Fusing modalities by multiplexed graph neural networks for outcome prediction from medical data and beyond.

With the emergence of multimodal electronic health records, the evidence for diseases, events, or findings may be present across multiple modalities ranging from clinical to imaging and genomic data. Developing effective patient-tailored therapeutic guidance and outcome prediction will require fusing evidence across these modalities. Developing general-purpose frameworks capable of modeling fine-grained and multi-faceted complex interactions, both within and across modalities is an important open problem in multimodal fusion. Generalized multimodal fusion is extremely challenging as evidence for outcomes may not be uniform across all modalities, not all modality features may be relevant, or not all modalities may be present for all patients, due to which simple methods of early, late, or intermediate fusion may be inadequate. In this paper, we present a novel approach that uses the machinery of multiplexed graphs for fusion. This allows for modalities to be represented through their targeted encodings. We model their relationship between explicitly via multiplexed graphs derived from salient features in a combined latent space. We then derive a new graph neural network for multiplex graphs for task-informed reasoning. We compare our framework against several state-of-the-art approaches for multi-graph reasoning and multimodal fusion. As a sanity check on the neural network design, we evaluate the multiplexed GNN on two popular benchmark datasets, namely the AIFB and the MUTAG dataset against several state-of-the-art multi-relational GNNs for reasoning. Second, we evaluate our multiplexed framework against several state-of-the-art multimodal fusion frameworks on two large clinical datasets for two separate applications. The first is the NIH-TB portals dataset for treatment outcome prediction in Tuberculosis, and the second is the ABIDE dataset for Autism Spectrum Disorder classification. Through rigorous experimental evaluation, we demonstrate that the multiplexed GNN provides robust performance improvements in all of these diverse applications.

Standardized multi-omics of Earth's microbiomes reveals microbial and metabolite diversity.

Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth's environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from diverse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment.

Predicting Epitope Candidates for SARS-CoV-2.

Epitopes are short amino acid sequences that define the antigen signature to which an antibody or T cell receptor binds. In light of the current pandemic, epitope analysis and prediction are paramount to improving serological testing and developing vaccines. In this paper, known epitope sequences from SARS-CoV, SARS-CoV-2, and other Coronaviridae were leveraged to identify additional antigen regions in 62K SARS-CoV-2 genomes. Additionally, we present epitope distribution across SARS-CoV-2 genomes, locate the most commonly found epitopes, and discuss where epitopes are located on proteins and how epitopes can be grouped into classes. The mutation density of different protein regions is presented using a big data approach. It was observed that there are 112 B cell and 279 T cell conserved epitopes between SARS-CoV-2 and SARS-CoV, with more diverse sequences found in Nucleoprotein and Spike glycoprotein.

Transcriptomic Profiling of Plaque Psoriasis and Cutaneous T-Cell Subsets during Treatment with Secukinumab.

The IL-17A inhibitor secukinumab is efficacious for the treatment of psoriasis. To better understand its mechanism of action, we investigated its impact on psoriatic lesions from 15 patients with moderate-to-severe plaque psoriasis undergoing secukinumab treatment. We characterized the longitudinal transcriptomic changes of whole lesional skin tissue as well as cutaneous CD4+ and CD8+ T effector cells and CD4+ T regulatory cells across 12 weeks of treatment. Secukinumab was clinically effective and reduced disease-associated overexpression of IL17A , IL17F, IL23A, IL23R, and IFNG in whole tissue as soon as 2 weeks after initiation of treatment. IL17A overexpression in T-cell subsets, primarily CD8+ T cells, was also reduced. Although secukinumab treatment resolved 89‒97% of psoriasis-associated expression differences in bulk tissue and T-cell subsets by week 12 of treatment, we observed expression differences involved in IFN signaling and metallothionein synthesis that remained unresolved at this time point as well as potential treatment-associated expression differences involved in IL-15 signaling. These changes were accompanied by shifts in broader immune cell composition on the basis of deconvolution of RNA-sequencing data. In conclusion, our study reveals several phenotypic and cellular changes within the lesion that underlie clinical improvement from secukinumab.

