a1 Department of Genetics, North Carolina State University, Raleigh, NC 27695, USA
a2 W. M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, NC 27695, USA
a3 Departments of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853-2703, USA
Predicting functional gene annotations remains a significant challenge, even in well-annotated genomes such as yeast and Drosophila. One promising, high-throughput method for gene annotation is to use correlated gene expression patterns to annotate target genes based on the known function of focal genes. The Drosophila melanogaster transcriptome varies genetically among wild-derived inbred lines, with strong genetic correlations among the transcripts. Here, we leveraged the genetic correlations in gene expression among known seminal fluid protein (SFP) genes and the rest of the genetically varying transcriptome to identify 176 novel candidate SFPs (cSFPs). We independently validated the correlation in gene expression between seven of the cSFPs and a known SFP gene, as well as expression in male reproductive tissues. We argue that this method can be extended to other systems for which information on genetic variation in gene expression is available.
(Received May 11 2011)
(Revised October 10 2011)
(Accepted October 12 2011)
c1 Corresponding author: North Carolina State University, Raleigh, NC, USA.
† These authors contributed equally to this work.