Duncan Stuart Palmer

The rapid growth of omics studies offers new opportunities to uncover disease mechanisms, yet findings from individual studies or single omics may be influenced by cohort-, platform- and layer-specific variation. Integrating evidence across studies and omics layers can reveal robust biological signals that are reproducible across molecular layers, but differences in assay platforms, sample sizes and feature coverage complicate direct combination of summary statistics. Here we present the Omics Rank-Based Integration Tool (ORBIT), a direction-aware framework for prioritizing concordant signals across omics layers using only within-dataset feature ranks and effect directions. ORBIT tests directional rank consistency across omics layers against a closed-form variance-gamma null distribution while accounting for inter-dataset correlation and incomplete feature overlap. Simulations show that ORBIT maintains well-calibrated false-positive rates under inter-omics correlation, increasing numbers of omics layers and missingness, while detecting concordant signals with increasing power as the number of layers grows. We applied ORBIT to tubulointerstitial transcriptomic data from patients with chronic kidney disease in the C-PROBE cohort and proteomic data from the Kidney Precision Medicine Project, identifying a concordant injury programme marked by inflammatory and fibrotic remodeling together with impaired energy metabolism. We also used ORBIT to integrate ten transcriptomic, four proteomic and one translatomic datasets in dilated cardiomyopathy heart tissue, identifying extracellular matrix remodeling and mitochondrial energy failure as the dominant concordant biological programmes. In both diseases, ORBIT prioritized concordant genes and identified pathways that were not significant in any single omics layer alone. These results show that directional rank integration can be used for integrating heterogeneous omics summary data and prioritizing robust molecular mechanisms.

Studying the genetics of intelligence provides insight into the neurobiology of cognitive function and rare neurodevelopmental conditions. Here we integrated fluid intelligence (FI) tests across different UK Biobank measures and imputed FI for unmeasured individuals, increasing the sample size from ~270,000 to >455,000. We show through extensive validation that the imputed phenotype genetically resembles measured FI, although some noncognitive signal likely remains. Combining measured and imputed FI increases the number of independent significant common SNP associations from 390 to 550 and reduces the ascertainment bias introduced by FI test participants being, on average, more educated. Rare variant analyses identify 26 significantly associated genes (false discovery rate < 1%), including 8 without strong prior evidence of involvement in intelligence or neurodevelopmental conditions, which replicate in aggregate in external cohorts. Our results demonstrate that imputed intelligence can boost power for genetic discovery and support its use to help discover new neurodevelopmental condition-associated genes.

Additive genetic models are the default for genome-wide association studies, but deviations from additivity are crucial for understanding disease mechanisms and therapeutic responses. Yet existing methods for testing nonadditivity are computationally infeasible for large-scale analysis or rely on Hardy-Weinberg assumptions, making them unsuitable for rare variants in large biobanks. We use an orthogonal allelic recoding framework that enables scalable testing of nonadditive genetic effects without Hardy-Weinberg assumptions while integrating seamlessly with existing linear mixed models. Analyzing up to 399,943 UK Biobank individuals across 2906 plasma proteins and 55 quantitative traits, we demonstrate that approximately one-third of cognate gene-protein relationships exhibit nonlinear dose responses. We identify recessive effects, including FUT10 variants associated with reduced lung function, and validate these findings in the All of Us cohort. We show that many complex traits show partial recessivity, reconciling conflicting inheritance patterns in the literature, and that joint two-degree-of-freedom models substantially improve statistical power when the underlying trait is partially recessive. Our framework efficiently partitions additive and nonadditive heritability components at biobank scale, providing a quantitative map of gene dose-response relationships with direct implications for therapeutic target identification and precision medicine.

