Masahiro Kanai

Genetic effects on gene expression often vary across cell types, motivating expression quantitative trait loci (eQTL) mapping in single-cell RNA sequencing data. Existing approaches generally aggregate expression across cells, use Gaussian models poorly suited to read counts or do not scale. Here we show that SAIGE-QTL, a Poisson mixed model that directly models single-cell read counts, enables calibrated and computationally scalable eQTL mapping while accounting for donor-level and cell-level covariates and unequal numbers of cells per donor. In simulations and three single-cell datasets, SAIGE-QTL improved statistical efficiency and discovery of genes with significant eQTLs (eGenes) relative to pseudobulk methods and produced results consistent with existing single-cell models at substantially lower computational cost. In 1.2 million immune cells, SAIGE-QTL identified 48.8% more eGenes than pseudobulk analysis. SAIGE-QTL further enabled genome-wide trans -eQTL mapping and rare variant set-based testing, identifying rare variant associations independent of common eQTL signals. SAIGE-QTL therefore expands single-cell eQTL discovery across common, rare and trans -acting genetic variation.

Sex differences in human health are typically modeled as binary comparisons between males (XY) and females (XX). However, sex-linked molecular traits vary continuously and are shaped by environmental factors. Here, we introduce the Proteomic Sex Index (PSI), a continuous, plasma proteomics–based measure that quantifies the degree to which an individual’s proteome aligns with their chromosomal sex, with higher PSI values indicating profiles less typical for their sex. Leveraging data from 51,310 individuals from six demographically heterogeneous cohorts, we show that PSI captures broad, biologically meaningful variation in sex-enriched traits and diseases. Individuals with proteomic profiles shifted away from their chromosomal sex exhibited trait values less characteristic of that sex. For example, males with higher PSI were shorter, had lower grip strength, and showed reduced severity of COVID-19 (OR=0.59, p =3.0×10 ⁻⁷ ). Analyses of individuals undergoing gender-affirming hormone therapy showed that, although PSI correlated with circulating sex hormones, it also captured additional variation independent of hormone levels. PSI is partially heritable and genetically influenced. Individuals with Klinefelter syndrome (XXY) had markedly elevated PSI (OR=13.7, p =1.1×10 ⁻¹³ ), and genome-wide association studies of PSI identified sex-biased cis-pQTLs with evidence of causal links to sex-specific disease risk. PSI provides a nuanced, individualized measure of sex-related biology that enables the study of within-sex molecular variation and its links to health and disease.

Most disease-associated genetic variants lie in non-coding regions^1,2, yet mechanistic insights are limited by the lack of an empirical framework for characterizing the molecular consequences of regulatory variation. Single-cell molecular quantitative trait locus (QTL) mapping^3,4 connects variants to gene regulation but lacks the power and simultaneous measurements to trace mechanisms from chromatin to expression⁵. Here we show that population-scale simultaneous profiling of chromatin accessibility and gene expression across immune cells reveals regulatory architectures connecting variants to disease. From paired single-nucleus assay for transposase-accessible chromatin-sequencing (snATAC–seq) and single-nucleus RNA-sequencing (snRNA-seq) analysis of 10 million peripheral blood mononuclear cells in 1,108 Finnish individuals⁶, we identify 51,083 cis-expression QTLs for 20,829 genes, 338,100 cis-chromatin accessibility QTLs for 210,584 peaks, 119,094 putative causal variants and 593,765 peak–gene links. Variants completing chromatin-to-expression cascades show twice the disease colocalization of chromatin-only effects, with massively parallel reporter assays⁷ validating 10,428 fine-mapped molecular QTLs. At evolutionarily constrained genes, we identify multilayered regulatory buffering, in which chromatin accessibility changes occur with normal effect sizes, but transmission to expression is attenuated through weaker, more numerous enhancer–gene links. This reconciles why disease variants preferentially target constrained genes despite apparent expression QTL depletion^8,9,10,11. Analysis using a massively parallel reporter assay⁷ confirms that this buffering acts downstream of the regulatory element, with constraint operating at the chromatin-to-expression interface rather than on intrinsic cis-regulatory activity. Our atlas provides testable hypotheses for over half of immune disease associations, illustrated by cascades at autoimmune loci (TICAM1 and RHOH) and Finnish-enriched variants (TNRC18 and IL21R).

