Assessing the potential of polygenic scores to strengthen medical risk prediction models of COVID-19

As findings on the epidemiological and genetic risk factors for coronavirus disease-19 (COVID-19) continue to accrue, their joint power and significance for prospective clinical applications remains virtually unexplored. Severity of symptoms in individuals affected by COVID-19 spans a broad spectrum, reflective of heterogeneous host susceptibilities across the population. Here, we assessed the utility of epidemiological risk factors to predict disease severity prospectively, and interrogated genetic information (polygenic scores) to evaluate whether they can provide further insights into symptom heterogeneity. A standard model was trained to predict severe COVID-19 based on principal component analysis and logistic regression based on information from eight known medical risk factors for COVID-19 measured before 2018. In UK Biobank participants of European ancestry, the model achieved a relatively high performance (area under the receiver operating characteristic curve ~90%). Polygenic scores for COVID-19 computed from summary statistics of the Covid19 Host Genetics Initiative displayed significant associations with COVID-19 in the UK Biobank (p-values as low as 3.96e-9, all with R2 under 1%), but were unable to robustly improve predictive performance of the non-genetic factors. However, error analysis of the non-genetic models suggested that affected individuals misclassified by the medical risk factors (predicted low risk but actual high risk) display a small but consistent increase in polygenic scores. Overall, the results indicate that simple models based on health-related epidemiological factors measured years before COVID-19 onset can achieve high predictive power. Associations between COVID-19 and genetic factors were statistically robust, but currently they have limited predictive power for translational settings. Despite that, the outcomes also suggest that severely affected cases with a medical history profile of low risk might be partly explained by polygenic factors, prompting development of boosted COVID-19 polygenic models based on new data and tools to aid risk-prediction.

Genetically-informed prediction of short-term Parkinson's disease progression

Parkinson's disease (PD) treatments modify disease symptoms but have not been shown to slow progression, characterized by gradual and varied motor and non-motor changes overtime. Variation in PD progression hampers clinical research, resulting in long and expensive clinical trials prone to failure. Development of models for short-term PD progression prediction could be useful for shortening the time required to detect disease-modifying drug effects in clinical studies. PD progressors were defined by an increase in MDS-UPDRS scores at 12-, 24-, and 36-months post-baseline. Using only baseline features, PD progression was separately predicted across all timepoints and MDS-UPDRS subparts in independent, optimized, XGBoost models. These predictions plus baseline features were combined into a meta-predictor for 12-month MDS UPDRS Total progression. Data from the Parkinson's Progression Markers Initiative (PPMI) were used for training with independent testing on the Parkinson's Disease Biomarkers Program (PDBP) cohort. 12-month PD total progression was predicted with an F-measure 0.77, ROC AUC of 0.77, and PR AUC of 0.76 when tested on a hold-out PPMI set. When tested on PDBP we achieve a F-measure 0.75, ROC AUC of 0.74, and PR AUC of 0.73. Exclusion of genetic predictors led to the greatest loss in predictive accuracy; ROC AUC of 0.66, PR AUC of 0.66-0.68 for both PPMI and PDBP testing. Short-term PD progression can be predicted with a combination of survey-based, neuroimaging, physician examination, and genetic predictors. Dissection of the interplay between genetic risk, motor symptoms, non-motor symptoms, and longer-term expected rates of progression enable generalizable predictions.

Pan-ancestry exome-wide association analyses of COVID-19 outcomes in 586,157 individuals

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), a respiratory illness that can result in hospitalization or death. We used exome sequence data to investigate associations between rare genetic variants and seven COVID-19 outcomes in 586,157 individuals, including 20,952 with COVID-19. After accounting for multiple testing, we did not identify any clear associations with rare variants either exome wide or when specifically focusing on (1) 13 interferon pathway genes in which rare deleterious variants have been reported in individuals with severe COVID-19, (2) 281 genes located in susceptibility loci identified by the COVID-19 Host Genetics Initiative, or (3) 32 additional genes of immunologic relevance and/or therapeutic potential. Our analyses indicate there are no significant associations with rare protein-coding variants with detectable effect sizes at our current sample sizes. Analyses will be updated as additional data become available, and results are publicly available through the Regeneron Genetics Center COVID-19 Results Browser.

