Biology subjects

Bierman, R.

Publications and source records attributed to Bierman, R..

6 recordsLinked to original sources

bioRxiv2026

A Principled Framework for Using Correlated Traits to Improve Risk Prediction

Although many complex phenotypes and diseases are influenced by shared genetic and environmental factors, risk prediction methods typically rely on genetic information from a single trait, leaving a rich source of predictive information largely unexploited. Phenotypic correlations can potentially be used to improve the accuracy of polygenic scores (PGS), but the conditions under which correlated traits meaningfully enhance prediction remain poorly understood. Here, we develop a general theoretical and simulation framework that quantifies the extent to which correlated "helper" traits improve predictive accuracy and identifies the factors that determine the magnitude of these gains. We show that helper traits can substantially improve predictive accuracy, with the magnitude of these gains governed by baseline model performance, genetic and environmental correlations, and the heritability of the target and helper traits, providing principled guidance for helper-trait selection. Paradoxically, when the target trait is itself weakly heritable, helper traits need not be highly heritable to substantially improve the accuracy of PGS, because low-heritability traits can still capture non-redundant environmental factors shared with the target trait. We empirically evaluated the use of helper traits by developing PGS models to predict type 2 diabetes using data from the UK Biobank. Helper traits substantially improved predictive accuracy relative to a single-trait PGS (AUC-ROC = 0.907 versus 0.677) and achieved performance comparable to models that use HbA1c (AUC-ROC = 0.889), the current clinical gold-standard biomarker. Our results establish a general theoretical and practical framework for exploiting correlated traits to improve polygenic prediction, provide principled guidance for selecting informative helper traits, and demonstrate how shared genetic and environmental architecture can be leveraged to substantially increase predictive accuracy. Furthermore, we developed an interactive web application to estimate the expected gain in accuracy from candidate helper traits using empirically measurable quantities.

genomics↗

bioRxiv2026

An interpretable machine learning framework for dog breed inference and ancestry decomposition

The over 300 currently recognized breeds of domesticated dogs are the culmination of centuries of intense artificial selection and recurrent population bottlenecks. While breed labels are widely used in genetic and veterinary studies, inferring breed identity from genomic data remains challenging due to the high dimensionality of genotype data, uneven sampling across breeds, and admixture resulting in mixed-breed individuals. Here, we present an interpretable machine learning framework to infer dog breed labels from genome-wide SNP data. Our approach combines dimensionality reduction with a multi-output random forest model that maps genetic variation to a continuous representation of breed membership, enabling both classification and mixed-breed inference. We apply this framework to the Dog Aging Project (DAP) dataset of 6,572 purebred and mixed-breed dogs across 100 breed classes, achieving 91.7% accuracy with an overlap-based metric, outperforming an ADMIXTURE-based benchmark that achieved 87.8% accuracy. Notably, we find that as few as 150 informative SNPs are sufficient to achieve near-maximal predictive performance, highlighting the highly structured nature of canine genetic variation. We also introduce a SNP importance score metric that links model predictions back to individual genetic variants. Analysis of top-ranked variants reveals loci previously associated with morphological, pigmentation, and behavioral traits, as well as candidate loci lacking prior phenotypic annotation, supporting both the biological relevance and discovery potential of the framework. Together, these results demonstrate that our framework provides an accurate, flexible, and interpretable approach to predict breed ancestry, with applications in veterinary genomics, canine population genetics, and the identification of loci underlying hallmark breed phenotypes.

bioinformatics↗

bioRxiv2026

Blood biomarkers and breed genetics of aging in pet dogs

Pet dogs share human-like environments while aging on a compressed timescale, making them a powerful translational model for aging research. Using genomic and phenotypic data from 7,627 dogs in the Dog Aging Project, including 976 profiled for 159 blood metabolites and clinical analytes, we generated the first GWAS catalog in dogs. Blood traits map to orthologous loci in dogs and humans, indicating deeply conserved pathways. Breed ancestry explains substantial variance in blood traits, and selection on visible characteristics such as fur type has pleiotropic metabolic effects. Leveraging mosaic ancestry in mixed-breed dogs and longitudinal mortality data, we identify blood traits elevated in short-lived breeds that predict individual mortality risk -- including globulin and potassium -- and protective traits enriched in long-lived breeds, such as ethanolamine. Although some aging-associated traits relate to growth hormone pathways, many do not, indicating that aging in dogs is multifactorial. These findings establish dogs as a translational system for identifying genetic determinants and biomarkers of aging relevant to extending healthy lifespans.

