NIH Public Access Author Manuscript Biol Psychiatry. Author manuscript; available in PMC 2011 January 1.

Published in final edited form as

Analysis of PTPRK polymorphisms in association with risk and age at onset of Alzheimer’s disease, cancer risk, and cholesterol

The human receptor-type protein-tyrosine phosphatase kappa (PTPRK) gene is highly expressed in human brain and is previously associated with neuropsychiatric disorders and cancer. This study investigated the association of 52 single nucleotide polymorphisms (SNPs) in the PTPRK with the risk and age at onset (AAO) of Alzheimer’s disease (AD) in 791 AD patients and 782 controls. Five SNPs (top SNP rs4895829 with p=0.0125) were associated with the risk of AD based on a multiple logistic regression (p\u3c0.05); while 6 SNPs (top SNP rs1891150 with p=8.02×10−6) were associated with AAO by using a multiple linear regression analysis. Interestingly, rs2326681 was associated with both the risk and AAO of AD (p=4.65×10−2 and 5.18×10−3, respectively). In a replication study, the results from family-based association test - generalized estimating equation (GEE) statistics and Wilcoxon test showed that seven SNPs were associated with the risk of AD (top SNP rs11756545 with p=1.02×10−2) and 12 SNPs were associated with the AAO (top SNP rs11966128 with p=1.39×10−4), respectively. One additional sample showed that four SNPs were associated with risk of cancer (top SNP rs1339197 with p=4.1×10−3), 12 SNPs associated with LDL-cholesterol (top SNP rs4544930 with p=3.47×10−3), and 8 SNPs associated with total cholesterol (top SNP rs1012049 with p=6.09×10−3). In addition, the AD associated rs4895829 was associated with the gene expression level in the cerebellum (p=7.3×10−5). The present study is the first study providing evidence of several genetic variants within the PTPRK gene associated with the risk and AAO of AD, risk of cancer, LDL and total cholesterol levels

Principal component analysis of early alcohol, drug and tobacco use with major depressive disorder in US adults

Early alcohol, tobacco and drug use prior to 18 years old are comorbid and correlated. This study included 6239 adults with major depressive disorder (MDD) in the past year and 72,010 controls from the combined data of 2013 and 2014 National Survey on Drug Use and Health (NSDUH). To deal with multicollinearity existing among 17 variables related to early alcohol, tobacco and drug use prior to 18 years old, we used principal component analysis (PCA) to infer PC scores and then use weighted multiple logistic regression analyses to estimate the associations of potential factors and PC scores with MDD. The odds ratios (ORs) with 95% confidence intervals (CIs) were estimated. The overall prevalence of MDD was 6.7%. The first four PCs could explain 57% of the total variance. Weighted multiple logistic regression showed that PC1 (a measure of psychotherapeutic drugs and illicit drugs other than marijuana use), PC2 (a measure of cocaine and hallucinogens), PC3 (a measure of early alcohol, cigarettes, and marijuana use), and PC4 (a measure of cigar, smokeless tobacco use and illicit drugs use) revealed significant associations with MDD (OR = 1.12, 95% CI = 1.08-1.16, OR = 1.08, 95% CI = 1.04-1.12, OR = 1.13, 95% CI = 1.07-1.18, and OR = 1.15, 95% CI = 1.09-1.21, respectively). In conclusion, PCA can be used to reduce the indicators in complex survey data. Early alcohol, tobacco and drug use prior to 18 years old were found to be associated with increased odds of adult MDD

Systematically characterizing dysfunctional long intergenic noncoding RNAs in multiple brain regions of major psychosis