Motivation: Single-cell expression quantitative trait locus (eQTL) studies can resolve cell-type-specific genetic effects, but conventional gene-by-gene analyses do not directly capture coordinated genetic regulation of neighboring genes. Principal-component QTL (pcQTL) mapping can summarize such multi-gene effects, but existing approaches were developed for bulk expression and are not designed for sparse single-cell counts. Results: We developed sc-pcQTL, a framework that applies two-component hurdle modeling and sliding-window clustering to identify local co-expression clusters, summarizes each cluster using principal components, and maps cis-pcQTLs. In simulations, the individual hurdle components controlled type I error, while the component-union screening rule was substantially more powerful than donor-level pseudobulk correlation tests. Applied to 1.24 million peripheral blood mononuclear cells from 982 OneK1K donors across 10 cell types, sc-pcQTL identified 2,485 local co-expression clusters and conducted QTL mapping for 4,353 cluster-PC phenotypes at single-cell resolution, of which 2,040 had at least one significant cis-pcQTL association. Fine-mapping and colocalization with genome-wide association study loci across 1,163 phenotypes in the FinnGen study identified 394 colocalized QTL-GWAS signal groups. Each group comprised fine-mapped QTL and GWAS signals connected through one or more colocalization links within the same cell type and local gene cluster. Of these groups, 46 were pcQTL-specific and contained no colocalized single-gene eQTL from a constituent gene. Locus-level analyses further revealed cell-type-specific multi-gene regulatory effects. Thus, sc-pcQTL complements conventional single-gene eQTL analysis by identifying trait-relevant regulatory signals shared across neighboring genes. Availability and implementation: The sc-pcQTL software is openly available at https://github.com/ZhouLabGenetics/sc-pcQTL ; analysis and figure-generation scripts are available at https://github.com/ZhouLabGenetics/sc-pcQTL_code ; and the summary result tables are publicly available on Zenodo (DOI: https://doi.org/10.5281/zenodo.21222687 ). Contact: wzhou@broadinstitute.org Supplementary material: Supplementary material accompanies this preprint.

Many disease-associated variants are thought to act through gene regulation, yet conventional eQTL mapping explains only a fraction of GWAS loci, potentially because regulatory effects vary across cellular states and environments. We present CASTIE, a scalable Poisson mixed-model framework that directly models sparse single-cell read counts and enables genome-wide testing of genotype-by-context interactions without pre-screening for static effects. Applying CASTIE to 1.2 million peripheral blood mononuclear cells from 982 OneK1K donors identified 3,155 context-dependent eQTL associations, including 2,022 eGenes without detectable static effects. These associations yielded 374 colocalizations across 94 traits, representing 270 unique loci, of which 197 were not recovered using the corresponding static eQTLs. The colocalizations linked trait associations to specific cellular contexts and genes including GCHFR , RNASET2 and ATP1A3 . In adipose-derived mesenchymal stem cells exposed to metabolic stimulations, CASTIE increased eGene discovery by 36-92% across cell populations and identified stimulation-dependent regulatory effects at metabolic trait loci. Thus, modeling cellular context reveals disease-relevant regulatory variation beyond static eQTL mapping.

Autoimmune hypothyroidism (Hashimoto’s thyroiditis) is common and has a strong genetic component. Here we performed multi-ancestry genome-wide association meta-analyses encompassing 48,694 Hashimoto’s thyroiditis cases, using a precise case definition, and 1,044,134 controls. We identified 155 significant ( P < 5 × 10 ⁻⁸ ) independent genetic associations, of which 45 variants and 19 loci were not previously associated with hypothyroidism. Six loci were specific for individuals of European ancestry reference populations. Functional enrichment analyses of Hashimoto’s thyroiditis-associated genes highlighted immune cells and the spleen, underpinning the importance of T cells in Hashimoto’s thyroiditis development. This observation was further supported by 161 significant colocalizations with expression quantitative trait loci in immune cells and 40 in thyroid tissue (for example, TG, VAV3 , IRF5 ), highlighting the interplay between the immune system and the thyroid. Mendelian randomization indicated causal effects of Hashimoto’s thyroiditis on cardiovascular traits and expected associations with thyroid hormone levels.

