Biology subjects

Chen, C.-Y.

Publications and source records attributed to Chen, C.-Y..

At least 19 recordsLinked to original sources

bioRxiv2018

Comparative genetic architectures of schizophrenia in East Asian and European populations

Author summarySchizophrenia is a severe psychiatric disorder with a lifetime risk of about 1% world-wide. Most large schizophrenia genetic studies have studied people of primarily European ancestry, potentially missing important biological insights. Here we present a study of East Asian participants (22,778 schizophrenia cases and 35,362 controls), identifying 21 genome-wide significant schizophrenia associations in 19 genetic loci. Over the genome, the common genetic variants that confer risk for schizophrenia have highly similar effects in those of East Asian and European ancestry (rg=0.98), indicating for the first time that the genetic basis of schizophrenia and its biology are broadly shared across these world populations. A fixed-effect meta-analysis including individuals from East Asian and European ancestries revealed 208 genome-wide significant schizophrenia associations in 176 genetic loci (53 novel). Trans-ancestry fine-mapping more precisely isolated schizophrenia causal alleles in 70% of these loci. Despite consistent genetic effects across populations, polygenic risk models trained in one population have reduced performance in the other, highlighting the importance of including all major ancestral groups with sufficient sample size to ensure the findings have maximum relevance for all populations.

genetics↗

bioRxiv2018

GenEpi: Gene-based Epistasis Discovery Using Machine Learning

Genome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between genetic variants associated with phenotypes, are still limited. We believe that developing an efficient and effective GWAS method to detect epistasis will be a key for discovering sophisticated pathogenesis, which is especially important for complex diseases such as Alzheimers disease (AD). In this regard, this study presents GenEpi, a computational package to uncover epistasis associated with phenotypes by the proposed machine learning approach. GenEpi identifies both within-gene and cross-gene epistasis through a two-stage modeling workflow. In both stages, GenEpi adopts two-element combinatorial encoding when producing features and constructs the prediction models by L1-regularized regression with stability selection. The simulated data showed that GenEpi outperforms other widely-used methods on detecting ground-truth epistasis. As real data is concerned, this study uses AD as an example to reveal the capability of GenEpi in finding disease-related variants and variant interactions that show both biological meanings and predictive power. Availability: GenEpi is an open-source python package and available free of charge only for non-commercial users. The package can be downloaded from https://github.com/Chester75321/GenEpi, and has also been published on The Python Package Index.

bioinformatics↗

bioRxiv2018

Electroporation of short hairpin RNAs for rapid and efficient gene knockdown in the starlet sea anemone, Nematostella vectensis

A mechanistic understanding of evolutionary developmental biology requires the development of novel techniques for the manipulation of gene function in phylogenetically diverse organismal systems. Recently, gene-specific knockdown by microinjection of short hairpin RNA (shRNA) has been applied in the sea anemone Nematostella vectensis, a cnidarian model organism. Due to the unusual architecture of the cnidarian microRNA processing pathway, the shRNA approach is unusually effective for sequence-specific knockdown of a gene of interest. However, the time- and labor-intensive process of microinjection limits access to this technique and its application in large scale experiments. To address this issue, here we present an electroporation protocol for shRNA delivery into Nematostella eggs. This method leverages the speed and simplicity of electroporation, enabling users to manipulate gene expression in hundreds of Nematostella eggs or embryos within minutes. We provide a detailed description of the experimental procedure, including reagents, electroporation conditions, preparation of Nematostella vectensis eggs, and follow-up care of experimental animals. Finally, we demonstrate the knockdown of several endogenous and exogenous genes with known phenotypes and discuss the potential applications of this method.

developmental biology↗

bioRxiv2018

Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 90,000 patients across three healthcare systems

