Lithium is a first-line treatment in bipolar disorder, but individual response is variable. Previous studies have suggested that lithium response is a heritable trait. However, no genetic markers of treatment response have been reproducibly identified.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with strong genetic components. Several recent genome-wide association (GWA) studies in Caucasian samples have reported a number of gene regions and loci correlated with the risk of ASD--albeit with very little consensus across studies.

The identification of gene-environment interactions (G × E) may eventually guide health-related choices and medical interventions for complex diseases. More powerful methods must be developed to identify G × E. The "adaptive combination of Bayes factors method" (ADABF) has been proposed as a powerful genome-wide polygenic approach to detect G × E. In this work, we evaluate its performance when serving as a gene-based G × E test. We compare ADABF with six tests including the "Set-Based gene-EnviRonment InterAction test" (SBERIA), "gene-environment set association test" (GESAT), etc. With extensive simulations, SBERIA and ADABF are found to be more powerful than other G × E tests. However, SBERIA suffers from a power loss when 50% SNP main effects are in the same direction with the SNP × E interaction effects while 50% are in the opposite direction. We further applied these seven G × E methods to the Taiwan Biobank data to explore gene× alcohol interactions on blood pressure levels. The ADAMTS7P1 gene at chromosome 15q25.2 was detected to interact with alcohol consumption on diastolic blood pressure (p = 9.5 × 10-7, according to the GESAT test). At this gene, the P-values provided by other six tests all reached the suggestive significance level (p < 5 × 10-5). Regarding the computation time required for a genome-wide G × E analysis, SBERIA is the fastest method, followed by ADABF. Considering the validity, power performance, robustness, and computation time, ADABF is recommended for genome-wide G × E analyses.

We previously reported initial results in 102 multiple myeloma (MM) patients treated with sequential high-dose melphalan and autologous hematopoietic cell transplantation followed by 200 cGy total body irradiation with or without fludarabine 90 mg/m2 and allogeneic hematopoietic cell transplantation. Here we present long-term clinical outcomes among the 102 initial patients and among 142 additional patients, with a median follow up of 8.3 (range 1.0-18.1) years. Donors included human leukocyte antigen identical siblings (n=179) and HLA-matched unrelated donors (n=65). A total of 209 patients (86%) received tandem autologous-allogeneic upfront, while thirty-five patients (14%) had failed a previous autologous hematopoietic cell transplantation before the planned autologous-allogeneic transplantation. Thirty-one patients received maintenance treatment at a median of 86 days (range, 61-150) after allogeneic transplantation. Five-year rates of overall survival (OS) and progression-free survival (PFS) were 54% and 31%, respectively. Ten-year OS and PFS were 41% and 19%, respectively. Overall non-relapse mortality was 2% at 100 days and 14% at five years. Patients with induction-refractory disease and those with high-risk biological features experienced shorter OS and PFS. A total of 152 patients experienced disease relapse and 117 of those received salvage treatment. Eighty-three of the 117 patients achieved a clinical response, and for those, the median duration of survival after relapse was 7.8 years. Moreover, a subset of patients who became negative for minimal residual disease (MRD) by flow cytometry experienced a significantly lower relapse rate as compared with MRD-positive patients (P=0.03). Our study showed that the graft-versus-myeloma effect after non-myeloablative allografting allowed long-term disease control in standard and high-risk patient subsets. Ultra-high-risk patients did not appear to benefit from tandem autologous/allogeneic hematopoietic cell transplantation because of early disease relapse. Incorporation of newer anti-MM agents into the initial induction treatments before tandem hematopoietic cell transplantation and during maintenance might improve outcomes of ultra-high-risk patients. Clinical trials included in this study are registered at: clinicaltrials.gov identifiers: 00075478, 00005799, 01251575, 00078858, 00105001, 00027820, 00089011, 00003196, 00006251, 00793572, 00054353, 00014235, 00003954.

A genetic risk score could be beneficial in assisting clinical diagnosis for complex diseases with high heritability. With large-scale genome-wide association (GWA) data, the current study constructed a genetic risk model with a machine learning approach for bipolar disorder (BPD). The GWA dataset of BPD from the Genetic Association Information Network was used as the training data for model construction, and the Systematic Treatment Enhancement Program (STEP) GWA data were used as the validation dataset. A random forest algorithm was applied for pre-filtered markers, and variable importance indices were assessed. 289 candidate markers were selected by random forest procedures with good discriminability; the area under the receiver operating characteristic curve was 0.944 (0.935-0.953) in the training set and 0.702 (0.681-0.723) in the STEP dataset. Using a score with the cutoff of 184, the sensitivity and specificity for BPD was 0.777 and 0.854, respectively. Pathway analyses revealed important biological pathways for identified genes. In conclusion, the present study identified informative genetic markers to differentiate BPD from healthy controls with acceptable discriminability in the validation dataset. In the future, diagnosis classification can be further improved by assessing more comprehensive clinical risk factors and jointly analysing them with genetic data in large samples.

