Genetic diversity fuels gene discovery for tobacco and alcohol use.

Tobacco and alcohol use are heritable behaviours associated with 15% and 5.3% of worldwide deaths, respectively, due largely to broad increased risk for disease and injury1-4. These substances are used across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries5. Here we leveraged global genetic diversity across 3.4 million individuals from four major clines of global ancestry (approximately 21% non-European) to power the discovery and fine-mapping of genomic loci associated with tobacco and alcohol use, to inform function of these loci via ancestry-aware transcriptome-wide association studies, and to evaluate the genetic architecture and predictive power of polygenic risk within and across populations. We found that increases in sample size and genetic diversity improved locus identification and fine-mapping resolution, and that a large majority of the 3,823 associated variants (from 2,143 loci) showed consistent effect sizes across ancestry dimensions. However, polygenic risk scores developed in one ancestry performed poorly in others, highlighting the continued need to increase sample sizes of diverse ancestries to realize any potential benefit of polygenic prediction.

A map of cis-regulatory elements and 3D genome structures in zebrafish.

The zebrafish (Danio rerio) has been widely used in the study of human disease and development, and about 70% of the protein-coding genes are conserved between the two species1. However, studies in zebrafish remain constrained by the sparse annotation of functional control elements in the zebrafish genome. Here we performed RNA sequencing, assay for transposase-accessible chromatin using sequencing (ATAC-seq), chromatin immunoprecipitation with sequencing, whole-genome bisulfite sequencing, and chromosome conformation capture (Hi-C) experiments in up to eleven adult and two embryonic tissues to generate a comprehensive map of transcriptomes, cis-regulatory elements, heterochromatin, methylomes and 3D genome organization in the zebrafish Tübingen reference strain. A comparison of zebrafish, human and mouse regulatory elements enabled the identification of both evolutionarily conserved and species-specific regulatory sequences and networks. We observed enrichment of evolutionary breakpoints at topologically associating domain boundaries, which were correlated with strong histone H3 lysine 4 trimethylation (H3K4me3) and CCCTC-binding factor (CTCF) signals. We performed single-cell ATAC-seq in zebrafish brain, which delineated 25 different clusters of cell types. By combining long-read DNA sequencing and Hi-C, we assembled the sex-determining chromosome 4 de novo. Overall, our work provides an additional epigenomic anchor for the functional annotation of vertebrate genomes and the study of evolutionarily conserved elements of 3D genome organization.

Iron inhibits glioblastoma cell migration and polarization.

Glioblastoma is one of the deadliest malignancies facing modern oncology today. The ability of glioblastoma cells to diffusely spread into neighboring healthy brain makes complete surgical resection nearly impossible and contributes to the recurrent disease faced by most patients. Although research into the impact of iron on glioblastoma has addressed proliferation, there has been little investigation into how cellular iron impacts the ability of glioblastoma cells to migrate-a key question, especially in the context of the diffuse spread observed in these tumors. Herein, we show that increasing cellular iron content results in decreased migratory capacity of human glioblastoma cells. The decrease in migratory capacity was accompanied by a decrease in cellular polarization in the direction of movement. Expression of CDC42, a Rho GTPase that is essential for both cellular migration and establishment of polarity in the direction of cell movement, was reduced upon iron treatment. We then analyzed a single-cell RNA-seq dataset of human glioblastoma samples and found that cells at the tumor periphery had a gene signature that is consistent with having lower levels of cellular iron. Altogether, our results suggest that cellular iron content is impacting glioblastoma cell migratory capacity and that cells with higher iron levels exhibit reduced motility.

Sympathetic Blocks as a Predictor for Response to Ketamine Infusion in Patients with Complex Regional Pain Syndrome: A Multicenter Study.

