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Genomics

Explore bioRxiv genomics preprints on sequencing, gene expression and genetic variation. Read source abstracts and verify methods in the manuscript; preprints are not peer reviewed.

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bioRxiv2024

Integration of metagenome-assembled genomes with clinical isolates reveals genomic signatures of Klebsiella pneumoniae in carriage and disease

Klebsiella pneumoniae is an opportunistic pathogen causing diseases ranging from gastrointestinal disorders to severe liver abscesses. While clinical isolates of K. pneumoniae have been extensively studied, less is known about asymptomatic variants colonizing the human gut across diverse populations. Genome-resolved metagenomics has offered unprecedented access to metagenome-assembled genomes (MAGs) from diverse host states and geographical locations, opening opportunities to explore health-associated microbial features. Here we analysed 662 human gut-derived K. pneumoniae genomes (319 MAGs, 343 isolates) from 29 countries to investigate the population structure and genomic diversity of K. pneumoniae in carriage and disease. Only 9% of sequence types were found to be shared between healthy and disease states, highlighting distinct diversity across health conditions. Integrating MAGs nearly doubled gut-associated K. pneumoniae phylogenetic diversity, and uncovered 86 lineages without representation among >20,000 Klebsiella isolate genomes from various sources. Genomic signatures linked to pathogenicity and carriage included those involved in antibiotic resistance, iron regulation, restriction modification systems and polysaccharide biosynthesis. Notably, machine learning models integrating MAGs and isolates more accurately classified disease and carriage states compared to isolates alone. These findings showcase the value of metagenomics to understand pathogen evolution with implications for public health surveillance strategies.

microbiology↗

bioRxiv2025

Harnessing landscape genomics to evaluate genomic vulnerability and future climate resilience in an East Asia perennial

In this era of rapid climate change, understanding the adaptive potential of organisms is imperative for buffering biodiversity loss. Genomic forecasting provides invaluable insights into population vulnerability and adaptive potential under diverse climatic conditions, thereby facilitating management interventions, bolstering population resilience, and shaping germplasm conservation strategies tailored to specific species. Here we integrated population genomics and landscape genomics approaches, leveraging single-nucleotide polymorphisms obtained through whole-genome resequencing of 43 Rheum palmatum complex populations, to pinpoint adaptive variation and its significance in the context of future climates, delineate seed zones, and establish guidelines for ex situ germplasm conservation to capture the majority of existing adaptive diversity. Our analysis unveiled that the species complex comprised two distinct genetic clusters, exhibiting differential climate adaptation and genomic vulnerabilities across its distribution range. We also determined that the species range could be subdivided into three distinct seed zones, with varying sample requirements per seed zone corresponding to differed conservation efforts. Overall, our findings provide a genome-wide perspective on climate adaptation and valuable insights into germplasm conservation strategies aimed at enhancing population resilience in future climates, serving as a blueprint for restoration plans of other vulnerable species.

evolutionary biology↗

bioRxiv2025

Population Genomics of Giant Mice from the Faroe Islands: Hybridization, Colonization, and a Novel Challenge to Identifying Genomic Targets of Selection

Populations that colonize islands provide unique insights into demography, adaptation, and the spread of invasive species. House mice on the Faroe Islands evolved exceptionally large bodies after colonization, generating interest from biologists since Darwin. To reconstruct the evolutionary history of these mice, we sequenced genomes of population samples from three Faroe Islands (Sandoy, Nolsoy, and Mykines) and Norway as a mainland comparison. Mice from the Faroe Islands are hybrids between the subspecies Mus musculus domesticus and M. m. musculus, with ancestry alternating along the genome. Analyses based on the site frequency spectrum of single nucleotide polymorphisms and the ancestral recombination graph (ARG) indicate that mice arrived on the Faroe Islands on a timescale consistent with transport by Norwegian Vikings, with colonization of Sandoy likely preceding colonization of Nolsoy. Substantial reductions in nucleotide diversity and effective population size associated with colonization suggest that mice on the Faroe Islands evolved large body size during periods of heightened genetic drift. Genomic scans for positive selection uncover windows with unusual site frequency spectra, but this pattern is mostly generated by clusters of singletons in individual mice. Variants showing evidence of selection in both Nolsoy and Sandoy based on the ARG are enriched for genes with neurological functions. Our findings reveal a dynamic evolutionary history for the enigmatic mice from Faroe Island and emphasize the challenges that accompany population genomic inferences in island populations. Significance StatementPopulations that colonize islands are expected to have unusual histories compared to their mainland counterparts. Using population genomic data, we conclude that giant mice living on the Faroe Islands originated from hybrids, invaded the islands on a timescale consistent with transport by Vikings, and persisted despite drastic reductions in population size. We also uncover a novel challenge to scanning genomes for genes involved in adaptation.

