Biology subjects

Rivas, M. A.

Publications and source records attributed to Rivas, M. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Information-theoretic Limits on Programmatic Specification of Biological Systems

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.

biophysics↗

bioRxiv2026

A loss-of-function variant in GFRAL associates with increased alcohol consumption in humans

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. HighlightsO_LIGDF15 is markedly elevated in humans with alcohol dependence C_LIO_LIA truncating GFRAL variant associates with higher alcohol intake in UK Biobank C_LIO_LIGFRAL frameshift disrupts GDF15-RET signaling in vitro C_LIO_LIRecombinant GDF15 suppresses voluntary alcohol intake in mice C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=152 SRC="FIGDIR/small/709997v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@13630fborg.highwire.dtl.DTLVardef@c9bebforg.highwire.dtl.DTLVardef@109fd63org.highwire.dtl.DTLVardef@da971f_HPS_FORMAT_FIGEXP M_FIG C_FIG

physiology↗

bioRxiv2025

Joint Spherical-Harmonics Regression for PheWAS: Global Maps, Residual Localization, and Spherical-Cap Enrichment

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, l95, 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.

genetics↗

bioRxiv2025

Unified meta regression models for rare variant association studies

Rare variant association studies (RVAS) of complex traits have emerged as a powerful approach to advance drug discovery and diagnostics. Missense pathogenicity predictions from AlphaMissense based on structural context and protein language models improve the differentiation between benign and deleterious variants. Constraint metrics, on the other hand, allow researchers to pinpoint genomic regions under selective pressure that may not directly impact protein structure, but are more likely to contain functionally important mutations. Loss-of-function (LoF) variants, which result in the complete or partial loss of protein function, are particularly informative, as it is more straightforward to assess their downstream functional consequences. In this study, we present a unified meta regression model approach that incorporates the probability of pathogenicity, probability of constraint, and indicator whether a variant is a predicted loss-of-function or missense variant as features to model the observed effect size and uncertainty of effect size obtained from single-variant genetic analysis. We applied the unified meta regression model to 1,144 continuous phenotypes from UK Biobank using single variant summary statistics obtained from Genebass. We replicated our findings using the AllofUS cohort. For each gene discovery, we make available a characterization of whether constrained sites are associated with the phenotype, whether pathogenic sites determined by structural based predictions are associated with phenotype, and whether broader loss-of-function or missense variant annotation better explains the summary statistics observed. Our results are publicly available at Global Biobank Engine (https://biobankengine.shinyapps.io/phenome-wide-unified-model/).

genetics↗

bioRxiv2024

Integrative machine learning approaches for predicting disease risk using multi-omics data from the UK Biobank

We train prediction and survival models using multi-omics data for disease risk identification and stratification. Existing work on disease prediction focuses on risk analysis using datasets of individual data types (metabolomic, genomics, demographic), while our study creates an integrated model for disease risk assessment. We compare machine learning models such as Lasso Regression, Multi-Layer Perceptron, XG Boost, and ADA Boost to analyze multi-omics data, incorporating ROC-AUC score comparisons for various diseases and feature combinations. Additionally, we train Cox proportional hazard models for each disease to perform survival analysis. Although the integration of multi-omics data significantly improves risk prediction for 8 diseases, we find that the contribution of metabolomic data is marginal when compared to standard demographic, genetic, and biomarker features. Nonetheless, we see that metabolomics is a useful replacement for the standard biomarker panel when it is not readily available.

genomics↗

bioRxiv2024

Efficient storage and regression computation for population-scale genome sequencing studies

In the era of big data in human genetics, large-scale biobanks aggregating genetic data from diverse populations have emerged as important for advancing our understanding of human health and disease. However, the computational and storage demands of whole genome sequencing (WGS) studies pose significant challenges, especially for researchers from underfunded institutions or developing countries, creating a disparity in research capabilities. We introduce new approaches that significantly enhance computational efficiency and reduce data storage requirements for WGS studies. By developing algorithms for compressed storage of genetic data, focusing particularly on optimizing the representation of rare variants, and designing regression methods tailored for the scale and complexity of WGS data, we significantly lower computational and storage costs. We integrate our approach into PLINK 2.0. The implementation demonstrates considerable reductions in storage space and computational time without compromising analytical accuracy, as evidenced by the application to the AllofUs project data. We optimized the runtime of an exome-wide association analysis involving 19.4 million variants and the body mass index phenotype of 125,077 individuals, reducing it from 695.35 minutes (approximately 11.5 hours) on a single machine to just 1.57 minutes using 30 GB of memory and 50 threads (or 8.67 minutes with 4 threads). Additionally, we extended this approach to support multi-phenotype analyses. We anticipate that our approach will enable researchers across the globe to unlock the potential of population biobanks, accelerating the pace of discoveries that can improve our understanding of human health and disease.

