CCB Seminar: Dr. Latifa Jackson, Howard University: Using Multi-Omics Computational Approaches to Understand Human Health: Navigating data to make real inference
October 4 @ 2:00 pm - 3:00 pm
Dr. Latifa Jackson, Howard University
Zoom link: https://berkeley.zoom.us/j/96580389122
Abstract: Increasingly, computational scale data is being leveraged to gain insights into a host of human health issues. From environmental stressors to ancestry analyses, computational biology is becoming increasingly integral to our desire to gain insights into human biology. In this talk I would like to highlight several of the key ways that computational analyses have helped to identify novel insights into African American history and health. In particular, I will share recent findings related to using immune health, inflammation and their intersections with environmental stressors. Recent studies suggest that socioeconomics, psychological determinants and biology contribute to immune weathering in young adults. Few studies have examined violence exposure’s effect on healthy preclinical young adults who have had no diagnosed chronic diseases. We have shown that there are gender differences to experienced violence and that immune stress biomarker concentration is correlated to the experience of sexual or racial discrimination. Our more recent findings suggest that the processing and potential internalization of adverse experiences may be an even more important factor in modulating the biological stress responses in African American. At the same time, our understanding of African American populations and their health is based fundamentally on exploiting genomic data to make inference about ethnic group identity in the absence of familial pedigree data. We were interested in how building historical models based on actual family pedigree information could either recapitulate or negate our interpretation of who legacy African Americans. While examples of computational approaches are key to deeper study of human health and history, these same approaches require careful introspection on the part of computation biologists to ensure that our inferential ability is robust. I will use the research examples that I describe to extract some areas of concern and intellectual investment.