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Background: The cysteine-altering variants in NOTCH3 have been suggested to be associated with stroke, dementia, and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), where aberrant blood pressure levels represent the characteristics of these diseases. We aimed to assess whether the cysteine-altering p.Arg544Cys (p.R544C; rs201118034) variant and common single nucleotide variants (SNVs) in NOTCH3 could contribute to systolic and diastolic blood pressure and related phenotypes in the Taiwan Biobank. Methods: We employed a discovery sample of 68,925 individuals, an independent replication sample of 45,676 individuals, and a combined/total sample of 114,601 individuals; all from the Taiwan Biobank. Blood pressure, such as systolic and diastolic blood pressure, was measured for all participants. Association was evaluated using a general linear model, where results were considered statistically significant if the P value < 0.05 divided by the number of independent tests per model. Results: From our analysis, we identified and replicated three novel candidates for blood pressure that have not previously been reported: the cysteine-altering p.R544C variant for systolic blood pressure, the common SNV rs11669950 for diastolic blood pressure, and the common SNV rs4808235 for diastolic blood pressure. We also generalized two previously identified SNVs (i.e., rs10418305 and rs7408868) in NOTCH3 for blood pressure in European and non-Taiwanese East Asian populations to the Taiwanese population. Moreover, the participants with NOTCH3 p.R544C had an increased stroke frequency (P < 1.0 × 10–5) and a higher dementia frequency (P = 2.0 × 10–4) compared with the whole Taiwan Biobank population in the combined/total sample. Conclusion: NOTCH3 is a strong candidate for a role in stroke, dementia, and CADASIL, which has previously been linked to blood pressure changes. While our preliminary study suggests that NOTCH3 p.R544C may influence blood pressure, stroke, and dementia in the Taiwan Biobank, replication in a well-powered external sample is required. This study also underlines considerable prospects of detecting novel genetic biomarkers in underrepresented worldwide populations. [ABSTRACT FROM AUTHOR]

Background: Gamma-aminobutyric acid type A (GABAA) receptors mainly mediate the effects of gamma-aminobutyric acid, which is the primary inhibitory neurotransmitter in the central nervous system. Abundant evidence suggests that GABAA receptors play a key role in sleep-regulating processes. No genetic association study has explored the relationships between GABAA receptor genes and sleep duration, sleep quality, and sleep timing in humans. Methods: We determined the association between single-nucleotide polymorphisms (SNPs) in the GABAA receptor genes GABRA1, GABRA2, GABRB3, GABRA5, and GABRG3 and sleep duration, sleep quality, and sleep timing in the Taiwan Biobank with a sample of 10,127 Taiwanese subjects. There were 10,142 subjects in the original study cohort. We excluded 15 subjects with a medication history of sedative-hypnotics. Results: Our data revealed an association of the GABRB3-GABRA5-GABRG3 gene cluster with sleep duration, which has not been previously identified: rs79333046 (beta = − 0.07; P = 1.21 × 10–3) in GABRB3, rs189790076 (beta = 0.92; P = 1.04 × 10–3) in GABRA5, and rs147619342 (beta = − 0.72; P = 3.97 × 10–3) in GABRG3. The association between rs189790076 in GABRA5 and sleep duration remained significant after Bonferroni correction. A variant (rs12438141) in GABRB3 was also found to act as a potential expression quantitative trait locus. Additionally, we discovered interactions between variants in the GABRB3-GABRA5-GABRG3 gene cluster and lifestyle factors, such as tea and coffee consumption, smoking, and physical activity, that influenced sleep duration, although some interactions became nonsignificant after Bonferroni correction. We also found interactions among GABRB3, GABRA5, and GABRG3 that affected sleep duration. Furthermore, we identified an association of rs7165524 (beta = − 0.06; P = 2.20 × 10–3) in GABRA5 with sleep quality and an association of rs79465949 (beta = − 0.12; P = 3.95 × 10–3) in GABRB3 with sleep timing, although these associations became nonsignificant after Bonferroni correction. However, we detected no evidence of an association of individual SNPs in GABRA1 and GABRA2. Conclusions: Our results indicate that rs189790076 in GABRA5 and gene–gene interactions among GABRB3, GABRA5, and GABRG3 may contribute to sleep duration in the Taiwanese population. [ABSTRACT FROM AUTHOR]

