Leveraging Polygenic Architecture of Complex Traits to Inform Biology, Causality and Prediction

  • Nilanjan Chatterjee, PhD, Bloomberg Distinguished Professor, Dept. of Biostatistics, Bloomberg School of Public Health, JHU
  • An IDIES Bi-Monthly Seminar
  • When: October 10, 2018, 16:30
  • Where: 305 Olin Hall
    Homewood Campus, Johns Hopkins University
    Baltimore, MD 21218


Using results from modern genome-wide association studies (GWAS), we and others have now unequivocally demonstrated that complex traits are extremely polygenic, with each individual trait potentially involving thousands to tens of thousands of genetic variants. While each individual variant may have a small effect on a given trait, in combination, they can explain substantial variation of the trait in the underlying population. In the past, analyses of GWAS have mainly focused on modelling genetic susceptibility one-variant-at-a-time, and identifying those which reach stringent statistical significance for association. In the future, however, we advocate that analysis needs to focus more on polygenic modelling to exploit the power of diffused signals in GWAS. In this talk, I will review recent advances in statistical methods for polygenic analysis, as well as scientific knowledge gained through their applications, in three areas of major interest (i) understanding biology through genomic enrichment analysis (ii) exploring causality through Mendelian Randomization (Genetic Instrumental Variable) analysis and (iii) and informing precision medicine through development of risk prediction models.


Dr. Chatterjee is a Bloomberg Distinguished Professor at the Department of Biostatistics, Bloomberg School of Public Health and Department of Oncology School of Medicine at the Johns Hopkins University. Prior to joining Johns Hopkins, he led the Biostatistics Branch of the Division of Cancer Epidemiology and Genetics of the US National Cancer Institute during 2008-2015. He is known for foundational and methodological contributions to multiple areas of modern biomedical data science, including large scale analysis of genetic associations, gene-environment interactions and predictive model building by synthesis of information from multiple data sources. His collaborative research has led to understanding of genetic architecture and role of gene-environment interactions in the etiology of a variety of cancers. He has received numerous prestigious national and international awards, including the notable COPSS President Award (2011) that is sponsored by five major international statistical societies to recognize the outstanding contribution of a statistician under age 41.

IDIES Bi-Monthly Seminar

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