Biology subjects

Morris, J. A.

Publications and source records attributed to Morris, J. A..

14 recordsLinked to original sources

bioRxiv2026

Identification of target genes and regulatory networks for bone mineral density GWAS loci through systematic targeting and inhibition

Osteoporosis is characterized by low bone mineral density (BMD) and elevated fracture risk. Most BMD-associated GWAS variants lie in noncoding regions, complicating efforts to identify causal genes and mechanisms. To overcome this variant-to-function challenge, we previously developed STING-seq, a framework integrating biobank-scale GWAS with single-cell CRISPR inhibition (CRISPRi) screening to directly connect noncoding cis-regulatory elements (CREs) to their target genes. Applied to human fetal osteoblast (hFOB) cells across osteogenic differentiation, STING-seq linked 76 CREs to 75 target genes at BMD loci. Arrayed CRISPRi and activation validated key CRE-gene relationships, including long-range enhancers regulating CXCL12 (over 500 kb apart). We further uncovered trans-regulatory networks and characterized the DAP3-YY1AP1 bidirectional promoter, demonstrating a role for DAP3 in mineralization via mitochondrial pathways. Together, these findings provide mechanistic insight into how noncoding GWAS variants shape osteoblast activity and highlight the genes and pathways that mediate genetic effects on BMD.

genomics↗

bioRxiv2025

CRISPRi perturbation screens and eQTLs provide complementary and distinct insights into GWAS target genes

Most genetic variants associated with human traits and diseases lie in noncoding regions of the genome 1, and a key challenge is determining which genes they affect 2,3. A common approach has been to leverage associations between natural genetic variation and gene expression to identify expression quantitative trait loci (eQTLs) in the population 4,5. At the same time, a novel approach uses pooled CRISPR interference (CRISPRi) perturbations of noncoding loci with single-cell transcriptome sequencing 6,7. Here, we systematically harmonized and compared the results from these approaches across hundreds of genomic regions associated with blood cell traits. We find that while the two approaches sometimes identify the same target genes, there are considerable differences that affect biological inferences made from the data. CRISPRi preferentially maps highly proximal, constraint-enriched genes, whereas eQTLs recover multiple, often distal targets. By benchmarking against 1,075 gold-standard CRE-gene pairs linked to blood traits, we show that the two approaches identify largely distinct targets; when combined, they achieve a balance between accuracy and completeness of gene discovery. Our results offer guidance for improved design of CRISPRi and eQTL studies and highlight their joint potential as a powerful toolkit for interpreting disease-associated loci.

genomics↗

bioRxiv2024

Non-linear transcriptional responses to gradual modulation of transcription factor dosage

Genomic loci associated with common traits and diseases are typically non-coding and likely impact gene expression, sometimes coinciding with rare loss-of-function variants in the target gene. However, our understanding of how gradual changes in gene dosage affect molecular, cellular, and organismal traits is currently limited. To address this gap, we induced gradual changes in gene expression of four genes using CRISPR activation and inactivation. Downstream transcriptional consequences of dosage modulation of three master trans-regulators associated with blood cell traits (GFI1B, NFE2, and MYB) were examined using targeted single-cell multimodal sequencing. We showed that guide tiling around the TSS is the most effective way to modulate cis gene expression across a wide range of fold-changes, with further effects from chromatin accessibility and histone marks that differ between the inhibition and activation systems. Our single-cell data allowed us to precisely detect subtle to large gene expression changes in dozens of trans genes, revealing that many responses to dosage changes of these three TFs are nonlinear, including non-monotonic behaviours, even when constraining the fold-changes of the master regulators to a copy number gain or loss. We found that the dosage properties are linked to gene constraint and that some of these nonlinear responses are enriched for disease and GWAS genes. Overall, our study provides a straightforward and scalable method to precisely modulate gene expression and gain insights into its downstream consequences at high resolution.

