Biology subjects

Wallace, C.

Publications and source records attributed to Wallace, C..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Sharing GWAS summary statistics results in more citations: evidence from the GWAS catalog

Genome-wide association studies (GWAS) have been a crucial tool in genomics and an example of applied reproducible science principles for almost two decades.1 Their output, summary statistics, are especially suited for sharing, which in turn enables new hypothesis testing and scientific discovery. However, GWAS summary statistics sharing rates have been historically low due to a lack of incentives and strong data sharing mandates, privacy concerns and standard guidelines.2 Albeit imperfect, citations are a key metric to evaluate the research impact. We hypothesised that data sharing might benefit authors through increased citation rates and investigated this using GWAS catalog3 data. We found that sharers get on average ~75% more citations, independently of journal of publication and impact factor, and that this effect is sustained over time. This work provides further incentivises authors to share their GWAS summary statistics in standard repositories, such as the GWAS catalog.

genomics↗

bioRxiv2022

A semi-supervised Bayesian mixture modelling approach for joint batch correction and classification

AO_SCPLOWBSTRACTC_SCPLOWSystematic differences between batches of samples present significant challenges when analysing biological data. Such batch effects are well-studied and are liable to occur in any setting where multiple batches are assayed. Many existing methods for accounting for these have focused on high-dimensional data such as RNA-seq and have assumptions that reflect this. Here we focus on batch-correction in low-dimensional classification problems. We propose a semi-supervised Bayesian generative classifier based on mixture models that jointly predicts class labels and models batch effects. Our model allows observations to be probabilistically assigned to classes in a way that incorporates uncertainty arising from batch effects. By simultaneously inferring the classification and the batch-correction our method is more robust to dependence between batch and class than pre-processing steps such as ComBat. We explore two choices for the within-class densities: the multivariate normal and the multivariate t. A simulation study demonstrates that our method performs well compared to popular off-the-shelf machine learning methods and is also quick; performing 15,000 iterations on a dataset of 750 samples with 2 measurements each in 11.7 seconds for the MVN mixture model and 14.7 seconds for the MVT mixture model. We further validate our model on gene expression data where cell type (class) is known and simulate batch effects. We apply our model to two datasets generated using the enzyme-linked immunosorbent assay (ELISA), a spectrophotometric assay often used to screen for antibodies. The examples we consider were collected in 2020 and measure seropositivity for SARS-CoV-2. We use our model to estimate seroprevalence in the populations studied. We implement the models in C++ using a Metropolis-within-Gibbs algorithm, available in the R package batchmix. Scripts to recreate our analysis are at https://github.com/stcolema/BatchClassifierPaper.

bioinformatics↗

bioRxiv2022

Time-resolved proximity labeling of protein networks associated with ligand-activated EGFR

Ligand binding to the EGF receptor (EGFR) triggers multiple signal transduction processes and promotes endocytosis of the receptor. The mechanisms of EGFR endocytosis and its crosstalk with signaling are poorly understood. Here, we combined peroxidase-catalyzed proximity labeling, isobaric peptide tagging and quantitative mass-spectrometry to define the dynamics of the proximity proteome of ligand-activated EGFR. Using this approach, we identified a network of signaling proteins, which remain associated with the receptor during its internalization and trafficking through the endosomal system. We showed that Trk-fused gene (TFG), a protein known to function at the endoplasmic reticulum exit sites, was enriched in the proximity proteome of EGFR in early/sorting endosomes and localized in these endosomes, and demonstrated that TFG regulates endosomal sorting of EGFR. This study provides a comprehensive resource of time-dependent nanoscale environment of EGFR, thus opening avenues to discovering new regulatory mechanisms of signaling and intracellular trafficking of receptor tyrosine kinases.

systems biology↗

bioRxiv2021

fcfdr: an R package to leverage continuous and binary functional genomic data in GWAS

