  RNA-Seq technology allows for studying the transcriptional state of the cell
at an unprecedented level of detail. Beyond quantification of whole-gene
expression, it is now possible to disentangle the abundance of individual
alternatively spliced transcript isoforms of a gene. A central question is to
understand the regulatory processes that lead to differences in relative
abundance variation due to external and genetic factors. Here, we present a
mixed model approach that allows for (i) joint analysis and genetic mapping of
multiple transcript isoforms and (ii) mapping of isoform-specific effects.
Central to our approach is to comprehensively model the causes of variation and
correlation between transcript isoforms, including the genomic background and
technical quantification uncertainty. As a result, our method allows to
accurately test for shared as well as transcript-specific genetic regulation of
transcript isoforms and achieves substantially improved calibration of these
statistical tests. Experiments on genotype and RNA-Seq data from 126 human
HapMap individuals demonstrate that our model can help to obtain a more
fine-grained picture of the genetic basis of gene expression variation.