Evidence suggests that the neuro-inflammation mechanisms associated with interleukin-12 (IL-12) may be linked to Alzheimer's diseases and cognitive aging. In this study, we speculate that single nucleotide polymorphisms (SNPs) in IL-12-associated genes, such as IL12A, IL12B, IL12RB1 , and IL12RB2 genes, could be associated with cognitive aging individually and/or via complicated interactions in the elder Taiwanese population. There were totally 3,730 Taiwanese individuals with age ≥60 years from the Taiwan Biobank. Mini-Mental State Examination (MMSE) was analyzed for all participants. We employed MMSE scores to assess cognitive functions. Our analysis revealed that the IL12A gene (including rs116910715, rs78902931, and rs78569420), the IL12B gene (including rs730691), and the IL12RB2 gene (including rs3790558, rs4655538, rs75699623, and rs1874396) were associated with cognitive aging. Among these SNPs, the association with the IL12RB2 rs3790558 SNP remained significant after performing Bonferroni correction (P = 6.87 × 10−4). Additionally, we found that interactions between the IL12A and IL12RB2 genes influenced cognitive aging (P = 0.022). Finally, we pinpointed the effects of interactions between IL12A, IL12B , and IL12RB2 with physical activity (P < 0.001, = 0.002, and < 0.001, respectively). Our study suggests that the IL-12-associated genes may contribute to susceptibility to cognitive aging independently as well as through gene-gene and gene-physical activity interactions. [ABSTRACT FROM AUTHOR]

In the wake of recent advances in scientific research, personalized medicine using deep learning techniques represents a new paradigm. In this work, our goal was to establish deep learning models which distinguish responders from non-responders, and also to predict possible antidepressant treatment outcomes in major depressive disorder (MDD). To uncover relationships between the responsiveness of antidepressant treatment and biomarkers, we developed a deep learning prediction approach resulting from the analysis of genetic and clinical factors such as single nucleotide polymorphisms (SNPs), age, sex, baseline Hamilton Rating Scale for Depression score, depressive episodes, marital status, and suicide attempt status of MDD patients. The cohort consisted of 455 patients who were treated with selective serotonin reuptake inhibitors (treatment-response rate = 61.0%; remission rate = 33.0%). By using the SNP dataset that was original to a genome-wide association study, we selected 10 SNPs (including ABCA13 rs4917029, BNIP3 rs9419139, CACNA1E rs704329, EXOC4 rs6978272, GRIN2B rs7954376, LHFPL3 rs4352778, NELL1 rs2139423, NUAK1 rs2956406, PREX1 rs4810894, and SLIT3 rs139863958) which were associated with antidepressant treatment response. Furthermore, we pinpointed 10 SNPs (including ARNTL rs11022778, CAMK1D rs2724812, GABRB3 rs12904459, GRM8 rs35864549, NAALADL2 rs9878985, NCALD rs483986, PLA2G4A rs12046378, PROK2 rs73103153, RBFOX1 rs17134927, and ZNF536 rs77554113) in relation to remission. Then, we employed multilayer feedforward neural networks (MFNNs) containing 1–3 hidden layers and compared MFNN models with logistic regression models. Our analysis results revealed that the MFNN model with 2 hidden layers (area under the receiver operating characteristic curve (AUC) = 0.8228 ± 0.0571; sensitivity = 0.7546 ± 0.0619; specificity = 0.6922 ± 0.0765) performed maximally among predictive models to infer the complex relationship between antidepressant treatment response and biomarkers. In addition, the MFNN model with 3 hidden layers (AUC = 0.8060 ± 0.0722; sensitivity = 0.7732 ± 0.0583; specificity = 0.6623 ± 0.0853) achieved best among predictive models to predict remission. Our study indicates that the deep MFNN framework may provide a suitable method to establish a tool for distinguishing treatment responders from non-responders prior to antidepressant therapy. [ABSTRACT FROM AUTHOR]