The exploration of 'gene-environment interactions' (G × E) is important for disease prediction and prevention. The scientific community usually uses external information to construct a genetic risk score (GRS), and then tests the interaction between this GRS and an environmental factor (E). However, external genome-wide association studies (GWAS) are not always available, especially for non-Caucasian ethnicity. Although GRS is an analysis tool to detect G × E in GWAS, its performance remains unclear when there is no external information. Our 'adaptive combination of Bayes factors method' (ADABF) can aggregate G × E signals and test the significance of G × E by a polygenic test. We here explore a powerful polygenic approach for G × E when external information is unavailable, by comparing our ADABF with the GRS based on marginal effects of SNPs (GRS-M) and GRS based on SNP × E interactions (GRS-I). ADABF is the most powerful method in the absence of SNP main effects, whereas GRS-M is generally the best test when single-nucleotide polymorphisms main effects exist. GRS-I is the least powerful test due to its data-splitting strategy. Furthermore, we apply these methods to Taiwan Biobank data. ADABF and GRS-M identified gene × alcohol and gene × smoking interactions on blood pressure (BP). BP-increasing alleles elevate more BP in drinkers (smokers) than in nondrinkers (nonsmokers). This work provides guidance to choose a polygenic approach to detect G × E when external information is unavailable.

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.

Large-p-small-n datasets are commonly encountered in modern biomedical studies. To detect the difference between two groups, conventional methods would fail to apply due to the instability in estimating variances in t-test and a high proportion of tied values in AUC (area under the receiver operating characteristic curve) estimates. The significance analysis of microarrays (SAM) may also not be satisfactory, since its performance is sensitive to the tuning parameter, and its selection is not straightforward. In this work, we propose a robust rerank approach to overcome the above-mentioned diffculties. In particular, we obtain a rank-based statistic for each feature based on the concept of "rank-over-variable." Techniques of "random subset" and "rerank" are then iteratively applied to rank features, and the leading features will be selected for further studies. The proposed re-rank approach is especially applicable for large-p-small-n datasets. Moreover, it is insensitive to the selection of tuning parameters, which is an appealing property for practical implementation. Simulation studies and real data analysis of pooling-based genome wide association (GWA) studies demonstrate the usefulness of our method.

Nowadays diabetic nephropathy (DN) is the most common cause of end-stage renal disease (ESRD). Recent studies have demonstrated that the myosin, heavy chain 9, non-muscle (MYH9) gene is associated with ESRD in African Americans. In this study, we tested the hypothesis that a common single nucleotide polymorphism rs16996677 in the MYH9 gene may contribute to the etiology of DN in type 2 diabetes (T2D) in a Taiwanese population with T2D. There were 180 T2D patients diagnosed with DN and 178 age- and sex-similar T2D without DN controls. Single locus analyses showed no significant main effects of MYH9 rs16996677 on the risk of DN in T2D. The results suggest that the rs16996677 SNP in MYH9 may not contribute to the risk of DN in T2D in Taiwanese T2D patients.

Four serotonin-related genes including guanine nucleotide binding protein beta polypeptide 3 (GNB3), 5-hydroxytryptamine receptor 1A (HTR1A; serotonin receptor 1A), 5-hydroxytryptamine receptor 2A (HTR2A; serotonin receptor 2A), and solute carrier family 6 member 4 (SLC6A4; serotonin neurotransmitter transporter) have been suggested to be candidate genes for influencing antidepressant treatment outcome. The aim of this study was to explore whether interaction among these genes could contribute to the pharmacogenomics of short-term antidepressant response in a Taiwanese population with major depressive disorder (MDD).

A prospective study was carried out to identify the association of the DAZL (deleted in azoospermia-like) gene with major semen parameters in 210 men with normal and 467 men with abnormal semen parameters. Primer extension analysis for single nucleotide polymorphisms (SNP) of DAZL and quantitative trait analysis on the association of allele/genotype frequencies, linkage disequilibrium characteristics and DAZL haplotypes with semen parameters were investigated. Of five SNP (260A-->G, 386A-->G, 520+34c-->a, 584+28c-->t and 796+36g-->a) screened, 386A-->G was significantly correlated with sperm count (P<0.0001) and motility (P<0.005) and 584+28c-->t was marginally correlated with sperm morphology. After excluding 520+34c-->a, which was not in the Hardy-Weinberg equilibrium, the major haplotypes consisted of four SNP. One haplotype (260A-->G (major allele), 386A-->G (major allele), 584+28c-->t (minor allele) and 796+36g-->a (major allele)) was significantly associated with sperm count (P=0.003) and motility (P=0.04). This study suggests DAZL may be involved in regulating sperm counts, motility and possibly morphology.

