Considering Genetic Heterogeneity in the Association Analysis Finds Genes Associated With Nicotine Dependence

While substantial progress has been made in finding genetic variants associated with nicotine dependence (ND), a large proportion of the genetic variants remain undiscovered. The current research focuses have shifted toward uncovering rare variants, gene-gene/gene-environment interactions, and structural variations predisposing to ND, the impact of genetic heterogeneity in ND has been nevertheless paid less attention. The study of genetic heterogeneity in ND not only could enhance the power of detecting genetic variants with heterogeneous effects in the population but also improve our understanding of genetic etiology of ND. As an initial step to understand genetic heterogeneity in ND, we applied a newly developed heterogeneity weighted U (HWU) method to 26 ND-related genes, investigating heterogeneous effects of these 26 genes in ND. We found no strong evidence of genetic heterogeneity in genes such as CHRNA5. However, results from our analysis suggest heterogeneous effects of CHRNA6 and CHRNB3 on nicotine dependence in males and females. Following the gene-based analysis, we further conduct a joint association analysis of two gene clusters, CHRNA5-CHRNA3-CHRNB4 and CHRNB3-CHRNA6. While both CHRNA5-CHRNA3-CHRNB4 and CHRNB3-CHRNA6 clusters are significantly associated with ND, there is a much stronger association of CHRNB3-CHRNA6 with ND when considering heterogeneous effects in gender (p-value = 2.11E-07)

Abnormal regional signal in the left cerebellum as a potential neuroimaging biomarker of sudden sensorineural hearing loss

ObjectiveWhile prior reports have characterized visible changes in neuroimaging findings in individuals suffering from sudden sensorineural hearing loss (SSNHL), the utility of regional homogeneity (ReHo) as a means of diagnosing SSNHL has yet to be established. The present study was thus conducted to assess ReHo abnormalities in SSNHL patients and to establish whether these abnormalities offer value as a diagnostic neuroimaging biomarker of SSNHL through a support vector machine (SVM) analysis approach.MethodsResting-state functional magnetic resonance imaging (rs-fMRI) analyses of 27 SSNHL patients and 27 normal controls were conducted, with the resultant imaging data then being analyzed based on a combination of ReHo and SVM approaches.ResultsRelative to normal control individuals, patients diagnosed with SSNHL exhibited significant reductions in ReHo values in the left cerebellum, bilateral inferior temporal gyrus (ITG), left superior temporal pole (STP), right parahippocampal gyrus (PHG), left posterior cingulum cortex (PCC), and right superior frontal gyrus (SFG). SVM analyses suggested that reduced ReHo values in the left cerebellum were associated with high levels of diagnostic accuracy (96.30%, 52/54), sensitivity (92.59%, 25/27), and specificity (100.00%, 27/27) when distinguishing between SSNHL patients and control individuals.ConclusionThese data suggest that SSNHL patients exhibit abnormal resting-state neurological activity, with changes in the ReHo of the left cerebellum offering value as a diagnostic neuroimaging biomarker associated with this condition

Effects of Intranasal Oxytocin on Pup Deprivation-Evoked Aberrant Maternal Behavior and Hypogalactia in Rat Dams and the Underlying Mechanisms

Oxytocin (OT), a hypothalamic neuropeptide, applied through nasal approach (IAO), could improve maternal health during lactation that is disrupted by mother–baby separation; however, the regulation of IAO effects on maternal behaviors and lactation as well as the underlying mechanisms remain unclear. Using lactating rats, we observed effects of intermittent pup deprivation (PD) with and without IAO on maternal behaviors and lactation as well as the activity of OT neurons in the supraoptic nucleus (SON) and the activity of hypothalamic pituitary-adrenal axis, key factors determining the milk-letdown reflex during lactation and maternal behaviors. The results showed that PD reduced maternal behaviors and lactation efficiency of rat dams as indicated by significantly longer latency to retrieve their pups and low litter’s body weight gains during the observation, respectively. In addition, PD caused early involution of the mammary glands. IAO partially improved these changes in rat dams, which was not as significant as IAO effects on control dams. In the SON, PD decreased c-Fos and increased glial fibrillary acidic protein (GFAP) filaments significantly; IAO made PD-evoked c-Fos reduction insignificant while reduced GFAP filament significantly in PD dams. IAO tended to increase the levels of phosphorylated extracellular signal-regulated kinases (pERK) 1/2 in PD dams. Moreover, PD+IAO significantly increased plasma levels of dam adrenocorticotropic hormone and corticosterone but not OT levels. Lastly, PD+IAO tended to increase the level of corticotropin-releasing hormone in the SON. These results indicate that PD disrupts maternal behaviors and lactation by suppressing the activity of hypothalamic OT-secreting system through expansion of astrocytic processes, which are partially reversed by IAO through removing astrocytic inhibition of OT neuronal activity. However, the improving effect of IAO on the maternal health could be compromised by simultaneous activation of hypothalamic pituitary-adrenocortical axis

