 Limited Agreement of Independent RNAi Screens for Virus-Required Host Genes Owes More to False-Negative than False-Positive FactorsLinhui Hao, Qiuling He, Zhishi Wang, Mark Craven, Michael A Newton and Paul Ahlquist PLOS Computational Biology, 2013, vol. 9, issue 9, 1-20 Abstract: Systematic, genome-wide RNA interference (RNAi) analysis is a powerful approach to identify gene functions that support or modulate selected biological processes. An emerging challenge shared with some other genome-wide approaches is that independent RNAi studies often show limited agreement in their lists of implicated genes. To better understand this, we analyzed four genome-wide RNAi studies that identified host genes involved in influenza virus replication. These studies collectively identified and validated the roles of 614 cell genes, but pair-wise overlap among the four gene lists was only 3% to 15% (average 6.7%). However, a number of functional categories were overrepresented in multiple studies. The pair-wise overlap of these enriched-category lists was high, ∼19%, implying more agreement among studies than apparent at the gene level. Probing this further, we found that the gene lists implicated by independent studies were highly connected in interacting networks by independent functional measures such as protein-protein interactions, at rates significantly higher than predicted by chance. We also developed a general, model-based approach to gauge the effects of false-positive and false-negative factors and to estimate, from a limited number of studies, the total number of genes involved in a process. For influenza virus replication, this novel statistical approach estimates the total number of cell genes involved to be ∼2,800. This and multiple other aspects of our experimental and computational results imply that, when following good quality control practices, the low overlap between studies is primarily due to false negatives rather than false-positive gene identifications. These results and methods have implications for and applications to multiple forms of genome-wide analysis.Author Summary: Genome-wide RNA interference assays of gene functions offer the potential for systematic, global analysis of biological processes. A pressing challenge is to develop meta-analysis methods that effectively combine information from multiple studies. One puzzle is that implicated gene lists from independent studies of the same process often show relatively low overlap. This disagreement might arise from false-positive factors, such as imperfect gene targeting (off-target effects), or from false negatives if separate studies access different components of large, complex systems. We present new methods to examine the relations between individual genome-wide RNAi studies, using studies of host genes in influenza virus replication as a test case. We find that cross-study agreement is greater than suggested by overlap of reported gene lists. This better agreement is evidenced by the strong relation of independent gene lists in functional pathways and protein interaction networks, and by a statistical model that relates multi-study, gene-level findings to factors driving correct, false-negative, and false-positive gene identification. Our analysis of multiple genome-wide studies predicts that there are many undetected host genes important for influenza virus infection, and that false negatives are the major concerns for genome-wide studies. Date: 2013 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1003235 DOI: 10.1371/journal.pcbi.1003235 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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