Samuel K Handelman1, Jacob M Aaronson2, Michal Seweryn3, Igor Voronkin2, Jesse J Kwiek4, Wolfgang Sadee5, Joseph S Verducci6, Daniel A Janies7. 1. Department of Pharmacology, Ohio State University College of Medicine, 5072 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, United States; Mathematical Biosciences Institute, The Ohio State University, Jennings Hall 3rd Floor, 1735 Neil Avenue, Columbus, OH 43210, United States. Electronic address: handelman.9@osu.edu. 2. Department of Biomedical Informatics, Ohio State University College of Medicine, 3190 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, United States. 3. Mathematical Biosciences Institute, The Ohio State University, Jennings Hall 3rd Floor, 1735 Neil Avenue, Columbus, OH 43210, United States. 4. Department of Microbial Infection & Immunity and Department of Microbiology, The Ohio State University, 788 Biomedical Research Tower, 460 West 12th Avenue, Columbus, OH 43210, United States. 5. Department of Pharmacology, Ohio State University College of Medicine, 5072 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, United States. 6. Department of Statistics, The Ohio State University, 404 Cockins Hall, 1958 Neil Avenue, Columbus, OH 43210-1247, United States. 7. Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223-0001, United States.
Abstract
BACKGROUND: Associations between genotype and phenotype provide insight into the evolution of pathogenesis, drug resistance, and the spread of pathogens between hosts. However, common ancestry can lead to apparent associations between biologically unrelated features. The novel method Cladograms with Path to Event (ClaPTE) detects associations between character-pairs (either a pair of mutations or a mutation paired with a phenotype) while adjusting for common ancestry, using phylogenetic trees. METHODS: ClaPTE tests for character-pairs changing close together on the phylogenetic tree, consistent with an associated character-pair. ClaPTE is compared to three existing methods (independent contrasts, mixed model, and likelihood ratio) to detect character-pair associations adjusted for common ancestry. Comparisons utilize simulations on gene trees for: HIV Env, HIV promoter, and bacterial DnaJ and GuaB; and case studies for Oseltamavir resistance in Influenza, and for DnaJ and GuaB. Simulated data include both true-positive/associated character-pairs, and true-negative/not-associated character-pairs, used to assess type I (frequency of p-values in true-negatives) and type II (sensitivity to true-positives) error control. RESULTS AND CONCLUSIONS: ClaPTE has competitive sensitivity and better type I error control than existing methods. In the Influenza/Oseltamavir case study, ClaPTE reports no new permissive mutations but detects associations between adjacent (in primary sequence) amino acid positions which other methods miss. In the DnaJ and GuaB case study, ClaPTE reports more frequent associations between positions both from the same protein family than between positions from different families, in contrast to other methods. In both case studies, the results from ClaPTE are biologically plausible.
BACKGROUND: Associations between genotype and phenotype provide insight into the evolution of pathogenesis, drug resistance, and the spread of pathogens between hosts. However, common ancestry can lead to apparent associations between biologically unrelated features. The novel method Cladograms with Path to Event (ClaPTE) detects associations between character-pairs (either a pair of mutations or a mutation paired with a phenotype) while adjusting for common ancestry, using phylogenetic trees. METHODS: ClaPTE tests for character-pairs changing close together on the phylogenetic tree, consistent with an associated character-pair. ClaPTE is compared to three existing methods (independent contrasts, mixed model, and likelihood ratio) to detect character-pair associations adjusted for common ancestry. Comparisons utilize simulations on gene trees for: HIV Env, HIV promoter, and bacterial DnaJ and GuaB; and case studies for Oseltamavir resistance in Influenza, and for DnaJ and GuaB. Simulated data include both true-positive/associated character-pairs, and true-negative/not-associated character-pairs, used to assess type I (frequency of p-values in true-negatives) and type II (sensitivity to true-positives) error control. RESULTS AND CONCLUSIONS: ClaPTE has competitive sensitivity and better type I error control than existing methods. In the Influenza/Oseltamavir case study, ClaPTE reports no new permissive mutations but detects associations between adjacent (in primary sequence) amino acid positions which other methods miss. In the DnaJ and GuaB case study, ClaPTE reports more frequent associations between positions both from the same protein family than between positions from different families, in contrast to other methods. In both case studies, the results from ClaPTE are biologically plausible.
Authors: Art F Y Poon; Luke C Swenson; Winnie W Y Dong; Wenjie Deng; Sergei L Kosakovsky Pond; Zabrina L Brumme; James I Mullins; Douglas D Richman; P Richard Harrigan; Simon D W Frost Journal: Mol Biol Evol Date: 2009-12-02 Impact factor: 16.240
Authors: Jian Yang; Beben Benyamin; Brian P McEvoy; Scott Gordon; Anjali K Henders; Dale R Nyholt; Pamela A Madden; Andrew C Heath; Nicholas G Martin; Grant W Montgomery; Michael E Goddard; Peter M Visscher Journal: Nat Genet Date: 2010-06-20 Impact factor: 38.330
Authors: Christian Renaud; Alexandre A Boudreault; Jane Kuypers; Kathryn H Lofy; Lawrence Corey; Michael J Boeckh; Janet A Englund Journal: Emerg Infect Dis Date: 2011-04 Impact factor: 6.883