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Books > Medicine > Pre-clinical medicine: basic sciences > Medical genetics
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Predicting Breeding Values with Applications in Forest Tree Improvement (Paperback, Softcover reprint of hardcover 1st ed. 1989)
Loot Price: R5,879
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Predicting Breeding Values with Applications in Forest Tree Improvement (Paperback, Softcover reprint of hardcover 1st ed. 1989)
Series: Forestry Sciences, 33
Expected to ship within 10 - 15 working days
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In most breeding programs of plant and animal species, genetic data
(such as data from field progeny tests) are used to rank parents
and help choose candidates for selection. In general, all selection
processes first rank the candidates using some function of the
observed data and then choose as the selected portion those
candidates with the largest (or smallest) values of that function.
To make maximum progress from selection, it is necessary to use a
function of the data that results in the candidates being ranked as
closely as possible to the true (but always unknown) ranking. Very
often the observed data on various candidates are messy and
unbalanced and this complicates the process of developing precise
and accurate rankings. For example, for any given candidate, there
may be data on that candidate and its siblings growing in several
field tests of different ages. Also, there may be performance data
on siblings, ancestors or other relatives from greenhouse,
laboratory or other field tests. In addition, data on different
candidates may differ drastically in terms of quality and quantity
available and may come from varied relatives. Genetic improvement
programs which make most effective use of these varied, messy,
unbalanced and ancestral data will maximize progress from all
stages of selection. In this regard, there are two analytical
techniques, best linear prediction (BLP) and best linear unbiased
prediction (BLUP), which are quite well-suited to predicting
genetic values from a wide variety of sources, ages, qualities and
quantities of data.
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