Arby Abood

PhD candidate

University of Virginia

Center for Public Health Genomics

Data Science Training Program

Abdullah "Arby" Abood

Abdullah "Arby" Abood

PhD candidate

University of Virginia


I’m a PhD candidate, bioinformatician and data scientist at The University of Virginia School of Medicine. My research focuses on identifying theraputic targets for ostoeoporosis using systems genetics. I am currently integrating genome-wide assocition studies, splice quantitative trait loci, long-read RNA sequencing, and proteomics to identify novel isoforms as candidate causal mediators of disease.


  • Systems Genetics
  • Computational Biology
  • Genetic Epidemiology


  • PhD in Genomics & Bioinformatics, 2023

    University of Virginia

  • MSc in Microbial Genomics, 2018

    Clemson University

  • BSc in Biology, 2013

    George Mason University





Genetic epidemiology and statistics




PhD candidate

University of Virginia

Jul 2018 – Present Charlottesville, VA

Responsibilities include:

  • Informed bone mineral density (BMD) genome-wide association studies (GWAS) by integrating splice quantitative trait loci (sQTL) data with Bayesian colocalization analysis. The results were further refined by using data from long-read RNAseq, and proteomics followed by experimental validation in-vitro. This enabled us to implicate causal genes in osteoporosis.
  • Systematically identified long non-coding RNAs potentially responsible for the effect of BMD GWAS loci using expression quantitative trait loci (eQTL) Bayesian colocalization, transcriptome-wide association studies (TWAS), and allelic imbalance analyses. Due to this work, we were able to shed light on the non-coding aspects of the bone transcriptome and their role in osteoporosis.
  • Characterized the landscape of isoforms and proteoforms in the process of osteoblast differentiation by combining long-read RNAseq data and proteomics data and applying differential expression analysis, differential splicing analysis, differential isoform usage, and other statistical approaches.

Graduate assistant

Clemson University

Aug 2015 – May 2018 Clemson, SC
  • Investigated human oral and gut microbiome community dynamics using both 16S and Whole Genome Shotgun (WGS) metagenomics data analyses. This enabled us to understand host-microbiome interaction and provide detailed abundance counts at bacterial strain level resolution.
  • Implemented multiple bioinformatics pipelines and provided detailed instructions on their usage to assist lab partners and collaborators with their research needs.
  • Managed day-to-day laboratory operations including ordering, and Linux server maintenance.
  • Gained experience in the bash/Linux command line interface (CLI), Python, and R.


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