BACKGROUNDIndividuals at high risk schizophrenia may benefit from early intervention but few validated risk predictors are available. Genetic profiling is one approach to risk stratification that has been extensively validated in research cohorts, but its utility in clinical settings remains largely unexplored. Moreover, the broad health consequences of a high genetic risk of schizophrenia are poorly understood, despite being highly relevant to treatment decisions.\n\nMETHODSWe used electronic health records of 91,980 patients from three large healthcare systems to evaluate the penetrance and pleiotropy of genetic risk for schizophrenia. Polygenic risk scores (PRSs) for schizophrenia were calculated from meta-analysis summary statistics and tested for association with schizophrenia diagnostic codes and 1338 code-defined disease categories in a phenome-wide association study. Effect estimates were meta-analyzed across sites, and follow-up analyses evaluated the effect of a schizophrenia diagnosis.\n\nRESULTSPRSs were robustly associated with schizophrenia (odds ratio per standard deviation increase in PRS = 1.65 [95% confidence interval (CI), 1.5-1.8], p = 1.25 x 10-16) and patients in the highest risk decile of the PRS distribution had a four-fold increased odds of schizophrenia compared to those in the bottom decile (95% CI, 2.4-6.5, p = 4.43 x 10-8). PRSs were also associated with other psychiatric phenotypes, including anxiety disorders, bipolar disorder, depression, substance use disorders, personality disorders, and suicidal behavior. Non-psychiatric associations included heart palpitations, urinary syndromes, obesity, and nonspecific somatic symptoms. Most associations remained significant when conditioning on a diagnosis of schizophrenia, indicating genetic pleiotropy.\n\nCONCLUSIONSWe demonstrate that an available measure of genetic risk for schizophrenia is robustly associated with schizophrenia in healthcare settings and has pleiotropic effects on related psychiatric disorders as well as other medical symptoms and syndromes. Our results provide an initial indication of the opportunities and limitations that may arise with the future application of PRS testing in healthcare systems.

genetics↗

bioRxiv2018

Polygenic Prediction via Bayesian Regression and Continuous Shrinkage Priors

Polygenic prediction has shown promise in identifying individuals at high risk for complex diseases, and may become clinically useful as the predictive performance of polygenic risk scores (PRS) improves. Here, we present PRS-CS, a novel polygenic prediction method that infers posterior SNP effect sizes using GWAS summary statistics and an external linkage disequilibrium (LD) reference panel. PRS-CS utilizes a high-dimensional Bayesian regression framework, and is distinct from previous work by placing a continuous shrinkage (CS) prior on SNP effect sizes, which is robust to varying genetic architectures, provides substantial computational advantages, and enables multivariate modeling of local LD patterns. Simulation studies using data from the UK Biobank show that PRS-CS outperforms existing methods across a wide range of effect size distributions, especially when the training sample size is large. We apply PRS-CS to predict six complex diseases and six quantitative traits in the Partners HealthCare Biobank, and further demonstrate the improvement of PRS-CS in prediction accuracy over alternative methods.

genetics↗

bioRxiv2018

Contribution of rare copy number variants to bipolar disorder risk is limited to schizoaffective cases

BackgroundGenetic risk for bipolar disorder (BD) is conferred through many common alleles, while a role for rare copy number variants (CNVs) is less clear. BD subtypes schizoaffective disorder bipolar type (SAB), bipolar I disorder (BD I) and bipolar II disorder (BD II) differ according to the prominence and timing of psychosis, mania and depression. The factors contributing to the combination of symptoms within a given patient are poorly understood.\n\nMethodsRare, large CNVs were analyzed in 6353 BD cases (3833 BD I [2676 with psychosis, 850 without psychosis], 1436 BD II, 579 SAB) and 8656 controls. Measures of CNV burden were integrated with polygenic risk scores (PRS) for schizophrenia (SCZ) to evaluate the relative contributions of rare and common variants to psychosis risk.\n\nResultsCNV burden did not differ in BD relative to controls when treated as a single diagnostic entity. Burden in SAB was increased compared to controls (p-value = 0.001), BD I (p-value = 0.0003) and BD II (p-value = 0.0007). Burden and SCZ PRS were higher in SAB compared to BD I with psychosis (CNV p-value = 0.0007, PRS p-value = 0.004) and BD I without psychosis (CNV p-value = 0.0004, PRS p-value = 3.9 x 10-5). Within BD I, psychosis was associated with higher SCZ PRS (p-value = 0.005) but not with CNV burden.\n\nConclusionsCNV burden in BD is limited to SAB. Rare and common genetic variants may contribute differently to risk for psychosis and perhaps other classes of psychiatric symptoms.

