Biology subjects

Meyer, P.

Publications and source records attributed to Meyer, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Engineering growth-coupled metabolic biosensors for disease prognosis and diagnosis using full growth trajectories

Although metabolomics has shown considerable promise for biomarker discovery, and the development of diagnostic and prognostic applications, its translation into routine clinical practice remains limited by analytical complexity, cost, throughput, and standardization challenges. These limitations underscore the need for complementary tools, particularly in resource-limited settings. In this study, we developed a workflow for the engineering and characterization of growth-coupled metabolic sensors capable of disease detection (healthy vs. infected) and outcome prediction (mild vs. severe), which we illustrated using COVID-19 as a proof-of-concept application. We first generated a biomarker-guided library of 34 candidate sensors leveraging both auxotrophic phenotypes and less stringent metabolic dependencies. We then screened the library against patient plasma pools, identifying 19 sensor candidates with diagnostic and/or prognostic potential, including 14 with prognostic potential. Lastly, a selected subset of candidates was further evaluated on a patient cohort using two newly developed analytical frameworks designed to extract additional information from bacterial growth curves. The best-performing sensors achieved a balanced accuracy of 0.88{+/-} 0.06 for prognostic prediction (outer-test AUC = 0.89, 5-fold cross-validation, n = 37) and 1.00 for diagnostic classification (outer-test AUC = 1.00, 5-fold cross-validation, n = 56). Collectively, these findings establish a proof of concept for translating disease-associated plasmatic metabolic signatures into low-cost, growth-coupled biosensors with diagnostic and prognostic capabilities.

synthetic biology↗

bioRxiv2026

Systematic De-Risking of TCR-Mimic Therapeutics Through Proteome-Wide Off-Target Landscaping and a Generalizable Design Rule Framework

TCR-mimic (TCRm) antibodies targeting peptide-human leukocyte antigen (pHLA) complexes enable precision immunotherapy against intracellular antigens, including cancer-testis antigens (CTAs). Achieving high specificity, however, remains challenging because of the vast diversity of the human immunopeptidome and the associated risk of off-target recognition. Here, we introduce ValidaTe, a unified framework for the proteome-scale prediction, validation, and mitigation of off-target liabilities in pHLA-directed therapeutics. ValidaTe integrates rational target prioritization, peptide-centric binder selection, proteome-wide off-target prediction, and therapeutic engineering into a hierarchical de-risking workflow. Using the CTA MAGE-A4 as a proof-of-concept, we identify the TCRm antibodies VR-4 and VR-6 with superior specificity and demonstrate how this workflow enables the discovery of safer pHLA-targeted binders. Furthermore, ValidaTe establishes the basis for the WiFi (Widened Fingerprint) engineering principle, which rationally combines TCRms with complementary off-target fingerprints in trivalent T-cell engagers to minimize unintended interactions while preserving potent target-specific activity. Together, these findings establish a generalizable framework for the rational development of safer and more selective pHLA-targeted therapeutics. We further discuss how orthogonal proteomic characterization may complement this workflow as a final layer of translational safety assessment prior to clinical development. TeaserValidaTe accelerates safe pHLA-targeted immunotherapy through proteome-wide off-target mapping and WiFi design

immunology↗

bioRxiv2026

End-to-end mapping of membrane transport from chemical structure to microorganisms

Membrane transport is a fundamental biological process with profound implications for pharmacology, biotechnology, and microbiology. While computational approaches have largely adopted a protein-centric perspective to annotate transportomes, inferring transport function directly from the intrinsic properties of substrates remains a major challenge. Addressing transport at the compound level enables the systematic evaluation of whether molecules undergo active transport and by which mechanisms, independent of prior transporter annotation. Here, we introduce ChemProFlow, a comprehensive computational framework that redefines transport analysis from a substrate-centric perspective. By integrating geometric deep learning with orthology-based genomic mapping, ChemProFlow predicts molecular transportability, assigns transport mechanisms according to the Transporter Classification Database, and identifies the microorganisms encoding the corresponding transport systems. We show that this integrated pipeline enables scalable, end-to-end mapping of substrate-transporter-organism relationships, with broad applications in pharmacology for anticipating drug transport, in biotechnology for guiding strain engineering, and in microbiology for dissecting substrate utilization across diverse taxa. By capturing the chemical derminants of transportabiliy, ChemProFlow generalizes to previously unseen substrates and provides a high-throughput framework for systematic exploration of molecular transport across diverse biological contexts.

