About

Jason Ernst is a Professor of Biological Chemistry, Computer Science, and Computational Medicine at UCLA Samueli School of Engineering. His research interests include computational biology, bioinformatics, and machine learning. He is involved in advancing understanding and development of computational methods in biological and medical sciences, contributing to the fields through his work and publications. His lab, the Ernst Lab, focuses on these areas, and he maintains a professional presence online through his lab's website.

Research topics

• Genetics
• Evolutionary biology
• Biology
• Computational biology

Selected publications

• A single-cell and spatial 3D multi-omic atlas of developing human basal ganglia and inhibitory neurons

  DRYAD · 2026-01-31

  datasetOpen access

  The human basal ganglia (BG), subcortical nuclei fundamental to motor regulation and cognitive modulation, is constructed from neurons produced during gestation in the adjacent ganglionic eminences (GEs). GEs are transient structures in the ventral prenatal brain that also generate GABAergic inhibitory neurons, which migrate to destinations in the BG, cortex, and other destinations. This study aims to elucidate the epigenomic and 3D-genomic dynamics involved in the specification and maturation of GEs and GE-derived neurons, using single-nucleus methyl-3C sequencing (snm3C-seq), highly-multiplexed spatial transcriptomics, and chromatin+RNA single-molecule imaging. Our multi-modal data support a heterogeneous temporal progression across GE subregions, with the lateral GE (LGE) showing declining neurogenic activity in mid-gestation and caudal GE (CGE) exhibiting ongoing developmental progression through infancy. We identified regulatory programs that specify subtypes of BG principal cells, medium spiny neurons (MSN), via synchronized maturation of the 3D-epigenome. In infant brains, we found a transient short-range enriched (SE) chromatin conformation during the transition between oligodendrocyte progenitors (OPCs) and oligodendrocytes (ODCs), and a temporary shift toward Long-range Enriched (LE) chromatin conformation in projection neurons, extending previous works showing the differentiation of neurons and glial cells is associated with permanent SE and LE conformation, respectively. Lastly, we found that gene regulatory regions active in MSNs were enriched in loci associated with genetic risk for neuropsychiatric disease. Our study delineates the highly complex, lineage-specific 3D genomic dynamics in ventral progenitors and basal ganglia populations of the perinatal human brain.

  PublisherDOI

• Distinct mismatch-repair complex genes set neuronal CAG-repeat expansion rate to drive selective pathogenesis in HD mice

  Cell · 2025-02-11 · 47 citations

  articleOpen access

  Huntington's disease (HD) modifiers include mismatch-repair (MMR) genes, but their connections to neuronal pathogenesis remain unclear. Here, we genetically tested 9 HD genome-wide association study (GWAS)/MMR genes in mutant Huntingtin (mHtt) mice with 140 inherited CAG repeats (Q140). Knockout (KO) of genes encoding a distinct MMR complex either strongly (Msh3 and Pms1) or moderately (Msh2 and Mlh1) rescues phenotypes with early onset in striatal medium-spiny neurons (MSNs) and late onset in the cortical neurons: somatic CAG-repeat expansion, transcriptionopathy, and mHtt aggregation. Msh3 deficiency ameliorates open-chromatin dysregulation in Q140 neurons. Mechanistically, the fast linear rate of mHtt modal-CAG-repeat expansion in MSNs (8.8 repeats/month) is drastically reduced or stopped by MMR mutants. Msh3 or Pms1 deficiency prevents mHtt aggregation by keeping somatic MSN CAG length below 150. Importantly, Msh3 deficiency corrects synaptic, astrocytic, and locomotor defects in HD mice. Thus, Msh3 and Pms1 drive fast somatic mHtt CAG-expansion rates in HD-vulnerable neurons to elicit repeat-length/threshold-dependent, selective, and progressive pathogenesis in vivo.