genomics↗

bioRxiv2023

Large-scale causal discovery using interventional data sheds light on the regulatory network architecture of blood traits

Inference of directed biological networks is an important but notoriously challenging problem. We introduce inverse sparse regression (inspre), an approach to learning causal networks that leverages large-scale intervention-response data. Applied to 788 genes from the genome-wide perturb-seq dataset, inspre helps elucidate the network architecture of blood traits.

genetics↗

bioRxiv2022

Modeling protected species distributions and habitats to inform siting and management of pioneering ocean industries: A case study for Gulf of Mexico aquaculture

Marine Spatial Planning (MSP) provides a process that uses spatial data and models to evaluate environmental, social, economic, cultural, and management trade-offs when siting ocean industries. Aquaculture is the fastest-growing food sector in the world. The U.S. has substantial opportunity for offshore aquaculture development given the size of its exclusive economic zone, habitat diversity, and variety of candidate species for cultivation. However, many protected species rely upon habitats that overlap with promising aquaculture areas. Siting surveys, farm construction, operations, and decommissioning can alter the habitat and behavior of animals in the vicinity of these activities. Vessel activity, underwater noise, and physical interactions between protected species and farms can potentially increase the risk of injury or cause direct mortality. In 2020, the U.S. Gulf of Mexico was identified as one of the first regions to be evaluated for offshore aquaculture opportunities as directed by a Presidential Executive Order. We developed a generalized scoring model for protected species data layers that captures vulnerability using species conservation status and demographic information. We applied this approach to data layers for eight species listed under the Endangered Species Act, including five species of sea turtles, Rices Whale, Smalltooth Sawfish, and Giant Manta Ray. We evaluated several methods for scoring (e.g., arithmetic mean, geometric mean, product, lowest scoring layer) and created a combined protected species data layer that was used within a multi-criteria decision-making modeling framework for MSP. The product approach for scoring provided the most logical ordering of and the greatest contrast in site suitability scores. This approach provides a transparent and repeatable method to identify aquaculture site alternatives with the least conflict with protected species. These modeling methods are transferable to other regions, to other sensitive or protected species, and for spatial planning for other ocean-uses.

scientific communication and education↗

bioRxiv2022

Efficient combinatorial targeting of RNA transcripts in single cells with Cas13 RNA Perturb-seq

Pooled CRISPR screens coupled with single-cell RNA-sequencing have enabled systematic interrogation of gene function and regulatory networks. Here, we introduce Cas13 RNA Perturb-seq (CaRPool-seq) which leverages the RNA-targeting CRISPR/Cas13d system and enables efficient combinatorial perturbations alongside multimodal single-cell profiling. CaRPool-seq encodes multiple perturbations on a cleavable array which is associated with a detectable barcode sequence, allowing for the simultaneous targeting of multiple genes. We compared CaRPool-seq to existing Cas9-based methods, highlighting its unique strength to efficiently profile combinatorially perturbed cells. Finally, we apply CaRPool-seq to perform multiplexed combinatorial perturbations of myeloid differentiation regulators in an acute myeloid leukemia (AML) model system and identify extensive interactions between different chromatin regulators that can enhance or suppress AML differentiation phenotypes.

genomics↗

bioRxiv2021

Discovery of target genes and pathways of blood trait loci using pooled CRISPR screens and single cell RNA sequencing

The majority of variants associated with complex traits and common diseases identified by genome-wide association studies (GWAS) map to noncoding regions of the genome with unknown regulatory effects in cis and trans. By leveraging biobank-scale GWAS data, massively parallel CRISPR screens and single cell transcriptome sequencing, we discovered target genes of noncoding variants for blood trait loci. The closest gene was often the target gene, but this was not always the case. We also identified trans-effects networks of noncoding variants when cis target genes encoded transcription factors, such as GFI1B and NFE2. We observed that GFI1B trans-target genes were enriched for GFI1B binding sites and fine-mapped GWAS variants, and expressed in human bone marrow progenitor cells, suggesting that GFI1B acts as a master regulator of blood traits. This platform will enable massively parallel assays to catalog the target genes of human noncoding variants in both cis and trans.

