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In
situ
Conservation Methodology for Farm Animals
T.
Furukawa1, H. Takeda2, M. Satoh1, K. Ishii1
and C. Hicks1
1 National Institute of
Animal Industry, PO Box 5, Norindanchi, Tsukuba, 305-0901, Japan
2 National Institute of
Agrobiological Resources, Kannondai, Tsukuba, 305-8602, Japan
There
are three major methods used in conservation of farm animal genetic resources
(Hansen, 1992). The first involves conservation of living ova, embryo, semen or
somatic cell stored cryogenically in liquid nitrogen. The second encompasses
preservation of genetic information in form of DNA, stored in frozen samples of
blood or other animal tissue or as DNA segments. The third involves
conservation of living population, i.e. in
situ conservation.
Hansen
(1992) pointed out that there is no single method of preservation which is
optimal for all situations. However, in
situ conservation has a number of advantages, and may be the only option
available in some instances. In situ
conservation is very flexible in its application and allows for the development
and utilization of breeds (Weiner, 1989). However, because of limited
facilities and budget constraints, in
situ conservation may be restricted to a small population. The genetic
properties in a small population change rapidly as generations advance causing
two problems namely, loss of genetic peculiarities and a reduction in genetic
variability. It is therefore imperative to use a sustainable conservation
program like in situ that maintain
genetic peculiarities and genetic variability of a population.
Genetic
peculiarities of a breed usually do not change if the population is kept as a
pure breed. This problem becomes a matter of concern when crossbreeding is use
to in order to restore genetic variability. Therefore, the most important
problem in considering in situ
conservation is how to keep genetic variability within the population while
maintaining genetic peculiarities without reducing allelic or genotype
frequencies. Discussion in this paper will focus on random drift, evaluation of genetic variability and
methods for keeping genetic variability in farm animals.
1. Random
drift and reduction of genetic variability
An
offspring inherits one allele from both parents at each locus. It is, however,
at random that it inherits an allele from a pair of alleles from each parent.
That means, the number of offspring which inherit a certain allele of an
individual parent could be larger or smaller by chance, even if each individual
has a constant number of offspring. For example, one allele of the pair will
fail to be transmitted to the next generation with the probability of 0.25, if
the individual has 3 offspring. The range of the probability would be wider if
the variation in the number of offspring is taken into account. That is taking
into account changes in allele or genotype frequencies. The phenomenon
involving changes in allele frequencies by chance is called "random drift".
The
influence of random drift is little in a large population. But it causes a
severe reduction in genetic variability in a small population. For example, if
we assume a population consisting of four individuals and with two types of alleles
in the same number, the probability that the next generation will lose one type
of allele is 1/128. This may not be considered to be so severe. However, this
probability reaches 1/2 after only nine generations. Moreover, considering that
there are many loci, we can safely say that many types of allele are lost in a
small population after a few generations. A reduction in genetic variability
causes the following problems:
1)
Increase of homozygous loci
As
the genetic variability decreases, the proportion of the homozygous loci in an
individual becomes larger. It means the appearance of homozygous recessive
genes with undesired effects, loss of dominant effects, and it results in
inbreeding depression which affects viability, adaptability and reproductivity.
Inbreeding depression will cause a decrease in population size and may lead to
extinction.
2)
Homogeneity of the population
As
the genetic variability decreases, the genetic properties of the individuals in
the population become more homogeneous causing uniformity in the population. It
means that most individuals are likely to be sensitive to the same kind of
disease or environment. So, if they become homogeneous and are faced with the
undesirable disease or environment, the risk of extermination may increase. The
homogeneity of the population makes it difficult to show efficient selection
response that is required to make genetic improvement of the population viable.
3)
Loss of the useful genes
Wild
or local breeds probably have many useful alleles which are rare in other
breeds, even though many of them are not recognized. The reduction of genetic
variability increases the probability that they may be lost.
2. Evaluation
of genetic diversity
The
first step in maintaining genetic diversity is to evaluate and establish the
level of genetic diversity in the population in question. Discussion in the
following sections will therefore focus on the methods for evaluating ideal
genetic diversity of a population, some procedures popular in population genetics
and some practical simple methods for evaluating genetic diversity.
1)
Gene diversity or average heterozygosity
The
gene diversity or the average heterozygosity is the best index of the genetic
variability of a population (Nei, 1987). In a random mating population, if a
frequency of allele i of one locus is
xi, the heterozygosity (h) of this locus is calculated using the
sum of squares of frequencies in all alleles as shown in the following
equation:
h
= 1 – Sxi2.