genetics↗

bioRxiv2023

ChromaFold predicts the 3D contact map from single-cell chromatin accessibility

The identification of cell-type-specific 3D chromatin interactions between regulatory elements can help to decipher gene regulation and to interpret the function of disease-associated non-coding variants. However, current chromosome conformation capture (3C) technologies are unable to resolve interactions at this resolution when only small numbers of cells are available as input. We therefore present ChromaFold, a deep learning model that predicts 3D contact maps and regulatory interactions from single-cell ATAC sequencing (scATAC-seq) data alone. ChromaFold uses pseudobulk chromatin accessibility, co-accessibility profiles across metacells, and predicted CTCF motif tracks as input features and employs a lightweight architecture to enable training on standard GPUs. Once trained on paired scATAC-seq and Hi-C data in human cell lines and tissues, ChromaFold can accurately predict both the 3D contact map and peak-level interactions across diverse human and mouse test cell types. In benchmarking against a recent deep learning method that uses bulk ATAC-seq, DNA sequence, and CTCF ChIP-seq to make cell-type-specific predictions, ChromaFold yields superior prediction performance when including CTCF ChIP-seq data as an input and comparable performance without. Finally, fine-tuning ChromaFold on paired scATAC-seq and Hi-C in a complex tissue enables deconvolution of chromatin interactions across cell subpopulations. ChromaFold thus achieves state-of-the-art prediction of 3D contact maps and regulatory interactions using scATAC-seq alone as input data, enabling accurate inference of cell-type-specific interactions in settings where 3C-based assays are infeasible.

bioinformatics↗

bioRxiv2023

Cooperative super-enhancer inactivation caused by heterozygous loss of CREBBP and KMT2D skews B cell fate decisions and yields T cell-depleted lymphomas

Mutations affecting enhancer chromatin regulators CREBBP and KMT2D are highly co-occurrent in germinal center (GC)-derived lymphomas and other tumors, even though regulating similar pathways. Herein, we report that combined haploinsufficiency of Crebbp and Kmt2d (C+K) indeed accelerated lymphomagenesis. C+K haploinsufficiency induced GC hyperplasia by altering cell fate decisions, skewing B cells away from memory and plasma cell differentiation. C+K deficiency particularly impaired enhancer activation for immune synapse genes involved in exiting the GC reaction. This effect was especially severe at super-enhancers for immunoregulatory and differentiation genes. Mechanistically, CREBBP and KMT2D formed a complex, were highly co-localized on chromatin, and were required for each-others stable recruitment to enhancers. Notably, C+K lymphomas in mice and humans manifested significantly reduced CD8+ T-cell abundance. Hence, deficiency of C+K cooperatively induced an immune evasive phenotype due at least in part to failure to activate key immune synapse super-enhancers, associated with altered immune cell fate decisions. SIGNIFICANCEAlthough CREBBP and KMT2D have similar enhancer regulatory functions, they are paradoxically co-mutated in lymphomas. We show that their combined loss causes specific disruption of super-enhancers driving immune synapse genes. Importantly, this leads to reduction of CD8 cells in lymphomas, linking super-enhancer function to immune surveillance, with implications for immunotherapy resistance.

cancer biology↗

bioRxiv2021

Bayesian mixture model for clustering rare-variant effects in human genetic studies

Rare-variant aggregate analysis from exome and whole genome sequencing data typically summarizes with a single statistic the signal for a gene or the unit that is being aggregated. However, when doing so, the effect profile within the unit may not be easily characterized across one or multiple phenotypes. Here, we present an approach we call Multiple Rarevariants and Phenotypes Mixture Model (MRPMM), which clusters rare variants into groups based on their effects on the multivariate phenotype and makes statistical inferences about the properties of the underlying mixture of genetic effects. Using summary statistic data from a meta-analysis of exome sequencing data of 184,698 individuals in the UK Biobank across 6 populations, we demonstrate that our mixture model can identify clusters of variants responsible for significantly disparate effects across a multivariate phenotype; we study three lipid and three renal traits separately. The method is able to estimate (1) the proportion of non-null variants, (2) whether variants with the same predicted consequence in one gene behave similarly, (3) whether variants across genes share effect profiles across the multivariate phenotype, and (4) whether different annotations differ in the magnitude of their effects. As rare-variant data and aggregation techniques become more common, this method can be used to ascribe further meaning to association results.