Increased risk of developing metabolic syndrome (MetS) has been associated with the circadian clock genes. In this study, we assessed whether 29 circadian clock-related genes (including ADCYAP1, ARNTL, ARNTL2, BHLHE40, CLOCK, CRY1, CRY2, CSNK1D, CSNK1E, GSK3B, HCRTR2, KLF10, NFIL3, NPAS2, NR1D1, NR1D2, PER1, PER2, PER3, REV1, RORA, RORB, RORC, SENP3, SERPINE1, TIMELESS, TIPIN, VIP, and VIPR2) are associated with MetS and its individual components independently and/or through complex interactions in a Taiwanese population. We also analyzed the interactions between environmental factors and these genes in influencing MetS and its individual components. A total of 3,000 Taiwanese subjects from the Taiwan Biobank were assessed in this study. Metabolic traits such as waist circumference, triglyceride, high-density lipoprotein cholesterol, systolic and diastolic blood pressure, and fasting glucose were measured. Our data showed a nominal association of MetS with several single nucleotide polymorphisms (SNPs) in five key circadian clock genes including ARNTL, GSK3B, PER3, RORA, and RORB; but none of these SNPs persisted significantly after performing Bonferroni correction. Moreover, we identified the effect of GSK3B rs2199503 on high fasting glucose (P = 0.0002). Additionally, we found interactions among the ARNTL rs10832020, GSK3B rs2199503, PER3 rs10746473, RORA rs8034880, and RORB rs972902 SNPs influenced MetS (P < 0.001 ~ P = 0.002). Finally, we investigated the influence of interactions between ARNTL rs10832020, GSK3B rs2199503, PER3 rs10746473, and RORB rs972902 with environmental factors such as alcohol consumption, smoking status, and physical activity on MetS and its individual components (P < 0.001 ~ P = 0.002). Our study indicates that circadian clock genes such as ARNTL, GSK3B, PER3, RORA, and RORB genes may contribute to the risk of MetS independently as well as through gene-gene and gene-environment interactions. [ABSTRACT FROM AUTHOR]

Evidence indicates that the pathophysiologic mechanisms associated with insulin resistance may contribute to cognitive aging and Alzheimer’s diseases. In this study, we hypothesize that single nucleotide polymorphisms (SNPs) within insulin resistance-associated genes, such as the ADAM metallopeptidase with thrombospondin type 1 motif 9 (ADAMTS9), glucokinase regulator (GCKR), and peroxisome proliferator activated receptor gamma (PPARG) genes, may be linked with cognitive aging independently and/or through complex interactions in an older Taiwanese population. A total of 547 Taiwanese subjects aged over 60 years from the Taiwan Biobank were analyzed. Mini-Mental State Examinations (MMSE) were administered to all subjects, and MMSE scores were used to measure cognitive functions. Our data showed that four SNPs (rs73832338, rs9985304, rs4317088, and rs9831846) in the ADAMTS9 gene were significantly associated with cognitive aging among the subjects (P = 1.5 x 10−6 ~ 0.0002). This association remained significant after performing Bonferroni correction. Additionally, we found that interactions between the ADAMTS9 rs9985304 and ADAMTS9 rs76346246 SNPs influenced cognitive aging (P < 0.001). However, variants in the GCKR and PPARG genes had no association with cognitive aging in our study. Our study indicates that the ADAMTS9 gene may contribute to susceptibility to cognitive aging independently as well as through SNP-SNP interactions. [ABSTRACT FROM AUTHOR]

