Expansion of biological pathways based on evolutionary inference.

The availability of diverse genomes makes it possible to predict gene function based on shared evolutionary history. This approach can be challenging, however, for pathways whose components do not exhibit a shared history but rather consist of distinct "evolutionary modules." We introduce a computational algorithm, clustering by inferred models of evolution (CLIME), which inputs a eukaryotic species tree, homology matrix, and pathway (gene set) of interest. CLIME partitions the gene set into disjoint evolutionary modules, simultaneously learning the number of modules and a tree-based evolutionary history that defines each module. CLIME then expands each module by scanning the genome for new components that likely arose under the inferred evolutionary model. Application of CLIME to ∼1,000 annotated human pathways and to the proteomes of yeast, red algae, and malaria reveals unanticipated evolutionary modularity and coevolving components. CLIME is freely available and should become increasingly powerful with the growing wealth of eukaryotic genomes.

Systematic identification of barriers to human iPSC generation.

Reprogramming of somatic cells to induced pluripotent stem cells (iPSCs) holds enormous promise for regenerative medicine. To elucidate endogenous barriers limiting this process, we systematically dissected human cellular reprogramming by combining a genome-wide RNAi screen, innovative computational methods, extensive single-hit validation, and mechanistic investigation of relevant pathways and networks. We identify reprogramming barriers, including genes involved in transcription, chromatin regulation, ubiquitination, dephosphorylation, vesicular transport, and cell adhesion. Specific a disintegrin and metalloproteinase (ADAM) proteins inhibit reprogramming, and the disintegrin domain of ADAM29 is necessary and sufficient for this function. Clathrin-mediated endocytosis can be targeted with small molecules and opposes reprogramming by positively regulating TGF-ß signaling. Genetic interaction studies of endocytosis or ubiquitination reveal that barrier pathways can act in linear, parallel, or feedforward loop architectures to antagonize reprogramming. These results provide a global view of barriers to human cellular reprogramming.

Measuring DNA mechanics on the genome scale.

Mechanical deformations of DNA such as bending are ubiquitous and have been implicated in diverse cellular functions1. However, the lack of high-throughput tools to measure the mechanical properties of DNA has limited our understanding of how DNA mechanics influence chromatin transactions across the genome. Here we develop 'loop-seq'-a high-throughput assay to measure the propensity for DNA looping-and determine the intrinsic cyclizabilities of 270,806 50-base-pair DNA fragments that span Saccharomyces cerevisiae chromosome V, other genomic regions, and random sequences. We found sequence-encoded regions of unusually low bendability within nucleosome-depleted regions upstream of transcription start sites (TSSs). Low bendability of linker DNA inhibits nucleosome sliding into the linker by the chromatin remodeller INO80, which explains how INO80 can define nucleosome-depleted regions in the absence of other factors2. Chromosome-wide, nucleosomes were characterized by high DNA bendability near dyads and low bendability near linkers. This contrast increases for deeper gene-body nucleosomes but disappears after random substitution of synonymous codons, which suggests that the evolution of codon choice has been influenced by DNA mechanics around gene-body nucleosomes. Furthermore, we show that local DNA mechanics affect transcription through TSS-proximal nucleosomes. Overall, this genome-scale map of DNA mechanics indicates a 'mechanical code' with broad functional implications.

A phylogenetic method linking nucleotide substitution rates to rates of continuous trait evolution.

Genomes contain conserved non-coding sequences that perform important biological functions, such as gene regulation. We present a phylogenetic method, PhyloAcc-C, that associates nucleotide substitution rates with changes in a continuous trait of interest. The method takes as input a multiple sequence alignment of conserved elements, continuous trait data observed in extant species, and a background phylogeny and substitution process. Gibbs sampling is used to assign rate categories (background, conserved, accelerated) to lineages and explore whether the assigned rate categories are associated with increases or decreases in the rate of trait evolution. We test our method using simulations and then illustrate its application using mammalian body size and lifespan data previously analyzed with respect to protein coding genes. Like other studies, we find processes such as tumor suppression, telomere maintenance, and p53 regulation to be related to changes in longevity and body size. In addition, we also find that skeletal genes, and developmental processes, such as sprouting angiogenesis, are relevant.

