Manuel Antonio Rivas

Genome-wide association studies have successfully identified thousands of genetic associations, yet their predominant reliance on European-descent populations limits insights into the full spectrum of human genetic diversity and its impact on disease. Admixture mapping offers a powerful, complementary approach by leveraging differences in haplotype frequencies across ancestral backgrounds to identify risk loci for complex traits. Here, we perform a large-scale, multi-ancestry admixture mapping study across 415,792 unrelated individuals in the UK Biobank, examining associations between local haplotype ancestry and 108 phenotypes. Our approach identifies 13 genome-wide significant ancestry-phenotype associations, recovering previously reported signals while uncovering four novel ancestry-associated findings, including new risk loci for atrial fibrillation, dermatitis, and angina pectoris. To overcome the limited resolution of traditional admixture mapping, we implemented a conditional fine-mapping framework, which enabled us to localize four putatively causal variants. In silico variant effect prediction and eQTL integration revealed regulatory and missense effects predominantly localized to lung, and immune tissues, aligning with captured phenotypes such as asthma, dermatitis, and hypothyroidism. Notably, our findings demonstrate striking genetic heterogeneity, revealing how the same clinical phenotype can arise through distinct genetic pathways depending on the ancestral background. Overall, this work highlights the critical importance of modeling local ancestry structure to refine genetic associations, uncover novel disease mechanisms, and improve the equitable translation of genomic medicine.

The central problem of biology is the origin of biological organization. We show, using information theory, that an organism does not contain enough organism-specific information to specify its own fully functioning microscopic organization. The organized machinery of life is therefore not the execution of a fully prewritten organism-specific program under favorable conditions. Rather, it is the compilation of a coarse organism-specific specification by a shared physical background that is constitutive of biological organization. We formalize this as a coarse-graining information threshold on biological specification, with two complementary entropy faces — a Shannon face controlling stochastic generation and a Hartley face controlling zero-error deterministic addressability. Above the threshold, organism-controlled information is sufficient to specify structural and functional ensembles; below it, programmed microstate determinism is impossible: deterministic addressability fails by pigeonhole, and any algorithm producing sub-threshold outputs must consume runtime randomness proportional to the information deficit. The threshold follows from two information-theoretic constraints — finite specification capacity and causal locality — supplemented by a mixing lemma showing that initial-condition information decays exponentially under thermal dynamics. We establish the threshold as a family of maximal capacity-compatible coarse-grainings, distinguish the proven impossibility below the threshold from the empirically realized coarse mappings above it, and locate the threshold empirically through worked cases of protein folding, E. coli, Drosophila early development, and C. elegans , together with computational verification using AlphaFold-2, the JCVI-syn3A 4D whole-cell simulation, and canonical stochastic gene network models. We further show that no known naturally realized environmental channel can close the gap. The result rules out programmed microstate determinism while leaving physical determinism untouched, reframes the genome as a generator specification rather than a trajectory program, and unifies gene-centric, developmental, and field-theoretic (bioelectric, morphogenetic, and related continuum) views of biological specification under a single coarse-graining framework.

Biobank-scale genomic analyses remain computationally expensive, CPU-bound workflows, particularly when adjusting for confounding. Here, we present CuGen, a GPU-accelerated framework for large-scale genomics. CuGen uses UltraLasso, a novel hierarchical application of univariate-guided sparse regression (uniLasso), to select a compact, phenotype-informed active set of fewer than 30,000 variants. This achieves robust leave-one-chromosome-out (LOCO) confounding control, enabling both downstream GWAS and in-sample fine-mapping. Additionally, we introduce the .cugen file format, a genotype representation designed for memory-optimized, high-throughput streaming and random access on GPU hardware. Building on this substrate, we provide a general GPU-accelerated genomics toolkit handling polygenic prediction, data manipulation, quality control, analysis, and visualization. We demonstrate CuGen’s efficacy in the UK Biobank with up to 408,624 individuals, where the full GWAS pipeline and fine-mapping against 6.8 million imputed variants completes in approximately 10 minutes on a single high-throughput GPU with 80 GB of memory. The pipeline scales efficiently to massive phenome-wide analyses with sublinear resource consumption.

