1. Part 5 Genetics and Fisheries Management
Genetic variation in fish stocks;
Use of molecular tools
Estimation of effective population size and population dynamics' parameters;
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2. 1 - Overview of the applications of population genetics to marine resources management
Fisheries Management = the application of scientific knowledge to the problems of providing the optimum yield of commercial fisheries products or angling pleasure (Everhart & Youngs 1981)
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3. Historical Perspective
Vast majority of fish stock harvested through fisheries is wild – a conservation problem
Until recently there was very limited application of basic genetics principles to fisheries management:
Field dominated by taxonomists that care little about differences between individuals
Fish are more difficult to observe and so the study of the relationships between phenotype and genotype is more difficult
Fish are the ‘last’ major food source captured from wild stocks, with hard to define boundaries
The amounts of phenotypic variation found in fish are very wide when compared with other food source animals
First genetic results often contradicted ecological and ethological understanding of species
So, the long-term perspective that genetic analysis provides has been missing from Fisheries Management theory
The result is that many fisheries stocks were the victim of over-exploitation
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4. Genetic and Phenotypic Variation in Fish Species
Include table 1.2 and 1.2 of Pop Gen and Fish. Man.
1 - It appears that fish are phenotypically more variable than other vertebrates;
2 - Specifically, economically important traits such as growth rate vary much between as well as within populations;
3 – Variation between con-specific populations is also very large, artic char 4000%, whereas Darwin's’ finches 15 x less
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5. Heritability in Fish Species
Include table 1.3 of Pop Gen & Fish. Man.
Heritability – proportion of variation that is of genetic origin
Ranges from 0 (no genetic component) to 1 (no environmental component)
Usually lower in fishes than in other vertebrates
Fish appear more susceptible to environmental factor:
Indeterminate growth – permits greater adjustment to env. Factors
Poikilothermic – more sensitive to temperature variations than birds and mammals
plasticity in age and size at maturity
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6. Genetic Divergence Between Populations
Classic problem in Fisheries Management:
FROM the identification of a fishing stock
BECOMES the identification of genetically meaningful management units
Many ecological and behavioral differences are due to environmental differences (freshwater/seawater, different spawning times and places, etc.)
A considerable part of the difference between populations is also due to environmental differences - similar environmental conditions mold variation within populations and so the differences between populations tend to be due to differences in the environment of each one – The role of genetics in the differentiation has been unclear until recently
Markers not available until the mid 70’s
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7. First Biochemical Studies
Identification of previously unrecognized systematic groups:
Population units of Pacific herring in the Alaskan peninsula (Grant & Utter, 1984)
Reproductively isolated sympatric populations of brown trout (Ferguson & Mason, 1981)
Identification of new species of rockfish (Seeb, 1986)
Inconsistencies with previous assumptions of genetic divergence
Conspecificity of anadromous and landlocked forms of char in North America (Kornfield et al., 1981)
No apparent genetic divergence between Fall and Spring spawning Atlantic herring (Ryman et al., 1984)
Conspecificity (and local random breeding) of distinct morphological types of Ilyodon previously considered separate species (Turne & Grosse, 1980)
Conspecificity of sympatric but trofically specialized forms of Mexican cichlids (Kornfield et al., 1982)
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8. Management Goals
Conservation of genetic variation between and within natural populations
Maintenance of the genetic characteristics of stocks that are artificially propagated in hatcheries (e.g. Pacific salmon species in the Northwest USA)
Stock enhancement
Selective breeding for production traits
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9. The Role of Genetics in Fisheries Management
Differential Harvesting Among Populations
Differential Harvesting Within Populations
Hatchery Populations
Release of Hatchery Fish
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10. 2 - Basic concepts in population and molecular genetics
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11. Gene Frequencies and Hardy-Weinberg Equilibrium
Simplified model for character determination;
Applicable to simple traits, such as blood groups or disease resistance;
We will assume diploidy;
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12. Phenotypes and Genotypes
Phenotypes are the appearances of characters (visible/measurable);
Genotypes are the genetic compositions that cause the phenotypes;
Often one phenotype is the product of many genes – For example FCR depends on digestive enzymes in the gut, on hundreds of metabolic enzymes in cells, etc., in total it is estimated that hundreds or thousands of genes will determine FCR (a phenotype we can measure);
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13. Recessive, Dominant and Codominant Alleles
In diploid organisms each gene (locus) is present in two copies (alleles);
If both are equal we say the locus is homozygous, otherwise it is termed heterozygous;
Recessive alleles do not express themselves in the presence of a dominant one. Difficult to detect… the problem with disease ‘carriers’;
Codominance means that to a degree the phenotype of the heterozygote is the product of the expression of both alleles;
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14. Describing the Genetic Make-up of a Population Genotype Frequencies
When we know the relationship between phenotypes and genotypes we can estimate the genotype frequencies in a population;
E.g. blood group typing, eye color in fruit flies, etc.