Lineage-specific transcriptional regulation of DICER by MITF in melanocytes.

DICER is a central regulator of microRNA maturation. However, little is known about mechanisms regulating its expression in development or disease. While profiling miRNA expression in differentiating melanocytes, two populations were observed: some upregulated at the pre-miRNA stage, and others upregulated as mature miRNAs (with stable pre-miRNA levels). Conversion of pre-miRNAs to fully processed miRNAs appeared to be dependent upon stimulation of DICER expression--an event found to occur via direct transcriptional targeting of DICER by the melanocyte master transcriptional regulator MITF. MITF binds and activates a conserved regulatory element upstream of DICER's transcriptional start site upon melanocyte differentiation. Targeted KO of DICER is lethal to melanocytes, at least partly via DICER-dependent processing of the pre-miRNA-17 approximately 92 cluster thus targeting BIM, a known proapoptotic regulator of melanocyte survival. These observations highlight a central mechanism underlying lineage-specific miRNA regulation which could exist for other cell types during development.

PhyloAcc-GT: A Bayesian Method for Inferring Patterns of Substitution Rate Shifts on Targeted Lineages Accounting for Gene Tree Discordance.

An important goal of evolutionary genomics is to identify genomic regions whose substitution rates differ among lineages. For example, genomic regions experiencing accelerated molecular evolution in some lineages may provide insight into links between genotype and phenotype. Several comparative genomics methods have been developed to identify genomic accelerations between species, including a Bayesian method called PhyloAcc, which models shifts in substitution rate in multiple target lineages on a phylogeny. However, few methods consider the possibility of discordance between the trees of individual loci and the species tree due to incomplete lineage sorting, which might cause false positives. Here, we present PhyloAcc-GT, which extends PhyloAcc by modeling gene tree heterogeneity. Given a species tree, we adopt the multispecies coalescent model as the prior distribution of gene trees, use Markov chain Monte Carlo (MCMC) for inference, and design novel MCMC moves to sample gene trees efficiently. Through extensive simulations, we show that PhyloAcc-GT outperforms PhyloAcc and other methods in identifying target lineage-specific accelerations and detecting complex patterns of rate shifts, and is robust to specification of population size parameters. PhyloAcc-GT is usually more conservative than PhyloAcc in calling convergent rate shifts because it identifies more accelerations on ancestral than on terminal branches. We apply PhyloAcc-GT to two examples of convergent evolution: flightlessness in ratites and marine mammal adaptations, and show that PhyloAcc-GT is a robust tool to identify shifts in substitution rate associated with specific target lineages while accounting for incomplete lineage sorting.

Leveraging epigenomes and three-dimensional genome organization for interpreting regulatory variation.

Understanding the impact of regulatory variants on complex phenotypes is a significant challenge because the genes and pathways that are targeted by such variants and the cell type context in which regulatory variants operate are typically unknown. Cell-type-specific long-range regulatory interactions that occur between a distal regulatory sequence and a gene offer a powerful framework for examining the impact of regulatory variants on complex phenotypes. However, high-resolution maps of such long-range interactions are available only for a handful of cell types. Furthermore, identifying specific gene subnetworks or pathways that are targeted by a set of variants is a significant challenge. We have developed L-HiC-Reg, a Random Forests regression method to predict high-resolution contact counts in new cell types, and a network-based framework to identify candidate cell-type-specific gene networks targeted by a set of variants from a genome-wide association study (GWAS). We applied our approach to predict interactions in 55 Roadmap Epigenomics Mapping Consortium cell types, which we used to interpret regulatory single nucleotide polymorphisms (SNPs) in the NHGRI-EBI GWAS catalogue. Using our approach, we performed an in-depth characterization of fifteen different phenotypes including schizophrenia, coronary artery disease (CAD) and Crohn's disease. We found differentially wired subnetworks consisting of known as well as novel gene targets of regulatory SNPs. Taken together, our compendium of interactions and the associated network-based analysis pipeline leverages long-range regulatory interactions to examine the context-specific impact of regulatory variation in complex phenotypes.

