Life Science 200A

Introduction to population genetics, genetic structure and gene flow

Victoria Sork, October 30 - November , 2003

References

  1. Austerlitz, F., and P. Smouse. 2001. Two-generation analysis of pollen flow across a landscape. II. Relation between fft, pollen dispersal and interfemale distance. Genetics 157:851-857.
  2. Dick, C. W., G. Etchelecu, and F. Austerlitz. 2003. Pollen dispersal of tropical trees (Dinizia excelsa: Fabaceae) by native insects and African honeybees in pristine and fragmented Amazonian rainforest. Molecular Ecology 12:753-764.
  3. Excoffierj L, Smouse PE, Quattro JM 1992. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics, 131: 479-491.
  4. Futuyma, Douglas. 1998. Evolutionary Biology, 3rd Edition. Sinauer Associates, Inc. Sunderland, MA
  5. Godoy, J. A., and P. Jordano. 2001. Seed dispersal by animals: exact identification of source trees with endocarp DNA microsatellites. Molecular Ecology 10:2275-2283.
  6. Hartl, DL and AG Clark. 1997. Principles of Population Genetics. 3rd Edition. Sinauer Associates, Inc. Sunderland, MA
  7. Hedrick, PW, Gilpin ME (1997) Genetic effective size of a metapopulation In: Metapopulation Biology. Ecology, Genetics, and Evolution (Eds, Hanski IA, Gilpin ME), pp. 166-181. San Diego.
  8. Heuertz, M., X. Vekemans, J. F. Hausman, M. Palada, and O. J. Hardy. 2003. Estimating seed vs. pollen dispersal from spatial genetic structure in the common ash. Molecular Ecology 12:2483-2495.
  9. Hutchison, DW, AR.Templeton. 1999. Correlation of pairwise genetic and geographic distance measures: inferring the relative influences of gene flow and drift on the distributrion of genetic variability. Evolution 53:1898-1914.
  10. Kirkpatrick, M., and N. H. Barton. 1997. Evolution of a species' range. American Naturalist 150:1-23.
  11. Levin DA 1978. Genetic variation in annual Phlox: Self-compatible versus self-incompatible species. Evolution32: 245-263.
  12. Rousset, F. 1997. Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145:1219-1228.
  13. Ruckelshaus, M. H. 1996. Estimation of genetic neighborhood parameters from pollen and seed dispersal in the marine angiosperm Zostera marina L. Evolution 50:856-864.
  14. Slatkin, M. 1985. Gene flow in natural populations. Annual Review of Ecology and Systematics 16:393-430.
  15. Slatkin, M. 1987. Gene flow and the geographical structure of natural populations. Science 239:787-792.
  16. Schaal, BA 1975. Population structure and local differentiation in Liatris cylindracea American Naturalist 109: 511-528.
  17. Smouse PE, Dyer RJ, Westfall RD, Sork VL .2001. Two-generation analysis of pollen flow across a landscape. I. Male gamete heterogeneity among females. Evolution 55:260-271.
  18. Sork, V. L., D. R. Campbell, R. J. Dyer, J. F. Fernandez, J. Nason, R. Petit, P. E. Smouse, and E. Steinberg. 1998. Proceedings from a workshop on gene flow in fragmented, managed, and continuous populations. National Center for Ecological Analysis and Synthesis Research Paper No. 3: http://www.nceas.ucsb.edu/nceas-web/projects/2057/nceas-paper3/.
  19. Sork, V. L., F. W. Davis, P. E. Smouse, V. J. Apsit, R. J. Dyer, J. F. Fernandez, and B. Kuhn. 2002. Pollen movement in declining populations of California Valley oak, Quercus lobata: where have all the fathers gone? Molecular Ecology V11:1657-1668.
  20. Streiff R, Ducousso A, Lexer C et al. 1999.Pollen dispersal inferred from paternity analysis in a mixed oak stand of Quercus robur L. and Q. petraea (Matt.) Liebl. Molecular Ecology, 8:831-841.
  21. Whitlock, MC and DE McCauley. 1999. Indirect measures of gene flow and migration: FST not equaly to 1/(4Nm + 1). Heredity 82: 117-125
  22. Weir BS, Cockerham CC 1984 Estimating F-statistics for the analysis of population structure. Evolution 38:1358-1370.
  23. Wright, S. 1946. Isolation by distance under diverse systems of mating. Genetics 31:39-59.
  24. Wright, S. 1951. The genetical structure of populations. Annals of Eugenics 15:323-354.
  25. Wright, S. 1965. The interpretation of population structure by F-statistics with special regard to systems of mating. Evolution 19:395-420.