Rare coding variants can have large effects on disease risk and provide direct routes from human genetics to disease mechanisms and therapeutic targets, but their discovery is constrained by sample size, particularly for low-prevalence diseases. Here we establish the Biobank Rare Variant Analysis (BRaVa) consortium, a global rare variant association resource that integrates sequencing and linked health-record data from ten biobanks and cohorts comprising over 1.2 million individuals across diverse ancestries. We performed gene-based meta-analyses of rare coding variation across 33 clinical endpoints and 11 quantitative traits. Aggregating evidence across biobanks and ancestries identified 514 gene-trait associations, including 31 not previously reported in prior studies or curated association resources following systematic literature review. Notably, 36.1% of gene-level associations were undetectable in any individual biobank, and 91 emerged only through cross-ancestry meta-analysis, demonstrating that federated integration enables discovery beyond the reach of single cohorts. Similar gains were observed at the variant level, where 25.0% of phenotype-locus associations were detectable only through meta-analysis. Effect size estimates were correlated across ancestries with concordant directions of effect, supporting the generalizability of rare variant associations. The identified signals implicate pathways involved in transcriptional and epigenetic regulation, metabolism, vascular and epithelial biology, and immune function, highlighting rare coding variation as an engine for biological discovery across medical record phenotypes. For example, damaging variation in ANKRD12 implicates inflammatory transcriptional dysregulation in asthma and chronic obstructive pulmonary disease, and ultra-rare predicted loss-of-function variants in NAA15 link protein acetylation processes to type 2 diabetes risk. BRaVa establishes a scalable framework and freely available community resource for rare variant meta-analysis across global biobanks. Public release of gene- and variant-level association summary statistics provides a reference map of rare coding variant associations to support disease gene discovery, biological interpretation, and therapeutic target prioritization as sequencing-linked health-record resources continue to expand.

Polygenic scores (PGS) predict complex traits and stratify disease risk but often fail to fully capture individual-level variation. “Misaligned” individuals, whose observed phenotypes deviate from their genetically expected values based on polygenic scores (PGS), provide a powerful model for identifying factors beyond common-variant effects, including additional genetic factors. Here, we apply misalignment classification and enrichment testing frameworks to seven continuous and three dichotomous traits, assessing whether misaligned individuals in the UK Biobank are enriched for rare (minor allele frequency (MAF) < 0.1%) damaging genetic variation. We identify significant enrichment (false discovery rate (FDR)-adjusted P < 0.05) of predicted loss-of-function (pLoF) variants in COPB2 and GORAB among individuals misaligned for lower-than-expected bone mineral density. We refine previously observed grouped-gene enrichment in individuals with misaligned stature to the single-gene level: shorter-than-expected individuals are enriched for pLoF variants in ACAN and IGF1 , and taller-than-expected individuals are enriched for predicted damaging missense in FBN1 . Using an individual’s misalignment classification as a phenotype, we perform an exome-wide scan across seven traits, resulting in 74 FDR- significant genes. We identify KANK1 as a gene associated with later age at menopause, potentially protective against primary ovarian insufficiency. For dichotomous disease status traits, we demonstrate evidence for the liability threshold model in the context of counteracting conditionally-orthogonal common and rare variant pathogenic/protective effects. Among individuals diagnosed with type 2 diabetes, carriers of rare pathogenic pLoF variants in HNF1A and HNF4A had significantly lower polygenic risk than non- carriers (FDR-adjusted one-sided t -test P < 5 × 10 ^{− 3} ). We also show that coronary artery disease controls carrying rare protective pLoF variants in ANGPTL3 had nominally higher polygenic risk (one-sided t -test P = 0.03) than non-carriers. This study highlights the power of misalignment-based analyses in complex continuous phenotypes and disease, with the potential to validate known genetic contributors to traits and identify novel genes. This work paves the way for better molecular diagnoses and targeted therapeutic discovery.

Genome-wide association studies (GWASs) may help inform the etiology of infertility. Here, we perform GWAS meta-analyses across seven cohorts in up to 42,629 cases and 740,619 controls and identify 25 genetic risk loci for male and female infertility. We additionally identify up to 269 genetic loci associated with follicle-stimulating hormone, luteinizing hormone, estradiol and testosterone through sex-specific GWAS meta-analyses (n = 6,095–246,862). Exome sequencing analyses reveal that women carrying testosterone-lowering rare variants in some genes are at risk of infertility. However, we find no local or genome-wide genetic correlation between female infertility and reproductive hormones. While infertility is genetically correlated with endometriosis and polycystic ovary syndrome, we find limited genetic overlap between infertility and obesity. Finally, we show that the evolutionary persistence of infertility-risk alleles may be explained by directional selection. Taken together, we provide a comprehensive view of the genetic determinants of infertility across multiple diagnostic criteria.