evolutionary biology↗

bioRxiv2025

The genome of the vining fern Lygodium microphyllum highlights genomic and functional differences between life phases of an invasive plant

Functional and genomic studies on the differences between gametophyte and sporophyte life phases remain scarce, yet unraveling these dynamics is crucial to understanding the biology of plants and the success of each phase under different environments. Here, we provide a novel reference genome for the highly invasive fern Lygodium microphyllum and compare the transcriptomic and epigenomic landscapes of the gametophyte and sporophyte life phases. We found differential regulation of developmental genes (homeobox and MADS-box clades) and usage of alternative isoforms that may play a role in the genomic determination of the haploid and diploid life stages. We further generated the first base pair-resolution methylome of a fern gametophyte, and determined that methylation patterns are remarkably similar between vegetative tissues despite their morphological and functional differences. By examining the physiological and transcriptomic responses of gametophytes and sporophytes to freezing stress, the most likely abiotic factor preventing further expansion of this invasive species, we show that life phases and tissues use alternative molecular pathways to respond to this stressor, underscoring the need to incorporate both life phases when developing effective mitigation strategies. These new genomic resources fill a gap in our understanding of fundamental plant biology and inform invasive species research. SIGNIFICANCEAll land plants undergo an alternation of generations between haploid gametophyte and diploid sporophyte life phases. How these disparate life phases are generated from a single genome, and the functional implications of these differences for plant success, is largely unknown. Moreover, understanding life-phase-specific differences in rapidly evolving populations, such as invasive species, is critical to developing effective management strategies. We assembled a chromosome-level genome and generated epigenomic and transcriptomic resources from gametophyte and sporophyte phases of the invasive fern Lygodium microphyllum, which is estimated to cost the U.S. more than $2 million annually. We highlight transcriptomic, epigenomic, and physiological variation between life phases and find support for the crucial role of the often overlooked gametophyte in the invasion process.

plant biology↗

bioRxiv2025

Empirical validation of the nearly neutral theory at divergence and population genomic scale using 150 mammals genomes

By limiting the efficacy of selection, random drift is expected to play a major role in genome evolution. Formalizing this idea, the nearly-neutral theory predicts that the ratio of non-synonymous over synonymous polymorphism ({pi}N /{pi}S) within populations, and divergence (dN /dS) between species, should both correlate negatively with Ne. This has previously been tested in mammals and other groups. However, most studies have focused on either dN /dS or on{pi} N /{pi}S, thus not addressing the problem across evolutionary scales. In addition, many studies at the macro scale have used life-history traits (LHT) as a proxy of Ne, assuming that large-bodied organisms have lower Ne than small-bodied species. However, this assumption itself has rarely been validated against more objective measures of Ne, such as genetic diversity{pi} S = 4Ne{micro}, in part because{pi} S estimates are scarce. Here we propose an integrative test of the nearly-neutral predictions on 150 mammalian species, using 6000 orthologous genes, spanning the macro and the micro-evolutionary scale, using for the latter a measure of heterozygosity on each of the assembled diploid genomes. At the micro scale, we observe, for the first time in mammalian nuclear genomes, a relationship between{pi} N /{pi}S and{pi} S. At the macro scale, we confirm the positive correlation between dN /dS and LHT but, more importantly, establish that LHT and dN /dS are correlated with{pi} S, although weakly so. Together, these results provide the first global test of the nearly-neutral theory in mammals across time scales, suggesting all variables are correlated with a single hidden variable: Ne. Significance StatementNatural selection eliminates large-effect deleterious mutations but has limited efficacy for purging mutations of small effects. The effective population size (called Ne) is what determines the cutoff below which selection is inefficient. Since Ne varies between species, the prediction is that more mutations of intermediate effect should segregate and reach fixation in species with small populations. This has been widely studied both between species (macro-evolution) and within species (micro-evolution), although never in a fully integrated manner. Here, we analysed 144 genomes of placental mammals and 6002 orthologous genes, using the original sequence reads produced for genome assembly to estimate within-species genetic diversity based on the heterozygosity of the diploid individual whose genome has been sequenced. We show consistent relationships between genetic diversity within species, life-history traits such as body mass or longevity and selection efficacy simultaneously at the micro- and macro-evolutionary levels. Our analysis provides a globally consistent picture confirming the role of effective population size in tuning selection efficacy in placental mammals.