Recent prospective studies have shown that dysregulation of the immune system may precede the development of B-cell lymphomas (BCL) in immunocompetent individuals. However, to date, the studies were restricted to a few immune markers, which were considered separately. Using a nested case-control study within two European prospective cohorts, we measured plasma levels of 28 immune markers in samples collected a median of 6 years before diagnosis (range 2.01-15.97) in 268 incident cases of BCL (including multiple myeloma [MM]) and matched controls. Linear mixed models and partial least square analyses were used to analyze the association between levels of immune marker and the incidence of BCL and its main histological subtypes and to investigate potential biomarkers predictive of the time to diagnosis. Linear mixed model analyses identified associations linking lower levels of fibroblast growth factor-2 (FGF-2 p = 7.2 × 10-4 ) and transforming growth factor alpha (TGF-α, p = 6.5 × 10-5 ) and BCL incidence. Analyses stratified by histological subtypes identified inverse associations for MM subtype including FGF-2 (p = 7.8 × 10-7 ), TGF-α (p = 4.08 × 10-5 ), fractalkine (p = 1.12 × 10-3 ), monocyte chemotactic protein-3 (p = 1.36 × 10-4 ), macrophage inflammatory protein 1-alpha (p = 4.6 × 10-4 ) and vascular endothelial growth factor (p = 4.23 × 10-5 ). Our results also provided marginal support for already reported associations between chemokines and diffuse large BCL (DLBCL) and cytokines and chronic lymphocytic leukemia (CLL). Case-only analyses showed that Granulocyte-macrophage colony stimulating factor levels were consistently higher closer to diagnosis, which provides further evidence of its role in tumor progression. In conclusion, our study suggests a role of growth-factors in the incidence of MM and of chemokine and cytokine regulation in DLBCL and CLL.

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.

This study aimed to examine the association of dopamine-related genes with mental and motor development and the gene-environment interaction in preterm and term children. A total of 201 preterm and 111 term children were examined for their development at 6, 12, 18, 24, and 36 months and were genotyped for 15 single-nucleotide polymorphisms (SNPs) in dopamine-related genes (DRD2, DRD3, DAT1, COMT, and MAOA). An independent sample of 256 preterm children was used for replication. Since the developmental age trends of preterm children differed from those of term children, the analyses were stratified by prematurity. Among the 8 SNPs on the MAOA gene examined in the whole learning sample, the results of linkage disequilibrium analysis indicated that they were located in one block (all D' > 0.9), and rs2239448 was chosen as the tag (r2 > 0.85). In the analysis of individual SNPs in each dopamine-related gene, the tag SNP (rs2239448) in MAOA remained significantly associated with the mental scores of preterm children for the interaction with age trend (p < 0.0001; largest effect size of 0.65 at 24 months) after Bonferroni correction for multiple testing. Similar findings for rs2239448 were replicated in the independent sample (p = 0.026). However, none of the SNPs were associated with the motor scores of preterm children, and none were related to the mental or motor scores of term children. The genetic variants of the MAOA gene exert influence on mental development throughout early childhood for preterm, but not term, children.

Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders.

The priority of interventions to alleviate cognitive deficits in patients with bipolar disorder (BD) is inconclusive. We systematically evaluate the efficacy of pharmacological or neurostimulation interventions for cognitive function in BD through a network meta-analysis.

It has been suggested that the relationship between cognitive function and functional outcome in schizophrenia is mediated by clinical symptoms, while functional outcome is assessed by the Quality of Life Scale (QLS) and the Global Assessment of Functioning (GAF) Scale. To determine the outcome assessed by QLS and GAF, we established a bagging ensemble framework with a feature selection algorithm resulting from the analysis of factors such as 3 clinical symptom scales and 11 cognitive function scores of 302 patients with schizophrenia in the Taiwanese population. We compared our bagging ensemble framework with other state-of-the-art algorithms such as multilayer feedforward neural networks, support vector machine, linear regression, and random forests. The analysis revealed that the bagging ensemble model with feature selection performed best among predictive models in predicting the QLS functional outcome by using 20-item Scale for the Assessment of Negative Symptoms (SANS20) and 17-item Hamilton Depression Rating Scale (HAMD17). Moreover, to predict the GAF outcome, the bagging ensemble model with feature selection performed best among predictive models by using SANS20 and the Positive and Negative Syndrome Scale-Positive (PANSS-Positive) subscale. The study indicates that there are synergistic effects between negative (SANS20) and depressive (HAMD17) symptoms as well as between negative and positive (PANSS-Positive) symptoms in influencing functional outcome of schizophrenia using the bagging ensemble framework with feature selection.