Neopetrotaurines A-C, Isoquinoline Alkaloids with an Unprecedented Taurine Bridge from the Sponge Neopetrosia sp.

Neopetrotaurines A-C (1-3), unusual alkaloids possessing two isoquinoline-derived moieties that are linked via a unique taurine bridge, were isolated from a Neopetrosia sp. marine sponge. These new compounds have proton-deficient structural scaffolds that are difficult to unambiguously assign using only conventional 2- and 3-bond 1H-13C and 1H-15N heteronuclear correlation data. Thus, the application of LR-HSQMBC and HMBC NMR experiments optimized to detect 4- and 5-bond long-range 1H-13C heteronuclear correlations facilitated the structure elucidation of these unusual taurine-bridged marine metabolites. Neopetrotaurines A-C (1-3) showed significant inhibition of transcription driven by the oncogenic fusion protein PAX3-FOXO1, which is associated with alveolar rhabdomyosarcoma, and cytotoxic activity against PAX3-FOXO1-positive cell lines.

Diet quality from mid to late life and its association with physical frailty in late life in a cohort of Chinese adults.

BACKGROUND: It is unclear if improving diet quality after midlife could reduce the risk of physical frailty at late life. We aimed to associate changes in diet quality after midlife with physical frailty at late life. METHODS: Diet quality in 12,580 participants from the Singapore Chinese Health Study was assessed with the Dietary Approaches to Stop Hypertension (DASH) scores at baseline (1993-1998; mean age 53 years) and follow-up 3 (2014-2016; mean age 73 years). Physical frailty was assessed using the modified Cardiovascular Health Study phenotype at follow-up 3. Multivariable logistic regressions examined associations between DASH scores and physical frailty. RESULTS: Comparing participants in extreme quartiles of DASH scores, the odds ratios (OR) [95% confidence interval (CI)] for physical frailty were 0.85 (0.73,0.99) at baseline and 0.49 (0.41, 0.58) at follow-up 3. Compared to participants with consistently low DASH scores, participants with consistently high scores (OR 0.74, 95% CI: 0.59, 0.94) and those with > 10% increase in scores (OR 0.78, 95% CI: 0.64, 0.95) had lower odds of frailty. Compared to those in the lowest DASH tertiles at both time-points, significantly lower odds of physical frailty were observed in those who were in the highest DASH tertiles at both time points [0.59 (0.48, 0.73)], and in those who improved their scores from the lowest [0.68 (0.51, 0.91)] or second tertile at baseline [0.61 (0.48, 0.76)] to the highest tertile at follow-up 3. CONCLUSIONS: Maintaining a high diet quality or a substantial improvement in diet quality after midlife could lower the risk of physical frailty at late life.

Relation of preconception eating behaviours to dietary pattern trajectories and gestational weight gain from preconception to late pregnancy.

Studies examining preconception eating behaviours with longitudinal dietary patterns from preconception to late pregnancy as well as gestational weight gain (GWG) are limited. We derived dietary pattern trajectories from preconception to late-pregnancy, and related preconception eating behaviours to these trajectories and GWG. Preconception eating behaviours were assessed using the Three-Factor Eating Questionnaire measuring cognitive restraint (CR) - conscious restriction of food intake, emotional eating (EE) - overeating in response to negative emotions, and uncontrolled eating (UE) - overeating with a feeling of lack of control. Dietary intakes were measured at preconception, 20-21 and 34-36 weeks' gestation with food frequency questionnaires. Dietary patterns were determined using factor analysis, and trajectories derived using group-based trajectory modelling. Inadequate and excessive GWG were defined according to Institute of Medicine guidelines based on weights at preconception and the last antenatal visit (median: 38 weeks' gestation). Two dietary patterns were derived: 'Fast Food, Fried Snacks and Desserts (FFD)' and 'Soup, Fish and Vegetables (SFV)'. Adherence trajectories from preconception to late-pregnancy were characterised as consistently high ("stable-high") and low ("stable-low"). Women with higher UE scores had higher odds of being in the "stable-high" trajectory (n = 34) of the FFD pattern [Odds Ratio (OR): 1.25, 95% Confidence Interval (CI): 1.03, 1.51], compared to "stable-low" (n = 260). Percentages of women with inadequate, adequate or excessive GWG were 21.7% (n = 70), 25.8% (n = 83), and 52.5% (n = 169), respectively; women with higher EE scores had a higher likelihood of excessive GWG [Relative Risk Ratio (RRR): 1.35, 95% CI: 1.02, 1.80], but this association was attenuated after adjusting for preconception body mass index. Eating behaviour interventions to improve dietary patterns among pregnant women may need to start as early as preconception, incorporating strategies to manage UE.

