Abatacept increases T cell exhaustion in early RA individuals who carry HLA risk alleles.

Exhausted CD8 T cells (TEX) are associated with worse outcome in cancer yet better outcome in autoimmunity. Building on our past findings of increased TIGIT+KLRG1+ TEX with teplizumab therapy in type 1 diabetes (T1D), in the absence of treatment we found that the frequency of TIGIT+KLRG1+ TEX is stable within an individual but differs across individuals in both T1D and healthy control (HC) cohorts. This TIGIT+KLRG1+ CD8 TEX population shares an exhaustion-associated EOMES gene signature in HC, T1D, rheumatoid arthritis (RA), and cancer subjects, expresses multiple inhibitory receptors, and is hyporesponsive in vitro, together suggesting co-expression of TIGIT and KLRG1 may broadly define human peripheral exhausted cells. In HC and RA subjects, lower levels of EOMES transcriptional modules and frequency of TIGIT+KLRG1+ TEX were associated with RA HLA risk alleles (DR0401, 0404, 0405, 0408, 1001) even when considering disease status and cytomegalovirus (CMV) seropositivity. Moreover, the frequency of TIGIT+KLRG1+ TEX was significantly increased in RA HLA risk but not non-risk subjects treated with abatacept (CTLA4Ig). The DR4 association and selective modulation with abatacept suggests that therapeutic modulation of TEX may be more effective in DR4 subjects and TEX may be indirectly influenced by cellular interactions that are blocked by abatacept.

Cell subtype-specific effects of genetic variation in the Alzheimer's disease brain.

The relationship between genetic variation and gene expression in brain cell types and subtypes remains understudied. Here, we generated single-nucleus RNA sequencing data from the neocortex of 424 individuals of advanced age; we assessed the effect of genetic variants on RNA expression in cis (cis-expression quantitative trait loci) for seven cell types and 64 cell subtypes using 1.5 million transcriptomes. This effort identified 10,004 eGenes at the cell type level and 8,099 eGenes at the cell subtype level. Many eGenes are only detected within cell subtypes. A new variant influences APOE expression only in microglia and is associated with greater cerebral amyloid angiopathy but not Alzheimer's disease pathology, after adjusting for APOEε4, providing mechanistic insights into both pathologies. Furthermore, only a TMEM106B variant affects the proportion of cell subtypes. Integration of these results with genome-wide association studies highlighted the targeted cell type and probable causal gene within Alzheimer's disease, schizophrenia, educational attainment and Parkinson's disease loci.

Blended learning in biochemistry: The development of pre-class and post-class learning aids for electron transport chain and oxidative phosphorylation.

Electron transport chain and oxidative phosphorylation are always a challenging topic for students studying metabolism. We had adopted blended learning in metabolism teaching and evaluated the learning experiences of students. In this project, a pre-class learning aid the Story Mode and a post-class learning aid the Revision Mode in the Powerland was developed that facilitated students learning electron transport chain and oxidative phosphorylation. In the Story Mode, pathways were presented by short animations and simplified diagram that allowed students to understand basic concepts and recall simple facts of the topic. Students were asked to watch the animations before class to acquire lower level of cognitive learning first, and this facilitated students in understanding more complicated concepts later on during class. Another challenge that students faced was that they were especially weak at integrating metabolic pathways and understand the relationships between these pathways. A metro map was designed in the Revision Mode that aided students in knowledge integration, and the functions of biomolecules were summarized in flashcards that helped students in revising the concepts. This interactive self-learning tool was packaged as a courseware using the Articulate Storyline.

The molecular genetic landscape of human brain size variation.

Human brain size changes dynamically through early development, peaks in adolescence, and varies up to 2-fold among adults. However, the molecular genetic underpinnings of interindividual variation in brain size remain unknown. Here, we leveraged postmortem brain RNA sequencing and measurements of brain weight (BW) in 2,531 individuals across three independent datasets to identify 928 genome-wide significant associations with BW. Genes associated with higher or lower BW showed distinct neurodevelopmental trajectories and spatial patterns that mapped onto functional and cellular axes of brain organization. Expression of BW genes was predictive of interspecies differences in brain size, and bioinformatic annotation revealed enrichment for neurogenesis and cell-cell communication. Genome-wide, transcriptome-wide, and phenome-wide association analyses linked BW gene sets to neuroimaging measurements of brain size and brain-related clinical traits. Cumulatively, these results represent a major step toward delineating the molecular pathways underlying human brain size variation in health and disease.

Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings.

Deep learning methods have recently become the state of the art in a variety of regulatory genomic tasks1-6, including the prediction of gene expression from genomic DNA. As such, these methods promise to serve as important tools in interpreting the full spectrum of genetic variation observed in personal genomes. Previous evaluation strategies have assessed their predictions of gene expression across genomic regions; however, systematic benchmarking is lacking to assess their predictions across individuals, which would directly evaluate their utility as personal DNA interpreters. We used paired whole genome sequencing and gene expression from 839 individuals in the ROSMAP study7 to evaluate the ability of current methods to predict gene expression variation across individuals at varied loci. Our approach identifies a limitation of current methods to correctly predict the direction of variant effects. We show that this limitation stems from insufficiently learned sequence motif grammar and suggest new model training strategies to improve performance.

Robust, scalable, and informative clustering for diverse biological networks.

Clustering molecular data into informative groups is a primary step in extracting robust conclusions from big data. However, due to foundational issues in how they are defined and detected, such clusters are not always reliable, leading to unstable conclusions. We compare popular clustering algorithms across thousands of synthetic and real biological datasets, including a new consensus clustering algorithm-SpeakEasy2: Champagne. These tests identify trends in performance, show no single method is universally optimal, and allow us to examine factors behind variation in performance. Multiple metrics indicate SpeakEasy2 generally provides robust, scalable, and informative clusters for a range of applications.

A Molecular Basis of Human Brain Connectivity.

Neuroimaging is commonly used to infer human brain connectivity, but those measurements are far-removed from the molecular underpinnings at synapses. To uncover the molecular basis of human brain connectivity, we analyzed a unique cohort of 98 individuals who provided neuroimaging and genetic data contemporaneous with dendritic spine morphometric, proteomic, and gene expression data from the superior frontal and inferior temporal gyri. Through cellular contextualization of the molecular data with dendritic spine morphology, we identified hundreds of proteins related to synapses, energy metabolism, and RNA processing that explain between-individual differences in functional connectivity and structural covariation. By integrating data at the genetic, molecular, subcellular, and tissue levels, we bridged the divergent fields of molecular biology and neuroimaging to identify a molecular basis of brain connectivity. One-Sentence Summary: Dendritic spine morphometry and synaptic proteins unite the divergent fields of molecular biology and neuroimaging.

Circulating cell-free mitochondrial DNA as a diagnostic and prognostic biomarker in chronic and late acute graft-versus-host disease in children.

In an earlier study, we found that mitochondrial DNA (mtDNA) concentration is elevated in adults with chronic graft-versus-host disease (cGvHD), acting as an endogenous source of TLR9 agonists to augment B-cell responses. To validate this in children, we evaluated mtDNA plasma expression in a large pediatric cohort (ABLE/PBMTC 1202 study). Plasma cell-free mtDNA (cf-mtDNA) copy numbers were measured in 202 pediatric patients using quantitative Droplet Digital polymerase chain reaction (ddPCR). Two evaluations were performed: 1) before the onset of cGvHD or late acute graft-versus-host disease (aGvHD) at day 100 ± 14 days and 2) at the time of cGvHD onset compared with time-matched non-cGvHD controls. We found that cf-mtDNA copy numbers were not affected by immune reconstitution post-hematopoietic stem cell transplantation but were higher on day 100 before the onset of late aGvHD and at the onset of cGvHD. We found that cf-mtDNA was not impacted by previous aGvHD, but correlated with the early onset, NIH moderate/severe cGvHD, and did not correlate with other immune cell populations, cytokines, or chemokines but did with the metabolites spermine and taurine. Similar to adults, children have elevated plasma cf-mtDNA concentrations at the early onset of cGvHD, especially in NIH moderate/severe cGvHD, elevation with late aGvHD, and associated with metabolites involved in mitochondrial function.

Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings.

Deep learning methods have recently become the state-of-the-art in a variety of regulatory genomic tasks1-6 including the prediction of gene expression from genomic DNA. As such, these methods promise to serve as important tools in interpreting the full spectrum of genetic variation observed in personal genomes. Previous evaluation strategies have assessed their predictions of gene expression across genomic regions, however, systematic benchmarking is lacking to assess their predictions across individuals, which would directly evaluates their utility as personal DNA interpreters. We used paired Whole Genome Sequencing and gene expression from 839 individuals in the ROSMAP study7 to evaluate the ability of current methods to predict gene expression variation across individuals at varied loci. Our approach identifies a limitation of current methods to correctly predict the direction of variant effects. We show that this limitation stems from insufficiently learnt sequence motif grammar, and suggest new model training strategies to improve performance.

