-chain amino acids (BCAAs), even though they have shown heterogeneous effectiveness in diabetes-related factors. So in this study, we aim to investigate whether dietary BCAAs interact with the genetic predisposition in relation to T2D risk and fasting glucose in Chinese adults. Methods In a case-control study nested in the Harbin Cohort Study on Diet, Nutrition and Chronic Non-Communicable Diseases, we obtained data for 434 incident T2D cases and 434 controls matched by age and sex. An unweighted genetic risk score (GRS) was calculated for 25 T2D-related single nucleotide polymorphisms by summation of the number of risk alleles for T2D. Multivariate logistic regression models and general linear regression models were used to assess the interaction between dietary BCAAs and GRS on T2D risk and fasting glucose. Results Significant interactions were found between GRS and dietary BCAAs on T2D risk and fasting glucose (p for interaction = 0.001 and 0.004, respectively). Comparing with low GRS, the odds ratio of T2D in high GRS were 2.98 (95% CI 1.54–5.76) among those with the highest tertile of total BCAA intake but were non-significant among those with the lowest intake, corresponding to 0.39 (0.12) mmol/L versus − 0.07 (0.10) mmol/L fasting glucose elevation per tertile. Viewed differently, comparing extreme tertiles of dietary BCAAs, the odds ratio (95% CIs) of T2D risk were 0.46 (0.22–0.95), 2.22 (1.15–4.31), and 2.90 (1.54–5.47) (fasting glucose elevation per tertile: − 0.23 (0.10), 0.18 (0.10), and 0.26 (0.13) mmol/L) among participants with low, intermediate, and high genetic risk, respectively. Conclusions This study indicated that dietary BCAAs could amplify the genetic association with T2D risk and fasting glucose. Moreover, higher BCAA intake showed positive association with T2D when genetic predisposition was also high but changed to negative when genetic predisposition was low.

scholarly journals Research on a novel data-driven aging estimation method for battery systems in real-world electric vehicles

2021 ◽  
Vol 13 (7) ◽  
pp. 168781402110277
Author(s):  
Yankai Hou ◽  
Zhaosheng Zhang ◽  
Peng Liu ◽  
Chunbao Song ◽  
Zhenpo Wang
Keyword(s):  
Electric Vehicles ◽  
Real World ◽  
Regression Models ◽  
Estimation Method ◽  
Recursive Least Squares ◽  
Data Driven ◽  
Accurate Estimation ◽  
Support Vector ◽  
Battery Degradation ◽  
Operational Data

Accurate estimation of the degree of battery aging is essential to ensure safe operation of electric vehicles. In this paper, using real-world vehicles and their operational data, a battery aging estimation method is proposed based on a dual-polarization equivalent circuit (DPEC) model and multiple data-driven models. The DPEC model and the forgetting factor recursive least-squares method are used to determine the battery system’s ohmic internal resistance, with outliers being filtered using boxplots. Furthermore, eight common data-driven models are used to describe the relationship between battery degradation and the factors influencing this degradation, and these models are analyzed and compared in terms of both estimation accuracy and computational requirements. The results show that the gradient descent tree regression, XGBoost regression, and light GBM regression models are more accurate than the other methods, with root mean square errors of less than 6.9 mΩ. The AdaBoost and random forest regression models are regarded as alternative groups because of their relative instability. The linear regression, support vector machine regression, and k-nearest neighbor regression models are not recommended because of poor accuracy or excessively high computational requirements. This work can serve as a reference for subsequent battery degradation studies based on real-time operational data.

scholarly journals Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis

2021 ◽  
Vol 13 (4) ◽  
pp. 581 ◽  
Author(s):  
Yuanyuan Fu ◽  
Guijun Yang ◽  
Xiaoyu Song ◽  
Zhenhong Li ◽  
Xingang Xu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Least Squares ◽  
Aboveground Biomass ◽  
Regression Models ◽  
Digital Images ◽  
Predictive Performance ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Biomass Estimation ◽  
High Definition

Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics “Mean” from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass.

scholarly journals Effects of polygenic risk for major mental disorders and cross-disorder on cortical complexity

2021 ◽  
pp. 1-12
Author(s):  
Simon Schmitt ◽  
Tina Meller ◽  
Frederike Stein ◽  
Katharina Brosch ◽  
Kai Ringwald ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Mental Disorders ◽  
Cortical Thickness ◽  
Genetic Risk ◽  
Right Hemisphere ◽  
Autism Spectrum ◽  
Risk Scores ◽  
Grey Matter Volume ◽  
Polygenic Risk ◽  
Risk For Depression

Abstract Background MRI-derived cortical folding measures are an indicator of largely genetically driven early developmental processes. However, the effects of genetic risk for major mental disorders on early brain development are not well understood. Methods We extracted cortical complexity values from structural MRI data of 580 healthy participants using the CAT12 toolbox. Polygenic risk scores (PRS) for schizophrenia, bipolar disorder, major depression, and cross-disorder (incorporating cumulative genetic risk for depression, schizophrenia, bipolar disorder, autism spectrum disorder, and attention-deficit hyperactivity disorder) were computed and used in separate general linear models with cortical complexity as the regressand. In brain regions that showed a significant association between polygenic risk for mental disorders and cortical complexity, volume of interest (VOI)/region of interest (ROI) analyses were conducted to investigate additional changes in their volume and cortical thickness. Results The PRS for depression was associated with cortical complexity in the right orbitofrontal cortex (right hemisphere: p = 0.006). A subsequent VOI/ROI analysis showed no association between polygenic risk for depression and either grey matter volume or cortical thickness. We found no associations between cortical complexity and polygenic risk for either schizophrenia, bipolar disorder or psychiatric cross-disorder when correcting for multiple testing. Conclusions Changes in cortical complexity associated with polygenic risk for depression might facilitate well-established volume changes in orbitofrontal cortices in depression. Despite the absence of psychopathology, changed cortical complexity that parallels polygenic risk for depression might also change reward systems, which are also structurally affected in patients with depressive syndrome.

scholarly journals Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ganna Leonenko ◽  
Emily Baker ◽  
Joshua Stevenson-Hoare ◽  
Annerieke Sierksma ◽  
Mark Fiers ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Genetic Risk ◽  
Prediction Accuracy ◽  
Genetic Risk Score ◽  
Low Risk ◽  
Risk Scores ◽  
Sample Mean ◽  
Polygenic Risk ◽  
Reliable Identification

AbstractPolygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals’ scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals’ scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.

Effects of genetic risk for alcohol dependence and onset of regular drinking on the progression to alcohol dependence: A polygenic risk score approach

2021 ◽  
pp. 109117
Author(s):  
Ellen W. Yeung ◽  
Kellyn M. Spychala ◽  
Alex P. Miller ◽  
Jacqueline M. Otto ◽  
Joseph D. Deak ◽  
...  
Keyword(s):  
Alcohol Dependence ◽  
Risk Score ◽  
Genetic Risk ◽  
Polygenic Risk Score ◽  
Polygenic Risk
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