BG2: Bayesian variable selection in generalized linear mixed models with nonlocal priors for non-Gaussian GWAS data.

BACKGROUND: Genome-wide association studies (GWASes) aim to identify single nucleotide polymorphisms (SNPs) associated with a given phenotype. A common approach for the analysis of GWAS is single marker analysis (SMA) based on linear mixed models (LMMs). However, LMM-based SMA usually yields a large number of false discoveries and cannot be directly applied to non-Gaussian phenotypes such as count data. RESULTS: We present a novel Bayesian method to find SNPs associated with non-Gaussian phenotypes. To that end, we use generalized linear mixed models (GLMMs) and, thus, call our method Bayesian GLMMs for GWAS (BG2). To deal with the high dimensionality of GWAS analysis, we propose novel nonlocal priors specifically tailored for GLMMs. In addition, we develop related fast approximate Bayesian computations. BG2 uses a two-step procedure: first, BG2 screens for candidate SNPs; second, BG2 performs model selection that considers all screened candidate SNPs as possible regressors. A simulation study shows favorable performance of BG2 when compared to GLMM-based SMA. We illustrate the usefulness and flexibility of BG2 with three case studies on cocaine dependence (binary data), alcohol consumption (count data), and number of root-like structures in a model plant (count data).

Bayesian model selection for generalized linear mixed models.

We propose a Bayesian model selection approach for generalized linear mixed models (GLMMs). We consider covariance structures for the random effects that are widely used in areas such as longitudinal studies, genome-wide association studies, and spatial statistics. Since the random effects cannot be integrated out of GLMMs analytically, we approximate the integrated likelihood function using a pseudo-likelihood approach. Our Bayesian approach assumes a flat prior for the fixed effects and includes both approximate reference prior and half-Cauchy prior choices for the variances of random effects. Since the flat prior on the fixed effects is improper, we develop a fractional Bayes factor approach to obtain posterior probabilities of the several competing models. Simulation studies with Poisson GLMMs with spatial random effects and overdispersion random effects show that our approach performs favorably when compared to widely used competing Bayesian methods including deviance information criterion and Watanabe-Akaike information criterion. We illustrate the usefulness and flexibility of our approach with three case studies including a Poisson longitudinal model, a Poisson spatial model, and a logistic mixed model. Our proposed approach is implemented in the R package GLMMselect that is available on CRAN.

BGWAS: Bayesian variable selection in linear mixed models with nonlocal priors for genome-wide association studies.

BACKGROUND: Genome-wide association studies (GWAS) seek to identify single nucleotide polymorphisms (SNPs) that cause observed phenotypes. However, with highly correlated SNPs, correlated observations, and the number of SNPs being two orders of magnitude larger than the number of observations, GWAS procedures often suffer from high false positive rates. RESULTS: We propose BGWAS, a novel Bayesian variable selection method based on nonlocal priors for linear mixed models specifically tailored for genome-wide association studies. Our proposed method BGWAS uses a novel nonlocal prior for linear mixed models (LMMs). BGWAS has two steps: screening and model selection. The screening step scans through all the SNPs fitting one LMM for each SNP and then uses Bayesian false discovery control to select a set of candidate SNPs. After that, a model selection step searches through the space of LMMs that may have any number of SNPs from the candidate set. A simulation study shows that, when compared to popular GWAS procedures, BGWAS greatly reduces false positives while maintaining the same ability to detect true positive SNPs. We show the utility and flexibility of BGWAS with two case studies: a case study on salt stress in plants, and a case study on alcohol use disorder. CONCLUSIONS: BGWAS maintains and in some cases increases the recall of true SNPs while drastically lowering the number of false positives compared to popular SMA procedures.

Mapping Genetic Variation in Arabidopsis in Response to Plant Growth-Promoting Bacterium Azoarcus olearius DQS-4T.

Plant growth-promoting bacteria (PGPB) can enhance plant health by facilitating nutrient uptake, nitrogen fixation, protection from pathogens, stress tolerance and/or boosting plant productivity. The genetic determinants that drive the plant-bacteria association remain understudied. To identify genetic loci highly correlated with traits responsive to PGPB, we performed a genome-wide association study (GWAS) using an Arabidopsis thaliana population treated with Azoarcus olearius DQS-4T. Phenotypically, the 305 Arabidopsis accessions tested responded differently to bacterial treatment by improving, inhibiting, or not affecting root system or shoot traits. GWA mapping analysis identified several predicted loci associated with primary root length or root fresh weight. Two statistical analyses were performed to narrow down potential gene candidates followed by haplotype block analysis, resulting in the identification of 11 loci associated with the responsiveness of Arabidopsis root fresh weight to bacterial inoculation. Our results showed considerable variation in the ability of plants to respond to inoculation by A. olearius DQS-4T while revealing considerable complexity regarding statistically associated loci with the growth traits measured. This investigation is a promising starting point for sustainable breeding strategies for future cropping practices that may employ beneficial microbes and/or modifications of the root microbiome.

