Effective population size

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The effective population size is the number of individuals that an idealised population would need to have, in order for some specified quantity of interest to be the same in the idealised population as in the real population. Idealised populations are based on unrealistic but convenient simplifications such as random mating, simultaneous birth of each new generation, constant population size, and equal numbers of children per parent. In some simple scenarios, the effective population size is the number of breeding individuals in the population. However, for most quantities of interest and most real populations, the census population size N of a real population is usually larger than the effective population size Ne.[1] The same population may have multiple effective population sizes, for different properties of interest, including for different genetic loci.


The effective population size is most commonly measured with respect to the coalescence time. In an idealised diploid population with no selection at any locus, the expectation of the coalescence time in generations is equal to twice the census population size. The effective population size is measured as within-species genetic diversity divided by four times the mutation rate, because in such an idealised population, the heterozygosity is equal to 4Nμdisplaystyle 4Nmu displaystyle 4Nmu . In a population with selection at many loci and abundant linkage disequilibrium, the coalescent effective population size may not reflect the census population size at all, or may reflect its logarithm.


The concept of effective population size was introduced in the field of population genetics in 1931 by the American geneticist Sewall Wright.[2][3]




Contents





  • 1 Overview: Types of effective population size

    • 1.1 Empirical measurements



  • 2 Variance effective size

    • 2.1 Theoretical examples

      • 2.1.1 Variations in population size


      • 2.1.2 Dioeciousness


      • 2.1.3 Variance in reproductive success


      • 2.1.4 Non-Fisherian sex-ratios




  • 3 Inbreeding effective size

    • 3.1 Theoretical example: overlapping generations and age-structured populations

      • 3.1.1 Haploid


      • 3.1.2 Diploid




  • 4 Coalescent effective size


  • 5 Selection effective size


  • 6 See also


  • 7 References


  • 8 External links




Overview: Types of effective population size


Depending on the quantity of interest, effective population size can be defined in several ways. Ronald Fisher and Sewall Wright originally defined it as "the number of breeding individuals in an idealised population that would show the same amount of dispersion of allele frequencies under random genetic drift or the same amount of inbreeding as the population under consideration". More generally, an effective population size may be defined as the number of individuals in an idealised population that has a value of any given population genetic quantity that is equal to the value of that quantity in the population of interest. The two population genetic quantities identified by Wright were the one-generation increase in variance across replicate populations (variance effective population size) and the one-generation change in the inbreeding coefficient (inbreeding effective population size). These two are closely linked, and derived from F-statistics, but they are not identical.[4]


Today, the effective population size is usually estimated empirically with respect to the sojourn or coalescence time, estimated as the within-species genetic diversity divided by the mutation rate, yielding a coalescent effective population size.[5] Another important effective population size is the selection effective population size 1/scritical, where scritical is the critical value of the selection coefficient at which selection becomes more important than genetic drift.[6]



Empirical measurements


In Drosophila populations of census size 16, the variance effective population size has been measured as equal to 11.5.[7] This measurement was achieved through studying changes in the frequency of a neutral allele from one generation to another in over 100 replicate populations.


For coalescent effective population sizes, a survey of publications on 102 mostly wildlife animal and plant species yielded 192 Ne/N ratios. Seven different estimation methods were used in the surveyed studies. Accordingly, the ratios ranged widely from 10-6 for Pacific oysters to 0.994 for humans, with an average of 0.34 across the examined species.[8] A genealogical analysis of human hunter-gatherers (Eskimos) determined the effective-to-census population size ratio for haploid (mitochondrial DNA, Y chromosomal DNA), and diploid (autosomal DNA) loci separately: the ratio of the effective to the census population size was estimated as 0.6–0.7 for autosomal and X-chromosomal DNA, 0.7–0.9 for mitochondrial DNA and 0.5 for Y-chromosomal DNA.[9]



Variance effective size


References missing
In the Wright-Fisher idealized population model, the conditional variance of the allele frequency p′displaystyle p'p', given the allele frequency pdisplaystyle pp in the previous generation, is


var⁡(p′∣p)=p(1−p)2N.displaystyle operatorname var (p'mid p)=p(1-p) over 2N.operatorname var(p'mid p)=p(1-p) over 2N.