genetics↗

bioRxiv2018

Effect of de novo transcriptome assembly on transcript quantification

BackgroundCorrect quantification of transcript expression is essential to understand the functional products of the genome in different physiological conditions and developmental stages. Recently, the development of high-throughput RNA sequencing (RNA-Seq) allows the researchers to perform transcriptome analysis for the organisms without the reference genome and transcriptome. For such projects, de novo transcriptome assembly must be carried out prior to quantification. However, a large number of erroneous contigs produced by the assemblers might result in unreliable estimation on the abundance of transcripts. In this regard, this study comprehensively investigates how assembly quality affects the performance of quantification for RNA-Seq analysis based on de novo transcriptome assembly.\n\nResultsSeveral important factors that might seriously affect the accuracy of the RNA-Seq analysis were thoroughly discussed. First, we found that the assemblers perform comparatively well for the transcriptomes with lower biological complexity. Second, we examined the over-extended and incomplete contigs, and then demonstrated that assembly completeness has a strong impact on the estimation of contig abundance. Lastly, we investigated the behavior of the quantifiers with respect to sequence ambiguity which might be originally present in the transcriptome or accidentally produced by assemblers. The results suggest that the quantifiers often over-estimate the expression of family-collapse contigs and under-estimate the expression of duplicated contigs. For organisms without reference transcriptome, it remained challenging to detect the inaccurate abundance estimation on family-collapse contigs. On the contrary, we observed that the situation of under-estimation on duplicated contigs can be warned through analyzing the read distribution of the duplicated contigs.\n\nConclusionsIn summary, we explicated the behavior of quantifiers when erroneous contigs are present and we outlined the potential problems that the assemblers might cause for the downstream analysis of RNA-Seq. We anticipate the analytic results conducted in this study provides valuable insights for future development of transcriptome assembly and quantification.\n\nAvailabilitywe proposed an open-source Python based package QuantEval that builds connected components for the assembled contigs based on sequence similarity and evaluates the quantification results for each connected component. The package can be downloaded from https://github.com/dn070017/QuantEval.

bioinformatics↗

bioRxiv2018

Testing Causal Bidirectional Influences between Physical Activity and Depression using Mendelian Randomization

BackgroundBurgeoning evidence from randomized controlled trials and prospective cohort studies suggests that physical activity protects against depression, pointing to a potential modifiable target for prevention. However, the direction of this inverse association is not clear: physical activity may reduce risk for depression, and/or depression may result in decreased physical activity. Here, we used bidirectional two-sample Mendelian randomization (MR) to test causal influences between physical activity and depression.\n\nMethodsFor genetic instruments, we selected independent top SNPs associated with major depressive disorder (MDD, N = 143,265) and two physical activity phenotypes--self-reported (N = 377,234) and objective accelerometer-based (N = 91,084)--from the largest available, non-overlapping genome-wide association results. We used two sets of genetic instruments: (1) only SNPs previously reported as genome-wide significant, and (2) top SNPs meeting a more relaxed threshold (p < 1x10-7). For each direction of influence, we combined the MR effect estimates from each instrument SNP using inverse variance weighted (IVW) meta-analysis, along with other standard MR methods such as weighted median, MR-Egger, and MR-PRESSO.\n\nResultsWe found evidence for protective influences of accelerometer-based activity on MDD (IVW odds ratio (OR) = 0.74 for MDD per 1 SD unit increase in average acceleration, 95% confidence interval (CI) = 0.59-0.92, p =.006) when using SNPs meeting the relaxed threshold (i.e., 10 versus only 2 genome-wide significant SNPs, which provided insufficient data for sensitivity analyses). In contrast, we found no evidence for negative influences of MDD on accelerometer-based activity (IVW b = 0.04 change in average acceleration for MDD versus control status, 95% CI = -0.43-0.51, p =.87). Furthermore, we did not see evidence for causal influences between self-reported activity and MDD, in either direction and regardless of instrument SNP criteria.\n\nDiscussionWe apply MR for the first time to examine causal influences between physical activity and MDD. We discover that objectively measured--but not self-reported--physical activity is inversely associated with MDD. Of note, prior work has shown that accelerometer-based physical activity is more heritable than self-reported activity, in addition to being more representative of actual movement. Our findings validate physical activity as a protective factor for MDD and point to the importance of objective measurement of physical activity in epidemiological studies in relation to mental health. Overall, this study supports the hypothesis that enhancing physical activity is an effective prevention strategy for depression.

