Biology subjects

Gjorgjieva, T.

Publications and source records attributed to Gjorgjieva, T..

3 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↗

bioRxiv2025

Focus on single gene effects limits discovery and interpretation of complex trait-associated variants

Standard QTL mapping approaches consider variant effects on a single gene at a time, despite abundant evidence for allelic pleiotropy, where a single variant can affect multiple genes simultaneously. While allelic pleiotropy describes variant effects on both local and distal genes or a mixture of molecular effects on a single gene, here we specifically investigate allelic expression "proxitropy": where a single variant influences the expression of multiple, neighboring genes. We introduce a multi-gene eQTL mapping framework--cis-principal component expression QTL (cis-pc eQTL or pcQTL)--to identify variants associated with shared axes of expression variation across a cluster of neighboring genes. We perform pcQTL mapping in 13 GTEx human tissues and discover novel loci undetected by single-gene approaches. In total, we identify an average of 1396 pcQTLs/tissue, 27% of which were not discovered by single-gene methods. These novel pcQTL colocalized with an additional 142 GWAS trait-associated variants and increased the number of colocalizations by 34% over single-gene QTL mapping. These findings highlight that moving beyond single-gene-at-a-time approaches toward multi-gene methods can offer a more comprehensive view of gene regulation and complex trait-associated variation.

genetics↗

bioRxiv2024

Specificity, length, and luck: How genes are prioritized by rare and common variant association studies

Standard genome-wide association studies (GWAS) and rare variant burden tests are essential tools for identifying trait-relevant genes. Although these methods are conceptually similar, we show by analyzing association studies of 209 quantitative traits in the UK Biobank that they systematically prioritize different genes. This raises the question of how genes should ideally be prioritized. We propose two prioritization criteria: 1) trait importance -- how much a gene quantitatively affects a trait; and 2) trait specificity -- a genes importance for the trait under study relative to its importance across all traits. We find that GWAS prioritize genes near trait-specific variants, while burden tests prioritize trait-specific genes. Because non-coding variants can be context specific, GWAS can prioritize highly pleiotropic genes, while burden tests generally cannot. Both study designs are also affected by distinct trait-irrelevant factors, complicating their interpretation. Our results illustrate that burden tests and GWAS reveal different aspects of trait biology and suggest ways to improve their interpretation and usage.

genomics↗