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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 />

109

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