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Working Group

Teaching

Reference

  Jinko Graham

  Professor       Office TLX-10553
  Statistics and Actuarial Science       IRMACS Lab
  Simon Fraser University       Tel: (778)782-3155
  Burnaby, BC V5A 1S6      

A core activity in science is to collect data to develop and evaluate theories about the way things work. To address research questions, we design studies and then look at the resulting data to find meaning in the context of randomness. Statistics provides a principled framework and powerful set of methods to help in these efforts.

As a statistical geneticist, my interests lie at the interface with genomic science. A central theme in my research is that the genomic data on DNA sequence variation of individuals reflects their underlying genealogical relationships. These relationships can tell us about individual susceptibility to traits that run in families or populations, and so are of use in mapping disease genes. Another theme is that the 'omic datasets generated by high-throughput technologies can be integrated with trait data to obtain insights into an individual's susceptibility to diseases with an inherited component. However, with big data come issues in statistical interpretation. Does the way the data have been collected allow us to answer our research questions? What are the relevant patterns and what are blind alleys that result from unrecognized biases in sampling. What are the sources of variation in our data? Are we seeing merely chance patterns in random data that has no systematic component?

To help understand susceptibility to disease, we can incorporate fundamental genetics principles and statistical thinking into our study designs, models and methods for the analysis of genomic and trait data. Recent developments in statistical computing and Bayesian modelling of data structures with complex dependencies have enabled and enriched this effort. Additionally, advances in high-dimensional data analysis have enabled 'omic data to be integrated with trait data from imaging and clinical testing, in recent collaborations with neuro-imaging and pediatrics experts. Broadly, my focus is on developing, applying and evaluating analytic tools to understand trait and/or disease susceptibility, while taking into account the way the data have been sampled, the randomness and dependencies in the data, and the genetics principles underlying the research problem.