Long term temporal trends in synoptic-scale weather conditions favoring significant tornado occurrence over the central United States.

We perform a statistical climatological study of the synoptic- to meso-scale weather conditions favoring significant tornado occurrence to empirically investigate the existence of long term temporal trends. To identify environments that favor tornadoes, we apply an empirical orthogonal function (EOF) analysis to temperature, relative humidity, and winds from the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) dataset. We consider MERRA-2 data and tornado data from 1980 to 2017 over four adjacent study regions that span the Central, Midwestern, and Southeastern United States. To identify which EOFs are related to significant tornado occurrence, we fit two separate groups of logistic regression models. The first group (LEOF models) estimates the probability of occurrence of a significant tornado day (EF2-EF5) within each region. The second group (IEOF models) classifies the intensity of tornadic days either as strong (EF3-EF5) or weak (EF1-EF2). When compared to approaches using proxies such as convective available potential energy, our EOF approach is advantageous for two main reasons: first, the EOF approach allows for the discovery of important synoptic- to mesoscale variables previously not considered in the tornado science literature; second, proxy-based analyses may not capture important aspects of three-dimensional atmospheric conditions represented by the EOFs. Indeed, one of our main novel findings is the importance of a stratospheric forcing mode on occurrence of significant tornadoes. Other important novel findings are the existence of long-term temporal trends in the stratospheric forcing mode, in a dry line mode, and in an ageostrophic circulation mode related to the jet stream configuration. A relative risk analysis also indicates that changes in stratospheric forcings are partially or completely offsetting increased tornado risk associated with the dry line mode, except in the eastern Midwest region where tornado risk is increasing.

Long-term recovery from opioid use disorder: recovery subgroups, transition states and their association with substance use, treatment and quality of life.

BACKGROUND AND AIMS: Limited information exists regarding individual subgroups of recovery from opioid use disorder (OUD) following treatment and how these subgroups may relate to recovery trajectories. We used multi-dimensional criteria to identify OUD recovery subgroups and longitudinal transitions across subgroups. DESIGN, SETTING AND PARTICIPANTS: In a national longitudinal observational study in the United States, individuals who previously participated in a clinical trial for subcutaneous buprenorphine injections for treatment of OUD were enrolled and followed for an average of 4.2 years after participation in the clinical trial. MEASUREMENTS: We identified recovery subgroups based on psychosocial outcomes including depression, opioid withdrawal and pain. We compared opioid use, treatment utilization and quality of life among these subgroups. FINDINGS: Three dimensions of the recovery process were identified: depression, opioid withdrawal and pain. Using these three dimensions, participants were classified into four recovery subgroups: high-functioning (minimal depression, mild withdrawal and no/mild pain), pain/physical health (minimal depression, mild withdrawal and moderate pain), depression (moderate depression, mild withdrawal and mild/moderate pain) and low-functioning (moderate/severe withdrawal, moderate depression and moderate/severe pain). Significant differences among subgroups were observed for DSM-5 criteria (P < 0.001) and remission status (P < 0.001), as well as with opioid use (P < 0.001), treatment utilization (P < 0.001) and quality of life domains (physical health, psychological, environment and social relationships; Ps < 0.001, Cohen's fs ≥ 0.62). Recovery subgroup assignments were dynamic, with individuals transitioning across subgroups during the observational period. Moreover, the initial recovery subgroup assignment was minimally predictive of long-term outcomes. CONCLUSIONS: There appear to be four distinct subgroups among individuals in recovery from OUD. Recovery subgroup assignments are dynamic and predictive of contemporaneous, but not long-term, substance use, substance use treatment utilization or quality of life outcomes.

BICOSS: Bayesian iterative conditional stochastic search for GWAS.

