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1 Choosing mutation models in population genetics Joachim Mergeay Research Institute for Nature and Forest BELGIUM POPGROUP 47 Bath, 10.01.2014 Old school.

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Presentation on theme: "1 Choosing mutation models in population genetics Joachim Mergeay Research Institute for Nature and Forest BELGIUM POPGROUP 47 Bath, 10.01.2014 Old school."— Presentation transcript:

1 1 Choosing mutation models in population genetics Joachim Mergeay Research Institute for Nature and Forest BELGIUM POPGROUP 47 Bath, Old school POPGEN

2 2 Processes affecting genetic diversity Mutation, µ Migration, m Selection, S Drift, N e

3 3 Processes affecting genetic diversity When all parameters (µ, m, S, N e ) stable  expected equilibrium genetic diversity, H eq At this stage H ≈ 4N e µ (for the entire population) 75% equilibrium: how long does it take? Crow & Aoki 1984, PNAS: time to halfway equilibrium is Ln(2) / (2µ +1/(2N)) µ=10 -9 (SNP)t ≈ 2.8 N e generations µ=10 -4 (µsat)t ≈ 2.0 N e generations

4 4 Processes affecting genetic diversity When all parameters (µ, m, S, Ne) stable  expected equilibrium genetic diversity, H eq Equilibrium Elusive Illusion

5 5 Processes affecting genetic diversity When all parameters (µ, m, S, Ne) stable  expected equilibrium genetic diversity, H eq Most populations

6 6 Processes affecting genetic diversity When all parameters (µ, m, S, Ne) stable  expected equilibrium genetic diversity, H eq Deviation from expected pattern H eq ?  inference on underlying processes causing the deviation Different processes can lead to similar patterns

7 7 Mutation models Genetic structure: F-statistics & co F ST, G ST, R ST, D, H ST,  ST,  ST,  ST,  ST, N ST,  ST Demographic changes (expansion – contraction = bottlenecks) Selection (Relatedness – phylogeography – Phylogenetics)

8 Types of mutation models Models that take evolutionary relations among alleles into account  SMM, TPM (only µsats)  DNA sequence evolution models (e.g., GTR+G+I) e.g. allele AAA is more related to ATA than to GGC Explicit mutation models Models that don’t  IAM - oldest model  KAM SNPs: K ≤ 4(IAM: K=∞) Implicit mutation models 8

9 Explicit mutation models Give extra weight to relation among alleles SMM/TPM is considered “best for microsatellite studies” rather than IAM. “The Two-Phase Model (TPM) was used since it’s more appropriate and realistic for microsatellites (Luikart & Cornuet 1998; Piry et al. 1999) ” Whenever we can use explicit models, we tend to think we should use them because we can. “Parametric tests are better for experimental studies, non-parametric tests are better for field surveys” 9

10 Explicit mutation models Is this extra information (evolutionary relations among alleles) informative for the biological question asked? Time & space: recent small-scale versus old and large scale processes? 10

11 Explicit model or not? 11 Landscape with three pops Phylogeography: explicit MM Current landscape genetics & gene flow: implicit MM

12 T=0: 3 phylogroups 12

13 T=1: 4 populations founded from different sources (admixed populations) 13 In the admixed populations we integrate allele histories that originated in the source populations. Is this informative for the tested hypothesis?

14 Conflicts between population history and allele history Assumption of EMM in population genetics Mutation is the prime source of genetic diversity in a population Population history // allele history Spatial and temporal scale of the study: new allele may (not) have entered (meta)population through migration.  Allele ancestry confuses signal of migration  Allele ancestry represents more ancient process of mutation that happened elsewhere  Explicit MM wrongly assumes that immigrant allele has local common ancestor with other resident alleles  We cannot make the difference between mutation and migration as causal process in the make-up of the genetic structure of populations, but wrongly assume that it was mutation  Equivalence to homoplasy: the allele is identical by state, but not identical by descent in the sense that it did not descend from another allele within the population, but from outside the population. 14

15 Implicit mutation models No assumption related to ancestry of alleles Do not make assumptions on origin of diversity: m or µ Choice of mutation model essential in tests for deviations of equilibrium 15

16 16 Detecting selection based on deviation from mutation-selection equilibrium Tajima’s D (1989a), Fu’s Fs (1997) Watterson’s neutrality test (1975,77,78) Assumes constant Ne, µ and m