Let var^(p′∣p)displaystyle widehat operatorname var (p'mid p)widehat operatorname var(p'mid p) denote the same, typically larger, variance in the actual population under consideration. The variance effective population size Ne(v)displaystyle N_e^(v)N_e^(v) is defined as the size of an idealized population with the same variance. This is found by substituting var^(p′∣p)displaystyle widehat operatorname var (p'mid p)widehat operatorname var(p'mid p) for var⁡(p′∣p)displaystyle operatorname var (p'mid p)operatorname var(p'mid p) and solving for Ndisplaystyle NN which gives


Ne(v)=p(1−p)2var^(p).displaystyle N_e^(v)=p(1-p) over 2widehat operatorname var (p).N_e^(v)=p(1-p) over 2widehat operatorname var(p).


Theoretical examples


In the following examples, one or more of the assumptions of a strictly idealised population are relaxed, while other assumptions are retained. The variance effective population size of the more relaxed population model is then calculated with respect to the strict model.



Variations in population size


Population size varies over time. Suppose there are t non-overlapping generations, then effective population size is given by the harmonic mean of the population sizes[10]:


1Ne=1t∑i=1t1Nidisplaystyle 1 over N_e=1 over tsum _i=1^t1 over N_i1 over N_e=1 over tsum _i=1^t1 over N_i

For example, say the population size was N = 10, 100, 50, 80, 20, 500 for six generations (t = 6). Then the effective population size is the harmonic mean of these, giving:











1Nedisplaystyle 1 over N_e1 over N_e

=110+1100+150+180+120+15006displaystyle =beginmatrixfrac 110endmatrix+beginmatrixfrac 1100endmatrix+beginmatrixfrac 150endmatrix+beginmatrixfrac 180endmatrix+beginmatrixfrac 120endmatrix+beginmatrixfrac 1500endmatrix over 6=beginmatrixfrac 110endmatrix+beginmatrixfrac 1100endmatrix+beginmatrixfrac 150endmatrix+beginmatrixfrac 180endmatrix+beginmatrixfrac 120endmatrix+beginmatrixfrac 1500endmatrix over 6


=0.19456displaystyle =0.1945 over 6=0.1945 over 6


=0.032416667displaystyle =0.032416667=0.032416667

Nedisplaystyle N_eN_e

=30.8displaystyle =30.8=30.8

Note this is less than the arithmetic mean of the population size, which in this example is 126.7. The harmonic mean tends to be dominated by the smallest bottleneck that the population goes through.



Dioeciousness


If a population is dioecious, i.e. there is no self-fertilisation then


Ne=N+12displaystyle N_e=N+beginmatrixfrac 12endmatrixN_e=N+beginmatrixfrac 12endmatrix

or more generally,


Ne=N+D2displaystyle N_e=N+beginmatrixfrac D2endmatrixN_e=N+beginmatrixfrac D2endmatrix

where D represents dioeciousness and may take the value 0 (for not dioecious) or 1 for dioecious.


When N is large, Ne approximately equals N, so this is usually trivial and often ignored:


Ne=N+12≈Ndisplaystyle N_e=N+beginmatrixfrac 12approx NendmatrixN_e=N+beginmatrixfrac 12approx Nendmatrix


Variance in reproductive success


If population size is to remain constant, each individual must contribute on average two gametes to the next generation. An idealized population assumes that this follows a Poisson distribution so that the variance of the number of gametes contributed, k is equal to the mean number contributed, i.e. 2:


var⁡(k)=k¯=2.displaystyle operatorname var (k)=bar k=2.operatorname var(k)=bar k=2.

However, in natural populations the variance is often larger than this. The vast majority of individuals may have no offspring, and the next generation stems only from a small number of individuals, so


var⁡(k)>2.displaystyle operatorname var (k)>2.operatorname var(k)>2.

The effective population size is then smaller, and given by:


Ne(v)=4N−2D2+var⁡(k)displaystyle N_e^(v)=4N-2D over 2+operatorname var (k)N_e^(v)=4N-2D over 2+operatorname var(k)

Note that if the variance of k is less than 2, Ne is greater than N. In the extreme case of a population experiencing no variation in family size, in a laboratory population in which the number of offspring is artificially controlled, Vk = 0 and Ne = 2N.