(1)
The
average heterozygosity of a population is defined as the average over
heterozygosities of all loci. Since we cannot know the genotypes of every locus
in all the individuals, this method is thought as the concept or the ideal
value of genetic diversity to be estimated. Without the knowledge of the
genotypes of each individual, the best way to keep genetic diversity may be by
keeping the alleles derived from different alleles in the first generation as
many as possible. The methods for evaluating the possibility that a pair of
alleles is identical by descent are discussed in the subsequent sections.
2)
Effective population size
In
order to illustrate the random drift, the idealized population was proposed
(Falconer and Mackay, 1996). There is one large base population under random
mating and a large number of subpopulation subdivided from the base population.
The subpopulations are referred to as lines. The base population has infinite
size and each line has size N. The
idealized population is assumed as follows: (1) mating is restricted within the
subpopulation without migration, (2) no overlapping generation, (3) the number
of breeding individuals in each line is the same for all lines and in all
generations, (4) mating is random within each line, including
self-fertilization, (5) no selection, (6) no mutation. Under the assumptions of
this idealized population, the basic theory of population genetics was
developed. Unfortunately the actual animal population cannot satisfy the above
conditions. Therefore, we have to convert the population size to the size of
the idealized population showing an equivalent degree of random drift. This is
the effective number of breeding individuals, or the effective population size,
Ne. The effective population size can
be derived from the actual number as follows.
- With self-fertilization excluded,
Ne = N
+ 1/2. (approx.)
(2)
- With sib-mating also excluded,
Ne = N
+ 2. (approx.) (3)
- With different number of males and females, N = Nm
+ Nf, where Nm and Nf are the number
of males and females respectively,
1
/ Ne = 1 / 4Nm + 1 / 4Nf. (approx.) (4)
- With unequal numbers in successive generations,
1
/ Ne = (1/N1 + 1/N2
+ ... + 1/Nt) / t. (approx.) (5)
- With non-random distribution of family size (Wright ,
1938),
Ne
= (4N - 2) / (Vk + 2)
(approx.) (6)
where, Vk is the variance of family size.
- With different variance of family size by sex (Hill, 1972),
1
/ Ne = [2 + Vkmm + 2(Nm/Nf) Cov(kmm, kmf) + (Nm/Nf)2 Vkmf] / 16Nm
+ [2 + Vkff + 2(Nm/Nf) Cov(kfm,
kff) + (Nf/Nm)2 Vkfm]
/ 16Nf (7)
where Vksm
and Vksf are the
respective variances of male and female progeny contributed by a parent of sex s, Cov(ksm, ksf) are the covariances of numbers
of male and female progeny, respectively, contributed by a parent of sex s. The larger number of population size
makes it easier to maintain genetic diversity in the population than a smaller
one.
3)
Inbreeding coefficient
The
inbreeding coefficient is the probability that the two genes at any locus in an
individual are identical by descent, under the condition that the two genes of
parents are randomly transferred to progenies with the probability of 1/2. The
inbreeding coefficient is not the true probability of homozygosity because the
probability is biased due to selection, the probability refers only to a
distinct generation, the probability of homozygosity without identical by
descent i.e. identical in state is not considered and the possibility of
mutation is neglected. But, the inbreeding coefficient is also a good measure
of homozygosity. The rate of
inbreeding (DF)
in an idealized population is,
DF
= 1 / 2N.
(8)
The
expected inbreeding coefficient of individuals in the tth generation is therefore,
Ft =
DF
+ (1 - DF)
Ft-1
= 1 - (1 - DF)t.
(9)
The
general formula of the inbreeding coefficient of an individual with pedigree is
Fx
= S(1/2)n (1+FA)
(10)
where n
is the number of individuals in any path of relationship counting the parents
of X, the common ancestor, and all individuals in the path connecting parents
to common ancestor; summation is over all paths of relationship; FA is the inbreeding
coefficient of a common ancestor (Falconer and Mackay, 1996). The inbreeding
coefficient is also an important indicator of inbreeding depression. Therefore,
preventing an increase in the level of inbreeding coefficient using in situ conservation is important aspect
in maintaining genetic diversity. Inbreeding coefficient can be computed by the
method of coancestry as described below.
4)
Coancestry
The
coancestry of two individuals is the probability that two gametes taken
randomly, one from each, carry alleles that are identical by descent. This
value is identical with the inbreeding coefficient of their offspring. Consider
individual X, its parents P and Q, and grandparents A, B, C, and D. The
coancestry f is under the following
rules:
fPQ = (fAC + fAD
+ fBC + fBD) / 4
(11)
FX = fPQ
(12)
fPC
= (fAC + fAD) / 2
(13)
fXX
= (1 + FX) / 2
(14)
The
coancestry is important because it indicates the inbreeding coefficient of the next
generation. Furthermore, coancestry has a high correlation with the gene
diversity, so it is a good index for measuring genetic diversity using in situ conservation (Furukawa et al., 1996).