Improving Primary Care Adolescent Depression Screening and Initial Management: A Quality Improvement Study.

Although recommended, adolescent depression screening with appropriate initial management is challenging. This project aimed to improve adolescent depression screening rates during preventive care visits in 12 primary care clinics from 65.4% to 80%, increase the proportion of documented initial management for those with a positive screen from 69.5% to 85%, then sustain improvements for 12 months. Methods: This quality improvement project involved 12 urban primary care clinics serving >120,000 mostly Medicaid-enrolled patients and targeted adolescents 12-17 years. Interventions included standardized depression screening using tablets with electronic health record (EHR) capture and automated scoring, embedding screening results and initial management actions into the EHR, provider education, and individual clinician and clinic performance feedback. Results: After standardizing the approach to screening, the process mean depression screening rate was 91.9%. However, after adopting tablets into the clinic flow, there was an unexpected initial decrease in proportion with appropriately documented initial management plans, from 89.7% to 67.6%. In response to this special cause variation, there was additional provider feedback and education, and a redesign of the EHR flow related to the presentation of results and prompts for action after a positive screen. As a result, the proportion with appropriately documented initial management was 87.3% by project completion. Conclusions: Tablet-based screening with EHR scoring capture effectively increased depression screening rates but required significant additional work to improve initial management after a positive screen. A full system approach, including EHR modification, clinician education, and performance feedback, is needed to make meaningful, sustained improvements in comprehensive adolescent depression screening.

Functional Genomics Platform, A Cloud-Based Platform for Studying Microbial Life at Scale.

The rapid growth in biological sequence data is revolutionizing our understanding of genotypic diversity and challenging conventional approaches to informatics. With the increasing availability of genomic data, traditional bioinformatic tools require substantial computational time and the creation of ever-larger indices each time a researcher seeks to gain insight from the data. To address these challenges, we pre-computed important relationships between biological entities spanning the Central Dogma of Molecular Biology and captured this information in a relational database. The database can be queried across hundreds of millions of entities and returns results in a fraction of the time required by traditional methods. In this paper, we describe Functional Genomics Platform (formerly known as OMXWare), a comprehensive database relating genotype to phenotype for bacterial life. Continually updated, the Functional Genomics Platform today contains data derived from 200,000 curated, self-consistently assembled genomes. The database stores functional data for over 68 million genes, 52 million proteins, and 239 million domains with associated biological activity annotations from Gene Ontology, KEGG, MetaCyc, and Reactome. The Functional Genomics Platform maps all of the many-to-many connections between each biological entity including the originating genome, gene, protein, and protein domain. Various microbial studies, from infectious disease to environmental health, can benefit from the rich data and connections. We describe the data selection, the pipeline to create and update the Functional Genomics Platform, and the developer tools (Python SDK and REST APIs)which allow researchers to efficiently study microbial life at scale.

Semi-Supervised Pipeline for Autonomous Annotation of SARS-CoV-2 Genomes.

SARS-CoV-2 genomic sequencing efforts have scaled dramatically to address the current global pandemic and aid public health. However, autonomous genome annotation of SARS-CoV-2 genes, proteins, and domains is not readily accomplished by existing methods and results in missing or incorrect sequences. To overcome this limitation, we developed a novel semi-supervised pipeline for automated gene, protein, and functional domain annotation of SARS-CoV-2 genomes that differentiates itself by not relying on the use of a single reference genome and by overcoming atypical genomic traits that challenge traditional bioinformatic methods. We analyzed an initial corpus of 66,000 SARS-CoV-2 genome sequences collected from labs across the world using our method and identified the comprehensive set of known proteins with 98.5% set membership accuracy and 99.1% accuracy in length prediction, compared to proteome references, including Replicase polyprotein 1ab (with its transcriptional slippage site). Compared to other published tools, such as Prokka (base) and VAPiD, we yielded a 6.4- and 1.8-fold increase in protein annotations. Our method generated 13,000,000 gene, protein, and domain sequences-some conserved across time and geography and others representing emerging variants. We observed 3362 non-redundant sequences per protein on average within this corpus and described key D614G and N501Y variants spatiotemporally in the initial genome corpus. For spike glycoprotein domains, we achieved greater than 97.9% sequence identity to references and characterized receptor binding domain variants. We further demonstrated the robustness and extensibility of our method on an additional 4000 variant diverse genomes containing all named variants of concern and interest as of August 2021. In this cohort, we successfully identified all keystone spike glycoprotein mutations in our predicted protein sequences with greater than 99% accuracy as well as demonstrating high accuracy of the protein and domain annotations. This work comprehensively presents the molecular targets to refine biomedical interventions for SARS-CoV-2 with a scalable, high-accuracy method to analyze newly sequenced infections as they arise.