bioinformatics↗

bioRxiv2026

Alzheimers Disease Brain Phenotypes are Age-dependent

A widespread view of neurodegenerative disorders, including Alzheimers Disease (AD), frames their effects as accelerated aging, with the brain-age gap (BAG, the deviation of predicted brain age from chronological age) as a staple biomarker. However, BAG relies on a fundamental, untested assumption: that AD can be identified via age-invariant brain phenotypes. Using invariant representation learning on brain MRI from 44,178 individuals, we created neural representations that optimally convey age information (age-aware) or conversely remove it (age-invariant) while minimizing reconstruction distortion. We provide the first causal evidence that age information is necessary in brain biomarkers for AD detection: age-aware representations achieve competitive state-of-the-art performance and significantly outperform age-invariant ones (0.84 vs. 0.77 AUC, p < 0.001, with external validation). This necessity reveals a conceptual flaw in BAG: by subtracting chronological age, it discards the very information essential for accurate detection. Using conditional decoders to simulate aging trajectories, we found that healthy aging and AD operate along multiple independent anatomical dimensions (deep gray matter, frontoparietal, temporal). AD patients diverge from rather than accelerate healthy aging, showing pathological temporal shifts alongside, remarkably, relative frontoparietal preservation. Furthermore, representational similarity analysis suggests that even models pretrained on non-age tasks (e.g., sex or BMI) implicitly converge toward age-related features when optimized for AD. Given that the AD phenotype cannot be decoupled from age, our results establish a hard limit for age-independent biomarkers and favor multidimensional models that preserve aging structure over unidimensional summaries like BAG.

neuroscience↗

bioRxiv2026

An integrated synthetic biology and robotics approach for neutralising landmines in post-war communities

Unexploded ordnances (UX.Os) and landmines endanger lives and hinder the economic progress of communities living in post-conflict zones. Currently, the primary method for clearing UX.Os relies on metal detection and manual removal of UX.Os - an expensive, time-consuming, and hazardous process. This study, derived from the 2024 EPFL iGEM project SYNPLODE, presents a new approach that integrates synthetic biology and aerial drone robotics, proposing a novel, end-to-end, safe, and efficient solution to address UX.Os. Starting from bacteria engineered to detect and degrade 2,4,6-trinitrotoluene (TNT), a common explosive in landmines, our solution is designed for three main tasks: detecting TNT and RDX, breaking these compounds down into non-explosive byproducts, and confirming explosive neutralisation. To deploy this solution safely in UXO-contaminated areas, we designed, built, and tested an aerial drone capable of spraying explosive-degrading bacteria. Combining synthetic biology, robotics, mathematical modelling, and affected community engagement, our solution aims to improve UXO and landmine clearance by offering a scalable and cost-effective approach for deactivating UX.Os without risking human lives.

synthetic biology↗

bioRxiv2026

Structural basis of HSP90C, a highly active chloroplastic HSP90 chaperone from A. thaliana

Chloroplasts are the main energy organelles in plants, primary through photosynthesis. Thereby, they are responsible for CO2 fixation and dioxygen production, which are essential for living species on Earth. To ensure these processes, numerous proteins encoded from the nuclear DNA need to be imported inside the chloroplast, and eventually to the thylakoids. Whereas the translocation systems from both chloroplastic and thylakoids membranes have been studied in recent years, the stromal route between these two membranes is largely unknown. Notably, the chloroplastic HSP90 (HSP90C) is likely to play an important role in this process, but its structure and molecular mechanisms remain to be unveiled. In this study, we used a combination of structural and biophysical approaches to elucidate the features of Arabidopsis thalianas HSP90C. Principally, we found that HSP90C has a remarkably high ATPase activity among the HSP90 family proteins. Further investigation allowed us to pinpoint atypical mechanisms responsible for this high activity. First, the N-terminal cap is involved in a disulfide bond that accelerates the ATPase activity of HSP90C. Second, its C-terminal domain features an extension that is mandatory for its dimerization. Third, our crystal structures reveal a wide opening of the HSP90Cs dimer with reduced intermonomeric interfaces. Lastly, we identified a helical switch which is required for HSP90Cs high activity. Three of these four features are due to sequence signatures of HSP90C, which we found to be shared by most of green plants representatives. Our study provides first insights of HSP90Cs non-canonical mechanisms, which will help in the understanding of processes related to protein import in the chloroplast.