genomics↗

bioRxiv2020

An Effector Index to Predict Causal Genes at GWAS Loci

Drug development and biological discovery require effective strategies to map existing genetic associations to causal genes. To approach this problem, we began by identifying a set of positive control genes for 12 common diseases and traits that cause a Mendelian form of the disease or are the target of a medicine used for disease treatment. We then identified a widely-available set of genomic features enriching GWAS-associated single nucleotide variants (SNVs) for these positive control genes. Using these features, we trained and validated the Effector Index (Ei), a causal gene mapping algorithm using the 12 common diseases and traits. The area under Eis receiver operator curve to identify positive control genes was 80% and area under the precision recall curve was 29%. Using an enlarged set of independently curated positive control genes for type 2 diabetes which included genes identified by large-scale exome sequencing, these areas increased to 85% and 61%, respectively. The best predictors were coding or transcript altering SNVs, distance to gene and open chromatin-based metrics. We provide the Ei algorithm for its widespread use and have created a web-portal to facilitate understanding of results. This work outlines a simple, understandable approach to prioritize genes at GWAS loci for functional follow-up and drug development. Author summaryIn order to derive biological insight, or develop drugs based on genome-wide association studies (GWAS) data, causal genes at associated loci need to be identified. GWAS usually identify large genome regions containing many genes, but seldomly identifies specific causal genes. We have developed an algorithm to predict which genes in a region of disease association are likely causal and have named this algorithm the Effector Index. The Effector Index was optimized on diseases that have known causal or drug target genes, and further validated to predict these types of genes in independent datasets. The Effector Index formalizes these predictive features into a tool that can be used by researchers, and results from the traits and diseases studied here are available via the Accelerating Medicine Partnership web-portal at http://hugeamp.org/effectorgenes.html.

bioinformatics↗

bioRxiv2020

Automated design of CRISPR prime editors for thousands of human pathogenic variants

Prime editors (PEs) are CRISPR-based genome engineering tools that can introduce precise base-pair edits at specific locations in the genome. These programmable gene editors have been predicted to repair 89% of known human pathogenic variants in the ClinVar database, although these PE constructs do not presently exist. Towards this end, we developed an automated pipeline to correct (therapeutic editing) or introduce (disease modeling) human pathogenic variants that optimizes the design of several RNA constructs required for prime editing and avoids predicted off-targets in the human genome. However, using optimal PE design criteria, we find that only a small fraction of these pathogenic variants can be targeted. Through the use of alternative Cas9 enzymes and extended templates, we increase the number of targetable pathogenic variants to >50,000 variants and make these pre-designed PE constructs accessible through a web-based portal (http://primeedit.nygenome.org). Given the tremendous potential for therapeutic gene editing, we also assessed the possibility of developing universal PE constructs. By examining the overlap of different PE components with common human genetic variants in dbSNP, we find that common variants affect only a small minority of designed PEs.

genomics↗

bioRxiv2020

Osteocyte Transcriptome Mapping Identifies a Molecular Landscape Controlling Skeletal Homeostasis and Susceptibility to Skeletal Disease

Osteocytes are master regulators of the skeleton. We mapped the transcriptome of osteocytes from different skeletal sites, across age and sexes in mice to reveal genes and molecular programs that control this complex cellular-network. We define an osteocyte transcriptome signature of 1239 genes that distinguishes osteocytes from other cells. 77% have no previously known role in the skeleton and are enriched for genes regulating neuronal network formation, suggesting this program is important in osteocyte communication. We evaluated 19 skeletal parameters in 733 knockout mouse lines and reveal 26 osteocyte transcriptome signature genes that control bone structure and function. We showed osteocyte transcriptome signature genes are enriched for human orthologs that cause monogenic skeletal disorders (P=2.4x10-22) and are associated with the polygenic diseases osteoporosis (P=1.8x10-13) and osteoarthritis (P=1.6x10-7). Thus, we reveal the molecular landscape that regulates osteocyte network formation and function and establish the importance of osteocytes in human skeletal disease.

