Current Training Program Attendees

Enrolled: 2023

University of Wisconsin-Madison

Gage Moreno is a postdoctoral fellow in the Sabeti lab, where he is interested in viral transmission within and between hosts, the evolution of viral pathogens, and using genomics to better understand the factors promoting viral spread.

Gage completed degrees in Genetics, with a certificate in Global Health at the University of Wisconsin-Madison. He received his Ph.D. in 2021 from the Department of Pathology and Laboratory Medicine at the University of Wisconsin-Madison under David O’Connor. During his PhD, Gage’s research interests were initially rooted in developing metagenomic sequencing techniques to detect novel pathogens before their emergence. After the onset of the SARS-CoV-2 pandemic, his work focused on using viral genome sequencing to characterize SARS-CoV-2 transmission dynamics.

Outside of the lab, you can find Gage running along the Esplanade, walking his two dogs, and trying new restaurants.

Genomic Epidemiology and Data Science

In his research, Gage works to combine large-scale genomic surveillance data and individualized epidemiological metadata to better understand drivers of new SARS-CoV-2 waves. More broadly, he hopes to develop systems that can be applied to detect, monitor, and understand epidemiological drivers behind the spread of other infectious diseases.

Enrolled: 2023

Drexel University

Kayla (she/her) is a Postdoctoral Research Fellow within the Rare Disease Group at the Center for Mendelian Genomics. Prior to joining the Translational Genomics Group, she completed her doctorate at Drexel University. There, where she built platforms to identify novel biomarkers for several vector-borne pathogens for molecular-based diagnostics.

Currently, Kayla is working on establishing a federated variant-level matching platform aimed at identifying the impact of variants of uncertain significance. This would facilitate data sharing, increased variant interpretation, and patient diagnosis. Whenever Kayla isn’t doing research, she spends her time creating art pieces through various mediums, reading novels, or traveling.

Establishing a federated platform for variant-level searching across all databases

The development of genomic sequencing platforms has allowed for an abundance of high-quality genomes that can be queried to better understand health and disease. As a result, the use of this data through various platforms resulted in 4,000 genes being identified as causal for several diseases. This progress however is limited in the case of variants that are not identified as casual for a disease due to conflicting information on presenting phenotype in carriers. To rectify this and continue the process of further understanding the human genome in health and disease, matching variants across several platforms must be done. Our group is focused on establishing a federated, variant-level platform utilizing BeaconV2 API to search across multiple participating nodes. Additionally, we will define the consent/permissions needed to link variants to presented phenotypes. Through this work, we anticipate that the construction and implementation of a variant matching platform will facilitate the field’s progression toward widespread genomic data sharing, much needed for rare disease variant interpretation and patient diagnosis.

Enrolled: 2022

The University of Hong Kong

Justin Tubbs studied biology and psychology at Virginia Commonwealth University, where my interest in psychiatric genetics was sparked through my work with Drs. Jeanne Savage, Amy Adkins, and Danielle Dick. Afterwards, he completed a post-baccalaureate fellowship at the NCCIH, where he contributed to studies examining the psychological and physiological mechanisms of pain and affective touch. Justin’s PhD studies at The University of Hong Kong with Prof. Pak Sham have focused on developing and applying methods for estimating genetic nurture, as well as identifying genetic risk factors for complex traits including depression and psychosis. Justin is looking forward to the next stage of his journey as a T32 fellow in Dr. Jordan Smoller’s lab.

In his free time, he enjoys traveling, cooking, and music.

Leveraging genomics and big health data to advance precision psychiatry

Enrolled: 2023

University of Chicago

Sarah Urbut is a current cardiology fellow at Massachusetts General Hospital in Boston, MA with an interest in statistics, genomics, and preventive cardiology.  For her postdoctoral years, she plans to combine new efforts in Bayesian risk modeling with causal inference to infer individualized lifetime risk prediction and therapeutic benefit for truly precision medicine.

Outside of science and medicine, she enjoys road cycling, traveling, comedy and supporting the Chicago White Sox.

Lifetime risk prediction; Heterogeneity of Treatment effects; Bayesian statistics, variable selection, method development

Existing guidelines for cardiovascular risk-reducing therapy calculate static fixed window estimates of risk for coronary artery disease. Under the current approach using a limited set of known cardiovascular risk factors the covariate with the greatest influence on this 10-year risk is age. These 10-year equations fail to accurately estimate risk in select populations, in particular younger individuals and those with hereditary risk. Critically, even the revised Pooled Cohort Equations (PCE) of 2018 critically mention that reducing overestimation of certain groups it was not sufficient to correct misestimation problems; the statistical methods also required revision to improve equation accuracy but do little to resolve the static and fixed versus dynamic longitudinal modeling framework. This leaves a sizable portion of the population without adequate prevention. Such models and the inherent assumptions within assume a fixed effect of traditional risk factors, or at best, a linear interaction with time.  Furthermore, a sizable number of people are also classified as being at intermediate risk, for whom the optimal preventive strategy should be more precise.

Our goal is to ultimately provide individual trajectories of overall coronary artery disease risk for patients conditional on their time-varying covariates, recognizing time varying effects of the predictors, updated predictors, competing risks and accumulated exposure.The goal will be to add the importance of accumulated risk to inherent primordial ‘genomic risk’ and to see how the additive importance of acquired risk changes total prediction over the life course. Critically, we aim to provide a trajectory over time in contrast to commonly available static five- or ten-year risk projections, and critically, to offer covariate specific counterfactual representations of the alternative course. These counterfactual predictions provide ideal proposition for practical therapeutic preventive strategies, which has been critically absent from existing work.

Enrolled: 2023

Johns Hopkins University

Cristian is a postdoctoral fellow at the Raychaudhuri lab where he is applying genetics and genomics to understand the interindividual variability on immune-related phenotypes. Cristian is originally from Peru where he got his BA in biology from Universidad Nacional Federico Villarreal in 2012. Cristian completed his PhD at Johns Hopkins School of Public Health in 2021 under the supervision of Dr. Priya Duggal and Dr. Benjamin Larman where he studied the genetics of the antibody response to Epstein-Barr virus and Human Adenovirus at the epitope level.

Outside the lab Cristian enjoys hiking, exploring the local food scene, pastries and he is a black coffee lover.

Lab website: https://immunogenomics.hms.harvard.edu/people/cristian-valencia

Immuno-genomics

Cristian is a postdoctoral fellow that is interested in integrating genetics and genomics to better understand how genetic risk factors can influence immune-mediated diseases. Cristian joined the Raychaudhuri lab in 2021 and he is currently working on identifying expression quantitative trait loci (eQTLs), whose magnitudes change in response to perturbation (response eQTL) such as immune activation. Our framework provides a model for better detection of response eQTLs and underscores the importance of modeling cell-level variation. Improving these models will ultimately advance our understanding of the biologic mechanisms that underlie disease heritability.