Efficient computation of Faith's phylogenetic diversity with applications in characterizing microbiomes.

The number of publicly available microbiome samples is continually growing. As data set size increases, bottlenecks arise in standard analytical pipelines. Faith's phylogenetic diversity (Faith's PD) is a highly utilized phylogenetic alpha diversity metric that has thus far failed to effectively scale to trees with millions of vertices. Stacked Faith's phylogenetic diversity (SFPhD) enables calculation of this widely adopted diversity metric at a much larger scale by implementing a computationally efficient algorithm. The algorithm reduces the amount of computational resources required, resulting in more accessible software with a reduced carbon footprint, as compared to previous approaches. The new algorithm produces identical results to the previous method. We further demonstrate that the phylogenetic aspect of Faith's PD provides increased power in detecting diversity differences between younger and older populations in the FINRISK study's metagenomic data.

Depression and Suicide-Risk Screening Results in Pediatric Primary Care.

BACKGROUND AND OBJECTIVES: Depression is common, and suicide rates are increasing. Adolescent depression screening might miss those with unidentified suicide risk. Our primary objective in this study was to compare the magnitude of positive screen results across different approaches. METHODS: From June 2019 to October 2020, 803 mostly Medicaid-enrolled adolescents aged ≥12 years with no recent history of depression or self-harm were screened with the Patient Health Questionnaire-9 Modified for Adolescents (PHQ-9A) and the Ask Suicide-Screening Questions (ASQ) across 12 primary care practices. Two PHQ-9A screening strategies were evaluated: screening for any type of depression or other mental illness (positive on any item) or screening for major depressive disorder (MDD) (total score ≥10). RESULTS: Overall, 56.4% of patients screened positive for any type of depression, 24.7% screened positive for MDD, and 21.1% screened positive for suicide risk. Regardless of PHQ-9A screening strategy, the ASQ identified additional subjects (eg, 2.2% additional cases compared with screening for any type of depression or other mental illness and 8.3% additional cases compared with screening positive for MDD). Of those with ≥6 month follow-up, 22.9% screened positive for any type of depression (n = 205), 35.6% screened positive for MDD (n = 90), and 42.7% with a positive ASQ result (n = 75) had a depression or self-harm diagnosis or an antidepressant prescription. CONCLUSIONS: Suicide risk screening identifies cases not identified by depression screening. In this study, we underscore opportunities and challenges in primary care related to the high prevalence of depression and suicide risk. Research is needed regarding optimal screening strategies and to help clinicians manage the expected number of screening-identified adolescents.

DNA Extraction and Host Depletion Methods Significantly Impact and Potentially Bias Bacterial Detection in a Biological Fluid.