SummaryGWAS discovery is limited in power to detect associations that exceed the stringent genome-wide significance threshold, but this limitation can be alleviated by leveraging relevant auxiliary data. Frameworks utilising the conditional false discovery rate (cFDR) can be used to leverage continuous auxiliary data (including GWAS and functional genomic data) with GWAS test statistics and have been shown to increase power for GWAS discovery whilst controlling the FDR. Here, we describe an extension to the cFDR framework for binary auxiliary data (such as whether SNPs reside in regions of the genome with specific activity states) and introduce an all-encompassing R package to implement the cFDR approach, fcfdr, demonstrating its utility in an application to type 1 diabetes. Availability and implementationThe fcfdr R package is freely available at: https://github.com/annahutch/fcfdr. Scripts and data to reproduce the analysis in this paper are freely available at: https://annahutch.github.io/fcfdr/articles/t1d_app.html

genetics↗

bioRxiv2021

In-field genetic stock identification of overwintering coho salmon in the Gulf of Alaska: Evaluation of Nanopore sequencing for remote real-time deployment

Genetic stock identification (GSI) by single nucleotide polymorphism (SNP) sequencing has become the gold standard for stock identification in Pacific salmon, which are found in mixed-stocks during the oceanic phase of their lifecycle. Sequencing platforms currently applied require large batch sizes and multi-day processing in specialized facilities to perform genotyping by the thousands. However, recent advances in third-generation single-molecule sequencing platforms, like the Oxford Nanopore minION, provide base calling on portable, pocket-sized sequencers and hold promise for the application of real-time, in-field stock identification on variable batch sizes. Here we report and evaluate utility and comparability of at-sea stock identification of coho salmon Oncorhynchus kisutch based on targeted SNP amplicon sequencing on the minION platform during the International Year of the Salmon Signature Expedition to the Gulf of Alaska in the winter of 2019. Long read sequencers are not optimized for short amplicons, therefore we concatenate amplicons to increase coverage and throughput. Nanopore sequencing at-sea yielded stock assignment for 50 of the 80 assessed individuals. Nanopore-based SNP calls agreed with Ion Torrent based genotypes in 83.25%, but assignment of individuals to stock of origin only agreed in 61.5% of individuals highlighting inherent challenges of Nanopore sequencing, such as resolution of homopolymer tracts and indels. However, poor representation of assayed coho salmon in the queried baseline dataset contributed to poor assignment confidence on both platforms. Future improvements will focus on lowering turnaround time, accuracy, throughput, and cost, as well as augmentation of the existing baselines, specifically in stocks from coastal northern BC and Alaska. If successfully implemented, Nanopore sequencing will provide an alternative method to the large-scale laboratory approach. Genotyping by amplicon sequencing in the hands of diverse stakeholders could inform management decisions over a broad expanse of the coast by allowing the analysis of small batches in remote areas in near real-time.

genetics↗

bioRxiv2021

Flashfm: A Flexible and Shared Information Fine-mapping Approach for Multiple Quantitative Traits

Joint fine-mapping that leverages information between quantitative traits could improve accuracy and resolution over single-trait fine-mapping. Using summary statistics, flashfm (FLexible And SHared information Fine-Mapping) fine-maps signals for multiple traits, allowing for missing trait measurements and use of related individuals. In a Bayesian framework, prior model probabilities are formulated to favour model combinations that share causal variants to capitalise on information between traits. Simulation studies demonstrate that both approaches produce broadly equivalent results when traits have no shared causal variants. When traits share at least one causal variant, flashfm reduces the number of potential causal variants by 30% compared with single-trait fine-mapping. In a Ugandan cohort with 33 cardiometabolic traits, flashfm gave a 20% reduction in the total number of potential causal variants from single-trait fine-mapping. Flashfm is computationally efficient and can easily be deployed across publicly available summary statistics for signals in up to six traits.

genetics↗

bioRxiv2021

A more accurate method for colocalisation analysis allowing for multiple causal variants

In genome-wide association studies (GWAS) it is now common to search for, and find, multiple causal variants located in close proximity. It has also become standard to ask whether different traits share the same causal variants, but one of the popular methods to answer this question, coloc, makes the simplifying assumption that only a single causal variant exists for any given trait in any genomic region. Here, we examine the potential of the recently proposed Sum of Single Effects (SuSiE) regression framework, which can be used for fine-mapping genetic signals, for use with coloc. SuSiE is a novel approach that allows evidence for association at multiple causal variants to be evaluated simultaneously, whilst separating the statistical support for each variant conditional on the causal signal being considered. We show this results in more accurate coloc inference than other proposals to adapt coloc for multiple causal variants based on conditioning. We therefore recommend that coloc be used in combination with SuSiE to optimise accuracy of colocalisation analyses when multiple causal variants exist.