genetics↗

bioRxiv2025

Landscape of human protein-coding somatic mutations across tissues and individuals

Although somatic mutations are fundamentally important to human biology, disease, and aging, many outstanding questions remain about their rates, spectrum, and determinants in apparently healthy tissues. Here, we performed high-coverage exome sequencing on 265 samples from 14 GTEx donors sampled for a median of 17.5 tissues per donor (spanning 46 total tissues). Using a novel probabilistic method tailored to the unique structure of our data, we identified 8,470 somatic variants. We leverage our compendium of somatic mutations to quantify the burden of deleterious somatic variants among tissues and individuals, identify molecular features such as chromatin accessibility that exhibit significantly elevated somatic mutation rates, provide novel biological insights into mutational mechanisms, and infer developmental trajectories based on patterns of multi-tissue somatic mosaicism. Our data provides a high-resolution portrait of somatic mutations across genes, tissues, and individuals.

genomics↗

bioRxiv2022

Unsupervised reference-free inference reveals unrecognized regulated transcriptomic complexity in human single cells

Myriad mechanisms diversify the sequence content of eukaryotic transcripts at both the DNA and RNA levels, leading to profound functional consequences. Examples of this diversity include RNA splicing and V(D)J recombination. Currently, these mechanisms are detected using fragmented bioinformatic tools that require predefining a form of transcript diversification and rely on alignment to an incomplete reference genome, filtering out unaligned sequences, potentially crucial for novel discoveries. Here, we present SPLASH+, significantly advancing biological discovery possible with SPLASH, our recently introduced efficient, reference-free statistical approach. Integrating a micro-assembly and biological interpretation framework, SPLASH+ enables new discoveries including broad and novel examples of transcript diversification in single cells de novo, without the need for cell type metadata, which is impossible with current algorithms. Applied to 10,326 primary human single cells across 19 tissues profiled with SmartSeq2, SPLASH+ discovers a set of splicing and histone regulators with highly conserved intronic regions that are themselves subject to complex splicing regulation. Additionally, it reveals unreported transcript diversity in the heat shock protein HSP90AA1, as well as diversification in centromeric RNA expression, V(D)J recombination, RNA editing, and repeat expansion, all missed by existing methods. SPLASH+ is highly efficient, enabling the discovery of an unprecedented breadth of RNA regulation and diversification in single cells through a new automated paradigm of unbiased transcriptomic analysis.

bioinformatics↗

bioRxiv2022

Statistical analysis supports pervasive RNA subcellular localization and alternative 3' UTR regulation

Targeted low-throughput studies have previously identified subcellular RNA localization as necessary for cellular functions including polarization, and translocation. Further, these studies link localization to RNA isoform expression, especially 3 Untranslated Region (UTR) regulation. The recent introduction of genome-wide spatial transcriptomics techniques enable the potential to test if subcellular localization is regulated in situ pervasively. In order to do this, robust statistical measures of subcellular localization and alternative poly-adenylation (APA) at single cell resolution are needed. Developing a new statistical framework called SPRAWL, we detect extensive cell-type specific subcellular RNA localization regulation in the mouse brain and to a lesser extent mouse liver. We integrated SPRAWL with a new approach to measure cell-type specific regulation of alternative 3 UTR processing and detected examples of significant correlations between 3 UTR length and subcellular localization. Included examples, Timp3, Slc32a1, Cxcl14, and Nxph1 have subcellular localization in the brain highly correlated with regulated 3 UTR processing that includes use of unannotated, but highly conserved, 3 ends. Together, SPRAWL provides a statistical framework to integrate multi-omic single-cell resolved measurements of gene-isoform pairs to prioritize an otherwise impossibly large list of candidate functional 3 UTRs for functional prediction and study. SPRAWL predicts 3 UTR regulation of subcellular localization may be more pervasive than currently known.

bioinformatics↗