BACKGROUND: Ketamine infusions are frequently employed for refractory complex regional pain syndrome (CRPS), but there are limited data on factors associated with treatment response. Sympathetic blocks are also commonly employed in CRPS for diagnostic and therapeutic purposes and generally precede ketamine infusions. OBJECTIVES: We sought to determine whether demographic and clinical factors, and technical and psychophysical characteristics of sympathetic blocks are associated with response to ketamine infusion. METHODS: In this multi-center retrospective study, 71 patients who underwent sympathetic blocks followed by ketamine infusions at 4 hospitals were evaluated. Sympathetically maintained pain (SMP) was defined as ≥ 50% immediate pain relief after sympathetic block and a positive response to ketamine was defined as ≥ 30% pain relief lasting over 3 weeks. RESULTS: Factors associated with a positive response to ketamine in univariable analysis were the presence of SMP (61.0% success rate vs 26.7% in those with sympathetically independent pain; P = .009) and post-block temperature increase (5.66 ± 4.20 in ketamine responders vs 3.68 ± 3.85 in non-responders; P = .043). No psychiatric factor was associated with ketamine response. In multivariable analysis, SMP (OR 6.54 [95% CI 1.83, 23.44]) and obesity (OR 8.75 [95% 1.45, 52.73]) were associated with a positive ketamine infusion outcome. CONCLUSIONS: The response to sympathetic blocks may predict response to ketamine infusion in CRPS patients, with alleviation of the affective component of pain and predilection to a positive placebo effect being possible explanations.

Versatile genetic assembly system (VEGAS) to assemble pathways for expression in S. cerevisiae.

We have developed a method for assembling genetic pathways for expression in Saccharomyces cerevisiae. Our pathway assembly method, called VEGAS (Versatile genetic assembly system), exploits the native capacity of S. cerevisiae to perform homologous recombination and efficiently join sequences with terminal homology. In the VEGAS workflow, terminal homology between adjacent pathway genes and the assembly vector is encoded by 'VEGAS adapter' (VA) sequences, which are orthogonal in sequence with respect to the yeast genome. Prior to pathway assembly by VEGAS in S. cerevisiae, each gene is assigned an appropriate pair of VAs and assembled using a previously described technique called yeast Golden Gate (yGG). Here we describe the application of yGG specifically to building transcription units for VEGAS assembly as well as the VEGAS methodology. We demonstrate the assembly of four-, five- and six-gene pathways by VEGAS to generate S. cerevisiae cells synthesizing ß-carotene and violacein. Moreover, we demonstrate the capacity of yGG coupled to VEGAS for combinatorial assembly.

Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes.

Transcriptome-wide association study (TWAS) is a popular approach to dissect the functional consequence of disease associated non-coding variants. Most existing TWAS use bulk tissues and may not have the resolution to reveal cell-type specific target genes. Single-cell expression quantitative trait loci (sc-eQTL) datasets are emerging. The largest bulk- and sc-eQTL datasets are most conveniently available as summary statistics, but have not been broadly utilized in TWAS. Here, we present a new method EXPRESSO (EXpression PREdiction with Summary Statistics Only), to analyze sc-eQTL summary statistics, which also integrates 3D genomic data and epigenomic annotation to prioritize causal variants. EXPRESSO substantially improves existing methods. We apply EXPRESSO to analyze multi-ancestry GWAS datasets for 14 autoimmune diseases. EXPRESSO uniquely identifies 958 novel gene x trait associations, which is 26% more than the second-best method. Among them, 492 are unique to cell type level analysis and missed by TWAS using whole blood. We also develop a cell type aware drug repurposing pipeline, which leverages EXPRESSO results to identify drug compounds that can reverse disease gene expressions in relevant cell types. Our results point to multiple drugs with therapeutic potentials, including metformin for type 1 diabetes, and vitamin K for ulcerative colitis.

Analysis of anemia and iron supplementation among glioblastoma patients reveals sex-biased association between anemia and survival.

The association between anemia and outcomes in glioblastoma patients is unclear. We analyzed data from 1346 histologically confirmed adult glioblastoma patients in the TriNetX Research Network. Median hemoglobin and hematocrit levels were quantified for 6 months following diagnosis and used to classify patients as anemic or non-anemic. Associations of anemia and iron supplementation of anemic patients with median overall survival (median-OS) were then studied. Among 1346 glioblastoma patients, 35.9% of male and 40.5% of female patients were classified as anemic using hemoglobin-based WHO guidelines. Among males, anemia was associated with reduced median-OS compared to matched non-anemic males using hemoglobin (HR 1.24; 95% CI 1.00-1.53) or hematocrit-based cutoffs (HR 1.28; 95% CI 1.03-1.59). Among females, anemia was not associated with median-OS using hemoglobin (HR 1.00; 95% CI 0.78-1.27) or hematocrit-based cutoffs (HR: 1.10; 95% CI 0.85-1.41). Iron supplementation of anemic females trended toward increased median-OS (HR 0.61; 95% CI 0.32-1.19) although failing to reach statistical significance whereas no significant association was found in anemic males (HR 0.85; 95% CI 0.41-1.75). Functional transferrin-binding assays confirmed sexually dimorphic binding in resected patient samples indicating underlying differences in iron biology. Anemia among glioblastoma patients exhibits a sex-specific association with survival.

Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets.

Genetic mechanisms of blood pressure (BP) regulation remain poorly defined. Using kidney-specific epigenomic annotations and 3D genome information we generated and validated gene expression prediction models for the purpose of transcriptome-wide association studies in 700 human kidneys. We identified 889 kidney genes associated with BP of which 399 were prioritised as contributors to BP regulation. Imputation of kidney proteome and microRNAome uncovered 97 renal proteins and 11 miRNAs associated with BP. Integration with plasma proteomics and metabolomics illuminated circulating levels of myo-inositol, 4-guanidinobutanoate and angiotensinogen as downstream effectors of several kidney BP genes (SLC5A11, AGMAT, AGT, respectively). We showed that genetically determined reduction in renal expression may mimic the effects of rare loss-of-function variants on kidney mRNA/protein and lead to an increase in BP (e.g., ENPEP). We demonstrated a strong correlation (r = 0.81) in expression of protein-coding genes between cells harvested from urine and the kidney highlighting a diagnostic potential of urinary cell transcriptomics. We uncovered adenylyl cyclase activators as a repurposing opportunity for hypertension and illustrated examples of BP-elevating effects of anticancer drugs (e.g. tubulin polymerisation inhibitors). Collectively, our studies provide new biological insights into genetic regulation of BP with potential to drive clinical translation in hypertension.

Factors associated with pregnancy termination in women of childbearing age in 36 low-and middle-income countries.

Lack of access to safe, affordable, timely and adequate pregnancy termination care, and the stigma associated with abortion in low-middle income countries (LMICs), pose a serious risk to women's physical and mental well-being throughout the lifespan. Factors associated with pregnancy termination and their heterogeneity across countries in LMICs previously have not been thoroughly investigated. We aim to determine the relative significance of factors associated with pregnancy termination in LMICs and its variation across countries. Analysis of cross-sectional nationally representative household surveys carried out in 36 LMICs from 2010 through 2018. The weighted population-based sample consisted of 1,236,330 women of childbearing aged 15-49 years from the Demographic and Health Surveys. The outcome of interest was self-report of having ever had a pregnancy terminated. We used multivariable logistic regression models to identify factors associated with pregnancy termination. The average pooled weighted prevalence of pregnancy termination in the present study was 13.3% (95% CI: 13.2%-13.4%), ranging from a low of 7.8 (95% CI: 7.2, 8.4%) in Namibia to 33.4% (95% CI: 32.0, 34.7%) in Pakistan. Being married showed the strongest association with pregnancy termination (adjusted OR, 2.94; 95% CI, 2.84-3.05; P < 0.001) compared to unmarried women. Women who had more than four children had higher odds of pregnancy termination (adjusted OR, 2.45; 95% CI, 2.33-2.56; P < 0.001). Moreover, increased age and having primary and secondary levels of education were associated with higher odds of pregnancy termination compared to no education. In this study, married women, having one or more living children, those of older age, and those with at least primary level of education were associated with pregnancy termination in these 36 LMICs. The findings highlighted the need of targeted public health intervention to reduce unintended pregnancies and unsafe abortions.

Real-World Data: Applications and Relevance to Cancer Clinical Trials.