  PublisherDOI

• Identifying associations of de novo noncoding variants with autism through integration of gene expression, sequence, and sex information

  Genome biology · 2025-06-04

  articleOpen accessSenior author

  BACKGROUND: Whole-genome sequencing (WGS) data has facilitated genome-wide identification of rare noncoding variants. However, elucidating these variants' associations with complex diseases remains challenging. A previous study utilized a deep-learning-based framework and reported a significant brain-related association signal of autism spectrum disorder (ASD) detected from de novo noncoding variants in the Simons Simplex Collection (SSC) WGS cohort. RESULTS: We revisit the reported significant brain-related ASD association signal attributed to deep-learning and show that local GC content can capture similar association signals. We further show that the association signal appears driven by variants from male proband-female sibling pairs that are upstream of assigned genes. We then develop Expression Neighborhood Sequence Association Study (ENSAS), which utilizes gene expression correlations and sequence information, to more systematically identify phenotype-associated variant sets. Applying ENSAS to the same set of de novo variants, we identify gene expression-based neighborhoods showing significant ASD association signal, enriched for synapse-related gene ontology terms. For these top neighborhoods, we also identify chromatin state annotations of variants that are predictive of the proband-sibling local GC content differences. CONCLUSIONS: Overall, our work simplifies a previously reported ASD signal and provides new insights into associations of noncoding de novo mutations in ASD. We also present a new analytical framework for understanding disease impact of de novo mutations, applicable to other phenotypes.

  PublisherOA PDFDOI

• Genome-wide identification and analysis of recurring patterns of epigenetic variation across individuals

  Communications Biology · 2025-06-07

  articleOpen accessSenior author

  Epigenetic mapping studies across individuals have identified many positions of epigenetic variation across the human genome. However the relationships between these positions, and in particular global patterns that recur in many regions of the genome, remains understudied. In this study, we use a stacked chromatin state model to systematically learn global patterns of epigenetic variation across individuals and annotate the human genome based on them. We apply this framework to histone modification data across individuals in lymphoblastoid cell lines and across autism spectrum disorder cases and controls in prefrontal cortex tissue. We find that global patterns are correlated across multiple histone modifications and with gene expression. We use the global patterns as a framework to predict trans-regulators and study a complex disorder. The frameworks for identifying and analyzing global patterns of epigenetic variation are general and we expect will be useful in other systems.

  PublisherOA PDFDOI

• EXPLORING FOUNDATIONAL SKILLS IN INFORMATION TECHNOLOGY SECTOR CO-OPERATIVE EDUCATION JOB POSTINGS IN CANADA

  ICERI proceedings · 2025-11-01

  article
  PublisherDOI

• The impact of ambient contamination on demultiplexing methods for single-nucleus multiome experiments

  Research Square · 2025-02-10

  preprintOpen access
  PublisherOA PDFDOI

• The impact of ambient contamination on demultiplexing methods for single-nucleus multiome experiments

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-02-08 · 1 citations

  preprintOpen access

  Abstract Sample multiplexing has become an increasingly common design choice in droplet-based single-nucleus multi-omic sequencing experiments to reduce costs and remove technical variation. Genotype-based demultiplexing is one popular class of methods that was originally developed for single-cell RNA-seq, but has not been rigorously benchmarked in other assays, such as snATAC-seq and joint snRNA/snATAC assays, especially in the context of variable ambient RNA/DNA contamination. To address this, we develop ambisim, a genotype-aware read-level simulator that can flexibly control ambient molecule proportions and generate realistic joint snRNA/snATAC data. We use ambisim to evaluate demultiplexing methods across several important parameters: doublet rate, number of multiplexed donors, and coverage levels. Our simulations reveal that methods are variably impacted by ambient contamination in both modalities. We then applied the demultiplexing methods to two joint snRNA/snATAC datasets and found highly variable concordance between methods in both modalities. Finally, we develop a new metric, variant consistency , which we show is correlated with cell-level ambient molecule fractions in singlets. Applying our metric to two multiplexed joint snRNA/snATAC datasets reveals variable ambient contamination across experiments and modalities. We conclude that improved modelling of ambient material in demultiplexing algorithms will increase both sensitivity and specificity.