Untargeted sequencing of nucleic acids present in food can inform the detection of food safety and origin, as well as product tampering and mislabeling issues. The application of such technologies to food analysis may reveal valuable insights that are simply unobtainable by targeted testing, leading to the efforts of applying such technologies in the food industry. However, before these approaches can be applied, it is imperative to verify that the most appropriate methods are used at every step of the process: gathering of primary material, laboratory methods, data analysis, and interpretation. The focus of this study is on gathering the primary material, in this case, DNA. We used bovine milk as a model to (i) evaluate commercially available kits for their ability to extract nucleic acids from inoculated bovine milk, (ii) evaluate host DNA depletion methods for use with milk, and (iii) develop and evaluate a selective lysis-propidium monoazide (PMA)-based protocol for host DNA depletion in milk. Our results suggest that magnetically based nucleic acid extraction methods are best for nucleic acid isolation of bovine milk. Removal of host DNA remains a challenge for untargeted sequencing of milk, highlighting the finding that the individual matrix characteristics should always be considered in food testing. Some reported methods introduce bias against specific types of microbes, which may be particularly problematic in food safety, where the detection of Gram-negative pathogens and hygiene indicators is essential. Continuous efforts are needed to develop and validate new approaches for untargeted metagenomics in samples with large amounts of DNA from a single host. IMPORTANCE Tracking the bacterial communities present in our food has the potential to inform food safety and product origin. To do so, the entire genetic material present in a sample is extracted using chemical methods or commercially available kits and sequenced using next-generation platforms to provide a snapshot of the microbial composition. Because the genetic material of higher organisms present in food (e.g., cow in milk or beef, wheat in flour) is around 1,000 times larger than the bacterial content, challenges exist in gathering the information of interest. Additionally, specific bacterial characteristics can make them easier or harder to detect, adding another layer of complexity to this issue. In this study, we demonstrate the impact of using different methods for the ability to detect specific bacteria and highlight the need to ensure that the most appropriate methods are being used for each particular sample.

SARS-CoV-2 detection status associates with bacterial community composition in patients and the hospital environment.

BACKGROUND: SARS-CoV-2 is an RNA virus responsible for the coronavirus disease 2019 (COVID-19) pandemic. Viruses exist in complex microbial environments, and recent studies have revealed both synergistic and antagonistic effects of specific bacterial taxa on viral prevalence and infectivity. We set out to test whether specific bacterial communities predict SARS-CoV-2 occurrence in a hospital setting. METHODS: We collected 972 samples from hospitalized patients with COVID-19, their health care providers, and hospital surfaces before, during, and after admission. We screened for SARS-CoV-2 using RT-qPCR, characterized microbial communities using 16S rRNA gene amplicon sequencing, and used these bacterial profiles to classify SARS-CoV-2 RNA detection with a random forest model. RESULTS: Sixteen percent of surfaces from COVID-19 patient rooms had detectable SARS-CoV-2 RNA, although infectivity was not assessed. The highest prevalence was in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples more closely resembled the patient microbiome compared to floor samples, SARS-CoV-2 RNA was detected less often in bed rail samples (11%). SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity in both human and surface samples and higher biomass in floor samples. 16S microbial community profiles enabled high classifier accuracy for SARS-CoV-2 status in not only nares, but also forehead, stool, and floor samples. Across these distinct microbial profiles, a single amplicon sequence variant from the genus Rothia strongly predicted SARS-CoV-2 presence across sample types, with greater prevalence in positive surface and human samples, even when compared to samples from patients in other intensive care units prior to the COVID-19 pandemic. CONCLUSIONS: These results contextualize the vast diversity of microbial niches where SARS-CoV-2 RNA is detected and identify specific bacterial taxa that associate with the viral RNA prevalence both in the host and hospital environment. Video Abstract.

Analysis and forecasting of global real time RT-PCR primers and probes for SARS-CoV-2.

Rapid tests for active SARS-CoV-2 infections rely on reverse transcription polymerase chain reaction (RT-PCR). RT-PCR uses reverse transcription of RNA into complementary DNA (cDNA) and amplification of specific DNA (primer and probe) targets using polymerase chain reaction (PCR). The technology makes rapid and specific identification of the virus possible based on sequence homology of nucleic acid sequence and is much faster than tissue culture or animal cell models. However the technique can lose sensitivity over time as the virus evolves and the target sequences diverge from the selective primer sequences. Different primer sequences have been adopted in different geographic regions. As we rely on these existing RT-PCR primers to track and manage the spread of the Coronavirus, it is imperative to understand how SARS-CoV-2 mutations, over time and geographically, diverge from existing primers used today. In this study, we analyze the performance of the SARS-CoV-2 primers in use today by measuring the number of mismatches between primer sequence and genome targets over time and spatially. We find that there is a growing number of mismatches, an increase by 2% per month, as well as a high specificity of virus based on geographic location.

