Spring 2025 Seminar Schedule

Wednesday, February 5: Richard Border, Carnegie Mellon University

Richard Border, PhD, is an Assistant Professor in the Department of Computational Biology at the Carnegie Mellon University School of Computer Science. He studies problems at the intersections of human genetics, statistics, quantitative psychology, and applied mathematics. His primary research interests include nonrandom mating and other forms of population structure, particularly how they impact inference about genetic architecture, scalable Bayesian inference and other methods for the efficient and flexible analysis and simulation of genome-wide data, and metascience, measurement, model misspecification, and sensitivity analysis.

(somewhat less un)Realistic models of the genetic architecture of complex traits

There is recognition among human complex-trait geneticists that not only are many common assumptions made for statistical tractability (e.g., random mating, independence of parent/offspring environments) unlikely to apply in many contexts, but that methods reliant on such assumptions can yield misleading results. Investigations of the consequences of violating these assumptions so far have focused on individual perturbations operating in isolation. Here, I analyze widely used estimators of genetic architectural parameters, including LD-score regression and both population-based and within-family GWAS, across a broad array of perturbations to classical assumptions, such as multivariate assortative mating and vertical transmission (parental effects on offspring phenotypes not mediated by genetic inheritance). I demonstrate that widely-used statistical approaches are unreliable across a broad range of perturbations, and that structural sources of confounding often operate synergistically to distort conclusions. For example, mild multivariate assortative mating and vertical transmission together can dramatically inflate heritability estimates and GWAS false positive rates, even when each accounts for only 5% of the phenotypic variance. Analysis of UK Biobank data reveals that mate selection operates across multiple independent dimensions: eight canonical vectors are required to explain 90% of cross-mate phenotypic correlations across 34 traits. Further, GWAS will become progressively more polluted by off-target associations as sample sizes increase, with false positive rates up to 1.98 times higher than expected at 66% statistical power under 5-variate assortative mating. Even within-family GWAS estimates, while more robust to these confounds in representative samples, can be substantially biased under non-random sampling. Finally, I introduce xftsim, a forward time simulation library capable of modeling a wide range of genetic architectures, mating regimes, and transmission dynamics, to facilitate the systematic comparison of existing approaches and the development of robust methods. Together, these findings illustrate the importance of comprehensive sensitivity analysis and present a valuable tool for future research.

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Wednesday, March 5: Jacob Scott, CWRU School of Medicine and Cleveland Clinic

Jacob Scott, MD, DPhil, is an Associate Professor and Staff Physician-Scientist at the Case Western Reserve University School of Medicine and the Cleveland Clinic. A veteran of the US Navy submarine force turned academic physician-scientist, Dr. Scott's cancer research lab uses mathematical modeling to explain the complexity of cancer, and biological and clinical investigations to validate those models. Jacob's background in physics, medicine, mathematics and engineering gives him a unique perspective on cancer and systems biology, allowing him to communicate and collaborate with professionals across many disciplines. His clinical practice involves using radiation therapy in the care of adults and adolescents with sarcomas: cancers of the bone and soft tissues.

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Wednesday, March 19: Raji Balasubramanian, UMass-Amherst

Raji Balasubramanian, PhD, is an Associate Professor in Biostatistics and Epidemiology at UMass-Amherst. She is a biostatistician working at the interface of molecular epidemiology, biostatistics and women’s health. Her research focus is on network models of genomic and metabolomic data, models for error-prone outcomes such as self-reports and flexible survival models with application to pediatric HIV studies. Her group has active collaborations with medical investigators studying various aspects of women’s health including cardiovascular disease, stroke, chronic distress and breast cancer. Her current collaborations include studies nested within the Women’s Health Initiative and the Nurses’ Health Study cohorts.

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Wednesday, April 2: Ben Peter, University of Rochester

Ben Peter, PhD, is an Assistant Professor in the Biology Department at the University of Rochester. Since November 2017,  he has also been a Research Group Leader at the Max-Planck-Institute for Evolutionary Anthropology in Leipzig (Germany). Before that, he was a postdoc with John Novembre at the University of Chicago, and he received my PhD from the University of California, Berkeley, jointly advised by Rasmus Nielsen and Monty Slatkin. He is widely interested in methods and applications of population and evolutionary genetics. Currently, he is primarily focussed on analyzing Neandertal DNA from fossils with minimal DNA preservation, and studying the interactions of Neandertals, Denisovans and early humans through time. He is also interested in theory and methods to conceptualize, formalize and estimate population structure, particularly from large, heterogeneous samples.
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Wednesday, April 16: Sriram Sankararam, University of California Los Angeles

Sriram Sankararam, PhD, is a Professor of Computer Science, Human Genetics, and Computational Medicine at UCLA. He is  broadly interested in problems at the intersection of computer science, statistics, and biomedicine.
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Wednesday, April 30: Shoba Vasudevan, Brown University

Shobha Vasudevan, PhD, joined Brown University in 2024 as Associate Professor of Molecular Biology, Cell Biology and Biochemistry, and Director of Technology and Innovation at the Brown RNA Center. She was Associate Professor at the Department of Medicine, Harvard Medical School (HMS), Massachusetts General Hospital, and faculty at Harvard Stem Cell Institute (HSCI), Harvard Initiative for RNA Medicine (HIRM), Dana Farber/Harvard Cancer Center (DF/HCC), and Broad Institute, which she joined as an Assistant professor 15 years ago. Her research is focused on uncovering the role of RNA mechanisms underlying refractory cancer, as a basis for developing new therapeutics to curtail intractable cancers. Her lab discovered that quiescent cancer cells use specialized post-transcriptional mechanisms to enable tumor persistence. These mechanisms alter the roles and expression of coding and noncoding RNAs and RNA complexes such as ribosomes, to express survival regulators that persist refractory acute myeloid leukemia and other tumors. She completed her doctorate in molecular genetics with Dr. S.W. Peltz at Rutgers University-UMDNJ, where she uncovered a key mRNA stability pathway upon metabolic stress, with implications in cancer. Her postdoctoral fellowship in biochemistry with Dr. J. A. Steitz at Yale University, uncovered a new role for microRNAs in quiescent acute myeloid leukemia, which express regulators for leukemia persistence. Dr. Vasudevan has received several awards for her research, mentoring, and community leadership, including from the AACR, and from the RNA Society such as the Scaringe and the Excellence in Inclusive leadership awards. Her studies are funded by the NIH, Leukemia and Lymphoma Society, CRI, AACR, Leukemia Research and V Foundations, as well as by industries and philanthropic funding agencies.

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