ChatGPT versus NASS clinical guidelines for degenerative spondylolisthesis: a comparative analysis.

BACKGROUND CONTEXT: Clinical guidelines, developed in concordance with the literature, are often used to guide surgeons' clinical decision making. Recent advancements of large language models and artificial intelligence (AI) in the medical field come with exciting potential. OpenAI's generative AI model, known as ChatGPT, can quickly synthesize information and generate responses grounded in medical literature, which may prove to be a useful tool in clinical decision-making for spine care. The current literature has yet to investigate the ability of ChatGPT to assist clinical decision making with regard to degenerative spondylolisthesis. PURPOSE: The study aimed to compare ChatGPT's concordance with the recommendations set forth by The North American Spine Society (NASS) Clinical Guideline for the Diagnosis and Treatment of Degenerative Spondylolisthesis and assess ChatGPT's accuracy within the context of the most recent literature. METHODS: ChatGPT-3.5 and 4.0 was prompted with questions from the NASS Clinical Guideline for the Diagnosis and Treatment of Degenerative Spondylolisthesis and graded its recommendations as "concordant" or "nonconcordant" relative to those put forth by NASS. A response was considered "concordant" when ChatGPT generated a recommendation that accurately reproduced all major points made in the NASS recommendation. Any responses with a grading of "nonconcordant" were further stratified into two subcategories: "Insufficient" or "Over-conclusive," to provide further insight into grading rationale. Responses between GPT-3.5 and 4.0 were compared using Chi-squared tests. RESULTS: ChatGPT-3.5 answered 13 of NASS's 28 total clinical questions in concordance with NASS's guidelines (46.4%). Categorical breakdown is as follows: Definitions and Natural History (1/1, 100%), Diagnosis and Imaging (1/4, 25%), Outcome Measures for Medical Intervention and Surgical Treatment (0/1, 0%), Medical and Interventional Treatment (4/6, 66.7%), Surgical Treatment (7/14, 50%), and Value of Spine Care (0/2, 0%). When NASS indicated there was sufficient evidence to offer a clear recommendation, ChatGPT-3.5 generated a concordant response 66.7% of the time (6/9). However, ChatGPT-3.5's concordance dropped to 36.8% when asked clinical questions that NASS did not provide a clear recommendation on (7/19). A further breakdown of ChatGPT-3.5's nonconcordance with the guidelines revealed that a vast majority of its inaccurate recommendations were due to them being "over-conclusive" (12/15, 80%), rather than "insufficient" (3/15, 20%). ChatGPT-4.0 answered 19 (67.9%) of the 28 total questions in concordance with NASS guidelines (P = 0.177). When NASS indicated there was sufficient evidence to offer a clear recommendation, ChatGPT-4.0 generated a concordant response 66.7% of the time (6/9). ChatGPT-4.0's concordance held up at 68.4% when asked clinical questions that NASS did not provide a clear recommendation on (13/19, P = 0.104). CONCLUSIONS: This study sheds light on the duality of LLM applications within clinical settings: one of accuracy and utility in some contexts versus inaccuracy and risk in others. ChatGPT was concordant for most clinical questions NASS offered recommendations for. However, for questions NASS did not offer best practices, ChatGPT generated answers that were either too general or inconsistent with the literature, and even fabricated data/citations. Thus, clinicians should exercise extreme caution when attempting to consult ChatGPT for clinical recommendations, taking care to ensure its reliability within the context of recent literature.