Elevated ADA2 Enzyme Activity at the Onset of Chronic Graft-versus-Host Disease in Children.

Adenosinergic signaling has potent, context-specific effects on immune cells, particularly on the dysregulation of lymphocytes. This in turn may have a role in immune activation and loss of tolerance in such diseases as chronic graft-versus-host disease (chronic GVHD). We assessed whether changes in the enzymatic activity of adenosine deaminase 2 (ADA2), an enzyme that depletes adenosine in the extracellular space via conversion to inosine, may be associated with the onset of chronic GVHD. ADA2-specific enzyme activity was measured in plasma samples from 230 pediatric hematopoietic stem cell transplantation (HSCT) recipients enrolled on the Applied Biomarkers of Late Effects of Childhood Cancer (ABLE)/Pediatric Blood and Marrow Transplant Consortium (PBMTC) 1202 study and compared between patients developing chronic GVHD and those not developing chronic GVHD within 12 months of transplantation. ADA2 and its relationships with 219 previously measured plasma-soluble proteins, metabolites, and immune cell populations were evaluated as well. Plasma ADA2 enzyme activity was significantly elevated in pediatric HSCT recipients at the onset of chronic GVHD compared to patients without chronic GVHD and was not associated with prior history of acute GVHD or generalized inflammation as measured by C-reactive protein concentration. ADA2-specific enzyme activity met our criteria as a potential diagnostic biomarker of chronic GVHD (effect ratio ≥1.30 or ≤.75; area under the receiver operating characteristic curve ≥.60; P < .05) and was positively associated with markers of immune activation previously identified in pediatric chronic GVHD patients. These results support the potential of ADA2 enzyme activity, in combination with other biomarkers and subject to future validation, to aid the diagnosis of chronic GVHD in children post-HSCT.

A diagnostic classifier for pediatric chronic graft-versus-host disease: results of the ABLE/PBMTC 1202 study.

The National Institutes of Health Consensus criteria for chronic graft-versus-host disease (cGVHD) diagnosis can be challenging to apply in children, making pediatric cGVHD diagnosis difficult. We aimed to identify diagnostic pediatric cGVHD biomarkers that would complement the current clinical criteria and help differentiate cGVHD from non-cGVHD. The Applied Biomarkers of Late Effects of Childhood Cancer (ABLE) study, open at 27 transplant centers, prospectively evaluated 302 pediatric patients after hematopoietic cell transplant (234 evaluable). Forty-four patients developed cGVHD. Mixed and fixed effect regression analyses were performed on diagnostic cGVHD onset blood samples for cellular and plasma biomarkers, with individual markers declared relevant if they met 3 criteria: an effect ratio ≥1.3 or ≤0.75; an area under the curve (AUC) of ≥0.60; and a P value <5.814 × 10-4 (Bonferroni correction) (mixed effect) or <.05 (fixed effect). To address the complexity of cGVHD diagnosis in children, we built a machine learning-based classifier that combined multiple cellular and plasma biomarkers with clinical factors. Decreases in regulatory natural killer cells, naïve CD4 T helper cells, and naïve regulatory T cells, and elevated levels of CXCL9, CXCL10, CXCL11, ST2, ICAM-1, and soluble CD13 (sCD13) characterize the onset of cGVHD. Evaluation of the time dependence revealed that sCD13, ST2, and ICAM-1 levels varied with the timing of cGVHD onset. The cGVHD diagnostic classifier achieved an AUC of 0.89, with a positive predictive value of 82% and a negative predictive value of 80% for diagnosing cGVHD. Our polyomic approach to building a diagnostic classifier could help improve the diagnosis of cGVHD in children but requires validation in future prospective studies. This trial was registered at www.clinicaltrials.gov as #NCT02067832.

CD56bright CD16- natural killer cells as an important regulatory mechanism in chronic graft-versus-host disease.