evolutionary biology↗

bioRxiv2025

Developing a general AI model for integrating diverse genomic modalities and comprehensive genomic knowledge

Advances in next-generation sequencing technologies have vastly expanded the availability of diverse genomic, epigenomic and transcriptomic data, presenting the opportunity to develop a general AI model that integrates comprehensive genomic knowledge into a unified model. Unlike previous predictive models, which are typically specialized to certain tasks, our general AI model unifies a wide range of genomic modalities, such as nascent RNA and ultra-high-resolution chromatin organization, within a multi-task architecture. Using ATAC-seq and DNA sequences as inputs, we incorporated diverse genomic modalities as output, and the model exhibits strong generalizability across different cell types and tissues in all tasks we trained. It accurately predicts gene-level transcription measured by various nascent RNA assays, and effectively captures enhancer-associated transcription. Additionally, it also accurately captures the potential functions of non-coding genetic variants and regulatory elements. Additionally, we extended the model trained on human data to a mouse general model, achieving accurate predictions of genomic modalities, such as high resolution chromatin contact maps with limited data availability, which are further validated using an established mouse inner-ear study. This comprehensive approach offers a powerful tool for understanding genome regulation in both human and mouse species.

bioinformatics↗

bioRxiv2025

IDEAL GENOM: Integrated Downstream Analytical Pipeline for Genomic Analysis

There has been an exponential increase in the development of technologies that have offered unprecedented opportunities to unravel the genetic underpinnings of rare and complex diseases. However, despite the progress, and availability of tools, there are still several challenges to over-come, such as the development of a unified framework that facilitates downstream analysis. To address this challenge, we present IDEAL-GENOM (Integrated Downstream Analytical Toolkit for Genomic Analysis), a Python-based wrapper designed to streamline the analytical workflow commonly implemented in genome-wide association studies (GWAS) settings. IDEAL-GENOM integrates widely used tools such as PLINK, GCTA and bcftools along with custom-developed functionalities, enabling reproducible results through parameter sharing. IDEAL-GENOM provides a simplified framework for quality control (QC) and GWAS and downstream analysis that will enable beginners and advanced users to leverage the in-built functionalities to per-form GWAS analysis for complex diseases. IDEAL-GENOM is a customizable tool that enables users to efficiently analyze genotyping data with reproducible QC, post-imputation, and GWAS pipelines.

bioinformatics↗

bioRxiv2025

Genome-wide signatures of reproductive isolation shape the varied genomic landscape of the roundtail horned lizard (Phrynosoma modestum)

Population divergence is promoted and inhibited by gene flow and divergent selection, but the mechanisms of and relationship between these two processes remain poorly understood. Modelling the selective pressures at play in a natural population requires a thorough understanding of species structure and demographic history, which can be helpful in hypothesizing the genetic and evolutionary factors that underlie divergence. In this study, we assess whole genome sequences of round-tailed horned lizards (Phyrnosoma modestum) from throughout the species range and combine phylogenetic analyses with genetic landscape scans to understand how current genetic diversity has been influenced by demographic histories and evolutionary pressures. Maximum likelihood phylogenetic analysis supports two lineages within the species, correlating with a North/South population divide that likely diverged about 7 Ma and displayed little migration. Intermediate gdi values indicate that the two lineages may be in the gray area of speciation, yet our results also support significant isolation-by-distance (IBD). Negative values of Tajimas D offer support for selection acting on P. modestum, but may stem from recent population expansions. Genomic diversity within populations and islands of divergence between populations are also detected across the P. modestum genome, specifically exhibiting differentiation patterns linked to reproductive isolation and within-population selection. We posit potential explanations for the genomic landscape characterized here, namely that allopatry plays a large role in shaping genomic divergence across the species range. Taken together, our results provide a picture of a species currently maintaining species integrity despite significant genomic signatures of geographic structure and reproductive isolation.

evolutionary biology↗

bioRxiv2018

Database-integrated genome screening (DIGS): exploring genomes heuristically using sequence similarity search tools and a relational database.