Non-Fisherian sex-ratios


When the sex ratio of a population varies from the Fisherian 1:1 ratio, effective population size is given by:


Ne(v)=Ne(F)=4NmNfNm+Nfdisplaystyle N_e^(v)=N_e^(F)=4N_mN_f over N_m+N_fN_e^(v)=N_e^(F)=4N_mN_f over N_m+N_f}

Where Nm is the number of males and Nf the number of females. For example, with 80 males and 20 females (an absolute population size of 100):









Nedisplaystyle N_eN_e

=4×80×2080+20{displaystyle =4times 80times 20 over 80+20=4times 80times 20 over 80+20


=6400100displaystyle =6400 over 100=6400 over 100


=64displaystyle =64=64

Again, this results in Ne being less than N.



Inbreeding effective size


Alternatively, the effective population size may be defined by noting how the average inbreeding coefficient changes from one generation to the next, and then defining Ne as the size of the idealized population that has the same change in average inbreeding coefficient as the population under consideration. The presentation follows Kempthorne (1957).[11]


For the idealized population, the inbreeding coefficients follow the recurrence equation


Ft=1N(1+Ft−22)+(1−1N)Ft−1.displaystyle F_t=frac 1Nleft(frac 1+F_t-22right)+left(1-frac 1Nright)F_t-1.F_t=frac 1Nleft(frac 1+F_t-22right)+left(1-frac 1Nright)F_t-1.

Using Panmictic Index (1 − F) instead of inbreeding coefficient, we get the approximate recurrence equation


1−Ft=Pt=P0(1−12N)t.displaystyle 1-F_t=P_t=P_0left(1-frac 12Nright)^t.1-F_t=P_t=P_0left(1-frac 12Nright)^t.

The difference per generation is


Pt+1Pt=1−12N.displaystyle frac P_t+1P_t=1-frac 12N.frac P_t+1P_t=1-frac 12N.

The inbreeding effective size can be found by solving


Pt+1Pt=1−12Ne(F).displaystyle frac P_t+1P_t=1-frac 12N_e^(F).frac P_t+1P_t=1-frac 12N_e^(F).

This is


Ne(F)=12(1−Pt+1Pt)displaystyle N_e^(F)=frac 12left(1-frac P_t+1P_tright)N_e^(F)=frac 12left(1-frac P_t+1P_tright)

although researchers rarely use this equation directly.



Theoretical example: overlapping generations and age-structured populations


When organisms live longer than one breeding season, effective population sizes have to take into account the life tables for the species.



Haploid


Assume a haploid population with discrete age structure. An example might be an organism that can survive several discrete breeding seasons. Further, define the following age structure characteristics:



vi=displaystyle v_i=v_i= Fisher's reproductive value for age idisplaystyle ii,

ℓi=displaystyle ell _i=ell _i= The chance an individual will survive to age idisplaystyle ii, and

N0=displaystyle N_0=N_0= The number of newborn individuals per breeding season.

The generation time is calculated as



T=∑i=0∞ℓivi=displaystyle T=sum _i=0^infty ell _iv_i=T=sum _i=0^infty ell _iv_i= average age of a reproducing individual

Then, the inbreeding effective population size is[12]


Ne(F)=N0T1+∑iℓi+12vi+12(1ℓi+1−1ℓi).displaystyle N_e^(F)=frac N_0T1+sum _iell _i+1^2v_i+1^2(frac 1ell _i+1-frac 1ell _i).N_e^(F)=frac N_0T1+sum _iell _i+1^2v_i+1^2(frac 1ell _i+1-frac 1ell _i).


Diploid


Similarly, the inbreeding effective number can be calculated for a diploid population with discrete age structure. This was first given by Johnson,[13] but the notation more closely resembles Emigh and Pollak.[14]


Assume the same basic parameters for the life table as given for the haploid case, but distinguishing between male and female, such as N0ƒ and N0m for the number of newborn females and males, respectively (notice lower case ƒ for females, compared to upper case F for inbreeding).