Randomized controlled trials (RCTs) are the gold standard for comparative-effectiveness research (CER). Since the 1980s, there has been a rise in the creation and utilization of large national cancer databases to provide readily accessible "real-world data" (RWD). This review article discusses the role of RCTs in oncology, and the role of RWD from the national cancer database in CER. RCTs remain the preferred study type for CER because they minimize confounding and bias. RCTs have challenges to conduct, including extensive time and resources, but these factors do not impact the internal validity of the result. Generalizability and external validity are potential limitations of RCTs. RWD is ideal for studying cancer epidemiology, patterns of care, disparities in care delivery, quality-of-care evaluation, and applicability of RCT data in specific populations excluded from RCTs. However, retrospective databases with RWD have limitations in CER due to unmeasured confounders and are often suboptimal in identifying causal treatment effects.

Future trends in incidence and long-term survival of metastatic cancer in the United States.

BACKGROUND: Previous studies have demonstrated epidemiological trends in individual metastatic cancer subtypes; however, research forecasting long-term incidence trends and projected survivorship of metastatic cancers is lacking. We assess the burden of metastatic cancer to 2040 by (1) characterizing past, current, and forecasted incidence trends, and (2) estimating odds of long-term (5-year) survivorship. METHODS: This retrospective, serial cross-sectional, population-based study used registry data from the Surveillance, Epidemiology, and End Results (SEER 9) database. Average annual percentage change (AAPC) was calculated to describe cancer incidence trends from 1988 to 2018. Autoregressive integrating moving average (ARIMA) models were used to forecast the distribution of primary metastatic cancer and metastatic cancer to specific sites from 2019 to 2040 and JoinPoint models were fitted to estimate mean projected annual percentage change (APC). RESULTS: The average annual percent change (AAPC) in incidence of metastatic cancer decreased by 0.80 per 100,000 individuals (1988-2018) and we forecast an APC decrease by 0.70 per 100,000 individuals (2018-2040). Analyses predict a decrease in metastases to liver (APC = -3.40, 95% CI [-3.50, -3.30]), lung (APC (2019-2030) = -1.90, 95% CI [-2.90, -1.00]); (2030-2040) = -3.70, 95% CI [-4.60, -2.80]), bone (APC = -4.00, 95% CI [-4.30, -3.70]), and brain (APC = -2.30, 95% CI [-2.60, -2.00]). By 2040, patients with metastatic cancer are predicted to have 46.7% greater odds of long-term survivorship, driven by increasing plurality of patients with more indolent forms of metastatic disease. CONCLUSIONS: By 2040, the distribution of metastatic cancer patients is predicted to shift in predominance from invariably fatal to indolent cancers subtypes. Continued research on metastatic cancers is important to guide health policy and clinical intervention efforts, and direct allocations of healthcare resources.

Cancer that has spread beyond the area where it originated and into different organs is called metastatic cancer. This study analyzed trends in metastatic cancer incidence, the proportion of those with metastatic cancer surviving 5 years after diagnosis and the locations in the body each cancer had spread to. The incidence of metastatic cancer decreased between 1988 and 2018 and is expected to continue to decrease until 2040. Some of the most common locations cancer spreads to is the lung, liver, brain, and bone. Metastatic cancer incidence to these areas is predicted to decrease. Also, the likelihood of surviving for more than 5 years after diagnosis with metastatic cancer is predicted to increase by 2040. This research should facilitate optimal planning of future healthcare resources and policy.

Multi-ancestry and multi-trait genome-wide association meta-analyses inform clinical risk prediction for systemic lupus erythematosus.

Systemic lupus erythematosus is a heritable autoimmune disease that predominantly affects young women. To improve our understanding of genetic etiology, we conduct multi-ancestry and multi-trait meta-analysis of genome-wide association studies, encompassing 12 systemic lupus erythematosus cohorts from 3 different ancestries and 10 genetically correlated autoimmune diseases, and identify 16 novel loci. We also perform transcriptome-wide association studies, computational drug repurposing analysis, and cell type enrichment analysis. We discover putative drug classes, including a histone deacetylase inhibitor that could be repurposed to treat lupus. We also identify multiple cell types enriched with putative target genes, such as non-classical monocytes and B cells, which may be targeted for future therapeutics. Using this newly assembled result, we further construct polygenic risk score models and demonstrate that integrating polygenic risk score with clinical lab biomarkers improves the diagnostic accuracy of systemic lupus erythematosus using the Vanderbilt BioVU and Michigan Genomics Initiative biobanks.

Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing.