A RIPK1-regulated inflammatory microglial state in amyotrophic lateral sclerosis.

Microglial-derived inflammation has been linked to a broad range of neurodegenerative and neuropsychiatric conditions, including amyotrophic lateral sclerosis (ALS). Using single-cell RNA sequencing, a class of Disease-Associated Microglia (DAMs) have been characterized in neurodegeneration. However, the DAM phenotype alone is insufficient to explain the functional complexity of microglia, particularly with regard to regulating inflammation that is a hallmark of many neurodegenerative diseases. Here, we identify a subclass of microglia in mouse models of ALS which we term RIPK1-Regulated Inflammatory Microglia (RRIMs). RRIMs show significant up-regulation of classical proinflammatory pathways, including increased levels of Tnf and Il1b RNA and protein. We find that RRIMs are highly regulated by TNFα signaling and that the prevalence of these microglia can be suppressed by inhibiting receptor-interacting protein kinase 1 (RIPK1) activity downstream of the TNF receptor 1. These findings help to elucidate a mechanism by which RIPK1 kinase inhibition has been shown to provide therapeutic benefit in mouse models of ALS and may provide an additional biomarker for analysis in ongoing phase 2 clinical trials of RIPK1 inhibitors in ALS.

Somatic structural variation targets neurodevelopmental genes and identifies SHANK2 as a tumor suppressor in neuroblastoma.

Neuroblastoma is a malignancy of the developing sympathetic nervous system that accounts for 12% of childhood cancer deaths. Like many childhood cancers, neuroblastoma shows a relative paucity of somatic single-nucleotide variants (SNVs) and small insertions and deletions (indels) compared to adult cancers. Here, we assessed the contribution of somatic structural variation (SV) in neuroblastoma using a combination of whole-genome sequencing (WGS) of tumor-normal pairs (n = 135) and single-nucleotide polymorphism (SNP) genotyping of primary tumors (n = 914). Our study design allowed for orthogonal validation and replication across platforms. SV frequency, type, and localization varied significantly among high-risk tumors. MYCN nonamplified high-risk tumors harbored an increased SV burden overall, including a significant excess of tandem duplication events across the genome. Genes disrupted by SV breakpoints were enriched in neuronal lineages and associated with phenotypes such as autism spectrum disorder (ASD). The postsynaptic adapter protein-coding gene, SHANK2, located on Chromosome 11q13, was disrupted by SVs in 14% of MYCN nonamplified high-risk tumors based on WGS and 10% in the SNP array cohort. Expression of SHANK2 was low across human-derived neuroblastoma cell lines and high-risk neuroblastoma tumors. Forced expression of SHANK2 in neuroblastoma cells resulted in significant growth inhibition (P = 2.6 × 10-2 to 3.4 × 10-5) and accelerated neuronal differentiation following treatment with all-trans retinoic acid (P = 3.1 × 10-13 to 2.4 × 10-30). These data further define the complex landscape of somatic structural variation in neuroblastoma and suggest that events leading to deregulation of neurodevelopmental processes, such as inactivation of SHANK2, are key mediators of tumorigenesis in this childhood cancer.

MiRACLe: an individual-specific approach to improve microRNA-target prediction based on a random contact model.