BACKGROUND: Single marker analysis (SMA) with linear mixed models for genome wide association studies has uncovered the contribution of genetic variants to many observed phenotypes. However, SMA has weak false discovery control. In addition, when a few variants have large effect sizes, SMA has low statistical power to detect small and medium effect sizes, leading to low recall of true causal single nucleotide polymorphisms (SNPs). RESULTS: We present the Bayesian Iterative Conditional Stochastic Search (BICOSS) method that controls false discovery rate and increases recall of variants with small and medium effect sizes. BICOSS iterates between a screening step and a Bayesian model selection step. A simulation study shows that, when compared to SMA, BICOSS dramatically reduces false discovery rate and allows for smaller effect sizes to be discovered. Finally, two real world applications show the utility and flexibility of BICOSS. CONCLUSIONS: When compared to widely used SMA, BICOSS provides higher recall of true SNPs while dramatically reducing false discovery rate.

Mechanistic Aspects on [3+2] Cycloaddition (32CA) Reactions of Azides to Nitroolefins: A Computational and Kinetic Study.

[3+2] cycloadditions of nitroolefins have emerged as a selective and catalyst-free alternative for the synthesis of 1,2,3-triazoles from azides. We describe mechanistic studies into the cycloaddition/rearomatization reaction sequence. DFT calculations revealed a rate-limiting cycloaddition step proceeding via an asynchronous TS with high kinetic selectivity for the 1,5-triazole. Kinetic studies reveal a second-order rate law, and 13 C kinetic isotopic effects at natural abundance were measured with a significant normal effect at the conjugated olefinic centers of 1.0158 and 1.0216 at the α and ß-carbons of ß-nitrostyrene. Distortion/interaction-activation strain and energy decomposition analyses revealed that the major regioisomeric pathway benefits from an earlier and less-distorted TS, while intermolecular interaction terms dominate the preference for 1,5- over 1,4-cycloadducts. In addition, the major regioisomer also has more favorable electrostatic and dispersion terms. Additionally, while static DFT calculations suggest a concerted but highly asynchronous Ei-type HNO2 elimination mechanism, quasiclassical direct-dynamics calculations reveal the existence of a dynamic intermediate.

Musashi1 Contribution to Glioblastoma Development via Regulation of a Network of DNA Replication, Cell Cycle and Division Genes.

RNA-binding proteins (RBPs) function as master regulators of gene expression. Alterations in their levels are often observed in tumors with numerous oncogenic RBPs identified in recent years. Musashi1 (Msi1) is an RBP and stem cell gene that controls the balance between self-renewal and differentiation. High Msi1 levels have been observed in multiple tumors including glioblastoma and are often associated with poor patient outcomes and tumor growth. A comprehensive genomic analysis identified a network of cell cycle/division and DNA replication genes and established these processes as Msi1's core regulatory functions in glioblastoma. Msi1 controls this gene network via two mechanisms: direct interaction and indirect regulation mediated by the transcription factors E2F2 and E2F8. Moreover, glioblastoma lines with Msi1 knockout (KO) displayed increased sensitivity to cell cycle and DNA replication inhibitors. Our results suggest that a drug combination strategy (Msi1 + cell cycle/DNA replication inhibitors) could be a viable route to treat glioblastoma.

Synthesis of 3-Carbonyl Trisubstituted Furans via Pd-Catalyzed Aerobic Cycloisomerization Reaction: Development and Mechanistic Studies.

Herein, we report the synthesis of 3-carbonyl-trisubstituted furans via Pd-catalyzed oxidative cycloisomerization reactions of 2-alkenyl-1,3-dicarbonyl scaffolds, using molecular oxygen as the sole oxidant to regenerate active palladium catalytic species, featuring good functional tolerance and mild reaction conditions. Deep investigation of intermediates and transition states of the reaction mechanism were conducted via experimental and DFT studies, providing a detailed mechanistical profile. The new developed methodology presents a greener alternative to Wacker-type cycloisomerizations and avoids the use of stoichiometric amounts of oxidants and strong acid additives.

Direct access to tetrasubstituted cyclopentenyl scaffolds through a diastereoselective isocyanide-based multicomponent reaction.

An efficient strategy combining the stereocontrol of organocatalysis with the diversity-generating character of multicomponent reactions is described to produce structurally unique, tetrasubstituted cyclopentenyl frameworks. An asymmetric Michael addition-hemiacetalization between α-cyanoketones and α,ß-unsaturated aliphatic aldehydes was performed for constructing cyclic hemiacetals, which were next employed as chiral bifunctional substrates in a new diastereoselective intramolecular isocyanide-based multicomponent reaction. This approach furnished a diversity of structurally complex compounds - including peptidomimetics and natural product hybrids in high stereoselectivity (up to >99% ee and up to >99 : 1 dr) and in moderate to high yields.