genetics↗

bioRxiv2018

Prospective Study of Polygenic Risk, Protective Factors, and Incident Depression Following Combat Deployment in US Army Soldiers

BackgroundWhereas genetic susceptibility increases risk for major depressive disorder (MDD), non-genetic protective factors may mitigate this risk. In a large-scale prospective study of US Army soldiers, we examined whether trait resilience and/or unit cohesion could buffer against the onset of MDD following combat deployment, even in soldiers at high polygenic risk.\n\nMethodsData were analyzed from 4,182 soldiers of European ancestry assessed before and after their deployment to Afghanistan. Incident MDD was defined as no MDD episode at predeployment, followed by a MDD episode following deployment. Polygenic risk scores were constructed from the largest available MDD genome-wide association study. We first examined main effects of the MDD PRS and each protective factor on incident MDD. We then tested effects of each protective factor on incident MDD across strata of polygenic risk.\n\nResultsPolygenic risk showed a dose-response relationship to depression, such that soldiers at high polygenic risk had greatest odds for incident MDD. Both unit cohesion and trait resilience were prospectively associated with reduced risk for incident MDD. Notably, the protective effect of unit cohesion persisted even in soldiers at highest polygenic risk.\n\nConclusionsPolygenic risk was associated with new-onset MDD in deployed soldiers. However, unit cohesion--an index of perceived support and morale--was protective against incident MDD even among those at highest genetic risk, and may represent a potent target for promoting resilience in vulnerable soldiers. Findings illustrate the value of combining genomic and environmental data in a prospective design to identify robust protective factors for mental health.

genetics↗

bioRxiv2018

Propofol inhibits endogenous formyl peptide-induced neutrophil activation and alleviates lung injury

Critically ill patients have a high risk of sepsis. Various studies have demonstrated that propofol has anti-inflammatory effects that may benefit critically ill patients who require anesthesia. However, the mechanism and therapeutic effect remain incompletely understood. Our previous data suggest that propofol can act as a formyl peptide receptor 1 (FPR1) antagonist. Here, we hypothesize that propofol mitigates sepsis-induced acute lung injury (ALI) by inhibiting mitochondria-derived N-formyl peptide-mediated neutrophil activation. In human neutrophils, propofol competitively reduced the release of elastase, superoxide, and reactive oxygen species induced by fMMYALF, a human mitochondria-derived N-formyl peptide. In addition, propofol significantly inhibited fMMYALF-induced chemotaxis, calcium mobilization, and phosphorylation of protein kinase B and mitogen-activated protein kinases. These results indicate that propofol suppresses neutrophil activation by blocking the interaction between endogenous N-formyl peptide and its receptor, FPR1, thus inhibiting downstream signaling. Furthermore, propofol alleviated alveolar wall disruption, edematous changes, and neutrophil infiltration in lipopolysaccharide-induced ALI in mice. Noticeably, propofol improved the survival of sepsis mice. This study indicates that the anti-neutrophil effects of propofol may benefit critically ill septic patients.

pharmacology and toxicology↗

bioRxiv2018

GWAS meta-analysis highlights the hypothalamic-pituitary-gonadal axis (HPG axis) in the genetic regulation of menstrual cycle length