Chronic graft-versus-host disease (cGvHD) is a major cause of morbidity after hematopoietic stem cell transplantation (HSCT). In large patient populations, we have shown a CD56bright natural killer (NK) population to strongly associate with a lack of cGvHD and we hypothesize that these cells function to suppress cGvHD. We aimed to isolate and define the characteristics of regulatory NK (NKreg) cells associated with suppression of cGvHD. Immunophenotypic evaluation of a large pediatric population found the CD56bright NK population associated with a lack of cGvHD to be perforin-, Granzyme B-, and CD335+. Transcriptome analysis of a small patient cohort of CD56bright compared to CD56dim NK cells found the NKreg cells to also overexpress Granzyme K, IL-7R, GPR183, RANK, GM-CSFR, TCF7, and IL23A. Further analysis of this CD56bright NKreg population found a subpopulation that overexpressed IRF1, and TNF. We also found that viable NKreg cells may be isolated by sorting on CD56+ and CD16- NK cells, and this population can suppress allogeneic CD4+ T cells, but not Treg cells or CD8+ T cells through a non-cytolytic, cell-cell contact dependent mechanism. Suppression was not reliant upon the NKp44, NKp46, or GPR183 receptors. Additionally, NKreg cells do not kill leukemic cells. Moreover, this is the first paper to clearly establish that a CD56brightCD3-CD16-perforin- NKreg population associates with a lack of cGvHD and has several unique characteristics, including the suppression of helper T-cell function in vitro. With further investigation we may decipher the mechanism of NKreg suppression and operationalize expansion of NKreg cells associated with cGvHD suppression.

Predictive models for thromboembolic events in giant cell arteritis: A US veterans health administration population-based study.

Giant cell arteritis (GCA) that affects older patients is an independent risk factor for thromboembolic events. The objective of this study was to identify predictive factors for thromboembolic events in patients with GCA and develop quantitative predictive tools (prognostic nomograms) for pulmonary embolism (PE) and deep venous thrombosis (DVT). A total of 13,029 patients with a GCA diagnosis were included in this retrospective study. We investigated potential predictors of PE and DVT using univariable and multivariable Cox regression models. Nomograms were then constructed based on the results of our Cox models. We also assessed the accuracy and predictive ability of our models by using calibration curves and cross-validation concordance index. Age, inpatient status at the time of initial diagnosis of GCA, number of admissions before diagnosis of GCA, and Charlson comorbidity index were each found to be independent predictive factors of thromboembolic events. Prognostic nomograms were then prepared based on these predictors with promising prognostic ability. The probability of developing thromboembolic events over an observation period of 5 years was estimated by with time-to-event analysis using the method of Kaplan and Meier, after stratifying patients based on predicted risk. The concordance index of the time-to-event analysis for both PE and DVT was > 0.61, indicating a good predictive performance. The proposed nomograms, based on specific predictive factors, can accurately estimate the probability of developing PE or DVT among patients with GCA.

A practical guide to applying machine learning to infant EEG data.

Electroencephalography (EEG) has been widely adopted by the developmental cognitive neuroscience community, but the application of machine learning (ML) in this domain lags behind adult EEG studies. Applying ML to infant data is particularly challenging due to the low number of trials, low signal-to-noise ratio, high inter-subject variability, and high inter-trial variability. Here, we provide a step-by-step tutorial on how to apply ML to classify cognitive states in infants. We describe the type of brain attributes that are widely used for EEG classification and also introduce a Riemannian geometry based approach for deriving connectivity estimates that account for inter-trial and inter-subject variability. We present pipelines for learning classifiers using trials from a single infant and from multiple infants, and demonstrate the application of these pipelines on a standard infant EEG dataset of forty 12-month-old infants collected under an auditory oddball paradigm. While we classify perceptual states induced by frequent versus rare stimuli, the presented pipelines can be easily adapted for other experimental designs and stimuli using the associated code that we have made publicly available.

Telemedicine Use During the COVID-19 Pandemic by Resilient Rheumatology Providers: A National Veterans Affairs Follow-up Survey.

OBJECTIVE: To assess rheumatology provider experience and practices at Veterans Affairs (VA) facilities during the coronavirus disease 2019 (COVID-19) pandemic. METHODS: We performed an anonymized follow-up national cross-sectional survey (November 5, 2020 to January 1, 2021) to assess provider resilience, experience, practices, views, and opinions about changes to medications and laboratory monitoring of veterans with rheumatic diseases. RESULTS: Of the 143 eligible VA rheumatology providers, 114 (80%) responded. Compared to the original survey, fewer providers reported using telephone visits (78% vs 91%, P = 0.009), and more used clinical video telehealth (CVT; 16% vs 7%, P = 0.04) or in-person visits (76% vs 59%, P = 0.007). Most providers were somewhat or very comfortable with the quality of clinical encounters for established but not new patients for telephone, video-based VA Video Connect (VVC), and CVT. The mean 2-item Connor-Davidson Resilience Scale score was 6.85 (SD 1.06, range 0-8), significantly higher than the original April-May 2020 survey score of 6.35 (SD 1.26; P = 0.004). When adjusted for age, sex, and ethnicity, high provider resilience was associated with significantly higher odds of comfort with technology and the quality of the VVC visit for the following: (1) established patients (odds ratio [OR] 1.72, 95% CI, 0.67-4.40 and OR 4.13, 95% CI 1.49-11.44, respectively) and (2) new patients (OR 2.79, 95% CI 1.11-7.05, and OR 2.69, 95% CI 1.06-6.82, respectively). CONCLUSION: Reassuringly, VA rheumatology providers became increasingly comfortable with video visits during the first 10 months of the COVID-19 pandemic. High provider resilience, and its association with better quality CVTs, raise the possibility that video visits might be an acceptable substitute for in-person visits under appropriate circumstances.