A significant fraction of most genomes is comprised of DNA sequences that have been incompletely investigated. This genomic dark matter contains a wealth of useful biological information that can be recovered by systematically screening genomes in silico using sequence similarity search tools. Specialized computational tools are required to implement these screens efficiently. Here, we describe the database-integrated genome-screening (DIGS) tool: a computational framework for performing these investigations. To demonstrate, we screen mammalian genomes for endogenous viral elements (EVEs) derived from the Filoviridae, Parvoviridae, Circoviridae and Bornaviridae families, identifying numerous novel elements in addition to those that have been described previously. The DIGS tool provides a simple, robust framework for implementing a broad range of heuristic, sequence analysis-based explorations of genomic diversity.\n\nAvailabilityhttp://giffordlabcvr.github.io/DIGS-tool/\n\nContactrobert.gifford@glasgow.ac.uk\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

Systematic characterization of genome editing in primary T cells reveals proximal genomic insertions and enables machine learning prediction of CRISPR-Cas9 DNA repair outcomes

The Streptococcus pyogenes Cas9 (SpCas9) nuclease has become a ubiquitous genome editing tool due to its ability to target almost any location in DNA and create a double-stranded break1,2. After DNA cleavage, the break is fixed with endogenous DNA repair machinery, either by non-templated mechanisms (e.g. non-homologous end joining (NHEJ) or microhomology-mediated end joining (MMEJ)), or homology directed repair (HDR) using a complementary template sequence3,4. Previous work has shown that the distribution of repair outcomes within a cell population is non-random and dependent on the targeted sequence, and only recent efforts have begun to investigate this further5-11. However, no systematic work to date has been validated in primary human cells5,7. Here, we report DNA repair outcomes from 1,521 unique genomic locations edited with SpCas9 ribonucleoprotein complexes (RNPs) in primary human CD4+ T cells isolated from multiple healthy blood donors. We used targeted deep sequencing to measure the frequency distribution of repair outcomes for each guide RNA and discovered distinct features that drive individual repair outcomes after SpCas9 cleavage. Predictive features were combined into a new machine learning model, CRISPR Repair OUTcome (SPROUT), that predicts the length and probability of nucleotide insertions and deletions with R2 greater than 0.5. Surprisingly, we also observed large insertions at more than 90% of targeted loci, albeit at a low frequency. The inserted sequences aligned to diverse regions in the genome, and are enriched for sequences that are physically proximal to the break site due to chromatin interactions. This suggests a new mechanism where sequences from three-dimensionally neighboring regions of the genome can be inserted during DNA repair after Cas9-induced DNA breaks. Together, these findings provide powerful new predictive tools for Cas9-dependent genome editing and reveal new outcomes that can result from genome editing in primary T cells.

cell biology↗

bioRxiv2019

Nuclear Genome Size is Positively Correlated with Mean LTR Insertion Date in Fern and Lycophyte Genomes

AO_SCPLOWBSTRACTC_SCPLOWNuclear genome size is highly variable in vascular plants. The composition of long terminal repeat retrotransposons (LTRs) is a chief mechanism of long term change in the amount of nuclear DNA. Compared to flowering plants, little is known about LTR dynamics in ferns and lycophytes. Drawing upon the availability of recently sequenced fern and lycophyte genomes we investigated these dynamics and placed them in the context of vascular plants. We found that similar to seed plants, mean LTR insertion dates were strongly correlated with haploid nuclear genome size. Fern and lycophyte species with small genomes such as those of the heterosporous Selaginella and members of the Salviniaceae had recent mean LTR insertion dates, whereas species with large genomes such as homosporous ferns had old mean LTR insertion dates intermediate between angiosperms and gymnosperms. This pattern holds despite methylation and life history differences in ferns and lycophytes compared to seed plants, and our results are consistent with other patterns of structural variation in fern and lycophyte genomes.

evolutionary biology↗

bioRxiv2019

The genome design suite: enabling massive in-silico experiments to design genomes.

