Inferring transcriptional compensation interactions in yeast via stepwise structure equation modeling

<p>Abstract</p> <p>Background</p> <p>With the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. However, few approaches have studied subtle and indirect interaction such as genetic compensation, the existence of which is widely recognized although its mechanism has yet to be clarified. Furthermore, when inferring gene networks most models include only observed variables whereas latent factors, such as proteins and mRNA degradation that are not measured by microarrays, do participate in networks in reality.</p> <p>Results</p> <p>Motivated by inferring transcriptional compensation (TC) interactions in yeast, a stepwise structural equation modeling algorithm (SSEM) is developed. In addition to observed variables, SSEM also incorporates hidden variables to capture interactions (or regulations) from latent factors. Simulated gene networks are used to determine with which of six possible model selection criteria (MSC) SSEM works best. SSEM with Bayesian information criterion (BIC) results in the highest true positive rates, the largest percentage of correctly predicted interactions from all existing interactions, and the highest true negative (non-existing interactions) rates. Next, we apply SSEM using real microarray data to infer TC interactions among (1) small groups of genes that are synthetic sick or lethal (SSL) to SGS1, and (2) a group of SSL pairs of 51 yeast genes involved in DNA synthesis and repair that are of interest. For (1), SSEM with BIC is shown to outperform three Bayesian network algorithms and a multivariate autoregressive model, checked against the results of qRT-PCR experiments. The predictions for (2) are shown to coincide with several known pathways of Sgs1 and its partners that are involved in DNA replication, recombination and repair. In addition, experimentally testable interactions of Rad27 are predicted.</p> <p>Conclusion</p> <p>SSEM is a useful tool for inferring genetic networks, and the results reinforce the possibility of predicting pathways of protein complexes via genetic interactions.</p

Homotypic and heterotypic psychopathological continuity: a child cohort study

Background: Heterotypic psychopathological continuity (i.e. one disorder predicting another at a later time point) contradicts the conventional view that psychiatric disorders are discrete, static entities. Studying this phenomenon may help to tease out the complex mechanisms that underpin psychiatric comorbidity. To date, no studies have explicitly compared heterotypic effects within and across higher order dimensions of psychopathology. // Methods: Patterns of homotypic and heterotypic psychopathological continuity were examined using cohort data from the Avon Longitudinal Study of Parents and Children (ALSPAC, N = 4815). Eight common psychiatric disorders were assessed at age 7.5 and again at age 14 years using the maternal report version of the Development and Well-Being Assessment (DAWBA). Cross-lagged models were used to compare patterns of homotypic and heterotypic continuity within and across three higher order dimensions of psychopathology; internalizing-fear, internalizing-distress, and externalizing. // Results: Homotypic continuity was universal. Considerable heterotypic continuity was observed even after controlling for homotypic continuity and the presence of all disorders at baseline. Heterotypic continuity was more common within higher order dimensions, but a number of significant cross-dimension effects were observed, with ADHD acting as a strong predictor of subsequent internalizing disorders. // Conclusions: Heterotypic continuity may reflect elements of shared aetiology, or local-level interactions between disorders

The Latent Structure of Autistic Traits:A Taxometric, Latent Class and Latent Profile Analysis of the Adult Autism Spectrum Quotient

Autistic traits are widely thought to operate along a continuum. A taxometric analysis of Adult Autism Spectrum Quotient data was conducted to test this assumption, finding little support but identifying a high severity taxon. To understand this further, latent class and latent profile models were estimated that indicated the presence of six distinct subtypes: one with little probability of endorsing any autistic traits, one engaging in ‘systemising’ behaviours, three groups endorsing multiple components of Wing and Gould’s autistic triad, and a group similar in size and profile to the taxon previously identified. These analyses suggest the AQ (and potentially by extension autistic traits) have a categorical structure. These findings have important implications for the analysis and interpretation of AQ data

Opportunities to Learn Mathematics Pedagogy and Connect Classroom Learning to Practice: A Study of Future Teachers in the United States and Singapore

In this study, we conducted secondary analyses using the TEDS-M database to explore future mathematics specialists teachers’ opportunities to learn (OTL) how to teach mathematics. We applied latent class analysis techniques to differentiate among groups of prospective mathematics specialists with potentially different OTL mathematics pedagogy within the United States and Singapore. Within the United States, three subgroups were identified: (a) Comprehensive OTL, (b) Limited OTL, and (c) OTL Mathematics Pedagogy. Within Singapore, four subgroups were identified: (a) Comprehensive OTL, (b) Limited Opportunities to Connect Classroom Learning with Practice, (c) OTL Mathematics Pedagogy, and (d) Basic OTL. Understanding the opportunities different prospective teachers had to learn from and their experiences with different components of instructional practice in university and practicum settings has implications for teacher preparation programs

A latent class analysis of trauma based on a nationally representative sample of US adolescents

Purpose Traumatic events in adolescence rarely occur in isolation. Multiple traumatic experiences are prevalent, diverse and a well-established risk factor for mental health disorders. The aim of this study was to explore and explain the heterogeneity in trauma profiles in a nationally representative sample of US adolescents. Method Using latent class analysis, data on 10,123 adolescents aged between 13 and 18 from the National Comorbidity Survey Adolescent Supplement were examined. In addition, the relationships between the emergent classes and demographic and clinical variables were explored. Results A four-class solution was the best fit of adolescent trauma patterns, with classes labelled as low risk, sexual assault risk, non-sexual risk and high risk. When compared to the low risk class, those in the other classes were significantly more likely not to live with either biological parent, display symptoms indicative of mood and anxiety disorders, and to have higher rates of disorder comorbidity. Conclusions This provides evidence of four distinct groups of adolescents who have experienced a variety of traumas. Evidence demonstrates the increased risk of adolescents with a history of trauma meeting the diagnostic criteria for not only individual disorders but also comorbidity across disorde