Association of Use of Electronic Appointment Reminders With Waiting Times in the Veterans Affairs Health System.

Importance: Electronic appointment reminder systems are increasingly used across health systems. However, their association with patients' waiting times for their appointments, a measure of timely access to care, has yet to be assessed. Objective: To assess the associations between the introduction of an electronic appointment reminder system and the number of days patients had to wait from appointment booking to appointment completion in patients in the Veterans Affairs Health System. Design, Setting, and Participants: Cohort study of patients who completed appointments from January 1, 2018, to October 13, 2018, inclusive in all 130 Veterans Affairs (VA) health centers in the US. The study population comprised a census of all patients who received care at any VA health center during the period of the study for outpatient, procedural, rehabilitation, or radiology services. Data were analyzed from May 15, 2021, to December 15, 2021. Exposures: Phased introduction of an electronic appointment reminder system (VEText) in 6 waves spread across the study period. Main Outcomes and Measures: The unit of observation in this study was a completed appointment made by any such patients. Observations were excluded if the appointment was booked before but completed after the exposure, or if data were duplicated, missing, or incomplete. For each completed appointment, the number of days between which the appointment was booked and when it was completed. Results: The number of observations after exclusion comprised 39.5 million completed appointments from 5.1 million patients (91.1% male) with a mean (SD) age of 62.57 (16.24) years. The adoption of VEText was associated with an estimated reduction in patient waiting time by a mean of 6.51 days (95% CI, 5.51-7.52 days). Adoption of VEText was also associated with an increase of 8.54 (95% CI, 7.65-9.44) days of additional waiting per incomplete booking. Conclusions and Relevance: Results of this study suggest that appointment reminder systems may be associated with decreases in the mean number of days patients in the VA system have to wait for their appointments but can potentially lengthen waiting times for patients who miss their bookings. Further study is warranted to assess whether these findings may be generalizable to other populations.

Risk of venous and arterial thromboembolism in patients with giant cell arteritis and/or polymyalgia rheumatica: A Veterans Health Administration population-based study in the United States.

BACKGROUND: Giant cell arteritis (GCA) and polymyalgia rheumatica (PMR) are systemic inflammatory diseases that primarily affect elderly women. OBJECTIVES: To compare the risk of thromboembolic events and retinal vascular occlusions in GCA and/or PMR with that in osteoarthritis (OA), evaluating a veteran-based population. METHODS: A total of 1535 patients with GCA, 10,265 with PMR, and 1203 with overlapping disease, as well as 39,009 age- and sex-matched patients with OA were identified in this retrospective study. The incidence rate ratios (IRRs) of pulmonary embolism (PE), deep venous thrombosis (DVT), arterial thromboembolism, central retinal artery occlusion, and central retinal vein occlusion were calculated and examined over time. The cumulative incidence was plotted and hazard ratios (HRs) of thromboembolic events were calculated, adjusting for independent risk factors of thromboembolism. RESULTS: Patients with GCA and overlapping disease exhibited higher IRRs for all thromboembolic events compared to patients with OA. Patients with GCA had a higher risk of developing DVT and retinal vascular occlusions than those with overlapping disease (HR: 2.01, 95% confidence interval [CI]: 1.35-2.99, p < 0.001; HR: 2.37, 95% CI: 1.23-4.53, p = 0.009, respectively) or PMR alone (HR: 1.89, 95% CI: 1.50-2.41, p < 0.001; HR: 4.68, 95% CI: 3.10-7.07, p < 0.001, respectively). Patients with GCA had a higher risk of developing PE than those with PMR (HR: 1.55, 95% CI: 1.1-2.18, p = 0.01). CONCLUSION: The risk of thromboembolic events differs between GCA, PMR, and overlapping diseases. Our findings may help predict the risk of thromboembolic events based on disease phenotype.