genetics↗

bioRxiv2021

Characterization of cognitive-motor function in women who have experienced intimate partner violence-related brain injury

Intimate partner violence (IPV) affects at least 1 in 3 women worldwide and up to 92% report symptoms consistent with brain injury (BI). Although a handful of studies have examined different aspects of brain structure and function in this population, none has characterized potential deficits in cognitive-motor function. This knowledge gap was addressed in the current study by having participants who had experienced IPV complete the bimanual Object Hit & Avoid (OHA) task on a Kinarm End-Point Lab. BI load, as assessed by the Brain Injury Severity Assessment (BISA) tool as well as measures of comorbidities (PTSD, anxiety, depression, substance use, history of abuse) were also collected. Results demonstrated BI load accounted for a significant amount of variability in the number of targets hit and average hand speed. PTSD, anxiety, and depression also contributed significantly to the variability in these measures as well as to the number and proportion of distractor hits, and the object processing rate. Taken together, these findings suggest IPV-related BI, as well as comorbid PTSD, anxiety, and depression, disrupt the processing required to quickly and accurately hit targets while avoiding distractors. This pattern of results reflects the complex interaction between the physical injuries induced by the episodes of IPV and the resulting impacts these experiences have on mental health.

neuroscience↗

bioRxiv2020

Consensus clustering for Bayesian mixture models

AO_SCPLOWBSTRACTC_SCPLOWCluster analysis is an integral part of precision medicine and systems biology, used to define groups of patients or biomolecules. Consensus clustering is an ensemble approach that is widely used in these areas, which combines the output from multiple runs of a non-deterministic clustering algorithm. Here we consider the application of consensus clustering to a broad class of heuristic clustering algorithms that can be derived from Bayesian mixture models (and extensions thereof) by adopting an early stopping criterion when performing sampling-based inference for these models. While the resulting approach is non-Bayesian, it inherits the usual benefits of consensus clustering, particularly in terms of computational scalability and providing assessments of clustering stability/robustness. In simulation studies, we show that our approach can successfully uncover the target clustering structure, while also exploring different plausible clusterings of the data. We show that, when a parallel computation environment is available, our approach offers significant reductions in runtime compared to performing sampling-based Bayesian inference for the underlying model, while retaining many of the practical benefits of the Bayesian approach, such as exploring different numbers of clusters. We propose a heuristic to decide upon ensemble size and the early stopping criterion, and then apply consensus clustering to a clustering algorithm derived from a Bayesian integrative clustering method. We use the resulting approach to perform an integrative analysis of three omics datasets for budding yeast and find clusters of co-expressed genes with shared regulatory proteins. We validate these clusters using data external to the analysis. These clusters can help assign likely function to understudied genes, for example GAS3 clusters with histones active in S-phase, suggesting a role in DNA replication. Our approach can be used as a wrapper for essentially any existing sampling-based Bayesian clustering implementation, and enables meaningful clustering analyses to be performed using such implementations, even when computational Bayesian inference is not feasible, e.g. due to poor exploration of the target density (often as a result of increasing numbers of features) or a limited computational budget that does not along sufficient samples to drawn from a single chain. This enables researchers to straightforwardly extend the applicability of existing software to much larger datasets, including implementations of sophisticated models such as those that jointly model multiple datasets.

systems biology↗

bioRxiv2020

Comparison of sparse biclustering algorithms for gene expression datasets

Gene clustering and sample clustering are commonly used to find patterns in gene expression datasets. However, in heterogeneous samples (e.g. different tissues or disease states), genes may cluster differently. Biclustering algorithms aim to solve this issue by performing sample clustering and gene clustering simultaneously. Existing reviews of biclustering algorithms have yet to include a number of more recent algorithms and have based comparisons on simplistic simulated datasets without specific evaluation of biclusters in real datasets, using less robust metrics. In this study we compared four classes of sparse biclustering algorithms on a range of simulated and real datasets. In particular we use a knockout mouse RNA-seq dataset to evaluate each algorithms ability to simultaneously cluster genes and cluster samples across multiple tissues. We found that Bayesian algorithms with strict sparsity constraints had high accuracy on the simulated datasets and didnt require any post-processing, but were considerably slower than other algorithm classes. We assessed whether non-negative matrix factorisation algorithms can be repurposed for biclustering and found that, although the raw output was poor, after using a sparsity-inducing post-processing procedure we introduce, one such algorithm was one of the most highly ranked on real datasets. We also exhibit the limitations of biclustering algorithms by varying the complexity of simulated datasets. The algorithms generally struggled on simulated datasets with a large number of implanted factors, or with a large number of genes. In real datasets, the algorithms rarely returned clusters containing samples from multiple tissues, which highlights the need for further thought in the design and analysis of multi-tissue studies to avoid differences between tissues dominating the analysis. Code to run the analysis is available at https://github.com/nichollskc/biclust_comp, including wrappers for each algorithm, implementations of evaluation metrics, and code to simulate datasets and perform pre- and post-processing. The full tables of results are available at https://doi.org/10.5281/zenodo.4317556

