Efforts to identify the genetic underpinnings of rare undiagnosed diseases increasingly involve the use of next-generation sequencing and comparative genomic hybridization methods. These efforts are limited by a lack of knowledge regarding gene function, and an inability to predict the impact of genetic variation on the encoded protein function. Diagnostic challenges posed by undiagnosed diseases have solutions in model organism research, which provides a wealth of detailed biological information. Model organism geneticists are by necessity experts in particular genes, gene families, specific organs, and biological functions. Here, we review the current state of research into undiagnosed diseases, highlighting large efforts in North America and internationally, including the Undiagnosed Diseases Network (UDN) (Supplemental Material, File S1) and UDN International (UDNI), the Centers for Mendelian Genomics (CMG), and the Canadian Rare Diseases Models and Mechanisms Network (RDMM). We discuss how merging human genetics with model organism research guides experimental studies to solve these medical mysteries, gain new insights into disease pathogenesis, and uncover new therapeutic strategies.

RNA polymerase II (RNAPII) lies at the core of dynamic control of gene expression. Using 53 RNAPII point mutants, we generated a point mutant epistatic miniarray profile (pE-MAP) comprising ∼60,000 quantitative genetic interactions in Saccharomyces cerevisiae. This analysis enabled functional assignment of RNAPII subdomains and uncovered connections between individual regions and other protein complexes. Using splicing microarrays and mutants that alter elongation rates in vitro, we found an inverse relationship between RNAPII speed and in vivo splicing efficiency. Furthermore, the pE-MAP classified fast and slow mutants that favor upstream and downstream start site selection, respectively. The striking coordination of polymerization rate with transcription initiation and splicing suggests that transcription rate is tuned to regulate multiple gene expression steps. The pE-MAP approach provides a powerful strategy to understand other multifunctional machines at amino acid resolution.