Deciphering microRNA (miRNA) targets is important for understanding the function of miRNAs as well as miRNA-based diagnostics and therapeutics. Given the highly cell-specific nature of miRNA regulation, recent computational approaches typically exploit expression data to identify the most physiologically relevant target messenger RNAs (mRNAs). Although effective, those methods usually require a large sample size to infer miRNA-mRNA interactions, thus limiting their applications in personalized medicine. In this study, we developed a novel miRNA target prediction algorithm called miRACLe (miRNA Analysis by a Contact modeL). It integrates sequence characteristics and RNA expression profiles into a random contact model, and determines the target preferences by relative probability of effective contacts in an individual-specific manner. Evaluation by a variety of measures shows that fitting TargetScan, a frequently used prediction tool, into the framework of miRACLe can improve its predictive power with a significant margin and consistently outperform other state-of-the-art methods in prediction accuracy, regulatory potential and biological relevance. Notably, the superiority of miRACLe is robust to various biological contexts, types of expression data and validation datasets, and the computation process is fast and efficient. Additionally, we show that the model can be readily applied to other sequence-based algorithms to improve their predictive power, such as DIANA-microT-CDS, miRanda-mirSVR and MirTarget4. MiRACLe is publicly available at https://github.com/PANWANG2014/miRACLe.

A data-adaptive Bayesian regression approach for polygenic risk prediction.

MOTIVATION: Polygenic risk score (PRS) has been widely exploited for genetic risk prediction due to its accuracy and conceptual simplicity. We introduce a unified Bayesian regression framework, NeuPred, for PRS construction, which accommodates varying genetic architectures and improves overall prediction accuracy for complex diseases by allowing for a wide class of prior choices. To take full advantage of the framework, we propose a summary-statistics-based cross-validation strategy to automatically select suitable chromosome-level priors, which demonstrates a striking variability of the prior preference of each chromosome, for the same complex disease, and further significantly improves the prediction accuracy. RESULTS: Simulation studies and real data applications with seven disease datasets from the Wellcome Trust Case Control Consortium cohort and eight groups of large-scale genome-wide association studies demonstrate that NeuPred achieves substantial and consistent improvements in terms of predictive r2 over existing methods. In addition, NeuPred has similar or advantageous computational efficiency compared with the state-of-the-art Bayesian methods. AVAILABILITY AND IMPLEMENTATION: The R package implementing NeuPred is available at https://github.com/shuangsong0110/NeuPred. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Spectral clustering of single-cell multi-omics data on multilayer graphs.

MOTIVATION: Single-cell sequencing technologies that simultaneously generate multimodal cellular profiles present opportunities for improved understanding of cell heterogeneity in tissues. How the multimodal information can be integrated to obtain a common cell type identification, however, poses a computational challenge. Multilayer graphs provide a natural representation of multi-omic single-cell sequencing datasets, and finding cell clusters may be understood as a multilayer graph partition problem. RESULTS: We introduce two spectral algorithms on multilayer graphs, spectral clustering on multilayer graphs and the weighted locally linear (WLL) method, to cluster cells in multi-omic single-cell sequencing datasets. We connect these algorithms through a unifying mathematical framework that represents each layer using a Hamiltonian operator and a mixture of its eigenstates to integrate the multiple graph layers, demonstrating in the process that the WLL method is a rigorous multilayer spectral graph theoretic reformulation of the popular Seurat weighted nearest neighbor (WNN) algorithm. Implementing our algorithms and applying them to a CITE-seq dataset of cord blood mononuclear cells yields results similar to the Seurat WNN analysis. Our work thus extends spectral methods to multimodal single-cell data analysis. AVAILABILITY AND IMPLEMENTATION: The code used in this study can be found at https://github.com/jssong-lab/sc-spectrum. All public data used in the article are accurately cited and described in Materials and Methods and in Supplementary Information. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform.

We present Knowledge Engine for Genomics (KnowEnG), a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis, and expression signature analysis. The system specializes in "knowledge-guided" data mining and machine learning algorithms, in which user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge bases and encoded in a massive "Knowledge Network." KnowEnG adheres to "FAIR" principles (findable, accessible, interoperable, and reuseable): its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution, and are interoperable with other computing platforms. The analysis tools are made available through multiple access modes, including a web portal with specialized visualization modules. We demonstrate the KnowEnG system's potential value in democratization of advanced tools for the modern genomics era through several case studies that use its tools to recreate and expand upon the published analysis of cancer data sets.