The electrocardiogram (ECG) encodes the electrical activity of the heart across multiple timescales, yet standard clinical analysis collapses this rich signal into a handful of scalar measurements that discard most of the waveform’s structure. Whether the frequency signals lost in this reduction carry heritable biological information relevant to cardiovascular disease risk remains unclear. Here we decompose resting 12-lead ECGs from 47,052 White British UK Biobank participants into 84 frequency-specific energy features using Daubechies-6 wavelet analysis across 12 leads and 7 decomposition levels, and perform independent genome-wide association analyses on each feature. We identify 67 independent loci (p < 5 × 10⁻⁸) and refine these to 101 high-confidence causal variants (posterior inclusion probability > 0.80) through Bayesian fine-mapping; associated loci converge on genes governing cardiac conduction and myocardial integrity, including SCN5A, TTN, KCNQ1, and DSP, alongside less-characterized cardiomyopathy candidates. SNP-based heritability estimates range from 0.03 to 0.26, with the strongest signals in mid-frequency bands (D6–D4, ∼4–32 Hz) of Lead I and aVR, and strong inter-lead genetic correlations indicate a coordinated genetic architecture underlying the waveform. Integrating these features with FinnGen R12 cardiovascular phenotypes reveals genetic correlations reaching 0.56 with heart failure, driven predominantly by energy in the highest-frequency band (D1, 125–250 Hz) — a spectral range routinely filtered from clinical ECGs and previously regarded as acquisition noise. These results reframe the electrocardiogram as a multi-frequency genetic phenotype, expand the set of cardiac loci discoverable from ECG data, and implicate high-frequency cardiac electrical activity as an underexplored dimension of cardiovascular disease risk.

Background: Clinical trials are essential for therapeutic development but increasingly face challenges due to imprecise inclusion criteria, leading to low event rates and the need for large sample sizes. This inefficiency makes modern trials costly and time-consuming. Despite the availability of extensive clinical, genomic, and biological data, current trial enrollment strategies do not fully leverage this information. Incorporating genomic information into trial design could enable risk-based participant enrichment by preferentially enrolling individuals with higher disease risk, thereby increasing event rates and improving trial efficiency. Methods: In this study, we developed an in silico framework for evaluating prognostic enrichment guided by polygenic risk scores (PRS) in clinical trial design using genomic and electronic health record data from large-scale biobanks. Naturally occurring protective genetic variants were used as analogs of therapeutic interventions, with variant carriers treated as ’treatment’ arms and non-carriers as ’control’ arms. We compared unenriched designs, in which carriers and non-carriers were drawn from the full population, against PRS-enriched designs in which both arms were restricted to participants in the upper 75%, 50%, or 25% of the PRS distribution, respectively. Across these four designs, we quantified disease prevalence, statistical power, sample size requirements, and time-to-event accrual. Results: We applied this approach to the UK Biobank using three model gene–disease pairs: the protective variant p.Arg46Leu in PCSK9 for coronary artery disease (CAD), p.Gln175His in ANGPTL7 for glaucoma, and p.Arg381Gln in IL23R for inflammatory bowel disease (IBD). Across all three disease contexts, PRS-enriched designs increased disease prevalence, improved empirical power, and accelerated event accrual relative to unenriched cohorts. At 80% power, restricting enrollment to the upper 25% of the PRS distribution reduced required per-arm sample sizes by approximately 60% for CAD-PCSK9 and 78% for IBD-IL23R. Consistent reductions in time-to-event were also observed across enriched strata, suggesting that PRS-enriched trials could achieve target event counts with both smaller sample sizes and shorter follow-up. However, for glaucoma-ANGPTL7, the most restrictive threshold did not yield additional gains over moderate enrichment, as reduced sample size attenuated the detectable difference between arms. These results highlight the need to balance enrichment for higher-risk participants against retaining a sufficient eligible population, and underscore that optimal PRS thresholds are disease-context dependent. Conclusions: These findings establish a generalizable, data-driven framework for prospectively evaluating PRS-guided prognostic enrichment prior to trial initiation. In general, PRS-guided study designs lead to improved empirical power, lower required sample sizes, and faster trials. As population-scale genomic data become increasingly available within healthcare systems and biobanks, this framework provides a scalable foundation for integrating genetic risk information into clinical trial design.

Alcohol is an ancient and enduring component of the human diet, yet it is a dose-dependent cytotoxin and teratogen, raising the possibility that endogenous, state-dependent mechanisms constrain intake. Growth differentiation factor 15 (GDF15) is an endocrine hormone that rises during pregnancy—predominantly via secretion from blastocyst-derived placental trophoblasts into the maternal circulation—and is also induced in other tissues, particularly hepatocytes, by toxins and cellular stress. However, its function in humans remains unclear. Here, we show that circulating GDF15 levels are elevated 5-fold in individuals with alcohol dependence, identify a rare loss-of-function variant in the GDF15 receptor gene GFRAL associated with approximately 2.6 additional UK alcohol units (∼21 g ethanol) per week, and demonstrate that recombinant GDF15 reduces alcohol drinking in mice. Collectively, these findings support a model in which GDF15 acts as an endocrine signal induced by chronic alcohol exposure—and potentially during pregnancy—to limit alcohol intake in humans. Highlights: GDF15 is markedly elevated in humans with alcohol dependence A truncating GFRAL variant associates with higher alcohol intake in UK Biobank GFRAL frameshift disrupts GDF15–RET signaling in vitro Recombinant GDF15 suppresses voluntary alcohol intake in mice Graphical