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

Construction and Application of Polygenic Risk Scores in Autoimmune Diseases.

Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with autoimmune diseases and provided unique mechanistic insights and informed novel treatments. These individual genetic variants on their own typically confer a small effect of disease risk with limited predictive power; however, when aggregated (e.g., via polygenic risk score method), they could provide meaningful risk predictions for a myriad of diseases. In this review, we describe the recent advances in GWAS for autoimmune diseases and the practical application of this knowledge to predict an individual's susceptibility/severity for autoimmune diseases such as systemic lupus erythematosus (SLE) via the polygenic risk score method. We provide an overview of methods for deriving different polygenic risk scores and discuss the strategies to integrate additional information from correlated traits and diverse ancestries. We further advocate for the need to integrate clinical features (e.g., anti-nuclear antibody status) with genetic profiling to better identify patients at high risk of disease susceptibility/severity even before clinical signs or symptoms develop. We conclude by discussing future challenges and opportunities of applying polygenic risk score methods in clinical care.

Association of Spinal Cord Stimulator Implantation With Persistent Opioid Use in Patients With Postlaminectomy Syndrome.

Importance: The results of studies evaluating spinal cord stimulation (SCS) for postlaminectomy syndrome (PLS) have yielded mixed results. This has led to an increased emphasis on objective outcome measures such as opioid prescribing. Objective: To determine the association between SCS and long-term opioid therapy (LOT) for PLS. Design, Setting, and Participants: In this cohort study, adults with PLS were identified using the TriNetx Diamond Network and separated based on whether they underwent SCS. Patients were stratified according to baseline opioid use (opioid-naive or receiving LOT) and subsequent opioid therapy over the 12-month period ranging from 3 to 15 months post-SCS implantation or post-PLS index date. Statistical analysis was performed from June to December 2021. Exposure: SCS. Main Outcomes and Measures: The main outcome was cessation of opioid use among patients receiving LOT or abstinence from opioids among opioid-naive patients. Opioid-naive patients were defined as those receiving at most 2 opioid prescriptions per year, and patients on LOT were those receiving at least 6 opioid prescriptions per year. Results: Among 552 937 eligible patients treated between December 2015 and May 2021, 26 179 with PLS received an SCS implant. The median (IQR) patient age was 60 (51-69) years; 305 802 patients (55.3%) were female. Among those reporting racial identify (37.0% [204 758 patients]), 9.3% (18 971 patients) were African American, 0.3% (648 patients) were Asian, and 90.4% (185 139 patients) were White. Compared with those who did not receive an SCS, individuals who received an SCS were more likely to be using opioids preimplantation (mean [SD] prescriptions: 4.3 [8.5] vs 4.1 [9.3]; P < .001) but less likely to be using opioids after SCS implantation (mean [SD] prescriptions: 3.8 [8.2] vs 4.0 [9.4]; P = .006). In the 12-month study period, similar proportions in the SCS and no-SCS groups receiving baseline LOT remained on LOT (70.3% [n = 74 585] vs 69.2% [n = 3882], respectively; P = .10). In opioid-naive patients, SCS was associated with a small decreased likelihood of patients subsequently receiving LOT (7.6% vs 7.0%; difference, -0.6% [95% CI, -1.0% to -0.2%]; P = .003). In multivariable analysis, SCS was associated with an increased likelihood of not being on opioids in both opioid-naive (adjusted odds ratio [OR], 0.90 [95% CI, 0.85-0.96]; P < .001) and LOT patients (adjusted OR, 0.93 [95% CI, 0.88-0.99]; P = .02). White patients were significantly more likely to be diagnosed with PLS (ie, underwent surgery) (90.4% vs 85.2%; difference, 5.2% [95% CI, 5.1%-5.4%]; P < .001) and receive an SCS (93.7% vs 90.3%; difference, 3.4% [95% CI, 2.9% to 4.0%]; P < .001) than patients of other racial identities. Conclusions and Relevance: These findings suggest that under real-life conditions, SCS was associated with small, clinically questionable associations with opioid discontinuation and not starting opioids in the context of PLS.

Integrating 3D genomic and epigenomic data to enhance target gene discovery and drug repurposing in transcriptome-wide association studies.