The inbreeding effective number is


1Ne(F)=14T1N0f+1N0m+∑i(ℓi+1f)2(vi+1f)2(1ℓi+1f−1ℓif)+∑i(ℓi+1m)2(vi+1m)2(1ℓi+1m−1ℓim).displaystyle beginalignedfrac 1N_e^(F)=frac 14Tleftfrac 1N_0^f+frac 1N_0^m+sum _ileft(ell _i+1^fright)^2left(v_i+1^fright)^2left(frac 1ell _i+1^f-frac 1ell _i^fright)right.,,,,,,,,&\left.+sum _ileft(ell _i+1^mright)^2left(v_i+1^mright)^2left(frac 1ell _i+1^m-frac 1ell _i^mright)right.&endalignedbeginalignedfrac 1N_e^(F)=frac 14Tleftfrac 1N_0^f+frac 1N_0^m+sum _ileft(ell _i+1^fright)^2left(v_i+1^fright)^2left(frac 1ell _i+1^f-frac 1ell _i^fright)right.,,,,,,,,&\left.+sum _ileft(ell _i+1^mright)^2left(v_i+1^mright)^2left(frac 1ell _i+1^m-frac 1ell _i^mright)right.&endaligned


Coalescent effective size


According to the neutral theory of molecular evolution, a neutral allele remains in a population for Ne generations, where Ne is the effective population size. An idealised diploid population will have a pairwise nucleotide diversity equal to 4μdisplaystyle mu mu Ne, where μdisplaystyle mu mu is the mutation rate. The sojourn effective population size can therefore be estimated empirically by dividing the nucleotide diversity by the mutation rate.[5]


The coalescent effective size may have little relationship to the number of individuals physically present in a population.[15] Measured coalescent effective population sizes vary between genes in the same population, being low in genome areas of low recombination and high in genome areas of high recombination.[16][17] Sojourn times are proportional to N in neutral theory, but for alleles under selection, sojourn times are proportional to log(N). Genetic hitchhiking can cause neutral mutations to have sojourn times proportional to log(N): this may explain the relationship between measured effective population size and the local recombination rate.



Selection effective size


In an idealised Wright-Fisher model, the fate of an allele, beginning at an intermediate frequency, is largely determined by selection if the selection coefficient s ≫ 1/N, and largely determined by neutral genetic drift if s ≪ 1/N. In real populations, the cutoff value of s may depend instead on local recombination rates.[6][18] This limit to selection in a real population may be captured in a toy Wright-Fisher simulation through the appropriate choice of Ne. Populations with different selection effective population sizes are predicted to evolve profoundly different genome architectures.[19][20]



See also


  • Minimum viable population

  • Small population size


References




  1. ^ "Effective population size". Blackwell Publishing. Retrieved 4 March 2018..mw-parser-output cite.citationfont-style:inherit.mw-parser-output qquotes:"""""""'""'".mw-parser-output code.cs1-codecolor:inherit;background:inherit;border:inherit;padding:inherit.mw-parser-output .cs1-lock-free abackground:url("//upload.wikimedia.org/wikipedia/commons/thumb/6/65/Lock-green.svg/9px-Lock-green.svg.png")no-repeat;background-position:right .1em center.mw-parser-output .cs1-lock-limited a,.mw-parser-output .cs1-lock-registration abackground:url("//upload.wikimedia.org/wikipedia/commons/thumb/d/d6/Lock-gray-alt-2.svg/9px-Lock-gray-alt-2.svg.png")no-repeat;background-position:right .1em center.mw-parser-output .cs1-lock-subscription abackground:url("//upload.wikimedia.org/wikipedia/commons/thumb/a/aa/Lock-red-alt-2.svg/9px-Lock-red-alt-2.svg.png")no-repeat;background-position:right .1em center.mw-parser-output .cs1-subscription,.mw-parser-output .cs1-registrationcolor:#555.mw-parser-output .cs1-subscription span,.mw-parser-output .cs1-registration spanborder-bottom:1px dotted;cursor:help.mw-parser-output .cs1-hidden-errordisplay:none;font-size:100%.mw-parser-output .cs1-visible-errorfont-size:100%.mw-parser-output .cs1-subscription,.mw-parser-output .cs1-registration,.mw-parser-output .cs1-formatfont-size:95%.mw-parser-output .cs1-kern-left,.mw-parser-output .cs1-kern-wl-leftpadding-left:0.2em.mw-parser-output .cs1-kern-right,.mw-parser-output .cs1-kern-wl-rightpadding-right:0.2em


  2. ^ Wright S (1931). "Evolution in Mendelian populations" (PDF). Genetics. 16 (2): 97–159. PMC 1201091. PMID 17246615.