biochemistry↗

bioRxiv2025

Prediction of transformative breakthroughs in biomedical research

The ability to predict scientific breakthroughs at scale would accelerate the pace of discovery and improve the efficiency of research investments. Recent advances in artificial intelligence, graph theory, and computing power have provided new ways to pursue this elusive goal. We have identified a common signature within co-citation networks that accurately predicts the occurrence of breakthroughs in medical research, on average more than 5 years in advance of the subsequent publication(s) that announced the discovery. A combination of features produces these diagnostic signals: a burst of papers exploring a novel scientific concept, an unusually high number of very influential papers in specialty journals, and low topical cohesion of the associated content. We analyzed two different periods separated by 20 years to show that the kinetics of breakthrough formation are conserved, suggesting that our approach can be used to predict which topics will produce future transformative discoveries. Significance statementScientific breakthroughs are rare, as is contemporaneous recognition of their initial expression. Faster, more efficient identification of topics likely to produce future breakthroughs would speed scientific and technological progress. We introduce an AI/ML-detected signature in co-citation networks that recognizes such topics up to twelve years before the breakthrough itself occurs. Our findings illustrate how a better understanding of the scientific process may lead to greater scientific returns.

scientific communication and education↗

bioRxiv2025

GENET: AI-Powered Interactive Visualization Workflows to Explore Biomedical Entity Networks

Formulating hypotheses about gene-disease associations requires logical inference from prior data, followed by a laborious literature review. AI models trained on curated datasets (e.g., GWAS Catalog) can suggest SNP-disease links, but validating these predictions still demands manual evidence extraction. To streamline this process, we present GENET (Genomic Evidence Network Exploration Tool), an AI-enhanced, end-to-end visual analytics workflow applied to Age-Related Macular Degeneration (AMD). GENET comprises four sequential steps: (1) biomedical network analysis: a dual-encoder neural model identifies genes or SNPs associated with a target disease and vice versa; (2) literature evidence mining pipeline: a pipeline retrieves relevant papers and, using large language models, extracts biomedical entities and relations; (3) clustering: embeddings from pre-trained biomedical language models (BioBERT, BioLinkBERT) are clustered to group related concepts; (4) interactive visualizations: clusters and their networks are visualized with interactive features for hypothesis testing and insight generation. The workflow enables iterative hypothesis formulation and evidence validation, uncovering novel associations. GENET is open-sourced (https://github.com/BiomedSciAI/genet) and available for demonstration at https://genet.pythonanywhere.com.

bioinformatics↗

bioRxiv2025

High-Fidelity Tuning of Olfactory Mixture Distances in the Perceptual Space of Smell Through a Community Effort

A central goal in sensory science is to establish quantitative mappings between physical stimuli and perceptual responses. While such mappings are well characterized in vision and audition, they remain poorly defined in olfaction, limiting progress toward understanding the representations of smell. Predicting perceptual similarity between odor mixtures offers a promising route to formalize these relationships. To advance this effort, the DREAM (Dialogue for Reverse Engineering Assessment and Methods) Olfactory Mixtures Prediction Challenge assembled a curated, cross-study dataset describing the similarity of 507 mixture pairs and an unpublished test set of 46 mixture pairs. Teams competed to predict the perceptual similarity of mixture pairs, and then collaborated post-challenge to create an ensemble combining top-performing models that notably improves predictions over the existing state-of-the-art models. Moreover, ensemble model maintains high predictive accuracy in novel validation set. Our model provides a reproducible framework for neuroscientists, chemists, and engineers to compare odor mixtures and provides a foundation for future efforts towards better understanding the olfactory properties of mixtures.

neuroscience↗

bioRxiv2025

EpiTox: A Multi-Modular Framework for Population-Aware Off-Target Prediction Highlighting MAGE-A3 Cross-Reactivity