17 17 Detection of N e change (expansion/contraction)  Test for deviation of mutation-drift eq. Same principle, different assumptions Tajima’s D (1989b), Fu’s Fs (1997),Nei et al. (1975), Chakraborty & Nei (1977), Cornuet & Luikart (1996), etc.... Assumes constant µ, m=0, S=0

18 Who ever did a bottleneck test?

19 Ne change: Bottleneck Tests for population contraction / expansion are tests for deviations from mutation-drift equilibrium Assumption: m = 0, s = 0. Population was in MSMD equilibrium prior to disturbance Disturbance  disequilibrium  march to new equilibrium Time for new equilibrium is function of µ and N, assuming m=0 and s=0 Crow & Aoki (1984) Cornuet & Luikart (1996) Piry, Luikart & Cornuet (1999) 19

20 “Bottleneck” tests with migration If gene flow (“migration”) is clearly primary source of genetic diversity m>>µ If markers are neutral (most anonymous markers are on average) mutation-selection-migration-drift 20  Test for migration-drift equilibrium  change in N e or m Evol relationships among alleles add NOISE to the data  explicit mutation models are useless

21 “Bottleneck” tests with migration Broquet et al. (2010) simulated sudden drops in m in equilibrium populations “signals akin to genetic bottlenecks” “excess in gene diversity relative to mutation-drift equilibrium” 21  Actually deviation of migration-drift equilibrium  Change in landscape genetic structure (fragmentation!)

22 Considering assumptions Bottleneck tests & related tests can be used for deviation from equilibrium from selection-mutation mutation-drift migration-drift Caveat: underlying assumptions change 22

23 Considering assumptions Are you sure you know which deviation you are testing for? Decision tool for demography change test: If µ << m  migration-drift  implicit model! If µ >> m  mutation-drift  no recent or past admixture  explicit model;  with recent or past admixture, or no information  implicit model Test for admixture using phylogeographic data exploration: mtDNA & nDNA, individual-based clustering approaches, population homogeneity tests, … Hypothesis should drive the sampling design, mutation model, and then marker design 23

24 Fishing for patterns… “To call in the statistician after the experiment is done may be no more than asking him to perform a post- mortem examination: he may be able to say what the experiment died of” R.A. Fisher 24

25 Who considers assumptions? Who chooses mutation model based on question? 25 1/20 mentions assumption of no migration 0/20 attempted to check assumption of no migration 17/20 have probable migration among tested pops 16/20 chose mutation model based on marker type 3/20 test all models and cherry-pick afterwards 1/17 using SMM or TPM has evidence that µ >> m in the genetic make-up before and after the putative bottleneck 1/20 did not violate the migration assumption, and chose the MM that least likely violated other assumptions Screening of 20 most recent studies citing Piry et al. (1999) and looking for population bottlenecks

26 Who considers assumptions? Who chooses mutation model based on question? 26 Referee = author of other paper Statistical machismo: complex models = better, long computation times = better but by all means, don’t bother about the assumptions Screening of 20 most recent studies citing Piry et al. (1999) and looking for population bottlenecks

27 Phylogeography and recent genetic structure in an expanding damselfly Swaegers et al. JEB, nearly there 27 mtDNA whole range: EMM  Expansion (Tajima’s D)  After LGM µsats: per phylocluster test for expansion/contraction, TPM W-EU: Expansion E-EU: Expansion N-Afr: inconclusive (no effect) In recent range: IAM No evidence of disequilibrium

28 “Bottleneck” tests with migration Lower N e m does not directly affect rate of local drift, but impacts how much drift is compensated by gene flow 28 Connected pops, same local size Experienced Ne is larger due to compensation of drift through gene flow Isolated pops Square size ~ Ne Total metapop Ne size = sum Total metapop size still the same Average proportion of N e not shared among pops =1-F ST

29 “Bottleneck” tests with migration Relation between Fst and gene flow is hyperbolic Change in Fst per extra unit of gene flow largest around Nm=1  Nm=1 is considered minimal required gene flow for functional connectivity  A small change in Nm around the minimal threshold yields a comparably large deviation of migration-drift equilibrium  Useful in conservation genetics to detect genetic extinction debt / early warning for change in demography (N and m)  Only with IAM

30 Thanks for the attention, & thanks to 30 Organizers, for letting me fill an orphaned slot Janne Swaegers, Joost Vanoverbeke, Marc Ventura, Joost Raeymaekers Luc De Meester (KULeuven), Maurice Hoffmann (INBO) Water fleas, for being awesome model organisms


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