Biology subjects

Ungar, R. A.

Publications and source records attributed to Ungar, R. A..

5 recordsLinked to original sources

bioRxiv2026

Recommendations for the ethical and accurate use of population descriptors: a trainee-led survey of early-career researchers

Despite the importance of population descriptors in human genomics research, many scientists struggle to translate evolving ethical guidelines into their computational workflows. To characterize this gap between recommendations and implementation, we conducted a mixed-methods survey of early-career researchers to assess how they understand and implement the landmark 2023 NASEM report on the use of population descriptors in human genetics research. We show that while exposure to the report fosters ethical awareness, fundamental misconceptions about race and ancestry persist across academic disciplines, and trainees face structural bottlenecks, including legacy data constraints and a lack of technical confidence. To address this gap, we offer actionable, stakeholder-specific recommendations across the research lifecycle ranging from decision-support tools to "bring-your-own-data" workshops to leadership from academic journals, scientific societies, and trainee mentors. Ultimately, we argue that to promote scientific rigor and reduce bias in genetic discoveries, the scientific ecosystem must invest in the infrastructure necessary to empower the next generation of researchers.

genetics↗

bioRxiv2026

Building an Interoperable Rare Disease Multi-omic Resource: The GREGoR Data Model and Dataset

Rare disease research and diagnosis rely on the integration of genomic and phenotypic data generated across diverse clinical sites; however, the absence of widely adopted standards for representing genomic data and associated metadata has limited data interoperability, reuse, and cross-study analysis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was established to investigate challenging rare disease cases and evaluate emerging multi-omic technologies for clinical translation. To support coordinated data integration across distributed research sites, we developed a common Consortium Data Model in partnership with domain experts to standardize the capture of participant-, family-, phenotype- and assay-level metadata, with a particular emphasis on using a modular architecture to support linking of multiple data versions from multiple omic technologies to a single individual and attribution of a genetic finding to the specific technology used for its initial discovery. Adoption of the GREGoR Data Model has enabled continued generation and public release of a harmonized, analysis-ready Consortium Dataset. The most recent release includes phenotypic, family and multi-omic data from 12,292 participants in 5,029 families. Other rare disease data sharing efforts are beginning to adopt this data model which will facilitate cross consortium analyses and empower rare disease research. This work demonstrates that a collaborative, flexible, and scalable data model can enable large-scale rare disease research, facilitate cross-center data harmonization, and enable data interoperability.

genomics↗

bioRxiv2025

Functional impact of rare variants and sex across the X-chromosome and autosomes

The human X-chromosome contains hundreds of genes and has well-established impacts on sex differences and traits. However, the X-chromosome is often excluded from many genetic analyses, limiting broader understanding of variant effects. In particular, the functional impact of rare variants on the X-chromosome is understudied. To investigate functional rare variants on the X-chromosome, we use observations of outlier gene expression from GTEx consortium data. We show outlier genes are enriched for having nearby rare variants on the X-chromosome, and this enrichment is stronger for males. Using the RIVER model, we identified 753 rare variants in 449 genes predicted to have functional differences between males and females. We examined the pharmacogenetic implications of these variants and observed that 25% of drugs with a known sex difference in adverse drug reactions were connected to genes that contained a sex-biased rare variant. We further identify that sex-biased rare variants preferentially impact transcription factors with predicted sex-differential binding, such as the XIST-modulated SIX1. Combined, our study investigates functional rare variants on the X-chromosome, and further details how sex-stratification of variant effect prediction improves identification of rare variants with predicted sex-biased effects, transcription factor biology, and pharmacogenomic impacts.

genomics↗

bioRxiv2023

Increasing equity in science requires better ethics training: a course by trainees, for trainees

Despite the profound impacts of scientific research, few scientists have received the necessary training to productively discuss the ethical and societal implications of their work. To address this critical gap, we - a group of predominantly human genetics trainees - developed a course on genetics, ethics, and society. We intend for this course to serve as a template for other institutions and scientific disciplines. Our curriculum positions human genetics within its historical and societal context and encourages students to evaluate how societal norms and structures impact the conduct of scientific research. We demonstrate the utility of this course via surveys of enrolled students and provide resources and strategies for others hoping to teach a similar course. We conclude by arguing that if we are to work towards rectifying the inequities and injustices produced by our field, we must first learn to view our own research as impacting and being impacted by society.

genetics↗

bioRxiv2023

Transcriptomics and chromatin accessibility in multiple African population samples

Mapping the functional human genome and impact of genetic variants is often limited to European-descendent population samples. To aid in overcoming this limitation, we measured gene expression using RNA sequencing in lymphoblastoid cell lines (LCLs) from 599 individuals from six African populations to identify novel transcripts including those not represented in the hg38 reference genome. We used whole genomes from the 1000 Genomes Project and 164 Maasai individuals to identify 8,881 expression and 6,949 splicing quantitative trait loci (eQTLs/sQTLs), and 2,611 structural variants associated with gene expression (SV-eQTLs). We further profiled chromatin accessibility using ATAC-Seq in a subset of 100 representative individuals, to identity chromatin accessibility quantitative trait loci (caQTLs) and allele-specific chromatin accessibility, and provide predictions for the functional effect of 78.9 million variants on chromatin accessibility. Using this map of eQTLs and caQTLs we fine-mapped GWAS signals for a range of complex diseases. Combined, this work expands global functional genomic data to identify novel transcripts, functional elements and variants, understand population genetic history of molecular quantitative trait loci, and further resolve the genetic basis of multiple human traits and disease.

genomics↗