Heart failure with preserved ejection fraction (HFpEF) affects over 30 million people and lacks disease-modifying therapies. Although genomic-led drug discovery increases success by more than 2.6-fold, HFpEF genomic discovery remains constrained by imprecise phenotyping in biobanks, with only two loci identified to date. Biobanks lack HFpEF diagnostic codes and echocardiograms, yet HFpEF diagnosis exists along a continuum and is inherently probabilistic, presenting an opportunity for multimodal prediction. Here we introduce TRI-modal Assessment and Discovery of HFpEF (TRIAD-HFpEF), a machine learning framework integrating electrocardiograms, cardiac magnetic resonance imaging, and biomarkers to assign HFpEF probabilities. Deployed in UK Biobank, these probabilities validate with respect to mortality, hospitalizations, and structural and functional HFpEF features. Genome-wide and proteomic analyses reveal over 90 novel loci, a 45-fold expansion, and distinguish causal proteins from non-causal biomarkers of disease progression, prioritizing 11 therapeutic targets and 7 non-causal biomarkers. We identify FLT3 as one of the 11 therapeutic targets, consistent with the reported 7-fold increased heart failure risk from FLT3 inhibitors in leukemia. We validate this finding by demonstrating significant worsening of diastolic function following FLT3 inhibitor treatment in an independent clinical cohort. Conversely, MPO emerged as one of the 7 non-causal biomarkers, aligning with three recent negative MPO inhibitor trials. TRIAD-HFpEF demonstrates that machine learning-derived phenotypes can unlock genetic discovery in complex syndromes, identifying actionable targets while deprioritizing associations reflecting disease consequences rather than causes.

Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals, underscoring a critical gap in genetic research. Prediction models trained primarily on European ancestry often fail to generalize to diverse populations, leading to reduced accuracy and potential health disparities. Here, we assess whether incorporating interaction modeling and pretraining into disease prediction models can improve performance. We evaluated the performance of Group-LASSO INTERaction-NET (glinternet) and pretrained lasso in disease prediction focusing on diverse ancestries in the UK Biobank. Models were trained on multiomic data from White British and other ancestries and validated in a cohort of more than 96,000 individuals for 8 diseases. Of the 96 trained models, we report 16 with statistically significant incremental predictive performance in terms of ROC-AUC scores ( p -value < 0.05 ), found for diabetes, arthritis, gall stones, cystitis, asthma, and osteoarthritis. Our findings suggest that interaction terms and pre-training can modestly improve prediction accuracy, but these effects are not consistent across all diseases. Our code is available at ( https://github.com/rivas-lab/AncestryOmicsUKB ).

We present an application for analyzing variant-by-phenotype association summaries from PheWAS [10] using spherical harmonics (SH) [5, 4]. The method embeds phenotypes onto the unit sphere, fits a joint SH map across all variants via weighted ridge regression [3, 8, 9] with a Laplacian penalty, and then assesses per-variant residual localization beyond this global structure. Rotation-invariant descriptors (degree power spectrum, l ₉₅ , entropy, and a localization index) summarize spatial complexity. We detect localized genetic effects with a sign-aware spherical-cap enrichment test that scans cap radii around SH extrema and identifies phenotypes driving hotspots via inverse-variance fixed-effect meta-analysis [7]. Model selection uses BIC [2], and significance for high-degree structure uses a nested F -test within the linear-model framework [8, 9] with BH-FDR across variants [1]. The implementation provides 2D maps and an optional 3D globe, supports standard long and matrix data formats, and exports all artifacts in a single ZIP for reproducibility.

Pre-training is a powerful paradigm in machine learning to pass information across models. For example, suppose one has a modest-sized dataset of images of cats and dogs and plans to fit a deep neural network to classify them. With pre-training, we start with a neural network trained on a large corpus of images of not just cats and dogs but hundreds of classes. We fix all network weights except the top layer(s) and fine tune on our dataset. This often results in dramatically better performance than training solely on our dataset. Here, we ask: ‘Can pre-training help the lasso?’. We propose a framework where the lasso is fit on a large dataset and then fine-tuned on a smaller dataset. The latter can be a subset of the original, or have a different but related outcome. This framework has a wide variety of applications, including stratified and multi-response models. In the stratified model setting, lasso pre-training first estimates coefficients common to all groups, then estimates group-specific coefficients during fine-tuning. Under appropriate assumptions, support recovery of the common coefficients is superior to the usual lasso trained on individual groups. This separate identification of common and individual coefficients also aids scientific understanding.