systems biology↗

bioRxiv2019

Delivering on the Vision of Bench to Bedside: A Rare Disease Funding Community Collaboration to Develop Effective Therapies for Neurofibromatosis Type 1 Tumors

The time from target identification to new drug approval is often measured in decades. This can be even more challenging for rare diseases. Indeed, 95% of rare diseases do not have a specific therapy approved. Coordinated efforts to support research along the drug development pipeline can provide long term and comprehensive support to enable scientific breakthroughs for rare diseases. However, this requires coordination across multiple stakeholders. The present article analyzes the funding efforts of four major federal and philanthropic organizations to accelerate the advancement of MEK inhibitors to human clinical trials for NF1-associated tumors.

scientific communication and education↗

bioRxiv2018

Machine Learning to Predict Osteoporotic Fracture Risk from Genotypes

BackgroundGenomics-based prediction could be useful since genome-wide genotyping costs less than many clinical tests. We tested whether machine learning methods could provide a clinically-relevant genomic prediction of quantitative ultrasound speed of sound (SOS)--a risk factor for osteoporotic fracture.\n\nMethodsWe used 341,449 individuals from UK Biobank with SOS measures to develop genomically-predicted SOS (gSOS) using machine learning algorithms. We selected the optimal algorithm in 5,335 independent individuals and then validated it and its ability to predict incident fracture in an independent test dataset (N = 80,027). Finally, we explored whether genomic pre-screening could complement a UK-based osteoporosis screening strategy, based on the validated tool FRAX.\n\nResultsgSOS explained 4.8-fold more variance in SOS than FRAX clinical risk factors (CRF) alone (r2 = 23% vs. 4.8%). A standard deviation decrease in gSOS, adjusting for the CRF-FRAX score was associated with a higher increased odds of incident major osteoporotic fracture (1,491 cases / 78,536 controls, OR = 1.91 [1.70-2.14], P = 10-28) than that for measured SOS (OR = 1.60 [1.50-1.69], P = 10-52) and femoral neck bone mineral density (147 cases / 4,594 controls, OR = 1.53 [1.27-1.83], P = 10-6). Individuals in the bottom decile of the gSOS distribution had a 3.25-fold increased risk of major osteoporotic fracture (P = 10-18) compared to the top decile. A gSOS-based FRAX score, identified individuals at high risk for incident major osteoporotic fractures better than the CRF-FRAX score (P = 10-14). Introducing a genomic pre-screening step into osteoporosis screening in 4,741 individuals reduced the number of required clinical visits from 2,455 to 1,273 and the number of BMD tests from 1,013 to 473, while only reducing the sensitivity to identify individuals eligible for therapy from 99% to 95%.\n\nInterpretationThe use of genotypes in a machine learning algorithm resulted in a clinically-relevant prediction of SOS and fracture, with potential to impact healthcare resource utilization.\n\nResearch in ContextO_ST_ABSEvidence Before this StudyC_ST_ABSGenome-wide association studies have identified many loci associated with risk of clinically-relevant fracture risk factors, such as SOS. Yet, it is unclear if such information can be leveraged to identify those at risk for disease outcomes, such as osteoporotic fractures. Most previous attempts to predict disease risk from genotypes have used polygenic risk scores, which may not be optimal for genomic-prediction. Despite these obstacles, genomic-prediction could enable screening programs to be more efficient since most people screened in a population are not determined to have a level of risk that would prompt a change in clinical care. Genomic pre-screening could help identify individuals whose risk of disease is low enough that they are unlikely to benefit from screening.\n\nAdded Value of this StudyUsing a large dataset of 426,811 individuals we trained and tested a machine learning algorithm to genomically-predict SOS. This metric, gSOS, had performance characteristics for predicting fracture risk that were similar to measured SOS and femoral neck BMD. Implementing a gSOS-based pre-screening step into the UK-based osteoporosis treatment guidelines reduced the number of individuals who would require screening clinical visits and skeletal testing by approximately 50%, while having little impact on the sensitivity to identify individuals at high risk for osteoporotic fracture.\n\nImplications of all of the Available EvidenceClinically-relevant genomic prediction of heritable traits is feasible using the machine learning algorithm presented here in large sample sizes. Genome-wide genotyping is now less expensive than many clinical tests, needs to be performed once over a lifetime and could risk stratify for multiple heritable traits and diseases years prior to disease onset, providing an opportunity for prevention. The implementation of such algorithms could improve screening efficiency, yet their cost-effectiveness will need to be ascertained in subsequent analyses.