The normal menstrual cycle requires a delicate interplay between the hypothalamus, pituitary, and ovary. Therefore, its length is an important indicator of female reproductive health. Menstrual cycle length has been shown to be partially controlled by genetic factors, especially in the follicle stimulating hormone beta-subunit (FSHB) locus. GWAS meta-analysis of menstrual cycle length in 44,871 women of European ancestry confirmed the previously observed association with the FSHB locus and identified four additional novel signals in, or near, the GNRH1, PGR, NR5A2 and INS-IGF2 genes. These findings confirm the role of the hypothalamic-pituitary-gonadal axis in the genetic regulation of menstrual cycle length, but also highlight potential novel local regulatory mechanisms, such as those mediated by IGF2.

genetics↗

bioRxiv2018

Programmed Variations of Cytokinesis Contribute to Morphogenesis in the C. elegans embryo

While cytokinesis has been intensely studied, how it is executed during development is not well understood, despite a long-standing appreciation that various aspects of cytokinesis vary across cell and tissue types. To address this, we investigated cytokinesis during the invariant C. elegans embryo lineage and found several reproducibly altered parameters at different stages. During early divisions, furrow ingression asymmetry and midbody inheritance is consistent, suggesting specific regulation of these events. During morphogenesis, we find several unexpected alterations including migration of midbodies to the apical surface during epithelial polarization in different tissues. Aurora B kinase, which is essential for several aspects of cytokinesis, remains localized to the apical membrane after internalization of other midbody components. Inactivation of Aurora B causes cytokinesis failure, which disrupts polarization and tissue formation. Therefore, cytokinesis shows surprising diversity during development and is required during epithelial polarization to establish cellular architecture during morphogenesis.

cell biology↗

bioRxiv2018

Structural insights into DNA sequence recognition by Arabidopsis ETHYLENE RESPONSE FACTOR 96

The phytohormone ethylene is widely involved in many developmental processes and is a crucial regulator of defense responses against biotic and abiotic stresses in plants. Ethylene-responsive element binding protein (EREBP), a member of the APETALA2/ethylene response factor (AP2/ERF) superfamily, is a transcription factor that regulates stress-responsive genes by recognizing a specific cis-acting element of target DNA. A previous study showed only the NMR structure of the AP2/ERF domain of AtERF100 in complex with a GCC box DNA motif. In this report, we determined the crystal structure of AtERF96 in complex with a GCC box at atomic resolution. We analyzed the binding residues of the conserved AP2/ERF domain in the DNA recognition sequence. In addition to the AP2/ERF domain, an N-terminal -helix of AtERF96 participates in DNA interaction in the flanking region. We also demonstrated the structure of AtERF96 EDLL motif, a unique conserved motif in the group IX of AP2/ERF family, is critical for the transactivation of defense-related genes. Our study establishes the structural basis of the AtERF96 transcription factor in complex with the GCC box, as well as the DNA binding mechanisms of the N-terminal -helix and AP2/ERF domain.

plant biology↗

bioRxiv2018

GWAS identifies novel risk locus for erectile dysfunction and implicates hypothalamic neurobiology and diabetes in etiology

GWAS of erectile dysfunction (ED) in 6,175 cases among 223,805 European men identified one new locus at 6q16.3 (lead variant rs57989773, OR 1.20 per C-allele; p = 5.71x10-14), located between MCHR2 and SIM1. In-silico analysis suggests SIM1 to confer ED risk through hypothalamic dysregulation; Mendelian randomization indicates genetic risk of type 2 diabetes causes ED. Our findings provide novel insights into the biological underpinnings of ED.

genomics↗

bioRxiv2018

Genome-wide study identifies 611 loci associated with risk tolerance and risky behaviors

Humans vary substantially in their willingness to take risks. In a combined sample of over one million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. We identified 611 approximately independent genetic loci associated with at least one of our phenotypes, including 124 with general risk tolerance. We report evidence of substantial shared genetic influences across general risk tolerance and risky behaviors: 72 of the 124 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is moderately to strongly genetically correlated ([Formula] to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near general-risk-tolerance-associated SNPs are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We find no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

genetics↗

bioRxiv2018

Aberrant calcium signaling in astrocytes inhibits neuronal excitability in a human Down syndrome stem cell model

