Data For Good: Dr. Eimear Kenny

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Tuesday, November 19, 2019
9:00 AM - 12:30 PM
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Workshop: 9:00am to 11:00am
Talk: 11:00am to 12:30pm


Workshop Details:
Topic: Diversity in Healthcare and Health Research

Panelists:

- Dr. Arbel Harpak, Biological Sciences
- Dr. Linda Valeri, Biostatistics
- Dr. Lynn Petukhova, Dermatology & Epidemiology
- Dr. Emily Groopman, Nephrology/Institute of Genomic Medicine




Talk Details:

Population Genetics in An Era of Precision Medicine


Abstract:
Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry1,2,3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific4,5,6,7,8,9,10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States—where minority populations have a disproportionately higher burden of chronic conditions13—the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities.


Speaker: Dr. Eimear Kenny, Founding Director of the Center of Genomic Health

Speaker Bio:
Dr. Eimear Kenny, PhD, is Founding Director of the Center for Genomic Health, and Associate Professor of Medicine and Genetics at Mount Sinai. She is a statistical and population geneticist. She leads a multidisciplinary team of geneticists, computer scientists, clinicians, and other medical professionals, working on problems at the interface of computational genomics and medicine. Her goal is to accelerate the integration of genomics into clinical care, particularly in diverse and underserved populations. She is Principal Investigator in multiple NIH-funded consortia focused on genomic research, medicine and health, including NHGRI Clinical Sequencing Evidence-based Research, NHGRI Genome Sequencing Project, and NHLBI Trans-omics in Precision Medicine. She is a scientific advisor to many genomic and genomic medicine initiatives in government, non-profit and industry arenas. She has published over 70 papers in leading journals like Science, Nature, Nature Genetics, NEJM, and her work has been featured by the New York Times, Australian Broadcasting Company and at the Smithsonian Human Genome Exhibit in Washington DC.

She has a BA in Biochemistry from Trinity College Dublin, a PhD in computational genomics from Rockefeller University, and did her postdoctoral training in population genomics at Stanford University.
Event Contact Information:
Data Science Institute
212-854-5660
[email protected]
LOCATION:
  • Morningside
TYPE:
  • Seminar
CATEGORY:
  • Engineering
EVENTS OPEN TO:
  • Faculty
  • Graduate Students
  • Postdocs
  • Public
  • Staff
  • Students
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