Blood group
Number of individuals M MN N
Frequency (%)
Greenland
83.5
15.6
0.9
569
Iceland
31.2
51.5
17.3
747
Mourant(1954) in Falconer 1989
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15. Describing the Genetic Make-up of a Population Gene Frequencies
Frequency (%)
Greenland
91.3
8.7
Iceland
57.0
43.0
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16. MN blood Group Genotypes
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17. Hardy-Weinberg Equilibrium
Assumes:
Large population;
Random mating;
No selection;
No migration;
No mutation;
Predicts:
Stable gene frequencies from generation to generation;
Simple relationship between gene frequencies (allele frequencies) and genotype frequencies.
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18. H-W Equilibrium Genes in parents Genes in progeny A1 A2 A1A1 A1A2 A2A2
Frequencies p q p2
2pq q2
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19. Applications of the H-W Law
Determination of the gene frequency of a recessive allele;
Frequency of ‘carriers’;
Test of H-W equilibrium;
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20. Changes in gene frequency
Random drift;
Migration;
Mutation;
Selection;
Assortative Mating;
Computer simulation - PopG
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21. Assortative Mating
When mated pairs are of the same phenotype more often than expected by chance (common in humans, e.g. stature, intelligence, etc.);
The opposite is called disassortative mating (common in plants with self-sterility systems);
Increases frequency of homozygotes (although most characters are multiple loci coded);
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22. Migration q1 = m qm + (1 – m) q0 = m (qm – q0) + q0 Δq = q1 – q0
Thus teh rate of change depends on:
Immigration rate
Difference of gene frequencies between immigrants and natives
q1 = m qm + (1 – m) q0
= m (qm – q0) + q0
Δq = q1 – q0
= m (qm – q0)
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23. Mutation
Non-recurrent mutation – low chances of mutant allele survival
Recurrent mutation – more relevant
If u is mutation rate from A1 to A2
and v is mutation rate for reciprocal mutation (A2 to A1)
and p0 and q0 are frequencies of A1 and A2
then
The change in gene frequency in one generation is
Δq = up0 – vq0
And at equilibrium
q = u / (u + v)
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24. Selection Occurs when some genotypes are more fit than others
Degrees of dominance with respect to fitness:
No dominance;
Partial dominance;
Complete dominance;
Overdominance.
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25. Changes in Gene Frequency Under Selection
If s is the selective advantage of the A1 dominant allele (p), then the frequency of A2 after one generation (q1) is:
So, the change of gene frequency from one generation of selection Δq = q1 – q is
and substituting p = (1 – q)
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26. Polymorphism – Possible Causes
Heterozygote advantage;
Frequency-dependent selection;
Heterogeneous environment;
Transition stages in evolution (due to environment changes, for example);
Neutral mutation (allele);
Heterozygosity – a measure of the amount of polymorphism:
Proportion of polymorphic loci;
Frequency of heterozygotes averaged over all loci tested = average Heterozygosity;
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27. Small Populations Random drift Sampling effects:
Variance in the change of gene frequency
Variance of gene frequency among lines
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28. Random Drift and N MASTERS IN AQUACULTURE AND FISHERIES
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29. Inbreeding
The mating together of individuals that are related to each other by ancestry.
In a bisexual population the number of ancestors of a individual t generations ago is 2t
So, in a small population the relatedness of individuals will be greater than in a large population;
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30. Measuring Inbreeding The coefficient of Inbreeding is F
F1 is the inbreeding coefficient of generation 1 and F2 that of generation 2
Probability that a gamete pairs with another of different sort
Probability that a gamete pairs with another of the same sort
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31. } New Inbreeding or the rate of inbreeding
General Inbreeding Coefficient for individuals in generation t }
New Inbreeding or
the rate of inbreeding
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32. Effective Population Size Ne
Real-life populations are not ideal:
Structured;
Assortative / disassortative mating;
Breeding structure;
Etc.
Ne is the number of individuals that in an ideal population would give rise to the calculated sample variance or rate of inbreeding.
So, in an ideal population Ne=1/(2 ΔF)
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33. Formulas for Ne Self-feritilization Sib-mating
Diferent numbers of males and females
Unequal numbers in successive generations
Non-random distribution of family size
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34. Complicating Matters
In real-life populations populations are far from ideal.
Small effective sizes, various mating structures, mutation, selection and migration, all act together to produce the phenotypes and genotypes we see or measure.