The anti-apoptotic protein B-cell CLL/lymphoma 2 (Bcl-2) gene is a major regulator of neural plasticity and cellular resilience. Recently, the Bcl-2 rs956572 single nucleotide polymorphism was proposed to be a functional allelic variant that modulates cellular vulnerability to apoptosis. Our cross-sectional study investigated the genetic effect of this Bcl-2 polymorphism on age-related decreases in gray matter (GM) volume across the adult lifespan. Our sample comprised 330 healthy volunteers (191 male, 139 female) with a mean age of 56.2±22.0 years (range: 21–92). Magnetic resonance imaging and genotyping of the Bcl-2 rs956572 were performed for each participant. The differences in regional GM volumes between G homozygotes and A-allele carriers were tested using optimized voxel-based morphometry. The association between the Bcl-2 rs956572 polymorphism and age was a predictor of regional GM volumes in the right cerebellum, bilateral lingual gyrus, right middle temporal gyrus, and right parahippocampal gyrus. We found that the volume of these five regions decreased with increasing age (all P<.001). Moreover, the downward slope was steeper among the Bcl-2 rs956572 A-allele carriers than in the G-homozygous participants. Our data provide convergent evidence for the genetic effect of the Bcl-2 functional allelic variant in brain aging. The rs956572 G-allele, which is associated with significantly higher Bcl-2 protein expression and diminished cellular sensitivity to stress-induced apoptosis, conferred a protective effect against age-related changes in brain GM volume, particularly in the cerebellum. [ABSTRACT FROM AUTHOR]

In light of recent advancements in machine learning, personalized medicine using predictive algorithms serves as an essential paradigmatic methodology. Our goal was to explore an integrated machine learning and genome-wide analysis approach which targets the prediction of probable major depressive disorder (MDD) using 9828 individuals in the Taiwan Biobank. In our analysis, we reported a genome-wide significant association with probable MDD that has not been previously identified: FBN1 on chromosome 15. Furthermore, we pinpointed 17 single nucleotide polymorphisms (SNPs) which show evidence of both associations with probable MDD and potential roles as expression quantitative trait loci (eQTLs). To predict the status of probable MDD, we established prediction models with random undersampling and synthetic minority oversampling using 17 eQTL SNPs and eight clinical variables. We utilized five state-of-the-art models: logistic ridge regression, support vector machine, C4.5 decision tree, LogitBoost, and random forests. Our data revealed that random forests had the highest performance (area under curve = 0.8905 ± 0.0088; repeated 10-fold cross-validation) among the predictive algorithms to infer complex correlations between biomarkers and probable MDD. Our study suggests that an integrated machine learning and genome-wide analysis approach may offer an advantageous method to establish bioinformatics tools for discriminating MDD patients from healthy controls. [ABSTRACT FROM AUTHOR]

The serotonin [5-hydroxytryptamine (5-HT)] system has been implicated in the pathogenesis of major depressive disorder (MDD). Among the 5-HT receptor subtypes, 5-HT2 is one of the major pharmacological therapeutic targets for MDD. There have been inconsistent findings in previous pharmacogenetic studies investigating the antidepressant therapeutic response using one or several 5-HT2A (HTR2A) genetic polymorphisms. By using gene-based association analysis, we hope to identify genetic variants of HTR2A which are related to MDD susceptibility and its antidepressant therapeutic response. 288 HTR2A single nucleotide polymorphisms in MDD susceptibility have been investigated through a case–control (455 MDD patients and 2, 998 healthy controls) study, as well as in antidepressant efficacy (n = 455) in our current research. The 21-item Hamilton Rating Scale for Depression was used to evaluate measures of antidepressant therapeutic efficacy. From two MDD groups in the antidepressant therapeutic response, by using gene-based analyses, we have identified 14 polymorphisms as suggestive markers for therapeutic response (13 for remission and 1 for response) in both meta- and mega-analyses. All of these HTR2A reported polymorphisms did not reach statistical significance in the case–control association study. This current investigation supported the link between HTR2A variants and antidepressant therapeutic response in MDD but not with MDD susceptibility. [ABSTRACT FROM AUTHOR]