Accumulation of mutant mitochondrial DNA (mtDNA) heteroplasmy is among the strongest signatures of ageing ¹ . Here we investigated the underlying mechanism by calling mtDNA sequence, mtDNA abundance and mtDNA heteroplasmic variants in human blood using whole-genome sequences from approximately 750,000 individuals. We observed that mtDNA single-nucleotide variants (mtSNVs) accumulate sharply at age 60 years, occur at low levels of heteroplasmy, exhibit little evidence of positive selection and are likely to be predominantly neutral. The mutational spectrum of mtSNVs does not reflect oxidative lesions, as is commonly invoked, but is more consistent with mtDNA replication errors. To understand why mtSNVs become detectable with age, we performed a genome-wide association study for heteroplasmic mtSNV burden, identifying germline variants near TERT , TCL1A and SMC4 , all of which have been linked to clonal haematopoiesis (CH) ² . Rare-variant analysis also showed that high mtSNV burden is associated with mutations in numerous CH driver genes. These genetic associations persisted even after exclusion of individuals with known CH driver mutations. Our results support a model in which ‘cryptic’ mtDNA mutations initially arise randomly as replication errors but are undetectable in bulk. They then become apparent only through age-related expansion of cellular clones in blood. We propose that the high copy number and mutation rate of mtDNA make it a sensitive blood-based marker of somatic mosaicism due to CH. Our work mechanistically unifies three prominent signatures of ageing: common germline variants in TERT , CH and observed accrual of mtDNA mutations.

The high prevalence (>5%) of autoimmune hypothyroidism (AIHT) provides a unique opportunity to dissect genetic contributions to systemic and organ-specific autoimmunity. Here we performed a genome-wide association meta-analysis of 81,718 AIHT cases in FinnGen and the UK Biobank, identifying 418 independent signals ( P < 5 × 10 ⁻⁸ ). At 48 of these loci, a protein-coding variant is, or is highly correlated ( r ² > 0.95) with, the lead variant, including Finnish-enriched coding variants in LAG3 , ZAP70 and TG . We demonstrated that ZAP70 :T155M reduces T cell activation and broadly compare large-scale scans of nonthyroid autoimmunity and thyroid-stimulating hormone levels with a Bayesian classifier to assign loci into distinct groupings, estimating that 38% are involved in general autoimmunity whereas 20% are thyroid specific. We further identified substantial antagonistic pleiotropy, with 10% of AIHT loci showing a consistent protective effect against skin cancer. The AIHT results, including numerous genes encoding checkpoint proteins, support the causal role of natural immune variation influencing cancer outcomes.

Identifying the causal variants and mechanisms that drive complex traits and diseases remains a core problem in human genetics ^1–5 . Most of these variants individually have weak effects ⁶ and lie in non-coding gene-regulatory elements ^7–10 , for which we lack a complete understanding of how single-nucleotide alterations modulate transcriptional processes to affect human phenotypes ^5,11–15 . To address this problem, we measured the activity of 221,412 fine-mapped trait-associated variants using a massively parallel reporter assay ^16–20 in 5 diverse cell types. We show that this assay effectively discriminates between likely causal variants and controls, and identified 13,121 regulatory variants with high precision. Although the effects of these variants largely agree with orthogonal measures of function, only 69% of them can plausibly be explained by the disruption of a known transcription factor binding motif. We investigated the mechanisms of 136 variants using saturation mutagenesis and assigned affected transcription factors for 91% of variants without a clear canonical mechanism. Finally, we detected regulatory epistasis at 11% of tested regulatory variants in close proximity and identified multiple functional variants on the same haplotype at a small, but important, subset of trait-associated loci. Overall, our study provides a systematic functional characterization of likely causal common variants that underlie complex and molecular human traits, enabling new insights into the regulatory grammar underlying disease risk.