A catalog of associations between rare coding variants and COVID-19 outcomes

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease-19 (COVID-19), a respiratory illness that can result in hospitalization or death. We investigated associations between rare genetic variants and seven COVID-19 outcomes in 543,213 individuals, including 8,248 with COVID-19. After accounting for multiple testing, we did not identify any clear associations with rare variants either exome-wide or when specifically focusing on (i) 14 interferon pathway genes in which rare deleterious variants have been reported in severe COVID-19 patients; (ii) 167 genes located in COVID-19 GWAS risk loci; or (iii) 32 additional genes of immunologic relevance and/or therapeutic potential. Our analyses indicate there are no significant associations with rare protein-coding variants with detectable effect sizes at our current sample sizes. Analyses will be updated as additional data become available, with results publicly browsable at https://rgc-covid19.regeneron.com.

NDEx, the Network Data Exchange

Networks are a powerful and flexible methodology for expressing biological knowledge for computation and communication. Network-encoded information can include systematic screens for molecular interactions, biological relationships curated from literature, and outputs from analysis of Big Data. NDEx, the Network Data Exchange (www.ndexbio.org), is an online commons where scientists can upload, share, and publicly distribute networks. Networks in NDEx receive globally unique accession IDs and can be stored for private use, shared in pre-publication collaboration, or released for public access. Standard and novel data formats are accommodated in a flexible storage model. Organizations can use NDEx as a distribution channel for networks they generate or curate. Developers of bioinformatic applications can store and query NDEx networks via a common programmatic interface. NDEx helps expand the role of networks in scientific discourse and facilitates the integration of networks as data in publications. It is a step towards an ecosystem in which networks bearing data, hypotheses, and findings flow easily between scientists.

ConsensusCluster: a software tool for unsupervised cluster discovery in numerical data

We have created a stand-alone software tool, ConsensusCluster, for the analysis of high-dimensional single nucleotide polymorphism (SNP) and gene expression microarray data. Our software implements the consensus clustering algorithm and principal component analysis to stratify the data into a given number of robust clusters. The robustness is achieved by combining clustering results from data and sample resampling as well as by averaging over various algorithms and parameter settings to achieve accurate, stable clustering results. We have implemented several different clustering algorithms in the software, including K-Means, Partition Around Medoids, Self-Organizing Map, and Hierarchical clustering methods. After clustering the data, ConsensusCluster generates a consensus matrix heatmap to give a useful visual representation of cluster membership, and automatically generates a log of selected features that distinguish each pair of clusters. ConsensusCluster gives more robust and more reliable clusters than common software packages and, therefore, is a powerful unsupervised learning tool that finds hidden patterns in data that might shed light on its biological interpretation. This software is free and available from http://code.google.com/p/consensus-cluster .

Robust gene network analysis reveals alteration of the STAT5a network as a hallmark of prostate cancer

We develop a general method to identify gene networks from pair-wise correlations between genes in a microarray data set and apply it to a public prostate cancer gene expression data from 69 primary prostate tumors. We define the degree of a node as the number of genes significantly associated with the node and identify hub genes as those with the highest degree. The correlation network was pruned using transcription factor binding information in VisANT (http://visant.bu.edu/) as a biological filter. The reliability of hub genes was determined using a strict permutation test. Separate networks for normal prostate samples, and prostate cancer samples from African Americans (AA) and European Americans (EA) were generated and compared. We found that the same hubs control disease progression in AA and EA networks. Combining AA and EA samples, we generated networks for low low (<7) and high (≥7) Gleason grade tumors. A comparison of their major hubs with those of the network for normal samples identified two types of changes associated with disease: (i) Some hub genes increased their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with gain of regulatory control in cancer (e.g. possible turning on of oncogenes). (ii) Some hubs reduced their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with loss of regulatory control in cancer (e.g. possible loss of tumor suppressor genes). A striking result was that for both AA and EA tumor samples, STAT5a, CEBPB and EGR1 are major hubs that gain neighbors compared to the normal prostate network. Conversely, HIF-lα is a major hub that loses connections in the prostate cancer network compared to the normal prostate network. We also find that the degree of these hubs changes progressively from normal to low grade to high grade disease, suggesting that these hubs are master regulators of prostate cancer and marks disease progression. STAT5a was identified as a central hub, with ~120 neighbors in the prostate cancer network and only 81 neighbors in the normal prostate network. Of the 120 neighbors of STAT5a, 57 are known cancer related genes, known to be involved in functional pathways associated with tumorigenesis. Our method is general and can easily be extended to identify and study networks associated with any two phenotypes.