Reading for presentations:

Thursday, October 30, 2003:

Background reading:

For November 4 and 6, please read two of three articles each day closely, but you only need to critique one of the six articles. The critique is due on November 6.

Tuesday, November 4, 2003:

Thursday, November 6, 2003:

I. Major Evolutionary Forces (Review)

A. Random genetic drift

1. Chance fluctuations in allele frequency which occur as a results of random sampling among gametes.

2. Important in small populations

3. Loss of genetic variation

B. Gene flow or migration

1. Movement of genes among populations

2. Homogenizes differences among populations

3. Source of variation for single populations

C. Mutation

1. Changes in genetic material

2. Includes single nucleotide insertions, deletions, changes, chromosomal changes, and spontaneous polyploidy.

3. Source of genetic variation

D. Natural selection

1. Process by which genotypes with greater fitness leave, on the average, more offspring than do less fit genotypes.

2. Genetic composition gradually changes to promote greater adaptation to the environment

3. Usually results in a loss of genetic variation (but not always)

II. Genetic Drift

A. Binomial probability

1. For a population of diploid individuals (2N), the probability that a population will contain a specific number, i, of one type of allele.

2. Equation:

3. Probability of fixation is the probability that a given allele will have 100% frequency.

4. Probability of loss of allele is probability that it will have 0% frequency.

5. For example, a plant population of N=4 and 6 A alleles.

p=6/8=.75

q=2/8=.25

6. Note that example shows high likelihood of loss of allele in this small population.

(Figure taken from Felsensetein, NOAA Tech Memo NMFS NWFSC-30: Genetic Effects of Straying (http://research.nwfsc.noaa.gov/pubs/tm/tm30/felsenstein.html)

B. Opportunities for Genetic drift

1. Continuous drift; random effects are often the most important factor contributing to evolutionary change in populations that are always small.

a. Endangered species, e.g. California condors

b. Insular species (small islands, fragmented habitats

c. Skewed mating systems - many individuals but few breeders.

2. Intermittent drift: large fluctuations in population size.

3. Bottleneck effects - e.g. northern elephant seals, cheetahs (or metapopulation)

4. Founder effects

a. if a small group of individuals becomes geographically isolated from the remainder of the population or a small group of individuals colonize a new site.

b. random effects will significantly determine the frequencies of genes in the new population. e.g. Hawaiian Drosophila

C. Simulations of genetic drift and natural selection over time

See Felsenstein's PopG Genetic Simulation Program

ftp://evolution.gs.washington.edu/pub/popgen/popg.html

1. As time goes on, more and more populations become fixed

2. Population show the effects of "inbreeding", that is an overall deficiency of heterozygotes.

3. Within subpopulations, allele frequencies fit HW expectations.

4. For large populations that become subdivided due to restricted gene flow, genetic drift will influence loss of local genetic variation but, if sufficient number of subpopulations, global genetic variation will remain constant.

 

D. Discussion: Gene flow and selection

III. Population genetic structure

A. Measures of heterozygosity (Sewall Wright)

1. HI= heterozygosity for an individual in some subpopulation

=average heterozygosity of all loci for individual

(usually, we calculate the average observed heterozygosity across all individuals in a subpopulation.

2. HS =heterozygosity of a randomly mating subpopulation

= 2pq

3. HT = heterozygosity of a randomly mating total population

=2poqo

B. Levels of genetic structure

1. Inbreeding coefficient

a. measures reduction in heterozygosity of an individual due to nonrandom mating within a subpopulation.

b. equation:

2. Fixation index

a. measures reduction in heterozygosity due to genetic drift within subpopulations

b. measures amount of genetic differentiation among populations

equation:

3. Overall inbreeding coefficient

a. measure of reduction in heterozygosity of an individual relative to total population

b. reflects of the effect of inbreeding and genetic drift

c. equation

C. Example from Hartl: Levin 1978

1. Phlox cuspidata, single locus, PGM-, with two alleles, a and b.

2. 43 subpopulations

3. results

population freq(b) H
1-40 1.0 0
41 .49 .17
42 .83 .06
43 .91 .06
means: .9821 .067

Note: the high degree of both inbreeding and population subdivision

Be careful of studies with only one locus.