Alzheimer\u27s disease (AD), the most common form of dementia, is a chronic neurodegenerative disease. The HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1 (HACE1) gene is expressed in human brain and may play a role in the pathogenesis of neurodegenerative disorders. Till now, no previous study has reported the association of the HACE1 gene with the risk and age at onset (AAO) of AD; while few studies have checked the proportional hazards assumption in the survival analysis of AAO of AD using Cox proportional hazards model. In this study, we examined the associations of 14 single nucleotide polymorphisms (SNPs) in the HACE1 gene with the risk and the AAO of AD using 791 AD patients and 782 controls. Multiple logistic regression model identified one SNP (rs9499937 with p = 1.8×10-3) to be associated with the risk of AD. For survival analysis of AAO, both classic Cox regression model and Bayesian survival analysis using the Cox proportional hazards model were applied to examine the association of each SNP with the AAO. The hazards ratio (HR) with its 95% confidence interval (CI) was estimated. Survival analysis using the classic Cox regression model showed that 4 SNPs were significantly associated with the AAO (top SNP rs9499937 with HR=1.33, 95%CI=1.13-1.57, p=5.0×10-4). Bayesian Cox regression model showed similar but a slightly stronger associations (top SNP rs9499937 with HR=1.34, 95%CI=1.11-1.55) compared with the classic Cox regression model. Using an independent family-based sample, one SNP rs9486018 was associated with the risk of AD (p=0.0323) and the T-T-G haplotype from rs9786015, rs9486018 and rs4079063 showed associations with both the risk and AAO of AD (p=2.27×10-3 and 0.0487, respectively). The findings of this study provide first evidence that several genetic variants in the HACE1 gene were associated with the risk and AAO of AD

Bayesian Cox Proportional Hazards Model in Survival Analysis of HACE1 Gene with Age at Onset of Alzheimer’s Disease

Alzheimer’s Disease (AD), the most common form of dementia, is a chronic neurodegenerative disease. The HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1 (HACE1) gene is expressed in human brain and may play a role in the pathogenesis of neurodegenerative disorders. Till now, no previous study has reported the association of the HACE1 gene with the risk and Age at Onset (AAO) of AD; while few studies have checked the proportional hazards assumption in the survival analysis of AAO of AD using Cox proportional hazards model. In this study, we examined the associations of 14 Single Nucleotide Polymorphisms (SNPs) in the HACE1 gene with the risk and the AAO of AD using 791 AD patients and 782 controls. Multiple logistic regression model identified one SNP (rs9499937 with p = 1.8 × 10-3) to be associated with the risk of AD. For survival analysis of AAO, both classic Cox regression model and Bayesian survival analysis using the Cox proportional hazards model were applied to examine the association of each SNP with the AAO. The Hazards Ratio (HR) with its 95% Confidence Interval (CI) was estimated. Survival analysis using the classic Cox regression model showed that 4 SNPs were significantly associated with the AAO (top SNP rs9499937 with HR = 1.33, 95% CI = 1.13-1.57, p = 5.0 × 10-4). Bayesian Cox regression model showed similar but a slightly stronger associations (top SNP rs9499937 with HR = 1.34, 95% CI = 1.11-1.55) compared with the classic Cox regression model. Using an independent family-based sample, one SNP rs9486018 was associated with the risk of AD (p = 0.0323) and the T-T-G haplotype from rs9786015, rs9486018 and rs4079063 showed associations with both the risk and AAO of AD (p = 2.27 × 10-3 and 0.0487, respectively). The findings of this study provide first evidence that several genetic variants in the HACE1 gene were associated with the risk and AAO of AD