In the wake of recent advances in machine learning research, the study of pharmacogenomics using predictive algorithms serves as a new paradigmatic application. In this work, our goal was to explore an ensemble machine learning approach which aims to predict probable antidepressant treatment response and remission in major depressive disorder (MDD). To discover the status of antidepressant treatments, we established an ensemble predictive model with a feature selection algorithm resulting from the analysis of genetic variants and clinical variables of 421 patients who were treated with selective serotonin reuptake inhibitors. We also compared our ensemble machine learning framework with other state-of-the-art models including multi-layer feedforward neural networks (MFNNs), logistic regression, support vector machine, C4.5 decision tree, naïve Bayes, and random forests. Our data revealed that the ensemble predictive algorithm with feature selection (using fewer biomarkers) performed comparably to other predictive algorithms (such as MFNNs and logistic regression) to derive the perplexing relationship between biomarkers and the status of antidepressant treatments. Our study demonstrates that the ensemble machine learning framework may present a useful technique to create bioinformatics tools for discriminating non-responders from responders prior to antidepressant treatments. [ABSTRACT FROM AUTHOR]

Background: Sleep is a key factor for health-related quality of life since sleep disturbances are a significant and common problem for patients with various human diseases such as psychiatric disorders. While single nucleotide polymorphisms (SNPs) in circadian clock genes have been indicated to be associated with sleep duration, most of the association studies have been investigated in populations with European ancestry. It is believed that no studies have been conducted to investigate a link between sleep duration and the circadian clock genes RORA and RORB, which play a key role, with NR1D1, in an additional feedback loop for the circadian rhythm machinery.Methods: In this study, we assessed the relationships between sleep duration and SNPs in the circadian clock genes NR1D1, RORA, and RORB in the Taiwan Biobank with a sample of 10,112 Taiwanese subjects.Results: From our data, we revealed a novel significant association in sleep duration with the rs75981965 SNP (P = 9.93 × 10-5) in the RORA gene that has not been previously identified. The association of sleep duration with this SNP remained significant after performing Bonferroni correction. RORA is a potential candidate for sleep duration as RORA has been suggested to play a key role in the regulation of sleep disorders. Additionally, we pinpointed the effects of interactions between RORA rs75981965 and environmental factors such as tea consumption (P = 0.0015), coffee consumption (P = 0.0029), physical activity (P = 0.011), alcohol consumption (P = 0.0146), and smoking (P = 0.0223) in influencing sleep duration. We also found interactions between RORA and NR1D1 (P = 0.0023) as well as between RORA and RORB (P = 0.0061) in affecting sleep duration.Conclusions: Our results indicate that the circadian clock gene RORA may contribute to sleep duration independently as well as through gene-gene and gene-environment interactions in the Taiwanese population. [ABSTRACT FROM AUTHOR]

• The EXO1 rs1776181, rs1776177, rs1635510, and rs2526698 SNPs are associated with cognitive aging. • Interactions between the EXO1 gene and physical activity influence cognitive aging. • Interactions between the DCLRE1C gene and physical activity influence cognitive aging. • Interactions between the EXO1 and RAD51C genes contribute to cognitive aging. Evidence indicates that the age-related neuropathological mechanisms associated with DNA repair genes may contribute to cognitive aging and Alzheimer's disease. In this study, we hypothesize that single nucleotide polymorphisms (SNPs) within 155 DNA repair genes may be linked to cognitive aging independently and/or through complex interactions in an older Taiwanese population. A total of 3,730 Taiwanese subjects aged over 60 years from the Taiwan Biobank were analyzed. Mini-Mental State Examination (MMSE) was administered to all subjects, and MMSE scores were used to measure cognitive functions. Our data showed that out of 1,652 SNPs, the rs1776181 (P = 1.47 × 10−5), rs1776177 (P = 8.42 × 10-7), rs1635510 (P = 7.97 × 10-6), and rs2526698 (P = 7.06 × 10-6) SNPs in the EXO1 gene were associated with cognitive aging. The association with these SNP remained significant after performing Bonferroni correction. Additionally, we found that interactions between the EXO1 and RAD51C genes influenced cognitive aging (P = 0.002). Finally, we pinpointed the influence of interactions between EXO1 and physical activity (P < 0.001) as well as between DCLRE1C and physical activity (P < 0.001). Our study indicated that DNA repair genes may contribute to susceptibility in cognitive aging independently as well as through gene-gene and gene-physical interactions. [ABSTRACT FROM AUTHOR]