genetics↗

bioRxiv2021

Fast Numerical Optimization for Genome Sequencing Data in Population Biobanks

We develop two efficient solvers for optimization problems arising from large-scale regularized regressions on millions of genetic variants sequenced from hundreds of thousands of individuals. These genetic variants are encoded by the values in the set {0, 1, 2, NA}. We take advantage of this fact and use two bits to represent each entry in a genetic matrix, which reduces memory requirement by a factor of 32 compared to a double precision floating point representation. Using this representation, we implemented an iteratively reweighted least square algorithm to solve Lasso regressions on genetic matrices, which we name snpnet-2.0. When the dataset contains many rare variants, the predictors can be encoded in a sparse matrix. We utilize the sparsity in the predictor matrix to further reduce memory requirement and computational speed. Our sparse genetic matrix implementation uses both the compact 2-bit representation and a simplified version of compressed sparse block format so that matrix-vector multiplications can be effectively parallelized on multiple CPU cores. To demonstrate the effectiveness of this representation, we implement an accelerated proximal gradient method to solve group Lasso on these sparse genetic matrices. This solver is named sparse-snpnet, and will also be included as part of snpnet R package. Our implementation is able to solve group Lasso problems on sparse genetic matrices with more than 1, 000, 000 columns and almost 100, 000 rows within 10 minutes and using less than 32GB of memory.

bioinformatics↗

bioRxiv2021

Nonsense-mediated decay is highly stable across individuals and tissues.

Precise interpretation of the effects of protein-truncating variants (PTVs) is important for accurate determination of variant impact. Current methods for assessing the ability of PTVs to induce nonsense-mediated decay (NMD) focus primarily on the position of the variant in the transcript. We used RNA-sequencing of the Genotype Tissue Expression v8 cohort to compute the efficiency of NMD using allelic imbalance for 2,320 rare (genome aggregation database minor allele frequency <=1%) PTVs across 809 individuals in 49 tissues. We created an interpretable predictive model using penalized logistic regression in order to evaluate the comprehensive influence of variant annotation, tissue, and inter-individual variation on NMD. We found that variant position, allele frequency, including ultra-rare and singleton variants, and conservation were predictive of allelic imbalance. Furthermore, we found that NMD effects were highly concordant across tissues and individuals. Due to this high consistency, we demonstrate in silico that utilizing peripheral tissues or cell lines provides accurate prediction of NMD for PTVs.

genomics↗

bioRxiv2020

LPA and APOE are associated with statin selection in the UK Biobank

Genetics plays a key role in drug response, affecting efficacy and toxicity. Pharmacogenomics aims to understand how genetic variation influences drug response and develop clinical guidelines to aid clinicians in personalized treatment decisions informed by genetics. Although pharmacogenomics has not been broadly adopted into clinical practice, genetics influences treatment decisions regardless. Physicians adjust patient care based on observed response to medication, which may occur as a result of genetic variants harbored by the patient. Here we seek to understand the genetics of drug selection in statin therapy, a class of drugs widely used for high cholesterol treatment. Genetics are known to play an important role in statin efficacy and toxicity, leading to significant changes in patient outcome. We performed genome-wide association studies (GWAS) on statin selection among 59,198 participants in the UK Biobank and found that variants known to influence statin efficacy are significantly associated with statin selection. Specifically, we find that carriers of variants in APOE and LPA that are known to decrease efficacy of treatment are more likely to be on atorvastatin, a stronger statin. Additionally, carriers of the APOE and LPA variants are more likely to be on a higher intensity dose (a dose that reduces low-density lipoprotein cholesterol by greater than 40%) of atorvastatin than non-carriers (APOE: p(high intensity) = 0.16, OR = 1.7, P = 1.64 x 10-4, LPA: p(high intensity) = 0.17, OR = 1.4, P = 1.14 x 10-2). These findings represent the largest genetic association study of statin selection and statin dose association to date and provide evidence for the role of LPA and APOE in statin response, furthering the possibility of personalized statin therapy.

genetics↗

bioRxiv2020

Fast Sparse-Group Lasso Method for Multi-response Cox Model with Applications to UK Biobank

We propose a Sparse-Group regularized Cox regression method to improve the prediction performance of large-scale and high-dimensional survival data with few observed events. Our approach is applicable when there is one or more other survival responses that 1. has a large number of observed events; 2. share a common set of associated predictors with the rare event response. This scenario is common in the UK Biobank (Sudlow et al. 2015) dataset where records for a large number of common and rare diseases of the same set of individuals are available. By analyzing these responses together, we hope to achieve higher prediction performance than when they are analyzed individually. To make this approach practical for large-scale data, we developed an accelerated proximal gradient optimization algorithm as well as a screening procedure inspired by Qian et al. (2019). We provide a software implementation of the proposed method and demonstrate its efficacy through simulations and applications to UK Biobank data.

bioinformatics↗

bioRxiv2020

Pervasive additive and non-additive effects within the HLA region contribute to disease risk in the UK Biobank