A green metal-free "one-pot" microwave assisted synthesis of 1,4-dihydrochromene triazoles.

The synthesis of several 4-aryl-1,4-dihydrochromene-triazoles was achieved via a metal-free "one-pot" procedure using PEG400 as the sole solvent in an eco-friendly process. Using microwave irradiation, the triazole derivatives were obtained in good yields and short reaction times starting from readily accessible building blocks.

Molecular-level insight in supported olefin metathesis catalysts by combining surface organometallic chemistry, high throughput experimentation, and data analysis.

A combination of high-throughput experimentation (HTE), surface organometallic chemistry (SOMC) and statistical data analysis provided the platform to analyze in situ silica-grafted Mo imido alkylidene catalysts based on a library of 35 phenols. Overall, these tools allowed for the identification of σ-donor electronic effects and dispersive interactions and as key drivers in a prototypical metathesis reaction, homodimerization of 1-nonene. Univariate and multivariate correlation analysis confirmed the categorization of the catalytic data into two groups, depending on the presence of aryl groups in ortho position of the phenol ligand. The initial activity (TOFin) was predominantly correlated to the σ-donor ability of the aryloxy ligands, while the overall catalytic performance (TON1 h) was mainly dependent on attractive dispersive interactions with the used phenol ligands featuring aryl ortho substituents and, in sharp contrast, repulsive dispersive interactions with phenol free of aryl ortho substituents. This work outlines a fast and efficient workflow of gaining molecular-level insight into supported metathesis catalysts and highlights σ-donor ability and noncovalent interactions as crucial properties for designing active d0 supported metathesis catalysts.

Modeling allele-specific expression at the gene and SNP levels simultaneously by a Bayesian logistic mixed regression model.

BACKGROUND: High-throughput sequencing experiments, which can determine allele origins, have been used to assess genome-wide allele-specific expression. Despite the amount of data generated from high-throughput experiments, statistical methods are often too simplistic to understand the complexity of gene expression. Specifically, existing methods do not test allele-specific expression (ASE) of a gene as a whole and variation in ASE within a gene across exons separately and simultaneously. RESULTS: We propose a generalized linear mixed model to close these gaps, incorporating variations due to genes, single nucleotide polymorphisms (SNPs), and biological replicates. To improve reliability of statistical inferences, we assign priors on each effect in the model so that information is shared across genes in the entire genome. We utilize Bayesian model selection to test the hypothesis of ASE for each gene and variations across SNPs within a gene. We apply our method to four tissue types in a bovine study to de novo detect ASE genes in the bovine genome, and uncover intriguing predictions of regulatory ASEs across gene exons and across tissue types. We compared our method to competing approaches through simulation studies that mimicked the real datasets. The R package, BLMRM, that implements our proposed algorithm, is publicly available for download at https://github.com/JingXieMIZZOU/BLMRM . CONCLUSIONS: We will show that the proposed method exhibits improved control of the false discovery rate and improved power over existing methods when SNP variation and biological variation are present. Besides, our method also maintains low computational requirements that allows for whole genome analysis.

Noncovalent Interactions Drive the Efficiency of Molybdenum Imido Alkylidene Catalysts for Olefin Metathesis.

High-throughput experimentation and multivariate modeling allow identification of noncovalent interactions (NCIs) in monoaryloxy-pyrrolide Mo imido alkylidene metathesis catalysts prepared in situ as a key driver for high activity in a representative metathesis reaction (homodimerization of 1-nonene). Statistical univariate and multivariate modeling categorizes catalytic data from 35 phenolic ligands into two groups, depending on the substitution in the ortho position of the phenol ligand. The catalytic activity descriptor TON1h correlates predominantly with attractive NCIs when phenols bear ortho aryl substituents and, conversely, with repulsive NCIs when the phenol has no aryl ortho substituents. Energetic span analysis is deployed to relate the observed NCI and the cycloreversion metathesis step such that aryloxide ligands with no ortho aryls mainly impact the energy of metallacyclobutane intermediates (SP/TBP isomers), whereas aryloxides with pendant ortho aryls influence the transition state energy for the cycloreversion step. While the electronic effects from the aryloxide ligands also play a role, our work outlines how NCIs may be exploited for the design of improved d0 metathesis catalysts.