genomics↗

bioRxiv2020

Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR

Genome-wide association studies (GWAS) have identified thousands of genetic variants that are associated with complex traits. However, a stringent significance threshold is required to identify robust genetic associations. Leveraging relevant auxiliary covariates has the potential to boost statistical power to exceed the significance threshold. Particularly, abundant pleiotropy and the non-random distribution of SNPs across various functional categories suggests that leveraging GWAS test statistics from related traits and/or functional genomic data may boost GWAS discovery. While type 1 error rate control has become standard in GWAS, control of the false discovery rate can be a more powerful approach. The conditional false discovery rate (cFDR) extends the standard FDR framework by conditioning on auxiliary data to call significant associations, but current implementations are restricted to auxiliary data satisfying specific parametric distributions, typically GWAS p-values for related traits. We relax these distributional assumptions, enabling an extension of the cFDR framework that supports auxiliary covariates from arbitrary continuous distributions ("Flexible cFDR"). Our method can be applied iteratively, thereby supporting multi-dimensional covariate data. Through simulations we show that Flexible cFDR increases sensitivity whilst controlling FDR after one or several iterations. We further demonstrate its practical potential through application to an asthma GWAS, leveraging various functional genomic data to find additional genetic associations for asthma, which we validate in the larger, independent, UK Biobank data resource. Author summaryGenome-wide association studies (GWAS) detect regions of the human genome that are associated with various traits, including complex diseases, but the power to detect these genomic regions is currently limited by sample size. The conditional false discovery rate (cFDR) provides a tool to leverage one GWAS study to improve power in another. The motivation is that if two traits have some genetic correlation, then our interpretation of a low but not significant p-value for the trait of interest will differ depending on whether that SNP shows strong or absent evidence of association with the related trait. Here, we describe an extension to the cFDR framework, called "Flexible cFDR", that controls the FDR and supports auxiliary data from arbitrary distributions, surpassing current implementations of cFDR which are restricted to leveraging GWAS p-values from related traits. In practice, our method can be used to iteratively leverage various types of functional genomic data with GWAS data to increase power for GWAS discovery. We describe the use of Flexible cFDR to supplement data from a GWAS of asthma with auxiliary data from functional genomic experiments. We identify associations novel to the original GWAS and validate these discoveries with reference to a larger, more highly-powered GWAS of asthma.

genetics↗

bioRxiv2020

EPISPOT: an epigenome-driven approach for detecting and interpreting hotspots in molecular QTL studies

We present EPISPOT, a fully joint framework which exploits large panels of epigenetic annotations as variant-level information to enhance molecular quantitative trait locus (QTL) mapping. Thanks to a purpose-built Bayesian inferential algorithm, EPISPOT accommodates functional information for both cis and trans actions, including QTL hotspot effects. It effectively couples simultaneous QTL analysis of thousands of genetic variants and molecular traits, and hypothesis-free selection of biologically interpretable annotations which directly contribute to the QTL effects. This unified, epigenome-aided learning boosts statistical power and sheds light on the regulatory basis of the uncovered hits; EPISPOT therefore marks an essential step towards improving the challenging detection and functional interpretation of trans-acting genetic variants and hotspots. We illustrate the advantages of EPISPOT in simulations emulating real-data conditions and in a monocyte expression QTL study, which confirms known hotspots and finds other signals, as well as plausible mechanisms of action. In particular, by highlighting the role of monocyte DNase-I sensitivity sites from > 150 epigenetic annotations, we clarify the mediation effects and cell-type specificity of major hotspots close to the lysozyme gene. Our approach forgoes the daunting and underpowered task of one-annotation-at-a-time enrichment analyses for prioritising cis and trans QTL hits and is tailored to any transcriptomic, proteomic or metabolomic QTL problem. By enabling principled epigenome-driven QTL mapping transcriptome-wide, EPISPOT helps progress towards a better functional understanding of genetic regulation.