MotivationComputational biology is a rapidly developing field, and in-silico methods are being developed to aid the design of genomes to create cells with optimised phenotypes. Two barriers to progress are that in-silico methods are often only developed on a particular implementation of a specific model (e.g. COBRA metabolic models) and models with longer simulation time inhibit the large-scale in-silico experiments required to search the vast solution space of genome combinations.\n\nResultsHere we present the genome design suite (PyGDS) which is a suite of Python tools to aid the development of in-silico genome design methods. PyGDS provides a framework with which to implement phenotype optimisation algorithms on computational models across computer clusters. The framework is abstract allowing it to be adapted to utilise different computer clusters, optimisation algorithms, or design goals. It implements an abstract multi-generation algorithm structure allowing algorithms to avoid maximum simulation times on clusters and enabling iterative learning in the algorithm. The initial case study will be genome reduction algorithms on a whole-cell model of Mycoplasma genitalium for a PBS/Torque cluster and a Slurm cluster.\n\nAvailabilityThe genome design suite is written in Python for Linux operating systems and is available from GitHub on a GPL open-source licence.\n\nContacto.chalkley@bristol.ac.uk, lacsg@bristol.ac.uk, and lucia.marucci@bristol.ac.uk.

systems biology↗

bioRxiv2025

Genome-Level Hierarchical Attention Transformer with Multi-Head Attention Weighted Sum for Broad-Spectrum Antimicrobial Resistance Prediction and Discovery of Resistance-Related Genomic Contexts

Antimicrobial resistance is a growing global health concern, requiring reliable tools for predicting resistance across a wide range of bacteria and antibiotics. In this study, we introduce a genome-level hierarchical attention transformer (GL-HAT) that integrates a pretrained genomic foundation model with hierarchical attention mechanisms to analyze the full protein sequence context of bacterial genomes. GL-HAT is designed to deliver both accurate resistance predictions and interpretable insights into the genomic features associated with resistance using specialized modules. Trained and validated on a comprehensive dataset of bacterial genomes and antimicrobial susceptibility profiles, the model outperformed traditional machine learning method, with an F1-score of 0.845 and AUROC of 0.953. Moreover, GL-HAT could demonstrate the resistance-related contexts in the genome, such as vanA operon and nearby resolvase genes in vancomycin-resistant Enterococcus and tet(M) gene adjacent to conjugation proteins in tetracycline-resistant Streptococcus pneumoniae. By combining high accuracy with interpretability, GL-HAT offers a practical approach for advancing AMR diagnostics and research, supporting more informed clinical and public health decisions.

bioinformatics↗

bioRxiv2026

MirMachine 2: a scalable, evolutionarily informed pipeline for microRNA annotation and comparative genomics across thousands of animal genomes

Genome sequencing is rapidly outpacing the annotation of conserved regulatory elements, limiting the evolutionary and comparative insights that can be extracted from expanding genome collections. MicroRNAs are among the most conserved and phylogenetically informative genes, yet automated annotation has remained difficult to scale while preserving evolutionary interpretability. Here we present MirMachine 2, an evolutionarily informed framework that combines curated reference models, lineage-aware scoring, and adaptive filtering to enable robust genome-wide microRNA annotation at scale. Applying this to thousands of animal genomes reveals that many apparent absences of conserved microRNAs reflect methodological bias rather than biological loss, particularly in underrepresented lineages. By enabling consistent and interpretable comparison of microRNA complements across large datasets, MirMachine 2 establishes scalable microRNA annotation as a practical foundation for genome-scale evolutionary and comparative genomics.

bioinformatics↗

bioRxiv2026

Genomic properties representing plant sex chromosome evolution interpreted with genome language models

Plants have repeatedly evolved chromosomal sex-determining systems from hermaphroditic ancestors, providing a powerful natural framework for studying convergent evolution. However, sex chromosomes undergo extensive structural divergence, degeneration, and repeat accumulation, making direct comparisons across distant lineages difficult. Here, we apply a genome language model (gLM), which encodes genomic sequences into high-dimensional representations of their contextual properties, to independently evolved sex chromosomes from the distantly related genera Silene and Humulus. Without relying on sequence alignment or gene orthology, we identify convergent genomic signatures shared among plant Y chromosomes, including elevated GC content and depletion of specific trinucleotide motifs. Directionality analyses of latent genomic vectors further reveal common evolutionary trajectories associated with recombination suppression and Y chromosome differentiation. These properties differ from those observed in animal sex chromosomes, suggesting lineage-specific modes of sex chromosome evolution in plants. Our results demonstrate that genome language models can transform structurally incomparable chromosomes into quantitatively comparable evolutionary entities, allowing the interpretation of common genomic principles underlying convergent evolution across deeply diverged plant lineages.

evolutionary biology↗

bioRxiv2026

Leveraging genome-wide association studies and genomic prediction for distinctness, uniformity, and stability (DUS) testing in maize