5)
Coefficient of relationship
The
coefficient of relationship shows the correlation of breeding values between
two individuals. The coefficient of relationship of relatives in a random
mating population is about twice their coancestry. The coefficient of
relationship can be computed from the following formula,
R = 2fPQ
/ [(1 + FP)(1 + FQ)]1/2.
(15)
6)
Genetic conservation index
The
objective of a conservation program is to retain the full range of alleles
possessed by the base population. The ideal animal would receive equal
contribution from all the founder ancestors in the population. From this view
point the value of an animal can be determined by calculating the effective
number of founders in its pedigree using the following genetic conservation
index, GCI (Alderson, 1992).
GCI = 1 / SPi2
(16)
where Pi
is the proportion of genes of founder animal i in the pedigree.
The
GCI can be used either by individual
breeders as an aid to the selection of
a herd sire, or within an overall breed policy to formulate a group
breeding program. However, the index has limitations such as not accounting for
any concentration of breeding to non-founder animals in subsequent generations
in a pedigree and is inapplicable without pedigree records (Alderson, 1992).
7)
Coefficient of genetic contributory variation
Pi
in formula (16) is the genetic contributory of founder animal i in the subsequent generations. A
similar idea of GCI was applied to
population level. That is maintaining subsequent populations while keeping the
genetic structure in the base population. In the ideal genetic structure, every
founder animal would keep the same contribution to subsequent populations. In
order to indicate the bias between an actual genetic contributory and an ideal
genetic contributory, the coefficient of genetic contributory variation, CGCV was proposed by Abe and Furukawa
(1982, mimeograph).
CGCV = 2Nm SPmi2
+ 2Nf SPfj2
– 1
(17)
where Nm
and Nf are the respective numbers of
founders in male and female, Pmi
and Pfj are the genetic
contributory of male founder i and
female founder j, respectively. CGCV is used to maintain the designated
pig strains in Japan by the Japanese Pig Registration Association (Obata et al., 1994). CGCV shows high correlation with the true genetic diversity in the
initial generations, however the correlation becomes lower in the later
generations (Furukawa et al., 1996).
The
methods of evaluating genetic diversity discussed above have different
characteristics. The gene diversity of all loci cannot be estimated actually,
but if many DNA markers are genotyped the average heterozygosity could be used
as the index of the genetic diversity. The effective population size, the
inbreeding coefficient, the coancestry and the coefficient of relationship have
been popular in population genetics. The effective population size is useful to
compare the structures of different population. The inbreeding coefficient
indicates the degree of gene fixation. The coancestry includes both parameters
of inbreeding and relationship, so the coefficient of relationship is less
useful. GCI and CGCV are useful in practice because they are easy to calculate in a
pedigree population. However, their application to evaluation of genetic
diversity is limited to initial generations.
3. Maintaining
of genetic diversity
In
order to maintain genetic diversity in a population, the effect of random drift
should be suppressed and the probability of gene fixation should be minimized.
For this objective, the simplest method is not to allow the replacement of
generation. The methodology will be described in the paper concerning ex situ conservation. The effect of
random drift and the probability of gene fixation are lower in a larger
population, but the number of animals kept for in situ conservation is limited. Here some procedures to retain the
genetic diversity with an infinite population size are discussed.
1)
Mating to avoid inbreeding
Mating
between relatives such as sib mating produces progenies with high coefficient
of inbreeding. Therefore mating to avoid relatives can suppress an increase of
inbreeding coefficient and can prevent harmful effects of inbreeding. The
effective population size avoiding sib mating was described above as follows,
Ne
= N + 2.
If the population size is not large,
Ne
= N + 1
(18)
should be approximated (Wang, 1995).
However, as understood from formula (18), in case that population size is large
enough, mating to avoid relatives does not affect effective population size.
To
suppress the increase in inbreeding coefficient in a population, "maximum
avoidance of inbreeding" mating was proposed by Wright (1921) where
matings between most unrelated individuals are systematically carried out in
every generation. The rate of inbreeding under the mating with maximum
avoidance of inbreeding is predicted approximately as follows,
DF
= 1 / 4N.
(19)
This is a half of the rate of inbreeding
with random mating. However, it becomes difficult to keep DF
small in the later generations.
Later,
Kimura and Crow (1963) showed that there were some mating systems that can
reduce inbreeding coefficient in the later generations. However, these methods
produce higher inbreeding coefficient in initial generations than mating with
maximum avoidance of inbreeding. The circular mating is one example of them.
The rate of inbreeding in a large population under circular mating is
approximated as follows,
DF
= p2
/ 4(Ne + 2)2.