Transcriptome-wide association studies (TWAS) are popular approaches to test for association between imputed gene expression levels and traits of interest. Here, we propose an integrative method PUMICE (Prediction Using Models Informed by Chromatin conformations and Epigenomics) to integrate 3D genomic and epigenomic data with expression quantitative trait loci (eQTL) to more accurately predict gene expressions. PUMICE helps define and prioritize regions that harbor cis-regulatory variants, which outperforms competing methods. We further describe an extension to our method PUMICE +, which jointly combines TWAS results from single- and multi-tissue models. Across 79 traits, PUMICE + identifies 22% more independent novel genes and increases median chi-square statistics values at known loci by 35% compared to the second-best method, as well as achieves the narrowest credible interval size. Lastly, we perform computational drug repurposing and confirm that PUMICE + outperforms other TWAS methods.

Medical Service Use and Charges for Cancer Care in 2018 for Privately Insured Patients Younger Than 65 Years in the US.

Importance: Currently, there are limited published data regarding resource use and spending on cancer care in the US. Objective: To characterize the most frequent medical services provided and the associated spending for privately insured patients with cancer in the US. Design, Setting, and Participants: This cohort study used data from the MarketScan database for the calendar year 2018 from a sample of 27.1 million privately insured individuals, including patients with a diagnosis of the 15 most prevalent cancers, predominantly from large insurers and self-insured employers. Overall societal health care spending was estimated for each cancer type by multiplying the mean total spending per patient (estimated from MarketScan) by the number of privately insured patients living with that cancer in 2018, as reported by the National Cancer Institute's Surveillance, Epidemiology, and End Results program. Analyses were performed from February 1, 2018, to July 8, 2021. Exposures: Evaluation and management as prescribed by treating care team. Main Outcomes and Measures: Current Procedural Terminology and Healthcare Common Procedure Coding System codes based on cancer diagnosis code. Results: The estimated cost of cancer care in 2018 for 402 115 patients with the 15 most prevalent cancer types was approximately $156.2 billion for privately insured adults younger than 65 years in the US. There were a total of 38.4 million documented procedure codes for 15 cancers in the MarketScan database, totaling $10.8 billion. Patients with breast cancer contributed the greatest total number of services (10.9 million [28.4%]), followed by those with colorectal cancer (3.9 million [10.2%]) and prostate cancer (3.6 million [9.4%]). Pathology and laboratory tests contributed the highest number of services performed (11.7 million [30.5%]), followed by medical services (6.3 million [16.4%]) and medical supplies and nonphysician services (6.1 million [15.9%]). The costliest cancers were those of the breast ($3.4 billion [31.5%]), followed by lung ($1.1 billion [10.2%]) and colorectum ($1.1 billion [10.2%]). Medical supplies and nonphysician services contributed the highest total spent ($4.0 billion [37.0%]), followed by radiology ($2.1 billion [19.4%]) and surgery ($1.8 billion [16.7%]). Conclusions and Relevance: This analysis suggests that patients with breast, colorectal, and prostate cancers had the greatest number of services performed, particularly for pathology and laboratory tests, whereas patients with breast, lung, lymphoma, and colorectal cancer incurred the greatest costs, particularly for medical supplies and nonphysician services. The cost of cancer care in 2018 for the 15 most prevalent cancer types was estimated to be approximately $156.2 billion for privately insured adults younger than 65 years in the US.

A supervised learning framework for chromatin loop detection in genome-wide contact maps.

Accurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepening our understanding of proper gene regulation. Current approaches are mainly focused on searching for statistically enriched dots on a genome-wide map. However, given the availability of orthogonal data types such as ChIA-PET, HiChIP, Capture Hi-C, and high-throughput imaging, a supervised learning approach could facilitate the discovery of a comprehensive set of chromatin interactions. Here, we present Peakachu, a Random Forest classification framework that predicts chromatin loops from genome-wide contact maps. We compare Peakachu with current enrichment-based approaches, and find that Peakachu identifies a unique set of short-range interactions. We show that our models perform well in different platforms, across different sequencing depths, and across different species. We apply this framework to predict chromatin loops in 56 Hi-C datasets, and release the results at the 3D Genome Browser.