A Hierarchical Modeling Strategy for Identifying Gene Expression Heterosis

• Activity Number: 186
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): William Landau*+ and Jarad Niemi and Peng Liu and Dan Nettleton
• Companies: Iowa State University and Iowa State University and Iowa State University and Iowa State University
• Keywords: heterosis ; next-generation sequencing ; RNA-seq ; hierarchical models ; parallel computing ; graphics processing units

Abstract

Heterosis, or hybrid vigor, occurs when the mean trait value of offspring is more extreme than that of either parent. Well before heterosis was first scientifically described by Darwin in 1876, people used heterosis for practical purposes. Within the last century, heterosis has been used to improve many crop species for food, feed, and fuel industries. Despite intensive study and successful utilization of heterosis, the basic molecular genetic mechanisms responsible for heterosis remain unclear. To better understand these mechanisms, researchers have begun to measure the expression levels of thousands of genes in parental lines and their hybrid offspring. The expression level of each gene can be viewed as a trait alongside more traditional traits like plant height, grain yield, and drought tolerance. This presentation features the challenges in testing for heterosis with data from a single trait and in the simultaneous analysis of thousands of gene expression traits. The main focus is a hierarchical modeling strategy for modeling count-based expression data from next-generation RNA sequencing. Also featured are the high performance computing methods that make this method tractable.