genetics↗

bioRxiv2020

RapidoPGS: A rapid polygenic score calculator for summary GWAS data without validation dataset

MotivationPolygenic scores (PGS) aim to genetically predict complex traits at an individual level. PGS are typically trained on genome-wide association summary statistics and require an independent test dataset to tune parameters. More recent methods allow parameters to be tuned on the training data, removing the need for independent test data, but approaches are computationally intensive. Based on fine-mapping principles, we present RapidoPGS, a flexible and fast method to compute PGS requiring summary-level GWAS datasets only, with little computational requirements and no test data required for parameter tuning. ResultsWe show that RapidoPGS performs slightly less well than two out of three other widely-used PGS methods (LDpred2, PRScs, and SBayesR) for case-control datasets, with median r2 difference: -0.0092, -0.0042, and 0.0064, respectively, but up to 17,000-fold faster with reduced computational requirements. RapidoPGS is implemented in R and can work with user-supplied summary statistics or download them from the GWAS catalog. Availability and implementationOur method is available with a GPL license as an R package from GitHub.

genetics↗

bioRxiv2020

BaseQTL: a Bayesian method to detect eQTLs from RNA-seq data with or without genotypes

Available methods to detect molecular quantitative trait loci (QTL) require study individuals to be genotyped. Here, we describe BaseQTL, a Bayesian method that exploits allele-specific expression to map molecular QTL from sequencing reads even when no genotypes are available. When used with genotypes, BaseQTL has lower error rates and increased power compared with existing QTL mapping methods. Running without genotypes limits how many tests can be performed, but due to the proximity of QTL variants to gene bodies, the 2.8% of variants within a 100kB-window that could be tested, contained 26% of QTL variants detectable with genotypes. eQTL effect estimates were invariably consistent between analyses performed with and without genotypes. Often, sequencing data may be generated in absence of genotypes on patients and controls in differential expression studies, and we identified an apparent psoriasis-specific effect for GSTP1 in one such dataset, providing new insights into disease-dependent gene regulation.

genomics↗

bioRxiv2020

Multi-tissue transcriptome-wide association studies

Many genetic mutations affecting phenotypes are presumed to do so via altering gene expression in particular cells or tissues, but identifying the specific genes involved has been challenging. A transcriptome-wide association study (TWAS) attempts to identify disease associated genes by first learning a predictive model on an eQTL dataset and then imputing gene expression levels into a larger genome-wide association study (GWAS). Finally, associations between predicted gene expressions and GWAS phenotype are identified. Here, we compared tree-based machine learning (ML) method of random forests (RF) with more widely used linear methods of lasso, ridge, and elastic net regression, for prediction of gene expression. We also developed a multi-task learning extension to RF which simultaneously makes use of information from multiple tissues (RF-MTL) and compared it to a multi-dataset version of lasso, the joint lasso, and to a single tissue RF. We found that for prediction of gene expression, RF, in general, outperformed linear approaches on our chosen eQTL dataset and that multi-tissue methods generally outperformed their single-tissue counterparts, with RF-MTL performing the best. Simulations showed that these benefits generally propagated to the next steps of the analysis, although highlighted that joint lasso had a tendency to erroneously identify genes in one tissue if there existed a disease signal for that gene in another. We tested all four methods on type 1 diabetes (T1D) GWAS and expression data for several immune cells and found that 46 genes were identified by at least one method, though only 7 by all methods. Joint lasso discovered the most T1D-associated genes, including 15 unique to that method, but this may reflect its higher false positive rate due to "overborrowing" information across tissues. RF-MTL found more unique associated genes than RF for 3 out 5 tissues. Compared to lasso-based analysis, the RF gene list was more likely to relate to T1D in an analysis of independent data types. We conclude that RF, both single- and multi-task version, is competitive and, for some cell types, superior to linear models conventionally used in the TWAS studies. Author summaryA transcriptome-wide association study (TWAS) is a way of integrating expression data and genome-wide association studies (GWAS), which allows for discovery of genes, rather than mutations, associated to traits of interest. In the TWAS framework, we first train predictive models on an eQTL dataset, then use these models to impute gene expression into a GWAS dataset. Finally, we look for significant associations between predicted gene expression and a GWAS trait. In this work, we compare non-linear method of random forests (RF) to linear models, customarily used in TWAS. Furthermore, we demonstrate that TWAS framework can naturally be extended to, and potentially benefit from, a multi-tissue setting, thereby taking advantage of the correlation between gene expression in different tissue types. We applied the RF, a selection of linear models, and the multi-tissue approaches to an eQTL dataset of monocytes and B cells and a large T1D GWAS. We found that RF outperform lasso in terms of predictive accuracy and the number of differentially expressed genes found, and that multi-dataset version of lasso discovered the most T1D-associated genes. Analysis of the gene lists produced for each method in independent data types (excluding genetic association data) showed all related to T1D, but that the RF methods ranked T1D higher in their lists than the linear methods. We conclude that RF is a useful addition to the TWAS tool box.

