Variable Selection and Prediction for High-Dimensional Genetic Data with Complex Structures

点击翻译按钮获取中文摘要

The challenge of precision medicine is to appropriately fit treatments or recommendations to each individual. Large amounts of resources are being used to generate genetic data with the hope that it will provide tailored decision making. Since genotyping costs have now dropped below those of several routine clinical tests, at least seven large health care systems have invested in genome-wide genotyping of a large proportion of their population, within whom electronic health record data are available. This data is being used to develop polygenic risk scores (PRS), which can predict complex diseases on the basis of genetic data, and thus have the potential to improve clinical care via precision medicine which would be of great interest to the Canadian population. Analytic tools increasing prediction accuracy are needed to maximize the productivity of these investments. In the context of clinical decision making, there is also a need to understand which variables are driving these predictions. Indeed, there is a reluctance among substantive experts to use so-called black-box algorithms from the machine learning literature because there is a lack of interpretability and transparency. It is difficult to know how the algorithm is making its decisions which can have serious ethical consequences. On the other hand, while many of the models developed in the statistical literature are interpretable and provide measures of uncertainty around their parameter estimates, they are not scalable to the massive amounts of data being generated today. It is becoming increasingly important for statisticians to not only develop theoretically justified methods, but also consider practical issues such as computational algorithms, data format and software. Considering each component in tandem is a step towards more appropriate methods being used in practice. To this end, the goal of this proposal is focused around three Themes: 1) to develop the theory and computational algorithms for new high-dimensional linear mixed models for variable selection and prediction in correlated or groped data; 2) propose interaction models between a key exposure and a high-dimensional dataset (e.g. gene-environment interactions); and 3) develop prediction tools from high-dimensional data for survival time endpoints. Our methods will be implemented in user friendly software, with careful considerations of data format and storage, in order to promote wider uptake of more complex models by data analysts. The results from this project will help to establish me as a new researcher with expertise in variable selection and prediction models for high-dimensional data with complex structures and make me competitive nationally and internationally.