Variation at APOE and STH loci and Alzheimer's disease

BACKGROUND: The apolipoprotein E (APOE) and tau proteins play important roles in the pathological development of Alzheimer's disease (AD). Many studies have shown an association between the APOE gene and AD. Association between AD and the newly discovered saitohin (STH) gene, nested within the intron of the tau gene, has been reported. The present study aimed to elucidate the association between APOE and AD, and between STH and AD in our sample. METHODS: The functional polymorphisms, rs429358 and rs7412, in the APOE gene (which together define the ε2, ε3, and ε4 alleles), and the Q7R SNP in the STH gene, were genotyped in 369 patients with AD and 289 healthy European-Americans. The associations between these two genes and AD were analyzed in a case-control design. RESULTS: Consistent with previously reported results, the frequencies of the APOE ε4 allele, ε4/ε4 genotype and ε3/ε4 genotype were significantly higher in AD cases than controls; the ε4/ε4 genotype frequency was significantly higher in early-onset AD (EOAD) than late-onset AD (LOAD); the frequencies of the ε2 allele, ε3 allele, ε3/ε3 genotype and ε2/ε3 genotype were significantly lower in AD cases than controls. Positive likelihood ratios (LRs(+)) of APOE alleles and genotypes increased in a linear trend with the number of ε4 alleles and decreased in a linear trend with the number of ε2 or ε3 alleles. There was no significant difference in the STH allele and genotype frequency distributions between AD cases and controls. CONCLUSION: This study confirmed that the ε4 allele is a dose-response risk factor for AD and the ε4/ε4 genotype was associated with a significantly earlier age of onset. Moreover, we found that the ε2 allele was a dose-response protective factor for AD and the ε3 allele exerted a weaker dose-response protective effect for risk of AD compared with ε2. In a clinical setting, APOE genotyping could offer additional biological evidence of whether a subject may develop AD, but it is not robust enough to serve as an independent screening or predictive test in the diagnosis of AD. STH variation was not significantly associated with AD in our sample

Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification

Convolutional neural networks excel in histopathological image classification, yet their pixel-level focus hampers explainability. Conversely, emerging graph convolutional networks spotlight cell-level features and medical implications. However, limited by their shallowness and suboptimal use of high-dimensional pixel data, GCNs underperform in multi-class histopathological image classification. To make full use of pixel-level and cell-level features dynamically, we propose an asymmetric co-training framework combining a deep graph convolutional network and a convolutional neural network for multi-class histopathological image classification. To improve the explainability of the entire framework by embedding morphological and topological distribution of cells, we build a 14-layer deep graph convolutional network to handle cell graph data. For the further utilization and dynamic interactions between pixel-level and cell-level information, we also design a co-training strategy to integrate the two asymmetric branches. Notably, we collect a private clinically acquired dataset termed LUAD7C, including seven subtypes of lung adenocarcinoma, which is rare and more challenging. We evaluated our approach on the private LUAD7C and public colorectal cancer datasets, showcasing its superior performance, explainability, and generalizability in multi-class histopathological image classification

Serum BDNF levels and state anxiety are associated with somatic symptoms in patients with panic disorder

BackgroundWe aimed to explore the predictive role of serum BDNF and anxiety-related variables in changes in somatic symptoms post-escitalopram treatment in panic disorder (PD) patients.MethodsNinety PD patients and 99 healthy controls (HCs) were enrolled. PD patients received an 8-week escitalopram treatment. All patients were administered the Panic Disorder Severity Scale–Chinese Version (PDSS-CV) and State-Trait Anxiety Inventory (STAI) to assess panic and anxiety-related symptoms, respectively. Patient Health Questionnaire 15-item scale (PHQ-15) was performed to measure somatic symptoms, and the blood sample was collected to detect serum BDNF levels in all participants. We performed partial correlation analysis and multiple linear regression to explore correlates of PHQ-15 and predictors of PHQ-15 changes post-escitalopram treatment after controlling for age, gender, education levels (set as a dummy variable), the current duration, comorbid AP, and/or GAD.ResultsCompared to HCs, PD patients had lower serum BDNF levels and higher PHQ-15 scores that could be improved post-escitalopram treatment. Lower baseline STAI state (b = −0.07, p = 0.004), and PDSS-CV scores (b = −0.25, p = 0.007), but higher baseline serum BDNF levels (b = 0.35, p = 0.007) contributed to the prediction of PHQ-15 changes post-escitalopram treatment.ConclusionState anxiety, serum BDNF levels, and panic severity could predict changes in somatic symptoms post-escitalopram treatment, our results highlighted that serum BDNF could serve as a biological indicator for improving somatic symptoms in PD patients