Robust prediction of nonhome discharge following elective anterior cervical discectomy and fusion using explainable machine learning.

PURPOSE: Predict nonhome discharge (NHD) following elective anterior cervical discectomy and fusion (ACDF) using an explainable machine learning model. METHODS: 2227 patients undergoing elective ACDF from 2008 to 2019 were identified from a single institutional database. A machine learning model was trained on preoperative variables, including demographics, comorbidity indices, and levels fused. The validation technique was repeated stratified K-Fold cross validation with the area under the receiver operating curve (AUROC) statistic as the performance metric. Shapley Additive Explanation (SHAP) values were calculated to provide further explainability regarding the model's decision making. RESULTS: The preoperative model performed with an AUROC of 0.83 ± 0.05. SHAP scores revealed the most pertinent risk factors to be age, medicare insurance, and American Society of Anesthesiology (ASA) score. Interaction analysis demonstrated that female patients over 65 with greater fusion levels were more likely to undergo NHD. Likewise, ASA demonstrated positive interaction effects with female sex, levels fused and BMI. CONCLUSION: We validated an explainable machine learning model for the prediction of NHD using common preoperative variables. Adding transparency is a key step towards clinical application because it demonstrates that our model's "thinking" aligns with clinical reasoning. Interactive analysis demonstrated that those of age over 65, female sex, higher ASA score, and greater fusion levels were more predisposed to NHD. Age and ASA score were similar in their predictive ability. Machine learning may be used to predict NHD, and can assist surgeons with patient counseling or early discharge planning.

The association between exposure to community violence and somatic symptoms through bullying victimization among African American adolescents in Chicago: A developmental trauma approach.

BACKGROUND: The developmental trauma theory suggests that traumatic events impede the ability of individuals to form interpersonal relationships, impair cognition, affect, and increase the vulnerability of adolescents to develop psychopathology. However, few studies have examined the applicability of this theory among African American adolescents who were exposed to community violence and adverse outcomes, including bullying victimization and somatic symptoms. AIMS: Therefore, the purpose of the present study is to first test the association between exposure to community violence and somatic symptoms. Then, we explored whether bullying victimization mediated the relationship between exposure to community violence and somatic symptoms while controlling for gender differences and substance use (n = 622). RESULTS: Results suggest that exposure to community violence is positively associated with somatic symptoms. Moreover, the association between exposure to community violence and somatic symptoms was mediated by bullying victimization. DISCUSSION AND CONCLUSION: These results were consistent with the developmental trauma theory, such that trauma could increase the vulnerability for future victimization, thus, leading to somatic symptoms. Implications for research and practice are discussed.

Openness weighted association studies: leveraging personal genome information to prioritize non-coding variants.

MOTIVATION: Identification and interpretation of non-coding variations that affect disease risk remain a paramount challenge in genome-wide association studies (GWAS) of complex diseases. Experimental efforts have provided comprehensive annotations of functional elements in the human genome. On the other hand, advances in computational biology, especially machine learning approaches, have facilitated accurate predictions of cell-type-specific functional annotations. Integrating functional annotations with GWAS signals has advanced the understanding of disease mechanisms. In previous studies, functional annotations were treated as static of a genomic region, ignoring potential functional differences imposed by different genotypes across individuals. RESULTS: We develop a computational approach, Openness Weighted Association Studies (OWAS), to leverage and aggregate predictions of chromosome accessibility in personal genomes for prioritizing GWAS signals. The approach relies on an analytical expression we derived for identifying disease associated genomic segments whose effects in the etiology of complex diseases are evaluated. In extensive simulations and real data analysis, OWAS identifies genes/segments that explain more heritability than existing methods, and has a better replication rate in independent cohorts than GWAS. Moreover, the identified genes/segments show tissue-specific patterns and are enriched in disease relevant pathways. We use rheumatic arthritis and asthma as examples to demonstrate how OWAS can be exploited to provide novel insights on complex diseases. AVAILABILITY AND IMPLEMENTATION: The R package OWAS that implements our method is available at https://github.com/shuangsong0110/OWAS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.