Targeting intracellular tumor antigens presented by peptide human leukocyte antigen (pHLA) complexes offers a promising immunotherapeutic strategy for patients with limited treatment options. However, development of pHLA-targeted drugs such as T-cell receptor (TCR) mimic antibodies (TCRm) and TCR-based therapeutics remains challenging due to severe offtumor and off-target toxicities arising from incomplete pHLA off-target profiling. The fatal neurological toxicities observed in the MAGEA3 TCR-T cell trial, potentially linked to unanticipated cross-reactivity with EPS8L2- and MAGEA12-derived peptides, underscore the urgent need for more comprehensive safety assessment. While current preclinical de-risking methods utilize sequence similarity searches, they lack the layered integration of genetic context, HLA binding profiles, and structured risk assessment needed to comprehensively evaluate peptide cross-reactivity. To address these limitations, we developed EpiTox, a computational multi-modular platform that systematically identifies and evaluates pHLA off-targets. EpiTox integrates proteome-wide sequence similarity, genetic context, HLA binding profiles, and layered risk assessment to provide a holistic evaluation of potential cross-reactive peptides. Using MAGEA3 as a model, EpiTox identified known toxic off-targets and predicted novel epitopes. Notably, an anti-MAGEA3 therapeutic TCRm antibody bound several newly predicted off-targets, including three SNP-derived peptides. By enabling comprehensive and predictive preclinical safety screening, Epi-Tox supports safer TCRm drug development and may help prevent future clinical failures.

bioinformatics↗

bioRxiv2025

R2TP-like Quaternary Chaperones: a comprehensive overview to understand the dynamic R2SP complex

The human R2SP complex belongs to the R2TP-like quaternary chaperone family and consists of RUVBL1, RUVBL2, SPAG1 and PIH1D2. R2SP is crucial for the correct assembly of motile cilia (SPAG1 null mutations cause Primary Ciliary Dyskinesia) and the organization of the synaptic zone. RUVBL1/2 ATPases are the powerhouse of this molecular machinery, while SPAG1 and PIH1D2 would be adaptors that interact with specific clients to promote their quaternary assembly. Despite these functional data, little is known about the structure of R2SP and the precise mode of action of these R2TP-like complexes. We have combined biochemical and structural approaches (NMR, structural mass spectrometry and cryo-EM) to investigate the 3D organization of the human R2SP complex, its mode of assembly and ATPase activity. Our study reveals a three-dimensional structure similar to that of the canonical R2TP complex, but also highlights differences in the mode of action of its RUVBL1/2 ATPase core as well as the binding of its adaptors SPAG1 and PIH1D2.

biophysics↗

bioRxiv2024

DNA metabarcoding diet analysis in ruminants is quantitative and integrates feeding over several weeks

Dietary DNA metabarcoding is an established method, especially useful for resolving the diverse diets of large mammalian herbivores (LMH). However, despite longstanding research interest on the topic, we still lack unequivocal evidence on the potential of DNA metabarcoding to reflect proportions of ingested dietary plants in LMH. One major aspect to consider is the time window during which ingested diet remains detectable in faecal samples. This parameter is currently unknown for LMH, thus potentially hindering the scope of ecological conclusions. Another unknown factor is quantitative performance, i.e. the extent to which the amount of ingested biomass can be assessed based on sequence read abundances. We assessed DNA metabarcoding quantitative performance and DNA half-life detectability for plants with different digestibilities in a controlled feeding experiment with three female Eurasian tundra reindeer (Rangifer tarandus tarandus). Reindeer were fed birch twigs (Betula pubescens) and increasing biomass of lichen (mainly Cladonia stellaris). Relative read abundance positively correlated with ingested lichen biomass, suggesting potential for deriving dietary proportions in free-ranging reindeer on natural pastures. Dietary DNA was consistently detected within a few hours upon ingestion, with a mean half-life detectability of 30 and 14 hours for birch and lichen, respectively. However, dietary DNA remained detectable in faeces for at least 26 days post-feeding, indicating that a single faecal sample can provide an unsuspectedly integrative estimate of diet in ruminants. Together, our findings provide novel empirical validation of DNA metabarcoding as a tool for diet analysis in LMH.

ecology↗

bioRxiv2023

Genome-wide association study and sequence similarity analysis for unilateral renal agenesis using heterogeneous stock rats undercovers the KIT gene and AHR, ATF3, GATA3, HNF1B, POU2F2, and TFCP2 transcription factors as potential candidates to explain incomplete penetrance