Diastereoselectivity in the boron aldol reaction of α-alkoxy and α,ß-bis-alkoxy methyl ketones.

In this work, using DFT calculations, we investigated the 1,4 and 1,5 asymmetric induction in boron enolate aldol reactions of α-alkoxy and α,ß-bisalkoxy methyl ketones. We evaluated the steric influence of alkyl substituents at the α position and the stereoelectronic influence of the oxygen protecting groups at the α and ß positions. Theoretical calculations revealed the origins of the 1,4 asymmetric induction in terms of the nature of the ß-substituent. The synergistic effect between the α,ß-syn and α,ß-anti-bisalkoxy stereocenters was elucidated. In the presence of the ß-alkoxy center, the reaction proceeds through the Goodman-Paton 1,5-stereoinduction model, experiencing a minor influence of the α-alkoxy center.

A stereoselective sequential organocascade and multicomponent approach for the preparation of tetrahydropyridines and chimeric derivatives.

A stereoselective multicomponent approach leading to a novel class of pentasubstituted tetrahydropyridines is described. Variation of the components enabled the incorporation of peptide, sugar and steroid moieties to access chimeric derivatives. DFT calculations provide insights about the unprecedented high diastereoselectivity of the MCR.

Cobalt-Catalyzed Stereoselective Synthesis of 2,5- trans-THF Nitrile Derivatives as a Platform for Diversification: Development and Mechanistic Studies.

A straightforward protocol integrating a sustainable approach for the synthesis of new 2,5- trans-THF nitrile derivatives enabling an easy diversification of its side chain scaffolds is described. The reaction tolerated different aromatic and alkyl substituents, affording the corresponding 2,5- trans-THFs in high diastereoselectivity. A detailed mechanistic study using DFT calculation reveals details of the ligand-exchange step, suggesting an inner-sphere syn attack to form the 2,5- trans stereochemistry as the most likely pathway, excluding the previous cation radical intermediate. The formation of a Co-C intermediate is suggested on the basis of the homolytic cleavage to give the previously proposed free carbon radical intermediate.

Organocatalyzed Asymmetric Vinylogous Addition of Oxazole-2(3H)-thiones to α,ß-Unsaturated Ketones: An Additive-Free Approach for Diversification of Heterocyclic Scaffold.

A straightforward organocatalyzed asymmetric addition of oxazole-2(3H)-thiones to α,ß-unsaturated ketones is described. This additive-free Michael reaction in the presence of chiral cinchonine-derived primary amines as catalysts has proven to be highly effective for a wide range of cyclic and acyclic enones, leading to the Michael adducts in very good yields and excellent enantioselectivities. The absolute configuration (R) of compound 5j was unambiguously assigned by X-ray diffraction analysis. Furthermore, experimental and theoretical studies were performed and a mechanism is presented and discussed for this novel reaction.

miR-124, -128, and -137 Orchestrate Neural Differentiation by Acting on Overlapping Gene Sets Containing a Highly Connected Transcription Factor Network.

The ventricular-subventricular zone harbors neural stem cells (NSCs) that can differentiate into neurons, astrocytes, and oligodendrocytes. This process requires loss of stem cell properties and gain of characteristics associated with differentiated cells. miRNAs function as important drivers of this transition; miR-124, -128, and -137 are among the most relevant ones and have been shown to share commonalities and act as proneurogenic regulators. We conducted biological and genomic analyses to dissect their target repertoire during neurogenesis and tested the hypothesis that they act cooperatively to promote differentiation. To map their target genes, we transfected NSCs with antagomiRs and analyzed differences in their mRNA profile throughout differentiation with respect to controls. This strategy led to the identification of 910 targets for miR-124, 216 for miR-128, and 652 for miR-137. The target sets show extensive overlap. Inspection by gene ontology and network analysis indicated that transcription factors are a major component of these miRNAs target sets. Moreover, several of these transcription factors form a highly interconnected network. Sp1 was determined to be the main node of this network and was further investigated. Our data suggest that miR-124, -128, and -137 act synergistically to regulate Sp1 expression. Sp1 levels are dramatically reduced as cells differentiate and silencing of its expression reduced neuronal production and affected NSC viability and proliferation. In summary, our results show that miRNAs can act cooperatively and synergistically to regulate complex biological processes like neurogenesis and that transcription factors are heavily targeted to branch out their regulatory effect.