Testing for distinctness, uniformity, and stability (DUS) is a requirement for plant variety registration and based on phenotypic traits, which is time-consuming and sensitive to environmental variation. Advances in genomics allow to complement DUS testing with molecular markers, for which two models in DUS testing were proposed by the Union for the Protection of New Varieties of Plants (UPOV). A use cases was described for maize, but an implementation has been hindered by a lack of suitable markers and validated analytical frameworks. We address these challenges by integrating historical DUS characteristics scores from 352 European hybrid maize varieties with high-density genome-wide single nucleotide polymorphism (SNP) data. Using genome-wide association studies (GWAS), we identified 18 genomic regions and candidate genes associated with 12 DUS characteristics, enabling the development of diagnostic markers consistent with the UPOV model "Characteristic-Specific Molecular Markers". Since most DUS traits are polygenic, we combined GWAS-informed marker selection with XG-Boost-based machine learning to predict notes of DUS characteristics. This approach achieved strong predictive performance across multiple traits (mean accuracy 0.67), demonstrating its potential for managing reference collections under UPOV model "Combining phenotypic and molecular distances in the management of variety collections". Both approaches were validated for two characteristics using independent public USDA-NPGS maize datasets (>1,700 accessions) highlighting the value of public data for method validation. We also identify key limitations of historical DUS data, including imbalanced and sparse trait representation, and discuss mitigation strategies. Despite these constraints, our results demonstrate that molecular markers may improve maize DUS testing, enabling faster, more accurate variety registration and supporting accelerated crop improvement. Key messageHistorical DUS datasets can be used to identify marker-trait associations of DUS characteristics using genome-wide association study (GWAS) and to develop a genomic prediction framework for an accurate prediction of DUS character notes from marker data.

genetics↗

bioRxiv2026

An ONT sequencing framework for hantavirus genome assembly, variant analysis, and defective viral genome characterization from whole-segment amplicons

Orthohantaviruses are zoonotic, tri-segmented negative-sense RNA viruses of rodents and other small mammals that can cause severe human disease. Whole genome sequencing remains difficult because of low viral loads, high sequence diversity, and an inherent instability of the viral L segment. Current tiled amplicon and hybrid-capture approaches do not capture the heterogeneity of virus populations, including defective viral genomes (DVGs), which have never been described for hantaviruses. Here, we present an Oxford Nanopore Technologies sequencing framework based on universal reverse transcription primers targeting the conserved genome termini of orthohantaviruses and a single species-specific primer pair per segment, enabling amplification and sequencing of complete S, M, and L segments. We also developed an accompanying bioinformatic pipeline that performs reference-guided assembly of hantavirus genomes, consensus generation, variant calling, and DVG classification using the newly developed ORCHID classification pipeline. We applied this approach to diverse Puumala virus strains from infected cell cultures and bank voles, to representative Old World and New World orthohantaviruses, and to highly pathogenic New World viruses in vivo. In all cases we were able to accurately reconstruct full hantavirus genomes. Notably, we found that hantavirus populations contain highly abundant DVGs and conserved L segment deletions, whereas the S and M segments remained largely intact. Together, this work provides a low-cost, broadly applicable and easily customisable workflow for hantavirus genomics and reveals DVGs as a previously unrecognized feature of hantavirus populations.

microbiology↗

bioRxiv2013

Joint analysis of functional genomic data and genome-wide association studies of 18 human traits

Annotations of gene structures and regulatory elements can inform genome-wide association studies (GWAS). However, choosing the relevant annotations for interpreting an association study of a given trait remains challenging. We describe a statistical model that uses association statistics computed across the genome to identify classes of genomic element that are enriched or depleted for loci that influence a trait. The model naturally incorporates multiple types of annotations. We applied the model to GWAS of 18 human traits, including red blood cell traits, platelet traits, glucose levels, lipid levels, height, BMI, and Crohns disease. For each trait, we evaluated the relevance of 450 different genomic annotations, including protein-coding genes, enhancers, and DNase-I hypersensitive sites in over a hundred tissues and cell lines. We show that the fraction of phenotype-associated SNPs that influence protein sequence ranges from around 2% (for platelet volume) up to around 20% (for LDL cholesterol); that repressed chromatin is significantly depleted for SNPs associated with several traits; and that cell type-specific DNase-I hypersensitive sites are enriched for SNPs associated with several traits (for example, the spleen in platelet volume). Finally, by re-weighting each GWAS using information from functional genomics, we increase the number of loci with high-confidence associations by around 5%.

Genomics↗