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The relationship between genotype and phenotype remains an outstanding question for organism-level traits because these traits are generallycomplex. The challenge arises from complex traits being determined by a combination of multiple genes (or loci), which leads to an explosion of possible genotype–phenotype mappings. The primary techniques to resolve these mappings are genome/transcriptome-wide association studies, which are limited by their lack of causal inference and statistical power. Here, we develop an approach that combines transcriptional data endowed with causal information and a generative machine learning model designed to strengthen statistical power. Our implementation of the approach—dubbed transcriptome-wide conditional variational autoencoder (TWAVE)—includes a variational autoencoder trained on human transcriptional data, which is incorporated into an optimization framework. Given a trait phenotype, TWAVE generates expression profiles, which we dimensionally reduce by identifying independently varying generalized pathways (eigengenes). We then conduct constrained optimization to find causal gene sets that are the gene perturbations whose measured transcriptomic responses best explain trait phenotype differences. By considering several complex traits, we show that the approach identifies causal genes that cannot be detected by the primary existing techniques. Moreover, the approach identifies complex diseases caused by distinct sets of genes, meaning that the disease is polygenicandexhibits distinct subtypes driven by different genotype–phenotype mappings. We suggest that the approach will enable the design of tailored experiments to identify multigenic targets to address complex diseases. 

Pooled single-cell perturbation screens represent powerful experimental platforms for functional genomics, yet interpreting these rich datasets for meaningful biological conclusions remains challenging. Most current methods fall at one of two extremes: either opaque deep learning models that obscure biological meaning, or simplified frameworks that treat genes as isolated units. As such, these approaches overlook a crucial insight: gene co-fluctuations in unperturbed cellular states can be harnessed to model perturbation responses. Here we present CIPHER (Covariance Inference for Perturbation and High-dimensional Expression Response), a framework leveraging linear response theory from statistical physics to predict transcriptome-wide perturbation outcomes using gene co-fluctuations in unperturbed cells. We validated CIPHER on synthetic regulatory networks before applying it to 11 large-scale single-cell perturbation datasets covering 4,234 perturbations and over 1.36M cells. CIPHER robustly recapitulated genome-wide responses to single and double perturbations by exploiting baseline gene covariance structure. Importantly, eliminating gene-gene covariances, while retaining gene-intrinsic variances, reduced model performance by 11-fold, demonstrating the rich information stored within baseline fluctuation structures. Moreover, gene-gene correlations transferred successfully across independent experiments of the same cell type, revealing stereotypic fluctuation structures. Furthermore, CIPHER outperformed conventional differential expression metrics in identifying true perturbations while providing uncertainty-aware effect size estimates through Bayesian inference. Finally, most genome-wide responses propagated through the covariance matrix along approximately three independent and global gene modules. CIPHER underscores the importance of theoretically-grounded models in capturing complex biological responses, highlighting fundamental design principles encoded in cellular fluctuation patterns. 

Complex systems can convert energy imparted by nonequilibrium forces to regulate how quickly they transition between long-lived states. While such behavior is ubiquitous in natural and synthetic systems, currently there is no general framework to relate the enhancement of a transition rate to the energy dissipated or to bound the enhancement achievable for a given energy expenditure. We employ recent advances in stochastic thermodynamics to build such a framework, which can be used to gain mechanistic insight into transitions far from equilibrium. We show that under general conditions, there is a basic speed limit relating the typical excess heat dissipated throughout a transition and the rate amplification achievable. We illustrate this tradeoff in canonical examples of diffusive barrier crossings in systems driven with autonomous and deterministic external forcing protocols. In both cases, we find that our speed limit tightly constrains the rate enhancement.