Andy Dahl

Crohn’s disease is a complex inflammatory bowel disease resulting from an interplay of genetic, microbial and environmental factors. Cell-type-specific contributions to Crohn’s disease etiology and genetic risk are incompletely understood. Here we built a comprehensive atlas of cell-type-resolved chromatin accessibility comprising 557,310 candidate cis-regulatory elements (cCREs) in terminal ileum and ascending colon from 23 patients with active and inactive Crohn’s disease and 16 healthy controls. We identified cell-type-specific, anatomical location-specific and context-specific cCREs and characterized the regulatory programs underlying inflammatory responses in the intestinal mucosa of patients with Crohn’s disease. These cell-type-resolved data confirmed that Crohn’s disease heritability is primarily enriched in adaptive immune cells, in particular T cells, and in innate immune cells, including neutrophils. Using fine-mapped, noncoding Crohn’s disease variants, we identified 30 variants located within cCREs. Our atlas provides a comprehensive resource to study gene-regulatory effects in Crohn’s disease and health and highlights the cellular complexity underlying Crohn’s disease risk.

Gene-environment interaction ( G × E ) studies hold promise for identifying genetic loci mediating the effects of environmental risk on disease. However, interpretation of G × E effects is often confounded by two fundamental issues: the dependence of interaction estimates on outcome scale and the presence of endogenous treatment effects, in which genetic liability influences environmental exposure. These factors can induce apparent G × E signals—even when genetic and environmental contributions are purely additive on an unobserved scale. In this work, we demonstrate that any monotone convex transformation of an outcome induces sign-consistent G × E effects: the sign of the interaction term aligns with the sign of the corresponding main genetic effect. Convex transformations are a broad class of functions that include many commonly used data transformations, such as exponential and logarithmic functions, the square root, and other power transformations. We further show that endogenous treatment effects, modeled as threshold-based interventions, generate G × E effects with a similar directional signature. Exploiting this property, we propose a simple diagnostic: sign consistency across G × E estimates can signal when interactions are driven by outcome scaling or exposure endogeneity. We validate our framework in the UK Biobank using transcriptome-wide interaction studies (TxEWAS) across multiple trait–environment pairs, observing widespread sign consistency in some settings—suggesting confounding by scaling or treatment bias. Our results provide both a theoretical foundation and a practical tool for interpreting G × E findings, enabling researchers to assess whether the observed G × E signal may depend substantially on outcome scaling or be influenced by exposure endogeneity.

The importance of gene-environment interactions (G×E) for complex human traits is heavily debated. Recently, biobank-based GWAS have revealed many statistically significant G×E signals, though most lack clear evidence of biological significance. Here, we partly explain this discrepancy by showing that many G×E signals simplify to additive effects on a different phenotype scale, a classical concern that is currently underappreciated. Our results clearly distinguish G×Sex effects on height, which vanish on the log scale, from G×Sex effects on testosterone, where the log scale uncovers biologically meaningful female-specific effects. Across 32 phenotypes in UK Biobank, we find that scaling by a power transformation can explain 46% of PGS×Sex interactions, and that simple log transformation can explain 23%, with similar results for other environments. We also show that phenotype scale can substantially impact GWAS discovery and the construction and evaluation of polygenic scores. Finally, we provide a set of guidelines to consider and choose phenotype scale in modern genetic studies.

Genetic studies of psychiatric disorders have typically assumed that all genetic effects contribute additively to disease liability. However, it is likely that psychiatric disorders have unrecognized subtypes, where synergistic sets of risk variants co-occur within certain cases more than expected under additivity. The existence of synergistic sets induces a structured form of statistical interactions called coordinated epistasis. We test for these interactions in five psychiatric disorders and find evidence for synergistic sets, and by extension, disorder subtypes. We further find that synergistic sets contributing to comorbidities are mostly disorder-specific, despite high genetic correlations between disorders, supporting current diagnostic distinctions between disorders. Finally, we find that genetic risk factors shared across disorders identify a cross-disorder subtype that is likely the result of heritable confounders, rather than disorder-specific etiology. Our results show that genetic risk effects for psychiatric disorders act in sets, implying the existence of subtypes, and re-interpret the importance of shared genetic effects in understanding disease biology and classification.