 

D. Example from Schaal 1975

1. Liatris cylandacea, herbaceous perrenial

2. study site: 18 m x 13 m plot in sand prairie, Illinois, 66 quadrats of 3 m2

3. 27 loci, 15 polymorphic

4. Results:

Locus FIS FST FIT
GOT .3773 .1084 .3885
MDH .4853 .0903 .5318
ADH .4508 .0452 .4755
AP-1 .4669 .2240 .5863
AP-2 .5050 .0438 .5267
Est-1 .5020 .0464 .5249
Est-2 .5059 .2190 .4110
Pep .3025 .0256 .3203
G-6PGDH .4013 .0677 .4419
6-PGDH .3629 .0756 .4110
Per-- .1004 .0139 .1121
Per+ .2579 .0395 .1004
PGI .4401 .0767 .4830
AlkP .4148 .0361 .4358
Est-3 .4289 .0091 .4344
MEANS: .4070 .0687 .4257

5. Conclusions:

High inbreeding, and moderate population differentiation

Careful: both measures are a function of her quadrat size.

Question: what would happen if quadrat size was too large relative to area of

E. Other models to estimate genetic structure

  1. Weir and Cockerham 1984
  2. Analysis of Molecular Data, e.g. AMOVA (Excoffier et al 1992)

IV. Gene flow estimated from population structure

A. Wright's island model

1. Treats all populations as having equal probability of gene exchange.

2. FST= 1 / [4Nm + 1]

3. Estimate of average effective number of migrants per generation

 

B. Slatkin's isolation by distance model

1. Uses equation 1 to estimate pairwise gene exchange,

2. Based on Kimura's stepping stone model that individuals are dispersed from neighboring populations.

3. Isolation by distance predicts that gene exchange should decrease with interpopulation distance.

4. The ibd approach can incorporate landscape features by estimating various interpopulation pathways.

C. Advantages and limitations of Indirect Methods

  1. Provide insight about evolutionary equilibrium of gene flow and genetic drift
  2. Genetic structure may be confounded by other evolutionary forces, which would mislead us about extent of gene flow

D. Discussion

  1. Rucklehaus 1996
  2. Whitlock and McCauley 1999

V. Other types of genetic structure

A. Fine scale genetic structure

1. Measures distribution of genotypes within a population

2. Spatially explicit

3. Most plant populations show clusters of individuals with shared alleles

a. presumed to be due to restricted gene flow

b. affects estimates of mating system.

 

See Rousett 1997 for sophisticated use of spatial autocorrelation statistics to examine gene flow

 

B. Metapopulation genetic structure (source: Hedrick and Gilpin 1997)

1. Dynamics of colonization and extinction create different genetic structure than predicted by conventional population genetic models (e.g. island, mainland-continent, stepping stone)

2. Hedrick and Gilpin estimated the effective size of a metapopulation and show that metapopulation dynamics have the following impact effects:

a. heterozygosity declines to some lower level

b. FST increases to some lower level

c. Metapopulation effective size is reduced

d. Number of subpopulations increases effective population size and FST

Table IV. Estimated effective population sizes for different numbers of subpopulations when local patches have infinite size, c=.2 and e=.05.
NP Ne(S) Ne(T) Ne(S)/ Ne(T) FST
5 43.3 57.2 .756 .166
10 40.7 766 .531 .312
20 39.4 148.8 .265 .386
40 39.5 306.1 .124 .418

 

e. Gene flow increases metapopulation effective population size and reduces FST

Table V. The estimated effective population sizes for different levels of gene flow between patches when K is infinite, c=.2, e=.05, and NP=10.
m Ne(S) Ne(T) Ne(S)/ Ne(T) FST
.00 40.7 76.6 .531 .312
.00125 66.5 95.1 .699 .224
.0025 88.9 115.7 .769 .167
.005 125.8 140.1 .898 .114
.01 156.7 172.4 .909 .069
.02 217.7 219.7 .991 .040

3. Metapopulation dynamics may cause loss of heterozygosity can be lost more quickly than predicted by traditional estimates of effective population size.

4. Metapopulation dynamics may be a better explanation for low genetic variation in cheetah than the bottleneck hypothesis.

C. Topics not covered that should be

  1. Coalescent theory

  2. Phylogenetic approach

  3. Phylogeography

 

VI. Contemporary gene flow (Direct measures)

A. Background

1. Estimates gene movement per reproductive episode

2. Estimates are not the same as Nm; more similar to Wright's Neighborhood model:

3. Early approaches tracked animal movement or documented pollen and seed movement

4. Most genetically based plant population studies quantify pollen-mediated gene movement, but seed-mediated gene flow is possible.

 

B. Parentage model

B. Genetic structure appoach to estimate contemporary pollen-mediated gene flow

 

 

 

C. Contemporary seed movement

 

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