The human orthologue of murine Mpzl3 with predicted adhesive and immune functions is a potential candidate gene for immune-related hereditary hair loss

We have recently reported a mutation within the conserved immunoglobulin V-type domain of the predicted adhesion protein Mpzl3 (MIM 611707) in rough coat (rc) mice with severe skin abnormalities and progressive cyclic hair loss. In this study, we tested the hypothesis that the human orthologue MPZL3 on chromosome 11q23.3 is a candidate for similar symptoms in humans. The predicted conserved MPZL3 protein has two transmembrane motifs flanking an extracellular Ig-like domain. The R100Q rc mutation is within the Ig-domain recognition loop that has roles in T-cell receptors and cell adhesion. Results of the rc mouse study, 3D structure predictions, homology with Myelin Protein Zero and EVA1, comprehensive database analyses of polymorphisms and mutations within the human MPZL3 gene and its cell, tissue expression and immunostaining pattern indicate that homozygous or compound heterozygous mutations of MPZL3 might be involved in immune-mediated human hereditary disorders with hair loss.

New features and enhancements in the X-PLOR computer program

This article describes new methods for X-ray crystallographic refinement and nuclear magnetic resonance (NMR) structure determination that are available in the recent release of the X-PLOR software, X-PLOR 98.0. The major new features of the X-PLOR 98.0 software are: (i) the introduction of maximum likelihood methods (Pannu and Read, Acta Crystallogr 1996;A52:659-668) for X-ray crystallographic refinement with structure factor amplitude, intensity and phase probability targets, (ii) the addition of the Andersen thermal coupling method for temperature control during simulated annealing refinements, (iii) a new utility function for converting reflection data in to the X-PLOR format, (iv) validated scripts and performance enhancements for structure determination from NMR distance restraints using torsion angle dynamics, (v) fast code for direct nuclear Oberhauser effect (NOE) refinement using matrix doubling and gaussian quadratures, (vi) methodologies for using ambiguous restraint information to perform automated iterative peak assignment and structure determination (Nilges et al., J Mol Biol 1997;269: 408-422). Additional developments in methodology for refining crystal structures from poor initial models include the implementation of a fast adaptive bulk solvent scattering correction and an energy minimization routine that makes use of second derivative information. Trial crystallographic refinements with an energy minimization protocol that includes these enhancements indicate significantly improved convergence. The quality of the resulting models appears comparable to models obtained from refinement protocols that incorporate torsion angle dynamics. Test applications of the new energy minimizer to NMR structure refinement with using NOE calculations also show improved convergence, leading to more optimized final models.

Determination of (3)J(H (infi) (supN) ,C (infi) (sup') ) coupling constants in proteins with the C'-FIDS method

We introduce the C'-FIDS-(1)H,(15)N-HSQC experiment, a new method for the determination of (3)J(H (infi) (supN) ,C (infi) (sup') ) coupling constants in proteins, yielding information about the torsional angle ϕ. It relies on the (1)H,(15)N-HSQC or HNCO experiment, two of the the most sensitive heteronuclear correlation experiments for isotopically labeled proteins. A set of three (1)H,(15)N-HSQC or HNCO spectra are recorded: a reference experiment in which the carbonyl spins are decoupled during t(1) and t(2), a second experiment in which they are decoupled exclusively during t(1) and a third one in which they are coupled in t(1) as well as t(2). The last experiment yields an E.COSY-type pattern from which the (2)J(H (infi) (supN) ,C (infi-1) (sup') ) and (1)J(N(i),C (infi-1) (sup') ) coupling constants can be extracted. By comparison of the coupled multiplet (obtained from the second experiment) with the decoupled multiplet (obtained from the first experiment) convoluted with the (2)J(H (infi) (supN) ,C (infi-1) (sup') ) coupling, the (3)J(H (infi) (supN) ,C (infi) (sup') ) coupling can be found in a one-parameter fitting procedure. The method is demonstrated for the protein rhodniin, containing 103 amino acids. Systematic errors due to differential relaxation are small for (n)J(H(N),C') couplings in biomacromolecules of the size currently under NMR spectroscopic investigation.