1Human unilateral renal agenesis is a congenital urinary tract malformation. Affected individuals have only one kidney, which is often an asymptomatic developmental defect. A total of 5,585 male and female HS rats were assessed for unilateral renal agenesis and genotyped for 3513,321 markers. The R package SAIGEgds was used for the association analysis. The adjusted p-value threshold for the association analysis determined by permutation was equal to 5.6 (-log10). Two additional datasets were used as validation tests. Population two included 1,577 rats genotyped for 7,425,889 markers and a case-control imbalance equal to 1:174; population three included 1,407 rats, genotyped for 254,932 markers and case-control ratio equal to 1:38. The python package GxTheta was used to perform a polygenic epistasis analysis for the analyzed HS rat population. A founder haplotype mosaic determination was performed using the R package QTL2. Associated regions were selected for further analysis, including long-read PacBio sequencing for founder individuals and a founder haplotype prediction test. A similarity analysis at a genomic level and for loci encoding transcription factors predicted to interact with selected sequences inside the associated loci were accomplished. A total of 1,181 polymorphisms were associated with URA. All associated polymorphisms were located on chromosome 14 between 32.9 and 36.6 Mb. The most significant polymorphism was chr14:36,411,266, a G/T transversion. The same associated region was identified in population three. Polygenic epistasis was determined as not predominant for the presentation of URA. Based on the haplotype mosaic probability estimation, cases display a higher probability of inheriting the ACI allele. The long-read sequencing analysis showed the presence of an Erv insertion inside the intron one of the KIT gene located inside the associated region. The Erv insertion comprises one Erv sequence and two Ltr sequences located downstream and upstream of the former. No Erv insertion was identified for the founder strain BN. For ACI and HSRA, only one Ltr sequence was identified. One hundred and seven genes encoding TFs that recognize binding sites on the Erv insertion were analyzed for sequence similarity against the reference HSRA. The TF similarity score analysis for the interaction genotype and phenotype showed significance after FDR correction for 20 TFs, including AHR, HNF1B, JUNB, RARG, and RXRA. A mechanism identifying URA as a threshold phenotype is suggested in HS rats. It implies the existence of a minimum threshold for the final number of nephrons and kidney associated structures required for stalling the apoptotic process of the metanephric rudiments. Animals exhibiting a quantitative cumulative defect would express URA, being this malformation identified as a phenotype with decreased penetrance in the assessed population of HS rats. All these processes are described as mediated by KIT and TFs able to interact with sequences of the Erv insertion.

genetics↗

bioRxiv2023

Identification of the Intracellular Protein Targets of a Bio-active Clickable Half-Sandwich Iridium Complex by Chemical Proteomics

Identification of intracellular targets of anticancer drug candidates provides key information on their mechanism of action. Exploiting the ability of the anticancer (C^N)-chelated half-sandwich iridium(III) complexes to covalently bind proteins, click chemistry with a bioorthogonal azido probe was used to localize a phenyloxazoline-chelated iridium complex within cells and profile its interactome at the proteome-wide scale. Proteins involved in protein folding and actin cytoskeleton regulation were identified as high affinity targets. Upon iridium complex treatment, HSP90 folding activity was inhibited in vitro and major cytoskeleton disorganization was observed. We used a wide array of imaging and biochemical methods to validate selected targets and obtain a multiscale overview of the effects of this complex on live human cells. We demonstrate that it behaves as a dual agent, inducing both electrophilic and oxidative stresses in cells that account for its cytotoxicity.

biochemistry↗

bioRxiv2023

Evaluation and optimization of sequence-based gene regulatory deep learning models

Neural networks have emerged as immensely powerful tools in predicting functional genomic regions, notably evidenced by recent successes in deciphering gene regulatory logic. However, a systematic evaluation of how model architectures and training strategies impact genomics model performance is lacking. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast, to best capture the relationship between regulatory DNA and gene expression. For a robust evaluation of the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. While some benchmarks produced similar results across the top-performing models, others differed substantially. All top-performing models used neural networks, but diverged in architectures and novel training strategies, tailored to genomics sequence data. To dissect how architectural and training choices impact performance, we developed the Prix Fixe framework to divide any given model into logically equivalent building blocks. We tested all possible combinations for the top three models and observed performance improvements for each. The DREAM Challenge models not only achieved state-of-the-art results on our comprehensive yeast dataset but also consistently surpassed existing benchmarks on Drosophila and human genomic datasets. Overall, we demonstrate that high-quality gold-standard genomics datasets can drive significant progress in model development.

genomics↗

bioRxiv2023

Exponential increase in QTL detection with increased sample size.