The human leukocyte antigen (HLA) region is one of the most disease-associated regions of the human genome, yet even well-studied alleles in the HLA region have unknown impact on disease. Here, we study the effect of 156 HLA alleles on 677 binary phenotypes for 337,138 individuals in the UK Biobank. We assess HLA allele associations and subsequently use Bayesian Model Averaging for conditional analysis, a) replicating 88 known associations between HLA alleles and binary disease phenotypes such as cancer, and b) discovering 90 novel associations to phenotypes such as skin and reproductive tract cancers and to other phenotypes not previously associated with the HLA region (e.g. anemias and acne). We find several non-additive effects, suggesting a more complex landscape of disease-modifying effects throughout the region. Finally, we discover associations between homozygous HLA allele burden and several cancer and other phenotypes, suggesting that peptide presentation spectra as coded for by the HLA region are important in determining disease risk. Our results demonstrate the HLA regions complexity and richness while underscoring its clinical relevance.

genetics↗

bioRxiv2020

Large-Scale Sparse Regression for Multiple Responses with Applications to UK Biobank

In high-dimensional regression problems, often a relatively small subset of the features are relevant for predicting the outcome, and methods that impose sparsity on the solution are popular. When multiple correlated outcomes are available (multitask), reduced rank regression is an effective way to borrow strength and capture latent structures that underlie the data. Our proposal is motivated by the UK Biobank population-based cohort study, where we are faced with large-scale, ultrahigh-dimensional features, and have access to a large number of outcomes (phenotypes): lifestyle measures, biomarkers, and disease outcomes. We are hence led to fit sparse reduced-rank regression models, using computational strategies that allow us to scale to problems of this size. We use an iterative algorithm that alternates between solving the sparse regression problem and solving the reduced rank decomposition. For the sparse regression component, we propose a scalable iterative algorithm based on adaptive screening that leverages the sparsity assumption and enables us to focus on solving much smaller sub-problems. The full solution is reconstructed and tested via an optimality condition to make sure it is a valid solution for the original problem. We further extend the method to cope with practical issues such as the inclusion of confounding variables and imputation of missing values among the phenotypes. Experiments on both synthetic data and the UK Biobank data demonstrate the effectiveness of the method and the algorithm. We present multiSnpnet package, available at http://github.com/junyangq/multiSnpnet that works on top of PLINK2 files, which we anticipate to be a valuable tool for generating polygenic risk scores from human genetic studies.

bioinformatics↗

bioRxiv2020

GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background

Genome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. We describe UK Biobank GWAS results for three molecular traits--urate, IGF-1, and testosterone--with better-understood biology than most other complex traits. We find that many of the most significant hits are readily and surprisingly interpretable. We observe huge enrichment of associations near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of each trait, including differences in testosterone regulation between females and males. At the same time, even these molecular traits are highly polygenic, with many thousands of variants spread across the genome contributing to trait variance. In summary, for these three molecular traits we identify strong enrichment of signal in putative core gene sets, even while most of the SNP-based heritability is driven by a massively polygenic background.

genetics↗

bioRxiv2020

Fast Lasso method for Large-scale and Ultrahigh-dimensional Cox Model with applications to UK Biobank

We develop a scalable and highly efficient algorithm to fit a Cox proportional hazard model by maximizing the L1-regularized (Lasso) partial likelihood function, based on the Batch Screening Iterative Lasso (BASIL) method developed in (Qian et al. 2019). The output of our algorithm is the full Lasso path, the parameter estimates at all predefined regularization parameters, as well as their validation accuracy measured using the concordance index (C-index) or the validation deviance. To demonstrate the effectiveness of our algorithm, we analyze a large genotype-survival time dataset across 306 disease outcomes from the UK Biobank (Sudlow et al. 2015). Our approach, which we refer to as snpnet-Cox, is implemented in a publicly available package.

bioinformatics↗

bioRxiv2019

Medical relevance of common protein-altering variants in GPCR genes across 337,205 individuals in the UK Biobank study

G protein-coupled receptors (GPCRs) drive an array of critical physiological functions and are an important class of drug targets, though a map of which GPCR genetic variants are associated with phenotypic variation is lacking. We performed a phenome-wide association analysis for 269 common protein-altering variants in 156 GPCRs and 275 phenotypes, including disease outcomes and diverse quantitative measurements, using 337,205 UK Biobank participants and identified 138 associations. We discovered novel associations between GPCR variants and migraine risk, hypothyroidism, and dietary consumption. We also demonstrated experimentally that variants in the {beta}2 adrenergic receptor (ADRB2) associated with immune cell counts and pulmonary function and variants in the gastric inhibitory polypeptide receptor (GIPR) associated with food intake and body size affect downstream signaling pathways. Overall, this study provides a map of genetic associations for GPCR coding variants across a wide variety of phenotypes, which can inform future drug discovery efforts targeting GPCRs.

genetics↗