Genome-wide Association Meta-analysis of Childhood and Adolescent Internalizing Symptoms

Objective: To investigate the genetic architecture of internalizing symptoms in childhood and adolescence. Method: In 22 cohorts, multiple univariate genome-wide association studies (GWASs) were performed using repeated assessments of internalizing symptoms, in a total of 64,561 children and adolescents between 3 and 18 years of age. Results were aggregated in meta-analyses that accounted for sample overlap, first using all available data, and then using subsets of measurements grouped by rater, age, and instrument. Results: The meta-analysis of overall internalizing symptoms (INToverall) detected no genome-wide significant hits and showed low single nucleotide polymorphism (SNP) heritability (1.66%, 95% CI = 0.84-2.48%, n(effective) = 132,260). Stratified analyses indicated rater-based heterogeneity in genetic effects, with self-reported internalizing symptoms showing the highest heritability (5.63%, 95% CI = 3.08%-8.18%). The contribution of additive genetic effects on internalizing symptoms appeared to be stable over age, with overlapping estimates of SNP heritability from early childhood to adolescence. Genetic correlations were observed with adult anxiety, depression, and the well-being spectrum (vertical bar r(g)vertical bar > 0.70), as well as with insomnia, loneliness, attention-deficit/hyperactivity disorder, autism, and childhood aggression (range vertical bar r(g)vertical bar = 0.42-0.60), whereas there were no robust associations with schizophrenia, bipolar disorder, obsessive-compulsive disorder, or anorexia nervosa. Conclusion: Genetic correlations indicate that childhood and adolescent internalizing symptoms share substantial genetic vulnerabilities with adult internalizing disorders and other childhood psychiatric traits, which could partially explain both the persistence of internalizing symptoms over time and the high comorbidity among childhood psychiatric traits. Reducing phenotypic heterogeneity in childhood samples will be key in paving the way to future GWAS success.Peer reviewe

Genetic association study of childhood aggression across raters, instruments, and age

Childhood aggressive behavior (AGG) has a substantial heritability of around 50%. Here we present a genome-wide association metaanalysis (GWAMA) of childhood AGG, in which all phenotype measures across childhood ages from multiple assessors were included. We analyzed phenotype assessments for a total of 328 935 observations from 87 485 children aged between 1.5 and 18 years, while accounting for sample overlap. We also meta-analyzed within subsets of the data, i.e., within rater, instrument and age. SNP-heritability for the overall meta-analysis AGGoverall was 3.31% (SE= 0.0038). We found no genome-wide significant SNPs for AGGoverall. The gene-based analysis returned three significant genes: ST3GAL3 (P= 1.6E-06), PCDH7 (P= 2.0E-06), and IPO13 (P= 2.5E-06). All three genes have previously been associated with educational traits. Polygenic scores based on our GWAMA significantly predicted aggression in a holdout sample of children (variance explained = 0.44%) and in retrospectively assessed childhood aggression (variance explained = 0.20%). Genetic correlations rg among rater-specific assessment of AGG ranged from rg= 0.46 between self- and teacher-assessment to rg= 0.81 between mother- and teacher-assessment. We obtained moderate-to-strong rgs with selected phenotypes from multiple domains, but hardly with any of the classical biomarkers thought to be associated with AGG. Significant genetic correlations were observed with most psychiatric and psychological traits (range |rg|: 0.19-1.00), except for obsessive-compulsive disorder. Aggression had a negative genetic correlation (rg=∼-0.5) with cognitive traits and age at first birth. Aggression was strongly genetically correlated with smoking phenotypes (range |rg| : 0.46-0.60). The genetic correlations between aggression and psychiatric disorders were weaker for teacher-reported AGG than for mother- and self-reported AGG. The current GWAMA of childhood aggression provides a powerful tool to interrogate the rater-specific genetic etiology of AGG.</p

Small-Molecule Organic Photovoltaic Devices: Applications and Reliability.