genomics↗

bioRxiv2018

An Atlas of Human and Murine Genetic Influences on Osteoporosis

Osteoporosis is a common debilitating chronic disease diagnosed primarily using bone mineral density (BMD). We undertook a comprehensive assessment of human genetic determinants of bone density in 426,824 individuals, identifying a total of 518 genome-wide significant loci, (301 novel), explaining 20% of the total variance in BMD--as estimated by heel quantitative ultrasound (eBMD). Next, meta-analysis identified 13 bone fracture loci in ~1.2M individuals, which were also associated with BMD. We then identified target genes from cell-specific genomic landscape features, including chromatin conformation and accessible chromatin sites, that were strongly enriched for genes known to influence bone density and strength (maximum odds ratio = 58, P = 10-75). We next performed rapid throughput skeletal phenotyping of 126 knockout mice lacking eBMD Target Genes and showed that these mice had an increased frequency of abnormal skeletal phenotypes compared to 526 unselected lines (P < 0.0001). In-depth analysis of one such Target Gene, DAAM2, showed a disproportionate decrease in bone strength relative to mineralization. This comprehensive human and murine genetic atlas provides empirical evidence testing how to link associated SNPs to causal genes, offers new insights into osteoporosis pathophysiology and highlights opportunities for drug development.

genomics↗

bioRxiv2018

Population Genomics of Rapidly Invading Lionfish in the Caribbean Reveals Signals of Range Expansion in the Absence of Spatial Population Structure

Range expansions driven by global change and species invasions are likely to have significant genomic, evolutionary, and ecological implications. During range expansions, strong genetic drift characterized by repeated founder events can result in decreased genetic diversity with increased distance from the center of the historic range, or the point of invasion. The invasion of the Indo-Pacific lionfish, Pterois volitans, into waters off the U.S. East Coast, Gulf of Mexico, and Caribbean Sea provides a natural system to study rapid range expansion in an invasive marine fish with high dispersal capabilities. We report results from 12,759 loci sequenced by restriction enzyme associated DNA sequencing for nine P. volitans populations in the invaded range, including Florida and other Caribbean sites, as well as mitochondrial control region D-loop data. Analyses revealed low to no spatially explicit metapopulation genetic structure in the study area, which is partly consistent with previous finding of little structure within ocean basins, but partly divergent from reports of between-basin structure. Genetic diversity, however, was not homogeneous across all sampled sites. Patterns of genetic diversity correlate with invasion pathway. Observed heterozygosity, averaged across all loci within a population, decreases with distance from Florida while expected heterozygosity is mostly constant throughout sampled populations, indicating population genetic disequilibrium correlated with distance from the point of invasion. Using an FST outlier analysis and a Bayesian environmental correlation analysis, we identified 256 and 616 loci, respectively, that could be experiencing selection or genetic drift. Of these, 24 loci were shared between the two methods.

evolutionary biology↗