genetics↗

bioRxiv2020

Cytoplasmic CPSF6 regulates HIV-1 capsid trafficking and infection in a cyclophilin A-dependent manner

Human immunodeficiency virus type 1 (HIV-1) capsid binds host proteins during infection, including cleavage and polyadenylation specificity factor 6 (CPSF6) and cyclophilin A (CypA). We observe that HIV-1 infection induces higher-order CPSF6 formation and capsid-CPSF6 complexes co-traffic on microtubules. CPSF6-capsid complex trafficking is impacted by capsid alterations that reduce CPSF6 binding or by excess cytoplasmic CPSF6 expression, both of which are associated with decreased HIV-1 infection. Higher-order CPSF6 complexes bind and disrupt HIV-1 capsid assemblies in vitro. Disruption of HIV-1 capsid binding to CypA leads to increased CPSF6 binding and altered capsid trafficking, resulting in reduced infectivity. Our data reveal an interplay between CPSF6 and CypA that is important for cytoplasmic capsid trafficking and HIV-1 infection. We propose that CypA prevents HIV-1 capsid from prematurely engaging cytoplasmic CPSF6 and that differences in CypA cellular localization and innate immunity may explain cell-specific variations in HIV-1 capsid trafficking and uncoating. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=131 SRC="FIGDIR/small/136697v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@646829org.highwire.dtl.DTLVardef@1f25cf9org.highwire.dtl.DTLVardef@b5e696org.highwire.dtl.DTLVardef@3593a7_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2020

Resolving mechanisms of immune-mediated disease in primary CD4 T cells

Deriving mechanisms of immune-mediated disease from GWAS data remains a formidable challenge, with attempts to identify causal variants being frequently hampered by linkage disequilibrium. To determine whether causal variants could be identified via their functional effects, we adapted a massively-parallel reporter assay for use in primary CD4 T-cells, key effectors of many immune-mediated diseases. Using the results to guide further study, we provide a generalisable framework for resolving disease mechanisms from non-coding associations - illustrated by a locus linked to 6 immune-mediated diseases, where the lead functional variant causally disrupts a super-enhancer within an NF-{kappa}B-driven regulatory circuit, triggering unrestrained T-cell activation.

genetics↗

bioRxiv2020

Characterisation of the genetic architecture of immune-mediated disease through informed dimension reduction

Integration of genome-wide association study (GWAS) data has been used to generate new hypotheses of biological mechanism, aetiological relationships between traits, or test causality of one factor for another. However, such approaches have typically been limited to pairwise comparisons of traits. We propose a generally applicable method, that exploits ideas from Bayesian genetic fine mapping to define a "lens" that focuses relevant variants before dimension reduction of a set of related GWAS summary statistics. We applied this technique to immune-mediated diseases, deriving 13 components which summarise the multidimensional patterns of genetic risk. Projection of independent datasets demonstrated the specificity and accuracy of our reduced dimension basis, enabled us to functionally characterise individual components, identify disease-discriminating components and suggest novel associations in rare diseases where classical GWAS approaches are challenging. Our approach summarises the genetic architectures underlying any range of aetiologically-related traits in fewer dimensions, facilitating more nuanced multidimensional comparative analyses.

genetics↗