The development of organic optoelectronic devices has moved forward at an incredible pace over the past three decades. Prototype organic solar cell panels have emerged in the personal electronic market, and displays using organic light-emitting diodes have become an essential part of consumer electronics. This thesis focuses on the application and reliability of organic photovoltaic cells (OPVs), as they are promising candidates as low-cost, flexible solar energy conversion sources. Top-illuminated organic photovoltaic cells present opportunity to widen OPV applications, with potential use as power generating coatings on flexible and low-cost substrates. In the first part of the thesis, we explore inverted small-molecule organic photovoltaic cells on reflective metal substrates. We investigate the design of inverted OPVs. We demonstrate methods to overcome challenges in device performance and achieve inverted OPV on metal substrates with comparable efficiency to conventional devices. In the second part of the thesis, we study the reliability of small-molecule OPVs. We start with an extensive overview of characterization and reporting of OPV operational lifetime, device packaging and current research progress. We present a fully-automated, compact experimental setup for long-term reliability testing. We identify exciton-induced product as a dominant degradation mechanism in organic solar cells, and describe the physical theory that accurately predicts the burn-in period of device degradation. Device reliability can be greatly improved by reducing exciton lifetime, such as employing a mixed donor-acceptor active layer. The degradation mechanism also applies to photodegradation of neat OPV materials, and the stability is substantially improved in the mixed donor-acceptor film. In long-term lifetime study, we show that oxygen diffusion into the active layer is the primary cause of degradation, leading to increased recombination current.PhDMaterials Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110445/1/xrtong_1.pd

An integrative U method for joint analysis of multi-level omic data

Abstract Background The advance of high-throughput technologies has made it cost-effective to collect diverse types of omic data in large-scale clinical and biological studies. While the collection of the vast amounts of multi-level omic data from these studies provides a great opportunity for genetic research, the high dimensionality of omic data and complex relationships among multi-level omic data bring tremendous analytic challenges. Results To address these challenges, we develop an integrative U (IU) method for the design and analysis of multi-level omic data. While non-parametric methods make less model assumptions and are flexible for analyzing different types of phenotypes and omic data, they have been less developed for association analysis of omic data. The IU method is a nonparametric method that can accommodate various types of omic and phenotype data, and consider interactive relationship among different levels of omic data. Through simulations and a real data application, we compare the IU test with commonly used variance component tests. Conclusions Results show that the proposed test attains more robust type I error performance and higher empirical power than variance component tests under various types of phenotypes and different underlying interaction effects

Research on Power Enterprise Supplier Management System Based on Supplier Deep Portrait

In current supplier management business status of power enterprises, exists many common pain points, such as weak supplier data management, weak risk management awareness. With the intention of solving these problems, this research proposes a deep portrait system of suppliers of power enterprises by constructing full information database of suppliers, establishing a comprehensive evaluation index system of suppliers, and building a supplier label library. With the application of this system, this paper designs the business mechanism optimization scheme of the whole life cycle auxiliary management, which includes supplier qualification and performance verification, professional performance evaluation, market performance insight and risk control. This system proposed is of great significance to improve the lean level and informatization process of supplier management in power enterprises

Detecting Rare Mutations with Heterogeneous Effects Using a Family-Based Genetic Random Field Method

The genetic etiology of many complex diseases is highly heterogeneous. A complex disease can be caused by multiple mutations within the same gene or mutations in multiple genes at various genomic loci. Although these disease-susceptibility mutations can be collectively common in the population, they are often individually rare or even private to certain families. Family-based studies are powerful for detecting rare variants enriched in families, which is an important feature for sequencing studies due to the heterogeneous nature of rare variants. In addition, family designs can provide robust protection against population stratification. Nevertheless, statistical methods for analyzing family-based sequencing data are underdeveloped, especially those accounting for heterogeneous etiology of complex diseases. In this article, we introduce a random field framework for detecting gene-phenotype associations in family-based sequencing studies, referred to as family-based genetic random field (FGRF). Similar to existing family-based association tests, FGRF could utilize within-family and between-family information separately or jointly to test an association. We demonstrate that FGRF has comparable statistical power with existing methods when there is no genetic heterogeneity, but can improve statistical power when there is genetic heterogeneity across families. The proposed method also shares the same advantages with the conventional family-based association tests (e.g., being robust to population stratification). Finally, we applied the proposed method to a sequencing data from the Minnesota Twin Family Study, and revealed several genes, including SAMD14, potentially associated with alcohol dependence