Power analyses are often used to determine the number of animals required for a genome wide association analysis (GWAS). These analyses are typically intended to estimate the sample size needed for at least one locus to exceed a genome-wide significance threshold. A related question that is less commonly considered is the number of significant loci that will be discovered with a given sample size. We used simulations based on a real dataset that consisted of 3,173 male and female adult N/NIH heterogeneous stock (HS) rats to explore the relationship between sample size and the number of significant loci discovered. Our simulations examined the number of loci identified in sub-samples of the full dataset. The sub-sampling analysis was conducted for four traits with low (0.15 {+/-} 0.03), medium (0.31 {+/-} 0.03 and 0.36 {+/-} 0.03) and high (0.46 {+/-} 0.03) SNP-based heritabilities. For each trait, we sub-sampled the data 100 times at different sample sizes (500, 1,000, 1,500, 2,000, and 2,500). We observed an exponential increase in the number of significant loci with larger sample sizes. Our results are consistent with similar observations in human GWAS and imply that future rodent GWAS should use sample sizes that are significantly larger than those needed to obtain a single significant result.

genetics↗

bioRxiv2023

Automated quantitative trait locus analysis (AutoQTL)

BackgroundQuantitative Trait Locus (QTL) analysis and Genome-Wide Association Studies (GWAS) have the power to identify variants that capture significant levels of phenotypic variance in complex traits. However, effort and time are required to select the best methods and optimize parameters and pre-processing steps. Although machine learning approaches have been shown to greatly assist in optimization and data processing, applying them to QTL analysis and GWAS is challenging due to the complexity of large, heterogenous datasets. Here, we describe proof-of-concept for an automated machine learning approach, AutoQTL, with the ability to automate many complex decisions related to analysis of complex traits and generate diverse solutions to describe relationships that exist in genetic data. ResultsUsing a dataset of 18 putative QTL from a large-scale GWAS of body mass index in the laboratory rat, Rattus norvegicus, AutoQTL captures the phenotypic variance explained under a standard additive model while also providing evidence of non-additive effects including deviations from additivity and 2-way epistatic interactions from simulated data via multiple optimal solutions. Additionally, feature importance metrics provide different insights into the inheritance models and predictive power of multiple GWAS-derived putative QTL. ConclusionsThis proof-of-concept illustrates that automated machine learning techniques can be applied to genetic data and has the potential to detect both additive and non-additive effects via various optimal solutions and feature importance metrics. In the future, we aim to expand AutoQTL to accommodate omics-level datasets with intelligent feature selection strategies.

genetics↗

bioRxiv2022

Polygenic Transcriptome Risk Scores Can Translate Genetic Results Between Species

Genome-wide association studies (GWAS) have implicated specific alleles and genes as risk factors for numerous complex traits. However, translating GWAS results into biologically and therapeutically meaningful discoveries remains extremely challenging. Most GWAS results identify noncoding regions of the genome, suggesting that differences in gene regulation are the major driver of trait variability. To better integrate GWAS results with gene regulatory polymorphisms, we previously developed PrediXcan (also known as "transcriptome-wide association studies" or TWAS), which maps SNPs to predicted gene expression using GWAS data. In this study, we developed RatXcan, a framework that extends this methodology to outbred heterogeneous stock (HS) rats. RatXcan accounts for the close familial relationships among HS rats by modeling the relatedness with a random effect that encodes the genetic relatedness. RatXcan also corrects for polygenic-driven inflation because of the equivalence between a relatedness random effect and the infinitesimal polygenic model. To develop RatXcan, we trained transcript predictors for 8,934 genes using reference genotype and expression data from five rat brain regions. We found that the cis genetic architecture of gene expression in both rats and humans was sparse and similar across brain tissues. We tested the association between predicted expression in rats and two example traits (body length and BMI) using phenotype and genotype data from 5,401 densely genotyped HS rats and identified a significant enrichment between the genes associated with rat and human body length and BMI. Thus, RatXcan represents a valuable tool for identifying the relationship between gene expression and phenotypes across species and paves the way to explore shared biological mechanisms of complex traits. Author SummaryUnderstanding how genetic variation affects phenotypic variation is critical to leveraging the wealth of genetic studies to make biologically and therapeutically useful discoveries. Since most of the genetic loci associated with complex diseases are regulatory in nature--meaning that they do not alter protein coding but rather subtly affect gene expression--transcriptome-wide association studies have been developed. However, these apply only to human data where large samples of unrelated individuals are available. For animal models, relatedness is much higher, causing higher false-positive rates. We propose a computationally efficient method to address this problem and find shared biology between humans and rats. Taken together, our development paves the way to further explore shared biological mechanisms of complex traits across species.

genomics↗