Why People with the Same Pathogenic Variant Can Have Totally Different Outcomes

We just published in Nature Communications (June 5, 2025):
“Investigating the sources of variable impact of pathogenic variants in monogenic metabolic conditions” This was a collaboration between our lab and the Zaitlen Lab, as well as Kenny Lab in the Mt. Sinai Biobank bioMe

Full paper here: https://rdcu.be/epzLx

What we did:

  1. Analyzed ~230,000 exome‑sequenced individuals from UK Biobank and Mt. Sinai BioMe.

  2. Identified carriers of pathogenic variants in cardiometabolic genes (e.g., LDLR, MC4R).

  3. Quantified three key contributors to clinical variability:

    • Variant effect size via ESM1b scores

    • Polygenic background via PRS

    • Marginal epistasis using the FAME method

Major insights:

  • ESM1b scores correlated with phenotype severity and distinguished gain‑ vs loss‑of‑function variants in 6/10 genes

  • High PRS can drive phenotypes as extreme—or more extreme—than monogenic variants.

  • Epistasis (genetic background interactions) boosted predictive power by up to 170% in traits like LDL and triglycerides

Why it matters:
Clinical sequencing often flags rare variants, but predicting who actually develops disease is unreliable. Our work shows that integrating rare variant effects, polygenic context, and genetic interactions delivers far more accurate predictions.

Shout‑out: This whole effort was powered by graduate student Angela Wei, whose resilience and bioinformatics prowess drove the analyses from start to finish—seeing massive cohorts through to publication.

What’s next: We aim to map specific epistatic modifiers and integrate these models into clinical pipelines—pushing precision medicine beyond simple risk flags.

With every June we celebrate the New Graduates!

Congrats to Angela for successfully defensing her PhD Thesis! Thank you to all of our graduating undergraduate students: Yaneth Perez, Mili Sinvhal, Rashmi Parker, Isabella Cardenas, Miracle Ogbonnaya, David Jin, Lesly Leal, Suzy Sarkissian and Sawyer Findley!

New Preprints: Using multiomics to study the role of ASXL1 in Bohring Optiz Syndrome and AML

We just published two new preprints— the hard work of Isabella Lin, MSTP student in her PhD 3 year in the lab! These are a long term projects we have exploring the gene regulatory role of ASXL1 in patient-derived cells. here we identify how ASXL1 mutations change the epigenetic landscape and activate Wnt signaling pathtways in differentiated cells. Specifically we highlight both canonical signaling pathways and non-canoncial planar cell polarity pathways are affected by BOS. For the full preprint see here:

Truncating ASXL1 mutations in Bohring-Opitz Syndrome dysregulate canonical and non-canonical Wnt Signaling

We also explored whether the same observations in patient germline syndomes with ASXL1 mutations affect the same pathways in Acute myeloid leukemia with ASXL1 mutations.

ASXL1 mutations that cause Bohring Opitz Syndrome (BOS) or acute myeloid leukemia share epigenomic and transcriptomic signatures

Congrats to Isabella Cardenas for her CARE Fellowship Program acceptance!

Isabella Cardenas, an undergraduate research volunteer, has been named a CARE Fellow for the 22-23’ academic year. The CARE Fellowship Program funds sophomores in research at UCLA and provides professional development on research writing and presentations. As a CARE fellow in the Arboleda Lab, Isabella will continue working with PhD student, Aileen Nava, on KAT6 loss-of-function and gain-of-function experiments in human stem cells."

Genetics and Genomics Graduate Student, Leroy Bondhus publishes his first Paper!

Leroy Bondhus, PhD student along with two UCLA undergraduate students: Yenifer Hernandez and Roshni Varma, published a paper in Briefings in Bioinformatics this week: Balancing the transcriptome: leveraging sample similarity to improve measures of gene specificity

The spatial and temporal domain of a gene's expression can range from ubiquitous to highly specific. Quantifying the degree to which this expression is unique to a specific tissue or developmental timepoint can provide insight into the etiology of genetic diseases. However, quantifying specificity remains challenging as measures of specificity are sensitive to similarity between samples in the sample set. For example, in the Gene-Tissue Expression project (GTEx), brain subregions are overrepresented at 13 of 54 (24%) unique tissues sampled. In this dataset, existing specificity measures have a decreased ability to identify genes specific to the brain relative to other organs. To solve this problem, we leverage sample similarity information to weight samples such that overrepresented tissues do not have an outsized effect on specificity estimates. We test this reweighting procedure on 4 measures of specificity, Z-score, Tau, Tsi and Gini, in the GTEx data and in single cell datasets for zebrafish and mouse. For all of these measures, incorporating sample similarity information to weight samples results in greater stability of sets of genes called as specific and decreases the overall variance in the change of specificity estimates as sample sets become more unbalanced. Furthermore, the genes with the largest improvement in their specificity estimate's stability are those with functions related to the overrepresented sample types. Our results demonstrate that incorporating similarity information improves specificity estimates' stability to the choice of the sample set used to define the transcriptome, providing more robust and reproducible measures of specificity for downstream analyses.

Contact us: 

David Geffen School of  Medicine, UCLA
Department of Pathology and Laboratory Medicine
615 Charles E. Young Drive South
lab: BSRB 346
Los Angeles, CA 90095
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Join Us

We are looking for highly motivated and enthusiastic postdocs, graduate students, and undergraduates. Please see our Opportunities page or contact Valerie by email if you are interested in joining the lab.