Exploring the Effects of Coworker Ostracism on Feedback Inquiry and Voice: The Mediating Role of Proactive Motivation

Coworker ostracism occurs when employees perceive that their coworkers are ignoring or excluding them. The current work examines how coworker ostracism predicts feedback inquiry and voice, two proactive behaviors important for individual and organizational success. Specifically, I explored whether these relationships are mediated by three proactive motivation states: organizational identification, control appraisal, and positive affect. I also examined whether political skill and perceived organizational support (POS) moderated the relationships between coworker ostracism and proactive motivation states, as well as the proposed indirect effects. I collected data from 309 participants via TurkPrime, using a two-wave lagged design with measurements separated by two months. Results showed that, consistent with the proactive motivation model, the indirect effects of coworker ostracism on feedback inquiry via organizational identification and positive affect were significant, while the indirect effects of coworker ostracism on voice via organizational identification, control appraisal, and positive affect were significant. Most moderation hypotheses were not supported, with POS buffering the relationship between coworker ostracism and positive affect and the associated indirect effects as exceptions. The current study’s results suggest that coworker ostracism can potentially reduce proactive behavior by lowering proactive motivation. Organizations may benefit from reducing coworker ostracism so that it does not impact proactive motivation and subsequent proactive behavior. Supporting their employees so they develop high POS may also be a useful tactic for mitigating the effects of coworker ostracism on positive affect, and in turn proactive behavior

REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants

Supplemental Data Supplemental Data include one figure and five tables and can be found with this article online at http://dx.doi.org/10.1016/j.ajhg.2016.08.016. Supplemental Data Document S1. Figure S1 and Tables S1–S5 Download Document S2. Article plus Supplemental Data Download Web Resources ClinVar, https://www.ncbi.nlm.nih.gov/clinvar/ dbNSFP, https://sites.google.com/site/jpopgen/dbNSFP Human Gene Mutation Database, http://www.hgmd.cf.ac.uk/ REVEL, https://sites.google.com/site/revelgenomics/ SwissVar, http://swissvar.expasy.org/ The vast majority of coding variants are rare, and assessment of the contribution of rare variants to complex traits is hampered by low statistical power and limited functional data. Improved methods for predicting the pathogenicity of rare coding variants are needed to facilitate the discovery of disease variants from exome sequencing studies. We developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of individual tools: MutPred, FATHMM, VEST, PolyPhen, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP, SiPhy, phyloP, and phastCons. REVEL was trained with recently discovered pathogenic and rare neutral missense variants, excluding those previously used to train its constituent tools. When applied to two independent test sets, REVEL had the best overall performance (p < 10−12) as compared to any individual tool and seven ensemble methods: MetaSVM, MetaLR, KGGSeq, Condel, CADD, DANN, and Eigen. Importantly, REVEL also had the best performance for distinguishing pathogenic from rare neutral variants with allele frequencies <0.5%. The area under the receiver operating characteristic curve (AUC) for REVEL was 0.046–0.182 higher in an independent test set of 935 recent SwissVar disease variants and 123,935 putatively neutral exome sequencing variants and 0.027–0.143 higher in an independent test set of 1,953 pathogenic and 2,406 benign variants recently reported in ClinVar than the AUCs for other ensemble methods. We provide pre-computed REVEL scores for all possible human missense variants to facilitate the identification of pathogenic variants in the sea of rare variants discovered as sequencing studies expand in scale

Molecularly defined cortical astroglia subpopulation modulates neurons via secretion of Norrin

Despite expanding knowledge regarding the role of astroglia in regulating neuronal function, little is known about regional or functional subgroups of brain astroglia and how they may interact with neurons. We use an astroglia-specific promoter fragment in transgenic mice to identify an anatomically defined subset of adult gray matter astroglia. Using transcriptomic and histological analyses, we generate a combinatorial profile for the in vivo identification and characterization of this astroglia subpopulation. These astroglia are enriched in mouse cortical layer V; express distinct molecular markers, including Norrin and leucine-rich repeat-containing G-protein-coupled receptor 6 (LGR6), with corresponding layer-specific neuronal ligands; are found in the human cortex; and modulate neuronal activity. Astrocytic Norrin appears to regulate dendrites and spines; its loss, as occurring in Norrie disease, contributes to cortical dendritic spine loss. These studies provide evidence that human and rodent astroglia subtypes are regionally and functionally distinct, can regulate local neuronal dendrite and synaptic spine development, and contribute to disease

REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants

The vast majority of coding variants are rare, and assessment of the contribution of rare variants to complex traits is hampered by low statistical power and limited functional data. Improved methods for predicting the pathogenicity of rare coding variants are needed to facilitate the discovery of disease variants from exome sequencing studies. We developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of individual tools: MutPred, FATHMM, VEST, PolyPhen, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP, SiPhy, phyloP, and phastCons. REVEL was trained with recently discovered pathogenic and rare neutral missense variants, excluding those previously used to train its constituent tools. When applied to two independent test sets, REVEL had the best overall performance (p < 10(−12)) as compared to any individual tool and seven ensemble methods: MetaSVM, MetaLR, KGGSeq, Condel, CADD, DANN, and Eigen. Importantly, REVEL also had the best performance for distinguishing pathogenic from rare neutral variants with allele frequencies <0.5%. The area under the receiver operating characteristic curve (AUC) for REVEL was 0.046–0.182 higher in an independent test set of 935 recent SwissVar disease variants and 123,935 putatively neutral exome sequencing variants and 0.027–0.143 higher in an independent test set of 1,953 pathogenic and 2,406 benign variants recently reported in ClinVar than the AUCs for other ensemble methods. We provide pre-computed REVEL scores for all possible human missense variants to facilitate the identification of pathogenic variants in the sea of rare variants discovered as sequencing studies expand in scale