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35. Main Tools Available Allozymes Mitochondrial DNA (mtDNA)
Nuclear DNA (nDNA)
Microsatellites and minisatellites
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36. Allozymes
Allozymes are electrophoretically distinguishable protein variants
First used in fish stocks in late 60’s
DISADVANTAGES
Needs relatively large amounts of tissue in order to yield enough proteins for visualization
Many enzyme systems available (>75) although for each study usually only a small fraction shows polymorphism
Potentially subject to selection pressures
ADVANTAGES
Simple to use and applicable to all species (just needs a source of soluble proteins)
Standard protocols that require only minor adjustments from species to species
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37. MASTERS IN AQUACULTURE AND FISHERIES Genetics and Selection

38. Mitochondrial DNA
First studies (80’s) revealed high levels of sequence diversity
Useful for inter and intra-specific analysis
Occurs in multiple copies per cell (>1000)
Uniparental transmission, no recombination
Transmitted via the maternal line – useful in the analysis of sex-specific gene-flow patterns
Evolves faster than coding regions of nDNA
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39. Techniques to Analyze mtDNA
RFLPs
Restriction fragment length polymorphisms
Uses restriction enzymes to cut mtDNA in pieces and the separates them using agarose or acrylamide gel electrophoresis
PCR
Polymerase Chain Reaction
Amplification of the number of copies of a target sequence defined by two flanking primers
DNA sequencing
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40. Nuclear DNA RFLPs nDNA sequences Tandemly repeated DNA RAPDs AFLPs
VNTRs -DNA fingerprints (minisatellite sequences)
STRs - Microsatellites
RAPDs
Randomly amplified polymorphic DNA
AFLPs
Amplified fragment length polymorphisms
QTLs
Quantitative trait loci
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41. Summary of the Common Genetic Markers Used for Fisheries Management Studies
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42. Information Provided by Molecular Tools
Population structure
Deviations from H-W Law
Inbreeding, migration, selection, mating structure, demographic history, phylogeographic history, etc.
Genealogical and phylogenetic relationships
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43. Statistical Methods Used
Allozymes, RFLPs, AFLPs, DNA sequences, mini and microsatellites all provide us with allele frequency data (with different levels of polymorphism)
Most statistical analysis are based on the analysis of genetic variation and its partition into the various hierarchical levels of structure, from families to demes, to sub-populations and the population as a whole.
Most are base in Wright’s F-statistics (FST), adapted by Nei for molecular data (GST) and further adapted to newer markers.
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44. Variation
Usually measured by polymorphism (P, the proportion of polymorphic loci) and heterozygosity (H, the proportion of heterozygous individuals)
If the population is in H-W equilibrium then H can be calculated as
Where NAa is the number of heterozygote individuals in the sample and N is the sample size and n is number of loci, including the monomorphic ones
Where xi is the frequency of the ith allele
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45. Genetic Distance
It is a measure of the gene diversity between populations expressed as a function of genotype diversity
According to Nei,
For 2 populations X and Y, the probability of identity of 2 randomly chosen genes at a single locus (jk) is
The probability of identity of a gene at the same locus in populations X and Y is
The normalized identity between populations X and Y with respect to all loci is:
The Genetic Distance between populations is then:
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46. Inter-Population Diversity
Determine jk for each population and then the gene identity within all populations (JS)
The average gene diversity within populations is
The gene identity for the total population is
And the inter-population gene diversity is
The coefficient of differentiation can be defined as:
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47. Demographic History
Aims at uncovering past population bottlenecks or booms
Uses DNA sequence data and plots histograms of pair-wise differences between sequences
Needs estimate of molecular clock in order to time events
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48. Genealogy Theory
Uses genealogy data, mainly from DNA sequences but also from microsatellites, RFLPs etc., to reconstruct the genealogies of alleles.
Can provide information about population structure as well as past demographic events
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49. Analysis of Phylogeny
Using different data types but preferably DNA sequences together with specific algorithms for grouping the data (maximum likelihood, parsimony, etc.), it estimates the relationships between samples or alleles
Software:
PAUP – Phylogeny Analysis Using Parsimony
PHYLIP
MacClade
TreeView
CAIC - Comparative Analysis of Independent Contrasts
Etc… (
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50. Some Applications MASTERS IN AQUACULTURE AND FISHERIES
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51. Conservation MASTERS IN AQUACULTURE AND FISHERIES
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52. Determining Family Relationships and Ne
Herbinger, C.M., R.W. Doyle, C.T. Taggart, S. Lochmann, A.L. Brooker, J.M. Wright and D. Cook Family relationships and effective population size in a natural cohort of cod larvae. Can. J. Fish. Aquat. Sci. 54 (Suppl-1): Abstract
Sibship relationships within a naturally spawned cohort of Atlantic cod (Gadus morhua) larvae on the Western Bank of the Scotian Shelf were investigated by a likelihood ratio method that estimates relationships is among individuals using microsatellite (DNA fingerprint) information. We found no evidence of any temporal or spatial family structure among the larvae from seven different sample collections taken at sequential time intervals during a 21-d period of sampling the larval cohort. There was no evidence that larvae were more related within sample collections than across sample collections. Within each sample collection, there was no evidence of a family structure within or among the depths sampled. Similarly, there was no apparent change in the potential occurrence of sibship with time (successive sample collections), or in association with the passage of a storm during the sampling period. This cohort of cod larvae appears to have been a fairly homogeneous mixture of larvae that were not siblings and came from a large genetic pool. The minimum estimate of the inbreeding effective population size is 2800 individual spawners.