  3. ^ Wright S (1938). "Size of population and breeding structure in relation to evolution". Science. 87 (2263): 430–431. doi:10.1126/science.87.2263.425-a.


  4. ^ James F. Crow (2010). "Wright and Fisher on Inbreeding and Random Drift". Genetics. 184 (3): 609–611. doi:10.1534/genetics.109.110023. PMC 2845331. PMID 20332416.


  5. ^ ab Lynch, M.; Conery, J.S. (2003). "The origins of genome complexity". Science. 302 (5649): 1401–1404. doi:10.1126/science.1089370. PMID 14631042.


  6. ^ ab R.A. Neher; B.I. Shraiman (2011). "Genetic Draft and Quasi-Neutrality in Large Facultatively Sexual Populations". Genetics. 188 (4): 975–996. doi:10.1534/genetics.111.128876. PMC 3176096. PMID 21625002.


  7. ^ Buri, P (1956). "Gene frequency in small populations of mutant Drosophila". Evolution. 10: 367–402. doi:10.2307/2406998.


  8. ^ R. Frankham (1995). "Effective population size/adult population size ratios in wildlife: a review". Genetics Research. 66 (2): 95–107. doi:10.1017/S0016672300034455.


  9. ^ S. Matsumura; P. Forster (2008). "Generation time and effective population size in Polar Eskimos". Proc Biol Sci. 275 (1642): 1501–1508. doi:10.1098/rspb.2007.1724. PMC 2602656. PMID 18364314.


  10. ^ Karlin, Samuel (1968-09-01). "Rates of Approach to Homozygosity for Finite Stochastic Models with Variable Population Size". The American Naturalist. 102 (927): 443–455. doi:10.1086/282557. ISSN 0003-0147.


  11. ^ Kempthorne O (1957). An Introduction to Genetic Statistics. Iowa State University Press.


  12. ^ Felsenstein J (1971). "Inbreeding and variance effective numbers in populations with overlapping generations". Genetics. 68: 581–597.


  13. ^ Johnson DL (1977). "Inbreeding in populations with overlapping generations". Genetics. 87: 581–591.


  14. ^ Emigh TH, Pollak E (1979). "Fixation probabilities and effective population numbers in diploid populations with overlapping generations". Theoretical Population Biology. 15 (1): 86–107. doi:10.1016/0040-5809(79)90028-5.


  15. ^
    Gillespie, JH (2001). "Is the population size of a species relevant to its evolution?". Evolution. 55 (11): 2161–2169. doi:10.1111/j.0014-3820.2001.tb00732.x. PMID 11794777.



  16. ^ Hahn, Matthew W. (2008). "Toward a selection theory of molecular evolution". Evolution. 62 (2): 255–265. doi:10.1111/j.1558-5646.2007.00308.x. PMID 18302709.


  17. ^ Masel, Joanna (2012). "Rethinking Hardy–Weinberg and genetic drift in undergraduate biology". BioEssays. 34 (8): 701–10. doi:10.1002/bies.201100178. PMID 22576789.


  18. ^ Daniel B. Weissman; Nicholas H. Barton (2012). "Limits to the Rate of Adaptive Substitution in Sexual Populations". PLoS Genetics. 8 (6): e1002740. doi:10.1371/journal.pgen.1002740. PMC 3369949. PMID 22685419.


  19. ^ Lynch, Michael (2007). The Origins of Genome Architecture. Sinauer Associates. ISBN 0-87893-484-7.


  20. ^ Rajon, E.; Masel, J. (2011). "Evolution of molecular error rates and the consequences for evolvability". PNAS. 108 (3): 1082–1087. doi:10.1073/pnas.1012918108. PMC 3024668. PMID 21199946.



External links



  • Holsinger, Kent (2008-08-26). "Effective Population Size". University of Connecticut. Archived from the original on 2005-05-24.


  • Whitlock, Michael (2008). "The Effective Population Size". Biology 434: Population Genetics. The University of British Columbia.


  • https://web.archive.org/web/20050524144622/http://www.kursus.kvl.dk/shares/vetgen/_Popgen/genetics/3/6.htm — on Københavns Universitet.










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