The dopamine receptor-related loci have been suggested to be associated with cognitive functions and neurodegenerative diseases. It is unknown whether genetic variants such as single nucleotide polymorphisms (SNPs) in the dopamine receptor-related loci could contribute to cognitive aging independently as well as by virtue of complicated interplays in the elder population. To assess whether SNPs in the dopamine receptor-related loci are associated with cognitive aging in the elder population, we evaluated SNPs in the DRD1, NCAM1-TTC12-ANKK1-DRD2, DRD3-LOC107986115-ZNF80-TIGIT-MIR568-ZBTB20, DRD4, and DRD5-SLC2A9 loci from 25,195 older Taiwanese individuals from the Taiwan Biobank. Mini-Mental State Examination (MMSE) was scrutinized for all participants, where MMSE scores were employed to evaluate cognitive functions. From our analysis, we identified three novel genes for cognitive aging that have not previously been reported: ZBTB20 on chromosome 3 and NCAM1 and TTC12 on chromosome 11. NCAM1 and ZBTB20 are strong candidates for having a role in cognitive aging with mutations in ZBTB20 resulting in intellectual disability, and NCAM1 previously found to be associated with associative memory in humans. Additionally, we found the effects of interplays between physical activity and these three novel genes. Our study suggests that genetic variants in the dopamine receptor-related loci may influence cognitive aging individually and by means of gene-physical activity interactions.

The etiology of sudden sensorineural hearing loss (SSNHL) has been unclear until now. Understanding its potential etiology is crucial for the development of preventive medicine. In this study, we investigated the association between acute upper respiratory tract infections (URIs) and SSNHL risk. We conducted a case-crossover study by using the longitudinal health insurance database derived from the National Health Insurance Research Database in Taiwan. Individual acute URI between the case and control periods was reviewed. Multivariable conditional logistic regression models were used to estimate the adjusted odds ratios (aORs) of SSNHL risk associated with acute URIs after adjustments for potential confounders. In total, 1131 patients with SSNHL between 2010 and 2013 fulfilled our inclusion criteria and were included. The aOR (95% confidence interval [CI]) for SSNHL was 1.57 (1.20-2.05) in relation to acute URIs one month before the index date. Moreover, the aORs (95% CIs) of the female and young to middle-aged (≤65 years) populations were 1.63 (1.13-2.36) and 1.76 (1.29-2.40), respectively. In addition, the association between SSNHL and acute URIs was decreased over time. The aOR for SSNHL was 1.25 (1.01-1.56) in relation to acute URIs three months before the index date. Acute URIs increase SSNHL risk and are a potential risk factor for SSNHL. The establishment of a feasible health policy for the prevention of acute URIs is crucial for SSNHL prevention, particularly in female, and young to middle-aged populations.

Several genes that are involved in the regulation of circadian rhythms are implicated in the susceptibility to bipolar disorder (BD). The current study aimed to investigate the relationships between genetic variants in NR1D1 RORA, and RORB genes and BD in the Han Chinese population. We conducted a case-control genetic association study with two samples of BD patients and healthy controls. Sample I consisted of 280 BD patients and 200 controls. Sample II consisted of 448 BD patients and 1770 healthy controls. 27 single nucleotide polymorphisms in the NR1D1, RORA, and RORB genes were genotyped using GoldenGate VeraCode assays in sample I, and 492 markers in the three genes were genotyped using Affymetrix Genome-Wide CHB Array in sample II. Single marker and gene-based association analyses were performed using PLINK. A combined p-value for the joining effects of all markers within a gene was calculated using the rank truncated product method. Multifactor dimensionality reduction (MDR) method was also applied to test gene-gene interactions in sample I. All markers were in Hardy-Weinberg equilibrium (P>0.001). In sample I, the associations with BD were observed for rs4774388 in RORA (OR = 1.53, empirical p-value, P = 0.024), and rs1327836 in RORB (OR = 1.75, P = 0.003). In Sample II, there were 45 SNPs showed associations with BD, and the most significant marker in RORA was rs11639084 (OR = 0.69, P = 0.002), and in RORB was rs17611535 (OR = 3.15, P = 0.027). A combined p-value of 1.6×10-6, 0.7, and 1.0 was obtained for RORA, RORB and NR1D1, respectively, indicting a strong association for RORA with the risk of developing BD. A four way interaction was found among markers in NR1D1, RORA, and RORB with the testing accuracy 53.25% and a cross-validation consistency of 8 out of 10. In sample II, 45 markers had empirical p-values less than 0.05. The most significant markers in RORA and RORB genes were rs11639084 (OR = 0.69, P = 0.002), and rs17611535 (OR = 3.15, P = 0.027), respectively. Gene-based association was significant for RORA gene (P = 0.0007). Our results support for the involvement of RORs genes in the risk of developing BD. Investigation of the functional properties of genes in the circadian pathway may further enhance our understanding about the pathogenesis of bipolar illness.