Cladograms with Path to Event (ClaPTE): A novel algorithm to detect associations between genotypes or phenotypes using phylogenies

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 Eve...

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Published in:	Computers in biology and medicine Vol. 58; pp. 1 - 13
Main Authors:	Handelman, Samuel K, Aaronson, Jacob M., Seweryn, Michal, Voronkin, Igor, Kwiek, Jesse J., Sadee, Wolfgang, Verducci, Joseph S., Janies, Daniel A.
Format:	Journal Article
Language:	English
Published:	United States Elsevier Ltd 01.03.2015 Elsevier Limited
Subjects:	Algorithms Computational Biology - methods Correlated evolution Drug resistance E coli Evolution, Molecular Experiments Genealogy Genetic simulation Genotype Genotype & phenotype Genotype-phenotype association HIV evolution Human immunodeficiency virus Hypotheses Influenza Influenza A Virus, H1N1 Subtype - genetics Influenza evolution Internal Medicine Methods Models, Genetic Mutation Other Phenotype Phylogenetics Phylogeny Protein evolution Proteins Proteins - genetics Studies Trees Protein evolution FIC HIV evolution Correlated evolution Fig. or Figs Drug resistance Genetic simulation PCA R.O.C Influenza evolution Eq. or Eqs Phylogenetics ClaPTE Genotype-phenotype association Figure or Figures principal components analysis Felsenstein’s independent contrasts Cladogram with Path to Event Equation or Equations receiver operating characteristic
ISSN:	0010-4825, 1879-0534, 1879-0534
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Abstract	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. 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. 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. •Novel method identifies associated mutations, accounting for common ancestry.•Controls false positive rates when a multiple hypothesis correction is applied.•Computationally efficient method, suitable for large data sets.
AbstractList	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. 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. 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. 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. 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. 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. 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. •Novel method identifies associated mutations, accounting for common ancestry.•Controls false positive rates when a multiple hypothesis correction is applied.•Computationally efficient method, suitable for large data sets. 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. 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.BACKGROUNDAssociations 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.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.METHODSClaPTE 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.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.RESULTS AND CONCLUSIONSClaPTE 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 ofp-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.
Author	Sadee, Wolfgang Kwiek, Jesse J. Verducci, Joseph S. Handelman, Samuel K Seweryn, Michal Aaronson, Jacob M. Voronkin, Igor Janies, Daniel A.
AuthorAffiliation	c Department of Biomedical Informatics, Ohio State University College of Medicine, 3190 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, United States b Mathematical Biosciences Institute, The Ohio State University, Jennings Hall 3rd Floor, 1735 Neil Avenue, Columbus, OH 43210, United States d 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 e Department of Statistics, The Ohio State University, 404 Cockins Hall, 1958 Neil Avenue, Columbus, OH 43210-1247, United States f Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223-0001, United States a Department of Pharmacology, Ohio State University College of Medicine, 5072 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, United States
AuthorAffiliation_xml	– name: b Mathematical Biosciences Institute, The Ohio State University, Jennings Hall 3rd Floor, 1735 Neil Avenue, Columbus, OH 43210, United States – name: f Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223-0001, United States – name: a Department of Pharmacology, Ohio State University College of Medicine, 5072 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, United States – name: e Department of Statistics, The Ohio State University, 404 Cockins Hall, 1958 Neil Avenue, Columbus, OH 43210-1247, United States – name: d 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 – name: c Department of Biomedical Informatics, Ohio State University College of Medicine, 3190 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, United States
Author_xml	– sequence: 1 givenname: Samuel K surname: Handelman fullname: Handelman, Samuel K email: handelman.9@osu.edu organization: Department of Pharmacology, Ohio State University College of Medicine, 5072 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, United States – sequence: 2 givenname: Jacob M. surname: Aaronson fullname: Aaronson, Jacob M. organization: Department of Biomedical Informatics, Ohio State University College of Medicine, 3190 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, United States – sequence: 3 givenname: Michal surname: Seweryn fullname: Seweryn, Michal organization: Mathematical Biosciences Institute, The Ohio State University, Jennings Hall 3rd Floor, 1735 Neil Avenue, Columbus, OH 43210, United States – sequence: 4 givenname: Igor surname: Voronkin fullname: Voronkin, Igor organization: Department of Biomedical Informatics, Ohio State University College of Medicine, 3190 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, United States – sequence: 5 givenname: Jesse J. surname: Kwiek fullname: Kwiek, Jesse J. organization: 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 – sequence: 6 givenname: Wolfgang orcidid: 0000-0003-1894-6374 surname: Sadee fullname: Sadee, Wolfgang organization: Department of Pharmacology, Ohio State University College of Medicine, 5072 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, United States – sequence: 7 givenname: Joseph S. surname: Verducci fullname: Verducci, Joseph S. organization: Department of Statistics, The Ohio State University, 404 Cockins Hall, 1958 Neil Avenue, Columbus, OH 43210-1247, United States – sequence: 8 givenname: Daniel A. surname: Janies fullname: Janies, Daniel A. organization: Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223-0001, United States
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/25577610$$D View this record in MEDLINE/PubMed
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Keywords	Protein evolution FIC HIV evolution Correlated evolution Fig. or Figs Drug resistance Genetic simulation PCA R.O.C Influenza evolution Eq. or Eqs Phylogenetics ClaPTE Genotype-phenotype association Figure or Figures principal components analysis Felsenstein’s independent contrasts Cladogram with Path to Event Equation or Equations receiver operating characteristic
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Snippet	Associations between genotype and phenotype provide insight into the evolution of pathogenesis, drug resistance, and the spread of pathogens between hosts.... Abstract Background Associations between genotype and phenotype provide insight into the evolution of pathogenesis, drug resistance, and the spread of... Background Associations between genotype and phenotype provide insight into the evolution of pathogenesis, drug resistance, and the spread of pathogens between...
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SubjectTerms	Algorithms Computational Biology - methods Correlated evolution Drug resistance E coli Evolution, Molecular Experiments Genealogy Genetic simulation Genotype Genotype & phenotype Genotype-phenotype association HIV evolution Human immunodeficiency virus Hypotheses Influenza Influenza A Virus, H1N1 Subtype - genetics Influenza evolution Internal Medicine Methods Models, Genetic Mutation Other Phenotype Phylogenetics Phylogeny Protein evolution Proteins Proteins - genetics Studies Trees
Title	Cladograms with Path to Event (ClaPTE): A novel algorithm to detect associations between genotypes or phenotypes using phylogenies
URI	https://www.clinicalkey.com/#!/content/1-s2.0-S001048251400359X https://www.clinicalkey.es/playcontent/1-s2.0-S001048251400359X https://dx.doi.org/10.1016/j.compbiomed.2014.12.013 https://www.ncbi.nlm.nih.gov/pubmed/25577610 https://www.proquest.com/docview/1654962197 https://www.proquest.com/docview/1657316378 https://www.proquest.com/docview/1664203948 https://pubmed.ncbi.nlm.nih.gov/PMC4331246
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