Down syndrome (DS) is a devastating genetic disorder causing severe cognitive impairment. The staggering array of effects associated with an extra copy of human chromosome 21 (HSA21) complicates mechanistic understanding of DS pathophysiology. We developed an in vitro system to examine the interplay of neurons and astrocytes in a fully recapitulated HSA21 trisomy model differentiated from DS patient-derived induced pluripotent stem cells (iPSCs). By combining calcium imaging with genetic approaches, we utilized this system to investigate the functional defects of DS astroglia and their effects on neuronal excitability. We found that, compared with control isogenic astroglia, DS astroglia exhibited more-frequent spontaneous calcium fluctuations, which reduced the excitability of co-cultured neurons. DS astrocytes exerted this effect on both DS and healthy neurons. Neuronal activity could be rescued by abolishing astrocytic spontaneous calcium activity either chemically by blocking adenosine-mediated astrocyte-neuron signaling or genetically by knockdown of inositol triphosphate (IP3) receptors or S100{beta}, a calcium binding protein coded on HSA21. Our results suggest a novel mechanism by which DS alters the function of astrocytes, which subsequently disturbs neuronal excitability. Furthermore, our study establishes an all-optical neurophysiological platform for studying human neuron-astrocyte interactions associated with neurological disorders.\n\nSignificant statementDown syndrome (DS) is the most common genetic disorder caused by trisomy of chromosome 21 (HSA21). Problems with cognitive impairment, have not been properly addressed due to the inability to fully recapitulate HSA21, which is further confounded by the snapshot views of morphological changes of brain cells in isolation obtained from current studies. The brain develops neural networks consisting of neurons and glial cells that work together. To understand how DS affects the neural networks, we used DS patient-derived stem cells and calcium imaging to investigate functional defects of DS astrocytes and their effects on neuronal excitability. Our study has significant implication in understanding functional defects during brain development underlying DS.

neuroscience↗

bioRxiv2018

The Shared Genetic Basis of Human Fluid Intelligence and Brain Morphology

Individual differences in educational attainment are linked to differences in intelligence, and predict important social, economic and health outcomes. Previous studies have found common genetic factors that influence educational achievement, cognitive performance and total brain volume (i.e., brain size). Here, in a large sample of participants from the UK Biobank, we investigate the shared genetic basis between educational attainment and fine-grained cerebral cortical morphological features, and associate this genetic variation with a related aspect of cognitive ability. Importantly, we execute novel statistical methods that enable high-dimensional genetic correlation analysis, and compute high-resolution surface maps for the genetic correlations between educational attainment and vertex-wise morphological measurements. We conduct secondary analyses, using the UK Biobank verbal-numerical reasoning score, to confirm that variation in educational attainment that is genetically correlated with cortical morphology is related to differences in cognitive performance. Our analyses reveal the genetic overlap between cognitive ability and cortical thickness measurements in bilateral primary motor cortex and predominantly left superior temporal cortex and proximal regions. These findings may contribute to our understanding of the neurobiology that connects genetic variation to individual differences in educational attainment and cognitive performance.

genetics↗

bioRxiv2017

SeqsLab: an integrated platform for cohort-based annotation and interpretation of genetic variants on Spark

SummarySeqsLab is a platform that helps researchers to easily annotate and interpret genetic variants derived from a large quantity of personal genomes. It provides an integrated interface to annotate the variants based on curated databases as well as in silico estimation on the effects of the variants. SeqsLab adopts the scalable cluster computing framework, Spark, and incorporates several customized algorithms to speed up the process of variant annotation and interpretation. The key features of SeqsLab include efficient annotation on large structural variations, diverse combinations of variant filters, easy incorporation with a vast amount of public databases, and scalable architecture of analyzing hundreds of human whole genomes simultaneously.\n\nAvailability and ImplementationSeqsLab is implemented with JAVA. The generated annotation will then be stored in Elasticsearch for real-time query and exploratory analysis. SeqsLab can be accessed by web browsers and is freely available at http://portal.seqslab.net/.\n\nContactchungtsai_su@atgenomix.com\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