  PublisherDOI

• Whole-genome sequence-based association analysis of African American individuals with bipolar disorder and schizophrenia

  Human Genetics and Genomics Advances · 2025-08-30

  articleOpen access

  In studies of individuals of primarily European genetic ancestry, common and low-frequency variants and rare coding variants have been found to be associated with the risk of bipolar disorder (BD) and schizophrenia (SZ). However, less is known for individuals of other genetic ancestries or the role of rare non-coding variants in BD and SZ risk. We performed whole-genome sequencing (∼27X) of African American individuals: 1,598 with BD, 3,295 with SZ, and 2,651 unaffected controls (InPSYght study). We increased power by incorporating 14,812 jointly called psychiatrically unscreened ancestry-matched controls from the Trans-Omics for Precision Medicine (TOPMed) Program for a total of 17,463 controls (∼37X). To identify variants and sets of variants associated with BD and/or SZ, we performed single-variant tests, gene-based tests for singleton protein truncating variants, and rare and low-frequency variant annotation-based tests with conservation and universal chromatin states and sliding windows. We found suggestive evidence of the association of BD with single variants on chromosome 18 and of lower BD risk associated with rare and low-frequency variants on chromosome 11 in a region with multiple BD genome-wide association study loci, using a sliding window approach. We also found that chromatin and conservation state tests can be used to detect differential calling of variants in controls sequenced at different centers and to assess the effectiveness of sequencing metric covariate adjustments. Our findings reinforce the need for continued whole-genome sequencing in additional samples of African American individuals and more comprehensive functional annotation of non-coding variants.

  PublisherDOI

• The impact of ambient contamination on demultiplexing methods for single-nucleus multiome experiments

  eLife · 2025-07-08

  preprintOpen access

  Abstract Sample multiplexing has become an increasingly common design choice in droplet-based single-nucleus multi-omic sequencing experiments to reduce costs and remove technical variation. Genotype-based demultiplexing is one popular class of methods that was originally developed for single-cell RNA-seq, but has not been rigorously benchmarked in other assays, such as snATAC-seq and joint snRNA/snATAC assays, especially in the context of variable ambient RNA/DNA contamination. To address this, we develop ambisim, a genotype-aware read-level simulator that can flexibly control ambient molecule proportions and generate realistic joint snRNA/snATAC data. We use ambisim to evaluate demultiplexing methods across several important parameters: doublet rate, number of multiplexed donors, and coverage levels. Our simulations reveal that methods are variably impacted by ambient contamination in both modalities. We then applied the demultiplexing methods to two joint snRNA/snATAC datasets and found highly variable concordance between methods in both modalities. Finally, we develop a new metric, variant consistency, which we show is correlated with cell-level ambient molecule fractions in singlets. Applying our metric to two multiplexed joint snRNA/snATAC datasets reveals variable ambient contamination across experiments and modalities. We conclude that improved modelling of ambient material in demultiplexing algorithms will increase both sensitivity and specificity.

  PublisherDOI

• CMImpute: cross-species and tissue imputation of species-level DNA methylation samples across mammalian species

  Genome biology · 2025-05-20 · 3 citations

  articleOpen accessSenior author

  The large-scale application of the mammalian methylation array has substantially expanded the availability of DNA methylation data in mammalian species. However, this data captures only a small portion of species-tissue combinations. To address this, we develop CMImpute (Cross-species Methylation Imputation), a method based on a conditional variational autoencoder, to impute DNA methylation representing species-tissue combinations. We demonstrate that CMImpute achieves strong sample-wise correlation between imputed and observed values. Using CMImpute and data from 348 species and 59 tissue types, we impute methylation data for 19,786 new species-tissue combinations. We expect CMImpute will be a useful resource for DNA methylation analyses.

  PublisherOA PDFDOI

Recent grants

• Postdoctoral Research Fellowships in Biology for FY 2009

  NSF · $123k · 2009–2011

• Deciphering the Relationship between Substance Use and Psychiatric Disorders from Whole Genome Sequencing Data

  NIH · $2.3M · 2017–2024

• CAREER: Expanding the Dimensions of Computational Epigenomic Modeling and Analysis

  NSF · $991k · 2013–2019

• EAGER: Understanding the genomes and strain mutations of SARS-CoV-2 and other viruses using comparative and population genetic approaches

  NSF · $300k · 2021–2024

Frequent coauthors

• Manolis Kellis

  Massachusetts Institute of Technology

  74 shared

• Petko Fiziev

  Illumina (United States)

  42 shared

• Ha Vu

  University of California, Los Angeles

  33 shared

• Steve Horvath

  University of California, San Diego

  32 shared

• Pouya Kheradpour

  26 shared

• Julie A. Mattison

  National Institutes of Health

  24 shared

• Kathrin Plath

  21 shared

• B Bernstein

  Broad Institute

  21 shared