Functional Genomics: Cardiovascular GeneticsRight now, I haven't published any work from my postdoc, but I'll hopefully be filling this space with publications soon (early-mid 2019).
During graduate school, I was involved with some research in atrial fibrillation (AF) with the Texas Cardiac Arrhythmia Institute (TCAI). My collaborators enrolled around 400 patients with AF who were slated to undergo a cardiac ablation, a procedure to reduce or eliminate AF by scarring ectopic foci in the left atrium and pulmonary veins. We genotyped these individuals for 16 loci associated with AF from genome-wide association studies to see if their genotype was associated with outcomes of catheter ablation. This study is entitled Novel association of polymorphic genetic variants with predictors of outcome of catheter ablation in atrial fibrillation: new directions from a prospective study (DECAF). While this study was small, I enjoyed much of the work, and it provided me with an introduction to AF genetics, which would serve me well in my postdoc (though I didn't know that at the time).
Functional Genomics: Glioblastoma multiformeAfter I defended my dissertation, I worked primarily on getting my project on chromatin state profiling in primary glioblastoma multiforme tumors published in a peer reviewed journal. In March of 2018, our manuscript was accepted to Cancer Research, thus providing some closure to my graduate school years in terms of research. Bivalent Chromatin Domains in Glioblastoma Reveal a Subtype-Specific Signature of Glioma Stem Cells. The coolest part about this paper was identifying a "stem-cell" like signature in bulk tumors. That this signature is split between proneural and classical/mesenchymal subtypes of tumors, and that the signal is associated with bivalency in an adult tumor, really feels like a new way of using chromatin information to see what a tumor might do, when a selective pressure is applied (like chemotherapy or radiation).
In the run up to that publication, I collaborated on a lot of projects, including an array-based eQTL study of glioblastoma: An eQTL analysis of the human glioblastoma multiforme genome. Previous to this, during my rotation (!) I worked on a project to test variant calling in ChIP-seq data. Turns out you can call variants in peak regions, which opens up some cool techniques, such as identifying binding eQTLs (where a variant can affect transcription factor binding): Simultaneous SNP identification and assessment of allele-specific bias from ChIP-seq data.