(20)
2)
Group mating
In
the in situ conservation program, since
the number of animals kept in one place is usually limited, the population is
sometimes maintained with divided subpopulations. In this case, it is difficult
to carry out random mating over all subpopulations and it is effective to
change males among the subpopulations instead of overall random mating (Yamada,
1980). Maintenance of animals in different locations has the additional merit
of reducing the risk of accidental loss of the population (Nomura and Yonezawa,
1996).
An
example of a group mating scheme based on circular subpopulation mating was
proposed by Kimura and Crow (1963). Under this mating system, subpopulations
are located circular and reproductive males in subpopulations are provided
systematically from the neighboring subpopulations. The rate of inbreeding in
the overall population with a large number of subpopulations is approximated as
follows,
DF
= p2
/ 16(k/2 + Ne + 1)2
(21)
where k
is a number of subpopulations, Ne is the
effective population size of subpopulation.
Robertson
(1964) generalized that the mating together of close relatives within the
population leads to greater initial inbreeding but a lower final rate of
approach to the limit. Recently, Nomura and Yonezawa (1996) theoretically
compared some circular group mating systems where the cyclical system may
restrict the rate of inbreeding in initial generations more than the circular
subpopulation mating.
3)
Uniformity of the family size
One
of the most efficient techniques to keep genetic variability is to make the
family size as equal as possible. It may be easy to imagine that the extinction
probability of a certain allele is less in the case that every reproductive
animal produces two progenies for the next generation than in the other case
that one reproductive animal produce ten progenies and the others produce non.
It is usual in a population of domestic animals that males are extremely less
than females, but the difference in the number of males and females is not
desirable for the small population of the genetic resources, since it means the
extreme difference in the number of progenies. When the number of population is
fixed, we can minimize the reduction of genetic diversity by equalizing the
number of progeny from each individual.
The
effect of uniformalizing of family size can be predicted from formula (6). If
the individuals are chosen equally from all families, there is no variation in
family size, and Vk=0.
Then the effective population size is
Ne = 2N – 1
(22)
which is about twice the size of a
population bred from the equal numbers of males and females. If
the numbers are not equal in each sex, the variance of family size within sex
as defined in formula (7) can be reduced to zero by choosing one male from each
sire's progeny and one female from each dam's progeny as parents for the next
generation. The rate of inbreeding is given by the following formula (Gowe et
al., 1959).
1
/ Ne = 3 / 32Nm + 1 / 32Nf
(23)
The
effect of simultaneously uniformalizing of family size and maximum avoidance of
inbreeding was investigated by Robison and Bray (1965). If both procedures are
carried out simultaneously, the inbreeding coefficient in the initial
generations remain lower but the population with only uniformalizing family
size could keep lower inbreeding coefficient finally.
4)
Use of information from genetic markers
The
degree of inbreeding is estimated from pedigree information based on the
probability that the alleles of parents are transmitted to the next generation
with the probability of 1/2, since we cannot know which of the pair have been
transmitted. Recent developments in genome analysis provide linkage maps of a
lot of genetic markers like microsatellite for many livestock species. When the
genotypes of parents and their progenies are distinct, we can know which allele
of the parents has been transmitted to the progenies. By using this information
combined with the pedigree information, we can estimate the degree of
inbreeding in progenies more exactly (Takeda et al., 1997).
The
correlation between the average heterozygosity of genetic markers and the
realized genetic diversity is expected to be constant over generations. If we
can use a lot of genetic markers to calculate the average heterozygosity it is
useful as the index of the genetic diversity. And this information would assist
to select suitable mating pairs to retain the genetic diversity in the
population (Takeda et al., 1998).
In
order to maintain genetic diversity using in
situ conservation program, it is better to maintain a large number of
animals. However, maintaining a large number of animals is costly, in view of
this, maintaining a relatively smaller number under a well managed in situ conservation program would be
more economically efficient. The most effective way of conserving genetic
resources is through economical utilization of the animals in the production
system. However, improved breeds from Western countries tend to outperform
native breeds in developing countries in terms of productivity, this makes
promotion of utilization of pure native breeds difficult. This problem may be
overcome by crossbreeding native breeds with improved breeds as shown by
Furukawa et al. (1998).
In situ conservation is the easiest way
from methodological viewpoint and can be applied to any species of livestock.
On the other hand, ex situ
preservation is a technique that allows keeping genetic diversity of the
population permanently. However, ex situ
preservation of germplasm can be adapted and used to supplement in situ conservation program of animal
genetic resources. Cryopreservation of males in the base population could help
recovering of the genetic diversity of animal genetic resources. Therefore, in
conclusion, it is suggested that the efficient combination of in situ and ex situ conservation programs should be considered.
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