EMPress Enables Tree-Guided, Interactive, and Exploratory Analyses of Multi-omic Data Sets.

Standard workflows for analyzing microbiomes often include the creation and curation of phylogenetic trees. Here we present EMPress, an interactive web tool for visualizing trees in the context of microbiome, metabolome, and other community data scalable to trees with well over 500,000 nodes. EMPress provides novel functionality-including ordination integration and animations-alongside many standard tree visualization features and thus simplifies exploratory analyses of many forms of 'omic data.IMPORTANCE Phylogenetic trees are integral data structures for the analysis of microbial communities. Recent work has also shown the utility of trees constructed from certain metabolomic data sets, further highlighting their importance in microbiome research. The ever-growing scale of modern microbiome surveys has led to numerous challenges in visualizing these data. In this paper we used five diverse data sets to showcase the versatility and scalability of EMPress, an interactive web visualization tool. EMPress addresses the growing need for exploratory analysis tools that can accommodate large, complex multi-omic data sets.

Microbiome Metadata Standards: Report of the National Microbiome Data Collaborative's Workshop and Follow-On Activities.

Microbiome samples are inherently defined by the environment in which they are found. Therefore, data that provide context and enable interpretation of measurements produced from biological samples, often referred to as metadata, are critical. Important contributions have been made in the development of community-driven metadata standards; however, these standards have not been uniformly embraced by the microbiome research community. To understand how these standards are being adopted, or the barriers to adoption, across research domains, institutions, and funding agencies, the National Microbiome Data Collaborative (NMDC) hosted a workshop in October 2019. This report provides a summary of discussions that took place throughout the workshop, as well as outcomes of the working groups initiated at the workshop.

Monitoring the microbiome for food safety and quality using deep shotgun sequencing.

In this work, we hypothesized that shifts in the food microbiome can be used as an indicator of unexpected contaminants or environmental changes. To test this hypothesis, we sequenced the total RNA of 31 high protein powder (HPP) samples of poultry meal pet food ingredients. We developed a microbiome analysis pipeline employing a key eukaryotic matrix filtering step that improved microbe detection specificity to >99.96% during in silico validation. The pipeline identified 119 microbial genera per HPP sample on average with 65 genera present in all samples. The most abundant of these were Bacteroides, Clostridium, Lactococcus, Aeromonas, and Citrobacter. We also observed shifts in the microbial community corresponding to ingredient composition differences. When comparing culture-based results for Salmonella with total RNA sequencing, we found that Salmonella growth did not correlate with multiple sequence analyses. We conclude that microbiome sequencing is useful to characterize complex food microbial communities, while additional work is required for predicting specific species' viability from total RNA sequencing.

Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World.

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), artificial intelligence (AI)-including machine learning (ML) and Big Data analytics-as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This article provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas, where IoT can contribute are discussed, namely: 1) tracking and tracing; 2) remote patient monitoring (RPM) by wearable IoT (WIoT); 3) personal digital twins (PDTs); and 4) real-life use case: ICT/IoT solution in South Korea. Second, the role and novel applications of AI are explained, namely: 1) diagnosis and prognosis; 2) risk prediction; 3) vaccine and drug development; 4) research data set; 5) early warnings and alerts; 6) social control and fake news detection; and 7) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including: 1) crowd surveillance; 2) public announcements; 3) screening and diagnosis; and 4) essential supply delivery. Finally, we discuss how distributed ledger technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19.

Single-cell RNA sequencing of psoriatic skin identifies pathogenic Tc17 cell subsets and reveals distinctions between CD8+ T cells in autoimmunity and cancer.

