RA 00110.pdf - OAR@ICRISAT
RA 00110.pdf - OAR@ICRISAT
RA 00110.pdf - OAR@ICRISAT
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from 8-64, and s either 1/8, 1/4, or 1/2. It is apparent that<br />
with a low initial gene frequency many favorable<br />
alleles will not be fixed, even with strong selection<br />
pressure. With lower selection pressure and small<br />
effective population size as well, those alleles are not<br />
sure to get fixed even at intermediate initial frequencies.<br />
These examples are somewhat artificial since<br />
they assume that the loci are independent, that they<br />
only have additive effects, and that each locus has<br />
very small effects. However, the probabilities tell us<br />
that effective population size is very important if we<br />
are going to achieve much of the potential in a<br />
breeding population.<br />
H o w Many Traits Should Be Selected for<br />
This question has to be dealt with carefully because<br />
of the dilution effect. For example, in a sequential<br />
selection for first one trait, then another, followed<br />
by still another, the selection intensity for each<br />
is determined by the equation where N is the<br />
number of traits and P is the selected proportion.<br />
The proportion selected for each trait is shown in<br />
Table 2. In a test of 100 traits to select 10 (P = 0.1)<br />
with a goal to improve three, choose a 0.46 proportion<br />
for each trait, i.e., 0.46 x 100 = 46 for trait 1,0.46<br />
x 46 = 21 for trait 2, and 0.46 x 21 = 10 for trait 3. The<br />
important point is that the selection pressure for<br />
each trait is much reduced by adding more traits.<br />
Even though selection may not be in sequence as in<br />
this illustration, the dilution of selection pressure<br />
will be true with any kind of selection with additional<br />
traits. The solution is to include only important<br />
traits. If it is unclear that a trait is important,<br />
eliminate it from the selection criterion.<br />
H o w Does a Breeder Choose the Population<br />
to be Improved<br />
This is an easy question to pose, but can be quite<br />
difficult to answer. A simple example can be used to<br />
illustrate what can be involved. Suppose there is a<br />
potential choice among three different populations:<br />
• an adapted elite population that does not have as<br />
much genetic variation as the other two;<br />
• a population with a lower initial yield potential<br />
but a somewhat greater amount of genetic variation;<br />
and<br />
• a population with the lowest yield potential of the<br />
three but the largest amount of genetic variation.<br />
If it is assumed that the trait to be improved is<br />
controlled by a very large number of genes, each<br />
with very small effects so that response will continue<br />
linearly over the long term, and that the rate of<br />
response is proportional to the amount of genetic<br />
variation, the response patterns in Figure 1 might be<br />
expected. The shorter the time available, the more<br />
weight should be placed on the initial mean of<br />
important traits. The more one is interested in ultimate<br />
selection limits, the more weight should be<br />
placed on genetic variation. It is desirable, of course,<br />
to have both a high initial mean and a high amount<br />
of initial genetic variance, but this may not often be<br />
possible. In practice, some populations might be<br />
chosen to satisfy short term needs and others for<br />
longer term programs. Ultimately, it probably depends<br />
upon the source of funding and program<br />
objectives.<br />
What Should be the Target Environment<br />
It seems logical to subdivide a large potential set of<br />
macroenvironments into subsets, each having some<br />
cohesive internal relationship. In quantitative genetics,<br />
the reasoning is that genetic variation for a population<br />
is defined relative to these environmental<br />
subsets. If this environment is too broad, more of the<br />
genetic variation would go into G * E interaction,<br />
but if the target environment is narrow, the useful<br />
Table 2. Selected proportions of a population for each trait selected in sequence with the proportion for each trait equal 1 .<br />
Over-all selected<br />
proportion (P) 1<br />
0.05 0.05<br />
0.10 0.10<br />
0.20 0.20<br />
1. Calculated from the Nth root of P: P N or<br />
1<br />
2<br />
0.22<br />
0.32<br />
0.44<br />
Number of traits (N) to be improved<br />
3 4<br />
0.37<br />
0.46<br />
0.59<br />
0.47<br />
0.56<br />
0.67<br />
8 16<br />
0.69 0.83<br />
0.75 0.87<br />
0.82 0.90<br />
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