Synonymous Site-to-Site Substitution Rate Variation Dramatically Inflates False Positive Rates of Selection Analyses: Ignore at Your Own Peril

• Published in: Molecular biology and evolution Vol. 37; no. 8; pp. 2430 - 2439
• Main Authors: Wisotsky, Sadie R, Kosakovsky Pond, Sergei L, Shank, Stephen D, Muse, Spencer V
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 01.08.2020
• Subjects: Animals; Methods; Models, Genetic; Phylogeny; Rhodopsin - genetics; Selection, Genetic; Silent Mutation; Vertebrates - genetics; episodic selection; codon model; synonymous rate variation; evolutionary model
• ISSN: 0737-4038; 1537-1719; 1537-1719
• DOI: 10.1093/molbev/msaa037

Most molecular evolutionary studies of natural selection maintain the decades-old assumption that synonymous substitution rate variation (SRV) across sites within genes occurs at levels that are either nonexistent or negligible. However, numerous studies challenge this assumption from a biological perspective and show that SRV is comparable in magnitude to that of nonsynonymous substitution rate variation. We evaluated the impact of this assumption on methods for inferring selection at the molecular level by incorporating SRV into an existing method (BUSTED) for detecting signatures of episodic diversifying selection in genes. Using simulated data we found that failing to account for even moderate levels of SRV in selection testing is likely to produce intolerably high false positive rates. To evaluate the effect of the SRV assumption on actual inferences we compared results of tests with and without the assumption in an empirical analysis of over 13,000 Euteleostomi (bony vertebrate) gene alignments from the Selectome database. This exercise reveals that close to 50% of positive results (i.e., evidence for selection) in empirical analyses disappear when SRV is modeled as part of the statistical analysis and are thus candidates for being false positives. The results from this work add to a growing literature establishing that tests of selection are much more sensitive to certain model assumptions than previously believed.