Statistical issues in mapping genetic determinants for expression level polymorphisms
Abstract
Unraveling the underlying mechanism of inheritance for complex traits has been of great interest to the scientific community for many years. Complex traits are typically controlled by multiple genes that are organized into networks and may behave differently under varying environmental conditions. With advances in technologies, namely microarray technology, it is now possible to comprehensively dissect complex traits at a molecular level by identifying genetic determinants of expression level polymorphism (ELP; the quantified variation in mRNA transcripts by way of microarray technology), and thus provides a systems biological way to investigate gene networks. To date, the identification of ELP determinants has been pursued via existing statistical methods for association mapping and/or quantitative trait locus (QTL) mapping. Although these analytical methodologies address many important statistical issues ( e.g., sample size and replication), they fail to deal with the unification of QTL mapping and microarray technology for the purpose of molecularly dissecting a complex trait. A framework for designing, understanding, and analyzing ELP experiments is proposed with focus on three major components: experimental design; genetic mapping or locating of ELP determinants; and the construction of gene regulatory networks. A novel multivariate mixture linear model (MMLM) with mixed effects is proposed in an interval mapping setting for the purpose of mapping genetic determinants of ELPs. The performance of existing QTL methods and the proposed MMLM method is investigated using both simulated and real data. Practical recommendations for future ELP studies are given, and suggestions regarding the incorporation of ELP mapping results to gene network construction are made.
Degree
Ph.D.
Advisors
Doerge, Purdue University.
Subject Area
Statistics
Off-Campus Purdue Users:
To access this dissertation, please log in to our
proxy server.