Gene-environment interaction ( G × E ) studies hold promise for identifying genetic loci mediating the effects of environmental risk on disease. However, interpretation of G × E effects is often confounded by two fundamental issues: the dependence of interaction estimates on outcome scale and the presence of endogenous treatment effects, in which genetic liability influences environmental exposure. These factors can induce spurious G × E signals—even when genetic and environmental contributions are purely additive on an unobserved scale. In this work, we demonstrate that any monotone convex transformation of an outcome induces sign-consistent G × E effects: the sign of the interaction term aligns with the sign of the corresponding main genetic effect. We further show that endogenous treatment effects, modeled as threshold-based interventions, generate G × E effects with a similar directional signature. Exploiting this property, we propose a simple diagnostic: sign consistency across G × E estimates can identify artifacts driven by outcome scaling or exposure endogeneity. We validate our framework in the UK Biobank using transcriptome-wide interaction studies (TxEWAS) across multiple trait–environment pairs, observing widespread sign consistency in some settings—suggesting confounding by scaling or treatment bias. Our results provide both a theoretical foundation and a practical tool for interpreting G × E findings, enabling researchers to distinguish biologically meaningful interactions from those induced by statistical artifacts.

Single-cell RNA sequencing (scRNA-seq) has been widely used to characterize cell types based on their average gene expression profiles. However, most studies do not consider cell type-specific variation across donors. Modelling this cell type-specific inter-individual variation could help elucidate cell type-specific biology and inform genes and cell types underlying complex traits. We therefore develop a new model to detect and quantify cell type-specific variation across individuals called CTMM (Cell Type-specific linear Mixed Model). We use extensive simulations to show that CTMM is powerful and unbiased in realistic settings. We also derive calibrated tests for cell type-specific interindividual variation, which is challenging given the modest sample sizes in scRNA-seq. We apply CTMM to scRNA-seq data from human induced pluripotent stem cells to characterize the transcriptomic variation across donors as cells differentiate into endoderm. We find that almost 100% of transcriptome-wide variability between donors is differentiation stage-specific. CTMM also identifies individual genes with statistically significant stage-specific variability across samples, including 85 genes that do not have significant stage-specific mean expression. Finally, we extend CTMM to partition interindividual covariance between stages, which recapitulates the overall differentiation trajectory. Overall, CTMM is a powerful tool to illuminate cell type-specific biology in scRNA-seq.

Identifying the factors responsible for variability of treatment response is a central objective of clinical research. Specially designed pharmacogenomic studies have identified a handful of individual mutations modulating the effect of specific drugs. However, the extent to which drug response variability is driven by genetics is largely unknown, partly due to the small sample sizes of prospective pharmacogenomic trials. In this work, we develop a framework to study the genetic architecture of response to commonly prescribed drugs in large biobanks coupled with electronic health records. Our framework leverages concepts from gene-environment interaction testing, including novel interaction models at the level of genes, polygenic scores (PGS), and genome-wide heritability. We quantified the heritability of response to statins, metformin, warfarin, and methotrexate in 342,257 UK Biobank participants. Our results show that genetic variation modifies the primary effect of statins on LDL cholesterol (9% heritable) as well as its side effects on hemoglobin A1c and blood glucose (10% and 11% heritable, respectively). Next, we identified dozens of specific genes that modify drug response, which we then replicated in a retrospective pharmacogenomic study. Finally, we found that PGS accuracy varies up to 2-fold depending on treatment status, showing that the current approach of building PGS using mostly healthy individuals is likely to underperform in clinical contexts. Together, our results provide a framework for characterizing the genetic architecture of drug response using cross-sectional data.

Attention deficit hyperactivity disorder (ADHD) is a complex disorder that manifests variability in long-term outcomes and clinical presentations. The genetic contributions to such heterogeneity are not well understood. Here we show several genetic links to clinical heterogeneity in ADHD in a case-only study of 14,084 diagnosed individuals. First, we identify one genome-wide significant locus by comparing cases with ADHD and autism spectrum disorder (ASD) to cases with ADHD but not ASD. Second, we show that cases with ASD and ADHD, substance use disorder and ADHD, or first diagnosed with ADHD in adulthood have unique polygenic score (PGS) profiles that distinguish them from complementary case subgroups and controls. Finally, a PGS for an ASD diagnosis in ADHD cases predicted cognitive performance in an independent developmental cohort. Our approach uncovered evidence of genetic heterogeneity in ADHD, helping us to understand its etiology and providing a model for studies of other disorders.