Tumor necrosis factor inhibitor (TNFi) persistence and reasons for discontinuation in a predominantly male cohort with axial spondyloarthritis.

Although tumor necrosis factor inhibitors (TNFi) have favorably altered the treatment landscape for patients with axial spondyloarthritis (axSpA), there is limited data regarding TNFi persistence and reasons for discontinuation. This is an observational time-to-event study utilizing data collected for a prospective multiple-disease registry of US Veterans with axSpA treated with TNFi therapies and recruited over a 10 year period. Clinical, serological, and comorbid parameters were collected. Corporate Data Warehouse Pharmacy files provided courses of the 5 TNFi agents, and response to treatment was documented. Individual TNFi persistence was established utilizing univariate and multivariate Cox proportional models, and reasons for discontinuation were obtained by physician chart review. Two-hundred and fifty-five axSpA patients received 731 TNFi courses. A majority of patients (84.3%) had TNFi persistence at 12 months; 63.5% and 47.1% at 24 and 36 months, respectively. Compared to adalimumab, infliximab demonstrated greater persistence, certolizumab the least. Age, smoking status, BMI, comorbidity burden, inflammatory markers and HLA-B27 did not predict TNFi persistence or discontinuation. Stroke and peripheral arterial disease increased the probability of TNFi discontinuation. Secondary non-response (SNR) was the most common reason for discontinuation (46% of all courses); non-adherence (6%) and clinical remission (2%) were uncommon. Pain score at enrollment, myocardial infarction, African American race and inflammatory bowel disease (IBD) predicted TNFi response. While initial persistence of TNFi treatment was high, a large proportion of the patients discontinued initial TNFi therapy by 3 years, primarily due to loss of efficacy. While further research identifying potential predictors of TNFi discontinuation in axSpA is warranted, access to alternate disease-modifying therapies is needed.

Metabolomic identification of α-ketoglutaric acid elevation in pediatric chronic graft-versus-host disease.

Chronic graft-versus-host disease (cGVHD) is the most common cause for non-relapse mortality postallogeneic hematopoietic stem cell transplant (HSCT). However, there are no well-defined biomarkers for cGVHD or late acute GVHD (aGVHD). This study is a longitudinal evaluation of metabolomic patterns of cGVHD and late aGVHD in pediatric HSCT recipients. A quantitative analysis of plasma metabolites was performed on 222 evaluable pediatric subjects from the ABLE/PBMTC1202 study. We performed a risk-assignment analysis at day + 100 (D100) on subjects who later developed either cGVHD or late aGVHD after day 114 to non-cGVHD controls. A second analysis at diagnosis used fixed and mixed multiple regression to compare cGVHD at onset to time-matched non-cGVHD controls. A metabolomic biomarker was considered biologically relevant only if it met all 3 selection criteria: (1) P ≤ .05; (2) effect ratio of ≥1.3 or ≤0.75; and (3) receiver operator characteristic AUC ≥0.60. We found a consistent elevation in plasma α-ketoglutaric acid before (D100) and at the onset of cGVHD, not impacted by cGVHD severity, pubertal status, or previous aGVHD. In addition, late aGVHD had a unique metabolomic pattern at D100 compared with cGVHD. Additional metabolomic correlation patterns were seen with the clinical presentation of pulmonary, de novo, and progressive cGVHD. α-ketoglutaric acid emerged as the single most significant metabolite associated with cGVHD, both in the D100 risk-assignment and later diagnostic onset analysis. These distinctive metabolic patterns may lead to improved subclassification of cGVHD. Future validation of these exploratory results is needed. This trial was registered at www.clinicaltrials.gov as #NCT02067832.

Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants.

The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcriptome and eventually to the phenome in identifying target genes influenced by risk alleles. This cascading epigenomic analysis for GWAS, which we refer to as CEWAS, comprises two types of models: one for linking cis genetic effects to epigenomic variation and another for linking cis epigenomic variation to gene expression. Applying these models in cascade to GWAS summary statistics generates gene level statistics that reflect genetically-driven epigenomic effects. We show on sixteen brain-related GWAS that CEWAS provides higher gene detection rate than related methods, and finds disease relevant genes and gene sets that point toward less explored biological processes. CEWAS thus presents a novel means for exploring the regulatory landscape of GWAS variants in uncovering disease mechanisms.