BACKGROUND: Psoriasis is an inflammatory, IL-17-driven skin disease in which autoantigen-induced CD8+ T cells have been identified as pathogenic drivers. OBJECTIVE: Our study focused on comprehensively characterizing the phenotypic variation of CD8+ T cells in psoriatic lesions. METHODS: We used single-cell RNA sequencing to compare CD8+ T-cell transcriptomic heterogeneity between psoriatic and healthy skin. RESULTS: We identified 11 transcriptionally diverse CD8+ T-cell subsets in psoriatic and healthy skin. Among several inflammatory subsets enriched in psoriatic skin, we observed 2 Tc17 cell subsets that were metabolically divergent, were developmentally related, and expressed CXCL13, which we found to be a biomarker of psoriasis severity and which achieved comparable or greater accuracy than IL17A in a support vector machine classifier of psoriasis and healthy transcriptomes. Despite high coinhibitory receptor expression in the Tc17 cell clusters, a comparison of these cells with melanoma-infiltrating CD8+ T cells revealed upregulated cytokine, cytolytic, and metabolic transcriptional activity in the psoriatic cells that differed from an exhaustion program. CONCLUSION: Using high-resolution single-cell profiling in tissue, we have uncovered the diverse landscape of CD8+ T cells in psoriatic and healthy skin, including 2 nonexhausted Tc17 cell subsets associated with disease severity.

Microbial context predicts SARS-CoV-2 prevalence in patients and the hospital built environment.

Synergistic effects of bacteria on viral stability and transmission are widely documented but remain unclear in the context of SARS-CoV-2. We collected 972 samples from hospitalized ICU patients with coronavirus disease 2019 (COVID-19), their health care providers, and hospital surfaces before, during, and after admission. We screened for SARS-CoV-2 using RT-qPCR, characterized microbial communities using 16S rRNA gene amplicon sequencing, and contextualized the massive microbial diversity in this dataset in a meta-analysis of over 20,000 samples. Sixteen percent of surfaces from COVID-19 patient rooms were positive, with the highest prevalence in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples increasingly resembled the patient microbiome throughout their stay, SARS-CoV-2 was less frequently detected there (11%). Despite surface contamination in almost all patient rooms, no health care workers providing COVID-19 patient care contracted the disease. SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity across human and surface samples, and higher biomass in floor samples. 16S microbial community profiles allowed for high classifier accuracy for SARS-CoV-2 status in not only nares, but also forehead, stool and floor samples. Across these distinct microbial profiles, a single amplicon sequence variant from the genus Rothia was highly predictive of SARS-CoV-2 across sample types, and had higher prevalence in positive surface and human samples, even when comparing to samples from patients in another intensive care unit prior to the COVID-19 pandemic. These results suggest that bacterial communities contribute to viral prevalence both in the host and hospital environment.

Evaluation of a Genetic Risk Score for Diagnosis of Psoriatic Arthritis.

BACKGROUND: Diagnosis of psoriatic arthritis (PsA) can be challenging, resulting in delays that contribute to irreversible joint damage, reduced quality of life, and increased mortality. OBJECTIVE: Use genetic markers to develop and evaluate a PsA genetic risk score (GRS) for its ability to discriminate between psoriasis (PsO) only and PsO with PsA among a psoriatic cohort with full genome-wide genotype data. METHODS: Genome-wide single-nucleotide polymorphism genotyping was performed on 724 psoriatic patients. A set of 11 candidate risk genes previously shown to be preferentially associated with PsO or PsA were selected. To evaluate the cumulative effects of these risk loci, a PsA GRS was developed using an unweighted risk allele count (cGRS) and a weighted (wGRS) approach. Additional analyses included only human leukocyte antigen (HLA) risk alleles. RESULTS: The discriminative power attributable to each GRS was evaluated by calculating the areas under the receiver operator characteristic curve (AUROC). The AUROC for the wGRS is 56.2% versus 54.1% for the cGRS, and the AUROC for the HLA-only wGRS model was 56.9% versus 55.7% for the HLA-only cGRS. CONCLUSION: The AUROC of 56.9% for HLA-only wGRS indicates that this approach has the greatest power in discriminating PsA from PsO among these models. Given that an AUROC of 56.9% is quite modest, this study suggests that using a small number of well-validated genetic loci provides limited predictive power for PsA, and that future approaches may benefit from using a larger number of genetic loci.