Slide 1Slide 2Slide 3Slide 4Slide 5Slide 6Slide 7Slide 8Slide 9Slide 10Slide 11Slide 12Slide 13Slide 14Slide 15Slide 16Slide 17Slide 18Slide 19Slide 20Slide 21Slide 22Slide 23Slide 24Slide 25Slide 26Slide 27Slide 28Slide 29Slide 30Slide 31Slide 32Slide 33Slide 34Click to edit Master subtitle style 1/12/13 Chapter 9The history in our genes1/12/13 Genetic loci have their own genealogy1/12/13 Alleles in populations coalesce to a common ancestor1/12/13 Coalescence time varies for different genes1/12/13 Key Concepts•It is possible to trace gene genealogies and reconstruct the rise and spread of alleles1/12/13 Gene trees do not always match species treesData from several genes used to construct phylogenies1/12/13 Key Concepts•Gene trees and species trees do not always match–Data from multiple genes used in phylogeny construction1/12/13 Building a phylogeny with genetic data•Each nucleotide a potentially informative character•But, homoplasy common–Only four possible character states•Genes differ in rate of evolution–Slowly evolving genes useful for distantly related species–Rapidly evolving genes useful for closely related lineages1/12/13 Common methods•Maximum parsimony–Simplest explanation favored•Distance matrix (e.g. neighbor joining)–Clusters taxa based on genetic distance•Maximum likelihood–Finds most likely tree given specific model of molecular evolution•Bayesian methods–Looks at probability that a tree is correct given a specific model of molecular evolution1/12/13 Neighbor-joining example1/12/13 Key Concepts•Phylogenetic trees are hypotheses that are constantly reevaluated when new data become available1/12/13 Experimental phylogenetics validates methods1/12/13 A large number of tree topologies are possible1/12/13 All methods performed well1/12/13 Key Concepts•Scientists can test the effectiveness of phylogenetic methodologies1/12/13 Case study: origin of tetrapods1/12/13 Case study: human evolution•Multiregional model of human origins–Hominins across Old World were a single species connected by gene flow–Multiregional differences the result of local adaptation•Out-of-Africa model of human origins–All human populations are derived from recent African ancestry1/12/13 Phylogenetic data supports Out-of-Africa model1/12/13 Case study: origin of Darwin’s finches•Multiple colonizations by different finch species–Different species of Darwin’s species should have closest relatives elsewhere•Single colonization then diversification–Darwin’s finches should form monophyletic group•Colonization from Isla de Coco–Cocos finch should be closest relative of all Darwin’s finches1/12/13 Phylogenetic evidence supports single colonization1/12/13 Case study: human immunodeficiency virus (HIV)Three separate introductions from chimpanzees1/12/13 Neutral theory of molecular evolution•Motoo Kimura (1968): most evolution at the molecular level is neutral (due to drift)–Neutral substitutions should accrue in a clock-like fashion–Different types of DNA should evolve at different rates1/12/13 Support for clock-like substitution1/12/13 Rate of evolution in different types of DNA does differ1/12/13 The molecular clock can be used to estimate date of common ancestor1/12/13 Molecular clock can be used to estimate diversification events1/12/13 Two peaks in mammalian diversification rates1/12/13 Detecting selection on DNA sequences•Synonymous substitutions: do not change protein–Should evolve at a neutral rate•Nonsynonymous substitutions: change protein–Faster evolution than synonymous sites indicates positive selection–Slower evolution than synonymous sites indicates purifying selection1/12/13 Signature of positive selection1/12/13 Positive selection on FOXP21/12/131/12/13 Key Concepts•The neutral theory of molecular evolution describes patterns of nucleotide substitution predicted under drift alone•Neutral variation should accumulate in a clock-like fashion•Both positive and purifying selection leave distinctive genetic signatures that can be detected1/12/13 Genome alignments reveal conserved elements with important functions1/12/13 Key Concepts•Genome alignments provide valuable information about functionally important coding and non coding