title: Estimate of within population incremental selection through branch imbalance in lineage trees.
abstract: Incremental selection within a population, defined as limited fitness changes following mutation, is an important aspect of many evolutionary processes. Strongly advantageous or deleterious mutations are detected using the synonymous to non-synonymous mutations ratio. However, there are currently no precise methods to estimate incremental selection. We here provide for the first time such a detailed method and show its precision in multiple cases of micro-evolution. The proposed method is a novel mixed lineage tree/sequence based method to detect within population selection as defined by the effect of mutations on the average number of offspring. Specifically, we propose to measure the log of the ratio between the number of leaves in lineage trees branches following synonymous and non-synonymous mutations. The method requires a high enough number of sequences, and a large enough number of independent mutations. It assumes that all mutations are independent events. It does not require of a baseline model and is practically not affected by sampling biases. We show the method's wide applicability by testing it on multiple cases of micro-evolution. We show that it can detect genes and inter-genic regions using the selection rate and detect selection pressures in viral proteins and in the immune response to pathogens.
authors.affiliation: Gonda Multidisciplinary Brain Research Center, Bar Ilan University, Ramat-Gan 5290002, Israel.
authors.forename: Gilad
authors.initials: G
authors.lastname: Liberman
authors.affiliation: Department of Mathematics, Bar Ilan University, Ramat-Gan 5290002, Israel.
authors.forename: Jennifer I C
authors.initials: JI
authors.lastname: Benichou
authors.affiliation: Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06520-8011, USA Howard Hughes Medical Institute, New Haven, CT 06519, USA.
authors.forename: Yaakov
authors.initials: Y
authors.lastname: Maman
authors.affiliation: Program in Computational and Systems Immunology, Stanford University, Stanford, CA 94305, USA Department of Pathology, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA Program in Immunology, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA Distributed Bio, San Francisco, CA 94080, USA.
authors.forename: Jacob
authors.initials: J
authors.lastname: Glanville
authors.affiliation: Department of Mathematics, Bar Ilan University, Ramat-Gan 5290002, Israel.
authors.forename: Idan
authors.initials: I
authors.lastname: Alter
authors.affiliation: Gonda Multidisciplinary Brain Research Center, Bar Ilan University, Ramat-Gan 5290002, Israel Department of Mathematics, Bar Ilan University, Ramat-Gan 5290002, Israel ylouzoun@gmail.com.
authors.forename: Yoram
authors.initials: Y
authors.lastname: Louzoun
mesh_terms: D000465:Algorithms; D052685:Alphapapillomavirus / Q000145:classification / Q000235:genetics; D000818:Animals; D001483:Base Sequence; D005075:Biological Evolution; D003198:Computer Simulation; D000939:Epitopes / Q000737:chemistry / Q000235:genetics; D006678:HIV / Q000145:classification / Q000235:genetics; D006515:Hepatitis B virus / Q000145:classification / Q000235:genetics; D006801:Humans; D007136:Immunoglobulins / Q000145:classification / Q000235:genetics; D009980:Influenza A virus / Q000145:classification / Q000235:genetics; D051379:Mice; D008822:Mice, Transgenic; D008957:Models, Genetic; D008969:Molecular Sequence Data; D009154:Mutation; D010375:Pedigree; D010802:Phylogeny; D012367:RNA, Viral / Q000737:chemistry / Q000235:genetics; D011947:Receptors, Antigen, B-Cell / Q000145:classification / Q000235:genetics; D012641:Selection, Genetic; D016415:Sequence Alignment
chemical_list: D000939:Epitopes; D007136:Immunoglobulins; D012367:RNA, Viral; D011947:Receptors, Antigen, B-Cell