Samuel Handelman, Ph.D.

Research Assistant Professor
Medical SchoolInternal MedicineGastroenterology & Hepatology

Biography

Dr. Handelman is an interdisciplinary biologist and data scientist studying the effects of population genetic differences on health, disease, and drug/treatment efficacy. Within human populations, twin studies indicate a large heritability gap specifically for phenotypes crucial to treatment decisions, such as drug pharmacokinetics. Between populations, although health disparities cannot generally be attributed to genetic "main effects," genetics may play a role in differential-treatment efficacy, for example reduced statin efficacy in African-ancestry patients.

  • Ph.D., Biological Sciences, Columbia University
  • B.S., Biochemistry, Cell and Molecular Biology, University of California Santa Cruz
  • B.A., Computer Science, University of California Santa Cruz

Population Focus

Featured Member Profile

What are you thinking about?

I'm thinking about what might replace the "thrifty gene" hypothesis in explaining the genetic origins of health, susceptibility, and disease. I think that features of the innate immune system are where the rubber hits the road in most human populations: now that we understand the role of the microbiome in Western lifestyle diseases, this makes more sense. I'm also interested in how these results inform omics applications (related to the sciences of genomics, transcriptomics, proteomics and metabolomics) at the point of care.

Why is this interesting to you?

My scientific monomania is for compensatory mechanisms and trade-offs in evolution: real understanding of these questions goes all the way into the clinic. Anything less than clinical translation indicates that we don't understand what drove the genetics of human populations. Also, health disparities generally compound - for example, lack of follow-up care is a proportionally bigger risk factor in vulnerable populations, across a range of indications. Therefore, any genetic drivers of disparities in treatment efficacy can be expected to have outsized effects on outcomes.

What are the practical implications for healthcare?

Practical implications arise in two domains: precision health, and health disparities. In precision health, we need evolutionary foundations both to optimize individual gene:treatment models and to understand what types of gene or omics-based medicine is likely to have significant impact. In health disparities, we need to understand genetic drivers of population differentiated treatment efficacy in order both to make optimal treatment decisions and to optimize the development pipeline for pharmaceutics.

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