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53. Mixed Stock Management
To determine what impact the winter fishery that takes place in the 3Pn and 3Ps regions has on migratory groups (4T,4R,4S,4Vn and 4Vs) we are looking at the genetic makeup of fish from 3Pn and 3Ps areas during the mixed period and comparing this to the genetic make up of the stocks that are thought to migrate here. From these comparisons it will be possible to determine the level of exploitation of the migratory stocks and to have some estimate of the impact the winter fishery has on the migratory stocks and how this will effect their ability of these stocks to recover.
The graphs below illustrates two possible mixing profiles as they may exist.
In Figure A the outcome of the fishery would be a harvest which would be mostly focused on fish which come from the inner Gulf region (4T,4R and 4S);
Figure B would be a fishery which was mostly focused on resident populations from 3Pn and 3Ps.
With either case, consideration of the winter fishery impact must be taken and adjustments made as to how the affected stocks are managed.
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54. Assigning Individuals to Populations - Forensics
Enormous expense is currently incurred by enforcement officers while patrolling the fishing grounds with boats and aircraft. This method of enforcement is inefficient in terms of time, money, and the amount of catch actually monitored. With MGPL's DNA Fingerprinting capabilities, catch restrictions can be enforced at the dock instead of at sea on the basis of forensic analysis of the catch.
The genetic information carried by the animals themselves will identify the stock from which they were taken.
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55. Estimating changes in population size
Use of coalescent theory and of pair wise sequence comparisons in order to study and date demographic events;
Use of gene diversity estimates in order to detect past bottlenecks in populations;
Etc.
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56. Estimating gene flow among populations
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57. Software
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58. Brief summary of computer programs and data analysis approaches
LAMARC - Likelihood Analysis with Metropolis Algorithm using Random Coalescence
Lamarc is a program for doing Likelihood Analysis with Metropolis Algorithm using Random Coalescence. Lamarc estimates effective population sizes, population exponential growth rates, a recombination rate, and past migration rates for one to n populations assuming a migration matrix model with asymmetric migration rates and different subpopulation sizes. This version can use DNA or RNA sequence data, SNPs, microsatellites, or electrophoretic data. The program can produce: estimates of recombination rate, migration rates between each population pair, population sizes (assuming constant mutation rates among loci), population exponential growth rates, profile likelihood tables, and percentiles.If you know that there is no recombination in your data (for example in mtDNA) you might look also at the other programs: Fluctuate or Migrate.
Whichrun 4.1
A computer program for population assignment of individuals based on multilocus genotype data.
Microsatellite DNA provides essentially limitless, highly varied information within species.  That this provides a means for distinguishing not only among populations but also individuals has not escaped current theoretic interest (Smouse and Chevillon 1998, Waser and Strobeck 1998).  Here, we present a C++ computer program named WHICHRUN that uses multilocus genotypic data to allocate individuals to their most likely source population.
Genetic Mixture Analysis (GMA)
Software for estimating the stock proportions within mixed stock fisheries
BOTTLENECK version  (16.II.1999)
Bottleneck is a program for detecting recent effective population size reductions from allele data frequencies.
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59. Structure
The program structure is a free software package for using multi-locus genotype data to investigate population structure. Its uses include inferring the presence of distinct populations, assigning individuals to populations, studying hybrid zones, identifying migrants and admixed individuals, and estimating population allele frequencies in situations where many individuals are migrants or admixed. It can be applied to most of the commonly-used genetic markers, including microsatellites, RFLPs and SNPs. This method was described in an article by Pritchard, Stephens & Donnelly (2000). Extensions to the method were published by Falush, Stephens and Pritchard (2003). An interesting example from the original paper is shown here.
                                                                                                                                           
- RST Calc –
A program to calculate unbiased estimates
of Slatkin's RST and Goldstein et al's
(delta-mu)^2 distance for microsatellite data
GENEPOP is a population genetics software package originally designed by Michel Raymond and Francois Rousset at the Laboratiore de Genetique et Environment, Montpellier, France.
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60. Presentations Duration 15’, groups of 2-3 (all should participate)
When – Friday 17th, h
What: Results of selection programs in:
Salmon
Trout
Nile Tilapia
Catfish
Oyster
Shrimps
Structure:
The species and its farming
Important traits
Heritabilities
Structure of selection program
Performance (response) of program
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