Frank Wendt, PhD

My research embodies both basic and applied sciences to understand the genetics of complex traits and how race, ethnicity, and genetically determined ancestry influence these findings. My overall research goal is to understand the shared genetic influence for mental health, psychopathology, and behavior. This interest in trait relationships is typically anchored around the internalizing spectrum of disorders (major depressive disorder, generalized anxiety disorder, posttraumatic stress disorder) and trans-diagnostic internalizing domains (neuroticism).

As a first-generation college student, I was not exposed to academic research until college. One morning in autumn of 2010, I bundled up in my warmest clothing and snapped crampons onto my boots for my first site visit to the northern facing slopes of the Appalachian Mountains to collect ice cores from caves frozen for >10 months/year. Mentored by Dr. Corien Bakermans at Penn State University (PSU), I amplified 16S rRNA of archaea from ice cores for comparison to Antarctic cores. Though my excitement for field work faded, I mastered primer design and polymerase chain reaction which were essential skills for my successful research career. In 2011, I joined Dr. Mitchell Holland’s group at PSU to develop human genetics bench skills related to short tandem repeat (STR) assay development. We collaborated with the start-up company IntegenX to alpha test an instrument for rapid amplification (~90 minutes) and size separation of STR amplicons.

In 2014, I moved to Texas for doctoral training with Dr. Bruce Budowle at University of North Texas Health Science Center (UNTHSC). I developed machine learning models predicting response to the synthetic opioid agonist tramadol using a pathway-driven approach. In collaboration with Dr. Antti Sajantila (University of Helsinki), I (i) characterized the population genetic diversity of pharmacogenomic (PGx) SNPs, (ii) tested for shared inheritance patterns within and between genes, and (iii) used sequence data from CYP2D6, UGT2B7, ABCB1, OPRM1, and COMT for machine learning prediction of tramadol:primary metabolite ratio. Tramadol metabolism was best predicted with a five-gene model with highest accuracy estimates using 16 SNPs. My dissertation work formed the foundation for my interest in complex traits and the concepts of polygenicity (the additive effects of many loci on a phenotype) and pleiotropy (when multiple traits share liability loci).

My major scientific contribution outside of my dissertation was in STR population genetics. In collaboration with Drs. Sree Kanthaswamy (Arizona State University), Nicole Novroski (University of Toronto), Katherine Gettings (NIST), and Bruce Budowle (UNTHSC), I (i) characterized STR sequence diversity in US populations, (ii) expanded the STR allele identification tool (STRait Razor software) to assign STR genotypes from massively parallel sequencing data, and (iii) published the first studies of STR and microhaplotype (e.g., STR+SNP, SNP+SNP) variation in the Yavapai and Chachapoyas populations. These studies expanded allele frequency databases at Yale, the Federal Bureau of Investigation, and NIST.

To expand my interest in polygenic prediction of complex traits, I joined Yale for rigorous training in the genetics of psychiatric disorders. Under the mentorship of Dr. Renato Polimanti, I have coauthored >25 manuscripts and obtained an NRSA F32 fellowship through NIMH. As a fellow, I investigate sex differences in genetic liability for psychiatric disorders. Among the studies completed, I lead a study of sex-stratified gene-by-environment interactions influencing genetic risk for suicidality. We identified relationships between (i) physical violence and male suicidality genetic risk and (ii) post-trauma avoidance and upset feelings and female suicidality genetic risk. I also established analytic pipelines to understand trait relationships including (i) polygenic architecture, (ii) causal inference, (iii) latent genetic factors, (iv) PGx risk factors, and (v) cross-population effects. In response to COVID-19, I volunteered my expertise to studies of host genetic liability resulting in collaborations with the COVID-19 Host Genetics Initiativ^e and the Million Veteran Program (MVP) COVID-19 PGx Work Group. I currently lead MVP initiatives to understand genetic and environmental risks for Gulf War Veterans Illness and the effects of cryptic genetic heterogeneity within ancestry strata on gene discovery of complex traits.

Though currently focused on mental health, I remain actively involved in forensic science research. I am currently leading/supervising several projects to understand biases in DNA mixture match statistics as a consequence of the growing prevalence of admixture in the United States and Canada. These studies aim to reduce prejudicial reporting of DNA "matches" in the presence/absence of contributors from low genetic diversity populations.